15235 results about "Frequency band" patented technology

A headset antenna and a connector for the same are provided. The headset antenna includes an audio signal line, an antenna and a high impedance element in specified application frequency ranges. The audio signal line is adapted for transmitting an audio signal and the antenna is adapted for receiving an RF signal. The high impedance element is disposed on a transmission path of the audio signal and generates a high impedance at a specified frequency band of the RF signal, so that the audio signal line is equivalent to an open circuit and the antenna obtains a better receiving capability.

The invention relates to a high-altitude large steel structure corridor safety risk monitoring method and system and a medium, and belongs to the technical field of civil engineering structure health monitoring, and the monitoring method comprises the steps: collecting original monitoring data through a multi-source sensor group disposed at each node of a steel structure corridor; performing space-time alignment processing on the original monitoring data to generate a sensor data matrix; performing dynamic noise suppression on the sensor data matrix based on an environmental noise transfer function model, and outputting a pure signal matrix and a damage characteristic frequency band identifier; calculating a thermal stress sensitive factor, extracting a multi-physical field coupling feature associated with the damage feature frequency band identifier in the pure signal matrix, fusing to generate a high-dimensional damage feature tensor, inputting the high-dimensional damage feature tensor into a pre-constructed digital twinborn risk assessment model, and outputting an assessment result including a risk level label and a damage position coordinate; and matching the regulation and control strategy according to the risk level label, and generating a corresponding equipment control instruction. According to the invention, the scientificity and accuracy of risk decision can be improved.

An electric power assisted steering assembly comprises a steering mechanism which operatively connects a steering wheel to the roadwheels of a vehicle, a torque sensing means adapted to produce a first output signal indicative of the torque carried by a portion of the steering mechanism, a means for producing a second output signal indicative from a signal indicative of the angular velocity of the portion of the steering mechanism, an electric motor operatively connected to the steering mechanism, a signal processing unit adapted to receive the two output signals and to produce therefrom an overall torque demand signal representative of a torque to be applied to the steering mechanism by the motor, and a motor drive stage adapted to provide a drive current to the motor responsive to the torque demand signal. The overall torque demand signal includes a damping component that is dependent upon the second output signal. The means for producing the yaw damping component includes a filter having one pole and one zero, and in use the second signal is passed through the filter to produce the damping component, the frequency of the pole and zero being selected such that the filter provides a band between the pole and zero within which the phase of the filtered second signal and the velocity signal are further out of phase or in phase than within the band.

The invention discloses a smart power grid line loss intelligent detection method based on Internet of Things big data, and relates to the technical field of smart power grid electric energy metering, and the method comprises the following steps: S1, extracting a voltage signal and a current signal of each electric energy metering node in a set time window, carrying out the frequency domain transformation to obtain a frequency spectrum, calculating the energy concentration index of a high-frequency band, and calculating the energy concentration index of the high-frequency band; and identifying a high-frequency harmonic interference time section according to the change of the energy concentration degree. According to the method, a closed-loop mechanism of high-frequency harmonic interference identification, error traceability, cause discrimination and dynamic correction is constructed, fragment-level and node-level error compensation is realized, and the accuracy and the stability of line loss anomaly identification of the smart grid are improved by combining electric quantity consistency verification and trend model updating; and the reliability and the intelligent level of the system in a complex interference environment are obviously enhanced.

The invention discloses a method for realizing data communication by a multi-band adaptive antenna based on 5G communication. The method comprises the steps of dynamic sensing and multi-mode signal fusion, multi-band intelligent analysis, hybrid beam forming and dynamic reconstruction, cross-layer parameter joint tuning, real-time monitoring and self-learning compensation, multi-band adaptive switching and fault diagnosis and error calculation. The problems that in traditional 5G communication, the antenna frequency band is fixed, the beam adjusting capacity is limited, the anti-interference performance is poor, and the communication performance is unstable in a complex environment are solved. According to the method, the channel state is accurately evaluated and predicted through fusion of the extended Kalman filtering algorithm and the convolutional neural network, and frequency band resources are intelligently allocated and optimized through the deep Q network DQN and the non-orthogonal multiple access NOMA principle, so that omnibearing optimization and management of the antenna are realized, the communication efficiency and stability are improved, and the method is suitable for large-scale popularization and application. And the efficient and stable operation of the communication system in various complex environments is ensured.

The invention relates to the technical field of medical image processing, and provides a medical image segmentation method based on wavelet enhancement and multi-scale feature fusion. According to the method, a CNN-Transform double-branch coding structure is combined, a multi-scale wavelet fusion module is provided, from the perspective of a frequency domain, Haar wavelet transform is adopted to extract an image high-frequency sub-band so as to enhance edge and texture detail expression, dynamic weighting is performed on different frequency band features through grouping convolution and a sub-band attention mechanism, and the discrimination capability is improved; meanwhile, a multi-scale cavity pyramid structure is fused in a spatial domain, and after cross attention dynamic fusion is introduced, a feature alignment mechanism of a wavelet domain and the spatial domain is established; and collaborative fusion of frequency domain and space domain features is realized. The method effectively improves the segmentation precision of the fuzzy boundary and the fine-grained structure under the complex background, has good universality and adaptability, and is suitable for various medical image segmentation tasks.

The invention discloses a super-resolution image enhancement system and method based on a variational mode decomposition algorithm. The system comprises an adaptive decomposition module, an enhancement processing module, a fusion module and an optimization module. The adaptive decomposition module receives low-resolution image signals, generates modal component signals containing different frequency band characteristics, and outputs modal quantity parameter signals according to image frequency domain energy distribution. The enhancement processing module comprises a high-frequency enhancement unit and a low-frequency reconstruction unit, and generates a high-frequency enhancement signal and a low-frequency reconstruction signal. And the fusion module receives the modal quantity parameter signal, the high-frequency enhanced signal and the low-frequency reconstructed signal, and performs spatial adaptive weighted fusion on the high-frequency signal and the low-frequency signal through a dynamic weight coefficient to generate an initial high-resolution signal. And the optimization module carries out adaptive nonlinear filtering processing on the initial high-resolution signal. The super-resolution image enhancement system based on the variational mode decomposition algorithm can solve the problem that the prior art is difficult to adapt to a complex image structure.

A system for dynamic latent space adaptation using spatiotemporal kernel context for multiscale rendering with hierarchical and Lorentzian autoencoders. The Spatiotemporal Kernel Estimator (SKE) analyzes media through motion field, temporal recurrence, frequency band, and scene semantics analyzers to generate adaptive kernel parameters encoding content-specific importance distributions. The system dynamically adapts latent manifold geometry by modifying metric tensor properties according to kernel context, enabling content-aware compression that allocates representational capacity based on visual significance. A multiscale cache implements kernel-adaptive retention policies prioritizing important regions. An adaptive renderer provides intelligent level-of-detail selection based on zoom level and kernel-estimated importance, optimizing processing allocation. The self-optimizing architecture continuously refines kernel context and geometric adaptation based on user interaction and performance feedback, achieving superior compression ratios and perceptual quality. Applications include bandwidth-efficient video streaming, virtual reality, scientific visualization, and cognitive video analytics requiring intelligent context-aware visual processing.

The invention relates to the technical field of biomedical engineering, in particular to a multi-mode brain anomaly detection method and system based on machine learning. The method comprises the following steps: acquiring brain medical image data of different modalities, and realizing spatial registration and alignment through a multi-modal registration algorithm based on mutual information; a multi-branch feature extraction model including a convolutional neural network, a converter and a state space model is utilized to perform feature embedding on the original image of each modal; performing frequency decoupling on the features of each mode through adaptive approximate wavelet transform, and decomposing the features into high-frequency detail information and low-frequency global information; a frequency band fusion strategy based on an attention mechanism is implemented on high and low frequency features of different modal images, and fused frequency sub-band features are input into a space-frequency Mama module. Through the adaptive frequency domain decomposition and cross-modal fusion mechanism, the multi-modal brain image information is effectively integrated, and the accuracy and robustness of brain anomaly detection are remarkably improved.

The invention discloses a lithium ion battery thermal runaway early warning method, and belongs to the technical field of battery safety monitoring, and the method comprises the steps: synchronously obtaining a low-frequency sound wave signal, a temperature signal, a voltage signal and a stress-strain signal of a lithium ion battery; noise reduction processing is carried out on the low-frequency sound wave signals, time-frequency feature extraction is carried out on the low-frequency sound wave signals after noise reduction processing, and feature frequency band energy corresponding to a thermal runaway early event is obtained; a thermal runaway sensitivity coefficient is calculated based on the characteristic frequency band energy, and when the thermal runaway sensitivity coefficient is larger than a first coefficient threshold value, thermal runaway early warning is triggered; and on the basis of the thermal runaway sensitivity coefficient, the temperature signal, the voltage signal and the stress-strain signal, constructing a feature vector, and inputting the feature vector into a trained long-short-term memory network and attention mechanism hybrid model for classification to obtain a thermal runaway early warning level. The method can solve the problems of installation difficulty, detection lag and high false alarm rate in the prior art.

The invention relates to the field of heart rate monitoring, and discloses a millimeter wave radar breath and heart rate synchronous monitoring method and system, and the method comprises the steps: transmitting a linear frequency modulation continuous wave signal according to a millimeter wave radar, and collecting original echo data reflected by a target region; baseband signal demodulation and phase information extraction are carried out on the original echo data to obtain original phase time sequence data, and the original phase time sequence data comprise thoracic cavity micro-motion features; and according to a Butterworth band-pass filter, preprocessing the original phase time sequence data through human body physiological signal frequency band characteristics to obtain a breathing frequency band signal and a heart rate frequency band signal. According to the method, the apnea event triggering threshold value and the arrhythmia early warning index are updated in real time through Kalman filtering and extended Kalman filtering, so that the monitoring system can dynamically adjust the health parameters, which means that the monitoring system can be optimized in real time and the abnormal health event can be accurately responded in different physiological states.

The invention discloses a stability analysis method of a network tracking / constructing hybrid station based on an impedance model, and relates to a modeling and stability evaluation technology of a wind power plant grid-connected system. The method comprises the following steps: firstly, respectively establishing sequence impedance models of a following network type direct-driven fan and a construction network type direct-driven fan based on a harmonic linearization method, and verifying the accuracy of the established models in different frequency bands by combining an impedance sweep frequency method; secondly, on the basis of considering the influence of line impedance, deducing to form an impedance network structure of a hybrid wind power plant grid-connected system, and constructing a stability judgment condition suitable for system analysis; and finally, based on the established impedance stability criterion, identifying possible oscillation risks of the system in subsynchronous and supersynchronous frequency bands. The method is suitable for wind power system stability analysis in various hybrid access scenes, and provides theoretical basis and technical support for subsequent system parameter setting, control strategy optimization and oscillation suppression measures.

The invention relates to the technical field of line arc abnormity monitoring, in particular to a data center operation and maintenance service environment monitoring system which comprises a multi-mode sensing unit, a dynamic baseline modeling unit, a transition state abnormity extraction unit and a grading early warning unit. The dynamic baseline modeling unit builds a three-layer safety baseline model, the base layer builds harmonic references in a segmented mode according to the load rate, the environment layer generates voiceprint feature template libraries of different temperature and humidity intervals, the time layer fits a 24-hour change trend envelope line, and the transition state anomaly extraction unit separates and decouples an arc voiceprint feature frequency band through a blind source and calculates the energy ratio. The harmonic abrupt change phase deviation amplitude is analyzed in combination with window sliding correlation, the abnormal evolution index is generated through fusion, the graded early warning unit triggers response according to the index, transition state abnormity is accurately recognized, the perspectiveness and reliability of operation and maintenance early warning are improved, and the method is suitable for safe operation and maintenance of data center equipment.

The invention belongs to the technical field of bearing fault diagnosis, and particularly relates to a transmission casing typical small bearing fault weak vibration signal extraction method, which comprises the following steps: reconstructing a bearing vibration signal to obtain a modulation impact signal, and calculating a weighted kurtosis value based on the modulation impact signal to improve a Protugram algorithm; noise interference is added to a bearing fault signal, and extraction of a small bearing fault weak impact signal is realized by optimizing a filtering frequency band based on a weighted kurtosis value improved Protugram algorithm; bearing impact characteristics can be enhanced based on a simulation sensor resonance enhancement algorithm (SPM); the impact quantification of the bearing can eliminate the influence of different rotating speeds and load working conditions, and realizes the quantitative diagnosis of bearing faults. And finally, the fault data of the rolling body, the inner ring and the outer ring of the flight attachment case small bearing part test bed are verified, and effective extraction and enhancement of bearing fault weak signals can be realized.

The invention discloses a glass ceramic thickness detection method and a related device, and the method comprises the steps: obtaining a vibration signal generated in a glass ceramic thinning and grinding process, and extracting a vibration dominant frequency, a frequency spectrum centroid frequency and a frequency band energy distribution ratio as thickness characteristic parameters through fast Fourier transform processing; three-dimensional position coordinates of the grinding head are obtained, space mapping and weighted average processing are conducted on the thickness characteristic parameters, and space distribution of the thickness characteristic parameters is obtained; performing process interference compensation and sliding window time sequence smoothing processing according to the grinding process parameters to obtain stable thickness characteristic distribution; based on the stable thickness feature distribution and the current grinding process, final thickness distribution is predicted, and a thickness qualification pre-judgment result is obtained; and extracting a time sequence change characteristic of the thickness characteristic parameter as a vibration evolution index, and performing comprehensive quality evaluation in combination with a thickness eligibility pre-judgment result. According to the scheme, the thickness can be detected in real time in the thinning and grinding process of the microcrystalline glass, and the production efficiency is improved.

The invention relates to the technical field of voice interaction, particularly discloses an AI intelligent robot voice interaction method and system, and aims to solve the problems of low voice interaction accuracy, insufficient reliability and lack of authority control in a complex noise environment. A dynamic noise feature library containing steady-state noise, impact noise and human voice interference features and a pre-stored gesture instruction library are constructed, audio signals are collected in real time, low-frequency-band, middle-frequency-band and high-frequency-band differential noise reduction is executed, Mel-frequency cepstral coefficient features are extracted, noise scenes are matched, corresponding voice recognition models are switched, and voice recognition is achieved. And calculating a confidence value of the voice instruction, outputting multi-modal verification data in combination with a dynamic confidence threshold, and outputting an authority control signal through voiceprint matching, authority verification and instruction consistency judgment. Through multi-modal fusion, dynamic adaptation and authority control, the voice recognition accuracy and interaction safety in a complex noise environment are remarkably improved, and the method is suitable for scenes such as factory intelligent inspection.

The invention relates to the technical field of fault detection, in particular to a heating and ventilation system fault positioning system and method based on big data, and the system comprises a multi-source sensing module, a disturbance feature module, a path modeling module, a frequency spectrum matching module and a fault positioning module. In the method, a real-time disturbance sequence is constructed through time window segmentation and parameter offset calculation, separation of an active response chain and an abnormal propagation path is realized through a directional joint state vector and a topological relation table, and time asynchronism of multi-device signal transmission is eliminated by adopting a dynamic time warping algorithm. In combination with a real-time parameter bidirectional verification mechanism of a frequency domain main frequency band energy mark, a valve opening degree and a pump rotating speed, the problem of path confusion in a multi-node parameter coupling scene of a traditional method is solved, the tracing efficiency of concurrent faults in a complex pipe network system is improved, the adaptability limitation of a single-dimensional threshold mechanism to equipment performance degradation is overcome, and the method is suitable for a complex pipe network system. And the error positioning probability caused by signal delay superposition is reduced.

The invention provides a multi-band radio frequency power amplifier dynamic range adaptive test method and a system thereof. The method comprises the following steps: acquiring an output signal frequency spectrum of a tested radio frequency power amplifier, extracting characteristic frequency points including a center frequency and a bandwidth, matching the characteristic frequency points with a preset frequency band database, and automatically loading a test scheme of a corresponding frequency band. According to the method, the frequency bands are automatically matched through spectrum sensing, the test scheme is loaded, the multi-frequency-band characteristics can be rapidly adapted without manual parameter configuration, index missing test is avoided, and the switching efficiency is improved; dynamically optimizing test parameters by using a genetic algorithm, and accurately capturing a nonlinear distortion critical point while balancing coverage and efficiency; by combining multi-dimensional signal analysis and a deep learning model, the problem of missing detection of traditional single-domain analysis is solved, and the abnormal signal recognition capability is remarkably enhanced; through linear model correction, impedance dynamic matching and index threshold iteration, full automation and high precision of the test are realized, the environmental interference influence is effectively reduced, and a self-adaptive test system is constructed.

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health, voice navigation and the like, and discloses a voice processing method, device, equipment and medium based on voiceprint feature screening. The method comprises the following steps: screening voice of a near-field main speaker based on voiceprint similarity and signal intensity, executing duration filtering and confidence verification, dynamically constructing a voiceprint feature library, generating a voiceprint mask matrix based on the voiceprint feature library, performing frequency band suppression on a to-be-processed voice signal, and outputting a purified voice signal. According to the method, the voiceprint library is dynamically constructed, the voiceprint mask matrix is generated based on the voiceprint feature library, frequency band suppression is performed on the to-be-processed voice signal, non-target voiceprints are effectively shielded, the voice signal quality is improved, and thus target voice is accurately captured in a high-noise environment.

The invention relates to the technical field of electrical monitoring, provides a general generator electrical monitoring system with a fault self-diagnosis function, and aims to deeply excavate potential correlation between electrical and mechanical parameters by judging a coherence coefficient and a phase difference between a current harmonic component and a bearing vibration frequency band and marking a fault coupling identifier by using a coupling mode library. Electrical and mechanical coupling faults can be accurately identified, and the identification capability of complex faults can be greatly improved; meanwhile, the diagnosis threshold is dynamically adjusted in combination with the load rate and the winding temperature, so that the system can better adapt to different operation conditions of the generator, and the diagnosis accuracy and reliability are improved; the fault causal chain is analyzed through the causal inference algorithm, and the fault source is positioned, so that compared with the existing fault tracing mode lacking systematicness, the fault generation reason and process can be analyzed more comprehensively and deeply, the fault source can be positioned quickly and accurately, operation and maintenance personnel can take targeted measures in time, and the fault tracing efficiency is improved. And the operation safety and reliability of the generator are improved.

The invention relates to the technical field of motor fault diagnosis, in particular to a motor fault diagnosis algorithm based on multi-sensor fusion, and the algorithm comprises the steps: injecting a step excitation signal into a motor, synchronously collecting the original response waveforms of vibration and current sensors, and calculating the inherent response delay. Establishing a mapping relation library of delay values and current sensor filtering parameters, calling the delay values in real time according to the filtering parameters, performing reverse time offset compensation on a current harmonic signal time sequence, performing time alignment on the two types of data, finally performing cross-domain coupling analysis on the aligned data, extracting vibration pulse peak frequency and current harmonic fluctuation quantity, and determining the vibration pulse peak frequency and the current harmonic fluctuation quantity. Early faults are judged by combining the bearing outer ring fault characteristic frequency band and the load rate dynamic threshold value, graded alarm is generated by tracking characteristics, the problem of fault false judgment and missed judgment caused by sensor data space-time dislocation is solved, and the early fault diagnosis accuracy of the motor is improved.

The invention relates to the technical field of radar sensing, and discloses a sensing method and system based on millimeter radar waves. The method comprises the steps that millimeter wave radar receiving signals are subjected to low-noise amplification, frequency bands are screened through a band-pass filter and then mixed with local oscillation signals to obtain distance and speed information, after analog-to-digital conversion digitization, features are extracted to distinguish a human body target in a closed / open space, and finally multi-scene adaptability analysis is executed to generate human body position state information. According to the invention, through construction of a signal processing link, from signal amplification, filtering, frequency mixing and digital processing to target identification and multi-scene adaptability analysis, comprehensive perception of a human body target is realized, a static human body and a non-human body interference source can be effectively distinguished, a processing strategy is automatically adjusted according to different space environment characteristics, and the processing efficiency is improved. And the accuracy and reliability of human body perception are greatly improved.

The invention relates to the technical field of data processing, in particular to an accelerometer vibration rectification error analysis method, which comprises the steps of receiving multiple paths of accelerometer original current signals, processing acquired data and outputting a feature point coordinate sequence with confidence; according to the confidence and the optimization algorithm combination in the frequency band distribution characteristic dynamic scheduling knowledge base, calculating a local statistical data set; inputting the data set into a mechanical dynamics model to execute trajectory simulation, and inputting a control model reconstruction signal to compare a dual-path positioning drift distance difference to generate an error correction coefficient matrix; outputting a knowledge base updating instruction and a parameter resetting instruction by adopting a matrix reconstruction vibration rectification error quantized value; a quantized value is imported to calculate theoretical angle deviation and verify convergence, and the neural network is triggered to be retrained when the deviation exceeds a threshold value. Through a non-smooth feature extraction algorithm and a closed-loop feedback mechanism, the problem of signal distortion caused by smooth operation in the prior art is solved.

The invention provides a body armor composite interlayer defect positioning method and system based on multi-modal nondestructive testing, electronic equipment and a storage medium, and relates to the technical field of nondestructive testing. Ultrasonic guided wave, pulsed eddy current and infrared thermal wave excitation signals are synchronously applied to a body armor composite interlayer, and multi-modal response signals of all positions are collected; and generating an original multi-modal response signal set. And inputting the signal into a pre-trained deep convolutional neural network, determining a target frequency band parameter and a target noise suppression parameter of each modal response signal, and outputting an adaptive modulation parameter group. Performing frequency domain filtering and noise suppression processing on an original signal set to generate an optimized signal set, inputting spatial frequency features into a cross-modal feature fusion model, adaptively associating defect sensitive features of different modals through an attention mechanism in the model, and generating a three-dimensional defect distribution diagram of the composite interlayer, thereby realizing millimeter-level precision defect positioning. The precision and efficiency of body armor composite interlayer defect positioning can be improved.

The invention discloses a bearing fault diagnosis method based on multi-scale frequency sensing dynamic enhancement, and the method comprises the steps: collecting a vibration signal in a bearing operation state, carrying out the preprocessing of the vibration signal, obtaining a time-frequency matrix, and dividing the time-frequency matrix into a training set and a test set; building a multi-scale frequency network sensing model, inputting a time-frequency matrix in a training set into the model to realize extraction of multi-scale features, then performing pooling, time sequence compression, flattening and dimension reduction on the extracted multi-scale features, and then outputting fault category probability distribution through a classifier; and finally, testing the trained model by using a test set, and calculating evaluation indexes such as accuracy, a confusion matrix, an ROC curve and the like. The method has high accuracy while keeping light weight, breaks through double limitations of fixed frequency band and sensitive rotating speed of a traditional method, and can provide a high-precision and low-cost light-weight solution for engineering application of variable-rotating-speed mechanical fault diagnosis.

The invention provides a joint denoising method and system based on multi-source noise suppression and sparse representation, and belongs to the technical field of noise suppression in ultrasonic detection of power equipment, and the method comprises the steps: synchronously collecting an ultrasonic echo signal, a vibration signal and an electromagnetic signal of a composite insulator; decomposing the ultrasonic echo signal into a plurality of modal components and undecomposed signal margins; extracting a dominant frequency component in the vibration signal, and identifying a resonance noise feature overlapped with the frequency band of the ultrasonic signal; detecting a pulse interference event in the electromagnetic signal, marking occurrence time and a frequency range, and generating a pulse event feature; performing frequency band suppression and time domain threshold processing on the modal component according to the resonance noise characteristics and the pulse event characteristics; performing sparse representation coding on the processed modal components, executing multiple rounds of iterative screening and orthogonalization processing, and screening an atom set matched with defect echoes; and reconstructing the de-noised ultrasonic signal by using the screened atom set, and combining the de-noised ultrasonic signal with the undecomposed signal margin to output a final result.

The invention discloses a gradient welding strength control method and system for a welded galvanized steel pipe for fire fighting, particularly relates to the technical field of welding automation control, and aims to solve the problem of abnormal welding seam strength gradient caused by instantaneous unbalance of an electric field and a thermal field during dynamic parameter switching in the prior art. Electromagnetic distortion characteristic quantity is extracted through frequency domain energy analysis, and thermodynamic offset state quantity is deduced in combination with a thermal diffusion trend; on the basis of frequency band correlation mapping of electromagnetic and thermodynamic parameters, thermoelectric cooperative imbalance levels are divided through mutual information entropy evolution, and a self-adaptive harmonic attenuation weight tuning instruction is generated; analyzing a mismatching relation between transient disturbance of an environment magnetic field and arc voltage modulation distortion in real time, dynamically generating a magnetic field compensation factor and reversely superposing the magnetic field compensation factor to a control instruction; and in combination with the matching degree of molten pool oscillation energy and a harmonic frequency spectrum, correcting wire feeding rate calibration, reconstructing a multi-band harmonic energy proportion spectrum, outputting an anti-interference welding control signal, and realizing dynamic balance between arc stability and molten pool heat input.

The invention relates to an AI server cooling water pump vibration quantity self-adaptive calibration test method and system, an intelligent test platform integrates a six-dimensional force sensor and a temperature compensation vibration exciter, the platform rigidity is automatically calibrated, a water pump-pipeline system transfer function is obtained, and a rotating speed-lift-modal frequency three-dimensional mathematical model is established; a three-axis MEMS accelerometer array is arranged at sensitive parts such as a water pump bearing seat and a motor shell, and vibration, current and pressure signals are synchronously collected; carrying out time-frequency domain signal processing by adopting variational mode decomposition in combination with self-adaptive S transformation, and extracting a 128-dimensional full-frequency domain feature vector containing a modulation side frequency band; effective values of vibration acceleration, speed and displacement are calculated through a frequency domain integration algorithm, and current harmonic interference is corrected through an electromagnetic vibration compensation model; and finally, a vibration health degree evaluation model containing 18 characteristic parameters is established based on a support vector machine, the test system comprises an intelligent test platform unit, a multi-source sensing unit, an edge calculation unit and a data management unit, and microsecond-level synchronous acquisition and GB-level data throughput are realized through a time sensitive network. And online incremental learning and automatic generation of a test report are supported. The method has the effect of improving the water pump vibration quantity test precision.

The invention belongs to the technical field of nondestructive testing, and particularly relates to a detection method for quantitatively evaluating fatigue damage in a pipeline through a nonlinear ultrasonic technology. 316 stainless steel and SA335GRP-11 ferrite steel samples are adopted, a welding seam area is formed through V-shaped groove welding, fatigue loading is conducted, and actual pipeline fatigue damage is simulated; for the samples with different fatigue cycles, nonlinear response signals of the samples are obtained; fourier transform is carried out on the obtained signal to obtain an energy spectrum of the signal, and energy changes of the signal on a fundamental frequency band and a second harmonic frequency band are analyzed; and calculating a nonlinear coefficient, and analyzing a change rule of the nonlinear coefficient along with fatigue cycles, so that the evolution process of fatigue damage in the pipeline is evaluated. According to the method, energy is used for replacing the amplitude value, on the basis of the formula principle, integration is used for replacing the amplitude value to improve the intensity of the characterization nonlinear ultrasonic signal, the early stage of damage and the later stage of crack initiation are detected, and reliable guarantee is provided for safe operation of an industrial pipeline system.

The invention discloses a tunnel fire air curtain smoke prevention control method and system, and relates to the technical field of intelligent fire fighting, and the method comprises the steps: building a digital twinborn prediction model, and generating a fire source position coordinate and a smoke diffusion trend prediction map through multi-physics field coupling simulation; analyzing the stability requirement of the air curtain jet flow through a Brapare-Prandtt boundary layer analysis algorithm, and generating a jet flow enhancement requirement parameter table in combination with temperature gradient correction; optimizing an acoustic vortex main frequency band by adopting a Slodhaar number, and generating an acoustic vortex control parameter set in combination with a matched sound pressure level and a configured resonator phase difference; and converting the acoustic vortex control parameter set into an execution instruction, monitoring the jet velocity, the vortex street stability and the smoke retardation effect in real time, and dynamically correcting the parameters of the digital twinborn prediction model. According to the method, physical quantity outline matching of the blocking intensity and the sound pressure level is achieved through the sound intensity-momentum equivalent method, and the optimal phase configuration is determined in combination with POD modal analysis.