Network connection stability detection method and system, terminal and server

Abstract

The invention discloses a network connection stability detection method and system, a terminal and a server, and relates to the technical field of data transmission, and the method comprises the steps that terminal equipment monitors local network conditions in real time, including signal strength, packet loss rate and delay time, and uploads the monitored data to the server; the server receives the network condition information from the plurality of terminals, and evaluates the stability of the whole network environment in real time based on a big data analysis algorithm; and the server dynamically adjusts a network resource allocation strategy according to the evaluation result, and sends an optimization instruction to the terminal so as to improve the data transmission quality. The network connection stability is finely detected and optimally adjusted, the data transmission quality can be remarkably improved, the network failure rate is reduced, the utilization efficiency of network resources is improved, and the method is suitable for various scenes with high requirements for the network stability and is an important component in a modern intelligent network management system.

Description

Technical Field

[0001] The present invention relates to the field of data transmission technology, and in particular to a network connection stability detection method, system, terminal and server. Background Art

[0002] With the rapid development of modern internet technologies, data transmission quality requirements are becoming increasingly stringent across various network environments. This is especially true for applications such as video streaming, real-time voice communications, and online gaming, which place high demands on network latency, bandwidth, and stability. Traditional network management methods often rely on static network resource allocation, which struggles to adapt to dynamically changing network environments. This leads to unstable network connections, poor user experience, and compromised service quality. To improve network stability and optimize data transmission quality, a growing number of technologies are introducing dynamic network resource allocation mechanisms based on big data analysis and intelligent algorithms. These mechanisms monitor network status in real time and dynamically adjust network resource allocation, thereby optimizing data transmission, reducing latency and packet loss, and improving bandwidth utilization.

[0003] Although existing network connection stability detection methods and systems have solved the network stability problem to a certain extent, they still have the following shortcomings: they are unable to capture instantaneous network fluctuations in real time, resulting in untimely adjustment of network resources; they lack prediction of network environment change trends, making it difficult to provide early warning when network failures or performance bottlenecks occur; therefore, a network connection stability detection method based on the collaborative work of terminal devices and servers, combined with big data analysis, time series prediction and cluster analysis, is proposed. This method monitors the network status in real time, dynamically evaluates the stability of the overall network environment, and improves data transmission quality by optimizing network resource allocation strategies. Summary of the Invention

[0004] In order to solve the above technical problems, a network connection stability detection method, system, terminal and server are provided. This technical solution solves the above problems.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is: Network connection stability detection method, including: The terminal device monitors the local network status in real time, including signal strength, packet loss rate and delay time, and uploads the monitored data to the server; The server receives network status information from multiple terminals and evaluates the stability of the overall network environment in real time based on big data analysis algorithms; The server dynamically adjusts the network resource allocation strategy based on the evaluation results and sends optimization instructions to the terminal to improve data transmission quality.

[0006] Preferably, the terminal device monitors the local network status in real time, including signal strength, packet loss rate and delay time, and uploads the monitored data to the server, specifically including: Start the terminal device and initialize the network connection; The terminal device continuously collects signal strength through the network interface and records the real-time changes in signal quality. Based on the signal strength value, it determines the stability of the network connection; The terminal device periodically sends data packets to the server and waits for confirmation feedback, obtaining the proportion of sent data packets that do not receive confirmation responses; The terminal device sends a ping command, records the round-trip delay, and calculates and monitors the network delay based on the response time; The terminal device performs a local preliminary analysis based on the above-mentioned signal strength, packet loss rate, and delay time data to filter out valid network status data. If data deviates from the preset threshold, the terminal device triggers the optimization mechanism; The terminal device uploads the collected network status data to the server through a secure network protocol, and uploads the data regularly according to a predetermined time interval.

[0007] Preferably, the server receives network status information from multiple terminals, and evaluates the stability of the overall network environment in real time based on a big data analysis algorithm, specifically including: Use time series analysis algorithms to process and evaluate collected data in real time; Evaluate the stability of the overall network based on the data from each terminal and historical data; Analyze the network status of multiple terminal devices based on cluster analysis algorithms, identify similar device groups and areas, and evaluate the network performance of these areas; Based on time series data, predict network status trends and identify possible failure points; Based on the analysis results, the server generates multiple network stability evaluation indicators, including the average signal strength, packet loss rate and latency of the entire network.

[0008] Preferably, the evaluating the stability of the overall network based on the data of each terminal in combination with historical data specifically includes: The stability index of the entire network is calculated by weighted average, where the network stability index calculation formula is: Where w s 、w P 、w L is the weight of signal strength, packet loss rate and delay, N is the number of terminal devices, S i,t is the signal strength of the i-th terminal device at time t, P i,tis the packet loss rate of the i-th terminal device at time point t, L i,t is the delay of the i-th terminal device at time t.

[0009] Preferably, analyzing the network status of multiple terminal devices based on a cluster analysis algorithm, finding similar device groups and areas, and evaluating the network performance of these areas specifically includes: Clustering is performed by minimizing the following objective function, where the clustering algorithm formula is: Where J is the sum of the square distances from the data points in the cluster to the cluster center, K is the total number of clusters, k is the index of the current cluster, and C k is the set of all data points in the kth cluster, x i is the i-th data point in the data set, μ k is the cluster center of the kth cluster.

[0010] Preferably, predicting the trend of network conditions based on time series data and identifying possible fault points specifically include: Use the exponential smoothing method to predict the future trend of the network, and use the time series to predict the future network stability index. The formula of the exponential smoothing method is: Where, is the predicted value at time point t+h, x t is the actual observation value at time point t, is the predicted value at time point t-1, and α is the smoothing coefficient.

[0011] Preferably, the server dynamically adjusts the network resource allocation strategy according to the evaluation result and sends an optimization instruction to the terminal to improve the data transmission quality, specifically including: Based on the prediction of future network trends, the bandwidth allocation of each terminal is dynamically adjusted. The server detects that certain data flows are more important to the business and increases the priority of these flows; The server generates specific optimization instructions based on the adjusted network resource allocation policy, including bandwidth adjustment, priority setting, route change, and data retransmission; The server sends these optimization instructions to the corresponding terminal devices through the control channel. After receiving the optimization instructions, the terminal devices adjust their data transmission methods according to the instructions and may feedback the execution results to the server. The terminal device adjusts its network configuration according to the received optimization instructions. The terminal continuously monitors the network environment and adapts to the strategy provided by the server based on the evaluation results.

[0012] A network connection stability detection system, comprising: Terminal devices, used to monitor local network conditions in real time and upload network data; The server receives network status information from multiple terminals, evaluates network stability based on big data analysis algorithms, and dynamically adjusts network resource allocation strategies. An intelligent prediction module is used to combine historical data to predict future network status and allocate resources in advance; The service type identification module is used to customize network monitoring strategies according to different data transmission service types.

[0013] A terminal device for executing the method described in any one of claims 1 to 7, wherein the terminal device includes a signal strength detection module, a packet loss rate detection module, a delay detection module, and a data upload module for uploading monitored network data to a server.

[0014] A server, comprising: A network data receiving module is used to receive network status data from multiple terminals; Big data analysis module, used to evaluate the overall network environment stability in real time; Resource allocation adjustment module, used to dynamically adjust network resource allocation strategy according to evaluation results; Intelligent prediction module, used to combine historical network data to predict future network status and allocate resources in advance; The service type identification module is used to customize network monitoring strategies according to different service types.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention proposes to significantly improve the quality of data transmission, reduce network failure rate, and enhance the utilization efficiency of network resources by performing refined detection and optimization adjustment on the stability of network connections. It is suitable for various scenarios with high requirements on network stability, such as high-definition video conferencing, real-time gaming, large-scale online services, etc., and is an important component of modern intelligent network management systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a step flow chart of the present invention; Figure 2 This is a system framework diagram of the present invention. DETAILED DESCRIPTION

[0017] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0018] Reference Figure 1As shown, the network connection stability detection method includes: The terminal device monitors the local network status in real time, including signal strength, packet loss rate and delay time, and uploads the monitored data to the server; The server receives network status information from multiple terminals and evaluates the stability of the overall network environment in real time based on big data analysis algorithms; The server dynamically adjusts the network resource allocation strategy based on the evaluation results and sends optimization instructions to the terminal to improve data transmission quality.

[0019] The terminal device monitors the local network status in real time, including signal strength, packet loss rate and delay time, and uploads the monitored data to the server. Specifically, it includes: Start the terminal device and initialize the network connection; The terminal device continuously collects signal strength through the network interface and records the real-time changes in signal quality. Based on the signal strength value, it determines the stability of the network connection; The terminal device periodically sends data packets to the server and waits for confirmation feedback, obtaining the proportion of sent data packets that do not receive confirmation responses; The terminal device sends a ping command, records the round-trip delay, and calculates and monitors the network delay based on the response time; The terminal device performs a local preliminary analysis based on the above-mentioned signal strength, packet loss rate, and delay time data to filter out valid network status data. If data deviates from the preset threshold, the terminal device triggers the optimization mechanism; The terminal device uploads the collected network status data to the server via a secure network protocol and uploads the data regularly according to a predetermined time interval; This method collects multi-dimensional data from terminal devices, including signal strength, packet loss rate, delay time, etc., and combines it with a local preliminary analysis mechanism to accurately capture changes in network status and trigger optimization mechanisms in a timely manner. Unlike traditional methods, this method ensures data security when uploading data, and the regular upload strategy improves the timeliness of data, enhancing the real-time and adaptability of the system.

[0020] The server receives network status information from multiple terminals and uses big data analysis algorithms to evaluate the stability of the overall network environment in real time. Specifically, it includes: Use time series analysis algorithms to process and evaluate collected data in real time; Evaluate the stability of the overall network based on the data from each terminal and historical data; Analyze the network status of multiple terminal devices based on cluster analysis algorithms, identify similar device groups and areas, and evaluate the network performance of these areas; Based on time series data, predict network status trends and identify possible failure points; Based on the analysis results, the server generates multiple network stability evaluation indicators, including the average signal strength, packet loss rate, and latency of the entire network; This method uses technical means such as time series analysis, big data algorithms, and cluster analysis to conduct real-time evaluation of massive data from different terminals. It can comprehensively reflect the stability of the network environment. By analyzing different device groups and regions, it can not only identify current network problems, but also predict and prevent potential network failures in advance, thereby reducing the risk of network interruption or quality degradation.

[0021] Based on the data from each terminal and combined with historical data, the overall network stability is evaluated, specifically including: The stability index of the entire network is calculated by weighted average, where the network stability index calculation formula is: Where w s 、w P 、w L is the weight of signal strength, packet loss rate and delay, N is the number of terminal devices, S i,t is the signal strength of the i-th terminal device at time point t, P i,t is the packet loss rate of the i-th terminal device at time point t, L i,t is the delay of the i-th terminal device at time t.

[0022] Analyze the network status of multiple terminal devices based on cluster analysis algorithms, identify similar device groups and areas, and evaluate the network performance of these areas. Specifically, Clustering is performed by minimizing the following objective function, where the clustering algorithm formula is: Where J is the sum of the square distances from the data points in the cluster to the cluster center, K is the total number of clusters, k is the index of the current cluster, and C k is the set of all data points in the kth cluster, x i is the i-th data point in the data set, μ k is the cluster center of the kth cluster.

[0023] Based on time series data, the trend of network status is predicted and possible failure points are identified. Specifically, the exponential smoothing method is used to predict the future trend of the network and the time series is used to predict the future network stability index. The formula of the exponential smoothing method is: Where, is the predicted value at time point t+h, xt is the actual observation value at time point t, is the predicted value at time point t-1, and α is the smoothing coefficient.

[0024] The server dynamically adjusts the network resource allocation strategy based on the evaluation results and sends optimization instructions to the terminal to improve data transmission quality. Specifically, the following steps are performed: Based on the prediction of future network trends, the bandwidth allocation of each terminal is dynamically adjusted. The server detects that certain data flows are more important to the business and increases the priority of these flows; Based on the adjusted network resource allocation strategy, the server generates specific optimization instructions, including bandwidth adjustment, priority setting, route change, and data retransmission; The server sends these optimization instructions to the corresponding terminal devices through the control channel. After receiving the optimization instructions, the terminal devices adjust their data transmission methods according to the instructions and may feedback the execution results to the server. The terminal device adjusts its network configuration according to the received optimization instructions. The terminal continuously monitors the network environment and adapts to the strategy provided by the server based on the evaluation results. This method combines historical data with the weighted average method to evaluate the overall network stability, avoiding the one-sidedness of a single data source. It can more accurately calculate the stability indicators of the overall network, thereby providing a more precise optimization solution. The weighted average method can more flexibly adjust the evaluation weights and improve sensitivity to changes in network stability.

[0025] Reference Figure 2 As shown, a network connection stability detection system includes: Terminal devices, used to monitor local network conditions in real time and upload network data; The server receives network status information from multiple terminals, evaluates network stability based on big data analysis algorithms, and dynamically adjusts network resource allocation strategies. An intelligent prediction module is used to combine historical data to predict future network status and allocate resources in advance; The service type identification module is used to customize network monitoring strategies according to different data transmission service types.

[0026] A terminal device for executing the method of any one of claims 1 to 7, the terminal device comprising a signal strength detection module, a packet loss rate detection module, a delay detection module, and a data upload module for uploading monitored network data to a server.

[0027] A server, comprising: A network data receiving module is used to receive network status data from multiple terminals; Big data analysis module, used to evaluate the overall network environment stability in real time; Resource allocation adjustment module, used to dynamically adjust network resource allocation strategy according to evaluation results; Intelligent prediction module, used to combine historical network data to predict future network status and allocate resources in advance; The service type identification module is used to customize network monitoring strategies according to different service types.

[0028] In summary, the advantages of the present invention are: By continuously monitoring data such as network signal strength, packet loss rate, and delay time, terminal devices can reflect local network conditions in real time and provide more accurate network quality information; By regularly uploading network status data to the server, the server can obtain real-time network performance data, thereby making timely assessments and adjustments; The server uses big data analysis algorithms, combined with real-time and historical data from multiple terminals, to comprehensively assess the overall network environment, identify network problems, and make precise adjustments. Time series analysis algorithms can be used to perform real-time processing and trend analysis on historical data, effectively predicting changing trends in network performance and providing a basis for fault warning and resource allocation. The server can dynamically adjust network resource allocation strategies based on real-time evaluation results and network status predictions. For example, when network load is high or bottlenecks occur, the server can intelligently adjust bandwidth allocation, optimize routing, or adjust traffic priorities, thereby improving overall network performance. The terminal device adjusts its own network configuration according to the optimization instructions, effectively improving data transmission quality, reducing delays and packet loss, and enhancing user experience; Through cluster analysis algorithms, the server can identify groups of terminal devices with similar network conditions and take targeted network optimization measures for different groups and regions. This dynamic optimization based on geographical regions or device groups can significantly improve the efficiency of network resource utilization. Through time series prediction methods, the server can predict the future status of the network and identify possible failure points or performance bottlenecks in advance, so as to make resource adjustments in advance and avoid network failures. Using forecasting methods such as exponential smoothing can effectively reduce network performance fluctuations and improve service stability; The present invention is highly flexible and can formulate personalized network monitoring and resource allocation strategies according to different types of data transmission services, which makes the allocation of network resources more in line with the needs of different applications and further optimizes the user experience.

[0029] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A network connection stability detection method, characterized in that: include: The terminal device monitors the local network status in real time, including signal strength, packet loss rate and delay time, and uploads the monitored data to the server; The server receives network status information from multiple terminals and evaluates the stability of the overall network environment in real time based on big data analysis algorithms; The server dynamically adjusts the network resource allocation strategy based on the evaluation results and sends optimization instructions to the terminal to improve data transmission quality.

2. The network connection stability detection method according to claim 1, characterized in that: The terminal device monitors the local network status in real time, including signal strength, packet loss rate and delay time, and uploads the monitored data to the server. Specifically, the terminal device is started to initialize the network connection; The terminal device continuously collects signal strength through the network interface and records the real-time changes in signal quality. Based on the signal strength value, it determines the stability of the network connection; The terminal device periodically sends data packets to the server and waits for confirmation feedback, obtaining the proportion of sent data packets that do not receive confirmation responses; The terminal device sends a ping command, records the round-trip delay, and calculates and monitors the network delay based on the response time; The terminal device performs a local preliminary analysis based on the above-mentioned signal strength, packet loss rate, and delay time data to filter out valid network status data. If data deviates from the preset threshold, the terminal device triggers the optimization mechanism; The terminal device uploads the collected network status data to the server through a secure network protocol, and uploads the data regularly according to a predetermined time interval.

3. The network connection stability detection method according to claim 2, characterized in that: The server receives network status information from multiple terminals and evaluates the stability of the overall network environment in real time based on a big data analysis algorithm, specifically including: Use time series analysis algorithms to process and evaluate collected data in real time; Evaluate the stability of the overall network based on the data from each terminal and historical data; Analyze the network status of multiple terminal devices based on cluster analysis algorithms, identify similar device groups and areas, and evaluate the network performance of these areas; Based on time series data, predict network status trends and identify possible failure points; Based on the analysis results, the server generates multiple network stability evaluation indicators, including the average signal strength, packet loss rate and latency of the entire network.

4. The network connection stability detection method according to claim 3, characterized in that: The evaluation of overall network stability based on the data of each terminal and historical data specifically includes: The stability index of the entire network is calculated by weighted average, where the network stability index calculation formula is: Where w s 、w P 、w L is the weight of signal strength, packet loss rate and delay, N is the number of terminal devices, S i,t is the signal strength of the i-th terminal device at time point t, P i,t is the packet loss rate of the i-th terminal device at time point t, L i,t is the delay of the i-th terminal device at time t.

5. The network connection stability detection method according to claim 4, characterized in that: The cluster analysis algorithm is used to analyze the network status of multiple terminal devices, identify similar device groups and areas, and evaluate the network performance of these areas. Specifically, the following steps are involved: Clustering is performed by minimizing the following objective function, where the clustering algorithm formula is: Where J is the sum of the square distances from the data points in the cluster to the cluster center, K is the total number of clusters, k is the index of the current cluster, and C k is the set of all data points in the kth cluster, x i is the i-th data point in the data set, μ k is the cluster center of the kth cluster.

6. The network connection stability detection method according to claim 5, characterized in that: Predicting network status trends based on time series data and identifying possible failure points specifically include: Use the exponential smoothing method to predict the future trend of the network, and use the time series to predict the future network stability index. The formula of the exponential smoothing method is: Where, is the predicted value at time point t+h, x t is the actual observation value at time point t, is the predicted value at time point t-1, and α is the smoothing coefficient.

7. The network connection stability detection method according to claim 6, characterized in that: The server dynamically adjusts the network resource allocation strategy based on the evaluation results and sends optimization instructions to the terminal to improve the data transmission quality, specifically including: Based on the prediction of future network trends, the bandwidth allocation of each terminal is dynamically adjusted. The server detects that certain data flows are more important to the business and increases the priority of these flows; The server generates specific optimization instructions based on the adjusted network resource allocation policy, including bandwidth adjustment, priority setting, route change, and data retransmission; The server sends these optimization instructions to the corresponding terminal devices through the control channel. After receiving the optimization instructions, the terminal devices adjust their data transmission methods according to the instructions and may feedback the execution results to the server. The terminal device adjusts its network configuration according to the received optimization instructions. The terminal continuously monitors the network environment and adapts to the strategy provided by the server based on the evaluation results.

8. A network connection stability detection system, characterized in that: include: Terminal devices, used to monitor local network conditions in real time and upload network data; The server receives network status information from multiple terminals, evaluates network stability based on big data analysis algorithms, and dynamically adjusts network resource allocation strategies. An intelligent prediction module is used to combine historical data to predict future network status and allocate resources in advance; The service type identification module is used to customize network monitoring strategies according to different data transmission service types.

9. A terminal device, configured to execute the method according to any one of claims 1 to 7, characterized in that: The terminal device includes a signal strength detection module, a packet loss rate detection module, a delay detection module, and a data upload module, which are used to upload the monitored network data to the server.

10. A server, characterized in that: include: A network data receiving module is used to receive network status data from multiple terminals; Big data analysis module, used to evaluate the overall network environment stability in real time; Resource allocation adjustment module, used to dynamically adjust network resource allocation strategy according to evaluation results; Intelligent prediction module, used to combine historical network data to predict future network status and allocate resources in advance; The service type identification module is used to customize network monitoring strategies according to different service types.

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