Intersatellite multipoint transmission and link dynamic optimization method based on Bayesian situational awareness

Abstract

The present invention relates to the field of link transmission optimization technology, specifically to an intersatellite multipoint transmission and link dynamic optimization method based on Bayesian situational awareness. The method comprises the following steps: accessing an interstellar communication network interface, collecting real-time link data, including power, frequency, and signal-to-noise ratio, while recording the data transmission rate and delay of each link, as well as the packet loss rate, to generate real-time link status information. In the present invention, by monitoring the link status in real time and using Bayesian statistics for performance prediction, potential delays and anomalies are effectively predicted. By applying the Dijkstra algorithm to optimize the transmission path, the system adapts to the ever-changing environment of interstellar communication and ensures data transmission efficiency under extreme conditions. Dynamic routing adjustment strategies are updated according to real-time network load to ensure maximum data transmission efficiency. Automated network topology reconstruction further enhances the system's self-healing capability, reducing system downtime and potential performance degradation through rapid response.

Description

Technical Field

[0001] The present invention relates to the technical field of link transmission optimization, and in particular to an inter-satellite multipoint transmission and link dynamic optimization method based on Bayesian situational awareness. Background Art

[0002] The field of link transmission optimization technology involves improving and enhancing the efficiency and reliability of data packet transmission in data communication networks. Its core goal is to reduce latency, improve bandwidth utilization, and ensure stable and secure data transmission within the network through optimized algorithms and strategies. Link optimization strategies may include dynamic routing algorithms, load balancing techniques, error correction mechanisms, and real-time monitoring and adaptive adjustment of link status. These strategies are widely used in both wired and wireless communication networks, and are particularly critical in high-demand environments such as satellite communications and interplanetary networks.

[0003] Among them, the inter-satellite multi-point transmission and link dynamic optimization method is a technical topic specifically for link optimization in interstellar or satellite communication systems. Its main purpose is to achieve efficient and reliable data transmission in the interstellar environment. By dynamically adjusting link parameters and routing strategies to adapt to the high volatility and extreme conditions in interstellar communications, it can greatly improve the data transmission capacity of the interstellar network, optimize resource allocation, reduce signal attenuation, and enhance the performance and stability of the entire communication system. In addition, it also helps to support data communication between the earth and other celestial bodies, providing key technical support for deep space exploration missions.

[0004] The shortcomings of existing technologies are mainly reflected in their poor adaptability to dynamic environments, especially in high-demand and rapidly changing scenarios such as interstellar communications. Static routing strategies and fixed link parameters often lead to poor connection stability and low data transmission efficiency when faced with extreme conditions. In addition, existing error correction and load balancing technologies perform poorly in complex environments and fail to effectively handle link failures or network congestion, often resulting in signal attenuation and data loss. For example, in the absence of a dynamic adaptation mechanism, signal attenuation and delay problems in satellite communications can lead to increased packet loss rates, directly affecting the accuracy of data reception, and thus affecting the execution of deep space exploration missions and the quality of data analysis, resulting in an inability to effectively cope with rapidly changing external conditions and internal load fluctuations. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose an inter-satellite multi-point transmission and link dynamic optimization method based on Bayesian situational awareness.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The method for dynamic optimization of inter-satellite multipoint transmission and links based on Bayesian situational awareness includes the following steps:

[0008] S1: Accesses the interstellar communication network interface and collects real-time link data, including power, frequency, and signal-to-noise ratio. It also records the data transmission rate and delay of each link, as well as the packet loss rate, to generate real-time link status information.

[0009] S2: Based on the real-time link status information, perform Bayesian statistical analysis to evaluate link performance, calculate potential delay time in the future, compare it with baseline data, identify anomalies, and predict link performance to obtain link performance prediction results;

[0010] S3: Based on the link performance prediction results, perform path calculation, search for the lowest-cost transmission path in the network graph, adjust the path weight according to the current network load, select the optimal transmission line, and update the Bayesian model parameters to generate a Bayesian model with optimized parameters;

[0011] S4: Based on the Bayesian model after the parameter optimization, dynamic routing adjustment is performed to distribute the data to the optimal transmission line, and the routing table is updated. The transmission effect is monitored in real time to confirm that the data is transmitted along the optimal transmission line, and the dynamic adjustment result of the data flow is obtained;

[0012] S5: Based on the dynamic adjustment result of the data flow, recollect link performance data, compare the performance difference before and after the adjustment, re-evaluate the link status using the Bayesian model after the parameter optimization, detect the effectiveness of the adjustment operation, and obtain a link performance re-evaluation result;

[0013] S6: Based on the link performance reassessment results, any potential link failures and performance degradation are detected, and network topology reconstruction is automatically triggered. Problem areas are avoided by activating backup links and reconfiguring the network. The network status monitoring information is synchronously updated to generate self-healing network architecture adjustment results.

[0014] Optionally, the real-time link status information includes link power indicators, signal frequency data and signal-to-noise ratio levels; the link performance prediction results include delay time estimation records and performance anomaly indications; the parameter-optimized Bayesian model includes updated model parameters and calculated optimal transmission lines; the data flow dynamic adjustment results include updated routing configurations, the amount of data allocated to the optimal path and the monitored transmission efficiency; the link performance re-evaluation results include performance comparison records before and after adjustment, adjustment effect verification results and re-evaluated performance indicators; the self-healing network architecture adjustment results include activated backup links, reconfigured network structures and updated monitoring status records.

[0015] Optionally, access the interstellar communication network interface to collect real-time link data, including power, frequency, and signal-to-noise ratio, while recording the data transmission rate and delay of each link, as well as the packet loss rate. The specific steps for generating real-time link status information are as follows:

[0016] S101: Accesses the interstellar communication network interface, captures data streams of power, frequency, and signal-to-noise ratio in real time, analyzes signal parameters, and generates real-time link monitoring data;

[0017] S102: Based on the real-time link monitoring data, calculate the data transmission rate of each link, monitor data delay, count packet loss events, and obtain link performance evaluation data;

[0018] S103: Based on the link performance evaluation data, the status data of all links are integrated to reveal the dynamic changes of transmission rate, delay and data packet loss rate, and generate real-time link status information.

[0019] Optionally, based on the real-time link status information, Bayesian statistical analysis is performed to evaluate the link performance, calculate the potential delay time in the future, compare it with the benchmark data, identify anomalies and predict the link performance, and obtain the link performance prediction result in the following specific steps:

[0020] S201: Based on the real-time link status information, collect the transmission rate, delay and packet loss rate data of the current link, organize the data, calculate the average value and standard deviation, and generate link performance baseline data;

[0021] S202: Based on the link performance baseline data, perform trend analysis to predict future delay time, compare current data with the baseline performance data, determine the increase or decrease trend of delay, and obtain future delay prediction analysis results;

[0022] S203: Using the future delay prediction analysis results, identify link performance that does not match the prediction model, mark potential performance degradation and anomalies, and predict the future performance of the link to generate a link performance prediction result.

[0023] Optionally, based on the link performance prediction results, path calculation is performed to search for the lowest-cost transmission path in the network graph, path weights are adjusted according to the current network load, the optimal transmission line is selected, and the Bayesian model parameters are updated. The specific steps for generating the Bayesian model after parameter optimization are as follows:

[0024] S301: Based on the link performance prediction results, analyze the prediction data of all links in the network diagram, monitor the link delay and packet loss rate in real time, dynamically calculate the adjusted path weight, and generate path weight data;

[0025] S302: Using the path weight data, identifying the lowest-cost transmission path in the network graph, and combining it with real-time network load changes to determine the optimal path that matches the current network state, thereby obtaining an optimal transmission line selection result;

[0026] S303: Based on the optimal transmission line selection result, update the Bayesian model parameters, adjust the model prediction algorithm based on the latest link performance data and path selection results, and generate a Bayesian model with optimized parameters.

[0027] Optionally, the adjusted path weight is calculated according to the formula:

[0028]

[0029] Calculate, where W new Represents the new weight, W old represents the original path weight, which affects the current transmission efficiency of the network; d represents the average delay time of the link, which is a direct indicator of the network transmission efficiency; p represents the packet loss rate of the link, which directly reflects the reliability of the network; W avg Represents the average weight of all links and is used to compare the deviation of each link weight from the network average.

[0030] Optionally, based on the Bayesian model after parameter optimization, dynamic routing adjustment is performed to distribute data to the optimal transmission line, and the routing table is updated to monitor the transmission effect in real time to confirm that the data is transmitted along the optimal transmission line. The specific steps for obtaining the dynamic adjustment result of the data flow are as follows:

[0031] S401: Based on the Bayesian model after the parameter optimization, data is distributed according to the optimal transmission line, the data flow is adjusted to match the new line configuration, the balance of the data flow is simultaneously checked, and a data distribution strategy is generated;

[0032] S402: Adopting the data distribution strategy, implementing data flow redirection, updating the routing table to match the optimal transmission line, ensuring that all data packets are transmitted along the optimal transmission line, and obtaining updated routing table information;

[0033] S403: Continuously monitor the data transmission status on the optimal transmission line through the updated routing table information, verify the efficiency and correctness of the data along the predetermined path, and generate a data flow dynamic adjustment result.

[0034] Optionally, based on the dynamic adjustment result of the data flow, link performance data is recollected, the performance difference before and after the adjustment is compared, the link status is re-evaluated using the Bayesian model after the parameter optimization, and the effectiveness of the adjustment operation is detected. The specific steps for obtaining the link performance re-evaluation result are:

[0035] S501: Based on the dynamic adjustment result of the data flow, re-collect the current transmission rate, delay time and packet loss rate data of all links, record and organize the performance indicators, and generate a link performance data snapshot;

[0036] S502: Analyze the link performance difference before and after the adjustment measures using the link performance data snapshot, calculate the performance deviation value by comparing the new and old data sets, determine the actual areas of performance improvement and degradation, and obtain performance comparison analysis results;

[0037] S503: Based on the performance comparison and analysis results, use the parameter-optimized Bayesian model to perform an in-depth evaluation of the link status, check the performance of each link after adjustment, confirm the effectiveness of the adjustment operation, and obtain the link performance re-evaluation result.

[0038] Optionally, the performance deviation value is calculated according to the formula:

[0039]

[0040] Calculate, where ΔP represents the performance deviation value, y i represents the actual performance value of the i-th data point, represents the prediction performance value of the i-th data point, N represents the total number of data points, d i Represents the link delay of the i-th data point.

[0041] Optionally, based on the link performance reassessment results, detecting any potential link failures and performance degradation, automatically triggering network topology reconstruction, activating backup links and reconfiguring the network to avoid problem areas, and synchronously updating network status monitoring information to generate a self-healing network architecture adjustment result are the following specific steps:

[0042] S601: Based on the link performance reassessment results, analyze each link in the network one by one, identify links showing signs of performance degradation and failure, record the actual performance indicators and failure types of the relevant links, and generate a list of failed links;

[0043] S602: Using the faulty link list, activate pre-set backup links in the network, adjust the network configuration, avoid the identified problem areas, and obtain a network topology reconstruction plan;

[0044] S603: According to the network topology reconstruction plan, synchronously update the network status monitoring information, check all monitoring nodes, correctly reflect the new network structure and traffic path, and generate a self-healing network architecture adjustment result.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are:

[0046] In the present invention, by monitoring the link status in real time and using Bayesian statistics for performance prediction, potential delays and anomalies can be effectively predicted. By applying the Dijkstra algorithm to optimize the transmission path, the ever-changing environment of interstellar communication can be adapted to ensure the transmission efficiency of data under extreme conditions. The dynamic routing adjustment strategy is updated according to the real-time network load to ensure the maximum efficiency of data transmission. The automated network topology reconstruction further enhances the self-healing ability of the system and reduces the system downtime and potential performance degradation through rapid response. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 Schematic diagram of the steps of the present invention;

[0048] Figure 2 is a flow chart of the steps of S1 of the present invention;

[0049] Figure 3 This is a flow chart of the steps of S2 of the present invention;

[0050] Figure 4 This is a flow chart of the steps of S3 of the present invention;

[0051] Figure 5 This is a flow chart of the steps of S4 of the present invention;

[0052] Figure 6 This is a flow chart of the steps of S5 of the present invention;

[0053] Figure 7 This is a flow chart of the steps of S6 of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0056] See also Figure 1As shown, an embodiment of the present invention provides a method for inter-satellite multipoint transmission and link dynamic optimization based on Bayesian situational awareness, comprising the following steps:

[0057] S1: Accesses the interstellar communication network interface and collects real-time link data, including power, frequency, and signal-to-noise ratio. It also records the data transmission rate and delay of each link, as well as the packet loss rate, to generate real-time link status information.

[0058] S2: Based on real-time link status information, Bayesian statistical analysis is performed to evaluate link performance, calculate potential delays over a period of time, compare this with baseline data, identify anomalies, and predict link performance to obtain link performance prediction results.

[0059] S3: Based on the link performance prediction results, perform path calculation, search for the lowest-cost transmission path in the network graph, adjust the path weight according to the current network load, select the optimal transmission line, and update the Bayesian model parameters to generate a Bayesian model with optimized parameters.

[0060] S4: Based on the Bayesian model after parameter optimization, dynamic routing adjustments are performed to allocate data to the optimal transmission line, and the routing table is updated. The transmission effect is monitored in real time to confirm that the data is transmitted along the optimal transmission line, and the dynamic adjustment results of the data flow are obtained;

[0061] S5: Based on the dynamic data flow adjustment results, link performance data is collected again, and the performance difference before and after the adjustment is compared. The link status is re-evaluated using the Bayesian model after parameter optimization to test the effectiveness of the adjustment operation and obtain the link performance re-evaluation results.

[0062] S6: Based on the link performance reassessment results, detect any potential link failures and performance degradation, automatically trigger network topology reconstruction, activate backup links and reconfigure the network to avoid problem areas, synchronously update the network status monitoring information, and generate self-healing network architecture adjustment results.

[0063] Real-time link status information includes link power indicators, signal frequency data and signal-to-noise ratio levels; link performance prediction results include delay time estimation records and performance anomaly indications; the Bayesian model after parameter optimization includes updated model parameters and calculated optimal transmission lines; data flow dynamic adjustment results include updated routing configuration, the amount of data allocated to the optimal path and the monitored transmission efficiency; link performance reassessment results include performance comparison records before and after adjustment, adjustment effect verification results and reassessed performance indicators; self-healing network architecture adjustment results include activated backup links, reconfigured network structures and updated monitoring status records.

[0064] See also Figure 2As shown, the specific steps of S1 are:

[0065] S101: Accesses the interstellar communication network interface, captures data streams of power, frequency, and signal-to-noise ratio in real time, analyzes signal parameters, and generates real-time link monitoring data;

[0066] In interstellar communication network interfaces, capturing data streams of power, frequency, and signal-to-noise ratio in real time involves several technical details. First, capturing power and frequency relies on precisely calibrated sensors installed on communication equipment that continuously monitor and record the intensity and periodicity of incoming electrical signals. The sensor output data is fed into a data processing unit, which includes a preprocessing stage, where the signal undergoes preliminary amplification and filtering to remove environmental noise and non-signal interference. Next, the enhanced signal is fed into a signal decoder, which analyzes the signal format and structure according to the preset communication protocol, extracting specific values ​​for power, frequency, and signal-to-noise ratio. This generates real-time link monitoring data, providing a basis for comprehensive assessment and management of network status.

[0067] S102: Based on the real-time link monitoring data, calculate the data transmission rate of each link, monitor the data delay, count the packet loss events, and obtain link performance evaluation data;

[0068] In the above content, based on real-time link monitoring data, according to the formula:

[0069]

[0070] Calculate the data transmission rate for each link, where R represents the data transmission rate, V represents the data volume, M represents the packet loss rate, and T represents the transmission time. Consider that the data transmission rate R is calculated using link monitoring data obtained through real-time monitoring, while the data volume V can be collected in real time by the monitoring system. The packet loss rate M is also calculated from real-time data. Assuming that a link transmits 100MB of data within a certain period of time, the packet loss rate is 5%, or 0.05, and the transmission time is 10 seconds, the data transmission rate calculation process is as follows:

[0071]

[0072] The calculation results show the transmission efficiency and quality of the link under current conditions.

[0073] S103: Based on the link performance evaluation data, the status data of all links are integrated to reveal the dynamic changes of transmission rate, delay and packet loss rate, and generate real-time link status information;

[0074] The process of integrating all link statuses using link performance assessment data involves several key steps. The first is data integration, where performance data from each link, such as transmission rate, latency, and packet loss rate, is collected and sent to a central processing unit. This data is acquired using advanced data collection techniques, including data synchronization and timestamp alignment to ensure data consistency. Statistical analysis is then performed on the collected data to reveal dynamic trends. Data visualization tools, such as dynamic charts and real-time dashboards, are then used to visualize these changes for network administrators, enabling them to intuitively understand the health and performance fluctuations of each link in the network. This comprehensive real-time link status information provides a scientific basis for adjusting and optimizing network configurations.

[0075] See also Figure 3 As shown, the specific steps of SS2 are:

[0076] S201: Based on the real-time link status information, collect the transmission rate, delay, and packet loss rate data of the current link, organize the data, calculate the average and standard deviation, and generate link performance baseline data;

[0077] During real-time link status monitoring, network monitoring first collects real-time data on the link's transmission rate, latency, and packet loss rate. This data is captured every second and recorded for subsequent processing. Next, the collected data is organized and analyzed. This involves calculating the values ​​of each data point, such as transmission rate and latency, and calculating the mean and standard deviation of these data points. A performance baseline is generated by comparing the mean latency and standard deviation over several consecutive time windows. This provides a reference for quickly identifying data that deviates from normal performance patterns in future monitoring.

[0078] S202: Based on the link performance baseline data, trend analysis is performed to predict future delay times. Current data is compared with the baseline performance data to determine the increase or decrease trend of delay, and a future delay prediction analysis result is obtained.

[0079] Based on baseline link performance data, latency trend analysis is performed. First, historical trend analysis is performed on the collected latency data. This includes smoothing the historical data using methods such as moving average and exponential smoothing to eliminate the impact of short-term fluctuations. The resulting smoothed data provides a basis for predicting future latency. This data is then compared with previously established baseline performance data, using regression analysis to identify trends in latency increases and decreases. This analysis helps predict potential link latency performance over time, enabling network managers to prepare in advance.

[0080] S203: Using the future delay prediction analysis results, identify link performance that does not match the prediction model, mark potential performance degradation and anomalies, and predict the future performance of the link to generate a link performance prediction result;

[0081] Future latency forecast analysis results are used to identify and flag link performance that doesn't match the prediction model. This process begins with an in-depth analysis of the forecast results to identify latency metrics that exceed the normal forecast range. This is achieved by setting thresholds. For example, when the actual latency exceeds 10% of the predicted latency, the system automatically flags this performance as abnormal. Next, the system analyzes the possible causes behind these abnormal data, such as network congestion or hardware failure, and their potential impact on future link performance. Finally, the analysis is consolidated into a link performance forecast report. This report not only includes the current performance status but also predicts possible future performance trends, providing decision support for network operations and maintenance.

[0082] See also Figure 4 As shown, the specific steps of S S3 are:

[0083] S301: Based on the link performance prediction results, analyze the predicted data of all links in the network diagram, monitor the link delay and packet loss rate in real time, dynamically calculate the adjusted path weight, and generate path weight data;

[0084] The adjusted path weight is according to the formula:

[0085]

[0086] Calculate, where W new Represents the new weight, W old represents the original path weight, which affects the current transmission efficiency of the network; d represents the average delay time of the link, which is a direct indicator of the network transmission efficiency; p represents the packet loss rate of the link, which directly reflects the reliability of the network; W avg Represents the average weight of all links and is used to compare the deviation of each link weight from the network average.

[0087] W old Indicates the weight of the original path, which is assumed to be obtained by averaging historical network data, such as the average weight of the link over a period of time.

[0088] d is the average delay of the link, which can be measured in real time by a network monitoring system, such as the average delay over the past hour.

[0089] p is the packet loss rate of the link. Similarly, this is also measured in real time by the network monitoring system, such as the packet loss rate in the past hour.

[0090] W avgis the average weight of all links, which is calculated by averaging the weights of all links in the network.

[0091] Get the following data: old =5, d = 0.3 seconds, p = 0.02 (i.e., 2% packet loss rate), W avg =4.

[0092] Substitute the values ​​into the formula to calculate W new :

[0093] calculate

[0094] First calculate:

[0095]

[0096] Then calculate:

[0097]

[0098] It turns out that:

[0099] e -0.159 ≈0.853

[0100] Compute the product:

[0101]

[0102] Calculate the denominator:

[0103] 1+|W old -W avg |=1+|5-4|=2

[0104] Final calculation:

[0105]

[0106] The results show that the newly calculated link weight is 2.1325, a significant decrease from the previous weight of 5, reflecting the actual change in link performance, especially when considering latency and packet loss. A decrease in weight indicates a decrease in performance for this link, and consideration should be given to avoiding or reducing the use of this path to optimize network performance.

[0107] S302: Using the path weight data, identify the lowest-cost transmission path in the network graph. Simultaneously, based on real-time network load changes, determine the optimal path that matches the current network state and obtain the optimal transmission line selection result.

[0108] Using the newly generated path weight data, the lowest-cost transmission path in the network graph is identified. This process is implemented through a series of complex algorithmic calculations. First, based on the weight data collected from each link, a shortest path algorithm from graph theory, such as Dijkstra's algorithm, is used to analyze and compare the total weights of different paths to determine the cost-effectiveness of each path. Next, path selection is based on real-time changes in network load. This step includes monitoring traffic distribution and changing trends in the network to ensure that the selected path can maintain optimal performance even under high load conditions. Through comprehensive analysis, the optimal path that best matches the current network status is ultimately determined, which not only improves data transmission efficiency but also reduces the risk of network congestion and delays.

[0109] S303: Based on the optimal transmission line selection result, update the Bayesian model parameters, adjust the model prediction algorithm based on the latest link performance data and path selection results, and generate a Bayesian model with optimized parameters;

[0110] Based on the results of the optimal transmission path selection, the parameters of the Bayesian model used to predict network performance are updated. This update process first involves collecting the latest link performance data and path selection results, then using this data to adjust the parameters of the Bayesian model. During this process, statistical methods are used to estimate the probability distribution of various parameters, such as the average link latency and packet loss rate. These estimates are then applied to the Bayesian model to optimize its predictive accuracy. This not only considers historical data but also incorporates new performance data to ensure that the model can more accurately predict future network performance. The optimized Bayesian model is more sensitive and accurate, enabling dynamic adjustments to network policies and improving overall network performance.

[0111] See also Figure 5 As shown, the specific steps of S S4 are:

[0112] S401: Based on the optimized Bayesian model, data is distributed according to the optimal transmission line, the data flow is adjusted to match the new line configuration, the balance of data flow is simultaneously verified, and a data distribution strategy is generated;

[0113] When using a parameter-optimized Bayesian model for data distribution, the model first evaluates the optimal transmission path predicted by the model and adjusts data flows based on the prediction to adapt to the new path configuration. This process involves capturing real-time operational data for each link, such as transmission rate and latency, and feeding this data into the updated model. The model analyzes this data and provides recommendations for data flow distribution for each link. Based on these recommendations, routing is dynamically adjusted and packet flows are distributed to ensure that all data flows are transmitted along the most efficient and lowest-latency paths at any given time. Balance checks are also performed to ensure that data flows are evenly distributed across the network, avoiding performance bottlenecks caused by overloading certain links.

[0114] S402: Adopting a data distribution strategy, implementing data flow redirection, updating the routing table to match the optimal transmission path, ensuring that all data packets are transmitted along the optimal transmission path, and obtaining updated routing table information;

[0115] During data flow redirection, routing tables are updated based on the newly generated data distribution policy to match the optimal transmission path. This includes detailed routing configuration, programming routers to ensure that data packets are transmitted along the new optimized paths. This allows administrators to visualize the performance metrics of each link and select the most appropriate route based on the data distribution policy. Whenever the routing table is updated, the changes are automatically pushed to every router in the network, ensuring that routing information on all devices is up to date and that data packets can flow along the predetermined optimal path. Furthermore, a series of network tests are performed to ensure that the new routing configuration does not introduce any connection errors or data transmission delays.

[0116] S403: Continuously monitor the data transmission status on the optimal transmission line using the updated routing table information, verify the efficiency and correctness of the data along the predetermined path, and generate a dynamic data flow adjustment result;

[0117] The updated routing table continuously monitors the transmission status of data on the optimal transmission line. The monitoring work is completed through sensors deployed at key nodes in the network. The sensors can record detailed indicators of data transmission in real time, such as traffic, rate, delay and packet loss. At the same time, the real-time data is summarized and compared with the predetermined optimal path performance to verify whether the data flow is flowing correctly on the designated path as planned. Any deviation from the predetermined path or performance indicator will be immediately identified and reported to the network operation center. The technical team will analyze it, identify the root cause of the problem, and take appropriate measures to make adjustments. Continuous monitoring and real-time feedback mechanisms ensure the efficient operation of the network and the optimal performance of data transmission, while also providing valuable data support for possible future network optimization.

[0118] See also Figure 6 As shown, the specific steps of S S5 are:

[0119] S501: Based on the dynamic adjustment results of the data flow, re-collect the current transmission rate, delay time and packet loss rate data of all links, record and organize the performance indicators, and generate a link performance data snapshot;

[0120] Based on the dynamic adjustments to the data flow, the current transmission rate, latency, and packet loss rate of all links are recollected, capturing link performance data in real time. The collected data first undergoes preliminary cleaning and verification to remove any inaccuracies caused by sensor errors or data transmission anomalies. The data is then recorded in a performance database, providing detailed records of every performance metric for each link, including rate, latency, and packet loss at each point in time. This compilation process generates a time-stamped performance data snapshot, capturing a complete view of link performance at a specific moment and providing foundational data for subsequent performance analysis.

[0121] S502: Analyze the link performance difference before and after the adjustment measures using the link performance data snapshot. By comparing the new and old data sets, calculate the performance deviation value, determine the actual areas of performance improvement and degradation, and obtain the performance comparison analysis results.

[0122] Performance deviation value, according to the formula:

[0123]

[0124] Calculate, where ΔP represents the performance deviation value, y i represents the actual performance value of the i-th data point, represents the prediction performance value of the i-th data point, N represents the total number of data points, d i Represents the link delay of the i-th data point.

[0125] y i : The actual performance value of the i-th data point, obtained through the real-time monitoring system of link performance, such as the transmission speed or bandwidth utilization of a link.

[0126] The predicted performance value of the i-th data point is predicted based on historical performance data.

[0127] d i : The link delay of the i-th data point is obtained from the real-time monitoring data in the network management system.

[0128] N: The total number of data points, representing the total number of link data points considered during the analysis.

[0129] Assume that there are three data points with performance and latency data as follows:

[0130] y = [100, 150, 200] Mbps (actual performance value)

[0131] (Predicted performance value)

[0132] d = [20, 30, 15] ms (delay)

[0133] Calculate the absolute performance difference for each data point:

[0134]

[0135] Summing gives the sum of absolute differences:

[0136]

[0137] Calculate the square root sum of the delays:

[0138]

[0139] Substitute into the main formula:

[0140]

[0141] The results show that the standardized performance deviation value is 3.72, representing the average deviation in performance before and after the adjustment. This value can be used to evaluate the effectiveness of link adjustment measures. A lower value indicates that the adjusted performance is closer to the expected performance, while a higher value indicates a significant deviation from the expected performance.

[0142] S503: Based on the performance comparison and analysis results, the link status is deeply evaluated using the parameter-optimized Bayesian model. The performance of each link after adjustment is checked to confirm the effectiveness of the adjustment operation and obtain the link performance re-evaluation result.

[0143] Based on the performance comparison analysis, the parameter-optimized Bayesian model is used to conduct a more in-depth assessment of the status of each link. In this step, the model predicts and analyzes the performance of each link based on the latest performance data. Model evaluation involves comparing the actual observed performance data with the expected values ​​predicted by the model to check whether each link meets the expected performance standards after adjustment. In addition, the evaluation process includes further optimization of the model parameters to ensure that the model accurately reflects the current status and potential problems of the link. In this way, the network management team can confirm the effectiveness of each adjustment operation and ensure the continuous optimization of network performance. At the same time, the generated link performance reassessment results provide guidance for future network improvement measures.

[0144] See also Figure 7 As shown, the specific steps of SS6 are:

[0145] S601: Based on the link performance reassessment results, analyze each link in the network one by one, identify links showing signs of performance degradation and failure, record the actual performance indicators and failure types of the relevant links, and generate a list of failed links;

[0146] Based on the link performance reassessment results, each link in the network is analyzed individually to identify those showing signs of performance degradation and failure. This process is carried out through detailed performance log review and troubleshooting, deeply analyzing performance data to identify any unusual patterns or sudden changes in metrics. Once potential performance issues or failure indicators are identified, such as persistent high latency or frequent packet loss, detailed information about the link is recorded. This record includes the link's actual performance metrics and the possible fault type. This list is compiled into a faulty link list, which lists all links requiring attention and provides a basis for further network maintenance and troubleshooting.

[0147] S602: Using the faulty link list, activate pre-set backup links in the network, adjust the network configuration, avoid the identified problem areas, and obtain a network topology reconstruction plan;

[0148] Using the generated list of faulty links, the network management team activated pre-configured backup links within the network. This process involved precisely adjusting the network configuration to circumvent the identified problem areas. Through network management, data traffic was rerouted to the backup links to ensure that data transmission was not affected. Furthermore, network configuration adjustments included optimizing routing protocols and updating switching equipment configuration settings to ensure that the backup links could effectively assume the data transmission responsibilities of the primary links. Throughout the entire process, network performance changes were closely monitored to ensure that the adjustments did not introduce new problems, thereby completing a comprehensive network topology reconstruction plan.

[0149] S603: Synchronously update the network status monitoring information according to the network topology reconstruction plan, verify all monitoring nodes, correctly reflect the new network structure and traffic path, and generate the self-healing network architecture adjustment results;

[0150] Based on the network topology reconstruction plan, network status monitoring information is updated synchronously. This includes verifying all monitoring nodes to ensure they accurately reflect the new network structure and traffic paths. Through network monitoring, node monitoring configurations are updated, including monitoring frequency, performance metrics, and alarm thresholds. Simultaneously, the real-time visualization of network status is updated to reflect the new traffic paths and network topology. This ensures that the network monitoring system can provide accurate feedback, enabling timely identification and response to any issues arising from the structural adjustments. The resulting self-healing network architecture adjustments help the network operations team maintain an efficient and stable network environment.

[0151] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for dynamic optimization of inter-satellite multipoint transmission and links based on Bayesian situational awareness, characterized in that: The following steps are involved: Access the interstellar communication network interface to collect real-time link data, including power, frequency, and signal-to-noise ratio. It also records the data transmission rate and delay of each link, as well as the packet loss rate, to generate real-time link status information. Based on the real-time link status information, Bayesian statistical analysis is performed to evaluate the link performance, calculate the potential delay time in the future, compare it with the benchmark data, identify anomalies and predict the link performance to obtain the link performance prediction result; Based on the link performance prediction results, path calculation is performed to search for the lowest-cost transmission path in the network graph, path weights are adjusted according to the current network load, the optimal transmission line is selected, and the Bayesian model parameters are updated to generate a Bayesian model with optimized parameters; Based on the Bayesian model after parameter optimization, dynamic routing adjustment is performed to distribute data to the optimal transmission line, and the routing table is updated to monitor the transmission effect in real time to confirm that the data is transmitted along the optimal transmission line, thereby obtaining the dynamic adjustment result of the data flow; Based on the dynamic adjustment results of the data flow, link performance data is collected again, the performance difference before and after the adjustment is compared, the link status is re-evaluated using the Bayesian model after the parameter optimization, the effectiveness of the adjustment operation is detected, and the link performance re-evaluation result is obtained; Based on the link performance reassessment results, potential link failures and performance degradation are detected, and network topology reconstruction is automatically triggered. By activating backup links and reconfiguring the network to avoid problem areas, network status monitoring information is synchronously updated to generate self-healing network architecture adjustment results; Based on the link performance prediction results, path calculation is performed to search for the lowest-cost transmission path in the network graph. Path weights are adjusted according to the current network load to select the optimal transmission line. The Bayesian model parameters are updated. The specific steps for generating the parameter-optimized Bayesian model are as follows: Based on the link performance prediction results, analyze the predicted data of all links in the network diagram, monitor the link delay and packet loss rate in real time, dynamically calculate the adjusted path weight, and generate path weight data; Using the path weight data, identifying the lowest-cost transmission path in the network graph, and combining it with real-time network load changes to determine the optimal path that matches the current network state, thereby obtaining the optimal transmission line selection result; According to the optimal transmission line selection result, the Bayesian model parameters are updated, and according to the latest link performance data and path selection results, the model prediction algorithm is adjusted to generate a Bayesian model with optimized parameters.

2. The inter-satellite multipoint transmission and link dynamic optimization method based on Bayesian situational awareness according to claim 1, characterized in that: The real-time link status information includes link power indicators, signal frequency data and signal-to-noise ratio levels; the link performance prediction results include delay time estimation records and performance anomaly indications; the parameter-optimized Bayesian model includes updated model parameters and calculated optimal transmission lines; the data flow dynamic adjustment results include updated routing configurations, the amount of data allocated to the optimal path and the monitored transmission efficiency; the link performance reassessment results include performance comparison records before and after adjustment, adjustment effect verification results and reassessed performance indicators; the self-healing network architecture adjustment results include activated backup links, reconfigured network structures and updated monitoring status records.

3. The inter-satellite multipoint transmission and link dynamic optimization method based on Bayesian situational awareness according to claim 1, characterized in that: Access the interstellar communication network interface and collect real-time link data, including power, frequency, and signal-to-noise ratio. Simultaneously, record the data transmission rate and delay of each link, as well as the packet loss rate. The specific steps to generate real-time link status information are as follows: Access the interstellar communication network interface to capture data streams of power, frequency, and signal-to-noise ratio in real time, analyze signal parameters, and generate real-time link monitoring data; Based on the real-time link monitoring data, the data transmission rate of each link is calculated, the data delay is monitored, and the packet loss events are counted to obtain link performance evaluation data; Based on the link performance evaluation data, the status data of all links are integrated to obtain the dynamic changes of transmission rate, delay and data packet loss rate, and generate real-time link status information.

4. The inter-satellite multipoint transmission and link dynamic optimization method based on Bayesian situational awareness according to claim 1, characterized in that: Based on the real-time link status information, Bayesian statistical analysis is performed to evaluate the link performance, calculate the potential delay time in the future, compare it with the benchmark data, identify anomalies and predict the link performance. The specific steps for obtaining the link performance prediction result are as follows: Based on the real-time link status information, collect the transmission rate, delay and packet loss rate data of the current link, organize the data, calculate the average and standard deviation, and generate link performance baseline data; Based on the link performance baseline data, trend analysis is performed to predict future delay times, and current data is compared with the baseline performance data to determine the increase or decrease trend of delay, thereby obtaining future delay prediction analysis results; The future delay prediction analysis results are used to identify link performance that does not match the prediction model, mark potential performance degradation and anomalies, and predict the future performance of the link to generate a link performance prediction result.

5. The inter-satellite multipoint transmission and link dynamic optimization method based on Bayesian situational awareness according to claim 1, characterized in that: Based on the Bayesian model after parameter optimization, dynamic routing adjustment is performed to distribute data to the optimal transmission line, and the routing table is updated. The transmission effect is monitored in real time to confirm that the data is transmitted along the optimal transmission line. The specific steps for obtaining the dynamic adjustment result of the data flow are as follows: Based on the parameter-optimized Bayesian model, data is distributed according to the optimal transmission line, the data flow is adjusted to match the new line configuration, the balance of data flow is simultaneously verified, and a data distribution strategy is generated; Adopting the data distribution strategy, implementing data flow redirection, updating the routing table to match the optimal transmission line, ensuring that all data packets are transmitted according to the optimal transmission line, and obtaining updated routing table information; The updated routing table information is used to continuously monitor the transmission status of data on the optimal transmission line, verify the efficiency and correctness of the data along the predetermined path, and generate a dynamic adjustment result of the data flow.

6. The inter-satellite multipoint transmission and link dynamic optimization method based on Bayesian situational awareness according to claim 1, characterized in that: Based on the dynamic adjustment result of the data flow, link performance data is collected again, the performance difference before and after the adjustment is compared, the link status is re-evaluated using the Bayesian model after the parameter optimization, and the effectiveness of the adjustment operation is tested. The specific steps for obtaining the link performance re-evaluation result are as follows: Based on the dynamic adjustment results of the data flow, re-collect the current transmission rate, delay time and packet loss rate data of all links, record and organize the performance indicators, and generate a link performance data snapshot; Utilizing the link performance data snapshots, analyzing the link performance differences before and after the adjustment measures, calculating the performance deviation value by comparing the new and old data sets, determining the actual areas of performance improvement and degradation, and obtaining performance comparative analysis results; Based on the performance comparison and analysis results, the link status is deeply evaluated using the parameter-optimized Bayesian model, the performance of each link after adjustment is checked, the effectiveness of the adjustment operation is confirmed, and the link performance re-evaluation results are obtained.

7. The inter-satellite multipoint transmission and link dynamic optimization method based on Bayesian situational awareness according to claim 1, characterized in that: Based on the link performance reassessment results, any potential link failures and performance degradation are detected, network topology reconstruction is automatically triggered, backup links are activated, the network is reconfigured to avoid problem areas, and network status monitoring information is updated simultaneously. The specific steps for generating the self-healing network architecture adjustment results are as follows: Based on the link performance reassessment results, analyze each link in the network one by one, identify links showing signs of performance degradation and failure, record the actual performance indicators and failure types of the relevant links, and generate a list of failed links; Using the fault link list, pre-set backup links in the network are activated, network configuration is adjusted to avoid identified problem areas, and a network topology reconstruction plan is obtained; According to the network topology reconstruction plan, the network status monitoring information is synchronously updated, all monitoring nodes are checked, the new network structure and traffic path are correctly reflected, and the self-healing network architecture adjustment result is generated.

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