400 results about "Multiscale decomposition" patented technology

The invention provides a fatigue life prediction and residual life evaluation method for a high-power heavy-duty gearbox, and belongs to the technical field of intelligent operation and maintenance based on computer data processing. Comprising the following steps: acquiring dynamic data in an operation process, and performing multi-scale decomposition to form multi-source multi-scale data; inputting the multi-source multi-scale data into a designed multi-scale fatigue feature extraction module and a health state prediction module to obtain a multi-scale health index sequence and a health state label; establishing a fatigue damage evolution model, introducing the generated health index sequence for self-adaptive updating, outputting a comprehensive damage value, performing staged evaluation of fatigue degradation to obtain a damage label set, and performing multi-scale health index sequence and health state labels as well as the comprehensive damage value and the damage label set to obtain a multi-scale health index sequence and health state labels; inputting into a designed double-source fusion fatigue life prediction model, and outputting residual life prediction quantity; according to the invention, high-precision prediction and residual life evaluation of the fatigue life of the high-power heavy-duty gearbox are realized.

The invention discloses an intelligent detection method and device for fusing a medical image learning image, and relates to the technical field of medical image processing. The method comprises the following steps: acquiring and preprocessing a bimodal medical image, and extracting a feature map through multi-scale decomposition; constructing a cross-modal correlation model, and setting a modal attention mechanism (embedding anatomical structure prior guidance feature complementation) and a morphological attention mechanism (setting lesion morphological constraint weight); the method comprises the following steps: collecting multiple types of image samples, pairing according to a focus form and an imaging mode to construct a bimodal joint data set, and correlating and labeling to generate a training data set with modal attributes; after a multi-stage iteration training model, inputting the preprocessed image to carry out feature fusion so as to obtain a fused image; and generating a lesion probability graph according to the fused image, positioning a lesion area through multi-threshold segmentation, and outputting a detection result. The system comprises a data acquisition module, a preprocessing module and the like. The method improves the accuracy and reliability of medical image detection, and is suitable for clinical multi-modal image analysis.

The invention discloses a reservoir dam safety monitoring method and system based on edge calculation, and relates to the technical field of hydraulic engineering safety monitoring, and the method comprises the steps: obtaining monitoring data by each edge calculation node, carrying out the preprocessing, and generating a standardized data matrix; establishing a reference database, executing anomaly detection, and generating a labeling time sequence data matrix; performing multi-scale decomposition on the labeled time sequence data matrix, and constructing a node response feature matrix; the central processing unit receives data of each edge computing node, analyzes a multi-parameter spatial propagation mode by constructing a parameter-spatial correlation matrix, and obtains a spatial correlation feature matrix; establishing a Bayesian risk assessment model, predicting a dam risk level and outputting a risk evolution trend; and issuing a differentiated early warning instruction according to the risk assessment result. Through a distributed architecture combining edge calculation and central processing, real-time monitoring, intelligent analysis and accurate early warning of the dam safety state are realized, and the monitoring efficiency and the risk identification accuracy are improved.

The invention relates to the technical field of safety monitoring, and particularly discloses an embankment danger rapid identification method and system, and the method comprises the steps: collecting multi-modal data in real time through arranging a multi-source sensor network; according to the phase space trajectory, extracting a Lyapunov exponent spectrum, correlating the dimension and the Kolmogorov entropy, and forming a structure response chaos degree index; calculating a hydrogeological coupling coefficient in combination with multi-scale decomposition and mutual information analysis; and fusing the two into a three-dimensional dangerous case feature tensor, inputting the three-dimensional dangerous case feature tensor into a pre-training model based on a deep convolutional neural network and a long-short-term memory network, realizing intelligent discrimination of high, medium and low risk levels, generating an adaptive monitoring instruction for a low-risk working condition, outputting a risk evolution trend map, and supporting closed-loop management and control.

The invention discloses a method and a device for detecting abnormity of an electric power inspection image, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring the electric power inspection image; identifying and cutting the electric power inspection image based on the type of to-be-inspected equipment to obtain a target inspection image; performing multi-scale decomposition and extraction on the target inspection image to obtain scale features; mapping the target inspection image based on the scale features, and determining a target detection area image; performing enhancement processing on the target detection area image to obtain an enhanced image; and performing comparative analysis on the enhanced image and a normal image at the target detection area image to obtain an anomaly detection result. Through the method and the device provided by the embodiment of the invention, accurate anomaly detection of the electric power inspection image is realized.

The invention discloses a shipping industry carbon emission prediction method based on fusion of multi-scale feature extraction and time sequence dependence analysis, and belongs to the field of carbon emission prediction and time sequence analysis. The method comprises the steps that historical shipping carbon emission data are acquired and preprocessed; performing multi-scale decomposition on the preprocessed data by adopting a complete ensemble empirical mode decomposition adaptive noise technology, generating n intrinsic mode functions and a residual sequence, inputting the n intrinsic mode functions and the residual sequence into a Transform model, obtaining data features of the intrinsic mode functions, inputting the data features into an LSTM model, and obtaining predicted n intrinsic mode functions and n residual sequences; and superposing all prediction results, carrying out smoothing processing to obtain a final carbon emission prediction result, and then obtaining a trained Transform model and a trained LSTM model to predict the carbon emission of the shipping industry. The method is suitable for practical application scenes such as shipping carbon emission supervision, carbon emission reduction policy evaluation and carbon neutralization path planning.

The invention relates to the technical field of equipment fault intelligent diagnosis, in particular to an equipment fault intelligent diagnosis method and system based on a Modbus protocol. The method comprises the following steps: acquiring real-time operation data from target industrial equipment through a Modbus protocol, dynamically adjusting an initial sampling frequency based on an equipment operation state, and performing multiple verification and compensation correction on the acquired data to obtain a stable data stream; performing multi-scale decomposition and feature enhancement processing on the stable data stream, extracting a time-frequency domain mixed feature set, and constructing a feature evolution trajectory; inputting the feature evolution trajectory into a double-branch diagnosis model integrating equipment state prediction and fault classification, and outputting an equipment health degree score and fault type probability distribution; and constructing a dynamic fault threshold curved surface, carrying out multi-dimensional fusion decision by combining the equipment health degree score and the fault type probability distribution, and generating a graded fault early warning and maintenance strategy. According to the invention, the accuracy, timeliness and adaptability of industrial equipment fault diagnosis can be greatly improved.

The invention relates to the technical field of bridge health monitoring, and discloses a method for judging rigidity change of a bridge structure based on bridge health monitoring deformation data. The method comprises the following steps: establishing an initial data set of bridge deformation monitoring data and performing multi-scale decomposition processing to generate deformation component data of different time scales; inputting the deformation component data of different time scales into a pattern recognition engine, and recognizing a characteristic pattern data stream associated with the structural rigidity; constructing a rigidity influence factor sequence based on the characteristic mode data flow, and calculating a statistical characteristic quantity of the rigidity influence factor sequence through a sliding time window; performing multi-dimensional matching analysis on the statistical characteristic quantity and a historical reference database, and outputting a stiffness anomaly probability index; and activating a hierarchical verification mechanism according to the stiffness anomaly probability index, and confirming a stiffness change trend through a cross validation algorithm. Reliable data support is provided for bridge structure health condition evaluation.

The invention provides an oil and gas equipment leakage acoustic characteristic intelligent identification early warning method and system, and relates to the technical field of oil and gas equipment safety monitoring, and the method comprises the steps: arranging a sonic sensor array to collect signals, calculating a wavefront propagation path, optimizing the wavefront curvature, carrying out the multi-scale decomposition of a sonic signal, and extracting time-frequency characteristics; and reconstructing a sound field by using an acoustic propagation function to determine a leakage point space coordinate. The method can accurately identify the acoustic characteristics of oil and gas equipment leakage, improves the leakage point positioning precision, achieves the early warning of leakage, and guarantees the safe operation of oil and gas equipment.

The invention discloses a wind power multi-scale decomposition prediction method. At present, single-point prediction is not comprehensive and accurate enough, and cannot adapt to quantitative accurate requirements of a wind power plant and a power grid dispatching mechanism in risk management. The method comprises the following steps of: forming an original wind power sequence from actually acquired wind power data, sequentially performing feature selection and data decomposition processing to form multi-scale modal data, and constructing a depth prediction model according to the multi-scale modal data; a probability prediction interval determination process is completed in the residual error distribution mode depth prediction model through adaptive bandwidth kernel density estimation; after actually obtained wind power data form an original wind power sequence, an initial model is established, feature selection processing is performed on the initial model, that is, weighted marginal contribution is calculated for each feature of the initial model according to all involved feature subsets by using an SHAP algorithm based on a Shapley value in a game theory, and the weighted marginal contribution of each feature of the initial model is calculated; and completing a feature data acquisition process of accurately quantifying interdependence and interaction effect between features.

The invention discloses a tunnel surrounding rock deformation monitoring method based on a time sequence neural network, and the method comprises the steps: obtaining mountain tunnel surrounding rock deformation monitoring data, carrying out the preprocessing of the data through a time sequence preprocessing method, and obtaining a preprocessed time sequence data set; for the preprocessed time series data set, performing multi-scale decomposition on the data by adopting a wavelet transform method to obtain a decomposed multi-scale feature set; obtaining a plurality of feature matrixes according to the decomposed multi-scale feature set; carrying out matrix splicing by adopting a feature fusion method to generate a comprehensive feature matrix; according to the comprehensive characteristic matrix, time sequence modeling is carried out through a long and short term memory neural network model, the model is trained to monitor the surrounding rock deformation trend of the future time step, and a monitored deformation trend sequence is obtained. According to the method, through multi-scale feature extraction and fusion, the accuracy and reliability of surrounding rock deformation trend monitoring are improved by utilizing time sequence characteristics of monitoring data and combining external influence factors.

The invention discloses a multivariable predictive control energy consumption adjustment method and system, and belongs to the technical field of automatic control, and the method comprises the steps: collecting a parameter adjustment log in real time through an edge node, carrying out the intervention behavior recognition, and verifying the validity, so as to detect a manual parameter adjustment event; once an artificial parameter adjustment event is detected, multivariable data before and after intervention are extracted, and parameters of the local prediction model are dynamically corrected; performing rehearsal intervention based on the manual intervention parameters, generating space-time coupling constraints, and constructing a hybrid neural network to generate an energy consumption prediction trajectory; dividing types according to operator behavior modes, fusing prediction data to generate a comprehensive state vector, adjusting a reward function, focusing a sensitive variable, and generating a control instruction; and acquiring an actual energy consumption value in real time, comparing the actual energy consumption value with an energy consumption prediction track, calculating an energy consumption deviation, performing secondary optimization, positioning an error root cause through multi-scale decomposition in combination with a semantic tag and a knowledge graph, and performing layered compensation.

The invention discloses a mixed Mamb-Attention air quality prediction model based on multi-scale decomposition and a construction method of the mixed Mamb-Attention air quality prediction model. The method comprises the following steps: firstly, constructing a multi-scale decomposition module (MSD), decomposing an input time sequence into a trend term, a season term and a residual term through a parallel sliding window group, and realizing cross-scale feature fusion by utilizing group normalization and convolution; then designing a periodic pyramid module, extracting multi-level periodic features based on fast Fourier transform (FFT), and enhancing the perception ability of the model to different time scale periodic laws; the Mama branch is used for capturing long-range dependence, the self-attention branch is used for extracting a local dynamic mode, and the output of the Mama branch and the output of the self-attention branch are fused through residual connection and layer normalization; and finally, the prediction head module completes feature aggregation and result output. The model gives consideration to long sequence modeling capability and calculation efficiency, can accurately capture multi-scale dynamic change and non-stationary features in air quality data, improves the precision and stability of air quality prediction, and has good practical value and popularization prospect.

The invention belongs to the technical field of image processing and deep learning, and particularly relates to an image defogging method based on dynamic wavelet prior and double-domain learning. Aiming at the requirements of all-weather clear imaging in the fields of intelligent traffic systems, safety monitoring and the like, and in order to overcome the defect that a static convolution kernel adopted by a traditional defogging method is difficult to adapt to different haze degradation, the invention provides a method for dynamically generating a convolution kernel by using haze priori contained in a multi-scale wavelet LL sub-band; and an efficient, robust and accurate image defogging model is constructed. According to the invention, based on a multi-scale U-shaped coding-decoding architecture, a dynamic wavelet depth separable convolution module DyWConv is embedded in front of each level of a coder to realize content adaptive feature extraction, and a double-domain feature learning module SPAFormer Block cooperatively utilizing Fourier domain global modulation and wavelet domain multi-scale decomposition is designed. And double-domain features are fully fused through an adaptive gating fusion mechanism, and finally a clear image is reconstructed and output step by step. According to the method, a method for explicitly encoding frequency domain degradation prior into dynamic convolution kernel parameters is innovatively provided, the complementary advantages of Fourier transform and wavelet transform are cooperatively utilized, spatial non-uniform haze can be effectively removed, image details can be recovered, leading performance is achieved in a synthetic data set and a real scene, and the method has a wide application prospect.

The invention relates to the technical field of mineral processing visual detection, and discloses a visual analysis system for detecting the grade of a phosphorite flotation froth layer. The system comprises an image acquisition and decomposition module, a parallel feature extraction module, a dynamic feature fusion module, a foam evolution analysis module and a grade decision output module. The system performs multi-scale decomposition on a foam image, extracts physical and semantic features in parallel, constructs a dynamic fusion network based on bidirectional mapping to perform iterative interaction, and generates a multi-modal feature descriptor. Therefore, self-organizing growth of a foam evolution graph is driven, an evolution track of a key foam primitive is positioned and tracked, a foam grade state vector is formed, and finally, a regulation and control decision is output in combination with external control parameters. According to the system, deep fusion of physical and semantic features and deep analysis of the foam dynamic evolution process are achieved, the accuracy and predictability of foam grade state sensing are improved, and an effective means is provided for accurate control over the flotation process.

The invention belongs to the technical field of time sequence modeling and prediction, and particularly relates to a time sequence prediction method based on multi-scale decomposition and gating fusion. Performing multi-level down-sampling operation on the original meteorological time sequence data, and outputting a meteorological time sequence of a time scale after down-sampling; on the basis of the down-sampled meteorological time sequence, applying a neural Fourier trend decomposition mechanism to extract trend components and seasonal components of original meteorological time sequence data under different scales; carrying out fusion processing on the trend components and the seasonal components under different scales through a gating multi-layer sensing network to form meteorological time characteristics of a multi-periodic structure; normalizing the meteorological time characteristics of the multi-periodic structure, then generating a prediction result through each scale prediction branch, and finally obtaining multi-scale prediction output through weighted fusion; and combining various loss functions to form a total loss function for training optimization.

The invention relates to the technical field of power load prediction, in particular to a long-term power system load prediction method and system based on multi-scale decomposition fusion. The method comprises the steps of performing data preprocessing based on time sequence data; performing multi-scale decomposition and feature embedding on the preprocessed data to obtain a multi-scale load feature vector set; performing gating adaptive filtering and attention double-path fusion under the multi-scale load characteristics based on the multi-scale load characteristic vector set; performing independent prediction and prediction fusion on a fusion result based on a space-time attention gating mechanism; and evaluating a result after prediction fusion. According to the multi-scale prediction result space-time attention fusion mechanism provided by the invention, prediction information on different scales can be adaptively integrated, deviation caused by a single scale is avoided, the comprehensive performance of long-term prediction is further improved, and the method is suitable for various power system planning and operation scenes.

The invention discloses a BIM (Building Information Modeling)-based large-span post-tensioned bonded prestressed beam construction method. The BIM-based large-span post-tensioned bonded prestressed beam construction method comprises the following steps: collecting a vibration signal of a working rib and a matt rib reference signal of a matt rib; inputting the dummy rib reference signal into an adaptive filtering algorithm to which phase preserving constraint is applied, and performing noise cancellation processing on the working rib vibration signal to generate a purified pressure wave signal; performing multi-scale decomposition on the purified signal, extracting propagation time delay of the direct wave based on coherence analysis among components, and generating a phase-time delay characteristic matrix; and based on pre-configured geometric parameters of the BIM three-dimensional model, a real-time stress distribution field is calculated through inversion according to the phase-time delay characteristic matrix, and the real-time stress distribution field is synchronously updated to the BIM three-dimensional model. According to the method, the phase distortion problem under noise interference is solved through a physical reference channel and phase preserving filtering, the accuracy of time delay extraction is improved through a multi-path separation technology, and high-precision and high-robustness real-time monitoring of the prestress is achieved.

The invention belongs to the field of photovoltaic panel hot plate image processing, and particularly relates to a photovoltaic panel defect detection method fusing multi-scale wavelets and a lightweight attention mechanism. According to the method, an RHDWT discrete wavelet transform module based on multi-scale decomposition is added in a model input stage to strengthen image edge and texture detail representation; a Mix Structure Block module is introduced into a backbone network of the YOLOv11, so that multi-scale features are fused, and the feature expression capability is improved; an LWGA lightweight global attention mechanism is introduced into a neural network connection layer to enhance the context modeling capability, and the detection effect on small target defects such as fine cracks and hot spots is improved. According to the model, through collaborative optimization in three aspects of input preprocessing, feature extraction and an attention mechanism, the precision and robustness of defect detection in a complex photovoltaic module infrared or visible light image are remarkably improved, and the model is suitable for scenes such as high-precision photovoltaic panel image detection and intelligent maintenance.

The invention discloses a wind power gear box online fault diagnosis method based on multi-source data fusion, and particularly relates to the field of mechanical fault detection, and the method comprises the steps: S1, collecting high-frequency dynamic, medium-frequency working condition and low-frequency thermal state data, and outputting a standardized data set through time alignment, quality verification and physical constraint verification; s2, performing multi-scale decomposition to retain a fault sensitive frequency band, inverting physical parameters such as gear contact stress and the like, constructing a physical cause and effect graph, and determining fault sensitive characteristics and threshold values; s3, constructing a multi-modal feature tensor, and obtaining a low-dimensional health representation vector through CP decomposition fusion, graph neural network reasoning and variational auto-encoder dimension reduction; s4, calculating a weight by using an entropy weight method, calculating a dynamic health degree in combination with a health benchmark, predicting a trend by using LSTM, and establishing a five-level health system; s5, judging a fault mode through double-layer identification, analyzing a root cause and formulating a hierarchical operation and maintenance suggestion; the method is based on multi-source fusion and data mechanism dual drive, and precise diagnosis and operation and maintenance guidance are achieved.

The invention relates to the technical field of bridge health monitoring, and discloses a method for monitoring the bearing capacity state of a bridge latticed column structure. The method comprises the following steps: constructing an original monitoring field according to a sensor time sequence signal, and generating a state evolution sequence representing the overall dynamic evolution of the bridge latticed column through multi-dimensional space reconstruction, so as to capture the nonlinear characteristics of the structure behavior and provide a basis for anomaly detection. And positioning a potential abnormal region based on the deviation degree from the reference model, and extracting a time-varying characteristic spectrum of the potential abnormal region to drive the digital twin model to perform simulation so as to obtain a simulation response spectrum. The simulation response spectrum and the actual measurement state evolution sequence are subjected to space-time fusion, a mixed state field fusing real data and physical mechanism inference is generated, a monitoring blind area is made up, and the data reliability is improved. And by performing multi-scale decomposition on the mixed state field, the specific state mode of the bearing capacity of the bridge latticed column is identified, and more accurate and more complete evaluation of the bearing capacity abnormity is realized.

The invention relates to a simulation optimization method, device and equipment for a data assimilation driven gas compressor and a medium. The method comprises the following steps: acquiring internal flow field data of the gas compressor, and performing multi-scale decomposition by adopting wavelet transform to obtain a plurality of disturbance components; calculating the velocity gradient distribution of each disturbance component in the radial direction, and screening out disturbance components meeting preset velocity gradient constraints; aiming at the screened disturbance components, applying a space-time correlation rule by utilizing a convolutional neural network, and generating an adjusted disturbance component set; extracting space-time coupling features from the adjusted disturbance component set, fusing the velocity gradient constraint conditions, and constructing an initial model of a reconstructed flow field through a convolutional neural network; simulating the dynamic evolution process of the gas compressor based on a preset training set, and comparing the simulation result with the training set to judge the adaptation degree of the flow field drift; the initial model parameters are iteratively optimized based on the adaptation degree, the optimized flow field model is obtained, and the simulation accuracy and the calculation efficiency are improved.

The invention discloses an air pollution intelligent early warning method and system based on machine learning, and the method comprises the steps: obtaining the multi-source data of environment, wind direction, geography, traffic, emission, population and the like, carrying out the multi-scale decomposition of the environment data, extracting the short-term fluctuation and long-term trend, and achieving the time series prediction in combination with a historical mode; constructing a regional association graph by using a graph convolutional network, identifying cross-regional diffusion features, and fusing wind direction and space information to determine a high-risk region and a potential diffusion path; carrying out weighted calculation in combination with traffic and emission data to obtain a comprehensive risk score, and completing region division and early warning level determination; and finally, generating a regional visual early warning report. According to the invention, pollution trend accurate prediction and diffusion path identification can be realized.

The embodiment of the invention discloses a building engineering crack detection method and system based on image recognition, and the method comprises the steps: obtaining a building surface image, carrying out the preprocessing of the image, obtaining a standardized image, and carrying out the multi-scale decomposition extraction and integration of various features, and forming a multi-dimensional feature descriptor set; after feature importance is evaluated, a compact feature vector is generated through dimension reduction, quantization coding and compression, and then a multi-level feature index mechanism for optimized compression is constructed. A query feature vector is extracted from a newly collected image, searching and screening are completed by means of an index mechanism and a tolerance threshold, and a crack matching result is obtained; and based on the result, positioning cracks, classifying types, measuring parameters and evaluating severity, and generating a crack state report. The crack trend is analyzed in combination with the historical data time sequence, a multi-stage early warning mechanism is designed, maintenance suggestions are provided, and a real-time monitoring and early warning system is formed. According to the embodiment of the invention, the technical problems of high storage pressure and low real-time detection efficiency in the prior art can be effectively solved.

The invention discloses a power load prediction method and system. The method comprises the following steps: acquiring historical power attribute data and constructing a time sequence; carrying out standardization processing on the sequence; performing multi-scale decomposition and reconstruction on the standardized sequence by using a wavelet transform convolution module, and extracting a reconstructed sequence fused with multi-scale features; and inputting the reconstructed sequence into an xLSTM-Informer hybrid network, capturing time sequence dependence characteristics through xLSTM, and outputting a load prediction result through introducing an Informer model of a probability sparse attention mechanism. According to the method, the problems of insufficient long sequence dependence capture, single multi-scale feature extraction and low calculation efficiency are effectively solved, and the precision and stability of long-time prediction are improved.

The invention relates to the technical field of ocean current data analysis, in particular to a multi-scale modeling and verification method and system based on ocean current spring layer structure analysis. And obtaining the thermocline and halocline parameters of the target sea area and carrying out abnormal value elimination and spatial interpolation to form a thermocline data set. A convolutional neural network is used to extract spring layer space structure features, and based on Gaussian curvature quantification boundary complexity, a thermocline and halocline equivalent geometric model is constructed. On the basis, taking the equivalent geometric model as a boundary condition, and performing multi-scale decomposition on the Navier-Stokes equation set to generate a dynamic approximate equation; and then inputting the approximate equation and the equivalent geometric model into a physical information neural network for constraint training to obtain a multi-scale flow field prediction model, and comparing the multi-scale flow field prediction model with actually measured flow field data to complete verification. According to the method, automation, refinement and multi-scale coupling of spring layer structure modeling are achieved, and scientificity and engineering applicability of ocean current modeling are improved.

The invention relates to a TCN-BiGRU model-based wind power cluster power micro-prediction method in the technical field of new energy power management, and the method comprises the steps: carrying out the multi-scale decomposition of a wind speed signal through successive variational mode decomposition (SVMD), and extracting the feature components of different frequency bands; and then combining the decomposed wind speed characteristic component with other data characteristics to carry out PCA dimension reduction processing, optimizing key parameters of a DB algorithm through a fruit fly optimization algorithm FOA, realizing efficient clustering of the fan and selecting a representative machine. And finally, an SVMD-TCN-BiGRU-MSA-GJO hybrid micro-prediction model is constructed. A long-term dependency relationship is extracted through a time convolution network TCN, a bidirectional gating cycle unit BiGRU models time sequence dynamic characteristics, a multi-head self-attention mechanism MSA is introduced to optimize feature weight distribution, and a GJO algorithm is used to carry out adaptive tuning on model hyper-parameters. The method shows higher micro-prediction accuracy.

The invention relates to the technical field of data detection, in particular to an unmanned aerial vehicle atmospheric data anomaly detection and correction system based on deep learning, and provides the following scheme: an air mass reference coordinate system is constructed based on the attitude of an unmanned aerial vehicle and a relative wind direction; re-projecting the original measurement data to obtain an atmosphere data sequence with a stable reference direction; then, generating a frequency mask dynamically changing along with time, wherein the frequency mask is used for constraining positioning of disturbance energy of the rotor wing in the time-frequency analysis process; performing multi-scale decomposition on the time-frequency coefficient matrix based on a multi-channel frequency mask, and respectively extracting an aerodynamic disturbance estimation signal, a background atmosphere signal and a turbulence structure signal; and inputting the three types of signals into a preset deep learning model to realize identification and corresponding correction of atmospheric data anomaly. According to the invention, rotor disturbance and a real atmospheric structure can be distinguished, and the quality and reliability of atmospheric observation data are improved.

The invention provides a photovoltaic power prediction method and system, a computer and a storage medium. The method comprises the following steps: acquiring related data of a photovoltaic power station; non-linear feature screening is carried out, redundant and weak correlation features are removed, a main influence feature set is obtained, and cloud picture semantic features are extracted by using a convolution self-attention encoder; performing multi-scale decomposition and reconstruction to obtain a high-frequency sub-sequence, an intermediate-frequency sub-sequence and a low-frequency sub-sequence; based on the multi-dimensional meteorological variables, dividing weather modes through a clustering algorithm; constructing a physical information neural network model, and obtaining physical enhancement features based on the multi-modal input data set; and constructing a mixed time sequence framework fusing expanded long short-term memory network sparse self-attention and multi-head attention mechanisms, and performing short-term photovoltaic power prediction based on multi-source heterogeneous input. The prediction precision and robustness of the model under complex meteorological and data sparse conditions are significantly improved, and the method is suitable for a high-proportion photovoltaic grid-connected scene.

The invention provides a cable water-blocking buffer layer ablation degree detection method. The method comprises the following steps: acquiring a visible light image and an infrared image of a water-blocking buffer layer of a cable; performing weighted fusion on the visible light image and the infrared image to obtain a target image; performing multi-scale decomposition on the target image to obtain a plurality of spatial frequency images corresponding to the target image, the multi-scale decomposition being decomposition of the image into different spatial frequencies; and under the condition that the spatial frequency corresponding to the at least one spatial frequency image is greater than a preset frequency threshold, determining the ablation of the water-blocking buffer layer of the cable, and determining the ablation degree of the water-blocking buffer layer of the cable according to the target image. According to the scheme, the problem that the ablation degree cannot be accurately evaluated by adopting single-dimensional data in the prior art is solved.