A high-density wireless local area network access optimization management method and system

Abstract

The present invention discloses a high-density wireless local area network access optimization management method and system, which is suitable for high-density device access environments to improve network performance and user experience; first, through real-time network status monitoring, based on data such as channel utilization, device access density, and interference conditions, channel allocation is dynamically adjusted; a channel switching mechanism based on device priority is combined to ensure the connection stability of key devices; adaptive transmission power control and channel linkage optimization are used to reduce signal overlap and interference; multi-level load balancing is achieved between multiple access points to optimize network resource allocation; and through intelligent user access optimization, device behavior and movement trajectory are predicted to ensure seamless access. The present invention can reduce channel conflicts and improve network stability and user experience.

Description

Technical Field

[0001] The present application belongs to the field of digital information transmission technology, and in particular, relates to a high-density wireless local area network access optimization management method and system. Background Art

[0002] With the development of wireless communication technology, WLAN (wireless local area network) technology has become an important part of modern communication and is widely used in office buildings, shopping malls, airports, campuses and other scenarios. Especially in high-density access environments, such as conference centers, large exhibitions, shopping malls, airport terminals and campuses, WLAN provides high-speed, low-latency wireless access services for tens of thousands of users and devices. However, with the increase in access devices, changes in network load and the increasing demand of users for high-quality network experience, WLAN faces many technical challenges in these high-density access environments, mainly including channel conflicts, low spectrum utilization, network congestion and signal interference.

[0003] The WLAN management in the prior art has the following defects: 1. Most current WLAN systems use a static channel allocation scheme when deployed, that is, a fixed channel is pre-allocated to each access point when the access point is installed. This static channel allocation method is usually based on a preliminary assessment of the physical environment, such as the distance between access points, signal coverage, and interference areas. However, with the dynamic changes in network usage, especially in high-density environments, where device access and mobility occur frequently, static channel allocation often has difficulty coping with these complex dynamic scenarios.

[0004] 2. Load imbalance in WLAN networks is also one of the common problems in high-density access scenarios. As the number of device accesses changes, some access points may receive too many connection requests due to their geographical location or environmental characteristics, resulting in the network load being concentrated on some access points. Access points in other areas may be relatively idle and unable to fully utilize their channel resources. This imbalance in load distribution not only affects the user experience in high-load areas, but also leads to a waste of spectrum resources.

[0005] 3. In high-density environments, the mobility of user devices places higher demands on seamless network access. As users move in space, devices need to frequently switch between different access points. However, traditional WLAN management methods lack intelligent analysis of user movement trajectories, which may cause devices to frequently switch access points during roaming, resulting in connection interruptions or signal quality degradation. Especially in large-scale open environments such as airports and shopping malls, the high-speed mobility of user devices poses higher challenges to network access. Summary of the invention

[0006] The embodiment of the present application provides a high-density wireless local area network access optimization management method and system, which combines intelligent network status monitoring, adaptive learning algorithm and dynamic power control technology, and realizes adaptive optimization of WLAN network in complex high-density environment through multi-dimensional and multi-level network management. This method can not only respond to network load changes in real time, but also predict future network trends through historical data, and optimize channel and power configuration in advance, thereby effectively improving network performance, reducing channel conflicts, and optimizing user access experience.

[0007] In a first aspect, an embodiment of the present application provides a high-density wireless local area network access optimization management method, comprising the following steps: Step S100: deploying a monitoring module on each access point, and performing multi-level monitoring and prediction in combination with historical data to generate global network status data; Step S200: Based on the generated global network status visual data, combined with the multi-layer channel utilization model, monitoring is established at different levels to obtain channel-related data and determine the current channel utilization at different levels; Step S300: Based on the current channel utilization rates of different levels, in the dynamic channel allocation process, the priority of the device is differentiated; Step S400: Based on the real-time channel configuration result, combined with the changes in the physical environment and the distribution of devices, dynamically adjust the transmit power of each access point to ensure a balance between signal coverage and interference suppression, and achieve linkage optimization of channels and power; Step S500: Based on the transmission power of each access point, monitoring is performed at each level through a multi-level load balancing mechanism to obtain load indicators at different levels.

[0008] Step S600: Based on the load indicators of the different levels, the access point and channel configuration are optimized in advance for the mobile user equipment through the user behavior prediction model to ensure seamless access of the device during movement and improve the user experience.

[0009] It is further configured that step S100 includes the following sub-steps: the monitoring module monitors a single access point, an access point group, and all access points in the entire network in real time; it not only collects real-time data of each access point, but also performs special analysis on areas with dense device access and predicts the mobility pattern of devices; The monitoring module can collect multi-dimensional data, including channel interference, network traffic patterns, movement trajectories of user devices and other information. It analyzes historical access data through deep learning algorithms and predicts possible changes in network load in a specific time period. This prediction function is based on time series analysis, mobile patterns and usage characteristics of device types, and makes preventive optimization adjustments to high-load areas in advance to ensure reasonable allocation of channel resources and power adjustment before the load surges.

[0010] It is further configured that step S200 includes the following sub-steps: by establishing a multi-layer channel utilization model, when performing channel allocation, not only the real-time channel utilization of each access point is considered, but also the influence of the geographical location and physical environment between the access points on the channel allocation is analyzed; The multi-layer channel utilization model combines the coverage of each access point, the channel configuration of adjacent access points, and the interference level to perform cross-analysis to ensure that channel allocation can not only maximize the utilization of channel resources, but also effectively reduce interference within the same physical area; The multi-layer channel utilization model allocates channel resources of different priorities to devices with different application requirements. Through intelligent classification of application requirements, the channel allocation priority is dynamically adjusted to ensure that high-bandwidth devices or delay-sensitive applications are always connected to the access point with the least interference and the longest channel idle time. Based on the global network status data, determine the current access point level channel utilization, the average channel utilization within the access point group, and the average channel utilization within the sub-network; If the access point level channel utilization, the average channel utilization within the access point group, and the average channel utilization within the subnet are greater than utilization thresholds at different levels, it is determined that the access point utilization is too high.

[0011] It is further configured that step S300 includes the following sub-steps: determining the current channel utilization of different levels based on the generated global network status visual data, including: performing differentiated channel switching strategies for devices with different priorities by assigning different priorities to each connected device; During the channel switching process, priority is given to ensuring the stability of the channel connection of key devices. By reserving additional channel capacity or delaying channel switching, the connection of these key devices is not affected during periods of high network load or interference peaks. Low-priority devices gradually switch channels according to network load conditions. By real-time analysis of the signal strength and network requirements of low-priority devices, channel switching is prioritized during periods that do not affect the operation of their applications.

[0012] A further configuration is that step S400 includes the following sub-steps: dynamically adjusting the transmission power through multi-dimensional signal sensing technology in combination with changes in the physical environment; in indoor scenarios, optimizing the transmission angle and power output of the access point by using reflection and refraction analysis to ensure uniform signal coverage, and adjusting the transmission power according to changes in the physical environment to avoid signal dead spots; and intelligently adjusting the transmission power in combination with dynamic changes in device access density and signal strength; when devices are densely accessed in a specific area, automatically reducing the transmission power of the access points in the area to reduce signal overlap and interference; and when the number of device accesses decreases or is dispersed, increasing the power coverage range to ensure the signal quality of more distant devices.

[0013] Further, step S500 includes the following sub-steps: establishing a multi-level load balancing model, performing load analysis on device access, network traffic distribution and channel utilization at different levels; the levels include: access point level, access point group level, and sub-network level; the model can perform load balancing across multiple levels, dynamically allocate devices between different levels by analyzing the load status of each level in real time, and ensure load balancing at each level; During the channel reconfiguration process, the channel resource allocation of each level is dynamically adjusted by real-time monitoring of the device's access mode and traffic demand to ensure that cross-level channel overlap is minimized, and the overload and interference of local access points are reduced through dynamic channel adjustment; through cross-level channel configuration optimization, high load conditions in a single access point group or subnetwork are reduced.

[0014] It is further configured that step S500 includes the following sub-steps: based on the transmit power of each access point, obtaining an access point level load indicator, an access point group load indicator, and a sub-network load indicator through a multi-level load balancing mechanism; When the access point level load indicator, the access point group load indicator, and the sub-network load indicator are greater than load thresholds of different levels, it is determined that the level is overloaded.

[0015] Further, step S600 includes the following sub-steps: pre-allocating access points for high-speed moving user devices by building a user behavior prediction model, combining the user's historical network usage habits, device movement trajectory and current network load status; predicting the movement direction and access requirements of the user device, and optimizing the access point and channel configuration in advance, to ensure that the user seamlessly switches access points during movement; At the same time, taking into account the network load situation, priority access optimization is performed for user devices with high bandwidth requirements or delay sensitivity. Under high load conditions, channel resources and access points are reserved for these devices in advance to ensure their signal quality and connection stability, while other devices with low bandwidth requirements are connected by delaying access or switching to suboptimal access points.

[0016] In a second aspect, an embodiment of the present application also provides a high-density wireless local area network access optimization management system, comprising a control device, the control device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the computer program.

[0017] The beneficial effects of the present invention are: 1. The present invention can pre-adjust channels and power based on device flow patterns and historical trends before network load surges; this predictive adjustment mechanism based on dynamic data breaks through the traditional real-time response method and can optimize in advance to avoid sudden network congestion.

[0018] 2. The present invention introduces a multi-layer channel utilization model, intelligently allocates channels in combination with the physical environment and device application scenarios, and provides differentiated channel resource allocation in combination with the priority requirements of the devices. This approach can optimize channel utilization in complex environments, minimize co-channel interference, and improve network performance.

[0019] 3. The differentiated channel switching mechanism based on device priority of the present invention enables mission-critical devices to maintain high reliability in network congestion or interference environments, while low-priority devices complete channel switching at appropriate times through intelligent scheduling. This differentiated channel switching strategy improves the overall utilization of channel resources while ensuring the continuity and reliability of key applications.

[0020] 4. The present invention can flexibly adjust the transmission power of access points according to the dynamic changes of the environment and equipment to ensure uniformity of signal coverage and minimize interference. In particular, the intelligent perception of changes in the physical environment enables the network to adapt to external changes in complex environments and improve overall network performance and coverage.

[0021] 5. The present invention breaks through the traditional single-layer load balancing method by introducing multi-level load balancing, and can achieve global optimization in a multi-level network structure; in access point groups or sub-networks at different levels, it dynamically adjusts equipment and channel resources to ensure cross-regional and cross-floor load balancing, which is particularly effective in large buildings or complex network environments.

[0022] 6. The present invention uses a user behavior prediction model to allocate access points and channel resources to mobile user devices in advance, ensuring the network connection quality of high-speed mobile users; this intelligent access optimization based on behavior prediction greatly improves the user's network experience in a dynamic environment, especially in high-speed mobile scenarios that require seamless switching. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a functional block diagram of an embodiment. DETAILED DESCRIPTION

[0024] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0025] As attached Figure 1 As shown; This embodiment discloses a high-density wireless local area network access optimization management method, including the following steps: Step S100: deploying a monitoring module on each access point, and performing multi-level monitoring and prediction in combination with historical data to generate global network status data; Step S200: Based on the global network status data generated in step S100 and the predicted network load, combined with the multi-layer channel utilization model, establish monitoring at different levels to obtain channel-related data and calculate the current access point level channel utilization , the average channel utilization within the access point group , the average channel utilization within the subnetwork ,in is the current access point level channel utilization, is the average channel utilization within the access point group, is the average channel utilization within the subnetwork; The model algorithm is as follows: , in is the current channel utilization, For the actual bandwidth used, is the available bandwidth, i is the number of currently associated devices; Access point group level channel utilization: , in is the average channel utilization rate in the group, G is the total number of connected devices in the group, Sub-network level channel utilization: = , in is the average channel utilization in the sub-network, and N is the total number of devices in the sub-network; Then, based on the real-time channel utilization information, dynamically allocate and adjust channel configurations and set utilization thresholds at different levels. ,when > , it is believed that the access point channel utilization is too high ( is the utilization threshold of the i-th level), optimizes the utilization of channel resources and reduces channel conflicts and interference between adjacent access points; Step S300: In the dynamic channel allocation process, by performing differentiated processing on the priorities of devices, the channel stability of key devices is prioritized, and the impact of channel switching on high-priority devices is minimized. Low-priority devices are gradually adjusted when the network load allows. Step S400: Based on the real-time channel configuration result in step S300, combined with the changes in the physical environment and the distribution of devices, dynamically adjust the transmit power of each access point to ensure a balance between signal coverage and interference suppression, and achieve linkage optimization of channels and power; Step S500: Through the multi-level load balancing mechanism, monitoring is implemented at each level to obtain the access point level load index , access point group load indicator , sub-network load index ,in Access point level load indicators, is the access point group load indicator, is the sub-network load indicator; The model algorithm is as follows: = (Ratio of traffic to channel utilization), in is the network traffic per unit time, is the access point load indicator; = , = , = (Group traffic is evenly distributed to the number of devices), in is the number of currently connected devices, is the total traffic of access points in the group, is the total number of devices connected to the group, is the access point group load indicator; = , = , = (Sub-network traffic is evenly distributed among the number of devices), in is the total flow in the current sub-network, is the total number of devices connected to the subnet, is the sub-network load indicator; Set thresholds and adjust policies based on real-time load information: Set a threshold like > , the access point is considered to be overloaded. like > , then the access point group is overloaded, like > , then the subnetwork is overloaded; Combined with the real-time device access and channel resource allocation, the load of the access point and access point group is balanced, and the channel is reconfigured across layers to reduce local overload and channel conflicts. Step S600: Through the user behavior prediction model, combined with the user's historical usage habits and real-time network load conditions, the access point and channel configuration are optimized in advance for the mobile user device to ensure seamless access of the device during movement and improve the user experience.

[0026] Specifically, step S100 includes the following sub-steps: the monitoring module performs real-time monitoring on a single access point, an access point group (such as multiple access points on the same floor), and all access points in the entire network; it not only collects real-time data of each access point, but also performs special analysis on areas with dense device access and predicts the mobility pattern of devices; The monitoring module can collect multi-dimensional data, including channel interference, network traffic patterns, movement trajectories of user devices, and other information. It analyzes historical access data through deep learning algorithms and predicts possible changes in network load during specific time periods (such as rush hour and meeting peak hours). This prediction function is based on time series analysis, mobility patterns, and usage characteristics of device types (such as mobile devices), and makes preventive optimization adjustments to high-load areas in advance to ensure reasonable allocation of channel resources and power adjustments before the load surges.

[0027] Specifically, step S200 includes the following sub-steps: by establishing a multi-layer channel utilization model, when performing channel allocation, not only the real-time channel utilization of each access point is considered, but also the influence of the geographical location and physical environment (such as walls, obstacles, etc.) between the access points on the channel allocation is analyzed; The multi-layer channel utilization model combines the coverage of each access point, the channel configuration of adjacent access points, and the interference level to perform cross-analysis to ensure that channel allocation can not only maximize the utilization of channel resources, but also effectively reduce interference within the same physical area; The multi-layer channel utilization model allocates channel resources of different priorities to devices with different application requirements; through intelligent classification of application requirements, the channel allocation priority is dynamically adjusted to ensure that high-bandwidth devices or delay-sensitive applications (such as video conferencing) are always connected to the access point with the least interference and the longest channel idle time. For access points with high channel utilization, you can consider guiding new devices to connect to access points with lighter loads. You can optimize channel allocation and reduce the load by changing channel parameters (such as frequency, bandwidth, etc.). Monitor the channel utilization of the entire access point group and dynamically allocate channels. Monitor the channel utilization of the entire access point group and dynamically allocate channels. At the entire sub-network level, conduct an overall review of channel resources, dynamically adjust the working frequency band of each access point, and optimize channel usage through spectrum management.

[0028] Specifically, step S300 includes the following sub-steps: by assigning different priorities to each connected device (based on device type, application requirements, and network sensitivity), a differentiated channel switching strategy is implemented for devices with different priorities; During the channel switching process, priority is given to ensuring the stability of the channel connection of key devices (such as devices used for online meetings or security monitoring equipment). By reserving additional channel capacity or delaying channel switching, the connection of these key devices is not affected during periods of high network load or interference peaks. Low-priority devices gradually switch channels according to the network load. Through real-time analysis of the signal strength and network requirements of low-priority devices, channel switching is prioritized during periods that do not affect the operation of their applications.

[0029] Specifically, step S400 includes the following sub-steps: dynamically adjusting the transmission power through multi-dimensional signal sensing technology in combination with physical environment changes (such as door and window switches, dynamic obstacles, weather changes, etc.); in indoor scenarios, using reflection and refraction analysis to optimize the transmission angle and power output of the access point to ensure uniform signal coverage, and adjusting the transmission power according to changes in the physical environment to avoid signal dead spots; and intelligently adjusting the transmission power in combination with dynamic changes in device access density and signal strength; when devices are densely accessed in a specific area, automatically reducing the transmission power of the access points in the area to reduce signal overlap and interference; and when the number of device accesses decreases or is dispersed, the power coverage range is increased to ensure the signal quality of farther devices.

[0030] Specifically, step S500 includes the following sub-steps: establishing a multi-level load balancing model, performing load analysis on device access, network traffic distribution and channel utilization at different levels; the levels include: access point level, access point group level (such as a set of access points on the same floor or in the same area), and sub-network level (such as a building or a specific area); the model can perform load balancing across multiple levels, dynamically allocate devices between different levels by analyzing the load status of each level in real time, and ensure load balancing at each level; During the channel reconfiguration process, the channel resource allocation of each layer is dynamically adjusted by real-time monitoring of the device's access mode and traffic demand to ensure that cross-layer channel overlap is minimized and that overload and interference of local access points are reduced through dynamic channel adjustment; high load conditions in a single access point group or subnetwork are reduced through cross-layer channel configuration optimization; When an access point is overloaded, its connected devices are reallocated to access points with lighter loads. Strategies can be used to guide new access devices to select access points with lighter loads. The load situation within the group can be checked and, if necessary, the traffic distribution within the group can be dynamically adjusted to direct part of the traffic to other groups or access points. At the sub-network level, resources are reallocated, which may involve configuration adjustments to access points to ensure a uniform global load.

[0031] Specifically, step S600 includes the following sub-steps: pre-allocating access points for high-speed moving user devices by building a user behavior prediction model, combining the user's historical network usage habits, device movement trajectory and current network load status; predicting the movement direction and access requirements of the user device, and optimizing the access point and channel configuration in advance, to ensure that the user can seamlessly switch access points during movement; At the same time, taking into account the network load situation, priority access optimization is performed for user devices with high bandwidth requirements or delay sensitivity (such as video conferencing equipment or real-time gaming equipment); under high load conditions, channel resources and access points are reserved for these devices in advance to ensure their signal quality and connection stability, while other devices with low bandwidth requirements are connected by delaying access or switching to suboptimal access points.

[0032] This embodiment also discloses a high-density wireless local area network access optimization management system, including a control device, the control device including a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0033] The following examples are based on network test data in real scenarios, and show in detail the application effects of the present invention in different scenarios. All data are measured values ​​in simulated actual network environments, which are reasonable and practical, and fully demonstrate the superiority of the present invention.

[0034] Application Example 1 The dynamic changes of device access on university campuses are large, especially during peak hours of classes and exams. Traditional static channel allocation is difficult to cope with the sharp increase in the number of device access, resulting in increased network delays in hot spots such as teaching buildings and libraries. The present invention ensures network stability during peak hours through multi-level load balancing, dynamic channel allocation and power control optimization.

[0035] During peak hours (such as between classes), each access point is connected to 30 devices, and the total number of connected devices is about 6,000. In particular, the library, teaching buildings and dormitories are densely populated with devices.

[0036] Initial network delay: The network delay during peak hours is 200ms, and the delay in some areas exceeds 300ms.

[0037] Initial throughput: The network throughput at peak time was 5 Gbps, and the network performance was poor.

[0038] After adopting the solution of this embodiment, load distribution can be performed across floors and regions. The access points and channel resources of the equipment can be dynamically adjusted between different areas such as teaching buildings, dormitories and libraries to ensure that the load in local areas is not too high and to avoid network performance degradation caused by channel conflicts. Through an intelligent channel allocation algorithm, combined with the physical environment of the access point (such as building structure, partition walls, etc.), channel allocation and transmission power are dynamically adjusted to reduce channel overlap, ensuring that the network maintains efficient operation under high load conditions, especially in equipment-intensive areas such as libraries, teaching buildings and dormitories, and automatically adjusts power to reduce signal interference. Through the self-learning engine, future channel allocation strategies can be optimized based on historical data, equipment access needs during peak hours can be predicted in advance, and power and channel resources can be adjusted before the load increases to ensure that the network continues to maintain the best state.

[0039] The effect is as follows; Network delay: During peak hours, network delay is reduced from 200ms to 90ms. In extreme cases, network delay is controlled within 130ms, making campus network operation more stable.

[0040] Throughput: Network throughput increased from 5Gbps to 7.5Gbps, an overall increase of 50%. Especially during peak hours of classes, network congestion was greatly reduced.

[0041] Application Example 2 The traditional static management method of shopping malls is difficult to quickly respond to the dynamic access and mobility changes of devices, resulting in network congestion in hot spots of shopping malls. The present invention effectively optimizes channel resource allocation and device access in hot spots and high-load periods through user behavior prediction and multi-level load balancing mechanism, avoiding channel conflicts and network overload.

[0042] Number of connected devices: On average, each access point is connected to 30 devices. During peak hours, each access point in densely populated areas is connected to 50 devices. The total number of connected devices is 10,000, and 80% of the devices are concentrated in the catering area.

[0043] Initial network delay: The average network delay is 250ms, and the delay in hot spots exceeds 400ms, seriously affecting the user experience.

[0044] Initial throughput: The network throughput at peak time was 4 Gbps, but channel conflicts caused the actual throughput to be much lower than expected.

[0045] After adopting the solution of this embodiment, the present invention predicts the device usage behavior and traffic demand based on historical access data through a user behavior prediction model, and can allocate channel resources to the dining area in the device-intensive area in advance before the lunch and dinner peaks to ensure that channel conflicts in high-load areas are minimized. It is possible to load balance device access points and channel resources across areas (such as shopping areas and rest areas). When the load is high, the channels in the hot spot area can be adjusted in real time, and part of the load can be transferred to the access of the adjacent area to avoid overloading of a single access point.

[0046] The effect is as follows; Network latency: Network latency is reduced from 250ms to 100ms, and latency in hotspot areas is reduced to less than 150ms.

[0047] Throughput: Network throughput increased from 4 Gbps to 6.5 Gbps, an increase of 62.5%, and network performance became more stable during peak hours.

[0048] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A high-density wireless local area network access optimization management method, characterized in that: The following steps are included: Deploy monitoring modules on each access point and combine historical data for multi-level monitoring and prediction to generate global network status data; Based on the generated global network status visual data, combined with the multi-layer channel utilization model, monitoring is established at different levels to obtain channel-related data and determine the current channel utilization at different levels; Based on the current channel utilization rates of the different levels, in the dynamic channel allocation process, the priorities of the devices are differentiated; Based on the real-time channel configuration results, combined with changes in the physical environment and device distribution, dynamically adjust the transmit power of each access point to ensure a balance between signal coverage and interference suppression, and achieve linkage optimization of channels and power; Based on the transmission power of each access point, monitoring is implemented at each level through a multi-level load balancing mechanism to obtain load indicators at different levels; Based on the load indicators at different levels, the access point and channel configuration are optimized in advance for the mobile user equipment through the user behavior prediction model to ensure seamless access of the device during movement and improve the user experience.

2. A high-density wireless local area network access optimization management method as claimed in claim 1, characterized in that: The monitoring module is deployed on each access point, and multi-level monitoring and prediction are performed in combination with historical data to generate global network status data, including: The monitoring module monitors a single access point, a group of access points, and all access points in the entire network in real time. It not only collects real-time data for each access point, but also performs special analysis on areas with dense device access and predicts device mobility patterns. The monitoring module can collect multi-dimensional data, including channel interference, network traffic patterns, movement trajectories of user devices and other information. It analyzes historical access data through deep learning algorithms and predicts possible changes in network load in a specific time period. This prediction function is based on time series analysis, mobile patterns and usage characteristics of device types, and makes preventive optimization adjustments to high-load areas in advance to ensure reasonable allocation of channel resources and power adjustment before the load surges.

3. A high-density wireless local area network access optimization management method as claimed in claim 2, characterized in that: Based on the generated global network status visual data, combined with a multi-layer channel utilization model, monitoring is established at different levels to obtain channel-related data, including: by establishing a multi-layer channel utilization model, when performing channel allocation, not only the real-time channel utilization of each access point is considered, but also the influence of the geographical location and physical environment between the access points on the channel allocation is analyzed; The multi-layer channel utilization model combines the coverage of each access point, the channel configuration of adjacent access points, and the interference level to perform cross-analysis to ensure that channel allocation can not only maximize the utilization of channel resources, but also effectively reduce interference within the same physical area; The multi-layer channel utilization model allocates channel resources of different priorities to devices with different application requirements. Through intelligent classification of application requirements, the channel allocation priority is dynamically adjusted to ensure that high-bandwidth devices or delay-sensitive applications are always connected to the access point with the least interference and the longest channel idle time.

4. A high-density wireless local area network access optimization management method as claimed in claim 3, characterized in that: Based on the generated global network status visual data, current channel utilization rates at different levels are determined, including: Based on the global network status data, determine the current access point level channel utilization, the average channel utilization within the access point group, and the average channel utilization within the sub-network; If the access point level channel utilization, the average channel utilization within the access point group, and the average channel utilization within the subnet are greater than utilization thresholds at different levels, it is determined that the access point utilization is too high.

5. A high-density wireless local area network access optimization management method as claimed in claim 4, characterized in that: Based on the generated global network status visual data, determining the current channel utilization of different levels, including: by assigning different priorities to each connected device, executing differentiated channel switching strategies for devices with different priorities; During the channel switching process, priority is given to ensuring the stability of the channel connection of key devices. By reserving additional channel capacity or delaying channel switching, the connection of these key devices is not affected during periods of high network load or interference peaks. Low-priority devices gradually switch channels according to network load conditions. By real-time analysis of the signal strength and network requirements of low-priority devices, channel switching is prioritized during periods that do not affect the operation of their applications.

6. A high-density wireless local area network access optimization management method as claimed in claim 5, characterized in that: Based on the real-time channel configuration results, combined with the changes in the physical environment and the distribution of devices, the transmission power of each access point is dynamically adjusted, including: through multi-dimensional signal perception technology, combined with changes in the physical environment, the transmission power is dynamically adjusted; in indoor scenarios, reflection and refraction analysis is used to optimize the transmission angle and power output of the access point to ensure uniform signal coverage, and the transmission power is adjusted according to changes in the physical environment to avoid signal dead spots; and the transmission power is intelligently adjusted in combination with the dynamic changes in device access density and signal strength; when devices are densely accessed in a specific area, the transmission power of the access points in the area is automatically reduced to reduce signal overlap and interference; and when the number of device accesses decreases or the distribution is dispersed, the power coverage range is increased to ensure the signal quality of farther devices.

7. A high-density wireless local area network access optimization management method as claimed in claim 6, characterized in that: Based on the transmission power of each access point, monitoring is implemented at each level through a multi-level load balancing mechanism to obtain load indicators at different levels, including: establishing a multi-level load balancing model to perform load analysis at different levels on device access, network traffic distribution and channel utilization; the levels include: access point level, access point group level, and sub-network level; the model can perform load balancing across multiple levels, dynamically allocate devices between different levels by analyzing the load status of each level in real time, and ensure load balancing at each level; During the channel reconfiguration process, the channel resource allocation of each level is dynamically adjusted by real-time monitoring of the device's access mode and traffic demand to ensure that cross-level channel overlap is minimized, and the overload and interference of local access points are reduced through dynamic channel adjustment; through cross-level channel configuration optimization, high load conditions in a single access point group or subnetwork are reduced.

8. A high-density wireless local area network access optimization management method as claimed in claim 7, characterized in that: Based on the transmission power of each access point, obtaining access point level load indicators, access point group load indicators, and sub-network load indicators through a multi-level load balancing mechanism; When the access point level load indicator, the access point group load indicator, and the sub-network load indicator are greater than load thresholds of different levels, it is determined that the level is overloaded.

9. A high-density wireless local area network access optimization management method as claimed in claim 8, characterized in that: Based on the load indicators at different levels, the access point and channel configuration are optimized in advance for the mobile user equipment through the user behavior prediction model to ensure seamless access of the device during movement and improve the user experience, including: pre-allocating access points for high-speed mobile user equipment by building a user behavior prediction model, combining the user's historical network usage habits, device movement trajectory and current network load status; by predicting the movement direction and access needs of the user equipment, and optimizing the access point and channel configuration in advance, ensuring that the user can seamlessly switch access points during movement; At the same time, taking into account the network load situation, priority access optimization is performed for user devices with high bandwidth requirements or delay sensitivity. Under high load conditions, channel resources and access points are reserved for these devices in advance to ensure their signal quality and connection stability, while other devices with low bandwidth requirements are connected by delaying access or switching to suboptimal access points.

10. A high-density wireless local area network access optimization management system, applied to a high-density wireless local area network access optimization management method according to any one of claims 1 to 9, characterized in that: The method comprises a control device, wherein the control device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

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