146 results about "Stream network" patented technology

The invention belongs to the technical field of logistics supply chain management, and discloses a logistics supply chain dynamic risk identification method based on a large language model. A real-time heterogeneous data stream is subjected to space-time normalization processing through a dynamic sliding window mechanism, a three-dimensional space-time data tensor set with entity types, timestamps and space grid codes as dimensions is constructed, dynamic entities in a logistics supply chain and the incidence relation of the dynamic entities are recognized through an entity-relation-time triple extraction module, and the real-time heterogeneous data stream is obtained. Constructing a dynamic knowledge graph with space-time attributes; generating an incremental graph version according to the change event of the entity state, performing multi-dimensional anomaly detection in combination with a corresponding graph change log, and generating a structured risk tag; when a risk event occurs, the time-space coordinates of the root cause of the risk event are accurately positioned through a version backtracking function. The real-time performance, the accuracy and the interpretability of supply chain risk identification are remarkably improved, and a systematic solution is provided for dynamic risk management of a complex logistics network.

The invention provides a damage assessment method based on heterogeneous double-flow network and multi-strategy adaptive decision fusion. The method aims at solving the technical defects of an existing method in the aspects of dual-time-phase feature alignment, multi-scale perception and fusion strategy self-adaption. The method comprises the following steps: registering and enhancing unmanned aerial vehicle images before and after damage; afterwards, feature extraction is carried out through a heterogeneous double-current feature extraction module, a cross-temporal window alignment mechanism is introduced into a global context awareness stream, strict spatial alignment of double-temporal features is ensured, and capture of multi-scale damage details is enhanced through multi-receptive-field optimization of a local detail enhancement stream; finally, through a multi-strategy self-adaptive decision fusion module, a strategy selector is used for dynamically calculating the weight, weighted decision is carried out on output of the association perception fusion strategy, the explicit change detection strategy and the robust weighted fusion strategy, and finally the damage level is output through a classifier. According to the invention, the accuracy, robustness and adaptive ability of damage assessment in a complex battlefield environment are effectively improved.

The invention discloses a method for optimizing micro-crack segmentation based on deep learning and super-resolution reconstruction, and the method comprises the steps: cutting an image in real time, obtaining an image block which takes a component as a target main body, and synchronously recording a homography matrix for geometric mapping; selecting an amplification strategy to improve the resolution, and recording a scale mapping relation; inputting the enhanced image block into a double-flow network; adaptive fusion and reconstruction are carried out on the two branch features, and a high-resolution texture image is output; generating a geometrically corrected ortho-image, fusing the geometrically corrected ortho-image with original illumination information, and outputting a corrected image with a known pixel size; identifying cracks, spalling and honeycomb diseases in parallel; generating a unified defect confidence map; calculating real geometric parameters of the BIM in a BIM global coordinate system through coordinate back projection; and generating quantitative defect reports and maintenance suggestions. The method has the advantage that seamless connection between the detection result and the BIM global coordinates is realized.

The invention relates to a geological disaster change detection method and system based on an improved twin U-Net and a central surrounding double-flow network. According to the method, an improved twin U-Net network is constructed as a feature extraction trunk, and a deformable convolution module is integrated in an encoder to adaptively adjust a receptive field; a central surrounding double-flow network is embedded in a decoding path, detail features are extracted through a central flow path, and a global context is obtained through a surrounding flow path; a feature fusion module is designed to realize double-path feature deep fusion, and a gating attention unit is adopted to calibrate feature response; introducing a contrast feature learning mechanism to reinforce feature space clustering characteristics; multi-modal degradation enhancement training is implemented to improve the robustness of the model; and an objective function containing difference perception loss and feature comparison loss is minimized through end-to-end joint optimization. The method can accurately identify geological disaster change areas such as landslide, debris flow and land subsidence, and has the advantages of high detection precision and strong anti-interference capability.

The invention discloses a heterogeneous data processing method and system for energy big data, and the method comprises the steps: obtaining different types of energy data, inputting the different types of energy data into a pre-trained directed association graph model, and obtaining a cross-type dynamic contribution flow network node forest; a cross-type linkage report is obtained, and a basic directed positioning dynamic correction chain is constructed through report analysis positioning and causal analysis; and inputting the cross-type dynamic contribution flow network node forest and the basic directed positioning dynamic correction chain into a pre-trained positioning correction path model, and performing real-time positioning error correction adjustment on the cross-type linkage report to obtain an updated and adjusted cross-type linkage report. According to the invention, cross-type linkage analysis and report real-time error correction of the energy data are realized, and the accuracy and efficiency of energy data processing are improved.

The invention discloses a visual angle robust traffic accident detection method based on space-time attention domain adaptation. The method comprises the following steps: constructing a double-flow network architecture; the progressive multi-granularity spatial domain adaptation module processes appearance change caused by a visual angle through global and local feature alignment, and uses SSAM to generate a semantic mask and entropy-guided mobility weight to realize accurate spatial domain adaptation; the time collaborative attention module realizes dynamic alignment of accident related time information between different visual angles through cross-domain video clip correlation calculation and a collaborative attention mechanism; a comprehensive contrast learning strategy is introduced, and noise robust motion feature learning is enhanced; a multi-view-angle joint training strategy is adopted, cross-domain knowledge migration is carried out by using monitoring view angle, vehicle-mounted view angle and unmanned aerial vehicle view angle data, and model optimization is realized through a comprehensive loss function. According to the method, the performance is remarkably improved in a cross-view migration task, and an efficient solution is provided for a unified multi-view traffic accident detection system.

The invention provides a machine vision microdefect detection method and system based on spectrum-polarization-phase three-domain collaborative perception, and the method comprises the steps: constructing a third-order tensor through synchronously obtaining the spectrum, polarization and phase information of the surface of a to-be-detected object, and extracting features through a lightweight multi-stream network; and dynamic fusion of three-domain information is realized by adopting a cross attention mechanism, and finally defect category, position and size information is output. According to the method, the problem of insufficient single-mode information is solved, the surface defect characteristics of the object can be more comprehensively reflected by combining three-domain information, and the capability of identifying the defects of complex objects such as high-reflective surfaces and transparent materials is improved; a lightweight multi-stream network and a cross attention mechanism are adopted, the multi-domain information fusion efficiency is improved, the network parameter quantity is reduced, deployment at a production line edge calculation unit is facilitated, and the detection speed and cost requirements are met; the method can better adapt to surface form changes of complex objects such as curved surface parts and flexible materials, the defect omission ratio is reduced, and the product yield is improved.

The invention discloses a cross-modal pedestrian re-identification method and system based on a multi-scale joint learning network, and relates to the technical field of pedestrian re-identification, and the method comprises a four-flow network architecture which enables a model to extract diversified semantic features through a mode of separating an application data enhancement branch from an original branch. Random channel selection and self-adaptive graying are respectively applied to the data enhancement branch, so that the robustness of the model to color change and the adaptive capacity of the model to different thermal imaging conditions are improved. Important channels are enhanced through a channel attention mechanism, irrelevant channels are inhibited, and two substreams are guided to internally enhance modal specific features. Richer semantic information is reserved through features extracted by a four-flow network, a joint semantic learning module is designed, a group of learnable vectors are defined and spatial position codes are added, global features of original branches and color invariant features of channel data enhancement are fully learned under the guidance of loss, and the overall feature of the original branches is optimized; and the features among different modes have higher semantic consistency.

The invention provides a double-current comparison and DHI combined equipment fault detection method and system, and belongs to the technical field of power equipment state monitoring and fault diagnosis. The method comprises a time sequence perception adversarial enhancement module, a generative adversarial network (GAN) data expansion module, a double-flow contrast attention network (D-CAN) feature extraction module and a dynamic health index (DHI) calculation module. The core lies in that a fault sample with time sequence correlation is supplemented through a physically constrained GAN, robust features are extracted by using a ResNet1D and Transform fused double-flow network, and a health state is quantified in combination with unsupervised clustering and mahalanobis distance. Through simulation and experimental verification, zero-delay detection of the early fault of the transformer can be realized, the output health index and the 3D visualization result can provide an accurate basis for operation and maintenance of the transformer, and the diagnosis accuracy, the data utilization rate and the dynamic adaptability of state evaluation are remarkably improved.

The invention discloses a traffic anomaly detection method based on a space-time double-flow network and multi-modal feature fusion, relates to the technical field of intelligent video analysis and traffic behavior recognition, and is used for solving the problem of high false detection rate of existing traffic anomaly detection in a complex environment. A video image sequence is extracted through a fixed and dynamic combined frame sampling strategy, and motion features are constructed. And in combination with the direction consistency coefficient and the perturbation evolution trend, repairing direction perception distortion by using a mirror image mapping mechanism. Target appearance features are extracted through spatial branches, behavior evolution trajectories are extracted through time branches, and meanwhile, a modal weight dynamic adjustment mechanism is introduced to enhance the adaptability to complex scenes. The multi-target interaction relation is quantified based on a graph structure modeling mode, an adjacent structure is reconstructed through a graph self-encoder, and local behavior mutation is recognized. And finally realizing high-confidence anomaly marking by combining state transition analysis and spatial aggregation characteristics. The method has the advantages of high real-time performance, high robustness and wide scene adaptability.

The invention discloses a cross-modal fusion target detection method and system for a low-light storage environment, and is applied to the technical field of cross-modal fusion target detection. The method comprises the following steps: obtaining a cross-modal image data set, and carrying out timestamp alignment and spatial registration preprocessing on an image; designing a cross-modal dynamic fusion state space model; training a cross-modal dynamic fusion state space model based on the preprocessed cross-modal image data set to obtain a CMDS cross-modal detection model; and inputting a to-be-identified image into the CMDS cross-modal detection model to obtain each target detection result of the to-be-identified image. According to the invention, through optimization of multi-modal training data and a double-flow network flow, the problem of feature dislocation existing in cross-modal target detection of an existing system is solved, and especially in complex environments of low light, no light and the like, the detection precision and robustness are remarkably improved.

The invention discloses a watershed ditch and pond-road low-cost ecological sand control method based on hydrological connectivity, which belongs to the field of ecological environment protection and mainly comprises the steps of data acquisition, DEM data correction, confluence network construction, hydrological structure connectivity identification, SWAT model construction, key source area identification, scene analysis and cost benefit analysis. Under the influence of a ditch pond-road system, the key source area identification technology based on hydrological structure connectivity and the drainage basin key source area ditch pond road low-cost water regulation and soil conservation method achieve the dual purposes of reducing the water and soil regulation and control cost and continuously improving the ecological benefit by identifying the key source area.

The invention discloses a footprint image human body attribute automatic inference method based on a multi-modal large model, and relates to the technical field of mode recognition. The inference method comprises the following steps: constructing a CLIP-based footprint image and text analysis multi-modal large model: the model adopts a double-flow network structure, a graphic encoder processes image features, a text encoder processes text features, and cross-modal feature fusion is realized through a feature fusion layer; and constructing a Grad-CAM interpretable visualization model, wherein Grad-CAM is a gradient-based feature visualization technology in deep learning. The gradient of the feature map is output through the calculation model, an attention map is generated, and key areas concerned by the convolutional neural network model during decision making are displayed; the correlation between the footprint image and the human body attribute is deeply discussed, a new multi-modal feature fusion method and model architecture are provided, and the algorithm, the model and the application method thereof formed in the research process also promote the industrial application of the technology.

The application provides a dark light image enhancement method and system based on a normalized flow model, and belongs to the technical field of computer vision.The application provides a conditional normalized flow network model based on a transformer, the model uses image structure information as a condition, and realizes image decomposition and reconstruction through a reversible normalized flow.The normal light image is decomposed into multi-scale conditional features and hidden features through the reversible conditional normalized flow network.The high-dimensional conditional features more fully express the image structure information irrelevant to light, and the hidden features conforming to a normal distribution express the light features of the normal light image in a simple form.The image is decomposed and recombined in the feature space, and the method can restore a more real image enhancement result in light and color.

The present disclosure is generally related to edge computing technologies (ECTs), communications networking, network slicing, and in particular, to techniques and technologies for providing flow-specific network slices. In particular, the present disclosure describes mechanisms that expand existing end-to-end architectures in order to include quality of service and monitoring mechanisms that connect network slicing technologies with infrastructure and / or network data center quality of service provider domains. The described mechanisms provide data center bridging to enable network, edge computing, and cloud computing domains.

The invention discloses a self-supervised optimization and self-adaptive deployment method for an optical flow network, and the method enables the effective region endpoint of a KITTI data set and the error of a self-built complex scene data set to be effectively reduced through the collaborative design of a self-supervised optimization method and a self-adaptive computing power frame based on the accumulation and circulation consistency constraint of a reverse optical flow, and improves the precision. A feature multiplexing mechanism enables the inference speed of embedded equipment to be improved and the memory occupation to be reduced, a dynamic pyramid iteration module supports flexible balance precision and time delay improvement efficiency according to a computing power threshold, a progressive training strategy effectively inhibits error accumulation, the cross-domain migration precision is improved, and the optical flow direction estimation accuracy in a low-resolution scene is improved. The problem of performance degradation caused by a fixed structure in the prior art is solved, and an optical flow estimation solution considering real-time performance and high precision is provided for mobile platforms such as an unmanned aerial vehicle.

The invention provides a time sequence action nomination generation method based on an action progress point applied to automatic driving, and belongs to the field of automatic driving, and the method comprises the following steps: generating a candidate segment containing an action generation interval from an untrimmed long video collected by a vehicle-mounted camera; a double-flow network is adopted to carry out feature extraction on a video, extracted video features are respectively sent to an action progress evaluation module, a background-action separation module and a candidate nomination generation module, action progress points or background points form a progress area nomination or a background area in a post-processing stage, score fusion is carried out on the progress area nomination or the background area and candidate action nominations, and the candidate action nominations are obtained. And the Soft-NMS is adopted to suppress the redundancy nomination. According to the method, the problems that an existing time sequence action nomination method mostly utilizes action starting points, action ending points and corresponding interval scores in action boundary prediction, action internal information is not fully mined, noise interference is likely to happen, and candidate nomination boundaries and confidence scores are affected are solved.

The application discloses a surveying-based binary structure small watershed division method and system, and particularly relates to the field of watershed division. Basic data processing of the application includes collecting and preprocessing topography, remote sensing and other data, establishing a basic database, converting vector contour line data into DEM and filling in depressions, generating a non-depression DEM, determining water flow direction by using a D8 algorithm, calculating cumulative flow, and generating a river network. The river channel is converted into a vector runoff network, the sub-watershed range is extracted and mapped, the outcrop point of underground river or karst spring is taken as a corrosion reference, the watershed outlet is determined in combination with the main river channel of a large river, and a complete correlation system is constructed. Finally, the development of the underground water system pipeline is determined through hydrogeological investigation, geophysical prospecting, drilling and pumping test, the results of comprehensive investigation, geophysical prospecting, drilling and pumping test are comprehensively considered to accurately correct the boundary of the underground water system, and the underground water system distribution map conforming to the actual conditions is formed in combination with remote sensing images and administrative division maps.

The invention relates to a multipath transmission scheduling method, system, device, equipment, medium and product, and specifically relates to the technical field of data transmission. The method comprises the following steps: acquiring network state parameters of substreams corresponding to different paths and performance indexes related to substream network transmission, accurately calculating expected arrival duration of a data packet on the substreams according to the network state parameters, and correcting the expected arrival duration by using the performance indexes. According to the method provided by the invention, the sub-streams with shorter time length are selected from the corrected time length to transmit the data after the influence factors related to network transmission are fully considered, so that the sub-streams can be more accurately scheduled based on the transmission time length under the condition of considering the influence of the network environment on the transmission performance, the network resources are utilized to the maximum extent, and the transmission efficiency is improved.

The application provides a method for detecting a fake video, comprising the following steps: S1. obtaining a set of fake videos, marking the authenticity of the target objects in the set of fake videos, and performing video data preprocessing to obtain a training sample set, a test sample set and a verification sample set; S2. constructing a single-stream residual dilated 3D convolution network; S3. inputting the training sample set into the constructed network and performing training to obtain optimal parameters; S4. inputting the verification sample set into the constructed network and loading the optimal parameters, detecting and classifying the authenticity of the target objects in the test sample set and the verification sample set to obtain a classification result; and S5. comprehensively classifying the classification result to obtain the judgment probability of the authenticity of the target objects in the test sample set and the verification sample set. The application constructs a single-stream residual dilated 3D convolution network, removes the optical flow network in the double-stream dilated 3D convolution network to reduce it to an RGB single-stream network, thereby improving the adaptability of the detection of the fake video, and adds a residual module to improve the training ability of the network in the deep layer.

The invention discloses an audio and video synchronization detection method and system based on deep learning, and relates to the technical field of digital audio and video processing, and the method comprises the steps: carrying out the video stream and audio stream separation of a collected to-be-detected audio and video file, and generating multi-modal audio and video features through face detection and feature extraction; according to the pure data packet, a SyncNet deep double-flow network is adopted to carry out synchronism discrimination, lip movement features and voice features are extracted respectively, and a synchronization discrimination result packet is generated; and according to the detection report, verifying the audio and video synchronism by adopting a time sequence alignment algorithm, and finishing result solidification through timestamp anchoring to obtain a synchronism judgment result. According to the invention, the SyncNet deep double-flow network is adopted to carry out synchronism judgment, efficient extraction and synchronism judgment of the audio and video lip movement features and the voice features are realized in combination with deep learning, the matching degree between the target voice and the mouth shape can be accurately recognized, and the accuracy of synchronism detection is improved.

This invention discloses a dual-stream collaborative reconstruction method and system based on template frame initialization, belonging to the field of image processing technology. The method includes: recovering sparse point clouds from multi-view images and segmenting them to generate template clothing meshes and their UV unwrapping; initializing Gaussian elements and binding mesh patches using a nearest neighbor strategy, and extracting physical and Gaussian cue features to form a feature matrix; using a spectral domain low-frequency and spatial domain high-frequency dual-stream network to predict global deformation and local wrinkles respectively, and updating the clothing mesh; then jointly optimizing the human-clothing mesh through rendering and collision loss; and achieving high-fidelity clothing appearance reconstruction based on UV domain Gaussian representation and appearance refinement network, combined with differentiable rendering. This invention achieves high-fidelity and dynamically consistent 3D reconstruction of clothing images through template initialization and spectral-spatial dual-domain collaborative deformation prediction.

The application relates to the technical field of logistics network reconstruction, and discloses a logistics network reconstruction method and device, an electronic device and a storage medium, wherein the method comprises the following steps: constructing a space-time heat map based on relevant geopolitical events; constructing a port layer penetration model, an airspace layer penetration model and a land layer penetration model, and performing cascading to generate a combined model and convert the combined model into a mixed integer linear programming model; and solving the model by using a preset cascading analysis solving algorithm to obtain a reconstruction scheme. The application has the beneficial effects that: by constructing the space-time heat map and the penetration model, the interconnection between the layers can quickly evaluate and adjust the transportation scheme when an event occurs, the stability and efficiency of the supply chain are ensured, the optimization capability of the mixed integer linear programming is utilized, the feasibility and efficiency of the reconstruction scheme are improved, a scientific and data-driven strategy selection is provided for decision makers, and the adaptability and pressure resistance of the global logistics network are enhanced.

The present application belongs to the technical field of space-to-earth observation (satellite remote sensing) and watershed hydrology, geographical environment disciplines, and discloses a watershed scale high spatio-temporal resolution river network remote sensing information extraction method and system. After cloud and cloud shadow processing of optical remote sensing images, a simple non-iterative clustering superpixel segmentation algorithm is used to segment homogeneous landscape objects, and a hierarchical decision tree is constructed using a water body index and a vegetation index. In the area where the optical remote sensing image is seriously covered by clouds or the image is missing, the SAR image is filtered, the dual polarization water body index is calculated using the VV and VH polarization images, the maximum inter-class variance method is executed, and the image is binarized. After obtaining the water body result extracted from the remote sensing image, the river cut-off is repaired in combination with the water system diagram generated by the DEM model. The present application can automatically extract accurate, continuous and high spatio-temporal resolution river network remote sensing information, and can be expanded to global scale river network feature extraction.

The invention discloses a binary structure small watershed division method and system based on surveying and mapping, and particularly relates to the field of watershed division. Basic data processing comprises the steps of collecting and preprocessing data such as terrain and remote sensing, establishing a basic database, converting vector contour line data into DEM and filling depression, generating a non-depression DEM, and then determining the water flow direction by adopting a D8 algorithm; calculating accumulated flow to generate a river network; converting a river channel into a vector runoff network, extracting a sub-basin range and drawing, determining a basin outlet by taking an underground river dew point or a karst spring as a dissolution reference and combining a main river channel of a large river, and constructing a complete association system; and finally, through hydrogeological survey, geophysical prospecting, drilling and water pumping tests, underground water system pipeline development conditions are determined, comprehensive survey, geophysical prospecting, drilling and water pumping test results are obtained, an underground water drainage basin boundary is accurately corrected, and an underground water drainage basin distribution diagram meeting actual conditions is formed by combining remote sensing images and an administrative map.

In an approach for efficient flood water analysis from spatio-temporal data fusion and statistics, a processor classifies regular waters by using cartographic data in a first location. A processor generates a water stream network including a watershed based on elevation data. A processor performs statistical analysis of spectral information from a multi-spectral satellite imagery over water bodies including the regular waters and flood waters. A processor correlates the spectral statistics of the multi-spectral satellite imagery to kinetic energy of the flood waters using machine learning techniques and physical modeling. A processor builds a learning model based on the correlation between the spectral statistics and the flood waters with the kinetic energy. A processor estimates kinetic energy of flood waters in a second location using the learning model. A processor evaluates a flooding risk for the second location based on the estimated flood waters kinetic energy.

The invention discloses a new energy logistics network intelligent management system and method based on digital twinning, and relates to the technical field of logistics network management.The system senses abnormal events in a logistics network in real time, maps the events to a digital twinning body through semantic analysis, and adjusts the topological structure and operation parameters of the digital twinning network; on the basis of the digital twin network, a seepage model of abnormal event influence propagation is constructed, the infection probability and seepage centrality of network nodes are analyzed, and an abnormal influence thermodynamic diagram is generated; constructing a scene tree according to the abnormal influence thermodynamic diagram and the current scheduling plan, performing parallel simulation on each branch of the scene tree in a digital twin environment, and selecting an optimal scheduling strategy through target evaluation; and tracking an execution effect in real time, constructing an experience case library, and matching similar historical scenes through analogy reasoning to realize self-adaptive optimization of an exception coping strategy. The response speed is improved; and the robustness of the system is improved.

The application relates to an automatic program optimization method and device based on a flow network generation model and a storage medium, wherein the method comprises the following steps: S1, an initial tensor program is acquired, a plurality of candidate tensor programs with the same logical function are obtained through calculation graph extraction, subgraph segmentation and transformation based on the acquired initial tensor program, and a data set composed of a plurality of samples is constructed based on the obtained candidate tensor programs, wherein the sample comprises a binary tuple composed of a calculation subgraph and a candidate tensor program, and the hardware execution time corresponding to the binary tuple; S2, a plurality of samples are selected from the data set, and a GFlowNet sampling model is trained offline; and S3, the trained GFlowNet sampling model is used to optimize a program to be optimized. Compared with the prior art, the GFlowNet adopts a non-iterative probabilistic sampling strategy, generates a plurality of high-performance programs, apportions the sampling overhead from one mode to another mode, and accelerates the convergence speed.

A computer system is provided in which one or more data channels can be established in the same connection as any streaming channel using the same setup protocol. The data channel enabled protocol layer can establish any number and type of channels, whether only one or more channels are one or more data channels. An application using a data channel enabled protocol layer may choose to establish any number and type of channels. Integration of streaming media and related data into multiple different lanes within a single connection provides significant advantages over maintaining a separate connection and supports multiple different use cases.

In some embodiments, a system can include a first stream to be opened with an arbiter device, the first stream associated with a first set of capabilities, a second stream to be opened with the arbiter device, the second stream associated with a second set of capabilities, and a wireless network architecture that determines a network messaging schedule based on the first set of capabilities and the second set of capabilities. The network messaging schedule can be sent to one or more client devices associated with the first stream or the second stream.