A communication network optimization control system and method based on multi-dimensional data drive

Abstract

The present invention discloses a multi-dimensional data-driven communication network optimization control system and method. The system comprises a communication acquisition module that collects multiple network flow data, user behavior data, and exchange pattern data at each communication node in the communication network in real time; a first analysis unit that inputs each network flow data into a pre-selected and trained flow prediction model to predict the network flow after a preset time period; a second analysis unit that analyzes each user behavior data and processes it to obtain each user's network communication habits; a third analysis unit that classifies each exchange pattern data according to a preset flow classification algorithm to obtain multiple classified data; a comprehensive processing unit that processes the network predicted flow, network communication habits, and each classified data to obtain a comprehensive analysis result; and a communication adjustment module that dynamically adjusts bandwidth resources, routing selection, and flow diversion at each communication node based on the comprehensive analysis result. The present invention achieves optimized control of network communication resources.

Description

Technical Field

[0001] The present invention relates to the field of network communication technology, and in particular to a communication network optimization control system and method based on multi-dimensional data drive. Background Art

[0002] With the rapid development of the internet and communications technologies, global network traffic continues to grow, and the complexity of network communications is also increasing. Especially driven by emerging technologies such as 5G and the Internet of Things (IoT), the number of users, devices, data traffic, and communication patterns in networks has exploded. Traditional network management methods and resource allocation strategies are no longer able to meet the requirements of modern communication networks for efficiency, intelligence, and dynamic adjustment.

[0003] In traditional network management, traffic monitoring and control often rely on simple traffic thresholds and static policies. While these methods ensure basic network stability to a certain extent, they are unable to cope with constantly changing network demands and complex communication patterns. For example, when a network experiences a traffic spike, traditional methods often fail to predict traffic changes in advance and thus fail to respond in a timely manner, resulting in increased network latency, wasted bandwidth, or localized network congestion. Furthermore, when a large number of user behaviors change within the network (such as sudden high bandwidth demands or changing user habits), traditional methods are even less able to adapt flexibly.

[0004] Furthermore, switching patterns in modern communications networks are becoming increasingly complex. The varying congestion levels, switching frequencies, and path selection along different network paths make network congestion even more unpredictable. As network topologies evolve, static routing and bandwidth allocation methods cannot guarantee optimal network performance in all situations.

[0005] To address these issues, network management systems require more intelligent and dynamic technologies to process and analyze data from various network nodes in real time. This data includes not only traditional network traffic data but also user behavior data and exchange pattern data. By comprehensively analyzing this multi-dimensional data, network management systems can more accurately predict network traffic, identify user needs, and discover potential network bottlenecks. Based on this, they can make dynamic adjustments, effectively improving network resource utilization and enhancing user experience.

[0006] Although some network optimization technologies based on traffic prediction and user behavior analysis have been developed, these technologies still face several key challenges:

[0007] Existing network control technologies are usually based on simple analysis of historical data, lacking real-time and intelligence, and unable to cope with the instantaneous changes in the network environment;

[0008] In complex networks, how to combine different types of data for comprehensive analysis and optimize network resource allocation accordingly remains a technical challenge.

[0009] Therefore, how to flexibly respond to changes in user behavior and optimize network resource allocation through comprehensive analysis of multi-dimensional data has become a key technical issue that needs to be urgently addressed in the field of network communication management. Summary of the Invention

[0010] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a communication network optimization control system and method driven by multi-dimensional data, which is used to realize comprehensive analysis of multi-dimensional data and achieve optimal control of network communication resources.

[0011] To achieve the above objectives, the present invention provides the following technical solutions: a communication network optimization control system based on multi-dimensional data drive, comprising:

[0012] Communication collection module, used to collect multiple network traffic data, user behavior data and exchange pattern data at each communication node in the communication network in real time;

[0013] The data analysis module is connected to the communication acquisition module and includes:

[0014] A first analysis unit is configured to input each of the network traffic data into a pre-selected and trained traffic prediction model to predict the network traffic after a preset time period;

[0015] A second analysis unit is used to analyze the user behavior data and obtain the network communication habits of each user;

[0016] a third analyzing unit, configured to classify each of the exchange mode data according to a preset traffic classification algorithm to obtain a plurality of classification data;

[0017] a comprehensive processing unit, connected to the first analysis unit, the second analysis unit, and the third analysis unit, respectively, for obtaining a comprehensive analysis result based on the network predicted traffic, the network communication habits, and the classification data;

[0018] The communication adjustment module is connected to the data analysis module and is used to dynamically adjust the bandwidth resources, routing selection and traffic diversion at each communication node according to the comprehensive analysis results.

[0019] Furthermore, the network traffic data includes bandwidth utilization, packet loss rate, delay, data packet size and traffic distribution; the user behavior data includes access frequency, access type, access time, device type and geographic location; and the exchange mode data includes data packet exchange frequency, exchange path and path congestion level.

[0020] Furthermore, it also includes:

[0021] A detection module, connected to the data analysis module, for detecting the device load at each of the communication nodes in real time;

[0022] a calculation module, connected to the communication acquisition module and the data analysis module, configured to input the bandwidth utilization, the packet loss rate, the delay, and the data packet size into a preset comprehensive congestion calculation formula to calculate a communication congestion index;

[0023] a storage module, connected to the data analysis module, for storing a plurality of historical training data of each of the communication nodes, the historical training data including historical load, historical congestion index and historical traffic data;

[0024] The first analyzing unit includes:

[0025] a training subunit, configured to introduce an initial traffic model, take the historical loads, the historical congestion indexes, and the historical traffic data as input, take the historical traffic data after the next time period as output, retrain the initial traffic model, and obtain the traffic prediction model;

[0026] The prediction subunit is connected to the training subunit and is used to input the device load, the network traffic data and the communication congestion index at the current moment into the traffic prediction model to predict the network predicted traffic after a preset time period.

[0027] Furthermore, the comprehensive congestion calculation formula is configured as:

[0028]

[0029] Among them, C i Used to represent the communication congestion index, U b (t) is used to represent the bandwidth utilization at time t, L p (t) is used to represent the packet loss rate at time t, L t (t) is used to represent the delay at time t, S p (i) is used to represent the size of the i-th data packet, T is used to represent the time window of calculation, α, β, γ, and δ are respectively used to represent constants that control the influence of various parameters on the communication congestion index, and N is used to represent the total number of data packets.

[0030] Furthermore, the comprehensive processing unit includes:

[0031] A fusion subunit is configured to fuse the network predicted traffic, the network communication habits and the classified data into a preset data fusion model to perform multi-source data fusion to obtain multi-source fused data;

[0032] The analysis subunit is connected to the fusion subunit and is used to analyze the multi-source fusion data to obtain comprehensive analysis data.

[0033] Furthermore, the formula configuration of the data fusion model is:

[0034]

[0035] Among them, M f Used to represent the multi-source fusion data, N f (t) is used to represent the network predicted traffic at time t, C f (t) is used to represent the traffic classification data at time t, R i It is used to represent the data source signal strength at the communication node, α2, β2, γ2, δ2, η2, κ2 are used to represent the flow prediction influence constant, communication habit influence constant, flow classification influence constant, time attenuation influence constant and signal strength influence constant respectively, μ(H c (i)) is used to indicate the volatility of the network communication habits.

[0036] Furthermore, the detection module is further configured to detect multiple external environment data and multiple network security event data at each of the communication nodes in real time;

[0037] The integrated processing unit further includes an optimization subunit, connected to the fusion subunit, configured to input each of the external environment data and the communication habit influence constant into a preset first constant optimization formula to obtain a communication habit optimization influence constant, and input each of the network security event data and the traffic prediction influence constant into a preset second constant optimization formula to obtain a traffic prediction optimization influence constant;

[0038] The data fusion model is updated based on the communication habit optimization influence constant and the traffic prediction optimization influence constant.

[0039] Furthermore, the external environment data includes temperature variation, terrain interference, environmental magnetic field, and environmental noise;

[0040] The network security event data includes password cracking data and virus infection data.

[0041] Furthermore, the first constant optimization formula is configured as:

[0042]

[0043] Among them, β2′ is used to represent the communication habit optimization influence constant, ΔT is used to represent the temperature change, φ is used to represent the influence weight of the temperature change, G d is used to represent the terrain interference amount, χ is used to represent the influence weight of the terrain interference amount, B e is used to represent the environmental magnetic field, ψ is used to represent the influence weight of the environmental magnetic field, N e is used to represent the environmental noise, and ω is used to represent the influence weight of the environmental noise;

[0044] The second constant optimization formula is configured as:

[0045]

[0046] Among them, α2′ is used to represent the flow prediction optimization influence constant, P k Used to represent the password cracking data, T k Used to represent the time delay associated with password cracking, a k 、b k They are used to represent the first cracking related constant and the second cracking related constant related to password cracking, V j Used to represent the virus infection data, E j Used to represent environmental factors data related to viral infection, c j d j are used to represent a first infection-related constant and a second infection-related constant related to virus infection, n is used to represent the total number of the password cracking data, and m is used to represent the total number of the virus-infected data.

[0047] A communication network optimization control method based on multidimensional data drive, applied to the above-mentioned communication network optimization control system based on multidimensional data drive, comprising:

[0048] Step S1, the communication collection module collects multiple network traffic data, user behavior data and exchange pattern data at each communication node in the communication network in real time;

[0049] In step S2, the first analysis unit inputs each of the network traffic data into a pre-selected and trained traffic prediction model to predict the network traffic after a preset time period. The second analysis unit analyzes each of the user behavior data to obtain the network communication habits of each user. The third analysis unit classifies each of the exchange mode data according to a preset traffic classification algorithm to obtain a plurality of classification data.

[0050] Step S3, a comprehensive processing unit obtains a comprehensive analysis result based on the network predicted traffic, the network communication habits, and the classification data;

[0051] Step S4: The communication adjustment module dynamically adjusts the bandwidth resources, routing selection, and traffic diversion at each of the communication nodes according to the comprehensive analysis results.

[0052] Beneficial effects of the present invention:

[0053] This invention optimizes and controls network communication resources by comprehensively analyzing multi-dimensional data (including network traffic, user behavior, and exchange pattern data). It can predict future network traffic based on real-time data and dynamically adjust bandwidth, routing, and traffic distribution to avoid network congestion and overload, ensuring stable and efficient network operation.

[0054] Furthermore, the present invention intelligently analyzes user behavior and accurately identifies the communication habits of different users, enabling personalized allocation of network resources based on user needs. This not only improves user experience but also increases resource utilization efficiency, ensuring high-quality network services.

[0055] Ultimately, the present invention refines traffic classification and dynamic adjustment mechanisms, so that different types of network traffic can be given reasonable priority and bandwidth allocation, avoiding resource waste and reducing network operating costs, thereby improving the overall network service quality and operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a structural diagram of the communication network optimization control system based on multi-dimensional data drive in the present invention;

[0057] Figure 2 It is a schematic structural diagram of the first analysis unit in the present invention;

[0058] Figure 3 It is a structural diagram of the comprehensive processing unit in the present invention;

[0059] Figure 4 It is a schematic diagram of the steps of the communication network optimization control method based on multi-dimensional data drive in the present invention.

[0060] Figure numerals: 1. Communication acquisition module; 2. Data analysis module; 21. First analysis unit; 211. Training subunit; 212. Prediction subunit; 22. Second analysis unit; 23. Third analysis unit; 24. Comprehensive processing unit; 241. Fusion subunit; 242. Analysis subunit; 243. Optimization subunit; 3. Communication adjustment module; 4. Detection module; 5. Computation module; 6. Storage module. DETAILED DESCRIPTION

[0061] The present invention will be described in further detail below with reference to the accompanying drawings and embodiments. Identical components are denoted by the same reference numerals. It should be noted that the terms "front," "rear," "left," "right," "upper," and "lower" used in the following description refer to directions in the accompanying drawings, and the terms "bottom," "top," "inner," and "outer" refer to directions toward or away from the geometric center of a particular component, respectively.

[0062] Example 1, reference Figure 1 , which is the first embodiment of the present invention, provides a communication network optimization control system driven by multi-dimensional data, which can realize comprehensive analysis of multi-dimensional data and realize optimized control of network communication resources, including:

[0063] Communication collection module 1, used to collect multiple network traffic data, user behavior data and exchange pattern data at each communication node in the communication network in real time;

[0064] The data analysis module 2 is connected to the communication acquisition module 1 and includes:

[0065] The first analysis unit 21 is used to input the network traffic data into the pre-selected and trained traffic prediction model to predict the network traffic after a preset time period;

[0066] The second analysis unit 22 is used to analyze the behavior data of each user and obtain the network communication habits of each user;

[0067] The third analysis unit 23 is used to classify the exchange mode data according to a preset traffic classification algorithm to obtain a plurality of classification data;

[0068] The comprehensive processing unit 24 is connected to the first analysis unit 21, the second analysis unit 22 and the third analysis unit 23 respectively, and is used to obtain comprehensive analysis results based on the network predicted traffic, network communication habits and each classification data processing;

[0069] The communication adjustment module 3 is connected to the data analysis module 2 and is used to dynamically adjust the bandwidth resources, routing selection and traffic diversion at each communication node according to the comprehensive analysis results.

[0070] Preferably, network traffic data includes bandwidth utilization, packet loss rate, delay, data packet size and traffic distribution, user behavior data includes access frequency, access type, access time, device type and geographic location, and exchange mode data includes data packet exchange frequency, exchange path and path congestion level.

[0071] Working principle of embodiment 1:

[0072] The communication acquisition module 1 is used to collect various data from multiple nodes in the communication network in real time. Specifically, the communication acquisition module 1 collects network traffic data, user behavior data, and exchange pattern data at each communication node in real time through network sensors or network devices (such as switches and routers).

[0073] Data analysis module 2 consists of three analysis units: first analysis unit 21, second analysis unit 22, and third analysis unit 23. First analysis unit 21 uses a trained traffic prediction model to process collected network traffic data and predict network traffic for a specific period of time. The prediction results can be used to dynamically adjust network bandwidth and resource allocation to avoid network congestion.

[0074] The second analysis unit 22 collects statistics and analyzes user communication behaviors to extract information about their communication habits. For example, some users may have higher bandwidth requirements during specific time periods, while others may have lower bandwidth requirements. Based on these analysis results, appropriate network resources can be allocated to different user groups, providing personalized network services.

[0075] The third analysis unit 23 categorizes the exchange pattern data using a pre-set traffic classification algorithm, distinguishing different types of network traffic, such as real-time traffic, batch traffic, and video traffic. Based on these classifications, the system can prioritize high-priority traffic, such as real-time video or voice communications, to ensure their quality.

[0076] The integrated processing unit 24 receives the output data from the three analysis units, including network traffic prediction results, user behavior analysis results, and traffic classification data. It combines this data and performs a multi-dimensional comprehensive analysis to generate a comprehensive analysis result. This comprehensive analysis provides decision support for network resource allocation, ensuring that the system achieves optimal resource allocation.

[0077] Based on the comprehensive analysis results output by the comprehensive processing unit 24, the communication regulation module 3 dynamically adjusts each communication node in the communication network. Specifically, the communication regulation module 3 can adjust bandwidth allocation, routing selection, and traffic diversion strategies. For example, if it is predicted that the traffic of a node will exceed its bandwidth limit, the system will automatically adjust the routing path of that node or prioritize important traffic based on traffic classification results to avoid network performance degradation.

[0078] Preferably, it also includes:

[0079] Detection module 4, connected to data analysis module 2, for real-time detection of device load at each communication node;

[0080] The calculation module 5 is connected to the communication collection module 1 and the data analysis module 2, and is used to input the bandwidth utilization, packet loss rate, delay and data packet size into a preset comprehensive congestion calculation formula to calculate the communication congestion index;

[0081] The storage module 6 is connected to the data analysis module 2 and is used to store a plurality of historical training data of each communication node, the historical training data including historical load, historical congestion index and historical traffic data;

[0082] like Figure 2 As shown, the first analysis unit 21 includes:

[0083] The training subunit 211 is used to introduce the initial traffic model, and use the historical load, the historical congestion index and the historical traffic data as input, and use the historical traffic data after the next time period as output, to retrain the initial traffic model and obtain a traffic prediction model;

[0084] The prediction subunit 212 is connected to the training subunit 211 and is used to input the current device load, network traffic data and communication congestion index into the traffic prediction model to predict the network traffic after a preset time period.

[0085] Specifically, in this embodiment, the detection module 4 is connected to the data analysis module 2 and is used to monitor the device load of each communication node in the network in real time. This module performs real-time detection of the device load of each node by acquiring the device operating status of each node (such as CPU utilization, memory usage, and other indicators). This ensures that the data analysis module 2 can accurately grasp the device status of each node, providing an important basis for subsequent traffic prediction and resource adjustment. The calculation module 5 is connected to the communication acquisition module 1 and the data analysis module 2. Its main function is to calculate the congestion index of the communication network. The calculation module 5 uses bandwidth utilization, packet loss rate, latency, and packet size as inputs and substitutes them into the comprehensive congestion calculation formula to obtain the communication congestion index of each node. The congestion index is an important indicator reflecting the load and communication quality of network nodes, providing decision support for traffic prediction and dynamic resource allocation. The storage module 6 is connected to the data analysis module 2 and is used to store and manage historical training data for each communication node. Historical training data includes historical load, historical congestion index, and historical traffic data. This data is used to train the traffic prediction model and help analyze the long-term operating trends of the node. Through the storage module 6, the system can continuously optimize the flow forecast based on historical data and improve the accuracy of the forecast.

[0086] Preferably, the comprehensive congestion calculation formula is configured as:

[0087]

[0088] Among them, C iUsed to represent the communication congestion index, U b (t) is used to represent the bandwidth utilization at time t, L p (t) is used to represent the packet loss rate at time t, L t (t) is used to represent the delay at time t, S p (i) is used to represent the size of the i-th data packet, T is used to represent the time window of calculation, α, β, γ, and δ are used to represent the constants that control the influence of each parameter on the communication congestion index, and N is used to represent the total number of data packets.

[0089] Specifically, in this embodiment, bandwidth utilization is obtained through SNMP (Simple Network Management Protocol) or traffic monitoring tools (such as NetFlow, sFlow, etc.). Packet loss rate is obtained through ICMP echo request (ping) or the traffic counting function of the network device. Latency is obtained by measuring the round-trip time from the network source to the destination using the ping command or a delay measurement tool based on a protocol (such as ICMP, TCP). Packet size can be obtained through a protocol analysis tool (such as Wireshark) or the traffic analysis function of the device. During the data preprocessing stage, the collected raw data is converted into a form suitable for formula calculation: the bandwidth utilization, packet loss rate, delay, and packet size data are normalized or standardized to have a consistent scale in the calculation. For time-varying indicators such as packet loss rate and delay, a sliding window (for example, every 5 seconds) is used to calculate and store the current network status. The comprehensive congestion calculation formula is then input for calculation. Bandwidth utilization is usually in the range of [0, 1], packet loss rate is usually in the range of [0, 1], and delay, in milliseconds, is usually a positive number. The size of each packet is in bytes. In this embodiment, the constants are selected as follows: α = 1.2: bandwidth utilization has a significant impact on congestion; β = 2.5: packet loss rate has a strong impact on the congestion index; γ = 0.05: latency has a mild negative impact on the congestion index; and δ = 0.8: packet size has a small, but still significant, impact on congestion. Through simulations or actual network environment testing, the network congestion index can be calculated and output in real time. Results show that when bandwidth utilization exceeds 90%, packet loss rate exceeds 5%, and latency exceeds 100ms, the communication congestion index rises sharply, prompting network administrators to intervene.

[0090] In a specific embodiment, under a network environment with high bandwidth utilization and high packet loss rate, the comprehensive congestion index may reach 0.85, while under a normal network environment, the congestion index is below 0.3.

[0091] Preferably, Figure 3 As shown, the integrated processing unit 24 includes:

[0092] The fusion subunit 241 is used to fuse the network predicted traffic, network communication habits and the classified data into a preset data fusion model to obtain multi-source fusion data;

[0093] The analysis subunit 242 is connected to the fusion subunit 241 and is used to analyze the multi-source fusion data to obtain comprehensive analysis data.

[0094] Specifically, in this embodiment, the fusion subunit 241 receives multiple input data sources from the data analysis module 2, including predicted network traffic, network communication habits, and classified data derived from traffic classification analysis. It uses a pre-set data fusion model to perform multi-source data fusion on this data, unifying heterogeneous data from different sources to produce comprehensive multi-source fused data. This multi-source data fusion process enables the system to comprehensively consider the relevance and complementarity of different data sources, thereby improving the accuracy and comprehensiveness of the analysis results. The analysis subunit 242, connected to the fusion subunit 241, is responsible for analyzing the fused multi-source fused data. Through multi-dimensional analysis, the analysis subunit 242 extracts comprehensive analysis data, which in turn provides a basis for subsequent network resource scheduling and dynamic adjustment. This comprehensive analysis data can include network load predictions, user demand analysis, network bottleneck identification, and other information, providing strong support for the decision-making of the communication regulation module 3. A specific application scenario involves a high-traffic online video platform that needs to process viewing data from millions of users in real time and dynamically adjust according to varying user needs and network conditions.

[0095] Multi-source data fusion: Fusion subunit 241 receives data from network traffic forecasts, user behavior analysis, and traffic classification. For example, network traffic forecasts indicate peak platform traffic during certain periods, user behavior analysis reveals different users' viewing preferences during peak hours, and traffic classification distinguishes between different types of traffic, such as video traffic and real-time interactive traffic. Fusion subunit 241 fuses this information to generate multi-source fused data, helping the system fully understand current network conditions and user needs.

[0096] Comprehensive Analysis: The analysis subunit 242 performs in-depth analysis of multi-source fused data to extract key metrics. For example, the analysis subunit 242 may discover that certain video content has seen a surge in viewership during a specific time period, or that certain user groups prefer HD playback during specific times. Based on this comprehensive analysis data, the system can predict future network load and take measures to optimize resources.

[0097] Dynamic Adjustment: Based on the comprehensive analysis data provided by the analysis subunit 242, the communication adjustment module 3 dynamically adjusts network resources. For example, if the system foresees network congestion during a certain period of time, the communication adjustment module 3 will pre-allocate more bandwidth to high-priority traffic (such as high-definition video streams) and use load balancing strategies to direct some traffic to nodes with lower network loads to avoid network bottlenecks.

[0098] Optimizing the User Experience: By integrating and analyzing multi-source data, the system can respond in real time to network changes and user needs, ensuring a smooth viewing experience even during peak hours. The system intelligently optimizes video playback based on user viewing habits, improving video quality, reducing latency and lag, and ensuring efficient use of platform resources.

[0099] Preferably, the formula configuration of the data fusion model is:

[0100]

[0101] Among them, M f Used to represent multi-source fusion data, N f (t) is used to represent the network predicted traffic at time t, C f (t) is used to represent the traffic classification data at time t, R i It is used to represent the signal strength of the data source at the communication node. α2, β2, γ2, δ2, η2, and κ2 are used to represent the traffic prediction influence constant, the communication habit influence constant, the traffic classification influence constant, the time attenuation influence constant, and the signal strength influence constant, respectively. c (i)) is used to indicate the volatility of online communication habits.

[0102] Specifically, in this embodiment, the parameters are selected as follows: α2=2, β2=0.5, γ2=1.2, δ2=0.1, η2=1.5, κ2=0.8;

[0103] When N f (t) = 100Mbps, H c (t) = 0.8, C f When (t) = 0.4, then M f (t)=100 2 ·(1+e -0 . 5·0.8 )·e -1.2·0.4 ·e -0.1·|t-12| , when time t = 12, assuming M = 3, R1 = 0.9, R2 = 1.1, R3 = 1.2, the final multi-source fusion data is the sum of the integral part and the signal source weighted part:

[0104]

[0105] Finally, the fused network traffic prediction data M is calculated f =320.75Mbps.

[0106] Embodiment 2 is the second embodiment of the present invention. Unlike the previous embodiment, this embodiment provides an optimization subunit 243, which can optimize and correct the communication habit influence constant and the traffic prediction influence constant based on the external environment data and the network security event data, respectively, to improve the calculation accuracy of the multi-source fusion data. In particular, the detection module 4 is further used to detect multiple external environment data and multiple network security event data at each communication node in real time.

[0107] The integrated processing unit 24 further includes an optimization subunit 243 connected to the fusion subunit 241, configured to input the external environment data and the communication habit influence constant into a preset first constant optimization formula to obtain the communication habit optimization influence constant, and input the network security event data and the traffic prediction influence constant into a preset second constant optimization formula to obtain the traffic prediction optimization influence constant;

[0108] The data fusion model is updated based on the communication habit optimization influence constant and the traffic prediction optimization influence constant;

[0109] External environmental data include temperature change, terrain interference, environmental magnetic field, and environmental noise;

[0110] Cybersecurity incident data includes password cracking data and virus infection data.

[0111] Working principle of embodiment 2:

[0112] Changes in the external environment can cause performance degradation in communication equipment and unstable signal propagation, which in turn can affect users' communication habits. By introducing external environmental data into the first constant optimization formula, we can optimize the constant affecting communication habits and obtain the communication habit optimization constant. This eliminates the impact of the external environment on the communication habit constant, improving the computational accuracy of multi-source fusion data. Network security incidents can significantly impact traffic usage during network communications. For example, when a network communication node suffers a severe network security incident, user traffic usage may drop sharply in the short term, severely interfering with traffic data prediction. By introducing the network security event input into the second constant optimization formula, we can correct the traffic prediction constant and obtain the traffic prediction optimization constant. This eliminates the impact of network security time on the traffic prediction constant, thereby improving the computational accuracy of multi-source fusion data.

[0113] Preferably, the first constant optimization formula is configured as:

[0114]

[0115] Among them, β2′ is used to represent the influence constant of communication habit optimization, ΔT is used to represent the temperature change, φ is used to represent the influence weight of the temperature change, G d is used to represent the amount of terrain interference, χ is used to represent the influence weight of the terrain interference, and B e It is used to represent the environmental magnetic field, ψ is used to represent the influence weight of the environmental magnetic field, N e It is used to represent the environmental noise, and ∈ is used to represent the impact weight of the environmental noise;

[0116] Specifically, in this embodiment, if the communication habit optimization influence constant is close to 0, it indicates that the influence of environmental factors is very strong and the communication habit influence constant has been significantly optimized; if the communication habit optimization influence constant is close to the communication habit influence constant, it indicates that the influence of environmental factors is small, and the optimized constant is basically the same as the initial value. The above-mentioned first constant optimization formula uses an exponential decay function to describe the influence of environmental factors. The weight of each environmental factor can be flexibly adjusted according to actual conditions, so that the formula can adapt to different network environments and communication needs, and the optimization effect can be fine-tuned for specific situations. This formula can take into account the impact of multiple factors such as temperature changes, terrain interference, environmental magnetic fields, and noise on communication habits, ensuring that the communication system can operate stably in different environments, especially in environments with complex interference, and has strong environmental adaptability.

[0117] The second constant optimization formula is configured as:

[0118]

[0119] Among them, α2′ is used to represent the flow prediction optimization influence constant, P k Used to represent password cracking data, T k Used to represent the time delay associated with password cracking, a k 、b k They are used to represent the first cracking related constant and the second cracking related constant related to password cracking, V j Used to represent virus infection data, E j Used to represent environmental factors data related to viral infection, c j d j are used to represent the first infection-related constant and the second infection-related constant related to virus infection, n is used to represent the total number of password cracking data, and m is used to represent the total number of virus-infected data.

[0120] Specifically, in this embodiment, the first infection-related constant and the second infection-related constant represent the intensity of the impact of virus infection data on traffic prediction and the regulating factor of the impact of environmental factors on traffic prediction, respectively. If the traffic prediction optimization impact constant is close to 0, it indicates that the impact of security events (such as password cracking, network intrusion, etc.) on traffic prediction is very large, and the system's traffic prediction ability is greatly suppressed; if the traffic prediction optimization impact constant is close to the traffic prediction impact constant, it indicates that the impact of these security events is small, the traffic prediction constant is close to the initial value, and the system can operate normally. The above formula comprehensively considers the impact of security events on traffic prediction by combining multiple data sources such as password cracking, network intrusion, leaked data, and virus infection data. It is not limited to the consideration of a single factor, so that the above formula can be widely applied to traffic prediction in complex network environments. The constants in the above formula can be optimized and adjusted according to the actual network environment, historical data or experimental results, so as to accurately adapt to different communication scenarios.

[0121] A communication network optimization control method based on multi-dimensional data driving is applied to the above communication network optimization control system based on multi-dimensional data driving, such as Figure 2 Shown, including:

[0122] Step S1, the communication collection module 1 collects multiple network traffic data, user behavior data and exchange pattern data at each communication node in the communication network in real time;

[0123] In step S2, the first analysis unit 21 inputs each network traffic data into a pre-selected and trained traffic prediction model to predict the network traffic after a preset time period. The second analysis unit 22 analyzes each user behavior data to obtain each user's network communication habits. The third analysis unit 23 classifies each exchange mode data according to a preset traffic classification algorithm to obtain multiple classification data.

[0124] Step S3, the comprehensive processing unit 24 obtains a comprehensive analysis result based on the network predicted traffic, network communication habits and each classification data processing;

[0125] In step S4, the communication adjustment module 3 dynamically adjusts the bandwidth resources, routing selection, and traffic diversion at each communication node according to the comprehensive analysis results.

[0126] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that improvements and modifications that do not depart from the principles of the present invention are within the scope of protection of the present invention.

Claims

1. A communication network optimization control system based on multi-dimensional data drive, characterized in that: include: A communication collection module (1) is used to collect multiple network traffic data, user behavior data and exchange pattern data at each communication node in the communication network in real time; The data analysis module (2) is connected to the communication acquisition module (1) and comprises: A first analysis unit (21) is used to input each of the network traffic data into a pre-trained traffic prediction model to predict the network traffic after a preset time period, wherein the network traffic data includes bandwidth utilization, packet loss rate, delay, data packet size and traffic distribution; A second analysis unit (22) is used to analyze the user behavior data and obtain the network communication habits of each user; A third analysis unit (23) is used to classify each of the exchange mode data according to a preset traffic classification algorithm to obtain a plurality of classification data; a comprehensive processing unit (24), connected to the first analysis unit (21), the second analysis unit (22) and the third analysis unit (23), respectively, for obtaining a comprehensive analysis result based on the network predicted traffic, the network communication habits and the classification data; A communication adjustment module (3), connected to the data analysis module (2), is used to dynamically adjust bandwidth resources, routing selection, and traffic diversion at each of the communication nodes according to the comprehensive analysis results; Also includes: A detection module (4), connected to the data analysis module (2), for detecting the device load at each of the communication nodes in real time; a calculation module (5), connected to the communication acquisition module (1) and the data analysis module (2), for inputting the bandwidth utilization, the packet loss rate, the delay and the data packet size into a preset comprehensive congestion calculation formula to calculate a communication congestion index; A storage module (6), connected to the data analysis module (2), for storing a plurality of historical training data of each of the communication nodes, the historical training data including historical load, historical congestion index and historical traffic data; The first analyzing unit (21) comprises: A training subunit (211) is used to introduce an initial traffic model, and take the historical loads, the historical congestion indexes and the historical traffic data as inputs, and take the historical traffic data after the next time period as outputs, to retrain the initial traffic model and obtain the traffic prediction model; The prediction subunit (212) is connected to the training subunit (211) and is used to input the device load, the network traffic data and the communication congestion index at the current moment into the traffic prediction model to predict the network predicted traffic after a preset time period.

2. The communication network optimization control system based on multi-dimensional data drive according to claim 1, characterized in that: The comprehensive congestion calculation formula is configured as follows: Among them, C i Used to represent the communication congestion index, U b (t) is used to represent the bandwidth utilization at time t, L p (t) is used to represent the packet loss rate at time t, L t (t) is used to represent the delay at time t, S p (i) is used to represent the size of the i-th data packet, T is used to represent the time window of calculation, α, β, γ, and δ are respectively used to represent constants that control the influence of various parameters on the communication congestion index, and N is used to represent the total number of data packets.

3. The communication network optimization control system based on multi-dimensional data drive according to claim 1, characterized in that: The comprehensive processing unit (24) comprises: A fusion subunit (241) is used to fuse the network predicted traffic, the network communication habits and the classified data into a preset data fusion model to obtain multi-source fusion data; The analysis subunit (242) is connected to the fusion subunit (241) and is used to analyze the multi-source fusion data to obtain comprehensive analysis data.

4. The communication network optimization control system based on multi-dimensional data drive according to claim 3, characterized in that: The formula configuration of the data fusion model is: Among them, M f Used to represent the multi-source fusion data, N f (t) is used to represent the network predicted traffic at time t, C f (t) is used to represent the traffic classification data at time t, R i It is used to represent the data source signal strength at the communication node, α2, β2, γ2, δ2, η2, κ2 are used to represent the flow prediction influence constant, communication habit influence constant, flow classification influence constant, time attenuation influence constant and signal strength influence constant respectively, μ(H c (i)) is used to indicate the volatility of the network communication habits.

5. The communication network optimization control system based on multi-dimensional data drive according to claim 4, characterized in that: The detection module (4) is also used to detect multiple external environment data and multiple network security event data at each communication node in real time; The integrated processing unit (24) further includes an optimization subunit (243), connected to the fusion subunit (241), for inputting each of the external environment data and the communication habit influence constant into a preset first constant optimization formula to obtain a communication habit optimization influence constant, and inputting each of the network security event data and the traffic prediction influence constant into a preset second constant optimization formula to obtain a traffic prediction optimization influence constant; The data fusion model is updated based on the communication habit optimization influence constant and the traffic prediction optimization influence constant.

6. The communication network optimization control system based on multi-dimensional data drive according to claim 5, characterized in that: The external environment data includes temperature variation, terrain interference, environmental magnetic field, and environmental noise; The network security event data includes password cracking data and virus infection data.

7. The communication network optimization control system based on multi-dimensional data drive according to claim 6, characterized in that: The first constant optimization formula is configured as: Among them, β2′ is used to represent the communication habit optimization influence constant, ΔT is used to represent the temperature change, φ is used to represent the influence weight of the temperature change, G d is used to represent the terrain interference amount, χ is used to represent the influence weight of the terrain interference amount, B e is used to represent the environmental magnetic field, ψ is used to represent the influence weight of the environmental magnetic field, N e is used to represent the environmental noise, and ω is used to represent the influence weight of the environmental noise; The second constant optimization formula is configured as: Among them, α2′ is used to represent the flow prediction optimization influence constant, P k Used to represent the password cracking data, T k Used to represent the time delay associated with password cracking, a k 、b k They are used to represent the first cracking related constant and the second cracking related constant related to password cracking, V j Used to represent the virus infection data, E j Used to represent environmental factors data related to viral infection, c j d j are used to represent a first infection-related constant and a second infection-related constant related to virus infection, n is used to represent the total number of the password cracking data, and m is used to represent the total number of the virus-infected data.

8. A communication network optimization control method based on multidimensional data driving, applied to the communication network optimization control system based on multidimensional data driving according to any one of claims 1 to 7, characterized in that: include: Step S1, the communication collection module (1) collects multiple network traffic data, user behavior data and exchange pattern data at each communication node in the communication network in real time; In step S2, the first analysis unit (21) inputs each of the network traffic data into a pre-trained traffic prediction model to predict the network traffic after a preset time period, the second analysis unit (22) analyzes each of the user behavior data to obtain the network communication habits of each user, and the third analysis unit (23) classifies each of the exchange mode data according to a preset traffic classification algorithm to obtain a plurality of classification data; Step S3, the comprehensive processing unit (24) obtains a comprehensive analysis result based on the network predicted traffic, the network communication habits and the classification data; Step S4, the communication adjustment module (3) dynamically adjusts the bandwidth resources, routing selection and traffic diversion at each of the communication nodes according to the comprehensive analysis results.

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