An intelligent allocation optimization method and system for traffic bandwidth

Abstract

The present invention provides a method and system for intelligent allocation and optimization of traffic bandwidth, which relates to the technical field of intelligent allocation and optimization of traffic bandwidth. An information transmission network is constructed, node information transmission paths are obtained, traffic change data is analyzed to obtain path risk nodes; information of path intersection nodes is obtained to generate intersection risk nodes, risk characteristic data is obtained, and a multi-level traffic risk network is constructed; the first-level anomaly coefficient and the second-level anomaly coefficient are calculated, and the first-level anomaly coefficient and the second-level anomaly coefficient are respectively compared with the first-level preset threshold and the second-level preset threshold, and then traffic anomaly determination is performed to obtain a traffic anomaly determination result; the traffic bandwidth allocation coefficients of path nodes and intersection nodes are respectively calculated, and then the allocation and adjustment of traffic bandwidth are performed. The present invention solves the problems of lack of flexibility, intelligence and comprehensive evaluation in traffic management in the prior art, etc.

Description

Technical Field

[0001] The present invention provides a method and system for intelligent allocation and optimization of traffic bandwidth, which relates to the technical field of intelligent allocation and optimization of traffic bandwidth. Background Art

[0002] Traditional traffic management methods often rely on fixed bandwidth allocation strategies, lacking flexibility and being difficult to cope with sudden traffic changes and risk events in the network. In the prior art, although there are some means of traffic monitoring and bandwidth management, most of them only focus on the network status at a single level, such as only monitoring node traffic or only considering bandwidth allocation at the path level, lacking a multi-level and all-round assessment of the overall traffic risk of the network. This results in network managers being difficult to quickly and accurately locate the root cause of problems and take effective countermeasures when traffic anomalies or risk events occur. Traditional bandwidth allocation methods often rely on manual experience and fixed threshold settings, lacking adaptability and intelligence. When the network traffic pattern changes or new types of risks are encountered, these methods may not be able to make timely adjustments, resulting in a decline in network performance or waste of resources. Summary of the Invention

[0003] The present invention provides a method and system for intelligent allocation and optimization of traffic bandwidth to solve the above problems:

[0004] A method and system for intelligent allocation and optimization of traffic bandwidth proposed by the present invention, the allocation and optimization method includes:

[0005] S1. Construct an information transmission network, obtain information transmission nodes, and then obtain node information transmission paths, generate path node traffic packets, analyze traffic change data, and obtain path risk nodes;

[0006] S2. Obtain path intersection node information according to the node information transmission path information, generate intersection risk nodes, obtain risk feature data, label the first-level risk and the second-level risk, and construct a multi-level traffic risk network;

[0007] S3. Calculate the first-level anomaly coefficient and the second-level anomaly coefficient, compare the first-level anomaly coefficient and the second-level anomaly coefficient with the first-level preset threshold and the second-level preset threshold respectively, and then perform traffic anomaly determination to obtain a traffic anomaly determination result;

[0008] S4. Calculate the traffic bandwidth allocation coefficients of path nodes and intersection nodes respectively, and then perform allocation adjustment of traffic bandwidth.

[0009] Further, the S1 includes:

[0010] Obtain the network information transmission requirements, and construct an information transmission network according to the network information transmission requirements;

[0011] Obtain the information transmission nodes of the information transmission network, and obtain the node traffic information and node information transmission paths of each information transmission node;

[0012] Obtain the node traffic information on the node information transmission path, and generate multiple path node traffic packets corresponding to the node information transmission path;

[0013] Generate a traffic packet group corresponding to the node information transmission path according to the multiple path node traffic packets;

[0014] Obtain the traffic change data of the traffic packet group, compare the traffic change data with a preset change threshold, and obtain a change comparison result;

[0015] Determine abnormal nodes for the information transmission nodes according to the change comparison result, and obtain path risk nodes.

[0016] Further, the S2 includes:

[0017] Obtain multiple pieces of node information transmission path information, and obtain path intersection node information according to the node information transmission path information;

[0018] Obtain the path risk nodes of the path intersection node information, and generate intersection risk nodes;

[0019] Obtain the risk characteristic data of the path risk nodes, and obtain path node risk characteristic data;

[0020] Obtain the risk characteristic data of the intersection risk nodes, and obtain intersection node risk characteristic data;

[0021] Label the path node risk characteristic data as the first-level risk;

[0022] Label the intersection node risk characteristic data as the second-level risk; construct a multi-level traffic risk network according to the first-level risk information combined with the second-level risk information.

[0023] Further, the S3 includes:

[0024] Obtain the multi-level traffic risk network information and calculate the first-level anomaly coefficient;

[0025] Compare the first-level anomaly coefficient with the first-level preset threshold, and obtain a first-level comparison result;

[0026] Determine traffic anomalies for the first-level traffic according to the first-level comparison result, and obtain a first traffic anomaly determination result;

[0027] Calculate the second-level anomaly coefficient according to the first-level traffic anomaly determination;

[0028] Compare the second - level anomaly coefficient with the second - level preset threshold to obtain the second - level comparison result;

[0029] Determine traffic anomalies for the second - level traffic based on the second - level comparison result to obtain the second traffic anomaly determination result.

[0030] Further, the S4 includes:

[0031] When the first traffic anomaly determination result is an anomaly determination result, calculate the traffic bandwidth allocation coefficient of the corresponding path risk node according to the first - level anomaly coefficient combined with the path node risk characteristic data;

[0032] When the second traffic anomaly determination result is an anomaly determination, calculate the traffic bandwidth allocation coefficient of the corresponding intersection risk node according to the second - level anomaly coefficient combined with the intersection node risk characteristic data;

[0033] Obtain the node anomaly data after traffic bandwidth allocation, and perform continuous monitoring and adjustment.

[0034] Further, the allocation optimization system includes:

[0035] A network risk node determination module, used to construct an information transmission network, obtain information transmission nodes, then obtain the node information transmission path, generate path node traffic packets, analyze traffic change data, and obtain path risk nodes;

[0036] An intersection node determination module, used to obtain intersection node information of the path according to the node information transmission path information, generate intersection risk nodes, obtain risk characteristic data, label the first - level risk and the second - level risk, and construct a multi - level traffic risk network;

[0037] A multi - level anomaly analysis module, used to calculate the first - level anomaly coefficient and the second - level anomaly coefficient, compare the first - level anomaly coefficient and the second - level anomaly coefficient with the first - level preset threshold and the second - level preset threshold respectively, and then perform traffic anomaly determination to obtain the traffic anomaly determination result;

[0038] An allocation module, used to calculate the traffic bandwidth allocation coefficients of path nodes and intersection nodes respectively, and then perform the allocation and adjustment of traffic bandwidth.

[0039] Further, the network risk node determination module includes:

[0040] A network construction module, used to obtain the network information transmission requirements and construct an information transmission network according to the network information transmission requirements;

[0041] A path acquisition module, configured to acquire information transmission nodes of an information transmission network, and acquire node traffic information and node information transmission paths of each information transmission node;

[0042] A traffic change comparison module, configured to acquire node traffic information on the node information transmission path, and generate a plurality of path node traffic packets corresponding to the node information transmission path;

[0043] Generate a traffic packet group corresponding to the node information transmission path according to the plurality of path node traffic packets;

[0044] Acquire traffic change data of the traffic packet group, compare the traffic change data with a preset change threshold, and obtain a change comparison result;

[0045] A risk determination module, configured to perform an abnormal node determination on the information transmission node according to the change comparison result, and obtain a path risk node.

[0046] Further, the intersection node determination module includes:

[0047] An intersection node generation module, configured to acquire information on multiple node information transmission paths, and acquire path intersection node information according to the node information transmission path information;

[0048] Acquire path risk nodes of the path intersection node information, and generate intersection risk nodes;

[0049] A feature data acquisition module, configured to acquire risk feature data of the path risk node, and obtain path node risk feature data;

[0050] Acquire risk feature data of the intersection risk node, and obtain intersection node risk feature data;

[0051] A multi-layer network monitoring module, configured to label the path node risk feature data as a first-level risk;

[0052] Label the intersection node risk feature data as a second-level risk;

[0053] Construct a multi-level traffic risk network according to the first-level risk information in combination with the second-level risk information.

[0054] Further, the network risk node determination module includes:

[0055] A first calculation and determination module, configured to acquire multi-level traffic risk network information and calculate a first-level anomaly coefficient;

[0056] Compare the first-level anomaly coefficient with a first-level preset threshold, and obtain a first-level comparison result;

[0057] Perform traffic anomaly determination on the first - level traffic according to the first - level comparison result to obtain the first traffic anomaly determination result;

[0058] A second calculation and determination module, configured to calculate a second - level anomaly coefficient according to the first - level traffic anomaly determination;

[0059] Compare the second - level anomaly coefficient with the second - level preset threshold to obtain a second - level comparison result;

[0060] Perform traffic anomaly determination on the second - level traffic according to the second - level comparison result to obtain a second traffic anomaly determination result.

[0061] Further, the allocation module includes:

[0062] A first calculation and allocation module, configured to, when the first traffic anomaly determination result is an anomaly determination result, calculate a traffic bandwidth allocation coefficient for the corresponding path risk node according to the first - level anomaly coefficient in combination with the path node risk characteristic data;

[0063] A second calculation and allocation module, configured to, when the second traffic anomaly determination result is an anomaly determination, calculate a traffic bandwidth allocation coefficient for the corresponding intersection risk node according to the second - level anomaly coefficient in combination with the intersection node risk characteristic data;

[0064] A monitoring module, configured to obtain node anomaly data after traffic bandwidth allocation and perform continuous monitoring and adjustment.

[0065] Advantages of the present invention: By identifying risk nodes and performing intelligent allocation of traffic bandwidth, the risk of network failures can be effectively reduced, and the stability and reliability of the network can be improved. Bandwidth allocation based on the risk level of nodes and actual requirements can make more reasonable use of network resources, avoid waste and over - concentration of resources. By optimizing traffic bandwidth allocation, data transmission delay and packet loss rate can be reduced, and the efficiency and quality of data transmission can be improved. This method can adapt to network environments of different scales and complexities, providing strong support for network expansion and upgrade. Through the automated risk identification and bandwidth allocation process, the cost and complexity of manual intervention can be reduced, realizing the intelligence and automation of network management. Description of the Drawings

[0066] Figure 1 It is a schematic diagram of an intelligent allocation optimization method for traffic bandwidth;

[0067] Figure 2 It is a schematic diagram of a multi - level traffic risk network. Detailed Embodiments

[0068] The preferred embodiments of the present invention will be described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present invention, and are not intended to limit the present invention.

[0069] In one embodiment of the present invention, an intelligent allocation optimization method and system for traffic bandwidth are proposed. The allocation optimization method includes:

[0070] S1. Construct an information transmission network, obtain information transmission nodes, and then obtain the node information transmission paths, generate path node traffic packets, analyze the traffic change data, and obtain path risk nodes;

[0071] S2. Obtain the path intersection node information according to the node information transmission path information, generate intersection risk nodes, obtain the risk characteristic data, label the first-level risk and the second-level risk, and construct a multi-level traffic risk network;

[0072] S3. Calculate the first-level anomaly coefficient and the second-level anomaly coefficient, compare the first-level anomaly coefficient and the second-level anomaly coefficient with the first-level preset threshold and the second-level preset threshold respectively, and then perform traffic anomaly determination to obtain the traffic anomaly determination result;

[0073] S4. Calculate the traffic bandwidth allocation coefficients of the path nodes and the intersection nodes respectively, and then perform the allocation adjustment of the traffic bandwidth, as Figure 1 shown.

[0074] The working principle of the above technical solution is as follows: Construct an information transmission network, which consists of multiple information transmission nodes and the connection paths between the nodes. Identify each node in the network and determine the information transmission paths between the nodes. Generate path node traffic packets, which represent the data traffic in the network. By analyzing the traffic change data, identify the nodes in the path that may have risks. The nodes are affected by various reasons (such as equipment failures, network congestion, etc.) resulting in data transmission interruption or delay. According to the node information transmission path information, obtain the information of the path intersection nodes, that is, those nodes that are common to multiple paths in the network. Generate intersection risk nodes. Since these nodes are located at the intersection of multiple paths, their failures can have a greater impact on the entire network. Obtain risk characteristic data, including the traffic change, delay, packet loss rate, etc. of the nodes, for evaluating the risk level of the nodes. According to the risk characteristic data, label the first-level risk and the second-level risk. The first-level risk usually refers to the risk that directly affects a single path, while the second-level risk may involve the intersection of multiple paths and have a greater impact on the entire network. Construct a risk network with multiple levels to more comprehensively understand the risk distribution in the network. Calculate the anomaly coefficients of the first level and the second level, which reflect the deviation degree of the nodes at the first level from the normal state in the current state. Compare the calculated anomaly coefficients with the preset thresholds. If the anomaly coefficient exceeds the threshold, it is determined that the traffic is abnormal. According to the determination result, potential problems in the network can be discovered in time, and corresponding measures can be taken for intervention. Calculate the traffic bandwidth allocation coefficients of the path nodes and the intersection nodes respectively. It reflects the relative importance of the nodes in the network and the bandwidth resources they need. According to the calculated allocation coefficients, adjust the allocation of the traffic bandwidth. For the nodes with higher risks, their bandwidth resources can be appropriately increased to improve the stability and efficiency of data transmission.

[0075] The technical effects of the above technical solution are as follows: By identifying risk nodes and performing intelligent allocation of traffic bandwidth, the risk of network failures can be effectively reduced, and the stability and reliability of the network can be improved. Allocating bandwidth according to the risk level and actual needs of the nodes can make more reasonable use of network resources, avoiding waste and over-concentration of resources. By optimizing the allocation of traffic bandwidth, the delay and packet loss rate of data transmission can be reduced, and the efficiency and quality of data transmission can be improved. This method can adapt to network environments of different scales and complexities, providing strong support for the expansion and upgrade of the network. Through the automated risk identification and bandwidth allocation process, the cost and complexity of manual intervention can be reduced, realizing the intelligentization and automation of network management.

[0076] In an embodiment of the present invention, the S1 includes:

[0077] Obtain the network information transmission requirements and construct an information transmission network according to the network information transmission requirements;

[0078] Obtain the information transmission nodes of the information transmission network, and obtain the node traffic information and node information transmission paths of each information transmission node;

[0079] Obtain the node traffic information on the node information transmission path, and generate multiple path node traffic packets corresponding to the node information transmission path;

[0080] Generate a traffic packet group corresponding to the node information transmission path according to the multiple path node traffic packets;

[0081] Obtain the traffic change data of the traffic packet group, compare the traffic change data with a preset change threshold, and obtain a change comparison result; the traffic change data is the traffic difference between every two adjacent nodes;

[0082] Determine abnormal nodes for the information transmission nodes according to the change comparison result, and obtain path risk nodes.

[0083] When the traffic change data is greater than the preset change threshold, the node with relatively larger traffic in the traffic difference is the path risk node.

[0084] The working principle of the above technical solution is as follows: According to business requirements or network planning, clarify the specific requirements for network information transmission, such as the amount of data transmitted, transmission speed, transmission stability, etc. According to the network information transmission requirements, construct the corresponding information transmission network. This network consists of multiple information transmission nodes and the connection paths between the nodes. In the constructed information transmission network, identify and obtain the node traffic information of each information transmission node, which includes the data sending amount, receiving amount, etc. of the node. Obtain the information transmission path of each node, that is, the path of data flow between the nodes. For each node information transmission path, generate multiple path node traffic packets according to the traffic information of the nodes on the path. These traffic packets represent the data transmission amounts between different nodes on the path. Combine the multiple path node traffic packets on the same path into a traffic packet group. Obtain the traffic change data of the traffic packet group, which mainly calculates the traffic difference between every two adjacent nodes, that is, the change in the data transmission amount between adjacent nodes. Compare the calculated traffic change data with the preset traffic change threshold. If the traffic change data is greater than the preset change threshold, it indicates that there is a significant change in the data transmission amount between adjacent nodes, and there may be an abnormality. The node with relatively larger traffic in the traffic difference is determined as the path risk node. This is because the node with larger traffic is more likely to have a transmission bottleneck or failure, thus affecting the data transmission of the entire path.

[0085] The technical effects of the above technical solution are as follows: By monitoring the traffic changes between nodes in real time, abnormal situations in the network can be detected in a timely manner, improving the accuracy and timeliness of network monitoring. By identifying path risk nodes, these nodes can be monitored and optimized with emphasis, thereby improving the performance and stability of the entire network. Detecting and handling path risk nodes in a timely manner can reduce the risk of network failures and problems such as service interruptions and data loss caused by network failures. By analyzing and monitoring the node traffic information, network resources can be allocated more reasonably, improving the utilization rate and efficiency of resources.

[0086] In an embodiment of the present invention, S2 includes:

[0087] Obtain information on multiple node information transmission paths, and obtain path intersection node information according to the node information transmission path information;

[0088] Obtain the path risk nodes of the path intersection node information, and generate intersection risk nodes;

[0089] Obtain the risk characteristic data of the path risk nodes to obtain path node risk characteristic data; the path node risk characteristic data is the data of the traffic characteristics of the path nodes.

[0090] Obtain the risk characteristic data of the intersection risk nodes to obtain intersection node risk characteristic data; the intersection node risk characteristic data is the data of the traffic characteristics of the intersection nodes.

[0091] Label the path node risk characteristic data as the first-level risk; the first-level risk is the risk annotation of all path risk nodes in the network.

[0092] Label the intersection node risk characteristic data as the second-level risk; the second-level risk is the risk annotation of all intersection risk nodes in the network.

[0093] Construct a multi-level traffic risk network according to the first-level risk information combined with the second-level risk information, as Figure 2 shown. This figure is only a schematic diagram, and the specific node positions are determined by the data.

[0094] The working principle of the above technical solution is as follows: Obtain information on multiple node information transmission paths through network monitoring or data analysis tools. This information includes the nodes on the paths and the connection relationships between the nodes. Based on this path information, identify the path intersection nodes, that is, the nodes located at the intersection of multiple paths in the network. For each path, obtain the path risk node information. The path risk nodes may be nodes with potential risks determined based on previous traffic analysis, fault history, or other metrics. Extract the risk nodes located at the path intersections to generate a list of intersection risk nodes. Since these nodes are located at the intersection of multiple paths, their failures may have a greater impact on the entire network. For the path risk nodes and the intersection risk nodes, obtain their risk characteristic data respectively. These characteristic data are mainly based on the traffic characteristics of the nodes, such as traffic volume, traffic change rate, packet loss rate, latency, etc. By analyzing these characteristic data, the risk level of the nodes can be evaluated. Label the characteristic data of the path risk nodes as the first-level risk. The risk at this level mainly reflects the potential risks of the nodes on each path in the network. Label the characteristic data of the intersection risk nodes as the second-level risk. This level of risk pays more attention to the risks of the intersection nodes in the network because the failures of these nodes may affect multiple paths simultaneously. Based on the risk information of the first level and the second level, construct a risk network with multiple levels. This network not only shows the risk levels of each node in the network but also reveals the way of risk propagation and diffusion in the network. Through this multi-level traffic risk network, a more comprehensive understanding of the risk distribution and potential threats in the network can be obtained.

[0095] The technical effects of the above technical solution are as follows: By constructing a multi-level traffic risk network, potential risks in the network can be identified and evaluated more accurately, improving the network risk management ability. By focusing on monitoring and optimizing the risk nodes, the risk of network failures can be reduced, and the stability and performance of the network can be improved. This method is applicable to network environments of different scales and complexities, can support the expansion and upgrade of the network, and ensure that the network remains efficient and secure during continuous development. Through the automated risk identification and annotation process, the cost and complexity of manual intervention can be reduced, and the efficiency and quality of network maintenance can be improved. By promptly discovering and handling potential risk nodes, the security of the network can be enhanced, preventing problems such as network paralysis and data leakage caused by node failures or attacks.

[0096] In one embodiment of the present invention, S3 includes:

[0097] Obtain multi-level traffic risk network information and calculate the first-level anomaly coefficient;

[0098] The calculation formula for the first-level anomaly coefficient is:

[0099]

[0100] Among them, YC is the first - level network anomaly coefficient, w is the total number of paths in the information transmission network, f is the total number of path risk nodes in the path, L i is the traffic data of the i - th path risk node, L i-1 is the (i - 1) - th path node of the i - th path risk node, (L i -L i-1 ) o is the total abnormal change in traffic of the path risk nodes of the o - th path in the information transmission network, y is the total number of path nodes in the path, L x is the traffic data of the x - th path node, L x-1 is the (x - 1) - th path node of the x - th path node, (L x -L x-1 ) o is the total abnormal change in traffic of the path nodes of the o - th path in the information transmission network;

[0101] Compare the first - level anomaly coefficient with the first - level preset threshold to obtain the first - level comparison result;

[0102] Determine traffic anomalies for the first - level traffic based on the first - level comparison result to obtain the first traffic anomaly determination result;

[0103] Calculate the second - level anomaly coefficient based on the first - level traffic anomaly determination;

[0104] Start calculating the second - level anomaly coefficient when the first - level determination result is abnormal.

[0105] The calculation formula for the second - level anomaly coefficient is:

[0106]

[0107] Among them, RC is the second - level network anomaly coefficient, w is the total number of paths in the information transmission network, j is the total number of intersecting risk nodes in the path, L a is the traffic data of the a - th intersecting risk node, (L a ) o is the traffic anomaly of the a - th intersecting risk node of the o - th path in the information transmission network, f is the total number of path risk nodes in the path, L i is the traffic data of the i - th path risk node, (L i ) o is the traffic anomaly of the a - th intersecting risk node of the o - th path in the information transmission network;

[0108] Compare the second - level anomaly coefficient with the second - level preset threshold to obtain the second - level comparison result;

[0109] Perform traffic anomaly determination on the second-tier traffic according to the second-tier comparison result to obtain a second traffic anomaly determination result.

[0110] The working principle of the above technical solution is as follows: Calculate the first-tier network anomaly coefficient, which comprehensively considers the risk node traffic data of all paths in the information transmission network, the total amount of abnormal traffic changes between adjacent nodes, and the total number of nodes in the path. Each variable in the formula represents different network traffic characteristics, such as L i and L i-1 respectively represent the traffic data of the i-th path risk node and its previous node, (L i -L i-1 ) o represents the total amount of abnormal traffic changes of the i-th risk node in the o-th path. Similarly, L x and L x-1 as well as (L x -L x-1 ) o represent the traffic data and the total amount of abnormal changes of the path nodes. Through the calculation of these data, a coefficient reflecting the abnormal degree of the first-tier network can be obtained. Compare the calculated first-tier anomaly coefficient with the preset first-tier threshold. If the anomaly coefficient exceeds the threshold, it is determined that the first-tier traffic is abnormal, otherwise it is determined to be normal. If the first-tier determination result is abnormal, further calculate the second-tier network anomaly coefficient. This coefficient is also based on a series of traffic characteristic data, but focuses more on the abnormal traffic volume of intersecting risk nodes. L a in the formula represents the traffic data of the a-th intersecting risk node, (L a ) o represents the abnormal traffic volume of the a-th intersecting risk node in the o-th path. At the same time, the traffic data of the path risk nodes (L i ) o is also considered; compare the calculated second-tier anomaly coefficient with the preset second-tier threshold. According to the comparison result, determine whether the second-tier traffic is abnormal.

[0111] The technical effects of the above technical solution are as follows: By calculating the anomaly coefficient at different levels, the abnormal degree in the network can be evaluated more carefully, improving the accuracy of detection. By real-time monitoring and calculating the anomaly coefficient, potential problems in the network can be discovered in a timely manner. The hierarchical anomaly detection mechanism helps to better identify and manage the risk points in the network. Through the automated anomaly detection and determination process, the cost and complexity of manual intervention can be reduced, and the efficiency of network maintenance can be improved. This method is applicable to network environments of different scales and complexities, can support the expansion and upgrade of the network, and ensure that the network remains efficient and secure during continuous development.

[0112] In one embodiment of the present invention, S4 includes:

[0113] When the first traffic anomaly determination result is an anomaly determination result, calculate the traffic bandwidth allocation coefficient of the corresponding path risk node according to the first-level anomaly coefficient and the path node risk characteristic data;

[0114] The calculation formula for the traffic bandwidth allocation coefficient of the corresponding path risk node is:

[0115]

[0116] where FP1 is the traffic bandwidth allocation coefficient of the corresponding path risk node, YC is the first-level anomaly coefficient, L d is the path node risk characteristic data of the corresponding path risk node, L z is the path node risk characteristic data of all path risk nodes, DK z is the total traffic bandwidth data;

[0117] When the second traffic anomaly determination result is an anomaly determination, calculate the traffic bandwidth allocation coefficient of the corresponding intersection risk node according to the second-level anomaly coefficient and the intersection node risk characteristic data;

[0118] The calculation formula for the traffic bandwidth allocation coefficient of the corresponding intersection risk node is:

[0119]

[0120] where FP2 is the traffic bandwidth allocation coefficient of the corresponding intersection risk node, RC is the second-level network anomaly coefficient, J d is the intersection node risk characteristic data of the current intersection risk node, J z is the intersection node risk characteristic data of all intersection risk nodes, DK z is the total traffic bandwidth data;

[0121] Obtain the node anomaly data after traffic bandwidth allocation, and perform continuous monitoring and adjustment.

[0122] The working principle of the above technical solution is as follows: When the first traffic anomaly determination result is abnormal, it indicates that there is a traffic anomaly in the path risk nodes in the network. At this time, it is necessary to calculate the traffic bandwidth allocation coefficient according to the first-level anomaly coefficient and the risk characteristic data of the corresponding path risk nodes. The calculation formula takes into account the first-level anomaly coefficient (reflecting the severity of network anomalies), the risk characteristic data of the corresponding path risk nodes (reflecting the specific risk status of the nodes), and the total traffic bandwidth data (representing the total bandwidth available for network allocation). Through this formula, a reasonable bandwidth can be allocated to each path risk node to cope with the pressure brought by its traffic anomaly. When the second traffic anomaly determination result is abnormal, it indicates that there is a traffic anomaly in the intersection risk nodes in the network. At this time, it is necessary to calculate the traffic bandwidth allocation coefficient according to the second-level anomaly coefficient and the risk characteristic data of the current intersection risk nodes. The calculation formula also takes into account the second-level anomaly coefficient (reflecting the severity of intersection node anomalies), the risk characteristic data of the intersection risk nodes, and the total traffic bandwidth data. Through this formula, a reasonable bandwidth can be allocated to each intersection risk node to alleviate the impact of its traffic anomaly on the overall network performance. After the traffic bandwidth is allocated, it is necessary to continuously monitor the anomaly data of the nodes, including indicators such as traffic changes, latency, and packet loss rate. According to the monitoring results, the bandwidth allocation coefficient can be dynamically adjusted to optimize the network performance and reduce the anomaly risk.

[0123] The technical effects of the above technical solution are as follows: Through hierarchical anomaly determination and bandwidth allocation coefficient calculation, abnormal traffic in the network can be quickly identified and responded to, improving the stability and reliability of the network. Dynamically allocating bandwidth resources according to the risk characteristics and anomaly levels of nodes can make more effective use of network resources and improve the overall performance of the network. By continuously monitoring and adjusting the anomaly data of nodes, potential network risk points can be discovered and processed in a timely manner, reducing the risk of network failures. The automated anomaly detection and bandwidth allocation process can reduce the cost and complexity of manual intervention, improving the efficiency and accuracy of network maintenance. This method is applicable to network environments of different scales and complexities, can support the expansion and upgrade of the network, and ensure that the network remains efficient and secure during continuous development.

[0124] In an embodiment of the present invention, the allocation optimization system includes:

[0125] A network risk node determination module, configured to construct an information transmission network, obtain information transmission nodes, and then obtain node information transmission paths, generate path node traffic packets, analyze traffic change data, and obtain path risk nodes;

[0126] An intersection node determination module, configured to obtain path intersection node information according to the node information transmission path information, generate intersection risk nodes, obtain risk characteristic data, label the first-level risk and the second-level risk, and construct a multi-level traffic risk network;

[0127] A multi - layer anomaly analysis module, which is used to calculate the first - layer anomaly coefficient and the second - layer anomaly coefficient, compare the first - layer anomaly coefficient and the second - layer anomaly coefficient with the first - layer preset threshold and the second - layer preset threshold respectively, and then conduct traffic anomaly determination to obtain a traffic anomaly determination result;

[0128] An allocation module, which is used to calculate the traffic bandwidth allocation coefficients of path nodes and intersection nodes respectively, and then conduct allocation adjustment of traffic bandwidth.

[0129] The working principle of the above - mentioned technical solution is as follows: Construct an information transmission network, which is composed of multiple information transmission nodes and connection paths between the nodes. Identify each node in the network and determine the information transmission paths between the nodes. Generate path - node traffic packets, which represent the data traffic in the network. By analyzing the traffic change data, identify the nodes in the path that may have risks. The nodes are caused by various reasons (such as equipment failure, network congestion, etc.) resulting in data transmission interruption or delay. According to the path information of node information transmission, obtain the information of path intersection nodes, that is, those nodes that are common to multiple paths in the network. Generate intersection risk nodes. Since these nodes are located at the intersection of multiple paths, their failures can have a greater impact on the entire network. Obtain risk characteristic data, including traffic changes, delays, packet loss rates, etc. of the nodes, which are used to evaluate the risk level of the nodes. According to the risk characteristic data, label the first - layer risk and the second - layer risk. The first - layer risk usually refers to the risk that directly affects a single path, while the second - layer risk may involve the intersection of multiple paths and pose a greater impact on the entire network. Construct a risk network with multiple layers to more comprehensively understand the risk distribution in the network. Calculate the anomaly coefficients of the first layer and the second layer, which reflect the degree of deviation of the nodes in the first layer from the normal state in the current state. Compare the calculated anomaly coefficients with the preset thresholds. If the anomaly coefficient exceeds the threshold, it is determined as traffic anomaly. According to the determination result, potential problems in the network can be timely discovered and corresponding measures can be taken for intervention. Calculate the traffic bandwidth allocation coefficients of path nodes and intersection nodes respectively. It reflects the relative importance of the nodes in the network and the bandwidth resources they need. According to the calculated allocation coefficients, conduct allocation adjustment of traffic bandwidth. For nodes with higher risks, their bandwidth resources can be appropriately increased to improve the stability and efficiency of data transmission.

[0130] The technical effects of the above technical solution are as follows: By identifying risk nodes and performing intelligent allocation of traffic bandwidth, the risk of network failures can be effectively reduced, and the stability and reliability of the network can be improved. Bandwidth allocation based on the risk level and actual requirements of nodes can make more reasonable use of network resources, avoiding waste and over-concentration of resources. By optimizing the allocation of traffic bandwidth, the latency and packet loss rate of data transmission can be reduced, and the efficiency and quality of data transmission can be improved. This method can adapt to network environments of different scales and complexities, providing strong support for the expansion and upgrade of the network. Through the automated process of risk identification and bandwidth allocation, the cost and complexity of manual intervention can be reduced, realizing the intelligent and automated network management.

[0131] In one embodiment of the present invention, the network risk node determination module includes:

[0132] A network construction module, configured to obtain the network information transmission requirements and construct an information transmission network according to the network information transmission requirements;

[0133] A path acquisition module, configured to acquire the information transmission nodes of the information transmission network and obtain the node traffic information and node information transmission paths of each information transmission node;

[0134] A traffic change comparison module, configured to obtain the node traffic information on the node information transmission path, generate multiple path node traffic packets corresponding to the node information transmission path;

[0135] Generate a traffic packet group corresponding to the node information transmission path according to the multiple path node traffic packets;

[0136] Obtain the traffic change data of the traffic packet group, compare the traffic change data with a preset change threshold to obtain a change comparison result; the traffic change data is the traffic difference between every two adjacent nodes;

[0137] A risk determination module, configured to perform an abnormal node determination on the information transmission nodes according to the change comparison result to obtain path risk nodes.

[0138] When the traffic change data is greater than the preset change threshold, the node with a relatively larger traffic in the traffic difference is the path risk node.

[0139] The working principle of the above technical solution is as follows: According to business requirements or network planning, clarify the specific requirements for network information transmission, such as the amount of data transmitted, transmission speed, transmission stability, etc. Based on the network information transmission requirements, construct the corresponding information transmission network. This network consists of multiple information transmission nodes and the connection paths between the nodes. In the constructed information transmission network, identify and obtain the node traffic information of each information transmission node, which includes the data transmission volume and reception volume of the node, etc. Obtain the information transmission path of each node, that is, the path of data flow between nodes. For each node information transmission path, generate multiple path node traffic packets according to the traffic information of the nodes on the path. These traffic packets represent the data transmission volume between different nodes on the path. Combine multiple path node traffic packets on the same path into a traffic packet group. Obtain the traffic change data of the traffic packet group, which mainly calculates the traffic difference between every two adjacent nodes, that is, the change in the data transmission volume between adjacent nodes. Compare the calculated traffic change data with a preset traffic change threshold. If the traffic change data is greater than the preset change threshold, it indicates that there has been a significant change in the data transmission volume between adjacent nodes, and there may be an abnormality. The node with a relatively larger traffic in the traffic difference is determined as the path risk node. This is because a node with a larger traffic may be more likely to have a transmission bottleneck or failure, thus affecting the data transmission of the entire path.

[0140] The technical effects of the above technical solution are as follows: By real-time monitoring of the traffic changes between nodes, abnormal situations in the network can be discovered in a timely manner, improving the accuracy and timeliness of network monitoring. By identifying path risk nodes, these nodes can be monitored and optimized key points, thereby improving the performance and stability of the entire network. Discovering and handling path risk nodes in a timely manner can reduce the risk of network failures and reduce problems such as service interruptions and data loss caused by network failures. By analyzing and monitoring the node traffic information, network resources can be allocated more reasonably, improving the utilization rate and efficiency of resources.

[0141] In an embodiment of the present invention, the intersection node determination module includes:

[0142] An intersection node generation module, configured to obtain information on multiple node information transmission paths, and obtain path intersection node information according to the node information transmission path information;

[0143] Obtain the path risk nodes of the path intersection node information, and generate intersection risk nodes;

[0144] A feature data acquisition module, configured to acquire risk feature data of path risk nodes to obtain path node risk feature data; the path node risk feature data is the data of the traffic feature of the path node.

[0145] Obtain the risk characteristic data of the intersection risk nodes to obtain the risk characteristic data of the intersection nodes; the risk characteristic data of the intersection nodes is the data of the traffic characteristics of the intersection nodes.

[0146] The multi-layer network monitoring module is used to label the path node risk characteristic data as the first-level risk; the first-level risk is the risk label of all path risk nodes in the network.

[0147] Label the risk characteristic data of the intersection nodes as the second-level risk; the second-level risk is the risk label of all intersection risk nodes in the network.

[0148] Construct a multi-level traffic risk network based on the first-level risk information combined with the second-level risk information.

[0149] The working principle of the above technical solution is as follows: Obtain the information of multiple node information transmission paths through network monitoring or data analysis tools. This information includes the nodes on the paths and the connection relationships between the nodes. According to these path information, identify the path intersection nodes, that is, the nodes located at the intersection of multiple paths in the network. For each path, obtain the path risk node information thereon. Path risk nodes may be nodes with potential risks determined based on previous traffic analysis, fault history, or other metrics. Extract the risk nodes located at the path intersections to generate a list of intersection risk nodes. Since these nodes are located at the intersection of multiple paths, their failures may have a greater impact on the entire network. For path risk nodes and intersection risk nodes, obtain their risk characteristic data respectively. These characteristic data are mainly based on the traffic characteristics of the nodes, such as traffic volume, traffic change rate, packet loss rate, delay, etc. By analyzing these characteristic data, the risk level of the nodes can be evaluated. Label the characteristic data of the path risk nodes as the first-level risk. The risk at this level mainly reflects the potential risks of the nodes on each path in the network. Label the characteristic data of the intersection risk nodes as the second-level risk. The risk at this level pays more attention to the risks of the intersection nodes in the network because the failures of these nodes may affect multiple paths simultaneously. According to the first-level and second-level risk information, construct a risk network with multiple levels. This network not only shows the risk levels of each node in the network but also reveals the propagation and diffusion methods of risks in the network. Through this multi-level traffic risk network, a more comprehensive understanding of the risk distribution and potential threats in the network can be obtained.

[0150] The technical effects of the above technical solution are as follows: By constructing a multi-level traffic risk network, potential risks in the network can be identified and evaluated more accurately, improving the ability of network risk management. By focusing on monitoring and optimizing risk nodes, the risk of network failures can be reduced, enhancing the stability and performance of the network. This method is applicable to network environments of different scales and complexities, can support the expansion and upgrade of the network, and ensure that the network remains efficient and secure during continuous development. Through the automated risk identification and annotation process, the cost and complexity of manual intervention can be reduced, improving the efficiency and quality of network maintenance. By promptly detecting and handling potential risk nodes, the security of the network can be enhanced, preventing problems such as network paralysis and data leakage caused by node failures or attacks.

[0151] In one embodiment of the present invention, the network risk node determination module includes:

[0152] A first calculation and determination module, configured to obtain multi-level traffic risk network information and calculate the first-level anomaly coefficient;

[0153] The calculation formula for the first-level anomaly coefficient is:

[0154]

[0155] Where YC is the first-level network anomaly coefficient, w is the total number of paths in the information transmission network, f is the total number of path risk nodes in the path, L i is the traffic data of the i-th path risk node, L i-1 is the (i - 1)-th path node of the i-th path risk node, (L i - L i-1 ) o is the total traffic anomaly change amount of the path risk nodes of the o-th path in the information transmission network, y is the total number of path nodes in the path, L x is the traffic data of the x-th path node, L x-1 is the (x - 1)-th path node of the x-th path node, (L x - L x-1 ) o is the total traffic anomaly change amount of the path nodes of the o-th path in the information transmission network;

[0156] Compare the first-level anomaly coefficient with the first-level preset threshold to obtain the first-level comparison result;

[0157] Based on the first-level comparison result, perform traffic anomaly determination on the first-level traffic to obtain the first traffic anomaly determination result;

[0158] A second calculation and determination module, configured to calculate the second-level anomaly coefficient according to the first-level traffic anomaly determination;

[0159] When the determination result of the first level is abnormal, the calculation of the second-level anomaly coefficient starts.

[0160] The calculation formula of the second-level anomaly coefficient is as follows:

[0161]

[0162] Among them, RC is the second-level network anomaly coefficient, w is the total number of paths in the information transmission network, j is the total number of intersecting risk nodes in the path, and L a is the traffic data of the a-th intersecting risk node, (L a ) o is the traffic anomaly amount of the a-th intersecting risk node of the o-th path in the information transmission network, f is the total number of path risk nodes in the path, and L i is the traffic data of the i-th path risk node, (L i ) o is the traffic anomaly amount of the a-th intersecting risk node of the o-th path in the information transmission network;

[0163] Compare the second-level anomaly coefficient with the second-level preset threshold to obtain the second-level comparison result;

[0164] Perform traffic anomaly determination on the second-level traffic according to the second-level comparison result to obtain the second traffic anomaly determination result.

[0165] The working principle of the above technical solution is as follows: Calculate the first-level network anomaly coefficient, which comprehensively considers the traffic data of risk nodes on all paths in the information transmission network, the total amount of traffic anomaly changes between adjacent nodes, and the total number of nodes in the path. Each variable in the formula represents different network traffic characteristics, such as L i and L i-1 respectively represent the traffic data of the i-th path risk node and its previous node, (L i -L i-1 ) o represents the total amount of traffic anomaly changes of the i-th risk node in the o-th path. Similarly, L x and L x-1 and (L x -L x-1 ) oRepresents the traffic data and total abnormal changes of path nodes. Through the calculation of these data, a coefficient reflecting the abnormal degree of the first-level network can be obtained. Compare the calculated first-level abnormal coefficient with the preset first-level threshold. If the abnormal coefficient exceeds the threshold, it is determined that there is an abnormality in the first-level traffic, otherwise it is determined to be normal. If the determination result of the first level is abnormal, further calculate the second-level network abnormal coefficient. This coefficient is also based on a series of traffic characteristic data, but focuses more on the abnormal traffic volume of intersecting risk nodes. L in the formula a Represents the traffic data of the a-th intersecting risk node, (L a ) o Represents the abnormal traffic volume of the a-th intersecting risk node in the o-th path. At the same time, the traffic data of the path risk node (L i ) o is also considered; compare the calculated second-level abnormal coefficient with the preset second-level threshold. According to the comparison result, determine whether there is an abnormality in the second-level traffic.

[0166] The technical effects of the above technical solutions are as follows: By calculating the abnormal coefficient level by level, the abnormal degree in the network can be evaluated more meticulously, improving the accuracy of detection. By real-time monitoring and calculating the abnormal coefficient, potential problems in the network can be discovered in a timely manner. The hierarchical abnormal detection mechanism helps to better identify and manage the risk points in the network. Through the automated abnormal detection and determination process, the cost and complexity of manual intervention can be reduced, and the efficiency of network maintenance can be improved. This method is applicable to network environments of different scales and complexities, can support the expansion and upgrade of the network, and ensure that the network remains efficient and secure during continuous development.

[0167] In an embodiment of the present invention, the allocation module includes:

[0168] The first calculation and allocation module is used to calculate the traffic bandwidth allocation coefficient of the corresponding path risk node according to the first-level abnormal coefficient in combination with the path node risk characteristic data when the first traffic abnormal determination result is an abnormal determination result;

[0169] The calculation formula for the traffic bandwidth allocation coefficient of the corresponding path risk node is:

[0170]

[0171] where FP1 is the traffic bandwidth allocation coefficient of the corresponding path risk node, YC is the first-level abnormal coefficient, L d is the path node risk characteristic data of the corresponding path risk node, L z is the path node risk characteristic data of all path risk nodes, and DK z is the total traffic bandwidth data;

[0172] A second calculation and allocation module, configured to calculate a traffic bandwidth allocation coefficient for a corresponding intersection risk node according to the second - level anomaly coefficient and the intersection node risk feature data when the second traffic anomaly determination result is an anomaly determination;

[0173] The calculation formula for the traffic bandwidth allocation coefficient of the corresponding intersection risk node is:

[0174]

[0175] wherein, FP2 is the traffic bandwidth allocation coefficient for the corresponding intersection risk node, RC is the second - level network anomaly coefficient, J d is the intersection node risk feature data of the current intersection risk node, J z is the intersection node risk feature data of all intersection risk nodes, DK z is the total traffic bandwidth data;

[0176] A monitoring module, configured to obtain node anomaly data after traffic bandwidth allocation and perform continuous monitoring and adjustment.

[0177] The working principle of the above - mentioned technical solution is as follows: When the first traffic anomaly determination result is an anomaly, it indicates that there is a traffic anomaly of path risk nodes in the network. At this time, it is necessary to calculate the traffic bandwidth allocation coefficient according to the first - level anomaly coefficient and the risk feature data of the corresponding path risk nodes. The calculation formula takes into account the first - level anomaly coefficient (reflecting the severity of network anomalies), the risk feature data of the corresponding path risk nodes (reflecting the specific risk status of the nodes), and the total traffic bandwidth data (representing the total bandwidth available for network allocation). Through this formula, a reasonable bandwidth can be allocated to each path risk node to cope with the pressure brought by its traffic anomaly. When the second traffic anomaly determination result is an anomaly, it indicates that there is a traffic anomaly of intersection risk nodes in the network. At this time, it is necessary to calculate the traffic bandwidth allocation coefficient according to the second - level anomaly coefficient and the risk feature data of the current intersection risk node. The calculation formula also takes into account the second - level anomaly coefficient (reflecting the severity of intersection node anomalies), the risk feature data of intersection risk nodes, and the total traffic bandwidth data. Through this formula, a reasonable bandwidth can be allocated to each intersection risk node to alleviate the impact of its traffic anomaly on the overall network performance. After traffic bandwidth allocation, it is necessary to continuously monitor the node anomaly data, including indicators such as traffic changes, latency, and packet loss rate. According to the monitoring results, the bandwidth allocation coefficient can be dynamically adjusted to optimize network performance and reduce anomaly risks.

[0178] The technical effects of the above technical solution are as follows: Through hierarchical anomaly determination and bandwidth allocation coefficient calculation, abnormal traffic in the network can be quickly identified and responded to, improving the stability and reliability of the network. Dynamically allocating bandwidth resources according to the risk characteristics and anomaly levels of nodes can make more effective use of network resources and improve the overall performance of the network. By continuously monitoring and adjusting the abnormal data of nodes, potential network risk points can be discovered and processed in a timely manner, reducing the risk of network failures. The automated anomaly detection and bandwidth allocation process can reduce the cost and complexity of manual intervention, and improve the efficiency and accuracy of network maintenance. This method is applicable to network environments of different scales and complexities, can support the expansion and upgrade of the network, and ensure that the network remains efficient and secure during continuous development.

[0179] Obviously, those skilled in the art can make various changes and deformations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and deformations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and deformations.

Claims

1. An intelligent allocation and optimization method for traffic bandwidth, characterized in that The described allocation optimization method includes: S1. Construct an information transmission network, obtain information transmission nodes, and then obtain the node information transmission paths, generate path node traffic packets, analyze the traffic change data, and obtain path risk nodes; Among them, S1 includes: Obtain the network information transmission requirements, and construct an information transmission network according to the network information transmission requirements; Obtain the information transmission nodes of the information transmission network, and obtain the node traffic information and node information transmission paths of each information transmission node; Obtain the node traffic information on the node information transmission path, and generate multiple path node traffic packets corresponding to the node information transmission path; Generate a traffic packet group corresponding to the node information transmission path according to the multiple path node traffic packets; Obtain the traffic change data of the traffic packet group, compare the traffic change data with a preset change threshold, and obtain a change comparison result; Determine abnormal nodes for the information transmission nodes according to the change comparison result, and obtain path risk nodes; S2. Obtain path intersection node information according to the node information transmission path information, generate intersection risk nodes, obtain risk characteristic data, label the first-level risk and the second-level risk, and construct a multi-level traffic risk network; Among them, S2 includes: Obtain the information of multiple node information transmission paths, and obtain path intersection node information according to the node information transmission path information; Obtain the path risk nodes of the path intersection node information, and generate intersection risk nodes; Obtain the risk characteristic data of the path risk nodes, and obtain path node risk characteristic data; Obtain the risk characteristic data of the intersection risk nodes, and obtain intersection node risk characteristic data; Label the path node risk characteristic data as the first-level risk; Label the intersection node risk characteristic data as the second-level risk; Construct a multi-level traffic risk network according to the first-level risk information combined with the second-level risk information; S3. Calculate the first-level anomaly coefficient and the second-level anomaly coefficient, compare the first-level anomaly coefficient and the second-level anomaly coefficient with the first-level preset threshold and the second-level preset threshold respectively, and then perform traffic anomaly determination to obtain a traffic anomaly determination result; S4. Calculate the traffic bandwidth allocation coefficients of the path nodes and the intersection nodes respectively, and then perform allocation adjustment of the traffic bandwidth.

2. The intelligent allocation and optimization method for traffic bandwidth according to claim 1, wherein The S3 includes: Obtain the multi-level traffic risk network information, and calculate the first-level anomaly coefficient according to the multi-level traffic risk network information; Compare the first-level anomaly coefficient with the first-level preset threshold to obtain a first-level comparison result; Perform traffic anomaly determination on the first-level traffic according to the first-level comparison result to obtain a first-level traffic anomaly determination result; Calculate the second-level anomaly coefficient according to the first-level traffic anomaly determination; Compare the second-level anomaly coefficient with the second-level preset threshold to obtain a second-level comparison result; Perform traffic anomaly determination on the second-level traffic according to the second-level comparison result to obtain a second-level traffic anomaly determination result.

3. The intelligent allocation optimization method for traffic bandwidth according to claim 1, wherein The S4 includes: When the first - level traffic anomaly determination result is an anomaly determination result, calculate the traffic bandwidth allocation coefficient of the corresponding path risk node according to the first - level anomaly coefficient and the path node risk characteristic data; When the second - level traffic anomaly determination result is an anomaly determination, calculate the traffic bandwidth allocation coefficient of the corresponding intersection risk node according to the second - level anomaly coefficient and the intersection node risk characteristic data; Obtain the node anomaly data after traffic bandwidth allocation, and conduct continuous monitoring and adjustment.

4. An intelligent allocation and optimization system for traffic bandwidth, characterized in that The allocation optimization system includes: A network risk node determination module, which is used to construct an information transmission network, obtain information transmission nodes, further obtain the node information transmission paths, generate path node traffic packets, analyze the traffic change data, and obtain path risk nodes; Among them, the network risk node determination module includes: A network construction module, which is used to obtain the information transmission requirements of the network and construct an information transmission network according to the information transmission requirements of the network; A path acquisition module, which is used to obtain the information transmission nodes of the information transmission network, and obtain the node traffic information and the node information transmission paths of each information transmission node; A traffic change comparison module, which is used to obtain the node traffic information on the node information transmission path, and generate multiple path node traffic packets corresponding to the node information transmission path; Generate a traffic packet group corresponding to the node information transmission path according to the multiple path node traffic packets; Obtain the traffic change data of the traffic packet group, compare the traffic change data with a preset change threshold, and obtain a change comparison result; A risk determination module, which is used to determine abnormal nodes for the information transmission nodes according to the change comparison result, and obtain path risk nodes; An intersection node determination module, which is used to obtain path intersection node information according to the node information transmission path information, generate intersection risk nodes, obtain risk characteristic data, label the first - level risk and the second - level risk, and construct a multi - level traffic risk network; Among them, the intersection node determination module includes: An intersection node generation module, which is used to obtain the information of multiple node information transmission paths, and obtain path intersection node information according to the node information transmission path information; Obtain the path risk nodes of the path intersection node information, and generate intersection risk nodes; A characteristic data acquisition module, which is used to obtain the risk characteristic data of the path risk nodes, and obtain path node risk characteristic data; Obtain the risk characteristic data of the intersection risk nodes, and obtain intersection node risk characteristic data; A multi - layer network monitoring module, which is used to label the path node risk characteristic data as the first - level risk; Label the intersection node risk characteristic data as the second - level risk; Construct a multi - level traffic risk network according to the first - level risk information combined with the second - level risk information; A multi - layer anomaly analysis module, which is used to calculate the first - level anomaly coefficient and the second - level anomaly coefficient, compare the first - level anomaly coefficient and the second - level anomaly coefficient with the first - level preset threshold and the second - level preset threshold respectively, and then conduct traffic anomaly determination to obtain a traffic anomaly determination result; An allocation module, which is used to calculate the traffic bandwidth allocation coefficients of path nodes and intersection nodes respectively, and then conduct the allocation and adjustment of traffic bandwidth.

5. The intelligent allocation optimization system for traffic bandwidth according to claim 4, wherein The network risk node determination module includes: A first calculation and determination module, configured to obtain multi-level traffic risk network information and calculate a first-level anomaly coefficient according to the multi-level traffic risk network information; Compare the first-level anomaly coefficient with a first-level preset threshold to obtain a first-level comparison result; Perform traffic anomaly determination on the first-level traffic according to the first-level comparison result to obtain a first-level traffic anomaly determination result; A second calculation and determination module, configured to calculate a second-level anomaly coefficient according to the first-level traffic anomaly determination; Compare the second-level anomaly coefficient with a second-level preset threshold to obtain a second-level comparison result; Perform traffic anomaly determination on the second-level traffic according to the second-level comparison result to obtain a second-level traffic anomaly determination result.

6. The intelligent allocation optimization system for traffic bandwidth according to claim 4, wherein The allocation module includes: A first calculation and allocation module, configured to, when the first-level traffic anomaly determination result is an anomaly determination result, calculate a traffic bandwidth allocation coefficient of a corresponding path risk node according to the first-level anomaly coefficient in combination with path node risk feature data; A second calculation and allocation module, configured to, when the second-level traffic anomaly determination result is an anomaly determination, calculate a traffic bandwidth allocation coefficient of a corresponding intersection risk node according to the second-level anomaly coefficient in combination with intersection node risk feature data; A monitoring module, configured to obtain node anomaly data after traffic bandwidth allocation and perform continuous monitoring and adjustment.

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