1223 results about "Frequency noise" patented technology

The invention relates to a power supply switching control optimization method and system, and the method comprises the steps: constructing a multi-dimensional data fusion monitoring system, integrating a real-time voltage ripple coefficient detection module of a main power supply, a load transient response analysis module and an environment temperature and humidity sensor array, and generating a dynamic health degree index; the method comprises the following steps: establishing a fault prediction model based on transfer learning, pre-training through multi-scene failure data in a historical database, receiving a health degree index in real time, outputting a reliability attenuation curve, and activating a dual verification mechanism when the slope of the attenuation curve exceeds a preset threshold; extracting characteristic energy distribution of a frequency band of 0.1-10 kHz; executing a transient load impact test, and measuring dynamic response parameters of voltage recovery time and overshoot amplitude; and generating a switching confidence score based on cross processing of the dual verification result and the attenuation curve, and triggering a hierarchical switching instruction. The accuracy of the switching time of the main power supply and the standby power supply can be improved.

The invention discloses a water quality heavy metal pollution detection method and system based on Raman spectrum.The water quality heavy metal pollution detection method comprises the steps that water body Raman scattering light is collected in situ through a miniature optical fiber probe, and a continuous time sequence spectrum signal flow is generated; wavelet transform is combined with self-adaptive threshold setting, and high-frequency noise and effective spectral signals are separated; dynamically strengthening the characteristic peak of the target heavy metal through a frequency domain characteristic screening module, and inhibiting a water molecule interference peak at the same time; generating a cross-domain fusion feature vector; processing the fusion feature vector through a pre-trained heavy metal concentration prediction model, and outputting concentration prediction values of various heavy metals; a confidence score is dynamically calculated based on a deviation between a current predicted value and historical data distribution, and model parameter update and system calibration are automatically triggered. The method has the advantages that through acousto-optic signal cross-domain fusion and closed-loop self-calibration, the target peak is dynamically strengthened while water molecule interference is inhibited, and the real-time performance, the anti-interference performance and the prediction precision of heavy metal detection are improved.

The invention relates to a water quality real-time detection system which comprises the following modules: a multi-source sensing module which is based on a multi-parameter sensing array, adopts a self-adaptive sampling strategy, realizes sensor time sequence synchronization through a state estimation algorithm, completes water body multi-dimensional parameter acquisition in combination with a micro-fluidic chip, generates a multi-modal sensing data set, and transmits the multi-modal sensing data set to a data processing module; the multi-source sensing module comprises a multi-source sensing sub-module, a signal conditioning sub-module, a time sequence synchronization sub-module and an anomaly capture sub-module. The method has the advantages that through the synergistic effect of the adaptive sampling strategy and the state estimation algorithm, the multi-sensor time sequence synchronization precision is remarkably improved, the phase deviation problem caused by traditional fixed frequency sampling is effectively eliminated, the sliding window polynomial fitting is combined with the wavelet threshold de-noising technology, and the multi-sensor time sequence synchronization precision is improved. High-frequency noise interference is greatly suppressed on the premise that effective components of the signals are reserved, and meanwhile, the abnormal value detection accuracy is improved through a dynamic threshold mechanism.

The invention relates to the technical field of computer vision and pattern recognition, in particular to a deep-forged face image detection method based on a double-flow CNN and ViT hybrid model. The method comprises the following steps: preprocessing a real video and a forged video to obtain an original face image; obtaining a high-frequency noise residual error feature image of the original face image based on an improved high-frequency noise residual error feature extraction module; performing first feature enhancement operation on the original face image and the high-frequency noise residual feature image based on a double-flow CNN; carrying out secondary feature enhancement operation on the original face image and the high-frequency noise residual feature image which are subjected to the primary feature enhancement operation on the basis of double-flow ViT; and fusing the features of the original face image and the high-frequency noise residual feature image after the secondary feature enhancement operation, performing true and false prediction on the fused features, and outputting a detection result. The objective of the invention is to solve the technical problems of feature splitting, local-global imbalance and insufficient cross-modal interaction in the prior art.

The invention relates to the technical field of fault detection of the Internet of Things, in particular to a valve path detection method and system based on the Internet of Things, and the method comprises the following steps: based on real-time data of a valve path, removing high-frequency noise and high-frequency components in temperature fluctuation through a low-pass filter, carrying out the mean value smoothing operation of pressure data, and carrying out the mean value smoothing operation; and performing data standardization conversion on the flow data to obtain valve operation state data. According to the method, through real-time collection and optimization processing of valve operation data, it is ensured that the data is accurate and stable, weighted calculation is carried out in combination with the data influence degree, the representativeness of state feature extraction and the perception ability of working condition changes are enhanced, and based on state feature trend analysis, the sensitivity of fault detection is improved; and health state evaluation is carried out in combination with the operation duration and the load change, so that health state scoring is realized, the predictability of the long-term operation state is improved, false alarms and missing alarms are reduced, and the accuracy of fault early warning and the equipment health monitoring capability are enhanced.

The invention discloses an intelligent electromagnetic induction heating method and system for multi-parameter collaborative frequency domain separation, and provides a synchronous parameter acquisition and frequency domain separation processing scheme for solving the problems of temperature detection lag, insufficient frequency domain interference processing and weak dynamic adaptation capability in the prior art. Transient current, voltage and change rate (a first parameter group) of an electromagnetic coil are synchronously acquired, and high-frequency fluctuation (greater than or equal to 10kHz) and low-frequency disturbance (1t, 1t) are converted into low-frequency fluctuation (greater than or equal to 10kHz) by combining environment temperature, cookware materials and user preferences (a second parameter group); 10kHz) is used for frequency spectrum separation. Phase difference features are extracted through band elimination filtering and FFT, heating priorities are corrected in combination with dynamic compensation factors, and a segmented heating curve is generated through time-frequency domain weighted fusion. And the system adjusts PWM parameters in real time, and triggers adaptive protection when detecting that the phase is abnormal or the power frequency exceeds the limit. According to the method, the limitation of single-frequency-band detection is broken through, high-frequency noise suppression and low-frequency disturbance compensation are realized, and accurate temperature control of ferromagnetic / non-ferromagnetic cookware is supported.

The invention discloses a self-adaptive noise reduction method for a positive pressure type air respirator based on airflow spectrum characteristics, and relates to the technical field of positive pressure type air respirators.The self-adaptive noise reduction method comprises the steps that a respirator air path pressure signal and an environment noise signal are synchronously collected through an airflow sensor and a microphone array; performing time-frequency analysis on the collected signals, and extracting airflow spectrum features; and establishing a mapping relation between the working state of the breathing machine and the noise spectrum based on the historical record according to the mapping relation between the working state of the breathing machine and the noise spectrum. Through real-time acquisition and analysis of airflow frequency spectrum characteristics, accurate identification of useful airflow signals and environmental noise signals, dynamic adjustment of noise reduction parameters, effective suppression of high-frequency noise and low-frequency vibration, and improvement of noise reduction effect, compared with a traditional fixed frequency band filtering or passive sound insulation material, the method can better cope with a non-stationary noise environment, and has good application prospects. The noise reduction strategy is adjusted in real time, clear auditory information is provided in a complex noise environment, and therefore the auditory perception ability of firemen is improved.

The invention discloses a bimodal neural network and spectral subtraction combined underwater acoustic signal noise suppression method. Comprising the following steps: firstly, constructing a simulation underwater sound data set containing self-noise; thirdly, constructing a bimodal neural network, then training the bimodal neural network until training is completed, and obtaining a platform self-noise estimation model; inputting a to-be-processed noise-containing underwater acoustic signal into the platform self-noise estimation model, and outputting estimation platform self-noise by the model; and finally, based on the self noise of the estimation platform, noise reduction processing is carried out on the noise-containing underwater acoustic signal to be processed by using the improved spectral subtraction method, and then a noise-reduced underwater acoustic signal is obtained. The method combines an advanced deep learning method and a classical spectral subtraction algorithm, has the capabilities of autonomous learning and efficient noise reduction, remarkably improves the signal-to-noise ratio of the underwater acoustic signal, is suitable for noise reduction of the underwater acoustic signal in a non-stationary and complex marine environment, remarkably improves the low-frequency noise suppression capability and the target signal fidelity, and has a good application prospect. The method is suitable for underwater acoustic target detection, underwater acoustic communication and other scenes.

The invention discloses a water source chlorophyll a concentration prediction model design method based on machine learning. The method comprises the following steps: acquiring chlorophyll a concentration data in a to-be-predicted region for a continuous period of time; carrying out data preprocessing on the chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing a concentration prediction model, carrying out data preprocessing on chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing different concentration prediction models, and inputting the processed chlorophyll a concentration data and physicochemical parameters into the prediction models to obtain a chlorophyll a concentration data prediction result; and comparing prediction results of different prediction models, and determining the prediction model. According to the prediction model design method, the WT-GRU model is adopted to preprocess the data through wavelet transform, the wavelet transform effectively extracts key time scale characteristics through signal decomposition, and the accuracy of chlorophyll a concentration prediction is remarkably improved.

The invention relates to the technical field of atmospheric remote sensing, in particular to a satellite-borne laser radar-based multi-feature attention cloud top height estimation algorithm, which comprises the following steps of: full-waveform laser radar data acquisition and preprocessing: acquiring an original echo signal through a satellite-borne full-waveform laser radar system; performing time domain alignment, noise suppression and saturated signal elimination on the signals; a median filtering and wavelet threshold denoising algorithm is combined to eliminate high-frequency noise, and background radiation interference is eliminated through dynamic baseline correction; according to the method, a multi-stage noise reduction strategy combining sliding window median filtering and self-adaptive wavelet denoising is introduced in the waveform feature extraction stage, pulse interference and signal oscillation are effectively restrained, meanwhile, through a terrain matching error compensation mechanism, the detection accuracy is improved, and the detection accuracy is improved. The physical consistency of the elevation residual error under the real terrain background is enhanced, and the authenticity and the checkability of the point elevation return are improved.

The invention relates to the field of power distribution network ground fault accurate judgment, and discloses a power distribution network ground fault accurate positioning method based on traveling wave detection and artificial intelligence, which comprises the following steps: (a) collecting a power distribution network traveling wave voltage signal through a multi-node synchronous sensor array, and generating a preprocessing signal through baseline correction and wavelet threshold denoising; (b) constructing a Gaussian random field model of line resistance, inductance and capacitance, and simulating a traveling wave propagation process in combination with a nonlinear wave equation; and (c) defining Finsler measurement on the traveling wave signal manifold, and separating the aliasing mode by minimizing path integration. According to the invention, the technical scheme of multi-node synchronous signal acquisition and self-adaptive preprocessing is adopted, so that the effect of eliminating coupling of power frequency interference and high-frequency noise is achieved. Compared with a scheme depending on a single sensor and a fixed filtering threshold in the prior art, the method solves the defect that the wave head time difference extraction is not accurate due to asynchronous sampling.

The invention discloses a chromatographic signal data processing method and device, equipment, a medium and a product, and relates to the technical field of data processing.The method comprises the steps that an original chromatographic signal is obtained; a Savitzky-Golay filter is adopted to filter out high-frequency noise in the original chromatographic signal, and a de-noised signal is obtained; fitting a base line of the de-noised signal by adopting cubic spline interpolation; deducting the baseline from the de-noised signal to obtain a corrected signal; and performing peak identification on the corrected signal through a symmetric zero area method to generate an identification result. According to the invention, the accuracy of chromatographic signal identification can be improved.

The invention discloses a torsion test method based on a Kalman filtering algorithm, and relates to the technical field of torsion test.According to the torsion test method, a dynamic noise model and a material feature library are combined to enable a filter to automatically adapt to acoustic characteristics of different substrates, and the problem of misjudgment caused by fluctuation of material parameters in a traditional method is solved; secondly, a sub-band processing mechanism can restrain high-frequency noise and meanwhile completely reserve transient characteristics before the material is broken, and effective signal loss is avoided; the transfer learning model eliminates batch differences through adversarial training, and the generalization ability of dynamic threshold judgment is ensured; the real-time compensation strategy generates a refastening scheme considering both mechanical safety and assembly precision in emergency.

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 discloses a pipeline high-frequency silencer based on acoustic mode modulation and a perforated pipe. The pipeline high-frequency silencer comprises a shell, and the interior of the shell is connected with a cylindrical inner pipe through a plurality of partition plates; the partition plates are parallel to the axial direction of the inner pipe and evenly arranged in the circumferential direction of the inner pipe. The partition plates divide an annular cavity formed between the shell and the inner pipe into a plurality of sub-cavities of the same size. Flanges are connected to two ends of the shell. The invention further discloses a design method of the pipeline high-frequency silencer based on acoustic mode modulation and the perforated pipe. The design method comprises a perforated design method of the inner layer of the shell and / or the inner layer of the inner pipe and a design method of the partition plate. According to the pipeline high-frequency silencer based on acoustic mode modulation and the perforated pipe, the problem that in the prior art, a sound absorption material which is prone to losing efficacy and harms human health is used for achieving high-frequency noise suppression is solved. High-frequency noise elimination is achieved through the partition plate and the perforated pipe, the structure is simple, and disassembly and replacement are convenient.

The invention discloses a time stretching sweep frequency interference high-precision distance measurement method and device based on double auxiliary interferometers, and relates to the technical field of non-cooperative target absolute distance measurement. In order to solve the technical defects that in the prior art, due to initial wavelength fluctuation of a light source and frequency sweeping nonlinearity, drift of a ranging result is large, and interference signals are interfered by zero-frequency noise, the technical scheme provided by the invention comprises the following steps: acquiring linear frequency modulation optical signals output by a common-source frequency sweeping laser; the signals are respectively transmitted to a measurement interferometer, an auxiliary interferometer and a resampling auxiliary interferometer to form a homologous three-interference structure; a dispersive medium is introduced through the reference arm, so that the reference light generates time stretching and interferes with the measurement light to form a chirp beat frequency signal; k-domain resampling and cross-correlation processing are carried out on the beat frequency signals, and target distance information is extracted. The method is suitable for high-precision and wide-range non-cooperative target absolute distance measurement work, and is especially suitable for the fields of laser radar three-dimensional modeling, precision industrial detection, robot calibration and the like.

The invention discloses an IV-type hydrogen cylinder stress monitoring system, a signal processing method thereof and an IV-type hydrogen cylinder, and relates to the technical field of manufacturing of high-pressure hydrogen storage equipment. The carbon fiber winding layer stress monitoring structure and the liner stress monitoring structure are respectively arranged in a strain generation area of a carbon fiber winding layer and a strain generation area of the outer surface of a plastic liner in the IV-type hydrogen cylinder, so that the surface stress of the liner of the gas cylinder and the stress between carbon fiber winding layers can be dynamically monitored in real time; therefore, the IV-type hydrogen cylinder has the function of dynamically monitoring the stress between the carbon fiber winding layers and the stress of the inner container in real time. According to the signal processing method of the IV-type hydrogen cylinder stress monitoring system, high-frequency noise signals can be effectively filtered, low and medium-frequency signals can be reserved, and the surface stress of a carbon fiber winding layer and a plastic inner container can be accurately measured. The IV-type hydrogen cylinder is provided with the IV-type hydrogen cylinder stress monitoring system and has the function of dynamically monitoring the stress between carbon fiber winding layers and the stress of an inner container in real time.

The invention relates to the technical field of debugging systems, in particular to an adaptive debugging system and method for touch screen FW parameters, and the system comprises a signal acquisition module, a trend triggering module, a temperature compensation calculation module, a window generation module and a parameter output module. According to the invention, the edge of the driving voltage is carefully collected, the sliding difference algorithm is introduced to filter out high-frequency noise, meanwhile, real-time sampling of environment temperature and baseline capacitance adjustment are combined, the influence of external variables on signals can be dynamically corrected, and accurate judgment of capacitance fluctuation is improved by utilizing periodic delay deviation and regression trend analysis. By means of main frequency feature extraction and zero crossing point sudden change detection, threshold setting can be refined, self-adaptive window expansion can be achieved, touch signal response is kept sensitive and consistent all the time, adjustment and optimization can still be automatically completed even under the condition that the temperature changes frequently or signal interference is complex, parameter drift and debugging failure are effectively prevented, and user experience is improved. And the parameter automatic matching efficiency and the finished product consistency in batch production are improved.

The invention relates to the technical field of permanent magnet synchronous motors, in particular to a method, device and equipment for reducing vibration noise of a permanent magnet synchronous motor, and the method comprises the steps: building an electromagnetic field model, extracting radial electromagnetic force data, carrying out the spectrum and modal analysis, recognizing a main noise source, generating an acoustic power level waterfall plot through the combination of acoustic simulation, and defining an optimization target. The optimal scheme is finally obtained by designing the shape of a rotor hole, adjusting multi-parameter collaborative optimization of stator slot width and air gap length and the like, and iteratively optimizing key variables by adopting an intelligent algorithm. According to the method, the space 0-order quadruplicated frequency noise and 24-order octupled frequency noise are remarkably reduced, the torque fluctuation is inhibited, the output performance is ensured, the method is suitable for large-scale production, and the NVH quality of the motor is effectively improved.

The invention relates to the technical field of mechanical vibration and fluid excitation coupling effect analysis, in particular to a runner crown modification pressure pulsation amplitude measurement method, which comprises the following steps of: extracting pressure gradient distribution and cavitation phase change characteristic parameters of a runner surface through fluid dynamics simulation; targeted arrangement of a high-frequency pressure sensor array in a vortex core area, a boundary layer separation critical position and a modification structure mutation area is guided, and the signal acquisition precision is improved by adopting an annular encryption and axial equal-interval arrangement strategy. And establishing a dynamic error feedback mechanism of actual measurement and simulation. And verifying an abnormal frequency band proportion through frequency domain kurtosis and analyzing envelope spectrum harmonic waves, and screening a modification parameter combination with the pressure pulsation amplitude decreasing to reach the standard and stable frequency band distribution. According to the method, the problem of measurement errors caused by multi-source high-frequency noise and effective signal frequency domain aliasing is solved, a high-precision quantification basis is provided for runner modification optimization, and the method is suitable for the field of mechanical system dynamic characteristic testing.

The invention discloses a cooperative control method and system for noise reduction and fire prevention of a transformer substation. The method comprises the following steps: determining the acoustic impedance mutation degree of an original composite board according to the frequency and amplitude distribution of target low-frequency noise of the transformer substation; when the acoustic impedance abrupt change degree exceeds an abrupt change threshold value, optimizing a first physical parameter of the original composite board; the heat flow density of the first composite board obtained through optimization is obtained, and if the heat flow density exceeds a fireproof threshold value, a second physical parameter of the first composite board is optimized; obtaining a noise penetration index of the target low-frequency-band noise in the second composite board obtained through optimization, and if the noise penetration index does not exceed a penetration threshold value, optimizing a third physical parameter of the second composite board; and determining final physical parameters corresponding to the acoustic-thermal performance balance point according to the acoustic impedance distribution and the thermal conductivity distribution of the third composite board obtained through optimization so as to adjust the structure of the third composite board. According to the invention, collaborative optimization of sound absorption frequency band characteristics and fireproof performance can be realized, and both noise reduction and fire prevention can reach an ideal state at the same time.

The invention provides a pulse wave signal (BVP signal) extraction method based on remote photoplethysmography. The method comprises the following specific steps: step 1, acquiring a face video and detecting a face area; in the process, the system captures, recognizes and extracts all facial feature points; 2, the rPPG signal intensities of different face areas are different, and in order to maximize the signal quality, the face areas are divided according to the intensities of remote photoplethysmography signals (rPPG signals); 3, training a convolutional neural network model, calculating a weighting coefficient of each region according to the characteristics of the signals of each region, and fusing the signals of each region into a single-channel signal; 4, removing low-frequency noise and interference signals from the fused signals by using a filtering technology, wherein the superposition operation helps to enhance the effective rPPG signals; through the processing steps, the quality of the BVP signals can be improved, and high-quality input data is provided for subsequent extraction of blood oxygen, respiration, heart rate variability and other signals.

The invention discloses a detection method for representing interface roughness of an interconnection structure by using low-frequency noise. According to the method, constant low current is applied to a to-be-tested structure, output current of the to-be-tested structure is measured, and then random current fluctuation generated by interaction of electrons and interconnection structure interfaces, defects and phonons is obtained; fourier transform is carried out on random current fluctuation to obtain frequency domain parameters of the current; calculating the power spectrum density of random current fluctuation according to the frequency domain parameter of the current; wherein the PSD is proportional to the f, and then fitting is carried out to obtain a slope k; and evaluating the interface roughness of the interconnection structure by using the slope k. According to the method, the experiment can be rapidly completed under the condition that the sample is not damaged, and the reusability of the sample is improved. In addition, the low-frequency noise characterization method does not need direct contact or imaging, is suitable for testing packaged chips, and can measure the overall roughness of long interconnection line segments at the same time.

The invention discloses a transformer area phase identification method and system based on non-contact voltage measurement. The method comprises the following steps: acquiring voltage data of each monitoring node on a power line of target power distribution network transformer area power transmission equipment; performing intelligent judgment on the voltage data, and generating a corresponding high-frequency noise scene signal; in response to the high-frequency noise scene signal, a monitoring result of the target power distribution network area power transmission equipment is obtained in the process of processing the voltage data based on a Kalman filtering algorithm, the processing process is configured to obtain a first prediction result corresponding to each monitoring node based on a state prediction equation, and the first prediction result is configured to obtain a second prediction result corresponding to each monitoring node; inputting a fusion analysis result of a measurement noise matrix determined by a Bayesian fusion algorithm for each first prediction result and a feature extraction result of the corresponding voltage data into an observation update equation; and carrying out transformer area phase identification on the target power distribution network transformer area power transmission equipment based on a monitoring result so as to realize accurate detection of weak signals.

The invention discloses a remote sensing image low-frequency noise correction method and system, relates to the field of remote sensing image processing, realizes accurate removal of stripe noise under the condition of reducing satellite image detail loss, and relieves the problems of unstable correction effect, easiness in losing image details, difficulty in accurately depicting prior characteristics and the like of the existing remote sensing image low-frequency noise correction technology. The remote sensing image low-frequency noise correction process takes a column mean value or a row mean value as a basic processing unit; fine noise intensity calibration is carried out; low-frequency noise of the image is corrected in a mean value compensation mode, and detail loss of the corrected image is avoided. The method is suitable for multispectral and panchromatic remote sensing images of a remote sensing satellite, and is not influenced by ground feature types.

The invention relates to the technical field of intelligent detection of power equipment, in particular to a complex illumination low-voltage electricity meter frequency sensing detection method, system and equipment and a medium, and aims to solve the problems of high-frequency noise interference, intra-class feature difference, insufficient edge end energy efficiency and the like existing in electricity meter detection in a high-brightness environment. Proposing a dynamic frequency domain compensation mechanism to suppress light noise and recover signal details, and optimizing dense scene feature consistency in combination with adaptive filtering and a multi-scale sampling strategy; the boundary fuzzy problem is solved through dynamic feature alignment and an edge compensation mechanism, and parallel acceleration of frequency domain decomposition and dynamic resampling is realized by adopting a heterogeneous hardware architecture; a multi-modal data training framework and a cross-domain transfer learning strategy are fused to enhance the generalization ability of the model; according to the invention, collaborative optimization of detection precision, real-time performance and low power consumption is realized in a complex illumination scene, and high-robustness technical support is provided for intelligent power grid inspection.

The invention discloses an electromagnetic shielding method for a high-density integrated wireless audio module, and the method comprises the steps: employing a metal material with good conductivity and corrosion resistance, such as aluminum alloy and copper alloy, to cover the outer surface of the module, and enabling the outer metal shielding layer to be used for isolating external electromagnetic interference; the thickness of the outer metal shielding layer is generally 0.3 mm to 1 mm, it is ensured that external electromagnetic radiation can be effectively reflected or absorbed, and the outer metal shielding layer is located between the outer metal shielding layer and the inner conductive coating and is made of a material with high-frequency absorption performance, such as a ferrite material, a conductive polymer or a carbon-based material. The thickness of the wave-absorbing layer can be designed according to the electromagnetic characteristics of a working frequency band and is generally 0.5 mm to 2 mm, so that high-frequency noise can be effectively attenuated. By adopting a multi-layer shielding structure and an intelligent heat dissipation design, electromagnetic interference is effectively reduced, miniaturization and light weight of the wireless audio module are maintained, and compared with the prior art in which a large number of expensive shielding materials are needed, the wireless audio module has the advantages that the selection of the shielding materials and the intelligent heat dissipation design are optimized.

The invention belongs to the technical field of current source control, and particularly relates to a self-adaptive intelligent current source output control system, and the system works cooperatively through a plurality of modules: a load detection module monitors the load access state in real time, converts a current signal into a voltage signal Udetect, and outputs the voltage signal Udetect; the state stabilization filtering module receives the signal, filters high-frequency noise through an RC low-pass filtering unit, amplifies the signal through an in-phase proportion amplification unit, and is internally provided with a signal monitoring and judging unit to extract a load characteristic signal INPUTIDE in a stable working state; the feedback calculation module generates a feedback control parameter through preset threshold comparison and logical operation according to the INPUTIDE; the analog switch control module selects a resistance potential-divider network input channel according to the feedback control parameters, and outputs a current source regulation and control signal VC; and the adjustable current source module outputs a working current adaptive to the load according to the VC. The self-adaptive intelligent current source output control system is superior to a traditional system in dynamic adaptation, protection mechanism, multi-scene adaptation, precision and stability.

The invention provides a multi-modal temperature control method and system for a three-in-one laminating plastic packaging machine, and the method comprises the steps: carrying out the synchronous collection of data of a temperature sensor, a pressure sensor and a visual sensor through a timestamp alignment technology, and guaranteeing the time sequence consistency of multi-modal data; original data of the temperature and pressure sensor are processed through a Gaussian filtering algorithm, high-frequency noise is filtered out, and smoothed sensor data are obtained; aiming at image data acquired by a visual system, a self-adaptive illumination compensation algorithm is adopted to eliminate the influence of environment illumination change on adhesive film detection; in the reflective area of the adhesive film, an image is preprocessed by using a polarization filtering technology, so that reflective interference is reduced, and the accuracy of visual detection is improved; inputting the filtered temperature and pressure data and the processed visual data into a preset data fusion model to generate a multi-modal feature vector; and optimizing the fuzzy control rule through a genetic algorithm, and generating an optimal control strategy by taking historical data of temperature and pressure as training samples.

The invention discloses a pipeline leakage monitoring method and system, and belongs to the technical field of pipe network leakage monitoring. Based on leakage and non-leakage signal spectrum comparison, a high-difference frequency band far away from low-frequency noise is extracted as a characteristic frequency band, high-frequency effective characteristics are focused, and low-frequency interference is avoided; savitzky-Golay smooth filtering is used to eliminate random noise and maintain spectrum peak shape characteristics at the same time, so that the representativeness of a characteristic curve is improved; amplitude difference caused by environmental factors is eliminated through normalization processing, so that the characteristic curves under different working conditions can be compared; shape and amplitude dual verification is carried out through a Pearson's correlation coefficient PPC and a mean square error RMES, and the misjudgment rate of pipeline leakage is reduced; and through a dynamic threshold setting mechanism, different pipe types and working condition changes are self-adapted, and the problem of sensitivity reduction caused by a fixed threshold is avoided.