WiFi equipment management method and system based on 5G

Abstract

The invention is suitable for the technical field of bandwidth dynamic adjustment, and provides a 5G-based WiFi device management method and system, and the method comprises the steps: obtaining an initial incremental bandwidth allocated to a client device by a target WiFi device, a frame rate change log applied by the client device in real time, and the current bandwidth and load data of the client device; and analyzing the frame rate change log and the load data applied by the client device in real time, and determining a corresponding critical load index when the client device has a frame drop phenomenon for the first time. According to the method, the initial incremental bandwidth, the frame rate change log and the current bandwidth and load data of the client equipment are acquired, the frame rate change log and the load data are analyzed, the critical load index during first frame drop is determined, the characteristic broken line graph is constructed, the future load index is predicted, and therefore optimization and adjustment of the incremental bandwidth are achieved. By judging whether the future load index is smaller than the critical load index or not, the incremental bandwidth is dynamically allocated or adjusted to meet equipment requirements.

Description

Technical Field

[0001] The present invention belongs to the technical field of dynamic bandwidth adjustment, and in particular, relates to a 5G-based WiFi device management method and system. Background Art

[0002] In modern network environments, with the continuous increase in real-time applications, such as cloud gaming, high-definition video conferencing, AR / VR interaction, etc., higher requirements are placed on the stability and dynamic regulation capabilities of network bandwidth. Existing bandwidth allocation technologies usually monitor the network usage of client devices in real time and gradually adjust the bandwidth based on load data to ensure device performance. However, most of these technologies rely on testing bandwidth one by one to evaluate the rationality of network resource allocation, which is inefficient and difficult to quickly respond to dynamic demands in complex network environments. Especially in scenarios with large load fluctuations, traditional methods are difficult to accurately predict and adjust in a timely manner.

[0003] Commonly used technologies include QoS (quality of service)-based network resource management methods, which dynamically allocate bandwidth by monitoring indicators such as bandwidth occupancy and latency. However, the bandwidth adjustment of this type of method is mostly based on static threshold rules, rather than targeted analysis of the load change characteristics of the device, which leads to frame drops, freezes and other problems when the load fluctuates violently. In addition, some technologies try to optimize the relationship between bandwidth and load, but lack visualization and rapid prediction capabilities, and usually require repeated bandwidth adjustments and testing and verification, which further increases the burden on equipment operation and wastes bandwidth utilization efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a 5G-based WiFi device management method and system, aiming to solve the problems raised in the background technology.

[0005] The present invention is implemented in this way: a 5G-based WiFi device management method, the method comprising: Obtain the initial incremental bandwidth allocated by the target WiFi device to the client device, the frame rate change log of the client device's real-time application, and the client device's current bandwidth and load data; Analyze the frame rate change log and load data of the real-time application of the client device to determine the critical load index corresponding to the first frame drop phenomenon of the client device; Perform several bandwidth fluctuation tests from low to high on the client device within a preset time, record the load fluctuation data during the period, and draw a characteristic line graph of bandwidth fluctuation and load change; Predict the future load index of client devices after receiving the initial incremental bandwidth based on the characteristic line graph: Determine whether the future load index is less than the critical load index. If so, continue to allocate initial incremental bandwidth to the client device. If not, optimize the initial incremental bandwidth according to the critical load index and the characteristic line graph to obtain an adaptive incremental bandwidth.

[0006] As a further limitation of the technical solution of the embodiment of the present invention, the step of parsing the frame rate change log and load data of the real-time application of the client device and determining the critical load index corresponding to the first time the frame drop phenomenon occurs on the client device includes: Synchronize the timestamps of the frame rate change logs of the client device and the load data; Parse the frame rate change log, identify the time point when the first frame drop occurred, and match the load data at that time point; Analyze the matched load data and extract the corresponding critical load index.

[0007] As a further limitation of the technical solution of the embodiment of the present invention, the steps of performing a plurality of bandwidth fluctuation tests from low to high on the client device within a preset time, recording the load fluctuation data during the period, and drawing a characteristic line graph of bandwidth fluctuation and load change include: Within a preset time, perform bandwidth adjustment operations from low to high on the client device several times, and record bandwidth fluctuation data and load change index during each bandwidth adjustment; Synchronize the bandwidth fluctuation data and the load change index through timestamps; With bandwidth fluctuation data as the X-axis and load change index as the Y-axis, a characteristic line graph of bandwidth fluctuation and load change is drawn.

[0008] As a further limitation of the technical solution of the embodiment of the present invention, the step of predicting the future load index of the client device after receiving the initial incremental bandwidth based on the characteristic line graph includes: Analyze the characteristic line graph, extract the key data points of bandwidth fluctuation and load change, analyze their changing trends, and establish the correlation model between bandwidth fluctuation and load change through fitting method; Add the current bandwidth data to the initial incremental bandwidth to calculate the proposed bandwidth value; The proposed bandwidth value is input into the association model to determine the future load index of the client device after receiving the initial incremental bandwidth.

[0009] As a further limitation of the technical solution of the embodiment of the present invention, the step of determining whether the future load index is less than the critical load index, if so, continuing to allocate the initial incremental bandwidth to the client device, and if not, optimizing the initial incremental bandwidth according to the critical load index and the characteristic line graph to obtain the adaptive incremental bandwidth includes: Determine whether the future load index is less than the critical load index; When it is determined that the future load index is less than the critical load index, the initial incremental bandwidth continues to be allocated to the client device; When it is determined that the future load index is not less than the critical load index, the bandwidth corresponding to a specific point with a preset margin before the critical load index is located through the characteristic line graph, and the difference between the bandwidth and the current bandwidth is calculated to obtain the adaptive incremental bandwidth; The preset margin refers to a load index safety margin set according to the load change characteristics of the client device. The safety margin ensures that after the bandwidth is adjusted, the actual load index of the client device will not be close to the critical load index, while providing a certain safety buffer to avoid frame drops caused by short-term load fluctuations.

[0010] A 5G-based WiFi device management system, the system comprising: a data acquisition module, a critical load index determination module, a characteristic line graph generation module, a future load index determination module and an initial incremental bandwidth optimization module, wherein: A data acquisition module is used to obtain the initial incremental bandwidth allocated by the target WiFi device to the client device, the frame rate change log of the real-time application of the client device, and the current bandwidth and load data of the client device; A critical load index determination module is used to parse the frame rate change log and load data of the real-time application of the client device to determine the critical load index corresponding to the first frame drop phenomenon of the client device; A characteristic line graph generation module is used to perform several bandwidth fluctuation tests from low to high on the client device within a preset time, record the load fluctuation data during the period, and draw a characteristic line graph of bandwidth fluctuation and load change; A future load index determination module is used to predict the future load index of the client device after receiving the initial incremental bandwidth based on the characteristic line graph: The initial incremental bandwidth optimization module is used to determine whether the future load index is less than the critical load index. If so, the initial incremental bandwidth continues to be allocated to the client device. If not, the initial incremental bandwidth is optimized according to the critical load index and the characteristic line graph to obtain an adaptive incremental bandwidth.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the critical load index determination module specifically includes: A first timestamp correspondence unit, used for performing timestamp synchronization correspondence between the frame rate change log of the client device and the load data; The log parsing unit is used to parse the frame rate change log, identify the time point of the first frame drop phenomenon, and match the load data at that time point; The critical load index extraction unit is used to analyze the matched load data and extract the corresponding critical load index.

[0012] As a further limitation of the technical solution of the embodiment of the present invention, the characteristic line graph generation module specifically includes: The bandwidth adjustment operation execution unit is used to perform several bandwidth adjustment operations from low to high on the client device within a preset time, and record the bandwidth fluctuation data and load change index during each bandwidth adjustment; A second timestamp corresponding unit, used for synchronously corresponding the bandwidth fluctuation data and the load change index through timestamps; The characteristic line graph drawing unit is used to draw a characteristic line graph of bandwidth fluctuation and load change with bandwidth fluctuation data as the X-axis and load change index as the Y-axis.

[0013] As a further limitation of the technical solution of the embodiment of the present invention, the future load index determination module specifically includes: The correlation model building unit is used to analyze the characteristic line graph, extract the key data points of bandwidth fluctuation and load change, analyze their change trends, and build a correlation model between bandwidth fluctuation and load change through a fitting method; A proposed bandwidth value calculation unit is used to add the current bandwidth data to the initial incremental bandwidth to calculate the proposed bandwidth value; The future load index determination unit is used to input the proposed bandwidth value into the association model to determine the future load index of the client device after receiving the initial incremental bandwidth.

[0014] As a further limitation of the technical solution of the embodiment of the present invention, the initial incremental bandwidth optimization module specifically includes: A future load index determination unit, used to determine whether the future load index is less than a critical load index; The initial incremental bandwidth allocation unit is used to continue to allocate the initial incremental bandwidth to the client device when it is determined that the future load index is less than the critical load index; The adaptive incremental bandwidth generating unit is used for locating the bandwidth corresponding to a specific point with a preset margin before the critical load index through the characteristic line graph when it is determined that the future load index is not less than the critical load index, and calculating the difference between the bandwidth and the current bandwidth to obtain the adaptive incremental bandwidth; The preset margin refers to a load index safety margin set according to the load change characteristics of the client device. The safety margin ensures that after the bandwidth is adjusted, the actual load index of the client device will not be close to the critical load index, while providing a certain safety buffer to avoid frame drops caused by short-term load fluctuations.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention obtains the initial incremental bandwidth, frame rate change log, and current bandwidth and load data of the client device, parses the frame rate change log and load data, determines the critical load index when the first frame is dropped, constructs a characteristic line graph and predicts the future load index, thereby realizing the optimization adjustment of the incremental bandwidth. By judging whether the future load index is less than the critical load index, the incremental bandwidth is dynamically allocated or adjusted to meet the device requirements.

[0016] The present invention uses a characteristic line graph to visualize the changing relationship between bandwidth and load, which not only solves the problem of optimizing the initial incremental bandwidth, but also can quickly adjust the subsequent incremental bandwidth through the line graph. This method greatly improves the efficiency and accuracy of bandwidth allocation, avoids the inefficient traditional method of testing one by one, and provides an innovative solution for dynamic bandwidth management in complex network environments. It is suitable for cloud gaming, high-definition video conferencing and other scenarios that require high bandwidth stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a method provided by an embodiment of the present invention; Figure 2 A flow chart of determining a critical load index corresponding to a client device in a method provided in an embodiment of the present invention; Figure 3 A flow chart of drawing a characteristic line graph of bandwidth fluctuation and load change in the method provided in an embodiment of the present invention; Figure 4 A flow chart of predicting the future load index of a client device in a method provided by an embodiment of the present invention; Figure 5 A flow chart of optimizing the initial incremental bandwidth in the method provided in an embodiment of the present invention; Figure 6 An application architecture diagram of a system provided by an embodiment of the present invention; Figure 7 A structural block diagram of a critical load index determination module in a system provided by an embodiment of the present invention; Figure 8 A structural block diagram of a characteristic line graph generating module in a system provided by an embodiment of the present invention; Fig. 9 A structural block diagram of a future load index determination module in a system provided by an embodiment of the present invention; Fig.10 This is a structural block diagram of an initial incremental bandwidth optimization module in a system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] 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.

[0019] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0020] Specifically, a 5G-based WiFi device management method comprises the following steps: Step S100, obtaining the initial incremental bandwidth allocated by the target WiFi device to the client device, the frame rate change log of the real-time application of the client device, and the current bandwidth and load data of the client device.

[0021] In an embodiment of the present invention, the initial incremental bandwidth allocated by the target WiFi device to the client device is realized based on the QoS (quality of service) technology and dynamic bandwidth control technology supported by the 5G network. In the 5G network environment, QoS technology divides network resources into different service levels through network slicing, and allocates higher bandwidth and lower latency for high-priority application scenarios. Dynamic bandwidth control combines the ultra-low latency characteristics and high-speed transmission capabilities of the 5G network to adjust bandwidth allocation in real time to meet the performance guarantee of client devices under high load or sudden demand. The present invention is particularly suitable for application objects with "frame rate that is easy to change", such as high-definition real-time game APP, cloud game services, 4K / 8K streaming media playback, and remote conferences and AR / VR applications that require high interactivity. These applications are more dependent on frame rate stability and low latency.

[0022] The frame rate change log of the real-time application of the client device is generated based on the 5G network environment to record the frame rate changes of the device under the support of high throughput and low latency network. These logs usually contain timestamps, frame rate values ​​and network status information, such as signal strength or network latency, which can fully reflect the fluctuation and stability of the frame rate. These data are crucial for optimizing bandwidth allocation strategies and ensuring frame rate quality.

[0023] The current bandwidth and load data of the client device can be obtained through the coordinated monitoring of the target WiFi device and the 5G network. The target WiFi device uses the edge computing capabilities of the 5G network to accurately obtain its current bandwidth usage data by analyzing the network behavior and load status of the client device in real time. The load data of the client device, such as CPU and memory usage and resource usage of specific tasks, can be obtained through the monitoring API interface of the device operating system or the built-in performance monitoring module, and transmitted to the target WiFi device through the ultra-low latency of the 5G network for further bandwidth allocation optimization and load regulation.

[0024] Furthermore, the 5G-based WiFi device management method further includes the following steps: Step S200, analyzing the frame rate change log and load data of the real-time application of the client device, and determining the critical load index corresponding to the first frame drop phenomenon of the client device.

[0025] Specifically, Figure 2 A flow chart for determining a critical load index corresponding to a client device is shown.

[0026] The step of parsing the frame rate change log and load data of the real-time application of the client device and determining the critical load index corresponding to the first frame drop phenomenon of the client device specifically includes the following steps: Step S201, synchronizing the frame rate change log of the client device with the load data by time stamp; Step S202, parsing the frame rate change log, identifying the time point of the first frame drop phenomenon, and matching the load data at the time point; Step S203: Analyze the matched load data and extract the corresponding critical load index.

[0027] In an embodiment of the present invention, the frame rate change log and load data of the real-time application of the client device are parsed to determine the critical load index corresponding to the first frame drop phenomenon of the client device. During the specific implementation process, the frame rate change log and load data are obtained through the built-in log monitoring module of the target WiFi device or the client device, and the two are synchronized with the timestamp to ensure that the frame rate change corresponds to the load change one by one. When parsing the frame rate change log, the specific time point of the first frame drop phenomenon is identified. It should be noted that the frame drop phenomenon in the present technical solution specifically refers to the frame rate drop caused by the excessive load of the client device, excluding the frame rate fluctuation caused by other external factors (such as network jitter, hardware limitations, etc.). The confirmation of the frame drop time point needs to be comprehensively analyzed in combination with the real-time load data of the client device to exclude interference from non-load reasons.

[0028] After identifying the time point of frame drop, match the time point with the load data and extract the corresponding load value as the candidate critical load index. To ensure the reliability of the candidate critical load index, it is necessary to confirm it through simulation verification. Specifically, the load generation tool is used to gradually increase the load of the client device, while monitoring the frame rate changes to verify whether the frame drop phenomenon stably appears near the candidate critical load index. During the simulation verification process, the range and rate of load growth must be strictly controlled to be consistent with the load change trend of the actual operating environment of the device, so as to avoid verification deviations due to test conditions not meeting the actual situation.

[0029] This technical solution is based on log acquisition and timestamp synchronization technology, and realizes the synchronous collection of frame rate and load data through operating system API, SNMP protocol or other log collection tools. The analysis of frame rate changes and frame drop identification rely on the frame rate monitoring module, which uses the set threshold method to screen the key frame rate drop points. Load simulation and verification use performance stress testing tools such as JMeter or Locust to gradually build load scenarios to verify the accuracy of candidate critical load indexes. Through such a verification process, it is ensured that the extracted critical load index can accurately reflect the frame drop phenomenon caused by excessive load, providing an accurate basis for subsequent bandwidth optimization and dynamic allocation.

[0030] Furthermore, the 5G-based WiFi device management method further includes the following steps: Step S300, performing several bandwidth fluctuation tests from low to high on the client device within a preset time, recording the load fluctuation data during the period, and drawing a characteristic line graph of bandwidth fluctuation and load change.

[0031] Specifically, Figure 3 A flow chart for plotting a characteristic line graph of bandwidth fluctuation and load variation is shown.

[0032] Among them, performing several bandwidth fluctuation tests from low to high on the client device within a preset time, recording the load fluctuation data during the period, and drawing a characteristic line graph of bandwidth fluctuation and load change specifically includes the following steps: Step S301, within a preset time, performing bandwidth adjustment operations from low to high on the client device for several times, and recording bandwidth fluctuation data and load change index during each bandwidth adjustment; Step S302, synchronizing the bandwidth fluctuation data and the load change index through timestamps; Step S303 , drawing a characteristic line graph of bandwidth fluctuation and load change with the bandwidth fluctuation data as the X-axis and the load change index as the Y-axis.

[0033] In an embodiment of the present invention, the specific implementation of the above scheme can be achieved in the following manner. First, within a preset time, the target WiFi device performs several bandwidth adjustment operations from low to high on the client device, and dynamically collects bandwidth fluctuation data and the corresponding load change index during each bandwidth adjustment. The bandwidth adjustment operation should be gradually increased in smaller steps to more finely capture the impact of bandwidth changes on the load index. To ensure the accuracy of the data, all data needs to be synchronized through timestamps to align the time relationship between bandwidth fluctuations and load changes. Preferably, during the bandwidth adjustment operation, the bandwidth size can include the current bandwidth and a bandwidth value that is larger than the current bandwidth, so as to more realistically simulate the bandwidth usage of the client device in the actual network environment, and ensure that the test results are consistent with the actual scenario.

[0034] The preset time should be set as short as possible to reduce the impact on the normal operation of the client device. For example, a time window of several seconds can be selected to avoid long-term bandwidth adjustments from significantly interfering with device performance and user experience. By setting a reasonable time range and adjustment frequency, the test can be completed and the required data can be obtained without significantly affecting normal operations.

[0035] The significance of performing this bandwidth adjustment operation is that it can dynamically simulate the impact of bandwidth fluctuations in the actual network environment on client devices and truly reflect the changing trend of device load under different bandwidth levels. This test can not only provide data support for bandwidth optimization and allocation, but also help identify device load bottlenecks and ensure that the device can maintain stable performance under high load conditions.

[0036] The novelty of the characteristic line graph is that it associates bandwidth fluctuation data with the load change index in an intuitive way, providing a visual reference for bandwidth optimization. By drawing a characteristic line graph with bandwidth fluctuation as the X-axis and load change index as the Y-axis, the influence of bandwidth change on the load index can be clearly presented. Compared with traditional static data analysis methods, the characteristic line graph can more intuitively reflect the dynamic response characteristics of the device to bandwidth adjustment, providing a scientific basis for subsequent prediction model construction and incremental bandwidth adjustment strategies. The novelty of this method is also reflected in its dynamic and real-time nature. It can capture key data and generate models in a short time, and is suitable for application in high real-time scenarios.

[0037] Furthermore, the 5G-based WiFi device management method further includes the following steps: Step S400 , predicting the future load index of the client device after receiving the initial incremental bandwidth based on the characteristic line graph.

[0038] Specifically, Figure 4 A flow chart for predicting future load indices for client devices is shown.

[0039] The method of predicting the future load index of the client device after receiving the initial incremental bandwidth based on the characteristic line graph specifically includes the following steps: Step S401, analyzing the characteristic line graph, extracting key data points of bandwidth fluctuation and load change, analyzing their changing trends, and establishing a correlation model between bandwidth fluctuation and load change by a fitting method; Step S402, adding the current bandwidth data to the initial incremental bandwidth to calculate the proposed bandwidth value; Step S403: input the proposed bandwidth value into the association model to determine the future load index of the client device after receiving the initial incremental bandwidth.

[0040] In the embodiment of the present invention, the specific implementation process of analyzing the characteristic line graph, establishing the correlation model, and predicting the future load index is as follows: First, the key data points of bandwidth fluctuation and load change are extracted through the characteristic line graph. These data points are generated by the client device in multiple bandwidth fluctuation tests, and the corresponding load changes are recorded while the bandwidth is adjusted. In order to extract accurate data points, it is necessary to ensure that the bandwidth data and the load change data are synchronized through timestamps and eliminate outliers or invalid data. Then, the changing trend between bandwidth and load is analyzed through fitting methods (such as linear regression, polynomial regression or machine learning algorithms) to establish a correlation model. This model uses bandwidth fluctuation as the input variable and the load change index as the output variable, which can intuitively reflect the influence of bandwidth adjustment on load change.

[0041] After the model is built, the current bandwidth data is added to the initial incremental bandwidth to calculate the proposed bandwidth value, and the proposed bandwidth value is input into the association model. The model outputs the corresponding future load index based on the input proposed bandwidth value, thereby predicting the load status of the client device after receiving the initial incremental bandwidth.

[0042] The significance of future load index prediction is that it can provide a scientific basis for dynamic bandwidth allocation. By sensing the load situation after bandwidth adjustment in advance, it can avoid performance degradation or frame drop of the device under high load. The prediction results can help optimize the bandwidth allocation strategy, realize efficient use of network resources, and ensure the stable operation of client devices. The accuracy of the prediction depends on the quality of the characteristic line chart data and the accuracy of the fitting model. By selecting a reasonable fitting method and combining it with sufficient data verification, the prediction ability of the model can be significantly improved to ensure that the deviation between the future load index and the actual load is minimized. In addition, real-time updating of model input data and adjustment of algorithm parameters can also further enhance the real-time and reliability of the prediction.

[0043] The prediction method based on the association model has higher generalization ability and adaptability. The association model established by the fitting method can capture the global trend of bandwidth fluctuations and load changes, making it adaptable to any proposed bandwidth value within different bandwidth ranges. Compared with the existing technology, this method can quickly obtain prediction results in the subsequent bandwidth allocation operations for specified client devices without the need for actual adjustment tests one by one. In a dynamic network environment, this generalization ability is particularly important and can efficiently cope with load prediction needs in complex scenarios.

[0044] Furthermore, the 5G-based WiFi device management method further includes the following steps: Step S500, determine whether the future load index is less than the critical load index, if so, continue to allocate initial incremental bandwidth to the client device, if not, optimize the initial incremental bandwidth according to the critical load index and the characteristic line graph to obtain an adaptive incremental bandwidth.

[0045] Specifically, Figure 5 A flow chart for optimizing the initial incremental bandwidth is shown.

[0046] Among them, judging whether the future load index is less than the critical load index, if so, continuing to allocate the initial incremental bandwidth to the client device, if not, optimizing the initial incremental bandwidth according to the critical load index and the characteristic line graph to obtain the adaptive incremental bandwidth specifically includes the following steps: Step S501, determining whether the future load index is less than the critical load index; Step S502, when it is determined that the future load index is less than the critical load index, the initial incremental bandwidth continues to be allocated to the client device; Step S503, when it is determined that the future load index is not less than the critical load index, the bandwidth corresponding to the specific point with a preset margin before the critical load index is located through the characteristic line graph, and the difference between the bandwidth and the current bandwidth is calculated to obtain the adaptive incremental bandwidth.

[0047] The preset margin refers to the load index safety margin set according to the load change characteristics of the client device. The safety margin ensures that after the bandwidth is adjusted, the actual load index of the client device will not be close to the critical load index, and provides a certain safety buffer to avoid frame drops caused by short-term load fluctuations. The size of the preset margin can be dynamically adjusted according to the load change trend in the historical data analysis or characteristic line chart to take into account both system performance stability and bandwidth utilization efficiency.

[0048] In the embodiment of the present invention, the technical solution is based on the high-speed and low-latency characteristics of the 5G network, and a correlation model between bandwidth fluctuations and load changes is established through a fitting method, thereby achieving efficient prediction and optimization of dynamic bandwidth allocation. Utilizing the real-time data transmission capability of the 5G network, the model can capture the global trend of bandwidth fluctuations and load changes, and quickly adapt to future load requirements with different proposed bandwidth values. Compared with the existing traditional method of testing bandwidths one by one, this solution can quickly obtain prediction results in subsequent bandwidth allocation operations without the need for actual adjustment operations, significantly improving efficiency, while avoiding interference with the normal operation of client devices, ensuring performance stability in load fluctuation scenarios, reducing frame drops, and optimizing user experience.

[0049] Combined with the dynamic resource scheduling characteristics of 5G networks, this solution is particularly suitable for scenarios with high requirements for bandwidth stability and prediction accuracy, such as 5G cloud games, high-definition video conferencing, AR / VR interactive applications, and real-time monitoring scenarios in smart manufacturing. In these high-real-time applications, the network slicing and dynamic scheduling capabilities of 5G networks, combined with the model prediction method of this solution, can further optimize the allocation efficiency of bandwidth resources and improve the performance stability of client devices. At the same time, this solution is highly adaptable and can be widely used in a variety of devices and scenarios in 5G networks, providing technical support for the next generation of intelligent network management.

[0050] In the existing technology, bandwidth allocation mostly relies on actual test adjustments, which cannot quickly adapt to the dynamic needs of complex scenarios, and frequent bandwidth adjustments may interfere with the normal operation of the equipment. In a 5G-based technical environment, this solution comprehensively quantifies the impact of bandwidth adjustment on load changes through efficient data collection and the generalization ability of the fitting model. Compared with traditional technologies, by utilizing the real-time data transmission and dynamic regulation capabilities of the 5G network, this solution can quickly predict future load indexes without testing one by one, significantly improving the efficiency and accuracy in dynamic networks, and solving the limitations of existing technologies in terms of low bandwidth allocation efficiency, poor adaptability, and lack of real-time performance.

[0051] Furthermore, Figure 6 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0052] Among them, in another preferred embodiment provided by the present invention, a 5G-based WiFi device management system includes: The data acquisition module 100 is used to acquire the initial incremental bandwidth allocated by the target WiFi device to the client device, the frame rate change log of the real-time application of the client device, and the current bandwidth and load data of the client device.

[0053] In an embodiment of the present invention, the initial incremental bandwidth allocated by the target WiFi device to the client device is realized based on the QoS (quality of service) technology and dynamic bandwidth control technology supported by the 5G network. In the 5G network environment, QoS technology divides network resources into different service levels through network slicing, and allocates higher bandwidth and lower latency for high-priority application scenarios. Dynamic bandwidth control combines the ultra-low latency characteristics and high-speed transmission capabilities of the 5G network to adjust bandwidth allocation in real time to meet the performance guarantee of client devices under high load or sudden demand. The present invention is particularly suitable for application objects with "frame rate that is easy to change", such as high-definition real-time game APP, cloud game services, 4K / 8K streaming media playback, and remote conferences and AR / VR applications that require high interactivity. These applications are more dependent on frame rate stability and low latency.

[0054] The frame rate change log of the real-time application of the client device is generated based on the 5G network environment to record the frame rate changes of the device under the support of high throughput and low latency network. These logs usually contain timestamps, frame rate values ​​and network status information, such as signal strength or network latency, which can fully reflect the fluctuation and stability of the frame rate. These data are crucial for optimizing bandwidth allocation strategies and ensuring frame rate quality.

[0055] The current bandwidth and load data of the client device can be obtained through the coordinated monitoring of the target WiFi device and the 5G network. The target WiFi device uses the edge computing capabilities of the 5G network to accurately obtain its current bandwidth usage data by analyzing the network behavior and load status of the client device in real time. The load data of the client device, such as CPU and memory usage and resource usage of specific tasks, can be obtained through the monitoring API interface of the device operating system or the built-in performance monitoring module, and transmitted to the target WiFi device through the ultra-low latency of the 5G network for further bandwidth allocation optimization and load regulation.

[0056] Furthermore, the 5G-based WiFi device management system also includes: The critical load index determination module 200 is used to parse the frame rate change log and load data of the real-time application of the client device, and determine the critical load index corresponding to the first frame drop phenomenon of the client device.

[0057] Specifically, Figure 7 It shows a structural block diagram of a critical load index determination module 200 in a system provided by an embodiment of the present invention.

[0058] In a preferred embodiment of the present invention, the critical load index determination module 200 specifically includes: A first timestamp correspondence unit 201 is used to synchronize the timestamps of the frame rate change log of the client device with the load data; The log parsing unit 202 is used to parse the frame rate change log, identify the time point of the first frame drop phenomenon, and match the load data at the time point; The critical load index extraction unit 203 is used to analyze the matched load data and extract the corresponding critical load index.

[0059] In an embodiment of the present invention, the frame rate change log and load data of the real-time application of the client device are parsed to determine the critical load index corresponding to the first frame drop phenomenon of the client device. During the specific implementation process, the frame rate change log and load data are obtained through the built-in log monitoring module of the target WiFi device or the client device, and the two are synchronized with the timestamp to ensure that the frame rate change corresponds to the load change one by one. When parsing the frame rate change log, the specific time point of the first frame drop phenomenon is identified. It should be noted that the frame drop phenomenon in the present technical solution specifically refers to the frame rate drop caused by the excessive load of the client device, excluding the frame rate fluctuation caused by other external factors (such as network jitter, hardware limitations, etc.). The confirmation of the frame drop time point needs to be comprehensively analyzed in combination with the real-time load data of the client device to exclude interference from non-load reasons.

[0060] After identifying the time point of frame drop, match the time point with the load data and extract the corresponding load value as the candidate critical load index. To ensure the reliability of the candidate critical load index, it is necessary to confirm it through simulation verification. Specifically, the load generation tool is used to gradually increase the load of the client device, while monitoring the frame rate changes to verify whether the frame drop phenomenon stably appears near the candidate critical load index. During the simulation verification process, the range and rate of load growth must be strictly controlled to be consistent with the load change trend of the actual operating environment of the device, so as to avoid verification deviations due to test conditions not meeting the actual situation.

[0061] This technical solution is based on log acquisition and timestamp synchronization technology, and realizes the synchronous collection of frame rate and load data through operating system API, SNMP protocol or other log collection tools. The analysis of frame rate changes and frame drop identification rely on the frame rate monitoring module, which uses the set threshold method to screen the key frame rate drop points. Load simulation and verification use performance stress testing tools such as JMeter or Locust to gradually build load scenarios to verify the accuracy of candidate critical load indexes. Through such a verification process, it is ensured that the extracted critical load index can accurately reflect the frame drop phenomenon caused by excessive load, providing an accurate basis for subsequent bandwidth optimization and dynamic allocation.

[0062] Furthermore, the 5G-based WiFi device management system also includes: The characteristic line graph generation module 300 is used to perform several bandwidth fluctuation tests from low to high on the client device within a preset time, record the load fluctuation data during the period, and draw a characteristic line graph of bandwidth fluctuation and load change.

[0063] Specifically, Figure 8 It shows a structural block diagram of a characteristic line graph generating module 300 in a system provided by an embodiment of the present invention.

[0064] In the preferred embodiment of the present invention, the characteristic line graph generating module 300 specifically includes: The bandwidth adjustment operation execution unit 301 is used to perform several bandwidth adjustment operations from low to high on the client device within a preset time, and record bandwidth fluctuation data and load change index during each bandwidth adjustment; A second timestamp corresponding unit 302 is used to synchronize the bandwidth fluctuation data with the load change index through timestamps; The characteristic line graph drawing unit 303 is used to draw a characteristic line graph of bandwidth fluctuation and load change with the bandwidth fluctuation data as the X-axis and the load change index as the Y-axis.

[0065] In an embodiment of the present invention, the specific implementation of the above scheme can be achieved in the following manner. First, within a preset time, the target WiFi device performs several bandwidth adjustment operations from low to high on the client device, and dynamically collects bandwidth fluctuation data and the corresponding load change index during each bandwidth adjustment. The bandwidth adjustment operation should be gradually increased in smaller steps to more finely capture the impact of bandwidth changes on the load index. To ensure the accuracy of the data, all data needs to be synchronized through timestamps to align the time relationship between bandwidth fluctuations and load changes. Preferably, during the bandwidth adjustment operation, the bandwidth size can include the current bandwidth and a bandwidth value that is larger than the current bandwidth, so as to more realistically simulate the bandwidth usage of the client device in the actual network environment, and ensure that the test results are consistent with the actual scenario.

[0066] The preset time should be set as short as possible to reduce the impact on the normal operation of the client device. For example, a time window of several seconds can be selected to avoid long-term bandwidth adjustments from significantly interfering with device performance and user experience. By setting a reasonable time range and adjustment frequency, the test can be completed and the required data can be obtained without significantly affecting normal operations.

[0067] The significance of performing this bandwidth adjustment operation is that it can dynamically simulate the impact of bandwidth fluctuations in the actual network environment on client devices and truly reflect the changing trend of device load under different bandwidth levels. This test can not only provide data support for bandwidth optimization and allocation, but also help identify device load bottlenecks and ensure that the device can maintain stable performance under high load conditions.

[0068] The novelty of the characteristic line graph is that it associates bandwidth fluctuation data with the load change index in an intuitive way, providing a visual reference for bandwidth optimization. By drawing a characteristic line graph with bandwidth fluctuation as the X-axis and load change index as the Y-axis, the influence of bandwidth change on the load index can be clearly presented. Compared with traditional static data analysis methods, the characteristic line graph can more intuitively reflect the dynamic response characteristics of the device to bandwidth adjustment, providing a scientific basis for subsequent prediction model construction and incremental bandwidth adjustment strategies. The novelty of this method is also reflected in its dynamic and real-time nature. It can capture key data and generate models in a short time, and is suitable for application in high real-time scenarios.

[0069] Furthermore, the 5G-based WiFi device management system also includes: The future load index determination module 400 is used to predict the future load index of the client device after receiving the initial incremental bandwidth based on the characteristic line graph.

[0070] Specifically, Fig. 9 It shows a structural block diagram of the future load index determination module 400 in the system provided by an embodiment of the present invention.

[0071] In a preferred embodiment of the present invention, the future load index determination module 400 specifically includes: The correlation model building unit 401 is used to analyze the characteristic line graph, extract key data points of bandwidth fluctuation and load change, analyze their change trends, and build a correlation model of bandwidth fluctuation and load change through a fitting method; The proposed bandwidth value calculation unit 402 is used to add the current bandwidth data to the initial incremental bandwidth to calculate the proposed bandwidth value; The future load index determination unit 403 is configured to input the proposed bandwidth value into the association model to determine the future load index of the client device after receiving the initial incremental bandwidth.

[0072] In the embodiment of the present invention, the specific implementation process of analyzing the characteristic line graph, establishing the correlation model, and predicting the future load index is as follows: First, the key data points of bandwidth fluctuation and load change are extracted through the characteristic line graph. These data points are generated by the client device in multiple bandwidth fluctuation tests, and the corresponding load changes are recorded while the bandwidth is adjusted. In order to extract accurate data points, it is necessary to ensure that the bandwidth data and the load change data are synchronized through timestamps and eliminate outliers or invalid data. Then, the changing trend between bandwidth and load is analyzed through fitting methods (such as linear regression, polynomial regression or machine learning algorithms) to establish a correlation model. This model uses bandwidth fluctuation as the input variable and the load change index as the output variable, which can intuitively reflect the influence of bandwidth adjustment on load change.

[0073] After the model is built, the current bandwidth data is added to the initial incremental bandwidth to calculate the proposed bandwidth value, and the proposed bandwidth value is input into the association model. The model outputs the corresponding future load index based on the input proposed bandwidth value, thereby predicting the load status of the client device after receiving the initial incremental bandwidth.

[0074] The significance of future load index prediction is that it can provide a scientific basis for dynamic bandwidth allocation. By sensing the load situation after bandwidth adjustment in advance, it can avoid performance degradation or frame drop of the device under high load. The prediction results can help optimize the bandwidth allocation strategy, realize efficient use of network resources, and ensure the stable operation of client devices. The accuracy of the prediction depends on the quality of the characteristic line chart data and the accuracy of the fitting model. By selecting a reasonable fitting method and combining it with sufficient data verification, the prediction ability of the model can be significantly improved to ensure that the deviation between the future load index and the actual load is minimized. In addition, real-time updating of model input data and adjustment of algorithm parameters can also further enhance the real-time and reliability of the prediction.

[0075] The prediction method based on the association model has higher generalization ability and adaptability. The association model established by the fitting method can capture the global trend of bandwidth fluctuations and load changes, making it adaptable to any proposed bandwidth value within different bandwidth ranges. Compared with the existing technology, this method can quickly obtain prediction results in the subsequent bandwidth allocation operations for specified client devices without the need for actual adjustment tests one by one. In a dynamic network environment, this generalization ability is particularly important and can efficiently cope with load prediction needs in complex scenarios.

[0076] Furthermore, the 5G-based WiFi device management system also includes: The initial incremental bandwidth optimization module 500 is used to determine whether the future load index is less than the critical load index. If so, the initial incremental bandwidth continues to be allocated to the client device. If not, the initial incremental bandwidth is optimized according to the critical load index and the characteristic line graph to obtain an adaptive incremental bandwidth.

[0077] Specifically, Fig.10 It shows a structural block diagram of the initial incremental bandwidth optimization module 500 in the system provided by an embodiment of the present invention.

[0078] In a preferred embodiment of the present invention, the initial incremental bandwidth optimization module 500 specifically includes: A future load index determination unit 501 is used to determine whether the future load index is less than a critical load index; The initial incremental bandwidth allocation unit 502 is used to continue to allocate the initial incremental bandwidth to the client device when it is determined that the future load index is less than the critical load index; The adaptive incremental bandwidth generating unit 503 is used to locate the bandwidth corresponding to a specific point with a preset margin before the critical load index through the characteristic line graph when it is determined that the future load index is not less than the critical load index, and calculate the difference between the bandwidth and the current bandwidth to obtain the adaptive incremental bandwidth; The preset margin refers to a load index safety margin set according to the load change characteristics of the client device. The safety margin ensures that after the bandwidth is adjusted, the actual load index of the client device will not be close to the critical load index, while providing a certain safety buffer to avoid frame drops caused by short-term load fluctuations.

[0079] In the embodiment of the present invention, the technical solution is based on the high-speed and low-latency characteristics of the 5G network, and a correlation model between bandwidth fluctuations and load changes is established through a fitting method, thereby achieving efficient prediction and optimization of dynamic bandwidth allocation. Utilizing the real-time data transmission capability of the 5G network, the model can capture the global trend of bandwidth fluctuations and load changes, and quickly adapt to future load requirements with different proposed bandwidth values. Compared with the existing traditional method of testing bandwidths one by one, this solution can quickly obtain prediction results in subsequent bandwidth allocation operations without the need for actual adjustment operations, significantly improving efficiency, while avoiding interference with the normal operation of client devices, ensuring performance stability in load fluctuation scenarios, reducing frame drops, and optimizing user experience.

[0080] Combined with the dynamic resource scheduling characteristics of 5G networks, this solution is particularly suitable for scenarios with high requirements for bandwidth stability and prediction accuracy, such as 5G cloud games, high-definition video conferencing, AR / VR interactive applications, and real-time monitoring scenarios in smart manufacturing. In these high-real-time applications, the network slicing and dynamic scheduling capabilities of 5G networks, combined with the model prediction method of this solution, can further optimize the allocation efficiency of bandwidth resources and improve the performance stability of client devices. At the same time, this solution is highly adaptable and can be widely used in a variety of devices and scenarios in 5G networks, providing technical support for the next generation of intelligent network management.

[0081] In the existing technology, bandwidth allocation mostly relies on actual test adjustments, which cannot quickly adapt to the dynamic needs of complex scenarios, and frequent bandwidth adjustments may interfere with the normal operation of the equipment. In a 5G-based technical environment, this solution comprehensively quantifies the impact of bandwidth adjustment on load changes through efficient data collection and the generalization ability of the fitting model. Compared with traditional technologies, by utilizing the real-time data transmission and dynamic regulation capabilities of the 5G network, this solution can quickly predict future load indexes without testing one by one, significantly improving the efficiency and accuracy in dynamic networks, and solving the limitations of existing technologies in terms of low bandwidth allocation efficiency, poor adaptability, and lack of real-time performance.

[0082] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0083] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0084] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0085] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0086] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A 5G-based WiFi device management method, characterized in that: The method comprises: Obtain the initial incremental bandwidth allocated by the target WiFi device to the client device, the frame rate change log of the client device's real-time application, and the client device's current bandwidth and load data; Analyze the frame rate change log and load data of the real-time application of the client device to determine the critical load index corresponding to the first frame drop phenomenon of the client device; Perform several bandwidth fluctuation tests from low to high on the client device within a preset time, record the load fluctuation data during the period, and draw a characteristic line graph of bandwidth fluctuation and load change; Predict the future load index of client devices after receiving the initial incremental bandwidth based on the characteristic line graph: Determine whether the future load index is less than the critical load index. If so, continue to allocate initial incremental bandwidth to the client device. If not, optimize the initial incremental bandwidth according to the critical load index and the characteristic line graph to obtain an adaptive incremental bandwidth.

2. The 5G-based WiFi device management method according to claim 1, characterized in that: The steps of parsing the frame rate change log and load data of the real-time application of the client device and determining the critical load index corresponding to the first time when the client device has a frame drop phenomenon include: Synchronize the timestamps of the frame rate change logs of the client device and the load data; Parse the frame rate change log, identify the time point when the first frame drop occurred, and match the load data at that time point; Analyze the matched load data and extract the corresponding critical load index.

3. The 5G-based WiFi device management method according to claim 1, characterized in that: The steps of performing a plurality of bandwidth fluctuation tests from low to high on a client device within a preset time, recording load fluctuation data during the period, and drawing a characteristic line graph of bandwidth fluctuation and load change include: Within a preset time, perform bandwidth adjustment operations from low to high on the client device several times, and record bandwidth fluctuation data and load change index during each bandwidth adjustment; Synchronize the bandwidth fluctuation data and the load change index through timestamps; With bandwidth fluctuation data as the X-axis and load change index as the Y-axis, a characteristic line graph of bandwidth fluctuation and load change is drawn.

4. The 5G-based WiFi device management method according to claim 1, characterized in that: The steps of predicting the future load index of the client device after receiving the initial incremental bandwidth based on the characteristic line graph include: Analyze the characteristic line graph, extract the key data points of bandwidth fluctuation and load change, analyze their changing trends, and establish the correlation model between bandwidth fluctuation and load change through fitting method; Add the current bandwidth data to the initial incremental bandwidth to calculate the proposed bandwidth value; The proposed bandwidth value is input into the association model to determine the future load index of the client device after receiving the initial incremental bandwidth.

5. The 5G-based WiFi device management method according to claim 1, characterized in that: The steps of determining whether the future load index is less than the critical load index, if so, continuing to allocate the initial incremental bandwidth to the client device, and if not, optimizing the initial incremental bandwidth according to the critical load index and the characteristic line graph to obtain the adaptive incremental bandwidth include: Determine whether the future load index is less than the critical load index; When it is determined that the future load index is less than the critical load index, the initial incremental bandwidth continues to be allocated to the client device; When it is determined that the future load index is not less than the critical load index, the bandwidth corresponding to a specific point with a preset margin before the critical load index is located through the characteristic line graph, and the difference between the bandwidth and the current bandwidth is calculated to obtain the adaptive incremental bandwidth; The preset margin refers to a load index safety margin set according to the load change characteristics of the client device. The safety margin ensures that after the bandwidth is adjusted, the actual load index of the client device will not be close to the critical load index, while providing a certain safety buffer to avoid frame drops caused by short-term load fluctuations.

6. A 5G-based WiFi device management system, characterized in that: The system comprises: a data acquisition module, a critical load index determination module, a characteristic line graph generation module, a future load index determination module and an initial incremental bandwidth optimization module, wherein: A data acquisition module is used to obtain the initial incremental bandwidth allocated by the target WiFi device to the client device, the frame rate change log of the real-time application of the client device, and the current bandwidth and load data of the client device; A critical load index determination module is used to parse the frame rate change log and load data of the real-time application of the client device to determine the critical load index corresponding to the first frame drop phenomenon of the client device; A characteristic line graph generation module is used to perform several bandwidth fluctuation tests from low to high on the client device within a preset time, record the load fluctuation data during the period, and draw a characteristic line graph of bandwidth fluctuation and load change; A future load index determination module is used to predict the future load index of the client device after receiving the initial incremental bandwidth based on the characteristic line graph: The initial incremental bandwidth optimization module is used to determine whether the future load index is less than the critical load index. If so, the initial incremental bandwidth continues to be allocated to the client device. If not, the initial incremental bandwidth is optimized according to the critical load index and the characteristic line graph to obtain an adaptive incremental bandwidth.

7. The 5G-based WiFi device management system according to claim 6, characterized in that: The critical load index determination module specifically includes: A first timestamp correspondence unit, used for performing timestamp synchronization correspondence between the frame rate change log of the client device and the load data; The log parsing unit is used to parse the frame rate change log, identify the time point of the first frame drop phenomenon, and match the load data at that time point; The critical load index extraction unit is used to analyze the matched load data and extract the corresponding critical load index.

8. The 5G-based WiFi device management system according to claim 7, characterized in that: The characteristic line graph generation module specifically includes: The bandwidth adjustment operation execution unit is used to perform several bandwidth adjustment operations from low to high on the client device within a preset time, and record the bandwidth fluctuation data and load change index during each bandwidth adjustment; A second timestamp corresponding unit, used for synchronously corresponding the bandwidth fluctuation data and the load change index through timestamps; The characteristic line graph drawing unit is used to draw a characteristic line graph of bandwidth fluctuation and load change with bandwidth fluctuation data as the X-axis and load change index as the Y-axis.

9. The 5G-based WiFi device management system according to claim 8, characterized in that: The future load index determination module specifically includes: The correlation model building unit is used to analyze the characteristic line graph, extract the key data points of bandwidth fluctuation and load change, analyze their change trends, and build a correlation model between bandwidth fluctuation and load change through a fitting method; A proposed bandwidth value calculation unit is used to add the current bandwidth data to the initial incremental bandwidth to calculate the proposed bandwidth value; The future load index determination unit is used to input the proposed bandwidth value into the association model to determine the future load index of the client device after receiving the initial incremental bandwidth.

10. The 5G-based WiFi device management system according to claim 9, characterized in that: The initial incremental bandwidth optimization module specifically includes: A future load index determination unit, used to determine whether the future load index is less than a critical load index; The initial incremental bandwidth allocation unit is used to continue to allocate the initial incremental bandwidth to the client device when it is determined that the future load index is less than the critical load index; The adaptive incremental bandwidth generating unit is used for locating the bandwidth corresponding to a specific point with a preset margin before the critical load index through the characteristic line graph when it is determined that the future load index is not less than the critical load index, and calculating the difference between the bandwidth and the current bandwidth to obtain the adaptive incremental bandwidth; The preset margin refers to a load index safety margin set according to the load change characteristics of the client device. The safety margin ensures that after the bandwidth is adjusted, the actual load index of the client device will not be close to the critical load index, while providing a certain safety buffer to avoid frame drops caused by short-term load fluctuations.

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