A network priority allocation system based on user behavior
By using a network priority allocation system based on user behavior, which dynamically calculates network priorities using deep learning and multi-objective optimization algorithms, the problem of network resource allocation failing to adapt to changes in user behavior in real time is solved, achieving accurate and robust resource allocation and an efficient user experience.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- RAYTHEON (WUHAN) NETWORK TECH CO LTD
- Filing Date
- 2026-04-29
- Publication Date
- 2026-07-31
AI Technical Summary
Existing technologies cannot adapt to subtle changes in user behavior in real time during network resource allocation, resulting in large priority mapping errors and failing to guarantee the accuracy and consistency of resource allocation.
A network priority allocation system based on user behavior is adopted. Multi-source data is generated through a user behavior collection module, and behavior patterns are identified using a deep learning model. Network priorities are dynamically calculated by combining multi-objective optimization algorithms and reinforcement learning algorithms. Differential service mechanism and QoS control are supported to achieve adaptive optimization and fine-grained scheduling.
It achieves real-time accuracy and improved response speed in network resource allocation, reduces resource allocation errors, enhances the robustness of network resource allocation and user experience, and solves the problems of resource waste and priority conflicts.
Smart Images
Figure CN122496560A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of big data technology, specifically to a network priority allocation system based on user behavior. Background Technology
[0002] User behavior refers to all activities that users engage in when visiting websites, using apps, and interacting digitally, including specific actions such as browsing pages, searching for information, clicking links, purchasing goods, and posting comments.
[0003] Currently, due to the diversity and time-varying nature of user behavior in the network environment, the static strategies and simple rules relied upon for dynamic allocation of network resources cannot adapt to subtle changes in behavioral patterns in real time. When behavioral feature identification deviations occur, it will cause large errors in priority mapping and cannot guarantee the accuracy of resource allocation.
[0004] Therefore, a network priority allocation system based on user behavior is proposed to solve the above problems. Summary of the Invention
[0005] To address the shortcomings of existing technologies, this invention provides a network priority allocation system based on user behavior, which solves the problem mentioned in the background that it cannot adapt to subtle changes in behavioral patterns in real time.
[0006] To achieve the above objectives, the present invention provides the following technical solution: a network priority allocation system based on user behavior, comprising: The user behavior acquisition module uses the behavior sensor unit to generate multi-source user behavior data, performs data cleaning and normalization through the data preprocessing unit, and outputs standardized behavior feature data through the feature extraction unit. The behavior analysis module receives the standardized behavior feature data, identifies user behavior patterns through the pattern recognition unit, maps the behavior patterns to initial priority levels using the priority mapping unit, and outputs priority suggestion data through the analysis engine. The network priority calculation module receives the priority suggestion data, calculates the final network priority by combining the weight calculation unit with real-time network status data and using a multi-objective optimization algorithm engine, and outputs resource allocation instructions through the decision unit. The resource allocation execution module receives the resource allocation instruction, dynamically allocates network bandwidth resources through the traffic scheduling unit, adjusts the quality of service parameters through the QoS controller unit, and outputs allocation result data through the real-time monitoring unit. The adaptive optimization module receives the allocation result data, evaluates the allocation effect through the performance evaluation unit, optimizes the calculation parameters based on historical data by the feedback adjustment unit, and generates adaptive optimization instructions through the machine learning engine. The report generation module integrates the allocation result data with adaptive optimization instructions, aggregates multi-dimensional indicators through the data fusion unit, and generates an interactive network priority report using the visualization rendering unit.
[0007] Preferably, the user behavior acquisition module further includes a behavior classification unit, which classifies the multi-source user behavior data into real-time interaction type, batch transmission type and background service type behavior data according to the user behavior type. The data preprocessing unit removes outliers through a noise filtering subunit and ensures data synchronization through a time series alignment subunit.
[0008] Preferably, the pattern recognition unit of the behavior analysis module uses a deep learning model to identify user behavior patterns, including a convolutional neural network subunit to extract spatial features and a recurrent neural network subunit to extract time series features. The priority mapping unit maps the identified behavior patterns to the corresponding initial priority levels by querying a preset priority rule library.
[0009] Preferably, the weight calculation unit of the network priority calculation module dynamically adjusts the user behavior weight and the network status weight, wherein the user behavior weight is calculated based on the historical frequency of the behavior, the network status weight is calculated based on the real-time bandwidth utilization and latency data, and the multi-objective optimization algorithm engine adopts the Pareto optimization algorithm to balance the objectives of fairness and efficiency.
[0010] Preferably, the traffic scheduling unit of the resource allocation execution module supports a differential service mechanism, including a priority queue management subunit and a congestion control subunit. The QoS controller unit applies the quality of service policy through the policy execution subunit and dynamically adjusts the policy parameters according to the adaptive optimization instructions.
[0011] Preferably, the performance evaluation unit of the adaptive optimization module calculates allocation effect indicators, including user satisfaction indicators and network efficiency indicators, and the feedback adjustment unit adopts a reinforcement learning algorithm to optimize the parameters of the network priority calculation module through a reward function.
[0012] Preferably, the data fusion unit of the report generation module integrates real-time data and historical data, and generates trend analysis data through the indicator aggregation subunit. The visualization rendering unit supports multiple chart types, including heatmaps and line charts, to display the dynamic priority allocation.
[0013] Preferably, the system further includes a security audit module, which receives standardized behavioral feature data output by the user behavior collection module, identifies malicious behavior patterns through an anomaly detection unit, and records security events using an audit log unit. The output of the security audit module is connected to the behavior analysis module to adjust priority suggestion data.
[0014] Preferably, the anomaly detection unit of the security audit module uses the isolated forest algorithm to detect abnormal behavior and calculates a risk score through the risk assessment subunit. The audit log unit supports encrypted storage and query functions.
[0015] Preferably, the system further includes an interface adaptation module, which receives network status data input from an external system through a standardized API interface unit and converts the data into an internal format using a protocol conversion unit. The output of the interface adaptation module is connected to the network priority calculation module.
[0016] Compared with the prior art, the present invention provides a network priority allocation system based on user behavior, which has the following beneficial effects: 1. In this invention, when dynamically allocating network resources, the behavior sensor unit of the user behavior acquisition module generates multi-source user behavior data in real time, and the data preprocessing unit performs data cleaning and normalization processing. Combined with the pattern recognition unit of the behavior analysis module, a deep learning model is used to dynamically identify user behavior patterns, ensuring that the system can detect changes in user behavior in real time and accurately map priority levels. This solves the problem of large priority mapping errors caused by the inability to adapt to changes in behavior in real time, reduces resource allocation errors, and improves the accuracy and response speed of network resource allocation.
[0017] 2. In this invention, when dynamically allocating network resources, the weight calculation unit of the network priority calculation module dynamically integrates user behavior weights and real-time network status data, and uses a multi-objective optimization algorithm engine for collaborative optimization calculation. Combined with the feedback adjustment unit of the adaptive optimization module, the allocation deviation is corrected in real time based on reinforcement learning algorithm, ensuring that the system can achieve collaborative analysis of user behavior and network status. This solves the problem of mismatch in resource allocation decisions caused by the inability to collaboratively optimize user behavior and network status, ensuring that the allocation strategy is consistent with the actual network conditions, and improving the robustness and efficiency of resource allocation.
[0018] 3. In this invention, when dynamically allocating network resources, the traffic scheduling unit of the resource allocation execution module supports a differential service mechanism to achieve priority queue management for multiple users and multiple services. Combined with the QoS controller unit, the service quality parameters are dynamically adjusted to ensure that the system can achieve differentiated scheduling based on real-time interactive, batch transmission, and background service data output by the behavior classification unit. This solves the problem of priority conflicts and resource waste caused by the inability to achieve differentiated and fine-grained scheduling, reduces resource competition in multi-service scenarios, and improves network service quality and user experience. Attached Figure Description
[0019] Figure 1 This is an architecture diagram of a network priority allocation system based on user behavior according to the present invention. Detailed Implementation
[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0021] Please see Figure 1 The network priority allocation system based on user behavior includes: The user behavior acquisition module uses the behavior sensor unit to generate multi-source user behavior data, performs data cleaning and normalization through the data preprocessing unit, and outputs standardized behavior feature data through the feature extraction unit. The behavior analysis module receives standardized behavior feature data, identifies user behavior patterns through the pattern recognition unit, maps the behavior patterns to initial priority levels using the priority mapping unit, and outputs priority suggestion data through the analysis engine. The network priority calculation module receives priority suggestion data, combines it with real-time network status data through the weight calculation unit, uses a multi-objective optimization algorithm engine to calculate the final network priority, and outputs resource allocation instructions through the decision unit. The resource allocation execution module receives resource allocation instructions, dynamically allocates network bandwidth resources through the traffic scheduling unit, adjusts service quality parameters through the QoS controller unit, and outputs allocation result data through the real-time monitoring unit. The adaptive optimization module receives allocation result data, evaluates the allocation effect through the performance evaluation unit, optimizes the calculation parameters based on historical data by the feedback adjustment unit, and generates adaptive optimization instructions through the machine learning engine. The report generation module integrates allocation result data with adaptive optimization instructions, aggregates multi-dimensional indicators through the data fusion unit, and generates an interactive network priority report using the visualization rendering unit.
[0022] The user behavior acquisition module also includes a behavior classification unit, which classifies multi-source user behavior data into real-time interaction type, batch transmission type and background service type behavior data according to the user behavior type. The data preprocessing unit removes outliers through the noise filtering subunit and ensures data synchronization through the time series alignment subunit. The core outlier detection algorithm employs a Z-score-based standard deviation method, identifying outliers by calculating the deviation of each data point from the mean of the data within the sliding window. The specific formula is as follows: ; in, Indicates the current data point. This represents the mean of the data within the window. Indicates standard deviation, This is the result of the calculation.
[0023] The pattern recognition unit of the behavior analysis module uses a deep learning model to identify user behavior patterns. This includes a convolutional neural network subunit to extract spatial features and a recurrent neural network subunit to extract time-series features. Specifically, the system first takes the standardized behavioral feature data from the user behavior acquisition module as input. This data is a multi-dimensional time series, containing fields such as user ID, application type, data packet size, request timestamp, and session duration. The data is then fed into two processing branches: The first branch targets the spatial and static attribute features of behavior, employing a one-dimensional convolutional neural network for local feature extraction. Its core operations are implemented by convolutional layers, and the formula can be expressed as: ; in, Represents the input behavioral feature sequence, For convolution kernel function, For time, For the summation variable; Convolutional kernels slide along the feature dimension of behavior to capture local correlation patterns and spatial combination features between different behavioral attributes; The second branch focuses on the temporal dependence of behavior, using a gated recurrent unit network (GRU) for processing. The GRU selectively remembers and forgets historical information through its unique update gate and reset gate mechanism, thereby accurately modeling the long-term temporal patterns of user behavior. Finally, the spatial feature vector extracted by the CNN path is concatenated with the temporal feature vector extracted by the GRU path to form a comprehensive "behavioral spatiotemporal feature vector". The priority mapping unit queries a preset priority rule base to map the identified behavior patterns to the corresponding initial priority levels.
[0024] The weight calculation unit of the network priority calculation module dynamically adjusts the user behavior weight and the network status weight. The user behavior weight is calculated based on the historical frequency of the behavior, and the network status weight is calculated based on real-time bandwidth utilization and latency data. The dynamic adjustment of the weights is achieved through the following process: First, a weight vector is maintained within the unit: ; in, express User behavior weight at any given moment express The network state weights at time 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 ... 1, For time, Indicates behavior, Represents a network; The initial values of the weights can be set to the system default values. The key dynamic adjustment process is achieved through a feedback control loop, and this unit continuously calculates the overall system status index. This metric is jointly determined by user satisfaction and network efficiency metrics. The core formula for weight adjustment is based on the gradient descent optimization principle and can be expressed as: ; in, It is the system's overall utility function. It's the learning rate. It is the partial derivative of the utility function with respect to the weights of user behavior. As a comprehensive status indicator, For time, express Time-based user behavior weighting; The changes in network state weights are determined by the constraints. Specifically, when the system detects network congestion, the calculated gradient of the utility function indicates that the network state weights need to be increased. Prioritizing network stability, the gradient direction tends to increase the weights of user behaviors as user behavior diversity increases. To optimize the personalized experience; The multi-objective optimization algorithm engine employs the Pareto optimization algorithm to balance fairness and efficiency objectives. Specifically, the engine first mathematically models the abstract optimization objectives, defining the decision variables as resource allocation vectors for all data streams to be scheduled, with fairness as the objective. This is quantified as minimizing the deviation between the priority assigned to all users and their historical average priority. A common formula expresses this as a variance-based measure, minimizing the weighted variance of the priority distribution: ; in, For users Real-time calculation priority, Prioritize it based on its historical benchmark. Weights for their behaviors For indexing; efficiency goals This is then quantified as an improvement in the overall resource utilization of the system: ; in, For link bandwidth utilization To represent a function of utilization efficiency, For indexing.
[0025] The traffic scheduling unit of the resource allocation execution module supports a differential service mechanism, including a priority queue management subunit and a congestion control subunit. The QoS controller unit applies the quality of service policy through the policy execution subunit and dynamically adjusts the policy parameters according to the adaptive optimization instructions.
[0026] The performance evaluation unit of the adaptive optimization module calculates allocation effect indicators, including user satisfaction indicators and network efficiency indicators. The user satisfaction indicator aims to quantify the degree to which the resource allocation results satisfy the user's perceived experience. Its calculation is not a simple average, but adopts a weighted aggregation method, which fully considers the priority differences of different user groups and business types. A typical implementation formula is as follows: ; in, Indicates currently active users. Indicates user During the evaluation period The quantification of the quality of the experience within the site, It is a standardized function. User The weight, For indexing, For periodicity; Network efficiency metrics aim to evaluate the utilization efficiency of network infrastructure resources. They go beyond simple average utilization, emphasizing utilization under certain performance constraints. A common implementation method is to calculate globally weighted efficiency. ; in, Indicates the number of critical network links. and Representing links respectively In the cycle Average utilization and average latency within the range, To represent a function of utilization efficiency, It is a link Importance weight, For indexing, For periodicity; The feedback adjustment unit employs a reinforcement learning algorithm to optimize the parameters of the network priority calculation module through a reward function. Its implementation framework strictly follows the reinforcement learning paradigm, first clearly defining its key elements: state( The state space comprehensively reflects the decision-making context of the system, including network state, service state, performance state, and current parameters. action( ): The action space corresponds to the adjustment of adjustable parameters in the network priority calculation module; award( The design of the reward function is the guiding principle for agent optimization; it must directly and quantitatively reflect the core objectives of the system. Based on the above definition, the unit adopts a deep reinforcement learning algorithm based on policy gradient for iterative optimization, and its real-time implementation process is as follows: Interaction and Storage: The agent observes the current state According to its current policy network Output Action This instruction is applied to the network priority calculation module, affecting its next decision cycle. Subsequently, the system transitions to a new state. And receive a reward , experience tuple Store in the experience replay buffer; Learning and updating: Periodically sample a batch of empirical data from the buffer and update the policy network by calculating the advantage function estimation method. parameters Its update gradient direction is towards the direction of increasing expected reward. For deterministic policies, the core formula for policy update can be simplified to: ; in, It is the expected total reward. It is an action value function. It is a state distribution. Indicates the expected value. This is the current state. Indicates an action, Representation strategy, For parameters, This represents the number of iterations. This update enables the agent to learn to select actions that yield higher long-term value evaluations in specific states; Deployment and Iteration: The updated policy network will gradually replace the old network online, thereby achieving continuous and adaptive optimization of priority calculation parameters. Through millions of such "observation-decision-feedback-learning" cycles, the system can automatically adapt to the complex changes in network traffic patterns and user behavior habits, and dynamically find the best balance between fairness and efficiency.
[0027] The report generation module's data fusion unit integrates real-time and historical data, generates trend analysis data through the indicator aggregation sub-unit, and the visualization rendering unit supports various chart types, including heatmaps and line charts, to display the dynamics of priority allocation.
[0028] The system also includes a security audit module, which receives standardized behavioral feature data output by the user behavior collection module, identifies malicious behavior patterns through the anomaly detection unit, and records security events using the audit log unit. The output of the security audit module is connected to the behavior analysis module to adjust priority and suggest data.
[0029] The anomaly detection unit of the security audit module uses the isolated forest algorithm to detect abnormal behavior. Its implementation process begins in the feature engineering stage. The standardized behavioral feature data received by the unit is further converted into numerical feature vectors suitable for tree model processing. For categorical features, embedding encoding is used, and for temporal features, its sliding window statistics are calculated. These features together constitute a multi-dimensional feature space, in which normal user behavior data points are expected to be densely clustered in specific areas, while abnormal behavior is sparse due to deviation from the mainstream pattern. The model building and training phases employ a combination of offline initialization and online incremental updates. Initially, during the early stages of system deployment, the unit uses accumulated historical behavior data to construct an initial isolated forest model. The isolated forest algorithm recursively and randomly selects features and split values to "isolate" each data point. Its basic idea is that outliers, due to their more extreme feature values, are more easily isolated through random partitioning, meaning the path length required to reach the leaf nodes of the tree is shorter. Multiple such "isolated trees" are constructed to form a forest. For each data point... Its average path length in the forest The anomaly score is calculated using the following formula. ; in, The number of training samples. Data points The expected value of the path length on all trees. It is given The average path length over time; Score The closer the value is to 1, the more abnormal the point is. During the online detection phase, the user behavior feature vector generated in real time is input into the pre-trained Isolation Forest model, and the model immediately outputs its anomaly score. The unit will then compare this score with a dynamic threshold. In comparison, this threshold is not a fixed value, but is dynamically calculated by the risk assessment subunit based on historical false alarm rates, current network threat levels, and system tolerance. If so, the behavior is judged as abnormal; The risk score is calculated through the risk assessment sub-unit, and a typical implementation formula is as follows: ; in, The final calculated comprehensive risk score, For dynamic weighting coefficients, The standardized outlier score. For behavioral context risk coefficient, As the external threat intelligence risk coefficient, As a historical risk attenuation factor, Score historical risk; Calculate the comprehensive risk score Then, the sub-unit will map it to a predefined risk level and trigger the corresponding handling action; The audit log unit supports encrypted storage and querying.
[0030] The system also includes an interface adaptation module, which receives network status data from external systems through a standardized API interface unit and converts the data into an internal format using a protocol conversion unit. The output of the interface adaptation module is connected to the network priority calculation module.
[0031] The operation steps of a network priority allocation system based on user behavior are as follows: Step 1: Collection and Standardization of Multi-Source User Behavior Data The initial step in the system's principle is the comprehensive perception and organization of data. Through behavior sensor units distributed across network nodes, the system captures raw user behavior data in real time. This data covers multi-dimensional information such as user ID, application type, request timestamp, data packet size, and session duration. Subsequently, the data preprocessing unit is activated, with its core task being to improve data quality. It first uses a noise filtering subunit based on the Z-score algorithm to remove outliers and jitter, ensuring data reliability. Then, it uses a time series alignment subunit to solve the time series synchronization problem between different data sources. The preprocessed data is transformed by the feature extraction unit to generate standardized behavioral feature data with a unified format and standardized dimensions, laying the foundation for subsequent in-depth analysis. This step ensures the accuracy and consistency of the input information and is the data cornerstone for the system to achieve accurate decision-making.
[0032] Step 2: User behavior pattern recognition and priority mapping based on deep learning: After obtaining high-quality standardized data, the system enters the intelligent analysis stage. The pattern recognition unit of the behavior analysis module starts working. It uses convolutional neural network subunits to extract spatial local features from the behavior data, and at the same time uses recurrent neural network subunits to capture the long-term dependencies of behavior in the time series. This "spatiotemporal dual-path" deep learning model can accurately identify typical behavior patterns of "real-time interaction", "batch transmission" and "background service". Next, the priority mapping unit maps the identified behavior patterns to the corresponding initial priority levels according to the preset priority rule base, forming structured priority suggestion data. This step abstracts the original user behavior into priority labels with clear business meaning, realizing the key transformation from "data" to "information".
[0033] Step 3: Calculating the priority of the dynamic network that integrates multi-objective optimization: This step is the core of the system's intelligent decision-making. The network priority calculation module receives the priority suggestion data from the previous step and inputs it along with real-time network status data into the weight calculation unit. This unit dynamically adjusts the user behavior weights and network status weights to balance user needs and network health. Subsequently, the multi-objective optimization algorithm engine is activated. It uses the Pareto optimization algorithm to simultaneously optimize the fairness and efficiency objectives. The engine searches for the Pareto optimal solution set in the solution space, ensuring that the other objective is not compromised when one objective cannot be further optimized, thus finding the best balance between fairness and efficiency. Finally, the decision-making unit selects the final solution from the optimal solution set and outputs resource allocation instructions. This step demonstrates the system's ability to make scientific decisions under multiple constraints.
[0034] Step 4: Fine-grained resource allocation and execution based on differential services: The decision-making instructions need to be executed accurately. Based on the instructions, the traffic scheduling unit of the resource allocation execution module uses its priority queue management subunit and congestion control subunit to allocate differentiated network bandwidth resources to data streams with different priorities. At the same time, the QoS controller unit dynamically adjusts the quality of service parameters according to the policy execution subunit to ensure that high-priority services receive low latency and high bandwidth. The real-time monitoring unit continuously tracks the allocation results and forms allocation result data. This step transforms the optimization decision at the algorithm level into actual traffic control actions in the network, realizing the implementation of the policy.
[0035] Step 5: System performance evaluation and adaptive parameter optimization based on reinforcement learning: The system constructs a complete closed-loop optimization circuit. The performance evaluation unit of the adaptive optimization module first performs quantitative analysis on the allocation result data, calculates user satisfaction index and network efficiency index, and objectively evaluates the effect of this allocation. Subsequently, the feedback adjustment unit adopts a reinforcement learning algorithm, treating the resource allocation process as an interaction between an agent and the environment. Based on the evaluation results, it automatically adjusts the key parameters in the network priority calculation module. Through the policy gradient algorithm, the agent learns to adjust the parameters under different network states and user behavior patterns to maximize long-term cumulative rewards. This step enables the system to learn from experience, continuously iterate, and become more intelligent with use.
[0036] Step 6: Full-process data integration and visualization report generation: To provide a global view and decision support, the report generation module aggregates and presents the data generated throughout the entire process. The data fusion unit integrates real-time allocation results, historical optimization records, and adaptive optimization instructions. The indicator aggregation sub-unit generates trend analysis data, while the visualization rendering unit uses various charts such as heatmaps and line charts to intuitively display the dynamic changes in priority allocation, the evolution trend of system performance, and the effect of optimization strategies, generating an interactive network priority report. This step makes the system's operating status transparent, providing an effective tool for administrators to monitor and intervene.
[0037] Step 7: Continuous security auditing and system interface adaptation: To ensure the safe and reliable operation of the system, the security audit module works continuously in the background. Its anomaly detection unit uses the isolated forest algorithm to perform unsupervised learning on user behavior, identify malicious behaviors that deviate from normal patterns, and calculate risk scores through the risk assessment subunit, thereby influencing priority recommendations and realizing the linkage between security and scheduling. At the same time, the interface adaptation module is responsible for interfacing with external systems. Through standardized APIs and protocol conversions, it ensures that external network status data can be smoothly accessed by this system. These two modules serve as auxiliary support, ensuring the robustness and scalability of the main process from the security and integration levels, respectively.
[0038] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0039] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A network priority allocation system based on user behavior, characterized in that, include: The user behavior acquisition module uses the behavior sensor unit to generate multi-source user behavior data, performs data cleaning and normalization through the data preprocessing unit, and outputs standardized behavior feature data through the feature extraction unit. The behavior analysis module receives the standardized behavior feature data, identifies user behavior patterns through the pattern recognition unit, maps the behavior patterns to initial priority levels using the priority mapping unit, and outputs priority suggestion data through the analysis engine. The network priority calculation module receives the priority suggestion data, calculates the final network priority by combining the weight calculation unit with real-time network status data and using a multi-objective optimization algorithm engine, and outputs resource allocation instructions through the decision unit. The resource allocation execution module receives the resource allocation instruction, dynamically allocates network bandwidth resources through the traffic scheduling unit, adjusts the quality of service parameters through the QoS controller unit, and outputs allocation result data through the real-time monitoring unit. The adaptive optimization module receives the allocation result data, evaluates the allocation effect through the performance evaluation unit, optimizes the calculation parameters based on historical data by the feedback adjustment unit, and generates adaptive optimization instructions through the machine learning engine. The report generation module integrates the allocation result data with adaptive optimization instructions, aggregates multi-dimensional indicators through the data fusion unit, and generates an interactive network priority report using the visualization rendering unit.
2. The network priority allocation system based on user behavior according to claim 1, characterized in that: The user behavior acquisition module also includes a behavior classification unit, which classifies the multi-source user behavior data into real-time interaction type, batch transmission type and background service type behavior data according to the user behavior type. The data preprocessing unit removes outliers through the noise filtering subunit and ensures data synchronization through the time series alignment subunit.
3. The network priority allocation system based on user behavior according to claim 1, characterized in that: The pattern recognition unit of the behavior analysis module uses a deep learning model to identify user behavior patterns, including a convolutional neural network subunit to extract spatial features and a recurrent neural network subunit to extract time series features. The priority mapping unit maps the identified behavior patterns to the corresponding initial priority levels by querying a preset priority rule library.
4. A network priority allocation system based on user behavior according to claim 1, characterized in that: The weight calculation unit of the network priority calculation module dynamically adjusts the user behavior weight and the network status weight. The user behavior weight is calculated based on the historical frequency of the behavior, and the network status weight is calculated based on the real-time bandwidth utilization and latency data. The multi-objective optimization algorithm engine uses the Pareto optimization algorithm to balance the objectives of fairness and efficiency.
5. A network priority allocation system based on user behavior according to claim 1, characterized in that: The traffic scheduling unit of the resource allocation execution module supports a differential service mechanism, including a priority queue management subunit and a congestion control subunit. The QoS controller unit applies the quality of service policy through the policy execution subunit and dynamically adjusts the policy parameters according to the adaptive optimization instructions.
6. A network priority allocation system based on user behavior according to claim 1, characterized in that: The performance evaluation unit of the adaptive optimization module calculates allocation effect indicators, including user satisfaction indicators and network efficiency indicators. The feedback adjustment unit uses a reinforcement learning algorithm to optimize the parameters of the network priority calculation module through a reward function.
7. A network priority allocation system based on user behavior according to claim 1, characterized in that: The data fusion unit of the report generation module integrates real-time data and historical data, and generates trend analysis data through the indicator aggregation subunit. The visualization rendering unit supports multiple chart types, including heatmaps and line charts, to display the dynamic priority allocation.
8. A network priority allocation system based on user behavior according to claim 1, characterized in that: The system also includes a security audit module, which receives standardized behavioral feature data output by the user behavior collection module, identifies malicious behavior patterns through an anomaly detection unit, and records security events using an audit log unit. The output of the security audit module is connected to the behavior analysis module to adjust priority suggestion data.
9. A network priority allocation system based on user behavior according to claim 1, characterized in that: The anomaly detection unit of the security audit module uses the isolated forest algorithm to detect abnormal behavior and calculates a risk score through the risk assessment subunit. The audit log unit supports encrypted storage and query functions.
10. A network priority allocation system based on user behavior according to claim 1, characterized in that: The system also includes an interface adaptation module, which receives network status data input from external systems through a standardized API interface unit and converts the data into an internal format using a protocol conversion unit. The output of the interface adaptation module is connected to the network priority calculation module.