A data communication method and system based on WiFi6

Abstract

The present application relates to the technical field of data communication, and especially relates to a data communication method and system based on WiFi6. The method comprises the following steps: obtaining the control authority of WiFi6 networking, collecting the signal frequency band state and identifying the frequency band load state; then, converting the load state into a spectrum diagram, carrying out throughput attenuation simulation and adjacent channel interference risk quantification; then, fitting the network congestion increment index according to the data packet loss risk and the throughput attenuation data, and planning the channel frequency band and optimizing the OFDMA subcarrier allocation according to the network congestion increment index; finally, designing the WiFi6 data communication strategy and sending it to the control center to implement the optimized data communication method. The present application optimizes the data communication technology, so that the data communication technology is more perfect.

Description

Technical Field

[0001] This invention relates to the field of data communication technology, and in particular to a data communication method and system based on WiFi 6. Background Technology

[0002] With the widespread adoption of smart devices and the increasing demand for high-bandwidth, low-latency networks, WiFi 6 (also known as 802.11ax) has emerged as a next-generation wireless communication technology, offering higher transmission rates, lower latency, and stronger anti-interference capabilities than previous WiFi standards. WiFi 6 employs several innovative technologies, including OFDMA (Orthogonal Frequency Division Multiple Access), MU-MIMO (Multi-User Multiple-Input Multiple-Output), and TWT (Target Wake-Up Time), enabling it to handle simultaneous access from a large number of devices more efficiently and meet the network demands of high-density environments. As the number of devices increases, competition for spectrum resources intensifies, easily leading to signal interference, bandwidth congestion, and network latency. WiFi 6 effectively solves these problems, especially in high-density environments (such as office buildings, public places, and large conferences), where it enables multiple devices to transmit efficiently simultaneously without mutual interference. However, despite the many advantages offered by WiFi 6, optimizing its performance, reducing interference, and improving throughput in real-world networks remains a challenge. Channel band planning, resource allocation, and interference management in WiFi 6 networks still require meticulous design and adjustment to achieve optimal communication performance. However, a traditional WiFi 6-based data communication method suffers from low accuracy in identifying signal interference, thus making it impossible to perform accurate WiFi 6 communication optimization. Summary of the Invention

[0003] Therefore, it is necessary to provide a data communication method and system based on WiFi 6 to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objective, a data communication method based on WiFi 6 is provided, the method comprising the following steps:

[0005] Step S1: Obtain WiFi 6 networking control permissions from the router; collect the operating signal frequency band status based on the WiFi 6 networking control permissions to obtain the operating signal frequency band status dataset; identify the frequency band load status from the operating signal frequency band status dataset to obtain the operating signal frequency band load status.

[0006] Step S2: Perform load frequency band spectrum conversion on the load status of the operating signal frequency band to obtain the load signal frequency band spectrum; perform resource unit throughput attenuation simulation on the load signal frequency band spectrum to obtain resource unit throughput attenuation data; perform adjacent channel cross-interference trigger risk quantification on the load signal frequency band spectrum to obtain adjacent channel cross-interference risk data.

[0007] Step S3: Fit the network congestion increment index based on the packet loss risk probability and resource unit throughput attenuation data to obtain the network congestion increment index; perform channel frequency band planning based on the network congestion increment index to obtain channel frequency band planning data; optimize OFDMA subcarrier allocation on the channel frequency band planning data to obtain OFDMA subcarrier allocation optimization data.

[0008] Step S4: Design a routing WiFi 6 data communication strategy based on channel frequency band planning data and OFDMA subcarrier allocation optimization data to obtain the routing WiFi 6 data communication strategy; send the routing WiFi 6 data communication strategy to the routing WiFi 6 control center to execute the WiFi 6 data communication method.

[0009] Preferably, step S1 includes the following steps:

[0010] Step S11: Obtain WiFi 6 networking control permissions on the router;

[0011] Step S12: Based on the WiFi 6 networking control permissions of the router, collect the operating signal frequency band status among multiple WiFi 6 devices to obtain the operating signal frequency band status dataset;

[0012] Step S13: Analyze the bandwidth occupancy ratio of the running signal frequency band status dataset to obtain the signal frequency band bandwidth occupancy ratio;

[0013] Step S14: Based on the signal band bandwidth occupancy ratio, identify the band load status of the operating signal band using the operating signal band status dataset to obtain the operating signal band load status.

[0014] Preferably, step S2 includes the following steps:

[0015] Step S21: Perform load frequency band spectrum conversion on the load status of the operating signal frequency band to obtain the load signal frequency band spectrum;

[0016] Step S22: Identify co-channel interference based on the load signal frequency band spectrum diagram to obtain the co-channel interference frequency band of the load signal;

[0017] Step S23: Simulate the throughput attenuation of resource units based on the load signal frequency band spectrum diagram according to the co-frequency interference band of the load signal, and obtain the throughput attenuation data of resource units;

[0018] Step S24: Perform spectral extension analysis on the co-frequency interference band of the load signal to obtain spectral extension data of the interference signal;

[0019] Step S25: Based on the interference signal spectrum extension data, the adjacent channel cross-interference trigger risk is quantified on the load signal frequency band spectrum map to obtain adjacent channel cross-interference risk data.

[0020] Preferably, step S23 includes the following steps:

[0021] Step S231: Perform interference spectrum bandwidth analysis on the same-frequency interference band of the load signal to obtain the same-frequency interference spectrum bandwidth data;

[0022] Step S232: Based on the co-frequency interference spectrum bandwidth data, identify the signal band bandwidth overlap of the load signal frequency band spectrum to obtain the signal band interference bandwidth overlap data;

[0023] Step S233: Calculate the transient interference frequency density from the overlapping interference bandwidth data of the signal frequency bands to obtain the transient interference frequency density;

[0024] Step S234: Based on the transient interference frequency density, perform interference signal-to-noise ratio increment fitting on the frequency spectrum of the load signal band to obtain interference signal-to-noise ratio increment data;

[0025] Step S235: Simulate the resource unit throughput attenuation based on the transient interference frequency density and interference signal-to-noise ratio increment data to obtain the resource unit throughput attenuation data.

[0026] Preferably, step S234 includes the following steps:

[0027] Based on the transient interference frequency density, the transient time-series interference power spectrum is calculated by segmenting the load signal frequency band spectrum to obtain the transient segmented time-series interference power spectrum.

[0028] An amplitude variation trend analysis was performed on the power spectrum of transient segmented time-series interference to obtain the amplitude variation trend of interference power.

[0029] Based on the trend of interference power amplitude variation, the transient segmented time-series interference power spectrum is interpolated and accumulated to obtain interference power interpolation accumulation data.

[0030] Based on the interpolated cumulative data of interference power, the interference signal-to-noise ratio increment is fitted to the transient segmented time-series interference power spectrum to obtain the interference signal-to-noise ratio increment data.

[0031] Preferably, step S3 includes the following steps:

[0032] Step S31: Estimate the probability of packet loss risk based on the adjacent channel cross-interference risk data to obtain the probability of packet loss risk;

[0033] Step S32: Fit the network congestion increment index based on the packet loss risk probability and resource unit throughput decay data to obtain the network congestion increment index;

[0034] Step S33: Based on data such as packet loss risk probability, resource unit throughput attenuation, and network congestion increment index, perform channel frequency band planning to obtain channel frequency band planning data;

[0035] Step S34: Optimize the OFDMA subcarrier allocation of the channel frequency band planning data to obtain OFDMA subcarrier allocation optimization data.

[0036] Preferably, step S32 includes the following steps:

[0037] Step S321: Perform correlation coefficient analysis on traffic load increment based on packet loss risk probability and resource unit throughput attenuation data to obtain traffic load increment coefficient;

[0038] Step S322: Based on the resource unit throughput attenuation data and traffic load increment coefficient, evaluate the increase in resource retransmission load to obtain the increase in resource retransmission load data.

[0039] Step S323: Perform frequency band channel polling contention rate increase analysis on the resource retransmission load increase data to obtain channel polling contention rate increase data;

[0040] Step S324: Fit the network congestion increment index based on the resource retransmission load increase data and the channel polling contention rate increase data to obtain the network congestion increment index.

[0041] Preferably, step S33 includes the following steps:

[0042] Step S331: Based on data such as packet loss risk probability, resource unit throughput attenuation, and network congestion increment index, perform weighted fusion processing to obtain weighted data of influencing factors;

[0043] Step S332: Based on the impact factor weighted data, dynamically allocate channel bandwidth using data such as packet loss risk probability and resource unit throughput attenuation to obtain dynamic channel bandwidth allocation data;

[0044] Step S333: Perform adaptive channel spacing selection on the channel bandwidth dynamic allocation data to obtain adaptive channel spacing data;

[0045] Step S334: Perform channel load balancing allocation processing on the network congestion increment index based on the influence factor weighted data to obtain channel load balancing allocation data;

[0046] Step S335: Based on the channel bandwidth dynamic allocation data, channel adaptive interval data, and channel load balancing allocation data, perform channel frequency band planning to obtain channel frequency band planning data.

[0047] Preferably, step S34 includes the following steps:

[0048] Step S341: Perform subcarrier availability analysis on the channel frequency band planning data to obtain subcarrier availability data;

[0049] Step S342: Evaluate the interference level of each subcarrier based on the subcarrier availability data to obtain interference level data for each subcarrier;

[0050] Step S343: Perform channel bandwidth classification processing based on the interference level data and subcarrier availability data of each subcarrier to obtain channel bandwidth classification data;

[0051] Step S344: Based on the interference level data, subcarrier availability data and channel bandwidth classification data of each subcarrier, perform peak-to-average power output matching between frequency band channels to obtain peak-to-average power output matching data;

[0052] Step S345: Based on the channel bandwidth classification data and peak-to-average power output matching data, perform OFDMA subcarrier allocation optimization on the channel frequency band planning data to obtain OFDMA subcarrier allocation optimization data.

[0053] Preferably, the present invention also provides a WiFi 6-based data communication system for performing the WiFi 6-based data communication method described above, the WiFi 6-based data communication system comprising:

[0054] The frequency band load status identification module is used to obtain router WiFi6 networking control permissions; based on the router WiFi6 networking control permissions, it collects the operating signal frequency band status to obtain the operating signal frequency band status dataset; and performs frequency band load status identification on the operating signal frequency band status dataset to obtain the operating signal frequency band load status.

[0055] The interference risk quantification module is used to convert the load frequency band spectrum diagram of the operating signal frequency band to obtain the load signal frequency band spectrum diagram; to simulate the resource unit throughput attenuation of the load signal frequency band spectrum diagram to obtain the resource unit throughput attenuation data; and to quantify the adjacent channel cross-interference trigger risk of the load signal frequency band spectrum diagram to obtain the adjacent channel cross-interference risk data.

[0056] The allocation optimization module is used to fit the network congestion increment index based on the probability of packet loss risk and resource unit throughput attenuation data to obtain the network congestion increment index; to perform channel frequency band planning based on the network congestion increment index to obtain channel frequency band planning data; and to perform OFDMA subcarrier allocation optimization on the channel frequency band planning data to obtain OFDMA subcarrier allocation optimization data.

[0057] The communication strategy design module is used to design a routing WiFi6 data communication strategy based on channel frequency band planning data and OFDMA subcarrier allocation optimization data, thereby obtaining the routing WiFi6 data communication strategy; and to send the routing WiFi6 data communication strategy to the routing WiFi6 control center to execute the WiFi6 data communication method.

[0058] The beneficial effects of this invention are that, by acquiring control over WiFi 6 network routing and collecting data on the operating signal frequency band status, the solution can monitor and evaluate the signal frequency band status in the WiFi 6 network in real time. This process helps to accurately understand the load situation of each frequency band, promptly identify the load situation of the signal frequency band, ensure that the network operates in an efficient and optimized frequency band environment, and provide key data support for subsequent optimization decisions. Through this data collection and load status identification, network bottlenecks can be anticipated in advance, reducing the risk of network congestion and interference. By converting the operating signal frequency band load status into a spectrum diagram, and further performing resource unit throughput attenuation simulation and adjacent channel cross-interference risk quantification on the spectrum diagram, the solution can analyze the network performance within the frequency band more deeply. In this process, throughput attenuation simulation provides a basis for evaluating signal attenuation under frequency band load, while interference risk quantification helps identify cross-interference between different channels. This precise quantification and simulation can provide more intuitive data support for spectrum optimization, reducing signal interference and resource waste. By combining packet loss risk probability and throughput attenuation data to fit the network congestion increment index, the solution can effectively assess the degree of network congestion and plan channel frequency bands accordingly. The fitted network congestion increment index provides a scientific evaluation standard for network optimization, offering data support for subsequent frequency band planning. Based on this, channel frequency band planning becomes more rational, avoiding excessive congestion or idleness, thereby improving the transmission efficiency and stability of the WiFi 6 network. Simultaneously, OFDMA subcarrier allocation optimization further enhances resource utilization efficiency, controlling interference between subcarriers and improving overall frequency band performance. Based on the aforementioned channel frequency band planning data and OFDMA subcarrier allocation optimization data, the design of WiFi 6 data communication strategies can be dynamically adjusted according to actual network load, ensuring optimal allocation of network resources. By formulating precise data communication strategies, network congestion and interference can be effectively reduced, improving data transmission speed and stability. Sending these strategies to the WiFi 6 control center and executing them ensures that all WiFi 6 devices in the network follow the optimal communication scheme, significantly improving overall network performance and providing a more efficient, low-latency communication experience under high-load conditions. Therefore, this invention is an optimization of a traditional WiFi 6-based data communication method, which solves the problem that the traditional WiFi 6-based data communication method has low accuracy in identifying signal interference, thus making it impossible to perform accurate WiFi 6 communication optimization. It reduces the accuracy in identifying signal interference, thereby enabling accurate WiFi 6 communication optimization. Attached Figure Description

[0059] Figure 1 A flowchart illustrating the steps of a data communication method based on WiFi 6;

[0060] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2.

[0061] Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3. Detailed Implementation

[0062] Please see Figures 1 to 3 A data communication method based on WiFi 6, the method comprising the following steps:

[0063] Step S1: Obtain WiFi 6 networking control permissions from the router; collect the operating signal frequency band status based on the WiFi 6 networking control permissions to obtain the operating signal frequency band status dataset; identify the frequency band load status from the operating signal frequency band status dataset to obtain the operating signal frequency band load status.

[0064] Step S2: Perform load frequency band spectrum conversion on the load status of the operating signal frequency band to obtain the load signal frequency band spectrum; perform resource unit throughput attenuation simulation on the load signal frequency band spectrum to obtain resource unit throughput attenuation data; perform adjacent channel cross-interference trigger risk quantification on the load signal frequency band spectrum to obtain adjacent channel cross-interference risk data.

[0065] Step S3: Fit the network congestion increment index based on the packet loss risk probability and resource unit throughput attenuation data to obtain the network congestion increment index; perform channel frequency band planning based on the network congestion increment index to obtain channel frequency band planning data; optimize OFDMA subcarrier allocation on the channel frequency band planning data to obtain OFDMA subcarrier allocation optimization data.

[0066] Step S4: Design a routing WiFi 6 data communication strategy based on channel frequency band planning data and OFDMA subcarrier allocation optimization data to obtain the routing WiFi 6 data communication strategy; send the routing WiFi 6 data communication strategy to the routing WiFi 6 control center to execute the WiFi 6 data communication method.

[0067] In this embodiment of the invention, reference is made to Figure 1 The above is a flowchart illustrating the steps of a WiFi 6-based data communication method according to the present invention. In this example, the WiFi 6-based data communication method includes the following steps:

[0068] Step S1: Obtain WiFi 6 networking control permissions from the router; collect the operating signal frequency band status based on the WiFi 6 networking control permissions to obtain the operating signal frequency band status dataset; identify the frequency band load status from the operating signal frequency band status dataset to obtain the operating signal frequency band load status.

[0069] In this embodiment of the invention, obtaining network control permissions for the WiFi 6 router involves identity authentication and device authorization management. First, the WPA3-SAE (Simultaneous Authentication of Equals) authentication method in the IEEE 802.11ax protocol ensures that the visitor has legitimate permissions. During the identity authentication process, the terminal device sends an authentication request, and the router performs a handshake verification based on the pre-stored encryption key. After ensuring key matching, device control permissions are granted. After authentication is completed, the current network topology and the status data of each node are obtained through the SNMP (Simple Network Management Protocol). After obtaining control permissions, the operating signal frequency band status acquisition operation is performed. The CSI (Channel State Information) data extraction method is used to monitor the amplitude response and phase response of each available channel in real time. Specifically, based on the HE (High Efficiency) long training field (LTF) in the IEEE 802.11ax frame format, the received signal is parsed, the channel gain is calculated, and the full-band channel state information is obtained through FFT (Fast Fourier Transform). The status of different signal frequency bands is periodically scanned at 50ms intervals, and signal strength is collected for the entire frequency band within each cycle to build a channel status database. After acquiring the channel status, frequency band load status identification is performed using RSSI (Received Signal Strength Indicator) measurement to analyze the amplitude of the collected signal data. By setting RSSI thresholds (e.g., above -50dBm indicates high load, below -70dBm indicates low load), channel occupancy is detected using CCA (Clear Channel Assessment), and the proportion of idle time is calculated. For high-load channels, the number of clients occupying the channel and the data transmission rate are further analyzed, and a mean filtering method is used to eliminate short-term burst interference, ultimately obtaining the load status dataset for each operating signal frequency band.

[0070] Step S2: Perform load frequency band spectrum conversion on the load status of the operating signal frequency band to obtain the load signal frequency band spectrum; perform resource unit throughput attenuation simulation on the load signal frequency band spectrum to obtain resource unit throughput attenuation data; perform adjacent channel cross-interference trigger risk quantification on the load signal frequency band spectrum to obtain adjacent channel cross-interference risk data.

[0071] In this embodiment of the invention, load frequency band spectrum transformation is performed on the load status data of the operating signal frequency band, and STFT (Short-Time Fourier Transform) is used to perform time-frequency domain analysis on different signal frequency bands. The time-domain data of each signal frequency band is processed in blocks using a sliding window method, with each window length set to 10ms. After FFT calculation, short-time spectrum data is obtained. Combined with the frequency band energy distribution, the obtained time-frequency map is transformed into a two-dimensional spectrum map, forming a load signal frequency band spectrum map dataset. Based on the spectrum map data, resource unit throughput attenuation simulation is performed. The OFDMA (Orthogonal Frequency Division Multiple Access) subcarrier allocation model is adopted, and the throughput attenuation of different resource units is analyzed by calculating the subcarrier signal-to-noise ratio (SNR) in the channel state information (CSI). During the calculation, the channel is divided into RB (Resource Block) units, and the entire bandwidth is divided into subcarrier groups according to different channel widths of 20MHz / 40MHz / 80MHz. The data rate attenuation is predicted by combining the MCS (Modulation and Coding Scheme) parameters. For example, with MCS Index 9, the theoretical throughput can reach 173.3 Mbps with a 20 MHz bandwidth, but under high load conditions, due to subcarrier interference and collisions, the actual throughput drops to 120 Mbps. During the simulation, the Rayleigh fading model is introduced to simulate the throughput of the mobile terminal under different channel conditions, ultimately obtaining resource unit throughput attenuation data. Simultaneously, the risk of adjacent channel cross-interference is quantified based on the load signal frequency band spectrum. The ACIR (Adjacent Channel Interference Ratio) measurement method is used to calculate the inter-channel interference power ratio. Specifically, the OOBE (Out-Of-Band Emission) of the inter-channel signal is analyzed to measure signal leakage and calculate the ACIR value. For example, with a 40 MHz channel bandwidth, an ACIR below 30 dB indicates severe adjacent channel interference, leading to an increased bit error rate (BER). Combining the throughput attenuation, the interference risk of each frequency band is calculated using an interference statistical model, ultimately obtaining adjacent channel cross-interference risk data.

[0072] Step S3: Fit the network congestion increment index based on the packet loss risk probability and resource unit throughput attenuation data to obtain the network congestion increment index; perform channel frequency band planning based on the network congestion increment index to obtain channel frequency band planning data; optimize OFDMA subcarrier allocation on the channel frequency band planning data to obtain OFDMA subcarrier allocation optimization data.

[0073] In this embodiment of the invention, a network congestion increment index is fitted based on the probability of packet loss risk and resource unit throughput attenuation data. An exponential regression analysis method is used to construct a mapping relationship between packet loss rate (PLR) and throughput attenuation. During the data fitting process, PLR data under different load conditions is extracted. For example, when the channel load exceeds 80%, the PLR ​​rises to 8%-10%. The fitting results are used to calculate the network congestion increment index. EMA (Exponential Moving Average) smoothing is used to eliminate data fluctuation interference, resulting in a smooth congestion increment index. Based on the congestion increment index, channel frequency band planning is performed. First, the average load percentage of each channel is calculated, and a channel migration threshold is set. For example, when the load of a certain channel exceeds 75%, a channel switching mechanism is triggered. K-means clustering is used to divide the channels into three categories: high load, medium load, and low load, and dynamic channel allocation is performed on the devices. The DFS (Dynamic Frequency Selection) algorithm is used to perform secondary screening of available channels to avoid radar signal interference. Finally, channel frequency band planning data is generated. OFDMA subcarrier allocation optimization is performed on the channel frequency band planning data using the Hungarian algorithm. The algorithm inputs constraints such as subcarrier utilization, channel load, and throughput requirements to calculate the optimal allocation scheme. For example, under 80MHz channel conditions, WiFi 6 supports a maximum of 234 subcarriers, which are dynamically allocated according to user needs to maximize throughput while ensuring fairness. Finally, the optimized OFDMA subcarrier allocation data is obtained.

[0074] Step S4: Design a routing WiFi 6 data communication strategy based on channel frequency band planning data and OFDMA subcarrier allocation optimization data to obtain the routing WiFi 6 data communication strategy; send the routing WiFi 6 data communication strategy to the routing WiFi 6 control center to execute the WiFi 6 data communication method.

[0075] In this embodiment of the invention, a WiFi 6 data communication strategy is designed based on channel band planning data and OFDMA subcarrier allocation optimization data. A QoS (Quality of Service) priority classification method is used to categorize different data streams; for example, real-time video data has the highest priority and is allocated using a low-latency channel, while ordinary web browsing data uses a lower-priority channel. During strategy formulation, TWT (Target Wake Time) technology is used to optimize the wake-up cycle of terminal devices to reduce power consumption and improve network utilization. After the communication strategy design is completed, the strategy data is sent to the WiFi 6 control center of the router, using the CAPWAP (Control and Provisioning of Wireless Access Points) protocol to ensure that the control center correctly parses and executes the strategy. After the strategy is issued, the router's internal scheduling module performs data forwarding optimization. For example, for real-time streaming media data, MU-MIMO (Multi-User Multiple Input Multiple Output) technology is used to achieve parallel transmission for multiple users and improve communication efficiency. Finally, the WiFi 6 network enters an optimized operating state, achieving efficient data communication.

[0076] Preferably, step S1 includes the following steps:

[0077] Step S11: Obtain WiFi 6 networking control permissions on the router;

[0078] Step S12: Based on the WiFi 6 networking control permissions of the router, collect the operating signal frequency band status among multiple WiFi 6 devices to obtain the operating signal frequency band status dataset;

[0079] Step S13: Analyze the bandwidth occupancy ratio of the running signal frequency band status dataset to obtain the signal frequency band bandwidth occupancy ratio;

[0080] Step S14: Based on the signal band bandwidth occupancy ratio, identify the band load status of the operating signal band using the operating signal band status dataset to obtain the operating signal band load status.

[0081] In this embodiment of the invention, obtaining WiFi 6 network control permissions involves authentication, key negotiation, and remote management permission configuration. First, WPA3-SAE (Simultaneous Authentication of Equals) is used to authenticate the terminal device with the WiFi 6 router. The terminal device sends an authentication request to the WiFi 6 router. The WiFi 6 router verifies the device credentials based on the PBKDF2-HMAC-SHA256 key derivation function and performs a four-way handshake to confirm the legitimacy of the identity. After successful authentication, the WiFi 6 network topology information is obtained via SNMPv3 (Simple Network Management Protocol version 3), and network measurement data is requested based on the IEEE 802.11k protocol, including a list of Access Point (AP) devices, BSSID (Basic Service Set Identifier), and signal strength information. After obtaining the topology data, an access control command is sent to the WiFi 6 router via the TR-069 (Technical Report 069) remote management protocol to enable network control permissions and set CLI (Command Line Interface) level permissions, allowing subsequent frequency band status collection and scheduling optimization operations. After obtaining WiFi 6 network control permissions, the system collects the operating signal frequency band status of multiple WiFi 6 devices (including the main router, Mesh nodes, and repeaters) within the same network. It employs BSS Coloring technology from the IEEE 802.11ax protocol, using different BSS (Basic Service Set) labels to distinguish the signal frequency bands occupied by each WiFi 6 device, avoiding interference and data confusion. Signal acquisition uses the CSI (Channel State Information) method to analyze the LTF (Long Training Field) information sent by different AP devices, calculating channel gain and phase characteristics. Each WiFi 6 device collects channel state information every 10ms, and uses FFT (Fast Fourier Transform) to convert the time-domain signal into frequency-domain data, recording parameters such as RSSI (Received Signal Strength Indicator), SNR (Signal-to-Noise Ratio), and Channel Utilization Time for each signal frequency band.To ensure data integrity, a Timing Synchronization Function (TSF) mechanism is employed to ensure all WiFi 6 devices collect data within the same time window, ultimately generating a multi-router WiFi 6 device operating signal frequency band status dataset. Bandwidth occupancy analysis is then performed on the collected operating signal frequency band status dataset. First, channel utilization data for each WiFi 6 device is statistically analyzed, and a Clear Channel Assessment (CCA) detection mechanism is used to calculate the idle time percentage for each signal frequency band. For each channel, the continuous transmission time and idle time are statistically analyzed, and the bandwidth occupancy ratio is calculated. For example, if a WiFi 6 router operates on an 80MHz channel with a measured occupied time of 70ms and an idle time of 30ms, the bandwidth occupancy ratio is calculated to be 70%. A sliding window filtering method is used to smooth the data, filtering out short-term fluctuation interference, and the average occupancy ratio within different time windows is calculated. For multiple WiFi 6 devices, a time alignment method is used to align the data from different devices to the same time axis, and the average bandwidth occupancy ratio for the entire network is calculated. For devices with overlapping channels, BSS Coloring data is used for interference cancellation to avoid misjudgments between different APs, ultimately obtaining signal band bandwidth occupancy data. Based on the bandwidth occupancy data, the operating signal band status dataset is used for band load status identification. A threshold-based classification method is employed to divide the load status of different signal bands into low load (bandwidth occupancy <50%), medium load (50%-75%), and high load (>75%). Simultaneously, combined with throughput data, a K-means clustering algorithm is used to classify channel load into multiple levels to determine which channels are at risk of overload. During the signal band load status identification process, the average throughput, packet loss rate (PLR), and jitter of each channel are calculated. Channels with a throughput drop exceeding 30% are marked as high load; channels with a PLR greater than 5% undergo further congestion analysis. During the calculation, the SlidingWindow method is used, with a window size of 1 second, recalculating the load status every second to adapt to real-time changes in different network environments. Finally, the load status data of the operating signal frequency band is output to provide a basis for subsequent channel optimization.

[0082] Preferably, step S2 includes the following steps:

[0083] Step S21: Perform load frequency band spectrum conversion on the load status of the operating signal frequency band to obtain the load signal frequency band spectrum;

[0084] Step S22: Identify co-channel interference based on the load signal frequency band spectrum diagram to obtain the co-channel interference frequency band of the load signal;

[0085] Step S23: Simulate the throughput attenuation of resource units based on the load signal frequency band spectrum diagram according to the co-frequency interference band of the load signal, and obtain the throughput attenuation data of resource units;

[0086] Step S24: Perform spectral extension analysis on the co-frequency interference band of the load signal to obtain spectral extension data of the interference signal;

[0087] Step S25: Based on the interference signal spectrum extension data, the adjacent channel cross-interference trigger risk is quantified on the load signal frequency band spectrum map to obtain adjacent channel cross-interference risk data.

[0088] As an example of the present invention, reference is made to... Figure 2 As shown, in this example, step S2 includes:

[0089] Step S21: Perform load frequency band spectrum conversion on the load status of the operating signal frequency band to obtain the load signal frequency band spectrum;

[0090] In this embodiment of the invention, after obtaining the load status of the operating signal frequency band, it is converted into a load signal frequency band spectrum for subsequent interference analysis. First, the time-domain data of the load signal is transformed into frequency domain data using FFT (Fast Fourier Transform), converting the original RSSI (Received Signal Strength Indicator) and SNR (Signal-to-Noise Ratio) data into spectral information. During the FFT processing, the sampling rate is set to 160MHz and the number of sampling points to 4096 to ensure a spectral resolution of 39.06kHz. Subsequently, the FFT-processed data is transformed using STFT (Short-Time Fourier Transform), and the signal is weighted using the Hanning window function to reduce the impact of spectral leakage. The window length is set to 256, and the step size is set to 128 to ensure a balance between time and frequency resolution. In the STFT results, the signal power density (PSD) for each time period is represented in dBm and stored as a two-dimensional matrix, where the horizontal axis represents time, the vertical axis represents frequency, and the matrix values ​​represent the signal strength within a specific time window. Ultimately, a frequency spectrum of the load signal is obtained, providing data support for subsequent signal interference identification.

[0091] Step S22: Identify co-channel interference based on the load signal frequency band spectrum diagram to obtain the co-channel interference frequency band of the load signal;

[0092] In this embodiment of the invention, co-channel interference is identified in the load signal frequency band spectrum, and the EVD (Eigenvalue Decomposition) method is used to analyze the eigenvalue distribution of each frequency band. First, the covariance matrix of the spectrum is extracted, and the ratio of the maximum eigenvalue λ_max to the average eigenvalue λ_avg is calculated. When λ_max / λ_avg > 10, it is determined that there is strong co-channel interference in that frequency band. Simultaneously, the autocorrelation characteristics of the signal are calculated using ACF (Autocorrelation Function), the spectral energy is normalized, and the signal similarity distribution is analyzed. In cases where the signal has strong periodicity and abrupt energy distribution changes, the SVD (Singular Value Decomposition) method is further used to extract the principal component vector of the signal to distinguish between normal service signals and interference signals. For the identified interference signals, the K-means clustering algorithm is used to classify interference from different sources, grouping them according to the center frequency, bandwidth, and power distribution of the interference signals. The co-channel interference frequency bands are stored as a dataset to provide a basis for subsequent throughput attenuation simulation.

[0093] Step S23: Simulate the throughput attenuation of resource units based on the load signal frequency band spectrum diagram according to the co-frequency interference band of the load signal, and obtain the throughput attenuation data of resource units;

[0094] In this embodiment of the invention, resource unit throughput attenuation simulation is performed on the load signal frequency band spectrum diagram based on the identified co-channel interference. WiFi6 OFDMA (Orthogonal Frequency Division Multiple Access) technology is used to divide each channel into multiple RUs (Resource Units), and throughput estimation is performed for the interfered RUs. First, the SNR of the interfered RUs is statistically analyzed, and the upper limit of channel capacity is derived using the Shannon capacity formula. Then, the probability distribution of RU throughput decrease is calculated using the Markov chain modeling method, and a state transition matrix is ​​set, where the state space includes four cases: normal state, slight attenuation, moderate attenuation, and severe attenuation. The state transition probability is dynamically adjusted according to the interference intensity. Furthermore, the LDPC (Low-Density Parity-Check) decoding error rate analysis method is used to evaluate the impact of interference signals on data packet transmission, calculate the retransmission probability, and adjust the modulation scheme based on the MCS (Modulation and Coding Scheme) table to simulate throughput attenuation. Finally, the throughput attenuation results are stored as resource unit throughput attenuation data, providing input for the next step of interference signal spectrum analysis.

[0095] Step S24: Perform spectral extension analysis on the co-frequency interference band of the load signal to obtain spectral extension data of the interference signal;

[0096] In this embodiment of the invention, the spectral extension of the interfering signal is analyzed for the identified co-channel interference bands to assess the potential impact of the interfering signal on adjacent channels. First, the WVD (Wigner-Ville Distribution) time-frequency distribution method is used to perform joint time-frequency analysis on the interfering signal to identify its bandwidth extension. Second, the OBW (Occupied Bandwidth) of the interfering signal is calculated, defined as the bandwidth range covering 99% of the signal energy, using the cumulative power integration method. For signals with OBW exceeding the standard bandwidth (e.g., exceeding 22MHz in a 20MHz channel), their spectral leakage characteristics are further analyzed. Based on this, a filter bank method is used to perform wavelet decomposition on the interfering signal, extracting high-frequency components and calculating their power contribution rate to quantify the high-frequency leakage of the interfering signal. Signals with leakage power higher than -30dBm are marked as high-extension interference, and the spectral extension data of the interfering signal is stored to provide input for the next step of adjacent channel interference risk assessment.

[0097] Step S25: Based on the interference signal spectrum extension data, the adjacent channel cross-interference trigger risk is quantified on the load signal frequency band spectrum map to obtain adjacent channel cross-interference risk data.

[0098] In this embodiment of the invention, the risk of adjacent channel cross-interference is quantified based on the spectral extension data of the interference signal and the frequency band spectrum of the load signal. First, the ACI (Adjacent Channel Interference) coefficient of the interference signal is calculated, and the interference intensity between adjacent channel signals is measured using a cross-correlation method. Then, the signal quality of the adjacent channel receiver is analyzed based on the SINR (Signal-to-Interference-plus-Noise Ratio), with a SINR threshold of 15dB set; adjacent channels below this threshold are considered to be in an interfered state. In the interfered adjacent channels, the bit error rate (BER) is further calculated, and the packet error rate is calculated using an LDPC decoding model, combined with the WiFi 6 MAC layer data retransmission rate for comprehensive evaluation. Finally, a Bayesian risk assessment method is used to classify different adjacent channel interference scenarios, calculating the probability of triggering interference based on parameters such as interference signal power, bandwidth extension, and adjacent channel receiver sensitivity. Adjacent channels with an interference probability exceeding 50% are marked as high-risk, and the adjacent channel cross-interference risk data is stored to support WiFi 6 channel optimization decisions.

[0099] Preferably, step S23 includes the following steps:

[0100] Step S231: Perform interference spectrum bandwidth analysis on the same-frequency interference band of the load signal to obtain the same-frequency interference spectrum bandwidth data;

[0101] Step S232: Based on the co-frequency interference spectrum bandwidth data, identify the signal band bandwidth overlap of the load signal frequency band spectrum to obtain the signal band interference bandwidth overlap data;

[0102] Step S233: Calculate the transient interference frequency density from the overlapping interference bandwidth data of the signal frequency bands to obtain the transient interference frequency density;

[0103] Step S234: Based on the transient interference frequency density, perform interference signal-to-noise ratio increment fitting on the frequency spectrum of the load signal band to obtain interference signal-to-noise ratio increment data;

[0104] Step S235: Simulate the resource unit throughput attenuation based on the transient interference frequency density and interference signal-to-noise ratio increment data to obtain the resource unit throughput attenuation data.

[0105] In this embodiment of the invention, interference spectrum bandwidth analysis is performed on the co-channel interference band of the load signal to obtain the spectral spread of the co-channel interference signal. First, the collected co-channel interference signal is analyzed using FFT (Fast Fourier Transform) with a sampling rate of 160MHz and 4096 FFT points to obtain a spectral resolution of 39.06kHz. The power spectrum data after FFT conversion is stored as a two-dimensional matrix, where the horizontal axis represents frequency and the vertical axis represents signal power intensity. Next, the OBW (Occupied Bandwidth) of the interference signal is calculated using the power integration method, defining OBW as the bandwidth range where the signal energy accumulates to 99%. The FFT results are integrated to find the frequency range covered by the main energy of the signal, thereby determining the main bandwidth of the interference signal. For interference signals with spectral spread, Gaussian filtering is further used to analyze their boundary bandwidth distribution, and the signal power at the boundary frequencies is weighted to obtain the accurate bandwidth range. In addition, to analyze the bandwidth characteristics of different types of interference signals, STFT (Short-Time Fourier Transform) is used to perform time-frequency analysis on the signal to observe the time-varying bandwidth characteristics of the signal. Using a 256-point Hanning window with a step size of 128 points, the bandwidth variation within different time segments was calculated. Finally, co-channel interference spectrum bandwidth data was obtained, providing a foundation for subsequent bandwidth overlap analysis. Based on the acquired co-channel interference spectrum bandwidth data, bandwidth overlap was identified in the load signal frequency band spectrum to quantify the spectral crossover between the interference signal and the load signal. First, the power spectrum data of the load signal frequency band was extracted to obtain the signal power level corresponding to each subcarrier. A threshold method was used to filter out the main signal frequency band range, setting the threshold to 10 dB above the noise baseline. Subsequently, the load signal frequency band and the co-channel interference spectrum bandwidth were matched and analyzed. A cross-correlation calculation method was used to calculate the similarity between the two signal spectra, and the maximum cross-correlation coefficient was taken as the bandwidth overlap metric. When the cross-correlation coefficient exceeded 0.7, it was determined that there was significant bandwidth overlap in the signal frequency band. Based on this, the power distribution of the overlapping bandwidth was calculated using a density estimation method. The overlapping portion of the signal spectrum was integrated to obtain the total power value in that region, and the proportion of this power to the entire signal frequency band was calculated. For overlapping frequency bands exceeding 30%, these are marked as high-interference regions, and the signal band interference bandwidth overlap data is stored to provide input for subsequent transient interference frequency density calculations. Transient interference frequency density calculations are then performed on the signal band interference bandwidth overlap data to analyze the energy distribution of the interference signal at different frequency locations.First, the power spectral density (PSD) of the interference signal is calculated using the Welch power spectral estimation method. A window length of 512 and an overlap rate of 50% are set, and a Hamming window is used to weight the signal to improve frequency resolution and reduce leakage effects. Then, the PSD data of the interference signal is discretized, and energy distribution statistics are performed at 10kHz frequency intervals. Histogram statistics are used to calculate the transient power level at each frequency point and store it as transient interference frequency density data. Further, the Gaussian kernel density estimation (KDE) method is used to smooth the transient interference frequency density data to obtain the main frequency distribution center of the interference signal and calculate its frequency drift range. For signals with a frequency drift exceeding 5MHz, the drift velocity is further calculated to evaluate the dynamic characteristics of the signal's spectrum, providing input for subsequent signal-to-noise ratio (SNR) incremental fitting. Based on the transient interference frequency density data, the interference SNR (Signal-to-Noise Ratio) is incrementally fitted to the load signal frequency band spectrum to analyze the impact of the interference signal on the quality of the effective signal. First, baseline SNR data for the load signal is extracted. A sliding window mean filtering method is used to calculate the background noise level and the baseline SNR value. The window length is set to 128 sampling points, and the step size is 64 points. Then, based on transient interference frequency density data, the interference signal power at different frequency points is superimposed and calculated. An energy normalization method is used to calculate the SNR change amplitude of the interference signal on the load signal. For the signal power on each subcarrier, a logarithmic operation is used to calculate a new SNR value, and an SNR increment data matrix is ​​constructed. The horizontal axis represents frequency, and the vertical axis represents time. Each matrix element stores the SNR change value within the corresponding time window. Subsequently, a polynomial fitting method is used to curve-fit the SNR increment data to obtain the overall trend of the interference signal's influence on SNR. For different interference scenarios, the least squares method is used to calculate the fitting error to ensure the accuracy of the fitted curve. During the fitting process, the highest order is set to 3 to balance fitting accuracy and computational complexity. Finally, the interference signal-to-noise ratio increment data is obtained, providing input for subsequent throughput attenuation simulation. Based on transient interference frequency density and interference signal-to-noise ratio increment data, resource unit (RU) throughput attenuation simulation is performed on the load signal frequency band spectrum to evaluate the impact of interference signals on WiFi 6 network data throughput. First, based on the WiFi 6 OFDMA (Orthogonal Frequency Division Multiple Access) modulation mechanism, the load signal frequency band is divided into multiple RUs, with each RU having a bandwidth of 2MHz.Next, based on incremental SNR data, the upper limit of channel capacity for different RUs is calculated using the Shannon capacity formula, and the modulation scheme is adjusted in conjunction with the MCS (Modulation and Coding Scheme) index table. For RUs with an SNR reduction of more than 3dB, the MCS level is reduced by one; for RUs with an SNR reduction of more than 6dB, the MCS level is reduced by two, and the throughput is recalculated. Subsequently, discrete event simulation is used to simulate packet transmission under different interference conditions. The initial throughput value for each RU is set to 600Mbps, and its throughput change is calculated under different SNR reduction conditions. For RUs with a throughput reduction of more than 50%, their packet error rate is calculated, and the data retransmission ratio is determined. Finally, the throughput attenuation data of the storage resource units is used to provide input for WiFi 6 channel optimization.

[0106] Preferably, step S234 includes the following steps:

[0107] Based on the transient interference frequency density, the transient time-series interference power spectrum is calculated by segmenting the load signal frequency band spectrum to obtain the transient segmented time-series interference power spectrum.

[0108] An amplitude variation trend analysis was performed on the power spectrum of transient segmented time-series interference to obtain the amplitude variation trend of interference power.

[0109] Based on the trend of interference power amplitude variation, the transient segmented time-series interference power spectrum is interpolated and accumulated to obtain interference power interpolation accumulation data.

[0110] Based on the interpolated cumulative data of interference power, the interference signal-to-noise ratio increment is fitted to the transient segmented time-series interference power spectrum to obtain the interference signal-to-noise ratio increment data.

[0111] In this embodiment of the invention, based on transient interference frequency density data, segmented transient time-series interference power spectrum calculation is performed on the load signal frequency band spectrum to obtain the interference power variation within different time segments. First, the load signal frequency band is divided into multiple sub-bands, with a sub-band width set to 1MHz to match the OFDMA (Orthogonal Frequency Division Multiple Access) subcarrier resource allocation method of WiFi 6. Then, a short-time Fourier transform (STFT) is performed on the interference signal in each sub-band, with a window length of 512 sampling points, a Hamming window function, and an overlap rate of 75%. The power spectral density (PSD) within each time window is calculated and stored as a three-dimensional matrix, where the horizontal axis represents frequency, the vertical axis represents time, and the matrix elements store the interference power within the corresponding time window. Subsequently, a time-series energy weighted calculation method is used to weight the transient interference frequency density data of each sub-band, with the weights determined based on the joint distribution of transient frequency density and power spectrum. For interference signals that suddenly increase within a short period, a time-moving mean filtering method is used for smoothing, with a window length of 5 time slices to reduce the impact of sudden signal changes on the overall power spectrum calculation. Finally, transient segmented time-series interference power spectrum data is generated to provide input for subsequent amplitude variation trend analysis. Amplitude variation trend analysis is performed on the transient segmented time-series interference power spectrum data to identify power change patterns within different time segments. First, power spectrum data for each sub-band is extracted, the average interference power within each time segment is calculated, and stored as a time series. Then, the first-order difference method is used to calculate the time rate of change of the power spectrum to measure the increase or decrease trend of the interference signal. For frequency bands with rapid power changes, wavelet transform is further used for multi-scale analysis. The Daubechies 4 (db4) wavelet basis is selected, and the power time series is decomposed into three levels to extract short-term power fluctuation characteristics. Then, based on Empirical Mode Decomposition (EMD), the power time series is decomposed into multiple Intrinsic Mode Functions (IMFs), and the instantaneous amplitude changes of each IMF component are calculated. For sub-bands with amplitude fluctuations exceeding 3dB, their temporal variation trends are recorded and stored as interference power amplitude variation trend data, providing input for subsequent time-series interpolation cumulative integration calculations. Based on the interference power amplitude variation trend data, time-series interpolation cumulative integration is performed on the transient segmented time-series interference power spectrum to calculate the cumulative interference power level. First, time-series data with a sample point interval of 10ms is selected, and a piecewise cubic spline interpolation method is used to perform smooth interpolation calculations on the interference power time series. Subsequently, a time-weighted integration method is used to calculate the cumulative interference power level of each sub-band.A time window length of 100ms is set, and the power value within each window is calculated using an exponentially weighted average, where the weighting coefficients are dynamically adjusted based on the trend of interference power amplitude changes in the previous moment. Sub-bands with a significant power growth trend are assigned higher weights to ensure the accuracy of the cumulative calculation. Then, a numerical integration method is used to integrate the time-interpolated power spectrum of each sub-band to calculate the total interference power over different time periods. For sub-bands where the power growth rate exceeds a threshold (e.g., 5% / ms), a second-order difference calculation is further performed to quantify the impact of sudden interference events. Finally, interference power interpolation cumulative data is generated to provide input for subsequent interference signal-to-noise ratio (SNR) increment fitting. Based on the interference power interpolation cumulative data, the transient segmented time-series interference power spectrum is subjected to interference SNR increment fitting to calculate the impact of the interference signal on the SNR of the WiFi 6 communication channel. First, the baseline SNR data of the WiFi 6 load signal is extracted, the ratio of initial signal power to background noise power is calculated, and stored as a baseline SNR matrix. Then, based on the interpolated cumulative data of interference power, the least squares method is used to fit curves of the interference signal impact at different time segments. The fitting order is set to a third-order polynomial to balance fitting accuracy and computational complexity. During the fitting process, the fitting residuals are calculated, and secondary optimization adjustments are performed on data points with residuals exceeding a threshold (e.g., 0.5 dB) to improve the accuracy of SNR increment fitting. Subsequently, the bandwidth normalization method is used to normalize the interference signal impact of different sub-frequency bands, ensuring the adaptability of the SNR increment data across different frequency ranges. For frequency bands with high interference signal power density, an adaptive step size adjustment strategy is adopted to improve the temporal resolution of the fitted data. Finally, interference signal-to-noise ratio increment data is generated, providing input for WiFi 6 data communication channel optimization.

[0112] Preferably, step S3 includes the following steps:

[0113] Step S31: Estimate the probability of packet loss risk based on the adjacent channel cross-interference risk data to obtain the probability of packet loss risk;

[0114] Step S32: Fit the network congestion increment index based on the packet loss risk probability and resource unit throughput decay data to obtain the network congestion increment index;

[0115] Step S33: Based on data such as packet loss risk probability, resource unit throughput attenuation, and network congestion increment index, perform channel frequency band planning to obtain channel frequency band planning data;

[0116] Step S34: Optimize the OFDMA subcarrier allocation of the channel frequency band planning data to obtain OFDMA subcarrier allocation optimization data.

[0117] As an example of the present invention, reference is made to... Figure 3 As shown, step S3 in this example includes:

[0118] Step S31: Estimate the probability of packet loss risk based on the adjacent channel cross-interference risk data to obtain the probability of packet loss risk;

[0119] In this embodiment of the invention, the probability of packet loss risk is estimated based on adjacent channel cross-interference risk data to quantify the packet loss caused by adjacent channel interference during data transmission in a WiFi 6 communication environment. First, adjacent channel interference intensity data in the WiFi 6 channel environment is acquired. Short-time Fourier Transform (STFT) is used to perform time-frequency analysis on the interference signal, with a window length of 1024 sampling points and an overlap rate of 50%, to calculate the instantaneous interference power spectral density on each subcarrier. Then, for the interference signal on each subcarrier, the Interference-to-Noise Ratio (INR) is calculated. A sliding window statistical analysis method is used, with a window length set to 100ms, to calculate the average INR within different time segments and construct time series data. For time segments where the INR is higher than a specific threshold (e.g., -3dB), the corresponding packet loss rate is statistically analyzed, and the loss probability distribution is fitted using Poisson regression to estimate the packet loss probability under different interference intensities. Subsequently, a Markov chain state transition method is used to establish a state transition matrix for successful packet transmission and loss. The matrix includes four states: "successful transmission," "retransmitted once," "retransmitted twice," and "data packet dropped." The transition probability of each state is calculated based on the statistically obtained data packet loss probability, and the steady-state probability calculation method is used to obtain the data packet loss risk probability. Finally, the calculated data packet loss risk probability is stored to provide input for subsequent network congestion increment index fitting.

[0120] Step S32: Fit the network congestion increment index based on the packet loss risk probability and resource unit throughput decay data to obtain the network congestion increment index;

[0121] In this embodiment of the invention, network congestion increment exponential fitting is performed based on packet loss risk probability and resource unit throughput decay data to quantify the impact of network load growth on the congestion level of the WiFi 6 communication system. First, resource unit (RU) throughput decay data is extracted, and throughput changes under different RU allocation schemes are statistically analyzed. A time sliding window analysis method is used, with a window length set to 500ms, to calculate the mean throughput and construct time series data. Then, the packet loss risk probability and RU throughput decay data are correlated. The Lowess method (Local Regression Weighted Scatter Smoothing) is used to smooth the relationship between packet loss rate and throughput decay, extracting the main trend features. For sudden congestion events causing a sharp drop in throughput, partial least squares regression (PLSR) is used to calculate its impact on overall network throughput, and the fitting weights are adjusted to improve the accuracy of the exponential fitting. Subsequently, based on the changing trend of packet loss risk probability, an exponential regression method is used to construct a network congestion increment exponential function, setting the exponential basis parameter as the throughput decay rate, and using the least squares method to optimize the parameters to minimize the fitting error. For data points where the fitting residual exceeds a preset threshold (e.g., 0.1), a piecewise regression method is used for secondary optimization to improve the model's adaptability. Finally, the network congestion increment index is calculated and stored as data input for subsequent channel frequency band planning.

[0122] Step S33: Based on data such as packet loss risk probability, resource unit throughput attenuation, and network congestion increment index, perform channel frequency band planning to obtain channel frequency band planning data;

[0123] In this embodiment of the invention, channel frequency band planning is performed based on data such as packet loss risk probability, resource unit throughput attenuation, and network congestion increment index to optimize the channel resource allocation strategy of the WiFi 6 communication system. First, a list of available channels in the WiFi 6 network is obtained, and the bandwidth information, interference level, packet loss probability, and throughput attenuation of the current channels are extracted to establish a channel load assessment matrix. Then, the analytic hierarchy process (AHP) is used to calculate the load weight of each channel. Evaluation indicators include channel utilization, interference intensity, throughput attenuation rate, and packet loss probability. The weight distribution of each indicator is determined according to an expert scoring method, and the maximum eigenvalue of the weight matrix is ​​calculated using eigenvalue decomposition to determine the consistency of the matrix. For cases where the consistency ratio (CR) exceeds 0.1, weight adjustments are made to ensure the rationality of the channel assessment results. Subsequently, based on the network congestion increment index, the Dynamic Channel Assignment (DCA) method is used to allocate optimal channels to different load areas. For high-load areas, channels with wider bandwidth (such as 80MHz or 160MHz) are preferentially allocated, and interference avoidance mechanisms are combined to avoid spectrum overlap with adjacent high-power APs. For low-load areas, a tiered bandwidth allocation strategy is adopted, dynamically adjusting channel width based on network traffic to improve spectrum utilization efficiency. Finally, channel band planning data is generated and stored to provide input for subsequent OFDMA subcarrier allocation optimization.

[0124] Step S34: Optimize the OFDMA subcarrier allocation of the channel frequency band planning data to obtain OFDMA subcarrier allocation optimization data.

[0125] In this embodiment of the invention, OFDMA subcarrier allocation optimization is performed on channel frequency band planning data to improve the spectrum utilization and transmission efficiency of the WiFi 6 communication system. First, the bandwidth information of the allocated channels is obtained. According to the WiFi 6 OFDMA subcarrier partitioning standard, each channel is divided into multiple RUs, and the interference level, throughput, and number of serving terminals for each RU are statistically analyzed. Then, an improved Hungarian algorithm is used for subcarrier allocation optimization. First, a subcarrier allocation cost matrix is ​​constructed, using packet loss rate, throughput attenuation rate, and terminal service requirements as optimization objectives. The cost function under different RU allocation schemes is calculated, and the optimal subcarrier allocation scheme is calculated using the Hungarian algorithm. For high-load RUs, larger bandwidth (e.g., RUs with 106 subcarriers) is prioritized, and frequency hopping technology is used to dynamically adjust the subcarrier allocation scheme at different time slices to reduce co-channel interference. Subsequently, for multi-user concurrent transmission scenarios, a dynamic scheduling method is used to optimize subcarrier allocation. Based on the traffic requirements of the serving terminals, the optimal number of RUs for each terminal is calculated, and the RU allocation strategy is dynamically adjusted in conjunction with Channel State Information (CSI). For terminals with poor CSI, the number of RUs allocated is reduced to mitigate the impact of interference on throughput. For terminals with good CSI, the number of RUs allocated is increased to improve overall network throughput. Finally, combined with the time allocation mechanism, a time slot allocation optimization algorithm is adopted to minimize scheduling delay and optimize the allocation of time slots for different terminals. The transmission delay requirements of different terminals are calculated, and the scheduling priority of each terminal is dynamically adjusted according to the Max-Min Fairness principle. For high-priority terminals, shorter scheduling periods are allocated to reduce transmission delay. Finally, the OFDMA subcarrier allocation optimization data is calculated and stored as the final channel resource configuration scheme for the WiFi 6 data communication system.

[0126] Preferably, step S32 includes the following steps:

[0127] Step S321: Perform correlation coefficient analysis on traffic load increment based on packet loss risk probability and resource unit throughput attenuation data to obtain traffic load increment coefficient;

[0128] Step S322: Based on the resource unit throughput attenuation data and traffic load increment coefficient, evaluate the increase in resource retransmission load to obtain the increase in resource retransmission load data.

[0129] Step S323: Perform frequency band channel polling contention rate increase analysis on the resource retransmission load increase data to obtain channel polling contention rate increase data;

[0130] Step S324: Fit the network congestion increment index based on the resource retransmission load increase data and the channel polling contention rate increase data to obtain the network congestion increment index.

[0131] In this embodiment of the invention, correlation coefficient analysis of incremental traffic load is performed based on packet loss risk probability and resource unit throughput decay data to quantify the impact of increased network load on resource unit throughput. First, historical packet loss risk probability sequences are extracted and sorted by timestamp, with a time granularity set to 1 second, to obtain the packet loss rate within each time segment. Then, resource unit (RU) throughput decay data is extracted, and the throughput decay trend within different time segments is calculated using the exponential moving average method. Subsequently, Pearson correlation analysis is used to calculate the correlation between packet loss risk probability and resource unit throughput decay. First, the two sets of data are normalized to eliminate the influence of dimensions, then their covariance is calculated and normalized to a correlation coefficient matrix. For correlation coefficients greater than 0.7, partial correlation analysis is further used to eliminate the influence of other potential factors, ensuring that the calculated correlation accurately reflects the direct relationship between packet loss risk probability and resource unit throughput decay. Then, Dynamic Time Warping (DTW) is used to calculate the temporal matching degree between packet loss risk probability and throughput decay data to determine the lag effect between the two. For cases with a time lag exceeding 2 seconds, the lag correction value is calculated, and the correlation coefficient calculation method is adjusted to improve the accuracy of time series matching. Finally, the traffic load increment coefficient is obtained and stored as data input for subsequent resource retransmission load increase assessment. Based on resource unit throughput attenuation data and the traffic load increment coefficient, a resource retransmission load increase assessment is performed to analyze the retransmission pressure on resource units caused by increased network load. First, resource unit throughput attenuation data is extracted, the time window is set to 500ms, the throughput rate decrease trend within each time segment is statistically analyzed, and the throughput attenuation slope is calculated. Cases with a throughput attenuation slope greater than a preset threshold (e.g., -10Mbps / s) are marked as high-load states. Then, combined with the traffic load increment coefficient, a multivariate regression analysis method is used to calculate the impact of throughput attenuation rate on data retransmission. First, a retransmission load prediction model is constructed, using the throughput attenuation slope, traffic load increment coefficient, and historical data packet loss rate as input variables. The ridge regression method is used to calculate the regression coefficients of each variable, and the least squares method is used to optimize the regression parameters to minimize the error. Subsequently, a time series anomaly detection method is used to identify throughput attenuation anomalies. For cases where throughput attenuation exceeds the expected range (e.g., 20%), an ARIMA (Autoregressive Integral Moving Average) model is used for trend prediction. This model calculates the throughput attenuation rate in the near future and combines the prediction results to calculate the upward trend of resource retransmission load. Cases where the predicted retransmission load increase exceeds 20% are marked as resource-scarce and the resource retransmission load increase data is stored for subsequent analysis of channel polling contention rate increases.To assess the changing trend of channel resource contention in the WiFi 6 communication environment, we analyzed the increase in resource retransmission load data and performed frequency band channel polling contention rate analysis. First, we extracted the resource retransmission load increase data, sorted it chronologically, and calculated the resource occupancy rate for each time segment. The time window was set to 100ms, and the channel utilization rate after throughput attenuation was calculated. Channel utilization exceeding 80% was marked as a high contention state. Next, we acquired frequency band channel polling request data, counted the number of contention requests in each time segment, and calculated the polling request growth rate using an exponential smoothing method. For growth rates exceeding 10%, we further calculated the contention success rate and used logistic regression to evaluate the probability of successful contention. Subsequently, we used an autoregressive moving average (ARMA) model to predict channel polling contention in the near future. First, we constructed time series data, using the number of channel contention requests as input variables, and used the ARMA model to calculate the contention growth trend within the next 500ms. For predicted contention rate increases exceeding 15%, we stored the channel polling contention rate increase data to provide input for subsequent network congestion increment exponential fitting. Based on the data on rising resource retransmission load and rising channel polling contention rate, a network congestion increment index is fitted to quantify the network congestion trend in the WiFi 6 communication environment. First, the data on rising resource retransmission load and rising channel polling contention rate are extracted and normalized to the [0,1] interval using a standardization method to ensure data dimensionality consistency. Then, Principal Component Analysis (PCA) is used to extract key influencing factors. First, the covariance matrix between variables is calculated, and eigenvalue decomposition is performed to extract principal components with a cumulative variance contribution rate exceeding 85%. For components with low variance contribution rates, dimensionality reduction is performed to reduce data redundancy and improve fitting efficiency. Subsequently, an exponential regression method is used to fit the network congestion increment index. First, an exponential regression equation is constructed, with the rate of increase in resource retransmission load and the rate of increase in channel polling contention rate as independent variables. The parameters of the exponential function are calculated using nonlinear least squares. For data points with a fitting error exceeding 5%, a weighted regression method is used for secondary optimization to improve fitting accuracy. Finally, the changing trend of the network congestion increment index was calculated using the sliding window method, with the window size set to 1 second. The rate of change of the index was calculated and the fitting results were stored as key data for dynamic resource scheduling of the WiFi 6 communication system.

[0132] Preferably, step S33 includes the following steps:

[0133] Step S331: Based on data such as packet loss risk probability, resource unit throughput attenuation, and network congestion increment index, perform weighted fusion processing to obtain weighted data of influencing factors;

[0134] Step S332: Based on the impact factor weighted data, dynamically allocate channel bandwidth using data such as packet loss risk probability and resource unit throughput attenuation to obtain dynamic channel bandwidth allocation data;

[0135] Step S333: Perform adaptive channel spacing selection on the channel bandwidth dynamic allocation data to obtain adaptive channel spacing data;

[0136] Step S334: Perform channel load balancing allocation processing on the network congestion increment index based on the influence factor weighted data to obtain channel load balancing allocation data;

[0137] Step S335: Based on the channel bandwidth dynamic allocation data, channel adaptive interval data, and channel load balancing allocation data, perform channel frequency band planning to obtain channel frequency band planning data.

[0138] In this embodiment of the invention, a weighted fusion process is performed based on data such as packet loss risk probability, resource unit throughput attenuation, and network congestion increment index to calculate the comprehensive effect of different influencing factors on channel resource allocation. First, network status data from the past 30 minutes is extracted from historical data records, including packet loss risk probability sequences, resource unit throughput attenuation data, and network congestion increment index, and sorted by timestamp to ensure time alignment. Then, the maximum-minimum normalization method is used to normalize the three sets of data, transforming all data to the [0,1] interval to eliminate the influence of different data dimensions. Subsequently, the Analytic Hierarchy Process (AHP) is used to calculate the weights of each influencing factor. First, an influencing factor weight matrix is ​​constructed, and the consistency ratio (CR) is calculated to ensure the matrix meets the consistency requirement (CR < 0.1). Then, the weights of the influencing factors are adjusted based on historical network data to dynamically adapt to changes in the network environment. For example, in high-congestion scenarios, the weight of the network congestion increment index is increased from 0.3 to 0.5, while the weights of data such as packet loss risk probability and resource unit throughput attenuation are adjusted from 0.4 and 0.3 to 0.3 and 0.2, respectively. Next, a weighted summation method is used to calculate the weighted data of the impact factors. First, the normalized data is weighted according to the weight matrix, and a moving average method is used to calculate the smoothed value within the time window to reduce the impact of short-term fluctuations. For example, if the packet loss risk probability in the past 10 seconds is 0.2, the resource unit throughput attenuation is 0.35, and the network congestion increment index is 0.6, then under the current weight settings, the calculated weighted data of the impact factors is 0.365. Finally, the calculated weighted data of the impact factors is stored as input data for dynamic channel bandwidth allocation. Based on the weighted data of the impact factors, dynamic channel bandwidth allocation is performed on data such as packet loss risk probability and resource unit throughput attenuation to optimize bandwidth utilization efficiency in the WiFi 6 communication system. First, the weighted data of the influence factors over the past 10 seconds is extracted and sorted by timestamp, and the trend of the weighted data is calculated. Then, the current bandwidth utilization of each channel is obtained, including the throughput rate, channel utilization, and time slot occupancy of each channel, and the channel bandwidth utilization trend is calculated using the exponential moving average method. For example, if the current channel utilization is 85% and the average utilization over the past 5 seconds is 82%, the calculated bandwidth utilization trend is upward. Next, an adaptive weighted allocation algorithm is used to dynamically adjust the channel bandwidth. First, the available bandwidth of each channel is calculated, and the total available bandwidth is allocated according to the proportion of the weighted data of the influence factors. For example, if the total available bandwidth is 160MHz and the influence factor weighted data of a certain channel is 0.4, then the allocated bandwidth of that channel is adjusted to 64MHz. For channels with bandwidth utilization exceeding 90%, their allocated bandwidth is further reduced, and the bandwidth allocation of other low-utilization channels is increased.Finally, a time window method is used to update the channel bandwidth allocation data, adjusting the bandwidth allocation value every second, and storing the final dynamic channel bandwidth allocation data for adaptive channel spacing selection. Adaptive channel spacing selection is performed on the dynamic channel bandwidth allocation data to adjust the channel spacing strategy in the WiFi 6 communication system and optimize channel multiplexing efficiency. First, the dynamic channel bandwidth allocation data from the past 5 seconds is extracted, and the bandwidth fluctuation range of each channel is calculated. For example, if the bandwidth of a channel fluctuates between 20MHz and 40MHz in the past 5 seconds, then this channel is considered a high-fluctuation channel. Then, the interference situation of each channel is obtained, including co-channel interference and adjacent channel interference, and the Fast Fourier Transform (FFT) method is used to analyze the spectral characteristics of channel interference. For example, if the interference signal of a certain channel is mainly concentrated around the center frequency of 5.18GHz, then the available spacing of this channel should be adjusted to a larger spacing to reduce the impact of interference. Next, the K-means clustering algorithm is used to classify the channel spacing, clustering all channels according to the dynamic bandwidth allocation data and interference situation. For example, channels with less interference and lower bandwidth fluctuation range are grouped into one category and assigned a smaller channel spacing (e.g., 20MHz); channels with greater interference and higher bandwidth fluctuation range are grouped into another category and assigned a larger channel spacing (e.g., 40MHz). Finally, the channel spacing configuration is updated, and the adaptive channel spacing data is stored for subsequent channel load balancing allocation. Based on the weighted data of influencing factors, channel load balancing allocation is performed on the network congestion increment index to optimize the load distribution in the WiFi 6 communication system. First, the weighted data of influencing factors over the past 10 seconds is extracted, and the balance of channel load distribution is calculated. Then, the current load of each channel is obtained, including throughput, channel utilization, and time slot occupancy, and a weighted smoothing method is used to calculate the channel load change trend. For example, if the utilization of a channel increases from 70% to 90%, the channel is marked as an overloaded channel. Next, the minimum cost path algorithm is used to perform load balancing calculations in a multi-channel environment. First, a channel load graph is constructed, with each channel as a node, the load transfer cost between channels as the edge weight, and Dijkstra's algorithm is used to calculate the optimal load allocation path. For example, if a channel is overloaded, the minimum-cost channel migration path is calculated, and some of the load is migrated to a less loaded channel. Finally, the load allocation strategy for each channel is adjusted, and channel load balancing allocation data is stored for use in channel frequency band planning. Channel frequency band planning is performed based on dynamic channel bandwidth allocation data, adaptive channel spacing data, and channel load balancing allocation data to optimize the frequency band usage strategy of the WiFi 6 communication system. First, the above data is extracted, and the currently available frequency band resources are calculated. Then, the usage of each channel is obtained, including channel utilization and dynamic bandwidth allocation, and the channels are grouped using a hierarchical clustering method.For example, channels with low load and stable bandwidth are grouped into one category and preferentially allocated to narrower frequency bands (such as 20MHz); channels with high load and large bandwidth fluctuations are grouped into another category and allocated to wider frequency bands (such as 80MHz). Finally, the channel frequency band planning configuration is updated and the channel frequency band planning data is stored to ensure that the frequency band utilization efficiency of the WiFi 6 system is maximized.

[0139] Preferably, step S34 includes the following steps:

[0140] Step S341: Perform subcarrier availability analysis on the channel frequency band planning data to obtain subcarrier availability data;

[0141] Step S342: Evaluate the interference level of each subcarrier based on the subcarrier availability data to obtain interference level data for each subcarrier;

[0142] Step S343: Perform channel bandwidth classification processing based on the interference level data and subcarrier availability data of each subcarrier to obtain channel bandwidth classification data;

[0143] Step S344: Based on the interference level data, subcarrier availability data and channel bandwidth classification data of each subcarrier, perform peak-to-average power output matching between frequency band channels to obtain peak-to-average power output matching data;

[0144] Step S345: Based on the channel bandwidth classification data and peak-to-average power output matching data, perform OFDMA subcarrier allocation optimization on the channel frequency band planning data to obtain OFDMA subcarrier allocation optimization data.

[0145] In this embodiment of the invention, the channel band planning data includes multiple frequency bands, and the number of subcarriers in each frequency band is divided according to the Orthogonal Frequency Division Multiple Access (OFDMA) technology of WiFi 6. After acquiring the channel band planning data, all subcarriers are first scanned, and the signal power level of each subcarrier is measured using a high-precision spectrum analyzer, and its current occupancy status is recorded. For subcarriers already occupied by other devices, the carrier power threshold is extracted by analyzing its power density spectrum (PSD) curve, and combined with the minimum detectable signal (MDS) parameter, the actual availability of the subcarrier is identified. For unoccupied subcarriers, the time-frequency characteristics of the subcarrier are calculated by short-time Fourier transform (STFT) to determine whether the subcarrier is affected by sudden interference. In this process, the Energy Detection (ED) method is used, a detection threshold is set, and available subcarriers are screened based on the detected energy level. Finally, all available subcarriers are counted to form subcarrier availability data, which records the number, center frequency, bandwidth range, and availability status of the available subcarriers. Using the subcarrier availability data obtained in step S341, the interference level of each subcarrier is quantitatively assessed. First, based on a real-time spectrum monitoring system, long-term signal power statistics are performed on each available subcarrier, calculating its average power, power variance, and instantaneous interference peak value. Second, an Adaptive Noise Estimation (ANE) method is used to calculate the noise power level of the subcarrier through a sliding window and extract a background noise model. For subcarriers with high interference intensity, correlation analysis is further employed to detect whether they are affected by neighboring subcarriers or external interference sources. Specifically, the Cross-Correlation Function (CCF) is used to calculate the correlation coefficient between adjacent subcarriers, and Kalman filtering is combined to predict burst interference. Finally, based on the interference level classification criteria, the interference levels of each subcarrier are categorized, forming interference level data for each subcarrier. This data includes parameters such as the subcarrier's interference power level, noise impact factor, interference category, and duration of impact. Channel bandwidth is then graded according to the subcarrier's interference level and availability. First, the available subcarriers are sorted according to their center frequencies. Based on interference level data, subcarriers with lower interference intensity are preferentially combined to form a wider channel bandwidth, while subcarriers with higher interference intensity are allocated only narrow bandwidth. A hierarchical clustering method is used, with the channel gain difference between subcarriers as the clustering criterion, to group the subcarriers.For subcarriers with high channel gain and low interference, frequency resource pooling is used to allocate them to higher-priority bandwidth resource groups and assign them a larger power weight. For subcarriers more susceptible to interference, bandwidth segmentation is used to divide them into isolation zones or redundant subcarriers to reduce the impact of interference on data transmission. Finally, channel bandwidth classification data is generated, including information such as subcarrier groups at different bandwidth levels, center frequencies, number of subcarriers, and bandwidth weight parameters. Based on the channel bandwidth classification data generated in step S343, the power output between each frequency band is adjusted to meet the peak-to-average power ratio (PAPR) matching requirement. First, for each bandwidth level of subcarrier group, the ratio of peak power to average power is calculated, and the power spectral density distribution is obtained using Fast Fourier Transform (FFT), extracting the maximum power peak. Then, clipping and filtering methods are used to adjust the power of subcarriers whose power peak exceeds a set threshold, while nonlinear equalization is used to compensate for signal distortion caused by clipping. For power allocation between different frequency bands, an Adaptive Power Control (APC) algorithm is adopted. Based on the PAPR calculation results of each frequency band, the power output of each band is dynamically adjusted to achieve power balance and avoid interference from high-power bands to low-power bands. Finally, peak-to-average power output matching data is generated, which includes the peak power, average power, adjusted power allocation parameters, and PAPR optimization coefficients for each subcarrier. Based on the channel bandwidth classification data and PAPR output matching data obtained in steps S343 and S344, the OFDMA subcarrier allocation scheme is optimized. First, all available subcarriers are obtained, and the priority of each subcarrier group is determined based on the bandwidth classification data. A binary search method is used to optimally allocate subcarriers while meeting interference control and power allocation requirements. Specifically, during the OFDMA resource block allocation process, the Subcarrier Dynamic Scheduling (SDS) method is used to allocate subcarrier resources with minimal interference and optimal power to high-priority users. For low-priority users, a low-power subcarrier allocation strategy is adopted, and time multiplexing is used to reduce the impact on high-priority users. In addition, during the subcarrier allocation process, an adaptive channel estimation method is used to adjust the allocation scheme in real time based on the current subcarrier signal-to-noise ratio and bandwidth requirements.Ultimately, OFDMA subcarrier allocation optimization data is generated, which records information such as subcarrier allocation status, bandwidth occupancy, power allocation parameters, and dynamic adjustment strategies for each user.

[0146] Preferably, the present invention also provides a WiFi 6-based data communication system for performing the WiFi 6-based data communication method described above, the WiFi 6-based data communication system comprising:

[0147] The frequency band load status identification module is used to obtain router WiFi6 networking control permissions; based on the router WiFi6 networking control permissions, it collects the operating signal frequency band status to obtain the operating signal frequency band status dataset; and performs frequency band load status identification on the operating signal frequency band status dataset to obtain the operating signal frequency band load status.

[0148] The interference risk quantification module is used to convert the load frequency band spectrum diagram of the operating signal frequency band to obtain the load signal frequency band spectrum diagram; to simulate the resource unit throughput attenuation of the load signal frequency band spectrum diagram to obtain the resource unit throughput attenuation data; and to quantify the adjacent channel cross-interference trigger risk of the load signal frequency band spectrum diagram to obtain the adjacent channel cross-interference risk data.

[0149] The allocation optimization module is used to fit the network congestion increment index based on the probability of packet loss risk and resource unit throughput attenuation data to obtain the network congestion increment index; to perform channel frequency band planning based on the network congestion increment index to obtain channel frequency band planning data; and to perform OFDMA subcarrier allocation optimization on the channel frequency band planning data to obtain OFDMA subcarrier allocation optimization data.

[0150] The communication strategy design module is used to design a routing WiFi6 data communication strategy based on channel frequency band planning data and OFDMA subcarrier allocation optimization data, thereby obtaining the routing WiFi6 data communication strategy; and to send the routing WiFi6 data communication strategy to the routing WiFi6 control center to execute the WiFi6 data communication method.

[0151] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A data communication method based on WiFi 6, characterized in that, Includes the following steps: Step S1: Obtain WiFi 6 networking control permissions on the router; Based on the WiFi 6 networking control permissions of the router, the operating signal frequency band status is collected to obtain the operating signal frequency band status dataset; the frequency band load status is identified from the operating signal frequency band status dataset to obtain the operating signal frequency band load status. Step S2: Perform load frequency band spectrum conversion on the load status of the operating signal frequency band to obtain the load signal frequency band spectrum; perform resource unit throughput attenuation simulation on the load signal frequency band spectrum to obtain resource unit throughput attenuation data; perform adjacent channel cross-interference trigger risk quantification on the load signal frequency band spectrum to obtain adjacent channel cross-interference risk data. Step S3: Fit the network congestion increment index based on the packet loss risk probability and resource unit throughput attenuation data to obtain the network congestion increment index; perform channel frequency band planning based on the network congestion increment index to obtain channel frequency band planning data; optimize OFDMA subcarrier allocation on the channel frequency band planning data to obtain OFDMA subcarrier allocation optimization data. Step S4: Design a routing WiFi 6 data communication strategy based on channel frequency band planning data and OFDMA subcarrier allocation optimization data to obtain the routing WiFi 6 data communication strategy; Send the WiFi 6 data communication policy of the router to the WiFi 6 control center to execute the WiFi 6 data communication method.

2. The data communication method based on WiFi 6 according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain WiFi 6 networking control permissions on the router; Step S12: Based on the WiFi 6 networking control permissions of the router, collect the operating signal frequency band status among multiple WiFi 6 devices to obtain the operating signal frequency band status dataset; Step S13: Analyze the bandwidth occupancy ratio of the running signal frequency band status dataset to obtain the signal frequency band bandwidth occupancy ratio; Step S14: Based on the signal band bandwidth occupancy ratio, identify the band load status of the operating signal band using the operating signal band status dataset to obtain the operating signal band load status.

3. The data communication method based on WiFi 6 according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Perform load frequency band spectrum conversion on the load status of the operating signal frequency band to obtain the load signal frequency band spectrum; Step S22: Identify co-channel interference based on the load signal frequency band spectrum diagram to obtain the co-channel interference frequency band of the load signal; Step S23: Simulate the throughput attenuation of resource units based on the load signal frequency band spectrum diagram according to the co-frequency interference band of the load signal, and obtain the throughput attenuation data of resource units; Step S24: Perform spectral extension analysis on the co-frequency interference band of the load signal to obtain spectral extension data of the interference signal; Step S25: Based on the interference signal spectrum extension data, the adjacent channel cross-interference trigger risk is quantified on the load signal frequency band spectrum map to obtain adjacent channel cross-interference risk data.

4. The data communication method based on WiFi 6 according to claim 3, characterized in that, Step S23 includes the following steps: Step S231: Perform interference spectrum bandwidth analysis on the same-frequency interference band of the load signal to obtain the same-frequency interference spectrum bandwidth data; Step S232: Based on the co-frequency interference spectrum bandwidth data, identify the signal band bandwidth overlap of the load signal frequency band spectrum to obtain the signal band interference bandwidth overlap data; Step S233: Calculate the transient interference frequency density from the overlapping interference bandwidth data of the signal frequency bands to obtain the transient interference frequency density; Step S234: Based on the transient interference frequency density, perform interference signal-to-noise ratio increment fitting on the frequency spectrum of the load signal band to obtain interference signal-to-noise ratio increment data; Step S235: Simulate the resource unit throughput attenuation based on the transient interference frequency density and interference signal-to-noise ratio increment data to obtain the resource unit throughput attenuation data.

5. The data communication method based on WiFi 6 according to claim 4, characterized in that, Step S234 includes the following steps: Based on the transient interference frequency density, the transient time-series interference power spectrum is calculated by segmenting the load signal frequency band spectrum to obtain the transient segmented time-series interference power spectrum. An amplitude variation trend analysis was performed on the power spectrum of transient segmented time-series interference to obtain the amplitude variation trend of interference power. Based on the trend of interference power amplitude variation, the transient segmented time-series interference power spectrum is interpolated and accumulated to obtain interference power interpolation accumulation data. Based on the interpolated cumulative data of interference power, the interference signal-to-noise ratio increment is fitted to the transient segmented time-series interference power spectrum to obtain the interference signal-to-noise ratio increment data.

6. The data communication method based on WiFi 6 according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Estimate the probability of packet loss risk based on the adjacent channel cross-interference risk data to obtain the probability of packet loss risk; Step S32: Fit the network congestion increment index based on the packet loss risk probability and resource unit throughput decay data to obtain the network congestion increment index; Step S33: Based on the probability of packet loss risk, resource unit throughput attenuation data, and network congestion increment index, perform channel frequency band planning to obtain channel frequency band planning data; Step S34: Optimize the OFDMA subcarrier allocation of the channel frequency band planning data to obtain OFDMA subcarrier allocation optimization data.

7. The data communication method based on WiFi 6 according to claim 6, characterized in that, Step S32 includes the following steps: Step S321: Perform correlation coefficient analysis on traffic load increment based on packet loss risk probability and resource unit throughput attenuation data to obtain traffic load increment coefficient; Step S322: Based on the resource unit throughput attenuation data and traffic load increment coefficient, evaluate the increase in resource retransmission load to obtain the increase in resource retransmission load data. Step S323: Perform frequency band channel polling contention rate increase analysis on the resource retransmission load increase data to obtain channel polling contention rate increase data; Step S324: Fit the network congestion increment index based on the resource retransmission load increase data and the channel polling contention rate increase data to obtain the network congestion increment index.

8. The data communication method based on WiFi 6 according to claim 7, characterized in that, Step S33 includes the following steps: Step S331: Perform weighted fusion processing based on packet loss risk probability, resource unit throughput attenuation data, and network congestion increment index to obtain weighted data of influencing factors; Step S332: Based on the impact factor weighted data, dynamically allocate channel bandwidth according to the probability of packet loss risk and the throughput attenuation data of resource unit, and obtain dynamic channel bandwidth allocation data; Step S333: Perform adaptive channel spacing selection on the channel bandwidth dynamic allocation data to obtain adaptive channel spacing data; Step S334: Perform channel load balancing allocation processing on the network congestion increment index based on the influence factor weighted data to obtain channel load balancing allocation data; Step S335: Based on the channel bandwidth dynamic allocation data, channel adaptive interval data, and channel load balancing allocation data, perform channel frequency band planning to obtain channel frequency band planning data.

9. The data communication method based on WiFi 6 according to claim 7, characterized in that, Step S34 includes the following steps: Step S341: Perform subcarrier availability analysis on the channel frequency band planning data to obtain subcarrier availability data; Step S342: Evaluate the interference level of each subcarrier based on the subcarrier availability data to obtain interference level data for each subcarrier; Step S343: Perform channel bandwidth classification processing based on the interference level data and subcarrier availability data of each subcarrier to obtain channel bandwidth classification data; Step S344: Based on the interference level data, subcarrier availability data and channel bandwidth classification data of each subcarrier, perform peak-to-average power output matching between frequency band channels to obtain peak-to-average power output matching data; Step S345: Based on the channel bandwidth classification data and peak-to-average power output matching data, perform OFDMA subcarrier allocation optimization on the channel frequency band planning data to obtain OFDMA subcarrier allocation optimization data.

10. A data communication system based on WiFi 6, characterized in that, For performing the WiFi 6-based data communication method as described in claim 1, the WiFi 6-based data communication system includes: The frequency band load status identification module is used to obtain router WiFi6 networking control permissions; based on the router WiFi6 networking control permissions, it collects the operating signal frequency band status to obtain the operating signal frequency band status dataset; and performs frequency band load status identification on the operating signal frequency band status dataset to obtain the operating signal frequency band load status. The interference risk quantification module is used to convert the load frequency band spectrum diagram of the operating signal frequency band to obtain the load signal frequency band spectrum diagram; to simulate the resource unit throughput attenuation of the load signal frequency band spectrum diagram to obtain the resource unit throughput attenuation data; and to quantify the adjacent channel cross-interference trigger risk of the load signal frequency band spectrum diagram to obtain the adjacent channel cross-interference risk data. The allocation optimization module is used to fit the network congestion increment index based on the probability of packet loss risk and resource unit throughput attenuation data to obtain the network congestion increment index; to perform channel frequency band planning based on the network congestion increment index to obtain channel frequency band planning data; and to perform OFDMA subcarrier allocation optimization on the channel frequency band planning data to obtain OFDMA subcarrier allocation optimization data. The communication strategy design module is used to design a routing WiFi6 data communication strategy based on channel frequency band planning data and OFDMA subcarrier allocation optimization data, thereby obtaining the routing WiFi6 data communication strategy; and to send the routing WiFi6 data communication strategy to the routing WiFi6 control center to execute the WiFi6 data communication method.

Images