677 results about "Noise component" patented technology

The invention relates to an intelligent microphone pickup and speech enhancement method, system and device, and the method comprises the steps: obtaining an audio signal collected by a dual-silicon microphone array, and carrying out the cross-correlation analysis of the audio signal, and obtaining a corresponding human voice signal correlation feature; performing sound source positioning analysis on the audio signal based on the human voice signal correlation feature to obtain a corresponding target human voice signal; performing frequency characteristic analysis on the target human voice signal, and performing segmented dynamic gain processing on the signal according to a preset frequency band range to obtain a corresponding voice enhancement signal; performing noise component adaptive filtering processing on the target human voice signal to obtain a corresponding noise suppression parameter; and inputting the speech enhancement signal and the noise suppression parameter into a preset echo cancellation model for joint optimization to obtain a corresponding output human voice signal. According to the invention, the coupling problem of multiple acoustic interferences can be effectively solved.

The invention relates to the technical field of audio processing, and discloses an audio processing method and system for speech spectrum reconstruction, and the method comprises the steps: carrying out the preprocessing of an original speech signal collected by a vehicle-mounted microphone, and obtaining a speech signal time-frequency matrix; performing singular value decomposition on the voice signal time-frequency matrix to obtain a sparse matrix representing voice components and a low-rank matrix representing noise components, and establishing a frequency band characteristic incidence matrix through a mutual information entropy calculation method; inputting the sparse matrix into a selection forgetting extreme learning machine for voice feature prediction to obtain predicted voice feature data; and based on the predicted voice feature data and the frequency band feature incidence matrix, performing voice probability calculation and feature interpolation on each time frequency point by adopting a logarithmic frequency spectrum amplitude calculation method to obtain reconstructed frequency spectrum data, thereby improving the accuracy of voice recognition and meeting the requirement of real-time processing in a vehicle-mounted environment at the same time.

The invention discloses a leakage sound signal denoising method based on an optimized VMD combined with an improved wavelet threshold, and aims to solve the problem of low recognition accuracy when an existing noise processing method is used for processing leakage sound signals of a water supply network. According to the method, the noise component and range in the leakage sound signal are analyzed by collecting the leakage sound signal of the actual water supply network; performing parameter optimization on the decomposition modal number K and the penalty factor alpha of the VMD based on an eagle optimization algorithm to obtain optimal parameters, and constructing a variational model by adopting the optimal parameters; decomposing the leakage sound signal by using a variational model to obtain a plurality of intrinsic mode components; screening the plurality of intrinsic mode components by using a correlation coefficient method to obtain high-frequency components and low-frequency components; performing wavelet threshold de-noising processing on the high-frequency component to obtain a de-noised high-frequency component; and reconstructing the low-frequency component and the denoised high-frequency component to obtain a denoised leakage sound signal. The beneficial effects are that the signal-to-noise ratio of denoising processing is improved, and the recognition accuracy is improved.

The invention relates to the technical field of equipment testing, and discloses a motor operation state vibration noise combined diagnosis generation method, which comprises the following steps: when a motor operates, performing bimodal data extraction on operation state data to obtain a vibration signal and a noise signal; performing order tracking analysis on the vibration signal to obtain an electric vibration characteristic order component; calculating a time domain energy envelope line, and performing synchronous time domain segmentation on the noise signal to obtain noise signal slices; filtering non-related components of the vibration characteristic order component in the noise signal slice, and extracting a vibration synchronous noise component of the vibration characteristic order component synchronous with the current rotation period of the motor according to a filtered result; constructing a sound-vibration fusion spectrum; analyzing the distribution rule of the sound and vibration intensity in the sound and vibration fusion spectrum, identifying a correlation mutation point, and diagnosing a specific operation state fault based on the distribution mode of the correlation mutation point; according to the invention, the efficiency of vibration noise combined diagnosis generation of the motor operation state can be improved.

A leakage sound signal denoising method based on a combination of optimized VMD and an improved wavelet threshold, for use in solving the problem in existing noise processing methods of low identification accuracy in processing leakage sound signals of water supply pipe networks. The present invention comprises: acquiring leakage sound signals of a real water supply pipe network, and analyzing noise components and ranges of the leakage sound signals; on the basis of a goshawk optimization algorithm, performing parameter optimization on the number K of decomposition modes and a penalty factor α of VMD to obtain optimal parameters, and using the optimal parameters to construct a variational model; using the variational model to decompose the leakage sound signals to obtain a plurality of intrinsic mode components; using a correlation coefficient method to screen the plurality of intrinsic mode components to obtain high-frequency components and low-frequency components; performing wavelet threshold denoising processing on the high-frequency components to obtain denoised high-frequency components; and reconstructing the low-frequency components and the denoised high-frequency components to obtain denoised leakage sound signals. The beneficial effects are that the signal-to-noise ratio of denoising processing is improved, and the identification accuracy is improved.

The invention provides an HPLC (High Performance Liquid Chromatography) dual-mode communication stability method, medium and system, and belongs to the technical field of dual-channel multiplexing communication, and the method comprises the following steps: firstly, collecting HPLC low-frequency / high-frequency carrier signals, working voltage / current, temperature and electromagnetic interference data in real time; stable, variable and noise components of a carrier signal are respectively obtained through empirical mode decomposition, and a correlation coefficient matrix of the variable components and interference factors is established by using multiple linear regression and singular value decomposition. And calculating a signal intensity, a signal-to-noise ratio and an error rate evaluation matrix based on the stability coefficient matrix, and constructing an abnormal feature matrix in combination with a communication protocol specification. And finally, inputting the features into a five-layer full-connection neural network, outputting fault type probability distribution through a softmax function, and taking the type corresponding to the maximum probability value as a diagnosis result. According to the invention, the technical problem of rapid and accurate diagnosis of HPLC communication stability in a complex and changeable low-voltage distribution network environment is solved.

A hearing system comprises a hearing device, e.g. a hearing aid or a headset, configured to be worn by a user. The hearing device comprises at least one input transducer for providing at least one electric input signal representative of sound in the environment of the hearing device, wherein said at least one electric input signal comprises a target signal component assumed to be of current interest to the user, and a noise component. The hearing device further comprises a noise control system configured to provide an estimate of said target signal component and an estimate of said noise component and to apply a statistical structure to said noise component to thereby provide a modified noise component comprising said statistical structure; and to determine a modified estimate of said target signal component in dependence of said modified noise component. Thereby an improved segregation of sound sources may be provided.

The invention belongs to the technical field of digital image processing, and discloses a low-light image enhancement method based on a zero-reference Retinex decomposition network, which comprises the following steps of: constructing a low-light image enhancement data set; building and training a low-light noise extraction network; building and training a low-light decomposition network, and estimating a reflection component and an illumination component; enhancing the low-light image; and testing the low light enhancement network. According to the low-light image enhancement method based on the zero-reference Retinex decomposition network, the illumination component, the reflection component and the noise component of the low-light image are learned through the deep learning network, the denoised low-light image is obtained, the illumination component after illumination adjustment is obtained through the gamma correction technology, and the low-light image enhancement method based on the zero-reference Retinex decomposition network is obtained through the Retinex decomposition theory. And obtaining a final low-light enhancement result. According to the method, training data is not needed, the low-light image is directly and rapidly enhanced, the image visual quality is improved, and the visual task precision is improved.

Apparatuses, systems, and techniques to selectively suppress noise in a conference call. In at least one embodiment, one or more neural networks may be used to identify a source of a noise component and cause a notification to be sent, informing a user of the noise source to confirm or decline suppression of the noise component.

The invention belongs to the technical field of space optical remote sensing, and relates to a high-resolution remote sensing image semantic segmentation method based on a diffusion model. The specific process is as follows: first-stage training: labeling the type of each pixel of a high-resolution remote sensing image to generate a label image, and training an auto-encoder comprising an encoder and a decoder by using the label image; in the second stage of training, on the basis of the auto-encoder, a conditional diffusion model is loaded and trained, the conditional diffusion model comprises a noise injection module, a conditional encoding module and a de-noising U-Net, and parameters of the auto-encoder are fixed during training; wherein the noise injection module adds noise to hidden variables output by the encoder, the conditional encoding module is used for performing multi-scale feature extraction on the high-resolution image, and the de-noised U-Net performs image component and noise component prediction and image reconstruction under the guidance of the multi-scale features; the decoder is used for decoding the reconstructed image; and image semantic segmentation: carrying out semantic segmentation on the high-resolution remote sensing image by using the trained network.

The invention relates to the technical field of audio noise reduction, in particular to a hybrid noise reduction method and system for a Bluetooth headset. The method comprises the following steps: obtaining multi-dimensional environment noise data based on an environment noise sensor, and carrying out noise component feature analysis to generate environment noise multi-frequency component features; performing intelligent noise reduction intensity and self-adaptive filtering noise reduction suppression on the multi-frequency component characteristics of the environmental noise so as to generate an external environmental noise reduction suppression strategy; a micro audio signal is sent based on an ear canal form sensor, and audio signal feedback information is collected; performing three-dimensional ear canal structure modeling according to the audio signal feedback information so as to construct a three-dimensional ear canal structure model of the user; and obtaining a noise reduction feedback log of the wearer, and carrying out personalized noise reduction demand analysis to obtain the noise reduction demand of the user in the current scene. According to the invention, through comprehensive and finer mixed noise suppression, the audio quality and stability of the Bluetooth earphone are improved.

The invention discloses a fan blade diagnosis method and system based on voiceprint perception, and relates to the technical field of intelligent monitoring of wind power equipment, and the method comprises the steps: carrying out frequency domain compensation processing, stripping environment wind noise components, and generating a pure voiceprint signal based on a wind flow characteristic parameter set; based on the pure voiceprint signal, identifying a sensitive acoustic frequency band of the fan blade, exciting a sound wave phase synergistic effect in the sensitive acoustic frequency band, and generating an enhanced voiceprint signal; quantifying the internal damage depth of the fan blade material according to the enhanced voiceprint signal, and calculating a sound energy flow disorder degree index; according to the sound energy flow disorder degree index, the blade health state of the fan is judged through a multi-dimensional acoustic characteristic state space mapping mechanism, and a three-dimensional health assessment report is generated; through a physical level wind noise stripping technology, a transfer function matrix is generated based on vortex interference modeling and sound wave-airflow phase offset correction, frequency domain energy reweighted decoupling is realized, and environmental wind noise is accurately stripped in a strong turbulence environment.

The invention discloses a low-dose CT image denoising method and system based on a conditional diffusion model, and the method comprises the steps: constructing a denoising network model, only employing a standard-dose CT image in a training process, and simulating a generation process of the standard-dose CT image through the training of the denoising network model. And abundant image priori knowledge is constructed. Meanwhile, the de-noising network model uses a U-Net encoder-decoder structure, a multi-scale cross axis attention mechanism is added in a decoder and an encoder, important image details are reserved in the de-noising process, irrelevant noise components are removed at the same time, and smooth areas and continuous boundaries of the image can be recovered. In addition, the frequency compensation block in the jump connection can compensate the insufficiency of medium-high frequency signals in the training process, so that the original structure and texture information of the image are recovered.

The invention discloses a low-light image enhancement method based on low-rank recovery and deep diffusion fusion, and relates to the technical field of image processing. Comprising the following steps: inputting a to-be-processed low-light image into a trained low-light image enhancement model, and transforming a pixel value of the to-be-processed low-light image through a discrete cosine transform module to obtain a spectrogram in a spectral domain; performing forward noise addition at each time step through a Markov chain mechanism, capturing global features of noise distribution through a Transform network, and removing noise components in the current time step according to the global features of the noise distribution to obtain an intermediate image; taking the intermediate image corresponding to the last time step as a new spectrogram; and performing inverse discrete cosine transform on the new spectrogram through an inverse discrete cosine transform module, and converting the new spectrogram from the frequency domain to the spatial domain to obtain an enhanced low-light image. The method pays attention to the globality of noise components, and can improve the definition and quality of the enhanced low-light image.

The invention relates to an air coupling ultrasonic signal processing method based on a COA-VMD combined improvement wavelet threshold, and the method specifically comprises the following steps: 1, employing a meta-heuristic algorithm Coati, and introducing a minimum envelope entropy as a fitness function of the algorithm, and taking the minimum envelope entropy as an evaluation criterion of an optimal solution; 2, decomposing a noisy part in the air coupling ultrasonic signal into a plurality of intrinsic mode function components by using the optimized VMD decomposition parameters; and 3, on the basis of the step 2, distinguishing an effective ultrasonic signal component f1 and a noise component f2 in the detected IMF components by adopting a correlation coefficient method. And step 4, processing the noise component f2 by adopting an improved wavelet threshold function algorithm to obtain a noise-reduced air coupling ultrasonic signal. According to the method, COA-VMD and an improved wavelet threshold noise reduction method are combined, the signal-to-noise ratio is effectively improved, and a technical basis is provided for high-precision detection of defects in the honeycomb sandwich structure.

Disclosed are systems, methods, computer program products, and other implementations, including a method for signal detection is disclosed that includes obtaining samples of observation data comprising a signal component produced by a source object, and a noise component, and generating based on at least one of the samples of the observation data, processed by a machine learning template derivation system, a filtering template to separate the signal component from the noise component, with the machine learning template derivation system including one or more trainable layers, and with at least one layer of the one or more trainable layers implementing a respective one of one or more iterations of an unrolled optimization process to determine optimized template parameters for the filtering template. The method further includes applying the filtering template to one or more of the samples of the observation data to obtain the signal component of the observation data.

The invention discloses a high-reliability planetary gearbox signal denoising method, system, medium and equipment, and the method comprises the steps: obtaining original vibration signals of a nuclear power circulating pump planetary gearbox in different health states, and carrying out the detrending and demean preprocessing of the signals; performing modal decomposition on the original vibration signal by adopting an empirical mode decomposition (EMD) algorithm, an ensemble empirical mode decomposition (EEMD) algorithm and a variational mode decomposition (VMD) algorithm to obtain a plurality of different modal components; iteratively optimizing a hyper-parameter value in the variational mode decomposition algorithm VMD by adopting a sparrow search algorithm SSA so as to realize the self-adaptive decomposition of the variational mode decomposition algorithm VMD on the signal; and carrying out modal decomposition on the gearbox vibration signal by adopting a variational modal decomposition algorithm VMD after iterative optimization, removing noise components in modal components, and reconstructing the signal to realize gearbox vibration signal denoising.

The invention provides a distribution network type lightning arrester leakage current optimization measurement method. The method comprises the steps of signal acquisition, preliminary signal denoising and preprocessing, wavelet transform signal decomposition, high-frequency noise component elimination and processing, input convolutional neural network CNN descrambling, integrated learning model optimization, output of descrambled leakage current signals, performance evaluation and result feedback. Through a multi-level processing method combining wavelet transform and a convolutional neural network, high-frequency noise in a weak leakage current signal is effectively removed, effective characteristics of the signal are reserved in the denoising process, and compared with a traditional method depending on a single signal processing technology, signal descrambling can be more accurate and effective, and the signal descrambling efficiency is improved. According to the wavelet transform signal decomposition method, the signal can be decomposed into components with different frequencies, high-frequency noise components are effectively eliminated, low-frequency and intermediate-frequency effective signals are reserved, then the convolutional neural network further improves the definition and reliability of the signal through depth feature extraction, and accurate detection of the leakage current signal is ensured.

The invention relates to the technical field of signal processing, and discloses a power equipment partial discharge signal denoising method and system, and the method comprises the steps: S1, generating an exponential decay type pulse signal simulating partial discharge, and superposing white noise and narrow-band interference into the exponential decay type pulse signal to generate a noisy signal; s2, decomposing the noisy signal into a plurality of intrinsic mode components through an ensemble empirical mode decomposition method, calculating a correlation coefficient and a kurtosis index of each intrinsic mode component, and identifying a noise component and a signal component based on the correlation coefficient and the kurtosis index; s3, wavelet threshold denoising is carried out on the noise components, and a wavelet threshold is dynamically adjusted for each noise component through a particle swarm optimization algorithm; and S4, reconstructing the de-noised noise component and the reserved signal component into a final de-noised signal, and evaluating the de-noising performance based on the signal-to-noise ratio, the root mean square error, the peak signal-to-noise ratio and the correlation coefficient.

To provide a technique for suppressing influence of sound other than target sound or a noise component.SOLUTION: A sound collection device 100 includes an adaptive filter 18, a coefficient updating section, and an adaptive control section 34. The adaptive filter 18 executes calculation on a vibration signal 202 acquired in a vibration sensor 14 by using a filter coefficient, and outputs a conversion sound signal 212. The coefficient updating section updates the filter coefficient based on a residual signal 210 being a difference between the sound signal 200 acquired in a microphone 10 and the conversion sound signal 212. The adaptive control section 34 adjusts a frequency of updating of the filter coefficient in the coefficient updating section based on the vibration signal 202 and the sound signal 200.SELECTED DRAWING: Figure 1

The invention discloses a geotechnical engineering slope stability real-time monitoring method and a geotechnical engineering slope stability real-time monitoring system, which are characterized in that a blind source separation technology combining independent component analysis and physical constraint is introduced, an original displacement time sequence and an environment temperature time sequence of a plurality of GNSS (Global Navigation Satellite System) measuring points are regarded as multi-channel mixed signals, and statistical independence among signal sources is utilized to monitor the stability of a slope in real time. Periodic environment noise, instrument random noise and drift and real slope deformation signals are effectively separated from the mixed observation signals; and then, through correlation verification with physical quantities such as the field environment temperature and the like, automatic calibration is performed on the separated source signals, and temperature effect source signals and long-term creep source signals with physical labels are accurately identified and extracted, so that accurate elimination of noise components and high-fidelity reconstruction of pure deformation signals are realized. Therefore, the accuracy of real-time monitoring of the slope stability of geotechnical engineering can be effectively improved, and a solid data basis and a decision basis are provided for early warning and prevention of slope disasters.

This disclosure provides methods, devices, and systems for audio signal processing. The present implementations more specifically relate to speech enhancement techniques that are agnostic to varying signal levels in near-end audio signals. In some aspects, a speech enhancement system may include a delay estimator, an input normalizer, and an acoustic echo and noise (AEN) decoupling filter. The delay estimator receives a near-end audio signal via a microphone and a far-end audio signal for output via a speaker and estimates a reference audio signal based on a delay between the near-end audio signal and the far-end audio signal. The input normalizer normalizes a loudness of the near-end audio signal and the reference audio signal. The AEN decoupling filter determines a set of masks based on the normalized audio signals and suppresses an echo component and a noise component of the near-end audio signal based on the set of masks.

The invention relates to the field of electrical measurement and testing, in particular to a fault monitoring method and early warning system for a bottom loading and unloading crane pipe, and the method comprises the steps: obtaining and preprocessing the current, voltage and vibration sequence of a servo motor, and obtaining a corresponding IMF component and an energy spectrum; and in combination with historical normal operation data and to-be-monitored data, the importance degree of the IMF component is evaluated, and the time domain anomaly degree is obtained based on the standard deviation of the sliding window variance and the DTW distance. Odd harmonic information is extracted from the energy spectrum, and the time domain anomaly degree is combined to obtain the comprehensive anomaly degree. And obtaining a fault degree through weighted integration, and inputting the fault degree into a control system to judge a fault. According to the method, the energy spectrum difference of the IMF components, the first-order difference sequence difference and the time-frequency domain abnormal degree are comprehensively analyzed, the operation state of the servo motor is comprehensively evaluated, key fault information loss and single time domain feature monitoring limitation caused by elimination of high noise components in a traditional method are avoided, and the accuracy and sensitivity of fault monitoring are remarkably improved.

The invention discloses an acceleration method, device and equipment for partial discharge detection of a switch cabinet and a storage medium, and relates to the technical field of power equipment fault detection, partial discharge signals are collected in real time through a multi-parameter sensor, multi-source signal alignment is realized by combining a hardware trigger mechanism and timestamp synchronization, noise components are separated through multi-scale wavelet transform, and the partial discharge detection of the switch cabinet is realized. The method comprises the following steps: extracting time-frequency joint features through discrete wavelet transform, reducing dimensionality by adopting a principal component analysis method, taking low-dimensional feature vectors as input limited by a Gaussian mixture model (GMM), taking feature vectors limited by the GMM as input of an SVM classifier, outputting a classification result, and performing online judgment according to the classification result. When the output is partial discharge, an alarm prompt is triggered through the interface module; and when the output is normal, real-time monitoring is continued. According to the invention, signal processing and classification are accelerated by using an FPGA parallel processing architecture, and real-time early warning of power equipment faults is realized.

The invention discloses an acceleration signal processing method, device and equipment for measurement while drilling and a medium. The method comprises the following steps: processing an acceleration signal by using two alternating filters to obtain two alternating filtering results; carrying out weighted summation on the two alternative filtering results by adopting a first weight and a second weight to obtain a weighted summation result; and performing adaptive adjustment on the first weight and the second weight based on the weighted summation result and an optimization target so as to obtain an optimized weighted summation result based on the optimized first weight and the optimized second weight after adaptive adjustment, and taking the optimized weighted summation result as a processing result of the acceleration signal. Accuracy and stability of acceleration signal processing results can be improved, noise components can be reduced, measurement precision of well track parameters such as well deviation can be improved, the drilling direction can be accurately fed back and adjusted, and the drilling rate of high-quality reservoirs can be effectively guaranteed.

To more easily obtain a desired image using a diffusion model.SOLUTION: An image processing part generates an output image by repeating a noise removal step for removing noise components from an input image, for a plurality of times. A user interface part outputs a noise-removed image acquired in the middle of the plurality of times of noise removal steps, and acquires parameters related to the removal of noise components. An image processing part executes the noise removal steps using the acquired parameters. The present embodiment can be realized in any form, such as an information processing system, information processor, or information processing method.SELECTED DRAWING: Figure 1

The invention discloses a method, a system and equipment for acquiring a high-measurement-precision Brillouin frequency shift distribution curve, and relates to the technical field of distributed optical fiber sensing. The method comprises the following steps: receiving an original time domain signal, carrying out frame-by-frame fixed step length scanning on the time domain signal by adopting a window function, and carrying out Fourier transform on each frame of signal segment to obtain an original time frequency distribution curve; according to the method, a correlation coefficient optimized variational mode decomposition algorithm is adopted, denoising is carried out on the same frequency point curve in the time frequency data obtained through STFT processing, interference of noise components on the whole time frequency signal analysis result is reduced, the definition and the signal-to-noise ratio of the time frequency signal are remarkably improved, the measurement precision of the system is further enhanced, and the measurement accuracy of the system is improved. Through the denoising means, the influence of noise on the time-frequency signal processing quality can be reduced to the greatest extent while the original characteristics of the signal are maintained, and reliable technical support is provided for accurate signal analysis and system performance optimization.

Systems and methods of removing motion-induced noise from datasets from a NMR tool. The method includes steps of generating an echo train from each dataset and identifying one or more datasets that were collected while a motion of the tool was below a predetermined threshold as stationary datasets. Datasets not identified as stationary are identified as moving datasets. The method further performs steps of building a statistical model of the echo train, estimating a motion-induced noise component of the echo train using the statistical model and the stationary datasets, calculating a distortion of the echo train for each datasets using a physical model of the tool within the wellbore, estimating a true signal of each moving dataset based on the estimated motion-induced noise component and the respective calculated distortion, estimating a true signal of each stationary dataset based on the respective calculated distortion, and combining the estimated true signals of the moving datasets and the stationary datasets to produce a superset of clean NMR data.

A computer-implemented method may include performing a traffic forecasting using traffic data of a first time period to generate a first traffic forecast for a target geographical region; performing the traffic forecasting using traffic data of a second other time period to generate a second traffic forecast for another geographical region; decomposing the first and second traffic forecast into components of seasonal, trend, and noise; comparing a first noise component of the first traffic forecast with a second noise component of the second traffic forecast to detect at least one anomaly, by comparing at least one deviation between the first and second noise components to an anomaly threshold. Emissions produced by traffic in the target geographical region may be predicted based on the first traffic forecast, including, when the at least one anomaly is detected, predicting an impact on the emissions of the at least one anomaly.

The invention discloses a data full-link dynamic anti-interference optimization transmission method. The method comprises the following steps: S1, constructing a data transmission system architecture comprising a sending end, a transmission link and a receiving end; the sending end detects environment disturbance characteristics in real time, and the receiving end collects receiving signals containing background noise and link distortion; s2, designing a self-adaptive waveform coding strategy at a sending end; s3, performing active channel compensation on a transmission link; channel characteristics are extracted, and channel interference is counteracted in real time through pre-distortion waveform adjustment; s4, deploying an intelligent noise reduction decoding algorithm at a receiving end; a depth feature separation network is constructed, and link residual interference is filtered out by analyzing the time-frequency domain difference between a target signal and a noise component. Through a three-level anti-interference mechanism of adaptive coding of the sending end, active compensation of a transmission link and intelligent noise reduction of the receiving end, the problem that the transmission reliability of signals is reduced due to environment disturbance, channel distortion and noise pollution in a complex environment is solved.