2781 results about "Image area" patented technology

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 discloses a data compression transmission method and system applied to a ferry inspection image, and the method comprises the steps: collecting and obtaining the ferry inspection image in real time, recognizing a key inspection target region in the image, and carrying out the segmentation and partitioning of the image; compressing the key inspection target area based on lossless compression coding; the quantization step size is dynamically adjusted by comparing statistical variances of background pixels between continuous frames, and lossy compression coding is carried out on a background area; based on the boundary distance between the key inspection target area and the background area, adaptive compression coding is carried out on the transition area; constructing a hierarchical data packet; and constructing a data transmission optimization model, dynamically allocating data transmission links, and obtaining a transmission scheme with the highest total transmission. The method has the advantages that efficient data compression transmission is realized by accurately segmenting the image area and adopting a lossless, lossy and adaptive compression technology, the overall transmission efficiency is improved through intelligent transmission optimization, and the definition and real-time performance of the inspection image are ensured.

The invention relates to the technical field of thermal power production, artificial intelligence and time sequence analysis, in particular to a measuring point time sequence anomaly analysis method and system based on a multi-modal large model. Calculating a cross-modal feature similarity weight based on a self-attention mechanism to obtain a fusion feature vector; comparing the fusion feature vector with the normal mode feature representation through a self-supervised learning algorithm to realize anomaly detection, wherein a result comprises anomaly time, a fault image area and a text keyword; and a result is input into a multi-modal large model, cross-modal attention mechanism association data is utilized, fault reasoning and causal relationship analysis are carried out, a visual diagnosis report is generated, and then the accuracy of measuring point screening and time sequence anomaly analysis is improved.

The invention discloses a foam concrete image processing method based on sub-pixel edge reconstruction, and particularly relates to the field of foam concrete image processing.The method comprises the steps that an image of foam concrete is obtained, and a boundary response feature set containing edge direction information, edge gradient change information and local intensity comparison information is extracted; performing edge structure classification on the image region based on boundary response feature set combined threshold judgment; and aiming at the image region which is judged to be the local direction convergence mutation region, identifying a cross mutation point set by fitting a direction tensor convergence trend, and executing directionally-guided corrosion and expansion image processing. By constructing a structure judgment variable based on image boundary response characteristics and guiding a corrosion expansion path and a vein backtracking path to execute sub-pixel-level aperture boundary separation, image structure processing and contour reconstruction of a porous adhesion area are realized.

The invention relates to the technical field of medical image detection, and discloses a medical image quality detection method based on image processing. The method comprises the following steps: acquiring medical image data to be detected, wherein the medical image data comprises a multi-modal scanning image sequence and corresponding acquisition parameters; the medical image data are preprocessed, standardized image data are generated, and the standardized image data comprise unified parameters of spatial resolution, gray scale range and noise level; extracting structural features of the standardized image data, wherein the structural features comprise tissue boundary gradient distribution, texture consistency and local contrast information; constructing a quality evaluation model according to the structural features, wherein the quality evaluation model analyzes a mapping relationship between the structural features and preset quality indexes through a dynamic convolutional network; and outputting a quality defect detection result based on the quality evaluation model, wherein the quality defect detection result marks an image region with artifacts, fuzziness or distortion.

The invention discloses an intelligent glasses image adjusting system based on eye movement tracking and gesture fusion, and relates to the technical field of intelligent equipment. A multi-modal sensing module is arranged to construct a multi-modal sensing layer to capture eyeball movement tracks and gesture actions; a fixation point prediction module is set to process a dynamic scene through a space-time attention mechanism to obtain a fixation point prediction area, a gesture semantic understanding module is set to process gesture actions based on a Transform architecture, and the gesture actions of a user are converted into image adjustment instructions. An image enhancement strategy setting module designs a multi-stage image enhancement strategy according to the fixation point prediction area and the image adjustment instruction, and sets a dynamic adjustment intensity control module to perform adaptive adjustment to obtain a dynamic adjustment intensity control result; an eye movement-gesture cooperative control module is arranged to provide an eye movement-gesture cooperative control mechanism to realize image area selection and parameter adjustment, and accurate image area selection and parameter adjustment are realized.

The invention relates to the technical field of data processing, and provides a target detection method and device, a model training method and device, electronic equipment and a medium. The target detection method comprises the steps that a to-be-recognized image and a query text are acquired, and the query text is used for querying a target object corresponding to the query text in the to-be-recognized image; performing image recognition on the to-be-recognized image to obtain image description features and region detection visual features; performing regional multi-modal fusion processing on the image description features and the regional detection visual features to obtain regional multi-modal fusion features; performing feature fusion processing on text features obtained based on the query text and the regional multi-modal fusion features to obtain text regional fusion features corresponding to the query text; and a target detection result is obtained based on the text features and the text region fusion features, so that the fusion degree of text semantics and image region features is improved, and the accuracy and robustness of target detection in a complex scene are improved.

The invention relates to the technical field of medical cosmetology and computer vision, in particular to a 3D skin measurement method based on high-precision measurement of a face concave-convex degree region, and the method comprises the following steps: arranging a structured light camera to collect a face RGB image and depth data, and carrying out the filtering and denoising to generate a smooth point cloud; detecting an RGB image based on FaceMesh to obtain face key points, and constructing a multi-region segmentation algorithm; segmenting an RGB local area and converting the RGB local area into point cloud data; iDW interpolation is carried out to generate a dense point cloud, and a two-dimensional contour line is extracted through a Marking Square algorithm; and mapping the color of the matched color card to an image area to generate a contour map. According to the method, the face recognition model is constructed through the FaceMesh architecture, accurate positioning of multiple face types is adapted, two-dimensional contour mapping is adopted to three-dimensional contour mapping, data are intercepted, local processing is combined, the operation load is reduced by 60%, and efficiency is remarkably improved.

The invention relates to the technical field of image recognition, in particular to a bridge pier underwater structure image intelligent analysis method and system, and the method comprises the following steps: obtaining RGB channel values of all pixels of a bridge underwater structure image region, converting a main color temperature, generating a color temperature map, dividing the region in a vertical direction, extracting a main color temperature track, and carrying out the linear fitting. And comparing the residual error with a continuity threshold, and marking a color temperature continuous section. According to the method, a two-dimensional map is constructed through fusion of main color temperature and space coordinates, spatial distribution expression of a structural region is enhanced, a stable region is extracted by combining residual and continuity discrimination, interval judgment and RGB finishing are introduced into gray scale lifting, image brightness balance and color consistency are enhanced, a gray scale trend model is constructed through radial chain grouping, and light attenuation interference stripping is achieved. Feature point continuous frame tracking is combined with disturbance vector mapping, structure dynamic changes are captured, abnormal behaviors are recognized through cross analysis of tension differences and response terms, and efficient recognition and interference isolation under multi-dimensional feature fusion are achieved.

The invention relates to the technical field of display screens, in particular to a liquid crystal display screen backlight local dimming method and device, a terminal and a medium. Comprising the steps of obtaining brightness and color distribution data of a display picture; calculating a target brightness value of each backlight block through the dimming algorithm module based on a dynamic partition control strategy and a preset brightness mapping model; wherein the dynamic partition control strategy is used for dividing adjacent backlight blocks into dynamic dimming groups according to brightness characteristics of a picture area on the premise that the number of the backlight blocks is not increased; the backlight control device drives an LED backlight block of the backlight module according to the target brightness value; and monitoring a display effect in real time, and adjusting parameters of the brightness mapping model based on feedback. According to the invention, the problems of cost increase and related derivation caused by increase of the number of partitions in the prior art can be solved.

The invention provides a safety production behavior monitoring method and system based on AI video analysis. The method comprises the steps of collecting a real-time video data stream of a production area; inputting each frame of video image in the real-time video data stream into a target detection model for target detection to obtain a personnel target output by the target detection model and a target position coordinate of the personnel target in each frame of video image; cutting out a local image area of the personnel target in each frame of video image based on the target position coordinate, and performing feature recognition based on the local image area to obtain personnel features; performing comparison on the basis of the personnel characteristics and the personnel standard behavior characteristics to obtain personnel behavior states, and performing track association on the basis of the personnel behavior states corresponding to the continuous multi-frame video images to obtain personnel behavior tracks; and performing safety production behavior monitoring based on the personnel behavior state and the personnel behavior track, and generating an abnormal behavior early warning signal. According to the method and the device, the real-time performance and the accuracy of safety monitoring in a production scene are improved.

The invention provides a digital evaluation method and system for fair-faced concrete based on Mockup, and belongs to the field of building construction. According to the technical scheme, the method comprises the steps of collecting a surface image of a bare concrete Mock sample plate, extracting a reference Lab color value and a reference texture feature from the collected sample plate image, and constructing an evaluation reference model; collecting a surface image of the to-be-evaluated bare concrete member, and generating image data; performing defect area identification on the image data, extracting an effective image area, and extracting a Lab color value to be evaluated and a texture feature to be evaluated from the effective image area; generating a comparison result based on the evaluation reference model; and based on the comparison result, generating evaluation output information. The method has the beneficial effects that the color and texture double-feature model is constructed, a defect identification and shielding mechanism is introduced, and a structured comparison algorithm and an output system are adopted, so that the automation of the whole process from data acquisition, feature extraction, defect avoidance to intelligent comparison and evaluation output is realized.

The invention discloses a lingual surface diagnostic information acquisition, diagnosis and analysis method and system, and belongs to the technical field of medical auxiliary diagnos.The method comprises the steps that an acquired lingual surface image is matched with patient information, a symptom associated lingual surface area is determined, the associated lingual surface area is subjected to priority division, and whether the acquired image meets a clear standard or not is judged; the method comprises the steps of constructing an image index evaluation model based on tongue vibration and image texture, performing tongue vibration, texture stability and water vapor fuzzy interference evaluation on an image needing to be processed, constructing a stable frame evaluation model, and importing vibration intensity, texture stability and a water vapor proportion into the stable frame evaluation model to evaluate image area stability. Image registration is carried out on the stable frame set, multi-frame registration and fusion are carried out based on an image stable region, region stability and processing information are recorded, a high-quality image for tongue picture analysis is generated, the image definition of a key diagnosis region is improved, and the accuracy and stability of tongue picture analysis are improved.

The invention relates to the technical field of target detection, in particular to a search and rescue task-oriented unmanned aerial vehicle thermal imaging visual target detection method, which comprises the following steps of: acquiring multiple frames of thermal imaging images of an unmanned aerial vehicle, extracting regional thermal difference characteristics according to a window, marking a non-background region to generate a candidate set, and fitting and reconstructing a suspected thermal target contour. And analyzing the track and the thermal change rate, screening background interference, identifying jump abnormity, positioning the gravity center, and generating a target repositioning signal. According to the method, the heat value range and variance index sequence in the image area is constructed, the thermal anomaly area is judged in combination with the temperature baseline difference, background disturbance comparison is executed in combination with the direction vector of the coordinate trajectory in the multi-frame image and the thermal change parameter, the false detection probability caused by background noise is reduced, and the detection accuracy is improved. The target jump identification is carried out according to the inter-frame heat value and area change rate in linkage with the thermal isoline closure degree, the target discrimination accuracy in a shielding scene is improved, and the robustness of thermal target extraction in a complex search and rescue environment is integrally improved.

The invention relates to the field of machine learning, and discloses a printing quality detection method and device based on machine learning, and the method comprises the steps: obtaining the original image data of a printed matter under the conditions of multiple batches, heterogeneous illumination and multi-angle imaging, and constructing a printing image training set through combining an image region equalization strategy and a light interference shielding mechanism; performing image block-level fine-grained division on the printing image training set, performing multi-channel feature extraction on each image block by adopting a deep convolution model based on a residual attention mechanism, and generating a composite feature map through a feature coupling structure; based on the composite characteristic spectrum, constructing a defect positioning network, and identifying a potential defect response area; and in combination with the initial defect candidate set, introducing an image background disturbance simulation mechanism and a local artifact inversion model, establishing a defect error maintenance positive frame, and eliminating an interference area caused by non-printing errors. The method has the advantage of improving the accuracy of defect identification.

The invention discloses a sludge treatment automatic monitoring method based on computer vision, and relates to the technical field of sludge treatment automatic monitoring, and the method comprises the following steps: collecting sludge image information through computer vision, extracting an edge integrity index and a brightness distribution index, and constructing a fusion judgment function based on the time sequence change of the two indexes, determining whether a thin water film exists on the sludge surface or not according to an output result of the fusion judgment function; under the condition of determining that a thin water film exists on the sludge surface, acquiring a pixel highlight gradient polymerization rate, a texture missing fluctuation frequency and an image saturation nonlinear deviation value of a corresponding image region, and establishing a specular reflection image feature recognition matrix; by constructing a multi-dimensional image feature recognition and dynamic regulation and control mechanism, the problem of image misjudgment caused by thin water film mirror reflection in the sludge dewatering process is solved, and accurate recognition and automatic closed-loop control of the real water-containing state of the sludge are achieved.

The invention provides a textile industry broken yarn identification method and system based on a neural network, and the method comprises the steps: collecting yarn multi-modal data, including a surface image, a fracture sound wave signal and tension change data; preprocessing the data, and extracting an image ROI region, sound wave spectrum features and a tension mutation sequence; performing space-time alignment and feature extraction on the extracted content to obtain a joint feature vector; inputting the broken yarn into a trained broken yarn identification model, wherein the model can identify broken yarn features; judging whether broken yarns exist or not according to the output result and outputting an identification result; and updating the model through online incremental learning. According to the method, multiple types of data are combined, the adaptive preprocessing and feature extraction technology is used, environmental noise is inhibited, key features are focused, and the problem of false alarm of a traditional sensor is solved; multi-modal features are fused through space-time alignment and a self-attention mechanism, and bidirectional LSTM modeling is combined, so that a broken yarn dynamic rule is accurately captured, and the problems of missing detection and delay of manual inspection are avoided.

The invention discloses a data labeling method and system based on user behaviors and attention tracking, and the method comprises the steps: synchronously collecting multi-source behavior signals of a mouse, a keyboard, eye movement and the like of a doctor in real time, combining identity and interface metadata, and carrying out the standardized normalization, abnormality elimination and short time sequence behavior unit division. And extracting individual behavior micro-modes by using unsupervised clustering, and constructing a behavior portrait library. Through multi-modal time sequence modeling and a self-adaptive space-time attention mechanism, behavior characteristics, an interface area and a report text are deeply fused, a multi-level correlation probability is output, and high-precision automatic tagging of content and an image area is realized.

The invention discloses a seal area character recognition method and device and a nonvolatile storage medium. The method comprises the following steps: determining a candidate seal image area in an image according to color information of pixel points in the image; point-by-point sliding convolution processing is carried out on the candidate seal image area through a multi-scale annular convolution kernel group, so that a target seal image area is determined in the candidate seal image area, and the multi-scale annular convolution kernel group comprises a plurality of convolution kernels which are of concentric ring structures and have different radius lengths; mapping the target seal image area into a rectangular expanded image, and identifying and extracting a character image to be identified in the rectangular expanded image; and performing identification processing on the character image to be identified to obtain a seal text corresponding to the target seal image area. The technical problem that the text image processing efficiency is low due to the fact that the text information of the seal area cannot be effectively recognized in the related technology is solved.

The invention discloses a method for identifying small target features in a ray detection image, and relates to the field of image processing, and the method comprises the steps: carrying out the size unification, pixel standardization and normalization preprocessing of an original ray image; constructing a training sample set through image region cutting, and introducing a dynamic sampling strategy to realize positive and negative sample proportion adaptive control; enhancing the number and diversity of small target samples in a training set by using point-shaped and linear artificial defect generation strategies; constructing an image segmentation model and introducing feature jump connection to fuse shallow space and deep semantic information; combining Dice loss and Focal loss to form a composite loss function, and guiding the model to pay attention to a target region with a small area and weak gray level; and finally, a segmentation result is optimized through morphological processing and connected domain analysis, and structured target detection information is output. According to the invention, the recognition accuracy and integrity of the tiny target in the ray image can be effectively improved, and the adaptive capacity of the detection method to the change of the imaging quality is enhanced.

A system may access an input image. The system may generate a plurality of segments based on one or more segmentation models and the input image, each segment from among the plurality of segments representing a corresponding salient object. The system may generate a depth map based on a depth estimation model. The system may layer the plurality of segments, based on the depth map and border regions between pairs of segments, to generate a plurality of ordered segments. The system may execute a vision-language model to generate a text annotation of the image based on the plurality of ordered segments.

The invention discloses an impregnated paper surface uniformity detection method based on texture image analysis, and particularly relates to the field of image enhancement and texture analysis, and the method comprises the steps: obtaining an impregnated paper image, constructing an edge image based on a gray difference value, extracting an image region with an edge value lower than a first threshold value in the edge image, and defining the image region as a fuzzy region; and edge value difference calculation between adjacent pixels is carried out on the fuzzy region to form a difference chart, and a pixel region with an edge change value greater than a second threshold value is extracted from the difference chart and is defined as a fracture region. According to the scheme, the weak boundary between the adhesive film and the fiber is extracted by recognizing the fuzzy region with the low edge value and tracking the direction path of the fuzzy region, the problem that the low-contrast edge cannot be recognized through a traditional method is solved, and the detection precision of the non-uniform region is improved.

The invention relates to a steel surface microscopic crack feature extraction method and device, equipment and a storage medium. The method comprises the following steps: acquiring steel surface features, performing down-sampling on the steel surface features twice, and then capturing long-distance feature association of a cross-image region by adopting a dynamic sparse attention mechanism to obtain dynamic attention features; after the dynamic attention features are coded, a dynamic position coding strategy is adopted for position coding, and a position coding result is obtained; carrying out cross-scale semantic fusion on a position coding result by adopting a self-attention mechanism of Transform, and then carrying out dimension remodeling to obtain fine defect features on the surface of the steel; and performing defect detection on the steel surface according to the fine defect characteristics of the steel surface. According to the method, a dynamic sparse attention mechanism is adopted, so that the robustness of the deep learning model in processing long-range dependence and diversified features is effectively enhanced, and the efficiency and accuracy of extracting the steel surface microscopic crack features are improved.

The invention relates to the technical field of image focus detection, in particular to a fiber endoscope image focus detection method and system, and the method comprises the following steps: providing a data quality guide interface; capturing narrow-band light images of the endoscope away from a first preset position and a second preset position of the intestinal wall in the axial movement process, calculating brightness gain values of a blue light channel and a green light channel, calculating an asymmetric scattering correction coefficient, and updating indication of color information calibration integrity; tracking the pixel moving speed of the interferent in the view in the propulsion operation process, calculating the relative depth of the interferent, and updating the indication of the validity of the depth data of the interference layer; identifying the image area which is not shielded by the interferent, and updating the indication of the information coverage degree of the background area; triggering an image synthesis operation; performing color compensation on the image information of the image area which is stored in the background canvas and is not shielded by the interferent; reconstructing a clear image; and performing focus detection on the clear image. The method improves the accuracy of focus detection.

The invention discloses a multi-modal remote sensing visual positioning method and device based on scene knowledge enhancement and a medium, and relates to the technical field of remote sensing visual positioning. The method comprises the following steps: firstly, preprocessing a plurality of remote sensing images, generating scene knowledge enhanced text description, forming a visual positioning data set, and dividing the visual positioning data set into a training set, a verification set and a test set; constructing a visual positioning model, and obtaining an optimal model through training, verification and testing; inputting a to-be-queried text to obtain remote sensing image coordinates. According to the method, cross-modal fusion of knowledge enhancement is realized, scene knowledge is embedded into a visual positioning framework, and the problem of inference of implicit semantics in a remote sensing scene is solved; multi-scale image features and scene knowledge are fused layer by layer through multi-round cross-modal attention iteration, and semantic understanding from coarse granularity to fine granularity is achieved; a similarity threshold is introduced to screen a high-correlation image region, and background interference is reduced in combination with loss constraints. The LLaMA2 is subjected to efficient fine tuning in combination with the LoRA technology, end-to-end coordinate generation is supported, and both performance and calculation efficiency are considered.

The invention discloses an image region analysis method based on entropy driving feature enhancement, and relates to the technical field of image analysis and feature enhancement. According to the method, the local channel information entropy is used as a core feature statistical magnitude, and adaptive weighting and strengthening of different importance region features are realized through explicit quantification of image feature information amount; weight distribution is dynamically adjusted according to the characteristic values, the response of a high-information dense area is remarkably enhanced, and meanwhile low-information and noise interference areas are effectively restrained. On the basis, a cross-layer attention mechanism based on entropy prior is designed, feature statistical information is embedded into a gating and weight generation process, and attention distribution with feature significance as guidance is achieved. According to the method, through an entropy-driven adaptive feature enhancement mechanism, the perception capability, the feature discrimination capability and the analysis precision of the model on the salient region of the image are effectively improved.

The invention relates to the technical field of image processing, in particular to a laminated slab surface crack intelligent detection method, which comprises the following steps: acquiring a surface image of a current laminated slab; clustering all the pixel points based on the texture roughness of each pixel point to obtain a plurality of image areas; calculating a first defect probability of each image area; obtaining a corresponding second defect probability based on the obtained sound wave signal of the surface of the laminated slab corresponding to each image area; fusing the first defect probability and the second defect probability corresponding to each image area by using the gray weight and the sound wave weight to obtain a defect degree; if the defect degree of at least one image area is greater than or equal to a threshold value, determining that the quality of the current laminated slab is unqualified; and if the defect degree of each image area is smaller than a threshold value, continuously performing quality judgment on the current laminated slab according to a set condition. According to the scheme, the surface crack of the laminated slab can be accurately detected.

The invention relates to the technical field of computers, in particular to an exponential segmentation method based on visual fine-grained semantic-driven cross-modal collaboration, which follows a basic normal form of research in the field of exponential segmentation, and designs a method for carrying out image fine-grained visual enhancement, then carrying out semantic-driven cross-modal collaboration and finally carrying out semantic-driven cross-modal collaboration. And finally, segmenting the mask prediction model. According to an input image, fine-grained visual understanding is enhanced, understanding on a complex spatial position is enhanced, semantic-driven cross-modal collaborative decoding is carried out on the image after fine-grained understanding is enhanced in combination with a text, and then the decoded image is used for final segmentation mask prediction. The method can solve the problems that the correlation between an image area and related language description cannot be fully mined through an existing representative segmentation method, so that fine-grained alignment is insufficient, and a model cannot understand fine-grained correlation such as space and position.

The invention provides a small target detection method and device based on a high-resolution fusion feature map. The method comprises the following steps: dividing an image to be detected into a plurality of overlapped sub-regions and recording position information of the overlapped sub-regions; performing feature extraction and multi-scale fusion on each sub-region to generate a high-resolution fusion feature map; generating geometric parameters and initial confidence of candidate frames based on each spatial position on the image, and performing preliminary screening to obtain a candidate frame set of the sub-regions; mapping candidate frames in all the candidate frame sets to a global coordinate system according to the position information, and combining repeated candidate frames pointing to the same small target to form a global candidate frame set; for each candidate frame in the global candidate frame set, cutting a local image area in the to-be-detected image to carry out fine judgment to obtain a fine trimming confidence coefficient; and fusing the initial confidence coefficient and the refined confidence coefficient to obtain a comprehensive confidence coefficient, and outputting a final small target detection result according to a comparison result of the comprehensive confidence coefficient and an adaptive threshold value. According to the invention, the tiny target in the image can be accurately captured.

The invention provides a method and a system for transmitting a photographic image of a security camera, relates to the technical field of image transmission, and effectively divides an image frame into a differential image block and a background image block by introducing a partition mechanism based on inter-frame pixel difference, thereby remarkably improving the detection efficiency of a moving target and the pertinence of data processing. Through embedding marks such as a direction vector and a motion timestamp in a differential image block set, priority coding of a dynamic target image area is realized, and further in combination with a periodic compression strategy of a background image frame, redundant data is compressed to the greatest extent, and the overall data transmission load is effectively reduced. Meanwhile, an image fragment priority model is jointly constructed through brightness saliency and texture complexity, and structure entropy and direction gradient analysis is assisted, so that redundant backup generation of key content is realized, and data integrity under a weak network condition is greatly improved.