An MBB multiplexed local aggregation and superposition transmission method and system

Abstract

The present invention proposes a method and system for MBB multi-channel local aggregation and superposition transmission. Belonging to the field of communication technologies, the method includes: establishing a plurality of physical links in a wireless communication system, and the system selects the optimal plurality of physical links for aggregation according to the current network environment, device status, and user requirements; splitting the data to be transmitted into a plurality of data packets, each data packet is encapsulated according to the characteristics of the selected physical link, and the encapsulated data packets are transmitted in parallel through the selected physical links; during the transmission process, the system monitors the transmission status of each link in real time, and dynamically adjusts the transmission rate and power allocation of each link according to the current network load and device status. By transmitting in parallel through a plurality of physical links, the network bandwidth can be fully utilized, the data transmission rate can be improved, and the transmission delay can be reduced.

Description

Technical Field

[0001] The present invention proposes a method and system for MBB multi-channel local aggregation and superposition transmission, belonging to the field of communication technology. Background Art

[0002] With the rapid development of the mobile Internet, users' requirements for data transmission rate, network stability, and power consumption control are increasing day by day. When facing complex scenarios such as large amounts of data and high-concurrency access, traditional single-channel transmission methods often suffer from problems such as bandwidth bottlenecks, transmission delays, and high power consumption. Therefore, developing an efficient, stable, and low-power MBB multi-channel local aggregation and superposition transmission method has become an urgent technical problem to be solved in the current field of communication technology. Summary of the Invention

[0003] The present invention provides a method and system for MBB multi-channel local aggregation and superposition transmission to solve the problems mentioned in the above background art:

[0004] A method for MBB multi-channel local aggregation and superposition transmission proposed by the present invention, the method includes:

[0005] S1. Establish multiple physical links in the wireless communication system, and the system selects the optimal multiple physical links for aggregation according to the current network environment, device status, and user requirements;

[0006] S2. Divide the data to be transmitted into multiple data packets, each data packet is encapsulated according to the characteristics of the selected physical link, and the encapsulated data packets are transmitted in parallel through the selected physical links;

[0007] S3. During the transmission process, the system monitors the transmission status of each link in real time, and dynamically adjusts the transmission rate and power allocation of each link according to the current network load and device status;

[0008] S4. After the receiving end receives the data packets from multiple physical links, it reorganizes the data packets according to the link identifier and timestamp, and verifies the reorganized data.

[0009] Further, the S1 includes:

[0010] S11. Scan the available wireless spectrum resources to identify potential physical links; evaluate the signal quality of each potential link; and calculate the correlation between the links;

[0011] S12. Monitor the status of the device in real time, and the device status includes battery power, processing capacity, and heat dissipation status;

[0012] S13. Analyze the current user requirements of the user, where the user requirements include data transmission rate, latency requirements, and data priority;

[0013] S14. Based on the above results, select multiple optimal physical links for aggregation through a multi-objective optimization algorithm.

[0014] Further, the S2 includes:

[0015] S21. Split the data to be transmitted into multiple data packets according to the data size, assign priorities to each data packet, and give priority to transmitting the data packets with high priorities;

[0016] S22. Add encapsulation information to each data packet according to the characteristics of the selected physical links, where the encapsulation information includes link identification, check code, and timestamp;

[0017] S23. Transmit the encapsulated data packets in parallel through a multi-channel transmission protocol, and dynamically adjust the transmission order and rate of the data packets according to the real-time status of each link during the transmission process.

[0018] Further, the S22 includes:

[0019] S221. Collect the detailed characteristics of all currently available physical links, where the detailed characteristics include bandwidth, latency, bit error rate, jitter rate, and stability;

[0020] S222. Evaluate the transmission efficiency of each link through a multi-objective optimization algorithm based on the collected link characteristic data, and formulate a data packet allocation strategy accordingly;

[0021] S223. Conduct periodic link status monitoring and evaluation based on the dynamic changes of the links, and dynamically adjust the link selection strategy based on the evaluation results;

[0022] S224. Add a link identification to each data packet so that the data packet can be correctly routed to the target receiving end in a complex network environment;

[0023] S225. Generate and append a check code to verify the integrity of the data packet at the receiving end, detect and correct possible errors during the transmission process;

[0024] S226. Embed timestamp information to record the encapsulation time of the data packet, and assign different priority levels to the data packet according to the priority identification of the data packet, business logic, or application requirements;

[0025] S227. And based on the existing redundancy coding scheme, dynamically adjust the redundancy according to the bit error rate of the link;

[0026] S228. Predict the change trend of link performance in a future period through a link characteristic prediction model, and pre-adjust the encapsulation format and redundancy of data packets based on the prediction results.

[0027] Further, the S228 includes:

[0028] Extract the relevant indicators of each physical link from the historical database. The relevant indicators include bandwidth utilization, delay variation, bit error rate history, jitter rate trend, and stability index.

[0029] Select the features most influential for link performance prediction through a feature selection algorithm and perform normalization processing; perform model training based on time series analysis, and adjust model parameters through cross-validation.

[0030] Take the current and recent link status data as input and input it into the trained prediction model.

[0031] The prediction model outputs the relevant prediction values of the link in a future period. The relevant prediction values include bandwidth availability, delay variation range, expected bit error rate, and jitter level; and calculate the uncertainty interval of the prediction result.

[0032] Dynamically adjust the segmentation size of data packets according to the predicted future bandwidth availability and delay. For links with a high bit error rate, enable additional error detection and correction mechanisms.

[0033] Optimize the routing path of data packets based on the prediction of link performance, and dynamically adjust the redundancy coding level in data packets according to the predicted bit error rate.

[0034] Calculate the optimal redundancy configuration through a redundancy optimization algorithm based on the link characteristic prediction result.

[0035] Adjust the redundancy strategy according to the actual performance of data packet transmission through a real-time feedback system.

[0036] Based on the prediction results, configure the encapsulation format and redundancy for the data packets to be transmitted, and continuously monitor the actual link performance during data transmission and compare it with the prediction results. If there is a deviation, immediately trigger an adaptive adjustment mechanism to dynamically adjust the data packet encapsulation and redundancy settings.

[0037] Further, the S23 includes:

[0038] S231. Configure the parameters of the parallel transmission protocol, and determine the transmission order of data packets on multiple links through a data packet scheduling algorithm according to the priority of data packets, link status, and service requirements.

[0039] S232. Transmit through a multi-channel transmission protocol. During the transmission process, monitor the load and signal quality of each link in real time, evaluate the real-time state of the link based on the real-time monitoring results, and dynamically adjust the transmission rate on each link based on the evaluation results.

[0040] S233. Dynamically adjust the distribution of data packets on multiple links through a load balancing algorithm according to the real-time state and transmission requirements of the links.

[0041] S234. Moreover, during the transmission process, use check codes and timestamps to detect the integrity and transmission delay of data packets. For the detected incorrect or lost data packets, recover them through a preset error recovery strategy.

[0042] Further, the S232 includes:

[0043] Set corresponding key parameters according to the network environment and service requirements, and initialize multiple parallel transmission channels based on the configured parameters.

[0044] Real-time collect the key performance indicators of the link through the link state monitors deployed on each transmission channel. The key performance indicators include bandwidth utilization, latency, and packet loss rate.

[0045] Preprocess the collected raw data, aggregate the preprocessed data to a unified monitoring platform, build a link state evaluation model based on historical data and real-time data, evaluate the current state of the link through the link state evaluation model, and predict the change trend in the next period of time.

[0046] Set reasonable warning thresholds for the performance indicators of the link according to the actual situation of the service requirements and network environment. If it is detected that a certain indicator exceeds the threshold, immediately trigger the warning mechanism.

[0047] Design a transmission rate adjustment strategy according to the link state evaluation results. Based on the adjustment strategy, make real-time adjustments to each channel.

[0048] Further, the S3 includes:

[0049] S31. Real-time monitor the key indicators of each link. The key indicators include signal quality, bit error rate, and packet loss rate. During the monitoring process, improve the accuracy and real-time performance of the monitoring through signal processing algorithms.

[0050] S32. Dynamically adjust the transmission rate and power distribution of each link according to the monitoring results. When the performance of a certain link deteriorates, automatically reduce the load of this link or switch to a standby link.

[0051] S33. Evenly distribute data packets to each link through a load balancing algorithm, and perform real-time evaluation on the resource utilization rate of each link.

[0052] Further, the S4 includes:

[0053] S41. The receiving end reorganizes the data packets from multiple physical links based on the link identifier, timestamp, sequence, and integrity of the data packets.

[0054] S42. Check the reorganized data through a cyclic redundancy check algorithm to detect whether there is data loss or error.

[0055] S43. If data packets with loss or error are detected, recover them through a retransmission mechanism.

[0056] S44. And cache the frequently requested data packets through an intelligent caching mechanism.

[0057] An MBB multi-channel local aggregation and superposition transmission system proposed by the present invention, the system includes:

[0058] Link aggregation module: Establish multiple physical links in a wireless communication system. The system selects the optimal multiple physical links for aggregation according to the current network environment, device status, and user requirements.

[0059] Data transmission module: Divide the data to be transmitted into multiple data packets. Each data packet is encapsulated according to the characteristics of the selected physical link, and the encapsulated data packets are transmitted in parallel through the selected physical links.

[0060] Status monitoring module: During the transmission process, the system monitors the transmission status of each link in real time, and dynamically adjusts the transmission rate and power allocation of each link according to the current network load and device status.

[0061] Data verification module: After the receiving end receives the data packets from multiple physical links, reorganize the data packets according to the link identifier and timestamp, and verify the reorganized data.

[0062] Advantages of the present invention: By parallelly transmitting multiple physical links, the network bandwidth can be fully utilized, the data transmission rate can be increased, and the transmission delay can be reduced; Through the real-time monitoring and analysis of the network environment, device status and user requirements, the aggregation of the selected physical links becomes more intelligent and can quickly adapt to network changes; Through the data packet recombination, checksum and retransmission mechanisms, the integrity and reliability of the data during transmission can be ensured, and the data loss caused by link failures can be reduced; By assigning priorities to data packets, important data can be transmitted preferentially, meeting the requirements of different application scenarios and improving the service quality; Implementing a load balancing algorithm to achieve dynamic allocation of data packets among links, improving the utilization rate of resources and avoiding overloading of some links while other links are idle; Adopting a multi-objective optimization algorithm and a link characteristic prediction model, the encapsulation format, redundancy and transmission path of data packets can be flexibly adjusted to adapt to different network conditions and service requirements; By intelligently caching frequently requested data packets, the burden of repeated transmission can be reduced and the system response speed can be increased; Through the real-time monitoring and dynamic adjustment of the performance indicators of each link, the efficient utilization of the links is achieved, ensuring the optimization of the overall network performance; By reducing latency, increasing the transmission rate and ensuring data integrity, the user experience in different scenarios is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flowchart of the method described in the present invention;

[0064] Figure 2 It is a block diagram of the system described in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present invention, and are not intended to limit the present invention.

[0066] An embodiment of the present invention, as Figure 1 shown, a method for MBB multi-channel local aggregation and superposition transmission, the method includes:

[0067] S1. Establish multiple physical links in the wireless communication system. The system selects the optimal multiple physical links for aggregation according to the current network environment, device status and user requirements;

[0068] S2. Split the data to be transmitted into multiple data packets. Each data packet is encapsulated according to the characteristics of the selected physical link, and the encapsulated data packets are transmitted in parallel through the selected physical links;

[0069] S3. During the transmission process, the system real-time monitors the transmission status of each link and dynamically adjusts the transmission rate and power allocation of each link according to the current network load and device status;

[0070] S4. After the receiving end receives the data packets from multiple physical links, it reorganizes the data packets according to the link identifier and timestamp, and verifies the reorganized data to detect whether there is data loss or error. For the lost or incorrect data, the system attempts to recover it through the retransmission mechanism.

[0071] The working principle of the above technical solution is as follows: In a wireless communication system, multiple physical links are first established as data transmission channels. The system selects the optimal multiple physical links for aggregation according to factors such as the current network environment (such as signal strength, interference situation), device status (such as transmit power, receive sensitivity), and user requirements (such as data transmission rate, reliability requirements). For example, the ones with the strongest signal, the highest receive sensitivity, and the top transmission rates are selected. The data to be transmitted is divided into multiple data packets, and each data packet is encapsulated according to the characteristics of the selected physical link, including adding information such as link identifier and timestamp. The encapsulated data packets are transmitted in parallel through the selected physical links, thus making full use of the bandwidth resources of multiple links. During the transmission process, the system monitors the transmission status of each link in real time, including key indicators such as signal quality, bit error rate, and packet loss rate. According to the current network load and device status, the system dynamically adjusts the transmission rate and power allocation of each link. After the receiving end receives the data packets from multiple physical links, it reorganizes the data packets according to the link identifier and timestamp. The reorganized data is verified to detect whether there is data loss or error. For the lost or incorrect data, the system attempts to recover it through the retransmission mechanism to ensure the reliability of data transmission.

[0072] The effects of the above technical solution are as follows: By establishing and aggregating multiple physical links, parallel data transmission is achieved. Compared with single-link transmission, the data transmission rate and throughput can be significantly improved. The system can select the optimal multiple physical links for aggregation according to the current network environment, device status, and user requirements, ensuring that data transmission always occurs on the best link, thereby improving the transmission efficiency. During the transmission process, the system monitors the transmission status of each link in real time, including key indicators such as signal quality, bit error rate, and packet loss rate. Based on these indicators, the system can dynamically adjust the transmission rate and power allocation of each link to ensure the stability and reliability of data transmission. After the receiving end receives the data packets from multiple physical links, it reorganizes the data packets according to the link identifier and timestamp. At the same time, the reorganized data is verified to detect whether there is data loss or error. For the lost or incorrect data, the system attempts to recover it through the retransmission mechanism, further enhancing the reliability of data transmission. The system can dynamically adjust the transmission rate of each link according to the current network load and device status. The flexible adjustment mechanism can ensure that when the network load is high, the transmission rate is reduced to avoid network congestion; when the network load is low, the transmission rate is increased to make full use of network resources. In addition to the transmission rate, the system can also dynamically adjust the power allocation of each link, ensuring that the power of each link is reasonably allocated during the transmission process, thereby improving the energy utilization efficiency and reducing energy consumption, and dynamically adjusting according to user requirements to meet the different requirements of different users for data transmission rate, reliability, and stability. The above technical solution can be widely applied to various wireless communication scenarios, such as video conferencing, online games, high-definition video transmission, etc. By improving the data transmission efficiency and reliability, the above technical solution can significantly enhance the user's service quality experience. For example, in video conferencing, users can enjoy a more smooth and clear video call experience; in online games, users can enjoy lower latency and higher game stability.

[0073] In one embodiment of the present invention, S1 includes:

[0074] S11. Scan the available wireless spectrum resources to identify all potential physical links; evaluate the signal quality of each potential link, including indicators such as signal strength, stability, and interference degree; and calculate the correlation between the links. Among them, the signal quality is evaluated by the following formula:

[0075]

[0076] Where S represents the current signal strength; S yrepresents the threshold of signal strength, used to determine whether the signal strength is sufficient; k1 and k2 represent shape parameters, used to adjust the steepness of the logarithmic and exponential functions; N represents the number of interference sources or the number of data points within the time window; H i represents the signal stability in the i-th time window or scenario; G j represents the interference level of the j-th interference source; w1, w2, w3 represent weight coefficients and their sum is 1;

[0077] S12. Monitor the status of the device in real time, where the device status includes battery power, processing capacity, and heat dissipation status;

[0078] S13. Analyze the current user requirements, where the user requirements include data transmission rate, latency requirements, and data priority;

[0079] S14. Based on the above results, select the optimal multiple physical links for aggregation through a multi-objective optimization algorithm.

[0080] The working principle of the above technical solution is as follows: Through a spectrum analysis tool or device, comprehensively search for the available wireless spectrum in the surrounding environment to identify all potential physical links. Evaluate the signal quality of each potential link, including measurements of indicators such as signal strength, stability, and interference level. Consider factors such as the relative positions of devices and obstacles, and calculate the correlation between links. The correlation between links may vary due to factors such as device location, obstruction by environmental obstacles, and the propagation characteristics of wireless signals. Links with high correlation may have an impact on each other's transmission performance, so this needs to be considered when selecting links; Monitor the devices participating in link aggregation in real time, including battery power, processing capacity, and heat dissipation status, etc. Ensure that the selected links do not exceed the physical limits of the devices, such as power and bandwidth, which can avoid a decline or failure in link performance caused by insufficient device capabilities; Deeply analyze the current user requirements such as data transmission rate, latency requirements, and data priority. According to the user requirements, select the links that can meet these requirements for aggregation. For example, if the user requires a high data transmission rate, based on the above scanning, evaluation, and monitoring results, use a multi-objective optimization algorithm to select the optimal multiple physical links for aggregation. When selecting links, consider the complementarity between links, such as the diversity of frequency bands, modulation methods, and coding techniques. Determine the link aggregation strategy according to the algorithm results, including which links to select for aggregation and how to allocate transmission tasks, etc.

[0081] The effects of the above technical solution are as follows: By comprehensively scanning the available wireless spectrum resources, this technical solution can identify all potential physical links, thus ensuring the maximum utilization of wireless resources. Evaluating the signal quality of each potential link helps to screen out links with excellent signal quality and high stability, providing strong guarantee for subsequent link aggregation. Considering factors such as the relative positions of devices and obstacles and calculating the correlation between links helps to avoid selecting highly correlated links, thereby reducing interference and mutual influence between links and improving the overall transmission performance and stability after link aggregation. Real-time monitoring of the battery power, processing capacity, and heat dissipation status of the device can promptly detect potential problems and hidden dangers of the device, ensuring that the selected links do not exceed the physical limits of the device, thus protecting the normal operation of the device and extending its service life. According to the changes in the device status, this technical solution can flexibly adjust the link selection strategy to meet the requirements in different scenarios, ensuring that the transmission performance after link aggregation always remains in the optimal state. In-depth analysis of the user's current data transmission rate, latency requirements, and data priorities helps to accurately grasp the specific needs of the user, ensuring that the transmission performance after link aggregation can meet the actual needs of the user. According to the changes in user needs, this technical solution can dynamically adjust the link aggregation strategy to provide personalized services, enhancing user satisfaction and loyalty. Through a multi-objective optimization algorithm, this technical solution can select the optimal multiple physical links for aggregation, ensuring that the transmission performance after link aggregation is maximized. When selecting links, fully consider the complementarity between links, such as the diversity of frequency bands, modulation methods, and coding technologies, to improve the overall performance and stability after link aggregation and reduce the risk of overall performance degradation caused by a single link failure.The above signal quality evaluation formula comprehensively considers the main factors affecting signal quality by introducing signal strength (S), signal stability (H), and interference level (G), making the evaluation results more comprehensive and accurate. The weight coefficients (w1, w2, w3) enable the influence of each factor to be adjusted according to the actual application scenario, providing flexibility. The shape parameters (k1, k2) allow the steepness of the evaluation formula to be adjusted for different environments and requirements, and can adapt to changes in signal conditions. For example, in a high-interference environment, a more stringent signal quality evaluation can be provided. By setting a signal strength threshold, the formula can effectively determine whether the signal is strong enough, thereby affecting the link selection. Introducing considerations of signal stability and interference sources helps to better identify unstable links and reduce transmission errors caused by interference, thus improving the reliability of link selection. When selecting multiple physical links, the correlation between different links can be calculated based on the signal quality evaluation results to optimize the link combination strategy and ensure the efficiency of data transmission. Combining the specific needs of users (such as data transmission rate, latency requirements, and data priority), signal quality evaluation can provide users with the optimal link selection and improve the data transmission experience. Through a multi-objective optimization algorithm, the best physical links are selected for aggregation according to different user needs and network conditions to ensure the rational use of resources. Real-time monitoring of the device's battery power, processing capacity, and heat dissipation status can timely adjust the link selection strategy to avoid signal quality degradation caused by poor device conditions. Based on the real-time feedback information, the link selection can be dynamically optimized to improve the flexibility and responsiveness of the network. Through the above formula, the evaluation of signal quality can not only enhance the scientificity and rationality of link selection, but also greatly enhance the adaptability of the wireless communication system under different environments and requirements, thereby optimizing the overall user experience.

[0082] In one embodiment of the present invention, the S2 includes:

[0083] S21. Divide the data to be transmitted into multiple data packets according to the data size, assign priorities to each data packet, and preferentially transmit the data packets with high priorities;

[0084] S22. Add encapsulation information to each data packet according to the characteristics of the selected physical link, where the encapsulation information includes a link identifier, a check code, and a timestamp;

[0085] S23. Parallelly transmit the encapsulated data packets through a multi-channel transmission protocol, and dynamically adjust the transmission order and rate of the data packets according to the real-time status of each link (such as load, signal quality, etc.) during the transmission process.

[0086] The working principle of the above technical solution is as follows: First, the data to be transmitted is segmented into multiple data packets according to the size of the data, ensuring efficient transmission on different physical links and facilitating independent management and monitoring of each data packet; priorities are assigned to each data packet based on factors such as the urgency, importance of the data, or the priority specified by the user. By assigning priorities to data packets, it is ensured that the most important data is transmitted first in case of limited resources; data packets with high priorities are preferentially transmitted. Encapsulation information is added to each data packet according to the characteristics of the selected physical link (such as bandwidth, latency, bit error rate, etc.); the encapsulation information includes a link identifier (used to identify the link to which the data packet belongs), a check code (used to detect whether an error occurs during the transmission of the data packet), and a timestamp (used to record the transmission time of the data packet); during the encapsulation process, the redundancy of the data packet also needs to be considered. Redundancy refers to the additional amount of information added to the data packet to cope with potential transmission errors. The encapsulation process combines the data packet with the encapsulation information to form a complete transmission unit. The encapsulated data packets are transmitted in parallel through a multi-channel transmission protocol. During the transmission process, the transmission order and rate of the data packets are dynamically adjusted according to the real-time status of each link (such as load, signal quality, etc.). The dynamic adjustment mechanism ensures that the transmission strategy can be adjusted in a timely manner to adapt to the new transmission environment when the link status changes; by adjusting the transmission order and rate of the data packets, the transmission performance can be optimized. For example, when the link load is high, data packets with high priorities can be preferentially transmitted and the transmission rate of other data packets can be reduced; when the link signal quality is poor, the redundancy of the data packet can be increased or a more reliable transmission path can be selected.

[0087] The effects of the above technical solution are as follows: By splitting the data to be transmitted into multiple data packets, the size of each data packet can be made more suitable for the transmission capacity of the selected physical link, thereby improving the transmission efficiency. Prioritizing the transmission of high-priority data packets can ensure that critical data reaches the destination faster, reducing the waiting time and further enhancing the overall transmission efficiency. Using a multi-channel transmission protocol for parallel transmission can make full use of the bandwidth resources of multiple physical links to achieve fast data transmission. Parallel transmission can also reduce the risk of transmission interruption caused by a single link failure and improve the reliability of transmission. By adding encapsulation information such as link identifiers, check codes, and timestamps to each data packet, the integrity and accuracy of the data packet during transmission can be ensured. The check code can be used to detect whether errors occur during the transmission of the data packet, and the timestamp can be used to record the sending and receiving times of the data packet, facilitating subsequent data processing and analysis. Considering the redundancy of data packets during encapsulation can increase the error resistance of data packets and reduce the risk of data loss. Redundancy can be achieved by adding additional data blocks or check information to ensure that even if partial data loss or errors occur during transmission, the original data can be restored through the redundant information. Dynamically adjusting the transmission order and rate of data packets according to the real-time status of each link (such as load, signal quality, etc.) during transmission can optimize resource utilization. For example, when the link load is high, high-priority data packets can be preferentially transmitted, and the transmission rate of other data packets can be appropriately reduced to avoid network congestion; when the link signal quality is poor, the redundancy of data packets can be increased or a more reliable transmission path can be selected to improve the transmission success rate. This technical solution can add encapsulation information and adjust the transmission strategy according to different physical link characteristics (such as bandwidth, delay, bit error rate, etc.), so as to adapt to the data transmission requirements in different link environments and maintain high-efficiency and reliable transmission performance in various network environments. Through mechanisms such as priority transmission and parallel transmission, the time for users to wait for the completion of data transmission can be reduced, enhancing the user experience. By increasing the redundancy of data packets and dynamically adjusting the transmission strategy and other measures, the success rate of data transmission can be improved, reducing problems such as data loss or delay caused by transmission errors or network failures.

[0088] In one embodiment of the present invention, S22 includes:

[0089] S221. Collect the detailed characteristics of all currently available physical links, where the detailed characteristics include bandwidth, delay, bit error rate, jitter rate, and stability;

[0090] S222. Based on the collected link characteristic data, evaluate the transmission efficiency of each link through a multi-objective optimization algorithm, and formulate a data packet allocation strategy accordingly;

[0091] S223. Based on the dynamic changes of the link (such as network congestion, equipment failure), perform periodic link status monitoring and evaluation, and dynamically adjust the link selection strategy based on the evaluation results;

[0092] S224. Add a link identifier to each data packet to correctly route the data packet to the target receiver in a complex network environment;

[0093] S225. Generate and append a check code to verify the integrity of the data packet at the receiving end, detect and correct possible errors during the transmission process;

[0094] S226. Embed timestamp information to record the encapsulation time of the data packet, and assign different priority levels to the data packet according to the priority identifier of the data packet, business logic or application requirements;

[0095] S227. And based on the existing redundancy coding scheme, dynamically adjust the redundancy according to the bit error rate of the link; wherein, the bit error rate is calculated by the following formula:

[0096]

[0097] Wherein, T represents the total monitoring time; R represents the transmission rate; M represents the number of independent time intervals recorded during the monitoring period; E i represents the number of error bits received in the i-th time interval; t i represents the start time of the i-th time interval; λ represents the time decay factor of the bit error rate, which is used to reduce the influence of errors in past time intervals;

[0098] S228. Through the link characteristic prediction model, predict the change trend of the link performance in the future period of time, and pre-adjust the encapsulation format and redundancy of the data packet based on the prediction results.

[0099] The working principle of the above technical solution is as follows: the system first collects detailed characteristics of all currently available physical links. The key indicators include bandwidth, delay, bit error rate, jitter rate and stability. Based on the collected link characteristic data, the system evaluates the transmission efficiency of each link through a multi-objective optimization algorithm. The evaluation results will serve as the basis for the data packet allocation strategy. Considering the possible dynamic changes of the link (such as network congestion, equipment failure, etc.), the system performs periodic link status monitoring and evaluation. The monitoring results will be used to dynamically adjust the link selection strategy to adapt to the changes in link status. Add a link identifier to each data packet. The link identifier is the "identity card" of the data packet transmitted in the network, ensuring that the data packet can reach the predetermined destination accurately. The system generates and attaches a checksum to the data packet for verifying the integrity of the data packet at the receiving end. The checksum can detect and correct errors that may occur during the transmission process to ensure the accuracy of the data. The timestamp information is embedded in the data packet to record the encapsulation time of the data packet. According to the priority identifier of the data packet, the system assigns different priority levels to the data packet according to the business logic or application requirements. The timestamp and priority identifier help the receiving end to process and prioritize the data packets in an orderly manner. Based on the existing redundant coding scheme, the system dynamically adjusts the redundancy of the data packet according to the bit error rate of the link. By increasing or decreasing redundant information, the system can ensure the error resistance of the data packet during transmission and reduce the risk of data loss. Using the link characteristic prediction model, the system predicts the trend of link performance changes in the future. Based on the prediction results, the system pre-adjusts the encapsulation format and redundancy of the data packet to adapt to changes in link performance.

[0100] The effects of the above technical solutions are as follows: By collecting the detailed characteristics of all currently available physical links, including bandwidth, latency, bit error rate, etc., the system can more accurately understand the transmission capabilities of each link. Based on this characteristic data, the transmission efficiency of the link is evaluated through a multi-objective optimization algorithm, and a data packet allocation strategy is formulated to ensure that data packets are allocated to the link with the highest transmission efficiency, thereby improving the transmission efficiency. The system can periodically monitor the link status and dynamically adjust the link selection strategy according to the evaluation results to adapt to possible dynamic changes in the link (such as network congestion, equipment failure), avoiding sending data packets to links with poor performance, thereby further improving the transmission efficiency and resource utilization rate. Adding a link identifier to each data packet can ensure that the data packet is correctly routed to the target receiver in a complex network environment, reducing the risk of data packet loss. Generating and attaching a check code can verify the integrity of the data packet at the receiving end, detect and correct possible errors during the transmission process, thereby improving the reliability of data transmission. The system can dynamically adjust the redundancy of data packets according to the bit error rate of the link to ensure the error resistance of data packets during transmission. By increasing or decreasing redundant information, the system can improve the stability and reliability of data transmission while ensuring the transmission efficiency. By embedding timestamp information, the encapsulation time of the data packet can be recorded, which helps the receiving end process the data packet in an orderly manner. According to the priority identifier of the data packet, the system can assign different priority levels to data packets according to business logic or application requirements, thereby optimizing the flexibility of data transmission, ensuring that critical data can reach the destination faster, and improving the overall transmission efficiency. Through the link characteristic prediction model, the system can predict the change trend of link performance in the future for a period of time. Based on the prediction results, the system can pre-adjust the encapsulation format and redundancy of data packets to adapt to the change of link performance. The above bit error rate formula can more accurately reflect the bit error situation of the link by considering the number of error bits and their time distribution within multiple time intervals. Especially in a network environment, the link state may change due to instantaneous fluctuations; by introducing the time decay factor λ, the influence of bit errors in earlier time periods on the current BER gradually weakens, which can reduce the interference of historical data on the current performance evaluation and pay more attention to the real-time performance of the link. Based on the calculation of BER, the system can adjust the redundancy of the redundant coding scheme according to the real-time network state. When it detects an increase in the bit error rate, the system can increase the redundancy to improve the reliability of data transmission, and vice versa, it can reduce the redundancy to improve the transmission efficiency. By evaluating the link characteristics (including BER) and combining with the multi-objective optimization algorithm, a more reasonable data packet allocation strategy can be formulated to improve the overall network performance, reduce latency and jitter, and at the same time ensure the reliability of data transmission. As an important indicator for link state monitoring. Once it is found that the bit error rate rises abnormally, the system can quickly identify possible link failures, and then take measures to recover, avoiding data loss or affecting subsequent data transmission.Based on the calculation results of this formula as the network conditions change, the system can adapt to the changes in the network state in real time. Through periodic link state monitoring, the link selection strategy can be adjusted in a timely manner to achieve the best data transmission performance. By applying this bit error rate formula, the network management system can more accurately and flexibly evaluate and optimize the link performance, thereby effectively improving the transmission efficiency and stability of the network.

[0101] In one embodiment of the present invention, S228 includes:

[0102] Extract the relevant metrics of each physical link from the historical database. The relevant metrics include bandwidth utilization, delay variation, bit error rate history, jitter rate trend, and stability index;

[0103] Select the features that have the most influence on link performance prediction through a feature selection algorithm and perform normalization processing; perform model training based on time series analysis and adjust the model parameters through cross-validation;

[0104] Use the current and recent (e.g., the most recent week) link state data (including bandwidth usage, delay measurements, bit error rate detection, etc.) as input and input it into the trained prediction model;

[0105] The prediction model outputs the relevant predicted values of the link within a future period of time (such as the next 1 hour, 24 hours, etc.); the relevant predicted values include bandwidth availability, delay variation range, expected bit error rate, and jitter level; and calculate the uncertainty interval of the prediction result;

[0106] Dynamically adjust the packet segmentation size according to the predicted future bandwidth availability and delay conditions. For links with a predicted high bit error rate, enable additional error detection and correction mechanisms;

[0107] Based on the prediction of link performance, optimize the routing path of the packet, select a link combination with better expected performance, avoid potential bottleneck or unstable links, and dynamically adjust the redundancy coding level in the packet (such as increasing or decreasing the length of the error correction code) according to the predicted bit error rate to balance the reliability and bandwidth efficiency of data transmission;

[0108] Calculate the optimal redundancy configuration through a redundancy optimization algorithm based on the link characteristic prediction results;

[0109] Adjust the redundancy strategy through a real-time feedback system according to the actual performance of packet transmission (such as successful reception rate, number of retransmissions, etc.);

[0110] Based on the prediction results, appropriately configure the encapsulation format and redundancy for the data packets to be transmitted in advance, and continuously monitor the actual performance of the link during data transmission, compare it with the prediction results. If there is a deviation, immediately trigger the adaptive adjustment mechanism to dynamically adjust the packet encapsulation and redundancy settings.

[0111] The working principle of the above technical solution is as follows: Extract the relevant indicators of each physical link from the historical database. The indicators include bandwidth utilization, latency variation, bit error rate history, jitter rate trend, and stability index, etc. Use feature selection algorithms (such as Lasso regression, random forest feature importance assessment) to screen out the features that have the most influence on link performance prediction and perform normalization processing. Based on time series analysis for model training, adjust the model parameters through cross-validation to optimize the prediction performance. The trained prediction model can receive the current and recent link status data as input and output the relevant predicted values of the link for a period of time in the future. The prediction model outputs the relevant predicted values of the link for a period of time in the future (such as the next 1 hour, 24 hours, etc.), including bandwidth availability, latency variation range, expected bit error rate, and jitter level, etc. Calculate the uncertainty interval of the prediction results to quantify the reliability of the prediction results and provide a basis for subsequent decisions. Dynamically adjust the packet segmentation size according to the predicted future bandwidth availability and latency to optimize the transmission efficiency. For links with a predicted high bit error rate, enable additional error detection and correction mechanisms to improve the reliability of data transmission. Optimize the routing path of the data packets based on the prediction results of the link performance, select a link combination with better expected performance, and avoid potential bottlenecks or unstable links. Dynamically adjust the redundancy coding level in the data packets according to the predicted bit error rate to balance the reliability and bandwidth efficiency of data transmission. Through the redundancy optimization algorithm based on the prediction results of link characteristics, comprehensively consider the bit error rate, latency, and bandwidth limitations to calculate the optimal redundancy configuration. Use a real-time feedback system to adjust the redundancy strategy according to the actual performance of the data packet transmission (such as successful reception rate, number of retransmissions, etc.) to achieve more refined transmission control. During data transmission, continuously monitor the actual performance of the link and compare it with the prediction results. If there is a deviation, immediately trigger the adaptive adjustment mechanism to dynamically adjust the packet encapsulation and redundancy settings to cope with the changes in link performance.

[0112] The effects of the above technical solution are as follows: By extracting relevant metrics from the historical database, screening out the most influential features using a feature selection algorithm, and then combining time series analysis for model training, accurate prediction of link performance can be achieved, enabling the system to understand in advance key information such as the bandwidth availability, latency variation range, expected bit error rate, and jitter level of the link over a period of time in the future. Based on the predicted future bandwidth availability and latency, the system can dynamically adjust the packet segmentation size, thereby optimizing the transmission efficiency. When the bandwidth is sufficient and the latency is low, the packet segmentation size can be appropriately increased to improve the transmission speed; conversely, it is reduced to reduce the transmission latency and error rate. For links with a predicted high bit error rate, the system can enable additional error detection and correction mechanisms, such as increasing the length of the error correction code or adopting a more complex coding scheme, to improve the reliability of data transmission. Based on the prediction results of link performance, the system can select a link combination with better expected performance for packet transmission, thereby avoiding potential bottlenecks or unstable links and reducing packet loss and latency during transmission. According to the predicted bit error rate, the system can dynamically adjust the redundancy coding level in the packet to balance the reliability of data transmission and bandwidth efficiency. When the bit error rate is high, the redundancy coding level is increased to improve the reliability of data transmission; when the bit error rate is low, the redundancy coding level is reduced to save bandwidth resources. This algorithm can comprehensively consider multiple factors such as the bit error rate, latency, and bandwidth limitation, calculate the optimal redundancy configuration, enabling the system to automatically adjust the redundancy setting according to changes in link characteristics, thereby improving the system's adaptability and intelligence level. Through the real-time feedback system, the system can adjust the redundancy strategy based on the actual performance of packet transmission (such as the successful reception rate, number of retransmissions, etc.), enabling the system to promptly discover and solve problems during transmission, further improving the reliability and stability of data transmission. During data transmission, the system can continuously monitor the actual performance of the link and compare it with the prediction results. If there is a deviation, the adaptive adjustment mechanism is immediately triggered to dynamically adjust the packet encapsulation and redundancy setting to adapt to changes in link characteristics. By accurately predicting link performance and dynamically adjusting the transmission strategy, the system can reduce unnecessary bandwidth waste and the number of retransmissions, thereby reducing the operating cost. At the same time, due to the system's high adaptability and intelligence level, the number of manual interventions and fault troubleshooting can be reduced, lowering the maintenance cost.

[0113] In an embodiment of the present invention, S23 includes:

[0114] S231. Configure the parameters of the parallel transmission protocol, and determine the transmission order of packets on multiple links through a packet scheduling algorithm according to the priority of the packets, the link status, and the service requirements.

[0115] S232. Transmit through a multi-channel transmission protocol. During the transmission process, monitor the load and signal quality of each link in real time, evaluate the real-time state of the link based on the real-time monitoring results, and dynamically adjust the transmission rate on each link based on the evaluation results.

[0116] S233. Through a load balancing algorithm, dynamically adjust the allocation of data packets on multiple links according to the real-time state and transmission requirements of the links.

[0117] S234. Moreover, during the transmission process, use check codes and timestamps to detect the integrity and transmission delay of data packets. For the detected incorrect or lost data packets, recover them through a preset error recovery strategy.

[0118] The working principle of the above technical solution is as follows: First, relevant parameters of the parallel transmission protocol need to be configured, including data packet fragmentation size, retransmission strategy, and flow control mechanism. Through the data packet scheduling algorithm, the system can dynamically determine the transmission order of data packets on multiple links according to the priority of data packets, link status, and service requirements. Using the multi-channel transmission protocol, data can be transmitted in parallel on multiple links. During the transmission process, the system will monitor the load and signal quality of each link in real time, including key indicators such as bandwidth utilization rate, delay, and packet loss rate. Based on the evaluation results of the link status, the system will dynamically adjust the transmission rate on each link to optimize the transmission performance. For example, on a link with a high bandwidth utilization rate, the transmission rate can be appropriately reduced to reduce the possibility of congestion and packet loss. Through the load balancing algorithm, the system can dynamically adjust the allocation of data packets on multiple links according to the real-time state and transmission requirements of the links. This adjustment aims to achieve an even distribution of traffic and avoid the situation where some links are overloaded while others are idle. In practical applications, the load balancing algorithm will dynamically adjust the data packet allocation strategy according to the real-time monitored link status information. For example, when the delay or packet loss rate of a certain link suddenly increases, the algorithm will quickly reduce the number of data packets allocated to this link to ensure the overall quality of data transmission. During the transmission process, the system will use check codes and timestamps to detect the integrity and transmission delay of data packets. The check code is used to verify whether the data packet has errors or is damaged during transmission, while the timestamp is used to measure the transmission delay of the data packet. For the detected incorrect or lost data packets, the system will recover them through a preset error recovery strategy, including retransmission mechanism, error correction coding, etc. The retransmission mechanism means that when a data packet is lost or in error, the system will retransmit the data packet to ensure data integrity. Error correction coding is a technology that embeds redundant information in data packets to detect and correct transmission errors.

[0119] The effects of the above technical solutions are as follows: By reasonably configuring the parameters of the parallel transmission protocol, such as the packet fragmentation size, retransmission strategy, and flow control mechanism, the data transmission process can be optimized, unnecessary waiting and retransmission can be reduced, and thus the transmission efficiency can be improved; The packet scheduling algorithm can dynamically adjust the transmission order of packets on multiple links to adapt to the real-time changes in the network environment, ensuring the stability and reliability of data transmission, especially in the case of unstable network conditions; The multi-channel transmission protocol allows data to be transmitted in parallel on multiple links, thus making full use of network resources and improving the overall transmission capacity; By real-time monitoring the load and signal quality of each link, the system can timely detect and solve potential network bottlenecks or problems. Dynamically adjusting the transmission rate based on the evaluation results helps to balance the network load, avoid congestion and packet loss; The load balancing algorithm can dynamically adjust the distribution of packets on multiple links according to the real-time state and transmission requirements of the links, which helps to optimize the network traffic distribution and improve the overall performance of the system; Through load balancing, the system can avoid over-reliance on a single link, thereby reducing the risk of single-point failure. Even if a certain link fails, other links can still continue to transmit data to ensure the continuous and stable operation of the system; Using checksum and timestamp to detect the integrity and transmission delay of packets can timely detect and correct errors or damaged packets during the transmission process, ensuring the accuracy and reliability of data; Through the preset error recovery strategies (such as retransmission, error correction coding, etc.), the system can automatically recover lost or incorrect packets, thereby improving the quality of data transmission and reducing the risks such as system downtime or data loss caused by data transmission errors.

[0120] In an embodiment of the present invention, the S232 includes:

[0121] According to the network environment and service requirements, perform corresponding key parameter settings, and initialize multiple parallel transmission channels based on the configured parameters;

[0122] Through the link status monitors deployed on each transmission channel, real-time collect the key performance indicators of the link, and the key performance indicators include bandwidth utilization rate, latency, and packet loss rate;

[0123] Preprocess the collected raw data, aggregate the preprocessed data to a unified monitoring platform, build a link status evaluation model based on historical data and real-time data, evaluate the current state of the link through the link status evaluation model, and predict the change trend in the next period of time;

[0124] According to the service requirements and the actual situation of the network environment, set reasonable warning thresholds for each performance indicator of the link. If it is monitored that a certain indicator exceeds the threshold, immediately trigger the warning mechanism;

[0125] Design a transmission rate adjustment strategy based on the link state assessment result; perform real-time adjustment on each channel based on the adjustment strategy.

[0126] The working principle of the above technical solution is as follows: Configure the packet fragmentation size, retransmission strategy (such as exponential backoff, limited number of retransmissions, etc.), and flow control mechanism (such as sliding window protocol) according to the network environment and service requirements. Initialize multiple parallel transmission channels based on the configured parameters. The establishment of parallel transmission channels allows data to be transmitted simultaneously on multiple links. Deploy link state monitors on each transmission channel to collect key performance indicators of the link in real time, including bandwidth utilization, latency, and packet loss rate. Preprocess the collected raw data, such as denoising, normalization, etc., and aggregate the preprocessed data to a unified monitoring platform for centralized management and analysis. Build a link state assessment model based on historical data and real-time data. Through the link state assessment model, evaluate the current state of the link and predict the change trend in the next period of time. Set reasonable warning thresholds for various performance indicators of the link according to the service requirements and the actual situation of the network environment. If a certain indicator is detected to exceed the threshold, immediately trigger the warning mechanism to notify relevant personnel to take corresponding measures. Design a transmission rate adjustment strategy based on the link state assessment result. When the link bandwidth utilization is detected to be too high, reduce the transmission rate to reduce congestion; when the link latency increases, optimize the transmission efficiency by adjusting the packet fragmentation size or retransmission strategy. Perform real-time adjustment on each channel based on the transmission rate adjustment strategy. Real-time adjustment can ensure that the transmission efficiency and quality of data on different links reach the optimal state.

[0127] The effects of the above technical solutions are as follows: By setting key parameters such as the data packet fragmentation size, retransmission strategy, and flow control mechanism according to the network environment and service requirements, the system can flexibly adapt to different network conditions and application scenarios; Initializing multiple parallel transmission channels improves the flexibility and reliability of data transmission. Even if some links fail, other links can still continue to transmit data; Through the link status monitors deployed on each transmission channel, key performance indicators such as bandwidth utilization, latency, and packet loss rate are collected in real time, providing comprehensive link status information for the system; The link status evaluation model built based on historical data and real-time data can accurately evaluate the current status of the link and predict the change trend in the next period of time, providing strong support for system optimization; Setting reasonable warning thresholds for various performance indicators of the link. Once a certain indicator is detected to exceed the threshold, the warning mechanism is immediately triggered to remind relevant personnel to take measures in a timely manner to avoid potential network problems; By adjusting the transmission rate, data packet fragmentation size, or retransmission strategy in real time, the system can quickly respond to network changes and ensure the stability and efficiency of data transmission; According to the link status evaluation results, the transmission rate is dynamically adjusted, which not only avoids network congestion but also improves data transmission efficiency; By adjusting the data packet fragmentation size and retransmission strategy, the system can make more reasonable use of network resources, reducing unnecessary transmission overhead and latency; Through real-time link status monitoring and adjustment, the system can ensure the stable transmission of data and reduce transmission interruptions or delays caused by network problems; The optimized transmission efficiency and resource utilization provide users with higher-quality data transmission services and enhance the user experience.

[0128] In one embodiment of the present invention, the S233 includes:

[0129] During the transmission process, continuously collect the real-time status information of each link. The real-time status information includes bandwidth utilization, latency, jitter, packet loss rate, and link stability, and preprocess the collected information;

[0130] Based on historical data and real-time data, build a link performance evaluation model. By comprehensively considering multiple performance indicators of the link through the link performance evaluation model, give a comprehensive performance score of the link;

[0131] According to the link performance evaluation results, dynamically adjust the distribution of data packets on multiple links through a load balancing algorithm;

[0132] After the data packet distribution is adjusted, continuously monitor the load conditions and transmission performance of each link, and establish a feedback mechanism to feedback the monitoring results to the load balancing algorithm in real time and make fine-tuning according to the actual situation.

[0133] The working principle of the above technical solution is as follows: During data transmission, the system continuously collects real-time status information of each link, including key performance indicators such as bandwidth utilization, latency, jitter, packet loss rate, and link stability. The collected raw data is preprocessed, and the system combines historical data and real-time data to build a link performance evaluation model. Historical data provides the long-term trend and benchmark of link performance, while real-time data reflects the current link status. The link performance evaluation model comprehensively considers multiple performance indicators of the link, such as bandwidth utilization, latency, jitter, packet loss rate, and link stability. These indicators jointly determine the comprehensive performance score of the link. According to the link performance evaluation results, the system can identify links with better performance and links with poorer performance. Based on the load balancing algorithm, the system dynamically adjusts the distribution of data packets across multiple links. Links with better performance will undertake more data transmission tasks, while links with poorer performance will correspondingly reduce the data transmission volume. After the adjustment of data packet distribution, the system continuously monitors the load conditions and transmission performance of each link, discovers and solves potential transmission problems. The system establishes a feedback mechanism to feedback the monitoring results to the load balancing algorithm in real time. According to the feedback results, the load balancing algorithm can be fine-tuned to further optimize data packet distribution and link performance.

[0134] The effects of the above technical solution are as follows: By continuously collecting real-time status information of each link, including bandwidth utilization rate, latency, jitter, packet loss rate, and link stability, etc., the system can comprehensively understand the current network status. The accuracy of the real-time status information helps the system make more reasonable decisions, thereby optimizing the data transmission path and improving the transmission efficiency. The link performance evaluation model constructed based on historical data and real-time data can comprehensively consider multiple performance indicators of the link and give a comprehensive performance score of the link. This model can accurately reflect the actual performance of the link and provide a reliable basis for packet allocation. According to the performance evaluation results of the link, the distribution of packets on multiple links is dynamically adjusted through a load balancing algorithm. When the performance of a certain link deteriorates, the system can automatically transfer the packets to other links with better performance, thereby avoiding data transmission interruption or delay. After the packet allocation is adjusted, the system continuously monitors the load conditions and transmission performance of each link and establishes a feedback mechanism. The monitoring results are fed back to the load balancing algorithm in real time for fine-tuning according to the actual situation to ensure that the network always maintains the best state. By dynamically adjusting the distribution of packets on multiple links, the system can ensure that resources are reasonably allocated. Links with better performance undertake more data transmission tasks, while links with poorer performance reduce the data transmission volume accordingly, thereby improving the overall resource utilization rate. The optimized data transmission path and resource allocation help reduce the energy consumption of network devices. By reducing unnecessary transmission overhead and latency, the system can reduce the overall energy consumption and improve the energy utilization efficiency. Through mechanisms such as real-time status information collection, link performance evaluation model, and dynamic load balancing, the system can ensure the stable transmission of data. Users can enjoy a more smooth and stable data transmission experience, improving satisfaction and loyalty. The system establishes a feedback mechanism that can promptly detect and solve potential transmission problems. In case of a failure, the system can quickly respond and resume data transmission, reducing the waiting time and losses of users.

[0135] In an embodiment of the present invention, the S3 includes:

[0136] S31. Real-time monitor the key indicators of each link, where the key indicators include signal quality, bit error rate, and packet loss rate; and during the monitoring process, improve the accuracy and real-time performance of the monitoring through signal processing algorithms;

[0137] S32. Dynamically adjust the transmission rate and power allocation of each link according to the monitoring results; when the performance of a certain link deteriorates, automatically reduce the load of this link or switch to a backup link;

[0138] S33. Evenly distribute the packets to each link through a load balancing algorithm and real-time evaluate the resource utilization rate of each link. Among them, the resource utilization rate is evaluated by the following formula:

[0139]

[0140] Among them, RUIξ(f) represents the resource utilization rate of the ξ-th link at time f; F is the time window length for evaluating the resource utilization rate; Cξ(τ) is the current transmission rate of the ξ-th link at time τ; Mξ is the maximum transmission capacity of the ξ-th link.

[0141] The working principle of the above technical solution is as follows: The system monitors the key indicators of each link in real time, including signal quality, bit error rate, and packet loss rate. During the monitoring process, the system improves the accuracy and real-time performance of monitoring by applying signal processing algorithms (such as adaptive filtering and blind source separation). According to the real-time monitoring results, the system dynamically adjusts the transmission rate and power allocation of each link. When the link performance is good, the system can increase the transmission rate and power allocation to make full use of the link resources. When the link performance deteriorates, the system automatically reduces the load of the link and decreases the transmission rate and power allocation. When a serious performance degradation occurs in a certain link, the system can automatically switch to a backup link, and the selection of the backup link is based on the real-time monitoring results and link performance evaluation. The system evenly distributes data packets to each link through a load balancing algorithm to balance the load of each link. The load balancing algorithm considers factors such as the real-time performance, transmission capacity, and resource utilization rate of the link to ensure that data packets can be transmitted efficiently and stably. The system evaluates the resource utilization rate of each link in real time, including bandwidth utilization rate, power utilization rate, etc. The evaluation results are used to guide the adjustment of the transmission rate and power allocation, and the optimization of the load balancing algorithm. Through continuous evaluation and optimization, the system can ensure that the resources of each link are fully utilized, while avoiding resource waste and performance bottlenecks.

[0142] The effects of the above technical solutions are as follows: By monitoring the key indicators (such as signal quality, bit error rate, packet loss rate, etc.) of each link in real time, the system can promptly detect problems in the link and take corresponding optimization measures to ensure the stability and reliability of data transmission. Applying signal processing algorithms such as adaptive filtering and blind source separation can further improve the accuracy and real-time performance of monitoring. The adaptive filtering algorithm can dynamically adjust the parameters of the filter to better suppress noise and interference, thereby improving the accuracy of signal quality monitoring. The blind source separation algorithm can separate each source signal from the mixed signal, which helps to accurately evaluate the bit error rate and packet loss rate of the link. According to the monitoring results, the system can dynamically adjust the transmission rate and power allocation of each link. When the link performance is good, the system can increase the transmission rate and power allocation to make full use of the link resources. When the link performance deteriorates, the system automatically reduces the load of that link to reduce the bit error rate and packet loss rate and ensure the stability of data transmission. When a serious performance degradation occurs in a certain link, the system can automatically switch to the backup link to ensure the continuity of data transmission. At the same time, through the load balancing algorithm, the system can evenly distribute data packets to each link, avoid overloading a single link, and improve the transmission efficiency and stability of the overall network. The system conducts real-time evaluation of the resource utilization rate of each link, including bandwidth utilization rate, power utilization rate, etc., can discover resource waste and performance bottlenecks, and take corresponding optimization measures. By dynamically adjusting the transmission rate and power allocation, and the application of the load balancing algorithm, the system can ensure that the resources of each link are fully utilized. Since the system can monitor the key indicators of the link in real time and take measures quickly when problems are found, it can quickly respond to link failures. Through continuous monitoring, evaluation, and optimization, the system can continuously improve the performance of the link. This helps to ensure that the network always remains in the best state and improves the stability of the network. The above resource utilization rate evaluation formula enables the system to make timely adjustments when the link load changes by calculating the resource utilization rate in real time. If it is found that the utilization rate of a certain link is too high, the system can actively transfer part of the traffic to other links to avoid performance degradation caused by overloading a single link. Combined with the load balancing algorithm, this formula enables data packets to be more evenly distributed to each link, improves the transmission efficiency of the overall network, reduces the congestion of a single link, and enhances the user experience. By monitoring and calculating the resource utilization rate of each link, the network management system can real-time evaluate the performance of the link. This provides important data support for subsequent network optimization and fault detection and can quickly identify links with performance degradation. Through the analysis of the link utilization rate, the management system can predict the future change of the load capacity of the link. This prediction ability can help network managers take measures in advance to prevent link overload and ensure the smooth operation of the network. Based on the calculation results of this formula, the network management system can provide a quantitative basis for decisions such as traffic scheduling and link selection. The data-driven decision-making method makes network management more intelligent and more adaptable.The introduction and application of the above formula not only improve the accuracy and real-time performance of the evaluation of network link resource utilization, but also provide an important theoretical basis and practical tool for network management and optimization, promoting the improvement of network performance and user experience.

[0143] In one embodiment of the present invention, S4 includes:

[0144] S41. The receiving end reorganizes the data packets from multiple physical links based on the sequence and integrity of the data packets according to information such as the link identifier and timestamp.

[0145] S42. The reorganized data is verified through a cyclic redundancy check algorithm to detect whether there is data loss or error.

[0146] S43. If it is detected that there are lost or incorrect data packets, they are recovered through a retransmission mechanism.

[0147] S44. And through an intelligent caching mechanism, the frequently requested data packets are cached.

[0148] The working principle of the above technical solution is as follows: At the receiving end, the system first identifies and processes the data packets from multiple physical links according to information such as the link identifier and timestamp; based on the sequence and integrity of these data packets, the system reorganizes them; the reorganized data will be verified through a cyclic redundancy check (CRC) algorithm; by comparing the data check codes at the sending end and the receiving end, the system can accurately identify whether the data has changed or been lost during transmission; once data loss or error is detected, the system will trigger the retransmission mechanism; the retransmission mechanism can be implemented based on protocols such as automatic repeat request (ARQ). For example, according to the priority of the data packets and the real-time status of the link, the system dynamically adjusts the retransmission strategy. Prioritizing the retransmission of high-priority data packets can ensure the timely transmission of critical data; and retransmitting on links with better signal quality can improve the success rate and efficiency of retransmission; to further improve the efficiency and reliability of data transmission, the technical solution also introduces an intelligent caching mechanism; the system caches the frequently requested data packets. When the same data packet is requested again, the system can directly read the data from the cache without transmitting it from the physical link again.

[0149] The effects of the above technical solutions are as follows: Based on information such as link identifiers and timestamps, the receiving end can accurately reconstruct the data packets from multiple physical links, ensuring the restoration of the order and integrity of the data packets during transmission and providing a reliable basis for subsequent data processing. Through precise data packet reconstruction, the system can avoid duplication and loss of data packets during transmission, thereby improving the efficiency of data transmission, reducing network congestion and latency, and enhancing the overall network performance. The cyclic redundancy check algorithm is used to verify the reconstructed data, which can reliably detect data loss or errors. Through data verification, the system can promptly detect and correct errors in data transmission, thereby reducing the error rate of data transmission, enhancing the accuracy and reliability of data processing, and ensuring the correctness and consistency of data. The retransmission mechanism is based on protocols such as ARQ or HARQ, but with innovative improvements. For example, according to the priority of data packets and the real-time status of the links, the retransmission strategy is dynamically adjusted, and the optimal retransmission method can be selected according to the actual situation, improving the efficiency of retransmission. By preferentially retransmitting high-priority data packets, the system can ensure the timely transmission of critical data, enhancing the response speed and stability of the system, especially prominent when dealing with emergency tasks or critical data. Retransmission on links with better signal quality can improve the success rate and efficiency of retransmission, optimize the utilization of link resources, and reduce unnecessary resource waste. Through the intelligent caching mechanism, the system can cache frequently requested data packets, reduce the repeated transmission of data, and improve the speed and efficiency of data transmission. The intelligent caching mechanism can reduce the latency and bandwidth occupancy of data transmission, thereby enhancing the user experience. Especially when dealing with a large number of data requests, the caching mechanism can significantly accelerate the data response speed.

[0150] An embodiment of the present invention, as Figure 2 shown, is a MBB multi-channel local aggregation and superposition transmission system, and the system includes:

[0151] Link aggregation module: Establish multiple physical links in the wireless communication system. The system selects the optimal multiple physical links for aggregation according to the current network environment, device status, and user requirements;

[0152] Data transmission module: Divide the data to be transmitted into multiple data packets. Each data packet is encapsulated according to the characteristics of the selected physical link, and the encapsulated data packets are transmitted in parallel through the selected physical links;

[0153] Status monitoring module: During the transmission process, the system monitors the transmission status of each link in real time, including signal quality, bit error rate, packet loss rate, etc., and dynamically adjusts the transmission rate and power allocation of each link according to the current network load and device status;

[0154] Data verification module: After the receiving end receives data packets from multiple physical links, it reorganizes the data packets according to the link identifier and timestamp, and verifies the reorganized data to detect whether there is data loss or error. For the lost or incorrect data, the system attempts to recover it through the retransmission mechanism.

[0155] The working principle of the above technical solution is as follows: In a wireless communication system, multiple physical links are first established as data transmission channels. The system selects the optimal multiple physical links for aggregation according to factors such as the current network environment (such as signal strength, interference situation), device status (such as transmit power, receive sensitivity), and user requirements (such as data transmission rate, reliability requirements), for example, selecting the ones with the strongest signal, the highest receive sensitivity, and the top transmission rates respectively. The data to be transmitted is divided into multiple data packets, and each data packet is encapsulated according to the characteristics of the selected physical link, including adding information such as link identifier and timestamp. The encapsulated data packets are transmitted in parallel through the selected physical links, thus making full use of the bandwidth resources of multiple links. During the transmission process, the system monitors the transmission status of each link in real time, including key indicators such as signal quality, bit error rate, and packet loss rate. According to the current network load and device status, the system dynamically adjusts the transmission rate and power allocation of each link. After the receiving end receives data packets from multiple physical links, it reorganizes the data packets according to the link identifier and timestamp. The reorganized data is verified to detect whether there is data loss or error. For the lost or incorrect data, the system attempts to recover it through the retransmission mechanism to ensure the reliability of data transmission.

[0156] The effects of the above technical solution are as follows: By establishing and aggregating multiple physical links, parallel data transmission is achieved. Compared with single-link transmission, the data transmission rate and throughput can be significantly improved. The system can select the optimal multiple physical links for aggregation according to the current network environment, device status, and user requirements, ensuring that data transmission always occurs on the best link, thereby improving the transmission efficiency. During the transmission process, the system monitors the transmission status of each link in real time, including key indicators such as signal quality, bit error rate, and packet loss rate. Based on these indicators, the system can dynamically adjust the transmission rate and power allocation of each link to ensure the stability and reliability of data transmission. After the receiving end receives the data packets from multiple physical links, it reorganizes the data packets according to the link identifier and timestamp. At the same time, the reorganized data is verified to detect whether there is data loss or error. For lost or incorrect data, the system attempts to recover it through a retransmission mechanism, further enhancing the reliability of data transmission. The system can dynamically adjust the transmission rate of each link according to the current network load and device status. The flexible adjustment mechanism can ensure that when the network load is high, the transmission rate is reduced to avoid network congestion; when the network load is low, the transmission rate is increased to fully utilize network resources. In addition to the transmission rate, the system can also dynamically adjust the power allocation of each link, ensuring that the power of each link is reasonably allocated during the transmission process, thereby improving the energy utilization efficiency and reducing energy consumption, and dynamically adjusting according to user requirements to meet the different needs of different users for data transmission rate, reliability, and stability. The above technical solution can be widely applied to various wireless communication scenarios, such as video conferencing, online gaming, high-definition video transmission, etc. By improving the data transmission efficiency and reliability, the above technical solution can significantly enhance the user's service quality experience. For example, in video conferencing, users can enjoy a smoother and clearer video call experience; in online gaming, users can enjoy lower latency and higher game stability.

[0157] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A method for MBB multi-path local aggregation and superposition transmission, characterized in that, The method includes: S1. Establish multiple physical links in a wireless communication system. The system selects the optimal multiple physical links for aggregation according to the current network environment, device status, and user requirements; S2. Split the data to be transmitted into multiple data packets. Each data packet is encapsulated according to the characteristics of the selected physical link, and the encapsulated data packets are transmitted in parallel through the selected physical links; S3. During the transmission process, the system monitors the transmission status of each link in real time and dynamically adjusts the transmission rate and power allocation of each link according to the current network load and device status; S4. After the receiving end receives the data packets from multiple physical links, it reorganizes the data packets according to the link identifier and timestamp, and verifies the reorganized data; The S2 includes: S21. Split the data to be transmitted into multiple data packets according to the data size, assign a priority to each data packet, and give priority to transmitting the data packets with a higher priority; S22. Add encapsulation information to each data packet according to the characteristics of the selected physical link. The encapsulation information includes a link identifier, a check code, and a timestamp; S23. Transmit the encapsulated data packets in parallel through a multi-channel transmission protocol, and dynamically adjust the transmission order and rate of the data packets according to the real-time status of each link during the transmission process.

2. The MBB multi-path local aggregation and superposition transmission method according to claim 1, characterized in that The S1 includes: S11. Scan the available wireless spectrum resources to identify potential physical links; evaluate the signal quality of each potential link; and calculate the correlation between the links; S12. Monitor the status of the device in real time. The device status includes battery power, processing capacity, and heat dissipation status; S13. Analyze the current user requirements of the user. The user requirements include data transmission rate, latency requirements, and data priority; S14. Based on the above results, select the optimal multiple physical links for aggregation through a multi-objective optimization algorithm.

3. The method for multiplexed MBB local aggregation and superposition transmission according to claim 1, wherein The S22 includes: S221. Collect the detailed characteristics of all currently available physical links. The detailed features include bandwidth, latency, bit error rate, jitter rate, and stability; S222. Evaluate the transmission efficiency of each link through a multi-objective optimization algorithm based on the collected link characteristic data, and formulate a data packet allocation strategy accordingly; S223. Based on the dynamic changes of the links, perform periodic link status monitoring and evaluation, and dynamically adjust the link selection strategy based on the evaluation results; S224. Add a link identifier to each data packet to enable the data packet to be correctly routed to the target receiving end in a complex network environment; S225. Generate and append a check code to verify the integrity of the data packet at the receiving end, detect and correct possible errors during the transmission process; S226. Embed timestamp information to record the encapsulation time of the data packet, and assign different priority levels to the data packet according to the priority identifier of the data packet, according to business logic or application requirements; S227. And based on the existing redundancy coding scheme, dynamically adjust the redundancy according to the bit error rate of the link; S228. Predict the change trend of link performance within a future period of time through a link characteristic prediction model, and pre-adjust the encapsulation format and redundancy of data packets based on the prediction results.

4. The MBB multi-channel local aggregation and superposition transmission method according to claim 3, wherein The S228 includes: Extract relevant metrics of each physical link from the historical database. The relevant metrics include bandwidth utilization rate, latency change, bit error rate history, jitter rate trend, and stability index. Filter out the features that have the most influence on link performance prediction through a feature selection algorithm and perform normalization processing; conduct model training based on time series analysis, and adjust model parameters through cross-validation. Use the current and recent link state data as input and input it into the trained prediction model. The prediction model outputs the relevant predicted values of the link within a future period of time. The relevant predicted values include bandwidth availability, latency change range, expected bit error rate, and jitter level; and calculate the uncertainty interval of the prediction result. Dynamically adjust the segmentation size of data packets according to the predicted future bandwidth availability and latency. For links with a high bit error rate, enable additional error detection and correction mechanisms. Optimize the routing path of data packets based on the prediction of link performance, and dynamically adjust the redundancy coding level in data packets according to the predicted bit error rate. Calculate the optimal redundancy configuration through a redundancy optimization algorithm based on the link characteristic prediction result. Adjust the redundancy strategy through a real-time feedback system according to the actual performance of data packet transmission. Based on the prediction results, configure the encapsulation format and redundancy for the data packets to be transmitted. During the data transmission process, continuously monitor the actual performance of the link and compare it with the prediction results. If there is a deviation, immediately trigger an adaptive adjustment mechanism to dynamically adjust the data packet encapsulation and redundancy settings.

5. The MBB multi-path local aggregation and superposition transmission method according to claim 1, wherein The S23 includes: S231. Configure the parameters of the parallel transmission protocol. Through a data packet scheduling algorithm, determine the transmission order of data packets on multiple links according to the priority of data packets, link status, and service requirements. S232. And perform transmission through a multi-channel transmission protocol. During the transmission process, real-time monitor the load and signal quality of each link, and evaluate the real-time state of the link based on the real-time monitoring results; based on the evaluation results, dynamically adjust the transmission rate on each link. S233. Dynamically adjust the distribution of data packets on multiple links through a load balancing algorithm according to the real-time state and transmission requirements of the links. S234. And, during the transmission process, use check codes and timestamps to detect the integrity and transmission latency of data packets. For the detected error or lost data packets, recover them through a preset error recovery strategy.

6. The method for multiplexed MBB local aggregation and superposition transmission according to claim 5, characterized in that, The S232 includes: Perform corresponding key parameter settings according to the network environment and service requirements, and initialize multiple parallel transmission channels based on the configured parameters. Through the link state monitors deployed on each transmission channel, collect the key performance metrics of the link in real time. The key performance metrics include bandwidth utilization rate, latency, and packet loss rate. Preprocess the collected raw data and aggregate the preprocessed data to a unified monitoring platform. Based on historical data and real-time data, construct a link status evaluation model, evaluate the current status of the link through the link status evaluation model, and predict the change trend in the next period of time; According to the business requirements and the actual situation of the network environment, set reasonable warning thresholds for various performance indicators of the link. If it is detected that a certain indicator exceeds the threshold, the warning mechanism will be triggered immediately; Design a transmission rate adjustment strategy according to the link status evaluation result; based on the adjustment strategy, perform real-time adjustment on each channel.

7. The method for multiplexed MBB multi-path local aggregation and superposition transmission according to claim 1, wherein The S3 includes: S31. Monitor the key indicators of each link in real time. The key indicators include signal quality, bit error rate, and packet loss rate; and improve the accuracy and real-time performance of the monitoring through signal processing algorithms during the monitoring process; S32. Dynamically adjust the transmission rate and power allocation of each link according to the monitoring results; when the performance of a certain link deteriorates, automatically reduce the load of this link or switch to the standby link; S33. Evenly distribute the data packets to each link through the load balancing algorithm, and perform real-time evaluation on the resource utilization rate of each link.

8. The method for multiplexed MBB multi-local aggregation and superposition transmission according to claim 1, characterized in that The S4 includes: S41. The receiving end reorganizes the data packets from multiple physical links based on the link identifier and timestamp, based on the order and integrity of the data packets; S42. Check the reorganized data through the cyclic redundancy check algorithm to detect whether there is data loss or error; S43. If it is detected that there are lost or incorrect data packets, recover them through the retransmission mechanism; S44. And through the intelligent caching mechanism, cache the frequently requested data packets.

9. A system for implementing the MBB multi-channel local aggregation and superposition transmission method as described in claim 1, characterized in that, The system includes: Link aggregation module: Establish multiple physical links in the wireless communication system. The system selects the optimal multiple physical links for aggregation according to the current network environment, device status, and user requirements; Data transmission module: Split the data to be transmitted into multiple data packets. Each data packet is encapsulated according to the characteristics of the selected physical link, and the encapsulated data packets are transmitted in parallel through the selected physical links; Status monitoring module: During the transmission process, the system monitors the transmission status of each link in real time and dynamically adjusts the transmission rate and power allocation of each link according to the current network load and device status; Data verification module: After the receiving end receives the data packets from multiple physical links, reorganize the data packets according to the link identifier and timestamp, and verify the reorganized data.

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