2908 results about "Wavelet" patented technology

Provided is a rolling bearing fault diagnosis method based on FFT coding and L-CNN, including: obtaining original bearing fault vibration data, extracting intrinsic mode components of different frequency bands in the original bearing fault vibration data, calculating a permutation entropy value corresponding to each of the intrinsic mode components, and performing wavelet threshold denoising according to the permutation entropy value to obtain a denoised reconstructed time domain signal; performing fast Fourier transform to the denoised reconstructed time domain signal to obtain a frequency domain signal and a phase angle corresponding to the time domain signal, reconstructing the frequency domain signal according to a preset rule, retaining frequency domain data of features of the phase angle, and drawing FFT-x heat maps of different fault type data according to an amplitude range; and constructing an model L-CNN, and inputting coded data into the L-CNN model, and obtaining fault diagnosis results.

The invention discloses a composite power quality disturbance identification method based on a time-frequency feature fusion classification network. The method comprises the following steps: carrying out noise reduction processing on a noise-containing disturbance signal based on self-adaptive noise complete mode decomposition in combination with a wavelet threshold method; obtaining frequency domain data of the noise reduction signal based on fast Fourier transform; based on a TCN-Transformer deep network, deep feature extraction is carried out on the denoised signals in parallel from a time domain and a frequency domain; and carrying out weighted fusion on the extracted signal time domain and frequency domain features based on a cross attention mechanism to finish classification and identification. The method is simple in principle, the average recognition accuracy rate in a 30dB noise environment can reach 97.86%, and compared with other mainstream deep learning recognition models, the method shows better recognition precision, recognition stability and generalization ability.

The invention discloses a cross-device clock correction method supporting accurate synchronization of meters, and aims to improve the data time consistency among multiple meter devices and meet the requirements of high-precision electric power measurement and event analysis. The method comprises the following steps: acquiring a network time protocol timestamp and a first sampling time sequence in a reference meter, generating a clock reference signal, and broadcasting the clock reference signal to a corrected meter; comparing the corrected meter with a local second sampling time sequence, and calculating a clock skew coarse value vector; further performing wavelet decomposition on the offset coarse value, extracting to form a drift feature vector, fusing the drift feature vector with a historical template, and calculating a unified correction parameter set by using a Bayesian recursion method; and generating a correction pulse sequence carrying a delay residual compensation code based on the parameter set, sending the correction pulse sequence to a corrected meter, calling an interpolation compensation function to construct a piecewise polynomial drift compensation table, and finally realizing dynamic correction and cross-device accurate synchronization of a local real-time clock.

The invention discloses a fault detection and root cause analysis system for a multi-source energy system, and relates to the technical field of energy management and fault diagnosis. The system obtains various operation data and image data of the multi-source energy system in real time; preprocessing the operation data and the image data; inputting the preprocessed data into a hybrid algorithm combining wavelet transform, CNN and LSTM, and detecting an abnormal mode and a potential fault in the multi-source energy system; the digital twinborn model is dynamically updated according to data acquired in real time; and in combination with the feature extraction capability of a digital twin model and a hybrid algorithm, positioning key causes causing the fault, simulating a system operation state, analyzing potential causes of the fault, and outputting an analysis result. According to the invention, the comprehensive analysis capability of the system on various data sources is effectively improved, and a comprehensive and accurate solution is provided for intelligent operation and maintenance of the energy system.

The invention relates to the technical field of production debugging control, and discloses a production debugging control method and system for a plastic container. The method comprises the following steps: arranging a plurality of different sensors in plastic container blow molding equipment, and simultaneously collecting blow molding process parameter data; performing wavelet threshold denoising and anomaly identification on the blow molding process parameter data to obtain a process parameter anomaly identification result; establishing a transfer function matrix between the process parameters and quality indexes according to the process parameter anomaly identification result, and obtaining a target process parameter set through multi-target optimization; and inputting the target process parameter set into a double-integral enhanced recurrent neural network for parameter regulation and control calculation to obtain a process parameter adjustment amount. According to the method, early detection and accurate positioning of process abnormity in the blow molding process are realized, the quality fluctuation risk and the defective product rate are greatly reduced, and the technical problem that different response characteristic parameters are difficult to coordinate is solved.

The invention relates to an intelligent power distribution harmonic monitoring and dynamic compensation system which comprises a monitoring unit, a correction unit and a compensation unit. The monitoring unit continuously collects high-frequency harmonic voltage and current data in a distribution line at a high sampling frequency, extracts transient harmonic components through wavelet packet transformation and empirical mode decomposition, and generates low-dimensional feature vectors based on sparse representation. And the correction unit decodes the low-dimensional feature vector, recovers harmonic time-frequency features, calculates a phase drift rate, predicts a harmonic propagation path and an accumulation node by combining real-time power distribution network topology construction and adopting a nonlinear dynamic prediction model, and generates a correction instruction when abnormality is detected. And the compensation unit adopts pulse sequence density modulation to dynamically adjust a compensation current phase according to the correction instruction, and meanwhile, an inductive coupling device is utilized to transfer harmonic energy to a low-risk node, so that harmonic voltage distortion of a target node is quickly recovered to a stable level in a fundamental wave period after early warning.

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

The invention discloses a bearing fault diagnosis method based on fusion of an improved capsule network and zero sample learning, and relates to the technical field of state monitoring and fault diagnosis of electromechanical equipment, and the method comprises the following steps: collecting a multi-mode signal during the operation of a bearing, employing an improved wavelet threshold denoising algorithm for the multi-mode signal to eliminate environmental noise, and then employing a zero sample learning algorithm for the multi-mode signal; according to the method, the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm are adopted to extract the time-frequency domain mixed features as sample data, and the GAN is combined to expand the bearing sample data, so that the data dependence of traditional deep learning is broken through, the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm, and the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm. Small sample data learning is realized, and by training a pyramid capsule network and optimizing a cross entropy loss function and combining cross-modal joint optimization and a zero sample inference engine, the diagnosis accuracy of known faults is greatly improved, and unknown fault types can be effectively inferred.

The invention provides a satellite-borne multi-band synthetic aperture radar signal processing optimization method and system, and relates to the technical field of signal processing, and the method comprises the steps: obtaining original echo data of a satellite-borne multi-band synthetic aperture radar, carrying out Doppler center frequency estimation, carrying out two-dimensional matrix product operation, and obtaining compensation echo data; distance direction position information and scattering intensity information are obtained based on a distance direction matching filtering algorithm of sub-band decomposition; determining spatial distribution density, performing adaptive block processing, and performing phase error modeling in each block to generate a continuous phase correction function; performing wavelet domain convolution operation to obtain corrected echo data, obtaining a multi-band two-dimensional image, extracting a multi-dimensional feature vector through a deep neural network, performing weight learning, performing nonlinear weighted fusion, and performing adaptive histogram equalization enhancement to determine a synthetic aperture radar imaging result.

The present invention provides a wind power off-grid hydrogen production power supply topology and control method without a step-down transformer, including: obtaining wind speed data, using a wavelet threshold algorithm for denoising, predicting power based on an LSTM-GRU hybrid neural network, and extracting power fluctuation characteristics through multi-scale entropy analysis; obtaining load data, using Kalman filtering and recursive least squares to establish an impedance model, and extracting dynamic characteristics in combination with Hilbert transform; using a differential evolution algorithm for multi-objective optimization to generate an optimal switching sequence and power allocation strategy; achieving system control through space vector modulation, balanced control, soft switch control and harmonic compensation; achieving state estimation and disturbance suppression based on a sliding mode observer and a nonlinear observer; and achieving fault diagnosis and protection using a deep belief network. The present invention improves system efficiency and reliability and improves dynamic performance.

A GIS partial discharge defect category intelligent identification method fusing optical and electrical characteristics comprises the following steps: acquiring an optical signal of partial discharge through an optical detection system, and extracting light intensity and spectral distribution characteristics; meanwhile, an ultrahigh frequency (UHF) sensor is used for collecting electromagnetic wave signals, and time domain and frequency domain characteristics of the electromagnetic wave signals are extracted; de-noising the small optical signal by adopting spatial filtering, and processing an electromagnetic wave signal by adopting a wavelet de-noising method; fusing the denoised signals by using a multi-modal deep neural network; splicing the two types of features and carrying out dimensionality reduction through a principal component analysis method to obtain a fusion feature vector; and finally, constructing a dual-channel CNN model for training and evaluation, optimizing the model by using a cross entropy loss function and an Adam optimizer, adjusting hyper-parameters and a network structure through a verification set, improving the generalization ability of the model, and finally obtaining a GIS partial discharge defect category classification model. According to the invention, precise and intelligent identification of GIS partial discharge defects is realized.

The invention discloses a risk early warning method and device constructed based on dynamic rules, equipment and a medium, and relates to the technical field of risk early warning, and the method comprises the steps: extracting a user static attribute and a dynamic behavior sequence, constructing an original data set, carrying out time sequence analysis, generating a behavior time sequence response feature, carrying out cross-modal feature fusion, and constructing a space-time perception behavior vector; constructing a dynamic rule graph structure according to the risk rule and the corresponding standard space-time perception behavior vector, iteratively updating a risk rule weight based on a graph convolutional network, constructing a wavelet domain risk quantification model, and generating a multi-scale risk value; and inputting the newly generated space-time perception behavior vector into the wavelet domain risk quantification model, generating a to-be-judged multi-scale risk value, and performing risk early warning when a threshold condition is not met. Through time sequence analysis and a multi-scale risk quantification model, the accuracy of risk identification is improved, and multi-dimensional and multi-scale risk analysis is supported, so that the accuracy and real-time performance of risk management are effectively improved.

The invention discloses a construction man-machine safety intelligent detection and early warning method based on deep learning. The method comprises the following steps: S1, introducing a wavelet convolution improved YOLOv11 target detection algorithm framework; s2, coordinate extraction of the instance segmentation module; s3, designing a monocular depth estimation network; s4, training the monocular deep network; and S5, converting the two-dimensional coordinates into the three-dimensional coordinates. According to the invention, the real distance between the worker and the equipment is obtained and real-time safety early warning is carried out; obtaining a man-machine actual distance, and judging the accurate position and posture of the machine by combining angle information of the construction machine with edge contour detection; target detection in a complex construction scene is realized by optimizing a network structure; the S2R-DepthNet depth estimation network is used for performing depth estimation on a single RGB image through a self-supervised learning method, the three-dimensional information acquisition process is simplified, and dependence on expensive hardware is eliminated.

The invention relates to a water quality real-time detection system which comprises the following modules: a multi-source sensing module which is based on a multi-parameter sensing array, adopts a self-adaptive sampling strategy, realizes sensor time sequence synchronization through a state estimation algorithm, completes water body multi-dimensional parameter acquisition in combination with a micro-fluidic chip, generates a multi-modal sensing data set, and transmits the multi-modal sensing data set to a data processing module; the multi-source sensing module comprises a multi-source sensing sub-module, a signal conditioning sub-module, a time sequence synchronization sub-module and an anomaly capture sub-module. The method has the advantages that through the synergistic effect of the adaptive sampling strategy and the state estimation algorithm, the multi-sensor time sequence synchronization precision is remarkably improved, the phase deviation problem caused by traditional fixed frequency sampling is effectively eliminated, the sliding window polynomial fitting is combined with the wavelet threshold de-noising technology, and the multi-sensor time sequence synchronization precision is improved. High-frequency noise interference is greatly suppressed on the premise that effective components of the signals are reserved, and meanwhile, the abnormal value detection accuracy is improved through a dynamic threshold mechanism.

The invention relates to the technical field of brain-computer interfaces, in particular to an electroencephalogram signal amplification method, device and equipment based on topological gradient field coding. The method comprises the following steps: acquiring a first data set corresponding to a scalp electroencephalogram of a testee through electroencephalogram acquisition equipment, and performing Hilbert transform to extract phase information so as to calculate a topological gradient field; key phase features meeting the energy concentration ratio feature and the duration feature are extracted from the topological gradient field, so that the key phase features with the energy concentration ratio threshold value larger than a preset value and the duration exceeding a time threshold value are screened out; inputting the key phase features to a pre-constructed generative adversarial network, and generating an enhanced phase field by a generator of the generative adversarial network through adversarial training; and combining the enhanced phase field with the low-frequency amplitude component, subjected to wavelet decomposition, of the first data set to construct a complex field, simulating spatio-temporal evolution of the complex field based on a nonlinear dynamic model, and generating a finally enhanced and amplified electroencephalogram signal. And efficient characterization of electroencephalogram phase information is realized.

The invention relates to a face depth forgery active defense method and device based on discrete wavelet transform, and relates to the technical field of image processing, and the method comprises the steps: carrying out the attribute editing of an original image and a disturbance-containing image through a depth forgery model, calculating the mean square error loss and structural similarity loss between the original image and the disturbance-containing image, different weights are endowed and fused into a total loss function; gradient information is obtained based on loss function back propagation, the optimal direction of disturbance application is determined, and an intermediate disturbance value is formed; low-frequency and high-frequency components are embedded into corresponding components of the disturbance-containing image through discrete wavelet transform, and then the image is reconstructed through inverse discrete wavelet transform; checking whether the number of iterations is reached or not, and if not, introducing a JPEG noise layer to continuously iterate and adjust disturbance; and if yes, outputting a final face image containing invisible disturbance information. According to the method, the robustness in the wavelet domain is fully utilized, and the resistance of disturbance to JPEG compression is improved on the premise of ensuring that the disturbance is invisible.

The invention provides a civil construction monitoring intelligent sensor information detection method and system under signal mismatch, and the method comprises the steps: carrying out the time-frequency analysis of temperature and strain fundamental frequency components, selecting a wavelet basis function adaptive to the concrete setting and strength increasing process, carrying out the self-adaptive threshold processing of a wavelet coefficient, removing high-frequency noise, and carrying out the detection of the temperature and strain fundamental frequency components. Low-frequency signal characteristics reflecting concrete strength increase are reserved; performing feature extraction on the denoised temperature and strain signals by adopting a singular value decomposition algorithm, detecting internal defects of the concrete through abrupt change of a singular value spectrum, and if crack and hole defects exist, causing strain signal abrupt change by stress concentration; if it is detected that defects exist in the concrete, that is, spatial heterogeneity of temperature and stress distribution exists, temperature and strain monitoring data of multi-point arrangement and a defect detection result serve as boundary conditions and constraints, and a temperature field and a stress field in the whole concrete structure are obtained through numerical simulation.

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 provides a power distribution network insulator early fault detection method and system considering an interference event. The method comprises the steps of obtaining a zero-sequence current of a power distribution network insulator, and calculating a zero-sequence current mutation rate based on the zero-sequence current; if the zero-sequence current abrupt change rate is greater than a set threshold value, judging that the power distribution network has a fault or a disturbance event; obtaining original recording data of the insulator of the power distribution network, and filtering the original recording data based on a wavelet reconstruction algorithm; for the filtered signal, identifying a peak waveform based on a fault phase voltage extreme point; the non-linear distortion waveform is identified based on zero-sequence current sine fitting, and based on an identification result, when a set first criterion or a set second criterion is satisfied, it is judged that an early fault of the insulator may occur; and calculating the wavelet energy entropy of the signal, if the wavelet energy entropy is greater than a threshold value of the set entropy, determining that the power distribution network insulator has an early fault, otherwise, determining that the power distribution network insulator has an interference event.

The invention discloses a multi-mode perception and reinforcement learning engineering machinery intelligent regulation and control method and system, and belongs to the technical field of intelligent control and industrial automation. According to the system, stratum data within the range of 20-50 meters in front of a shield tunneling machine are collected in real time through multi-mode sensing equipment such as a distributed optical fiber sensor, a cutterhead vibration sensor and an electromagnetic wave radar, and original data are processed by adopting a wavelet-Fourier combined noise reduction algorithm; and inputting the processed multi-modal data into a reinforcement learning intelligent decision-making model based on a CNN-LSTM hybrid network architecture. The model is trained through a dynamic reward function, and weight coefficient combinations can be automatically switched according to different construction scenes. According to the system, parameters such as the rotating speed, the thrust and the grouting amount of a shield cutter head are regulated and controlled in real time through the self-adaptive control module, and the slurry utilization rate is increased through the gradient pulse grouting technology. The precision, efficiency and safety of shield construction are remarkably improved, and meanwhile energy consumption and construction cost are reduced.

The invention relates to the technical field of industrial water system safety monitoring, in particular to an intelligent source tracing method for a process medium leaked in circulating water, which comprises the following steps of: acquiring multi-dimensional operating parameters such as conductivity, pH value, turbidity, dissolved oxygen, temperature, pressure and characteristic ion concentration; a standardized water quality parameter matrix is generated after space-time alignment and wavelet noise reduction; the method comprises the following steps: extracting an abnormal fluctuation signal by using a leakage feature recognition model based on transfer learning, simulating a diffusion process through a three-dimensional leakage diffusion model, realizing leakage source positioning by combining reverse particle tracking and kernel density estimation, associating a high-probability leakage region with upstream process equipment, extracting backtracking path features, and matching a process medium feature library, thereby realizing leakage source positioning. The leakage medium type is judged; and finally generating a structured traceability report. According to the method, high-precision identification, positioning and medium analysis of process leakage in a complex circulating water system can be realized, and the method has relatively high practicability and popularization value.

The invention discloses a new energy station harmonic emission level evaluation method and system based on recording data, and the method comprises the steps: carrying out the wavelet threshold de-noising processing of the recording data of line current and bus voltage in a power grid, and then carrying out the Fourier analysis, and obtaining the amplitude and phase angle of each harmonic current and voltage; on the basis of the amplitude and phase angle of each harmonic current and voltage, the harmonic order to be analyzed is determined through the harmonic voltage content rate and the harmonic distortion rate, an abnormal bus is screened as a concerned bus, and suspicious harmonic source users in the power grid are identified; establishing an equivalent model of the power grid, and solving harmonic transfer impedance between each suspicious harmonic source user and the concerned bus in the equivalent model by using a blind source separation algorithm; and quantifying the harmonic responsibility of each suspicious harmonic source user to the concerned bus. According to the invention, the accuracy of harmonic evaluation and the accuracy of tracing are improved, and the improvement of the electric energy quality of a new energy station and the stable operation of a power grid are facilitated.

The invention relates to the field of remote sensing image processing, and discloses a high-quality HRHS image generation method based on a double-subnetwork structure, and the method comprises the following steps: receiving an input low-resolution hyperspectral image and a panchromatic image, which are respectively used for providing spectral information and spatial information; performing noise filtering on the low-resolution hyperspectral image through an anti-noise pre-fusion sub-network, and generating a pre-fused hyperspectral image by using the correlation between the high spatial resolution information of the panchromatic image and the spectral characteristics of the low-resolution hyperspectral image; performing high-frequency detail reconstruction and enhancement on the pre-fused image based on a conditional diffusion model through a high-frequency enhancement sub-network; and fusing the low-frequency information of the pre-fused image and the high-frequency detail information generated by the high-frequency enhancement sub-network through wavelet inverse transformation, and outputting a final high-resolution hyperspectral image. According to the method, spectrum and space information can be effectively fused in various noise environments, and a high-resolution hyperspectral image with hyperspectral fidelity and space details is generated.

The invention discloses an unmanned aerial vehicle aerial image small target detection method based on RT-DETR, an end-to-end target detection algorithm RT-DETR is improved, an FCW-RTDETR algorithm is provided, and the method comprises the following steps: improving a backbone network by using adaptive frequency cavity convolution, and extracting unmanned aerial vehicle aerial image features; performing intra-scale feature interaction AIFI on a P5 layer in the backbone network, and inputting obtained features into a cross-scale feature fusion network CCFM; the method comprises the following steps: respectively improving a CCFM structure and an up-and-down sampling method by using a context and spatial feature calibration network CSFCN and wavelet pooling, and fusing features of different scales in the CCFM to obtain an image feature sequence; selecting a feature from a feature sequence output by an encoder as an initial target query of a decoder by using IoU-perceived query selection; and the decoder iteratively optimizes the target query through the auxiliary prediction header, generates a bounding box and a confidence score, and completes small target detection. According to the scheme, the small target detection efficiency and accuracy are improved.

The invention discloses a photovoltaic power generation day-ahead prediction method based on causal inference and multi-scale feature fusion, and belongs to the technical field of photovoltaic power generation prediction. According to the method, historical meteorological characteristic data is divided into a plurality of samples, and generated power of the samples in the future M steps is used as corresponding labels. A PCMCI causal inference method is used to calculate a causal inference weight between a sample and a label, the sample is weighted and then is input into three base learners of BiLSTM, TCN and GAF-SE-ResNet50 in parallel, the trend of an input sequence is recognized from three dimensions of global, local and periodical, and a photovoltaic power generation power day-ahead preliminary prediction value is obtained through corresponding training. Wavelet features of the local weather forecast sequence of the prediction point are extracted, and a final prediction value is obtained through fusion of a cross attention mechanism and the initial prediction value of the base learning device. Accurate and stable photovoltaic power generation prediction in a complex climate change environment is realized.

The invention relates to the technical field of power monitoring, in particular to a station power utilization system safety and energy efficiency monitoring system based on multi-source information fusion, which comprises a signal analysis module, an anomaly identification module, a risk assessment module and an emergency response module. According to the method, the fineness and the response speed of power station monitoring are improved by integrating wavelet transformation and information entropy analysis, the time-frequency characteristics of signals are deeply analyzed through wavelet transformation, the capturing capacity for short-term and long-term changes is improved, the integrity and the accuracy of data analysis are further ensured, and the accuracy of power station monitoring is improved. The information entropy is introduced to assess the stability and uncertainty of the power station, a scientific basis is provided for predicting and recognizing potential risks of the system, and by combining real-time anomaly recognition and dynamic risk assessment, equipment faults and system failures can be effectively predicted, and energy consumption is optimized, so that early warning and prevention and control are performed in advance, the downtime is remarkably shortened, and the system reliability is improved. The maintenance cost is reduced, and the level and effect of energy efficiency management are improved.

The invention provides a converter station in-station equipment energy consumption analysis model and method. Belongs to the field of converter station equipment energy consumption analysis. According to the method, a three-dimensional data acquisition system covering equipment in the whole station is established, electrical measurement data of the equipment is obtained in real time through a monitoring system, and meanwhile, a converter valve trigger pulse, a cooling water pump rotating speed adjusting signal and a circuit breaker opening and closing state are captured through an equipment digital twin interface. And carrying out real-time feature extraction by means of an edge computing node, and triggering high-speed data recording for a commutation failure event. And correcting abnormal data by combining a sliding window dynamic cleaning mechanism with a wavelet threshold denoising algorithm, and extracting a current harmonic distortion rate, a voltage fluctuation coefficient and equipment vibration spectrum characteristics. And further establishing a multi-dimensional characteristic matrix containing equipment operation conditions, environmental parameters and power grid interaction characteristics, performing energy consumption prediction by using a hybrid model, and generating a dynamic energy efficiency evaluation index which is applied to energy saving strategy optimization.

The invention discloses a multi-scale adaptive time sequence InSAR (Interferometric Synthetic Aperture Radar) earth surface deformation extraction method, and relates to the field of remote sensing signal processing. The method comprises the following steps of: constructing an unwrapped time sequence InSAR phase simulation data set, performing pixel-by-pixel time sequence empirical mode decomposition on a time domain, obtaining a time sequence intrinsic mode function at the same pixel position, and constructing a FastICA wavelet enhancement denoising algorithm on a space domain: performing two-dimensional multi-stage wavelet decomposition on the intrinsic mode function of each dimension by taking a power spectrum entropy as a cut-off condition, the method comprises the following steps of: acquiring a wavelet approximation coefficient of each level and a detail coefficient, performing noise reduction and dimension reduction processing and reconstruction on the wavelet approximation coefficient and the detail coefficient of each level, and finally performing wavelet reconstruction on the updated wavelet coefficient to obtain a processed time sequence InSAR phase simulation data set. The method provided by the invention is based on data self-driving, self-adaptive decomposition is carried out on phase signals from two scales of time and space, earth surface deformation related signals can be effectively captured, other phase signals can be separated and explained, and a scientific theoretical basis is provided for high-precision earth surface deformation monitoring.

The invention discloses a power distribution network three-core cable fault distance measurement analysis method and system, and relates to the technical field of fault distance measurement, and the key points of the technical scheme are that the method comprises the steps: carrying out the empirical mode decomposition of three-phase voltage and current signals; generating a dynamic correction wave velocity based on the cable type, the environment temperature and the soil humidity parameter; determining the first arrival time of the wave head of the initial traveling wave according to the instantaneous frequency abrupt change point of the alpha-mode component, screening the second arrival time of the first reflected wave by using the amplitude gradient of the zero-mode component, and calculating the arrival time difference; and calculating an initial fault distance, and calling the impedance matrix to iteratively correct the initial fault distance. According to the method, the dynamic wave velocity is coupled with multiple factors, the limitation of single parameter correction is broken through, and the problem of transient drift of the wave velocity is solved; and the STFT high-frequency resolution and the wavelet low-frequency stability are fused, and the ridge continuity is optimized in combination with dynamic programming, so that the traveling wave head detection precision can be improved.

The invention provides an electric heating gas energy storage collaborative charge state disturbance suppression optimization method, and relates to the technical field of information, and the method comprises the steps: carrying out the filtering processing of a second-level disturbance signal in the real-time operation data of a power grid through an adaptive Kalman filtering algorithm, so as to improve the signal-to-noise ratio; performing time-frequency domain feature extraction on the filtered disturbance signal by using a wavelet analysis method, and judging the type and severity of the disturbance signal in combination with a preset disturbance mode library; and according to the type and characteristics of the disturbance signal. According to the method, an electric energy storage collaborative optimization control model is constructed by adopting a robust optimization theory, the uncertainty of disturbance signals and electric energy storage characteristics is considered, an optimal power instruction is solved, in the control process, the state of an electric energy storage unit is dynamically evaluated, a strategy is adjusted, online optimization is carried out according to power grid index changes, the control effect is continuously improved, and the control efficiency is improved. Through cooperative optimization control of electric energy storage, second-level disturbance of the power grid is effectively suppressed, and safe and stable operation of the power grid is guaranteed.