Network data management system based on intelligent flow control

Abstract

The present invention relates to the field of network optimization technology, specifically to a network data management system based on intelligent traffic control, the system comprising: a traffic partitioning and sample extraction module that obtains time series information of network traffic and divides it into sub-traffic segments, filters data packets in the sub-traffic segments, and generates a load characteristic sample set based on the screening results. In the present invention, network traffic data is grouped and managed through the extraction of time series information and the division of sub-traffic segments, and the loads of its source nodes and target nodes are accurately marked and filtered, thereby generating more accurate load characteristic data and avoiding unnecessary data redundancy. In the calculation of the load weighted value, the multi-dimensional weight mechanism combining dynamic priority weights and traffic change amounts further improves the prediction accuracy of data stream loads, so that trend changes in subsequent time segments can be more scientifically predicted.

Description

Technical Field

[0001] The present invention relates to the technical field of network optimization, and in particular to a network data management system based on intelligent flow control. Background Art

[0002] The field of network optimization involves the dynamic allocation and management of network resources, improving network efficiency and service quality through appropriate scheduling and control mechanisms. Core areas of this technology include network traffic analysis and control, bandwidth resource allocation, packet routing and forwarding, network load balancing, and latency management.

[0003] Among them, the network data management system uses data collectors to comprehensively monitor the traffic information in the network, subdivides and marks the collected data through traffic classification rules, and then combines the scheduling algorithm to dynamically adjust the priority and allocate resources for the data stream, thereby ensuring the orderliness of network operation and the stability of data transmission.

[0004] Existing technologies mainly rely on traffic classification rules to segment and mark collected data. Although they can provide basic traffic management functions, they lack the ability to accurately predict real-time changing load trends, making it difficult to effectively deal with situations where traffic fluctuates greatly in a short period of time. In addition, existing scheduling algorithms cannot comprehensively consider the load distribution of source nodes and target nodes and the global resource occupancy of transmission paths in dynamic priority adjustments, which can easily lead to resource overload or inefficient operation of some paths. In terms of abnormal traffic management, existing technologies lack accurate tracking and isolation strategies for abnormal traffic propagation paths, which may cause abnormal traffic to affect the transmission performance of normal traffic. For example, in high-concurrency scenarios, abnormal traffic occupying key path resources will lead to a serious decline in overall network performance, and data delays and packet loss rates will increase significantly, limiting the efficiency and stability of network data management systems in complex network environments. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a network data management system based on intelligent flow control.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A network data management system based on intelligent flow control includes:

[0007] The traffic partitioning and sample extraction module obtains the time series information of network traffic and divides it into sub-traffic segments, filters the data packets in the sub-traffic segments, and generates a load feature sample set based on the screening results;

[0008] The distributed trend prediction and synchronization module extracts the load records of the sub-flow segments in the load feature sample set, calculates the load weight value corresponding to each sub-flow segment based on the load records, and predicts the load change trend of the sub-flow segment in subsequent time segments with reference to the load weight value, and generates a global load change trend list;

[0009] The path optimization allocation and dynamic adjustment module calculates the load deviation values ​​between the sub-flow segments according to the load trend information of the sub-flow segments in the global load change trend list, establishes the load distribution path of the sub-flow segments according to the load deviation values, and dynamically adjusts the load distribution path to obtain the dynamic path flow distribution result;

[0010] The abnormal behavior identification and isolation module extracts the real-time traffic information of each path in the dynamic path traffic allocation result, identifies abnormal traffic, reallocates the abnormal traffic to the isolated path, and generates network traffic data optimization management results.

[0011] As a further solution of the present invention, the step of dividing the sub-flow segments is specifically as follows:

[0012] Capture data packets in the network based on network traffic monitoring equipment, segment and archive the traffic data at fixed time intervals, and generate a set of time series segments;

[0013] Based on the time series segment set, by parsing the source IP address and the destination IP address of each data packet, the traffic data is grouped and the information is recorded according to the node pairing rule to generate a sub-traffic segment set.

[0014] As a further solution of the present invention, the steps of acquiring the load characteristic sample set are specifically as follows:

[0015] Based on the sub-flow segment set, extracting packet size, transmission frequency, and load occupancy ratio information of the source node and the target node from the data packet, screening the data packet in combination with the load occupancy ratio of the source node and the target node, and integrating the screening results into a load signature to generate load signature data;

[0016] The load characteristics in the load characteristic data are compared with preset load thresholds one by one, the load characteristics exceeding the threshold are marked, and the marking results are sorted to generate a load characteristic sample set.

[0017] As a further solution of the present invention, the step of calculating the load weighted value corresponding to each sub-flow segment is specifically as follows:

[0018] Based on the load feature sample set, retrieve the information of each sub-flow segment, divide each group of data into corresponding time segments according to the time segment, and extract the load change value and flow change of each sub-flow segment to generate the load record set of the sub-flow segment;

[0019] Based on the load record set of the sub-traffic segment, the formula is used:

[0020] ;

[0021] Calculate sub-traffic segments Load weighted value ;

[0022] in, It is a sub-traffic segment The total number of time segments, It is a sub-traffic segment In time segments The load value in It is a sub-traffic segment In time segments The traffic weight in It is a sub-traffic segment In time segments Dynamic priority weights.

[0023] As a further solution of the present invention, the steps of obtaining the global load change trend list are specifically as follows:

[0024] According to the load weighted value, the formula is used:

[0025] ;

[0026] Calculate sub-traffic segments In the next time segment The predicted load value ;

[0027] in, It's a time segment inner sub-traffic segment The load growth factor, is the adjustment coefficient of load change trend, It's a time segment The load increment, is the time segment length;

[0028] Based on the predicted load value, the load change trends of all sub-flow segments are sorted in time dimension and space dimension, and a global load change trend list is generated by classifying and integrating the load data of each sub-flow segment.

[0029] As a further solution of the present invention, the step of calculating the load deviation value between the sub-flow segments is specifically as follows:

[0030] Based on the global load change trend list, extract the predicted load value of each sub-flow segment, sort the load values ​​in combination with the time dimension, and generate a load trend data set according to the sorting results;

[0031] Based on the load trend data set, the formula is used:

[0032] ;

[0033] Calculate sub-traffic segments and In time segments Load deviation value ;

[0034] in, It is a sub-traffic segment In time segments The predicted load value.

[0035] As a further solution of the present invention, the step of obtaining the dynamic path flow distribution result is specifically as follows:

[0036] Based on the load deviation value, the load deviation values ​​between the sub-flow segments are integrated in a matrix form, paths with multiple load conditions are identified, and the corresponding load paths are adjusted to generate sub-flow segment path load distribution results;

[0037] Based on the load distribution results of the sub-flow segment paths, the formula is adopted:

[0038] ;

[0039] Calculate sub-traffic segments and Path flow distribution ratio , and according to the sub-flow segment and Path flow distribution ratio Calculate and adjust path flow , dynamically adjust the load distribution path according to the adjusted path flow, and obtain the dynamic path flow distribution result;

[0040] in, It is a sub-traffic segment The load weighted value of is the initially allocated path flow value.

[0041] As a further solution of the present invention, the steps for obtaining the network traffic data optimization management result are specifically as follows:

[0042] Based on the dynamic path traffic allocation result, by monitoring the data packet transmission rate within the path and the traffic value within the time interval, comparing the traffic fluctuation with the preset fluctuation threshold range, identifying the source node, target node and propagation path of the traffic anomaly, and generating an abnormal traffic identification result;

[0043] Based on the abnormal traffic identification results, combined with the real-time load status and bandwidth utilization of the currently available paths, the abnormal traffic is reallocated to the isolated paths, the operating status of the isolated paths is monitored, and the network traffic data optimization management results are generated.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, network traffic data is grouped and managed by extracting time series information and dividing it into sub-traffic segments, and the loads of its source nodes and target nodes are accurately marked and screened, generating more accurate load feature data and avoiding unnecessary data redundancy. In the calculation of the load weighted value, the multi-dimensional weight mechanism combining dynamic priority weights and traffic changes further improves the prediction accuracy of the data flow load, so that the trend changes in subsequent time segments can be more scientifically predicted. In the path optimization and dynamic adjustment stage, the problem of uneven path load distribution is effectively solved by identifying the load deviation values ​​between sub-traffic segments and calculating the path allocation ratio, ensuring efficient resource utilization. In the process of abnormal traffic identification and isolation, the source of traffic anomalies is detected in a timely manner by dynamically comparing traffic fluctuations with the threshold range, and combined with the redistribution strategy of the isolated path, the stable transmission of normal traffic is ensured, while the interference of abnormal traffic on network performance is isolated. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a system flow chart of the present invention;

[0047] Figure 2 Flowchart for dividing sub-flow segments of the present invention;

[0048] Figure 3 A flow chart of obtaining a load characteristic sample set according to the present invention;

[0049] Figure 4 A flow chart for calculating the load weighted value corresponding to each sub-flow segment of the present invention;

[0050] Figure 5 A flowchart for obtaining a global load change trend list for the present invention;

[0051] Figure 6 A flow chart showing calculation of load deviation values ​​between sub-flow segments according to the present invention;

[0052] Figure 7A flow chart of obtaining dynamic path flow distribution results according to the present invention;

[0053] Figure 8 This is a flow chart of the present invention for obtaining network traffic data optimization management results. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0056] See also Figure 1 , the network data management system based on intelligent flow control includes:

[0057] The traffic partitioning and sample extraction module obtains the time series information of network traffic and divides it into sub-traffic segments, filters the data packets in the sub-traffic segments, and generates a load feature sample set based on the screening results;

[0058] The distributed trend prediction and synchronization module extracts the load records of the sub-flow segments in the load feature sample set, calculates the load weight value corresponding to each sub-flow segment based on the load records, and predicts the load change trend of the sub-flow segment in subsequent time segments with reference to the load weight value, and generates a global load change trend list;

[0059] The path optimization allocation and dynamic adjustment module calculates the load deviation between sub-flow segments based on the load trend information of the sub-flow segments in the global load change trend list, establishes the load distribution path of the sub-flow segments based on the load deviation value, and dynamically adjusts the load distribution path to obtain the dynamic path traffic distribution result;

[0060] The abnormal behavior identification and isolation module extracts the real-time traffic information of each path in the dynamic path traffic allocation results, identifies abnormal traffic, and reallocates the abnormal traffic to the isolated path to generate network traffic data optimization management results;

[0061] The load feature sample set specifically includes data packet size, data packet transmission frequency, source node load occupancy ratio, and target node load occupancy ratio. The global load change trend list includes sub-traffic segment load weight values, time dimension distribution of sub-traffic segment load change trends, and spatial dimension distribution of sub-traffic segment load change trends. The dynamic path traffic allocation results specifically include sub-traffic segment path load allocation paths and dynamic adjustment records of load paths. The network traffic data optimization management results include abnormal traffic identification results, isolation path allocation results, and abnormal traffic propagation paths.

[0062] See also Figure 2 , the steps of dividing the sub-traffic segments are as follows:

[0063] Capture data packets in the network based on network traffic monitoring equipment, segment and archive the traffic data at fixed time intervals, and generate a set of time series segments;

[0064] First, use network traffic monitoring equipment to capture data packets in the network in real time, record the timestamp information of each data packet, sort the traffic data according to the timestamp information to ensure the continuity and accuracy of the time series, use the time interval segmentation method to split the traffic data according to fixed intervals (such as 5 seconds or 10 seconds), and package the traffic data in continuous time segments in the time series into independent time segment sets. At the same time, record the number of data packets in each time segment and their transmission interval. Use data storage format (such as CSV or JSON file format) to archive and store the time segment set to ensure that each time segment contains a complete set of data packets associated with the timestamp, and generate a time series segment set.

[0065] Based on the set of time series segments, the source IP address and destination IP address of each data packet are parsed, the traffic data is grouped and the information is recorded according to the node pairing rules to generate a set of sub-traffic segments.

[0066] The IP address information of each data packet is parsed through the network traffic collection system, and the source IP address and destination IP address of the data packet are extracted from the header information of the data packet. The source IP address and destination IP address are used to form a node association table of the data packet. The data packets in each time segment are grouped according to the pairing rules in the association table. After grouping, the traffic data of the source node and the destination node are stored as an independent sub-traffic segment, and the specific information of the source node and the destination node in each sub-traffic segment is recorded, including the total data volume of the traffic transmission, the transmission frequency and the average delay of the data packet transmission. The sub-traffic segment set after grouping is used to mark each time segment to generate a sub-traffic segment set.

[0067] See also Figure 3 ,The steps for obtaining the load feature sample set are as follows:

[0068] Based on the sub-traffic segment set, the packet size, transmission frequency, and the load occupancy ratio of the source node and the target node are extracted from the data packet. The data packet is filtered based on the load occupancy ratio of the source node and the target node, and the filtering results are integrated into the load signature to generate load signature data.

[0069] By calling the data packet transmission log recorded in the network monitoring device, the header information of each data packet is parsed one by one to obtain its transmission frequency and size. The load occupancy ratio of the source node and the target node is defined as the ratio of the data packet size processed by the source node or the target node per unit time to its theoretical maximum carrying capacity. The actual load occupancy ratio is obtained by counting the cumulative data processing volume of the source node and the target node in a specified time period and comparing it with its rated processing capacity. The load occupancy ratio threshold range of the source node and the target node is set through sampling rules. The load data packets that exceed the threshold are preferentially marked, and their corresponding data packet size, transmission frequency, and load occupancy ratio of the source node and the target node are integrated into load characteristic data.

[0070] Compare the load characteristics in the load characteristic data with the preset load thresholds one by one, mark the load characteristics that exceed the thresholds, and organize the marking results to generate a load characteristic sample set;

[0071] By comparing each characteristic value in the load characteristic data with the preset threshold value one by one, the threshold value includes the maximum allowable range of the source node load occupancy ratio, the maximum allowable range of the target node load occupancy ratio, the maximum allowable value of the data packet size, and the lower limit of the data packet transmission frequency. For the source node and target node load occupancy ratio, the ratio rule is used to judge the node load that exceeds the set threshold. For the data packet size and transmission frequency, it is directly judged whether it meets the screening conditions based on the numerical range. All data packets that exceed the threshold are marked as abnormal load characteristics, and the source nodes and target nodes are classified separately according to the classification rules to generate the final load characteristic sample set. The sample set includes the source node, target node, data packet size and transmission frequency that exceed the threshold.

[0072] See also Figure 4 ,The steps for calculating the load weighted value corresponding to each sub-traffic segment are as follows:

[0073] Based on the load feature sample set, retrieve the information of each sub-flow segment, divide each group of data into corresponding time segments according to the time segment, and extract the load change value and flow change of each sub-flow segment to generate the load record set of the sub-flow segment;

[0074] First, the associated information of each sub-traffic segment is retrieved one by one from the load feature sample set, including the source node, target node, packet size and transmission frequency. The data in the sample set is grouped using timestamps, and each group of data is mapped to its own time segment according to the time segment division. The total packet size, transmission frequency and load change value of each sub-traffic segment in each time segment are extracted, the load occupancy ratio of the source node and target node in the time segment is recorded, and the total traffic change of the source node and target node in the time segment is calculated. The sub-traffic segment load records in each time segment are summarized and stored in chronological order as a load time series set.

[0075] Based on the load record collection of sub-traffic segments, the formula is:

[0076] ;

[0077] Calculate sub-traffic segments Load weighted value , which indicates the comprehensive load level of the sub-traffic segment in all time segments, in Mbps.

[0078] in, It is a sub-traffic segment The total number of time segments included indicates the number of time segments for network traffic data. The entire monitoring period is divided by setting the time interval, for example, a time segment is divided every 5 seconds or 10 seconds. Taking the total monitoring time of 100 seconds and the time interval of 5 seconds as an example, the total number of time segments is 100 seconds. , It is a sub-traffic segment In time segments The load value in the time segment indicates the traffic size in Mbps. The total size of the data packet is directly extracted through the network traffic monitoring device. For example, in the time segment The total number of data packets monitored in Mbps, then , It is a sub-traffic segment In time segments The traffic weight in is used to measure the importance of traffic. It is calculated based on the transmission frequency of the sub-traffic segment within the time segment. The higher the transmission frequency, the greater the weight. It is a sub-traffic segment In time segments The dynamic priority weight reflects the real-time load fluctuation and congestion situation in the time segment. It is obtained by analyzing the load fluctuation amplitude and node congestion rate in the time segment. The fluctuation amplitude is determined by the difference between the maximum and minimum load values. The node congestion rate is determined by monitoring the congestion situation of the source node or the target node. For example, if the time segment The maximum internal load is 150Mbps, the minimum is 100Mbps, the fluctuation range is 50Mbps, and the node congestion rate is 80%. Combining the above parameters, we can determine .

[0079] If the parameter : The number of time segments is 3. :The load values ​​are Mbps, Mbps, Mbps. :The traffic weights are , , . :The dynamic priority weights are , , , put it into the formula:

[0080] ;

[0081] Calculate the numerator: ;

[0082] Calculate the denominator: ;

[0083] Calculate the weighted load value: ;

[0084] The results show that the sub-flow segment The weighted load value 126.87Mbps, by introducing dynamic priority weight , making the calculation results more in line with the actual dynamic change characteristics of the sub-traffic segment. This value is slightly higher than the benchmark load value (for example, the basic value of 125Mbps without dynamic adjustment), which can more accurately reflect the comprehensive load level of the sub-traffic segment and provide reliable basic data for global trend prediction.

[0085] See also Figure 5 , the steps for obtaining the global load change trend list are as follows:

[0086] According to the load weighted value, the formula is used:

[0087] ;

[0088] Calculate sub-traffic segments In the next time segment The predicted load value ;

[0089] in, It's a time segment inner sub-traffic segment The load growth coefficient is a dimensionless value that represents the relative ratio of the load value of the current time segment to the overall maximum load. It is used to measure the load proportion of the current time segment and is calculated based on the historical load value. The formula is as follows: , It is the adjustment coefficient of the load change trend. It is a dimensionless value used to balance the influence of the load change trend on the prediction results. It is set according to the dynamic characteristics of the network. For example, a larger adjustment coefficient is set when the network fluctuation is high. The specific value is verified by historical monitoring data experiments. For example, a higher value is set during congested periods to enhance the response to the change trend. It's a time segment The load increment, in Mbps, represents the difference between the load value of the current time segment and the load value of the previous time segment. The calculation formula is: , It is a sub-traffic segment In the previous time segment The load value in It is the length of the time segment in seconds, indicating the time interval between adjacent time segments. It is directly obtained according to the set time segment interval. For example, if the length of each time segment is set to 10 seconds, then .

[0090] Set parameter sub-traffic segment The weighted load value of: Mbps. Load growth factor: The current load value is Mbps, the historical maximum load value is 200Mbps, then , load increment: load of the previous time segment Mbps, the current load is Mbps, then , time segment length: Seconds, adjustment factor: , enter the formula to calculate:

[0091] ;

[0092] ;

[0093] The results show that the sub-flow segment In time segments The predicted load value is 96.65 Mbps, a slight increase from the previous time segment's load value (95 Mbps, used as a baseline), indicating that the load growth trend for this traffic segment in the network is gradually increasing. This result can be used to adjust network resource allocation for the next time segment to ensure network load balancing.

[0094] Based on the predicted load values, the load change trends of all sub-flow segments are sorted out in the time and space dimensions. By classifying and integrating the load data of each sub-flow segment, a global load change trend list is generated.

[0095] First, the predicted load value of each sub-flow segment in all time segments is extracted, for example, In time segments The predicted load value is Mbps, the predicted load values ​​of other sub-traffic segments are calculated and extracted in a similar way. The predicted load values ​​of each sub-traffic segment are sorted according to the time segment number to form a load change sequence in the time dimension. In the spatial dimension, according to the topological distribution of the source nodes and target nodes of the sub-traffic segments, the sub-traffic segments with the same source nodes and target nodes are classified into the same spatial domain, for example, the sub-traffic segment and The source node is A, the target node is B, and the predicted load values ​​are Mbps and Mbps, the total spatial domain load of the two is Mbps. For all sub-traffic segments in the spatial domain, after the total load is calculated, a global load change trend list is generated through a joint analysis of the time dimension and the spatial dimension. The list includes the load change sequence of each time segment, the total load value of each spatial domain, and the correlation information between the time and spatial domains. For example, if The total load value of a certain spatial domain AB is significantly higher than the average value Mbps, the spatial domain is marked as a high-load area. The path allocation or traffic scheduling strategy is optimized based on this marking information, and a global load change trend list is finally generated, including the load sequence of the time segment, the total load distribution of the spatial domain, and the identification information of the relevant high-load areas.

[0096] See also Figure 6 , the steps for calculating the load deviation value between sub-flow segments are as follows:

[0097] Based on the global load change trend list, the predicted load value of each sub-traffic segment is extracted, and the load values ​​are sorted according to the time dimension. The load trend dataset is generated based on the sorting results.

[0098] Collect the load trend information of the sub-flow segments in the global load change trend list, extract the predicted load value of each sub-flow segment from the global load change trend list, including the load sequence in the time dimension and the load distribution data in the spatial domain, sort the predicted load values ​​of each sub-flow segment according to the time dimension, and generate the load trend curve of each time segment, such as the sub-flow segment The load sequence is Mbps, combined with the spatial domain information of the source node and the target node, the load distribution data of all sub-flow segments in the same spatial domain are extracted, and the predicted values ​​of each sub-flow segment are grouped by spatial domain. For example, spatial domain AB contains sub-flow segments and , the predicted load values ​​are Mbps and Mbps, the total load of the two is calculated as Mbps, and mark the load trends of each spatial domain for subsequent analysis, such as marking high-load areas and low-load areas, to form a load trend dataset.

[0099] Based on the load trend data set, the formula is used:

[0100] ;

[0101] Calculate sub-traffic segments and In time segments Load deviation value , in Mbps, is used to measure the load distribution difference between two sub-traffic segments.

[0102] in, It is a sub-traffic segment In time segments The predicted load value in Mbps.

[0103] If the sub-flow segment The predicted load value is: Mbps, traffic sub-segment The predicted load value is: Mbps, enter the parameters:

[0104] ;

[0105] The results show that the sub-flow segment and In time segments The load deviation value of is 9.15 Mbps. The existence of the deviation value indicates that there is a difference in load distribution between the two. This value can be used to adjust the resource allocation ratio of sub-traffic segments when optimizing path allocation. For example, it can reduce the traffic on the path with higher load or increase the utilization of the path with lower load, thereby achieving the purpose of network load balancing.

[0106] See also Figure 7 ,The steps to obtain the dynamic path traffic allocation results are as follows:

[0107] Based on the load deviation value, the load deviation values ​​between the sub-flow segments are integrated in a matrix form to identify the paths of various load conditions, and the corresponding load paths are adjusted to generate the sub-flow segment path load distribution results;

[0108] The load distribution path of the sub-flow segment is established according to the load deviation value, and the load deviation value between the above sub-flow segments is extracted, for example, the sub-flow segment and The load deviation value is Mbps, the load deviation values ​​of all sub-flow segments are integrated into the load deviation matrix between sub-flow segments. For example, The load deviation matrix of the sub-flow segment set is: , high-load paths and low-load paths are identified by sorting the values ​​in the load deviation matrix, e.g., the maximum value in the deviation matrix Corresponding sub-traffic segment and , indicating that the load difference between the two is the largest, part of the traffic in the high-load path is redistributed to the low-load path, such as the sub-flow segment Part of the flow is allocated to the sub-flow segment ,form a preliminary path load distribution path table, record the mapping relationship between the source node and the target node of the sub-flow segment according to the distribution path table, and generate the sub-flow segment path load distribution result.

[0109] Based on the load distribution results of the sub-traffic segment path, the formula is used:

[0110] ;

[0111] Calculate sub-traffic segments and Path flow distribution ratio , and according to the sub-flow segment and Path flow distribution ratio Calculate and adjust path flow , dynamically adjust the load distribution path according to the adjusted path flow, and obtain the dynamic path flow distribution result;

[0112] in, It is a sub-traffic segment The load weighted value, in Mbps, represents the weight of the path. is the initial allocated path traffic value in Mbps, which represents the initial traffic allocation of the path and is extracted from the preliminary path allocation table, e.g. Mbps.

[0113] Set parameter sub-traffic segment The weighted value of: Mbps. Sub-traffic segment The weighted value of: Mbps. Sub-traffic segment and Preliminary path flow: Mbps.

[0114] Calculate the distribution ratio :

[0115] ;

[0116] Calculate the adjusted path flow based on the allocation ratio

[0117] ;

[0118] This result shows that the adjusted path flow Mbps is based on load weighted values Mbps and Mbps is calculated by calculating the dynamic allocation result and the path allocation ratio. , realizing the path and Traffic is redistributed under the current load conditions. Initial path traffic Mbps, the ratio under uniform distribution is 0.5, but the distribution ratio calculated based on the weighted value is , indicating the path Compared to the path The load pressure is greater, so more flow distribution is required. Mbps, in line with the calculation results of the allocation ratio, and the remaining traffic Mbps shifted to less loaded paths This result ensures that the path traffic distribution conforms to the weight of the load-weighted value, and dynamically adjusts to balance the load, reducing the pressure on high-load paths and improving the balance of the overall network.

[0119] See also Figure 8 ,The specific steps for obtaining the network traffic data optimization management results are:

[0120] Based on the dynamic path traffic distribution results, by monitoring the packet transmission rate within the path and the traffic value within the time interval, comparing the traffic fluctuation with the preset fluctuation threshold range, identifying the source node, target node and propagation path of the traffic anomaly, and generating the abnormal traffic identification result;

[0121] By monitoring the packet transmission rate and transmission interval within a path in real time, the traffic value of each path within a fixed time period is obtained. The obtained traffic data is compared and screened based on a preset traffic fluctuation threshold range (for example, 10% to 30%) to determine whether there are abnormal fluctuations. The difference between the average traffic value (for example, 100Mbps) and the real-time traffic value (for example, 120Mbps) of a path within the time interval is calculated and the difference ratio (for example, 20%) is compared with the fluctuation threshold range. If the difference exceeds the upper limit of 30%, it is determined to be abnormal. By organizing information such as the source node, destination node, and timestamp of the traffic, the propagation path of the abnormal traffic is further traced. For example, if the abnormal traffic fluctuation on a path originates from node A and is destined for node B, and the timestamps are concentrated during the daily peak period (for example, 6:00 PM to 7:00 PM), the source characteristics, path, and related parameters of the abnormal traffic are recorded by combining the transmission packet size, packet loss rate, and delay data on the abnormal path (for example, the packet loss rate is 5% and exceeds the normal range by 3% to 4%), and finally integrated into the abnormal traffic identification results.

[0122] Based on the abnormal traffic identification results and combined with the real-time load status and bandwidth utilization of the currently available paths, the abnormal traffic is reallocated to isolated paths, the operating status of the isolated paths is monitored, and network traffic data optimization management results are generated;

[0123] Based on the source node, target node, and traffic size of the abnormal traffic, the real-time load status and bandwidth utilization of all available paths in the current network are analyzed (for example, paths with bandwidth utilization below 50% are selected as isolation path candidates). Combined with the available transmission capacity of the isolation path (for example, the transmission capacity of an isolation path is 1Gbps, the current occupancy rate is 40%, and the remaining available bandwidth is 600Mbps), the traffic is preferentially allocated to the adapted path. The traffic distribution rate is further adjusted to ensure that the transmission rate meets the carrying capacity of the isolation path (for example, abnormal traffic with a flow rate of 200Mbps is distributed to two isolation paths at 150Mbps and 50Mbps respectively). The operating status of the isolation path after traffic redistribution is monitored in real time, including key parameters such as packet loss rate (for example, increased from the original 1% to 2%, which is still within the normal range) and latency (for example, increased from 20ms to 25ms). The performance change data of the isolation path is combined with the traffic redistribution strategy to form the final network traffic data optimization management result.

[0124] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A network data management system based on intelligent flow control, characterized in that: The system comprises: The traffic partitioning and sample extraction module obtains the time series information of network traffic and divides it into sub-traffic segments, filters the data packets in the sub-traffic segments, and generates a load feature sample set based on the screening results; The steps of dividing the sub-flow segments are specifically as follows: Capture data packets in the network based on network traffic monitoring equipment, segment and archive the traffic data at fixed time intervals, and generate a set of time series segments; Based on the set of time series segments, by parsing the source IP address and destination IP address of each data packet, the traffic data is grouped and recorded according to a node pairing rule to generate a set of sub-traffic segments; wherein the node pairing rule specifically forms a node association table of the data packet using the source IP address and the destination IP address; The steps for obtaining the load characteristic sample set are specifically as follows: Based on the sub-flow segment set, extracting packet size, transmission frequency, and load occupancy ratio information of the source node and the target node from the data packet, screening the data packet in combination with the load occupancy ratio of the source node and the target node, and integrating the screening results into a load signature to generate load signature data; The load occupancy ratio information of the source node and the target node is defined as the ratio of the size of the data packet processed by the source node or the target node in unit time to its theoretical maximum carrying capacity; The load characteristics include data packet size, transmission frequency, and load occupancy ratio of source node and destination node; Comparing each load feature in the load feature data with a corresponding preset load feature threshold value one by one, marking load features that exceed the threshold value, and collating the marking results to generate a load feature sample set; The distributed trend prediction and synchronization module extracts the load feature records of the sub-flow segments in the load feature sample set, calculates the load weight value corresponding to each sub-flow segment based on the load feature records, and predicts the load change trend of the sub-flow segment in subsequent time segments with reference to the load weight value, and generates a global load change trend list; The path optimization allocation and dynamic adjustment module calculates the load deviation values ​​between the sub-flow segments according to the load trend information of the sub-flow segments in the global load change trend list, establishes the load distribution path of the sub-flow segments according to the load deviation values, and dynamically adjusts the load distribution path to obtain the dynamic path flow distribution result; The abnormal behavior identification and isolation module extracts the real-time traffic information of each path in the dynamic path traffic allocation result, identifies abnormal traffic, reallocates the abnormal traffic to the isolated path, and generates network traffic data optimization management results.

2. The network data management system based on intelligent flow control according to claim 1, characterized in that: The steps of calculating the load weighted value corresponding to each sub-flow segment are specifically as follows: Based on the load feature sample set, retrieve the information of each sub-flow segment, divide each group of data into corresponding time segments according to the time segment, and extract the load change value and flow change of each sub-flow segment to generate the load record set of the sub-flow segment; Based on the load record set of the sub-traffic segment, the formula is used: ； Calculate sub-traffic segments Load weighted value ; in, It is a sub-traffic segment The total number of time segments, It is a sub-traffic segment In time segments The load value in represents the time slice The flow rate within It is a sub-traffic segment In time segments The traffic weight in It is a sub-traffic segment In time segments Dynamic priority weights.

3. The network data management system based on intelligent flow control according to claim 2, characterized in that: The steps for obtaining the global load change trend list are specifically as follows: According to the load weighted value, the formula is used: ； Calculate sub-traffic segments In the next time segment The predicted load value ; in, It's a time segment inner sub-traffic segment The load growth factor, is the adjustment coefficient of load change trend, It's a time segment The load increment, is the time segment length; Based on the predicted load value, the load change trends of all sub-flow segments are sorted in time dimension and space dimension, and a global load change trend list is generated by classifying and integrating the load data of each sub-flow segment.

4. The network data management system based on intelligent flow control according to claim 3, characterized in that: The step of calculating the load deviation value between the sub-flow segments is specifically as follows: Based on the global load change trend list, extract the predicted load value of each sub-flow segment, sort the load values ​​in combination with the time dimension, and generate a load trend data set according to the sorting results; Based on the load trend data set, the formula is used: ； Calculate sub-traffic segments and In time segments Load deviation value ; in, It is a sub-traffic segment In time segments The predicted load value.

5. The network data management system based on intelligent flow control according to claim 4, characterized in that: The steps for obtaining the dynamic path traffic allocation result are specifically as follows: Based on the load deviation value, the load deviation values ​​between the sub-flow segments are integrated in a matrix form, paths with multiple load conditions are identified, and the corresponding load paths are adjusted to generate sub-flow segment path load distribution results; Based on the load distribution results of the sub-flow segment paths, the formula is adopted: ； Calculate sub-traffic segments and Path flow distribution ratio , and according to the sub-flow segment and Path flow distribution ratio Calculate and adjust path flow , dynamically adjust the load distribution path according to the adjusted path flow, and obtain the dynamic path flow distribution result; in, It is a sub-traffic segment The load weighted value of is the initially allocated path flow value.

6. The network data management system based on intelligent flow control according to claim 5, characterized in that: The steps for obtaining the network traffic data optimization management results are specifically as follows: Based on the dynamic path traffic allocation result, by monitoring the data packet transmission rate within the path and the traffic value within the time interval, comparing the traffic fluctuation with the preset fluctuation threshold range, identifying the source node, target node and propagation path of the traffic anomaly, and generating an abnormal traffic identification result; Based on the abnormal traffic identification results, combined with the real-time load status and bandwidth utilization of the currently available paths, the abnormal traffic is reallocated to the isolated paths, the operating status of the isolated paths is monitored, and the network traffic data optimization management results are generated.

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