358 results about "Wavelet denoising" patented technology

The invention relates to the field of building structure safety detection, and discloses a building structure anti-seismic performance monitoring method and system. The method comprises the following steps: acquiring multi-source response data such as acceleration, displacement, strain and inclination angle; obtaining a structure standard response based on wavelet denoising and standardization processing; adopting a multi-order modal recognition algorithm to extract structural dynamic characteristic parameters; performing structural damage evaluation based on the modal change index, and combining the yield strength, the load index and the displacement margin to calculate the residual bearing capacity of the structure; and finally, outputting an anti-seismic grade early warning signal. The system comprises a structure response acquisition module, a modal identification and damage evaluation module and a residual bearing capacity calculation and early warning module. According to the method, real-time quantitative analysis of the performance of the structure under the earthquake or load effect can be achieved, the evaluation precision is high, the applicability is high, and the wide engineering popularization value is achieved.

The invention relates to the technical field of dairy product production, and discloses a dairy product quality safety detection method and system based on artificial intelligence. The detection method comprises the following steps: S10, sensor layout and initial process mapping; s20, carrying out data preprocessing and wavelet denoising; s30, regression scene construction and model prior; s40, multi-core collaborative optimization of the bitter fish algorithm; s50, carrying out online monitoring and distributed drift correction; s60, pollutant alarm and model feedback are carried out; and S70, at the end of each production stage or cycle, performing global evaluation on the method in the aspects of accuracy, real-time performance, system compatibility and the like. The core innovation of the invention lies in constructing a dynamic frequency domain processing framework and a multi-core self-adaptive modeling mechanism fusing process prior, and effectively solves the problems of incomplete signal noise reduction, risk feature coupling modeling deficiency and the like of a traditional method in a complex production environment.

The invention discloses a self-adaptive test parameter optimization method, and relates to the technical field of parameter optimization, and the method comprises the steps: collecting an original sensing signal in a mechanical test system, and carrying out the preprocessing of the original sensing signal; based on the preprocessed data set, constructing a four-dimensional space-time tensor, and executing improved parallel factor tensor decomposition to obtain a decoupled core factor and space-time feature component matrix; performing cross-modal alignment on the decoupled core factor and the time-space feature component matrix through a wear feature channel and an acoustic emission feature channel to obtain a fused cross-modal feature vector; performing crack growth rate prediction on the fused cross-modal feature vectors to obtain a crack risk level, and adjusting a strategy through dynamic parameters to obtain an optimized parameter set; the problem of signal noise and time mismatch is solved through multi-mode signal preprocessing, and high signal-to-noise ratio input is provided for subsequent analysis in combination with wavelet denoising, space-time alignment and double-domain feature extraction.

The invention relates to the technical field of engineering mechanical structure health monitoring, and discloses a carry-scraper structural member crack real-time detection system and method based on vibration characteristics, and the carry-scraper structural member crack real-time detection system based on vibration characteristics comprises a sensing acquisition module which is used for acquiring structural member vibration response and obtaining an original signal; the signal processing module is used for carrying out wavelet denoising on the original signal and extracting effective vibration data; the feature extraction and dynamic calibration module is used for extracting multi-domain features and performing dynamic calibration in combination with working conditions; the crack recognition and intelligent diagnosis module is used for inputting an intelligent model to recognize a crack state; and the crack positioning and alarm feedback module is used for positioning cracks and giving an alarm in combination with the diagnosis result. According to the invention, the multi-modal sensor array is constructed, piezoelectric and MEMS sensors are cooperatively arranged, a magnetic snap-in type installation structure is combined, broadband response signals are stably collected under different working conditions, and then front-end signal optimization is completed in cooperation with a multi-scale wavelet denoising and standardization mechanism.

The invention discloses a power transformer arc discharge multi-parameter detection simulation platform and a fault diagnosis method, and relates to the technical field of power system equipment state monitoring and fault diagnosis. The platform comprises a transformer body, a replaceable discharge module, a multi-parameter sensing unit and a signal processing and diagnosis module, and can truly reproduce various typical arc discharge faults of a needle plate, an air gap, a creeping surface, turn-to-turn and the like. The sensing unit is integrated with ultrahigh frequency and ultrasonic sensing probes, high-frequency current and voltage sensors, optical fiber temperature / pressure / strain sensors and the like, so that synchronous acquisition of multi-physical field signals is realized. According to the diagnosis method, through wavelet denoising and multi-dimensional feature extraction, a feature vector of multi-state parameter fusion in the process from partial discharge to arcing is constructed, and accurate classification of fault types is realized by using a support vector machine (SVM) model. The diagnosis method has high accuracy and early warning capability, effectively overcomes the limitation of single parameter diagnosis, and provides reliable technical support for transformer fault research and intelligent operation and maintenance.

The invention discloses a fan gearbox fault diagnosis method based on a multi-core wavelet denoising network, and belongs to the technical field of fault diagnosis. The method comprises the following steps: 1, collecting vibration signals to construct a data set, dividing a training set, a test set and a verification set, and carrying out normalization processing; 2, multi-scale pulse features are extracted by fusing a wavelet kernel convolution module, adaptive fusion of a time sequence channel attention mechanism is proposed, and redundant features are suppressed; a third step of integrating a multi-scale adaptive soft threshold module to realize accurate denoising and processing multi-scale information; 4, a feature-driven Transform encoder is adopted, and global information is captured by using a multi-head attention mechanism; and 5, inputting the extracted features into a classifier for classification, performing fine adjustment on the model by using a verification set, performing test set evaluation, and completing fault diagnosis. The method effectively solves the problems of low accuracy and low diagnosis precision caused by the fact that fault features are easily covered by noise and the long-term dependency relationship is difficult to capture in the fault diagnosis of the gearbox of the wind turbine generator.

The invention relates to the technical field of power supplies, and discloses a high-precision power supply control method based on a digital signal processor, which comprises the following steps of: acquiring data such as voltage, current, temperature, load parameters and the like of a power supply at a high frequency of kHz-level sampling frequency by using a high-precision sensor, transmitting the data to a DSP (Digital Signal Processor), and removing noise interference by applying wavelet transform. Sample quality is improved through high-frequency data acquisition and wavelet denoising, nonlinear and time-varying characteristics of a power supply system are identified by using a lightweight CNN and an LSTM, a reinforcement learning dynamic optimization control strategy is combined, self-adaptive switching between traditional control and an intelligent algorithm is realized through working condition identification, and the power supply system control method based on the LSTM is realized. The problems that noise interference influences data quality, system characteristic recognition is inaccurate, complex working condition control strategy optimization is insufficient, and output precision is unstable due to the fact that a control mode cannot be switched in a self-adaptive mode in an existing power supply can be effectively solved, and control precision, efficiency and device loss can be considered. And the stable operation requirement of the high-precision power supply under multiple working conditions is met.

The invention discloses a method for effectively studying and judging false alarm of a fire alarm system in a transformation power station in a multi-dimensional mode, and the method comprises the steps: collecting and fusing multi-channel sensor data, such as smoke concentration, temperature, humidity, electromagnetic field intensity, grounding state and the like, and constructing a scenarized data set in combination with an environment label and an operation and maintenance log; through multi-level feature processing such as normalization, wavelet denoising and dimension reduction, the data quality and discriminability are improved; according to the method, the space-time diagram neural network is used for achieving multi-period environment subarea and typical signal mode division, multi-period fusion confidence judgment is conducted on alarm signals based on the fuzzy set theory and Bayesian reasoning, dynamic judgment of false alarms and real alarms is achieved, and the accuracy and robustness of alarm judgment in the complex environment are effectively improved.

The invention discloses a reaction condition optimization control method and system for biological aviation kerosene production, and particularly relates to the field of biological aviation kerosene production. The method comprises the following steps: firstly, acquiring parameters such as temperature, pressure, flow, catalyst activity and raw material components of biological aviation kerosene in real time through a sensor module consisting of a temperature sensor, a pressure sensor, a flow sensor and the like, and then carrying out pretreatment operations such as median filtering and wavelet denoising on acquired data; constructing an LSTM-Transformer hybrid model based on the preprocessed data to predict the second-by-second value of each parameter in the future 10 minutes, correcting the predicted value according to a catalyst activity index formula, further optimizing the prediction model by using a shadow model, comparing the predicted value with a preset interval and a threshold value, judging the state of the predicted value, and determining the second-by-second value of each parameter in the future 10 minutes. And finally, generating a fixed fine-tuning, strengthening or emergency control strategy according to a judgment result. According to the method, the biological aviation kerosene production reaction conditions can be accurately predicted, regulated and controlled, and the production stability, safety and product quality are improved.

The invention relates to a hydrogen storage station safety monitoring method and system based on a wireless passive three-signal sensor, and belongs to the technical field of safety monitoring. Wireless passive three-signal sensor nodes are deployed in a hydrogen storage station to form a three-dimensional monitoring network; the temperature, strain and hydrogen detection units of the sensor are simultaneously excited through the multi-frequency-band signal transceiver, so that parallel acquisition of three-parameter signals is realized; performing signal decoupling, temperature compensation and primary risk assessment by using an edge computing gateway; deep data mining and risk assessment are carried out through a cloud early warning platform in combination with a digital twinborn model; wherein multi-parameter signal decoupling is realized by adopting a frequency division multiplexing and wavelet denoising algorithm, and safety early warning is carried out in combination with a three-level early warning decision tree. The hydrogen storage station electric spark risk can be eliminated, high-precision synchronous monitoring of temperature, strain and hydrogen concentration is achieved, and the hydrogen leakage early warning time is shortened.

The invention belongs to the technical field of Internet of Things prediction, and discloses a soil moisture content prediction method and system based on a BiLSTM-Transform dynamic weight hybrid architecture, and the method comprises the steps: collecting an observation data set of soil moisture content influence factors of an irrigation region, the observation data set comprising a meteorological element sequence and a soil parameter sequence; the method comprises the following steps: collecting time series data of soil moisture content influence factors, carrying out noise reduction processing on the time series data by adopting a wavelet noise reduction method to obtain noise reduction data, and filling missing values of the noise reduction data by adopting a linear interpolation method to obtain a continuous data set. According to the method, a feature fusion strategy is adaptively adjusted through a dynamic weight mechanism, the time sequence modeling capability of soil moisture content prediction is remarkably improved, parameter quantities are compressed while the prediction precision is kept, the feasibility of deployment on edge computing equipment is achieved, and the constructed framework has the advantages of improving the stability of a prediction result and improving the prediction efficiency. And the prediction result error of the soil moisture content can be effectively reduced.

The invention provides an underground structure boundary identification method and system based on distributed optical fiber sensing, and relates to the technical field of underground structure detection and boundary identification. The method comprises the following steps: firstly, arranging a sensing array in a to-be-detected area, establishing a channel corresponding to a space coordinate, and collecting a non-excitation base line; medium and noise are estimated through low-energy probe, excitation frequency band, energy and repetition rate are optimized, and the signals are emitted by an adaptive seismic source; reflection and transmission responses are synchronously collected under the unified time reference; performing wavelet denoising, temperature and dispersion compensation and time alignment on the data, and extracting amplitude, phase and frequency characteristics; gradient is calculated on the feature field, non-maximum suppression is carried out, and stable boundary points are obtained by adopting self-adaptive double thresholds based on local statistics and combining time continuity and space connectivity constraints; a two-dimensional section is obtained through spline fitting, a three-dimensional model is reconstructed under the constraint of multi-section consistency, a section map and the three-dimensional model are output, and high-precision, real-time and visual detection of small-size boundaries is achieved.

The invention provides an industrial air conditioner and waste heat recovery collaborative intelligent optimization control method and related equipment, and is applied to the technical field of data processing. Industrial air conditioner and waste heat recovery system data are fused through the Internet of Things and edge calculation, a standardized parameter sequence is generated through wavelet noise reduction and is converted into an energy flow network atlas, high-energy-consumption working conditions are subdivided through fuzzy clustering, and a dynamic energy efficiency coupling model is constructed. And based on the model, constructing an optimization equation by using an improved particle swarm algorithm, and obtaining the energy consumption of the key node in combination with an energy gradient utilization matrix. And an energy-saving potential coefficient is generated through the multi-dimensional feature matrix, a production plan grouping operation mode is combined, a self-adaptive optimization control model is constructed by using deep reinforcement learning, and finally a real-time regulation and control instruction and an energy efficiency scheme are generated to realize system collaborative optimization.

The invention provides a vibration reduction buffering and posture self-adaptive adjusting method in wind turbine generator cabin transportation, and relates to the technical field of wind turbine generator transportation, which comprises the following steps: collecting transportation vibration time sequence data, after wavelet denoising and normalization processing, adopting synchronous compression decomposition and nonlinear cross-scale feature mapping to extract a dominant frequency component, and then extracting the dominant frequency component; synchronously calculating time domain parameters (a root mean square value, a peak factor and a waveform factor) and frequency domain parameters (a frequency centroid and bandwidth energy distribution); inputting the multi-dimensional features into a graph convolutional neural network to realize vibration mode classification, constructing an attitude optimization model based on a classification result, performing cooperative solution through a multi-agent depth deterministic strategy gradient algorithm, and outputting an optimal attitude adjustment parameter; after the adjustment is implemented, new vibration data are collected to update network parameters, and closed-loop control of vibration feature recognition and attitude optimization is formed. Transportation vibration impact is effectively reduced, and the transportation stability and safety of wind power equipment are improved.

The invention discloses an internet of things-based amusement equipment light dynamic control method and system, and the method comprises the steps: collecting the acceleration, angular velocity and coordinate data of equipment, carrying out the wavelet denoising and Z-score standardization processing, and constructing a dynamic topology model based on a graph attention network and a graph convolution network. A spectral clustering algorithm is utilized to carry out space partitioning on equipment nodes to generate a topological sub-graph, and an LSTM network is combined to predict an equipment motion track and generate a continuous track sequence. Track correlation features are extracted through a graph attention network, a genetic algorithm is fused to optimize and generate a light conversion sequence, dynamic time warping alignment timestamps are synchronously adopted, and phase synchronization of the light sequence and the motion track is ensured. And finally, brightness, color and flicker frequency are adjusted in real time based on a PID control algorithm, and a closed-loop feedback mechanism is formed. According to the method, high-precision dynamic matching of light and equipment movement is realized, and immersive experience and visual interactivity are improved.

The invention relates to the technical field of beauty intelligent terminals, multi-modal deep learning and transfer learning, image processing and the like, and provides a skin condition evaluation method based on a skin detection function head and a beauty intelligent terminal. And synchronous acquisition of visible and near-infrared multi-channel skin images and impedance spectrums is realized. According to the method, multi-modal data are aligned by taking a unified timestamp as a reference, dark current correction, wavelet denoising and environment standardization are introduced in a preprocessing stage, and a unified feature vector is constructed. The multi-modal cross attention model subjected to transfer learning fine tuning is fused with each modal feature and then outputs four indexes of water-oil balance, pigmentation, texture roughness and inflammation risk; and if a high-risk state is detected, subsequent physiotherapy is automatically limited, and a voice alarm is given out, so that a detection, evaluation and protection closed loop is formed, and the intelligent beauty safety and evaluation accuracy are improved.

The invention discloses an electrocardiogram arrhythmia classification method and system based on a residual shrinkage network, and relates to the technical field of arrhythmia classification. According to the method, efficient electrocardiogram arrhythmia classification is achieved through multi-link cooperation, and a fine preprocessing, data balance strategy and multi-attention mechanism fusion model is designed; preprocessing provides high-quality input through wavelet denoising, precise R-wave detection and the like; the under-sampling-over-sampling mixed strategy is used for solving class imbalance and improving minority class recognition; the ResTCL-Net is fused with CNN, GRU, RCA, TSA and CLA modules, and signal features are mined in multiple dimensions; the optimization training strategy gives consideration to efficiency and stability, and is matched with comprehensive evaluation to guarantee performance. According to the scheme, the classification accuracy and generalization ability are remarkably improved, and abnormal heart beat recognition is enhanced.

A train-track-bridge coupling response prediction method based on a sparrow optimization algorithm and a long short-term memory network comprises the steps that data such as train speed, axle load, track vibration acceleration, bridge strain and environment temperature are collected in real time through a multi-source sensor, and a multivariable time series data set is constructed after wavelet denoising and standardized preprocessing; and designing an LSTM network architecture on this basis, introducing an attention mechanism to dynamically allocate feature weights of each time step so as to enhance the ability to capture key signals in the track irregularity mutation and bridge resonance interval, and adopting a sparrow optimization algorithm to globally search an optimal combination of a hidden layer neuron number, a learning rate and a time step length in order to solve the problem of LSTM hyper-parameter optimization. Through the dynamic adaptive step length strategy balance algorithm, the early-stage global exploration and later-stage local development capabilities are balanced, the local convergence defect of a traditional grid search or genetic algorithm is avoided, the calculation efficiency can be remarkably improved, errors can be reduced, and the prediction precision can be improved.

The invention discloses an underwater image enhancement method based on an improved MobileNetV4 (MobileNetV4). The method comprises the steps of firstly constructing a degraded underwater image data set and performing normalization preprocessing; a generator with MobileNetV4 as a trunk is provided, wavelet denoising (WD), color correction (CC), a multi-query attention mechanism (MQA) and a LayerScale layer are integrated, and high-frequency noise reduction, color temperature adjustment and feature stabilization are achieved; in cooperation with a multi-scale discriminator (MSD), Huber adversarial loss, Smooth L1 similarity loss and dual-channel content perception loss optimization training are adopted. Compared with a traditional method, the model is smaller in parameter quantity and calculation quantity, but better performance is achieved, the UIQM index is improved by more than 15%, the requirements for light weight and enhanced quality are remarkably balanced, and the method has practical value in the fields of ocean exploration, biological monitoring and the like.

The invention discloses a fan operation abnormal vibration monitoring method and system based on multi-sensor fusion, and relates to the technical field of fan operation monitoring, and the method comprises the following steps: deploying a plurality of types of sensors on a fan, collecting the data of each sensor in real time, carrying out the synchronous processing of the sensor data through employing an IEEE1588 protocol, carrying out the preprocessing through combining wavelet denoising, and carrying out the monitoring of the abnormal vibration of the fan. Extracting feature data by using a principal component PCA combined independent component ICA analysis method; according to the method, multiple types of sensors are deployed on the fan, the actual conditions of the offshore wind field are considered, fine processing and feature extraction are carried out after multi-source signals are collected in real time, the abnormal vibration condition of the fan in the coastal or offshore wind field under the complex environment is accurately monitored and reliably recognized, and therefore the early abnormal features of fan operation can be captured in time; normal changes and real fault anomalies caused by environmental factors are effectively distinguished, and early discovery of faults is further realized.

The invention provides a gait analysis method and an early warning system based on multi-source data fusion, three types of original signals are collected through a wearable inertial sensor, a plantar pressure insole and an edge calculation camera, and multi-dimensional motion characteristic parameters are fused through wavelet denoising, low-pass filtering, interpolation alignment and data consistency correction, so that the gait analysis method based on multi-source data fusion is realized. And constructing a user individualized gait feature prototype library. The method further adopts a dynamic causal graph network to model a motion relation between joints, determines an abnormal coupling mode based on causal weight and historical reference, and combines activity context and Bayesian rules to generate a dynamic threshold value to realize risk grading early warning, so that heterogeneous data collaborative analysis precision and abnormal gait detection sensitivity are improved, and the method is suitable for large-scale popularization and application. And support is provided for gait health monitoring and personalized risk management and control under multiple scenes.

The invention discloses a roasting workshop safety risk assessment system for carbon anode production, particularly relates to the field of data analysis, and comprises a multi-dimensional data entry module, a preprocessing module, an index calculation module and a safety risk assessment module. According to the invention, a process-equipment-environment-material multi-dimensional data acquisition system is constructed, a special sensor array and a slide rail type robot are combined to realize total-factor monitoring and eliminate a space blind area, preprocessing technologies such as wavelet denoising and dynamic standardization are innovatively adopted, the parameter sensitivity is enhanced, and the data quality is improved; based on four-dimensional state space modeling and a dynamic mahalanobis distance algorithm, multi-factor coupling risks are quantified, evaluation timeliness is ensured in combination with a quarterly updated sliding window standard deviation mechanism, the composite risks of process disturbance, equipment decline, environment abnormity and material deterioration can be accurately recognized, blue, yellow, orange and red four-level dynamic early warning is achieved, and the method is suitable for large-scale popularization and application. And multi-dimensional safety decision support covering the whole process is provided for the roasting workshop.

The invention discloses an intelligent pet health monitoring and intelligent evaluation system, which comprises a plurality of integrated sensors, and carries out real-time processing on multi-source data by adopting an improved unscented Kalman filtering and deep fusion algorithm. The system integrates a convolutional neural network, a long-short-term memory network and a Transform model at the same time, and realizes adaptive behavior recognition, health state evaluation and personalized health suggestion generation through online learning and incremental learning. The edge device is responsible for data acquisition and primary processing, including wavelet denoising, filtering and feature extraction, and performs data interaction and model updating with the cloud server through a standardized interface, such as MQTT or RESTful API. And the cloud server uses large-scale data to train a depth model, and continuously feeds back and optimizes an edge end algorithm. Through detailed parameter setting, interface design between modules and an early warning strategy, multi-dimensional data fusion, dynamic health assessment and multi-level early warning are realized, and an accurate, real-time and customized health management scheme is provided for pets.

The invention discloses a current detection method and system based on a double-ring TMR nested structure, and belongs to the technical field of current detection. The method comprises the following steps: based on a double-ring TMR system of a double-ring TMR nested structure, performing double-ring detection on the current of a to-be-detected wire in a current measurement scene; synchronously acquiring double-ring data obtained by double-ring detection through AD, and performing wavelet denoising on the double-ring data according to the structure data of the double-ring TMR nested structure; and based on a Kalman filtering algorithm, fusing the double-loop data after wavelet denoising to obtain current information of the to-be-measured wire in the current measurement scene. The method not only has outstanding advantages in the aspects of measurement accuracy and anti-interference performance, but also has a wide measurement range and stable linearity performance, and can be widely applied to complex electromagnetic environments with high requirements on current detection accuracy, such as new energy grid connection, electric vehicle charging piles and intelligent power grids.

The invention provides a lithium ion battery safety valve opening and failure early warning method based on expansive force, and belongs to the technical field of lithium ion batteries. Battery expansive force and cycle data under different pre-tightening force conditions are collected, statistical features are extracted to construct a state feature set, health state groups are divided by adopting a fuzzy clustering algorithm, and the early warning result is obtained. Establishing a segmented nonlinear mapping model of the expansive force and the internal pressure, performing wavelet denoising and robust differential calculation on expansive force signals, and optimizing an initial expansive force derivative threshold value by analyzing time dispersion at different heating rates; a multi-scale feature fusion algorithm based on hierarchical attention aggregation is utilized to construct a state self-adaptive early warning model to correct a threshold value, and a four-stage early warning mechanism is set to monitor the opening and failure states of the safety valve. The technical problem that the opening time of the safety valve cannot be accurately predicted and self-adaptive early warning cannot be realized under different battery health states and pretightening force working conditions is solved.

The invention provides a multi-modal information acquisition and roof global attitude monitoring method and system, and relates to the technical field of mining safety monitoring and roadway surrounding rock control. The method comprises the following steps of: acquiring acoustic, optical, electrical and mine pressure multi-source data by arranging a fan-shaped drill hole, an annular electrode array and various mine pressure monitoring devices; a wave velocity profile, a fracture parameter set and a resistivity equivalent cloud picture are generated through wavelet noise reduction, fracture recognition and resistivity inversion, space-time registration with a mine pressure data sequence is carried out, and roof partition probability distribution is output through a space-time feature pyramid network. Furthermore, improved XGBoost is adopted for joint inversion of the thickness of the loose circle, the Poisson's ratio and the elastic modulus, probability distribution serves as a space weight, the separation amount and anchoring stress data are fused, and a roof displacement field, a curvature field and a rotation field are reconstructed. And finally, generating an early warning based on a preset safety threshold value, outputting a posture partition map and a monitoring report, and realizing top plate posture transparent deduction and support coordinated regulation and control.

The invention discloses a broadband signal parameter identification method based on wavelet noise reduction and an improved VMD-Prony algorithm, and the method comprises the following steps: carrying out the noise reduction preprocessing of a broadband signal based on a soft threshold wavelet noise reduction method, so as to reduce the interference of the noise to a measurement process, and building an evaluation index according to a Pearson's correlation coefficient and a signal-to-noise ratio for evaluating the noise reduction effect; parameters of a mutual information entropy and an energy entropy optimization VMD algorithm are introduced, the noise-reduced signal is decomposed by using the parameter optimization VMD algorithm, a plurality of modes with center frequencies are decomposed, and the complexity of broadband signal processing is reduced; performing parameter estimation on each mode by using a Prony algorithm, selecting a proper model order p according to the characteristics of the modes, performing numerical calculation by constructing a plurality of functions, obtaining frequency, amplitude and phase information of each mode, and realizing parameter identification of the broadband signal; according to the invention, the problems of noise interference and insufficient identification precision of the broadband oscillation signal in the parameter identification process are solved.

The invention discloses a rice producing area identification method based on dynamic characteristic screening and a weighted integration model. Belongs to the technical field of agricultural product traceability. The prominent problems of large accuracy fluctuation, poor model mobility, low operation efficiency and the like of the existing mid-infrared spectrum identification method are solved. The method comprises the following steps: collecting a rice flour sample spectrum by using a mid-infrared spectrometer equipped with an ATR accessory, and carrying out spectrum standardization treatment; performing noise reduction and correction on the spectrum after standardization processing by using a wavelet denoising and improved SNV correction method to obtain a noise-reduced and corrected spectrum; performing dynamic feature screening on the noise reduction correction spectrum to obtain a low-redundancy and high-independence optimized feature subset; integrating a plurality of classification models into a multi-stage mixed classification model in a dynamic weight set manner by optimizing the feature subsets; and identifying the producing area of the rice flour sample by adopting a multi-stage mixed classification model.

The invention provides a Rowland time delay signal prediction method and system, electronic equipment, a program product and a storage medium. The method comprises the following steps: acquiring an initial Rowland time delay signal; extracting a periodic term of the initial Rowland time delay signal, and inputting the periodic term to a constructed multi-periodic term and trend term model; calculating a residual signal between the observed value of the initial Rowland time delay signal and the output of the multicycle term and trend term model; wavelet decomposition and threshold denoising are carried out on the residual signals; fusing the output of the multi-cycle term and trend term model and the denoised residual signal through an adaptive weight mechanism to obtain a predicted Rowland time delay signal; the deterministic periodic term and the long-term trend drift of the Rowland time delay signal are captured through a multi-periodic term and trend term model, and non-stationary disturbance is processed by using wavelet denoising, so that the Rowland time delay prediction precision is remarkably improved, and the method is particularly suitable for a high-precision timing scene in a complex electromagnetic environment.

The invention relates to the technical field of equipment fault prediction, in particular to a compressor mixed fault prediction method and system based on LSH and LSTM, and the method comprises the steps: data collection, preprocessing, LSTM model construction and training, incremental learning and fault prediction. Data preprocessing is combined with median filtering and wavelet denoising, and similar data clusters are formed through LSH processing; constructing an attention layer-containing LSTM network, and highlighting key features through attention scores; when the state of the compressor changes, new data are matched with corresponding data clusters through LSH, and only correlation model parameters are finely adjusted; the system comprises a data acquisition module, a preprocessing module, a model training module, an incremental learning module and a fault prediction module. According to the invention, data processing efficiency and model adaptability are improved, real-time performance and accuracy of fault prediction are enhanced, and equipment maintenance cost is reduced.