266 results about "Acoustic spectrum" patented technology

The invention relates to the technical field of automatic welding control, and particularly discloses a multi-station automatic welding cooperative control method for electric iron accessories, which realizes full-closed-loop accurate control of a welding process through multi-sensor data fusion and an intelligent optimization algorithm. According to the method, an infrared thermal imager, an acoustic emission sensor and an ultrasonic thickness measuring device are adopted to collect molten pool temperature field, sound wave frequency spectrum and fusion depth data in real time, after feature extraction, the data are input into a multi-target optimization model, and a Pareto optimal solution set is generated with the fusion depth, the width of a heat affected zone and phase change latent heat as optimization targets; dynamically adjusting a segmented welding-back path and process parameters on the basis of an optimization result, and triggering model iterative correction through ultrasonic online monitoring; the central control unit adopts space voxelization monitoring and a three-level collision avoidance strategy to coordinate operation of the multiple mechanical arms, and collision emergency protection is achieved in combination with flexible pressure sensing.

The invention relates to a multi-agent cooperation strategy generation method, device and equipment and a medium, and the method comprises the steps: separating acoustic spectrum features and text semantic features of conference voice through environment perception processing, solving a cross-modal information conflict problem, and generating an accurate semantic understanding result; identifying the essence of the problem based on task analysis, associating the responsibility field, and constructing a classifiable problem point set; calling an agent capability library to dynamically match problem requirements, and generating a candidate agent list; quantifying a problem influence range and a decision time limit through weighted emergency scores, and generating a priority-sorted agent sequence; screening and confirming a core problem point and a primary agent; and finally, generating an executable cooperation scheme through multi-agent collaborative optimization. According to the method, the problems of incomplete feature extraction, task allocation delay and resource conflict in the prior art are solved, and the operability and decision-making efficiency of a cooperation strategy are remarkably improved.

The invention discloses an unmanned aerial vehicle positioning method based on a Bayesian network, and the method comprises the steps: collecting unmanned aerial vehicle data, carrying out the preprocessing, obtaining sound wave data, motion data, infrared image data and environment data, carrying out the spectrum analysis based on the sound wave data, obtaining sound spectrum time sequence data, and carrying out the positioning of the unmanned aerial vehicle. Constructing a probability density thermodynamic diagram and extracting a first range through a sparse variational Gaussian process according to time, frequency and energy three-dimensional information of the sound spectrum time sequence data and environment data, and obtaining first positioning data through a Bayesian network according to the first range and motion data, and performing adaptive wavelet down-sampling on the infrared image data and the sound spectrum time sequence data in the lateral direction to obtain lateral positioning data, and performing deviation correction on the first positioning data by using the lateral positioning data to obtain second positioning. According to the method, through space-time synchronization, adaptive processing and probabilistic reasoning of sound spectrum time sequence data, the positioning precision and stability in a complex environment are improved.

The invention discloses a pipeline defect identification method and device, a storage medium and equipment, and belongs to the technical field of pipeline defect detection. The method comprises the following steps: S1, acquiring a time sequence sound signal acquired by an acoustic sensor arranged on the inner wall and / or the outer wall of a pipeline; s2, performing spectrogram extraction on the time sequence sound signal to obtain a Mel spectrogram and a LOFAR spectrogram; and S3, performing feature extraction on the Mel spectrogram, the LOFAR spectrogram and the time sequence sound signal through a deep learning model of multi-modal signal fusion to obtain a feature vector containing spectrogram features and time sequence features, and performing classification according to the feature vector to obtain a classification result of pipeline defects. According to the method, the acoustic signals are converted into different types of spectrograms, the original time sequence signals are combined, feature extraction, fusion and classification are carried out through a deep learning model, complementary information of the spectrograms and the time sequence signals is fully utilized, the limitation of a single recognition mode is overcome, and the accuracy and robustness of pipeline defect detection are improved.

The invention relates to the technical field of boiler temperature measurement, in particular to a waste heat boiler three-dimensional temperature field construction method and system and a medium. The method comprises the following steps: performing optical fiber sensor grid deployment on the waste heat boiler to obtain a distributed temperature acquisition network; sampling the distributed temperature acquisition network to obtain a boiler real-time temperature sequence; performing data preprocessing on the boiler real-time temperature sequence to obtain a boiler initial temperature field; based on the initial temperature field of the boiler, fluid dynamic model parameter construction is conducted on the waste heat boiler, and a boiler fluid characteristic parameter set is obtained; performing multi-physical field coupling on the boiler fluid characteristic parameter set to obtain a boiler theoretical temperature field; acoustic sensor array arrangement and data acquisition are carried out on the waste heat boiler, and acoustic spectrum acquisition data are obtained. By fusing various temperature field data, respective advantages and limitations can be complemented, and comprehensive and accurate temperature information of the waste heat boiler is obtained.

The invention discloses a spectrogram feature enhancement method for arc fault detection and a series AC arc fault detection method and system, and belongs to the technical field of arc fault detection. The invention aims to solve the problem that fault characteristic information carried by current is easily covered by a so-called shielding load in the existing arc fault detection. The method comprises the following steps: performing short-time Fourier transform on time sequence data of a current signal under a load condition to obtain time-frequency information Ct, f of the current signal, and visualizing the time-frequency information Ct, f to obtain a spectrogram of an original current signal; based on the spectrogram of the original current signal, the spectrogram is processed according to St, f = 10 * log10 (Ct, f), an enhanced spectrogram is obtained, and the enhanced spectrogram is used for arc fault detection. In the detection process, an arc fault detection model constructed based on the deep learning technology is adopted to carry out fault detection, and the arc fault detection model outputs an arc fault detection result.

The invention provides a gas concentration detection device and method, and relates to the technical field of acoustic spectrum gas detection. The device comprises an MEMS infrared radiation light source, an eccentric photoacoustic cell, a digital MEMS microphone and data acquisition and processing equipment, the eccentric photoacoustic cell comprises a cylindrical air chamber, a first spherical reflector, a second spherical reflector and a shell, and an eccentric hole is formed in the position, deviating from the axis, of the first spherical reflector; infrared light emitted by the MEMS infrared radiation light source enters the cylindrical gas chamber, passes through a preset optical path and is absorbed by gas to be detected in the cylindrical gas chamber to generate a photoacoustic signal; converting the photoacoustic signal into a digital signal; determining the concentration of the to-be-detected gas based on the digital signal; wherein the preset optical path is determined based on the distance of the eccentric hole deviating from the axis and the curvature radius of the first spherical reflector and the curvature radius of the second spherical reflector. According to the gas concentration detection device and method provided by the invention, the detection precision of the concentration of the gas to be detected can be improved.

The invention relates to the technical field of electric digital data processing, in particular to a sound cloning system and method, electronic equipment and a storage medium, and the system comprises an audio input module which is used for receiving a reference voice segment of any sound source; a semantic extraction module for identifying a linguistic discrete semantic representation of the reference speech segment based on the reference speech segment; the voiceprint feature extraction module is used for extracting voiceprint features of any sound source based on the reference voice segment; the acoustic synthesis module is used for reconstructing the linguistic discrete semantic representation and the voiceprint feature into a corresponding acoustic spectrum; and the vocoder is used for taking the acoustic spectrum as input so as to output voice with the tone of any sound source by using the reference voice segment. Therefore, the technical problems that in the related technology, a sound cloning model needs target sound source voice of a certain duration, instant cloning cannot be achieved, and the phenomenon of tone leakage exists, so that the synthesized voice still contains part of original sound source features and the like are solved.

The invention discloses an automatic processing device and method for PCB lamination, and the method comprises the steps: depositing an erodable film on a lamination material layer and arranging a cavity concentric with a through hole array before the lamination of a PCB, and collecting acoustic resonance frequency data to obtain a reference matrix; dynamic dot frequency tracking processing is carried out on the multi-point acoustic time sequence signals in the pressing heating stage, and a through hole instantaneous acoustic track library is obtained; performing coupling judgment on time-temperature-pressure ternary working condition data according to the trajectory library to obtain a blockage risk matrix; carrying out time delay and gradient adjustment on a pressing temperature and pressure curve based on the risk matrix; and carrying out three-dimensional inversion processing on the whole board broadband acoustic spectrum collected before cooling, calculating through hole filling rate distribution and judging the quality of the board. According to the scheme, the resin filling state of the micro through hole can be monitored in real time in the PCB pressing process, an acoustic detection channel is constructed through the erodable film and the cavity structure, early warning and active intervention of the blocking risk are achieved, and the pressing quality is ensured.

The invention relates to the technical field of oil and gas exploitation, and discloses an oil and gas well water production position positioning method, device and equipment based on distributed optical fiber sound waves and a medium, and acoustic signals corresponding to a plurality of production profiles can be collected through a distributed acoustic sensor arranged in an oil and gas shaft. And performing preprocessing, time-frequency conversion and normalization on the acoustic signal corresponding to each production profile to obtain a normalized acoustic spectrum corresponding to each production profile. And extracting a characteristic wave crest section from the normalized acoustic spectrum corresponding to each production profile. And carrying out reverse normalization on the characteristic wave crest segment corresponding to each production profile to obtain a reverse normalization wave crest segment. And based on the water phase response frequency band, determining a matched first wave crest section in the anti-normalized wave crest section corresponding to each production profile, and determining the production profile corresponding to each first wave crest section as a water production position. According to the invention, intelligent detection of the water production position of the oil and gas well can be realized, and the detection precision of the water production position is effectively improved.

The invention discloses a wind turbine generator fault diagnosis method and system based on a fractional order neural network. The method comprises the following steps: S1, converting a preprocessed original yaw system audio signal into time domain data, frequency domain data and sound spectrum data; s2, establishing a parallel multi-mode dense connection convolutional network, and capturing transient or periodic features of the fault from three dimensions; s3, performing feature fusion on the yaw abnormal sound fault features captured in different dimensions; s4, performing parallel multi-modal dense connection network model training and performance optimization by adopting a momentum fractional order optimization technology, and constructing a fractional order-multi-modal dense connection convolutional network; s5, continuously adjusting the weight and parameters of the model, and outputting a yaw abnormal sound fault diagnosis result of the wind turbine generator; the beneficial effects of the invention are that the method extracts and analyzes the data features of sound signals in different modes, proposes a fractional order-multi-mode dense connection convolution network, and carries out the accurate abnormal sound fault diagnosis through the small sample data.

The invention relates to the technical field of arc fault diagnosis, and provides an energy storage system arc fault diagnosis method and system based on voiceprint detection, and the method comprises the following steps: collecting a voiceprint signal in real time, and carrying out the anti-interference preprocessing, and obtaining a time-frequency domain spectrogram; identifying the time-frequency domain spectrogram based on a multi-branch convolutional neural network and weighting the time-frequency domain spectrogram to obtain a time sequence feature sequence; inputting the time sequence feature sequence into a time sequence convolutional network model, and outputting global context features; performing dynamic weighted pooling on the global context features to obtain key frame feature vectors representing the arc fault; and inputting the key frame feature vector into an arc fault diagnosis classification model to obtain an arc fault type, an arc fault severity grade and sound source positioning information, and generating a corresponding alarm signal and a control instruction based on the arc fault severity grade. According to the invention, the detection sensitivity and the anti-interference capability are improved, and the safety protection level and the fault early warning capability of the energy storage system are improved.

The invention belongs to the technical field of industrial equipment fault detection application, and particularly discloses a multi-channel audio acquisition and intelligent fusion coal mill fault voiceprint recognition system and method, and the system comprises a multi-channel audio acquisition module, an audio preprocessing and spectrogram conversion module, a weighted fusion module, a fault judgment and decision module, and an alarm push module. The identification method comprises the following steps: S1, multi-channel audio acquisition; s2, audio preprocessing and spectrogram conversion; s3, carrying out weighted fusion; s4, fault judgment and decision making; and S5, alarm pushing. The beneficial effects of the invention are that: 1) the multi-channel audio acquisition device comprehensively acquires sound signals of key parts of the coal mill, avoids information omission, can capture unique sound features of faults of different parts, provides rich data for accurate judgment, and improves the recognition capability; and 2) the weight fusion module based on the attention mechanism dynamically adjusts the weight according to the fault importance, highlights the key part information, and improves the fault detection accuracy.

The invention relates to the technical field of gas sensing detection, and discloses a gas detection method and system based on a quartz tuning fork enhanced photoacoustic spectrum. The method comprises the following steps: constructing a standardized continuous tuning fork signal data stream; dynamically segmenting the data stream based on the gas concentration interval, and extracting time domain and frequency domain features in parallel in each segment to form an initial feature vector; dynamically adjusting an anomaly judgment threshold according to the jump amplitude of the adjacent segment feature vectors, and scanning and screening out an effective analysis interval of a stable concentration state according to the anomaly judgment threshold; performing cross-domain coupling compensation on the feature vectors in the effective interval to generate enhanced feature vectors; and finally, performing reverse traceability reasoning through a gas leakage propagation model based on the enhanced feature vector, and outputting a leakage source position identifier. The device can adapt to a dynamically changing concentration field, and the reliability and traceability positioning capability of gas detection in a complex environment are improved.

The invention discloses a method for detecting and adjusting a photoacoustic cell with adjustable resonant frequency, which relates to the technical field of gas detection, and comprises the following steps: collecting environmental parameters of the photoacoustic cell, transmitting the environmental parameters to a control center, and dividing the collected environmental parameters into historical data and real-time data, the method comprises the following steps: collecting historical data and real-time data, preprocessing the collected historical data and real-time data, establishing a photoacoustic cell evaluation model to predict the resonant frequency of a photoacoustic cell under different environmental conditions, measuring the real-time resonant frequency of the photoacoustic cell by adopting a resonance sound spectrum method, recording the initial resonant frequency, analyzing the change trend of the resonant frequency, and obtaining environmental interference factors at the same time. According to the invention, the resonant frequency is monitored in real time, compared with the preset value, accurately identified and adjusted to the optimal matching state, so that the maximum output of the photoacoustic signal is ensured, the signal capturing capability of the system is enhanced, the interference of background noise is reduced, and the accuracy of the detection result is greatly improved.

The invention discloses a dynamic encryption transmission method and system for sound-light alarm signals, and relates to the field of data encryption, and the method comprises the steps: obtaining a dynamic environment entropy value; generating a pseudo-random chaotic sequence; obtaining a helium-oxygen mixed gas density parameter in the dynamic environment entropy value; segmenting a preset standard alarm waveform into a plurality of signal waveforms in a time domain, and generating a distorted signal waveform according to the helium-oxygen mixed gas density parameter; performing encryption modulation on the distorted signal waveform to generate an encrypted distorted signal waveform; determining a sending sequence and a sending interval of the encrypted distortion signal waveform by combining a pseudo-random chaos sequence based on an impedance fluctuation value and background acoustic spectrum characteristics in the dynamic environment entropy; generating a dynamic modulation signal based on the transmission sequence and the transmission interval; the dynamic modulation signal is sent to a water surface console through an umbilical cable; and receiving the guide pulse, performing phase conjugation processing on the guide pulse, generating an echo verification signal, and transmitting the echo verification signal back to the water surface console.

The invention relates to the field of intelligent analysis, and discloses a transformer operation state analysis system and method for transformer substation noise, and the method comprises the steps: collecting the sound signal and noise intensity of a transformer in each region during operation at a set frequency; performing feature extraction on the sound signal of each area to obtain corresponding waterfall spectrogram feature information; inputting the waterfall spectrogram feature information into a pre-trained convolutional neural network model, and outputting a transformer operation state of each region; the transformer operation state of each area in the transformer operation state analysis module is compared with the noise intensity, if the transformer operation state is matched with the noise intensity, it is judged that the current transformer operation state is accurate, and if the transformer operation state is not matched with the noise intensity, it is judged that the current transformer operation state is not accurate, all operation parameters of the transformer are obtained and then analyzed, and transformer operation coefficients are obtained; and updating the current transformer operation state according to the transformer operation coefficient.

The invention discloses an automatic charging processing method and device for a charging robot, and relates to the technical field of robot energy management, and the method comprises the following steps: constructing an acoustic array, collecting high-frequency background sound wave signals in a working area of the charging robot in real time, and extracting sound spectrum energy distribution; and identifying an interference frequency band coinciding with the working frequency of the ultrasonic sensor, and calculating the intensity level of the interference frequency band according to the sound spectrum energy intensity and the duration time in the interference frequency band. According to the method, a closed-loop system of interference perception-risk assessment-control response is constructed, and dynamic adjustment of perception confidence and output of blindness risk indexes are realized through multi-modal identification consistency analysis and historical deviation tracking. Once the threshold value is exceeded, the system executes frequency hopping and strategy optimization, frequency band shielding is effectively avoided, the sensing stability, path accuracy and operation safety of the charging robot in the sound interference environment are improved, and good application prospects are achieved.

The invention belongs to the technical field of geological analysis, and discloses a ground subsidence analysis method and device, and the method comprises the steps: matching a sensor layout strategy corresponding to a foundation subsidence risk and meteorological data of a monitoring region; acquiring land subsidence data of the monitoring area according to a sensor; performing time-frequency analysis on the land subsidence data to obtain a geoacoustic spectrogram corresponding to the land subsidence data; inputting the geoacoustic spectrogram into a preset neural network, and outputting a settlement semantic vector corresponding to the geoacoustic spectrogram; acquiring and analyzing historical land subsidence data, and constructing a subsidence rule corresponding to the historical land subsidence data; and inputting the settlement semantic vector and the settlement rule into a preset space-time analysis model, and predicting to obtain a future settlement trend of the monitored area. By using the method disclosed by the invention, the problem that all risks in a certain area cannot be checked when geological data is monitored based on local monitoring points can be solved, and the problem that the geological risk analysis efficiency is relatively low can be solved.

The invention relates to the field of photovoltaic equipment abnormity early warning, and provides a photovoltaic equipment temperature abnormity prediction method and device based on a digital twinborn model, and the method comprises the steps: obtaining an environment acoustic signal, meteorological data, and the actually measured temperature data of photovoltaic equipment; performing acoustic spectrum feature extraction according to the environmental acoustic signal to obtain a virtual environmental parameter set; sending the meteorological data and the virtual environment parameter set to a preset digital twinborn model for processing to obtain an initial temperature predicted value set; determining a temperature prediction value distribution diagram according to the initial temperature prediction value set and actually measured temperature data of the photovoltaic equipment, and comparing a temperature prediction result with an abnormal temperature threshold value to obtain a temperature abnormal risk point; and processing according to the temperature prediction value distribution diagram, the temperature anomaly risk points and the meteorological data to obtain temperature anomaly early warning information of the photovoltaic equipment, and improving the accuracy and foresight of temperature anomaly prediction of the photovoltaic equipment.

The invention discloses a chicken abnormal sound recognition method, system and device for a large-scale farm and a storage medium. The method comprises the following steps: acquiring sound data of a chicken as a first audio; taking the preprocessed first audio as a second audio; taking the second audio frequency after the secondary noise reduction as a third audio frequency; performing sound endpoint detection on the third audio, and taking an obtained effective sound fragment as a fourth audio; performing data enhancement on the fourth audio, and taking the fourth audio after data enhancement as a fifth audio; converting the audio clip of the fifth audio into a Mel spectrogram, extracting a corresponding first-order difference Mel spectrogram and a corresponding second-order difference Mel spectrogram based on the Mel spectrogram, and performing feature fusion to obtain corresponding three-channel Mel fusion features; and inputting the Mel fusion feature into a model for identification to obtain an event category result corresponding to the audio. According to the invention, abnormal chicken sounds in the chicken farm can be accurately identified, and the breeding efficiency is improved.

The invention discloses a keystroke identification method based on acoustic eavesdropping. The method comprises the following steps: S1, acquiring keystroke data; s2, performing pre-emphasis processing on the keystroke data, and removing background noise by using an adaptive spectral subtraction method to obtain a clean keystroke signal; s3, performing fast Fourier transform on the clean keystroke signal, calculating frequency spectrum energy, separating the keystroke signal into single keystroke segments, and ensuring that each segment only contains one keystroke behavior; s4, extracting spectrogram and Mel spectrogram features of each keystroke segment; s5, introducing a multi-scale channel attention module and an iterative attention feature fusion module, carrying out feature fusion on the spectrogram and Mel spectrogram features, and endowing key features with higher weights through an attention mechanism; s6, inputting the fused feature data into a convolutional neural network model for training, and using the model to classify and identify the keystroke behavior; according to the method, the accuracy and practicability of keystroke recognition are improved, and the method is more suitable for a complex acoustic environment.

For training a perception algorithm to detect an emergency vehicle, respective audio datasets are received from two microphones and respective spectrograms are generated. At least one interaural difference map is generated based on the spectrograms, audio source localization data is generated, which specifies a number of audio sources in respective grid cells of a spatial grid, by applying a CRNN to first input data containing the spectrograms and the least one interaural difference map. An image is received from a camera and output data comprising a bounding box for the emergency vehicle is predicted by applying at least one further ANN to second input data containing the image and the spectrograms. Network parameters are adapted depending on the output data and the audio source localization data.

To provide a technology to accurately identify abnormal sound.SOLUTION: A computer included in a vehicle abnormal sound diagnosis system acquires sound spectral data for a vehicle and vehicle data that describes a vehicle driving state. The computer receives user input in an evaluation time range for the sound spectral data. The computer sets a first time range before the evaluation time range and a second time range after the evaluation time range to the sound spectral data. The computer identifies as a candidate frequency bandwidth a frequency bandwidth in which a secular change in a sound pressure level does not correspond to a secular change in the vehicle driving state. The computer identifies as an abnormal sound frequency bandwidth the candidate frequency bandwidth including a frequency in which a projection amount of the sound pressure level in the evaluation time range relative to the first time range and the second time range satisfies a predetermined reference, from among the candidate frequency bandwidths.SELECTED DRAWING: Figure 2

The invention relates to the technical field of inspection process automation, in particular to an automatic process control system and method for a clinical laboratory, and the system comprises a pedigree depth analysis module, a sound spectrum precise positioning module, a virus load calculation module, a spectrum cell recognition module, a cell electrical performance analysis module, a sample comprehensive decision module and an intelligent sample processing module. According to the method, multiple advanced technologies such as image spectrum analysis, sound spectrum analysis, time-frequency domain analysis, spectrum technology and cell electrical impedance analysis are combined, so that high-precision analysis and deep learning processing of the sample are realized, and the accuracy and efficiency of sample analysis are improved; the system especially has obvious advantages in the aspects of virus load calculation, cell type identification, sample risk assessment and the like, the application of the intelligent sample processing module optimizes the sample processing flow, improves the working efficiency and the processing speed, and greatly improves the automation degree and the intelligent level of the work of the clinical laboratory.

The invention discloses a current sensor pin bending processing control system and processing equipment, and relates to the technical field of current sensor pin bending processing, and the current sensor pin bending processing control system comprises the following steps: fusing and collecting three types of disturbance parameters with different physical dimensions and response mechanisms, namely bending acceleration ZWa, pin surface contact impedance disturbance ZBd and friction sound spectrum response ZMc; the sensing coverage range of the system on the multi-source abnormal phenomenon in the machining process is remarkably expanded, the recognition blind area of a traditional system depending on a single force or displacement signal is made up, and the sensitivity to non-dominant disturbance characteristics is improved. Non-linear interaction modeling is carried out on the three disturbance factors through the perturbation coupling strength index psi, and the system not only can recognize single-variable fluctuation, but also can capture the co-evolution trend among variables. According to the structure, the system has the capability of recognizing disturbance structural imbalance and coupling activation trend in advance, and has higher fault perspectiveness and active response capability.

To provide technology which can appropriately calculate a rate of a period in which a frequency of maximum sound pressure and a generated frequency are substantially matched.SOLUTION: An abnormal sound diagnosis system includes at least one computer. The computer performs processing for acquiring sound spectrum data, processing for acquiring vehicle data, processing for setting a predicted frequency band and a time zone in which presence or absence of abnormal sound is evaluated for the sound spectrum data, processing for calculating a rate of a period in which a frequency of maximum sound pressure and a generated frequency of sound which a device generates are substantially matched in the predicted frequency band and the time zone, which are set, processing for extracting a time zone in which an operation mode in which the generated frequency is included in the predicted frequency band is described from the time zone which is set, when the rate of the period exceeds a first prescribed value, and processing for calculating the rate of the period in which the frequency of maximum sound pressure and the generated frequency of the device are substantially matched in the predicted frequency which is set and the extracted time zone.SELECTED DRAWING: Figure 2

The invention relates to the technical field of livestock and poultry product safety detection, in particular to a temperature-controllable crushing device for livestock and poultry product detection, which is characterized in that sound wave signals generated in a homogenizing process are acquired through a sound sensor arranged outside a detection cup, and spectrum drift and energy attenuation characteristics of the sound wave signals are analyzed by adopting a sound spectrum temperature inversion algorithm; inverting the temperature and distribution of the sample in real time; and meanwhile, a cooling air curtain is formed through nitrogen injection, the gas flow rate and the injection angle are intelligently adjusted through a control unit according to a temperature inversion result, directional focusing cooling of a local overheating area is achieved, and a closed-loop temperature control system driven by acoustic signals is formed. According to the device and the method, cross contamination and sensor adsorption effect caused by contact type temperature measurement are thoroughly avoided, the problems that the sensor is easy to pollute and difficult to maintain in a high-humidity and fat-rich environment are solved, heat-sensitive veterinary drug decomposition caused by friction temperature rise in the homogenizing process is effectively inhibited, and the accuracy, reliability and repeatability of veterinary drug residue detection are remarkably improved.

The invention relates to the field of mechanical automation, in particular to a drilling machine intelligent operation system and a deviation correction control method.According to the system, through multi-dimensional signal fusion and dynamic risk assessment, drill jamming recognition and disposal response time is remarkably shortened, and efficiency loss caused by traditional shutdown protection is avoided; the imaging and obstacle positioning technology is adopted to replace manual drill lifting inspection, and the no-load operation time and mechanical loss of equipment are reduced. The multi-physical field signal fusion technology breaks through the sensing limitation of a single sensor, and is beneficial to accurately identifying underground obstacle space distribution and geological interface mutation features; acoustic spectrum and thermal field distribution analysis can enhance the ability to analyze drill bit-stratum interaction dynamics, and multi-dimensional input is provided for intelligent decision making. And dynamic optimization of a control strategy is realized in cooperation with a virtual-real interaction mechanism to adapt to demand changes of different stratum conditions and construction stages.

The invention belongs to the field of vehicle-mounted acoustic signal processing and recognition, and particularly relates to an emergency vehicle detection method based on acoustic spectrum-time domain information fusion, which comprises the following steps: acquiring an audio signal from an environment through vehicle-mounted microphone equipment, and respectively expressing the audio signal by a frequency domain and a time domain; introducing a coordinate attention network to perform fusion calculation on the features in the time domain representation and the frequency domain representation to obtain attention fused features; and inputting the attention fused features into a lightweight detection neural network model, and calculating whether siren sound exists in the current signal. According to the invention, through fusion of time domain and frequency domain information and combination of a coordinate attention mechanism, high identification precision can be maintained in high-speed driving or other scenes with large noise interference; especially under the condition that the influence of background noise such as engine noise, wind noise and traffic flow is large, the siren sound of the emergency vehicle can still be accurately detected, and the robustness of the system is enhanced.