Dynamic network flow optimization system and method based on artificial intelligence

Abstract

The invention discloses a dynamic network traffic optimization system based on artificial intelligence, and the system comprises a data collection and preprocessing module which is used for collecting and cleaning multi-source network traffic data in real time; the artificial intelligence model training and optimizing module is used for training the prediction model by using a deep learning algorithm and optimizing the performance of the prediction model; the traffic prediction and dynamic scheduling module is used for predicting future network traffic based on the prediction model and dynamically adjusting resource allocation; the intelligent monitoring and fault early warning module is used for monitoring the network flow and the network state in real time and carrying out anomaly detection and early warning; the user feedback and model continuous optimization module is used for collecting user feedback and network performance data to continuously optimize the prediction model; the data privacy and security protection module is used for ensuring the privacy and security of the data; and the system expansion and compatibility module provides a system expansion interface and supports butt joint with other network management systems. The dynamic management and optimization of the network flow are realized, the utilization efficiency of network resources is improved, and the data privacy and security are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of computer network technology, and in particular to a dynamic network traffic optimization system and method based on artificial intelligence. Background Art

[0002] With the rapid development of Internet technology and the increasing number of network applications, network traffic has exploded, which has brought unprecedented challenges to network management and optimization. Traditional network traffic management methods often rely on static configuration and manual intervention, which are difficult to adapt to the dynamically changing network environment, resulting in inefficient network resource utilization and poor user experience.

[0003] In response to the above problems, researchers have proposed a variety of solutions, trying to improve the efficiency of network traffic management by introducing automated and intelligent means. For example, some existing network management systems monitor the real-time changes of network traffic and try to adjust resources according to preset rules or thresholds. However, these methods still have many limitations. On the one hand, they often rely on simple rule matching and lack the ability to predict future changes in network traffic, so they cannot make reasonable resource allocation and scheduling strategies in advance. On the other hand, existing network management systems often seem powerless when dealing with complex and changing network environments, and are unable to make dynamic adjustments based on real-time network status and user needs, resulting in limited optimization of network performance.

[0004] In addition, existing network traffic optimization technologies still have problems with insufficient data privacy and security protection. In the process of collecting, processing and analyzing network traffic data, if there is a lack of effective privacy and security protection measures, it may lead to the leakage of user information and increase the risk of network attacks. Summary of the invention

[0005] In order to solve the problems raised in the above background technology, the present invention provides a dynamic network traffic optimization system and method based on artificial intelligence. By introducing artificial intelligence and intelligent means, dynamic management and optimization of network traffic are realized, the utilization efficiency of network resources is improved, the user experience is enhanced, and data privacy and security are strengthened.

[0006] In order to solve the above technical problems, the technical solution proposed by the present invention is:

[0007] The present invention is a dynamic network traffic optimization system based on artificial intelligence, which includes: data acquisition and preprocessing module, data acquisition and preprocessing module, artificial intelligence model training and optimization module, traffic prediction and dynamic scheduling module, intelligent monitoring and fault warning module, user feedback and model continuous optimization module, data privacy and security protection module, system expansion and compatibility module.

[0008] The data collection and preprocessing module is used to collect and clean multi-source network traffic data in real time;

[0009] The artificial intelligence model training and optimization module uses a deep learning algorithm to train the prediction model and optimize its performance;

[0010] The traffic prediction and dynamic scheduling module predicts future network traffic based on the prediction model and dynamically adjusts resource allocation;

[0011] The intelligent monitoring and fault warning module monitors network traffic and network status in real time, and performs abnormality detection and warning;

[0012] The user feedback and model continuous optimization module collects user feedback and network performance data to continuously optimize the prediction model;

[0013] The data privacy and security protection module ensures the privacy and security of data;

[0014] The system expansion and compatibility module provides a system expansion interface and supports connection with other network management systems.

[0015] Preferably, the data collection and preprocessing module collects multi-source network traffic data from network devices, user terminals and servers in real time, including user behavior data, network status data, device status data, and cleans and preprocesses the data.

[0016] Preferably, the artificial intelligence model training and optimization module uses deep learning algorithms, combined with variants of convolutional neural networks and recurrent neural networks, to train historical network traffic data, build a prediction model, and evaluate and optimize the prediction model through model evaluation methods, including cross-validation, precision, recall rate and F1 score. Based on the evaluation results, the model is iteratively optimized to improve prediction accuracy and generalization ability.

[0017] Preferably, the artificial intelligence model training and optimization module also includes model transfer learning and model fusion technology, which utilizes the knowledge of trained models on similar tasks to accelerate the training process of new models and improve the adaptability and robustness of the models; and fuses the prediction results of multiple models to improve the accuracy and stability of the prediction.

[0018] Preferably, the dynamic scheduling module in the traffic prediction and dynamic scheduling module uses an adaptive scheduling algorithm to dynamically adjust resources including network bandwidth and server load according to traffic prediction results to optimize network performance.

[0019] Preferably, the traffic prediction and dynamic scheduling module predicts the network traffic in the future based on the trained prediction model, including the traffic size, traffic peak period, and traffic distribution; dynamically adjusts the allocation and scheduling strategies of network resources according to the prediction results, including bandwidth allocation, server load balancing, routing selection, etc., to optimize network performance; monitors changes in network traffic in real time, and fine-tunes the scheduling strategy according to real-time data, including timely adjusting the bandwidth resources of a certain area when a sudden increase in traffic is detected in the area, and timely transferring part of the traffic to other servers when it is detected that the load of a server is too high.

[0020] Preferably, the traffic prediction and dynamic scheduling module also includes an adaptive scheduling algorithm based on reinforcement learning, which can be dynamically adjusted according to real-time network status and user needs to achieve optimal resource allocation.

[0021] Preferably, the intelligent monitoring and fault warning module monitors network traffic and network status in real time, with key indicators including data transmission speed, delay, and packet loss rate; uses machine learning algorithms, including cluster analysis and anomaly detection, to perform real-time analysis and warning of network traffic; when abnormal traffic or potential faults are detected, timely issues warning signals, including sending emails, text messages, or SMS to notify management personnel, and taking corresponding emergency measures, including restricting access to abnormal traffic and switching to backup networks; introduces a network attack detection and defense mechanism based on a deep learning algorithm, identifies the characteristics and behavior patterns of network attacks by training deep learning models, and promptly detects and handles potential network threats.

[0022] Preferably, the user feedback and model continuous optimization module collects user usage feedback and network performance data, including user satisfaction, network delay, and bandwidth utilization; based on user feedback and network performance data, the prediction model is continuously optimized and adjusted to improve the accuracy of prediction and optimization, and the function of user-defined optimization strategy is provided, allowing the user to adjust the allocation and scheduling strategy of network resources according to actual needs.

[0023] Preferably, the system expansion and compatibility module provides a system expansion interface, supports docking and collaboration with other network management systems and data analysis platforms, adapts to network environments of different sizes and types, including enterprise networks, data center networks, and cloud computing networks, supports cross-platform and cross-device network traffic optimization, and ensures the flexibility and scalability of the system.

[0024] Preferably, the data privacy and security protection module encrypts the collected multi-source network traffic data to protect the confidentiality of the data and prevent the data from being stolen or tampered with during transmission and storage; establishes a strict data access control mechanism to limit access to and processing of sensitive data to authorized personnel or systems, and prevent unauthorized access and data abuse; desensitizes the data to remove sensitive information that may involve user privacy while ensuring the availability and analytical value of the data; monitors data usage to promptly detect and prevent abnormal data access or operation behavior; follows relevant privacy laws and policies, including the principle of data minimization, only collects and uses necessary data, and reduces the risk of data leakage and abuse; and uses secure multi-party computing, differential privacy and other technologies to perform data analysis and model training while protecting data privacy.

[0025] The dynamic network traffic optimization method based on artificial intelligence includes the following steps:

[0026] Collect multi-source network traffic data from network devices, user terminals and servers in real time, including user behavior data, network status data, and device status data, and clean and pre-process the collected data, including removing invalid data, redundant data and abnormal data, as well as formatting, normalizing and extracting features;

[0027] Using deep learning algorithms, combined with variants of convolutional neural networks and recurrent neural networks, historical network traffic data is trained to establish a prediction model. The prediction model is evaluated and optimized using a model evaluation method. The model is iteratively optimized based on the evaluation results to improve prediction accuracy and generalization capabilities. It also includes using model transfer learning and model fusion technology to accelerate the new model training process and improve adaptability and robustness, as well as fusing the prediction results of multiple models to improve accuracy and stability.

[0028] Predict future network traffic based on prediction models, including traffic size, peak traffic hours, and traffic distribution;

[0029] Dynamically adjust the allocation and scheduling strategies of network resources based on the prediction results, including bandwidth allocation, server load balancing, and routing selection, monitor network traffic changes in real time, and fine-tune the scheduling strategies based on real-time data;

[0030] Real-time monitoring of network traffic and network status, with key indicators including data transmission speed, latency, and packet loss rate. Machine learning algorithms are used to analyze and warn network traffic in real time. When abnormal traffic or potential failures are detected, warning signals are issued in a timely manner and corresponding emergency measures are taken. It also includes the introduction of network attack detection and defense mechanisms based on deep learning algorithms to identify network attack characteristics and behavior patterns and deal with potential network threats in a timely manner.

[0031] Collect user feedback and network performance data, including user satisfaction, network latency, and bandwidth utilization, and continuously optimize and adjust the prediction model based on user feedback and network performance data to improve the accuracy of prediction and optimization. It also provides users with the function of customizing optimization strategies, allowing users to adjust the allocation and scheduling strategies of network resources according to actual needs.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. The present invention utilizes deep learning algorithms to train prediction models, and combines model transfer learning and model fusion technology to more accurately predict future network traffic. Based on the prediction results, the present invention uses an adaptive scheduling algorithm to dynamically adjust network resources, including network bandwidth, server load, etc., effectively avoiding resource idleness and overload problems and improving resource utilization.

[0034] 2. The present invention can monitor network traffic and network status in real time, use machine learning algorithms for anomaly detection and early warning, and introduce network attack detection and defense mechanisms based on deep learning, thereby improving the security and stability of the network.

[0035] 3. The present invention collects user feedback and network performance data to continuously optimize and adjust the prediction model, thereby improving the accuracy of prediction and optimization, while allowing users to customize optimization strategies to meet the actual needs of different users.

[0036] 4. The present invention ensures the privacy and security of network traffic data through a data privacy and security protection module, thereby preventing leakage of user information and increasing the risk of network attacks. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a system architecture diagram of the present invention;

[0038] Figure 2 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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.

[0040] Example 1

[0041] refer to Figure 1 and Figure 2The dynamic network traffic optimization system based on artificial intelligence includes data acquisition and preprocessing module, artificial intelligence model training and optimization module, traffic prediction and dynamic scheduling module, intelligent monitoring and fault warning module, user feedback and model continuous optimization module, data privacy and security protection module, and system expansion and compatibility module.

[0042] In this embodiment, the data collection and preprocessing module first collects multi-source network traffic data from network devices, user terminals and servers in real time. These data include traffic size, traffic type, source address, and destination address, and these data are cleaned and preprocessed to remove invalid data, redundant data and abnormal data, and data formatting, normalization and feature extraction are performed at the same time.

[0043] Next, the AI ​​model training and optimization module uses deep learning algorithms, combined with variants of convolutional neural networks and recurrent neural networks, to train historical network traffic data and build a prediction model. The module also uses model evaluation methods to evaluate and optimize the prediction model, with indicators including cross-validation, precision, recall, and F1 score. In order to improve model training efficiency and performance, the module also introduces model transfer learning and model fusion technology, which uses the knowledge of trained models on similar tasks to accelerate the training process of new models, and fuses the prediction results of multiple models to improve accuracy and stability.

[0044] The traffic prediction and dynamic scheduling module predicts future network traffic based on the trained prediction model, including traffic size, traffic peak hours, and traffic distribution. Based on the prediction results, the dynamic scheduling module uses an adaptive scheduling algorithm to dynamically adjust resource allocation such as network bandwidth and server load to optimize network performance. In addition, the module also introduces an adaptive scheduling algorithm based on reinforcement learning, which can dynamically adjust resources such as network bandwidth and server load according to real-time network status and user needs.

[0045] The intelligent monitoring and fault warning module monitors network traffic and network status in real time, and uses machine learning algorithms to detect anomalies and provide warnings for network traffic data. The module also introduces a network attack detection and defense mechanism based on deep learning algorithms to improve network security performance.

[0046] The user feedback and model continuous optimization module collects user feedback and network performance data, and iteratively optimizes and adjusts the prediction model based on these data to improve the accuracy of prediction and optimization. At the same time, this module also allows users to customize optimization strategies to meet the actual needs of different users.

[0047] The data privacy and security protection module uses encryption technology and data desensitization measures during data collection, processing and analysis to protect the confidentiality and privacy of data. The module also establishes a strict data access control mechanism to restrict only authorized personnel or systems to access and process sensitive data, preventing unauthorized access and data abuse. At the same time, the module also monitors the use of data, promptly detects and prevents abnormal data access or operation behavior, and complies with relevant privacy laws and policies.

[0048] The system expansion and compatibility module provides a system expansion interface and supports docking with other network management systems to achieve more extensive network management and optimization functions.

[0049] Example 2

[0050] This embodiment describes in detail the artificial intelligence model training and optimization module of the artificial intelligence-based dynamic network traffic optimization system.

[0051] In this embodiment, the artificial intelligence model training and optimization module uses the convolutional neural network in the deep learning algorithm combined with a variant of the recurrent neural network to train the prediction model. The convolutional neural network is responsible for extracting useful feature information from complex network traffic data, while the recurrent neural network is responsible for processing sequence data and capturing temporal dependencies.

[0052] Specifically, we use historical network traffic data as input, build a convolutional neural network through multiple convolutional layers, pooling layers, and fully connected layers to extract the spatial features of traffic data. Then, we use the output of the convolutional neural network as the input of the recurrent neural network, and capture the temporal features of traffic data through the loop structure of the recurrent neural network. Finally, the predicted future network traffic is output through the fully connected layer, including traffic size, peak traffic hours, and traffic distribution.

[0053] During the training process, we use the cross entropy loss function to measure the difference between the predicted value and the true value. The cross entropy loss function can handle classification problems well and has a good measurement effect on the difference between probability distributions. By minimizing the cross entropy loss function, the predicted result is closer to the true value.

[0054] In order to optimize the model parameters, we use the back propagation algorithm. The back propagation algorithm calculates the gradient of the loss function with respect to the model parameters and updates the parameters in the opposite direction of the gradient, thereby gradually reducing the value of the loss function. During the back propagation process, we use the adaptive moment estimation optimization algorithm to accelerate the training process and improve the convergence speed of the model. The adaptive moment estimation optimization algorithm combines the advantages of the momentum method and the root mean square transfer algorithm, and can adaptively adjust the learning rate, making the model more stable and efficient during the training process.

[0055] In order to improve the training efficiency and performance of the model, this module also introduces model transfer learning and model fusion technology. Model transfer learning uses the knowledge of trained models on similar tasks to accelerate the training process of new models.

[0056] Specifically, we first train a base model on a large dataset, then migrate it to the current task and fine-tune it based on the data of the current task. This can greatly shorten the model training time and improve the generalization ability of the model.

[0057] In addition, we also fuse the prediction results of multiple models to improve the accuracy and stability of the prediction. Specifically, we use the weighted average method to fuse the prediction results of multiple models, where the weight of each model is assigned according to its prediction performance.

[0058] Example 3

[0059] This embodiment describes in detail the traffic prediction and dynamic scheduling module of the dynamic network traffic optimization system based on artificial intelligence.

[0060] Based on the trained prediction model, this module predicts future network traffic, including traffic size, traffic peak hours, and traffic distribution. Then, based on the prediction results, the dynamic scheduling module uses an adaptive scheduling algorithm to dynamically adjust resource allocation, including network bandwidth and server load.

[0061] The adaptive scheduling algorithm is dynamically adjusted according to the real-time network status and user needs. Specifically, we define a resource utilization threshold. When it is predicted that the future network traffic will exceed this threshold, the dynamic scheduling module will increase the network bandwidth or adjust the server load to cope with the upcoming traffic peak; on the contrary, when it is predicted that the future network traffic will be lower than this threshold, the dynamic scheduling module will reduce the network bandwidth or reduce the server load to save resources.

[0062] In order to implement the adaptive scheduling algorithm, we use the current network status and prediction results as the input status of the scheduling strategy network, including network bandwidth utilization, server load, and predicted traffic size. Define the scheduling action space, including increasing bandwidth, reducing bandwidth, and adjusting server load. Design a reward function to evaluate the quality of scheduling actions. Including, when the scheduling action successfully copes with the traffic peak and saves resources, give positive rewards; when the scheduling action leads to insufficient or wasted resources, give negative rewards. Use reinforcement learning to train the scheduling strategy network. Through continuous iterative training, the scheduling strategy network outputs the optimal scheduling action based on the current network status and prediction results.

[0063] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. Dynamic network traffic optimization system based on artificial intelligence, characterized by: The system includes: Data collection and preprocessing module, used to collect and clean multi-source network traffic data in real time; Artificial intelligence model training and optimization module, which uses deep learning algorithms to train prediction models and optimize their performance; Traffic prediction and dynamic scheduling module, which predicts future network traffic based on the prediction model and dynamically adjusts resource allocation; Intelligent monitoring and fault warning module, real-time monitoring of network traffic and network status, abnormal detection and warning; User feedback and model continuous optimization module, which collects user feedback and network performance data to continuously optimize the prediction model; Data privacy and security protection module to ensure data privacy and security; The system expansion and compatibility module provides a system expansion interface and supports docking with other network management systems.

2. The artificial intelligence-based dynamic network traffic optimization system according to claim 1, characterized in that: The data collection and preprocessing module collects multi-source network traffic data from network devices, user terminals and servers in real time, including traffic size, traffic type, source address, destination address, and cleans and preprocesses the data.

3. The dynamic network traffic optimization system based on artificial intelligence according to claim 1 is characterized in that: The artificial intelligence model training and optimization module uses deep learning algorithms, including variants of convolutional neural networks and recurrent neural networks, to train prediction models and optimize model performance through evaluation methods.

4. The artificial intelligence-based dynamic network traffic optimization system according to claims 1 and 3, characterized in that: The artificial intelligence model training and optimization module also includes model transfer learning and model fusion technology, which uses pre-trained models to accelerate the model training process and fuse the prediction results of multiple models.

5. The artificial intelligence-based dynamic network traffic optimization system according to claim 1, characterized in that: The dynamic scheduling module in the traffic prediction and dynamic scheduling module adopts an adaptive scheduling algorithm to dynamically adjust resources including network bandwidth and server load according to traffic prediction results.

6. The artificial intelligence-based dynamic network traffic optimization system according to claims 1 and 5, characterized in that: The traffic prediction and dynamic scheduling module also includes an adaptive scheduling algorithm based on reinforcement learning, which can be dynamically adjusted according to real-time network status and user needs.

7. The artificial intelligence-based dynamic network traffic optimization system according to claim 1, characterized in that: The intelligent monitoring and fault warning module monitors network traffic and network status in real time, uses machine learning algorithms to perform anomaly detection and warning on network traffic data, and introduces a network attack detection and defense mechanism based on deep learning algorithms.

8. The artificial intelligence-based dynamic network traffic optimization system according to claim 1, characterized in that: The user feedback and model continuous optimization module collects user usage feedback and network performance data, and iteratively optimizes and adjusts the prediction model based on these data.

9. The artificial intelligence-based dynamic network traffic optimization system according to claim 1, characterized in that: The data privacy and security protection module adopts encryption technology and data desensitization measures during data collection, processing and analysis, and complies with relevant laws, regulations and privacy policies.

10. A dynamic network traffic optimization method based on artificial intelligence, characterized in that: The following steps are involved: Collect and clean multi-source network traffic data in real time, and perform data cleaning and preprocessing; Use deep learning algorithms to train predictive models and use training data to optimize their performance; Predict future network traffic based on prediction models; Dynamically adjust the allocation and scheduling strategies of network resources based on the prediction results; Monitor network traffic and network status in real time, and conduct anomaly detection and early warning; Collect user feedback and network performance data to continuously optimize the prediction model.

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