264 results about "Information recovery" patented technology

The invention relates to the technical field of crop yield analysis, in particular to a crop yield prediction method based on a time sequence remote sensing image, and the method comprises the steps: obtaining an optical remote sensing image and a synthetic aperture radar image in a crop growth period, and constructing a time sequence image through complementary information fusion and under-cloud information recovery; the problems of cloud interference and data missing are solved, a multi-scale time regulation and control convolution module is used for processing a time sequence image, multi-scale time sequence features are extracted, feature fusion is carried out in combination with attention difference jump connection, and a rice distribution map is obtained; determining a rice planting area based on the rice distribution diagram, selecting an optimal assimilation time period in the rice planting area, and assimilating the leaf area index subjected to remote sensing inversion and a crop growth model simulation result to generate a simulated yield after assimilation; based on the multi-scale time sequence characteristics, a parameter dynamic response matrix related to crop growth model parameters is established, and residual error correction is performed on assimilated simulation yield, so that the rice yield prediction precision is improved.

The application discloses a ciphertext decryption method and related equipment applied to ciphertext encrypted according to an RSA algorithm, and the method comprises the following steps: acquiring a large number in ciphertext and a public key of the ciphertext; selecting a number smaller than the large number as a base of a discrete logarithm problem, selecting 1 as a modulus power operation result corresponding to the base, and converting the base and the modulus power operation result into an initial quantum state; inputting the initial quantum state into a quantum computing module for solving the discrete logarithm problem to obtain a first target quantum state in which a solution result of the discrete logarithm problem is stored; extracting the solution result from the first target quantum state, and calculating a private key of the ciphertext based on the solution result; and decrypting the ciphertext based on the private key to obtain plaintext of the ciphertext. Through the technical scheme, the private key can be recovered according to the disclosed information in the case that the private key is not disclosed, and then the ciphertext is decrypted, so that the related technical blank is filled.

The invention provides a hot upgrade method for a virtual machine monitor (VMM), which comprises the following steps of: pausing the operation of a virtual machine, storing state information of an original VMM, and reserving a kernel-based virtual machine KVM extension page table and a memory management structure of the original VMM; reserving a page global directory and a virtual memory area VMA corresponding to a virtual machine RAM area according to the memory management structure, and refreshing a process address space described by the memory management structure into a new VMM execution program so as to complete switching of the original VMM into the new VMM; and executing the new VMM, recovering the state information to the new VMM, and inheriting the KVM extension page table by the new VMM to recover the operation of the virtual machine. The invention further provides computer equipment, a computer readable medium and a computer program product.

The invention provides a large model-based text generation and information recovery method and system. The method comprises the following steps of: converting complex situation information into simple and accurate text description by utilizing a text generation model; converting the text description of the situation information into a semantic vector by using a text encoder of a pre-trained CLIP model, and capturing semantic information of the text; encoding the noise image into a hidden space representation by using an encoder of a variational auto-encoder, and learning a compressed representation of the image; fusing the semantic vector generated by the pre-trained CLIP model with the image hidden space representation generated by the encoder of the variational auto-encoder, and optimizing the fused hidden space representation by using the trained de-noising Unet network; the optimized hidden space representation is decoded into a high quality situational information image using a decoder of a variational auto-encoder. By applying the technical scheme of the invention, the technical problem of incomplete received information caused by inevitable information loss in the data compression process in the prior art is solved.

The invention discloses a screen shooting traceability method, device and equipment and a readable storage medium, which are applied to the field of computer software, and comprise the following steps: encoding watermark information to obtain a watermark information sequence; embedding the watermark information sequence into the image through an R channel, a G channel and a B channel of RGB (Red, Green, Blue) to obtain an image with a watermark; and acquiring the shot image with the watermark image, and tracing to obtain watermark information based on the shot image. According to the method, the watermark is generated by using the RGB channel of the single pixel image, so that the visual concealment of the watermark image is improved, the difficulty in perceiving of human eye recognition is ensured, and the visual quality of a user is not influenced; the watermark embedding position is not influenced by an original image carrier, and watermark information can be hidden in any area of the image; and the watermark information recovery success rate is high.

The invention provides an RAID (redundant array of independent disks) data processing method and device, a chip, electronic equipment, a storage medium and a computer program product, and is applied to RAID, the method comprises the following steps: detecting that at most three storage devices fail, determining at most three corresponding to-be-recovered areas in a stripe, and storing the to-be-recovered areas in the stripe; the to-be-recovered area is a data area, a verification area, a first hot standby area and / or a second hot standby area; determining a target area based on at most three areas to be recovered, wherein the target area is a data area, a verification area, a first hot standby area and / or a second hot standby area except the areas to be recovered; and target information in the target area is read, and the data information and / or the first verification information of the to-be-recovered area are / is recovered based on the target information.

The invention provides an emergency communication network generation method, a terminal device and an electronic device, the method is applied to a first node device in a wireless communication network, and when the communication of the wireless communication network is interrupted due to unforeseen circumstances, each node device monitors a neighbor node list broadcasted by other node devices to generate an emergency communication network. And comparing the received neighbor node list with the neighbor node list of the local machine, determining cluster head equipment, and forming a corresponding target communication cluster by taking the cluster head equipment as a central control node. And the cluster head device sends a cluster establishment message to the effective neighbor node in the neighbor node list, the effective neighbor node adds to the target communication cluster as the identity of the cluster member based on the received cluster establishment message, and finally each cluster member device carries out data receiving and sending according to the unified scheduling information sent by the cluster head device. Therefore, each node device can quickly form a communication cluster and then quickly recover communication according to the unified scheduling information sent by the cluster head device.

The invention relates to a data recovery method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: receiving a data reading command, and determining protection information of target data corresponding to the data reading command; the APP domain information in the protection information comprises bad sector mark BST information corresponding to a sector where the target data is located; under the condition that the BST information is damaged, verification information corresponding to the APP domain information is determined in the APP domain stripe; verifying the APP domain information according to the verification information to obtain recovered APP domain information; the recovered APP domain information contains the recovered BST information. By adopting the method, the data recovery efficiency can be improved.

The embodiment of the invention provides a low-illumination image enhancement method and system based on frequency decomposition and multi-scale fusion, and the method comprises the steps: obtaining a to-be-processed image, and decomposing the to-be-processed image into a low-frequency component feature map and a high-frequency component feature map through employing an iterative mean filter; a low-frequency tensor is generated based on the low-frequency component feature map, and a high-frequency tensor is generated based on the high-frequency component feature map. And fusing the high-frequency tensor and the low-frequency tensor to generate an enhanced feature map. According to the method, iterative mean filtering can be applied to separate frequency bands, and an image to be processed is decomposed into a high-frequency component and a low-frequency component. More accurate image enhancement is realized through different sub-networks, that is, a high-frequency enhancement network focuses on detail reconstruction and noise suppression, and a low-frequency enhancement network is responsible for recovery and transformation of illumination and color information, so that the image enhancement quality can be improved, and the problem of poor image enhancement effect is solved.

The invention discloses a geomagnetic anomaly data stable downward continuation method based on an SRGAN deep learning network, and belongs to the field of geophysical magnetic field data continuation. According to the invention, in combination with the continuation characteristic of geomagnetic anomaly data, a CBAM space attention mechanism module is added to improve the spatial perception capability of a network model to the magnetic anomaly data, and in combination with the characteristic that the downward continuation of potential field data can amplify the noise generated by a high-frequency signal; and the deep learning model is combined with adaptive threshold multiple wavelet transform to suppress noise data so as to further improve the continuation precision. In order to enable a model to fully learn detailed information of geomagnetic anomaly data, a training data set construction method combining a Kriging super-resolution algorithm with a sliding subarea mode is provided. By designing and synthesizing a simulation model experiment and comparing actual measurement data with other three algorithms, the model is highest in calculation precision and best in magnetic anomaly detail information recovery.

The invention relates to the technical field of over-the-air downloading, in particular to a remote OTA upgrading method and system, and the method comprises the steps: building an encrypted communication channel between a terminal and a server, and guaranteeing the precise and safe transmission of data; the terminal transmits version related information to the server through the communication channel, the server performs upgrade judgment according to the received information and stores the information as a backup for specific analysis of incremental files, and upgrade failure caused by data loss in the running process of the terminal is prevented; if upgrading is needed, the server compares the files to generate a class of file packages, and analyzes modular files in the incremental files to reduce the size of the file packages; after the file package is transmitted to the terminal, the completion of upgrading is confirmed through integrity verification and testing; in addition, when upgrading fails, the terminal can be recovered by utilizing backup information through a rollback mechanism, so that the upgrading process is accelerated, and the upgrading success rate is improved.

A mesh data decoding method according to embodiments may comprise: a step of receiving a base mesh bitstream, a displacement vector bitstream, a texture map bitstream, and signaling information; a base mesh processing step of restoring a base mesh from the base mesh bitstream; a displacement information processing step of restoring displacement information from the displacement vector bitstream; a restoration step of restoring a mesh on the basis of the base mesh and the displacement information; and a texture map processing step of restoring texture maps from the texture map bitstream.

The invention provides a communication method and a communication device. The method comprises the following steps: a first communication device compresses N pieces of second channel information based on X pieces of first channel information to obtain M pieces of compressed information, for example, the ith compressed information is obtained by compressing Pi pieces of second channel information based on the jth piece of first channel information, and the acquisition time of the first channel information is earlier than the acquisition time of the second channel information; the first communication device sends the ith compressed information, and the first communication device also sends the jth first channel information; the second communication device restores Pi pieces of second channel information based on the i-th piece of compression information and the j-th piece of first channel information that may be received at different times. Through the mode, the channel information (namely the second channel information) can be fed back in time, and the feedback overhead can be reduced.

The invention discloses a covert communication system based on a generative diffusion model, and belongs to the technical field of covert communication. In order to solve the problems of insufficient concealment and poor anti-noise performance in the prior art, hidden transmission and reliable recovery of information are realized by constructing a diffusion generation and classification discrimination module. The sending end carries out noise addition and shaping on the original bit waveform by using a forward diffusion process to form a noise-like hidden signal; and a receiving end recovers a signal structure through a reverse denoising process, and accurately judges bit information through a classifier. According to the method, a Gaussian approximation hidden waveform library is constructed by adopting a signal pair training model which is consistent in statistics but distinguishable in structure, and random sampling mapping and boundary smooth splicing are combined, so that transmission signals are difficult to distinguish from noise in a frequency domain and a feature space; therefore, the avoidance capability, the anti-interference robustness and the information recovery accuracy under the low signal-to-noise ratio of the detection means are remarkably improved.

The invention provides a gradient matching attack method of a Vision Transform (ViT) model aiming at noise disturbance, and relates to a privacy protection problem in a federated learning scene. The method specifically comprises the steps of providing a gradient denoising method based on a diffusion model, recovering disturbed gradient data through reverse noise prediction, and designing a gradient diffusion denoising network for attention perception to solve the problem of feature distortion caused by traditional Gaussian noise. On the basis, a gradient matching attack method for ViT is provided, and high-fidelity semantic information recovery of noise-added gradient is realized by analyzing gradient distribution characteristics of a ViT model and combining a position coding gradient extraction and optimization reconstruction technology. The method disclosed by the invention reveals the vulnerability of the ViT model with noise disturbance when facing gradient leakage attacks, provides a confrontation view angle and thinking enlightenment for constructing a ViT privacy protection mechanism, and has important application value in privacy sensitive fields such as medical image analysis and financial risk control.

The invention discloses a neural network structure search method for self-supervising image noise reduction, and belongs to the field of neural network structure search and image noise reduction. According to the method, a search space of a multi-scale encoder is designed, so that a network structure can fuse feature information extracted from different scales, more image details can be recovered, and the network structure with a better noise reduction effect can be found; based on a search space of a multi-scale encoder, an improved genetic algorithm is provided to search a network structure, a coarse-grained population initialization method, a distance-guided parent selection method and a modularization-based crossover mutation operation are designed, and the search capability of the genetic algorithm is improved. Compared with an existing self-supervised image noise reduction method and a neural network structure method, a better noise reduction effect can be achieved; experimental results prove that the self-supervised image noise reduction neural network structure searching method can effectively discover a network structure with a better noise reduction effect.

The invention relates to a recoverable visual encryption method for a human body privacy area in an image collected by an unmanned aerial vehicle. The method comprises the following steps: constructing a joint deep learning model integrating a pre-training target detection module, a self-adaptive visual encryption module and an enhanced visual decryption module; in the training stage, target detection module parameters are frozen, and an encryption and decryption module is optimized end to end through combined loss formed by a composite decryption loss function and an encryption strength loss function; during application, the target detection module accurately locates a human body sensitive area, the encryption module applies size-adaptive and strength-learnable pixelated transformation to generate a natural encryption image, and the decryption module realizes high-fidelity original information recovery through an enhanced U-Net network under an authorization condition. Compared with an existing traditional image encryption or irreversible desensitization technology, the method has the advantages that seamless integration with a mainstream target detection process is achieved, the integrity and restorability of information are guaranteed while high visual concealment is achieved, and the method has the advantages of being intelligent, high in efficiency and high in practicability.

The invention provides a database log analysis and recovery method and device, equipment, a medium and a program product, and relates to the field of data processing. Periodically generating a snapshot file of cache record data involved in a database log analysis process, and recording a snapshot identifier corresponding to the snapshot file; updating version information of a data dictionary, and binding the snapshot identifier with the version information of the data dictionary; when database log analysis is interrupted, matching corresponding target version information from the data dictionary according to a target snapshot identifier; recovering the state of the cache record data based on the target version information, and determining the initial position of database log analysis; according to the method, the snapshot file of the cache record data is periodically generated, and the snapshot identifier and the data dictionary version information are bound, so that the cache data state and the analysis starting position are quickly recovered based on the latest snapshot after the database log analysis is interrupted, and the fault tolerance and the recovery efficiency of log analysis are improved.

The invention provides a communication method and a communication device, and the method comprises the steps that a second device obtains first indication information from a first device, the first indication information is used for determining a time unit in which reset state information of a first artificial intelligence AI model takes effect, and the second device can determine according to the first indication information, and a time unit for enabling reset state information of the first artificial intelligence (AI) model. According to the scheme of the embodiment of the invention, the robustness of the recovery performance of the channel information can be improved.

The invention provides a semantic communication method and system for an ultra-high-definition video, and relates to the technical field of semantic communication, and the method comprises the steps: obtaining semantic features based on a to-be-coded video P frame and a channel state through employing a video P frame semantic feature extractor; based on the semantic features, obtaining four-step semantic importance by using a quadtree-based spatial domain entropy model; based on the semantic features and the four-step semantic importance, utilizing a four-way semantic feature codec to obtain the received semantic features; and reconstructing a video P frame by using a semantic information restorer and a de-noising enhancement module based on the received semantic features, the received channel state and the multi-scale reference context features. According to the method, the semantic features of the video frames are extracted and recovered by using the module constructed by the CNN, and the semantic features of the video frames are processed by using the coding and decoding module constructed by the Swinin-Transform, so that the coding and decoding efficiency is improved, the understanding ability of the model to the video content is enhanced, the high-quality video content can be better reconstructed, and the size of the model parameter is controlled.

The invention provides a model reasoning method and device, a storage medium and computer equipment. The method comprises the following steps: acquiring a pre-stored allocation table and an address recovery table of a target model; wherein the allocation table is a target model used for recording an execution sequence corresponding to nodes in a computational graph captured by the target model in a preprocessing process, and the address recovery table is a mapping relation table of a kernel and parameters constructed according to the computational graph; according to the execution sequence in the allocation table, an address recovery table is inquired, address information of nodes in the computational graph is obtained, and the address information comprises a kernel address and a parameter address of each node; according to the address information of each node, recovering a computational graph of the target model in the reasoning process; and based on the calculation graph, executing reasoning calculation of the target model. Therefore, the model can be inferred according to the computational graph, the operator graph is prevented from being recaptured when the model is inferred, the time delay of cold start of the model is reduced, and the cold start efficiency of the model is improved.

The invention discloses a single-photon single-pixel imaging method based on average pulse number estimation intensity. The method comprises the following steps: recording each single-photon event; averaging the recorded pulse number of each single photon event to obtain an average pulse number of different photon numbers; performing intensity estimation by using the average pulse number of different photon numbers to obtain average pulse number estimation intensity, and performing information recovery on the target object to obtain an image of the target object; based on the image of the target object, the imaging quality of the target object is evaluated by calculating different image quality indexes. An image is restored by counting the number of pulses before each photon arrives and averaging. In an experiment, only one single-photon avalanche diode is used as a single-photon detector to obtain photon information, the high efficiency of the method under the low-light condition is proved, and compared with a traditional time correlation single-photon counting imaging method, the method can achieve information recovery of a target object through fewer photons.

A method for information recovery includes providing a set of rules to hosts, where each host performs a backup operation using the set of rules by sending at least one file to at least one backup location. The method also includes receiving, from each host, a first metadata corresponding to files backed up in the backup operation, where the metadata from each host forms a set of metadata, providing the set of metadata to a learning module, receiving from the learning module metadata analysis, adjusting, using the metadata analysis, the set of rules to obtain an adjusted set of rules, and providing the adjusted set of rules to the plurality of hosts, where at least one host of the hosts uses the adjusted set of rules to identify a backup location for a file.

The invention belongs to the technical field of wireless communication, and particularly relates to a communication sensing integrated system based on non-orthogonal AFDM (Alternating Frequency Division Multiplexing). According to the system, a bandwidth compression factor is introduced into an AFDM framework to construct a non-orthogonal waveform, and controllable overlapping between chirp subcarriers is realized, so that the spectrum utilization rate is improved, and the robustness of the system under a high-speed dynamic channel is enhanced. And a receiving end realizes reliable information recovery through maximum likelihood detection, and processes echo signals in combination with a two-dimensional parameter estimation algorithm so as to realize high-precision sensing of the target distance and speed. The system has good anti-Doppler capability and perception precision performance, and is suitable for communication perception fusion application in a high-speed mobile scene.

The invention discloses a monocular vision-based structure displacement measurement method under a high-temperature working condition, and particularly relates to the field of monocular vision-based image analysis from a bit image to a non-bit image, and the method comprises the steps: extracting a local gray level feature set and carrying out the statistics of gray level distribution parameters based on a continuous frame gray level image sequence collected by a monocular vision system; judging and screening preliminary connected regions, establishing a region degradation probability graph, and constructing a standard Markov random domain graph structure; based on a standard Markov random domain graph structure, a gray gradient feature set is extracted, a local gradient disturbance vector field is established, and a disturbance potential energy graph is generated. By constructing a variation Markov random domain model based on local gray features and mutual information entropy adjustment and combining dynamic degradation characteristic extraction and reference point time sequence trajectory analysis, structural information recovery and displacement measurement of a local degradation region under a high-temperature working condition are realized. And the problems of image connectivity loss and structure tracking failure caused by optical window degradation are solved.

Provided are a feature encoding / decoding method and device, and a computer-readable recording medium generated by the feature encoding method. The feature decoding method according to the present disclosure is performed by a feature decoding device, and comprises the steps of: acquiring, from a bit-stream, parameter information related to a plurality of layers; and recovering feature information about the plurality of layers on the basis of the parameter information, wherein the parameter information can include information about the number of feature channels and / or information about a quantization parameter (QP).

The embodiment of the invention provides a software content layout data processing method and device, and the method comprises the steps: receiving template data, annotation data and configuration data of a preset software template, carrying out the structural analysis of the template data according to a template analysis engine, determining a structural analysis result, initializing the configuration data according to a rule engine, and carrying out the structural analysis of the configuration data. And determining a rule engine instance, based on the structure analysis result and the rule engine instance, performing standardized preprocessing on the template data through a template preprocessing engine, determining a standardized template structure, and replacing the template content of the standardized template structure according to the annotation data. According to the method and the device for replacing the small red book template, the template is replaced, the labeling information recovery operation, the spacing deviation recovery operation, the exceeding detection operation and the layout adjustment operation are sequentially executed on the basis of the replaced template, and a final template structure is obtained for a user to use, so that the small red book template typesetting efficiency and quality can be improved.

The present invention relates to the technical field of crop yield analysis, and specifically to a crop yield prediction method based on time-series remote sensing images. In the present invention, optical remote sensing images and synthetic aperture radar images during the crop growth period are acquired, and time-series images are constructed through complementary information fusion and cloud-underground information recovery. Cloud interference and data missing problems are addressed, and a multi-scale time-regulated convolution module is used to process the time-series images, extract multi-scale time-series features, and perform feature fusion in combination with attention differential jump connections to obtain a rice distribution map. Rice-planting areas are determined based on the rice distribution map, and an optimal assimilation period is selected within the rice-planting areas. A leaf area index obtained by remote sensing inversion is assimilated with crop growth model simulation results to generate an assimilated simulated yield. Based on the multi-scale time-series features, a parameter dynamic response matrix related to crop growth model parameters is established, and residual correction is performed on the assimilated simulated yield, thereby improving the accuracy of rice yield prediction.

The invention discloses a depth guidance image defogging method and system for deep ground medical rescue, and aims to improve the visibility of medical rescue monitoring images and the accuracy and real-time performance of structural information recovery in deep ground environments such as mines. By introducing a scale collaborative attention module, collaborative learning of multi-scale features is realized, scale confusion is relieved, and the cross-scale feature collaboration capability is enhanced; and meanwhile, an error perception guide module is adopted to fuse local feature difference and global dependence, and mutual promotion optimization is performed on defogging and depth estimation tasks, so that more accurate image restoration is realized in a region with a complicated structure or non-uniform fog distribution. According to the method, close collaborative optimization and performance improvement between image defogging and depth estimation tasks can be realized, and the problems of scale confusion and limited scale collaborative capability in multi-scale feature learning can be solved to a certain extent.

The invention provides a deep and large fracture depicting method based on low-frequency electromagnetic information recovery enhancement, and the method comprises the steps: S1, collecting electromagnetic data, and carrying out the preprocessing of the electromagnetic data, and obtaining the preprocessed electromagnetic data; s2, obtaining frequency domain apparent resistivity and phase according to the preprocessed electromagnetic data; s3, establishing a shallow stratum electrical model; s4, acquiring a depth domain apparent resistivity profile according to the shallow stratum electrical model; and S5, depicting deep and large fracture distribution based on the depth domain apparent resistivity profile. According to the invention, enhancement of low-frequency electromagnetic signals is realized, the inversion precision of electromagnetic data is improved, low-frequency geological information is effectively recovered, and deep and large fractures can be effectively described.