Monitoring terminal wireless access communication management system based on wireless authentication and privacy infrastructure (WAPI)

Abstract

The invention relates to the technical field of WAPI communication management, in particular to a monitoring terminal wireless access communication management system based on WAPI, which comprises a WAPI route optimization module, a signal monitoring module, a bandwidth prediction module, a bandwidth resource management module, a flow regulation and control module and a data performance feedback module. According to the method, the data routing is dynamically adjusted by analyzing the network delay and the bandwidth utilization rate in real time, so that the delay and the packet loss rate in data transmission are remarkably reduced, the system can intelligently adjust according to the real-time change of the network condition, the overall operation efficiency of the network is improved, and the bandwidth utilization trend is analyzed, so that the bandwidth utilization rate is improved. According to the method and the system, future requirements are predicted, resource allocation is optimized according to the future requirements, the bandwidth resource overload condition is relieved, system collapse is prevented, the LSTM model predicts the peak value and the valley value of the network flow, the accuracy and the efficiency of channel allocation are improved, and a WAPI wireless communication main body is safer, more efficient and wider in channel.

Description

Technical Field

[0001] The present invention relates to the technical field of WAPI communication management, and in particular to a monitoring terminal wireless access communication management system based on WAPI. Background Art

[0002] Communication management technology covers the management of various communication networks, equipment and their operations. This field focuses on improving data transmission efficiency, ensuring communication security, and ensuring the stable operation of the network. Specifically for communication management, the technologies involved include network configuration management, fault management, performance management, security management, and account management, etc., which ensure that the communication network can efficiently and securely handle various data and communication needs. It is an indispensable part of modern communication systems.

[0003] Among them, WAPI's monitoring terminal wireless access communication management system is a communication system designed specifically for substations. It uses the WAPI wireless security protocol to ensure the security of data transmission. Its main purpose is to achieve effective management and control of monitoring terminals. By accessing the network wirelessly, it can remotely monitor the operating status of substations and respond to changes and demands in power supply in a timely manner, which helps to improve the reliability of the power system while reducing maintenance costs and time caused by line problems.

[0004] Traditional systems are slow to respond and unable to provide immediate adjustments when dealing with rapidly changing network loads and external interference, which affects the real-time and reliability of communications. In terms of security management, traditional systems fail to fully cover the security risks caused by the increasing complexity of network threats. In addition, the inefficient management of large-scale equipment also reduces network management efficiency, resulting in untimely changes in power supply and demand response, resulting in inefficient operation of the power system and increased maintenance costs and time. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and propose a monitoring terminal wireless access communication management system based on WAPI.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A monitoring terminal wireless access communication management system based on WAPI includes: The WAPI routing optimization module is based on the WAPI standard, collects network status data of monitoring terminals, uses Q learning to analyze network delay and bandwidth utilization in real time, calculates the network status index, and refines the routing path of data packets to obtain routing parameter configuration; The signal monitoring module regularly collects network delay and packet loss of the monitoring terminal based on the routing parameter configuration, evaluates the network signal quality according to the real-time data, dynamically adjusts the routing parameters, and optimizes the signal transmission to obtain the signal transmission configuration; The bandwidth prediction module uses a support vector machine to collect historical bandwidth utilization data based on the signal transmission configuration, performs bandwidth usage trend analysis, predicts the bandwidth demand of the monitoring terminal in the short and long term, and obtains a bandwidth demand prediction record; The bandwidth resource management module adjusts the network resource allocation based on the bandwidth demand prediction record, controls the distribution of resources among multiple wireless access points of the monitoring terminal through real-time network performance data, and obtains an overview of resource configuration; Based on the resource configuration overview, the traffic control module uses the LSTM model to monitor the current network traffic status, predict traffic peaks and valleys, and adjust channel allocation and network resource configuration according to the prediction results to obtain a traffic management plan; Based on the traffic management plan, the data performance feedback module collects and analyzes the monitoring terminal network performance data, including response time and service quality, evaluates the improvement points of network performance, and re-optimizes the network adjustment process to generate network performance management results.

[0007] As a further solution of the present invention, the routing parameter configuration includes priority rules, traffic allocation process, and path selection process; the signal transmission configuration includes interference management, transmission power adjustment, and channel selection; the bandwidth demand forecast record includes time series analysis results, demand peak identification results, and historical data comparison results; the resource configuration overview includes allocation information, priority setting, and access point capacity; the traffic management plan includes traffic regulation parameters, budget control information, and bandwidth resource reallocation information.

[0008] As a further solution of the present invention, the WAPI route optimization module includes: The first data collection submodule collects network delay and bandwidth utilization data from the monitoring terminal based on the WAPI standard, and performs preliminary screening and classification on the collected data to obtain a network status data set; The learning analysis submodule applies Q learning to analyze network delay and bandwidth utilization in real time based on the network status data set, calculates the network status index, and matches the real-time changes of network behavior to obtain a network behavior model; The routing refinement submodule adjusts the routing path of the data packet based on the network behavior model, refines the transmission route of each data packet, and optimizes the delay and transmission efficiency to obtain the routing parameter configuration.

[0009] As a further solution of the present invention, the signal monitoring module includes: The delay data collection submodule regularly collects and records the network delay and packet loss of the monitoring terminal based on the routing parameter configuration, monitors data changes in real time, verifies the real-time nature of the data, and obtains delay and packet loss records; The signal quality assessment submodule assesses the stability and quality of the network signal based on the delay and packet loss records, calculates key indicators of network performance, and generates signal quality data; The transmission optimization submodule dynamically adjusts the routing parameters and signal strength in the network based on the signal quality data, optimizes signal transmission and transmission efficiency, and obtains signal transmission configuration.

[0010] As a further solution of the present invention, the bandwidth prediction module includes: The second data collection submodule collects historical bandwidth data of the monitoring terminal based on the signal transmission configuration, and obtains a bandwidth data set by screening effective bandwidth usage records, formatting the information and matching the analysis requirements; The trend analysis submodule performs time series analysis on the bandwidth data set, identifies periodic changes and abnormal points in bandwidth usage, determines key change trends in bandwidth demand, and generates bandwidth change analysis results; The demand prediction submodule uses a support vector machine to predict the bandwidth demand of the monitoring terminal in the short and long term based on the bandwidth change analysis result, and performs a quantitative evaluation on the future bandwidth demand to obtain a bandwidth demand prediction record.

[0011] As a further solution of the present invention, the support vector machine is used to predict the bandwidth demand in the short term and the long term according to the formula:

[0012] Get predicted bandwidth requirements , where represents support vector machine, represents the regularization parameter, represents the standard deviation of the Gaussian kernel, represents the Lagrange multiplier, represents the kernel function, represents the bias term, is the number of support vectors.

[0013] As a further solution of the present invention, the bandwidth resource management module includes: The resource analysis submodule analyzes the matching degree between the current resource configuration and the future demand based on the bandwidth demand forecast record, identifies the insufficient and excessive areas in the resource configuration through real-time performance data comparison, and generates resource configuration efficiency indicators; The allocation strategy submodule adjusts the resource allocation between wireless access points based on the resource allocation efficiency index, optimizes network bandwidth management, and matches the predicted bandwidth demand to obtain resource allocation configuration; The configuration overview submodule controls the distribution of bandwidth resources among multiple access points based on the resource allocation configuration, monitors the allocation execution effect, and determines the current resource demand of each node to obtain a resource configuration overview.

[0014] As a further solution of the present invention, the resource allocation between the wireless access points is adjusted to match the predicted bandwidth demand, according to the formula:

[0015] Calculate resource allocation configuration for each access point , where represents the predicted bandwidth demand, Represents the total available bandwidth resources, Represents the number of access points.

[0016] As a further solution of the present invention, the flow control module includes: The real-time data analysis submodule monitors the current network traffic in real time based on the resource configuration overview, analyzes the traffic data of multiple access points, identifies abnormal traffic distribution, and generates a traffic distribution map; The traffic prediction submodule processes the traffic data based on the traffic distribution map using the LSTM model, predicts the traffic peak and valley values ​​in the future time period, and analyzes the potential factors affecting network performance to obtain traffic trend prediction results; The configuration adjustment submodule adjusts channel allocation and network resource configuration according to the traffic trend prediction results, optimizes network resource distribution, matches traffic changes, and generates a traffic management plan.

[0017] As a further solution of the present invention, the data performance feedback module includes: The performance monitoring submodule continuously monitors network operation data based on the traffic management plan, collects key performance indicators related to response time and service quality, monitors network performance in real time, and generates performance monitoring indicators; The data analysis submodule analyzes network operation efficiency and problem points based on the performance monitoring indicators, explores performance bottlenecks, and determines key areas that need to be optimized to obtain performance improvement points; The optimization feedback submodule adjusts the network configuration and resource allocation based on the performance improvement points, including increasing bandwidth and adjusting routing, re-optimizing the network adjustment process, and generating network performance management results.

[0018] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, data routing is dynamically adjusted by real-time analysis of network delay and bandwidth utilization, thereby significantly reducing the delay and packet loss rate in data transmission, enabling the system to intelligently adjust according to real-time changes in network conditions, improving the overall operation efficiency of the network, and reducing bandwidth resource overload and preventing system crashes by analyzing bandwidth usage trends, predicting future demands and optimizing resource allocation accordingly. The LSTM model predicts the peak and valley values ​​of network traffic, improving the accuracy and efficiency of channel allocation, and continuous analysis and feedback optimization of performance data further improve the response speed and stability of the network, making the main body of WAPI wireless communication safer, more efficient, and with wider channels. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a system flow chart of the present invention; Figure 2 It is a schematic diagram of the system framework of the present invention; Figure 3 This is a flow chart of the WAPI routing optimization module of the present invention; Figure 4 This is a flow chart of the signal monitoring module of the present invention; Figure 5 is a flow chart of a bandwidth prediction module of the present invention; Figure 6 This is a flow chart of the bandwidth resource management module of the present invention; Figure 7 It is a flow chart of the flow control module of the present invention; Figure 8 It is a flow chart of the data performance feedback module of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0022] Embodiment 1 See also Figure 1 The present invention provides a technical solution: a monitoring terminal wireless access communication management system based on WAPI includes: The WAPI routing optimization module is based on the WAPI standard and collects network status data of monitoring terminals. It uses Q learning to analyze network delay and bandwidth utilization in real time, calculates the network status index, and refines the routing path of data packets to obtain routing parameter configuration. Monitoring terminals specifically refer to cameras, robots, and sensors. The terminals continuously send their current network status to the WAPI routing optimization module, including signal strength, network speed, and connection quality. The data reflects the network status of each device at different locations and times for subsequent communication management.

[0023] The signal monitoring module regularly collects and monitors the network delay and packet loss of the terminal based on the routing parameter configuration, evaluates the network signal quality based on real-time data, dynamically adjusts the routing parameters, and optimizes the signal transmission to obtain the signal transmission configuration; The bandwidth prediction module uses support vector machines to collect historical bandwidth utilization data based on signal transmission configuration, conduct bandwidth usage trend analysis, predict the bandwidth requirements of monitoring terminals in the short and long term, and obtain bandwidth demand prediction records; The bandwidth resource management module adjusts network resource allocation based on bandwidth demand forecast records, controls resource distribution among multiple wireless access points of the monitoring terminal through real-time network performance data, and obtains an overview of resource configuration; Based on the resource configuration overview, the traffic control module uses the LSTM model to monitor the current network traffic status, predict traffic peaks and valleys, and adjust channel allocation and network resource configuration according to the prediction results to obtain a traffic management plan; The data performance feedback module collects, analyzes and monitors terminal network performance data based on the traffic management plan, including response time and service quality, evaluates network performance improvement points, and re-optimizes the network adjustment process to generate network performance management results.

[0024] Routing parameter configuration includes priority rules, traffic allocation process, and path selection process. Signal transmission configuration includes interference management, transmission power adjustment, and channel selection. Bandwidth demand forecast records include time series analysis results, demand peak identification results, and historical data comparison results. Resource configuration overview includes allocation information, priority settings, and access point capacity. Traffic management plans include traffic regulation parameters, budget control information, bandwidth resource reallocation information, and real-time analysis information of data packet loss interference sources. For the analysis of data packet loss interference sources, data collection and initial monitoring are performed first, and detailed logs of all network activities are automatically recorded, including timestamps, data packet sizes, sending and receiving status, etc. A practical machine learning-based anomaly detection algorithm is used to identify abnormal patterns and network behaviors that cause data packet loss, check and analyze the routing path of the data packet, determine possible bottlenecks or configuration errors in the path, evaluate device overload, hardware failure, or firmware problems, and then automatically adjust the data route based on the real-time analysis results to avoid network paths with poor performance or frequent packet loss.

[0025] See also Figure 2 and Figure 3 ,The WAPI route optimization module includes a first data collection submodule, a learning and analysis submodule, and a route refinement submodule; The first data collection submodule collects network delay and bandwidth utilization data from the monitoring terminal based on the WAPI standard, and performs preliminary screening and classification on the collected data to obtain a network status data set; The WAPI standard is adopted to collect data on network delay and bandwidth utilization from the monitoring terminal system, including real-time capture of various communication states of the network through highly automated sensors and data interfaces, and screening and classification of data through preliminary data processing algorithms to filter out noise and irrelevant data, retaining key performance indicator data such as peak delay and average bandwidth utilization, and then grouping the data according to preset classification rules to form data sets for different network conditions. The data sets are organized and stored for further analysis and use, ensuring the accuracy and practicality of the collected data.

[0026] The learning analysis submodule uses Q learning to analyze network delay and bandwidth utilization in real time based on the network status data set, calculates the network status index, and matches the real-time changes in network behavior to obtain a network behavior model; Based on the network status data set, the Q learning algorithm is applied to analyze the data in real time. First, the state space of the learning model is defined, including all combinations of delay and bandwidth utilization. Then, the model parameters are adjusted according to the real-time data, and the mining and utilization strategies in reinforcement learning are used to continuously optimize the decision-making process. The optimal action path in each state, namely the network status index, is calculated, and the network settings are dynamically adjusted according to the index to adapt to traffic changes. The generated network behavior model describes the optimal action strategy and expected results under specific network conditions.

[0027] The routing refinement submodule adjusts the routing path of the data packet based on the network behavior model, refines the transmission route of each data packet, and optimizes the delay and transmission efficiency to obtain the routing parameter configuration; The routing path of the data packet is adjusted according to the network behavior model, the data path selection provided by the analysis model is refined, the transmission route of each data packet is determined by calculation, so as to reduce the overall network delay and improve the bandwidth utilization efficiency. The system monitors the effect of the route change in real time, and further optimizes the routing decision based on the real-time feedback of the network traffic, obtains the routing parameter configuration, records all the route adjustments and expected network performance improvements, and ensures that the network can maintain the best performance state under any given conditions.

[0028] See also Figure 2 and Figure 4 ,The signal monitoring module includes a delay data collection submodule, a signal quality assessment submodule, and a transmission optimization submodule; The delay data collection submodule regularly collects and records the network delay and packet loss of the monitoring terminal based on the routing parameter configuration, monitors data changes in real time, verifies the real-time and accuracy of the data, and obtains delay and packet loss records; Automatically collect data at specified time intervals, and query the network delay and packet loss data of each terminal device in a targeted manner based on the routing parameter configuration. Obtain relevant data through network management protocols such as SNMP or API calls. Each data point includes a timestamp, device identification, and measurement value. Perform time synchronization checks on the data to ensure the real-time and consistency of the data. At the same time, verify data integrity and eliminate any abnormal values ​​or damaged data packets. For packet loss, record the frequency and pattern of continuous and random packet loss. By analyzing the data, you can generate delay and packet loss records that reflect the real-time status and trends of the network.

[0029] The signal quality assessment submodule assesses the stability and quality of network signals based on delay and packet loss records, calculates key indicators of network performance, and generates signal quality data; After receiving the delay and packet loss records, the data is normalized to standardize the data of different devices and time to the same comparison benchmark. Then, standard deviation and variance analysis are applied to evaluate the data quality, and the mean and fluctuation range of network delay and the cumulative distribution of packet loss rate are calculated to help identify unstable nodes or connections in the network. Based on the analysis results, key indicators of network performance, such as effective throughput and reliability index, are further calculated to generate signal quality data including multiple dimensions to describe the stability and overall quality of network signals.

[0030] The transmission optimization submodule dynamically adjusts the routing parameters and signal strength in the network based on the signal quality data, optimizes signal transmission and transmission efficiency, and obtains the signal transmission configuration; Adjust network settings based on signal quality data. Dynamically adjust routing algorithms and signal strength based on specific indicators of quality data. Automatically adjust routing paths for detected low-quality signal connections and select alternate paths with higher signal quality. Automatically adjust network signal strength based on current data transmission requirements and signal packet loss records to automatically adjust the transmission power of related devices, reduce interference and enhance signal transmission stability. Through adjustments, data transmission paths and signal quality are optimized, the overall transmission efficiency and performance of the network are improved, and an optimized signal transmission configuration is obtained.

[0031] See also Figure 2 and Figure 5 , the bandwidth prediction module includes a second data acquisition submodule, a trend analysis submodule, and a demand prediction submodule; The second data collection submodule collects historical bandwidth data of the monitoring terminal based on the signal transmission configuration, and obtains a bandwidth data set by screening effective bandwidth usage records, formatting the information and matching the analysis requirements; After receiving the signal transmission configuration, the data collection process is started. Historical bandwidth data is collected regularly from the monitoring terminal through the configured routing parameters, and valid bandwidth usage records are automatically screened out. During the screening process, verification and cleaning are performed according to the timestamp and data integrity standards of the bandwidth records, and any abnormal or incomplete data records are eliminated. The screened data is then formatted and converted into a format that matches the analysis requirements, such as converting bandwidth data into hourly or daily usage peak and valley records. This data processing process ensures that the generated bandwidth data set is highly consistent and available.

[0032] The trend analysis submodule performs time series analysis on bandwidth data sets, identifies periodic changes and abnormal points in bandwidth usage, determines key change trends in bandwidth demand, and generates bandwidth change analysis results; Receive the collated bandwidth data set and conduct in-depth analysis of the data through time series analysis to identify periodic change patterns in the data and any atypical anomalies, including applying Fourier transform to analyze periodicity and using statistical anomaly detection algorithms to identify and mark outliers in the data set. Based on the analysis, key changing trends in bandwidth usage, such as growth, decrease, or periodic fluctuations, can be determined. In addition, trend lines are further refined through moving average and exponential smoothing techniques. The generated bandwidth change analysis results record the evolution of bandwidth demand and predict future changing trends.

[0033] The demand prediction submodule uses support vector machines to predict the bandwidth demand of monitoring terminals in the short and long term based on the results of bandwidth change analysis, and quantitatively evaluates future bandwidth demand to obtain bandwidth demand prediction records; Using support vector machines, we predict short-term and long-term bandwidth requirements according to the formula:

[0034] Get predicted bandwidth requirements , where represents support vector machine, represents the regularization parameter, represents the standard deviation of the Gaussian kernel, represents the Lagrange multiplier, represents the kernel function, represents the bias term, is the number of support vectors; There are three historical bandwidth data points located at Mbps, Mbps and Mbps. Assume that the input currently required for prediction is Mbps, known , , , , , calculate the value of each kernel function, the Gaussian kernel is also called the radial basis function (RBF) kernel, and the expression is :

[0035]

[0036]

[0037] Substitute the calculated results into the formula:

[0038]

[0039]

[0040] The results show that the predicted bandwidth requirement is close to 0.9Mbps, indicating that under the current model and parameter settings, the predicted output is significantly lower than the actual historical data, and it is necessary to adjust the kernel width or retrain the model to improve the prediction accuracy.

[0041] See also Figure 2 and Figure 6 ,The bandwidth resource management module includes a resource analysis submodule, an allocation strategy submodule, and a configuration overview submodule; The resource analysis submodule analyzes the matching degree between current resource allocation and future demand based on bandwidth demand forecast records, identifies insufficient and excessive areas in resource allocation through real-time performance data comparison, and generates resource allocation efficiency indicators; After receiving the bandwidth demand forecast record, the resource usage of each access point is evaluated by comparing it with the stored real-time performance data. The data is collected and analyzed, and the resource utilization rate of each node is compared with the future demand forecast. It is identified which resources are over-allocated and which are insufficient. For areas with inefficient resource utilization, they are marked as optimization candidates, and resource recovery evaluation is performed for areas with excess resources. Through data processing and analysis, a resource allocation efficiency index is generated. The index reflects the matching degree between the current resource allocation and future bandwidth demand.

[0042] The allocation strategy submodule adjusts the resource allocation between wireless access points based on the resource allocation efficiency index, optimizes network bandwidth management, and matches the predicted bandwidth demand to obtain resource allocation configuration; Adjust resource allocation between wireless access points to match the predicted bandwidth demand, according to the formula:

[0043] Calculate resource allocation configuration for each access point , where represents the predicted bandwidth demand, Represents the total available bandwidth resources, Represents the number of access points; Given a scenario, the predicted bandwidth requirements are known. The total available bandwidth is 100 Mbps. 1000Mbps, number of access points The number of access points is 10. According to the above formula, the resources to be allocated to each access point are calculated. :

[0044] The results show that in order to match the predicted bandwidth demand and optimize network bandwidth management, each access point is allocated 100Mbps of resources. This resource allocation ensures that all access points can share the total bandwidth resources equally, thereby meeting the predicted demand without causing resource waste or shortage.

[0045] The configuration overview submodule controls the distribution of bandwidth resources among multiple access points based on resource allocation configuration, monitors the allocation execution effect, and determines the current resource requirements of each node to obtain a resource configuration overview; Based on the new resource allocation configuration, control and optimize the reasonable distribution of bandwidth resources among various wireless access points. By tracking the resource usage of each access point in real time, evaluate the execution effect of resource allocation, re-evaluate the resource requirements of each node based on the allocated data, and confirm the effectiveness of resource allocation, including regular review and adjustment of bandwidth allocation execution. Through continuous monitoring, generate a resource configuration overview, record the resource usage and demand satisfaction of each access point, help understand the resource configuration status of the entire network for further optimization.

[0046] See also Figure 2 and Figure 7 ,The traffic control module includes a real-time data analysis submodule, a traffic prediction submodule, and a configuration adjustment submodule; The real-time data analysis submodule monitors the current network traffic in real time based on the resource configuration overview, analyzes the traffic data of multiple access points, identifies abnormal traffic distribution, and generates a traffic distribution map; After receiving the resource configuration overview, the traffic monitoring sensors deployed at multiple access points continuously collect data traffic information from each node, and use data aggregation technology to integrate the information to form a real-time traffic distribution status. By setting thresholds and duration standards, the system automatically identifies and marks traffic distribution that deviates from the normal range, such as abnormal points with sudden increases or decreases in traffic. The marked data points are further analyzed to determine their abnormal nature and potential causes, such as traffic anomalies caused by DDoS attacks or configuration errors. Through analysis, the generated traffic distribution map shows the traffic usage of each access point.

[0047] The traffic prediction submodule processes traffic data based on the traffic distribution map and applies the LSTM model to predict traffic peaks and valleys in the future time period, and analyzes potential factors affecting network performance to obtain traffic trend prediction results; After receiving the traffic distribution map, the long short-term memory network (LSTM) model is used to analyze and predict the traffic data. First, the traffic data is preprocessed, including standardization and denoising, to ensure the quality of the input data. Then, the LSTM network parameters are configured, such as the number of hidden nodes and the learning rate. The model is trained with historical data to capture the time dependency of traffic changes. After the model training is completed, the traffic peak and valley values ​​are predicted for future time periods. At the same time, the network performance influencing factors behind the trend, such as seasonal changes, large events or maintenance activities, are analyzed to generate traffic trend prediction results.

[0048] The configuration adjustment submodule adjusts channel allocation and network resource configuration according to the traffic trend prediction results, optimizes network resource distribution, matches traffic changes, and generates a traffic management plan; Dynamically adjust network resource configuration based on traffic trend forecast results, analyze traffic changes indicated in the forecast results, especially predicted peak and valley values, adjust channel allocation and resource allocation strategies based on the information, update channel and bandwidth resource distribution in real time through the communication management system, ensure that all adjustments can quickly respond to predicted traffic demands, evaluate the effectiveness of adjustments through simulation and actual traffic comparison tests, ensure that optimization measures can effectively respond to future traffic changes, generate a traffic management plan, specify resource allocation and channel usage strategies for each time period, and ensure reasonable optimization and effective management of network resources.

[0049] See also Figure 2 and Figure 8 ,The data performance feedback module includes a performance monitoring submodule, a data analysis submodule, and an optimization feedback submodule; The performance monitoring submodule continuously monitors network operation data based on the traffic management plan, collects key performance indicators related to response time and service quality, monitors network performance in real time, and generates performance monitoring indicators; After receiving the traffic management plan, sensors installed at each node of the network continuously collect data, including key performance indicators such as response time and service quality, process data in real time, identify and record the specific time and location of performance fluctuations or declines, and generate comprehensive performance monitoring indicators by comparing historical performance data with real-time data. The indicators intuitively display the performance status of the entire network and each individual node.

[0050] The data analysis submodule analyzes network operation efficiency and problem points based on performance monitoring indicators, discovers performance bottlenecks, and identifies key areas that need to be optimized to obtain performance improvement points; Utilize performance monitoring indicators to perform in-depth network performance analysis, apply data mining technology to identify performance bottlenecks in the network, such as nodes with high latency or links with frequent errors, and determine the trends and patterns of performance degradation by comparing performance data at different times and under different conditions. Then, based on the analysis results, identify key areas of inefficiency and frequent problems in network operation, not only identify current problems, but also predict future risk points and generate performance improvement points.

[0051] The optimization feedback submodule adjusts network configuration and resource allocation based on performance improvement points, including increasing bandwidth and adjusting routes, re-optimizing the network adjustment process, and generating network performance management results; Adjust and optimize network configuration based on performance improvement points, determine specific areas that require bandwidth enhancement or route adjustment, and reallocate resources and reconfigure routes as needed to adapt to changes in network traffic and service requirements. All adjustment measures are updated in real time in the communication management system to ensure that the adjustment measures can take effect in a timely manner, and the adjustment effects are verified through subsequent performance monitoring to generate network performance management results.

[0052] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A monitoring terminal wireless access communication management system based on WAPI, characterized in that: The system comprises: The WAPI routing optimization module is based on the WAPI standard, collects network status data of monitoring terminals, uses Q learning to analyze network delay and bandwidth utilization in real time, calculates the network status index, and refines the routing path of data packets to obtain routing parameter configuration; The signal monitoring module regularly collects network delay and packet loss of the monitoring terminal based on the routing parameter configuration, evaluates the network signal quality according to the real-time data, dynamically adjusts the routing parameters, and optimizes the signal transmission to obtain the signal transmission configuration; The bandwidth prediction module uses a support vector machine to collect historical bandwidth utilization data based on the signal transmission configuration, performs bandwidth usage trend analysis, predicts the bandwidth demand of the monitoring terminal in the short and long term, and obtains a bandwidth demand prediction record; The bandwidth resource management module adjusts the network resource allocation based on the bandwidth demand prediction record, controls the distribution of resources among multiple wireless access points of the monitoring terminal through real-time network performance data, and obtains an overview of resource configuration; Based on the resource configuration overview, the traffic control module uses the LSTM model to monitor the current network traffic status, predict traffic peaks and valleys, and adjust channel allocation and network resource configuration according to the prediction results to obtain a traffic management plan; Based on the traffic management plan, the data performance feedback module collects and analyzes the monitoring terminal network performance data, including response time and service quality, evaluates the improvement points of network performance, and re-optimizes the network adjustment process to generate network performance management results.

2. The monitoring terminal wireless access communication management system based on WAPI according to claim 1 is characterized in that: The routing parameter configuration includes priority rules, traffic allocation process, and path selection process; the signal transmission configuration includes interference management, transmission power adjustment, and channel selection; the bandwidth demand forecast record includes time series analysis results, demand peak identification results, and historical data comparison results; the resource configuration overview includes allocation information, priority settings, and access point capacity; the traffic management plan includes traffic regulation parameters, budget control information, and bandwidth resource reallocation information.

3. The monitoring terminal wireless access communication management system based on WAPI according to claim 1 is characterized in that: The WAPI route optimization module includes: The first data collection submodule collects network delay and bandwidth utilization data from the monitoring terminal based on the WAPI standard, and performs preliminary screening and classification on the collected data to obtain a network status data set; The learning analysis submodule applies Q learning to analyze network delay and bandwidth utilization in real time based on the network status data set, calculates the network status index, and matches the real-time changes of network behavior to obtain a network behavior model; The routing refinement submodule adjusts the routing path of the data packet based on the network behavior model, refines the transmission route of each data packet, and optimizes the delay and transmission efficiency to obtain the routing parameter configuration.

4. The monitoring terminal wireless access communication management system based on WAPI according to claim 1 is characterized in that: The signal monitoring module comprises: The delay data collection submodule regularly collects and records the network delay and packet loss of the monitoring terminal based on the routing parameter configuration, monitors data changes in real time, verifies the real-time nature of the data, and obtains delay and packet loss records; The signal quality assessment submodule assesses the stability and quality of the network signal based on the delay and packet loss records, calculates key indicators of network performance, and generates signal quality data; The transmission optimization submodule dynamically adjusts the routing parameters and signal strength in the network based on the signal quality data, optimizes signal transmission and transmission efficiency, and obtains signal transmission configuration.

5. The monitoring terminal wireless access communication management system based on WAPI according to claim 1 is characterized in that: The bandwidth prediction module comprises: The second data collection submodule collects historical bandwidth data of the monitoring terminal based on the signal transmission configuration, and obtains a bandwidth data set by screening effective bandwidth usage records, formatting the information and matching the analysis requirements; The trend analysis submodule performs time series analysis on the bandwidth data set, identifies periodic changes and abnormal points in bandwidth usage, determines key change trends in bandwidth demand, and generates bandwidth change analysis results; The demand prediction submodule uses a support vector machine to predict the bandwidth demand of the monitoring terminal in the short and long term based on the bandwidth change analysis result, and performs a quantitative evaluation on the future bandwidth demand to obtain a bandwidth demand prediction record.

6. The monitoring terminal wireless access communication management system based on WAPI according to claim 5 is characterized in that: Using the support vector machine, the bandwidth demand in the short term and long term is predicted according to the formula: Get predicted bandwidth requirements , where represents support vector machine, represents the regularization parameter, represents the standard deviation of the Gaussian kernel, represents the Lagrange multiplier, represents the kernel function, represents the bias term, is the number of support vectors.

7. The monitoring terminal wireless access communication management system based on WAPI according to claim 1 is characterized in that: The bandwidth resource management module includes: The resource analysis submodule analyzes the matching degree between the current resource configuration and the future demand based on the bandwidth demand forecast record, identifies the insufficient and excessive areas in the resource configuration through real-time performance data comparison, and generates resource configuration efficiency indicators; The allocation strategy submodule adjusts the resource allocation between wireless access points based on the resource allocation efficiency index, optimizes network bandwidth management, and matches the predicted bandwidth demand to obtain resource allocation configuration; The configuration overview submodule controls the distribution of bandwidth resources among multiple access points based on the resource allocation configuration, monitors the allocation execution effect, and determines the current resource demand of each node to obtain a resource configuration overview.

8. The monitoring terminal wireless access communication management system based on WAPI according to claim 7 is characterized in that: Adjust resource allocation between the wireless access points to match the predicted bandwidth demand, according to the formula: Calculate resource allocation configuration for each access point , where represents the predicted bandwidth demand, Represents the total available bandwidth resources, Represents the number of access points.

9. The monitoring terminal wireless access communication management system based on WAPI according to claim 1, characterized in that: The flow control module includes: The real-time data analysis submodule monitors the current network traffic in real time based on the resource configuration overview, analyzes the traffic data of multiple access points, identifies abnormal traffic distribution, and generates a traffic distribution map; The traffic prediction submodule processes the traffic data based on the traffic distribution map using the LSTM model, predicts the traffic peak and valley values ​​in the future time period, and analyzes the potential factors affecting network performance to obtain traffic trend prediction results; The configuration adjustment submodule adjusts channel allocation and network resource configuration according to the traffic trend prediction results, optimizes network resource distribution, matches traffic changes, and generates a traffic management plan.

10. The monitoring terminal wireless access communication management system based on WAPI according to claim 1, characterized in that: The data performance feedback module includes: The performance monitoring submodule continuously monitors network operation data based on the traffic management plan, collects key performance indicators related to response time and service quality, monitors network performance in real time, and generates performance monitoring indicators; The data analysis submodule analyzes network operation efficiency and problem points based on the performance monitoring indicators, explores performance bottlenecks, and determines key areas that need to be optimized to obtain performance improvement points; The optimization feedback submodule adjusts the network configuration and resource allocation based on the performance improvement points, including increasing bandwidth and adjusting routing, re-optimizing the network adjustment process, and generating network performance management results.

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