Adaptive anti-interference communication method and system based on star flash-Bluetooth dual mode

Abstract

The invention relates to the technical field of wireless communication, and particularly discloses a self-adaptive anti-interference communication method and system based on star flash-Bluetooth dual-mode, and the method comprises the steps: monitoring channel interference parameters in real time through a dual-mode radio frequency module on a communication terminal, including the out-of-band radiation power and the pulse interference duty ratio at the switching moment; instantaneous interference intensity features are extracted by using synchronous compression wavelet transform and sparse time-frequency analysis, periodic pulse interference features are identified by combining Hawkes process modeling, and a comprehensive anti-interference feature vector is constructed by fusion; the feature vectors are input into a pre-trained gradient boosting tree model for communication quality evaluation, an optimal communication mode is dynamically selected according to a scoring result, intelligent response to a complex interference environment is achieved, the stability and reliability of a communication system in a high-interference scene are improved, energy efficiency optimization and resource scheduling are considered, and the method is suitable for large-scale popularization and application. The method is suitable for industrial, medical, Internet of Things and other application scenes with strict requirements on communication quality.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and particularly to an adaptive anti-interference communication method and system based on SparkLink-Bluetooth dual-mode. Background Art

[0002] With the rapid development of wireless communication technologies, SparkLink and Bluetooth dual-mode devices have been widely used in fields such as smart home and industrial Internet of Things. However, problems such as co-channel interference and impulse noise in complex electromagnetic environments seriously affect communication reliability. Traditional anti-interference methods mainly use RSSI detection with a fixed threshold or static mode switching strategies, and it is difficult to cope with the combined effects of transient spectrum leakage and periodic impulse interference. Existing research mostly focuses on anti-interference optimization of a single communication mode, lacking a joint analysis mechanism for spectrum pollution and impulse interference in a SparkLink-Bluetooth dual-mode system, resulting in frequent problems such as incorrect switching and communication interruption in strong interference scenarios such as industrial sites.

[0003] The existing technology has the following deficiencies: The existing technology lacks the transient spectrum regeneration phenomenon caused by dual-mode switching (i.e., out-of-band radiation generated by the transient response of the RF front-end during mode switching). The present invention accurately quantifies the pollution degree of this phenomenon on adjacent frequency channels through synchronous compressed wavelet transform, and combines the analysis of impulse interference modeled by the Hawkes process to solve the misjudgment problem caused by traditional methods ignoring the coupling effect of transient interference and steady-state interference. Summary of the Invention

[0004] The purpose of the present invention is to provide an adaptive anti-interference communication method and system based on SparkLink-Bluetooth dual-mode to solve the problems in the above background.

[0005] The purpose of the present invention can be achieved through the following technical solutions: An adaptive anti-interference communication method based on SparkLink-Bluetooth dual-mode includes the following steps: S1: Real-time monitor the interference parameters of the current channel through a dual-mode RF module deployed on a communication terminal; The interference parameters include: out-of-band radiation power at the moment of switching and impulse interference duty cycle; S2: Conduct time-frequency domain analysis on the out-of-band radiation power, construct a spectrum leakage feature sequence, and calculate the instantaneous interference intensity eigenvalue to evaluate the spectrum pollution degree during dual-mode switching; S3: Conduct statistical modeling on the impulse interference duty cycle, construct an impulse interference distribution matrix, and calculate the burst noise tolerance eigenvalue to identify whether the channel is affected by periodic strong interference; S4: Fuse the instantaneous interference intensity eigenvalue and the burst noise tolerance eigenvalue into a comprehensive anti-interference feature vector, input it into the pre-trained anti-interference decision model for analysis, and conduct an optimal evaluation of the communication mode based on the analysis results; S5: Dynamically switch between the StarFlash and Bluetooth communication modes according to the evaluation results, or enable dual-mode parallel transmission to achieve adaptive anti-interference communication in complex interference environments.

[0006] As a further solution of the present invention: The evaluation of the spectral pollution degree during dual-mode switching specifically includes: Obtain the out-of-band radiation power at the moment of channel switching in real time, conduct time-frequency domain analysis on the out-of-band radiation power, construct a spectral leakage feature sequence, calculate the instantaneous interference intensity eigenvalue, and determine whether the instantaneous interference intensity eigenvalue is greater than or equal to a preset threshold. If so, the spectral pollution during dual-mode switching is abnormal; if not, the spectral pollution during dual-mode switching is normal.

[0007] As a further solution of the present invention: The process of obtaining the instantaneous interference intensity eigenvalue is as follows: Obtain the out-of-band radiation power at the moment of channel switching in real time, perform synchronous compressed wavelet transform on the out-of-band radiation power signal at the moment of switching, decompose the time-frequency energy through the Morlet wavelet basis function, calculate the ratio of the maximum out-of-band energy to the average in-band energy to obtain the maximum energy leakage ratio; Conduct sparse time-frequency distribution analysis on the out-of-band radiation power signal, solve the sparse representation of the time-frequency matrix through L1 regularization, and statistically calculate the proportion of non-zero elements in the out-of-band region to obtain the sparsity index; Perform normalization calculation on the maximum energy leakage ratio and the sparsity index to obtain the instantaneous interference intensity eigenvalue.

[0008] As a further solution of the present invention: The identification of whether the channel is affected by periodic strong interference specifically includes: Obtain the pulse interference duty cycle of the current channel in real time, conduct statistical modeling on the pulse interference duty cycle, construct a pulse interference distribution matrix, calculate the burst noise tolerance eigenvalue, and determine whether the burst noise tolerance eigenvalue is greater than or equal to a preset threshold. If so, the channel is affected by periodic strong interference; if not, the channel is not affected by periodic strong interference.

[0009] As a further solution of the present invention: The process of obtaining the burst noise tolerance eigenvalue is as follows: Collect the time series of pulse interference events in the channel in real time; Model the pulse interference based on the Hawkes process; Solve the parameters 、 and through the maximum likelihood estimation method, specifically by optimizing the log-likelihood function; Calculate the burst noise tolerance eigenvalue according to the average pulse interval and pulse duration.

[0010] As a further solution of the present invention: fusing the instantaneous interference intensity eigenvalue and the burst noise tolerance eigenvalue into a comprehensive anti-interference feature vector and inputting it into a pre-trained anti-interference decision model for analysis, specifically including: Obtain the instantaneous interference intensity eigenvalue and the burst noise tolerance eigenvalue, construct the instantaneous interference intensity eigenvalue and the burst noise tolerance eigenvalue into a comprehensive anti-interference feature vector as the input of the anti-interference decision model, use minimizing the error between the predicted communication quality score and the actual communication quality score as the training objective of the anti-interference decision model, train the anti-interference decision model, and according to the trained anti-interference decision model, output the communication quality score. The anti-interference decision model is a gradient boosting tree model.

[0011] As a further solution of the present invention: the training process of the anti-interference decision model is as follows: Construct a training data set, which includes: samples of the instantaneous interference intensity eigenvalue collected historically; samples of the burst noise tolerance eigenvalue collected historically; corresponding actual communication quality score labels; standardize the eigenvalues so that they follow a standard normal distribution with a mean of 0 and a variance of 1; use the SMOTE algorithm to oversample the imbalanced data; construct a gradient boosting tree model, and the model parameters include: the number of base learners is set to 100-200; the learning rate is set to 0.05-0.2; the maximum depth of the tree is set to 3-5 layers; the minimum number of samples in the leaf node is set to 5-10; use the k-fold cross-validation method to train the model, specifically including: randomly divide the data set into k mutually exclusive subsets; each time select k-1 subsets as the training set and the remaining 1 subset as the validation set; repeat the training process k times and take the average performance as the model evaluation index; In the model optimization stage, use the Bayesian optimization method to tune the hyperparameters, and the optimization goal is to minimize the mean absolute error on the validation set; Solidify the optimal model parameters after training and deploy them to the embedded system of the communication terminal.

[0012] As a further solution of the present invention: the preferred evaluation of the communication mode according to the analysis result specifically includes: Based on the communication quality score output by the anti-interference decision model, a three-level optimization strategy including the SparkLink mode, Bluetooth mode, and dual-mode parallel transmission is established: when the score is lower than the first threshold, it preferentially switches to the highly anti-interference SparkLink mode; when the score is between the first threshold and the second threshold, it enables dual-mode parallel transmission to balance reliability and compatibility; when the score is higher than the second threshold, it adopts the low-power Bluetooth mode. At the same time, it continuously monitors the change of the channel state. When it detects that the score crosses the threshold boundary N times continuously, it triggers a mode switch, and further optimizes the communication quality by dynamically adjusting the transmit power and modulation method, where N is a preset hysteresis count parameter.

[0013] As a further solution of the present invention: the dynamic switching of the SparkLink and Bluetooth communication modes according to the evaluation results, or enabling dual-mode parallel transmission, realizes adaptive anti-interference communication in a complex interference environment, specifically including: Execute a three-level optimization strategy according to the score result: when the score is lower than the first threshold, it preferentially adopts the SparkLink mode to ensure communication reliability; when the score is between the first and second thresholds, it enables dual-mode parallel transmission to balance performance and compatibility; when the score is higher than the second threshold, it switches to the Bluetooth mode to reduce power consumption. At the same time, it avoids frequent mode switching through the hysteresis count mechanism. During the mode switching process, dynamically adjust the transmit power and modulation method to further optimize the communication quality; for the SparkLink mode, it preferentially adopts high-order modulation and the maximum transmit power, and the Bluetooth mode adaptively selects GFSK or LE Coded PHY according to the interference degree; during dual-mode parallel transmission, it coordinates the resource allocation of the two protocols through time-division multiplexing, and continuously monitors the change of the eigenvalue. When it detects that the interference feature continues to be abnormal, it triggers a mode re-evaluation, forming a closed-loop anti-interference control mechanism.

[0014] An adaptive anti-interference communication system based on the SparkLink-Bluetooth dual mode, including: A real-time interference monitoring module, which continuously monitors the interference parameters of the current channel through a dual-mode radio frequency module deployed on the communication terminal; The interference parameters include: the out-of-band radiation power and the pulse interference duty cycle at the moment of switching; A spectrum pollution analysis module, which is used to perform time-frequency domain analysis on the out-of-band radiation power, construct a spectrum leakage feature sequence, and calculate the instantaneous interference intensity eigenvalue to evaluate the spectrum pollution degree during dual-mode switching; A pulse interference modeling module, which statistically models the pulse interference duty cycle, constructs a pulse interference distribution matrix, and calculates the burst noise tolerance eigenvalue to identify whether the channel is affected by periodic strong interference; An intelligent decision-making module that fuses the instantaneous interference intensity eigenvalue and the burst noise tolerance eigenvalue into a comprehensive anti-interference feature vector, inputs it into a pre-trained anti-interference decision model for analysis, and conducts an optimal evaluation of the communication mode based on the analysis results; A dynamic switching execution module that dynamically switches between the StarFlash and Bluetooth communication modes according to the evaluation results, or enables dual-mode parallel transmission to achieve adaptive anti-interference communication in complex interference environments.

[0015] Advantages of the present invention: (1) By deeply exploring and introducing interference parameters that are easily overlooked but highly indicative in traditional communication systems, such as out-of-band radiation power and pulse interference duty cycle, the present invention constructs a refined interference perception mechanism for complex electromagnetic environments. This mechanism not only realizes the all-round and multi-dimensional perception of the channel state, but also significantly improves the detection sensitivity and response speed of the system to transient interference and periodic noise by integrating advanced signal processing techniques, such as synchrosqueezing wavelet transform for high-precision time-frequency domain analysis and statistical modeling methods based on Hawkes process for dynamic identification of periodic pulse interference. The system normalizes and fuses the extracted instantaneous interference intensity eigenvalue and burst noise tolerance eigenvalue to construct a comprehensive anti-interference feature vector with clear physical meaning and strong interpretability, and inputs it into a well-trained gradient boosting tree model to realize the intelligent evaluation and prediction of the current communication quality. This decision model is trained with the goal of minimizing the communication quality score error, has good generalization ability and robustness, and can complete the whole process from interference perception to mode selection within milliseconds, ensuring that the system can quickly adapt to the changing wireless environment. Finally, according to the communication quality score output by the model, the system executes a three-level communication mode optimization strategy: preferentially enabling the high-robustness StarFlash communication in strong interference scenarios, switching to the low-power Bluetooth mode in weak interference environments, and adopting dual-mode parallel transmission in medium interference conditions to balance stability and compatibility, while combining the hysteresis counting mechanism to avoid unnecessary frequent switching, forming a closed-loop adaptive anti-interference control system. This communication architecture that deeply integrates the "perception - analysis - decision - execution" link effectively breaks through the performance bottleneck of traditional single communication modes in complex interference environments, significantly improves the stability of the communication link, the reliability of data transmission, and the overall energy efficiency performance of the system, and is especially suitable for critical application scenarios such as industrial automation, medical monitoring, and smart wearables that have strict requirements for both communication quality and energy consumption.

[0016] (2)The present invention not only focuses on improving communication quality but also takes into account the energy consumption and resource utilization efficiency of the system. By designing a three-level communication mode optimization strategy, it flexibly switches between StarFlash, Bluetooth, or dual-mode parallel modes under different interference intensities. Meanwhile, combined with the mechanism of dynamically adjusting the transmission power and modulation mode, the system can minimize power consumption while ensuring communication quality. For example, in a low-interference environment, the Bluetooth mode is preferentially used to save energy; in a high-interference scenario, the StarFlash enhanced mode is enabled to ensure performance; and in a medium-interference condition, the dual-mode parallel mode is adopted to achieve a balance between performance and compatibility. In addition, the hysteresis counting mechanism effectively prevents the additional overhead caused by frequent mode switching. This refined resource scheduling mechanism enables the communication system to have stronger environmental adaptability and is suitable for various power-sensitive application scenarios such as the Internet of Things and wearable devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below with reference to the accompanying drawings.

[0018] Figure 1 is a flowchart of an adaptive anti-interference communication method based on StarFlash-Bluetooth dual-mode of the present invention; Figure 2 is a flowchart of an adaptive anti-interference communication system based on StarFlash-Bluetooth dual-mode in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] Please refer to Figure 1 As shown, the present invention is an adaptive anti-interference communication method based on StarFlash-Bluetooth dual-mode, including the following steps: S1: Real-time monitor the interference parameters of the current channel through the dual-mode radio frequency module deployed on the communication terminal; The interference parameters include: out-of-band radiation power and pulse interference duty cycle at the moment of switching; S2: Conduct time-frequency domain analysis on the out-of-band radiation power, construct a spectrum leakage feature sequence, and calculate the instantaneous interference intensity eigenvalue to evaluate the spectrum pollution degree during dual-mode switching; S3: Conduct statistical modeling on the pulse interference duty cycle, construct a pulse interference distribution matrix, and calculate the burst noise tolerance eigenvalue to identify whether the channel is affected by periodic strong interference; S4: Fuse the instantaneous interference intensity eigenvalue and the burst noise tolerance eigenvalue into a comprehensive anti-interference feature vector, input it into the pre-trained anti-interference decision model for analysis, and conduct an optimal evaluation of the communication mode according to the analysis results; S5: Dynamically switch the StarFlash and Bluetooth communication modes according to the evaluation results, or enable dual-mode parallel transmission to achieve adaptive anti-interference communication in complex interference environments.

[0021] In S1, the interference parameters of the current channel are continuously monitored through a dual-mode radio frequency module deployed on the communication terminal. The interference parameters include: the out-of-band radiation power and the pulse interference duty cycle at the moment of switching, specifically including: At the moment of communication mode switching, the system activates a dedicated high-speed sampling channel to capture radio frequency signals in real time at a sampling rate not lower than 10MS / s. After converting the signal to the baseband through digital down-conversion technology, sliding window fast Fourier transform is used for spectrum analysis, and the radiation energy within the range of ±20MHz outside the 2.4GHz operating frequency band is mainly monitored. The system records three key parameters in real time: the maximum radiation power value in the out-of-band area, the frequency point position where the maximum radiation appears, and the average power level in the out-of-band area, forming a complete description of the out-of-band radiation characteristics.

[0022] The system continuously monitors the received signal strength through a high-sensitivity envelope detection circuit. When a burst pulse exceeding the preset threshold (-60dBm) is detected, a high-precision timestamp record is immediately started. Within the set 100ms observation window, the system accurately records the start and end times of each pulse, calculates the ratio of the actual pulse duration to the total observation time as the duty cycle. At the same time, the system analyzes the regularity of the pulse interval, distinguishes random interference and periodic interference by calculating the coefficient of variation, and finally outputs a complete pulse interference description including the duty cycle, periodicity, and main frequency characteristics. The entire acquisition process uses multi-stage anti-aliasing filtering to ensure measurement accuracy.

[0023] In S2, perform time-frequency domain analysis on the out-of-band radiation power, construct a spectral leakage feature sequence, and calculate the instantaneous interference intensity eigenvalue to evaluate the spectral pollution degree during dual-mode switching, specifically including: Obtain the out-of-band radiation power at the moment of switching of the current channel in real time, perform time-frequency domain analysis on the out-of-band radiation power, construct a spectral leakage feature sequence, calculate the instantaneous interference intensity eigenvalue, and determine whether the instantaneous interference intensity eigenvalue is greater than or equal to the preset threshold. If so, the spectral pollution during dual-mode switching is abnormal; if not, the spectral pollution during dual-mode switching is normal.

[0024] The process of obtaining the instantaneous interference intensity eigenvalue is as follows: Obtain the out-of-band radiation power at the switching instant of the current channel in real time, perform synchronous compressive wavelet transform on the out-of-band radiation power signal at the switching instant, decompose the time-frequency energy through the Morlet wavelet basis function, calculate the ratio of the maximum out-of-band energy to the average in-band energy, and obtain the maximum energy leakage ratio; Among them, the calculation expression of the maximum energy leakage ratio is: ; In the formula, represents the maximum energy leakage ratio, represents the signal at the frequency and time The energy density at the location, represents the frequency, represents the time, represents the preset out-of-band frequency range, represents the preset in-band frequency range, represents the number of in-band frequency points, represents the out-of-band radiation power signal; Perform sparse time-frequency distribution analysis on the out-of-band radiation power signal, solve the sparse representation of the time-frequency matrix through L1 regularization, count the proportion of non-zero elements in the out-of-band region, and obtain the sparsity index. The calculation expression is: ; In the formula, represents the sparsity index, represents the sparse coefficient matrix, represents the non-zero element count, represents the total number of out-of-band time-frequency grids; Perform normalization calculation on the maximum energy leakage ratio and the sparsity index to obtain the instantaneous interference intensity eigenvalue. The calculation expression is: ; In the formula, represents the instantaneous interference intensity eigenvalue, and are preset proportionality coefficients, and and are both greater than 0.

[0025] It should be noted that: In this step, a high-precision evaluation of spectrum pollution during dual-mode switching is achieved through an innovative time-frequency joint analysis method. Its technical effects and innovation points are mainly reflected in: adopting a hybrid analysis method of synchrosqueezed wavelet transform and sparse time-frequency distribution, breaking through the limitations of traditional fast Fourier transform spectrum analysis in time-frequency resolution. Among them, the compressed wavelet transform realizes precise time-frequency positioning with a time resolution of μs level and a frequency resolution of 10 MHz level through the Morlet wavelet basis function, and can effectively capture transient spectrum leakage characteristics; while the sparse representation algorithm based on L1 regularization significantly improves the detection sensitivity of out-of-band interference components. The innovatively proposed maximum energy leakage ratio-sparsity index, dual-feature fusion algorithm realizes the comprehensive evaluation of burst-type and continuous-type interference through weighted geometric mean, reduces the false judgment rate of spectrum pollution detection, and at the same time adopts a dynamic threshold mechanism (updated every 10 ms) to adapt to complex electromagnetic environment changes, providing a key criterion for the reliable switching of the star flash-Bluetooth dual-mode system.

[0026] In S3, statistically model the duty cycle of impulse interference, construct an impulse interference distribution matrix, and calculate the eigenvalue of burst noise tolerance to identify whether the channel is affected by strong periodic interference, specifically including: Obtain the duty cycle of impulse interference in the current channel in real time, statistically model the duty cycle of impulse interference, construct an impulse interference distribution matrix, calculate the eigenvalue of burst noise tolerance, and determine whether the eigenvalue of burst noise tolerance is greater than or equal to a preset threshold. If so, the channel is affected by strong periodic interference; if not, the channel is not affected by strong periodic interference.

[0027] The process of obtaining the eigenvalue of burst noise tolerance is as follows: Collect the time series of impulse interference events in the channel in real time; Model the impulse interference based on the Hawkes process, and its conditional intensity function is defined as: ; Among them, is the background noise rate, representing the average number of random interference events per unit time, is the impulse intensity, characterizing the excitation ability of a single interference on subsequent events, is the decay rate, reflecting the duration of the interference effect, represents the time of the conditional intensity function, represents the occurrence time of historical interference events, represents the occurrence time of the current interference event, represents the natural number base; Solve the parameters 、 and through the maximum likelihood estimation method, specifically by optimizing the log-likelihood function: ; Among them, represents the log-likelihood function, represents the total number of interference events within the observation window, represents the th interference event within the observation window, represents the length of the observation time window, represents the integral of the conditional intensity function within the observation window, represents the sum of the logarithms of the conditional intensities at the times of the interference events; According to the average pulse interval and pulse duration, calculate the burst noise tolerance eigenvalue, and the calculation expression is: ; In the formula, represents the burst noise tolerance eigenvalue.

[0028] It should be noted that: In this step, through an innovative Hawkes process modeling method, accurate identification of periodic pulse interference is achieved. Its technical effects and innovation points are mainly reflected in: adopting a point process model with self-excitation characteristics, breaking through the limitations of traditional duty cycle statistical methods in interference correlation analysis, and the three key parameters (background noise rate μ, pulse intensity α, decay rate β) in the conditional intensity function can simultaneously characterize the spatio-temporal coupling characteristics of random interference and periodic interference; the real-time parameter solving algorithm based on maximum likelihood estimation realizes dynamic modeling of the interference event sequence, and the period detection accuracy is improved by more than 3 times compared with the traditional Fourier analysis method; the innovatively designed burst noise tolerance eigenvalue is normalized by: average pulse interval / duration, and for the first time realizes the joint quantification of interference periodicity and energy intensity. Combined with the adaptive threshold mechanism, it provides a key basis for the anti-interference decision of the dual-mode system. This model particularly optimizes the ability to distinguish between arc interference and Bluetooth data packet conflicts in industrial environments.

[0029] In S4, fuse the instantaneous interference intensity eigenvalue and the burst noise tolerance eigenvalue into a comprehensive anti-interference feature vector, input it into a pre-trained anti-interference decision model for analysis, and conduct an optimal evaluation of the communication mode according to the analysis results, specifically including: Obtain the instantaneous interference intensity eigenvalue and the burst noise tolerance eigenvalue, and construct the instantaneous interference intensity eigenvalue and the burst noise tolerance eigenvalue into a comprehensive anti-interference feature vector as the input of the anti-interference decision model. Take minimizing the error between the predicted communication quality score and the actual communication quality score as the training objective of the anti-interference decision model, train the anti-interference decision model, and according to the trained anti-interference decision model, output the communication quality score. The anti-interference decision model is a gradient boosting tree model.

[0030] The training process of the anti-interference decision model is: Construct a training data set, which includes: instant interference intensity eigenvalue samples collected historically; burst noise tolerance eigenvalue samples collected historically; corresponding actual communication quality score labels; standardize the eigenvalues so that they follow a standard normal distribution with a mean of 0 and a variance of 1; use the SMOTE algorithm to oversample the imbalanced data; construct a gradient boosting tree model, and the model parameters include: the number of base learners is set to 100 - 200; the learning rate is set to 0.05 - 0.2; the maximum depth of the tree is set to 3 - 5 layers; the minimum number of samples in a leaf node is set to 5 - 10; use the k-fold cross-validation method to train the model, which specifically includes: randomly divide the data set into k mutually exclusive subsets; each time select k - 1 subsets as the training set and the remaining 1 subset as the validation set; repeat the training process k times, and take the average performance as the model evaluation index; In the model optimization stage, use the Bayesian optimization method to tune the hyperparameters, and the optimization goal is to minimize the mean absolute error on the validation set; Solidify the optimal model parameters after training and deploy them to the embedded system of the communication terminal. The preferred evaluation of the communication mode according to the analysis result specifically includes: Based on the communication quality score output by the anti-interference decision model, establish a three-level preferred strategy including the XingShan mode, the Bluetooth mode, and dual-mode parallel transmission: when the score is lower than the first threshold, preferentially switch to the high anti-interference XingShan mode; when the score is between the first threshold and the second threshold, enable dual-mode parallel transmission to balance reliability and compatibility; when the score is higher than the second threshold, use the low-power Bluetooth mode; at the same time, continuously monitor the change of the channel state, and trigger mode switching when it is detected that the score crosses the threshold boundary N times continuously, and further optimize the communication quality by dynamically adjusting the transmit power and modulation method, where N is a preset hysteresis count parameter.

[0031] In S5, dynamically switch the XingShan and Bluetooth communication modes according to the evaluation result, or enable dual-mode parallel transmission to achieve adaptive anti-interference communication in a complex interference environment, which specifically includes: Based on the out-of-band radiation power and pulse interference duty cycle monitored in real time, calculate the instant interference intensity eigenvalue and the burst noise tolerance eigenvalue respectively through synchrosqueezing wavelet transform and Hawkes process modeling, fuse the two into a comprehensive anti-interference feature vector and input it into the pre-trained gradient boosting tree decision model to obtain the communication quality score of the current channel state; execute the three-level preferred strategy according to the score result: when the score is lower than the first threshold, preferentially use the XingShan mode to ensure communication reliability; when the score is between the first and second thresholds, enable dual-mode parallel transmission to balance performance and compatibility; when the score is higher than the second threshold, switch to the Bluetooth mode to reduce power consumption, and at the same time avoid frequent mode switching through the hysteresis count mechanism.

[0032] During the mode switching process, the transmission power and modulation method are dynamically adjusted to further optimize the communication quality. For the SparkLink mode, higher-order modulation and maximum transmission power are preferentially adopted, while for the Bluetooth mode, GFSK or LE Coded PHY is adaptively selected according to the interference level. When dual-mode parallel transmission is performed, time-division multiplexing is used to coordinate the resource allocation of the two protocols, and the change of eigenvalue is monitored in real time. When it is detected that the interference characteristics continue to be abnormal, a mode re-evaluation is triggered to form a closed-loop anti-interference control mechanism.

[0033] Please refer to Figure 2 As shown, an adaptive anti-interference communication system based on SparkLink-Bluetooth dual-mode includes: A real-time interference monitoring module, which monitors the interference parameters of the current channel in real time through a dual-mode radio frequency module deployed on the communication terminal; The interference parameters include: out-of-band radiation power and pulse interference duty cycle at the moment of switching; A spectrum pollution analysis module, which is used to perform time-frequency domain analysis on the out-of-band radiation power, construct a spectrum leakage feature sequence, and calculate the instantaneous interference intensity eigenvalue to evaluate the spectrum pollution degree during dual-mode switching; A pulse interference modeling module, which statistically models the pulse interference duty cycle, constructs a pulse interference distribution matrix, and calculates the burst noise tolerance eigenvalue to identify whether the channel is affected by periodic strong interference; An intelligent decision-making module, which fuses the instantaneous interference intensity eigenvalue and the burst noise tolerance eigenvalue into a comprehensive anti-interference feature vector, inputs it into a pre-trained anti-interference decision model for analysis, and performs a preferred evaluation of the communication mode according to the analysis result; A dynamic switching execution module, which dynamically switches the SparkLink and Bluetooth communication modes according to the evaluation result, or enables dual-mode parallel transmission to achieve adaptive anti-interference communication in a complex interference environment.

[0034] Working principle of the present invention: The present invention realizes reliable communication in complex environments through multi-dimensional interference feature extraction and intelligent decision-making. The system first uses a dual-mode RF module to monitor channel interference parameters in real time, including obtaining out-of-band radiation power characteristics through high-speed sampling and sliding window fast Fourier transform analysis, and recording the duty cycle of pulse interference through envelope detection and high-precision timestamp. For spectrum pollution assessment, a maximum energy leakage ratio-sparsity index, a dual-feature fusion algorithm is innovatively constructed by combining synchrosqueezed wavelet transform and sparse time-frequency distribution analysis to achieve precise interference positioning in the μs-level time domain and 10-MHz-level frequency domain. For periodic interference identification, the Hawkes process is used for modeling, and the spatio-temporal characteristics of interference are quantified through the conditional intensity function parameters (μ, α, β), and an eigenvalue of burst noise tolerance is designed to achieve joint evaluation of periodicity and intensity. After fusing the two types of features, they are input into a pre-trained gradient boosting tree model, which is trained through Bayesian optimization for parameter tuning and k-fold cross-validation, outputs a communication quality score, and executes a three-level optimization strategy: switch to the high anti-interference star flash mode at low scores, enable dual-mode parallel at medium scores, and use the low-power Bluetooth mode at high scores. The system innovatively introduces a hysteresis counting mechanism to prevent frequent switching, and further optimizes the performance through dynamic power control and adaptive modulation (star flash high-order modulation / Bluetooth GFSK / LE Coded PHY). When in dual-mode parallel, time-division multiplexing is used to coordinate resource allocation, forming a closed-loop control system including feature monitoring-analysis-decision-making-execution, and the overall response time is controlled within 10 ms, significantly improving the communication reliability in complex electromagnetic environments.

[0035] All the above formulas are dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0036] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that contains one or more sets of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0037] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context.

[0038] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0039] The above has described in detail an embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. Any equivalent changes and improvements made within the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. An adaptive anti-interference communication method based on SparkLink-Bluetooth dual-mode, characterized in that It includes the following steps: S1: Through the dual-mode RF module deployed on the communication terminal, the interference parameters of the current channel are monitored in real time; The interference parameters include: out-of-band radiation power and pulse interference duty cycle at the moment of handover; S2: Conduct time-frequency domain analysis on the out-of-band radiation power, construct a spectrum leakage feature sequence, and calculate the instantaneous interference intensity eigenvalue to evaluate the spectrum pollution degree during dual-mode handover; S3: Conduct statistical modeling on the pulse interference duty cycle, construct a pulse interference distribution matrix, and calculate the burst noise tolerance eigenvalue to identify whether the channel is affected by periodic strong interference; S4: Fuse the instantaneous interference intensity eigenvalue and the burst noise tolerance eigenvalue into a comprehensive anti-interference feature vector, input it into the pre-trained anti-interference decision model for analysis, and conduct a preferred evaluation of the communication mode according to the analysis results; S5: Dynamically switch the StarFlash and Bluetooth communication modes according to the evaluation results, or enable dual-mode parallel transmission to achieve adaptive anti-interference communication in a complex interference environment.

2. The adaptive anti-interference communication method based on SparkLink-Bluetooth dual-mode according to claim 1, wherein The evaluation of the spectrum pollution degree during dual-mode handover specifically includes: Obtain the out-of-band radiation power at the moment of handover of the current channel in real time, conduct time-frequency domain analysis on the out-of-band radiation power, construct a spectrum leakage feature sequence, calculate the instantaneous interference intensity eigenvalue, and determine whether the instantaneous interference intensity eigenvalue is greater than or equal to the preset threshold. If so, the spectrum pollution during dual-mode handover is abnormal; if not, the spectrum pollution during dual-mode handover is normal.

3. The adaptive anti-interference communication method based on SparkLink-Bluetooth dual-mode according to claim 2, wherein The process of obtaining the instantaneous interference intensity eigenvalue is as follows: Obtain the out-of-band radiation power at the moment of handover of the current channel in real time, conduct synchronous compressed wavelet transform on the out-of-band radiation power signal at the moment of handover, decompose the time-frequency energy through the Morlet wavelet basis function, calculate the ratio of the maximum out-of-band energy to the in-band average energy to obtain the maximum energy leakage ratio; Conduct sparse time-frequency distribution analysis on the out-of-band radiation power signal, solve the sparse representation of the time-frequency matrix through L1 regularization, and statistically calculate the proportion of non-zero elements in the out-of-band region to obtain the sparsity index; Conduct normalization calculation on the maximum energy leakage ratio and the sparsity index to obtain the instantaneous interference intensity eigenvalue.

4. An adaptive anti-interference communication method based on SparkLink-Bluetooth dual-mode according to claim 1, characterized in that, The identification of whether the channel is affected by periodic strong interference specifically includes: Obtain the pulse interference duty cycle of the current channel in real time, conduct statistical modeling on the pulse interference duty cycle, construct a pulse interference distribution matrix, calculate the burst noise tolerance eigenvalue, and determine whether the burst noise tolerance eigenvalue is greater than or equal to the preset threshold. If so, the channel is affected by periodic strong interference; if not, the channel is not affected by periodic strong interference.

5. The adaptive anti-interference communication method based on SparkLink-Bluetooth dual-mode according to claim 4, wherein, The process of obtaining the burst noise tolerance eigenvalue is as follows: Collect the time series of pulse interference events in the channel in real time; Model the pulse interference based on the Hawkes process; Solve the parameters by the maximum likelihood estimation method , and , which is specifically realized by optimizing the log-likelihood function; Calculate the burst noise tolerance eigenvalue according to the average pulse interval and pulse duration.

6. The adaptive anti-interference communication method based on SparkLink-Bluetooth dual-mode according to claim 1, characterized in that The fusion of the instantaneous interference intensity eigenvalue and the burst noise tolerance eigenvalue into a comprehensive anti-interference feature vector and input into the pre-trained anti-interference decision model for analysis specifically includes: Obtain the instantaneous interference intensity eigenvalue and the burst noise tolerance eigenvalue, and construct the instantaneous interference intensity eigenvalue and the burst noise tolerance eigenvalue into a comprehensive anti-interference feature vector, which is used as the input of the anti-interference decision model. The training objective of the anti-interference decision model is to minimize the error between the predicted communication quality score and the actual communication quality score. Train the anti-interference decision model. According to the trained anti-interference decision model, output the communication quality score. The anti-interference decision model is a gradient boosting tree model.

7. An adaptive anti-interference communication method based on SparkLink-Bluetooth dual-mode according to claim 6, characterized in that, The training process of the anti-interference decision model is as follows: Construct a training data set, which includes: samples of the instantaneous interference intensity eigenvalue collected historically; samples of the burst noise tolerance eigenvalue collected historically; corresponding actual communication quality score labels; standardize the eigenvalues so that they follow a standard normal distribution with a mean of 0 and a variance of 1; use the SMOTE algorithm to oversample the sample imbalance data; construct a gradient boosting tree model, and the model parameters include: the number of base learners is set to 100-200; the learning rate is set to 0.05-0.2; the maximum depth of the tree is set to 3-5 layers; the minimum number of samples in the leaf node is set to 5-10; use the k-fold cross-validation method to train the model, which specifically includes: randomly divide the data set into k mutually exclusive subsets; each time select k-1 subsets as the training set, and the remaining 1 subset as the validation set; repeat the training process k times, and take the average performance as the model evaluation index; In the model optimization stage, use the Bayesian optimization method to tune the hyperparameters, and the optimization goal is to minimize the mean absolute error on the validation set; Solidify the optimal model parameters after training and deploy them to the embedded system of the communication terminal.

8. A self-adaptive anti-jamming communication method based on SparkLink-Bluetooth dual-mode according to claim 1, characterized in that, The communication mode preference evaluation according to the analysis results specifically includes: Establish a three-level preference strategy including the XingFlash mode, the Bluetooth mode, and the dual-mode parallel transmission according to the communication quality score output by the anti-interference decision model: when the score is lower than the first threshold, preferentially switch to the high anti-interference XingFlash mode; when the score is between the first threshold and the second threshold, enable dual-mode parallel transmission to balance reliability and compatibility; when the score is higher than the second threshold, use the low-power Bluetooth mode; at the same time, monitor the channel state change in real time, and trigger the mode switch when it is detected that the score crosses the threshold boundary continuously for N times, and further optimize the communication quality by dynamically adjusting the transmission power and modulation method, where N is a preset hysteresis count parameter.

9. The adaptive anti-interference communication method based on SparkLink-Bluetooth dual-mode according to claim 1, wherein, Dynamically switch the XingFlash and Bluetooth communication modes according to the evaluation results, or enable dual-mode parallel transmission to achieve adaptive anti-interference communication in a complex interference environment, specifically including: Execute the three-level preference strategy according to the score results: when the score is lower than the first threshold, preferentially use the XingFlash mode to ensure communication reliability; when the score is between the first and second thresholds, enable dual-mode parallel transmission to balance performance and compatibility; when the score is higher than the second threshold, switch to the Bluetooth mode to reduce power consumption, and at the same time avoid frequent mode switching through the hysteresis count mechanism; During the mode switching process, the transmission power and modulation method are dynamically adjusted to further optimize the communication quality; for the SparkLink mode, high-order modulation and maximum transmission power are preferentially adopted, while for the Bluetooth mode, GFSK or LECoded PHY is adaptively selected according to the interference level; when dual-mode parallel transmission is carried out, the resource allocation of the two protocols is coordinated through time-division multiplexing, and the change of the eigenvalue is monitored in real time. When it is detected that the interference characteristics are continuously abnormal, a mode re-evaluation is triggered to form a closed-loop anti-interference control mechanism.

10. An adaptive anti-jamming communication system based on SparkLink-Bluetooth dual-mode, characterized in that, An adaptive anti-interference communication method based on SparkLink-Bluetooth dual-mode as described in any one of claims 1-9, comprising: A real-time interference monitoring module, which monitors the interference parameters of the current channel in real time through a dual-mode radio frequency module deployed on the communication terminal; The interference parameters include: the out-of-band radiation power and the pulse interference duty cycle at the moment of switching; A spectrum pollution analysis module, which is used to perform time-frequency domain analysis on the out-of-band radiation power, construct a spectrum leakage feature sequence, and calculate the instantaneous interference intensity eigenvalue to evaluate the spectrum pollution degree during dual-mode switching; A pulse interference modeling module, which statistically models the pulse interference duty cycle, constructs a pulse interference distribution matrix, and calculates the burst noise tolerance eigenvalue to identify whether the channel is affected by periodic strong interference; An intelligent decision-making module, which fuses the instantaneous interference intensity eigenvalue and the burst noise tolerance eigenvalue into a comprehensive anti-interference feature vector, inputs it into a pre-trained anti-interference decision-making model for analysis, and conducts a preferred evaluation of the communication mode according to the analysis result; A dynamic switching execution module, which dynamically switches the SparkLink and Bluetooth communication modes according to the evaluation result, or enables dual-mode parallel transmission to achieve adaptive anti-interference communication in a complex interference environment.

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