425 results about "Dual domain" patented technology

The invention relates to the technical field of intelligent manufacturing and production management, in particular to a self-adaptive production scheduling system based on artificial intelligence, which comprises a data acquisition and reference construction module for analyzing process data to construct a directed acyclic graph representing a non-interference state as a reference map; the theoretical disturbance simulation module is used for converting the interference rule into a graph change instruction, generating a theoretical damaged state graph and obtaining a theoretical difference feature vector; the theoretical difference feature vector comprises, but is not limited to, a vector form obtained after a difference matrix is expanded according to rows or columns in terms of mathematical representation; the real deviation extraction module is used for collecting real-time state data to construct a real-time operation state diagram and calculating a real difference feature vector; a double-domain coupling decision module; an adaptive scheduling execution module; according to the method, the causal relationship is verified by comparing the form of theoretical deduction and actual observation, non-systematic noise is effectively filtered, and accurate response to real faults is realized while the stability of the production rhythm is maintained.

The invention discloses an industrial part defect sample accurate generation method based on a conditional diffusion model, and belongs to the field of image processing and artificial intelligence. The method forms a closed-loop cooperative system by constructing four deep coupling modules of physical constraint noise scheduling, multi-scale feature coupling, double-domain feedback optimization and adaptive weight adjustment; a defect physical forming mechanism is converted into a dynamic noise scheduling strategy, deep interaction between condition information and a feature map is established at multiple levels of a diffusion network, quality closed-loop optimization is achieved through dual evaluation of a pixel domain and a frequency domain, and training weight is dynamically adjusted according to defect scarcity. And multi-scale accurate control is realized, a quality guarantee closed loop is established, the problem of data imbalance is effectively solved, and the performance of an industrial defect detection model is remarkably improved.

The invention discloses a power transmission line state simulation and prediction method based on digital twinning, and the method comprises the following steps: accessing meteorological data, electrical data, space and asset data and historical state data, carrying out the preprocessing, and generating a multi-source spatial-temporal feature sequence set; constructing a double-domain cross-coupling time convolution twinborn model, and obtaining corresponding feature representation through a quick response path and a slow response path; physical consistency state representation is generated through a physical constraint cross gating layer; outputting a prediction result set through a simulation engine; online observation data are obtained, and the model is corrected based on virtual and real residual errors; and generating a current prediction result set by using the corrected model, and converting the current prediction result set into a scheduling and operation and maintenance strategy set. According to the method, the double-domain cross-coupling time convolution twin model and a virtual-real closed-loop correction mechanism are adopted, multi-scale state simulation prediction of the power transmission line is achieved, and the method has the advantages of being high in precision, robustness and performability.

The invention discloses a pole-mounted transformer state evaluation and fault early warning method fusing multi-source data, and relates to the technical field of intelligent power grid monitoring, and the method comprises the steps: executing dual state judgment according to a multi-source fusion feature set, extracting a statistical deviation degree of operation fluctuation abnormity, and calculating a physical deviation degree of multi-physical field coupling imbalance; the statistical deviation degree and the physical deviation degree are fused in a double-domain cooperation mode, and a real-time health index is obtained; the degradation rate and multi-scale energy characteristics of the real-time health index are extracted, a potential degradation aggregation mode is identified by referring to a historical slow-varying baseline, and a hidden danger trend factor is generated; performing composite attribution matching on the hidden danger trend factor and the multi-source fusion feature set, executing grading risk criteria according to a multi-physical field anomaly mechanism chain, and outputting an early warning grade and an early warning reason; according to the method, diagnosis is carried out by embedding a multi-physical field anomaly mechanism chain, and the physical root and evolution stage of the fault can be accurately positioned.

The invention discloses an artificial intelligence abnormity early warning diagnosis method for industrial equipment operation state monitoring, and belongs to the technical field of industrial equipment intelligent monitoring, and the method comprises the steps: collecting a multi-source heterogeneous signal of industrial equipment, and carrying out the time-frequency dual-domain feature extraction; constructing a multi-scale time window based on the time-frequency features, and executing self-supervised contrast learning by injecting a preset abnormal mode to obtain cross-scale contrast feature representation; constructing a dynamic adjacency matrix according to the comparison features, extracting space-time correlation features through a graph attention network, and determining an abnormal score based on joint evaluation; according to the method, a deep coupling closed-loop cooperative system is formed, multi-dimensional state characterization, adaptive anomaly detection and root cause diagnosis are realized, and the problems of single data source and lack of fault analysis capability in the prior art are effectively solved.

The invention discloses a double-domain artifact correction method based on multi-energy-level data and physical prior fusion, and the method achieves the efficient and precise removal of dispersive metal artifacts through the construction of a complete technical scheme of the combination of multi-energy-level data collection, double-domain cooperative correction and physical model constraint. The system has the beneficial effects that the system covers multiple fields of medical treatment, industry, security and protection, aerospace and the like, and has extremely high universality and adaptability. On the basis of a multi-energy-level slow switching scanning protocol, full-angle scanning of at least two energy levels is completed by dynamically adjusting radiation source parameters, and obtained complete multi-energy-level projection data is converted into a high-dimensional tensor through a channel dimension splicing image fusion method. The constructed multi-channel virtual image completely retains attenuation characteristics and structure information of a target object under each energy, provides a more comprehensive input source with discrimination for a deep learning network, builds a data basis for accurate correction in different fields fundamentally, and adapts to various imaging scenes containing metal targets.

The invention provides an unmanned aerial vehicle full-time perception image reconstruction method based on multi-mode cooperative reinforcement learning and degeneration decoupling. A frequency perception feature modulation model, a dual-mode dual-domain transformation module and a dynamic bidirectional guide mechanism are included. According to the system, firstly, feature information of different frequency bands is adaptively separated and modulated through a frequency sensing feature modulation model, and decoupling and compensation of composite unknown degradation are achieved; realizing cross-domain interaction and information fusion of visible light and infrared characteristics in a spatial domain and a channel domain by using a bimodal dual-domain transformation module; and finally, realizing collaborative enhancement of cross-modal degradation perception through a bidirectional dynamic guide mechanism, and generating an unmanned aerial vehicle visible light reconstruction image and an infrared super-resolution image with higher structural consistency and texture fidelity. According to the method, deep fusion and degeneration decoupling of multi-modal information can be realized in a complex degeneration environment, and the imaging quality and the environmental adaptability of an unmanned aerial vehicle full-time sensing system are remarkably improved.

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 field of physiological parameter detection and medical diagnosis measurement, in particular to a multi-mode psychological state quantitative evaluation method and device and a medium thereof. The method comprises the following steps: acquiring multi-source physiological and behavior signals and situation metadata, performing clock synchronization and equipment identifier mapping processing, performing quality feature extraction, artifact detection and slice completion processing, and generating a quality passing fragment set; de-noising normalization, time windowing and feature stack construction, modal gating and attention weighting processing are executed, and unified time sequence embedding is generated; through domain alignment parameter estimation, double-domain drift correction and multi-task inference, a psychological state quantitative index and uncertainty thereof are output; and finally, generating a structured evaluation report and updating parameters through consistency comparison, online self-distillation updating and individual baseline refreshing. According to the method, the anti-interference performance, the individual suitability and the result credibility of psychological state assessment in a natural scene are effectively improved.

The invention discloses a camouflage target detection method and system based on a double-domain fusion enhanced network, and relates to the technical field of target detection. Through the nonlinear double-domain fusion module, in combination with nonlinear mapping of a spatial domain and a frequency domain, key difference characteristics of a frequency domain amplitude spectrum and a phase spectrum are captured, the problem that the detection performance is reduced in a scene of low contrast and the like depending on an RGB spatial domain is solved, and the target discrimination degree is improved; based on a lightweight scale perception modulation converter and a double-feature fusion module, multi-scale features are extracted, aligned and fused, a semantic relation is integrated by means of cross attention, and the problems of detail loss and boundary fuzziness caused by scale diversity are solved; the context feature enhancement module integrates cross attention and edge auxiliary injection, accumulates multi-layer feature integration, gives consideration to a global boundary and a local structure, effectively reduces false detection, missing detection and edge roughness, and further enhances robustness through multi-layer auxiliary supervision.

The invention provides an unmanned aerial vehicle visible light-infrared image cooperative enhancement network and method based on dual-branch cooperation and frequency adaptive fusion, and relates to the technical field of unmanned aerial vehicle multi-modal image enhancement and super-resolution restoration. According to the method, visible light image enhancement and infrared image super-resolution reconstruction are respectively carried out by adopting a heterogeneous double-branch architecture; a frequency adaptive fusion module is embedded in a visible light branch, and spectrum decomposition and feature refining of degradation sensing are realized through a learnable frequency mask; in the infrared branch, a long-range dependency relationship is captured through a residual Transform module; bidirectional cross-modal guidance is realized through a multi-modal feature interaction module, the module integrates wavelet convolution transformation, frequency perception fusion and a cross-modal Transform mechanism, and feature alignment and semantic complementation of space-frequency double domains are realized. According to the method, the visual quality, the detail recovery capability and the cross-modal collaborative robustness of the visible light and infrared images of the unmanned aerial vehicle under complex illumination, weather and degradation conditions can be effectively improved.

The invention discloses a parallel U-Net-based dual-domain collaborative infrared and visible light image fusion method. The method comprises the following steps: firstly, extracting initial features of a source image by using dense connection blocks; then, parallel frequency domain branches and spatial domain branches are constructed, the frequency domain branches are combined with discrete wavelet transform and fast Fourier convolution to decompose and enhance multi-scale global frequency domain features, and the spatial domain branches capture long-distance spatial dependence with linear calculation complexity by using a convolutional layer and Mama based on a selective state space model; dynamic interaction and weighted fusion of double-domain information are realized through an adaptive feature fusion module; and finally, generating a fused image through an image reconstruction module. According to the method, the problems of high calculation overhead and video domain information negligence in the prior art are solved, and infrared heat radiation maintenance and visible light texture enhancement are effectively considered.

The invention discloses an automobile electronic wire harness insulation sheath crack detection system based on computer vision, and the system comprises a multi-channel input module which is used for obtaining and preprocessing multi-channel original data frames; the double-domain feature extraction module is used for extracting a double-domain feature pyramid; the double-domain fusion module is used for executing cross-domain attention and mutual information consistent weighting and inhibiting mirror surface highlight pseudo response; the topological structure output module is used for outputting a preliminary crack structuring result through the multi-head crack structure prediction head set; the communication repair module is used for carrying out breakpoint bridging and conflict rollback on the preliminary crack structuring result; and the geometric measurement module is used for measuring crack data and forming a structured detection report. According to the method, polarization reflection suppression and flattening geometric modeling are fused, a double-domain crack detection network is constructed, accurate identification and quantitative measurement of sheath cracks are achieved, and the method has the advantages of being high in reflection resistance, stable in topology and traceable in result.

The invention relates to the field of computer vision, and discloses a defect detection method, system, medium and equipment based on adaptive dual-domain filtering and Gaussian mixture prior constraint, and the method comprises the steps: carrying out the multi-scale feature extraction of an ultrasonic C-scan image through a Vision Transform network after the ultrasonic C-scan image is preprocessed; respectively inputting the shallow fusion features and the deep fusion features into a frequency-space double-domain adaptive feature filtering module for filtering, inputting the filtered features into a Gaussian mixture modeling module Ada-GMM, and modeling normal feature distribution; carrying out Ada-GMM-Guided decoding, and carrying out interactive fusion on the corresponding deep semantic features and shallow texture features by adopting a deep and shallow multi-scale feature interaction mechanism; performing optimization by adopting cosine reconstruction loss, filtering consistency, entropy regularization loss and distribution alignment loss, and adaptively learning normal distribution characteristics according to an optimization process to obtain a model weight; and reasoning the input ultrasonic C-scan image by using the trained network weight to realize anomaly detection and positioning, and outputting an interpretable anomaly thermodynamic diagram.

The invention discloses a time prediction method and system based on double-domain feature fusion. The method and system adapt to long and short term time series prediction requirements of multiple scenes such as weather forecast, energy scheduling, traffic flow and financial exchange rate. The method comprises the steps that a multi-field data set is obtained and preprocessed, and instance normalization is carried out; performing double-domain multi-scale characteristic decomposition by adopting down-sampling and discrete wavelet transform to obtain continuous trend and high-frequency mutation details; a local unit is obtained through patch cutting and embedding, local time sequence association is mined through depth separable convolution, cross-patch global interaction is achieved in combination with a multi-layer perceptron, and local-to-global progressive fusion is completed; and constructing bidirectional attention flow enhanced cross-domain and cross-scale collaboration, and combining with standardized data training to obtain a prediction model. According to the invention, the method can improve the depiction capability of non-stable and non-linear complex time sequence data containing abrupt change and multi-period superposition, gives consideration to the adaptability of long and short term prediction, remarkably improves the accuracy of multi-field time sequence prediction, and promotes the application of the prediction technology in multiple scenes.

The invention relates to a low-illumination image enhancement method based on double-domain collaboration, and the method comprises the following steps: collecting a low-illumination image in a real scene, obtaining a normal exposure image corresponding to the low-illumination image, and constructing a paired supervised training data set; performing double-domain characteristic decomposition on the input low-illumination image; constructing a double-domain cooperative modulation module: establishing a gating weight, and carrying out weighted fusion on the spatial domain and frequency domain features on the basis of the illumination distribution difference to obtain a fusion feature; constructing a multi-scale detail enhancement module to obtain fusion features with enhanced details; according to the noise confidence map, constructing a dual-domain collaborative denoising module based on signal-to-noise ratio estimation; and constructing a joint loss function, and performing end-to-end training on the whole model.

The invention discloses a Galileo HAS service receiving and positioning performance evaluation method and device, and belongs to the technical field of satellite positioning and navigation. The method comprises the steps that Galileo E6B frequency band signals are captured through USRP equipment, after radio frequency and direct current signals are separated through a bias device, self-adaptive layering processing is carried out through GNSS-SDR software, and the self-adaptive layering processing comprises dynamic sampling and interference suppression of a radio frequency adaptation layer, double-domain cooperative synchronization and self-adaptive decoding of a baseband intelligent processing layer and self-adaptive decoding of a baseband intelligent processing layer. And carrying out multi-dimensional feature mapping and robust verification on the HAS correction number extraction layer, and finally outputting a standardized HAS correction number. And in combination with a real-time precise single-point positioning technology, centimeter-level dynamic positioning is realized, and the positioning performance is evaluated through comparison with reference station data. According to the method, the robustness and precision of signal receiving and processing are improved, and the method is suitable for high-precision positioning requirements in a complex environment.

The invention relates to the technical field of intelligent equipment scheduling planning, and discloses a scene scheduling method and system for intelligent equipment, and the method comprises the steps: determining the task emergency degree and the motion complexity based on a dual-domain scheduling factor construction mechanism; constructing a scheduling priority weight; screening a conflict node set; generating an avoidance decision by adopting a double-case evaluation mechanism; and finally, synchronously issuing the avoidance decision to related intelligent equipment. In the prior art, there is no scheduling method of a priority guidance and fine conflict judgment mechanism, and especially in a dense operation scene of multiple mobile devices including an industrial AGV, a luggage transfer robot and a mobile charging vehicle, dynamic prediction and active avoidance of conflict risks are difficult to realize. Due to the fact that task and motion double-domain factor construction and a space and time double-condition conflict recognition mechanism are introduced, active avoiding and real-time response of device-level scheduling are achieved, and the cooperative operation stability of intelligent devices is improved.

The invention provides an image target intelligent tracking and positioning method and system under Beidou position constraint, and relates to the technical field of Beidou positioning, and the method comprises the steps: obtaining a multi-source heterogeneous data flow, constructing a multi-modal representation model, and building a coordinate system dynamic projection transformation relation; generating a search mask to limit a tracking range by using the Beidou position information; bayesian correction is carried out on a tracking result through double-domain consistency measurement; and dynamically updating projection transformation parameters and feature weights based on residual distribution. According to the invention, deep fusion of vision and Beidou information is realized, and the precision and robustness of target tracking in a complex scene are improved.

The invention discloses a multi-party supply chain sensing data encryption measurement and traceability method based on space-time coupling watermark embedding, which comprises the following steps of: constructing a double-domain coupling model of a time sequence and a space distribution characteristic, and embedding dynamic watermark information into a sensing data stream to realize data source identity identification and tamper-proof protection. A self-adaptive key generation and credible chain verification mechanism is introduced in an encryption metering stage, so that the data integrity and confidentiality can be guaranteed on the premise of not influencing the real-time transmission performance of the data; in the tracing stage, a data transmission path is reconstructed by using the watermark tracing matrix, and data responsibility definition and anomaly detection among multiple nodes are realized. The method is suitable for high-security data management and credible tracking in a supply chain multi-node collaborative environment.

The invention specifically discloses a satellite navigation interference source positioning method based on a distributed unmanned aerial vehicle, and relates to the technical field of satellite navigation interference resistance. According to the method, firstly, an unmanned aerial vehicle cluster carrying uniform linear array antennas is deployed in a target area, and after parameters of a system are initialized, all unmanned aerial vehicles synchronously collect interference signals and estimate the direction of arrival (DOA) of an interference source; then performing dual-domain interference suppression based on DOA information, calculating an interference source coordinate in combination with an unmanned aerial vehicle coordinate, and performing noise reduction; planning a path by using a path planning model of a multi-modal Markov decision process and a reinforcement learning algorithm to realize tracking of a dynamic interference source; and finally, judging and outputting a final coordinate through Euclidean distance convergence. The method solves the problems of limited coverage of a traditional fixed monitoring station and insufficient positioning precision of a single unmanned aerial vehicle, can efficiently position dynamic and multiple interference sources, improves the positioning precision and the anti-interference capability, reduces the deployment cost, and is suitable for satellite navigation interference source positioning in a complex environment.

The invention discloses a frequency modulation and wavelet sub-band guided double-domain collaborative Transformer X-ray image denoising method, which comprises the following steps of: acquiring a noise-containing digital ray original image and a corresponding clear reference image, and constructing a data set after preprocessing the noise-containing digital ray original image and the corresponding clear reference image; constructing a network model of a double-domain collaborative coding-decoding architecture; performing 3 * 3 deep convolution on an input image to extract shallow layer features; in the encoding stage, ETB and AFMB are alternately stacked to represent local and global information, a WB-LKED module is embedded to strengthen fine-grained features, and WDB executes down-sampling and transmits high-frequency features to a decoding end; in the decoding stage, the WUB recovers the resolution through double-path up-sampling, integrates the same-scale features of an encoder, enhances details by using high-frequency features, splices the features, then carries out ETB and AFMB refining, obtains output features through 3 * 3 deep convolution, and combines a global residual error connection optimization result; and training the model by using the data set, inputting a to-be-denoised image, and outputting a final result. According to the method, the problems of weak complex noise interference resistance, poor detail retention effect and limited CNR improvement can be solved.

The invention relates to the technical field of fully mechanized coal mining face automation control, and discloses a working face end straightness detection method and device based on a laser radar, and the method comprises the steps: building geometric and radiation references through collecting fixed reference target data; in real-time detection, a displacement vector and an atmospheric attenuation coefficient are calculated by using an instantaneous state of a target, and double-domain dynamic correction of coordinate rigid inverse transformation and intensity threshold adaptive adjustment is performed on an original point cloud. Then, clustering segmentation and slice centroid extraction are carried out on the corrected point cloud, and an ordered feature point set is obtained; and performing multi-factor weighted curve fitting on the feature point set in combination with geometric stability and environmental credibility, constructing a form fitting curve, and outputting a straightness deviation quantitative index according to a comparison result of the curve and an ideal reference straight line. According to the invention, the interference of equipment vibration and dust attenuation on measurement is eliminated, and the robustness and precision of the straightness detection of the scraper conveyor are improved.

The invention discloses a variable-viscosity UV ink system and an intelligent control method thereof, and relates to the technical field of data processing, and the system comprises a multi-dimensional sensing module which is used for executing multi-dimensional sensing data collection and establishing an ink state feature vector; the prediction module is used for outputting predicted viscosity distribution parameters by using the double-domain collaborative modeling unit; the fusion module is used for authenticating fusion and outputting fusion viscosity state parameters; the feedback module is used for performing self-adaptive feedback optimization according to a difference comparison result of the target viscosity curve and the fused viscosity state parameters; and the management module is used for performing intelligent control management according to the self-adaptive feedback optimization result. The technical problems that in the prior art, UV ink viscosity control is single and lagged, the multi-dimensional cooperative regulation and control capacity is lacked, and light curing and rheological property coupling cannot be dealt with are solved, and the technical effects that real-time, accurate and self-adaptive prediction and cooperative control over the ink viscosity are achieved, and therefore the printing quality and the process stability are improved are achieved.

The invention relates to a DAS-VSP seismic data noise suppression method based on a double-domain generative adversarial network, and belongs to the field of seismic exploration data denoising and deep learning. The method comprises the following steps: constructing a double-domain generative adversarial network, determining an optimal hyper-parameter combination by Bayesian optimization, adding actual noise to a pure seismic signal obtained by a forward modeling method to construct a complete training set, training the double-domain generative adversarial network, and testing the double-domain generative adversarial network. According to the method, DAS-VSP data which is low in signal-to-noise ratio and contains various kinds of complex noise can be effectively processed, the denoised seismic signals are clearer in structure, better in continuity, higher in signal-to-noise ratio and more thorough in noise suppression, effective signals are reserved to the maximum extent, high-quality basic data are provided for subsequent seismic data processing and explanation, and the method is suitable for large-scale popularization and application. The method meets the high-precision requirement of current seismic exploration, and has a wide application prospect in the field of oil and gas resource exploration and development.

The invention discloses an image cross-domain transmission method and a double-domain architecture platform. In the method, a component in a first operating system domain performs image rendering, writes rendering completion data into a cross-domain shared memory after rendering is completed, and sends a rendering completion notification to a second operating system domain; and after receiving the rendering completion notification, the component in the second operating system domain reads the rendering completion data from the cross-domain shared memory, realizes picture output based on the rendering completion data, and sends a vertical synchronization signal to the first operating system domain, so that the component in the first operating system domain performs next-round image rendering. According to the method and the device, cross-domain image transmission is realized, and the integrity of image data transmission and the fluency of image display are ensured by using the rendering completion notification and the vertical synchronization signal.

The invention discloses a current rotating speed mapping control method of a liquid cooling system, and relates to the technical field of liquid cooling systems, and the control method comprises the steps: constructing a double-domain model of a motor domain and a thermal load domain, carrying out the online identification of a liquid path impedance parameter in an operation process, and obtaining a current rotating speed mapping model; a target rotating speed instruction is generated in combination with a current-rotating speed mapping reference result and a thermal load flow estimation result, self-adaptive pump speed control is achieved through weight adjustment and smooth constraint driven by residual errors, and meanwhile baseline mapping and a pump family curve are dynamically updated through a memory buffering and batch reestimation mechanism; according to the invention, the self-adaptive capability and the cooling control stability of the liquid cooling system under complex working conditions are obviously improved, and the contradiction between insufficient cooling and energy consumption increase is effectively avoided.

The invention provides a CT metal artifact correction algorithm based on combination of double-domain diffusion and wavelet attention, and relates to the technical field of image correction. According to the method, the accuracy and effectiveness of CT metal artifact correction are improved by fusing the wavelet attention mechanism and the double-domain diffusion model, and the accuracy and effectiveness of CT metal artifact correction are improved by means of targeted extraction of the wavelet attention module on the high-frequency component of the image and quantitative analysis of the gradient direction consistency index and the curvature entropy. Accurate distinguishing of real edges and metal artifact fragments is achieved, and the problem of structure loss caused by confusion of edges and artifacts in a traditional method is effectively solved. Besides, on the basis of the design of edge geometric attribute dynamic distribution diffusion parameters, a small convolution kernel and a slow step length are adopted for a high-curvature edge to reserve a fine structure, and a large convolution kernel and a fast step length are adopted for an artifact area to strengthen the suppression effect, so that the artifact removal efficiency is improved, and the damage of excessive smoothness to key edge features is avoided; and the balance between the local fine structure and the global smooth demand is realized.

The invention provides an image enhancement method based on double-domain illumination prior and electronic equipment, and relates to the field of image processing. The method comprises the steps of obtaining a to-be-processed illumination image; according to local illumination distribution characteristics of the to-be-processed illumination image, multi-scale enhancement is carried out on the to-be-processed illumination image after global illumination enhancement, a first illumination image after spatial domain enhancement is obtained, and the local illumination distribution characteristics comprise a brightness mean value and a brightness standard deviation; amplitude information of the to-be-processed illumination image in the frequency domain is enhanced, and a second illumination image enhanced in the frequency domain is obtained; and fusing the to-be-processed illumination image, the first illumination image and the second illumination image to obtain an enhanced target illumination image.

The invention discloses a mining area vehicle-mounted ore real-time detection and size estimation method and system. The method comprises the following steps: acquiring an ore image to be detected; an ore image to be detected is input to the improved YOLOv11-Miner model, the ore category is obtained, and the improved YOLOv11-Miner model comprises a spatial domain and frequency domain dual-domain mixing module, a boundary-semantic fusion enhancement module and a shielding perception multi-head attention module; and based on a monocular photogrammetry algorithm, converting the pixel coordinates of the ore category into an actual physical size, and obtaining the size of the ore. According to the method, the monocular photogrammetry algorithm and the dynamically calibrated camera parameters are combined, the pixel coordinates of the ore image can be converted into the actual physical size, and the accuracy of size estimation is remarkably improved.