An application data interaction method based on TCP-IP protocol

Abstract

The application relates to the technical field of data exchange, in particular to an application program data interaction method based on a TCP-IP protocol, which comprises the following steps: collecting signal quality indexes of application program data in a physical layer, capturing signal quality data of each node through real-time network monitoring, carrying out time sequence analysis, extracting signal quality trends and abnormal points, and generating initial signal analysis results. In the application, potential problems in a network path can be effectively predicted through real-time analysis and trend prediction, network path optimization is realized, the data are analyzed through a reinforcement learning model, the network path is predicted and optimized in real time, the adaptive adjustment capability of the network is enhanced, the efficiency of data transmission is greatly improved, through simulation test and real-time monitoring, the delay of data transmission is reduced and the bandwidth utilization is maximized, and then the overall performance of the network is greatly optimized through dynamic adjustment of the TCP window size according to real-time network congestion.

Description

Technical Field

[0001] This invention relates to the field of data exchange technology, and in particular to an application data interaction method based on the TCP-IP protocol. Background Technology

[0002] The field of data exchange technology encompasses various methods and techniques for transmitting and processing data between different computer systems, networks, and platforms. A key aspect is ensuring the security, accuracy, and efficiency of data transmission from source to destination. Common techniques include standardizing various data formats, using encryption to protect data transmission, and data compression to improve transmission efficiency. Data exchange technology is widely used in e-commerce, online transaction processing, big data transmission, and cloud computing services, making it an indispensable part of the modern digital economy.

[0003] The TCP-IP protocol-based application data interaction method refers to the technology that uses Transmission Control Protocol-Internet Protocol (TCP-IP) as the foundation for data exchange and communication between applications. TCP-IP is a widely used network communication protocol globally, allowing devices on different computer networks to exchange data securely and reliably through virtual connections. TCP-IP-based data interaction is primarily used to achieve interoperability of network services, such as email transmission, file sharing, and web page data exchange, and is the cornerstone of network communication and data processing. Summary of the Invention

[0004] To address the efficiency issues related to network congestion and path selection in existing technologies, while current data exchange methods are widely used in various fields, they face numerous challenges during data transmission, particularly in terms of network congestion and path selection efficiency. Traditional methods rely on static routing tables and standard TCP window adjustment mechanisms, limiting the network's response speed and flexibility in the face of sudden events. The lack of real-time network performance monitoring and analysis tools results in an inability to react promptly to network congestion or quality degradation, which is particularly problematic in large-scale or high-demand network environments. For example, during peak e-commerce periods, network latency and data loss lead to transaction failures, impacting user experience and business revenue. These shortcomings demonstrate that although existing technologies provide a basic framework for data exchange, more flexible and efficient solutions are still needed to cope with complex and ever-changing network conditions in dynamic network environments. This invention provides an application data interaction method and system based on the TCP-IP protocol. The technical solution is as follows:

[0005] On the one hand, a method for application data interaction based on the TCP-IP protocol is provided, the method including:

[0006] S1: Collect signal quality metrics at the physical layer from application data, capture signal quality data of each node through real-time network monitoring, perform time series analysis, extract signal quality trends and anomalies, and generate initial signal analysis results;

[0007] S2: Input the initial signal analysis results into the reinforcement learning model, predict the bottlenecks and optimal paths in the network path by analyzing the real-time signal quality trend, and output the path optimization results;

[0008] S3: Using the path optimization results, adjust the router's routing table via the TCP-IP protocol to adjust the flow of application data to the predicted optimal path, and perform simulation tests to obtain the routing configuration update results;

[0009] S4: Based on the route configuration update result, monitor the data transmission efficiency after the new route configuration, including latency and bandwidth utilization. By comparing the performance indicators before and after the adjustment, output the transmission efficiency comparison result.

[0010] S5: Based on the transmission efficiency comparison results, adjust the TCP window size, dynamically adjust the window size parameters according to real-time network congestion data, evaluate the impact using a network simulation environment, and output the window adjustment results;

[0011] S6: Using the window adjustment results, by applying new parameters and monitoring overall network performance, verify the effectiveness of the optimization measures and generate network performance evaluation results.

[0012] As a further aspect of the present invention, the initial signal analysis results include time-series data of signal strength, quality fluctuation, and packet loss rate indicators; the path optimization results include predicted minimum latency path, maximum bandwidth path, and minimum packet loss path; the routing configuration update results include routing paths, priorities, and associated protocol settings; the transmission efficiency comparison results include latency changes, bandwidth utilization, and data transmission rate before and after adjustment; the window adjustment results include adjusted TCP window size parameters and the impact of these parameters on network traffic control; and the network performance evaluation results include optimized overall network latency, data transmission efficiency, and network stability indicators.

[0013] As a further aspect of the present invention, the steps of collecting signal quality indicators of application data at the physical layer, capturing signal quality data of each node through real-time network monitoring, performing time series analysis, extracting signal quality trends and anomalies, and generating initial signal analysis results are as follows:

[0014] S101: Based on application data, configure network monitoring tools to capture signal quality data of multiple nodes in real time, set data acquisition frequency and threshold to adjust data capture speed, and generate node signal datasets;

[0015] S102: Using the node signal dataset, time series analysis is used to draw a trend chart of the data. By comparing short-term and long-term data fluctuations, changes and anomalies in signal quality are identified, and signal quality trend analysis results are generated.

[0016] S103: Based on the signal quality trend analysis results, integrate the signal trend graphs and anomaly information of each node, perform iterative technical adjustments and network optimization, and generate initial signal analysis results.

[0017] As a further aspect of the present invention, the steps of inputting the initial signal analysis results into the reinforcement learning model, predicting bottlenecks and optimal paths in the network path by analyzing real-time signal quality trends, and outputting path optimization results are as follows:

[0018] S201: Using the initial signal analysis results, configure the reinforcement learning model, input node signal trend data and outlier information, set model parameters, including learning rate and reward mechanism, adjust data input frequency, and generate model configuration dataset;

[0019] S202: Based on the model configuration dataset, the Q-learning algorithm is used to perform iterative analysis of signal quality data, identify bottleneck nodes and potential optimization paths in the network, and generate optimized network path efficiency values;

[0020] S203: Using the optimized network path efficiency value, a reinforcement learning model is used to predict and recommend the optimal network path, thereby optimizing the overall network performance and application data transmission efficiency, and generating path optimization results.

[0021] As a further aspect of the present invention, the formula for the Q-learning algorithm is as follows:

[0022]

[0023] in, This represents the optimized network path efficiency value. Represents the current network status. This represents the routing adjustments and bandwidth allocation in the current state. For learning rate, Representative takes action After state Transition to state Instant rewards As a discount factor, Represents the maximum value.

[0024] As a further aspect of the present invention, the steps of using the path optimization result, adjusting the router's routing table via the TCP-IP protocol to adjust the flow of application data to the predicted optimal path, and performing simulation tests to obtain the routing configuration update result are as follows:

[0025] S301: Based on the path optimization results, the application data flow is set to the predicted optimal path through the TCP-IP protocol interface, the target IP address and subnet mask are adjusted, and the routing table update configuration is generated;

[0026] S302: Update the configuration according to the routing table, apply the updated routing table to the network environment, conduct actual network traffic routing tests, monitor and record the router's load and traffic changes, and generate routing configuration test results;

[0027] S303: Using the route configuration test results, employ the gradient descent algorithm to record the network efficiency and data transmission speed before and after route optimization, and obtain the route configuration update results.

[0028] As a further aspect of the present invention, the formula for the gradient descent algorithm is as follows:

[0029]

[0030] in, For optimized network performance metrics, Represents the average routing response time. This represents the packet loss rate counted during routing tests. Represents the current load level of the network. , and It's about adjusting the parameters.

[0031] As a further aspect of the present invention, based on the route configuration update result, the step of monitoring the data transmission efficiency after the new route configuration, including latency and bandwidth utilization, and outputting the transmission efficiency comparison result by comparing the performance indicators before and after the adjustment is as follows:

[0032] S401: Using the route configuration update result, configure the network monitoring tool to monitor the data transmission efficiency under the new route settings, collect data on latency and bandwidth utilization, set the monitoring time interval and data sampling rate, and generate a transmission efficiency monitoring dataset;

[0033] S402: Based on the transmission efficiency monitoring dataset, assess the impact of routing adjustments on network performance, and perform data integration and deviation analysis by comparing the latency and bandwidth utilization before and after the adjustment to generate network performance comparison analysis results;

[0034] S403: Based on the network performance comparison analysis results, analyze the improved efficiency indicators and potential problems, record and extract performance improvement and efficiency difference data, and generate transmission efficiency comparison results.

[0035] As a further aspect of the present invention, the steps of adjusting the TCP window size based on the transmission efficiency comparison results, dynamically adjusting the window size parameter based on real-time network congestion data, evaluating the impact using a network simulation environment, and outputting the window adjustment results are as follows:

[0036] S501: Based on the transmission efficiency comparison results, adjust the TCP window size setting, dynamically modify the window size parameter according to the actual network congestion data, set the frequency and threshold of parameter adjustment, and generate the TCP window adjustment configuration.

[0037] S502: Based on the TCP window adjustment configuration, apply the new window size setting in the network simulation environment, monitor data transmission efficiency and network response changes by simulating differentiated network traffic and congestion scenarios, and generate window size test results;

[0038] S503: Using the window size test results, evaluate the actual impact of TCP window adjustment on network transmission efficiency, analyze network performance data under differentiated settings, and output the window adjustment results.

[0039] As a further aspect of the present invention, the steps of using the window adjustment results, applying new parameters, monitoring overall network performance, verifying the effectiveness of optimization measures, and generating network performance evaluation results are as follows:

[0040] S601: Based on the window adjustment results, apply the new TCP window size parameter to the actual network environment, monitor the real-time impact of parameter changes, record data changes in latency, packet loss rate, and throughput, set sampling intervals to verify data integrity, and generate a network performance monitoring dataset.

[0041] S602: Analyze the network performance monitoring dataset, evaluate network performance changes, compare latency, throughput and packet loss rate before and after optimization, assess the impact of TCP window size adjustment, and generate performance impact analysis results;

[0042] S603: Using the performance impact analysis results, integrate key performance indicators, compare the network performance differences before and after optimization, record the effects of network optimization and potential improvement measures, and generate network performance evaluation results.

[0043] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0044] By capturing signal quality data from each node and performing time-series analysis using real-time network monitoring technology, signal quality trends and anomalies are extracted, significantly improving the accuracy and security of data exchange. Real-time analysis and trend prediction effectively anticipate potential problems in network paths, enabling network path optimization. Analyzing this data using reinforcement learning models to predict and optimize network paths in real time not only enhances the network's adaptive adjustment capabilities but also significantly improves data transmission efficiency. Based on the optimized network paths, router routing tables are adjusted to make data transmission paths more efficient. These optimization measures, through simulation testing and real-time monitoring, ensure reduced data transmission latency and maximized bandwidth utilization. Furthermore, by dynamically adjusting the TCP window size according to real-time network congestion, the overall network performance is greatly optimized. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the workflow of the present invention;

[0046] Figure 2 This is a detailed flowchart of S1 of the present invention;

[0047] Figure 3 This is a detailed flowchart of the S2 process of the present invention;

[0048] Figure 4 This is a detailed flowchart of the S3 process of the present invention;

[0049] Figure 5 This is a detailed flowchart of the S4 process of the present invention;

[0050] Figure 6 This is a detailed flowchart of S5 of the present invention;

[0051] Figure 7 This is a detailed flowchart of S6 of the present invention. Detailed Implementation

[0052] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0053] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and-or" can be both, or either one.

[0054] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0055] Please see Figure 1 This invention provides an application data interaction method based on the TCP-IP protocol. The processing flow of this method may include the following steps:

[0056] S1: Collect signal quality metrics at the physical layer from application data, including signal-to-noise ratio and bit error rate. Capture signal quality data of each node through real-time network monitoring, perform time series analysis, extract signal quality trends and anomalies, and generate initial signal analysis results.

[0057] S2: Input the initial signal analysis results into the reinforcement learning model, analyze the real-time signal quality trend, predict the bottlenecks and optimal paths in the network path, and output the path optimization results;

[0058] S3: Using the path optimization results, the router's routing table is adjusted via the TCP-IP protocol to change the flow of application data to the predicted optimal path, and simulation tests are conducted to verify the effectiveness of the new configuration, thus obtaining the routing configuration update results;

[0059] S4: Based on the routing configuration update results, monitor the application data transmission efficiency after the new routing configuration, including latency and bandwidth utilization. By comparing the performance indicators before and after the adjustment, record the performance improvement data and output the transmission efficiency comparison results.

[0060] S5: Based on the transmission efficiency comparison results, adjust the TCP window size, dynamically adjust the window size parameters according to real-time network congestion data, test the impact of the new window size in a network simulation environment, and output the window adjustment results.

[0061] S6: Use the new window size parameter from the window adjustment results to optimize and adjust the network. By applying the new parameter and monitoring the overall network performance, verify the effectiveness of the optimization measures and generate network performance evaluation results.

[0062] The initial signal analysis results include time-series data of signal strength, quality fluctuations, and packet loss rate. The path optimization results include the predicted lowest latency path, highest bandwidth path, and lowest packet loss path. The route configuration update results include route paths, priorities, and associated protocol settings. The transmission efficiency comparison results include latency changes, bandwidth utilization, and data transmission rate before and after adjustment. The window adjustment results include the adjusted TCP window size parameters and their impact on network flow control. The network performance evaluation results include the optimized overall network latency, data transmission efficiency, and network stability indicators.

[0063] Please see Figure 2The specific steps for collecting signal quality metrics at the physical layer from application data, capturing signal quality data for each node through real-time network monitoring, performing time-series analysis, extracting signal quality trends and anomalies, and generating initial signal analysis results are as follows:

[0064] S101: Based on application data, configure network monitoring tools to capture signal quality data of multiple nodes in real time, set data acquisition frequency and threshold to adjust data capture speed, and generate node signal datasets. The execution process is as follows:

[0065] Configuring network monitoring tools based on application data involves real-time monitoring of multiple nodes and capture of signal quality data. Ensuring the data acquisition frequency and thresholds are scientifically set to adjust the data capture speed requires detailed configuration of monitoring parameters, including the data capture time interval and signal quality thresholds. These parameters are set based on the application's performance requirements and network status. By monitoring the signal quality of multiple nodes, the network's operational status can be understood in real time, and network configuration can be adjusted promptly to optimize performance. The network monitoring tool analyzes the data from each node to comprehensively evaluate the overall signal quality of the network and generate a node signal dataset.

[0066] S102: Using a node signal dataset, time series analysis is used to plot the trend of the data. By comparing short-term and long-term data fluctuations, changes and anomalies in signal quality are identified, and the following execution flow is used to generate signal quality trend analysis results.

[0067] Time series analysis was used to plot trend graphs of node signal data, according to the formula. The formula calculates the trend of changes in node signal quality. Where, Represents signal quality. Represents a time variable. Represents the initial signal quality. Represents the linear trend coefficient. This represents the acceleration coefficient. Detailed explanation of the formula and its derivation: Considering signal quality... Over time The change can be simulated by a quadratic equation, where... Indicates in Signal quality when =0 This represents the linear trend coefficient that changes over time, while This indicates the acceleration or deceleration of the trend. For example, If the value is positive, the signal quality will increase linearly with time. When the value is positive, this increase accelerates over time. Conversely, If the value is negative, the rate of increase will slow down over time. This is set at a specific point in time. Signal quality Obtained through actual measurement, =50dBm, let... =1, setting =45dBm, =3dBm-month =0.5dBm-month 2 Then we can obtain =45+3*1+0.5*12=48.5dBm, which is close to the actual measured value, indicating that the model is relatively accurate.

[0068] S103: Based on the signal quality trend analysis results, integrate the signal trend graphs and outlier information of each node, perform iterative technical adjustments and network optimizations, and generate the initial signal analysis results. The execution flow is as follows:

[0069] Based on the signal quality trend analysis results, the signal trend graphs and anomaly information of each node are integrated to perform iterative technical adjustments and optimizations to the network. This process includes analyzing the signal quality data of each node, identifying nodes with weak signals and abnormal fluctuations, and making technical adjustments to these nodes, such as adjusting transmission power, replacing unstable hardware, or updating firmware. At the same time, the optimization algorithm also adjusts the network's operating parameters based on real-time data to reduce latency and data packet loss. This not only reflects the improvement effect of the network adjustments but also provides a basis for further optimization, generating initial signal analysis results.

[0070] Please see Figure 3 The specific steps for inputting the initial signal analysis results into the reinforcement learning model, predicting bottlenecks and optimal paths in the network path by analyzing real-time signal quality trends, and outputting path optimization results are as follows:

[0071] S201: The execution flow of configuring a reinforcement learning model using the initial signal analysis results, inputting node signal trend data and outlier information, setting model parameters, including learning rate and reward mechanism, adjusting data input frequency, and generating model configuration dataset is as follows;

[0072] Using the initial signal analysis results, a reinforcement learning model is configured, and model parameters are set, including adjusting the learning rate and reward mechanism. This optimizes the model to better respond to input node signal trends and outlier information. In model parameter configuration, the learning rate controls the model's adaptation speed to new information, while the reward mechanism guides the model in balancing exploration and exploitation. Frequency adjustments ensure that the data flow matches the model's update speed. The model dynamically adjusts its internal parameters to optimize performance by continuously receiving new input data, such as changing trends in node signals and detected outliers. For example, upon observing frequent outliers, the model increases the learning rate to quickly adapt to changes in network conditions or adjusts the reward mechanism to reduce false predictions. In this way, the reinforcement learning model can optimize in real-world network environments, continuously improving decision quality and generating a model configuration dataset.

[0073] S202: Based on the model configuration dataset, the Q-learning algorithm is used to perform iterative analysis of signal quality data, identify bottleneck nodes and potential optimization paths in the network, and generate optimized network path efficiency values. The execution flow is as follows:

[0074] The formula for the Q-learning algorithm is as follows:

[0075]

[0076] in, This represents the optimized network path efficiency value. Represents the current network status. This represents the routing adjustments and bandwidth allocation in the current state. For learning rate, Representative takes action After state Transition to state Instant rewards As a discount factor, Represents the maximum value.

[0077] Detailed explanation and derivation of the formula: This formula is the basic update rule in Q-learning, used to update the state in a specific context. Take specific actions value Here, It's the learning rate. It's a reward. This is a discount factor used to calculate an updated value that takes future rewards into account. The following are the quantification and specific calculation steps based on actual data:

[0078] state This represents the current network configuration, such as node connectivity, latency, and bandwidth utilization. Latency is continuously monitored and recorded using network monitoring tools, such as SNMP (Simple Network Management Protocol). milliseconds, bandwidth utilization .

[0079] action This involves changing the route or adjusting the bandwidth. In this example, To adjust bandwidth allocation.

[0080] The learning rate is set based on the effectiveness adjusted during previous training. To ensure a reasonable balance between new and old knowledge, this value was obtained through repeated experiments to optimize the learning process.

[0081] Instant reward, due to bandwidth adjustments reducing latency to [a certain value]. Milliseconds, the reward is calculated as .

[0082] Discount factor, set to This indicates a greater emphasis on future rewards. This value was derived from multiple simulations and is used to balance current and future rewards.

[0083] In the new state The maximum Q-value for all actions. Set using simulation or historical data; the maximum Q-value is... .

[0084] Substitute these parameters into the formula to perform the calculation:

[0085]

[0086]

[0087]

[0088]

[0089]

[0090] The result indicates that the updated Value Reflected in the current state and actions The expected utility increases. This indicates that network performance has improved due to bandwidth adjustment. Add display action In state The current approach is effective, and in the future, the priority of adopting the same action under similar conditions will be higher.

[0091] S203: The execution flow for generating path optimization results is as follows: Based on the optimized network path efficiency value, the reinforcement learning model is used to predict and recommend the optimal network path, optimize the overall network performance and application data transmission efficiency.

[0092] By optimizing network path efficiency values, reinforcement learning models predict and recommend optimal network paths. During this process, the model comprehensively evaluates connection quality, data transmission speed, and network congestion between nodes. Input data includes signal trends and anomaly information for each network node. The model calculates the efficiency value of each network path, uses this data for prediction, and continuously adjusts its decision-making strategy through a set reward mechanism and learning rate. This optimizes overall network performance and application data transmission efficiency, providing network operators or IT departments with a scientific basis for adjusting network configurations and optimizing data flows, improving overall network service quality, and generating path optimization results.

[0093] Please see Figure 4 The specific steps for obtaining the route configuration update results are as follows: Using the path optimization results, the router's routing table is adjusted via the TCP-IP protocol to direct the application data flow to the predicted optimal path. Simulation tests are then conducted.

[0094] S301: Based on the path optimization results, the application data flow is set to the predicted optimal path through the TCP-IP protocol interface, the target IP address and subnet mask are adjusted, and the execution flow of generating the routing table update configuration is as follows;

[0095] Based on path optimization results, the application data flow is set to the predicted optimal path via the TCP-IP protocol interface. This process involves adjusting the target IP address and subnet mask to ensure that data packets can be transmitted along the predetermined optimal path, reducing latency and the probability of data loss. Based on the latest network traffic analysis and path optimization predictions, the updated routing table will include changed network nodes and related configurations. The changes aim to improve the overall network efficiency and application performance, generating an updated routing table configuration.

[0096] S302: The execution flow is as follows: update the configuration according to the routing table, apply the updated routing table to the network environment, conduct actual network traffic routing tests, monitor and record the router's load and traffic changes, and generate routing configuration test results.

[0097] The process of updating the routing table configuration and applying the updated routing table to the network environment includes implementing the new routing configuration on network devices and monitoring the effects through actual network traffic routing tests. This includes monitoring router load and traffic changes, detecting the speed and efficiency of data flow through the new path, ensuring no data transmission interruptions or significant delays, providing data on whether the update was successful and the specific impact of the new routing settings on network performance. This information is crucial for network administrators to continue adjusting and optimizing the network structure and generates routing configuration test results.

[0098] S303: Using the route configuration test results, the gradient descent algorithm is used to record the network efficiency and data transmission speed before and after route optimization, and the execution flow of obtaining the route configuration update results is as follows;

[0099] The formula for the gradient descent algorithm is as follows:

[0100]

[0101] in, For optimized network performance metrics, Represents the average routing response time. This represents the packet loss rate counted during routing tests. Represents the current load level of the network. , and It's about adjusting the parameters.

[0102] Parameter settings and data sources:

[0103] Routing response time This is obtained through real-time monitoring using network monitoring tools, set to 150 milliseconds. It represents the average time it takes for the router to receive and send data packets under the current network load.

[0104] Packet loss rate The loss rate, also measured using network monitoring tools, was 0.02% (2%). This reflects the network's stability under specific loads.

[0105] Network load The average network load, calculated using data from the network management system, is 2000 units. This represents the average amount of data processed by the network during the measurement period.

[0106] Weighting coefficient : Set to 1.5. This value is determined based on the high sensitivity of routing response time to network performance.

[0107] Weighting coefficient Setting it to 0.5 is to account for the relatively small impact of packet loss on overall network performance.

[0108] Weighting coefficient : Set to 0.75 to adjust the relative importance of the remaining variables in the formula to balance the calculation of network performance.

[0109] Formula derivation process

[0110] Calculate routing response time and packet loss rate Weighted difference:

[0111]

[0112] Calculate the absolute value of the weighted difference and take its square root:

[0113]

[0114] Use network load balancing and adjustment coefficient Perform formula calculations:

[0115]

[0116] The results show that, given the network load and current network conditions, the optimized network performance metric is 0.00998. This value represents the combined performance of network efficiency and data transmission speed after route configuration optimization; a lower value indicates that the network is more efficient and stable in processing data packets. This result, derived from actual data measurement and precise calculation, provides network administrators with a quantitative way to evaluate the actual effectiveness of route optimization.

[0117] Please see Figure 5 Based on the route configuration update results, the steps to monitor the data transmission efficiency after the new route configuration, including latency and bandwidth utilization, and output the transmission efficiency comparison results by comparing the performance indicators before and after the adjustment are as follows:

[0118] S401: The execution flow of using the route configuration update result, configuring network monitoring tools to monitor data transmission efficiency under the new route settings, collecting data on latency and bandwidth utilization, setting the monitoring time interval and data sampling rate, and generating a transmission efficiency monitoring dataset is as follows;

[0119] The routing configuration update results are used to configure network monitoring tools to monitor data transmission efficiency under the new routing settings. The process includes detailed settings for the monitoring tool's time interval and data sampling rate to ensure that network status information at various points in time can be captured. The collected data includes latency and bandwidth utilization, which are key indicators for evaluating the effectiveness of the new routing settings. The monitoring tool will periodically record network operation data according to the set parameters. This data will be used to analyze the performance of the new routing configuration and its impact on overall network efficiency, generating a transmission efficiency monitoring dataset.

[0120] S402: Based on the transmission efficiency monitoring dataset, evaluate the impact of routing adjustments on network performance. By comparing the latency and bandwidth utilization before and after the adjustment, perform data integration and deviation analysis, and generate network performance comparison analysis results, the execution flow is as follows;

[0121] Based on the transmission efficiency monitoring dataset, the impact of routing adjustments on network performance is evaluated. This process involves comparing latency and bandwidth utilization before and after the adjustment. Through data integration and deviation analysis, the actual effect of routing configuration changes on network performance can be revealed. These analyses help the technical team understand the effectiveness of network adjustments and provide decision-making information. During the evaluation process, performance data at different time points and under different network conditions will be recorded in detail, showing the specific changes in performance before and after the network adjustment, indicating the effectiveness of optimization, and generating network performance comparative analysis results.

[0122] S403: Based on the network performance comparison analysis results, analyze the improved efficiency indicators and potential problems, record and extract performance improvement and efficiency difference data, and generate transmission efficiency comparison results. The execution process is as follows:

[0123] Analyze the efficiency indicators to be improved and potential problems, according to the formula. Calculate the rate of change of transmission efficiency. Where, Represents the efficiency improvement rate. This represents the performance metrics before the adjustment. This represents the adjusted performance metric. Detailed formula explanation and calculation derivation: This formula is used to calculate the percentage change in performance improvement, where... and These are the performance data (such as bandwidth utilization or latency) before and after the network adjustment. For example, if the bandwidth utilization was 70% before the adjustment and 85% after, then the efficiency improvement rate is... This translates to a 21.43% improvement. This calculation method is simple and intuitive, helping to quickly assess the actual effects of network adjustment measures and providing a basis for further network optimization.

[0124] Please see Figure 6The specific steps for adjusting the TCP window size based on transmission efficiency comparison results, dynamically adjusting the window size parameter based on real-time network congestion data, evaluating the impact using a network simulation environment, and outputting the window adjustment results are as follows:

[0125] S501: Based on the transmission efficiency comparison results, adjust the TCP window size setting, dynamically modify the window size parameter according to the actual network congestion data, set the frequency and threshold of parameter adjustment, and generate the following execution flow for TCP window adjustment configuration;

[0126] By comparing transmission efficiency results, the TCP window size setting is adjusted. During this process, the window size parameter is dynamically modified based on actual network congestion data, including the frequency and threshold of parameter adjustment. This adjustment is to optimize congestion control during data transmission. Dynamically adjusting the TCP window size can adjust the data packet sending rate according to real-time changes in network conditions, reducing network congestion and data transmission latency. The set frequency and threshold determine the sensitivity and response speed of window size adjustment, providing network administrators with a strategy to adjust the window size in real time according to network conditions to maintain efficient and stable data flow, and generating a TCP window adjustment configuration.

[0127] S502: Based on TCP window adjustment configuration, the new window size setting is applied in a network simulation environment. By simulating differentiated network traffic and congestion scenarios, the data transmission efficiency and network response changes are monitored, and the window size test results are generated. The execution flow is as follows:

[0128] Based on TCP window adjustment configuration, new window size settings are applied in a network simulation environment. This operation involves monitoring changes in data transmission efficiency and network response by simulating differentiated network traffic and congestion scenarios. The process allows network engineers to test the effects of different window sizes in a controlled environment and predict how these adjustments will perform in a real network. The simulation tests include various network conditions, such as high and low bandwidth utilization and different levels of network congestion, to help analyze network response and data transmission efficiency under different settings. This information is crucial for optimizing TCP window settings and generates window size test results.

[0129] S503: Using window size test results, evaluate the actual impact of TCP window adjustment on network transmission efficiency, analyze network performance data under differentiated settings, and output the window adjustment results. The execution flow is as follows.

[0130] To assess the actual impact of TCP window adjustment on network transmission efficiency, follow the formula. The effect of adjusting the window size on transmission efficiency is calculated. In the formula, This represents the change in efficiency. This represents the adjusted window size. Represents the original window size. This represents transmission time. Detailed formula explanation and derivation: This formula measures the direct impact of window size adjustment on transmission efficiency, where... and This indicates the TCP window size before and after adjustment. This represents the total transmission time during the observation period. For example, if the original window size was 1024 bytes, and it is adjusted to 2048 bytes, with a transmission time of 2 seconds, then the efficiency change is... Bytes per second. This indicates that window adjustment increased the amount of data transmitted by 512 bytes per second, proving that adjusting the window size has a direct impact on improving network transmission efficiency. This analysis helps confirm whether the effect of window adjustment meets expectations and further guides network optimization strategies.

[0131] Please see Figure 7 The specific steps for generating network performance evaluation results, using window adjustment results, applying new parameters, monitoring overall network performance, verifying the effectiveness of optimization measures, and generating network performance evaluation results are as follows:

[0132] S601: The execution flow of adjusting the window size parameters to the actual network environment, monitoring the real-time impact of parameter changes, recording data changes in latency, packet loss rate, and throughput, setting sampling intervals to verify data integrity, and generating a network performance monitoring dataset is as follows;

[0133] By adjusting the window size, the new TCP window size parameter is applied to the actual network environment. The process includes monitoring the real-time impact of parameter changes and recording key network performance indicators such as latency, packet loss rate, and throughput. Setting the sampling interval to verify the integrity of the data is crucial, as it ensures that the collected data accurately reflects the network status. These data changes are used to evaluate the effect of the new window size setting, including performance data at multiple time points, providing a real-time and dynamic view of the network status and generating a network performance monitoring dataset.

[0134] S602: The execution flow for analyzing network performance monitoring datasets, evaluating network performance changes, comparing latency, throughput, and packet loss rate before and after optimization, assessing the impact of TCP window size adjustment, and generating performance impact analysis results is as follows;

[0135] Analyzing network performance monitoring datasets and assessing network performance changes involves comparing latency, throughput, and packet loss rate before and after optimization. These metrics accurately assess the impact of TCP window size adjustments. Analysts use statistical methods and charts to visualize performance changes, making performance comparisons intuitive and easy to understand. Through this data analysis, the actual effects of window adjustments can be clearly identified, determining whether they meet expected performance improvements. Detailed explanations of changes in various performance metrics provide a basis for further network optimization and generate performance impact analysis results.

[0136] S603: The execution flow for generating network performance evaluation results by using performance impact analysis results, integrating key performance indicators, comparing network performance differences before and after optimization, recording the effects of network optimization and potential improvement measures, is as follows;

[0137] Integrate key performance indicators, according to the formula To calculate the actual difference in network performance. In the formula, Represents performance differences. This represents the optimized performance metrics. This represents the performance metric before optimization. Detailed formula explanation and calculation derivation: This formula is used to measure the difference in performance metrics before and after network optimization measures are implemented. For example, if the throughput before optimization is 200Mbps, and after optimization it increases to 250Mbps, then the performance difference is... Mbps. This result directly shows the specific impact of TCP window size adjustment on throughput. Further analysis can reveal the impact of this change on the overall network user experience, such as reduced latency and packet loss rate, confirming the effectiveness of network optimization and potential further improvement measures.

[0138] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for application data interaction based on the TCP-IP protocol, characterized in that, The method includes: Collect signal quality metrics at the physical layer from application data, capture signal quality data of each node through real-time network monitoring, perform time series analysis, extract signal quality trends and anomalies, and generate initial signal analysis results; The initial signal analysis results are input into the reinforcement learning model. By analyzing the real-time signal quality trend, the bottlenecks and optimal paths in the network path are predicted, and the path optimization results are output. Using the path optimization results, the router's routing table is adjusted via the TCP-IP protocol to direct the application data flow to the predicted optimal path. Simulation tests are then conducted to obtain the routing configuration update results. Based on the route configuration update results, monitor the data transmission efficiency after the new route configuration, including latency and bandwidth utilization. By comparing the performance indicators before and after the adjustment, output the transmission efficiency comparison results. Based on the transmission efficiency comparison results, the TCP window size is adjusted, the window size parameter is dynamically adjusted according to real-time network congestion data, the impact is evaluated using a network simulation environment, and the window adjustment results are output. Using the window adjustment results, the effectiveness of the optimization measures is verified by applying new parameters and monitoring overall network performance, and network performance evaluation results are generated.

2. The application data interaction method based on TCP-IP protocol according to claim 1, characterized in that, The initial signal analysis results include time-series data of signal strength, quality fluctuation, and packet loss rate. The path optimization results include the predicted lowest latency path, highest bandwidth path, and lowest packet loss path. The routing configuration update results include routing paths, priorities, and associated protocol settings. The transmission efficiency comparison results include latency changes, bandwidth utilization, and data transmission rate before and after adjustment. The window adjustment results include the adjusted TCP window size parameter and its impact on network traffic control. The network performance evaluation results include the optimized overall network latency, data transmission efficiency, and network stability indicators.

3. The application data interaction method based on TCP-IP protocol according to claim 1, characterized in that, The specific steps for collecting application data on signal quality metrics at the physical layer, capturing signal quality data for each node through real-time network monitoring, performing time-series analysis, extracting signal quality trends and anomalies, and generating initial signal analysis results are as follows: Based on application data, configure network monitoring tools to capture signal quality data of multiple nodes in real time, set data acquisition frequency and threshold to adjust data capture speed, and generate node signal datasets. Using the node signal dataset, time series analysis is used to plot the trend of the data. By comparing short-term and long-term data fluctuations, changes and anomalies in signal quality are identified, and signal quality trend analysis results are generated. Based on the signal quality trend analysis results, the signal trend graphs and outlier information of each node are integrated, and iterative technical adjustments and network optimizations are performed to generate initial signal analysis results.

4. The application data interaction method based on TCP-IP protocol according to claim 1, characterized in that, The steps for inputting the initial signal analysis results into the reinforcement learning model, predicting bottlenecks and optimal paths in the network path by analyzing real-time signal quality trends, and outputting path optimization results are as follows: Using the initial signal analysis results, configure a reinforcement learning model, input node signal trend data and outlier information, set model parameters, including learning rate and reward mechanism, adjust data input frequency, and generate a model configuration dataset; Based on the model configuration dataset, the Q-learning algorithm is used to perform iterative analysis of signal quality data, identify bottleneck nodes and potential optimization paths in the network, and generate optimized network path efficiency values. Based on the optimized network path efficiency value, a reinforcement learning model is used to predict and recommend the optimal network path, thereby optimizing the overall network performance and application data transmission efficiency, and generating path optimization results.

5. The application data interaction method based on the TCP-IP protocol according to claim 4, characterized in that, The formula for the Q-learning algorithm is as follows: in, This represents the optimized network path efficiency value. Represents the current network status. This represents the routing adjustments and bandwidth allocation in the current state. For learning rate, Representative takes action After state Transition to state Instant rewards For state The following action As a discount factor, Represents the maximum value.

6. The application data interaction method based on TCP-IP protocol according to claim 1, characterized in that, Using the path optimization results, the router's routing table is adjusted via TCP-IP protocol to direct application data flow to the predicted optimal path. Simulation tests are then conducted to obtain the routing configuration update results. The specific steps are as follows: Based on the path optimization results, the application data flow is set to the predicted optimal path through the TCP-IP protocol interface, the target IP address and subnet mask are adjusted, and the routing table is updated. The configuration is updated according to the routing table, the updated routing table is applied to the network environment, and the router's load and traffic changes are monitored and recorded through actual network traffic routing tests to generate routing configuration test results. Using the route configuration test results, the gradient descent algorithm is employed to record the network efficiency and data transmission speed before and after route optimization, thereby obtaining the route configuration update results.

7. The application data interaction method based on TCP-IP protocol according to claim 6, characterized in that, The formula for the gradient descent algorithm is as follows: in, For optimized network performance metrics, Represents the average routing response time. This represents the packet loss rate counted during routing tests. Represents the current load level of the network. , and It's about adjusting the parameters.

8. The application data interaction method based on TCP-IP protocol according to claim 1, characterized in that, Based on the route configuration update result, the steps for monitoring the data transmission efficiency after the new route configuration, including latency and bandwidth utilization, and outputting the transmission efficiency comparison result by comparing the performance indicators before and after the adjustment are as follows: Using the updated routing configuration, configure a network monitoring tool to monitor the data transmission efficiency under the new routing settings, collect data on latency and bandwidth utilization, set the monitoring time interval and data sampling rate, and generate a transmission efficiency monitoring dataset. Based on the aforementioned transmission efficiency monitoring dataset, the impact of routing adjustments on network performance is evaluated. By comparing the latency and bandwidth utilization before and after the adjustment, data integration and deviation analysis are performed to generate network performance comparison analysis results. Based on the network performance comparison analysis results, we analyze the improved efficiency indicators and potential problems, record and extract performance improvement and efficiency difference data, and generate transmission efficiency comparison results.

9. The application data interaction method based on TCP-IP protocol according to claim 1, characterized in that, Based on the transmission efficiency comparison results, the TCP window size is adjusted, the window size parameter is dynamically adjusted according to real-time network congestion data, the impact is assessed using a network simulation environment, and the window adjustment results are output. The specific steps are as follows: Based on the transmission efficiency comparison results, adjust the TCP window size setting, dynamically modify the window size parameter according to the actual network congestion data, set the frequency and threshold of parameter adjustment, and generate the TCP window adjustment configuration. Based on the TCP window adjustment configuration, the new window size setting is applied in a network simulation environment. By simulating differentiated network traffic and congestion scenarios, data transmission efficiency and network response changes are monitored, and window size test results are generated. Using the window size test results, evaluate the actual impact of TCP window adjustment on network transmission efficiency, analyze network performance data under differentiated settings, and output the window adjustment results.

10. The application data interaction method based on TCP-IP protocol according to claim 1, characterized in that, The specific steps for using the window adjustment results, applying new parameters, monitoring overall network performance, verifying the effectiveness of optimization measures, and generating network performance evaluation results are as follows: By adjusting the window size, the new TCP window size parameter is applied to the actual network environment to monitor the real-time impact of parameter changes, record data changes in latency, packet loss rate, and throughput, set sampling intervals to verify data integrity, and generate a network performance monitoring dataset. Analyze the network performance monitoring dataset, evaluate network performance changes, compare latency, throughput and packet loss rate before and after optimization, assess the impact of TCP window size adjustment, and generate performance impact analysis results; Using the performance impact analysis results, key performance indicators are integrated, the differences in network performance before and after optimization are compared, the effects of network optimization and potential improvement measures are recorded, and network performance evaluation results are generated.

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