26 results about "Product quantization" patented technology

The invention discloses an unsupervised semi-pairing cross-modal retrieval method and system based on deep learning, relates to the field of artificial intelligence, and is used for solving the problems of annotation data dependence, asymmetric semantic association and high-dimensional storage efficiency. According to the method, a double-branch visual encoder and a dynamic prompt text encoder are combined, dynamic weighting of visual-text features is achieved through gating cross attention, and modal redundancy interference is restrained. An enhancement strategy is generated through low-frequency semantic guidance, and the long-tail word coverage rate is increased; a dual-stage quantitative hierarchical index is constructed, coarse-grained clustering and fine-grained product quantitative compression feature storage is adopted, and million-level data real-time retrieval is supported. A degradation aware increment maintenance mechanism monitors data distribution offset through a KL divergence threshold, and triggers index reconstruction to maintain long-term update precision. According to the method, limitation of a traditional strong pairing model is broken through, cross-modal sensitive content second-level positioning is achieved, asymmetric semantic alignment is effectively solved, and retrieval efficiency is improved.

The invention relates to the technical field of artificial intelligence, can be applied to the fields of financial science and technology and medical science and technology, and discloses a voice coding and decoding method, device, equipment and medium. A continuous vector is obtained by encoding the data through an encoder trained by a first-stage mirror image architecture; segmenting a continuous vector into sub-vectors, matching the sub-vectors with corresponding sub-coding dictionaries through a quantizer to obtain sub-indexes, and combining the sub-indexes to generate an overall index; analyzing the overall index to obtain sub-indexes, calling sub-discrete vectors and splicing the sub-discrete vectors into a discrete vector; and reconstructing the discrete vector through a decoder trained by a second-stage non-mirror-image architecture to obtain a target speech spectrum feature, and converting the target speech spectrum feature into a target speech signal. According to the method, double-stage training is adopted, the first-stage mirror image architecture guarantees the coding stability, the second-stage non-mirror image architecture improves the decoding flexibility, and the product quantization technology is combined, so that the calculation efficiency and the storage overhead are balanced, and meanwhile, the voice reconstruction quality is improved.

A computational storage device for executing DiskANN search is provided by an aspect of the present disclosure. The computational storage device comprises a NAND memory array configured to storage node chunks corresponding to nodes in the DiskANN. The computational storage device also comprises a non-volatile memory array configured to store multiple product quantization (PQ) vectors corresponding to the nodes of the node chunks. The computational store device also comprises a processing unit coupled to the NAND memory array and the non-volatile memory array. The processing unit configured to execute DiskANN search (DiskANNs) among the node chunks in the NAND memory array and the multiple PQ vectors in the non-volatile memory array according to a received search instruction, and output a search result.

This invention discloses an unsupervised semi-paired cross-modal retrieval method and system based on deep learning, belonging to the field of artificial intelligence, to solve problems of labeled data dependency, asymmetric semantic association, and high-dimensional storage efficiency. This invention combines a dual-branch visual encoder and a dynamic cue text encoder, utilizing gated cross-attention to achieve dynamic weighting of visual-text features and suppress modal redundancy interference. A low-frequency semantic-guided generation enhancement strategy is used to improve long-tail keyword coverage; a two-stage quantized hierarchical index is constructed, employing coarse-grained clustering and fine-grained product quantization to compress feature storage, supporting real-time retrieval of millions of data points. A degradation-aware incremental maintenance mechanism monitors data distribution shifts through a KL divergence threshold, triggering index reconstruction to maintain long-term update accuracy. This method overcomes the limitations of traditional strong pairing models, achieving second-level localization of cross-modal sensitive content, effectively solving asymmetric semantic alignment, and improving retrieval efficiency.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input to generate a network output. In one aspect, one of the systems includes a neural network configured to perform the machine learning task, the neural network comprising an N-grammer layer and an output neural network, the N-grammer layer configured to: at each of one or more heads: receive a sequence of input embeddings; generate a discrete latent representation of the sequence of input embeddings by using a learned product quantization codebook; generate a plurality of n-gram indices from the discrete latent representation; and generate a latent n-gram representation of the sequence of input embeddings; and generate a sequence of output embeddings, and the output neural network configured to: receive the sequence of output embeddings; and process the sequence of output embeddings to generate the network output.

This invention is applicable to the fields of artificial intelligence and intelligent transportation, providing a modified vehicle identification method based on attribute fusion and fuzzy product quantization. In the comprehensive vector feature extraction, this invention employs vehicle and license plate detection, parallel feature extraction, and attribute fusion concatenation to obtain comprehensive features containing high-level license plate semantic information, as well as low-level vehicle model and color attributes. This eliminates background noise interference, enhances the response strength of three easily modified vehicle features, and helps compensate for the limitations of single features. Furthermore, it applies fuzzy clustering and cosine similarity distance calculation methods, which can accommodate the uncertainty of input information during modified vehicle retrieval. Compared to traditional Euclidean distance, cosine similarity is not affected by vector length, and can better handle differences in dimensions when retrieving modified vehicles. Compared to the original product vector method, this invention's method has higher retrieval accuracy and robustness in modified vehicle scenarios.

The invention discloses a product quantization method based on hierarchical fuzzy clustering, and the method comprises the steps: carrying out the spatial decomposition of a to-be-compressed high-dimensional vector of image or video data into a plurality of low-dimensional subspaces through employing a hierarchical decomposition strategy; in each low-dimensional subspace, a hierarchical fuzzy clustering method is adopted, so that each data point belongs to a plurality of fuzzy clustering centers at different membership degrees; and on the basis of a hierarchical fuzzy clustering result, a product quantization coding mechanism is adopted, so that the fuzzy clustering center of each subspace forms a corresponding codebook, data points are coded and compressed according to membership degrees of the data points in different clustering centers, and data compression of a high-dimensional vector of the image or video data is realized. The invention provides a compression method combining a hierarchical structure and a fuzzy clustering technology, and the efficiency, the accuracy and the robustness of vector compression are remarkably improved.

The application discloses a similar text retrieval method based on vector representation. The method first processes the text at the article level by using the word embedding + pooling method, obtains the text vector representation through Bert coding, constructs all text vector representations into a ball tree index by using the ball tree method, performs product quantization on all text vector representations and ball tree center points, and builds a PQ index; during retrieval, the query text is also converted into a vector representation, a distance matrix is calculated through product quantization, KNN retrieval is performed based on the distance matrix and the ball tree index structure, and k similar texts are obtained. The similar text retrieval method based on vector representation constructed by the application supports long text retrieval, can accurately and quickly retrieve massive text vector representations, has good effect on complex or multi-semantic text queries, is very friendly and helpful to users who are not familiar with the jargon of a certain field or want to do exploratory search, and finally, the search is more comprehensive and accurate compared with the IndexIVFPQ method in Faiss.

The present application relates to the technical field of power grid maintenance, and provides an intelligent generation method and electronic equipment for a power grid maintenance work ticket, comprising: in the case that the data amount of a business logic vector library is greater than a preset threshold, performing approximate nearest neighbor retrieval in the business logic vector library based on an inverted file index and a product quantization manner and a query vector to obtain a plurality of logic chain segments related to a current power grid maintenance task; in the case that the data amount is less than or equal to the preset threshold, performing accurate nearest neighbor retrieval based on a flat inner product index manner to obtain the plurality of logic chain segments; inputting the plurality of logic chain segments into a business logic reasoning model to perform task decomposition, step framework building and equipment correlation analysis, and obtaining a business logic thinking chain including equipment, equipment correlation constraints and operation rules, and combining original information to input work content generation model to obtain a work ticket. The present application solves the problem that related technologies cannot efficiently generate reliable work tickets.

The invention belongs to the technical field of information, and relates to a product quantization optimization method and system based on optimal subspace allocation. The coding process of the method comprises the following steps: dividing N input D-dimensional vectors into M subspaces, wherein each subspace comprises N D / M-dimensional subspace vectors; respectively mapping all the subspace vectors to d subspaces; executing a K-means clustering algorithm on the mapped subspaces to obtain approximate estimation code words of each subspace vector in d subspaces to which the subspace vector is mapped; for a subspace vector and d approximate estimation code words thereof in different subspaces, reserving a mapping position with a minimum residual absolute value to obtain a code of the subspace vector; and combining the codes of the subspace vectors to obtain a compression result of the input vector. The invention aims to improve and optimize a product quantization algorithm, can reduce quantization errors as much as possible while ensuring the decoding speed, and can be widely applied to the related fields of approximate nearest neighbor search and large-scale quantization models.

The invention discloses an old and ancient book cover image retrieval method based on dynamic partitioning and multi-modal feature fusion, and relates to the technical field of image recognition, old and ancient book cover images are used as query vectors to be input into a retrieval model, and a matched book list corresponding to old and ancient books is obtained according to the query vectors; the training process of the retrieval model is as follows: performing visual feature extraction and text feature extraction on the cover image of the ancient and old book, and partitioning; inputting the blocked visual features and text features into a cross attention mechanism, and generating an original high-dimensional fusion feature vector including a layout text structure and image layout information; performing subspace decomposition and quantization coding on the original high-dimensional fusion feature vector by adopting a product quantization algorithm to obtain a compressed feature piece vector, and constructing an ancient and old book feature database according to the compressed feature piece vector; taking cover images of the old and ancient books as input query vectors, indexing the old and ancient book database by adopting a three-level progressive query architecture, and returning a matched book list; the trainable parameters of the retrieval model are jointly optimized by constructing a total loss function; according to the retrieval method, multi-level understanding and digital reconstruction of ancient book covers are achieved, and rapid retrieval is achieved through efficient index construction.

The invention discloses a vector database-based big data real-time file gathering system, which comprises a data acquisition and access module used for acquiring multi-source heterogeneous data and establishing a data access channel; the data preprocessing module is used for preprocessing the multi-source heterogeneous data; the vector modeling module is used for generating a normalized object feature vector through the improved TabPFN; the file-gathering index construction module is used for constructing file-gathering index structure entries through an improved reverse index structure and a product quantization coding method, and writing the file-gathering index structure entries into a vector database; the file-gathering updating module is used for carrying out similar retrieval on the entity objects to be filed and updating or newly creating file-gathering index entries; the archive output and visualization module is used for constructing a structured data table and a JSON (JavaScript Object Notation) data document and generating a visual archive map; and the incremental feedback learning module is used for executing incremental training on the improved TabPFN network. According to the method, the accuracy, the real-time performance and the processing efficiency of big data file gathering are improved.

The invention discloses an edge deployment-oriented continuous learning industrial anomaly detection method and device, and the method comprises the steps: obtaining industrial normal sample data, and dividing training tasks and corresponding training sets and test sets according to classes; a teacher-student dual-network architecture is constructed, a lightweight convolutional neural network is adopted as a backbone network, dual-network front-segment parameter sharing is set, and only student network rear-segment parameters are updated; inputting samples according to a task sequence, calculating teacher-student feature matching loss based on a difference enhancement knowledge distillation mechanism, and updating student network parameters; a replay mechanism is designed, and catastrophic forgetting is avoided in combination with KNN Greedy and random sampling; compressing and replaying sample features by adopting a product quantization method; after training, test data features are extracted, and anomaly scores are calculated to realize anomaly classification and segmentation. The method can meet the requirements of unsupervised learning, continuous learning and edge deployment at the same time, gives consideration to the detection precision and the adaptability of edge equipment, and is suitable for various industrial detection scenes.

The invention discloses a multi-modal data approximate query method and system based on a hybrid block chain, and relates to the technical field of block chains. According to the method, a verifiable index structure is constructed and used for describing the mapping relation between the compressed representation of the feature vector of the multi-modal data and the data pointer under the chain of the multi-modal data, the index state change is recorded only by using a very small space on the chain, and huge storage overhead caused by directly storing the original high-dimensional feature vector or complete data in the block chain is avoided. According to the method, feature vectors are compressed into PQ codes through product quantization, a verifiable lightweight cryptographic proof is generated in combination with an MPT structure, authenticity verification of a multi-modal approximate query result is achieved, a Merkle proof is independently verified through on-chain root hash, and it is ensured that the result is not tampered; the client downloads original data, executes full-precision feature extraction and distance calculation, and compensates precision loss caused by PQ compression; and a complete and credible closed loop of'efficient approximate search + accurate result output 'is realized.

A text retrieval method, apparatus and device, and a storage medium, the method comprising: reading text information in a file set according to a preset code tool to extract original data in the text information to construct a text tree; processing the text tree according to a multi-scale analysis and recombination technology to generate a plurality of structured text fragments; inputting the plurality of text segments into a preset training language model, and obtaining a text embedding vector corresponding to the output of the preset training language model; decomposing and quantizing the text embedding quantity according to a product quantization technology, and constructing a vector index database; according to the obtained to-be-retrieved text and the vector index database, the distance between the to-be-retrieved text and each text fragment in the vector index database is calculated to obtain a target text fragment corresponding to the to-be-retrieved text, and the problems that in the related technology, training depends on a large amount of corpus data, key information is lost, and the retrieval efficiency is improved are solved. And the technical problem that the retrieved text effect is poor is solved.

The application discloses a kind of self-supervised remote sensing image retrieval methods, comprising the following steps: obtaining remote sensing image data;Remote sensing image data is carried out data enhancement, and training data set is obtained;Based on cross quantization and self-adapting error correction label, construct initial self-supervised remote sensing image retrieval model;Using training data set, initial self-supervised remote sensing image retrieval model is trained, and self-supervised remote sensing image retrieval model is obtained;Through self-supervised remote sensing image retrieval model, actual remote sensing image retrieval is carried out.The application further discloses a kind of system for implementing the self-supervised remote sensing image retrieval method.The method model of the application reduces the load of storage and processing data by product quantization compression high-dimensional data, effectively improves the efficiency of retrieval;The application method uses cross learning strategy to improve retrieval precision;The application also designs self-adapting error correction label, effectively solves the sampling bias problem in contrast learning, improves retrieval precision.

A computational storage device for executing DiskANN search is provided by an aspect of the present disclosure. The computational storage device comprises a NAND memory array configured to storage node chunks corresponding to nodes in the DiskANN. The computational storage device also comprises a non-volatile memory array configured to store multiple product quantization (PQ) vectors corresponding to the nodes of the node chunks. The computational store device also comprises a processing unit coupled to the NAND memory array and the non-volatile memory array. The processing unit configured to execute DiskANN search (DiskANNs) among the node chunks in the NAND memory array and the multiple PQ vectors in the non-volatile memory array according to a received search instruction, and output a search result.

This invention provides a computing storage device, a system, and a method for operating the memory device. The computing storage device includes a NAND flash memory array for storing multiple node chunks corresponding to multiple nodes in a disk approximate nearest point. The computing storage device also includes a non-volatile memory array for storing multiple product quantization (PQ) vectors corresponding to multiple nodes in the multiple node chunks. The computing storage device also includes a processing unit coupled to the NAND flash memory array and the non-volatile memory. The processing unit performs a disk approximate nearest point search on the multiple node chunks in the NAND flash memory array and the multiple product quantization vectors in the non-volatile memory array according to a received search instruction, and outputs the search result.

This invention discloses a method and system for approximate multimodal data query based on hybrid blockchain, relating to the field of blockchain technology. The invention constructs a verifiable index structure to describe the compressed representation of multimodal data feature vectors and their mapping relationship to off-chain data pointers. It uses only a minimal on-chain space to record index state changes, avoiding the huge storage overhead of directly storing the original high-dimensional feature vectors or complete data on the blockchain. By compressing the feature vectors into PQ codes through product quantization and combining them with an MPT structure to generate verifiable lightweight cryptographic proofs, the authenticity of the multimodal approximate query results is verified. The Merkle proof is independently verified using on-chain root hashes to ensure the results have not been tampered with. The client downloads the original data, performs full-precision feature extraction and distance calculation, compensating for the precision loss caused by PQ compression; thus achieving a complete and reliable closed loop of "efficient approximate search + accurate result output".

The application discloses a neural network joint quantization method for image classification, solves the problem that a deep neural network for image classification is difficult to be efficiently stored and run on a resource-limited embedded device, and realizes compression and acceleration of the neural network. Scalar quantization is used to convert element values of a mapping codebook obtained through product quantization into 8-bit integers. In combination with 8-bit integer activation values, fixed-point operation of the neural network is realized, and the purpose of calculation acceleration is achieved. In order to eliminate the activation value distribution error caused by different inputs, the saturation truncation method is used to reduce the quantization interval of the activation value, and 8-bit scalar quantization of the activation value is realized. Finally, a pseudo-quantity unit is inserted into the neural network, a scalar quantization error is introduced, parameters are updated through back propagation, and the error is eliminated. The application fully combines the advantages of scalar quantization and product quantization, and can significantly reduce the model size while reducing the network operation amount.

This invention relates to the field of artificial intelligence technology and can be applied to fintech and medical technology. It discloses a speech encoding and decoding method, apparatus, device, and medium. The method includes: acquiring an original speech signal and extracting its spectral features; encoding the signal using an encoder trained with a first-stage mirror architecture to obtain a continuous vector; segmenting the continuous vector into sub-vectors, matching them with corresponding sub-encoding dictionaries using a quantizer to obtain sub-indices, and combining them to generate a global index; parsing the global index to obtain sub-indexes, retrieving sub-discrete vectors and concatenating them into a discrete vector; reconstructing the target speech spectral features from the discrete vectors using a decoder trained with a second-stage non-mirror architecture, and then converting the target speech signal. This invention employs a two-stage training process: the first-stage mirror architecture ensures encoding stability, while the second-stage non-mirror architecture enhances decoding flexibility. Combined with product quantization technology, it improves speech reconstruction quality while balancing computational efficiency and storage overhead.

The invention relates to the technical field of power grid maintenance, and provides a power grid maintenance work ticket intelligent generation method and electronic equipment, and the method comprises the steps: under the condition that the data size of a business logic vector library is greater than a preset threshold value, querying the business logic vector library based on an inverted file index and product quantization mode and a query vector; performing approximate nearest neighbor retrieval in the business logic vector library to obtain a plurality of logic chain fragments related to the current power grid maintenance task; under the condition that the data volume is smaller than or equal to a preset threshold value, accurate nearest neighbor retrieval is carried out based on a flat inner product index mode, and the multiple logic chain fragments are obtained; and inputting the plurality of logic chain fragments into a business logic reasoning model, performing task disassembly, step framework construction and equipment association analysis to obtain a business logic thinking chain comprising equipment, equipment association constraints and operation rules, and inputting the business logic thinking chain into a work content generation model in combination with original information to obtain a work ticket. The problem that reliable work tickets are difficult to generate efficiently in related technologies is solved.

The application relates to a product quantization nearest neighbor search method based on optimal space decomposition, which comprises the following steps: obtaining a sample data set; solving the optimal solution of space decomposition of the sample data set; decomposing the sample data set into M subspaces according to the optimal solution of space decomposition, and respectively performing cluster analysis in each subspace to obtain a plurality of clusters; constructing an inverted order table by taking the cluster center as an index; obtaining data to be searched; decomposing the data to be searched into M subspaces according to the optimal solution of space decomposition, and respectively searching a plurality of nearest clusters in each subspace; calculating the Cartesian product of the plurality of nearest clusters to obtain a plurality of candidate code sets; and querying the inverted order table according to the plurality of candidate code sets to obtain a search result set. The application converts the space division problem of product quantization into a space decomposition problem of balancing the data distribution volume of each subspace, realizes reasonable division of the space, effectively reduces the quantization error, and improves the search precision.

The invention relates to the technical field of big data processing and information retrieval, and discloses a big data full life cycle quality control and efficient retrieval system, which comprises a data acquisition processing module, a hierarchical index construction module and a retrieval repair module, and is characterized in that the acquisition processing module generates a dynamic quality score based on static verification, dynamic activeness and data conflict degree; the high-quality data is stored in a hot storage area to construct a high-precision vector index, the data to be treated is stored in a cold storage area, and a low-precision vector index is constructed by using a self-adaptive product quantization algorithm; and when the retrieval of the hot area fails, the retrieval and restoration module generates virtual reference data based on the semantic intention, performs reverse detection and search on the cold area, discovers and restores potential to-be-treated data, and promotes the potential to-be-treated data back to the hot storage area. According to the method, on-demand allocation of resources and automatic discovery and dynamic optimization of hidden dirty data are realized through a quality-driven hierarchical index and reverse detection mechanism, and the data availability is remarkably improved.

Methods and systems for compressing a matrix are described. A matrix having a plurality of rows formed from a respective plurality of vectors is partitioned into a plurality of sub-matrices, each sub-matrix containing a respective set of sub-vectors from one or more consecutive columns of the matrix. For each given sub-matrix, the sub-vectors are clustered into a plurality of clusters. For each given cluster, a centroid and a variance are computed and stored based on the sub-vectors belonging to the given cluster. A mapping of each vector to a respective cluster in each sub-matrix is stored. The stored centroids, the stored variances, and the stored mappings form a compressed data set for reconstructing the matrix.

The application relates to a kind of approximate nearest neighbor text semantic retrieval method, device, equipment and storage medium, the method comprises: obtaining text data to be retrieved, the text data to be retrieved is preprocessed using bag-of-words strategy to generate text vector;The weight of the text vector in the text library is calculated;The text vector is subjected to product quantization coding processing, and product quantization coding data is generated;Based on the weight and the product quantization coding data, the text vector is processed using a multi-table product quantization algorithm, and an approximate nearest neighbor text semantic retrieval result set is generated.The application is based on the nearest neighbor retrieval problem of semantic text, and a fast approximate nearest neighbor retrieval method is designed, which can greatly improve the speed of semantic-based text approximate nearest neighbor retrieval in a large-scale text database while ensuring the accuracy of the nearest neighbor retrieval.