Multilink fusion method and system based on SD-WAN technology

Abstract

The invention discloses a multi-link fusion method and system based on an SD-WAN (Secure Digital Wireless Area Network) technology, which can timely discover and deal with the condition of network failure or performance reduction by monitoring network state parameters of a plurality of network links in real time, and is helpful for ensuring reliable network connection at any time point, thereby improving the reliability and the stability of the network, and improving the reliability and the reliability of the network. By dynamically adjusting distribution of data traffic, the optimal link combination can be selected for data transmission, so that congestion is avoided, delay is reduced, the packet loss rate is reduced, the utilization rate of network bandwidth is maximized, the data transmission efficiency is remarkably improved, the routing optimization step can quickly respond to service changes by utilizing flexibility and programmability of the SDN technology, and the service quality is improved. According to the method, the continuity and the high efficiency of services are ensured by optimizing data transmission routes, existing network resources can be fully utilized through multi-link fusion and intelligent scheduling, excessive dependence on a single link or an expensive high-performance link is avoided, and reduction of network construction and operation and maintenance cost is facilitated.

Description

Technical Field

[0001] The present invention relates to the field of multi-link fusion technology, and in particular to a multi-link fusion method and system based on SD-WAN technology. Background Art

[0002] With the deepening of enterprise digital transformation and the widespread application of technologies such as cloud computing, big data, and the Internet of Things (IoT), network traffic has shown explosive growth. Traditional network architecture and connection methods can no longer meet the rapidly growing data transmission needs, especially in scenarios such as multinational enterprises, remote offices, and branch office interconnection. Problems such as network bandwidth, latency, reliability, and security are becoming increasingly prominent.

[0003] Traditional network architecture usually relies on a single or a few fixed network links, which may be affected by multiple factors such as geographical location, operator policies, natural disasters, etc., resulting in unstable network performance, limited bandwidth, and poor fault recovery capabilities. In addition, the routing selection and traffic scheduling of traditional network architecture are often based on static configuration, lacking flexibility and intelligence, and are difficult to adapt to rapidly changing business needs and network environments. Summary of the invention

[0004] In view of this, the present invention proposes a multi-link fusion method and system based on SD-WAN technology, which can solve the defects of the prior art that it relies on a single or a few fixed network links, and that routing selection and traffic scheduling are often based on static configuration.

[0005] The technical solution of the present invention is achieved in this way:

[0006] A multi-link fusion method based on SD-WAN technology, specifically including:

[0007] Monitoring steps: Real-time monitoring of network status parameters of multiple network links through the SD-WAN controller;

[0008] Evaluation step: Based on the monitored network status parameters, the performance and quality of each network link are evaluated to generate link quality evaluation results;

[0009] Scheduling step: According to the link quality evaluation results and the preset scheduling strategy, dynamically adjust the distribution of data traffic and select the optimal link combination for data transmission;

[0010] Route optimization steps: Use SDN technology to dynamically configure network paths based on business needs and network status to optimize data transmission routes;

[0011] Security control step: Implement security policies during data transmission.

[0012] As a further optional solution of the multi-link fusion method based on SD-WAN technology, the real-time monitoring of network status parameters of multiple network links by the SD-WAN controller specifically includes:

[0013] Deploy SD-WAN controllers in enterprise networks;

[0014] Install SD-WAN agents at the access point of each network link in the enterprise network;

[0015] Define monitoring indicators and frequency;

[0016] Collect network status data of each link in real time based on SD-WAN agent, monitoring indicators and frequency, and transmit the data to SD-WAN controller;

[0017] The SD-WAN controller performs real-time and predictive analysis based on the built-in machine learning algorithm to obtain the network status parameters of each network link.

[0018] As a further optional solution of the multi-link fusion method based on SD-WAN technology, the performance and quality of each network link is evaluated based on the monitored network status parameters, and the link quality evaluation result is generated, which specifically includes:

[0019] Extracting key features that can reflect link performance and quality from the monitored network status parameters, wherein the key features include average delay, maximum delay, delay standard deviation, packet loss rate, and jitter rate;

[0020] Based on the key features, pairwise comparisons are performed to construct a judgment matrix;

[0021] Calculate the eigenvector corresponding to the maximum eigenvalue of each judgment matrix and perform normalization to obtain the weight of each indicator;

[0022] Calculate the comprehensive score of the link based on the weight of each indicator and the real-time monitored data;

[0023] According to the calculated comprehensive link score, the overall performance and quality of the link are evaluated to obtain a link quality evaluation result.

[0024] As a further optional solution of the multi-link fusion method based on SD-WAN technology, the eigenvector corresponding to the maximum eigenvalue of each judgment matrix is ​​calculated and normalized to obtain the weight of each indicator, specifically including:

[0025] Call the Python library function to calculate the eigenvalues ​​and eigenvectors of each judgment matrix to obtain the calculation results;

[0026] According to the calculation results, find the maximum eigenvalue of each judgment matrix and its corresponding eigenvector;

[0027] Calculate the sum of all elements in the eigenvector;

[0028] Divide each element in the feature vector by their sum to get the weight of each indicator.

[0029] As a further optional solution of the multi-link fusion method based on SD-WAN technology, dynamically adjusting the distribution of data traffic and selecting the optimal link combination for data transmission according to the link quality evaluation results and the preset scheduling strategy specifically includes:

[0030] Preset scheduling strategies, wherein the scheduling strategies include load balancing strategies, fault recovery strategies, and traffic shaping strategies;

[0031] According to the link quality evaluation results and the preset scheduling strategy, the most appropriate strategy is selected for execution;

[0032] When executing the scheduling strategy, a greedy algorithm is used to select the optimal link combination, and data transmission is performed based on the optimal link combination.

[0033] As a further optional solution of the multi-link fusion method based on SD-WAN technology, the use of SDN technology to dynamically configure network paths according to business needs and network status and optimize data transmission routes specifically includes:

[0034] Select an SDN controller based on business requirements and network status;

[0035] The SDN controller collects network traffic information in real time and uses data analysis tools to process the collected data to identify hot spots, potential bottlenecks, and traffic patterns in the network;

[0036] Dynamically generate routing strategies based on identification results;

[0037] The generated routing strategy is converted into network configuration instructions and sent to network devices for network path configuration, thereby optimizing data transmission routes.

[0038] A multi-link fusion system based on SD-WAN technology, including:

[0039] A monitoring module, which is used to monitor the network status parameters of multiple network links in real time through the SD-WAN controller;

[0040] An evaluation module, connected to the monitoring module, is used to evaluate the performance and quality of each network link based on the network status parameters monitored by the monitoring module, and generate a link quality evaluation result;

[0041] The scheduling module is connected to the evaluation module and dynamically adjusts the data flow distribution according to the link quality evaluation results and the preset scheduling strategy, and selects the optimal link combination for data transmission;

[0042] The routing optimization module uses SDN technology to work with the scheduling module to dynamically configure network paths based on business needs and network status and optimize data transmission routes;

[0043] The security control module is used to implement security policies during data transmission.

[0044] As a further optional solution of the multi-link fusion system based on SD-WAN technology, the system also includes:

[0045] The management interface is used to display monitoring data, link quality assessment results, scheduling status and route optimization, and provides a user interaction interface that allows administrators to configure and modify preset scheduling policies and security policies.

[0046] A computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the multi-link fusion method based on SD-WAN technology are implemented.

[0047] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the multi-link fusion method based on SD-WAN technology described above.

[0048] The beneficial effects of the present invention are as follows: by real-time monitoring of the network status parameters of multiple network links, network failures or performance degradation can be discovered and responded to in a timely manner. This real-time monitoring mechanism helps to ensure reliable network connections at any time point, thereby improving the reliability and stability of the network. In the evaluation step, the performance and quality of each link are evaluated according to the monitored network status parameters, which provides a scientific basis for subsequent scheduling decisions. By dynamically adjusting the distribution of data traffic, the optimal link combination can be selected for data transmission, thereby avoiding congestion, reducing delays, reducing packet loss rates, and maximizing the utilization of network bandwidth, significantly improving data transmission efficiency. The routing optimization step utilizes the flexibility and programmability of SDN technology to dynamically configure network paths according to business needs and network status, which means that it can quickly respond to business changes, such as adding new services, traffic surges, or changes in network topology, and ensure business continuity and efficiency by optimizing data transmission routes. Through multi-link fusion and intelligent scheduling, the system can make full use of existing network resources and avoid over-reliance on a single link or expensive high-performance links, which helps to reduce network construction and operation and maintenance costs and improve the return on investment of network resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 A schematic diagram of a multi-link fusion method based on SD-WAN technology of the present invention;

[0051] Figure 2 This is a schematic diagram of the composition of a multi-link fusion system based on SD-WAN technology in the present invention. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] refer to Figure 1 to Figure 2 , a multi-link fusion method based on SD-WAN technology, specifically including:

[0054] Monitoring steps: Real-time monitoring of network status parameters of multiple network links through the SD-WAN controller;

[0055] Evaluation step: Based on the monitored network status parameters, the performance and quality of each network link are evaluated to generate link quality evaluation results;

[0056] Scheduling step: According to the link quality evaluation results and the preset scheduling strategy, dynamically adjust the distribution of data traffic and select the optimal link combination for data transmission;

[0057] Route optimization steps: Use SDN technology to dynamically configure network paths based on business needs and network status to optimize data transmission routes;

[0058] Security control step: Implement security policies during data transmission.

[0059] In this embodiment, by real-time monitoring of the network status parameters of multiple network links, network failures or performance degradation can be discovered and responded to in a timely manner. This real-time monitoring mechanism helps to ensure that there is a reliable network connection at any time point, thereby improving the reliability and stability of the network. In the evaluation step, the performance and quality of each link are evaluated according to the monitored network status parameters, which provides a scientific basis for subsequent scheduling decisions. By dynamically adjusting the distribution of data traffic, the optimal link combination can be selected for data transmission, thereby avoiding congestion, reducing delays, reducing packet loss rates, and maximizing the utilization of network bandwidth, significantly improving data transmission efficiency. The routing optimization step utilizes the flexibility and programmability of SDN technology to dynamically configure network paths according to business needs and network status, which means that it can quickly respond to business changes, such as new services, traffic surges, or changes in network topology, and optimize data transmission routes to ensure business continuity and efficiency. Through multi-link fusion and intelligent scheduling, the system can make full use of existing network resources and avoid over-reliance on a single link or expensive high-performance links, which helps to reduce network construction and operation and maintenance costs and improve the return on investment of network resources.

[0060] Preferably, the real-time monitoring of network status parameters of multiple network links by the SD-WAN controller specifically includes:

[0061] Deploy SD-WAN controllers in enterprise networks;

[0062] Install SD-WAN agents at the access point of each network link in the enterprise network;

[0063] Define monitoring indicators and frequency;

[0064] Collect network status data of each link in real time based on SD-WAN agent, monitoring indicators and frequency, and transmit the data to SD-WAN controller;

[0065] The SD-WAN controller performs real-time and predictive analysis based on the built-in machine learning algorithm to obtain the network status parameters of each network link.

[0066] In this embodiment, first, an SD-WAN controller is deployed at the core of the enterprise network. The controller is responsible for centrally managing and controlling the status monitoring, evaluation, scheduling and route optimization of all network links. SD-WAN agents are installed at the access points or key nodes of each network link. These agents are responsible for collecting real-time network status data of the links and determining monitoring indicators. In addition to conventional bandwidth utilization, latency, packet loss rate and availability, more fine-grained monitoring indicators can be added, such as TCP / UDP connection quality, traffic patterns of specific applications, network jitter, etc. The monitoring frequency can be flexibly set according to business needs and network environment. For key links or high-variability The SD-WAN controller collects network status data of each link in real time through the agent, and transmits the data to the SD-WAN controller. On the controller side, the collected raw data is cleaned, denoised, and standardized to improve the accuracy and efficiency of subsequent analysis. The preprocessed data is analyzed in real time using machine learning or deep learning algorithms to quickly identify network anomalies, bottlenecks, and potential failure points. Based on historical data and real-time analysis results, time series prediction, association rule mining and other technologies are used to predict the network status in the future so that measures can be taken in advance to prevent potential problems.

[0067] It should be noted that the SD-WAN controller performs real-time and predictive analysis based on the built-in decision tree algorithm, including:

[0068] In the decision tree algorithm, feature selection is a core step. The SD-WAN controller needs to select the most representative features from a variety of network indicators as the input of the decision tree based on the needs and goals of network management;

[0069] Use information gain, information gain ratio and other methods to evaluate the importance of each feature and select the optimal feature combination;

[0070] Starting from the root node, recursively select the optimal features for splitting and generate child nodes until the stopping condition is met (such as the purity of the data in the node reaches a threshold, the preset tree depth is reached, etc.);

[0071] To avoid overfitting, the generated decision tree is pruned, unnecessary subtrees or nodes are deleted, and the model is simplified;

[0072] The network data collected in real time is input into the trained decision tree model for rapid analysis and prediction.

[0073] In addition, deploying SD-WAN controllers in enterprise networks and installing SD-WAN agents at the access points of each network link can achieve comprehensive monitoring of all network links. This deployment method improves network visibility and transparency, allowing network administrators to clearly understand the real-time status of each link and make more accurate decisions. Based on the results of real-time and predictive analysis, the SD-WAN controller can more accurately evaluate the performance and quality of each network link. During data transmission, the controller can dynamically select the optimal transmission path based on link status parameters, thereby optimizing data transmission routes, reducing delays and packet loss, and improving transmission efficiency and reliability. The entire monitoring and analysis process is automatically completed by the SD-WAN controller without human intervention. This automated management method not only reduces the complexity and cost of network management, but also improves the accuracy and timeliness of management. At the same time, due to the introduction of machine learning algorithms, the SD-WAN controller can continuously learn and optimize its own decision-making capabilities to achieve more intelligent network management.

[0074] Preferably, the step of evaluating the performance and quality of each network link based on the monitored network status parameters and generating a link quality evaluation result specifically includes:

[0075] Extracting key features that can reflect link performance and quality from the monitored network status parameters, wherein the key features include average delay, maximum delay, delay standard deviation, packet loss rate, and jitter rate;

[0076] Based on the key features, pairwise comparisons are performed to construct a judgment matrix;

[0077] Calculate the eigenvector corresponding to the maximum eigenvalue of each judgment matrix and perform normalization to obtain the weight of each indicator;

[0078] Calculate the comprehensive score of the link based on the weight of each indicator and the real-time monitored data;

[0079] According to the calculated comprehensive link score, the overall performance and quality of the link are evaluated to obtain a link quality evaluation result.

[0080] In this embodiment, by extracting key features that can reflect link performance and quality (such as average delay, maximum delay, delay standard deviation, packet loss rate and jitter rate), the actual performance status of the link can be more accurately grasped. These key features are directly related to the efficiency and reliability of data transmission and are an important basis for evaluating link quality. By comparing key features in pairs and constructing a judgment matrix, and then calculating the eigenvector corresponding to the maximum eigenvalue of the judgment matrix and normalizing it, the weight of each indicator can be scientifically determined. This method avoids the deviation caused by subjective judgment and makes the evaluation result more objective and accurate. According to the weight of each indicator and the data monitored in real time, the comprehensive score of the link is calculated, and the overall performance and quality of the link can be comprehensively and comprehensively evaluated. This comprehensive evaluation method not only considers the performance of a single indicator, but also considers the mutual influence and weight distribution between the indicators, making the evaluation result more comprehensive and reliable. The link quality evaluation result provides an important basis for subsequent scheduling decisions. By understanding the performance and quality status of each link, the optimal link combination can be selected more intelligently for data transmission, thereby achieving load balancing, fault recovery or improving data transmission efficiency. This decision optimization based on the evaluation results helps to improve the overall performance of the network and user experience.

[0081] Preferably, the eigenvector corresponding to the maximum eigenvalue of each judgment matrix is ​​calculated and normalized to obtain the weight of each indicator, specifically including:

[0082] Call the Python library function to calculate the eigenvalues ​​and eigenvectors of each judgment matrix to obtain the calculation results;

[0083] According to the calculation results, find the maximum eigenvalue of each judgment matrix and its corresponding eigenvector;

[0084] Calculate the sum of all elements in the eigenvector;

[0085] Divide each element in the feature vector by their sum to get the weight of each indicator.

[0086] In this embodiment, Python library functions (such as the linear algebra module in NumPy or Sc i Py) are used to calculate eigenvalues ​​and eigenvectors, which can greatly improve the computing efficiency. These libraries are optimized and can efficiently process large-scale matrix operations to ensure the accuracy and timeliness of the calculation results. The weight of each indicator is obtained by dividing each element in the eigenvector by its sum, which ensures the rationality of the weight distribution. This method takes into account the relative importance of each indicator and avoids the deviation caused by subjective weighting. At the same time, the normalization process also makes the weights of each indicator comparable, which is convenient for subsequent comprehensive evaluation. Based on the calculated weights and real-time monitored data, the performance and quality of each link can be evaluated more objectively. This data-driven evaluation method provides strong support for subsequent scheduling decisions, making the decision-making process more scientific, reasonable and effective.

[0087] Preferably, dynamically adjusting the allocation of data traffic and selecting the optimal link combination for data transmission according to the link quality evaluation result and the preset scheduling strategy specifically includes:

[0088] Preset scheduling strategies, wherein the scheduling strategies include load balancing strategies, fault recovery strategies, and traffic shaping strategies;

[0089] According to the link quality evaluation results and the preset scheduling strategy, the most appropriate strategy is selected for execution;

[0090] When executing the scheduling strategy, a greedy algorithm is used to select the optimal link combination, and data transmission is performed based on the optimal link combination.

[0091] In this embodiment, according to business needs and network environment, multiple scheduling strategies are designed, such as load balancing strategy, fault recovery strategy, traffic shaping strategy, etc. Each strategy specifies its application scenario, triggering condition, execution action and expected effect, and configures corresponding parameters for each strategy, such as weight distribution ratio, threshold setting, priority sorting, etc. The parameter configuration should be based on historical data and simulation tests to ensure the effectiveness and rationality of the strategy, and monitor the link quality evaluation results and traffic conditions in the network in real time. When the link quality changes significantly or the traffic demand exceeds the preset threshold, the execution of the scheduling strategy is triggered. According to the current network condition and the preset scheduling strategy, the most appropriate strategy is selected for execution. The execution actions include reallocating traffic, switching transmission paths, adjusting transmission parameters, etc. When executing the scheduling strategy, the comprehensive score of each link, the remaining bandwidth, the load condition and other factors are comprehensively considered to select the optimal link combination for data transmission, and the optimal link combination is selected by adopting a greedy algorithm.

[0092] It should be noted that when the greedy algorithm is used to select the optimal link combination, it specifically includes:

[0093] Based on the results of link quality assessment, a comprehensive score or priority is calculated for each link. This score can be based on the weighted sum of multiple factors such as latency, bandwidth, and packet loss rate.

[0094] In each step, the currently optimal link is selected from the remaining links and added to the link combination. The selection of the optimal link is based on its comprehensive score or priority. The link with a higher score or higher priority is more likely to be selected.

[0095] Remove the selected link from the set of selectable links and update the status of the link combination, including statistics such as the total bandwidth and total delay of the selected links;

[0096] Repeat the above selection process until a certain stopping condition is reached. The stopping condition can be that the link combination meets a specific performance requirement (such as the total bandwidth reaches a threshold, the total delay is lower than a certain value), or there are no more links to choose from;

[0097] In the selection process, various constraints also need to be considered, such as mutual exclusivity between links (some links cannot be used at the same time) and link capacity limitations.

[0098] In addition, through the preset scheduling strategies (including load balancing strategies, fault recovery strategies and traffic shaping strategies), the distribution of data traffic can be flexibly adjusted according to the current network status and business needs. This dynamic adjustment mechanism helps to balance the network load and avoid overloading of a single link, thereby improving the overall network performance and transmission efficiency; the fault recovery strategy is an important part of the scheduling strategy. When a link is detected to have a fault or performance degradation, it can quickly switch to other reliable links for data transmission to ensure business continuity and stability. This fault recovery capability is crucial to ensuring the normal operation of key business applications; when the greedy algorithm selects the optimal link combination, it will consider multiple factors such as link quality, bandwidth utilization, and delay, so as to select the optimal transmission path under the current state. This optimization choice not only improves the efficiency and speed of data transmission, but also maximizes the use of network resources and avoids resource waste.

[0099] Preferably, the use of SDN technology to dynamically configure network paths according to business requirements and network status to optimize data transmission routes specifically includes:

[0100] Select an SDN controller based on business requirements and network status;

[0101] The SDN controller collects network traffic information in real time and uses data analysis tools to process the collected data to identify hot spots, potential bottlenecks, and traffic patterns in the network;

[0102] Dynamically generate routing strategies based on identification results;

[0103] The generated routing strategy is converted into network configuration instructions and sent to network devices for network path configuration, thereby optimizing data transmission routes.

[0104] In this embodiment, the SDN controller can collect traffic information in the network in real time, and identify hot spots, potential bottlenecks and traffic patterns in the network through data analysis tools. Based on these identification results, the SDN controller can dynamically generate routing strategies and select the optimal transmission path, thereby avoiding network congestion and delays and improving the performance and efficiency of data transmission. By real-time monitoring and dynamic adjustment of network paths, SDN technology can promptly detect and respond to network failures or potential problems. When a link fails or performance degrades, the SDN controller can quickly adjust the routing strategy and transfer traffic to other reliable links to ensure business continuity and stability. SDN technology makes the management and scheduling of network resources more intelligent and refined. By dynamically configuring network paths and optimizing data transmission routes, SDN can maximize the use of network resources, avoid waste and idleness of resources, and improve overall resource utilization. SDN technology simplifies the complexity and workload of network management through centralized control and management. Network administrators can monitor and manage the network globally through SDN controllers without having to configure and adjust each network device one by one, thereby improving management efficiency and accuracy.

[0105] A multi-link fusion system based on SD-WAN technology, including:

[0106] A monitoring module, which is used to monitor the network status parameters of multiple network links in real time through the SD-WAN controller;

[0107] An evaluation module, connected to the monitoring module, is used to evaluate the performance and quality of each network link based on the network status parameters monitored by the monitoring module, and generate a link quality evaluation result;

[0108] The scheduling module is connected to the evaluation module and dynamically adjusts the data flow distribution according to the link quality evaluation results and the preset scheduling strategy, and selects the optimal link combination for data transmission;

[0109] The routing optimization module uses SDN technology to work with the scheduling module to dynamically configure network paths based on business needs and network status and optimize data transmission routes;

[0110] The security control module is used to implement security policies during data transmission.

[0111] Preferably, the system further comprises:

[0112] The management interface is used to display monitoring data, link quality assessment results, scheduling status and route optimization, and provides a user interaction interface that allows administrators to configure and modify preset scheduling policies and security policies.

[0113] A computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the multi-link fusion method based on SD-WAN technology are implemented.

[0114] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the multi-link fusion method based on SD-WAN technology described above.

[0115] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-link fusion method based on SD-WAN technology, characterized in that: Specifically include: Monitoring steps: Real-time monitoring of network status parameters of multiple network links through the SD-WAN controller; Evaluation step: Based on the monitored network status parameters, the performance and quality of each network link are evaluated to generate link quality evaluation results; Scheduling step: According to the link quality evaluation results and the preset scheduling strategy, dynamically adjust the distribution of data traffic and select the optimal link combination for data transmission; Route optimization steps: Use SDN technology to dynamically configure network paths based on business needs and network status to optimize data transmission routes; Security control step: Implement security policies during data transmission.

2. According to a multi-link fusion method based on SD-WAN technology according to claim 1, it is characterized in that: The real-time monitoring of network status parameters of multiple network links by the SD-WAN controller specifically includes: Deploy SD-WAN controllers in enterprise networks; Install SD-WAN agents at the access point of each network link in the enterprise network; Define monitoring indicators and frequency; Collect network status data of each link in real time based on SD-WAN agent, monitoring indicators and frequency, and transmit the data to SD-WAN controller; The SD-WAN controller performs real-time and predictive analysis based on the built-in machine learning algorithm to obtain the network status parameters of each network link.

3. According to the multi-link fusion method based on SD-WAN technology in claim 2, it is characterized in that: The evaluating the performance and quality of each network link based on the monitored network status parameters and generating a link quality evaluation result specifically includes: Extracting key features that can reflect link performance and quality from the monitored network status parameters, wherein the key features include average delay, maximum delay, delay standard deviation, packet loss rate, and jitter rate; Based on the key features, pairwise comparisons are performed to construct a judgment matrix; Calculate the eigenvector corresponding to the maximum eigenvalue of each judgment matrix and perform normalization to obtain the weight of each indicator; Calculate the comprehensive score of the link based on the weight of each indicator and the real-time monitored data; According to the calculated comprehensive link score, the overall performance and quality of the link are evaluated to obtain a link quality evaluation result.

4. According to the multi-link fusion method based on SD-WAN technology in claim 3, it is characterized in that: The eigenvector corresponding to the maximum eigenvalue of each judgment matrix is ​​calculated and normalized to obtain the weight of each indicator, specifically including: Call the Python library function to calculate the eigenvalues ​​and eigenvectors of each judgment matrix to obtain the calculation results; According to the calculation results, find the maximum eigenvalue of each judgment matrix and its corresponding eigenvector; Calculate the sum of all elements in the eigenvector; Divide each element in the feature vector by their sum to get the weight of each indicator.

5. According to the multi-link fusion method based on SD-WAN technology of claim 4, it is characterized in that: The method of dynamically adjusting the allocation of data traffic and selecting the optimal link combination for data transmission according to the link quality evaluation result and the preset scheduling strategy specifically includes: Preset scheduling strategies, wherein the scheduling strategies include load balancing strategies, fault recovery strategies, and traffic shaping strategies; According to the link quality evaluation results and the preset scheduling strategy, the most appropriate strategy is selected for execution; When executing the scheduling strategy, a greedy algorithm is used to select the optimal link combination, and data transmission is performed based on the optimal link combination.

6. The multi-link fusion method based on SD-WAN technology according to claim 5, characterized in that: The use of SDN technology to dynamically configure network paths according to business needs and network status and optimize data transmission routes specifically includes: Select an SDN controller based on business requirements and network status; The SDN controller collects network traffic information in real time and uses data analysis tools to process the collected data to identify hot spots, potential bottlenecks, and traffic patterns in the network; Dynamically generate routing strategies based on identification results; The generated routing strategy is converted into network configuration instructions and sent to network devices for network path configuration, thereby optimizing data transmission routes.

7. A multi-link fusion system based on SD-WAN technology, characterized in that: include: A monitoring module, which is used to monitor the network status parameters of multiple network links in real time through the SD-WAN controller; An evaluation module, connected to the monitoring module, is used to evaluate the performance and quality of each network link based on the network status parameters monitored by the monitoring module, and generate a link quality evaluation result; The scheduling module is connected to the evaluation module and dynamically adjusts the data flow distribution according to the link quality evaluation results and the preset scheduling strategy, and selects the optimal link combination for data transmission; The routing optimization module uses SDN technology to work with the scheduling module to dynamically configure network paths based on business needs and network status and optimize data transmission routes; The security control module is used to implement security policies during data transmission.

8. The multi-link fusion system based on SD-WAN technology according to claim 7, characterized in that: The system further comprises: The management interface is used to display monitoring data, link quality assessment results, scheduling status and route optimization, and provides a user interaction interface that allows administrators to configure and modify preset scheduling policies and security policies.

9. A computing device, characterized in that It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the multi-link fusion method based on SD-WAN technology described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the multi-link fusion method based on SD-WAN technology described in any one of claims 1 to 6.

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