Anti-interference automatic synchronization frequency hopping method suitable for wireless ad hoc network

Abstract

The invention relates to the technical field of wireless communication, and particularly discloses an anti-interference automatic synchronization frequency hopping method suitable for a wireless ad hoc network, which comprises the following steps of: acquiring signal strength, signal-to-noise ratio and bit error rate data of communication frequency points in real time, and utilizing advanced signal processing technologies such as empirical mode decomposition, fast Fourier transform and Haar wavelet transform to carry out frequency hopping on the signal strength, the signal-to-noise ratio and the bit error rate data of the communication frequency points; the method comprises the following steps: calculating key feature indexes reflecting frequency point states by combining machine learning algorithms such as K-means clustering and a random forest model, constructing a comprehensive feature vector, predicting whether frequency points are interfered in a period of time in the future, selecting non-interfered frequency points from a preset communication frequency point pool for replacement by a system when detecting that the current frequency point is interfered, and performing frequency point interference in the communication frequency point pool when detecting that the current frequency point is interfered. Continuously monitoring a new frequency point; according to the frequency point selection method, the scientificity and the accuracy of frequency point selection are improved, interference can be avoided, the synchronous adjustment capability of the whole network is enhanced, and the overall performance and the anti-interference capability of the wireless ad hoc network are improved.

Description

Technical Field

[0001] The invention relates to the technical field of wireless communications, and in particular to an anti-interference automatic synchronous frequency hopping method suitable for wireless ad hoc networks. Background Art

[0002] With the rapid development of wireless communication technology, wireless ad hoc networks have been widely used in emergency response, military applications, the Internet of Things and other fields because they can achieve autonomous communication between nodes without relying on fixed infrastructure. However, wireless communication spectrum resources are limited and susceptible to external interference, which makes it a major challenge to maintain high-quality communication in complex electromagnetic environments. Especially in high-density user scenarios or when multiple devices in the same frequency band coexist, the signal interference problem is particularly prominent, which seriously affects the stability and reliability of the network. To solve these problems, researchers have developed a variety of anti-interference technologies and frequency hopping mechanisms, aiming to avoid interference sources by dynamically adjusting the communication frequency and ensure the stability of the communication link. However, how to efficiently and accurately select and switch to the optimal frequency, and maintain the overall coordination and stability of the network in the process, is still a key problem that needs to be solved.

[0003] The prior art has the following deficiencies:

[0004] The existing technology still has many deficiencies in dealing with the anti-interference needs in wireless ad hoc networks. First, the traditional manual or simple rule-based frequency selection method lacks scientific basis and forward-looking evaluation capabilities, making it difficult to effectively identify potential interference and make timely adjustments, resulting in frequent communication interruptions or quality problems. Secondly, the existing dynamic adjustment mechanism often fails to make full use of advanced signal processing technology and machine learning algorithms for multi-dimensional data analysis, resulting in inaccurate judgment of the frequency status and inability to fully reflect the actual interference situation of the frequency. In addition, the existing technology also has defects in the synchronous adjustment of the entire network. When a node detects interference and switches the frequency, other nodes may not be able to obtain updated information in time, resulting in network fragmentation or data transmission failure. Finally, the lack of an effective intelligent feedback calibration mechanism makes it impossible to verify the validity of the new frequency hopping sequence in real time, making it difficult to ensure continuous high-quality communication during network operation. These deficiencies limit the performance of wireless ad hoc networks in complex electromagnetic environments, and there is an urgent need for a more intelligent and efficient solution to improve its anti-interference ability and overall reliability. Summary of the invention

[0005] The purpose of the present invention is to provide an anti-interference automatic synchronous frequency hopping method suitable for wireless ad hoc networks to solve the above-mentioned problems.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] An anti-interference automatic synchronous frequency hopping method applicable to a wireless ad hoc network comprises the following steps:

[0008] S1: collect the signal strength data of the current communication frequency in real time, and judge whether the current communication frequency is interfered according to the amplitude and frequency of signal strength change;

[0009] S2: According to the judgment result, the current communication frequency point is divided into: an interfered communication frequency point and an uninterfered communication frequency point;

[0010] S3: Based on the interfered communication frequency, the system selects an uninterfered communication frequency from a preset communication frequency pool to replace the interfered communication frequency, and continuously monitors the replaced communication frequency. If the replaced frequency is determined to be an interfered communication frequency for M consecutive times, it automatically switches to the backup frequency hopping sequence;

[0011] S4: Based on the undisturbed communication frequency, the signal-to-noise ratio characteristics and bit error rate characteristics of the current communication frequency are extracted and constructed into a comprehensive feature vector as the input of the comprehensive scoring model. According to the comprehensive scoring model, it is predicted whether the communication frequency will be interfered in the future, and the frequency that will remain undisturbed for a long time in the future is preferentially selected as the new communication frequency.

[0012] As a further solution of the present invention: the determining whether the current communication frequency point is interfered specifically includes:

[0013] The signal strength of the current communication frequency is collected in real time. The amplitude coefficient and frequency coefficient are calculated according to the signal strength change amplitude and change frequency respectively. The amplitude coefficient and frequency coefficient are normalized and calculated to obtain the interference coefficient. It is determined whether the interference coefficient of the current communication frequency is greater than or equal to the threshold. If so, it is marked as an interfered communication frequency. Otherwise, it is marked as an undisturbed communication frequency.

[0014] As a further solution of the present invention: the process of obtaining the amplitude coefficient is:

[0015] Collect signal strength data of current communication frequency in real time according to time series;

[0016] Performing empirical mode decomposition on the collected signal strength data to obtain a series of intrinsic mode functions and a residual term; based on the energy level of each intrinsic mode function, selecting the intrinsic mode function with the highest energy as the main component reflecting the change of signal strength;

[0017] For the selected main intrinsic mode function, the maximum and minimum values ​​of the intrinsic mode function are calculated to obtain the signal strength variation amplitude, and the signal strength variation amplitude is calculated to obtain the amplitude coefficient by ratio calculation of the average signal strength in the time window.

[0018] As a further solution of the present invention: the process of obtaining the frequency coefficient is:

[0019] Collect signal strength data of current communication frequency in real time according to time series;

[0020] Apply Haar wavelet transform to the acquired signal strength data to decompose it into approximate coefficients and detail coefficients at different scales;

[0021] The energy level of the detail coefficient at each scale is obtained by calculating the square sum of all detail coefficients at each scale and taking the average value;

[0022] The frequency coefficient is obtained by multiplying the energy level of each scale by its corresponding scale number and then dividing it by the sum of the energy levels at all scales.

[0023] As a further solution of the present invention: if the replaced frequency point is determined to be the interfered communication frequency point for M consecutive times, automatically switching to the standby frequency hopping sequence specifically includes:

[0024] If a communication frequency is determined to be interfered with for M consecutive times, a non-interfered frequency is immediately selected from the preset communication frequency pool as a replacement;

[0025] During the replacement process, the system not only updates the local frequency configuration, but also broadcasts the new frequency information to adjacent nodes through the frequency hopping channel to ensure that the entire network can be adjusted synchronously;

[0026] If the performance of the new sequence does not meet the standard, the alternative sequence is immediately adjusted and regenerated until the performance of the new sequence meets the standard.

[0027] As a further solution of the present invention: the prediction of whether the communication frequency point in the future period is interfered specifically includes:

[0028] Based on the undisturbed communication frequency, the signal-to-noise ratio data of the current communication frequency is obtained according to the time series, the difference between the signal-to-noise ratio of the current communication frequency and the preset threshold is calculated, and the signal-to-noise ratio anomaly coefficient is calculated according to the degree of change of the signal-to-noise ratio difference. The bit error rate data of the current communication frequency is obtained according to the time series, and the bit error rate anomaly coefficient is calculated according to the change rate of the bit error rate data in the time series. The signal-to-noise ratio anomaly coefficient and the bit error rate anomaly coefficient are constructed into a comprehensive feature vector as the input of the comprehensive scoring model. According to the output of the model, it is predicted whether the communication frequency will be interfered in the future.

[0029] As a further solution of the present invention: the process of obtaining the signal-to-noise ratio anomaly coefficient is:

[0030] Based on the undisturbed communication frequency point, a wireless network analyzer is used to collect the signal-to-noise ratio data of the communication frequency point in real time at a frequency of once per second to form a time series data;

[0031] Perform fast Fourier transform on the collected signal-to-noise ratio time series data to convert it into the frequency domain for analysis; based on the fast Fourier transform results, calculate the square of the amplitude of each frequency component to obtain the power spectrum density of each frequency component;

[0032] The mean and variance of the signal-to-noise ratio are calculated from the time domain data. According to the calculated mean signal-to-noise ratio, the difference between the mean signal-to-noise ratio and the preset threshold is calculated. According to the degree of change of the signal-to-noise ratio difference, the ratio of the absolute value of the difference between the mean signal-to-noise ratio and the preset threshold to the variance is calculated to obtain the signal-to-noise ratio anomaly coefficient.

[0033] As a further solution of the present invention: the process of obtaining the bit error rate abnormality coefficient is:

[0034] Based on the undisturbed communication frequency point, a wireless network analyzer is used to collect the bit error rate data of the communication frequency point in real time at a frequency of once per second to form a time series data;

[0035] For each time point, the difference between the current bit error rate and the bit error rate at the previous time point is calculated, and the ratio is calculated with the time interval to obtain the rate of change of the bit error rate;

[0036] The bit error rate change rate is used as the key feature of the bit error rate data and as input data for K-means clustering analysis. The number of clusters G is selected and the centroid position is initialized. Clustering is performed by minimizing the square distance between all points and the centroid of their clusters. Data points are assigned to the nearest cluster and the position of the cluster centroid is updated until convergence.

[0037] Based on the K-means clustering results, outliers are identified. For the rate of change of the bit error rate at each time point, the distance to the nearest cluster center is calculated, and the ratio is calculated with the maximum value of all distances to obtain the bit error rate anomaly coefficient.

[0038] As a further solution of the present invention: the construction process of the comprehensive scoring model is:

[0039] Based on each undisturbed communication frequency, the signal-to-noise ratio anomaly coefficient and the bit error rate anomaly coefficient are obtained, and a comprehensive feature vector is constructed as the input of the comprehensive scoring model. The comprehensive scoring model is trained with the minimization of the error between the prediction of whether the communication frequency in the future period is interfered with and the actual communication frequency in the future period being interfered as the training goal. According to the trained comprehensive scoring model, the output of the comprehensive scoring model is whether the current communication frequency is an interfered communication frequency or an undisturbed communication frequency. The comprehensive scoring model is a random forest model.

[0040] Beneficial effects of the present invention:

[0041] (1) The present invention achieves accurate quantification and intelligent prediction of the communication frequency status by integrating real-time data acquisition with advanced signal processing technologies, including empirical mode decomposition, fast Fourier transform and Haar wavelet transform, as well as machine learning algorithms such as K-means clustering and random forest model. Specifically, the system first uses a wireless network analyzer to collect signal strength, signal-to-noise ratio and bit error rate data of each frequency point in real time at a frequency of seconds, and analyzes the main components of signal strength changes through EMD, converts the signal-to-noise ratio to the frequency domain through FFT to reveal its internal pattern, and captures the high-frequency fluctuation characteristics in the bit error rate through Haar wavelet transform. Subsequently, these multi-dimensional features are constructed into a comprehensive feature vector and sent as input to the trained random forest model to predict whether the frequency point will be interfered in the future. This method not only significantly improves the scientificity and accuracy of frequency selection, but also can prospectively evaluate potential interference situations to ensure that the network provides stable and high-quality communication services in the long-term operation in the future. Compared with the traditional manual or simple rule-based frequency selection method, the present invention can more effectively identify and avoid interference sources with its highly intelligent data processing capabilities and accurate prediction models, greatly improving communication quality and network reliability, especially in complex and changeable electromagnetic environments, providing strong anti-interference capabilities and optimized performance guarantees for wireless ad hoc networks. Therefore, the present invention has shown significant advantages in improving communication efficiency and reducing interference effects, and is a key technological innovation for achieving efficient and reliable wireless communications.

[0042] (2) The present invention introduces an advanced dynamic adjustment mechanism. When the current communication frequency is detected to be interfered, the system can intelligently select an undisturbed frequency from the pre-set communication frequency pool to replace it, and continuously monitor the new frequency. If the new frequency is still interfered in multiple consecutive detections, it will automatically switch to the backup frequency hopping sequence to ensure the stability and reliability of the communication link. This process not only relies on real-time data collection and analysis, but also combines the intelligent feedback calibration mechanism with the sliding window technology to verify the effectiveness of the new frequency hopping sequence, so that it can still maintain efficient and stable communication quality in a high-interference environment. Through this mechanism, the wireless ad hoc network can show excellent adaptability and robustness in a complex and changeable electromagnetic environment, significantly reducing communication interruptions or quality problems caused by interference. More importantly, this method supports synchronous adjustment of the entire network, using the frequency hopping channel to broadcast the updated frequency information to ensure that all nodes can be synchronized in time, enhancing the overall coordination and stability of the network. This highly intelligent dynamic adjustment strategy not only improves the anti-interference capability of a single node, but also optimizes the performance of the entire network, enabling wireless ad hoc networks to have stronger self-repair and self-optimization capabilities when facing complex electromagnetic environment challenges, providing a solid technical guarantee for achieving seamless, high-quality data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention will be further described below in conjunction with the accompanying drawings.

[0044] Figure 1 The present invention is a flowchart of the specific steps of an anti-interference automatic synchronous frequency hopping method suitable for wireless ad hoc networks. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] See also Figure 1 As shown, the present invention is an anti-interference automatic synchronous frequency hopping method suitable for wireless ad hoc networks, comprising the following steps:

[0047] S1: collect the signal strength data of the current communication frequency in real time, and judge whether the current communication frequency is interfered according to the amplitude and frequency of signal strength change;

[0048] S2: According to the judgment result, the current communication frequency point is divided into: an interfered communication frequency point and an undisturbed communication frequency point;

[0049] S3: Based on the interfered communication frequency, the system selects an uninterfered communication frequency from a preset communication frequency pool to replace the interfered communication frequency, and continuously monitors the replaced communication frequency. If the replaced frequency is determined to be an interfered communication frequency for M consecutive times, it automatically switches to the backup frequency hopping sequence;

[0050] S4: Based on the undisturbed communication frequency, the signal-to-noise ratio characteristics and bit error rate characteristics of the current communication frequency are extracted and constructed into a comprehensive feature vector as the input of the comprehensive scoring model. According to the comprehensive scoring model, it is predicted whether the communication frequency will be interfered in the future, and the frequency that will remain undisturbed for a long time in the future is preferentially selected as the new communication frequency.

[0051] In S1, the signal strength data of the current communication frequency is collected in real time, and whether the current communication frequency is interfered is determined according to the signal strength change amplitude and change frequency, which specifically includes:

[0052] Use a wireless network analyzer with signal strength measurement function, configure it to monitor specific communication frequencies, ensure that the device settings include the correct center frequency, bandwidth, and required resolution bandwidth to meet the signal capture requirements, and set up an automatic collection script through its supporting software development kit to collect signal strength data of specific communication frequencies in real time at a frequency of once per second.

[0053] Collect the signal strength of the current communication frequency in real time, calculate the amplitude coefficient and frequency coefficient respectively according to the signal strength change amplitude and change frequency, normalize the amplitude coefficient and frequency coefficient to obtain the interference coefficient, and judge whether the interference coefficient of the current communication frequency is greater than or equal to the threshold. If so, mark it as the interfered communication frequency, otherwise mark it as the undisturbed communication frequency;

[0054] The process of obtaining the amplitude coefficient is as follows:

[0055] Collect signal strength data of current communication frequency in real time according to time series;

[0056] Performing empirical mode decomposition on the collected signal strength data to obtain a series of intrinsic mode functions and a residual term; based on the energy level of each intrinsic mode function, selecting the intrinsic mode function with the highest energy as the main component reflecting the change of signal strength;

[0057] Among them, the calculation expression of empirical mode decomposition is: ;

[0058] In the formula, Indicates at time The signal strength value measured on Indicates the time collection point, represents the number of intrinsic mode functions, represents the total number of intrinsic mode functions, represents the residual term, Indicates Intrinsic mode functions;

[0059] The energy level of each intrinsic mode function is calculated as follows: ;

[0060] In the formula, Indicates the starting time point of the collection period. Indicates the end time point of the collection period. Indicates The energy level of the intrinsic mode functions;

[0061] For the selected main intrinsic mode function, the maximum and minimum values ​​of the intrinsic mode function are calculated to obtain the signal strength variation amplitude, and the signal strength variation amplitude is calculated to obtain the amplitude coefficient by ratio calculation of the average signal strength in the time window.

[0062] It should be noted that: through the empirical mode decomposition technology, the components that best reflect the signal fluctuation characteristics can be extracted from the complex signal environment, and the amplitude coefficient can be calculated based on this to help the system accurately determine the frequency point state. This method improves the anti-interference ability and enhances the adaptability and stability of wireless ad hoc networks in complex environments.

[0063] The process of obtaining the frequency coefficient is as follows:

[0064] Collect signal strength data of current communication frequency in real time according to time series;

[0065] Apply Haar wavelet transform to the acquired signal strength data to decompose it into approximate coefficients and detail coefficients at different scales;

[0066] Calculate the energy level of the detail coefficient at each scale, and the calculation expression is: ;

[0067] In the formula, Indicates scale, represents the number of detail coefficients, Indicated in The scale of The detail coefficient, Indicates The energy level of detail coefficients at each scale, Indicates The total number of detail coefficients at each scale, Indicates Detail coefficient at each scale;

[0068] The frequency coefficient is obtained by multiplying the energy level of each scale by its corresponding scale number and then dividing it by the sum of the energy levels at all scales.

[0069] It should be noted that by applying the Haar wavelet transform to perform multi-resolution analysis on the signal strength data, it is possible to more accurately capture the high-frequency components in the signal and calculate the frequency coefficient based on this. The frequency coefficient not only reflects the frequency characteristics of the signal strength changing over time, but also provides an important basis for evaluating whether the communication frequency is interfered with.

[0070] The calculation process of the interference coefficient is:

[0071] ;

[0072] In the formula, represents the interference coefficient, represents the amplitude coefficient, represents the frequency coefficient, and represents the preset coefficient, and and Both are greater than 0.

[0073] In S3, based on the interfered communication frequency, the system selects an uninterrupted communication frequency from a preset communication frequency pool to replace the interfered communication frequency, and continuously monitors the replaced communication frequency. If the replaced frequency is determined to be an interfered communication frequency for M consecutive times, it automatically switches to the backup frequency hopping sequence, specifically including:

[0074] When it is detected that the current communication frequency is interfered with, the system selects an undisturbed communication frequency from a preset communication frequency pool for replacement, and continuously monitors the replaced communication frequency; specifically, the following steps are included: first, the system evaluates the interference status of each communication frequency in real time. Once it is found that the currently used communication frequency is judged to be interfered with for M consecutive times, an undisturbed frequency is immediately selected from the preset communication frequency pool as a replacement.

[0075] During the replacement process, the system not only updates the local frequency configuration, but also broadcasts the new frequency information to adjacent nodes through the frequency hopping channel to ensure that the entire network can be adjusted synchronously. In addition, for the replaced communication frequency, the system sets a continuous monitoring cycle. If the frequency is determined to be interfered in M ​​consecutive checks, the backup frequency hopping sequence switching mechanism is triggered, and the intelligent feedback calibration mechanism is used to dynamically adjust the frequency hopping sequence according to the actual communication quality feedback from the receiving end, and the sliding window technology is used to verify the effectiveness of the new sequence to ensure that high-quality data transmission can be maintained even in high-interference environments. This method greatly enhances the ability of wireless ad hoc networks to cope with complex electromagnetic environments and improves overall network performance.

[0076] The automatically switching to the standby frequency hopping sequence comprises:

[0077] If a communication frequency is determined to be interfered with for M consecutive times, a non-interfered frequency is immediately selected from the preset communication frequency pool as a replacement;

[0078] During the replacement process, the system not only updates the local frequency configuration, but also broadcasts the new frequency information to adjacent nodes through the frequency hopping channel to ensure that the entire network can be adjusted synchronously;

[0079] If the performance of the new sequence does not meet the standard, the alternative sequence is immediately adjusted and regenerated until the performance of the new sequence meets the standard.

[0080] In S4, based on the undisturbed communication frequency, the signal-to-noise ratio characteristics and bit error rate characteristics of the current communication frequency are extracted and constructed into a comprehensive feature vector as the input of the comprehensive scoring model. According to the comprehensive scoring model, it is predicted whether the communication frequency will be interfered in the future, and the frequency that will remain undisturbed for a long time in the future is preferentially selected as the new communication frequency, which specifically includes:

[0081] Based on the undisturbed communication frequency, the signal-to-noise ratio data of the current communication frequency is obtained according to the time series, the difference between the signal-to-noise ratio of the current communication frequency and the preset threshold is calculated, and the signal-to-noise ratio anomaly coefficient is calculated according to the degree of change of the signal-to-noise ratio difference. The bit error rate data of the current communication frequency is obtained according to the time series, and the bit error rate anomaly coefficient is calculated according to the change rate of the bit error rate data in the time series. The signal-to-noise ratio anomaly coefficient and the bit error rate anomaly coefficient are constructed into a comprehensive feature vector as the input of the comprehensive scoring model. The output of the model is whether the communication frequency will be interfered in the future.

[0082] The process of obtaining the signal-to-noise ratio anomaly coefficient is as follows:

[0083] Based on the undisturbed communication frequency point, a wireless network analyzer is used to collect the signal-to-noise ratio data of the communication frequency point in real time at a frequency of once per second to form a time series data;

[0084] The collected signal-to-noise ratio time series data is subjected to fast Fourier transform to be converted into the frequency domain for analysis; based on the fast Fourier transform results, the power spectral density of each frequency component is calculated, and the calculation expression is: ;

[0085] In the formula, represents the number of frequency components, Indicates frequency components, Indicates The complex value of the frequency components, Indicates The power spectral density of the frequency components;

[0086] The mean and variance of the signal-to-noise ratio are calculated from the time domain data. According to the calculated mean signal-to-noise ratio, the difference between the mean signal-to-noise ratio and the preset threshold is calculated. According to the degree of change of the signal-to-noise ratio difference, the ratio of the absolute value of the difference between the mean signal-to-noise ratio and the preset threshold to the variance is calculated to obtain the signal-to-noise ratio anomaly coefficient.

[0087] It should be noted that by collecting the signal-to-noise ratio data of the communication frequency points in real time and applying fast Fourier transform for frequency domain analysis, it is not only possible to accurately identify abnormal signal-to-noise ratio situations, but also to provide quantitative indicators to guide subsequent operations. The system can effectively identify and quantify abnormal signal-to-noise ratio situations, ensure communication quality, and improve overall network performance.

[0088] The process of obtaining the bit error rate abnormality coefficient is as follows:

[0089] Use a wireless network analyzer to collect bit error rate data of a specific communication frequency point in real time at a frequency of once per second to form a time series data;

[0090] For each time point, the difference between the current bit error rate and the bit error rate at the previous time point is calculated, and the ratio is calculated with the time interval to obtain the rate of change of the bit error rate;

[0091] The bit error rate change rate is used as the key feature of the bit error rate data and as input data for K-means clustering analysis. The number of clusters G is selected and the centroid position is initialized. Clustering is performed by minimizing the square distance between all points and the centroid of their clusters. Data points are assigned to the nearest cluster and the position of the cluster centroid is updated until convergence.

[0092] Based on the K-means clustering results, outliers are identified. For the rate of change of the bit error rate at each time point, the distance to the nearest cluster center is calculated, and the ratio is calculated with the maximum value of all distances to obtain the bit error rate anomaly coefficient.

[0093] It should be noted that by collecting the bit error rate data of the communication frequency points in real time, using the K-means clustering algorithm to identify abnormal patterns in the bit error rate, and calculating the bit error rate abnormality coefficient, a refined evaluation of the communication link quality is achieved.

[0094] The construction process of the comprehensive scoring model is as follows:

[0095] Based on each undisturbed communication frequency point, a signal-to-noise ratio abnormal coefficient and a bit error rate abnormal coefficient are obtained, and a comprehensive feature vector is constructed as the input of the comprehensive scoring model. The comprehensive scoring model is trained with minimizing the error between the prediction of whether the communication frequency point in the future period is interfered and the actual communication frequency point in the future period being interfered as the training goal. According to the trained comprehensive scoring model, the output of the comprehensive scoring model is that the current communication frequency point is an interfered communication frequency point or an undisturbed communication frequency point, and the comprehensive scoring model is a random forest model;

[0096] The training process of the random forest model is:

[0097] Collect historical data and build a training set. Each sample contains a comprehensive feature vector and its corresponding label (interfered or not interfered). If the communication frequency is determined to be interfered in the future, the label is set to 1; otherwise, it is set to 0. The training set is trained using the random forest algorithm. The random forest generates multiple sub-sample sets by sampling the training data, and trains a decision tree on each sub-sample set. The final prediction result is determined by the vote of all decision trees.

[0098] According to the trained comprehensive scoring model, the comprehensive feature vector model of the current communication frequency is input and the output of the current communication frequency is whether the current communication frequency is an interfered communication frequency or an undisturbed communication frequency. The frequency that will remain undisturbed for a long time in the future is preferentially selected as the new communication frequency.

[0099] It should be noted that by collecting the signal-to-noise ratio and bit error rate data of the communication frequency in real time, calculating the corresponding anomaly coefficient, and constructing it into a comprehensive feature vector as input, the random forest model is trained to predict whether the communication frequency will be interfered in the future. This method can not only accurately identify potential interference in the communication link, but also provide quantitative indicators to guide subsequent operations.

[0100] The working principle of the present invention is as follows: by collecting the signal strength data of the current communication frequency in real time, and calculating the interference coefficient according to the signal strength change amplitude and frequency, it is judged whether the frequency is interfered. Subsequently, the communication frequency is divided into two categories: interfered and undisturbed, and the interfered frequency is replaced. When it is detected that the current frequency is interfered, the system selects an undisturbed frequency from the pre-set communication frequency pool for replacement, and continuously monitors the replaced frequency; if it is judged to be interfered for M consecutive times, the standby frequency hopping sequence switching mechanism is triggered. Furthermore, based on the undisturbed frequency, the signal-to-noise ratio and bit error rate characteristics are extracted, and a comprehensive feature vector is constructed as the input of the random forest model to predict whether the frequency will be interfered in the future. Specifically, the signal-to-noise ratio and bit error rate data are collected in real time using a wireless network analyzer, and the signal-to-noise ratio anomaly coefficient and the bit error rate anomaly coefficient are calculated respectively, and accurate quantization is achieved by combining the fast Fourier transform and the K-means clustering algorithm. The trained random forest model outputs the frequency state prediction result, and preferentially selects the frequency that remains undisturbed for a long time in the future as the new communication frequency. This method not only improves the scientificity and accuracy of frequency selection, but also enhances the network's ability to cope with interference, ensures high-quality data transmission, and significantly improves the overall performance and stability of wireless ad hoc networks. In addition, the effectiveness of the new sequence is verified through an intelligent feedback calibration mechanism and sliding window technology, which can maintain efficient and stable communication quality even in high-interference environments.

[0101] 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.

[0102] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by 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 process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0103] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0104] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0105] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An anti-interference automatic synchronous frequency hopping method suitable for wireless ad hoc networks, characterized in that: The following steps are involved: S1: collect the signal strength data of the current communication frequency in real time, and judge whether the current communication frequency is interfered according to the amplitude and frequency of signal strength change; S2: According to the judgment result, the current communication frequency point is divided into: an interfered communication frequency point and an uninterfered communication frequency point; S3: Based on the interfered communication frequency, the system selects an uninterfered communication frequency from a preset communication frequency pool to replace the interfered communication frequency, and continuously monitors the replaced communication frequency. If the replaced frequency is determined to be an interfered communication frequency for M consecutive times, it automatically switches to the backup frequency hopping sequence; S4: Based on the undisturbed communication frequency, the signal-to-noise ratio characteristics and bit error rate characteristics of the current communication frequency are extracted and constructed into a comprehensive feature vector as the input of the comprehensive scoring model. According to the comprehensive scoring model, it is predicted whether the communication frequency will be interfered in the future, and the frequency that will remain undisturbed for a long time in the future is preferentially selected as the new communication frequency.

2. The anti-interference automatic synchronous frequency hopping method applicable to a wireless ad hoc network according to claim 1, characterized in that: The determining whether the current communication frequency point is interfered with specifically includes: The signal strength of the current communication frequency is collected in real time. The amplitude coefficient and frequency coefficient are calculated according to the signal strength change amplitude and change frequency respectively. The amplitude coefficient and frequency coefficient are normalized and calculated to obtain the interference coefficient. It is determined whether the interference coefficient of the current communication frequency is greater than or equal to the threshold. If so, it is marked as an interfered communication frequency. Otherwise, it is marked as an undisturbed communication frequency.

3. The anti-interference automatic synchronous frequency hopping method applicable to wireless ad hoc networks according to claim 2, characterized in that: The process of obtaining the amplitude coefficient is as follows: Collect signal strength data of current communication frequency in real time according to time series; Performing empirical mode decomposition on the collected signal strength data to obtain a series of intrinsic mode functions and a residual term; based on the energy level of each intrinsic mode function, selecting the intrinsic mode function with the highest energy as the main component reflecting the change of signal strength; For the selected main intrinsic mode function, the maximum and minimum values ​​of the intrinsic mode function are calculated to obtain the signal strength variation amplitude, and the signal strength variation amplitude is calculated to obtain the amplitude coefficient by ratio calculation of the average signal strength in the time window.

4. The anti-interference automatic synchronous frequency hopping method applicable to a wireless ad hoc network according to claim 2, characterized in that: The process of obtaining the frequency coefficient is as follows: Collect signal strength data of current communication frequency in real time according to time series; Apply Haar wavelet transform to the acquired signal strength data to decompose it into approximate coefficients and detail coefficients at different scales; The energy level of the detail coefficient at each scale is obtained by calculating the square sum of all detail coefficients at each scale and taking the average value; The frequency coefficient is obtained by multiplying the energy level of each scale by its corresponding scale number and then dividing it by the sum of the energy levels at all scales.

5. The anti-interference automatic synchronous frequency hopping method applicable to wireless ad hoc networks according to claim 1, characterized in that: If the replaced frequency point is determined to be the interfered communication frequency point for M consecutive times, automatically switching to the standby frequency hopping sequence specifically includes: If a communication frequency is determined to be interfered with for M consecutive times, a non-interfered frequency is immediately selected from the preset communication frequency pool as a replacement; During the replacement process, the system not only updates the local frequency configuration, but also broadcasts the new frequency information to adjacent nodes through the frequency hopping channel to ensure that the entire network can be adjusted synchronously; If the performance of the new sequence does not meet the standard, the alternative sequence is immediately adjusted and regenerated until the performance of the new sequence meets the standard.

6. The anti-interference automatic synchronous frequency hopping method applicable to wireless ad hoc networks according to claim 1, characterized in that: The predicting whether the communication frequency point in the future period is interfered specifically includes: Based on the undisturbed communication frequency, the signal-to-noise ratio data of the current communication frequency is obtained according to the time series, the difference between the signal-to-noise ratio of the current communication frequency and the preset threshold is calculated, and the signal-to-noise ratio anomaly coefficient is calculated according to the degree of change of the signal-to-noise ratio difference. The bit error rate data of the current communication frequency is obtained according to the time series, and the bit error rate anomaly coefficient is calculated according to the change rate of the bit error rate data in the time series. The signal-to-noise ratio anomaly coefficient and the bit error rate anomaly coefficient are constructed into a comprehensive feature vector as the input of the comprehensive scoring model. According to the output of the model, it is predicted whether the communication frequency will be interfered in the future.

7. The anti-interference automatic synchronous frequency hopping method applicable to wireless ad hoc networks according to claim 6, characterized in that: The process of obtaining the signal-to-noise ratio anomaly coefficient is as follows: Based on the undisturbed communication frequency point, a wireless network analyzer is used to collect the signal-to-noise ratio data of the communication frequency point in real time at a frequency of once per second to form a time series data; Perform fast Fourier transform on the collected signal-to-noise ratio time series data to convert it into the frequency domain for analysis; based on the fast Fourier transform results, calculate the square of the amplitude of each frequency component to obtain the power spectrum density of each frequency component; The mean and variance of the signal-to-noise ratio are calculated from the time domain data. According to the calculated mean signal-to-noise ratio, the difference between the mean signal-to-noise ratio and the preset threshold is calculated. According to the degree of change of the signal-to-noise ratio difference, the ratio of the absolute value of the difference between the mean signal-to-noise ratio and the preset threshold to the variance is calculated to obtain the signal-to-noise ratio anomaly coefficient.

8. The anti-interference automatic synchronous frequency hopping method applicable to wireless ad hoc networks according to claim 6, characterized in that: The process of obtaining the bit error rate abnormality coefficient is as follows: Based on the undisturbed communication frequency point, a wireless network analyzer is used to collect the bit error rate data of the communication frequency point in real time at a frequency of once per second to form a time series data; For each time point, the difference between the current bit error rate and the bit error rate at the previous time point is calculated, and the ratio is calculated with the time interval to obtain the rate of change of the bit error rate; The bit error rate change rate is used as the key feature of the bit error rate data and as input data for K-means clustering analysis. The number of clusters G is selected and the centroid position is initialized. Clustering is performed by minimizing the square distance between all points and the centroid of their clusters. Data points are assigned to the nearest cluster and the position of the cluster centroid is updated until convergence. Based on the K-means clustering results, outliers are identified. For the rate of change of the bit error rate at each time point, the distance to the nearest cluster center is calculated, and the ratio is calculated with the maximum value of all distances to obtain the bit error rate anomaly coefficient.

9. The anti-interference automatic synchronous frequency hopping method applicable to wireless ad hoc networks according to claim 6, characterized in that: The construction process of the comprehensive scoring model is as follows: Based on each undisturbed communication frequency, the signal-to-noise ratio anomaly coefficient and the bit error rate anomaly coefficient are obtained, and a comprehensive feature vector is constructed as the input of the comprehensive scoring model. The comprehensive scoring model is trained with the minimization of the error between the prediction of whether the communication frequency in the future period is interfered with and the actual communication frequency in the future period being interfered as the training goal. According to the trained comprehensive scoring model, the output of the comprehensive scoring model is whether the current communication frequency is an interfered communication frequency or an undisturbed communication frequency. The comprehensive scoring model is a random forest model.

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