18 results about "Wavelet noise" patented technology

The invention discloses a rainfall monitoring method and device based on millimeter wave radar, and relates to the technical field of meteorological monitoring. The method comprises the following steps: controlling a 24 / 60GHz commercial millimeter wave radar to transmit a millimeter wave signal of an adaptive parameter to a preset area; a raindrop echo signal is received, and clutters are removed through Kalman filtering and wavelet noise reduction; extracting a one-dimensional energy feature, a two-dimensional speed feature and a speed-energy two-dimensional distribution matrix; inputting a low-error model trained by a random forest algorithm, calculating a rainfall energy value and mapping the rainfall energy value into a rainfall level; data is output through a LoRa / ZIGBEE network; the device comprises a millimeter wave radar module, a signal processing module, a calculation output module and a wireless communication module. The problems of single monitoring dimension, high cost and difficult deployment in the prior art are solved, non-contact, low-cost, high-real-time and distributed rainfall monitoring is realized, the error is less than or equal to 5%, the response period is less than or equal to 100ms, and the method is suitable for traffic, municipal administration, emergency and other scenes.

The invention discloses a new energy station cable terminal partial discharge signal identification method and system, and the method comprises the following steps: collecting an original signal, carrying out the preprocessing of the original signal, and carrying out the wavelet threshold noise reduction processing, and obtaining a noise reduction signal; performing sliding slicing processing on the denoised one-dimensional long time sequence signal, and inputting the processed signal into the constructed partial discharge identification model to train the model; and inputting the target signal into the trained partial discharge identification model to obtain a time position and a continuous range of a real partial discharge event in the long time sequence signal, thereby realizing effective identification and separation of the partial discharge signal and corona interference of the cable terminal of the new energy field station. Wavelet noise reduction, a time window and one-dimensional convolutional network depth feature recognition are integrally designed, partial discharge signals can be stably recognized in the strong noise and mixed pulse scene of the new energy station cable terminal, and a reliable data basis is provided for insulation state evaluation and operation and maintenance early warning of the new energy station cable terminal.

The embodiment of the application provides a cascade anti-interference circuit suitable for an electrocardio monitoring device, which comprises a sliding mean filter module, a notch filter module, a lifting wavelet decomposition module, a threshold calculation module, a threshold processing module and a lifting wavelet reconstruction module; while ensuring a small circuit scale, the ECG signal collected by the wearable electrocardio monitoring device is subjected to hierarchical noise reduction processing, and high signal-to-noise ratio ECG signal output is realized; for the ECG signal collected by the wearable electrocardio monitoring device, first, sliding mean filtering is performed to filter out baseline drift noise caused by human respiratory movement and other activities; the power frequency interference caused by the wired and wireless connection of the electromagnetic environment around the device is suppressed by using the notch filter module; finally, wavelet noise reduction is realized by using the lifting wavelet decomposition and reconstruction and threshold noise reduction method, the electromyographic interference caused by the autonomous or unconscious movement of the wearer is suppressed, and high signal-to-noise ratio ECG signal is output, which is used for physiological parameter extraction and electrocardio diagnosis.

The invention discloses a wireless remote control and data return device of a coal bunker cleaning machine, relates to the technical field of wireless remote control of coal bunker cleaning machines, aims to solve the technical problem that the type and residual quantity of coal accumulated in a traditional bunker cannot be accurately recognized, and comprises a field sensing module, a data processing module, a wireless transmission module and a remote control module. In a noise reduction unit of a data processing module, a wavelet noise reduction algorithm is used for processing image data acquired by an AI visual camera. Mist noise generated by dust interference can be effectively filtered out, key features such as edges and textures of coal deposits can be clearly reserved, the problem that viscous coal and wet powdery coal as well as blocky coal and viscous coal are difficult to distinguish manually is solved, the type difference and distribution condition of the coal deposits in the bin can be clearly captured, a high-quality image basis is provided for subsequent accurate recognition of the types of the coal deposits, and the coal deposits in the bin can be accurately recognized. And the coal deposit characteristics are judged no longer depending on subjective experience of operators. The problem that the type and residual quantity of accumulated coal in a bin cannot be accurately recognized is solved.

The invention discloses a distributed ocean photovoltaic panel automatic anti-bird device and method. The method comprises the following steps: S1, networking and sensing acquisition; s2, identification and threshold adjustment; s3, linkage and expelling are carried out; s4, performing feedback and data recording; s5, performing optimization and operation and maintenance calibration; the problem of adaptation of special marine environments is solved in a targeted mode, environmental interference such as high salt fog and light intensity fluctuation is effectively resisted by deploying salt fog-resistant weather-resistant edge nodes, integrating a multi-module ad hoc network and adopting wavelet noise reduction processing, stable operation of equipment and data acquisition purity are guaranteed, and the blank that a conventional bird prevention scheme is insufficient in marine environment adaptability is filled up; the method overcomes the pain point of inaccurate bird recognition and intention judgment, dynamically adjusts a threshold value by means of deep learning, a behavior model and track time sequence analysis in combination with environmental parameters, accurately distinguishes passing birds from resident birds, accurately judges landing and excretion intentions, greatly reduces false triggering and missed triggering probabilities, and improves the reliability of recognition decision.

The invention discloses a distributed spectrum sensing noise stripping method based on wavelet gradient, and belongs to the technical field of communication and artificial intelligence crossing. The method comprises the following steps: acquiring original spectrum data through a distributed cognitive radio node; performing sliding window segmentation, Hanning window weighting and power spectrum density calculation on the data; the preprocessed data are input into a differentiable wavelet noise separation layer, the layer serves as a learnable one-dimensional convolution kernel through a parameterized biorthogonal wavelet filter, and end-to-end trainable multi-resolution analysis is achieved in a deep learning framework; calculating the power spectrum density gradient of the high-frequency detail coefficient in real time, and quantifying the noise abrupt change intensity based on a central difference method; dynamically generating a gating weight, and selectively suppressing the wavelet coefficient of the high-gradient frequency point; inputting the processed data into a one-dimensional convolutional neural network with residual connection for classification; and results are fused by adopting a clustering type collaborative decision-making mechanism.

The present application relates to the field of near-field wavelet processing imaging technology, and particularly discloses a kind of multi-source air gun excited passive source near-field wavelet processing imaging method and device, the method comprises: through near-field detector, near-field wavelet data is collected, and near-field wavelet data is matched with navigation positioning data, the loading of observation system is completed. Again using the difference of each channel type of active source and passive source, passive source data is extracted. Again according to the development characteristics of near-field wavelet noise, key noise such as bubble direct wave is suppressed, the signal-to-noise ratio of data is improved and seabed shallow layer image is obtained, so as to fill the blank of domestic near-field wavelet imaging field, promote the development of near-field wavelet imaging processing technology, and lay a foundation for the standardization and standardization of multi-source near-field wavelet data processing flow.

This invention relates to the field of bridge inspection technology, specifically to a bridge displacement detection method and system based on phase information. The method includes: acquiring acceleration vibration response signals from at least two measuring points on the bridge; filtering out noise and drift using a bandpass filter; performing a Hilbert transform on the filtered signals to obtain an analytical signal and extracting the instantaneous phase sequence; calculating the cross-correlation function of the instantaneous phase sequences of adjacent measuring points and determining the phase difference time sequence through the maximum value; correlating the phase difference time sequence with the distance between measuring points to convert it into a relative displacement time sequence; integrating the relative displacement time sequence twice to obtain a cumulative relative displacement value; using this cumulative value as a benchmark, and combining it with the absolute displacement calculation results of each measuring point to infer the overall bridge displacement; and performing wavelet noise reduction on the overall displacement to obtain the final detection result. This method can improve the accuracy and stability of displacement detection and is suitable for displacement monitoring of bridge structures.

The invention discloses a welding penetration control method and system based on LabView, and relates to the technical field of welding process control. The method comprises the steps that a welding voltage signal in the welding process is collected; a LabView processor is used for conducting data format conversion, noise reduction processing and frequency domain analysis on the welding voltage signals, and the oscillation frequency of a molten pool is extracted; the oscillation frequency of the molten pool is compared with the critical penetration frequency in an expert database, and the penetration state of the current welding process is judged; and the welding system and / or the motion control system are / is controlled to execute corresponding control actions according to the judgment result, so that welding heat input is adjusted, and closed-loop control over the penetration state is achieved. The noise reduction treatment adopts a self-adaptive multi-scale correlation weighted generalized threshold wavelet noise reduction method, and the oscillation frequency of the molten pool is obtained through peak group proportion analysis. According to the method, online recognition and real-time regulation and control of the welding penetration state can be achieved, and the method has the advantages of being high in anti-interference capacity, stable in feature extraction and high in control precision.

The invention discloses a photovoltaic power generation power prediction method and system based on entropy weight wavelet denoising, and the method comprises the steps: obtaining photovoltaic power generation power and meteorological historical data, and forming an original time series data set; entropy weight adaptive threshold wavelet denoising processing is carried out on each feature sequence, the threshold is adaptively adjusted by calculating wavelet coefficient energy entropy of each scale, and noise suppression and signal retention are realized by adopting an improved threshold function shrinkage coefficient; inputting the de-noised data into a hierarchical adaptive multi-branch activation bidirectional gating cycle unit network for training to obtain a prediction model; and finally, performing power prediction on the real-time noise reduction data by using the prediction model. According to the invention, the precision and robustness of photovoltaic power prediction are effectively improved.

The invention provides an intelligent identification system and method for micro defects of a cladding layer on the inner wall of a large metal pipeline based on an ultrasonic phased array, and relates to the technical field of energy storage power stations. In order to solve the problems that a cladding layer (with the thickness of 0.5-1.0 mm) on the inner wall of an injection-production pipe of a compressed air energy storage power station easily falls off to form micro defects (with the diameter smaller than or equal to 1 mm) after long-term service, traditional ultrasonic detection is greatly interfered by roughness, and the signal discrimination degree is poor, an ultrasonic phased array probe is used for scanning and collecting signals along the outer wall of a pipeline, and after wavelet noise reduction and Fourier time-frequency conversion, the cladding layer (with the thickness of 0.5-1.0 mm) on the inner wall of the injection-production pipe of the compressed air energy storage power station is obtained. And extracting a voiceprint feature vector by using a convolutional neural network, identifying the defect size in combination with a dynamic matching algorithm, and outputting a defect image through full-focus imaging. According to the method, the advantages of high precision of the ultrasonic phased array and AI intelligent identification are integrated, the micro cladding layer falling defect can be rapidly and accurately identified, the anti-interference capability is high, and a reliable detection means is provided for safe operation of an injection-production pipe.

The invention provides a method for realizing rapid evaluation of surface uranium content by using unmanned aerial vehicle aviation release measurement, and relates to the technical field of unmanned aerial vehicle aviation radioactivity measurement. The method for realizing rapid evaluation of the uranium content of the earth surface specifically comprises the following steps: S1, system background calibration; s2, correcting atmospheric radon; s3, calibrating air sensitivity; s4, uranium content inversion; s5, carrying out data noise reduction processing; and S6, three-dimensional mapping. According to the technology, cosmic rays and equipment background are synchronously deducted through water area measurement, the radon correction coefficient is dynamically adjusted in combination with morning and evening baseline flight, the problems that aerial work of an unmanned aerial vehicle aerial radioactivity measurement system is difficult and radon correction is not accurate are solved, a height attenuation coefficient three-dimensional correction model and a wavelet noise reduction algorithm are adopted, small crystal noise interference is overcome, and the radon correction accuracy is improved. Through uranium content grading drawing and geologic model fusion, the method achieves the quick positioning of an abnormal region, is small in inversion error, is high in verification goodness of fit, remarkably improves the uranium mine verification efficiency and safety of a plateau / unmanned region, and reduces the production cost.

The invention discloses a while-drilling ultrasonic device and method for detecting inherent frequency of hole bottom rock. The while-drilling ultrasonic device comprises a drill bit, a detection ultrasonic unit, a power ultrasonic unit, external control equipment and the like. The detection ultrasonic unit is arranged near a drill bit in a short section mode and comprises a multi-array-element transducer and an echo information processing and entropy weight-Topsis dynamic weighting algorithm module, db4 wavelet noise reduction and FFT transformation are sequentially carried out on echo signals through circumferential sector-by-sector, radial grading and axial layering three-dimensional frequency sweeping logic so as to extract formants, and the detection ultrasonic unit is used for detecting the ultrasonic signals. Calculating a weighted average frequency through a dynamic weighting algorithm; the power ultrasonic unit receives the frequency instruction and adjusts vibration parameters in real time. According to the device and the method, the hole bottom full-space inherent frequency is accurately detected, the importance difference of rock blocks on the rock breaking efficiency is dynamically distinguished, the frequency matching degree and the energy consumption efficiency of ultrasonic rock breaking are improved, the adaptability is high, and original factory installation and later transformation of various frequency-adjustable ultrasonic rock breaking equipment are supported.

The invention relates to the field of power battery data processing methods, in particular to a multi-working-condition sensor method and system suitable for a power battery, and the method comprises the steps: synchronously reading the voltage, current and temperature data of the power battery, and setting the sampling frequency and range; calculating a temperature gradient, calculating an equivalent multiplying power, and determining a working condition type; performing composite drift compensation by applying a temperature gradient and an aging drift term, executing wavelet noise reduction, and updating filter parameters; calculating the median of each sensor group and the standard deviation of all the sensors, combining the confidence components of the three parts, and calculating the confidence of each sensor through normalization processing; the weight of each sensor is calculated, a weighted average value is calculated, a second derivative is estimated, dynamic smooth constraint is applied to execute adaptive weighted fusion, and anomaly detection and isolation are carried out; issuing high-precision data, generating a health report, and triggering an early warning mechanism; checking calibration conditions, executing zero point / gain calibration, and updating compensation parameters. According to the invention, the abnormity of the power battery can be dynamically and accurately found in real time.

The application belongs to the technical field of electromagnetic compatibility test, and discloses an electromagnetic compatibility test system, method, medium, equipment and terminal, wherein a narrow-band signal extraction module is used to extract a narrow-band resident interference signal, a wide-band signal extraction module is used to extract a wide-band signal in a test result through threshold comparison, a narrow-band signal elimination module is used to eliminate the resident narrow-band interference signal in a test environment, a wide-band signal elimination module is used to eliminate the wide-band interference signal, wavelet analysis multi-resolution characteristics are used, a wavelet decomposition and reconstruction module is used to decompose and reconstruct the signal processed by the difference method, and secondary noise reduction is realized. The application uses the filtering method to reduce noise, can save the spikes and mutations in the useful signal, and retains the original signal; the difference noise reduction method and the wavelet noise reduction method are combined to reduce the environmental noise, the effectiveness of eliminating the noise in the environment is improved, and the accuracy of the electromagnetic compatibility test is improved.

The invention discloses a wavelet noise reduction and gradient feature extraction method for buried pipeline welding seam positioning, and relates to the technical field of nondestructive testing. The method comprises: acquiring an original magnetic field signal of a pipeline area through a magnetic field acquisition device; wavelet transformation is carried out on the signal, wavelet reconstruction is carried out after a noise coefficient is suppressed through threshold processing, and a denoised signal is obtained; finally, gradient feature extraction is conducted, specifically, the gradient of the denoised signal is calculated and standardized, extreme points in the standardized gradient are recognized by setting a threshold value to serve as welding seam feature points, and therefore accurate positioning of the welding seam position is achieved. According to the method, non-periodic noise is effectively filtered through wavelet transformation, gradient analysis is combined to strengthen and extract the magnetic field abrupt change features of the welding seam, the problem that a traditional frequency domain method is poor in adaptability in buried pipeline welding seam positioning is solved, and the method has the advantages of being good in filtering effect, accurate in feature extraction and high in robustness.

The invention provides a hydropower station key equipment maintenance method based on a voiceprint perception inspection robot. The hydropower station key equipment maintenance method comprises the steps of autonomous inspection and voiceprint collection, edge side preprocessing and feature extraction, cloud diagnosis, fault positioning imaging and decision maintenance. The inspection robot moves autonomously based on SLAM and an obstacle avoidance algorithm, voiceprint and equipment operation data are acquired through an acoustic sensor array, and time domain, frequency domain and Mel frequency cepstrum coefficient features are extracted after adaptive filtering and wavelet noise reduction processing; the cloud CNN-LSTM model compares health and fault voiceprint references, identifies the fault type and predicts the residual life through a particle filter algorithm; and when the fault is abnormal, generating a voiceprint thermodynamic diagram through a delay-summation beam forming algorithm to position the fault, automatically generating a diagnosis report and updating an equipment knowledge base. The problems that traditional maintenance coverage is incomplete, deployment is not flexible and early fault recognition is insufficient are solved, wide-area, accurate and prospective maintenance is achieved, equipment safety and operation and maintenance efficiency are improved, and maintenance cost is reduced.

This invention discloses a multi-track audio hybrid noise reduction method and system, relating to the field of audio signal processing technology, including the following steps: S1: Parameter configuration and preprocessing, setting audio sampling rate, window function, wavelet noise reduction parameters, spectral subtraction parameters, and noise estimation parameters; creating an output directory and checking if the directory exists; if not, creating it automatically; S2: Multi-track audio reading, reading the noisy audio track file and the clean audio track file respectively, converting the multi-channel audio to mono, verifying the consistency of the sampling rate of each track, and unifying the length of all track audio; S3: Noise segment visualization and confirmation, automatically or manually extracting the corresponding noise segment according to the noise estimation method, visualizing the noise segment through waveform and spectrum diagrams, prompting the user to confirm the applicability of the noise segment, and continuing the process after user confirmation. This multi-track audio hybrid noise reduction method and system achieves accurate noise reduction of multi-track audio through joint noise estimation, hybrid noise reduction process, and quantitative evaluation.