126 results about "Hash coding" patented technology

The embodiment of the invention relates to the technical field of data storage, and discloses a cross-K8S cluster configuration change storage method and device based on a block chain, and the method comprises the steps: obtaining a transaction request message passing the compliance verification of an intelligent contract engine, and the transaction request message comprises a configuration change request; obtaining log information of the configuration change request in the transaction request message executed by the target K8S cluster, wherein the log information further comprises configuration change cluster difference information; the log information is stored in a fragmented mode based on the IPFS network, and content hash codes of the log information are obtained; and taking the transaction request message and the content hash code as complete transaction information, and storing the transaction request message and the content hash code in a transaction pool. Decentralized storage is achieved based on an IPFS network fragmentation storage mode, the problem that a centralized database or a log system is maliciously modified or historical records are deleted easily in centralized storage is solved, and auditing integrity and data storage safety are guaranteed.

The invention discloses an Internet of Things data storage method and device based on a block chain and a medium, and relates to the technical field of data storage, and the method comprises the steps: collecting an Internet of Things terminal data set, carrying out the noise disturbance and differential aggregation, and generating privacy protection data; performing hash coding on the privacy protection data, and outputting a tamper-proof to-be-stored data packet; performing security level encryption on the tamper-proof to-be-stored data packet by using a homomorphic encryption algorithm to form an anti-attack ciphertext vector; and carrying out data fragmentation and storage node mapping on the attack-resisting ciphertext vector to obtain a fragmentation storage index. According to the invention, through a dual privacy protection mechanism of homomorphic encryption and differential privacy, the privacy protection strength in the data storage process is improved. And meanwhile, safe and efficient multi-chain collaborative storage in a high-concurrency scene is realized through the constructed hierarchical cross-chain collaborative model.

The invention discloses an internet consumption analysis method based on data analysis, and relates to the technical field of data analysis, and the method comprises the steps: a plurality of participation platforms generate local cause-effect sub-graphs according to user behavior data, convert the local cause-effect sub-graphs into Hash codes, upload the Hash codes to a central server, and aggregate the Hash codes to generate a global cause-effect skeleton graph; injecting a predefined sequential logic rule based on the global causal skeleton graph, dynamically adjusting a causal edge weight according to a user real-time behavior event, and incrementally updating nodes and edges in the dynamic knowledge graph; pre-training the policy network parameters by using each participation platform, and uploading the pre-trained policy network parameters to a central server for aggregation; and issuing the aggregated policy network parameters to each participation platform, executing an interpretable policy according to the user real-time behavior event, and returning user feedback behavior data to each participation platform. According to the method, collaborative optimization of privacy security, real-time response and causal interpretability is realized through federal causal discovery and a dynamic knowledge graph evolution architecture.

The invention discloses a dynamic airspace gridding management method and system for low-altitude economy, and belongs to the technical field of unmanned aerial vehicle traffic management, and the method comprises the steps: collecting airspace state data in real time through a multi-source sensing device, and constructing a four-dimensional space-time grid model; generating a four-dimensional space-time grid with a block chain hash code by fusing meteorological data, an airspace control rule and a real-time flight demand; receiving a space-time grid use request submitted by the aircraft through the smart contract, and calculating an optimal grid allocation scheme based on a deep reinforcement learning model; and the edge computing node executes local track prediction, issues a navigation instruction to the aircraft through the distributed account book, monitors a grid occupation state in real time, and triggers a dynamic grid recombination mechanism when sudden conflicts are detected. According to the method, the rigid constraint of static airspace division can be broken through, the cooperative conflict of multiple aircrafts is eliminated, and the marketization configuration of airspace resources is realized.

The invention discloses a conference content intelligent generation processing method and system based on a multi-modal large model, and the method comprises the steps: collecting the original data of a conference, and completing the standardization preprocessing; inputting a multi-modal large model, extracting multi-modal features and carrying out semantic alignment; executing cross-modal hash coding, generating binary codes and establishing an index database; performing hash retrieval on the related fragments, and constructing a conference content directed graph; based on a conference content directed graph structure, searching an optimized path by adopting a Monte Carlo tree; and generating structured conference content, and outputting a summary, an abstract and an action item. According to the method, efficient extraction, accurate retrieval and structured intelligent generation of the conference content are realized by fusing a multi-modal large model, cross-modal Hash coding and Monte Carlo tree search.

The invention discloses a neural radiation field rendering method based on dynamic hash coding, and the method comprises the steps: employing the feature sequence data as the input, calculating the density value and color value of each sampling point through the forward propagation of a neural network, carrying out the volume rendering integral operation according to the ray tracing principle in the direction of a ray, and obtaining the feature sequence data; judging a final color output result of the current pixel point; according to an error value between the color output result and a real image, updating a network parameter weight through a back propagation algorithm, and if the error value is greater than a convergence threshold, continuing to iterate the training process to adjust a feature coding strategy to obtain an optimized neural radiation field model parameter; and after the rendering performance configuration parameters are obtained, optimizing a storage allocation strategy of feature data through a memory pool management mechanism, and if the current memory occupancy rate exceeds a safety threshold, starting a data compression algorithm to reduce the storage space requirement, and obtaining a real-time rendering output result. According to the invention, high-quality real-time rendering of the dynamic scene is realized.

The invention relates to the technical field of image processing and information retrieval, and discloses an image feature extraction and appearance trademark retrieval method based on deep learning, which comprises a feature extraction unit, a cross-modal fusion module, a retrieval strategy module and a dynamic weight distribution unit. The feature extraction unit extracts multi-level semantic information through a multi-branch convolutional network and multi-scale pyramid pooling; the cross-modal fusion module integrates vision, text and color information by using space attention, channel attention and cross attention mechanisms; the retrieval strategy module adopts Hash coding and global self-attention to realize rapid screening and fine matching; the dynamic weight allocation unit suppresses background interference through learnable parameters. The trademark retrieval precision and efficiency can be effectively improved, and the method is suitable for a multi-modal data processing scene.

The invention relates to the technical field of digital health, in particular to an intelligent health data early warning method and system.The method comprises the steps that multi-channel signals such as electrocardio signals, pulse oximeter signals and continuous blood pressure signals which are synchronously filtered are injected into an erbium-doped annular cavity through light modulation to generate delay chaos, and sparse binary vectors are obtained through frequency domain hash coding; the vector predicts a future cardiovascular state through a spiking neural network and a reversible ordinary differential equation network; inputting the continuous prediction frame into a reversible tensor flow network, and obtaining a risk vector through anti-fact disturbance; splicing risks, states, codes and contexts to calculate risk energy, and outputting personalized intervention actions by means of a diffusion generation model; and synchronously updating model parameters and coupling coefficients according to the rewards. According to the invention, millisecond-level prospective early warning, low-power-consumption operation and interpretable intervention are realized, and the method is suitable for long-term wearable health monitoring.

The invention discloses a general model pre-training and self-adaptive optimization system and method for a remote sensing image in the technical field of remote sensing images. The method comprises the following steps: acquiring a structured data set containing multi-modal remote sensing core data and corresponding auxiliary geographic information; based on the structured data set, utilizing a two-channel feature extraction architecture to synchronously process visual spectral features and geographic spatial features, and performing model training in combination with innovative training tasks of geographic context contrast learning and multi-temporal mask reconstruction to construct a pre-training model with general characterization capability; and when the pre-training model is applied to a target domain, performing cross-domain feature distribution alignment by utilizing a dynamic gradient inversion layer, and selectively unfreezing part of model parameters by adopting a task perception unfreezing strategy to complete adaptive migration. According to the method, through a self-supervised pre-training architecture of geographic coordinate hash coding and multi-temporal mask reconstruction, the spatial-temporal feature extraction robustness is enhanced, and the problem of generalization of a small sample scene is solved at the same time.

The invention relates to a deep hash image retrieval method based on feature fusion and a dynamic converter, and relates to the technical field of artificial intelligence and computer vision, and the method comprises the steps of data preprocessing, feature fusion extraction, hash code generation and joint loss optimization. By combining the local feature extraction capability of CNN and the global modeling advantage of ViT, introducing dynamic convolution, a KAN module and a LoRA low-rank adaptation mechanism, and designing a joint optimized multi-branch loss function, the semantic consistency and retrieval performance of Hash codes are effectively improved, efficient and accurate large-scale image retrieval is realized, and the retrieval efficiency is improved. The problems that an existing image retrieval method is insufficient in feature expression, insufficient in retrieval precision and high in calculation overhead are solved.

The invention provides an immersive video coding method and system based on 3DGS, and the method comprises the steps: carrying out the sparse reconstruction of each frame of multi-view video data, and obtaining an initial point cloud; determining anchor points of three-dimensional Gaussian distribution in each frame; extracting spatial context information of the anchor points through multi-resolution hash coding, and splicing the spatial context information with the features of the anchor points to form fusion features; predicting parameters of three-dimensional Gaussian distribution corresponding to each anchor point through a neural network by using the fusion features and camera parameters, and obtaining a rendered image; calculating the color loss between the rendered image and the original video frame, and optimizing the parameters of the anchor points; performing quantization and entropy coding on the optimized parameters of the anchor points; in combination with the color loss and the coding rate, performing rate distortion optimization on the quantization parameter to generate compressed three-dimensional scene representation; and repeating the steps for each frame of the multi-view video, and finally outputting a compressed immersive video code stream. According to the invention, high-quality representation and efficient compression of the immersive video are realized, and the rate-distortion performance is improved.

The application provides a fishery resource dynamic monitoring method and system based on an underwater detection device, relates to the technical field of dynamic monitoring analysis, and realizes fine management of water area monitoring by collecting fish group density data in real time, constructing a fish group density matrix, and combining grid element division and geographic hash coding. The gradient value of adjacent grid elements is calculated based on the density matrix to generate a gradient matrix, which can accurately identify the fish group density mutation area, mark the abnormal area, and then dynamically adjust the sonar sampling frequency of the underwater detection device to improve the monitoring accuracy of the abnormal area. Compared with the prior art, the application has been significantly optimized in terms of monitoring accuracy, abnormal detection response capability and resource utilization efficiency, especially in terms of fish group migration trend prediction and dynamic adjustment of grid division, which improves the adaptability and prediction capability of the monitoring system.

The application belongs to the field of three-dimensional vision and digitization, and relates to a three-dimensional human body reconstruction method and system based on three-dimensional Gaussian splashing. The method steps are as follows: based on a layered hash coding parameter field, the center position of each Gaussian primitive in the constructed three-dimensional Gaussian primitive set is corrected, and the color of the Gaussian primitive under the current observation angle is predicted; based on the human body posture parameters and shape parameters corresponding to the monocular video sequence, linear mixed skin transformation is performed on the corrected Gaussian primitive to map to the posture space; based on the Gaussian primitive parameters mapped to the posture space, three-dimensional Gaussian differentiable rendering is performed on the Gaussian primitive mapped to the posture space to obtain a rendering image consistent with the corresponding view angle of the monocular video; based on the constructed joint loss function, the Gaussian primitive parameters and the layered hash coding parameter field are optimized to obtain a three-dimensional human body model. The application can quickly reconstruct an animatable three-dimensional human body model with stable contours and clear textures from a monocular video.

The invention provides a place identification method based on a background dictionary mechanism, and belongs to the technical field of image identification. According to the method, the accuracy and reliability of location identification are mainly improved, the calculation amount is reduced, the low-delay scene requirement is met, and the deployment cost and period are reduced; according to the scheme adopted by the invention, the location information and deep hash code mapping table is constructed through the feature extraction data set, and the accuracy and reliability of system location identification are improved by retrieving the location information and deep hash code mapping table. According to the location information and deep hash code mapping table, the input image can be accurately matched with the known location in the database through the pre-stored location feature information during location identification, and especially in a high-density city environment or a complex weather condition, the false detection and missed detection probability can be effectively reduced.

The application discloses a high-precision three-dimensional reconstruction method based on sparse depth, an RGB-D image sequence of sparse depth is input, the missing depth information in each frame of sparse depth map is effectively filled in by a depth completion network trained on a public data set, and a pre-trained visual large model Dinov2 is used to infer the semantic information of each frame; for each frame of RGB image, a joint sampling method based on feature point sampling and uniform sampling is used to extract the sampling points of light, and spatial three-dimensional points are generated through importance sampling and random sampling strategy; the position coding is carried out on the three-dimensional space sampling points to code the low-frequency features, the high-frequency features are processed by using hash coding, and the two are fused and input into a neural network to predict the color value, SDF value and semantic value; according to the current scene representation information, the pose of the current frame is rendered and optimized, the predicted value is compared with the real color, depth and semantic information of the current frame through the global key frame as a sliding window, the implicit scene representation is optimized, and a high-fidelity three-dimensional model is reconstructed from the sparse depth RGB-D image sequence. The application effectively solves the problem of insufficient reconstruction precision of the traditional three-dimensional reconstruction technology under the condition of sparse depth data, and has a wide application prospect, and is especially suitable for the field of three-dimensional reconstruction based on RGB-D data.

The present application discloses a file storage method, apparatus, device and medium, which relates to the field of computers, including: obtaining a current storage request initiated by a user terminal and a corresponding original file, and saving the hash code of the original file to a local database, so as to generate an encryption key for the original file based on the hash code and a preset key; encrypting the original file using the encryption key to obtain a corresponding encrypted file, and forwarding the encrypted file to a preset file storage middleware, so that the preset file storage middleware returns parameter information indicating that the encrypted file has been successfully stored; generating a target identifier for the original file based on the parameter information, and saving the parameter information and the target identifier to a local database, and then sending the target identifier to the user terminal. In this way, the present application connects the user terminal and the middleware, can adapt to the business needs of different applications during the file storage process, and can ensure data security by combining encryption with hash coding.

The large-scale image search method based on deep hash comprises the following steps: acquiring a query image uploaded by a user and preprocessing the query image to obtain a to-be-searched image; the to-be-searched image is input into a pre-trained image Hash coding model, the image Hash coding model comprises a feature extraction network and a Hash mapping network, and the feature extraction network is constructed based on Vision Transform and is used for carrying out feature extraction on the to-be-searched image to obtain image high-dimensional features; the Hash mapping network is used for performing Hash coding on the image high-dimensional features to obtain search Hash codes; calculating the similarity between the search hash code and the in-library hash code of the inventory image in the image library based on the Hamming distance; and screening the inventory images based on the similarity to obtain a target image and feeding back the target image to the user. The invention provides a deep hash-based large-scale image search method, an image recommendation method, a recommendation system and computer equipment, which have better search performance.

A method for generating a target function is provided. The method includes: performing normalization processing on a vector corresponding to each pixel in a target feature map set to generate a target vector, so as to obtain a target vector set; generating hash coding corresponding to each vector in the target vector set, to obtain a hash coding set; determining a prior probability of each hash coding in the hash coding set; and generating a target function based on an entropy of the prior probability.

The invention discloses a neural radiation field real-time rendering-oriented memory access optimization method and system, and mainly solves the problems of low rendering efficiency and difficulty in real-time rendering on edge equipment in the prior art. According to the scheme, the method comprises the steps that all zero value units of a trained model are removed, and index-effective value key value pairs of compressed grids are constructed and stored on a chip; the occupation grid query of all light advancing sampling points is completed on a chip through two-dimensional mapping; dividing a high-resolution spatial hash table in the multi-resolution hash code into sub-grids, and mapping spatially adjacent 3D points to a continuous memory address; all input light rays are partitioned, the light rays in each block query a hash table in parallel in a similar voxel region, and original random and scattered memory access is converted into a predictable access mode; and a two-stage Cache structure is adopted for caching and repeatedly using multi-resolution hash addresses and values. According to the method, the memory bandwidth can be reduced, the rendering speed can be increased, and the method can be applied to augmented reality, virtual reality and other scenes with high real-time requirements.

The present application relates to a kind of asteroid global terrain three-dimensional reconstruction method for illumination change, comprising the following steps: input asteroid optical image, estimate camera pose and generate sparse point cloud, initialize Gaussian radiation field;Using image segmentation large model outputs the segmentation mask of asteroid optical image, prunes Gaussian radiation field;In combination with the mixed mask output by Gaussian radiation field, identify the shadow area of asteroid surface;The camera position, spherical harmonic coefficient and hash coding are combined into incident light angle, input into neural network, and the color value of Gaussian cell of illumination and shadow area is regressed;Add depth normal consistency loss and rotation variance and scale constraint loss, combine photometric loss to jointly supervise the optimization of Gaussian radiation field, calculate Gaussian opacity field, extract the three-dimensional model of asteroid global terrain.The present application can identify the shadow area on asteroid, improve the three-dimensional reconstruction precision of asteroid with surface illumination change.

A phishing classifier is disclosed for classifying URLs and content pages as phishing or not, comprising a URL feature hasher that parses and hashes URLs into feature hashes, and a headless browser that visits and internally renders the pages of the URLs, extracts HTML tokens, and captures an image of the rendering. Also disclosed is a phishing classifier for classifying URLs and content pages accessed via the URLs as phishing or not, comprising a URL feature hasher that parses and hashes URLs into feature hashes, and a headless browser that visits and internally renders the pages of the URLs, extracts words from the rendering, and captures an image of the pages. Classifying URLs and content pages accessed via the URLs as phishing or not is further disclosed. In addition to one or more of the disclosures, there is a phishing classification layer, a URL embedder, and an HTML encoder.

The application discloses a kind of wisdom energy multi-energy evaluation visualization system and method for industry, it is related to energy big data visualization cross technical field, including, acquisition multi-source heterogeneous data and pre-processing, by space-time hash coding method, generate high-dimensional energy data of space-time alignment;Based on three-dimensional manifold coordinate model, the equipment energy efficiency parameter is converted into equivalent energy quality field, by energy field reconstruction and geometric optimization method, generate curved manifold space and identify optimal transmission path and distortion intensity cloud chart;Based on distortion intensity cloud chart, generate thermal color scale by thermal color scale mapping algorithm, and the three-dimensional gradient field distribution of equivalent energy quality field is rendered using ray tracing method, generate visual energy evaluation interface.The application generates space and time hash code by space-time hash coding method, establishes equipment-grid mapping relationship in combination with GeoHash coding, realizes the accurate space-time alignment of multi-source heterogeneous data.

The application provides a kind of audio-driven three-dimensional digital human generation method and system based on neural radiance field, first, a pose preserving prior generation model is constructed, by fusing audio features and facial pose features, the cross-modal interaction relationship between the two modal features is learned using self-attention mechanism and cross-attention mechanism, to generate facial pose prior information synchronized with input audio. Then, a neural radiance field rendering model is constructed, three-plane hash coding technology is used to efficiently sample the three-dimensional space, and the facial pose prior and audio features are dynamically fused through the attention mechanism. Finally, the generated pose prior is input into the neural radiance field rendering model for volume rendering, and the light cumulative transmittance calculation is introduced to generate high-fidelity three-dimensional digital humans. The application improves the realism and expressiveness of digital humans, reduces the rendering computation complexity, and realizes real-time digital human generation with dynamic coherence and lip-synchronized.

The invention provides a port function area mining method and system based on geographical hash coding. The method comprises the following steps: determining a ship berthing function area; performing berthing identification and classification on track points; different Geohash hierarchies are selected for the in-port area and the out-port area, and a clustering method based on connectivity is adopted to segment the parking area into a plurality of sub-areas; an outlier grid and an abnormal value are removed, and a functional area with actual operation significance is formed; determining a ship entry and exit main channel; identifying an area with significantly unbalanced entry and exit point distribution; extracting a track section of the ship from the port entry point to the first mooring point, and displaying the track section in a thermodynamic diagram form; and segmenting the region into a plurality of sub-regions by adopting a connectivity-based clustering method, removing an outlier grid, and finally dividing a main channel. The method has the advantages that accurate recognition and dynamic segmentation of multiple types of functional areas in a complex port environment are achieved, and the spatial index efficiency and the expandability of practical application are remarkably improved.

The present application relates to the technical field of image retrieval, and particularly to an image retrieval method based on a combination of a deep convolutional neural network and a local sensitive hashing algorithm, which has the following steps: step S1: training set and validation set in an open source data set of image retrieval; step S2: input of the model during training; step S3: test retrieval ranking; step S4: the loss function of image retrieval adopts a contrast loss function, and in addition to mAP, the model evaluation index also newly adds mP@k; the present method is based on a combination of a deep convolutional neural network and a local sensitive hashing algorithm, the algorithm extracts image features of a gallery library and a query library in a deep convolutional manner, carries out LSH hash coding, greatly improves the retrieval performance, and uses contrast learning in a twin network, thereby greatly improving the retrieval precision.

The invention discloses a style storage method and device, equipment and a storage medium, and the method comprises the steps: obtaining a cascading style sheet rule, and disassembling the cascading style sheet rule into atomic-scale style blocks; performing combination optimization processing on each atomic-scale style block to form each target style block; respectively generating each hash code corresponding to each target style block; constructing a style-code index table based on the Hash code of each target style block; and storing each target style block by utilizing the style-code index table. According to the method, the cascading style sheet rule is disassembled into the atomic-scale style blocks, so that the accuracy and the high efficiency of the subsequent storage process can be ensured, the size of the atomic-scale style blocks needing to be stored can be reduced through combined optimization processing, the hash codes are directly utilized for storage during subsequent storage, the size of a memory needing to be stored is reduced, and the storage efficiency is improved. By means of the mode, efficient storage optimization is achieved, pattern data redundancy is avoided, the loading performance is improved, and normal use of the page is guaranteed.

This invention provides a method, system, storage medium, and electronic device for real-time localization and mapping (SLAM) of medical mobile robots, relating to the field of SLAM technology. First, a monocular RGB camera, suitable for complex environments and with lower cost, is selected to capture video streams of real-world scenes. Then, a convolutional gated recurrent unit is used to estimate the depth information and camera pose corresponding to each keyframe image by utilizing the optical flow between different keyframe images. Next, the volume rendering capability of neural radiation fields is fused with the ability of hierarchical voxel meshes to capture global and local scene information. A hash encoding method, which requires less storage space and allows for easy searching of the corresponding mesh for sampling points, is used to quickly render larger scenes with limited resources. This not only improves localization and mapping accuracy but also enables medical mobile robots to adopt appropriate strategies for different terrain conditions, improving the safety and adaptability of medical mobile robots to complex terrain.

The application provides a multi-modal retrieval method based on deep learning and a hash algorithm, comprising: obtaining multi-modal data to be retrieved; performing text semantic retrieval on the multi-modal data based on a deep learning and hash coding method to obtain text data; performing picture retrieval on the multi-modal data based on a gray scale comparison method and an ElasticSearch technology to obtain picture data; mapping the text data and the picture data into a unified vector subspace by using a model based on a double encoder; obtaining a candidate set by calculating the similarity distance of the text data and the picture data in the vector subspace through a cosine similarity; and calculating the similarity on the candidate set by using a model based on a fusion encoder and performing sorting to obtain a retrieval result of the multi-modal data. By the method, the efficient and high-precision multi-modal retrieval task on a large-scale data set is realized.

The invention relates to a data retrieval method based on adaptive space division and hash coding, and relates to the technical field of data retrieval. The method comprises the following steps: extracting feature vectors of original data and to-be-queried data; dividing a feature space Rd formed by the feature vectors based on a preset adaptive space division method, and repeating T times to generate T groups of feature space subunits of the Rd; distributing the data points of the Rd to the feature space subunits of the Rd; coding the T psi-bit vector sets H (x) based on a preset Hash coding mechanism, and connecting to generate an L-bit binary Hash code of each data point; and calculating the distance between the original data and the L-bit binary hash code of the to-be-queried data, and taking the original data corresponding to the minimum distance as a target retrieval result. The compact binary code is generated, and rapid similarity retrieval is realized in the binary code space, so that the problems of complex data coding, high calculation cost, low retrieval efficiency and high memory occupation in the prior art are solved.

The invention provides a point cloud frame searching and matching method and device, electronic equipment and a storage medium, and the method comprises the steps: determining first descriptor information of each point cloud frame in a point cloud frame sample set, and determining first hash coding information corresponding to each piece of first descriptor information; determining second descriptor information of the input point cloud frame, and determining second hash coding information corresponding to the second descriptor information; according to the first Hash coding information and the second Hash coding information, a similar candidate subset is determined from the point cloud frame sample set, the similar candidate subset comprises M point cloud frames, and M is a positive integer; and determining a target point cloud frame from the similar candidate subset according to the input point cloud frame. According to the method, descriptor hash search is added for screening, so that a large amount of time consumed by directly traversing and searching descriptor information can be avoided, and the effect of greatly reducing the calculated amount of point cloud frame search matching based on descriptors is achieved.