290 results about "Wavelet denoising" patented technology

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 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 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 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 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 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 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.

The invention relates to a power grid equipment dispatching control method and device based on artificial intelligence, equipment and a medium, and the method comprises the steps: collecting equipment operation data through a multi-mode sensing network, and generating a noise reduction data set through wavelet noise reduction processing; fusing electrical, thermodynamic and mechanical characteristics to calculate a real-time aging factor; utilizing a matrix transformation function to dynamically correct equipment factory parameters to generate current operation parameters; an optimal power distribution path is constructed through graph neural network topology analysis, and a control instruction set is generated; and updating the mapping function by combining the historical aging factor sequence based on the deviation quantized value of the execution feedback result and the theoretical parameter. According to the method, the problems of parameter drift and scheduling model mismatch caused by equipment aging are solved, real-time sensing of the aging state, dynamic parameter correction and closed-loop optimization are achieved, and the accuracy and safety of power grid scheduling are remarkably improved.

The invention discloses a bridge disease detection method based on a diffusion model and a bitter fish optimization algorithm, and relates to the technical field of bridge detection. The method comprises the following steps: fixing a visual angle and a distance at an easy-to-peel or crack position of a bridge, and collecting and aligning visible light and near-infrared images; carrying out multi-scale downsampling on the image, and carrying out wavelet denoising, brightness correction and texture smoothing; inputting the preprocessing result into an improved diffusion model, weighting the edge during forward diffusion, and reversely generating and applying texture and contour smoothing; the multi-source feature channel and the reconstructed image are combined and input into a deep segmentation network, shadow and stain are eliminated by using a local difference function, and global search is performed on a segmentation threshold, a noise coefficient and the like based on a disease detection rate, a false detection rate and the like by using a bitter fish algorithm; training and correcting the high-noise area again according to the optimal parameters; and uniformly marking disease areas. According to the method, the recognition recall rate of tiny spalling and irregular cracks in an extreme environment can be greatly improved, and the intelligent level and the practical effect of bridge disease detection are improved.

The invention provides an ultrasonic detection method for internal defects of an insulating part of a power system based on a piezoelectric ultrasonic transducer, and belongs to the technical field of insulating part detection. After wavelet denoising and deep attenuation compensation are carried out on echo signals, sound path distance time delay and sound pressure amplitude parameters are extracted, defect identification and position estimation are carried out by utilizing a layered variational auto-encoder, a high-frequency transducer is switched to aiming at tiny defects, a harmonic component is extracted by adopting a nonlinear ultrasonic method, and a characteristic size is calculated; a defect distribution space feature matrix is generated based on a synthetic aperture focusing technology, a unified or personalized processing strategy is selected according to an acoustic characteristic homogenization degree evaluation value, a complete defect detection result is output, and the technical problem that deep weak defects in the electric insulation part are difficult to accurately detect and recognize is solved.

The invention relates to an infrared and visible light image frequency domain diffusion fusion method, and belongs to the technical field of image processing. Comprising the following steps: inputting an infrared image and a visible light image into a potential space encoder to form original bimodal features; a Gaussian noise is randomly sampled, the Gaussian noise and the original bimodal features are input into a wavelet denoising network together to be decomposed into a low-frequency component and three high-frequency components, the bimodal high-frequency components are fused through FMM, and the bimodal low-frequency components are fused through FMM at the lowest scale; carrying out inverse discrete wavelet transform on the fused low-frequency component and high-frequency component to obtain estimated noise, and then carrying out noise removal on the estimated noise to obtain de-noising features; carrying out iterative optimization and feature updating to obtain fusion features; and inputting the fusion features into a potential space decoder to obtain a fusion image. The fusion quality of the infrared and visible light images can be comprehensively improved.

The invention discloses a side slope displacement monitoring method and device based on quantum single photons and a medium, and belongs to the technical field of displacement monitoring. Rectangular light pulses are emitted periodically, echo detection is completed by using a single photon detector, a photon counting time sequence is constructed, wavelet denoising and reconstruction processing is performed on the photon counting time sequence, main peak position offset, energy change and form distortion are extracted to match multiple types of feature vectors, and feature fusion and resolving are performed through a multi-source feature fusion BP neural network. And finally outputting a high-precision slope displacement result. According to the method, the quantum single-photon detection technology is combined with the wavelet denoising and neural network fusion algorithm, noise interference is effectively suppressed, the displacement feature extraction precision is improved, and the method is suitable for high-precision and long-distance slope micro-deformation real-time monitoring.

The invention discloses a cable partial discharge signal noise separation mode identification method and system, and particularly relates to the technical field of cable partial discharge detection, in a multi-cable stacking environment, partial discharge signals are synchronously collected through multiple types of sensors such as a high-frequency current sensor, an ultrasonic sensor and an electromagnetic antenna; performing band-pass filtering, wavelet denoising and multi-channel synchronous alignment on the acquired signals to suppress background noise and retain key features of partial discharge pulses; separating the mixed signals by adopting independent component analysis; calculating a decision coefficient based on an inter-channel correlation coefficient, a signal-noise power ratio and an amplitude dynamic range, and judging whether to introduce a denoising method based on deep learning; for the waveform after noise separation, executing an amplitude re-calibration process so as to correct amplitude scaling uncertainty, and extracting multi-dimensional time domain, frequency domain and phase features; and a support vector machine and other machine learning algorithms are combined to realize automatic identification of partial discharge types.

A hydraulic hoist pump station fault diagnosis method based on sound and vibration signal fusion comprises the following steps that 1, a vibration sensor and a sound sensor are arranged on a preset point position of a hydraulic hoist pump station, and vibration signals and sound signals in the operation process of the pump station are synchronously collected; step 2, carrying out wavelet denoising processing of adaptive threshold optimization on the sound signal, then carrying out variational mode decomposition, and extracting a spectrum entropy feature of a mode component after decomposition; 3, performing adaptive empirical mode decomposition on the vibration signal, and extracting a composite feature formed by a local mean decomposition energy operator and a morphological gradient of an IMF component after decomposition; the method is used for solving the problems that in existing hydraulic hoist pump station fault diagnosis, signal diagnosis information of a single sensor is limited, the capacity of a traditional method for processing non-stable sound vibration signals is insufficient, noise separation is difficult, feature fusion is complex, diagnosis precision is low, and the requirement for on-site real-time monitoring cannot be met.

The invention belongs to the technical field of intelligent control of industrial heating equipment, and particularly relates to a big data self-learning evolution method for an intelligent burning furnace model of a hot blast stove. According to the method, multi-source data such as fuel components and temperature fields are collected in real time, and a standardized input data set is constructed after wavelet noise reduction and principal component analysis processing. The model can dynamically optimize the air-fuel ratio, predict the hot air temperature and correct the equipment parameter deviation, and stable combustion is achieved. When effective samples are accumulated to a threshold value, the system automatically triggers parameter iteration, key parameters are reserved and secondary parameters are updated by utilizing transfer learning, encryption gradient aggregation and knowledge sharing among multiple furnaces are realized by virtue of federated learning, and distribution and deployment are performed after a global optimization model is formed. And finally, according to the model output, a closed-loop regulation and control fuel valve, a fan and other execution mechanisms are realized, the temperature and energy consumption are monitored in real time to evaluate the evolution effect, and a continuously optimized intelligent control cycle is formed. According to the method, the control precision and the combustion efficiency are remarkably improved, and energy conservation and consumption reduction are effectively achieved.

The invention discloses a method for detecting heart rate, respiration and oxyhemoglobin saturation based on micro-vibration images, and belongs to the technical field of physiological detection and image processing. A camera collects facial micro-vibration videos of a testee in real time, uses an ROI algorithm to lock a facial key area, extracts RGB micro-vibration change signals to replace red light and near-red light signals, and determines whether the micro-vibration videos are abnormal or not; performing wavelet transform five-layer decomposition, three rounds of peak valley screening and abnormal value elimination on the RGB microvibration time sequence signal, performing fine tuning training by using a yov11 model, and calculating to obtain heart rate, respiration and blood oxygen physiological indexes. According to the invention, non-contact detection is realized through video acquisition and signal processing of micro-vibration of a face area, and the comfort level and the use convenience of a testee are improved; interference caused by motion artifacts, baseline drift and expression changes is eliminated through multi-scale wavelet denoising; the three-wheel peak-valley screening algorithm deeply excavates the features of the face micro-vibration signals, and the recognition accuracy of the signal peak-valley pairs is improved; and an abnormity elimination and self-feedback parameter adjustment mechanism improves the calculation precision.

The invention discloses a smart agricultural crop growth analysis system based on big data. Initial crop data is acquired through a sensor array; performing noise reduction processing on the initial crop data by using a wavelet denoising-Kalman filtering coupling algorithm, performing feature extraction on the image data based on an improved MobileNetV3 network, and outputting a crop morphological feature vector; constructing a sequential network through LSTM to perform correlation analysis on the environment and physiology, learning a dynamic relationship between parameters and photosynthetic efficiency, and establishing a crop state prediction model; inputting the noise-reduced crop data into a prediction model for prediction, and outputting a growth health score; performing crop health grade judgment according to the growth health score, and generating a crop growth intervention strategy according to a judgment result. The growth health score of the crop can be predicted more accurately, and the resource utilization efficiency is improved.