449 results about "Wave transformation" patented technology

The invention discloses an electric energy metering box fault prediction method and system based on big data analysis, relates to the technical field of smart power grids, and solves the problems of progressive aging missing detection and instantaneous interference misjudgment caused by dependence on single parameter threshold alarm and fault positioning misalignment caused by multi-source data isolated analysis in the prior art. According to the scheme, electrical, environment and equipment state parameters are collected in real time through a multi-dimensional sensing network; the error drift of the mutual inductor is dynamically predicted based on an LSTM-Kalman filtering model, and core breakdown early warning is realized in combination with wavelet transform; outputting a corrected resistance value and a fault mark by using a BP neural network; predicting the life of the piezoresistor by adopting a gradient boosting decision tree and fusing lightning overvoltage characteristics; the transient interference is suppressed through the combination of a Transform self-attention mechanism and dynamic time warping; according to the method, the aging detection precision and the complex environment adaptability are remarkably improved, the misjudgment rate is reduced, and the multi-fault associated positioning and active defense capability is realized.

The invention provides a marine ranch water quality parameter real-time correction and compensation method and system for a multi-source sensor, and relates to the technical field of multi-source sensors, and the method comprises the steps: constructing a double-layer edge computing network, and connecting a sensor through a micro-service architecture to collect water quality data. And carrying out data preprocessing in combination with wavelet transform. And establishing a sensor digital twinborn model, and calculating the real-time credibility. Establishing a multi-dimensional sensor association network, optimizing a weight coefficient by adopting federal learning, and establishing a self-evolution correction parameter matrix; and fusing the sensor data by using a multi-task deep learning model to generate an initial correction value. And calculating a theoretical reference value through a space-time sequence prediction model. A compensation coefficient is adaptively adjusted by adopting a fuzzy decision tree, hierarchical water quality parameter correction is realized, and a closed-loop self-optimization intelligent correction system is formed through verification of a digital twin model. The accuracy and reliability of marine ranch water quality monitoring data are effectively improved.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing target detection method and system for a low-visibility image. The method comprises the following steps: acquiring multi-modal remote sensing image data; carrying out defogging enhancement processing on the low-visibility input image; normalizing the defogged RGB image and the defogged IR image, and then splicing and fusing the RGB image and the IR image; carrying out layer-by-layer coding on the multi-modal fusion image by adopting a mixed trunk structure fusing Transform, Mamba and CNN (Convolutional Neural Network); performing frequency domain decomposition on the trunk output features based on two-dimensional wavelet transform; generating an HR feature map by adaptively selecting a key region; and carrying out cross-scale aggregation on the HR feature map to obtain a detection target frame. Through the multi-modal image defogging enhancement and feature distillation mechanism, the definition and contrast of the remote sensing image in severe weather such as haze and rainy days are effectively enhanced, the shielding interference of environmental degradation on small target detection is weakened, and the stability and adaptability of the model in complex weather scenes are enhanced.

The invention discloses a cross-modal eye fundus image generation method and system based on a generative adversarial network, relates to the technical field of medical image processing, and constructs an eye fundus focus perception and edge consistency generative adversarial network by taking a cyclic consistency generative adversarial network as a baseline. The core of the method is that a lesion perception mixed attention module is embedded in a bottleneck layer of a generator so as to strengthen the extraction capability of fine features of a lesion area; an edge information extraction module is designed, and key edge features are accurately extracted in combination with Roberts edge detection, wavelet transform and non-local mean denoising; and a joint loss function containing edge consistency loss is constructed, and the semantic consistency of a focus structure during cross-modal generation is ensured by minimizing the feature difference between the source image and the generated image. According to the method, the problems of disordered content, inconsistent structure and unstable training of the generated image in the prior art are effectively solved, and the simulation degree and clinical availability of the generated image are remarkably improved.

The invention provides a spatio-temporal data fusion-based power distribution network load prediction method and system, and relates to the technical field of power distribution network load prediction, and the method comprises the steps: collecting related data of a power distribution network, carrying out the wavelet transform decomposition of historical load data, obtaining a load feature matrix, constructing a hierarchical graph convolution network based on topological structure data, and extracting topological correlation features; and generating a spatial-temporal feature tensor through tensor decomposition fusion, training a depth probability prediction model adopting a variational auto-encoder structure, and adjusting prediction probability distribution in combination with environmental data. According to the method, the prediction precision is improved, a complex space-time dependency relationship can be captured, and reliable uncertainty quantization is provided.

The invention discloses a water source chlorophyll a concentration prediction model design method based on machine learning. The method comprises the following steps: acquiring chlorophyll a concentration data in a to-be-predicted region for a continuous period of time; carrying out data preprocessing on the chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing a concentration prediction model, carrying out data preprocessing on chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing different concentration prediction models, and inputting the processed chlorophyll a concentration data and physicochemical parameters into the prediction models to obtain a chlorophyll a concentration data prediction result; and comparing prediction results of different prediction models, and determining the prediction model. According to the prediction model design method, the WT-GRU model is adopted to preprocess the data through wavelet transform, the wavelet transform effectively extracts key time scale characteristics through signal decomposition, and the accuracy of chlorophyll a concentration prediction is remarkably improved.

The invention discloses a rainfall field image extraction method based on double-domain collaboration and progressive feature decoupling. The method comprises the following steps: acquiring a rain image; respectively inputting the rain image into the rain layer branch and the background branch, carrying out corresponding image block embedding operation, and generating an initial rain layer feature and an initial background feature; performing feature extraction on the initial rain layer features through a learnable wavelet transform algorithm to obtain rain layer features, and performing feature extraction on the initial background features through frequency domain separation convolution to obtain background features; performing multi-scale progressive coupling feature extraction on the rain stripe features and the background features to obtain processed rain layer features and processed background features; and performing multi-scale up-sampling on the processed rain layer features and the processed background features to reconstruct a rain layer image and a rain-free background image, and performing weighted fusion on the rain layer image and the rain-free background image through learnable residual gating to obtain a reconstructed rain image and a rainfall field image. According to the invention, accurate separation of the rainfall field can be realized.

The invention relates to the technical field of image processing, in particular to a road anti-corrosion effect detection and evaluation method based on an image processing technology, and the method comprises the steps: obtaining to-be-detected grayscale images corresponding to two different types of images on the surface of a road anti-corrosion layer, and carrying out the segmentation to obtain to-be-detected sub-images; respectively obtaining two difference images corresponding to the to-be-detected sub-image according to the row gray difference and the column gray difference of the gray difference image corresponding to the to-be-detected sub-image; obtaining the roughness degree of the to-be-detected sub-images, obtaining an optimal scale based on the roughness degree, decomposing the two to-be-detected grayscale images by using wavelet transform according to the optimal scale, and fusing according to decomposition results to obtain a fused feature image; and obtaining a defect region of the fused feature image, and obtaining a road quality detection result according to area features of the defect region. According to the invention, a more accurate road quality detection result can be obtained.

The invention discloses a high-resolution image semantic segmentation network for underwater scene design. A high-resolution image semantic segmentation network for an underwater scene is provided based on HRNetV2. The core of the method comprises: a WDCM feature extraction module for enhancing the anti-noise capability by using wavelet transform convolution, inhibiting irrelevant features by dynamic gating, and improving the performance of a complex scene in combination with an RCA module; a multi-scale slice segmentation head is adopted, global and local features are fused, and key information is dynamically selected; and 3) proposing a sliding combination loss function, and optimizing target boundary semantic information on the basis of variable weight focus (Focal) loss and dice (dice) loss. The target boundary segmentation precision is effectively improved, and the segmentation performance in a complex scene is improved.

The invention discloses an intelligent primary and secondary fusion pole-mounted circuit breaker fault monitoring method, relates to the technical field of power equipment monitoring, and is used for solving the problem of insufficient real-time performance of fault monitoring under complex environment interference. According to the invention, electrical, mechanical vibration and environmental data are synchronously acquired through a multi-mode sensor array, and a timestamp synchronization mechanism is applied; performing multi-source interference classification on the original data, separating noise by using wavelet transform, and identifying interference types by clustering; kalman filtering adaptive compensation is applied based on an interference result, and environment feedback is introduced to ensure low delay; extracting multi-dimensional fault features, and forming a robust matrix through time-frequency analysis and principal component dimensionality reduction; inputting a deep learning model to carry out space-time modeling and rapid classification; a response mechanism is triggered to execute isolation or alarm, and closed-loop optimization is formed. The method effectively solves the problem of insufficient real-time performance under the interference of a complex environment, and improves the monitoring precision and the response speed.

The invention relates to an image three-dimensional reconstruction method, in particular to a multi-view three-dimensional reconstruction method based on frequency perception feature enhancement and cost aggregation. The objective of the invention is to overcome the defects of lack of frequency sensing capability and limited processing capability for problems of weak texture, noise, illumination variation, color distortion and the like in an existing learning-based multi-view three-dimensional reconstruction method. According to the method, multi-view three-dimensional reconstruction is realized through the steps of acquiring a multi-view image, calculating a multi-scale frequency sensing feature, calculating an initial cost body, embedding frequency information into the initial cost body, calculating depth estimation, performing back projection and the like in sequence; when multi-scale frequency sensing features are calculated, a double-branch frequency component enhancement module is arranged to process wavelet transform layer decomposition to obtain lossless approximate low-frequency components and high-frequency components of an input image, so that global consistency and local detail expression of depth estimation are improved.

The invention relates to an AC-DC integrated grounding fault positioning method and system, and the method comprises the steps: collecting the potential change data of a DC power supply system and the frequency characteristic data of an AC power supply system, and determining the electrical signal characteristics of stray current corrosion; decomposing the electric signal by using wavelet transform, and extracting direct-current and alternating-current components; determining a fault type according to the component amplitude; analyzing the potential gradient and the stray current flow direction in combination with a line topological structure, and determining the position of a fault point; measuring resistance to determine a grounding loop, and analyzing the correlation between corrosion distribution and electrical parameter characteristics; evaluating the influence of corrosion on the grounding path in combination with the material resistivity; according to the method, faults can be accurately recognized, fault points can be accurately positioned, the corrosion degree and the influence of the corrosion degree on the system can be evaluated, and effective support is provided for safe operation and maintenance of a subway power supply system.

The invention discloses a wafer process link pollution source identification method based on FOUP inner interlayer AMC distribution, and relates to the technical field of pollution source identification, the method comprises the following steps: analyzing airflow disturbance and pollutant diffusion characteristics, formulating an external detector deployment strategy, and collecting pollutant concentration and associated equipment operation parameters in real time; establishing a space-time matrix of pollutant concentration, extracting gradient characteristics of pollutant concentration change by using wavelet transform, and obtaining initial position data of a pollution source in combination with operation parameters of associated equipment; constructing a space-time gridding input matrix, designing a double-branch deep learning model, generating a pollution source positioning probability graph, and obtaining position data of a pollution source; the contribution weight of each associated device to pollutant diffusion is evaluated, and a directional treatment strategy is formulated and executed. According to the invention, the position of the pollution source can be accurately positioned, and the accuracy and response speed of pollution source identification are improved.

The invention discloses a non-tracking freestyle three-dimensional ultrasonic image reconstruction method based on deep learning. The method comprises the following steps: obtaining an image data set; constructing an ultrasonic sequence coding module; constructing a wavelet transform convolution module; constructing a mixed attention module; constructing a loss function; constructing a deep learning model by combining an ultrasonic sequence coding module, a wavelet transform convolution module and a mixed attention module, and training the deep learning module by using the training set to obtain a three-dimensional ultrasonic image reconstruction model; scanning a diagnosis and treatment area of a patient by using an image acquisition system and adopting a non-linear scanning track to obtain two-dimensional ultrasonic image sequence data; and inputting the two-dimensional ultrasonic image sequence data as input data into a three-dimensional ultrasonic image reconstruction model for image reconstruction, and outputting a high-quality three-dimensional ultrasonic image. According to the method, the efficiency and the accuracy of three-dimensional reconstruction of the ultrasonic image can be remarkably improved, the adaptability and the robustness of processing the ultrasonic image under different conditions can be improved, and more accurate diagnosis information can be provided for clinic.

The invention relates to the technical field of metal surface treatment monitoring, and discloses a black titanium surface coloring intelligent monitoring system, which comprises a spectrum acquisition module, an environment disturbance acquisition module, an environment disturbance comparison module and the like. The spectrum acquisition module dynamically adjusts a sampling frequency and a wavelength range in combination with environmental disturbance parameters; the environment disturbance acquisition module acquires information such as a temperature gradient change rate and an environment humidity fluctuation rate, and the environment disturbance comparison module generates a comprehensive index according to the information and judges an environment disturbance level. And the equipment state acquisition and comparison module monitors internal operation parameters of the system, generates an equipment health index and deals with abnormity. The spectrum and model synchronization module matches and evaluates the coloring quality by using wavelet transform and a pre-training model, and the coloring abnormity analysis module identifies defects. In addition, the system is also provided with a dynamic calibration module. The system can accurately monitor black titanium surface coloring, and the production efficiency and the product quality are improved.

A road longitudinal slope estimation method based on unmanned aerial vehicle aerial photography video includes: collecting and correcting the traffic flow video of the road; taking the center line of the road as the reference line, extracting pixels on the reference line, and outputting the pixel gray space-time image on the reference line; performing the contour extraction of pixel gray space-time image; through the trajectory contour information, obtaining the complete trajectory data set; identifying the vehicle speed change point by using the energy distribution of wavelet transform; constructing the data set of road slope estimation, and based on the assumption of the actual length distribution of road pixels and the value of road longitudinal slope, applying a Bayesian network and a machine learning algorithm to iteratively obtain an expected value and variance of the actual length of the road pixel point and an estimated value of the road longitudinal slope.

The invention discloses a seismic surface wave suppression method based on EWT-Curvelet, and belongs to the technical field of seismic exploration data processing. The method comprises the following steps: firstly, performing empirical wavelet transform (EWT) on post-stack seismic data channel by channel, extracting a frequency localized modal component through adaptive spectrum segmentation, and constructing a surface wave noise model; then converting the model to a curvelet domain, performing physical constraint filtering at a specific scale and in a nearly horizontal direction, and combining pixel connectivity analysis to suppress a continuous noise region; and finally, reconstructing and optimizing noise through inverse transformation and removing the noise from the original data. The method has the advantages that the modal aliasing problem is solved through channel-by-channel adaptive spectrum segmentation, a surface wave energy dense region is accurately locked by curvelet domain scale-direction combined filtering, isolated effective signals are protected in combination with connectivity analysis, high-precision suppression of surface waves is efficiently achieved in a non-iterative mode, meanwhile, the structural integrity of reflected waves is kept, and high-precision suppression of the surface waves is achieved. The seismic section signal-to-noise ratio and the geological interpretation precision are remarkably improved, and the method is suitable for land seismic exploration data processing.

The invention discloses an automatic blasting scheme optimization system device, and particularly relates to the technical field of intelligent blasting analysis and processing, which comprises a parameter acquisition module, a data processing module, an optimization algorithm module, a simulation verification module, a dynamic control module, a communication module, a safety protection module and a man-machine interaction module, a visual interface is provided for parameter input, scheme adjustment and three-dimensional dynamic display of simulation results, and multi-terminal synchronous operation and remote cooperative control are supported; according to the invention, multi-source sensor groups such as a vibration sensor and a geological radar are integrated, wavelet transform and Kalman filtering algorithms are combined, dynamic acquisition and space-time alignment of parameters such as an internal structure and water content of a rock mass are realized, and a three-dimensional geological model constructed by a data fusion unit can update a rock mass damage state in a blasting process in real time, so that the blasting accuracy is improved. The method has the advantages that the limitation of a traditional static model is broken through, the precision of explosive load calculation is remarkably improved, and dynamic adaptation of complex geological conditions is supported.

The invention belongs to the field of time synchronization and data processing, and discloses a satellite and Rowland timing data fusion method and related device.Firstly, original data are intercepted and subjected to mean value removal to eliminate baseline offset, and a frequency domain matrix is reconstructed by combining frequency domain conversion with singular value decomposition; according to the method, periodic term interference signals in a frequency domain are accurately recognized and filtered out through a dynamic threshold strategy, then effective components are reserved through time domain conversion, finally, a noise covariance matrix and observation model parameters are dynamically adjusted based on an adaptive Kalman filtering algorithm, and dynamic weight fusion of double-source data is achieved. By the adoption of the method, the defects that a traditional weighted average method is insensitive in fixed weight, Kalman filtering parameters are rigid and global interference suppression of wavelet transformation is insufficient are effectively overcome, the suppression capacity for non-stationary noise and periodic interference is remarkably improved, fused data have the high precision of a satellite system and the anti-interference characteristic of a Rowland system, and the method is suitable for being applied to the field of satellite communication. And finally, high-reliability and high-stability time synchronization performance is realized.

The invention discloses a defect detection system and method for a glass bottle, and belongs to the field of industrial visual defect detection and analysis. The system comprises a GBDM module, the GBDM module is a defect detection module, and the GBDM module comprises a front attention enhancement structure, a rear attention enhancement structure and a defect detection structure, a multi-branch feature extraction structure is used for generating four feature branch diagrams; and the feature fusion structure is used for fusing the four feature branch diagrams. The GBDM module is embedded in the backbone network of the YOLOv8 network model, and multi-branch structures such as wavelet transform, Gabor direction filtering and specular reflection suppression are utilized, so that the feature extraction capability of multiple types of complex defects such as cracks, smudginess and damage on the surface of the glass bottle is enhanced, the generalization capability of the model in a small sample scene is remarkably improved, and the accuracy of the model is improved. The real-time requirement of an industrial scene is met, continuous self-optimization is achieved, and the method has high robustness, high accuracy and high application value.

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 discloses a distributed photovoltaic output prediction method and system in changeable weather, and the method comprises the steps: firstly obtaining historical photovoltaic station operation data, and carrying out the processing through a photovoltaic data preprocessing model: carrying out the feature engineering through timestamp feature extraction and multi-scale wavelet transformation, and combining a clustering algorithm with an anomaly detection algorithm, thereby achieving the prediction of the distributed photovoltaic output. Dividing and purifying the data into a plurality of weather type data sets; then, for each weather type data set, an independent photovoltaic output prediction model is trained, and the model is composed of an LSTM layer, a multi-head attention layer and a full connection layer; during prediction, the weather type attribution of the real-time data is firstly judged, and then the corresponding pre-training model is called to complete prediction. Weight calculation of multiple attention layers is dynamically guided by using frequency domain features extracted by wavelet transform, adaptive modeling of global features and local details is realized, and the accuracy and robustness of distributed photovoltaic output prediction are improved.

The invention relates to the technical field of automatic driving, in particular to a multi-modal three-dimensional target detection method, which comprises the following steps of: in a first stage, firstly acquiring neighbor depth information of point cloud to enhance image features, and then further enhancing image edges and reducing semantic confusion by utilizing wavelet transform; and then a cross attention mechanism is introduced to realize effective fusion of the enhanced image features and the point cloud, an initial region suggestion is obtained, and bounding box classification and prediction in the first stage are realized. And in the second stage, designing a double-attention module based on grid features to supplement more geometric detail information, acquiring more context information and position information by utilizing the initially suggested spatial features and channel features generated in the first stage, and then introducing a self-attention mechanism to dynamically distribute interaction weights of the grid features, so as to realize the self-attention interaction of the grid features. Rich context information is effectively captured, a local geometric structure is better coded, information loss is reduced, and the detection precision of the model is improved.

The present application relates to the technical field of data processing, and particularly relates to a power monitoring method and system for remote electric energy meter, the method comprising: constructing a time series factor graph model with voltage and current phasor as hidden variables and original electric parameter data as observation nodes; performing synchronous compression wavelet transform on current phasors in the electric energy state sequence to generate a time-frequency energy distribution graph and obtain a monitoring feature vector sequence; inputting the monitoring feature vector sequence into a deep auto-encoder pre-trained on normal power consumption working condition data to calculate a reconstruction error, simultaneously calculating a Lyapunov index of the sequence within a preset time window, and obtaining a negative log-likelihood probability according to a pre-established Gaussian mixture model describing the distribution of the index under normal working condition; and weighting and fusing the reconstruction error and the negative log-likelihood probability to generate a comprehensive abnormality index for judging power consumption events. The present application can realize high-precision and low-false-alarm-rate detection of power consumption events.

The invention discloses a gas leakage infrared detection algorithm for self-adaptive environmental noise suppression. The gas leakage infrared detection algorithm comprises the following steps: S1, preprocessing input data; s2, adaptive filtering is carried out; s3, carrying out multi-frame fusion, carrying out time-space domain analysis on continuous infrared image frames, separating high-frequency noise and low-frequency gas signals by utilizing wavelet transform, calculating a difference image of adjacent frames by combining an inter-frame difference method, enhancing gas diffusion morphological characteristics, distributing a weight for each frame according to a signal-to-noise ratio, and synthesizing a final image; and S4, post-treatment: concentration quantification and leakage area marking treatment are respectively carried out. According to the gas leakage infrared detection algorithm for self-adaptive environmental noise suppression disclosed by the invention, high sensitivity is kept through a self-adaptive environmental noise suppression algorithm, meanwhile, environmental noise interference is effectively eliminated, and the detection stability and accuracy in a complex scene are improved.

The invention discloses a cantilever heading machine excavation refined simulation method considering vibration. The method comprises the steps that design data and geological condition data of a tunnel are obtained; according to the design data and the geological condition data of the tunnel, a refined numerical model of tunnel excavation is established; vibration displacements of the cantilever heading machine in the X (drilling direction) direction, the Y (horizontal direction) direction and the Z (vertical direction) direction during construction under various different working conditions are obtained; noise reduction is carried out by using wavelet transform, a vibration trend term is extracted, and rigid body displacement and vibration waves are decomposed; and the construction working condition is obtained, the excavation process of the numerical model is adjusted according to the milling and excavation path of the construction working condition, the vibration waves are loaded, and the deformation result of tunnel excavation is obtained. According to the method, the influence of the vibration on the excavation process is also considered besides the influence of the construction process of block excavation of the cantilever tunneling machine, and the vibration is subjected to noise reduction by utilizing wavelet transform, so that the precision of cantilever tunneling excavation simulation is improved, and a basis is provided for design and construction of related tunnels.

The invention provides a hyperspectral target detection method based on generative self-supervised learning and wavelet transform. The hyperspectral target detection method comprises the following implementation steps: acquiring a pre-training / testing sample set and a fine tuning sample set; constructing a spatial-spectral reconstruction model based on wavelet transformation and performing generative self-supervised pre-training on the spatial-spectral reconstruction model; constructing a priori constrained hyperspectral target detection network model and carrying out fine tuning training on the model; and obtaining a hyperspectral target prediction result and a detection result. In the pre-training process, the multi-scale spatial and spectral features can be effectively extracted and fused by combining a dual-branch information embedding module of wavelet transform, so that an encoder can learn feature representation from multi-scale spatial-spectral information; and the hyperspectral target detection network model is finely adjusted by using a priori constrained cross entropy loss function, and nonlinear transformation is performed on a prediction result, so that background information can be suppressed by fully utilizing a priori target spectrum, and the hyperspectral target detection precision is improved.

The invention discloses a complex nonlinear MIMU (Micro Inertial Measurement Unit) signal noise reduction method based on improved modal decomposition. Dynamic adaptive optimization of noise parameters is realized by introducing Hilbert spectrum analysis into a CEEMDAN (Complex Empirical Empirical Mode Decomposition Number) algorithm; an IMF component discrimination model is constructed based on multi-dimensional feature fusion, and accurate classification of IMF components is realized; and aiming at a discrimination result, adopting a hierarchical processing strategy of combining variational mode decomposition and empirical wavelet transform for different types of mode components to realize high-quality signal reconstruction. Compared with the prior art, the method has the advantages that the signal decomposition quality is remarkably improved, the multi-feature fusion discrimination model is excellent in performance when the boundary fuzzy region is processed, the problem of discontinuity of a traditional hard threshold method at the feature boundary is solved, the signal-to-noise ratio is remarkably improved, the root-mean-square error is greatly reduced, and the noise reduction effect is remarkably improved.

The invention relates to a multi-dimensional space long-span bridge track vertical deviation temperature-sensitive component extraction method. The method comprises the steps of integrating long-span bridge track vertical deviation three-dimensional historical data added with an environment temperature label; adopting empirical wavelet transform to construct an MRA component four-dimensional data set containing a temperature label and a wavelength label; after mileage dimension resampling, projecting data to an amplitude-wavelength space, drawing an accumulated amplitude growth curve, judging the type of the amplitude-wavelength curve, and determining a boundary wavelength of bridge deformation and track irregularity; projecting the data to an amplitude-temperature space, and judging an amplitude curve and temperature sensitivity; and in combination with the boundary wavelength and the temperature sensitivity, extracting a temperature sensitive component of track irregularity and a construction deviation component in actual bridge deformation. According to the method, synchronous identification and extraction of the temperature sensitive component in the track vertical deviation and the bridge construction deviation component can be realized, and a quantitative basis is provided for accurate maintenance and repair of a long-span railway bridge.

The invention provides a construction method for installing a large-span special-shaped beam curved template system, and belongs to the technical field of building construction. According to the construction method, accurate three-dimensional modeling is conducted on the shape of a special-shaped beam through a special-shaped curved surface parametric modeling algorithm, and material prefabrication is guided; eight oscillation excitation devices and twelve vibration monitoring devices are optimally arranged by using experimental design and a graph theory analysis method to form a dynamic monitoring network, vibration response data are analyzed in real time by using a wavelet transform monitoring algorithm, and the energy ratio of each frequency band is calculated as a quality evaluation index. A game optimization model is established, and according to different ranges of energy ratios, the support spacing is automatically adjusted, a back ridge fixing mode is combined or a supporting system is reinstalled, so that a comprehensive quality assurance system of accurate geometric modeling-dynamic state monitoring-intelligent parameter adjustment is constructed. The technical problem that the installation quality of a large-span special-shaped beam curved template system is difficult to guarantee is solved.