30 results about "Wavelet filtering" patented technology

The invention relates to the technical field of relay protection loop detection, and discloses a relay protection loop fault detection method and system, comprising data acquisition, data processing, feature extraction, fault diagnosis and early warning and alarming. Multi-dimensional state data of current, voltage, temperature and vibration are collected in real time, wavelet filtering, mean or median filtering and normalization preprocessing are performed, comprehensive characteristic parameters are extracted in combination with time domain and frequency domain analysis and wavelet packet decomposition, a fault is accurately diagnosed and positioned through a fault diagnosis model, early warning and alarming are achieved based on graded thresholds, and the fault diagnosis accuracy is improved. The fault key information is synchronously transmitted to the operation and maintenance terminal, database storage and display terminal display are matched, the problems of poor real-time performance, one-sided feature extraction, no graded early warning and the like of traditional detection are effectively solved, the fault detection efficiency, the diagnosis accuracy and the operation and maintenance response speed are greatly improved, the operation risk of a power system is remarkably reduced, and the safety and stability of the power system are guaranteed.

The invention provides a hot rolled steel strip surface defect detection method based on target detection, and aims to solve the problems of difficulty in multi-scale small target recognition, insufficient feature fusion and complex background interference in hot rolled steel strip surface defect detection. Extracting multi-scale defect features by using wavelet convolution through decomposition, convolution and reconstruction processes; multi-scale features are adaptively fused based on channels and space attention through a dynamic feature fusion module (DFF); a dynamic hyperbolic tangent function (DyT) is used to replace a normalization layer to dynamically adjust the input range and compress extreme values to reduce the amount of calculation. The method has the effects of high detection average precision (mAP), low missing report rate and remarkably reduced calculation complexity, effectively improves the efficiency and reliability of identifying the surface defects of the hot rolled steel strip in a complex industrial environment, and is suitable for intelligent steel manufacturing quality control.

The invention belongs to the technical field of inertial navigation, and provides an inertial instrument anti-disturbance alignment method based on sliding window wavelet filtering to solve the problems that measurement information cannot be fully utilized, an alignment result cannot be globally optimal and is seriously influenced by sudden change interference in an existing alignment method. The anti-disturbance alignment method comprises the following steps: extracting a speed error of an inertial instrument; based on a sliding window wavelet filtering method, carrying out noise reduction processing on system noise of the inertial instrument; and carrying out anti-disturbance self-alignment on the inertial instrument according to the speed error and the noise-reduced output data of the inertial instrument. According to the method, the output data of the inertial instrument can be subjected to noise reduction processing, the holding time of the alignment precision of the inertial measurement unit is prolonged, the dispersion of a convergence value is reduced, and the navigation precision is improved.

The application discloses a kind of traffic image segmentation control method and system, it is related to image processing field, wherein the method comprises: the camera of interactive target vehicle is obtained first traffic image;According to wavelet filtering algorithm, first traffic image is carried out denoising processing, and second traffic image is obtained;Based on the second traffic image, ORB feature recognition is carried out, and ORB feature recognition result is obtained;Based on the ORB feature recognition result, clustering is carried out, and ORB feature clustering result is obtained;Based on the ORB feature clustering result, the second traffic image is carried out background isolation, and third traffic image is obtained, and, third traffic image has ORB feature mark;Traffic image segmentation channel is built;Based on the ORB feature mark, according to the traffic image segmentation channel, third traffic image is carried out segmentation control, and target traffic image segmentation result is obtained.The technical problem that present technique is low in accuracy for traffic image segmentation is solved.

The invention relates to the technical field of electrocardiosignal quality evaluation, and discloses an electrocardiosignal quality evaluation method based on an unsupervised cascade adaptive network, which comprises the following steps: S1, preprocessing an obtained original electrocardiosignal: removing power frequency interference through adaptive notch filtering, removing high-frequency myoelectricity noise through soft threshold wavelet filtering, and obtaining a pre-processed electrocardiosignal; keeping a potential pathological characteristic waveform in a filtering process to obtain a preprocessed electrocardiosignal; s2, unsupervised multi-modal feature extraction: an unsupervised feature extraction network is constructed based on an auto-encoder; a normal electrocardiosignal distribution rule is automatically learned through an auto-encoder, so that the dependence on labeling resources of professional doctors is reduced; meanwhile, by means of a two-stage self-adaptive evaluation network, significant noise is filtered first, then pathological signals and noise are accurately distinguished, it is effectively avoided that abnormal waveforms caused by diseases such as myocardial infarction and atrial fibrillation are misjudged as noise, the retention rate of the pathological signals is remarkably increased, and more complete effective data support is provided for clinical diagnosis.

The invention discloses a GPS (Global Positioning System) and inertial navigation fused high-precision navigation method, aiming at the problems that a GPS signal is easily shielded and interfered and an inertial navigation error is accumulated along with time, high-precision navigation is realized through data preprocessing, a self-adaptive fusion algorithm, GPS signal interruption processing and fusion result optimization. In the data preprocessing, multi-path effect detection and correction are performed on GPS data, and zero offset error compensation is performed on inertial navigation data; the adaptive fusion algorithm constructs a model based on a fuzzy logic system, and updates a fusion weight in real time; when a GPS signal is interrupted, map matching is adopted to assist navigation, and the prediction model is utilized to compensate an inertial navigation error; and a fusion result is subjected to wavelet filtering and smoothing processing, and the accuracy is ensured by using consistency check. The method can effectively overcome the limitation of independent use of GPS and inertial navigation, realizes high-precision, continuous and reliable navigation in a complex environment, and improves the adaptability and stability of a navigation system.

The invention provides an easily-confused emotion recognition method based on multi-path feature extraction and gating fusion. The method can be applied to scenes such as human-computer interaction, emotion calculation and psychological health auxiliary diagnosis. According to the method, a feature path is constructed for each group of emotions aiming at three groups of easily-confused emotions of happiness-angry, fear-surprising and sadness-neutral, each path adopts different wavelet parameter sets to generate a multi-scale wavelet filtering kernel, and multi-path time-frequency features are extracted from the same voice signal. And inputting each path output into a gating attention mechanism module, calculating a path weight and performing weighted summation to obtain a fusion feature for easily confused emotion discrimination, and inputting the fusion feature into a classifier to output an emotion category. According to the method, a wavelet filtering kernel group emphasizing different emotion categories is constructed, a multi-path feature extraction structure is generated, and fusion is performed by using a gating mechanism, so that the model can realize easy-to-confusion emotion recognition according to feature contributions of different paths, and the easy-to-confusion emotion recognition accuracy of the model is improved.

The invention belongs to the field of power distribution network governance, and relates to a power distribution network harmonic optimization method and system based on electrochemical energy storage, and the method comprises the steps: collecting initial harmonic data, and carrying out the preprocessing of the initial harmonic data, and obtaining the processed harmonic data; the harmonic data comprises power grid current, energy storage system parameters, PCS power and power grid voltage; the preprocessing comprises wavelet filtering processing and direct current component filtering processing on the initial harmonic data in sequence; based on the processed harmonic data, calculating to obtain a fundamental wave parameter; the fundamental wave parameters comprise fundamental wave active power, fundamental wave reactive power and fundamental wave apparent power; based on the fundamental wave parameter, calculating to obtain a fundamental wave component, and based on the fundamental wave component, determining a compensation current target value; the fundamental component comprises a fundamental active component, a fundamental reactive component and a harmonic component; based on the compensation current target value, a control instruction is generated through cooperation of model predictive control and rapid repetitive control; and the overall power quality and operation reliability of the power distribution network are improved.

The application discloses a small drilling rig multi-model drilling speed prediction method and equipment based on stratum lithology classification, considers the important factor of stratum lithology reflecting the drilling environment of the small drilling rig in the role of the drilling speed prediction, introduces stratum lithology information, establishes drilling speed prediction models of multiple lithologies, and is divided into three stages, in the first stage, according to actual engineering application experience, data is screened and resampled, then, the data is processed through wavelet filtering and normalization; in the second stage, three typical lithologies are selected from drilling data, and real-time drilling data is used for lithology classification; in the third stage, the lithology classification result of the second stage is used, drilling data of classification is introduced into the drilling speed model of the corresponding lithology for prediction, a good foundation is laid for intelligent control research in the drilling process of the small drilling rig, the problem that a single drilling speed model has low adaptability to different stratum lithologies is solved, and drilling speed prediction precision is improved.

The invention discloses a cornea thickness meter and a cornea thickness measuring method, and relates to the technical field of cornea thickness meters, the cornea thickness meter comprises a lower base, an upper base, a thickness meter, a face limiting frame, a transverse adjusting assembly and a face adjusting assembly; an optical transmitting module, an optical receiving module, an ultrasonic auxiliary module and a data processing module are arranged in the thickness meter, a physical positioning mark is formed through high-frequency ultrasonic waves to determine an optical measurement area, and cornea stimulation and measurement deviation are avoided; the transverse and face adjusting assemblies are matched with a distance sensor to achieve automatic and accurate adjustment of the head position of a tester, and in addition, the data processing module is combined with wavelet filtering, an FFT algorithm and a curvature correction formula to achieve real-time processing of multi-channel signals and accurate calculation of the thickness; the problems of measurement deviation and data processing delay caused by low manual adjustment precision and irregular cornea surface of a traditional thickness gauge are effectively solved, and the cornea thickness gauge has the advantages of operation convenience, measurement accuracy and data management convenience.

The invention relates to the technical field of crossing of artificial intelligence and tobacco processing, discloses a cut tobacco feeding moisture regulation and control method and system based on LightGBM, and aims to solve the problems of moisture signal distortion and low regulation and control precision caused by dust interference. The method comprises the following steps: acquiring dust concentration and noise-containing moisture signals of a production line in real time; dynamically generating an optimal wavelet filtering parameter through a nonlinear mapping model based on the dust concentration; performing adaptive wavelet filtering on the moisture signal to reconstruct a pure signal; inputting the pure signal into a pre-trained LightGBM model to predict a moisture value; and generating a regulation and control instruction according to the deviation between the predicted value and the target value to realize closed-loop control. The system comprises a working condition sensing module, a signal acquisition module, a filtering parameter dynamic generation module, an adaptive filtering module, a moisture prediction module and a regulation and control instruction generation module. Through decoupling integration of dynamic filtering and an existing LightGBM model, on the premise that an original model is not changed, the moisture regulation and control precision and the response speed are remarkably improved, and the engineering transformation cost is reduced.

The invention provides a target detection-based weld infrared thermal imaging image defect detection method. The target detection-based weld infrared thermal imaging image defect detection method comprises the following steps of: selecting an optimal filtering algorithm (Gaussian / median / mean / wavelet filtering) through peak signal-to-noise ratio comparison to pre-process an image; an improved YOLO-AttCrossW model is constructed, a backbone network of the improved YOLO-AttCrossW model fuses high and low dimension features by adopting a cross auxiliary connection module, a neck network is embedded into a deformable channel attention module to realize sampling position adaptive offset, and a detection head applies a dynamic weighting loss function; during training, efficiency and precision are calculated through lightweight convolution and hyper-parameter fine tuning balance. Multi-scale features are fused by means of the spanning auxiliary connection module, the detection confidence of the tiny pores is greatly improved, and the detection confidence is obviously improved compared with original YOLOv5s; the deformable channel attention module can dynamically track the defect contour, and even if interference such as splashing and oxide skin exists, a high average precision mean value can still be kept. And by dynamically adjusting the confidence coefficient weight of the background complex region, the occurrence of false detection conditions is remarkably reduced.

The invention discloses a method for accurately calculating passive discharge time, and the method comprises the steps: S1, carrying out the wavelet filtering processing, obtaining a time sequence tk and a voltage sequence vk of a passive discharge curve of a passive discharge resistor collected by an oscilloscope, carrying out the wavelet filtering of the voltage sequence vk, and obtaining a voltage sequence vfk after the wavelet filtering; s2, determining a passive discharge starting point, and obtaining a time point tstart corresponding to the starting point; s3, determining a passive discharge termination point and a time point tend corresponding to the termination point; and S4, calculating the passive discharge time. According to the method for accurately calculating the passive discharge time, the defects that in the prior art, manual point taking errors are large, and the accuracy of actually measured passive discharge time cannot be guaranteed are overcome.

The invention provides an actor-commentator training method and system based on Hamiltonian error wavelet filtering, and relates to the technical field of artificial intelligence and automatic control. The invention aims to solve the problems of slow training and low precision caused by noise contained in Hamiltonian-Jacobi-Bellman approximation errors. The method comprises the following steps: calculating an approximate error signal of a Hamiltonian-Jacobi-Bellman equation; filtering the signal and extracting main components for training; updating parameters of the actor network and the commentator network based on the filtered error signals; the invention further discloses a corresponding system. By filtering the core error signal updated by the driving network, the training mechanism is improved, and the loss function is smoother, so that the convergence speed is accelerated, and the control precision and robustness are improved.

The invention relates to the technical field of multi-source acoustic signal separation, in particular to a multi-source acoustic signal separation method based on Doppler wavelet filtering, and the method comprises the following steps: 1, collecting a trackside mixed acoustic signal: arranging a single microphone at a vertical distance d from a track, and collecting a mixed acoustic signal s (t) containing at least two sound sources; 2, calculating pseudo time-frequency distribution: performing improved Doppler wavelet transform (IDT) on s (t) to obtain pseudo time-frequency distribution FTFD: t0 is a time center, f0 is a characteristic frequency, and D (tau) is a dynamic distance between a sound source and a microphone; 3, sound source parameters are extracted, wherein time centers t01, t02,..., t0n and corresponding characteristic frequencies f0j of all sound sources are extracted from FTFD; and step 4, constructing a Doppler wavelet filter: aiming at the target sound source i. The invention provides a multi-source signal separation technology based on pseudo time-frequency distribution and Doppler wavelet filtering, can realize rail-side multi-source acoustic signal separation, and has a better application prospect in wayside acoustic fault diagnosis.

The invention provides a non-contact blood pressure prediction method in a natural state based on a multi-level model, which comprises the following steps: sequentially performing face recognition, region of interest selection, color signal extraction, signal enhancement, abrupt change noise removal and wavelet filtering on each frame of picture to obtain a filtered IPPG signal; carrying out expansion and label classification on the data set; constructing a residual bidirectional long-short-term memory network, wherein the network comprises a convolutional neural sub-network, a residual sub-network and a bidirectional long-short-term memory sub-network; inputting the data set with the label into the network, and training the network by using a callback function to obtain the trained network; inputting a one-dimensional signal obtained through image processing into the trained network so as to predict the blood pressure value of the human body; according to the distance and the attitude angle, the blood pressure value is corrected through a Kalman filtering algorithm, and the corrected blood pressure value is obtained. In this way, the situation of overfitting can be avoided, the method is suitable for more real scenes, and the accuracy of the predicted blood pressure value is high.

The application relates to the technical field of artificial intelligence and tobacco processing, and discloses a tobacco cut filler moisture control method and system based on LightGBM, which aims to solve the problems of distorted moisture signal and low control precision caused by dust interference. The method comprises the following steps: acquiring the dust concentration of a production line and a moisture signal with noise in real time; generating optimal wavelet filtering parameters through a nonlinear mapping model based on the dust concentration; performing adaptive wavelet filtering on the moisture signal to reconstruct a pure signal; inputting the pure signal into a pre-trained LightGBM model to predict the moisture value; generating a control instruction according to the deviation between the predicted value and a target value to realize closed-loop control. The system comprises six modules, namely, working condition sensing, signal acquisition, dynamic filtering parameter generation, adaptive filtering, moisture prediction and control instruction generation. The application realizes decoupling integration of dynamic filtering and the existing LightGBM model, significantly improves the moisture control precision and response speed without changing the original model, and reduces the engineering transformation cost.

This invention discloses a method, apparatus, and computer-readable storage medium for generating defect samples, relating to the field of deep learning technology. The method includes the following steps: inputting a preset defect image and a preset target image into a trained convolutional neural network to obtain defect image samples; the convolutional neural network includes a wavelet filtering module, a downsampling layer, and an upsampling layer connected in sequence, wherein the wavelet filtering module is used to perform preset wavelet filtering processing on the input image; wherein the downsampling layer includes at least one downsampling module and at least one wavelet filtering module alternately arranged; the upsampling layer includes at least one upsampling module and at least one wavelet filtering module alternately arranged; the number of downsampling modules in the downsampling layer is equal to and corresponds to the number of upsampling modules in the upsampling layer. This invention provides effective defect samples for training defect detection models.

The application discloses a segmented linear excitation source data point simplification method, wherein time sequences and data value sequences corresponding to waveform data points to be simplified are respectively t k and v k , k=1, 2,..., n, and the method comprises the following steps: S1, wavelet filtering; S2, screening special extreme points; S3, screening data points through difference; and S4, screening data points through slope. The segmented linear excitation source data point simplification method can extract core data points from actual waveform original data points, the core data points retain main features of the original data points, and the number of the core data points is far less than that of the original data points. Using the core data points as inputs of the segmented linear excitation source can significantly shorten simulation time and help simulation convergence.

The invention discloses a GIS isolation switch fault diagnosis method. According to the method, a voltage signal and a current signal of the GIS isolation switch are obtained and then noise reduction processing is carried out by using a wavelet filtering algorithm; modal function components of the current signals in different states are extracted through EMD to serve as frequency domain features, and the frequency domain features and time domain features are fused through a PCA algorithm to obtain a fused feature set; dividing the fusion feature set into a training sample set and a test sample set; optimizing kernel parameters and penalty factors of the support vector machine model by improving an original whale optimization algorithm, and training and testing the support vector machine model according to the training sample set and the test sample set to obtain a GIS isolation switch fault diagnosis model; inputting the fusion feature set of the GIS disconnecting switch into a GIS disconnecting switch fault diagnosis model, and identifying a GIS disconnecting switch fault; the high-efficiency fault diagnosis of the high-voltage isolation switch is realized by adopting the finite characteristic quantity when the fault samples are few.

The present invention discloses an energy efficiency optimization and control method and system combined with energy consumption data analysis, relating to the technical field of energy efficiency optimization and control; the present invention combines energy consumption data analysis with a support vector machine model to accurately capture the relationship between equipment and regional energy consumption, predict future energy efficiency change trends, and use a particle swarm optimization algorithm for global optimization, adjust parameters such as equipment usage time, load rate and power in real time, optimize energy efficiency management, and improve accuracy and flexibility; compared with traditional static control methods, the present invention can dynamically respond to energy efficiency requirements under different working conditions; by real-time collection of energy consumption data and wavelet filtering and normalization processing, data accuracy and consistency are ensured, and through particle swarm optimization, resource waste and operating costs are reduced, thereby reducing energy consumption and operating costs; a PID controller is combined to smoothly control the scheduling process, avoiding system instability problems.

The invention discloses a digital twin power prediction method and system based on adaptive multi-resolution wavelet filtering, and relates to the technical field of power prediction. Comprising the following steps: acquiring multivariable power time sequence data, and performing reversible instance normalization processing; inputting the normalized power time sequence data into an inverted embedding layer, and mapping the normalized power time sequence data to a high-dimensional feature space to obtain an initial sequence; carrying out adaptive multi-resolution discrete wavelet transform on the initial sequence, and extracting low-frequency and multi-scale high-frequency components of the initial sequence; performing element-by-element product on the frequency domain component and the learnable filtering kernel to complete adaptive wavelet filtering; reconstructing a filtered time domain sequence through inverse wavelet transform, and performing cross-channel aggregation and feature redistribution through a star aggregation-redistribution module; and inputting the extracted feature sequence into a feedforward neural network to obtain a power index prediction result of a future time step. According to the method, the modeling capability and prediction robustness of the non-stationary power data are effectively enhanced, and the method is high in adaptability and easy to deploy.

This invention relates to a cable fault location method and apparatus based on broadband impedance spectrum peak attenuation compensation. The method includes: acquiring a broadband impedance spectrum signal from the faulty cable; performing a discrete Fourier transform on the broadband impedance spectrum signal, and then normalizing it using the maximum reflection peak as the standard to obtain a location amplitude spectrum containing defect characteristics; processing the location amplitude spectrum using wavelet filtering, determining the coordinates of the fundamental frequency, second harmonic, and third harmonic of the faulty cable in the filtered location amplitude spectrum, and fitting it to obtain a compensation curve; performing difference processing on the location amplitude spectrum and the compensation curve to obtain a compensated location amplitude spectrum; and determining the fault location of the cable based on the peak characteristics of the compensated location amplitude spectrum. Compared with the prior art, this invention can locate various cable faults, has higher resolution, fewer interference peaks in the location spectrum, and low data redundancy.

The invention discloses a fault detection system and method for an electronic component, and relates to the technical field of fault detection of the electronic component, and the method comprises the steps: filtering the noise and missing values of a sensor through multi-source high-speed data collection and online preprocessing, and packaging the filtered noise and missing values into a micro-batch data set; a fault is judged in real time and parameters are incrementally updated through a lightweight intelligent fault detection model, then the lightweight intelligent fault detection model is closely integrated with an industrial control system, millisecond-level closed-loop protection is executed by using a multi-level risk function, and feedback is returned; and finally, uploading historical records and mark information to a cloud end, and realizing full-life-cycle management by applying big data analysis and model iteration. Through the technologies of wavelet filtering, incremental learning, cross-level data integration and the like, fault detection delay can be effectively shortened, shutdown loss can be reduced, guarantee is provided for predictive maintenance, the safety and efficiency of a production line are greatly improved, and therefore the requirements of industrial scenes for fault recognition and real-time response can be met.

The invention relates to the technical field of welding seam detection, in particular to a steel structure welding seam defect identification method and system based on a visual function, and the method comprises the following steps: S1, obtaining an assembly type steel structure welding seam image, and identifying a welding seam defect area which comprises fine crack features through a CCD camera acquisition module; s2, extracting initial noise pixel points in the defect area, and performing division processing on each initial noise pixel point according to an AI noise adaptive filtering strategy to obtain a plurality of initial noise pixel areas; the initial noise pixel region comprises a plurality of initial noise pixel points for determining the size of a filtering window; according to the method, accurate suppression of complex field noise is realized through an AI-driven noise adaptive weighted wavelet filtering strategy, accurate identification of fine crack features in the assembly type steel structure welding seam image is realized in combination with the U-Net deep segmentation network, and the assembly type steel structure welding seam defect identification precision and robustness are improved.

The invention provides a metal oxide arrester state monitoring method and system, and relates to the technical field of arresters. The method comprises the following steps: acquiring leakage current waveform data in the operation process of a lightning arrester, and decomposing by adopting a db2 wavelet filtering and displacement current method to obtain a resistance current waveform and a capacitance current waveform; performing multi-feature extraction and fusion based on the two types of waveforms, constructing a first feature vector set, and expanding the first feature vector set into a high-dimensional second feature vector set; and inputting the classification result into a classification system based on a decision tree-support vector machine for training and diagnosis, and outputting a classification result. According to the method, a combined strategy of wavelet filtering and displacement current decomposition is utilized to remarkably improve the denoising and separation precision of a leakage current signal, and high-sensitivity identification of the state of the lightning arrester is realized; the robustness and the diagnosis intelligence level under complex working conditions are enhanced through multi-feature fusion and a joint classification mechanism, and the method has high practicability and popularization value.

The present application belongs to the field of power distribution network management, and relates to a power distribution network harmonic optimization method and system based on electrochemical energy storage, comprising: collecting initial harmonic data and preprocessing the initial harmonic data to obtain processed harmonic data; the harmonic data includes power grid current, energy storage system parameters, PCS power and power grid voltage; the preprocessing includes sequentially performing wavelet filtering processing and direct current component filtering processing on the initial harmonic data; based on the processed harmonic data, fundamental wave parameters are calculated; the fundamental wave parameters include fundamental wave active power, fundamental wave reactive power and fundamental wave apparent power; based on the fundamental wave parameters, fundamental wave components are calculated, and based on the fundamental wave components, a compensation current target value is determined; the fundamental wave components include fundamental wave active component, fundamental wave reactive component and harmonic component; based on the compensation current target value, control instructions are generated through model predictive control and fast repetitive control coordination; the overall power quality and the reliability of operation of the power distribution network are improved.

The application discloses an ECG signal adaptive threshold wavelet filtering method and device, which is applied to ECG signal measurement, and adjusts the wavelet threshold of each layer of wavelet filtering from an initial value by a set step length, and calculates a self-correlation function according to a fast algorithm of the self-correlation function. After each filtering, the self-correlation value of the filtering result is recorded, and when the self-correlation value reaches a maximum value, the corresponding wavelet threshold is recorded as an optimal wavelet threshold. The self-correlation function is calculated from the sequence correlation of the signal itself after denoising according to the correlation and self-correlation characteristics of adjacent time instants of the ECG signal. Finally, the optimal wavelet threshold is used for adaptive filtering of the ECG signal. The application selects the optimal wavelet threshold of the wavelet filtering through the calculation result of the self-correlation function, can evaluate the filtering effect of the wavelet filtering method in the case that there is no clean and noise-free reference signal, and further optimizes the wavelet threshold parameter, and can better retain useful information and obtain a higher signal-to-noise ratio.

The application discloses a kind of electronic component fault detection system and method, it is related to electronic component fault detection technical field, relies on multi-source high-speed data acquisition and online pre-processing, sensor noise and missing value are filtered and packed into micro-batch dataset after, real-time determination fault is carried out again through light weight intelligent fault detection model and incremental update parameter, then with industrial control system is closely integrated, utilizes multi-level risk function to execute millisecond level closed loop protection and returns feedback;Finally, historical record and marked information are uploaded to cloud, and full life cycle management is realized using big data analysis and model iteration;By wavelet filtering, incremental learning and cross-level data integration etc., can effectively shorten fault detection delay, reduce downtime loss, and provide guarantee for predictive maintenance, greatly improve production line safety and efficiency, so as to meet the demand of industrial scene to fault identification and real-time response.

The application discloses a kind of railway roadbed security situation identification method and system, it is related to railway roadbed operation and maintenance field, wherein the method comprises: obtaining M area real-time radar information, and according to wavelet filtering algorithm, M area real-time radar information is denoised, and M area denoised radar information is obtained;Build roadbed disease identification model;Based on roadbed disease identification model, respectively, M area denoised radar information is identified, and M roadbed area disease identification result is obtained;Based on the first weather forecast data and the first forecast railway operation data, respectively, M roadbed area disease identification result is carried out development trend prediction, and M roadbed area disease prediction result is obtained, and target roadbed disease survey report is generated in combination with M roadbed area disease identification result.The technical problem that the disease identification accuracy of railway roadbed in prior art is low, comprehensive is poor, leading to the technical problem that the disease survey effect of railway roadbed is not good.