328 results about "Information compression" patented technology

The invention discloses a multi-modal multi-scale retrieval enhancement generation method, system and device applied to external knowledge questions and answers and a medium. The method comprises question perception, multi-modal multi-scale query fusion coding, dense recall and answer generation. Analyzing a key query phrase from the question through a fine-tuned instruction language model, and accurately positioning a region of interest corresponding to the phrase in an image by using an open set visual positioning model; multi-source information is compressed and distilled into an optimal query vector through a deep fusion network integrating multi-head self-attention and an information bottleneck theory; executing a maximum inner product search to recall related knowledge; guiding the large language model to synthesize all information to generate a final answer; the system, the equipment and the medium directly perform feature fusion in the vector space based on the method, so that challenges such as information loss and cascading errors caused by a traditional normal form can be effectively dealt with, high correlation and high accuracy of retrieval knowledge are ensured, and accurate and reliable image-text questions and answers are realized.

The invention discloses a regional medical information sharing method based on artificial intelligence, and relates to the field of information processing, and the method comprises the steps: obtaining regional medical information; extracting text information in the regional medical information; dividing the text information into a key text and a conventional text; text encryption of the key text is completed, and text encryption information is obtained; performing information compression encryption on the non-text information to obtain non-text encrypted information; performing global encryption on the conventional text, the text encryption information and the non-text encryption information to obtain medical encryption information; and uploading the medical encrypted information to a medical information sharing terminal. According to the method and the device, the medical information can be effectively shared in a state that the medical information is not leaked.

The invention relates to the technical field of emergency communication, in particular to a Beidou short message-based multi-mode emergency communication terminal, which comprises a Beidou communication module, a multi-source positioning unit, an information coding processor and a main control unit, the Beidou communication module is used for realizing short message communication with a Beidou satellite and supporting a BDS-3 protocol; the physical SOS button is provided with a mistaken touch prevention structure and comprises a mechanical pressing assembly with a triggering pressure threshold value larger than 2N and continuous pressing time verification logic; the multi-source positioning unit is used for acquiring position information including longitude, latitude and height; the information coding processor is configured to compress the collected help-seeking information to be within the capacity limit of a Beidou short message and generate a standard message of an emergency communication protocol; a standby power supply switching circuit is arranged in the main control unit, the main control unit is used for coordinating the work of each module and managing a power supply system, and the power supply system comprises a replaceable lithium thionyl chloride battery and a solar charging panel; and the low-power-consumption main control unit controls the Beidou communication module to transmit information to a Beidou communication satellite.

The invention relates to the technical field of information management, in particular to a safety production informatization data management method and system, and the method comprises the following steps: obtaining a synchronous monitoring record, screening field integrity and numerical value qualified information, marking and classifying abnormal items, filing qualified and abnormal data, constructing a time sequence, additionally recording and correcting abnormal contents, and storing the abnormal contents. Weight hierarchical storage information is extracted, low-frequency data is compressed, a field structure is optimized, and a management scheme is generated. According to the method, the monitoring information is preliminarily screened according to field integrity and numerical standard, the data distribution efficiency is improved, abnormal information is classified and labeled, the structure recognition precision is enhanced, missing supplementary pushing and numerical correction are realized through time sequence comparison, the data continuity is guaranteed, hierarchical storage is performed according to information weight, attributes are clearly reserved, and low-frequency data compression and integration are performed; and the storage burden is relieved, the field structure is automatically expanded and rearranged, the format uniformity and the management specification are improved, and refined processing and efficient circulation of data are realized.

The invention discloses a sparse dynamic 3D Gaussian splash method based on global-local feature extraction. According to the method, a global-local feature extraction module, an inter-frame feature flow aggregation network and depth prior are combined with a 4D pseudo pose supervision mechanism for cooperative work, and high-quality dynamic scene reconstruction and rendering are realized under the condition of a sparse camera visual angle. According to the method, the multi-view image information is compressed to the feature space for processing, so that the calculation complexity is reduced, and the geometric structure and the dynamic features of the scene are effectively reserved; the fused depth prior information and the 4D pseudo pose provide accurate geometric constraints, and the inter-frame feature flow aggregation network optimizes the time consistency of the dynamic scene. The method is particularly suitable for processing a complex dynamic scene under a sparse view angle, the sense of reality and the real-time performance of virtual view synthesis are remarkably improved, and a dynamic view with a more accurate geometric structure, higher time continuity and richer details can be generated.

The invention discloses a three-dimensional point cloud geometric information compression method based on implicit neural representation, and the method comprises the steps: constructing a trunk structure of an implicit neural network through a plurality of sine representation network layers which are connected in series, and introducing a variable-scale position coding mechanism on this basis, the method enables a network to obtain higher geometric reduction precision while keeping a compression ratio, and comprises the following steps: (1) inputting space coordinates of divided voxels into a position coding module with adjustable scale parameters; (2) feeding a coding result into an implicit neural network constructed by a network layer based on sine representation, and outputting the occupancy probability of the voxel through an activation function; (3) in a training stage, the model continuously optimizes parameters, so that the output probability distribution is highly consistent with a real occupied label; (4) after model training is completed, a method of combining an AdaRound second-order quantization optimization strategy and quantization perception training is introduced, network weight is finely adjusted, and quantization errors are reduced; (5) in a reasoning stage, judging whether the voxel is occupied or not according to a preset threshold value, and when the prediction probability exceeds the threshold value, regarding the voxel as occupied; and (6) all voxels judged to be occupied are aggregated, and reconstruction of the geometric structure of the point cloud is completed.

The invention discloses an underwater vortex optical communication anti-turbulence method based on a photoelectric hybrid deep neural network, and the method comprises the steps: carrying out the all-optical information compression at a front end through the diffraction deep neural network through the fusion of a diffraction deep neural network and a convolutional neural network, and transmitting the processed information to a rear end of the convolutional neural network, a model task is completed through training, a predicted coefficient of a Zernike polynomial is output, prediction and compensation of ocean turbulence are achieved, and the mode purity of vortex beams is improved. The front-end diffraction deep neural network is utilized to effectively improve the problem that the rear-end convolutional neural network is difficult to process massive information, the photoelectric hybrid deep neural network model is high in integration, high in operation speed and accurate in light field signal processing, the power overhead for achieving turbulence resistance through the machine learning technology is saved, operation is easy, implementation is easy, and the method is suitable for popularization and application. The method has a wide application prospect in the aspects of vortex beam underwater communication and the like.

The invention provides an information compression and pruning RAG database construction method. The method comprises the implementation steps that node objects and a hierarchical clustering tree are constructed; compressing the node object text information in the clustering tree; and pruning the clustering tree after the partial node object text information is compressed based on a QR-like decomposition method. According to the method, the sentence unit text information in the sub-nodes in the clustering tree is compressed, the embedded vectors of the compressed sub-nodes are updated, and the most valuable external information can be screened and stored according to the actual requirements of the large language model, so that invalid data storage in an RAG database is reduced, and the reliability of the RAG database is improved. According to the method, text information stored in the RAG database is guaranteed to have gain all the time, meanwhile, pruning is carried out on a clustering tree obtained after partial node object text information is compressed based on a QR-like decomposition method, redundant child node objects with high semantic similarity are effectively recognized and deleted, and the storage overhead of the node objects in the RAG database is reduced.

A remote sensing image-text retrieval method based on information processing comprises the steps of preprocessing before modal input, building of an image-text retrieval model and characterization alignment, and the step of building of the image-text retrieval model comprises designing a target information enhancement module and a noise information compression module. The target information enhancement module aims at features of multiple scales to integrate detail information into visual features through the detail guide information attention module so as to enrich detail expressions of images, and the noise information compression module compresses graphic and text information so as to filter redundant information. According to the remote sensing image-text retrieval method, the problems that in an existing remote sensing image-text retrieval method, remote sensing image-text information capture is insufficient, and redundant information is excessively concerned are solved, and the remote sensing image-text retrieval precision is improved.

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 federated learning image classification method based on time extension gradient cumulative average. Each client uses a local data set to train a local model to obtain gradient information of a current communication round, and uploads the gradient information to the central server after compression; the client continues to perform local training in a time period from the time when the client starts to upload the compression gradient information of the current communication round to the time when the server completes the compression gradient information aggregation of the current communication round; the central server side aggregates the compression gradient information to obtain average compression gradient information of the current communication round, and issues the average compression gradient information to the client side to update a local model of the (t + beta) th communication round; and repeating the steps until the training is completed, and inputting the to-be-detected image into the local model to obtain an image classification result. The method shows excellent performance under various data sets and different compression levels, has higher convergence speed and model precision, and solves the problem of low communication efficiency caused by network delay and bandwidth limitation in image classification of federated learning.

The invention discloses an efficient image analysis and question answering system for intelligent customer service, and aims to solve the commercialized problems of slow response and high cost when an existing AI customer service processes user picture questions. In order to improve customer service interaction experience, the system greatly reduces the amount of visual data input into a core language agent through an innovative visual information compression engine, for example, the amount of the visual data is compressed to one fourth of the original amount. By means of the key optimization, the intelligent customer service staff can respond to picture inquiry of customers in real time, and customer loss caused by waiting is avoided. Meanwhile, the system ensures high-precision identification of commodity details through a unique training strategy, provides reliable and accurate answers for customers, and successfully solves the problem that speed and precision are difficult to consider in customer service application in the industry. Finally, a practical solution with instant response and high reliability is provided for the fields such as e-commerce intelligent customer service and the like needing real-time interaction, and commercialized popularization of the advanced AI technology is powerfully promoted.

The present application relates to a log processing method, a control apparatus, and a computer storage medium. The method comprises: acquiring a plurality of abnormal log files, file names of at least a part of the abnormal log files comprising identifiers compressed on the basis of log information; on the basis of a preset condition, performing a deduplication operation on the abnormal log files to obtain deduplicated abnormal log files and a data set table, the data set table comprising statistical data in the deduplication operation; and, on the basis of the data set table and a preset rule, sending the deduplicated abnormal log files to a specified backend for processing, the specified backend being specified on the basis of the preset rule. In the implementation of the technical solution of the present application, an efficient and automated log deduplication mechanism is provided. Such a method significantly increases the speed and accuracy of log processing and reduces the waste of resources caused by duplicate or highly similar logs.

The invention discloses a hierarchical network topology estimation method based on an information bottleneck theory, and the method comprises the steps: carrying out the merging of end-to-end traffic time sequences, introducing comparative learning, generating random perturbation for the merged end-to-end traffic time sequences in a time domain and a frequency domain, and carrying out the estimation of the hierarchical network topology. The end-to-end traffic time sequence is comprehensively represented from the perspective of time-frequency consistency analysis through comparative learning, end-to-end traffic time sequence embedding is obtained, and finally, the length of a shared path is measured by adopting the Euclidean distance embedded by the end-to-end traffic time sequence, and the network topology is recovered from bottom to top. According to the method, an information bottleneck theory is adopted, information related to topology estimation is reserved, noise is compressed, end-to-end flow time sequences are combined based on confidence, time sequence data are effectively represented from a time domain and a frequency domain at the same time by adopting comparative learning, features capable of reflecting variation difference and consistency between different time sequence data are extracted, and the time sequence data are effectively represented. And the length of the shared path is better measured, so that the network topology structure is more accurately recovered.

The invention discloses a system security access control method based on deep learning and dynamic risk awareness, which comprises the following steps of: generating a behavior state vector by collecting user identity, operation, equipment state and environment information, constructing an access behavior map, and establishing an access behavior map through a Hebbian learning rule; the method comprises the following steps: dynamically updating connection strength between behaviors in combination with historical behavior data, after receiving an access request, selecting an association path from a map, calculating an activation potential value, selecting an optimal path as an access decision basis by applying a Winner-Take-All selection mechanism, executing an access control operation, generating a risk mark and a behavior evolution mark, and establishing an access control strategy; the method is used for adjusting a selection threshold and an atlas weight. And finally, compressing and coding the related information, and recording the information in an access log. According to the method, intelligent judgment on the access behavior is realized by using deep learning and a dynamic risk perception mechanism.

A monitoring system can generate compressed storage system monitoring data segments using monitoring data obtained from a storage system. The monitoring system can obtain storage system monitoring data and generate a segment of the storage system monitoring data. The monitoring system can determine time information, a compression technique, and a compression parameter set for the segment. In response to the user query, the monitoring system can identify the compression technique and the compression parameter set based on a time interval of the query and the time information and perform at least one of: reconstructing and providing at least a portion of the segment using the time information, the compression technique, and the compression parameter set; or providing the compression parameter set and the indication of the compression technique for reconstruction of the portion.

The invention provides a multi-modal large-model long-sequence information compression retrieval method, and relates to the technical field of data processing, and the method comprises the following steps: receiving multi-modal long-sequence original data of a target patient; all modal data in the multi-modal long sequence original data are uniformly mapped to the same hidden space through a pre-trained multi-modal large model, and a unified semantic representation sequence of semantic association between modals is obtained; performing time sequence feature analysis on the unified semantic representation sequence, regarding feature points in the semantic representation as a spatial point set, determining boundary distribution by constructing convex hulls of feature clusters, identifying key feature clusters in the semantic representation, and determining a key time sequence interval based on a convex hull distribution mode of the key feature clusters. According to the method, the problems of weak cross-modal semantic association, easy loss of key time sequence information and low compression and retrieval efficiency in the existing multi-modal long sequence data retrieval are solved.

The invention relates to a point cloud super-resolution method based on a selection state space model, and belongs to the field of image processing, and the method comprises the steps: projecting a low-resolution point cloud to an image through Hough Voting, and obtaining a low-resolution image; image information is compressed to low-dimensional potential features through block coding, and a network is guided to pay attention to feature association in the vertical direction; high-low dimension features are fused through an encoder-decoder and jump connection, potential long-distance dependence is learned by using a selected state space, and a high-resolution image is generated through prediction; and reversely projecting the obtained high-resolution image to a three-dimensional space to form a high-resolution point cloud. According to the method, long-distance context feature learning can be more effectively processed, a large number of disordered, fuzzy and geometric distortion areas of the generated point cloud are avoided, the detail performance and fidelity of the point cloud are remarkably improved, the calculation complexity is low, the requirement for calculation resources is small, and the method is more suitable for actual application scenes.

For example, a processor may be configured to generate compressed radar information by compressing radar values in a plurality of data bins of at least one radar processing dimension, the at least one radar processing dimension including a range dimension. For example, the processor may be configured to generate the compressed radar information by quantizing a plurality of normalized values corresponding to the radar values in the plurality of data bins. For example, a normalized value corresponding to a radar value in a data bin may be based on a normalization of the radar value with respect to a plurality of radar values in the data bin. For example, the processor may be configured to store the compressed radar information in a memory.

A communication method and related devices, in which a first communication device may transmit the second channel information, enabling a second communication device to communicate based on the second channel information. Wherein the second channel information is determined according to the first channel information, the time information of the first channel information and the characteristic information of the historical channel information, namely, the second channel information can reflect the time domain correlation corresponding to the time information of the first channel information and can reflect the time domain correlation corresponding to the time information of the historical channel information. And one or more of time domain correlation, space domain correlation and frequency domain correlation corresponding to the characteristic information of the historical channel information can be reflected. Therefore, the accuracy of the second channel information can be improved by utilizing various correlations, so that the performance of data transmission based on the second channel information is improved. In addition, the scheme can be applied to a channel information compression feedback scene, and the overhead of channel information feedback can be reduced.

The invention provides a sparse adaptation circuit and a network-on-chip sparse data processing method, the sparse adaptation circuit is embedded between a network interface unit (NIU) and a router of an NoC system, and the sparse adaptation circuit comprises an input processing module, a compression engine and an output processing module; the sparse detection unit is used for performing real-time zero value detection on the data stream received from the network interface unit NIU to generate sparse identification information; the compression engine is coupled with the sparse detection unit and is used for carrying out dynamic compression coding on the data stream based on the sparse identification information; and the output interface is used for sending the processed data flow to a router in a format conforming to the network-on-chip protocol. In the network-on-chip, a hardware method is adopted to identify a data packet containing a large number of zero values in AI calculation and compress the data packet into a data packet only containing valid data, so that the problems of low transmission efficiency, waste of power consumption and the like caused by the fact that on-chip bandwidth is occupied by invalid data are avoided.

An information processing method and apparatus, device and medium, the method comprising: receiving first interaction guidance information for an artificial intelligence model, the first interaction guidance information being used for guiding the artificial intelligence model to respond; a compression rule is obtained, the first interaction guide information is compressed into second interaction guide information based on the compression rule, and the number of lexical elements included in the second interaction guide information is smaller than that of the lexical elements included in the first interaction guide information; and sending second interaction guide information and guide additional information related to the compression rule to the artificial intelligence model to guide the artificial intelligence model to generate first response content based on the compression rule and the second interaction guide information. According to the method, in the interaction process of the client and the artificial intelligence model, the model input length and lexical element consumption can be reduced, the context window limitation in the interaction process is relieved, and the transmission efficiency of the interaction process is improved.

The invention provides a binocular video compression method based on parallax compensation and deep learning, and the method comprises the steps: firstly obtaining training data, carrying out the preprocessing, and then constructing a binocular video compression network; the binocular video compression network comprises a motion estimation network, a motion information compression network, a motion compensation network, a binocular video frame information compression network and a frame reconstruction network, then carrying out supervised training on the binocular video compression network, and carrying out video compression processing by using the trained binocular video compression network; according to the method, a binocular feature interaction model and a binocular entropy model are arranged in a motion information compression network and a binocular frame information compression network, the binocular feature interaction model aligns binocular visual angle features by using a mixed parallax compensation mechanism in the encoding and decoding process, and the binocular entropy model performs accurate probability distribution modeling through autoregression probability estimation. According to the method, the compression performance can be remarkably improved while the reconstruction quality is guaranteed, and the method is suitable for scenes such as virtual reality and automatic driving needing efficient binocular video processing.

The various implementations described herein include methods and systems for encoding video. In one aspect, a method includes receiving a mesh with polygons representing a surface of an object; detecting a first symmetric region in the mesh that includes a first symmetry line to divide the first symmetric region into a first partition and a second partition. The method includes recursively determining whether one of the first partition or the second partition includes a second symmetric region until no symmetric region is detected in both the first partition and the second partition. The method includes in response to detecting the second symmetric region within one of the first partition or the second partition: determining a second symmetry line within the second symmetric region to divide the first or the second partition into a third sub-partition and a fourth sub-partition; and compressing information of the third sub-partition, the second symmetry line and the first symmetry line into a bitstream.

The invention provides a portrait animation generation method and system based on a reversible encoder and dynamic optimization, and relates to the technical field of video portrait reconstruction, high-precision portrait animation generation is realized through multi-module collaborative innovation, multi-scale appearance features are extracted by using a VAE encoder, identity vectors extracted by ArcFace and global appearance embedding of CLIP are combined, and the portrait animation generation method and system based on the reversible encoder and dynamic optimization are realized. Constructing a multi-modal characteristic system; a high-precision attitude estimation algorithm is adopted to extract fine joint points, and an error between a generated attitude and an original attitude is calibrated in real time through a dynamic optimization strategy, so that the attitude driving accuracy is remarkably improved. A reversible residual block is designed to realize lossless two-way transmission of features, and the problem of information compression of a traditional encoder is avoided; a high-frequency detail injection mechanism is introduced, high-frequency features such as facial textures are extracted through a pre-training model and fused with shallow features, an attention mechanism guided by identity features is introduced based on the generation process of a diffusion model, and the consistency of character identities in the generation process is ensured.

The invention belongs to the technical field of landslide evaluation, and particularly relates to a modeling method of a landslide susceptibility evaluation coupling model fusing an auto-encoder and a feedforward neural network, and the method comprises the steps: obtaining slope unit data and historical landslide data, carrying out the preprocessing, extracting landslide-related evaluation factors, carrying out the correlation test and correlation analysis, and obtaining a landslide susceptibility evaluation coupling model. Screening out evaluation factors for subsequent evaluation; constructing a landslide data set, balancing samples by adopting a GPU acceleration oversampling technology, and dividing a training set and a test set; constructing a landslide susceptibility evaluation coupling model based on the auto-encoder and the feedforward neural network; training the landslide susceptibility evaluation coupling model by using the training set and the test set to obtain optimal parameters; and outputting a landslide susceptibility distribution result by using the trained landslide susceptibility evaluation coupling model. According to the method, feature dimension reduction and redundant information compression can be effectively realized, and the evaluation precision and generalization ability of the model are improved.

The invention discloses a method and a system for protecting privacy of source codes in memory error detection, belongs to the technical field of computer software security, and particularly relates to a technology for protecting confidentiality of the source codes in a third-party memory error analysis process of software. According to the method, a debugging information minimization processing system DIREDUCER is constructed, a selective deletion and type minimization technology is utilized, on the premise that complete source codes are not exposed, necessary debugging information used for memory error detection is effectively reserved, and dual guarantee of source code privacy and memory error detection effects is achieved. According to the method, the debugging information in the non-stripping binary file is compressed to be within 10% of the original volume, and the recognition capability of an analysis tool on the problems such as memory leak, buffer overflow and overhanging pointers is not remarkably reduced. Experimental results show that the method has good adaptability and expandability in actual deployment, is suitable for various debugging tools and binary analysis frameworks, and has wide application prospects and practical values.

The invention discloses a remote internet teaching system operation method. The method comprises the following steps: S1, executing edge caching and dynamic distribution; s2, implementing adaptive transmission protocol optimization; s3, P2P collaborative learning resource sharing is carried out; s4, implementing cloud edge collaborative intelligent interaction; and S5, constructing secure tunnel encryption transmission. According to the method, interaction data such as characters, voices and expressions generated by a user are preprocessed in real time through edge nodes, key information is extracted by means of a professional algorithm, the data volume is compressed, invalid information is filtered, and only necessary data is uploaded to the cloud. In a high-concurrency access scene, cloud computing and storage pressure is effectively reduced, service abnormity caused by data overload of the cloud is avoided, stable operation of cloud service is ensured, and a foundation is built for development of large-scale teaching activities.

A user equipment (UE) includes a transceiver and a processor configured to transmit, via the transceiver to a network, UE capability information that corresponds with performance monitoring for artificial intelligence (AI) model-based channel state information (CSI) compression at the UE. In accordance with the UE capability information, the processor is configured to receive, via the transceiver and from the network, configuration and activation instructions for performing the AI model-based CSI compression and a network configuration on assisted information for the performance monitoring. The processor is configured to monitor performance of the AI model-based CSI compression in accordance with the received UAI configuration, and based on the monitored performance of the AI model-based CSI compression, transmit, via the transceiver to the network, a preference of the UE for the AI model-based CSI compression.

Certain aspects of the present disclosure provide techniques for sharing machine learning models and an indication of transmission and reception points (TRPs) for which the machine learning models are applicable between wireless nodes such as user equipments (UEs) and base stations (BSs). For example, a UE receives an indication of one or more machine learning models associated with at least one TRP of one or more TRPs from a BS. The UE then uses the at least one of the one or more machine learning models for applications related to the at least one TRP. The applications may include a channel state information (CSI) compression, a cross frequency channel prediction, and a beam selection.