553 results about "Noise detection" patented technology

The invention discloses an equipment data acquisition and query optimization method based on a large model enabling ERP-MES system, and relates to the technical field of integrated enterprise resource planning and monitoring management systems, and the method comprises the following steps: carrying out the dynamic adaptive data acquisition and preprocessing of equipment: employing an edge node to deploy a lightweight noise detection model, identifying an electromagnetic interference mode in sensor data collected by the equipment in real time, and dynamically compensating and correcting by combining environment temperature and humidity data; according to the method, an edge node lightweight noise detection model is combined with environmental data compensation and correction, and a BERT large model is utilized to analyze a protocol document design middleware, so that accurate acquisition and efficient processing functions of dynamic self-adaptive data of equipment are realized; preprocessed data are mapped and fused through a multi-modal large model to construct a unified data warehouse, and a mapping strategy is optimized through reinforcement learning, so that the equipment data quality is improved, and the data fusion and utilization efficiency is enhanced while heterogeneous data fusion is achieved.

The invention discloses a complex underwater side-scan sonar exploration detection method and device based on a multi-dimensional attention collaborative lightweight anti-noise detection framework, and the device comprises an underwater side-scan sonar imaging device which is used for obtaining a sonar image of a detected target; the computer is connected with the underwater side-scan sonar imaging equipment and comprises a backbone feature extraction network module which is used for processing an input sonar image through a multi-scale edge refining module and outputting a three-scale feature map; the check feature fusion network module is used for realizing cross-channel and cross-space information fusion through the focusing space adaptive local attention module, performing adaptive modulation and deep information aggregation on multi-scale features through a channel frequency aggregation and attention mechanism, and outputting three enhanced feature maps; and the YOLOhead detection head module is used for generating a target detection frame according to the enhanced feature map, and outputting a detection result after non-maximum suppression processing.

The invention provides a magnetotelluric data denoising method and device based on a depth fusion model, is applied to a magnetotelluric data denoising system, and relates to the technical field of magnetotelluric data processing. A deep fusion model with a'noise feature recognition-signal selective reconstruction 'framework is established, input signals are pre-classified through a front noise detection module, the problem of false attenuation of effective signals in traditional end-to-end denoising is effectively avoided, a denoising auto-encoder containing a residual structure is utilized, and the denoising accuracy is improved. The deep feature extraction capability of the residual structure and the signal reconstruction advantage of the denoising auto-encoder are combined, the fidelity and integrity of the reconstructed signal are remarkably improved, the denoising process does not depend on manual intervention, and the efficiency and accuracy of data processing are greatly improved.

A system may include a resistive-inductive-capacitive sensor, a driver configured to drive the resistive-inductive-capacitive sensor at a driving frequency, a measurement circuit communicatively coupled to the resistive-inductive-capacitive sensor and configured to measure phase information and amplitude associated with the resistive-inductive-capacitive sensor, and a noise detection circuit communicatively coupled to the measurement circuit and configured to determine a presence of external interference in the system based on at least one of the phase information and the amplitude information.

The invention discloses a motor abnormal sound detection method and system based on contact acquisition and small sample learning, and the method comprises the steps: directly coupling a motor housing through a contact vibration sensor, collecting an original vibration signal, and generating an anti-interference vibration signal; inputting the anti-interference vibration signal into a nonlinear resonance enhancement module to generate an enhanced sound signal; performing wavelet packet decomposition on the enhanced sound signal, extracting a multi-scale frequency band energy entropy, and generating a motor abnormal sound feature matrix by combining singular value decomposition dimension reduction; on the basis of a dynamic weight distribution element learning algorithm, a small number of normal samples and abnormal samples are utilized to construct an abnormal sound classification model; and inputting the motor abnormal sound characteristic matrix into an abnormal sound classification model, detecting transient abnormality through a sliding window time sequence matching algorithm, and outputting an abnormal sound judgment result. According to the embodiment of the invention, high-precision and low-false-alarm motor abnormal sound detection under complex working conditions can be realized.

The embodiment of the invention relates to the field of time series anomaly detection, in particular to a time series data anomaly detection method and device of a dynamic condition diffusion model based on gradient noise. A specific embodiment of the method comprises the following steps: determining a GNDC-DM model; performing trend noise detection processing on the acquired initial industrial equipment data based on a trend noise diffusion model included in the GNDC-DM model to generate trend noise reduction data; performing seasonal noise detection processing on the obtained initial industrial equipment data based on a seasonal noise diffusion model included in the GNDC-DM model to generate seasonal noise reduction data; and performing hybrid anomaly detection processing on the acquired initial industrial equipment data, trend noise reduction data and seasonal noise reduction data based on a hybrid anomaly diffusion model included in the GNDC-DM model to generate equipment noise reduction data. According to the embodiment, the detection based on the reconstruction score can be more effective.

The invention discloses a multispectral and hyperspectral laser radar weak multi-echo signal extraction method. According to the method, a B-spline model is adopted to establish models for transmitted pulses and received echoes. Compared with a traditional Gaussian model and a B-spline model, the method has local control characteristics, is more suitable for modeling and analysis of complex waveforms, improves the adaptability to diversified signals, remarkably reduces the probability that noise is misjudged as echo signals by conducting preliminary screening and positioning on received echoes of all channels, and improves the accuracy of noise detection. Therefore, error accumulation is effectively reduced. The process not only improves the accuracy of signal identification, but also greatly reduces the complexity of subsequent data processing, reduces the calculation burden of the system, and lays a solid foundation for refined waveform analysis.

The invention provides a method and device for carrying out noise detection on a train, which can be applied to the field of railway and urban rail transit, and the method comprises the following steps: determining the initial noise reverberation duration and the sound source position of noise; iteratively executing the following operations to obtain the dynamic reverberation duration: determining the nth reverberation duration according to the sum of the (n-1) th noise reverberation duration and the preset duration; processing the nth noise reverberation duration and the reference reverberation duration according to tunnel shape parameters for the pipe tunnel to obtain an nth room coefficient; processing the nth room coefficient by using a target noise detection model corresponding to the sound source position to obtain an nth sound pressure level increment value of the noise; under the condition that a difference value between the Nth sound pressure level increment value and a preset standard sound pressure level increment value meets a preset convergence condition, determining the Nth noise reverberation duration as a dynamic reverberation duration; and processing the reference reverberation duration and the dynamic reverberation duration according to a reverberation noise prediction algorithm to obtain a noise value of the noise.

The present invention relates to a helical gear detection method and gear detection system based on waviness information. The helical gear detection method based on waviness information comprises the following steps: extracting the full tooth waviness information of the helical gear; processing the full tooth waviness information to obtain a frequency spectrum; calculating the angle α of the waviness on any single tooth of the helical gear; judging whether the gear meets the noise requirement based on the angle α of the waviness on the single tooth and the frequency spectrum. The present invention calculates and extracts the important factors affecting the gear meshing noise by analyzing the full tooth order of the involute helical gear and the three-dimensional angle of the single tooth topology. It can effectively detect the tooth surface waviness information, identify and discover ghost frequencies, and analyze the angle of the corrugation line. Compared with the existing detection method, it greatly improves the fitting quality of the data, thereby improving the detection accuracy of the gear, so that it meets the noise detection requirements, and provides a new detection method for the production of high-speed and low-noise gears.

The invention relates to the technical field of pipe network acoustic detection, in particular to a water supply pipe network leakage noise detection method and system and a medium. The method comprises the following steps: constructing a physical model of leakage noise based on fluid mechanics and a sound propagation rule; generating simulated leakage noise data based on physical model parameterization so as to simulate leakage noise data under extreme physical conditions; a set of the actually measured leakage noise data and the simulated leakage noise data is used as leakage noise sample data, and time domain statistical characteristics and frequency domain statistical characteristics of the signals are obtained through quantification according to the leakage noise sample data; based on the statistical characteristics, dynamically adjusting window function parameters and performing short-time Fourier transform processing to obtain a resolution-optimized time-frequency image; and constructing a CNN classification model, training the model based on the resolution-optimized time-frequency image, and outputting prediction probabilities of leakage noise and environmental noise. While the water supply pipe network leakage noise detection precision is improved, hardware dependence is eliminated, and efficient pure acoustic end-to-end pipe network leakage detection is realized.

The invention discloses a heterogeneous perception traffic prediction method based on federated learning, and the method employs federated learning to design a unified heterogeneous perception framework, and supports an existing centralized traffic prediction model. Clients with similar traffic flow data distribution are gathered together by utilizing multi-dimensional positive sample comparative learning, so that the clients of the same kind can cooperatively train a model, and the influence of data isomerism between different clients is avoided; the model of each stage is trained in sequence based on a time window by using data partition, so that the influence of data missing on traffic prediction is reduced; noise detection is used for global detection and local denoising, so that the quality of client data is ensured.

The invention relates to an attribution analysis method and device for active causes of a cloud disk APP user. The method comprises the following steps: acquiring behavior characteristic data of the cloud disk APP user in a current statistical period; preprocessing the behavior characteristic data, wherein preprocessing is used for noise detection and filtering and abnormal user detection; based on the preprocessed behavior characteristic data, training a fusion prediction model used for predicting whether the user is retained in the next statistical period; calculating the contribution degree of each behavior feature to a prediction result based on the prediction result of the fusion prediction model; and determining an attribution factor which causes the user to be active in the current statistical period based on the contribution degree. According to the scheme, through training the fusion prediction model, time sequence dependence and complex feature interaction in a behavior sequence are accurately captured, so that high-precision prediction of user retention is realized; and the contribution degree of each behavior feature to a prediction result is calculated, so that the attribution factors causing the user activity are reversely deduced and determined, and the real motivation of the user activity can be accurately captured.

A noise detector for use in a wireless network device is disclosed. The wireless network device may be used to scan a plurality of different frequency channels, which may be Bluetooth or Zigbee channels, for example. The noise detector is used to quickly detect the presence of noise on a frequency channel. This quick detection allows the wireless network device to switch to another frequency channel and continue scanning. In some embodiments, the noise detector uses a detection window and counts frequency outliers in that detection window to determine whether a valid signal is present on the wireless channel. The detection window may be fixed in duration, or may grow. Additionally, the detection window may be stationary or may be a sliding window.

The invention discloses a hyperspectral image noise detection and evaluation method and system, and relates to the technical field of image processing, and the method comprises the steps: firstly, carrying out the superpixel segmentation of an original hyperspectral image through employing an improved linear iteration clustering method, and obtaining a plurality of superpixel sub-blocks; then, calculating a de-trending cross correlation coefficient and a de-trending cross correlation coefficient for the spectral reflectivity sequences of every two superpixel sub-blocks by adopting a de-trending cross correlation method; meanwhile, obtaining a noise sequence characteristic value range according to a randomly generated Gaussian white noise sequence, and taking the noise sequence characteristic value range as a reference of a hyperspectral signal noise range; and finally, performing spectral decomposition on a correlation matrix constructed by the de-trending partial cross correlation coefficients of every two superpixel sub-blocks to obtain a partial correlation matrix characteristic value under each preset scale, and performing calculation according to a noise sequence characteristic value range to obtain a noise proportion value and a de-trending partial cross correlation coefficient matrix after de-noising. According to the invention, the accuracy of noise detection evaluation of the hyperspectral image can be optimized.

The invention discloses a character extraction method for a power transmission and transformation project document. The method comprises the following steps: acquiring data information of a target power transmission and transformation project document and preprocessing the data information to obtain a binary image of the target power transmission and transformation project document; noise detection, noise classification and noise reduction processing are carried out; carrying out character region positioning on the target power transmission and transformation project document; segmenting and extracting characters; character recognition is carried out based on a local binary pattern; and carrying out association and replacement of characters and completing character extraction of the target power transmission and transformation project document. The invention also discloses a system for realizing the character extraction method for the power transmission and transformation project document. According to the method, the image of the target power transmission and transformation project document is processed and extracted, and particularly the characters of the table part in the document are positioned, segmented, identified and replaced, so that the character extraction of the power transmission and transformation project document is realized, the reliability is higher, and the accuracy is better.

The invention discloses a noise detection method and device for a dual-mode communication network of a power system. The noise detection method comprises the following steps: acquiring an analog signal transmitted by the dual-mode communication network of the power system in a current detection period and filtering the analog signal; sequentially carrying out signal amplification and analog-to-digital conversion on the filtered analog signal according to a preset sequence of a plurality of preset gain values to obtain a digital signal, carrying out validity verification on the digital signal, and terminating after obtaining a first preset gain value which is verified to be valid; and calculating a noise intensity value of the dual-mode communication network of the power system based on the preset gain value which is verified to be valid for the first time. Through orderly traversal of a preset gain sequence and a first effective immediate stop strategy, orderly matching of effective gains is realized, an ADC signal overflow risk is thoroughly avoided, and complete waveform capture in a strong noise environment is ensured; and meanwhile, traversal times are compressed, and detection time consumption is remarkably reduced compared with a traditional multi-stage gain scheme.

The invention provides a new energy automobile cabin abnormal noise detection method and device based on sound image fusion, and belongs to the technical field of noise detection. The method comprises the following steps: synchronously acquiring multi-channel audio data and video data through a multi-channel microphone array and a camera respectively; determining an abnormal noise type and extracting an abnormal noise signal; an abnormal noise positioning position is obtained through an improved space contraction fast method based on random search, namely an improved SRC method; calibrating the extracted abnormal noise positioning position and an image in the video data, and displaying a noise source in the image in the form of a sound field thermodynamic diagram to obtain a sound field diagram after sound-image fusion; according to the method, more efficient and more visual noise anomaly detection of the new energy automobile cabin can be realized, the accuracy of noise source positioning can be remarkably improved, the efficiency of abnormal noise detection of the new energy automobile cabin can be greatly improved, and the method can be widely applied to noise monitoring and fault diagnosis of the new energy automobile cabin.

The invention provides a point cloud denoising method and device, a medium and equipment, and relates to the technical field of point cloud processing. According to the method, the non-ground point cloud is obtained by acquiring the laser point cloud data and performing ground segmentation on the laser point cloud data, so that interference of ground point cloud information is eliminated. Clustering the non-ground point clouds to obtain a plurality of clustering target blocks; for the target point cloud in any clustering target block, constructing aggregation feature information corresponding to the target point cloud; sequentially inputting the plurality of clustering target blocks into a preset noisy point detection model, and classifying the target point clouds by using the noisy point detection model based on the aggregation feature information corresponding to the target point clouds to obtain categories of the target point clouds; and deleting the target point cloud of which the category is the noisy point. Therefore, the noisy points in the clustering target block can be quickly found and filtered, the interference of the noisy point cloud on the automatic driving system can be eliminated, and the accuracy of the environmental perception of the automatic driving system can be improved.

The invention relates to the technical field of ship navigation observation equipment, in particular to a sonar and noise detection integrated marine ecological monitoring buoy system, which comprises a sonar array dynamic regulation and control module which comprises a sonar array with an adjustable spatial position and dynamically adjusts the dip angle of the sonar array according to the noise propagation direction through a stepping motor driving mechanism, obtaining sonar target data; a noise collaborative sensing module; a sonar-noise closed-loop control module: a noise feature hierarchical extraction unit; a sonar parameter dynamic generation unit; a multi-modal fusion decision unit; the self-adaptive resource scheduling module is used for calculating the effective detection radius of each sonar based on noise energy distribution and dynamically distributing transmitting power; through innovative measures of cooperative work of multiple sonar arrays, noise feature extraction, intelligent resource scheduling and the like, the adaptability and monitoring accuracy of the marine ecological monitoring buoy system in a complex environment are greatly improved, and meanwhile extraction of ecological information and effective suppression of noise are achieved.

The invention relates to a new energy driving shaft abnormal sound detection method, and relates to the technical field of abnormal sound detection, and the method comprises the following steps: arranging a road spectrum collection device: arranging the road spectrum collection device at a vehicle driving shaft for collecting working data of the driving shaft in the vehicle driving process, the working data comprising torque and rotating speed data; road spectrum acquisition: road spectrum acquisition equipment acquires working data of a driving shaft of the vehicle in the energy recovery process; importing the working data into an NVH rack: importing the driving shaft working data acquired by a road spectrum into the HVH rack, and driving a servo motor to reproduce a transient process of an actual sliding working condition; and abnormal sound verification: operating the NVH rack in the sound insulation environment, simulating the working condition of the driving shaft to be tested in the energy recovery process according to the imported working data, and reproducing the abnormal sound of the driving shaft. The method has the effects of reducing the test cost and shortening the test period.

The invention provides an intelligent park multi-dimensional environmental parameter real-time monitoring and intelligent early warning system, and relates to the technical field of environmental monitoring, and the system comprises a base, a bottom column is fixed at the center of the top of the base, and the top of the bottom column is rotatably provided with a central shaft; the top of the center shaft is rotationally connected with the top plate, a photovoltaic panel is fixedly installed at the top of the top plate, and a noise detection device is fixed to the periphery of the top of the top plate; the air detection assembly comprises a mounting ring, the mounting ring is rotationally mounted on the surface of the center shaft, a detection box is fixed to the outer side of the mounting ring, the air quality around the device is detected through the air detection assembly, and the pollution degree of a water area near a park is detected through the water quality detection assembly; the pollution degree and humidity of soil around the device are detected through the soil detection assembly, a detection result is fed back to the display screen, and when detection data exceed a preset value, early warning is conducted through the display screen.

The invention belongs to the technical field of air conditioner indoor unit detection, and discloses an air conditioner indoor unit abnormal sound detection method and system based on a variational auto-encoder model, and the method comprises the steps: converting the sound data, collected in real time, of an air conditioner indoor unit from a one-dimensional original sound signal into a two-dimensional time-frequency feature, standardizing the two-dimensional time-frequency features, inputting the two-dimensional time-frequency features into an anomaly detection model, and outputting reconstruction errors to construct an anomaly score; judging the abnormal score and a preset threshold value, and obtaining an abnormal detection result; inputting an anomaly detection result into the fault classification model, and outputting a known fault score; calculating an unknown fault probability based on entropy by using a known fault score; the known fault score and the unknown fault probability are combined into a final fault score, and the maximum value in the final fault score is output as an abnormal sound fault result of the air conditioner indoor unit. Wherein the anomaly detection model and the fault classification model are both variational auto-encoder models. Full-automatic online detection of the air conditioner indoor unit is achieved, the detection accuracy is high, and the detection speed is high.

The utility model relates to the technical field of noise detection, in particular to a noise detection device for a pipeline element. The noise detection device for the pipeline element comprises a mute chamber, a sealed cabin, a pressurization assembly and a detection assembly, and the sealed cabin is arranged in the mute chamber; the pressurizing assembly comprises a pressurizing part and an air inlet pipe fitting, the pressurizing part is arranged on the outer side of the mute chamber, the air inlet pipe fitting is communicated with the pressurizing part and the sealed cabin, the pressurizing part inputs air into the sealed cabin through the air inlet pipe fitting so as to change the air pressure in the sealed cabin, and the sealed cabin is used for being communicated with a to-be-tested element; the detection assembly comprises at least one noise detection piece, and the noise detection piece is arranged in the mute chamber and used for detecting noise of the to-be-detected element. According to the noise detection device for the pipeline element, noise generated by the pipeline element under different pressures can be detected, and the reliability of a detection result is improved.

Aspects of the subject disclosure may include, for example, a method including training a first machine learning model to recognize a predetermined named entity in a sentence and a corresponding label, receiving input sentences including a target named entity, receiving a plurality of few-shot examples in a support set, the plurality of few-shot examples including an annotated label for the target named entity, performing annotation on the input sentences with the trained first machine learning model using the plurality of few-shot examples and using no prompt, generating labeled data including the annotated input sentences, performing post-annotation on the labeled data with a second machine learning model that performs confident learning based label noise detection, and generating cleaned labeled data excluding one or more noisy labels from the labeled data. Other embodiments are disclosed.

The utility model discloses a limiting device for track noise detection, which relates to the technical field of track traffic noise detection and comprises a square mounting frame. The two mounting plates are symmetrically arranged on the mounting frame, and the two mounting plates are connected with the mounting frame in a sliding and locking mode; two first limiting mechanisms; two clamping jaw parts; and two second limiting mechanisms. According to the utility model, firstly, the detected track is supported by the second limiting mechanism to ensure that the detected track is in a horizontal detection position, and then the detected track is limited and fixed by the first limiting mechanism and the clamping jaw part, so that the connection condition between the clamping jaw part and the detected track can be observed; whether the detected track is flat and qualified or not is preliminarily observed, and then levelness detection is conducted on the detected track through flatness detection equipment. The purpose of detecting the flatness of the surface of the track is achieved.

A processing circuit that processes a detection signal detected by a sensor element includes a main circuit that generates an output signal in response to the detection signal, an alternative circuit that generates an alternative signal in response to the value of the detection signal at a timing different from that of the output signal, a noise detection unit that outputs a noise signal that indicates the presence or absence of noise in the detection signal, and a selection unit that selects and outputs either the output signal or the alternative signal in response to the noise signal, and the selection unit outputs the alternative signal for a period longer than the noise detection period.

The invention provides a vibration noise detection device and method of a wall breaking machine, and mainly relates to the field of vibration noise detection. A vibration noise detection device of a wall breaking machine comprises a positioning mechanism arranged in a detection chamber and a plurality of noise detection mechanisms, coaxial placement and height adjustment of the wall breaking machine are achieved through clamping and positioning of the positioning mechanism, and the wall breaking machine can be located at the detection height on different workbenches. At least four noise detection mechanisms are arranged, each noise detection mechanism comprises a supporting arm, a supporting cavity is formed in the inner side of the front end of each supporting arm, a telescopic arm is arranged on each supporting cavity in a sliding mode, and a sound level meter and a distance sensor are arranged at the front end of each telescopic arm. The invention has the following beneficial effects: the invention creatively provides a structure and a method for respectively detecting noise by a plurality of workbenches, so that the noise intensity of the wall breaking machine in the daily use process can be more reasonably represented.

The utility model provides a noise detector for multi-point noise detection, which relates to the technical field of noise detection and comprises a detector body, two sides of the detector body are respectively provided with at least one inlet and outlet, two sides of the lower portion of an inner cavity of the detector body are respectively connected with circular plates, and the number of the circular plates corresponds to that of the inlets and outlets. The middle part of the front end of the circular plate is rotatably connected with a rotating shaft, the rotating shaft is sleeved with a storage roller, the storage roller is wound and connected with a signal line, one end of the signal line extends into the inlet / outlet, and the extending end of the signal line is connected with a noise detection probe, and the upper part of the back surface of the detector body is rotatably connected with a rotating rod; the outer surface of the rotating rod is sleeved with a rotating plate, the interior of the rotating plate is rotationally connected with a threaded rod, and one end of the threaded rod extends to the outer side of the rotating plate. According to the utility model, the detector body can be fixedly mounted conveniently, and the safety of the detector body in use is improved.

The utility model relates to the technical field of motor noise detection, specifically provides a motor noise detection simulation load device and a motor noise detection device, and aims to solve the problem of inaccurate motor noise detection data in the prior art. In order to achieve the purpose, the motor noise detection simulation load device comprises a shell, a driving shaft, a transmission mechanism and a pressure adjusting mechanism, the driving shaft is rotatably arranged on the shell, one end of the driving shaft is connected with the driving end of the motor, the other end of the driving shaft is connected with the transmission mechanism, and the transmission mechanism is arranged in the shell; the pressure adjusting mechanism is arranged to be capable of making contact with the transmission mechanism and adjusting the acting force between the pressure adjusting mechanism and the transmission mechanism. According to the utility model, the actual load-carrying state of the motor can be simulated, no extra noise is generated like a fan, and the accuracy of motor noise detection is improved.

The application discloses an arc fault detection device for a low-voltage circuit breaker and relates to the technical field of low-voltage circuit breaker fault detection.The device comprises a workbench, wherein an elevated platform is installed on a reference plate of the workbench;an infrared temperature measurement probe is installed on a first supporting rod of a first support;an industrial frequency magnetic field measuring device is installed on a second supporting rod of a second support;first and second inclined surfaces are symmetrically arranged on the left and right sides of a third support;third and fourth supporting rods are respectively installed on the first and second inclined surfaces;first and second noise detection probes are respectively installed on the inner ends of the third and fourth supporting rods;and a fifth supporting rod is arranged between the third and fourth supporting rods, and a third noise detection probe is installed on the bottom end of the fifth supporting rod.The device eliminates the blind area of traditional single-point detection, can capture complete high-frequency noise spectrum radiated by various types of fault arcs in the circuit breaker without dead angles, ensures complete extraction of characteristic signals, and avoids misjudgment caused by signal attenuation or directionality omission.