767 results about "Discrete wavelet transform" patented technology

The invention relates to a power distribution network state estimation method based on an adaptive space-time diagram neural network, and the method mainly comprises the following steps: S1, collecting historical and real-time measurement data of a power distribution network, and carrying out the preprocessing of the data, so as to guarantee the integrity of the data, provide high-quality input data for a model, and improve the estimation precision and stability of the model; s2, discrete wavelet transform is carried out on historical measurement data, multi-scale decomposition is achieved, and low-frequency and high-frequency components are extracted; a double-branch time sequence fusion module is constructed, global trend and local fluctuation features are respectively captured through a dynamic attention mechanism and a time convolution network, and the features are efficiently fused by means of adaptive weights. S3, in the real-time data processing process, branch measurement features are extracted through a multi-layer perceptron (MLP) and mapped to nodes of the whole network, dynamic integration of historical data and real-time data is achieved, and therefore the real-time performance and accuracy of state estimation of the power distribution network are improved.

The invention discloses a bearing fault diagnosis method based on a parallel multi-fusion deep learning network model. And obtaining a sample data set, dividing the sample data set into a training data set and a test data set, carrying out parallel multi-fusion deep learning network model training by using the training data set, and inputting the test data set into the trained parallel multi-fusion deep learning network model to carry out fault diagnosis. Wherein the parallel multi-fusion deep learning network model comprises three parallel sub-networks, namely a bidirectional gating circulation unit sub-network, a fast Fourier transform-convolutional neural network sub-network and a discrete wavelet transform-enhanced depth separable ion network, and a dynamic multi-head attention mechanism module. According to the method, the three parallel sub-networks and the dynamic multi-head attention mechanism module are combined to automatically extract and identify the fault features of the rolling bearing under multiple working conditions, and self-adaptive fault feature extraction under different domain signals and rolling bearing fault diagnosis under complex working conditions are realized.

The invention provides a cluster network flow prediction method based on multi-scale time feature fusion, and belongs to the technical field of computer network flow prediction. The method comprises the following steps: determining a multi-index prediction sequence based on traffic load characteristics of cluster IP instances, and constructing a high-quality time sequence data set; fourier transform and discrete wavelet transform are used for time-frequency feature analysis, and noise filtering and data dimension reduction are completed; projecting sequences of different time granularities to a unified model dimension, performing one-dimensional channel convolution merging, inputting the merged sequences into a time encoder and a cross-channel encoder, and capturing cross-scale long-term time dependence and a coupling relationship between variables; in the loss function design, time domain and frequency domain loss are fused, double-domain error calculation is carried out on a prediction result and a label through Fourier transform, and the robustness of a model to non-stationary fluctuation is enhanced; and through linear layer decoding and reverse normalization processing, the abstract feature is converted into an actual flow prediction value. According to the invention, the precision and reliability of cluster network flow prediction are significantly improved.

The invention provides an infrared and visible light image perception enhancement fusion method and system based on deep learning, and the method employs a multi-stage training strategy, and comprises the steps: carrying out the single-mode reconstruction training of an encoder and a decoder, so as to guarantee the feature extraction and reconstruction capability; based on a pre-training encoder and a pre-training decoder, performing multi-scale decomposition by applying discrete wavelet transform, and performing enhancement and fusion on the basic features and the detail features respectively; and designing an illumination perception module and an illumination consistency loss function to optimize the brightness and contrast of the fused image. The method effectively improves detail retention, brightness uniformity and color expression of the image under the low-light condition, and is particularly suitable for application scenes such as night high-perception requirements and the like.

The invention discloses a laboratory detection data processing method and system based on a machine learning technology, and relates to the technical field of data processing. According to the method, the signal is decomposed through discrete wavelet transform, the noise standard deviation is calculated based on the median of the highest frequency coefficient, the high signal-to-noise ratio data is reconstructed through the dynamic threshold and the soft threshold function, and the signal quality is improved; a sliding window is used for extracting time sequence signal statistics and FFT frequency domain features, spatial features are extracted in combination with an SIFT algorithm, high-correlation features are reserved through mutual information screening, and redundancy is reduced; constructing a graph convolutional network anomaly detection model and an XGBoost-LightGBM weighted regression model, and eliminating pollution data through an anomaly probability threshold to obtain a normal regression predicted value; predicting a compensation amount according to the environmental parameters by using an LSTM network, and obtaining calibration laboratory data according to the normal regression predicted value and the predicted compensation amount; according to the method, the problems of noise sensitivity, feature splitting, model isolation and environment drifting of multi-source data are solved, and the laboratory analysis precision and robustness are remarkably improved.

The invention provides a time sequence prediction method and system for a KAN enabling graph attention parallel loop network, and relates to the technical field of time sequence prediction, and the method comprises the steps: obtaining time sequence input data; inputting the time sequence input data into a feature extraction layer based on fast Fourier transform and discrete wavelet transform, and preprocessing the time sequence input data by the feature extraction layer to obtain spliced feature extraction data; inputting the spliced feature extraction data into a feature aggregation layer of graph attention based on KAN, and preprocessing the spliced feature extraction data by the feature aggregation layer to obtain feature aggregation data; and inputting the feature aggregation data into a time sequence prediction model of the parallel gating circulation unit network based on KAN and attention, and outputting time sequence prediction data by the time sequence prediction model. According to the method, the calculation complexity of time sequence prediction is reduced, and the time sequence prediction efficiency and accuracy are improved.

The invention relates to the technical field of micro-grid distributed control, in particular to a micro-grid cluster distributed control method and system based on information interconnection. The method comprises the following steps: identifying a micro-grid cluster in a region, and extracting a plurality of micro-grid nodes; obtaining a plurality of microgrid operation state parameters; performing filtering standardization processing on the plurality of micro-grid operation state parameters, and constructing a micro-grid time sequence load change curve; discrete wavelet transform is carried out on the micro-grid time sequence load change curve, load fluctuation frequency trend mining is carried out, and micro-grid load fluctuation frequency trend characteristics are generated; carrying out data stream encapsulation on the load fluctuation frequency trend characteristics of the micro-grid, and constructing a cloud environment distributed computing framework; carrying out dynamic load demand calculation on the plurality of micro-grid nodes based on a cloud environment distributed calculation framework so as to obtain multi-period load demand prediction data of each micro-grid; according to the invention, high-efficiency microgrid cluster control with high energy utilization rate is realized.

The invention relates to the technical field of lithium battery safety management, and particularly discloses a lithium battery thermal runaway early warning and protection method based on an adaptive neural network, which comprises the following steps: acquiring temperature, internal resistance and voltage data of a lithium battery in real time, extracting multi-dimensional features based on discrete wavelet transform, singular value decomposition and principal component analysis algorithms, and performing early warning and protection on the lithium battery thermal runaway. The influence of internal resistance and voltage on abnormal temperature fluctuation is quantified; constructing a feature vector through the comprehensive influence coefficient, inputting the feature vector into an adaptive fuzzy neural network prediction model, outputting a temperature anomaly fluctuation coefficient, and accurately predicting the thermal runaway risk of the lithium battery; according to the lithium battery thermal runaway prediction method, multi-level protection measures such as early warning response triggering, system automatic charging and discharging strategy adjusting, power output reducing and heat dissipation system starting are taken based on the prediction result, development of the thermal runaway risk is restrained, multi-dimensional data fusion and an intelligent algorithm are combined, and the lithium battery thermal runaway prediction accuracy and the risk control real-time performance are improved.

The invention discloses a power distribution network high-resistance grounding fault identification method based on deep feature fusion, and belongs to the technical field of power distribution network fault diagnosis and artificial intelligence deep learning. Comprising the following steps: performing deep mining on the waveform of a high-resistance fault signal by using discrete wavelet transform, and constructing a multi-scale time-frequency feature map and a global statistical feature matrix to enhance the expression of high-resistance fault features; designing a deep feature extraction network in combination with a lightweight residual network structure and a self-attention mechanism to realize fusion extraction of local and global time-frequency features; metric element learning is introduced to calculate a class prototype and the distance between the class prototype and the sample for the characteristics of various samples in a metric space, so that the construction of a high-resistance fault classifier is realized. According to the method, high-resistance fault identification tasks under different complex power distribution network operation conditions can be achieved, a good classification effect is still achieved when field data samples are few, and theoretical technical support is provided for application of the high-resistance fault identification method based on data driving in an actual system.

The invention relates to the field of image data processing, and discloses a texture preserving type image denoising and enhancing method based on a generative adversarial network, which comprises the following steps: acquiring an original image signal, and calculating low-frequency sub-band data and high-frequency sub-band data by using discrete wavelet transform; calculating the gradient magnitude of the low-frequency sub-band data to generate a structural significance gradient map; establishing a reverse mapping relation based on the structure saliency gradient map, and generating a spatial self-adaptive dynamic gating threshold; performing statistical gating on the high-frequency sub-band data by using the dynamic gating threshold to generate a high-pass gain coefficient and a low-pass suppression coefficient; according to the method, cross-band modulation logic of the structure flow to the texture flow is established, so that the problem that weak texture signals are easy to lose under non-uniform illumination is solved, and non-structured noise filtering and structured micro texture restoration are realized on the premise of not depending on semantic tags.

The invention relates to the technical field of heart sound signal processing, and discloses a heart sound feature recognition method based on wavelet spectral analysis. The method comprises the following steps: collecting heart sound signal data and carrying out signal filtering and segmentation processing to obtain segmented cardiac cycle signals; performing n-level discrete wavelet transform decomposition to obtain an approximation coefficient and n detail coefficients; performing multi-dimensional statistical feature calculation to obtain a multi-dimensional feature vector; performing principal component analysis dimension reduction processing to obtain a dimension reduction feature vector; fitting feature distribution by using a Gaussian mixture model and introducing a dynamic adjustment factor to obtain an adaptive classification threshold; the hidden Markov model is constructed through the dimension reduction feature vectors, the likelihood probability is calculated through a forward algorithm, the likelihood probability is compared with a self-adaptive classification threshold value, and the heart sound feature recognition result is output. The time-frequency features of the heart sound signals are fully utilized, the feature distinction degree and the recognition accuracy are improved, and the recognition accuracy is improved. And meanwhile, the method has good robustness and generalization ability.

The invention discloses a method for constructing a remote sensing image defogging network based on wavelet frequency domain heterogeneous enhancement, the network adopts a U-shaped architecture as a basic framework, the network input is a foggy image, and firstly, shallow layer features are extracted through a convolution block; then, a symmetric codec structure is adopted to learn layered representation, a codec comprises up and down sampling and a wavelet frequency domain heterogeneous enhancement module, and the resolution of up and down sampling is controlled through step convolution and deconvolution; the wavelet frequency domain heterogeneous enhancement module separates the high and low frequency features of the image through discrete wavelet transform, and performs heterogeneous enhancement on the separated high and low frequency features by combining the dynamic receptive field advantage of deformable convolution and the global perception capability of Fourier transform; therefore, the recovery of high-frequency local texture details and the removal of low-frequency global haze are effectively promoted. And finally, reconstructing a clear fogless image through the convolution block. According to the research algorithm, the texture features and natural colors of the scene can be precisely reduced.

The invention discloses a feature matching method based on differential wavelet transform and dynamic multi-scale feature fusion. The method comprises the following steps: constructing a feature matching model; the feature matching model comprises a backbone network, a key point detection network and a descriptor generation network; inputting a to-be-matched image to the backbone network, and generating a feature graph and a reliability graph; inputting a to-be-matched image to the key point detection network, generating a key point heat map, inputting the key point heat map, the reliability map and the feature map into the descriptor generation network to generate an enhanced descriptor of the to-be-matched image, and finally performing violent matching on the enhanced descriptor of the to-be-matched image; the differential wavelet transform down-sampling module in the backbone network performs frequency domain decomposition on features by using discrete wavelet transform, separates noise from key features, and effectively removes redundant information, enhances the key features and improves the matching accuracy of subsequent features in combination with differential operation of calculating the features and a neighborhood average pooling difference value.

The invention provides an image super-resolution reconstruction method based on double-domain feature fusion and implicit representation, and relates to the field of image processing and computers, and the method comprises the steps: obtaining a low-resolution remote sensing image, and carrying out the preprocessing of the low-resolution remote sensing image; performing feature extraction on the preprocessed remote sensing image through Haar discrete wavelet transform and a Transform-based pyramid structure to obtain frequency domain features and spatial domain features; performing double-domain cross attention fusion on the frequency domain features and the spatial domain features to obtain local detail features and global structure features; and through an implicit representation network, the fused local detail features and global structure features are mapped to any space coordinates, a final three-channel high-resolution image is obtained, and high-quality reconstruction of a remote sensing image of any scale is realized. According to the technical scheme of the invention, the implicit neural representation is guided to realize higher-precision image reconstruction through the cooperative expression of the frequency domain information and the spatial domain information.

The invention relates to the field of image processing, in particular to an automobile part processing burr detection method, which comprises the following steps of: positioning an automobile part, acquiring an edge image to define an ROI (Region of Interest), performing sub-pixel extraction on an edge in the ROI, and performing multi-scale decomposition by applying discrete wavelet transform to obtain detail coefficients. The fractal dimension of the edge local contour is calculated, the expected detail energy of the edge local contour under the corresponding wavelet scale is deduced, then the adaptive threshold adjustment amount is obtained through calculation, the final local adaptive threshold is generated in combination with the basic threshold, and the threshold is used for judging the actual detail coefficient to accurately position burrs. And then carrying out feature quantification on the positioned burrs. And finally, comparing the quantized burr characteristics with a preset quality standard, judging whether the part is qualified or not, and outputting a result. According to the method, the wavelet threshold is adaptively adjusted by introducing fractal features, so that the accuracy and robustness of burr detection under different edge conditions are improved.

The invention relates to the technical field of frequency converter anomaly detection, in particular to a hydraulic engineering frequency converter state detection method and system. The invention aims to improve the accuracy of abnormal data point detection in the frequency converter performance parameter time sequence data, and particularly focuses on the influence of environmental factors on performance parameters. Performance parameter time sequence data and environment parameter time sequence data are synchronously obtained, discrete wavelet transform is used for decomposing performance parameters in a multi-layer mode, and approximation coefficients are mainly analyzed to insight the operation state of the frequency converter. And after the suspected abnormal data segments are preliminarily screened, environment abnormal indexes are calculated in combination with environment parameters, and fluctuation degree values of the suspected abnormal data segments are analyzed. And based on the environmental anomaly index and the fluctuation degree value, adaptively adjusting the initial threshold value of each approximation coefficient to obtain an adaptive threshold value, and finally detecting abnormal data based on the adaptive threshold value. According to the method, abnormal data points caused by environmental factors can be effectively eliminated, and abnormal data points caused by abnormity of the frequency converter can be accurately identified.

The invention relates to the technical field of mode recognition and data recognition, and discloses an intelligent visual detection method for non-standard precision part surface microdefects, which comprises the following steps: acquiring surface gray level image data of a to-be-detected part, physically abandoning low-frequency components through discrete wavelet transform, and reserving high-frequency detail components to construct a frequency domain input tensor; constructing a double-flow reconstruction model containing a space domain coding network and a frequency domain coding network, and minimizing the distribution difference of the same feature between double-domain characterization through potential feature space consistency constraint joint optimization; the method comprises the following steps of: calculating a spatial domain residual image and a frequency domain residual image, combining a texture topological residual image extracted by structural tensor characteristic decomposition, and generating a comprehensive abnormal response image through weighted fusion to judge the defect, and effectively inhibiting macroscopic geometric contour interference through frequency domain decoupling and a topological check mechanism on the premise of not needing a standard geometric template. And sensitive perception and accurate identification of weak texture defects on the surface of the non-standard part are realized.

The invention provides an image snow removal method based on dual-frequency domain enhancement, which adopts a discrete wavelet transform technology to decompose high and low frequencies and fuse high-frequency enhancement features and low-frequency enhancement features after the high and low frequencies pass through a feature enhancement network so as to enhance the recovery of detail textures of a snowflake-degraded image. According to the method provided by the invention, the high-frequency characteristics of the snow degradation image are enhanced by using a differential convolutional network, and meanwhile, the serious degradation influence caused by large-scale snowflakes is reduced by using a low-frequency enhancement network, so that the performance of the snow degradation image in different frequency domains during snowflake removal and image restoration is improved. And meanwhile, global modeling is carried out on a seriously degraded area through a degradation sensing routing attention module, so that seriously degraded snowflakes are further removed more thoroughly.

The invention provides a class-independent motion prediction network system and method combining space-time and frequency domain features, equipment and a medium, and belongs to the field of automatic driving. The problem that the space-time dependency relationship and the dynamic characteristics cannot be fully captured in a complex dynamic scene in the moving target prediction is solved; comprising construction of a feature coding module, a hierarchical space-time integration module, a frequency-space fusion module and a feature decoding module, and the HTSIM fuses space-time information step by step through a multi-level space-time feature interactive learning mechanism, and can capture a long-term space-time dependency relationship and local dynamic change, so that a complex motion mode is modeled more accurately; fSFM combines the characteristics of a frequency domain and a space domain, multiband decomposition is carried out on input characteristics through two-dimensional discrete wavelet transform, and the capturing capability of dynamic characteristics is enhanced through deep fusion of the characteristics of the frequency domain and the space domain; the method is suitable for real-time track prediction tasks in intelligent traffic systems such as automatic driving and the like.

A distribution network fault positioning method based on particle filter algorithm multi-information fusion comprises the following steps: extracting transient information, and extracting a transient zero-sequence current high-frequency component by using discrete wavelet transform; calculating a similar entropy and a skewness coefficient of a transient zero-sequence current high-frequency component of each measurement point to obtain a respective corresponding initial fault section position; fusing the two preliminary fault section positions by using a particle filtering algorithm to obtain a fault section position; determining head and tail fault steady-state zero-sequence voltage and power of the fault section; and cutting a fault section, obtaining the zero-sequence voltage and power of a next cutting point by using a particle filter algorithm, carrying out iterative cutting until a threshold condition is met, and outputting a fault position. According to the method, transient zero-sequence current high-frequency components and steady-state zero-sequence voltage power serve as fault characteristic quantities, and the fault positioning function under the conditions of different fault positions, fault closing angles and white noise interference of the active distribution network is achieved.

The invention provides a dense overlapping target detection method based on a wavelet enhancement sparse hybrid expert model. The method comprises the following steps: firstly, extracting multi-layer features through a backbone network to capture multi-scale spatial representation; secondly, discrete wavelet transform is introduced to each level of features, spatial features are decomposed into a frequency domain, collaborative modeling of frequency domain and spatial domain features is realized, the reservation capability of detail and texture information is improved, a lightweight dynamic hypergraph aggregation module is introduced into the deepest layer of features, a hyperedge structure is adaptively learned, and the feature fusion is realized; modeling a high-order incidence relation in a local area in an explicit manner; and thirdly, in the decoding process, candidate queries are screened and reweighted through an IoU perception query selection mechanism, and a dynamic routing mechanism of sparse hybrid experts is introduced, so that query self-adaptive specialized representation learning is realized, and the target detection precision and reliability in a complex scene are effectively improved.

The invention relates to the technical field of data encryption, and discloses an intelligent data security encryption method in an edge computing environment. The method comprises the following steps: acquiring an original data stream, decomposing through discrete wavelet transform, and adding a random disturbance sequence; constructing a dynamic encryption strategy network, generating a dynamic key matrix and distributing an access control strategy; according to the data security level and the node load, adaptively selecting an encryption algorithm and generating an encrypted metadata label; verifying data integrity by using a decentralized consensus mechanism and a zero-knowledge proof algorithm; a key is updated through a key updating model based on a time decay function, and the key is stored in combination with a block chain technology. According to the method, the problems of data encryption, access control, key management and the like in the edge computing environment are solved, the security and integrity of the data are guaranteed, and the security and reliability of data processing in the edge computing environment are improved.

The invention provides an infrared image frequency division enhancement method based on a diffusion model, and the method comprises the steps: firstly carrying out the multi-scale frequency domain decomposition of an infrared image through discrete wavelet transform, and dividing the image into a low-frequency component and a high-frequency component; in the low-frequency component processing process, a diffusion equation is constructed in combination with a thermal radiation physical model, and discretization solution is performed by using a finite difference method. The high-frequency component is converted from a space domain to a frequency domain through Fourier transform, energy distribution of the high-frequency component is analyzed, and a diffusion probability function is adjusted. In the fusion stage, according to the signal-to-noise ratio of the local area of the image, the fusion weight of the low-frequency component and the high-frequency component is dynamically adjusted, the processing results of all the local areas are integrated in a weighted fusion mode, and finally the high-quality enhanced infrared image is generated. The method has excellent performance in improving image contrast, definition and detail performance, is suitable for the fields of security monitoring, military reconnaissance, industrial detection and the like, and can effectively improve monitoring, analysis and detection effects.

The invention discloses a 3D medical image segmentation method based on a diffusion model and double-attention discrete wavelet transform, and relates to the technical field of medical image segmentation. Comprising the following steps: acquiring a 3D medical image data set, and dividing into a training set, a verification set and a test set in proportion; constructing a 3D medical image segmentation model based on a diffusion model and double-attention discrete wavelet transform; inputting the training set into a 3D medical image segmentation model for model training; inputting the verification set into the trained 3D medical image segmentation model for model verification; and inputting the test set into the verified 3D medical image segmentation model for image segmentation. According to the method, the diffusion model and the full convolutional neural network structure are organically fused, discrete wavelet transform and a double-attention mechanism are introduced, and the accuracy and robustness of 3D medical image segmentation are remarkably improved.

The invention provides a steam turbine set vibration fault diagnosis method and system, and belongs to the technical field of thermal power plant fault diagnosis. The method comprises the following steps: inputting a to-be-identified vibration signal of a turboset into a trained turboset vibration fault diagnosis model for vibration fault diagnosis; the training rule of the steam turbine set vibration fault diagnosis model comprises the following steps: extracting a discrete wavelet transform coefficient from a steam turbine set vibration signal to obtain a DWTC image, and converting the steam turbine set vibration signal into a vibration signal image; taking the DWTC image and the vibration signal image as inputs of two branches of a double-flow convolutional neural network to perform high-level feature learning, and fusing high-level feature images output by the two branches of the double-flow convolutional neural network after preset processing; and inputting the fused image features obtained by fusion into a preset classifier, and outputting a corresponding turboset vibration fault type. The method and the system have the advantages of high accuracy, good generalization ability and strong robustness.

Embodiments of the present application provide a video sharpness assessment method, apparatus and device, a storage medium, and a product. The method comprises: converting a video image frame to be assessed into a gray-scale map, and performing discrete wavelet transform on the gray-scale map to obtain a high-frequency sub-band map; then performing saliency calculation on the gray-scale map to obtain a saliency map, scaling the saliency map, and converting the scaled saliency map into a weight matrix with the same size as the high-frequency sub-band map; and finally, on the basis of the weight matrix and the high-frequency sub-band map, calculating a total high-frequency energy value corresponding to the video image frame, so as to represent a sharpness assessment result of the video image frame. According to the scheme, saliency detection is introduced into video sharpness assessment, so that the video sharpness assessment focuses more on the part of an image that attracts human eyes' attention and is more in line with the human eye perception, and the accuracy of sharpness estimation is improved, thereby better feeding back the definition of videos.

The invention relates to a full-route bump feature mining and EDR estimation method based on airborne QAR data. The method comprises the following steps: forming an EDR basic data set; mining and extraction of advanced features are realized; forming an optimized feature set; obtaining a trained neural network model; and calculating the full-route EDR value of the real route. Through combination of discrete wavelet transform and a neural network model, the invention provides an accurate and reliable EDR estimation method, and compared with a traditional single-parameter method, detail description is more obvious and is completely and automatically calculated by a computer without manual reasoning. According to the method, after model training and optimization, the EDR value on a single course can be estimated in real time, and the accuracy and practicability of analyzing an airplane bumping event through flight data in the aviation field are improved.

The invention relates to the technical field of digital watermarking, and discloses a self-adaptive image watermark embedding and recovering system, which comprises an image preprocessing module used for carrying out discrete wavelet transform on a carrier image to be embedded with a watermark, decomposing the image into a plurality of frequency sub-bands, and selecting a low-frequency sub-band as a watermark embedding region; the watermark preprocessing module is used for carrying out orthogonal transformation on the watermark image, so that orthogonality is kept among a plurality of watermarks, and mutual interference of the watermarks is reduced; and the watermark embedding module is used for embedding the watermark into the low-frequency sub-band of the carrier image based on the output of the watermark preprocessing module. According to the method, the concealment and robustness of the watermarks are improved through the technologies of self-adaptive optimization of the watermark strength, redundancy embedding and orthogonal transformation, and compared with the prior art, the interference between the watermarks is effectively reduced, and the resistance of the watermarks to attacks such as compression, noise, cutting and rotation is enhanced.

The invention discloses a multi-modal image fusion method and device based on frequency domain perception, a medium and a product, and relates to the field of image fusion, and the method comprises the steps: obtaining a to-be-fused target infrared image and a to-be-fused target visible light image; according to the to-be-fused target infrared image and the to-be-fused target visible light image, determining a reconstructed image and a fused image of the target by using a multi-modal image fusion model; wherein the multi-modal image fusion model is obtained by training a convolutional neural network by using a training data set; the training data set comprises an infrared image and a visible light image of the target for training and a corresponding actual reconstruction image and an actual fusion image. According to the method, multiple frequency domain components are obtained by using discrete wavelet transform, and the components are fused by using a frequency domain fusion strategy so as to generate a high-quality fusion result. The quality of the fused image is improved.

The invention belongs to the technical field of wastewater treatment, and provides a wastewater treatment equipment remote control system based on the Internet of Things, which aims at solving the problems that the traditional fixed DO concentration control cannot adapt to water inlet load fluctuation, the effluent is easy to be substandard or the aeration energy consumption is high, the monitoring data noise is large, and the time sequence is misplaced. According to the scheme, a multi-dimensional real-time sensing network is constructed, flow, COD, BOD, ammonia nitrogen, water temperature, distributed DO and sludge activity sensors are deployed, discrete wavelet transform is adopted for noise reduction, and a data time sequence is aligned; a three-layer load-DO-energy consumption dynamic correlation model is designed, a basic layer predicts a load trend through LSTM, a middle layer quantifies a DO demand through a microbial metabolism model, and an optimization layer outputs an optimal DO set value through a PPO algorithm; and developing a dynamic decision-making system, adaptively generating a DO interval according to a load state, and adjusting fan parameters and number in a linkage manner. According to the invention, load dynamic adaptation is realized, effluent ammonia nitrogen is guaranteed to reach the standard, aeration energy consumption is reduced, and remote control precision and equipment operation efficiency are improved.