290 results about "White noise" patented technology

The invention relates to a building structure health monitoring method and system based on machine learning, and the method specifically comprises the following steps: firstly, building a target building three-dimensional numerical model through finite element simulation, generating a simulation signal, injecting Gaussian white noise, and adjusting model parameters to form a data set with health category labels; performing data enhancement by combining adaptive wavelet denoising with dynamic normalization, and extracting and enhancing high-resolution time-frequency features through adaptive window short-time Fourier transform and adaptive frequency band enhancement; then, a neural network model fusing structure physical prior guidance and multi-scale space-time interaction is constructed, a feature matrix is modulated, fused and coded to obtain a refined feature vector, and damage state probability distribution output is achieved; and training is carried out by using a feature consistency and prediction smoothness regularization term constraint model, and finally, the trained model is deployed, so that building structure health state evaluation and safety early warning are realized, the monitoring accuracy and reliability are improved, and effective technical support is provided for building safety guarantee.

The invention provides a power load prediction method and system based on adaptive modal decomposition, and the method comprises the steps: obtaining an original load sequence and a multi-modal data set based on time sequence distribution, and the multi-modal data set comprises meteorological data, equipment state data, and economic and social activity data; copying the original load sequence into a plurality of copies, and adding adaptive white noise to each original load sequence to obtain a plurality of noise-added sequences; performing modal decomposition, component screening and modal reconstruction on each noise adding sequence, and performing superposition to obtain a de-noising load sequence; performing feature screening based on causal driving on the de-noising load sequence and the multi-modal data set to obtain a feature vector; and inputting the feature vector into a preset load prediction model, so that the load prediction model outputs a corresponding load prediction result based on an attention mechanism and a preset physical rule constraint, and the accuracy of power load prediction in a complex scene is improved.

The invention relates to the technical field of geodetic survey and structural health monitoring, and discloses a GNSS-RTK coordinate domain error correction method, which comprises the following steps: acquiring GNSS-RTK dynamic observation data to form a mixed signal time sequence; executing an improved adaptive noise complete empirical mode decomposition algorithm on the sequence to obtain an intrinsic mode function component; identifying and eliminating high-frequency components representing Gaussian white noise based on an energy coefficient, and reconstructing residual components into a signal sequence after primary noise reduction; inputting the noise reduction sequence into a rapid independent component analysis model for blind source separation; and finally, carrying out sorting and phase and amplitude uncertainty correction on the separated independent components, and outputting a multi-path error model and a structure dynamic deformation signal. According to the method, the problem of blind source separation failure or low precision caused by strong noise covering source signal statistical characteristics is solved through a strategy of first noise reduction and then separation, and an effective physical signal can be accurately extracted from a strong noise background.

The invention discloses a baby cry recognition method for white noise equipment, and relates to the field of audio processing and intelligent acoustic recognition, and the method comprises the steps: collecting a pure reference signal, a first-path signal and a second-path signal, carrying out the synchronization and preprocessing, and obtaining a mixed audio signal based on the first-path signal; a residual signal is calculated through an adaptive echo cancellation algorithm; calculating an acoustic masking parameter based on the residual signal, the pure reference signal, and the ambient noise estimate; constructing a three-level recognition processing path including lightweight feature detection, registration voiceprint comparison and multi-modal information fusion; based on the numerical range of the acoustic masking parameter, selecting an identification processing path, and determining a crying event of the target infant from the residual signal; and triggering a corresponding grading alarm based on a crying event confirmation result. A three-level identification path is scheduled through acoustic masking parameters, and accurate and low-power-consumption baby crying monitoring under strong interference is realized by fusing multi-modal information.

The invention discloses a GIS partial discharge fault positioning method based on multi-feature fusion and kernel density estimation optimization, and the method comprises the following steps: collecting a partial discharge original pulse signal, carrying out the signal processing, and outputting a first processing signal; performing adaptive robust baseline determination on the first processing signal, detecting a wave head position based on multi-feature fusion and a dynamic threshold voting mechanism, and outputting a wave head arrival time; carrying out time synchronization matching on the wave head arrival time, and calculating and outputting a preliminary positioning result based on a time difference of arrival method; and accumulating a plurality of preliminary positioning results, performing optimization by adopting a kernel density estimation method, and outputting a final partial discharge fault position. According to the method, impulse noise and white noise are effectively suppressed, stable signals are provided for subsequent processing, the detection precision of the wave head arrival time is remarkably improved, the problem that false detection and missing detection are caused by the fact that a traditional fixed threshold value or a single feature is prone to noise interference is solved, the random error of single-time positioning is greatly compressed to be within 0.07 m from 0.33 m on average, and the overall positioning precision is improved by 80%.

Methods are generally described for inaudible orthogonal signal communication. An example method includes determining, based on an input message, a series of symbols, where each symbol from the series of symbols represents a numerical value, wherein each symbol from the series of symbols corresponds to a band-limited white noise tone orthogonal to each other band-limited white noise tone corresponding to each other symbol. The example method also includes generating a sub-token comprising each symbol from the series of symbols, where each symbol overlaps in time with at least one other symbol. The example method also includes modulating a carrier wave with the sub-token to produce a signal audio waveform, where the modulation uses a spread spectrum technique, and providing the signal audio waveform to a television device, where the television emits the signal audio waveform as a low-amplitude audio signal.

The invention discloses a quantitative calculation method for analyzing injection-production dynamic response by using time sequence interference. The quantitative calculation method comprises the following steps: S1, acquiring time sequence data of output and water injection rate; s2, taking the change of the water injection rate as an intervention event, and establishing a virtual interference variable; s3, taking the oil well output as a target variable and substituting the target variable and the virtual interference variable in the S2 into an SARMAX time sequence model for fitting; s4, judging the significance degree of the model by using the residual error of the model, and if the residual error is a random time sequence, ending; if the residual sequence of the model is non-white noise, going to S5; s5, determining autoregression and moving average orders by using an ARMA (p, q) model; s6, substituting order information into the model in the S3 for re-fitting; and S7, repeating the steps S3-S6 until the model is significant. The method can quantitatively evaluate the influence degree of the injection well on the output. Quantifiable data are provided for evaluation of production and injection dynamic influences in oil reservoir dynamic management, reasonable injection and production adjustment countermeasures are formulated accordingly, oil reservoir production dynamic management is guided, and the oilfield exploitation effect is guaranteed.

The invention discloses a secondary path online modeling and real-time ANC system observation method without additional white noise, and the method comprises the following steps: monitoring the stability of an ANC system through a real-time observer, and preventing divergence caused by the change of a secondary path or an overlarge step length; a secondary path online modeling method is designed, secondary path online precise modeling without adding white noise is realized by combining an offline modeling initial value and online self-adaptive updating, and residual noise rise and human ear discomfort caused by introduction of the white noise in a traditional online modeling method are avoided; secondary path coupling is eliminated by using a loudspeaker time-sharing sounding strategy, and the modeling calculation amount is reduced; and an intelligent ANC system is constructed, a secondary path model is dynamically updated, the step length is adaptively adjusted, and stable operation of the system is ensured. According to the invention, the stability and the noise reduction performance of the ANC system are improved, and the calculation complexity and the human ear interference risk are reduced at the same time.

The invention discloses a ring stationary signal extraction method based on a multi-scale gating attention mechanism and BiLSTM, and belongs to the technical field of mechanical fault diagnosis and signal processing. The objective of the invention is to solve the problem that weak fault features are difficult to accurately extract in a strong background noise and non-Gaussian abnormal interference environment in the prior art. Comprising the steps that a deep neural network model is constructed, local waveforms of different receptive fields are captured through parallel convolution branches, channel masks are generated through an attention mechanism, and hierarchical adaptive suppression of background noise is achieved; biLSTM is embedded in a bottleneck layer, a bidirectional memory unit of the BiLSTM is used for carrying out complete sequence time sequence modeling on compression features, and random abnormal pulses which do not conform to periodic logic are accurately eliminated through a long-term evolution rule of the sequence features; and finally, a decoder is adopted to carry out nonlinear shaping and detail repairing on the up-sampling features after transposed convolution. Gaussian white noise and non-Gaussian outliers can be efficiently filtered out, and pure ring stationary fault impact signals are reconstructed in a high-fidelity mode.

The invention provides a differential microphone array speech enhancement device and method based on reference point optimization, and relates to the technical field of speech signal processing. The device comprises a microphone array, a pre-processing module, a reference point selection module and a signal processing module. According to the invention, the reference point of the microphone array is improved from the first microphone in the prior art to the geometric center of the invention, the maximum value of the distance rm from the microphones in the microphone array to the reference point is reduced by half, the high frequency band is significantly reduced, and the approximation precision of Jacobi-Anger expansion is ensured. Through reference point optimization, the invention aims to realize a beam pattern with invariable frequency in a broadband range and combined optimization of DF and WNG. While the beam pattern approximation requirement and no distortion constraint are met, the white noise gain can be maximized, and the robustness is improved.

The invention discloses a beam structure fault intelligent diagnosis method based on a DIC and a CNN. The method comprises the steps that a beam structure model is established in finite element analysis software, a plurality of node positions corresponding to DIC measuring points are arranged on the surface of a structure, loads are applied, time domain vibration response signals of the multiple measuring points are extracted, and Gaussian white noise is superposed; performing wavelet packet analysis and multi-band adaptive noise reduction on the acquired multi-channel vibration signals, judging the energy distribution characteristics of the signals by using the change rate of wavelet energy entropy, and adaptively adjusting the length of a segmented window to decompose original non-stationary signals into a plurality of stationary time periods; the inherent frequency, the damping ratio and the vibration mode modal parameters of the structure are obtained through working modal analysis; and constructing a feature sample set by taking the extracted modal parameters and time domain features as inputs, inputting the feature sample set into a convolutional neural network model for training and prediction, and realizing classification and identification of various states such as structure health, early cracks, middle cracks and late cracks.

The invention relates to the field of railway data analysis and processing, in particular to a railway irregularity data processing method based on cEEMDAN-Hilbert. The method comprises the following steps: acquiring and preprocessing track irregularity original signals to obtain a target track irregularity signal sequence; performing CEEMDAN (adaptive noise complete ensemble empirical mode decomposition) on the target track irregularity signal sequence, and performing iterative decomposition on the signal added with Gaussian white noise to obtain a plurality of IMF (intrinsic mode function) components and a residual component; performing Hilbert transformation on each IMF component, calculating an instantaneous frequency and an instantaneous amplitude, constructing an analysis signal in a complex form, and extracting time and frequency joint distribution information; according to the instantaneous frequency and the energy result, false wavelength components are removed, signal reconstruction is carried out, and reconstructed track irregularity signals are generated. According to the invention, the identification capability of local structure disturbance can be enhanced.

1. Name of the product in this design: White Noise Bone Conduction Sleep Aid Music Box (SMB-16). 2. Purpose of this product design: Utilizing bone conduction technology, sound is transmitted and amplified through vibrations to the user's bones, thereby achieving a sleep-aiding effect. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The application discloses a baby crying sound recognition method for white noise equipment, relates to the field of audio processing and intelligent acoustic recognition, and comprises the following steps: collecting pure reference signals, first signals and second signals for synchronization and preprocessing, and obtaining mixed audio signals based on the first signals; calculating residual signals through a self-adaptive echo cancellation algorithm; calculating acoustic masking parameters based on the residual signals, the pure reference signals and environmental noise estimation; constructing a three-level recognition processing path comprising light feature detection, registered voiceprint comparison and multi-modal information fusion; selecting a recognition processing path based on the numerical range of the acoustic masking parameters, confirming the crying event of the target baby from the residual signals; and triggering corresponding graded alarms based on the crying event confirmation result. The three-level recognition path is scheduled through the acoustic masking parameters, multi-modal information is fused, and accurate and low-power baby crying monitoring under strong interference is realized.

The utility model discloses a multi-mode fusion safety protection device in a non-shielding mode. The multi-mode fusion safety protection device comprises double box bodies, a drawer, a white noise generator, a plurality of ultrasonic transmitters, a double-door structure and an incoming call sensing assembly. The double box bodies comprise an outer box body and a middle box body, the middle box body is slidably connected into the outer box body, and an air interlayer is arranged between the outer box body and the middle box body; the drawer is slidably connected into the middle box body through a sliding assembly. The double-door structure is installed at the front end of the drawer and used for sealing the outer box body and the middle box body. Wherein the inner walls of the middle box body and the drawer are respectively provided with a flexible sound insulation layer, and sealing assemblies are respectively arranged between the double-door structure and the front side of the outer box body and between the front sides of the middle box body. White noise and ultrasonic waves can be emitted into the drawer, the noise isolation effect is remarkably improved, information can be effectively prevented from being stolen, and the normal communication function of a mobile phone is guaranteed.

The application provides a multi-excitation vibrator vibration test control method, device, terminal and medium, the method comprises the following steps: obtaining a multi-channel white noise signal and a multi-channel output signal, and obtaining an adaptive inverse matrix according to the multi-channel white noise signal and the multi-channel output signal; the driving signal of the current frame is applied to the test piece through the excitation vibrator, and the test piece response signal is obtained; the dynamic target phase is obtained according to the test piece response signal; the driving signal of the next frame is obtained according to the dynamic target phase, the test piece response signal, the driving signal of the current frame and the adaptive inverse matrix, and the test piece is controlled according to the driving signal of the next frame through the excitation vibrator, and the test piece response signal of the next frame is obtained. The inter-channel antagonistic effect is eliminated, the error is efficiently eliminated, the number of iterations required to reach the target spectrum tolerance band is shortened, and the control convergence speed is improved.

The application discloses a wave plate cascade disturbance device and method based on outer rotation and inner rotation forms, and the device comprises an optical transmitter, a disturbance module and a calculation module. The method comprises the following steps: acquiring polarized light with a fixed polarization state; determining a transmission matrix of an outer rotation model and a transmission matrix of an inner rotation model given a disturbance speed and a white noise distribution; performing decomposition conversion processing on the transmission matrix of the outer rotation model and the transmission matrix of the inner rotation model to obtain three-level electrical signals; and performing polarization state adjustment processing on the polarized light with the fixed polarization state according to the three-level electrical signals to obtain adjusted polarized light. The application can independently control the disturbance speed and the polarization probability distribution, improve the controllability of the disturbance model, and realize high-speed polarization disturbance with strong adjustability. The application can be widely applied to the technical field of polarized light disturbance.

The invention relates to the field of masks, and discloses a mask and a control method thereof, the mask comprises a mask body, a secondary sound source device and a voice signal acquisition part; the mask body comprises a mouth and nose wrapping structure and a mask shell, and the mouth and nose wrapping structure is connected with the mask shell; the voice signal acquisition part comprises a main microphone, and the main microphone is arranged on the inner side of the mouth and nose wrapping structure; the secondary sound source device is arranged on the outer side of the mask shell and works based on a control instruction. According to the mask, user sound or environmental noise is collected through the voice collecting part, the secondary sound source device sends out corresponding sound according to user requirements or conducts phase reversal processing on sound waves, and sound opposite to the user is sent out to achieve sound wave counteracting near the air outlet so as to achieve the purpose of active noise reduction; or scrambling signals such as white noise, environment sound and scene sound are generated, and user voice is hidden to the maximum extent; or the sound of the user is amplified, so that the sound is clearer and more stable.

The application discloses a partial discharge diagnosis method based on phase correlation and phase statistical characteristics of ultrasonic signals, comprising the following steps: S01, based on the RLS algorithm, the collected ultrasonic signals are subjected to noise reduction processing to eliminate white noise, and Fourier method is used to filter out interference signals below 20 kHz; S02, while the step 1 operation is being performed, the starting point of the collected ultrasonic signals is found out; S03, the original signal is down-sampled to 500 data points by using the discrete Fourier transform, the frequency spectrum is windowed and then inverse transformed, the fuzzy entropy of the down-sampled signal is calculated, and a first diagnosis model is constructed; S04, the sampling points of the original data are reduced to 3600 data points in the form of taking the maximum value at a certain interval. The partial discharge diagnosis method based on phase correlation and phase statistical characteristics of ultrasonic signals can effectively determine whether the switch cabinet has a partial discharge phenomenon, effectively reduces the false positive rate, and can be used for online detection and daily inspection of electrical equipment such as switch cabinets in multiple scenes.

The application discloses a kind of pulse shaping method and system of nuclear signal, belong to signal processing technical field, for the problems of existing pulse signal width in prior art Widening, thereby increasing the accumulation event, and there is no better anti-noise effect to Gaussian white noise, the present application includes: the output signal of detector is acquired, and output signal is structured into digital function signal;The Z transform is carried out after the discrete of the digital function signal, and output Z transform signal is obtained;Definition input signal is exponential signal, and the Z transform is carried out to the exponential signal, and input Z transform signal is obtained;According to the input Z transform signal and the output Z transform signal, the transfer function of input signal pulse shaping is obtained;The inverse Z transform is carried out to the transfer function, and the pulse shaping signal of input signal is obtained, and its time domain expression.The purpose is: the pulse shaping signal obtained has better signal-to-noise ratio, and it is convenient to obtain amplitude.

This application discloses a downhole nuclear magnetic resonance (NMR) inversion method, apparatus, equipment, and storage medium based on pruned Bayesian methods, relating to the field of NMR data processing technology. The method includes: constructing a forward dataset based on fluid relaxation time and acquisition parameters, and adding Gaussian white noise to obtain the inversion input signal; initializing prior parameters, posterior mean, and variance; constructing a kernel matrix based on relaxation time points; constructing a kernel-dictionary matrix using Gaussian functions; updating the fitted echo data and fitting residuals using hierarchical Bayesian methods, thereby updating the prior parameters; updating the posterior mean through evidence maximization and calculating the proportion of each fluid component; pruning the kernel-dictionary matrix according to a preset pruning threshold and proportion, iterating until a termination condition is met, and finally mapping to obtain a two-dimensional NMR relaxation spectrum. This method solves the problem of waiting time and loss of diffusion relaxation dimension information caused by improved logging efficiency, which makes accurate multidimensional NMR logging inversion difficult.

The invention discloses a graph signal blind source separation method in a white noise environment, and belongs to the technical field of signal processing. Aiming at the problem that the traditional blind source separation performance is reduced due to additive white noise, the invention provides a joint optimization scheme combining blind compression nonlinear noise suppression and image smoothing regularization. Firstly, a blind compression function is applied to a noisy observation image signal for preprocessing; secondly, carrying out mean value removal and whitening processing on the compressed signal; then, constructing a graph Laplacian matrix as a smoothing operator, establishing a joint diagonalization objective function fusing graph autocorrelation, a FastICA item and a graph smoothing regular item, and adopting a Givens rotation algorithm to iteratively optimize and solve a separation matrix; and finally, reconstructing a source signal through multiplication of the separation matrix and the whitening data. According to the method, noise interference is effectively suppressed through blind compression, signal structure consistency is maintained by using graph smoothing prior, and separation robustness, precision and convergence stability in a strong noise environment are remarkably improved.

The embodiment of the invention discloses a music-assisted sleep method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises the following steps: acquiring a first heart rate signal and a first electroencephalogram signal of a target object after audition audio playing is finished; generating initial audio and audio beat configuration information according to the first heart rate signal and an audio style portrait corresponding to the target object; determining brain wave beat configuration information according to the first electroencephalogram signal; determining to add a white noise audio track and a binaural beat audio track in the initial audio in response to the target object, and generating a white noise audio and a binaural beat audio; according to the initial audio, the brain wave beat configuration information, the white noise audio and the binaural beat audio, generating a first track combining audio; and playing the first track combining audio according to the audio beat configuration information. According to the implementation mode, the purpose of effectively helping sleep of the object to be subjected to music assisted sleep is achieved.

The invention discloses a double-mechanical-arm model prediction control method and system with disturbance compensation. The method comprises the steps that motion parameters formed between double mechanical arms and an object are collected, and kinematics and dynamics relation solving is conducted through the motion parameters; solving by utilizing a relation between kinematics and dynamics, and designing a double-mechanical-arm hybrid force controller; according to the double-mechanical-arm hybrid force controller, a nonlinear neural network variable structure controller is adopted to conduct Lyapunov model predictive control; through online optimization of model prediction control, system constraints are effectively enhanced, and then the robustness of the system and the performance and stability of a controller are remarkably improved; meanwhile, the overall performance of the system is further improved by combining approximation and compensation of the neural network to white noise.

1. The name of the design product: wake-up light alarm clock sound. 2. The use of the design product: sound for playing music, white noise, music alarm clock, wake-up light alarm clock, clock display, atmosphere light, and night light. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: Design 1 perspective view. 5. Design 1 is designated as the basic design.

The application provides a user anomaly monitoring method and device, electronic equipment and storage medium, the user anomaly monitoring method comprises: obtaining the target user's to-be-inspected log data, performing sequence division on the to-be-inspected log data to obtain a to-be-inspected time series data set and a to-be-inspected white noise data set, there is a time sequence relationship between the data in the time series data set, and there is no time sequence relationship between the data in the white noise data set; based on the deviation degree of the data in the time series data set and the dynamic baseline, the to-be-inspected time series data is monitored for abnormal operation, the dynamic baseline is obtained according to historical time series data; based on the outlier detection method, the to-be-inspected white noise data is monitored for abnormal operation, and the standard deviation value used by the outlier detection method is calculated according to historical white noise data.

Device and computer-implemented method for modeling a velocity profile, wherein the velocity profile is modeled by a mean velocity and a time series (216), wherein the time series characterizes a velocity fluctuation around the mean velocity, wherein a value of the mean velocity is specified for modeling the velocity profile (212), wherein the time series is defined by an unsteady bounded stochastic process, wherein the process is defined by an autocorrelation coefficient of the random variable of the process and by red noise, wherein the red noise is defined by an autocorrelation coefficient and by white noise, wherein the white noise is drawn from a normal distribution, wherein the values ​​of the parameters autocorrelation coefficient of the red noise, autocorrelation coefficient of the random variable,and in particular logarithmic standard deviation of the white noise is drawn from a given three-dimensional distribution, in particular a normal distribution or a uniform distribution (214), wherein the distribution is uniquely determined by the means and the covariance matrix of the distribution and is assigned to the given value of the mean velocity, in particular, and wherein the time series is determined with the bounded stochastic process parameterized by the values ​​of the parameters drawn from the three-dimensional distribution (216), wherein the value of the autocorrelation coefficient of the red noise and the value of the autocorrelation coefficient of the process are restricted to a value from the interval from zero to one.

The invention discloses a vehicle-mounted audio equalization method and system based on residual vector quantization, and relates to the technical field of vehicle-mounted electronic equipment.The method comprises the steps that a white noise audio signal is played through a vehicle-mounted loudspeaker, a microphone is used for collecting the audio signal lost through in-vehicle propagation, and time-frequency characteristics are obtained through digital preprocessing; performing discretization processing on the target IIR filter coefficient through a residual vector quantization encoder to generate a plurality of layers of token sequences; inputting the time-frequency feature matrix into a convolutional neural network for feature extraction to obtain a low-dimensional feature vector, and through a multi-layer perceptron prediction head, respectively obtaining classification probability distribution of each layer of token and predicting a token sequence; and inputting the predicted token sequence into a residual vector quantization decoder, reconstructing the token sequence into an IIR filter coefficient, loading the IIR filter coefficient into a graphic equalizer, and performing frequency response compensation on an input audio signal. According to the method, the audio frequency response curve can be quickly and adaptively corrected, and the in-vehicle tone quality and the user hearing comfort are remarkably improved.

The invention discloses a high-frequency oscillation suppression method and system for a flexible direct-current power transmission system, and belongs to the field of high-frequency oscillation suppression of a power system, and the method comprises the steps: adding a Gaussian white noise signal in current control of the flexible direct-current power transmission system, and continuously observing the voltage and current of a grid-connected point of the flexible direct-current power transmission system; acquiring the amplification factor of the oscillation signal in the frequency domain and the oscillation stabilization time in the time domain, and judging the current oscillation risk; when the oscillation risk exists, a differential first controller is introduced to compensate a selected current loop feedback signal, a positive torque signal is generated, and parameters of the differential first controller are obtained based on the current oscillation risk level; and performing feedforward compensation on the generated positive torque signal to an output link of a flexible converter in the flexible direct-current power transmission system. The negative damping phenomenon caused by the control delay effect is effectively compensated, and high-frequency oscillation of the flexible direct-current power transmission system is avoided.

The invention relates to a cable temperature detection method, device and system and a storage medium, and relates to the technical field of cable detection. The method comprises the following steps: respectively injecting a plurality of groups of Gaussian white noise signals into an original temperature signal to obtain a plurality of groups of noise adding signals; according to the intrinsic mode function, performing signal decomposition operation and mean value processing operation on each group of noise adding signals to obtain an initial mode signal and an initial residual signal; performing signal decomposition operation on each group of Gaussian white noise signals by circularly adopting an intrinsic mode function, after multiple groups of noise decomposition signals are obtained each time, injecting each group of noise decomposition signals into a residual signal output last time, and performing signal decomposition operation and mean value processing operation on each group of noise adding residual signals to obtain a residual signal output last time; and obtaining a modal signal output at this time and a residual signal output at this time, and performing signal fusion on the multiple modal signals and the final residual signal to judge whether the target temperature signal is abnormal or not.