2407 results about "Background noise" patented technology

The invention relates to the technical field of devices for adjusting, controlling or stabilizing power or frequency in a power grid, in particular to a rapid frequency modulation method for a wind turbine generator based on disturbance adaptive compensation, which comprises the following steps of: constructing a dynamic disturbance sensing data set by collecting frequency deviation, a frequency deviation change rate and fan state parameters; a window length and a weight coefficient of a short-time window sliding mean algorithm are dynamically optimized by adopting a genetic algorithm, background noise interference of a power grid is suppressed, and high-precision disturbance characteristics are extracted. And reducing frequency interference of inertia identification by using a reverse test signal. And updating boundary layer parameters of the sliding mode control model through an online incremental learning algorithm, dynamically adjusting the output priority based on the disturbance energy entropy, and eliminating power conflicts. A multi-island genetic algorithm is introduced to optimize a mode switching threshold value, and frequency modulation safe exit is realized in combination with adaptive power ramp rate limitation. According to the method, the frequency response speed and the multi-source cooperation efficiency in the dynamic multi-disturbance and inertia time-varying scene are remarkably improved, and the frequency secondary drop risk is reduced.

The invention discloses a data line surface defect rapid nondestructive detection method based on intelligent image recognition, relates to the technical field of image data processing, and aims to solve the technical problems of difficult defect feature separation and low detection accuracy under complex weaving texture noise interference, and the method comprises the following steps: S1, collecting a data line image and carrying out graying processing; s2, constructing a multi-scale image pyramid, accurately segmenting the image by adopting a self-adaptive threshold algorithm, and realizing rapid detection of surface defects of the data line in combination with Hough transform; s3, frequency domain separation of weaving texture and background noise is realized through Fourier transform, interference is filtered out in combination with a band-pass filtering technology, and then the defect contrast is enhanced through histogram equalization; according to the method, the three-layer Gaussian pyramid is constructed for multi-scale decomposition, and the band-pass filtering technology is combined, so that the weaving texture and the defect signal are effectively separated, the detection accuracy is greatly improved, and the problems of missing detection and misjudgment caused by frequency characteristic confusion are thoroughly solved.

The invention provides a thin film surface defect detection method and system, and relates to the technical field of defect detection.According to the thin film surface defect detection method and system, an intelligent secondary verification link is constructed by introducing a defect confidence evaluation mechanism based on form and energy distribution, so that the detection performance is fundamentally improved; according to the mechanism, real physical defects with regular forms and concentrated energy and pseudo defects caused by electromagnetic interference, instantaneous film wrinkles and the like can be accurately distinguished, and the problem of high false alarm rate caused by dependence on single signal strength in the prior art is effectively solved while the high detection rate of low-contrast defects is reserved; besides, the judgment model based on physical characteristics has natural robustness for background noise generated in high-speed motion, and an adjustable confidence threshold value endows the system with extremely high practical flexibility, so that the system can adapt to complex and changeable industrial environments and different quality control standards, and the method is suitable for large-scale popularization and application. And the accuracy, the reliability and the intelligent level of the whole detection system are obviously enhanced.

The invention discloses a real-time single-stage remote sensing image correction target detection method based on YOLOV8, and relates to the technical field of remote sensing image processing. According to the method, a deformable convolution dynamic prediction local geometric distortion parameter is embedded based on a YOLOv8 backbone network, an adaptive deformation field is generated, pixel-level real-time correction is realized, shallow details and high-level semantic features are fused through a bidirectional path aggregation network, and channel attention and a space gating mechanism are combined, so that the real-time correction of the image is realized. The small target detection capability is enhanced, background noise is suppressed, angle prediction is divided into discrete classification and continuous residual error regression tasks through a decoupling type rotation detection head, angle periodic errors are eliminated in combination with a direction sensitive loss function, and the rotation frame positioning precision is improved. And constructing a dynamic multi-task collaborative loss function, introducing gradient distribution consistency constraint to jointly optimize correction and detection tasks, and realizing feature semantic alignment and model self-enhancement through end-to-end closed-loop training. And the rotating target detection precision and the complex scene robustness are obviously improved.

The invention provides a microcrack precise segmentation method integrating feature fusion and convolution attention, and belongs to the field of image processing. According to the method, a crack segmentation network based on an encoder-decoder architecture is constructed, a convolution block attention module is introduced at an encoder end, background noise is adaptively suppressed and obvious characteristics of cracks are enhanced through a channel and space dual attention mechanism, and the method is suitable for the adaptive segmentation of the cracks on the premise of almost not increasing the calculation overhead. The sensitivity of the model to microcracks is improved; a feature fusion module is introduced at a decoder end, and cooperation of low-layer details and high-layer semantics is realized through cross-layer fusion, so that a semantic gap is effectively bridged, detail loss caused by traditional convolution stacking is avoided, and continuity and a complete topological structure of a long and narrow crack are ensured. According to the method, through collaborative optimization of multi-scale feature extraction and an attention mechanism, accurate capture of the saliency features of the crack and effective suppression of complex background interference are realized, and the detection sensitivity and overall segmentation consistency of the micro-crack are remarkably improved.

The invention relates to the technical field of AI toys, and provides an AI toy voiceprint recognition interaction method, device and equipment, and the method comprises the steps: obtaining a to-be-recognized target voice signal, extracting an original audio data set to generate a sound field estimation parameter and a background noise feature, carrying out the voiceprint feature extraction of the target voice signal, obtaining a voiceprint feature vector, and obtaining the voiceprint feature vector; and performing feature clustering on the voiceprint feature vector and a preset child voiceprint vector set to obtain user identity information, a behavior tag and an emotion tag so as to generate a corresponding multi-modal response instruction, and inputting the multi-modal response instruction into a control module of the AI toy. Robustness of a target voice signal in a complex environment is improved by combining sound field estimation parameters and background noise features, and through voiceprint feature vector extraction and clustering and similarity calculation, the target voice signal is improved under the condition that environmental noise interference is remarkable or semantic emotion interaction is complex. The problems of low identification accuracy and low user discrimination degree exist in the prior art.

The invention discloses a behavior analysis camera monitoring method and system based on deep learning, and the method comprises the following steps: S1, collecting an image sequence and environment parameters of a target object in a scene through a multi-modal sensor, the environment parameters comprising illumination intensity and background noise level; s2, performing priority division on the image sequence by using a dynamic region-of-interest weight calculation module, and generating a region-of-interest mask based on the movement track and posture change of the target object; s4, generating an abnormal behavior index according to an output result of the classifier; and S5, when the comprehensive abnormal index exceeds a preset threshold value, a cross-camera cooperative tracking mechanism is triggered, an alarm signal is generated, and meanwhile, high-risk behavior fragment marks are stored to a priority queue, and accurate perception of human body behaviors in a complex environment is realized through an innovative architecture combining multi-modal data fusion and dynamic weight adjustment.

The invention discloses a crack segmentation method and system based on a dynamic receptive field and multi-scale semantic aggregation, and relates to the technical field of computer vision. The method comprises the following steps: inputting a crack image, and simultaneously capturing local details and global structural features of a crack through a dynamic snakelike Mama module: dynamically adjusting the shape of a convolution kernel to adapt to the geometric change of the crack, inputting the obtained features into a spatial pyramid pooling layer to extract multi-scale context information, and outputting feature representation fused with a long-range dependency relationship; based on the feature representation fused with the long-range dependency relationship, an interaction relationship between local details and global semantics is established through a multi-scale semantic aggregation module, background noise interference is suppressed through a parallel supervision attention mechanism, and a pixel-level crack segmentation result is generated through a lightweight segmentation head. According to the method, fine cracks can be segmented more accurately in a complex background environment, and meanwhile, the conditions of wrong segmentation and missing segmentation are effectively relieved.

The invention relates to the technical field of traffic scene target detection, in particular to a traffic scene target detection method based on feature fusion and an attention mechanism, and the method comprises the steps: obtaining a public traffic scene target image training data set; training a traffic target detection model by using the traffic scene target image training data set; and inputting a to-be-detected traffic scene image into the trained traffic scene target detection model to obtain an output detection result graph. A hierarchical receptive field is constructed through a feature fusion module, a fine structure of a target edge is reserved in a deep convolution stage, cross-channel semantic information is dynamically aggregated through point convolution, an attention module is put forward to dynamically adjust a convolutional receptive field and a space attention weight, interference of background noise is suppressed, and a target is obtained. The local detail feature expression of the target is better enhanced, a PIoU loss function is adopted, and the positioning precision and robustness are further improved by combining a target size adaptive penalty factor and an anchor frame quality-based gradient adjustment strategy.

The invention provides a wavelength adjustment method, device and equipment for an LED detection light source and a storage medium, and the method comprises the steps: transmitting multiband incident light through an LED light source array, and receiving reflected light or transmission light of a detection object through a photoelectric detector array, thereby obtaining the spectral feature data of the detection object. And according to the spectral feature data, calculating a signal-to-noise ratio value and a feature response difference value of each wavelength interval, and performing multi-target parameter optimization in combination with a background noise coefficient of the detection object to obtain an optimal detection wavelength parameter group. And then, through linkage control of the driving current and the chip temperature of the LED light source array, matching output signals of multiple groups of wavelengths are obtained. And adjusting the wavelength of the LED light source array according to the compensated optimal detection wavelength parameter group. According to the method, adaptive wavelength adjustment can be carried out according to the spectral characteristics of the detected object, light source output is optimized, the signal-to-noise ratio is improved, the detection precision is further improved, and the method has remarkable advantages in optical detection application in a complex environment.

The invention belongs to the technical field of leakage detection, and particularly relates to a leakage detection method based on multi-sensor time shift correction and deep learning. A plurality of sensors sensitive to leakage sound waves or vibration are arranged at different positions of a monitored pipeline or container and used for collecting leakage related signals; time alignment is carried out on the time sequence signals collected by the multiple sensors according to the position difference of the time sequence signals in the space; comparing and differentiating the plurality of sensor signals subjected to time alignment; modeling and updating environmental noise in real time, dynamically adjusting detection parameters according to noise level, continuously monitoring statistical characteristics of background signals, and establishing a noise model; a pre-trained deep learning model is used to carry out mode identification and classification determination on the signal after noise reduction processing; and the alarm and display module is used for triggering alarm and recording leakage information when suspected leakage is detected. According to the invention, weak leakage signals can be reliably detected under the condition of strong background noise.

Automatic video dubbing methods and apparatuses automatically dub a video signal into a different target language than the original while controllably preserving vocal characteristics of original speakers in the corresponding audio, based on separating audio from the video, preserving audio timings, separating voice and non-voice segments from background noise, and using ASR, MT and signal processing techniques to generate properly sized and selectable speech translations into different languages corresponding to speaker voices in the video and emotions, voice and / or textual characteristics.

The invention relates to the technical field of face recognition, in particular to a face recognition method and system for a dynamic environment, and the method comprises the steps: collecting a face video stream through a multispectral imaging device, and carrying out the preprocessing of dynamic noise reduction, distortion correction and the like; constructing a multi-scale space-time fusion feature extraction network to extract dynamic space-time features and fuse cross-modal features; establishing an environment disturbance simulation generation model to generate a virtual sample, and performing domain adaptive alignment; designing an online incremental feature updating mechanism to optimize parameters of the feature encoder; deploying a heterogeneous graph neural network to carry out multi-modal decision fusion; and a hierarchical verification architecture is adopted to complete identity recognition. The system comprises a data acquisition and preprocessing module, a multi-scale space-time fusion feature extraction module and the like. According to the method, the problem of face recognition in a dynamic environment is effectively solved, the recognition accuracy, robustness, real-time performance and reliability can be remarkably improved in the scenes of complex illumination, posture expression change, shielding, background noise and the like, and the method has a wide application prospect.

The invention provides an efficient and accurate small target detection method aiming at the problems of low detection precision, high false detection rate and poor real-time efficiency of small target objects in the field of automatic driving. According to the method, a YOLOv8 network is used as a baseline model, a dynamic enhancement convolution module is introduced into a backbone feature extraction network, and more detailed target detail information is extracted. And an adaptive weighted receptive field fusion module is adopted, so that image feature enhancement of diversified receptive fields is realized, and the richness and adaptability of feature representation are improved. And a large kernel channel space attention module is utilized to highlight a small target area and suppress background noise, so that the distinguishing capability and the detection performance of the whole model are remarkably improved. According to the technical scheme, the precision of small target detection is effectively improved, the omission ratio and the false detection ratio are reduced, and the perception capability and the safety of the automatic driving system in a complex traffic environment are enhanced.

The invention discloses a multi-modal thermal imager intelligent monitoring method based on image processing. The method comprises the following steps: S1, performing dual-band synchronous imaging; s2, carrying out cross-modal image registration; s3, multispectral image fusion: performing smoothing processing on the infrared and visible light images through anisotropic diffusion to obtain a main structure layer image, calculating to obtain an infrared and visible light detail layer image, and linearly combining the detail layer image through PCA principal component weight, and finally, combining the detail layer image and the main structure layer image according to the self-defined weight to obtain a final fusion image. And S4, gas leakage detection is carried out, so that gas leakage detection and early warning can be realized. According to the invention, a gas enhancement algorithm is adopted to realize real-time detection of leaked gas, interference of most background noise can be eliminated, and the real-time performance and reliability of the system are improved.

The invention discloses an intelligent cleaning and feature extraction system and method for multi-modal industrial data, and relates to the technical field of equipment state monitoring. The method is used for solving the problems that multi-source heterogeneous signal time alignment is not accurate, fault features are easily covered by background noise, a causal chain is not clear under working condition changes, and feature stability is poor. Firstly, a matching window is dynamically adjusted based on the main vibration frequency of rotating equipment, temperature signal delay is calculated in combination with a material thermal expansion coefficient, and modal alignment is achieved; then, a fault sensitive frequency band is solved through a bearing pedestal kinetic equation, a frequency band protection window is constructed, frequency domain filtering and gradient truncation operation are executed, and microcrack high-frequency features are extracted; secondly, recognizing a fault propagation path by combining image definition and envelope spectrum kurtosis, and dynamically shrinking a frequency domain window bandwidth according to a real-time load; finally, the feature vector is reconstructed to a phase space, when the curvature change rate or the temperature drift exceeds the limit, parameter updating and frequency band readjustment feedback are triggered, and the stability and adaptability of the system are improved.

The invention provides a building material surface defect detection method based on infrared polarization imaging, which belongs to the field of image processing, and constructs a building material surface defect detection model through input, image preprocessing, a feature extraction module, a feature fusion module, a building material polarization enhancement image module, a building material surface defect detection module and output. Information of a polarization image, an infrared image and a depth image is fused, more comprehensive feature representation is provided, the model has higher recognition capability on fine defects, a self-attention mechanism further optimizes the focusing effect of the features, interference of background noise is avoided, it is ensured that a defect area is fully recognized, and the recognition efficiency is improved. The method has better adaptability and performance in the field of defect detection, can cope with more complex and variable building material detection tasks, and particularly shows stronger advantages when detecting tiny defects and complex surfaces.

The invention discloses a fire smoke image early recognition method based on multi-modal fusion. The method comprises the following steps: S1, collecting multi-modal data and carrying out time alignment; s2, constructing a visible light branch, an infrared branch and a photoacoustic branch, generating multi-modal features, and storing the multi-modal features in a shared feature pool; s3, embedding a space-time tracking module in the shared feature pool, and generating global feature representation; s4, generating a dynamic background template by adopting a low-rank matrix decomposition algorithm, and separating and removing background noise; s5, multi-modal features are fused in the shared feature pool through a lightweight self-attention mechanism, and missing features are compensated; s6, enhancing an early smoke signal through a sparse feature enhancement algorithm, and performing fire state classification and smoke diffusion path prediction by applying a multi-task classification network; and S7, updating the shared feature pool and the space-time tracking module through a dynamic feedback mechanism. According to the invention, early-stage accurate identification of fire smoke is realized by using multi-modal fusion, space-time tracking and dynamic feedback methods.

The invention discloses a concrete crack detection method based on a hybrid Mama attention segmentation model, and the method comprises the steps: 1), obtaining an original image, carrying out the preprocessing of the image, and dividing a data set; 2) constructing an MA-UNet model, adopting an encoder-decoder symmetric structure, extracting features by an encoder through convolution and pooling, recovering resolution by the decoder through up-sampling of transposed convolution, and fusing high-resolution detail features of the encoder with deep semantic features of the decoder through jump connection; (3) a visual state space module VSS is adopted to suppress background noise and reinforce crack features through a dynamic focusing mechanism; 4) performing grouping channel attention weighting on the feature map through a multi-head depth separable channel attention module MDCA; according to the method, the calculation complexity and the parameter quantity are reduced through the lightweight module design, the continuous segmentation capability of the model on the micro cracks is enhanced, and the anti-interference performance under the complex background is improved.

The invention discloses a multi-mode speech enhancement system based on audio and video, and the system comprises a data collection module which collects target audio, background noise and interference sound through a microphone; a camera is used for synchronously collecting environment visual information, including facial expressions and mouth shapes of speakers, and noise and interference sounds are normalized to ensure that the noise and the interference sounds are matched with the dynamic range of a target audio; the data analysis module is used for preprocessing the collected audio, including noise suppression and speech enhancement, and improving the speech definition by applying a deep learning model; through the noise reduction and voice enhancement technology, the interference of background noise can be effectively reduced, and the voice is clearer. Thus, audiences can more easily understand speech contents, especially in noisy environments, such as public transport means or busy streets; and the voice recognition accuracy is improved, and the quality of the voice signal can be improved.

The invention relates to the technical field of carrier communication, and particularly discloses a power line carrier channel simulation method and system based on a dynamic multi-dimensional interference model.The power line carrier channel simulation method based on the dynamic multi-dimensional interference model.The power line carrier channel simulation method based on the dynamic multi-dimensional interference model comprises the steps that a power line noise sample is collected, and characteristic parameters of periodic interference, transient noise and background noise are extracted; classifying and marking according to noise types and corresponding power grid working conditions, and establishing a noise database containing a mapping relation between noise characteristics and working conditions; and based on the noise database, respectively establishing a periodic interference model, a transient noise model and a background noise model, and forming a multi-dimensional noise interference model. According to the power line carrier channel simulation method based on the dynamic multi-dimensional interference model, through multi-dimensional noise modeling and dynamic environment adaptation, the precision, the real-time performance and the scene coverage capability of power line carrier communication simulation are remarkably improved, and the simulation bottleneck in a complex power grid environment in the prior art is solved.

The invention discloses a remote sensing image-oriented multi-scale adaptive small target detection system and method. The system comprises a backbone network backbone, a feature aggregation network Neck and a detection head Head, the backbone network backbone is used for extracting multi-scale effective features, the feature aggregation network Neck is used for fusing and enhancing the multi-scale effective features, and the detection head Head is used for making a decision for target detection. The method comprises the following steps: constructing a target detection data set by using remote sensing images, preprocessing the images, and dividing the images into training, testing and verification sets; a backbone network backbone is adopted to extract multi-scale effective features, and a neck network Neck is adopted to refine the extracted features; carrying out mixed loss training by adopting NWD loss; and the detection head Head outputs the category and location of the target according to the results of the classification branch and the regression branch. According to the method, the loss of information in the transmission process is reduced, the background noise is inhibited, and the accuracy of small target detection of the remote sensing image is improved.

The invention relates to the technical field of voice recognition, in particular to a multi-channel voice conversion and synchronous transmission system applied to simultaneous interpretation. Multi-language voice signals are collected through a multi-microphone array, background noise is dynamically eliminated by adopting a noise suppression technology combining spectral subtraction and deep learning, and a dynamic information source verification symbol is generated to ensure data synchronization integrity; a blind source separation technology is combined with time-frequency analysis and time delay estimation to realize multi-language signal separation and synchronization, and a time sequence is dynamically adjusted through voice activity detection; an end-to-end ASR-NMT-TTS model is constructed to realize voice real-time translation and synthesis, and low-delay transmission is carried out based on a 5G network; the real-time monitoring module is adopted to dynamically adjust the output delay and the signal-to-noise ratio, and the translation delay and the synchronization precision are optimized in combination with user feedback. According to the method, dynamic noise reduction, multi-source synchronization and a self-adaptive feedback mechanism are integrated, the problems of distortion and delay of multi-language simultaneous transmission in a complex noise environment are solved, and the obvious technical synergistic effect and practicability are achieved.

The invention discloses a traffic domain service voice adaptive generation method and system based on a thinking chain fine-tuning large model, and the method comprises the steps: converting an input voice signal into a high-dimensional voice feature signal through a voice encoder, and generating a text output signal according to the high-dimensional voice feature signal through a text decoder and a pinyin decoder; according to the invention, a variational de-quantization joint data enhancement mechanism, a dual-channel multi-mode discriminator architecture and a layered composite loss function are adopted to carry out high-fidelity emotional speech generation and robustness synthesis in a complex noise environment, and semantic-driven dynamic rhythm optimization and professional term precise pronunciation are synchronously supported; in an application scene in the traffic field, a multi-task speech recognition method can be adopted to realize efficient linkage of character-level recognition, audio-to-pinyin conversion and sentence-level accent classification modules, so that complex accent, more noise and traffic term polyphone challenges are effectively handled, and the method is suitable for being widely popularized and used.

The invention discloses a cross-modal foreground distillation method for multi-view three-dimensional target detection, and belongs to the technical field of automatic driving perception. According to the method, a laser radar or a multi-modal three-dimensional target detection model is used as a teacher model, a multi-view aerial view BEV perception three-dimensional target detection model is used as a student model, and the model performance is improved through multi-scale feature distillation; a space attention mechanism and a foreground mask module are introduced, and the student model can simulate feature responses of the teacher model in different space positions under multiple scales, focus on a foreground target and suppress background noise; the adaptive module ensures the consistency of the multi-scale features in feature dimensions and spatial resolution, and finally, the difference between the features and regression output is measured through loss functions such as mean square error and KL divergence, so that efficient cross-modal and multi-scale foreground feature distillation is realized. Finally, the multi-view three-dimensional target detection precision and efficiency are improved, and meanwhile, the distillation requirements of multi-scale features are fully considered.

The invention discloses a small sample target detection method based on target feature enhancement and semantic fusion perception, and relates to a computer vision technology. A data set is divided into a query set and a support set, after features are extracted through a backbone network, background noise in the support features is inhibited through a dynamic hypergraph construction module, and high-order semantic association of a target area is enhanced; fusing the category name text semantics and the image specific prototype by using a semantic fusion perception module to generate a high-discrimination category prototype; modeling semantic distribution by means of a variational auto-encoder, and extracting variational features; and fusing the region-of-interest features and the variation features through a channel attention mechanism to realize classification and regression. According to the method, the prototype characterization capability is effectively improved, and experiments show that the method remarkably improves the detection precision and is suitable for labeling sample scarce scenes. More accurate small sample target detection is realized by enhancing the feature expression of the support feature map and the semantic meaning of the category prototype, and higher robustness and recognition performance are shown in a complex scene.

The invention relates to the technical field of ground resistance detection, in particular to a single-core cable ground resistance detection method capable of resisting strong power frequency interference. High-frequency signals are injected in a non-contact mode through a coupling type capacitor clamp, background voltage and current signals are collected firstly, and injection frequency far away from power frequency and harmonic waves is selected through Fourier analysis; cancelling background noise by adopting a normalized least mean square adaptive filter to generate an enhanced signal; the signal is down-converted to a base band through digital quadrature demodulation, a Kalman filter estimates the amplitude and the phase in real time, and the state and noise parameters are periodically calibrated through a compressed sensing algorithm; and finally, loop impedance is calculated according to the voltage and current amplitudes and the phase difference to obtain grounding resistance. The method has the advantages of being non-contact, high in safety, strong in anti-interference capability, high in measurement precision and the like, and is suitable for monitoring the grounding state of the cable in a complex electromagnetic environment.

The invention provides a target tracking method, electronic equipment and a readable storage medium. According to the target tracking method provided by the invention, by combining generation and updating of the static template and the dynamic template, the problems of time-varying characteristics and background noise in infrared small target tracking can be effectively solved. Firstly, a generated static target image template is cut by multiple scales, so that the adaptability to different scales of a target is improved; and the generation of the dynamic target image template is combined with the dynamic template of the previous frame and the temporary template of the current frame, and updating is carried out through a fusion strategy, so that the morphological change and the thermal radiation fluctuation of the target can be reflected in time, and the problem of response lag caused by the time-varying characteristic of the target is solved. And finally, inputting the static and dynamic templates into a pre-trained target tracking model for processing, thereby effectively suppressing interference of complex background noise, and accurately positioning the target.

The invention discloses a defect identification system of an ultrasonic flaw detector, and relates to the technical field of nondestructive testing, the system comprises a signal acquisition and preprocessing module, a defect feature identification module, a physical modeling analysis module, a life prediction and evaluation module and an intelligent decision visualization module; the signal acquisition and preprocessing module adopts a multi-frequency-point phased array transducer array, obtains an original signal through low-noise amplification, band-pass filtering and analog-to-digital conversion, and outputs a time domain signal matrix through adaptive noise reduction and gain compensation processing; the multi-frequency-point phased array transducer array and the advanced signal processing technology are integrated, the defect recognition precision and efficiency are remarkably improved, the system obtains high-quality original signals through low-noise amplification, band-pass filtering and analog-to-digital conversion technologies at first, then the high-quality original signals are subjected to self-adaptive noise reduction and gain compensation processing, and the defect recognition accuracy is improved. The background noise interference is effectively eliminated, and the purity of the signal is ensured.

The invention relates to the technical field of bill intelligent verification, and discloses a financial bill intelligent identification and verification method based on machine learning. The method comprises the following steps: acquiring an original image data stream of financial bills in real time, monitoring the total quantity of input bills and judging whether the total quantity exceeds a system processing safety threshold; during overrun, processing bill image feature elements by using a machine learning algorithm to generate a feature interaction graph so as to evaluate a potential error risk level, analyzing a background noise distribution mode by using a signal decomposition technology, reconstructing a noise dynamic characteristic model, and calculating an interference degree index of the noise dynamic characteristic model to a key region; deciding whether to start an overall verification optimization process or not according to the grades and the indexes; when the system is started, all bill image recognition path attribute data are analyzed to generate recognition complex factors, verification priority values are calculated according to the recognition complex factors, and a verification sequence is sequenced and output. According to the method, the accuracy and efficiency of financial bill identification and verification can be improved, and the processing flow is optimized.