497 results about "Structural similarity" patented technology

The invention provides an AI-based post competency and recruitment post matching method, and relates to the technical field of big data processing, and the method comprises the steps: obtaining multi-source heterogeneous data, and extracting dominant skill entities and implicit ability entities of candidates; constructing a candidate ability evolution graph; analyzing a post dynamic demand by combining a time sequence prediction model, and forming a dynamic post portrait including a current demand, a hidden demand and a future evolution demand; calculating the structural similarity between a candidate map and a post portrait through a map neural network matching algorithm, and generating and sorting comprehensive matching scores in combination with adaptive dimensions such as core capability difference, future integrating degree and team integration potential; team cooperative effect simulation is introduced, the influence of candidate addition on a team capability structure and a cooperative network is predicted, and the model is optimized through a closed-loop feedback mechanism; the talent recognition accuracy and coverage range are remarkably improved, prospective strategic talent matching is achieved, and the matching efficiency is improved.

The invention relates to the technical field of power plant management and control, and discloses an intelligent power plant management and control system based on the Internet of Things, and the system comprises the steps: when a plurality of labels related to the same equipment or parameter exist in different subsystems, according to a similarity index between the labels and an equipment association relationship; on the basis of the detected conflict label group, combining equipment historical operation and maintenance data and an upstream and downstream parameter flow relationship, constructing a label semantic evolution graph, and performing reasoning analysis on label equipment through a fusion rule engine and a graph neural network; through mapping knowledge domain fusion and semantic embedding comparison, matching and clustering among conflict labels are completed based on structural similarity and semantic relevancy, and a label alignment rule is constructed; according to a label coordination result, designing a mapping rule of a data field; and performing inter-system synchronous verification on a result after structure conversion and label standardization processing, and writing a standardized label into a unified semantic database. The method has the advantage of improving data semantic consistency.

The invention relates to the technical field of multi-image fusion recognition, in particular to a data fusion method and system for CCD visual detection, and the method comprises the following steps: obtaining a horizontal pixel row calculation gradient construction trend sequence, repairing an edge fracture to generate an integrity index, extracting a gray value to detect feature mutation, and distributing fusion weights to establish a mapping relation. And executing image fusion and balancing the contrast to generate a fusion matrix result. According to the method, the fracture edge region is identified, interpolation compensation is executed, the structural similarity index of the local gray sequence in the image overlapping region and feature direction mutation detection are combined, accurate identification of the edge matching result is guided, and fusion weight factor mapping corresponding to signal-to-noise ratio distribution is introduced; according to the method, the distribution relation between the pixels in the region and the credible weight is effectively established, the edge transition among the multi-source images is more natural through Poisson constraint and contrast balance adjustment of the fusion region, and the structural fidelity and the judgment stability of the fusion image are remarkably enhanced.

The invention relates to a multi-stage filtering road spilled object detection method based on dynamic difference analysis, which is suitable for automatic identification of unstructured foreign matters in video monitoring. The method comprises the following steps: firstly, extracting a reference image road mask, eliminating vehicle and pedestrian interference by using YOLOv8 detection, and extracting a motion candidate area through a frame difference method and background modeling; and then context expansion and super-resolution reconstruction are carried out on the candidate region, the candidate region is converted into an HSV space, multi-dimensional features such as color similarity, structural similarity and shadow determination are synthesized for screening, false detection is further removed in combination with inter-frame time sequence consistency, and finally a stable detection result is output. The method provided by the invention has the advantages of strong anti-interference capability, high adaptability, high detection precision and the like, and is suitable for the intelligent recognition task of the expressway thrown objects in a complex environment.

The invention discloses a radar echo extrapolation method and system based on frequency domain enhancement, and the method mainly comprises the following steps: obtaining and preprocessing a historical radar echo grayscale image sequence, generating a sequence sample through a sliding window, and dividing the sequence sample into a training set, a verification set and a test set; the method comprises the following steps: constructing a frequency domain enhanced U-Net network comprising an encoder-decoder structure, introducing a multi-scale deep convolution structure into an encoder and a decoder, and enhancing frequency domain features by using a frequency domain dynamic attention mechanism in jump connection; inputting the training set into the model for training by adopting a composite loss function comprising intensity weighted loss, frequency domain consistency loss and structural similarity loss; and inputting the test set into the trained model, and outputting a radar echo prediction result at a future moment. The method can be effectively applied to the fields of short-term and temporary weather forecast, severe convection monitoring and the like, and provides more accurate and reliable radar echo prediction support for meteorological disaster early warning.

The invention discloses a microscopic image seamless splicing and enhanced reconstruction method based on deep learning, and the method comprises the following steps: S1, collecting a plurality of original images with overlapped regions, and recording the spatial position information and imaging parameters of the original images; s2, preprocessing the original image to generate a standardized image sequence; s3, inputting the standardized image into a structure perception feature extraction network, and extracting a feature map fusing textures and structures; s4, inputting the feature image and the original image into an image registration module; s5, inputting the registration image into the boundary attention splicing network; s6, inputting the seamless image into the residual hierarchy reconstruction network, and enhancing image details through hole convolution and multi-scale branches; s7, image quality evaluation is executed, and the structural similarity, the signal-to-noise ratio and the edge retention rate are calculated; and S8, constructing a training set and carrying out end-to-end training optimization based on a joint loss function. According to the method, a multi-module deep network is fused, and seamless splicing and high-quality enhanced reconstruction of microscopic images are realized.

The invention discloses an MRI-PET mode conversion method and system based on a generative adversarial network, and belongs to the technical field of artificial intelligence medical image generation. And the multi-scale structure representation injection module injects multi-scale anatomical prior information at different stages of the encoder, and overcomes the limitations of insufficient utilization of prior information and single injection scale. And the adaptive semantic residual fusion module adopts semantic attention guidance and double-branch attention weighting, adaptively fuses fine-grained local features and global context information, harmonizes the difference between the fine-grained local features and the global context information in an abstract level and a semantic category, and solves the problems of feature conflict and semantic fuzziness in a bottleneck region. The direction sensing space-frequency discriminator realizes multi-dimensional and fine-grained adversarial supervision through a space, frequency and local image block multi-branch collaborative discrimination mechanism, and improves the structural fidelity and spectrum authenticity of a synthetic image. And the generated image is superior to the existing method in indexes such as structural similarity and peak signal-to-noise ratio, and has higher clinical practical value.

The invention discloses a three-dimensional dynamic scene graph construction method based on 3D Gauss, and belongs to the field of computer body intelligence. The implementation method comprises the following steps of: realizing object perception and semantic feature extraction of open vocabularies by utilizing a visual basic model; a 3D Gaussian scene with high fidelity and continuous object semantics is constructed through multi-view multi-dimension optimization of 3D Gaussian representation; constructing a multi-level three-dimensional scene graph, extracting spatial levels and semantic relationships among objects by using 3D spatial positions and semantic tags of instance objects existing in a semantic Gaussian graph, and constructing a multi-level spatial semantic topology to accurately represent an environment layout; according to the method for realizing local updating for the Gaussian scene graph based on the environmental structural similarity, environmental change detection is carried out through real-time RGB-D observation and the structural similarity between high-quality rendering views of the Gaussian scene graph, and corresponding local updating is carried out by using rapid training and differentiable rendering of 3D Gaussian representation. And the capability of adapting to a complex dynamic environment of the 3D Gaussian scene graph is improved.

The invention relates to the technical field of content recognition, in particular to an Al-based video content analysis method and system, and the method comprises the following steps: carrying out the image segmentation learning based on a video sequence through employing a U-Net convolutional neural network, analyzing scenes and elements in a video frame, recognizing and isolating key visual elements in a video through network learning, and carrying out the recognition of the key visual elements in the video. Comprising objects and figures. According to the method, image segmentation learning is carried out by adopting the U-Net convolutional neural network, key visual elements in the video can be identified and isolated more accurately, a clearer basis is provided for follow-up scene change and key event tracking, video content analysis is carried out by applying the graph neural network, the identification capability of dynamic scenes and events is enhanced, and the identification efficiency is improved. Deeper structured understanding is provided for video content indexing, the video quality is analyzed through a structural similarity index evaluation method, and the reason of visual quality reduction can be accurately recognized and improved.

The invention discloses a retrieval enhanced warehouse-level code completion method based on semantic topology and disambiguation redundancy pruning. Firstly, a key value code library for accurate mapping of source code fragments and induction sub-graphs is constructed through a specific slicing algorithm; in the retrieval stage, four-level layered optimization is adopted, structural similarity is evaluated by extracting a deep semantic relationship, trimming accurate duplicate items and using a new graph-based measurement (tradeoff is performed on editing according to topological importance), results are reordered to maximize correlation and diversity, candidate items are systematically refined, and the retrieval efficiency is improved. The problems of retrieval redundancy solidification and surface similarity misleading are solved, meanwhile, cross-module dependence is analyzed through an external perception identifier disambiguator, and the problem of cross-file symbol ambiguity is solved; and finally, fusing an optimization result to generate a prompt to drive the LLM to generate higher-quality output. According to the invention, through coordination of semantic and structural signals, strong performance can be obtained even in a large-scale and resource-limited code library. Meanwhile, the design allows it to be orthogonally complementary to other cross-file methods, providing collaborative improvements when used in combination.

The invention relates to the technical field of three-dimensional reconstruction, in particular to a near-infrared assisted low-light scene three-dimensional reconstruction method based on 3D Gaussian splashing, which is characterized in that generation of a Gaussian ellipsoid is dominated by a near-infrared image with a high signal-to-noise ratio, and a stable geometric basis is provided for a visible light information missing area; in the rendering stage, a near-infrared rendering image and a normal visible light rendering image are respectively generated through Gaussian ellipsoid shared geometric parameters and respective opacity and color attributes of two modes; through cross-modal structure similarity loss, a normal visible light image which is forcibly rendered is aligned with a near-infrared light image in structure, and clear near-infrared structure information is used to strictly constrain the recovery process of the color of the visible light image, so that the accuracy and authenticity of a recovery result are ensured.

The invention discloses a retraining-free pruning and recombination method for a sparse expert hybrid large model, and belongs to the technical field of large model compression and optimization. The method aims at solving the problems that due to the fact that an existing sparse expert hybrid (SMoE) model needs to load all expert parameters, memory occupation is too high, and deployment is difficult. According to the method, firstly, redundant experts are identified and pruned based on routing activation statistics; then, decomposing the pruned experts into neuron-level functional fragments, and redistributing the fragments to the reserved experts according to structural similarity; and finally, original fragments and newly distributed fragments are merged in the reserved experts through a weighted clustering algorithm, so that compact experts with fewer parameters and stronger expression ability are reconstructed. According to the method, fine-grained operation is carried out at the neuron level, the inherent representation conflict and dislocation problems among experts are effectively solved, the performance of the compressed model is remarkably improved, and reliable technical support is provided for deploying a large-scale SMoE model.

The invention discloses a multi-modal face recognition method and system based on deep learning, and relates to the technical field of face recognition, 3D structured light, near-infrared images and PPG blood flow signals are synchronously fused, occlusion completion is achieved through a double-branch generation network, PPG spectrum constraints generate blood vessel distribution, and biological feature authenticity is ensured; low-confidence sub-regions are divided based on a structural similarity algorithm, the weight of a loss function is dynamically adjusted, only problem regions are iteratively generated, and calculation redundancy is reduced; the depth feature of the complemented image and the blood vessel frequency spectrum main frequency band overlapping rate are extracted, the identity authenticity is doubly verified, and 3D mask and photo attacks are resisted; the performance bottleneck of traditional single-mode identification in a shielding scene is broken through, closed-loop optimization is driven through data, precision, efficiency and safety are considered, and the method is particularly suitable for high-requirement scenes such as medical treatment and finance.

The invention provides a wind power data determination method and device based on geographical constraint attention. The wind power data determination method comprises the steps of obtaining topographic data, surface vegetation coverage data and wind power data; for the wind power data, combining the topographic data and the surface vegetation data, performing feature fusion through an adaptive attention mechanism to obtain a fusion feature containing multi-source information; performing shallow feature extraction, deep feature extraction and image up-sampling on the fusion features in sequence, wherein the deep feature extraction realizes multi-scale feature extraction and geographic constraint fusion through a multi-stage residual fusion module RFSTB, a wind field frequency domain decoupling module CFB and a geographic perception global attention module GAB; verifying the effectiveness of the model by using a peak signal-to-noise ratio (PSNR) and a structural similarity index (SSIM); and performing super-resolution reconstruction on the wind power data based on the trained model to generate wind power data with predetermined precision. The method provides support for fine evaluation and efficient development of wind energy resources in complex terrain areas.

The invention discloses a small sample remote sensing image classification method based on hierarchical spatial structure learning. The method comprises the following steps: firstly, extracting multi-scale features of a remote sensing image by using a ViT (Visual Transform) model, and capturing rich semantic information and spatial structure relationships in the image; secondly, constructing a graph structure based on spatial adjacency and attention weight to model a structured relationship between samples, and encoding graph node features through a graph convolutional network (GCN) so as to enhance the discrimination ability of the features in a structural semantic space; thirdly, a residual enhancement mechanism is introduced to fuse global semantic information, and the discrimination capability of graph embedding is improved; then, based on the structural similarity between the support set and the query set, performing classification decision, and realizing accurate classification under a small sample condition; and finally, carrying out joint optimization on the whole model by adopting a training strategy of a small sample meta learning task and a supervision loss function.

The invention relates to the technical field of software security, in particular to a vulnerability mining system and device based on source code similarity. The vulnerability mining system comprises a vulnerability feature library which comprises vulnerability code snippets and feature information associated with the vulnerability code snippets; the coarse granularity positioning module is used for matching the vulnerability code snippets in the vulnerability feature library with the to-be-analyzed source code aiming at the to-be-analyzed source code, determining suspicious code snippets and forming similar code pairs; the feature representation module is used for acquiring feature information of the suspicious code snippets; the fine granularity positioning module is used for obtaining the semantic similarity, the structural similarity and the subgraph matching degree of the similar code pair according to the feature information associated with the two code snippets in the similar code pair, and determining the comprehensive similarity of the similar code pair; and the judgment module is used for determining whether the suspicious code snippets in the similar code pairs have vulnerabilities or not according to the comprehensive similarity. The method has the beneficial effects that the processing efficiency can be improved while the detection precision can be ensured.

The invention provides a bearing signal expansion method and system based on a chaotic particle swarm algorithm and a generative adversarial network, and the method comprises the following steps: firstly, carrying out the preprocessing of a collected bearing acceleration signal, and carrying out the denoising through employing the discrete wavelet transform (DWT) in combination with a Bayesian soft threshold method; secondly, determining a proper signal sample length according to a Nyquist sampling theorem, slicing the signal by adopting a sliding window strategy, inputting the sliced data into a chaos particle swarm algorithm for optimization, and searching an optimized vector which is closest to the structural similarity of the sliced data; and finally, inputting slice data of a real sample into a discriminator, and superposing the optimized feature vector with random disturbance to serve as initial input of a generator. In the training process, the parameters of the generator and the discriminator are mutually confronted and updated until a preset training round is reached. And the small sample problem and the class imbalance problem in bearing fault diagnosis are effectively relieved.

The invention relates to an adaptive BM3D terahertz medical image denoising method based on KSVD and SSIM optimization, and belongs to the technical field of image processing. In the basic estimation stage, firstly, an 8 * 8 reference block is selected in a noise image, similar blocks are searched in a neighborhood with the reference block as the center to form a similar block group, and noise is removed and image details are recovered through hard filter value filtering processing. And finally, the BM3D algorithm performs weighted average processing on the estimation blocks in all the groups to generate a final de-noised image. And carrying out KSVD noise reduction on the image subjected to basic estimation, inputting the image subjected to noise reduction and an original noise image into final estimation, and carrying out block matching grouping, Wiener filtering and aggregation to form a final result. According to the method, local sparsity and non-local similarity of pictures can be fully utilized, cross-regional redundant information is utilized to suppress noise, the noise reduction capability is improved, meanwhile, more structural similarity is reserved, and a better effect is achieved in the face of high noise.

The invention discloses a small sample cable protection layer overfire temperature-image generation method based on meta-learning, and the method comprises the steps: constructing a multi-task small sample data set through a small number of fire resistance test images, proposing a meta-learning type image generation model, and achieving the quick adaption to different protection systems through an internal and external circulation meta-learning strategy; inputting a target protection configuration, a fire condition and a temperature condition, wherein the meta-learning type image generation model can generate a fire appearance image of each protection layer at a corresponding temperature grade; further performing physical consistency judgment based on temperature-damage trend, cross-layer association and protection system configuration to ensure that a generation result conforms to a real damage rule, and performing quantitative index verification by calculating a structural similarity index, learning and sensing image block similarity and a Frechet Inception distance; according to the method, the multi-working-condition high-quality fire passing image can be generated under the condition of image scarcity, the fire resistance test cost is reduced, and data support is provided for temperature inversion and damage evaluation after a bridge cable fire disaster.

The invention discloses a photon counting CT image noise reduction method based on multi-channel 3D U-net, relates to the technical field of medical image processing, and aims to solve the problems of insufficient spatial information utilization of a traditional 2D CNN, complex calculation of a 3DCNN and poor noise reduction effect of a traditional method. According to the method, high-energy, low-energy and all-energy images obtained by photon counting CT are used as three-dimensional multi-channel input, and a matrix containing space and energy spectrum dimensions is formed after preprocessing. Multi-scale three-dimensional features are extracted through a 3D U-net encoder, deep semantic information is aggregated through a bottleneck layer, a decoder carries out deconvolution up-sampling and is fused with an encoder feature map through jump connection, and meanwhile, an attention module is embedded in the jump connection to generate an attention mask so as to strengthen key area features; a mixed loss function training network including mean square error loss and structural similarity loss is adopted, and pixel-level precision and structural retention are balanced.

The invention relates to the cross technical field of artificial intelligence and medical image processing, in particular to a medical image segmentation system and method based on a wavelet bridge diffusion model and an efficient conditional random field. A WBDM-ECRF framework is constructed and comprises a discrete wavelet transform module, a BDM-T module, a BDM-S module and an ECRF module; decomposing the image through discrete wavelet transform, extracting a low-frequency sub-band, and enhancing the contrast ratio of a focus and normal tissues; the BDM-T takes U-Net as a backbone, integrates a FlashAttention mechanism, and optimizes a variance formula to realize efficient training; the BDM-S adopts a leapfrog sampling strategy, so that the reasoning time is greatly shortened; the ECRF introduces a multivariate potential function of a structural similarity index and smooth operation through edge expansion, and accurately optimizes edge segmentation. According to the method, the dependence of marked data is reduced, the training and reasoning efficiency is improved, the problem of fuzzy edge segmentation is solved, the Dice coefficient and intersection-union ratio performance on the ISIC data set is excellent, and reliable quantitative support is provided for disease diagnosis and treatment.

The invention relates to a method and system for correcting answer content based on image recognition, and belongs to the technical field of image recognition. The method comprises the following steps: acquiring an answer content image, and preprocessing the answer content image to obtain an answer content reference image; identifying the answer content reference image through a cascade identification architecture to extract the content to be corrected; obtaining a standard answer, and calculating the structural similarity between the standard answer and the to-be-corrected content through a semantic similarity model; and carrying out error step labeling based on the structural similarity to generate a visual correction report. The answer content is corrected based on image recognition.

The invention discloses a wind turbine generator impeller anomaly detection method and system based on sound vibration signal identification. According to the method, firstly, impeller response is collected through a microphone / vibration sensor, and a Mel spectrogram is generated; then, a Teager-Kaiser energy operator and self-correlation analysis are utilized, and under the condition of not depending on a rotating speed signal, the rotating period of the impeller is recognized, and frequency spectrum segmentation is carried out; calculating a cross-period Mel frequency spectrum dynamic deviation, and normalizing the cross-period Mel frequency spectrum dynamic deviation through an amplitude correction coefficient related to the rotating speed; generating a periodic coherent energy diagram by adopting an improved structural similarity algorithm; performing filtering enhancement by using a harmonic resonance template, performing morphological deconstruction and parameterization on an abnormal region in the graph, and extracting geometric features; and finally, calculating a comprehensive abnormal score based on the multi-dimensional features and realizing automatic early warning. According to the method, the problems of variable working condition interference and rotating speed dependence are effectively solved, and accurate and stable detection of early abnormality of the impeller can be realized.

The invention relates to the field of rotating machine fault diagnosis, in particular to a rotating machine data enhancement method based on an SCF-CVAE-GAN network, and the method comprises the steps: obtaining rotating machine vibration signals in different fault modes; the rotating machinery vibration signals are converted into a two-dimensional time-frequency image, and the SCF-CVAE-GAN network is trained by adopting the two-dimensional time-frequency image; generating a two-dimensional time-frequency image sample by adopting the trained SCF-CVAE-GAN, calculating the structural similarity and the distance score of the generated sample, and evaluating the quality of the generated sample; mixing the generated sample meeting the quality requirement and the two-dimensional time-frequency image to obtain an enhanced data set; according to the method, a double-self-correction network is provided, a frequency domain regularization strategy is integrated, global information and local textures of data are deeply mined, and the diversity and fidelity of generated samples are ensured by constraining distribution of the samples in a frequency domain.

The invention discloses an SPR (Surface Plasmon Resonance) image optimization processing method based on image segmentation and edge enhancement, which comprises the following steps: acquiring SPR image data, and preprocessing to generate standardized SPR image data; inputting a structure boundary extraction model constructed based on CGNet, generating a structure boundary label graph, and aligning the structure boundary label graph with the image; gradient amplitude and local entropy mutation detection is executed, and an artifact guide graph is generated; respectively inputting the standardized image into details and context branches of the improved CSDNet, and extracting edge and semantic feature maps; inputting a guide perception gating module, executing structure enhancement and artifact suppression fusion, and generating a fusion feature map; inputting into a multi-scale detail recovery module, and outputting an edge enhanced image; and executing structural similarity and marginal definition scoring based on the original image and the enhanced image, and generating an optimization result. According to the method, synchronous optimization of SPR image edge enhancement and structure maintenance is realized, and the image definition and diagnosis availability are remarkably improved.

A multi-feature three-dimensional reconstruction 3D Gaussian method based on laser vision comprises the steps that laser radar point cloud and camera images are aligned through space-time calibration, and a unified coordinate system is established; extracting geometric features by using point cloud data acquired by the Lidar point cloud, and initializing a Gaussian ellipsoid according to the Lidar point cloud; optimizing the brightness, the contrast ratio and the structural similarity of the rendered image and the real image by combining the mean absolute error L1 and the structural similarity SSIM; the curvatures of Gaussian ellipsoids of K-nearest neighbors are forced to be consistent, and long and short axes and line and surface features of the Gaussian ellipsoids are aligned to reduce geometric distortion; the distribution density of 3D Gaussian is dynamically adjusted through line / surface features and visual structure information extracted by Lidar, and balance between geometric detail enhancement and calculation efficiency is achieved. According to the method, the position, the scale and the rotation parameters of Gaussian are uniformly optimized, and the details and the calculation efficiency of the model are balanced while the consistency of the model structure is improved.

The invention discloses a multi-focal-length embryo image fusion method based on multi-gradient collaborative fusion and structural global decoding, which comprises the following steps of: 1, acquiring a plurality of images of the same embryo under different focal lengths, and preprocessing to generate a multi-channel input feature map; 2, a parallel multi-branch gradient feature extraction network is constructed, and each branch is composed of a multi-scale convolution attention module and used for extracting different types of gradient features; 3, performing dynamic weighted fusion on the multi-scale gradient features output by each branch through a gating mechanism to generate unified fusion features; 4, constructing a structural global decoding network, and reconstructing the fusion features into a clear focusing image; 5, jointly introducing self-supervised gradient alignment loss and structural similarity loss, and optimizing network parameters; and 6, training and testing are carried out on the constructed data set, the fusion task of the multi-focal-length embryo image is completed, and the focusing quality and the structure reduction degree of the embryo image can be improved.

The invention belongs to the technical field of charging gun detection, and discloses a charging gun damage detection method and computer equipment, and the method comprises the steps: obtaining a detection picture of a charging gun of which the damage condition is to be detected; calculating a structural similarity index SSIM of the detection picture and a preset standard picture, and drawing a damaged area, which is different from the standard picture, on the detection picture; calculating a coverage value of the damaged area covering a preset key area and a damage proportion value of the area of the damaged area in the whole area of the detection picture; performing preset weighted calculation based on the coverage value and the damage proportion value to obtain a damage evaluation value of the charging gun; and generating corresponding alarm information based on the damage evaluation value. Through the multi-dimensional damage assessment system, the problem that the misjudgment rate is high due to the fact that damage is judged only through pixel differences in the prior art is effectively solved.

The invention relates to a cross-domain image change detection method based on style randomization and similarity difference, and belongs to the technical field of remote sensing image processing and artificial intelligence. Inputting the two stages of images, extracting multi-layer features by using a twinborn convolutional neural network encoder sharing weight, and inputting the extracted multi-layer features step by step from a high layer to a low layer along a low-to-high transmission direction of a feature pyramid to realize fusion of shallow details and deep semantics; performing structural style randomization processing on the multi-layer fused features obtained in each period; inputting the two stages of feature maps after the structural styles are randomized under the same scale into a multi-scale structural similarity difference module to obtain fused difference features; performing convolutional decoding on the fused difference features to output a pixel-level change probability graph, and performing post-processing to obtain a final change detection result; and calculating a loss function by using the change probability graph output by the decoder, and carrying out optimization training to obtain a final model. According to the method, the accuracy and generalization ability of cross-domain remote sensing change detection can be remarkably improved.

The invention provides an edge device behavior security detection method for a swan gap system, and the method comprises the steps: extracting behavior expected data of each component from a source code or an intermediate representation, and constructing an expected behavior graph; collecting behavior data of each component, and constructing an actual behavior graph; comparing the actual behavior map with an expected behavior map, calculating the structural similarity between the actual behavior map and the expected behavior map, and marking a node or a behavior path deviating from the expectation; and comparing the capacity, authority and component calling relationship between the nodes or behavior paths deviating from the expectation and the expectation behavior map to obtain an abnormal behavior source, taking response measures of different levels according to the type, severity and influence range of the behavior paths deviating from the expectation, and generating an abnormal report. According to the method, deviation between behavior maps is identified through a structured comparison method, and abnormal behaviors such as unauthorized capability combination calling, implicit permission extension and service path hijacking are effectively discovered.