235 results about "Restoration method" patented technology

The invention provides a chemical restoration method for treating heavy metal-polluted terrain soil by ferromagnetic artificial nucleuses. In a ferromagnetic artificial nucleus soil restoration method, the ferromagnetic artificial nucleuses are applied to heavy metal-polluted soil; soil above a heavy metal pollution depth is overturned by cultivation equipment to uniformly mix with the ferromagnetic artificial nucleuses; and after heavy metal can be dispersed from the soil to the artificial nucleuses by a period of time, the soil containing the artificial nucleuses is overturned by using the cultivation equipment to increase the change of the artificial nucleuses contacting with other soil. After the soil overturning procedure is repeated by multiple times, the cultivation equipment provided with magnets can be used for separating the ferromagnetic artificial nucleuses, absorbed with the heavy metal, from the polluted soil; and the content of the heavy metal of the polluted soil is reduced due to shifting-out of the ferromagnetic artificial nucleuses, so that the purpose of soil restoration is gradually achieved.

The invention discloses a diffusion model character restoration method and device based on dual-condition guidance, and belongs to the field of artificial intelligence of character restoration technology, and the method comprises the steps: obtaining a first feature matrix and a second feature matrix; taking the first feature matrix as a query vector, and carrying out cross-attention fusion on the query vector and a key vector and a value vector of the second feature matrix to generate a target condition vector; injecting the target condition vector into a noise preset network of a diffusion model of which the initial input is the to-be-repaired character image, and dynamically fusing the local feature of the jump connection with the target condition vector through a cross attention block to generate an enhanced feature; and generating a repaired character image by combining weight scheduling of skeleton priori and stroke priori. According to the method, the structure and stroke features are obtained through the two feature matrixes, and character constraint conditions are dynamically introduced in the reverse denoising process of the diffusion model, so that the model can retain details of strokes and character style features while generating a high-quality repair result.

The invention discloses an ancient fresco restoration method based on progressive reconstruction and damage perception self-adaption, and relates to the technical field of computer vision and image processing, a damaged fresco image is input into a coarse restoration network for initial restoration, in the initial restoration process, a discriminator and the coarse restoration network are adopted for adversarial training, and a damaged fresco image is obtained; generating an initial restoration result of the mural image; inputting the initial repair result and the mask image into a mask-guided local information extraction network to obtain a local optimization result; and inputting the local optimization result into a global information extraction network to obtain an overall restoration result of the mural image. The problem that an existing network is low in efficiency in the aspect of capturing local details and global styles is solved. The method has good applicability to damaged wall paintings; the problem of fuzzy texture in the repairing result is effectively solved; local features are adaptively extracted and fused according to the damage degree of the mural, and the multi-stage residual information distillation module further refines details of the mural on different scales.

The invention belongs to the technical field of computer vision three-dimensional scene repair, and provides an automatic driving three-dimensional scene repair method based on color point cloud prior guidance, which comprises the following steps: acquiring multi-source heterogeneous data; constructing an incomplete semantic two-dimensional Gaussian field according to the multi-source heterogeneous data, and generating an incomplete image sequence, an incomplete depth image sequence and an opacity image sequence; obtaining a color point cloud based on the instance segmentation image, the opacity image, the incomplete image and the incomplete depth image of the reference image; generating a texture pseudo view angle sequence based on the color point cloud; generating a repaired image sequence by taking the texture pseudo view angle sequence as a condition signal of the fine-tuning video diffusion model; generating a repair depth sequence based on the color point cloud; calculating a repair Gaussian loss function based on the repair image sequence and the repair depth sequence, and performing iterative repair optimization on the incomplete semantic two-dimensional Gaussian field to obtain a complete repair two-dimensional Gaussian field; the invention further discloses a computer device. And texture-geometry collaborative automatic driving scene three-dimensional repair is realized.

The invention relates to the technical field of image processing and cultural relic protection, in particular to a cultural relic image virtual restoration method based on texture reconstruction and color correction, and the method comprises the following steps: S1, obtaining a digital image of the surface of a to-be-restored cultural relic, and carrying out the denoising and brightness equalization processing of the digital image; s2, identifying and segmenting a damaged area in the image; s3, selecting an optimal sample block which is most matched with the structure of the region to be filled; s4, performing adaptive affine transformation on the optimal sample block to generate a reconstructed texture block; s5, carrying out linear transformation to obtain a repaired texture block after color correction; and S6, seamlessly embedding the repaired texture block after color correction into the damaged area. According to the method, the texture direction alignment and the color distribution correction are combined, so that the consistency of the damaged area and the surrounding image in structure and color is realized, and the naturalness and integrity of cultural relic image restoration are remarkably improved.

The invention discloses a chapter-level Chinese ancient book image restoration method and related equipment, and belongs to the field of ancient book restoration. The method comprises the following steps: acquiring ancient book images, and screening out intact ancient book images and damaged ancient book images; for an intact ancient book image, simulating damage in a random mask mode, and constructing a training data pair; for the damaged ancient book image, marking the position of the incomplete character, and determining the specific content of the incomplete character; constructing a repair model based on the diffusion model, and training the repair model; and using the trained model to gradually realize the restoration of the chapter-level ancient book image in the form of an overlapped sliding window in an autoregression mode. According to the method, on the basis of the damaged positions and the damaged text content of the ancient books marked by human experts, the diffusion model technology is adopted, an autoregressive restoration method is combined, automatic restoration of the chapter-level damaged ancient book image is achieved, and the overall consistency of the font style and the background is effectively kept in the restoration process.

The invention belongs to the technical field of cultural relic virtual restoration, and discloses a cultural relic virtual restoration method and device, equipment and a storage medium. The method comprises the following steps: acquiring text information and image information of a to-be-restored cultural relic; analyzing the text information and the image information; the analyzed key information is fused, and a repairing instruction is generated; based on the restoration control instruction, generating a restored cultural relic image through a diffusion model; performing dynamic parameter optimization on the restored cultural relic image; and decoding the optimized image data into a visual image and outputting the visual image. Through the above mode, accurate capture of the damaged area of the cultural relic and generation of the personalized restoration scheme are realized, secondary damage possibly caused by a traditional restoration method to the cultural relic is avoided, the text information of the cultural relic is fused on the basis of the image, the original appearance and safety of the cultural relic are ensured, and the cultural relic after virtual restoration better conforms to the real situation.

The invention relates to the technical field of image restoration, in particular to a spatial frequency hybrid restoration method for JPEG (Joint Photographic Experts Group) compressed blurred image restoration, which is characterized in that a spatial frequency hybrid restoration network is constructed for compressed blurred image restoration and comprises three coding blocks and three decoding blocks, each of the coding block and the decoding block is composed of a plurality of spatial frequency mixing blocks; the spatial frequency mixing block comprises a spatial frequency double-branch mixing structure and an ISS block; the SFHRN provided by the invention can realize a more efficient restoration effect on the compressed blurred image with lower model parameter quantity and calculation complexity.

The invention discloses a video restoration data set construction and restoration method combined with generation of a large model, and belongs to the technical field of video restoration, and the method comprises the following steps: S1, slicing; s2, screening: performing aesthetic scoring and motion detection on the slices, and screening and retaining high-dynamic and high-image-quality fragments; s3, segmenting a core object; s4, extracting text features; s5, model input; s6, performing feature fusion; s7, model training: only training parameters of the cross attention layer, and locking gradients of other layers to reduce the calculation cost; s8, calculating a loss function; s9, video input: inputting a to-be-restored video, and generating an object Mask and text description of the to-be-restored video; and S10, outputting the restored video: inputting the Mask video, the object Mask and the text description into the improved generated large model, and outputting the restored video. According to the staged feature injection strategy, the global naturalness and the local reality sense are considered, and the visual consistency of the repaired content and the original video is remarkably improved.

The invention discloses a historical document restoration method and system based on implicit interpolation network enhancement. The method comprises the following steps: acquiring a damaged historical document image, a position mask image for identifying a to-be-restored area and a content guide image for guiding the semantic meaning of restored content; constructing a historical document repair model based on a de-noising diffusion probability model; training the historical document repair model by using a training data set containing damaged image and intact image pairs; and inputting the damaged historical document image to be repaired, the position mask image and the content guide image into the historical document repair model, and outputting the repaired historical document image. According to the method, the accuracy of the character content in the restoration area and the naturalness of the visual boundary are remarkably improved, and high-fidelity digital restoration of complex degraded literatures such as ancient books and inscription rubbings is realized.

The invention discloses a document image illumination recovery method and system based on a diffusion model, and the method comprises the following steps: constructing a conditional diffusion module which takes a shadow image as conditional input, inputs a noise image, and predicts a conditional scoring function; a document image illumination recovery module is designed, a coarse-to-fine processing strategy is adopted, a preliminary degradation map is predicted through a coarse network, and more accurate degradation mapping is predicted through a fine network in combination with a shadow image, a scoring function and preliminary degradation mapping; applying the accurate degradation mapping to the shadow image to realize illumination recovery of the document image; a high-resolution complex illumination recovery test set with rich layout is constructed, and the generalization ability of the model is evaluated. According to the method and the device, effective recovery of the illumination of the document image is realized, meanwhile, the efficiency of processing the high-resolution image is improved, and the problems of poor recovery effect, low processing efficiency and inconsistent generation results when the illumination of the document image is recovered in the prior art are solved.

The invention discloses a lightweight mural virtual restoration method based on an inactive generative model, and belongs to the technical field of digital image processing and computer vision. The objective of the invention is to solve the technical problem of semantic and texture recovery imbalance of an existing deep learning repair model in face of cultural heritage such as murals. A damaged image and a mask are stacked into a multi-channel tensor to be input into a pre-trained AFMNet model, the model adopts an encoder-bottleneck-decoder architecture, and an encoder uses a multi-domain partial convolution module without an activation function to synchronously extract and fuse local texture and frequency domain global structure information and synchronously update the mask; the bottleneck portion models the lowest resolution features and masks thereof through a lightweight multi-head attention module to capture remote dependencies, and a decoder upsamples in conjunction with the features of the jump connection transfer to reconstruct image details. The method is mainly used for carrying out efficient and high-fidelity digital virtual restoration on damaged visual cultural heritage such as ancient murals.

The invention discloses an urban shallow lake ecological protection and restoration method and system integrated with intelligent management and control, and relates to the technical field of water environment ecological management and intelligent water affairs, and the method comprises the steps: constructing a three-dimensional monitoring network to synchronously collect water quality, image and meteorological data, and carrying out the fusion to generate a multi-modal data set; a deep learning model is utilized to realize pollution source tracing, water quality prediction and algae bloom early warning in parallel; based on the prediction result, outputting a multi-facility cooperative regulation strategy through a reinforcement learning agent; and converting the strategy into a hierarchical instruction to drive an execution unit, and feeding back the treated environment state to the model and the intelligent agent to form closed-loop optimization. According to the invention, whole-course intelligent management and control from monitoring to execution are realized, systematicness, accuracy and perspectiveness of lake treatment are effectively improved, and water environment risk response capability and ecological restoration effect are significantly enhanced.

The invention relates to a compression method, a reduction method and a processing device for template files, and the compression method comprises the steps: carrying out decompression processing on a to-be-compressed target document, and obtaining an object folder in a preset format; performing decompression processing on a template document of the target document to obtain a template folder in the preset format; the target document and the template document have common elements; and generating a compressed file of the target document according to difference information between the object folder and the template folder. According to the compression method, a large amount of storage space is saved, and the document compression rate of a single document and batch documents is effectively improved. The reduction process consumes very short time, and a single document or batch documents can be efficiently executed.

The invention discloses a mural restoration method and system based on a pulse coupling neural network and a diffusion model, and relates to the technical field of mural restoration. The method comprises the following steps of: firstly, enhancing an image by adopting a three-dimensional pulse coupled neural network enhancement module (SMSPCNN), removing noise and reserving original features; then global and local features are fused through the diffusion reconstruction module, the extraction module is responsible for extracting corresponding features, and finally the attention feature fusion module is used for fusing the features and enhancing expression, so that local and global information fusion is realized, and the model performance is improved. According to the method, the damage of the Dunhuang mural can be effectively repaired, details and structures are reserved, the sense of reality is enhanced, multiple indexes in a quantitative contrast experiment are superior to those of other methods, and a new way is provided for digital repair of the Dunhuang mural.

The invention discloses a goaf ecological restoration method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-source remote sensing data and geological survey data of a goaf, and generating a three-dimensional geological damage model of the goaf through a space-time fusion technology; performing multi-scale feature extraction on the three-dimensional geological damage model by using a deep convolutional neural network to obtain a multi-dimensional ecological feature data set, identifying an ecologically fragile region and an instability risk region, and outputting an ecological risk level map; performing parallel processing on the multi-dimensional ecological characteristic data set to generate an ecological assessment report containing health degree scores of each ecological subsystem of the goaf; and based on the ecological risk grade map and the ecological assessment report, a multi-objective optimization algorithm is adopted to generate a vegetation-soil / rock mass-water body collaborative restoration strategy. By means of the goaf ecological restoration method and device, accurate assessment and collaborative strategy generation of goaf ecological restoration can be achieved, and scientificity, efficiency and overall ecological benefits of restoration engineering are improved.

The invention discloses a time sequence reconstruction and machine learning coupled chlorophyll monitoring data breakpoint restoration method, and belongs to the technical field of water quality monitoring. The invention discloses a chlorophyll monitoring data breakpoint restoration method based on coupling of time sequence reconstruction and machine learning, and the method comprises the following steps: S1, collecting water quality monitoring data, and cleaning the monitoring data to obtain preprocessed data; s2, performing time sequence reconstruction on the preprocessed data to obtain a weekly average 1 data set; s3, respectively constructing a radial basis function neural network model and a back propagation neural network model by taking the chlorophyll concentration as a response variable and the conventional water quality parameter as a predictive variable; s4, performing performance evaluation on each model by taking a root mean square error, an average absolute percentage error, goodness of fit and relative error distribution statistics as evaluation indexes, and screening out an optimal model; and S5, applying the conventional water quality parameters in the breakpoint interval of the chlorophyll monitoring data in the water body to the optimal model, and outputting the restored chlorophyll concentration value to complete the dynamic restoration of the breakpoint.

This invention is a multi-dimensional ancient building maintenance and restoration method based on 3D laser scanning, relating to the field of computer processing technology. The method comprises the following steps: S01, using a terrestrial laser scanner to acquire basic 3D point cloud data of the ancient building. This basic 3D point cloud data includes geometric information of the building facade, ground structure, and near-ground decorative components; S02, using an unmanned aerial vehicle equipped with a multi-line laser scanner to scan the building roof and high-altitude structures to generate supplementary point cloud data. This supplementary point cloud data covers the millimeter-level precision 3D coordinates of the sloping roof, tower eaves, and eaves brackets; S03, using a multispectral imaging device to simultaneously collect surface texture data of the ancient building. This invention achieves millimeter-level accuracy in 3D reconstruction of ancient buildings through multi-source data fusion and a hierarchical optimization algorithm. Combined with automated defect identification and multimodal analysis techniques, this method improves the efficiency of maintenance decision-making.

The invention relates to a relics restoration method based on a meta-universe and a digital twinborn technology, and aims to solve the problems of relics disappearance and historical memory deficiency in urban construction, and utilizes the meta-universe and the digital twinborn technology to build a relics 3D model on a meta-universe platform to realize scene restoration. A virtual scene is constructed through software such as 3DMAX and Unity 3D and related technologies, and the characteristics of identity, social contact, immersion and the like are achieved; the method has obvious advantages, the cost can be reduced, the efficiency can be increased, the real scene can be stably simulated, and multi-place policy support can be obtained. The market potential is large, and the method faces college teachers and students, culture enthusiasts and other groups. Popularization strategies such as social media propaganda and SEO are adopted, and the product has unique advantages and innovation and is flexible in pricing; in addition, the method has both campus and social public welfare, provides an innovative scheme for relics recovery, and is of great significance in the fields of cultural inheritance, education and the like.

The invention discloses an electric imaging logging blank area intelligent restoration method based on graph adversarial transfer learning. The method comprises the steps that a first training data set is established according to public image data; constructing an initial image restoration model based on the graph neural network and the generative adversarial network, and training and testing the initial image restoration model by using the first training data set to obtain a pre-trained image restoration model; acquiring conductivity logging data, and establishing a second training data set according to the conductivity logging data; carrying out migration training on the pre-trained image restoration model by utilizing the second training data set to obtain an optimized image restoration model; and inputting a to-be-repaired electric imaging logging image into the optimized image repair model to complete the repair of the electric imaging logging image. The method can effectively improve the restoration precision of the image of the electric imaging logging blank area, fills up the image missing area caused by the instrument problem, and also has remarkable advantages in the aspects of processing speed and adaptability.

The present application relates to a power distribution network restoration method and apparatus, and a device, a storage medium and a program product. The method comprises: by taking a target restoration amount that corresponds to a power distribution network to be restored in a preset restoration period being maximized as an objective, constructing an objective function for restoring said power distribution network; acquiring a constraint condition for constraining said power distribution network, wherein the constraint condition comprises a resource scheduling constraint condition for constraining the scheduling of power restoration resources, and a power distribution network constraint condition for constraining said power distribution network; on the basis of the objective function and the constraint condition, determining a scheduling policy for the power restoration resources; and on the basis of the scheduling policy, restoring power to said power distribution network. By using the method, the restoration effect of a power distribution network can be improved.

The invention relates to the technical field of image enhancement, in particular to a damaged archive restoration method and device, and the method comprises the following steps: selecting a group of damaged archive images and undamaged archive images, and collecting a color histogram of the undamaged archive images to obtain color histogram data; and based on the color histogram data, adjusting a color histogram of the damaged archive image through a histogram matching technology, matching histogram distribution of the undamaged archive image, and generating a color correction image. According to the method, through matching correction of the image color histograms of the damaged archives and the undamaged archives, the original color features of the archives are accurately reproduced, the interference of human subjective factors on the restoration result is effectively reduced, and the authenticity and color consistency of the restored image are improved; due to the introduction of the local adaptive enhancement technology, the contrast ratio of each local area of the archive image is more prominent, and the problem of overexposure or detail missing possibly caused by overall unified enhancement is avoided.

The invention relates to a cultural relic virtual restoration method and device based on a digital twinning technology, equipment and a medium. The method comprises the following steps: acquiring original data of overseas lost cultural relics; performing standardized preprocessing on the original data to obtain a cultural relic data packet; performing three-dimensional geometric reconstruction based on the cultural relic data packet to obtain a cultural relic three-dimensional digital model; identifying diseases appearing in the cultural relic three-dimensional digital model through an image segmentation model to obtain a cultural relic disease list; based on the cultural relic disease list, predicting the risk of the structural stability of the cultural relic three-dimensional digital model through mechanical simulation to obtain a cultural relic risk prediction map; and based on the cultural relic disease list and the cultural relic risk prediction map, parallel generation of restoration strategies with different emphasis points is carried out, and a virtual restoration scheme is obtained. By adopting the method, the initial state of the cultural relic can be reproduced in the virtual environment, the restoration effect can be reviewed, and the potential risk can be accurately predicted.

The invention provides a mining area geology restoration method. The mining area geology restoration method comprises the steps that environmental parameters and reaction state data of a to-be-restored mining area are acquired through a sensing network; based on the environmental parameters and the reaction state data, digital twinborn bodies are constructed and run, and the digital twinborn bodies are used for simulating the migration of carbon dioxide in the to-be-restored mining area and the reaction process of the carbon dioxide and minerals; generating an injection strategy for controlling a carbon sequestration reaction process based on a simulation result of the digital twin; according to the injection strategy, injection equipment is controlled to inject a reaction medium into the to-be-repaired mining area; and performing monitoring and fault prediction on the operation state of the injection equipment, and generating a maintenance instruction. According to the invention, intelligence, precision, high efficiency and multi-target collaboration of the geological restoration process of the mining area are realized.

The invention discloses a test and output restoration method for an instruction-guided image editing model based on a metamorphic test technology, and the method comprises the steps: collecting an original input image and an editing instruction as an original test case, collecting an output image of the original test case, and comparing the input image with the output image to obtain an editing region of the original test case; converting the input image according to the metamorphic relation to generate a candidate image set, selecting an equivalent derivative test image, and forming a new derivative test case together with an original editing instruction; collecting output images of all the derivative test cases, comparing the input images with the output images to obtain editing areas of the derivative test cases, and judging whether the editing areas of all the derivative test cases are consistent with the editing areas of the original test cases or not; carrying out cross validation on all derivative test cases and the output of the original test case, selecting the output of the optimal test case, and mapping the output of the optimal test case to the original test case to serve as the restored original test output; and summarizing a test result and outputting a repair result.

The invention provides a mine operation repairing method based on an unmanned aerial vehicle and a related device, and relates to the technical field of mine repairing. Based on factors such as a topographic relief factor, a wind field turbulence intensity factor, an unmanned aerial vehicle interference factor, a throwing angle, a wind direction angle, throwing delay and the like, firstly, an initial drop point of seed settlement is given through a physical model, and deviation caused by complex wind field turbulence, unmanned aerial vehicle interference, irregular topography and the like is corrected in combination with a machine learning algorithm; according to the method, the nonlinear and interaction problems in an actual scene are considered, a quadratic term and a small number of physical meaning interaction items are adopted for feature mapping, correction is conducted through a physical model in combination with a machine learning algorithm, and accurate prediction of the seed settlement drop point can be better achieved.

The invention provides an evaluation and repair method and system for a composite bushing umbrella skirt. The method comprises the following steps: detecting and evaluating the current aging state of the composite bushing umbrella skirt to obtain an aging grade; setting a repair decision according to the aging level; and repairing the composite sleeve umbrella skirt according to the result of the repairing decision. Through the processing of the steps, the evaluation and restoration method breaks through the limitation of detection decentralization, evaluation subjectivity and restoration manual operation in the prior art, establishes a standardized grading evaluation and decision-making mechanism, constructs a whole-process automatic restoration system, and improves the accuracy of the whole-process automatic restoration. According to the method, the aging state of the composite sleeve umbrella skirt can be automatically and accurately evaluated, the repairing process is intelligently and adaptively executed, the repairing effect is verified in a closed loop mode, and standardization, automation and precision of aging repairing of the composite sleeve umbrella skirt are achieved, so that the operation and maintenance cost and dependence on manpower are greatly reduced on the premise that the repairing quality is guaranteed, and the repairing efficiency is improved. And the repair efficiency and quality of the composite sleeve umbrella skirt are improved.

The application discloses a kind of based on U-Net model's missing audio automatic repair method, including the following contents: audio signal is handled to the sample of same length and constructs multiple audio data sets;All audio samples are handled into fixed length three parts, from left to right respectively left context part, gap part and right context part;Using short-time Fourier transform, audio context part and gap part are all converted into spectrum diagram;The encoder in deep learning model carries out feature learning to the spectrum diagram of context part, and the decoder realizes the automatic repair of gap part missing audio, and the two corresponding layers are connected by jump connection to expand network receiving range, improve network repair performance;Finally, the generated spectrum diagram is converted into audio signal using local weighted summation strategy.The application uses U-Net deep learning model and local weighted summation strategy to repair missing audio, and the repair effect is good, and the calculation cost is lower, suitable for the repair of audio missing situation in short audio.

The invention discloses a firmware backdoor detection and one-key security restoration method and system, and relates to the technical field of software detection. The method comprises the steps of obtaining an original byte sequence of a firmware upgrade package; performing segmented structure analysis and standardization on an original byte sequence of the firmware upgrade package to obtain a byte interval index of each segment and an upgrade package segment list; extracting ciphertext statistical characteristics of each segment based on the byte interval index and generating an upgrade package statistical fingerprint; comparing the statistical fingerprint of the upgrade package with a historical baseline to obtain a ciphertext anomaly score; when the ciphertext exception score exceeds a verification trigger threshold value, generating an equipment end verification task and collecting equipment end verification evidence; performing fusion judgment on the basis of the ciphertext anomaly score and the equipment end verification evidence, and outputting a comprehensive risk level and a recommended disposal action; according to the method, the risk detection can be realized without depending on a decryption key or a differential synthetic material, so that the security of firmware upgrading is ensured, and the operation and maintenance efficiency is also considered.

The application provides an attack path restoration method and device, which can be used in the field of computer technology. The method comprises the following steps: monitoring the running state of an application program in each server and collecting key data of the application program in the running process by using a RASP agent installed on each server; when an attack event is detected, generating an alarm information according to the key data, and determining a target server in each server which has a correlation with the attack event according to the alarm information; generating a server attack path according to the interaction relationship among the target servers; and generating an attack path corresponding to the attack event according to the server attack path and each component in the target server. The application can quickly and accurately restore the attack path of the attack event.