Wireless networking method and system for router and relay equipment

Abstract

The invention discloses a wireless networking method and system for a router and a relay device, and relates to the technical field of wireless communication, and the method comprises the following steps: the relay device firstly receives a wireless signal from the router, and amplifies the wireless signal to extend the network coverage range; by intelligently evaluating the signal quality and adjusting the amplification strategy, the scheme effectively avoids distortion caused by excessive signal amplification, reduces data packet loss, communication interruption and bandwidth efficiency reduction, improves the network signal quality and stability, and particularly guarantees the stability of real-time application under high load. In addition, by dynamically adjusting the amplification range, network resource utilization is optimized, network performance is improved, the coverage range is expanded, connection of more devices is supported, meanwhile, efficient bandwidth utilization is guaranteed, and expansibility and reliability of the network are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a wireless networking method and system for routers and relay devices. Background Art

[0002] Wireless networking of routers and relay devices refers to connecting routers and relay devices together through wireless signals to form an extended wireless network system. The router is responsible for providing network access and data routing functions, while the relay device, as an extended node of the network, receives and forwards the router's signal wirelessly, thereby expanding the network coverage or improving the signal quality. In this networking method, the relay device can improve the signal strength in areas far away from the router, reduce the dead angle or blind spot of the wireless signal, and ensure that the device can stably connect to the network. It is suitable for wireless network deployment in large areas or complex environments. This method eliminates the limitations of traditional wired networks through wireless connections and achieves more flexible and efficient network coverage.

[0003] The prior art has the following deficiencies: Relay devices usually extend network coverage by amplifying received signals, especially when covering a large area, trying to transmit signals far away from the router. However, if the signal amplification process is not effectively controlled, it may cause the amplifier to saturate and produce signal distortion. In this case, the receiving sensitivity of the device may exceed the normal range, and the output of the amplifier cannot match the quality of the input signal, resulting in a decrease in signal quality. Signal distortion affects the accuracy of data transmission and reduces bandwidth efficiency, especially when the network load is high, the data traffic increases, the signal distortion increases, and the probability of data packet loss increases. Lost data cannot be corrected or retransmitted in time, resulting in information loss, which in turn affects the reliability of the network. This signal distortion can cause communication interruption, especially in sensitive applications such as real-time video and voice calls that have extremely high requirements for network quality, which seriously affects the user experience; at the same time, during file transmission, signal distortion may cause file content to be damaged or lost, resulting in business interruption and data integrity issues.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The purpose of the present invention is to provide a wireless networking method and system for routers and relay devices. By intelligently evaluating signal quality and adjusting amplification strategies, this solution effectively avoids distortion caused by excessive signal amplification, reduces data packet loss, communication interruption, and bandwidth efficiency reduction, improves network signal quality and stability, and ensures the stability of real-time applications under high load. In addition, by dynamically adjusting the amplification range, network resource utilization is optimized, network performance is improved, coverage is expanded, and more device connections are supported, while ensuring efficient bandwidth utilization, enhancing the scalability and reliability of the network, so as to solve the problems in the above-mentioned background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a wireless networking method of a router and a relay device, comprising the following steps: The relay device first receives the wireless signal from the router and amplifies it to extend the network coverage; During the signal amplification process, real-time monitoring and acquisition of data related to signal quality; Pre-process the acquired signal quality data to improve the accuracy and reliability of the data; Extract key features reflecting signal quality from preprocessed signal data, analyze the extracted key features under the detection window, and quantify changes in signal quality; The analyzed features are input into the pre-learned support vector machine model, and the signal quality features after analysis are intelligently evaluated through the support vector machine model; Based on the evaluation results of the support vector machine model, the signal quality changes are divided into "normal amplification signal" and "amplification distortion signal"; When the signal is in the "amplified distorted signal" state, the amplification range is automatically reduced to prevent the signal from being over-amplified and further distorted, to ensure that the signal is amplified within a reasonable range; when the signal is in the "normal amplified signal" state, the current amplification strategy will be continued, while continuously monitoring the signal quality to ensure the stability and continuity of the amplification process.

[0007] Preferably, key features reflecting signal quality are extracted from the preprocessed signal data, the extracted features including the periodicity and amplitude variation of the signal and the degree of amplifier gain saturation. Within the detection window, the periodicity and amplitude variation of the signal and the degree of amplifier gain saturation are analyzed to generate an autocorrelation distortion factor and a gain saturation factor, respectively. The autocorrelation distortion factor is used to quantify the stability of the periodic variation of the signal, and the gain saturation factor quantifies the degree to which the amplifier enters a saturation state during the amplification process, reflecting the situation when the amplifier can no longer effectively amplify the signal.

[0008] Preferably, the specific steps of analyzing the periodicity and amplitude changes of the signal in the detection window to generate the autocorrelation distortion factor are as follows: In the detection window, first, the periodic characteristics of the signal are extracted from the signal data by calculating the autocorrelation function of the signal. The calculation expression is as follows: , where is the autocorrelation function, i.e., the signal Delay in time The autocorrelation value under, N is the number of signal samples, Indicates the signal at time point The value at is the signal at time The value at is the time index; In the autocorrelation function After calculation, the periodicity of the signal is evaluated by the autocorrelation function The changes of are analyzed and the autocorrelation distortion metric is calculated to quantify the degree of change of the signal periodicity. The calculation expression is as follows; , where is the autocorrelation distortion measure, T is the size of the detection window, It's a signal Delay in time The autocorrelation value under , that is, the autocorrelation value under the previous time delay; In order to comprehensively detect the distortion of the signal, the change of the signal amplitude is considered, and the existing changes are captured by the change of the signal amplitude. The calculation expression is as follows: , where is the signal amplitude variation index; The autocorrelation distortion measure Sum signal amplitude variation index Generate the autocorrelation distortion factor, the calculation expression is as follows: , where is the autocorrelation distortion factor, is the autocorrelation distortion measure Maximum value, is the signal amplitude variation index Maximum value.

[0009] Preferably, the specific steps of analyzing the amplifier gain saturation degree in the detection window to generate the gain saturation factor are as follows: Under the detection window, the received signal is analyzed for amplitude, and the peak value and instantaneous amplitude of the signal are extracted. The calculation expression is as follows: , where Indicates the signal at time point The value at It is at the moment The signal peak value of The nonlinearity analysis of the relationship between the extracted signal peak and the amplifier output is performed to quantify whether the signal has generated nonlinear distortion during the amplification process, and then evaluate whether the amplifier has entered the saturation state. The calculation expression is as follows: , where It's at the time The peak value of the signal output by the amplifier, It's at the time The peak value of the signal at the amplifier input, is the exponential factor, is the nonlinearity factor; Finally, based on the nonlinearity factor The calculation result is combined with the change of signal amplitude to generate the gain saturation factor. The calculation expression is as follows: , where is the gain saturation factor, is the saturation threshold, is the detection window length.

[0010] Preferably, after analyzing the signal quality characteristics, the analyzed autocorrelation distortion factor and gain saturation factor are input into a pre-learned support vector machine model, a signal quality fluctuation index is generated by the support vector machine model, and the analyzed signal quality characteristics are intelligently evaluated by the signal quality fluctuation index.

[0011] Preferably, the signal quality fluctuation index generated when the signal quality is intelligently evaluated by the pre-learned support vector machine model is compared and analyzed with a pre-set signal quality fluctuation index reference threshold, and the signal quality is divided, and the division steps are as follows: If the signal quality fluctuation index is greater than or equal to a preset signal quality fluctuation index reference threshold, the signal quality in the detection window is classified as an amplified distorted signal; If the signal quality fluctuation index is less than a preset signal quality fluctuation index reference threshold, the signal quality in the detection window is classified as a normal amplification signal.

[0012] Preferably, when the signal is in the "amplified distorted signal", the specific steps of automatically reducing the amplification range are as follows: When a signal is identified as an "amplified distorted signal", it is necessary to automatically reduce the amplification range of the signal to avoid excessive amplification and further distortion of the signal. The calculation expression is as follows: , where is the gain adjustment factor, is the maximum gain of the amplifier, is the signal quality fluctuation index, is the reference threshold of signal quality fluctuation index, It limits the maximum gain.

[0013] Preferably, when the signal is in the "normal amplification signal", the current amplification strategy will be continued while the specific steps of continuously monitoring the signal quality are as follows: When the signal quality is classified as "normal amplification signal", the normal amplification state is maintained while the signal quality fluctuation index is continuously monitored. value to ensure that the signal quality fluctuation is within the set safety range. The calculation expression is as follows: .

[0014] A wireless networking system of routers and relay devices, including a signal receiving and amplifying module, a signal quality monitoring module, a signal data preprocessing module, a signal quality feature extraction and analysis module, an intelligent evaluation module, a signal quality classification module, an amplification adjustment module and a stable amplification strategy module: Signal receiving and amplifying module, the relay device first receives the wireless signal from the router and amplifies it to extend the network coverage; Signal quality monitoring module, which monitors and obtains data related to signal quality in real time during the signal amplification process; The signal data preprocessing module preprocesses the acquired signal quality data to improve the accuracy and reliability of the data; The signal quality feature extraction and analysis module extracts key features reflecting signal quality from the preprocessed signal data, analyzes the extracted key features under the detection window, and quantifies the changes in signal quality; The intelligent evaluation module inputs the analyzed features into the pre-learned support vector machine model, and uses the support vector machine model to intelligently evaluate the analyzed signal quality features; The signal quality classification module divides the signal quality changes into "normal amplification signal" and "amplification distortion signal" based on the support vector machine model evaluation results; Amplification adjustment module, when the signal is in the "amplification distortion signal", automatically reduces the amplification range to prevent the signal from being over-amplified and further distorted, to ensure that the signal is amplified within a reasonable range; The stable amplification strategy module will continue the current amplification strategy when the signal is in the "normal amplification signal" and continuously monitor the signal quality to ensure the stability and continuity of the amplification process.

[0015] In the above technical solution, the technical effects and advantages provided by the present invention are: By intelligently evaluating signal quality and adjusting the signal amplification strategy in real time, it is possible to effectively avoid over-amplification of the signal. Especially under high load, the fluctuation of signal quality will be more severe. By evaluating the signal quality fluctuation index based on the support vector machine model, it is possible to accurately identify whether the signal has entered the amplification distortion state. At this time, if the signal is found to have entered the distortion state, the system will automatically adjust the amplification range to prevent signal saturation and distortion caused by over-amplification, thereby reducing the risk of data packet loss, erroneous transmission and decreased bandwidth efficiency. Through this mechanism, especially in application scenarios such as real-time video and voice calls that have high requirements for latency and stability, it can effectively avoid communication interruptions, improve user experience, and ensure the accuracy and continuity of data transmission.

[0016] The present invention can not only guarantee the signal quality, but also ensure the stability and continuity of the amplification process through continuous monitoring when the signal quality is in the "normal amplified signal" state, thereby avoiding unnecessary amplification adjustments. This dynamic adjustment strategy ensures that the relay device can amplify the signal within an appropriate range, which not only improves the connection capability of long-distance devices, but also avoids the waste of resources caused by excessive signal amplification. In addition, the process of automatically detecting and adjusting the amplified signal effectively reduces signal loss and system load, optimizes the utilization of network resources, and expands the network coverage while maintaining efficient bandwidth utilization. Through this intelligent signal management, the overall performance of the network is improved, especially in large-scale wireless networking environments, it can support more device connections and improve the scalability and reliability of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 The present invention is a method flow chart of a wireless networking method of a router and a relay device.

[0019] Figure 2 The present invention is a module schematic diagram of a wireless networking system of a router and a relay device. DETAILED DESCRIPTION

[0020] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0021] The present invention provides Figure 1 A wireless networking method of a router and a relay device is shown, comprising the following steps: The relay device first receives the wireless signal from the router and amplifies it to extend the network coverage; The core function of a relay device is to extend the coverage area of ​​a wireless network. The received signal is usually captured by an antenna and preliminarily amplified by a built-in RF front-end module. This step ensures that the signal can be effectively received at the physical level and provides a signal of sufficient strength for subsequent processing. The preliminarily amplified signal should be kept within the linear operating range of the amplifier to avoid significant distortion at the initial stage. In addition, this step also includes basic filtering and modulation of the signal to ensure that the signal quality is suitable for further processing.

[0022] The relay device first uses an antenna to capture the wireless signal from the router through its built-in wireless receiving module. The received signal is first processed by the RF front-end circuit, including filtering to remove unnecessary frequency interference and noise, and then enters the low noise amplifier (LNA) for signal enhancement to improve signal strength and ensure signal quality in the subsequent processing stage. The amplified signal is converted into a digital signal through the analog-to-digital converter (ADC) for further processing and analysis by the digital signal processor (DSP). The DSP demodulates, corrects errors and parses the digital signal to ensure the integrity and accuracy of the signal. Subsequently, the processed signal is remodulated and amplified again through the radio frequency amplifier (RFA) to ensure that the signal maintains sufficient strength during transmission to cover a wider area. Finally, the amplified wireless signal is rebroadcast through the antenna of the relay device, extending the coverage of the network, so that areas with weak or no coverage can also be stably connected to the network. This series of steps ensures that the signal maintains high quality and stability during reception, processing and retransmission, thereby effectively expanding the coverage of the wireless network.

[0023] During the signal amplification process, real-time monitoring and acquisition of data related to signal quality; In order to dynamically control the signal amplification process, the relay equipment needs to continuously monitor the signal quality. This includes capturing key indicators such as signal-to-noise ratio (SNR), bit error rate (BER), signal strength, modulation mode, and spectrum usage. These data are collected in real time through the built-in sensors and measurement modules of the equipment and transmitted to the signal processing unit. The purpose of obtaining signal quality data in real time is to be able to identify signal changes in a timely manner, especially in the case of network load fluctuations or environmental interference, to ensure that the signal quality during the amplification process is always within the controllable range.

[0024] Pre-process the acquired signal quality data to improve the accuracy and reliability of the data; Preprocessing is a key step in data analysis, which aims to improve the effect of subsequent feature extraction and model evaluation. The relay device uses digital signal processing (DSP) technology to filter, denoise and normalize the raw signal data. This includes applying a low-pass filter to remove high-frequency noise, using a median filter to reduce pulse interference, and smoothing the data to eliminate instantaneous fluctuations. In addition, data compression and format conversion may also be performed to meet the needs of subsequent processing steps. The preprocessed data is cleaner and more standardized, laying the foundation for accurate extraction of key features.

[0025] Extract key features reflecting signal quality from preprocessed signal data, analyze the extracted key features under the detection window, and quantify changes in signal quality; The key features reflecting the signal quality are extracted from the preprocessed signal data. The extracted features include the periodicity and amplitude changes of the signal and the gain saturation degree of the amplifier. Under the detection window, the periodicity and amplitude changes of the signal and the gain saturation degree of the amplifier are analyzed to generate the autocorrelation distortion factor and the gain saturation factor respectively. The autocorrelation distortion factor is used to quantify the stability of the periodic changes of the signal. The gain saturation factor quantifies the degree to which the amplifier enters the saturation state during the amplification process, reflecting the situation when the amplifier can no longer effectively amplify the signal.

[0026] The distortion of the periodicity and amplitude of the signal may indeed indicate that the signal is distorted due to over-amplification. When the relay device amplifies the signal, if the amplification process is not effectively controlled, especially when the signal is over-amplified, the amplitude of the signal may exceed the linear operating range of the amplifier. After the amplifier reaches its maximum gain, it cannot continue to linearly amplify the input signal, resulting in saturation of the output signal. At this time, the amplitude of the signal is reduced, the original waveform is distorted, and even loses its original periodicity, forming an irregular waveform. This distortion phenomenon is called amplitude distortion. In addition, the periodicity of the signal is also an important factor affected by the nonlinear characteristics of the amplifier. When the amplification process is excessive, the periodic characteristics of the signal will be disturbed, the originally stable periodic signal will no longer be symmetrical or regular, and the frequency characteristics will shift. This phenomenon usually indicates that the signal has exceeded the normal linear amplification range when passing through the amplifier, resulting in serious distortion of the periodicity and amplitude of the signal. By analyzing the periodicity and amplitude distortion of the signal, the signal distortion caused by over-amplification can be effectively detected, which has a negative impact on the performance of the system.

[0027] The specific steps for analyzing the periodicity and amplitude changes of the signal under the detection window to generate the autocorrelation distortion factor are as follows: Under the detection window, first, the periodic characteristics of the signal are extracted from the signal data by calculating the autocorrelation function of the signal. The autocorrelation function measures the similarity between the signal and itself at different time delays and can reflect the periodic characteristics of the signal. The calculation expression is as follows: , where is the autocorrelation function, i.e., the signal Delay in time The autocorrelation value under, N is the number of signal samples, Indicates the signal at time point The value at is the signal at time The value at which the signal is delayed in time The value after is a time index, indicating the value of the signal at different time points; If the signal is not over-amplified, its autocorrelation function should show obvious periodic characteristics and By extracting these periodic features, we can provide a basis for subsequent analysis.

[0028] In the autocorrelation function After calculation, the periodicity of the signal is evaluated by the autocorrelation function The changes of are analyzed and the autocorrelation distortion metric is calculated to quantify the degree of change of the signal periodicity. The calculation expression is as follows; , where is the autocorrelation distortion measure, T is the size of the detection window, It's a signal Delay in time The autocorrelation value under , that is, the autocorrelation value under the previous time delay; This formula is calculated by calculating the adjacent delay times in the autocorrelation function. The difference between the delays is used to evaluate the amplitude of the signal's periodic fluctuations. If the difference is large, it means that the periodicity of the signal has changed significantly, suggesting signal distortion. This formula quantifies the amplitude of the periodic change by adding the difference between each delay. The larger the difference, the more serious the periodic change of the signal, which may indicate that over-amplification has caused signal distortion.

[0029] In order to fully detect the distortion of the signal, the change of the signal amplitude is considered. The amplitude distortion of the signal is usually caused by over-amplification, especially when the amplifier enters the saturation state. The existing changes are captured by the amplitude change of the signal. The calculation expression is as follows: , where is the signal amplitude variation index; This formula calculates the difference between adjacent signal amplitude values ​​and accumulates these differences to quantify the amplitude change of the signal at different points in time. If the amplitude change is abnormally drastic, it means that the signal amplitude has been distorted, which may be caused by amplifier saturation or nonlinear effects. An increase in the amplitude change exponentially usually indicates a degradation of signal quality, resulting in signal distortion.

[0030] The autocorrelation distortion measure Sum signal amplitude variation index Generate the autocorrelation distortion factor, the calculation expression is as follows: , where is the autocorrelation distortion factor, is the autocorrelation distortion measure Maximum value, is the signal amplitude variation index Maximum value.

[0031] This formula combines the periodic distortion and amplitude distortion information of the signal. Sum signal amplitude variation index The value of makes them comparable under different signal conditions, and then weights the effect of amplitude change through an exponential function to ensure that the contribution of amplitude distortion to the overall signal distortion index is not too low. Autocorrelation distortion factor The larger the value, the more abnormal the periodicity and amplitude changes of the signal are, and the more serious the signal distortion is, which may be caused by over-amplification.

[0032] The larger the value of the autocorrelation distortion factor generated after analyzing the periodicity and amplitude changes of the signal under the detection window, the greater the distortion of the periodic changes of the signal. This is usually caused by over-amplification of the signal when it is amplified by the relay device. When the amplitude of the signal is over-amplified, especially in the nonlinear region of the amplifier, the originally stable periodic signal will be distorted, showing periodic imbalance or irregularity. The autocorrelation distortion factor detects the degree of distortion of these periodic changes by quantifying the autocorrelation characteristics of the signal. The larger the value, the more serious the destruction of the periodic regularity of the signal, reflecting the signal distortion caused by over-amplification. On the contrary, when the value of the autocorrelation distortion factor is small or close to zero, it indicates that the periodicity of the signal remains stable and is not affected by over-amplification. The periodicity and amplitude changes of the signal remain within the normal range, indicating that the signal is in a stable state.

[0033] Over-saturation of amplifier gain usually indicates that the current signal may be distorted due to over-amplification. When the relay device attempts to expand the signal coverage, the amplifier gain needs to amplify the input signal to a sufficient strength to cover a larger area. However, if the gain is set too high or not properly controlled, the amplifier may enter a saturated state and cannot continue to enhance the signal in a linear manner. This saturation state will cause the amplitude of the signal to be distorted because the amplifier cannot maintain the linear relationship between the input and output signals, resulting in a serious distortion of the waveform of the output signal, especially in the peak part of the signal. At this time, the amplifier has limited processing capabilities for strong signals and cannot effectively handle large changes, resulting in the loss or deformation of some detailed information. This signal distortion is often not easy to detect directly because the distortion of the signal is usually more significant only in certain specific frequencies or amplitude ranges. For high-frequency and high-power signals, the distortion may be more severe, affecting the stability and quality of data transmission, and even causing communication interruption or data packet loss. Signal distortion caused by over-amplification, especially in complex network environments, often manifests as reduced bandwidth, increased latency or frequent erroneous transmissions, which in severe cases affects the reliability and performance of the overall network.

[0034] The specific steps for analyzing the amplifier gain saturation degree under the detection window to generate the gain saturation factor are as follows: Under the detection window, the received signal is analyzed for amplitude, and the peak value and instantaneous amplitude of the signal are extracted. Through this process, the fluctuation range of the signal is evaluated, especially the maximum intensity in the time domain. The calculation expression is as follows: , where Indicates the signal at time point The value at It is at the moment The signal peak value of The purpose of this step is to identify the extreme peak of the signal as a benchmark for subsequent amplifier gain saturation analysis. The extraction of the signal peak provides a data basis for the subsequent evaluation of whether the amplifier has entered a saturated state. Excessive increase in the peak value may indicate that the signal is over-amplified and is approaching or has entered the nonlinear saturation region of the amplifier.

[0035] The nonlinearity analysis of the relationship between the extracted signal peak and the amplifier output is performed to quantify whether the signal has generated nonlinear distortion during the amplification process, and then evaluate whether the amplifier has entered the saturation state. The calculation expression is as follows: , where It's at the time The peak value of the signal output by the amplifier, It's at the time The peak value of the signal at the amplifier input, It is an exponential factor, which indicates the nonlinearity of the relationship between output and input during the signal amplification process. is the nonlinearity factor; Nonlinearity factor, The increase in indicates that the output of the amplifier gradually deviates from the linear relationship of the input signal, which usually means that the amplifier has entered the gain saturation region. By calculating this nonlinearity, it is possible to preliminarily identify whether the signal is over-amplified.

[0036] Finally, based on the nonlinearity factor The calculation result is combined with the change of signal amplitude to generate the gain saturation factor. The calculation expression is as follows: , where is the gain saturation factor, is the saturation threshold, is the detection window length.

[0037] The degree of signal distortion is quantified by cumulative calculation of the nonlinearity factor at all time points. The larger the value of this index, the closer the amplifier is to saturation and the more serious the signal distortion. On the contrary, if the gain saturation index is low, it means that the signal is in the normal amplification range, the amplifier works in its linear range, and the signal quality is relatively stable.

[0038] The larger the gain saturation factor performance value generated after analyzing the amplifier gain saturation degree under the detection window, the more it indicates that the current signal is distorted due to over-amplification, and vice versa, it indicates that the current signal is not over-amplified and is in a stable state. When the signal is amplified by the relay device, the gain saturation factor quantifies the degree to which the amplifier enters the saturation state. If the gain saturation factor value is high, it means that the amplifier's gain has exceeded its linear processing capability and entered the saturation zone, and it can no longer effectively amplify the signal, causing the amplitude of the signal to be distorted and distortion to occur. At this time, the quality of the signal decreases, and the frequency components and amplitude may be changed irregularly, seriously affecting the reliability of data transmission. Conversely, when the gain saturation factor value is low, it indicates that the amplifier is within the normal working range, the relationship between the gain and the signal remains linear, the amplitude and frequency characteristics of the signal are not affected by improper amplification, and it is in a stable transmission state.

[0039] The analyzed features are input into the pre-learned support vector machine model, and the signal quality features after analysis are intelligently evaluated through the support vector machine model; After analyzing the signal quality characteristics, the analyzed autocorrelation distortion factor and gain saturation factor are input into the pre-learned support vector machine model, and the signal quality fluctuation index is generated by the support vector machine model. The signal quality fluctuation index is used to intelligently evaluate the analyzed signal quality characteristics.

[0040] A pre-learned support vector machine (SVM) model refers to a machine learning model that can accurately classify or regress signal quality by training with a large amount of historical data and specific signal quality features. SVM is a commonly used supervised learning method that distinguishes signal features of different categories by constructing a hyperplane (or multiple hyperplanes in a high-dimensional space). In order to train a good support vector machine model, you first need to collect a large amount of sample data. This data contains signal quality features in different situations, such as parameters such as autocorrelation distortion factor and gain saturation factor, and these data have been annotated with corresponding signal quality labels (such as good signal quality, poor signal quality, signal distortion, etc.). By training these sample data, the SVM model can learn the mapping relationship between the input signal features and their corresponding signal quality.

[0041] Once the training process is complete, the SVM model can use the newly input signal quality features (such as autocorrelation distortion factor and gain saturation factor) to make predictions, thereby deriving a signal quality fluctuation index. The signal quality fluctuation index is an indicator that comprehensively quantifies the signal quality fluctuation, reflecting the possible fluctuation and distortion of the signal during the transmission process. The SVM intelligently evaluates the degree of change in signal quality by calculating the position of the input features in the classification or regression boundary it constructs. Based on these evaluation results, the model can determine whether the signal has problems such as over-amplification, distortion or saturation, thereby providing real-time feedback for network management. In practical applications, the pre-learned SVM model can not only help identify the health status of the signal, but also predict the future trend of signal quality changes, issue warnings or adjust the amplification strategy in time, and effectively avoid communication interruptions or data loss caused by signal quality degradation.

[0042] The above support vector machine model is not limited here, and can realize the autocorrelation distortion factor and gain saturation factor Perform comprehensive analysis to generate a signal quality fluctuation index The support vector machine model can be any of them. In order to realize the technical solution of the present invention, the present invention provides a specific implementation method; Signal quality fluctuation index The generation formula is as follows: , where , The autocorrelation distortion factor and gain saturation factor The preset scaling factor of , Both are greater than 0.

[0043] It can be seen from the signal quality fluctuation index that, under the detection window, the larger the autocorrelation distortion factor performance value generated after analyzing the periodicity and amplitude changes of the signal under the detection window, and the larger the gain saturation factor performance value generated after analyzing the amplifier gain saturation under the detection window, the larger the signal quality fluctuation index performance value generated when the signal quality is intelligently evaluated through the vector machine model, indicating that the current signal is distorted due to excessive amplification. Otherwise, it indicates that the signal is not over-amplified and is within a reasonable amplification range.

[0044] Based on the evaluation results of the support vector machine model, the signal quality changes are divided into "normal amplification signal" and "amplification distortion signal"; The signal quality fluctuation index generated by the pre-learned support vector machine model when the signal quality is intelligently evaluated is compared with the pre-set signal quality fluctuation index reference threshold to divide the signal quality. The division steps are as follows: If the signal quality fluctuation index is greater than or equal to a preset signal quality fluctuation index reference threshold, the signal quality in the detection window is classified as an amplified distorted signal; If the signal quality fluctuation index is less than a preset signal quality fluctuation index reference threshold, the signal quality in the detection window is classified as a normal amplification signal.

[0045] Normal amplified signal refers to the signal amplified by the relay equipment, and the signal quality remains within the stable and efficient transmission range. In this case, the signal is amplified moderately, the gain of the amplifier is not over-saturated, and the amplitude and periodic changes of the signal remain normal. Amplified distorted signal refers to the signal quality that has changed significantly after amplification by the relay equipment, and distortion, distortion or other nonlinear phenomena have occurred, resulting in the signal being unable to normally reflect the original information.

[0046] When the signal is in the "amplified distorted signal" state, the amplification range is automatically reduced to prevent the signal from being over-amplified and further distorted, so as to ensure that the signal is amplified within a reasonable range; When the signal is in the "amplified distorted signal" state, the specific steps to automatically reduce the amplification range are as follows: When a signal is identified as an "amplified distorted signal", it is necessary to automatically reduce the amplification range of the signal to avoid over-amplification and further distortion of the signal. This adjustment strategy is achieved by controlling the gain of the amplifier. The calculation expression is as follows: , where is the gain adjustment coefficient, which indicates the adjusted gain value and controls the actual gain of the signal amplifier. is the maximum gain of the amplifier, is the signal quality fluctuation index, is the reference threshold of signal quality fluctuation index, It is to limit the maximum gain, which is an operation of taking the minimum value to ensure that the calculated gain adjustment value does not exceed the maximum gain value of the amplifier. ; By calculating the gain adjustment coefficient, the gain output is controlled to adapt to changes in signal quality and ensure that the signal is amplified within a reasonable range. When it is greater than or equal to the reference threshold, the gain adjustment factor is reduced to prevent the amplifier from entering saturation.

[0047] When the signal is in the "normal amplification signal", the current amplification strategy will be continued, while the signal quality will be continuously monitored to ensure the stability and continuity of the amplification process; When the signal is in the "normal amplification signal" state, the current amplification strategy will be continued while the specific steps for continuously monitoring the signal quality are as follows: When the signal quality is classified as "normal amplification signal", the normal amplification state is maintained while the signal quality fluctuation index is continuously monitored. value to ensure that the signal quality fluctuation is within the set safety range. If abnormal fluctuations in signal quality are detected, the same mechanism is used to judge and automatically adjust the gain. The calculation expression is as follows: This continuous monitoring mechanism ensures that the relay equipment can dynamically adjust the gain, while ensuring signal quality, while minimizing signal distortion caused by over-amplification. Through this feedback mechanism, the system always keeps the signal within a stable amplification range, thereby improving network reliability and communication quality.

[0048] The present invention can effectively avoid the situation of excessive signal amplification by intelligently evaluating signal quality and adjusting the signal amplification strategy in real time. Especially under high load, the fluctuation of signal quality will be more intense. Through the evaluation of the signal quality fluctuation index based on the support vector machine model, it can be accurately identified whether the signal has entered the amplification distortion state. At this time, if it is found that the signal has entered the distortion state, the system will automatically adjust the amplification range to prevent signal saturation and distortion caused by excessive signal amplification, thereby reducing the risk of data packet loss, erroneous transmission and decreased bandwidth efficiency. Through this mechanism, especially in application scenarios with high requirements for delay and stability such as real-time video and voice calls, communication interruptions can be effectively avoided, user experience can be improved, and the accuracy and continuity of data transmission can be ensured.

[0049] The present invention can not only guarantee the signal quality, but also ensure the stability and continuity of the amplification process through continuous monitoring when the signal quality is in the "normal amplified signal" state, thereby avoiding unnecessary amplification adjustments. This dynamic adjustment strategy ensures that the relay device can amplify the signal within an appropriate range, which not only improves the connection capability of long-distance devices, but also avoids the waste of resources caused by excessive signal amplification. In addition, the process of automatically detecting and adjusting the amplified signal effectively reduces signal loss and system load, optimizes the utilization of network resources, and expands the network coverage while maintaining efficient bandwidth utilization. Through this intelligent signal management, the overall performance of the network is improved, especially in large-scale wireless networking environments, it can support more device connections and improve the scalability and reliability of the network.

[0050] The present invention provides Figure 2A wireless networking system of a router and a relay device shown in the figure includes a signal receiving and amplifying module, a signal quality monitoring module, a signal data preprocessing module, a signal quality feature extraction and analysis module, an intelligent evaluation module, a signal quality classification module, an amplification adjustment module and a stable amplification strategy module: Signal receiving and amplifying module, the relay device first receives the wireless signal from the router and amplifies it to extend the network coverage; Signal quality monitoring module, which monitors and obtains data related to signal quality in real time during the signal amplification process; The signal data preprocessing module preprocesses the acquired signal quality data to improve the accuracy and reliability of the data; The signal quality feature extraction and analysis module extracts key features reflecting signal quality from the preprocessed signal data, analyzes the extracted key features under the detection window, and quantifies the changes in signal quality; The intelligent evaluation module inputs the analyzed features into the pre-learned support vector machine model, and uses the support vector machine model to intelligently evaluate the analyzed signal quality features; The signal quality classification module divides the signal quality changes into "normal amplification signal" and "amplification distortion signal" based on the support vector machine model evaluation results; Amplification adjustment module, when the signal is in the "amplification distortion signal", automatically reduces the amplification range to prevent the signal from being over-amplified and further distorted, to ensure that the signal is amplified within a reasonable range; The stable amplification strategy module will continue the current amplification strategy when the signal is in the "normal amplification signal" and continuously monitor the signal quality to ensure the stability and continuity of the amplification process.

[0051] A wireless networking method for a router and a relay device provided in an embodiment of the present invention is implemented by the wireless networking system for a router and a relay device mentioned above. The specific method and process of the wireless networking system for a router and a relay device are detailed in the embodiment of the wireless networking method for a router and a relay device mentioned above, which will not be repeated here.

[0052] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0053] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0054] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A wireless networking method for a router and a relay device, characterized in that: The following steps are involved: The relay device first receives the wireless signal from the router and amplifies it to extend the network coverage; During the signal amplification process, real-time monitoring and acquisition of data related to signal quality; Pre-process the acquired signal quality data to improve the accuracy and reliability of the data; Extract key features reflecting signal quality from preprocessed signal data, analyze the extracted key features under the detection window, and quantify changes in signal quality; The analyzed features are input into the pre-learned support vector machine model, and the signal quality features after analysis are intelligently evaluated through the support vector machine model; Based on the evaluation results of the support vector machine model, the signal quality changes are divided into "normal amplification signal" and "amplification distortion signal"; When the signal is in the "amplified distorted signal" state, the amplification range is automatically reduced to prevent the signal from being over-amplified and further distorted, to ensure that the signal is amplified within a reasonable range; when the signal is in the "normal amplified signal" state, the current amplification strategy will be continued, while the signal quality will be continuously monitored to ensure the stability and continuity of the amplification process.

2. A wireless networking method of a router and a relay device according to claim 1, characterized in that: The key features reflecting the signal quality are extracted from the preprocessed signal data. The extracted features include the periodicity and amplitude changes of the signal and the gain saturation degree of the amplifier. Under the detection window, the periodicity and amplitude changes of the signal and the gain saturation degree of the amplifier are analyzed to generate the autocorrelation distortion factor and the gain saturation factor respectively. The autocorrelation distortion factor is used to quantify the stability of the periodic changes of the signal. The gain saturation factor quantifies the degree to which the amplifier enters the saturation state during the amplification process, reflecting the situation when the amplifier can no longer effectively amplify the signal.

3. A wireless networking method of a router and a relay device according to claim 2, characterized in that: The specific steps for analyzing the periodicity and amplitude changes of the signal under the detection window to generate the autocorrelation distortion factor are as follows: In the detection window, first, the periodic characteristics of the signal are extracted from the signal data by calculating the autocorrelation function of the signal. The calculation expression is as follows: , where is the autocorrelation function, i.e., the signal Delay in time The autocorrelation value under, N is the number of signal samples, Indicates the signal at time point The value at is the signal at time The value at is the time index; In the autocorrelation function After calculation, the periodicity of the signal is evaluated by the autocorrelation function The changes of are analyzed and the autocorrelation distortion metric is calculated to quantify the degree of change of the signal periodicity. The calculation expression is as follows; , where is the autocorrelation distortion measure, T is the size of the detection window, It's a signal Delay in time The autocorrelation value under , that is, the autocorrelation value under the previous time delay; In order to comprehensively detect the distortion of the signal, the change of the signal amplitude is considered, and the existing changes are captured by the change of the signal amplitude. The calculation expression is as follows: , where is the signal amplitude variation index; The autocorrelation distortion measure Sum signal amplitude variation index Generate the autocorrelation distortion factor, the calculation expression is as follows: , where is the autocorrelation distortion factor, is the autocorrelation distortion measure Maximum value, is the signal amplitude variation index Maximum value.

4. A wireless networking method of a router and a relay device according to claim 2, characterized in that: The specific steps for analyzing the amplifier gain saturation degree under the detection window to generate the gain saturation factor are as follows: Under the detection window, the received signal is analyzed for amplitude, and the peak value and instantaneous amplitude of the signal are extracted. The calculation expression is as follows: , where Indicates the signal at time point The value at It is at the moment The signal peak value of The nonlinearity analysis of the relationship between the extracted signal peak and the amplifier output is performed to quantify whether the signal has generated nonlinear distortion during the amplification process, and then evaluate whether the amplifier has entered the saturation state. The calculation expression is as follows: , where It's at the time The peak value of the signal output by the amplifier, It's at the time The peak value of the signal at the amplifier input, is the exponential factor, is the nonlinearity factor; Finally, based on the nonlinearity factor The calculation result is combined with the change of signal amplitude to generate the gain saturation factor. The calculation expression is as follows: , where is the gain saturation factor, is the saturation threshold, is the detection window length.

5. A wireless networking method of a router and a relay device according to claim 2, characterized in that: After analyzing the signal quality characteristics, the analyzed autocorrelation distortion factor and gain saturation factor are input into the pre-learned support vector machine model, and the signal quality fluctuation index is generated by the support vector machine model. The signal quality fluctuation index is used to intelligently evaluate the analyzed signal quality characteristics.

6. A wireless networking method of a router and a relay device according to claim 5, characterized in that: The signal quality fluctuation index generated by the pre-learned support vector machine model when the signal quality is intelligently evaluated is compared with the pre-set signal quality fluctuation index reference threshold to divide the signal quality. The division steps are as follows: If the signal quality fluctuation index is greater than or equal to a preset signal quality fluctuation index reference threshold, the signal quality in the detection window is classified as an amplified distorted signal; If the signal quality fluctuation index is less than a preset signal quality fluctuation index reference threshold, the signal quality in the detection window is classified as a normal amplification signal.

7. A wireless networking method of a router and a relay device according to claim 6, characterized in that: When the signal is in the "amplified distorted signal" state, the specific steps to automatically reduce the amplification range are as follows: When a signal is identified as an "amplified distortion signal", it is necessary to automatically reduce the signal's amplification range to avoid excessive signal amplification and further distortion. The calculation expression is as follows: , where is the gain adjustment factor, is the maximum gain of the amplifier, is the signal quality fluctuation index, is the reference threshold of signal quality fluctuation index, is to limit the maximum gain.

8. A wireless networking method of a router and a relay device according to claim 6, characterized in that: When the signal is in the "normal amplification signal" state, the current amplification strategy will be continued while the specific steps for continuously monitoring the signal quality are as follows: When the signal quality is classified as "normal amplification signal", the normal amplification state is maintained while the signal quality fluctuation index is continuously monitored. value to ensure that the signal quality fluctuation is within the set safety range. The calculation expression is as follows: 。 9. A wireless networking system of a router and a relay device, used to implement a wireless networking method of a router and a relay device according to any one of claims 1 to 8, characterized in that: It includes signal receiving and amplifying module, signal quality monitoring module, signal data preprocessing module, signal quality feature extraction and analysis module, intelligent evaluation module, signal quality classification module, amplification adjustment module and stable amplification strategy module: Signal receiving and amplifying module, the relay device first receives the wireless signal from the router and amplifies it to extend the network coverage; Signal quality monitoring module, which monitors and obtains data related to signal quality in real time during the signal amplification process; The signal data preprocessing module preprocesses the acquired signal quality data to improve the accuracy and reliability of the data; The signal quality feature extraction and analysis module extracts key features reflecting signal quality from the preprocessed signal data, analyzes the extracted key features under the detection window, and quantifies the changes in signal quality; The intelligent evaluation module inputs the analyzed features into the pre-learned support vector machine model, and uses the support vector machine model to perform intelligent evaluation on the analyzed signal quality features; The signal quality classification module divides the signal quality changes into "normal amplification signal" and "amplification distortion signal" based on the support vector machine model evaluation results; Amplification adjustment module, when the signal is in the "amplified distorted signal", automatically reduces the amplification range to prevent the signal from being over-amplified and further distorted, to ensure that the signal is amplified within a reasonable range; The stable amplification strategy module will continue the current amplification strategy when the signal is in the "normal amplification signal" and continuously monitor the signal quality to ensure the stability and continuity of the amplification process.

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