63 results about "Database index" patented technology

The invention discloses an SSD B + tree indexing method and system based on wear leveling, and relates to the technical field of database indexing structure optimizing.The method comprises the steps that B + tree logic nodes and SSD physical addresses are decoupled, and a dynamic node mapping table is introduced; replacing local updating with additional updating, and writing the new version of the node into a new position of the SSD; when a write-in address is allocated to the node, an allocation strategy considering a block wear state is adopted, and write operation is guided to an area with relatively light wear; during garbage collection, high-wear blocks are preferentially treated to integrate wear. According to the method, the problems of writing hot spots and unbalanced abrasion of the B + tree on the SSD are effectively solved on the database software level, the service life of the SSD can be remarkably prolonged while the high performance of the B + tree is kept, and the method has very high universality and practical value.

The invention belongs to the field of information processing, discloses an industrial multi-modal data semantic alignment method based on vector space and topological constraints, and aims to solve the problem that multi-modal data semantic segmentation in an industrial scene is difficult to unify and associate. The method comprises the following steps: performing feature extraction and structured analysis on modal data, and mapping the modal data to a unified semantic vector space through a projection layer; on the basis of vector similarity matching, topological structure constraints derived from process drawings and the like are introduced, neighbor relation verification is carried out on candidate entities, context logic verification is carried out in combination with a large model, and therefore the accuracy of cross-source entity alignment is remarkably improved; mixed retrieval is supported by establishing an efficient vector database index, and incremental updating of the knowledge graph is achieved through large-model-assisted reasoning and rule base verification. Vector semantics and specific structural dependency of an industrial system are effectively fused, and high-precision and evolvable cross-modal semantic alignment and intelligent association analysis are achieved.

The invention discloses a database index optimization method, system and equipment based on artificial intelligence. The method specifically comprises the steps of collecting performance data of a database in real time; performing preprocessing and feature extraction on the performance data to obtain a spatial-temporal feature matrix; taking the spatial-temporal characteristic matrix as input, performing index combination selection by using a hybrid solver formed by a quantum annealing algorithm and a classical genetic algorithm, and generating an index optimization strategy; on the basis of an index optimization strategy, potential performance risks are predicted according to an index change influence evaluation model, and an optimization decision explanation is generated through an SHAP value; and executing index change operation according to the index optimization strategy, evaluating database performance after index change, and feeding back to the hybrid solver for updating and optimization. According to the method, the database index is automatically and intelligently optimized, the database performance is improved, and the manual intervention cost is reduced.

The invention provides a multilingual semantic analysis and decision support system and method for an international climate negotiation scene. The system comprises a data processing module which is used for utilizing a natural language processing model to automatically generate structured data which is composed of multiple languages and corresponds to corpus data related to a target theme; the database construction module is used for carrying out vector database index construction of hierarchical indexing on the structured data according to an issue, a language and time, and generating a corpus vector database; the content generation module is used for responding to the received content generation instruction, converting the content generation instruction into a query vector, performing retrieval from the corpus vector database according to the query vector to obtain target corpus data corresponding to the content generation instruction, and generating the target corpus data according to a pre-trained language generation model; and performing language generation processing on the content generation indication and the target corpus data to obtain a content generation result associated with the content generation indication.

The invention relates to a database index optimization method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining key SQL performance constraint data and a query statement set, wherein the query statement set comprises key SQL statements corresponding to the key SQL performance constraint data; generating candidate database index configuration based on the query statement set and a preset rule; and if the performance data of the evaluated key SQL statement under the candidate database index configuration does not meet the key SQL performance constraint data, marking that the candidate database index configuration is invalid, and returning to the step of generating the candidate database index configuration based on the query statement set and the preset rule. By adopting the method, the execution efficiency of overall index optimization can be improved, and the performance of key services is guaranteed.

The invention belongs to the technical field of database indexing, and particularly relates to an online index tuning system with uncertainty perception. The system comprises an operator-level learning model module which is used for dynamically correcting the estimation cost of a query optimizer by establishing lightweight learning models for operators in a query execution plan and learning cost adjustment factors of various operators by utilizing limited query execution feedback data; the model uncertainty quantification module is used for evaluating the prediction credibility of each operator model based on the joint measurement of the information entropy and Monte Carlo Dropout; in the index recommendation stage, an uncertainty weighted index value function is introduced, the instant performance income and the long-term learning value of candidate indexes are comprehensively evaluated, and self-adaptive balance of exploration and utilization is achieved. According to the method, index configuration can be dynamically adjusted under the continuously-changing query workload, high stability and self-learning ability are achieved, and the query performance is continuously optimized.

The invention relates to the technical field of database indexing, in particular to a vector database-oriented online incremental learning indexing system and a construction method thereof, and the overall architecture of the system is composed of a storage layer, an index layer, a learning layer and a consistency layer; the method has the beneficial effects that the lightweight neural network (LNE) is embedded in a node entity of graph / inverted class vector indexes such as HNSW (approximate nearest neighbor search structure) or IVF (inverted file index), new data distribution characteristics are absorbed in real time in a vector insertion stage, index topology and a quantized codebook are dynamically updated in a mode of'insertion, learning and reconstruction ', and the method has the advantages of being high in efficiency, high in reliability and high in reliability. And sub-millisecond query delay is maintained.

The embodiment of the invention provides a sparse bitmap-based holographic grid storage method and a sparse bitmap-based holographic grid retrieval method, which are applied to the technical field of low-altitude airspace. The method comprises the steps that holographic grid data are obtained, a holographic grid comprises a plurality of grid units, and each grid unit has spatial position codes and attribute information; according to a preset parent grid level, performing block aggregation on the grid units with the same attribute information to generate grid blocks, and determining a unique ancestor grid for each generated grid block; for each grid block, taking the corresponding ancestor grid as a reference frame, generating a sparse bitmap representing the distribution state of the sub-grids in the grid block, and compressing the sparse bitmap; and associatively storing the code of the ancestor grid of each grid block, the attribute information and the compressed sparse bitmap into a database, and constructing a database index by taking the code of the ancestor grid and the attribute information as keys. In this way, the data storage scale can be effectively reduced, the index complexity is reduced, and efficient retrieval under the multi-dimensional query condition is supported.

The invention discloses a multi-source isomorphic data NL2SQL method, device and equipment based on retrieval enhancement and a medium, and relates to the technical field of natural language process.The method comprises the steps that query keywords are extracted, and a cross-source metadata mapping table is matched to determine a candidate database set from a plurality of isomorphic databases; the cross-source metadata mapping table is used for storing a global mapping relationship among metadata information of a database; generating a query intention object, and determining database retrieval configuration information from the global index mapping table; the global index mapping table is used for storing a vector database address and a database index structure; the database index structure is used for storing a mapping relation between a document keyword vector and a document original text; determining a target database information set based on the intention object vector according to database retrieval configuration information; and generating a target database retrieval statement based on the preset retrieval statement generation large model, and executing the target database retrieval statement so as to visually display a database retrieval result.

The present application relates to the technical field of computer data storage, and particularly provides a data storage method and device, electronic equipment and storage medium, aiming at solving the problem of consuming a large amount of cpu and disk io when using the existing B+ tree to flush. To this end, the data storage method comprises: constructing a database data layer, containing a plurality of shard pages, and storing complete KV key-value pair data on each shard page; constructing a database index layer, storing index information of the KV key-value pair data stored in the database data layer, the index information being stored in a tree structure, including a split key of each shard page as an index key, and ID information of the shard page corresponding to each split key as an index value. The present application stores data and index separately by constructing the database data layer and the index layer, so as to improve performance and reduce resource consumption.

The application provides an optimization method of database index, a storage medium and equipment. The method comprises the following steps: obtaining a database operation; determining an index according to the database operation; searching for an index partition needing to perform the operation in a preset index system table according to the index, the preset index system table being used for recording the correspondence between the index and a self partition; and performing a corresponding operation according to the type of the database operation. Through the method, the index is partitioned in the database, the index partition needing to perform the operation is found according to the preset system table when the interface calls the database, the high-concurrency pressure is dispersed into each index partition, the concurrent access conflict of each index is reduced, the performance of the database is improved, and the index partition can be determined according to the information in the interface calling the database, so that the convenience of using the database is improved.

The application discloses a distributed database index recommendation monitoring system and method, belongs to the field of distributed databases, and aims to solve the technical problem of how to perform real-time feedback on analysis recommendation functions, establish a closed loop index, and guarantee the health and efficiency of database indexes. The technical scheme adopted is as follows: the structure comprises an index analysis recommendation module, an index suggestion modification application module, an index usage monitoring module, an index usage analysis module, an index report generation module, and a self-adaptive index suggestion optimization module; the index analysis recommendation module is used to start from unhealthy statements, check related tables, access patterns, predicate categories and related fields, and propose candidate indexes based on each SQL statement according to a candidate index generation algorithm; after the generation of the candidate indexes that need to be created, the effectiveness of the candidate indexes is verified through two steps; the two steps specifically refer to verifying the benefits of the indexes on a single SQL statement and verifying the benefits of the indexes on all affected statements under the overall application load.

The invention belongs to the field of natural language processing and large language models, and particularly discloses an intelligent question and answer method and system for a document in the field of water taking permission, and the method comprises the steps: analyzing a water taking permission document through layout recognition, and obtaining a json format text; performing knowledge extraction on the text, namely identifying a paragraph main entity through a paragraph main entity dual-channel detection and entity identification iterative training method, complementing the content of an indication pronoun in the text through long-distance anaphora resolution based on an elastic sliding window, and guiding a large language model to extract a triple by using knowledge extraction Prompt, data cleaning is carried out on the data; constructing a vector database index based on the cleaned triples, and when a user question is received, retrieving candidate triples related to the user question by using the vector database index; and inputting the retrieved candidate triples into the large language model to generate answers to the user questions. According to the method, the knowledge can be efficiently and accurately extracted and retrieved from the water taking permission document.

Techniques for vector database indexing are described. In an example, a plurality of unstructured data is obtained. Vector embeddings are performed on each unstructured data from amongst the plurality of unstructured data to generate a plurality of vectors. Each vector from amongst the plurality of vectors is indicative of attributes of the unstructured data from amongst the plurality of unstructured data. The plurality of unstructured data and the plurality of vectors is stored in the vector database. A vector index corresponding to the plurality of vectors based on a first indexing mechanism is generated where the vector index includes at least one cluster of vectors with similar attributes. An index mapping is performed to store the vector index into a relational database.

Techniques for optimized LLM inference from structured data via intermediate documents (“LLMs”) are disclosed. In an example method, a computing device determines chunks from documents based on one or more topics identified in the documents. The method further involves, for each chunk of the determined chunks, generating an embedded vector representing the chunk. The method further involves, for each chunk, outputting the embedded vector of the chunk to a vector database indexed based on the one or more topics. The method further involves receiving a query including a topic included in the one or more topics. The method further involves determining a chunk based on the topic. The method further involves generating a response based on the query and the chunk. The method further involves outputting the response to the client device.

The application relates to a learning type index method and system based on time sequence data characteristics, and relates to the field of database indexing.The application is used to solve the problems of large memory occupation and long indexing time of the existing time sequence data indexing method.The application comprises the following steps: step one, obtaining time sequence data, dividing the time sequence data into multiple data segments according to time stamps, and constructing a linear model for each data segment;step two, training the linear model obtained in step one by using the time stamp data and offset of the time sequence data, and obtaining a trained linear model;step three, establishing a B+ index tree by using the trained linear model obtained in step two; and step four, based on the B+ index tree obtained in step three, performing time sequence data insertion, time sequence data deletion and index query operations.The application is used for establishing database indexes and performing data insertion, deletion and index operations.

The application discloses a method for enhancing position monitoring of a closed space PNT network and a high-precision map, and relates to the field of wireless positioning. The method comprises the following steps: establishing a semantic unit grid of a target closed space; establishing a mapping relationship database of semantic information of any one entity object element and position information of the semantic unit where the entity object element is located; acquiring a grid code of a semantic unit where a target entity object is located; and obtaining position semantic information of the target entity object based on the grid code of the semantic unit and the mapping relationship database. According to the technical scheme, the grid code of the semantic unit where the user is located is calculated only once, the semantic information is obtained by reading the mapping relationship database index based on the grid code of the semantic unit, and the calculation amount and response waiting time of the user position monitoring information are greatly reduced. Furthermore, the rationality of the user position monitoring information is improved, and the calculation amount of the generated user position density monitoring information is reduced.

The application provides an intelligent ship identity recognition method and system based on artificial intelligence, and the method comprises the following steps: training original samples through structure constraint enhancement, and constructing a model conforming to real maritime imaging conditions; generating stable target sequences by using joint segmentation and identification bearing area guide mechanism on input videos; continuously updating the frames by tracking feature extraction model in a global coordinate system to realize target tracking; detecting texts on the tracking sequences, and suppressing single-frame errors by using the probability fusion of the same character positions in multiple frames; and finally, matching the recognition results with an identity database index to complete verification. The application significantly improves the accuracy and stability of ship identity recognition in complex environments, and is mainly used in port monitoring, maritime supervision and ship automatic identification scenes.

Methods and systems, including computer-readable media, are described for verifying a purchase receipt. A computing system verifies a purchase receipt by implementing a method that includes extracting line-item purchase information data elements from an image of the purchase receipt. The method includes retrieving expected item pricing data and expected tax data from a retailer-specific database indexed by region and data for each item. The method includes identifying a data element having a difference between a unit price represented in the corresponding data element and an expected unit price represented in the corresponding expected item pricing data, in which the difference meets a configurable threshold and corresponds to a purchase price discrepancy. The method includes constructing a discrepancy report having a structured data format, transmitting the discrepancy report to a retailer system over a preferred communication channel determined by a retailer profile associated with the retailer.

This disclosure provides a database index generation method and apparatus, a data processing method and apparatus, relating to the field of computer technology, specifically to distributed systems, storage management, and other technical fields. The specific implementation scheme is as follows: based on the metadata of the metadata database in the distributed file system, determine the items to be aggregated related to file quotas; based on the items to be aggregated, determine the item aggregation function; create an aggregation index in the metadata database; perform statistics on the items to be aggregated according to the item aggregation function to obtain statistical data; and store the statistical data as aggregated data in the aggregation index.

A computer-implemented system processes store data from multiple retail locations and content posts from store-based users to provide location-filtered retail content to user devices. The system may employ geolocation to determine user proximity to retail stores and multi¬ criteria filtering algorithms to content posts based on user preferences and real-time location data. Technical innovations include optimized database indexing for rapid location -based content retrieval, adaptive notification algorithms that identify users meeting proximity and preference criteria, and efficient content aggregation protocols that consolidate multiple store¬ based user posts into unified feeds. The system implements near-field communication and / or QR code scanning to trigger content display. Processing optimizations may include compressed transmission protocols and caching mechanisms that pre-load location-relevant content based on user movement patterns, resulting in improved response times and reduced bandwidth consumption for mobile retail content delivery.

The application discloses a database index processing method, system, device and readable storage medium, and belongs to the technical field of data processing. The method comprises the following steps: a compressed value field hashId is added in a table of a database, and is associated with an index field in the table; the value of the hashId is calculated according to the field value of the index field; the value of the hashId is stored in the compressed value field hashId, and the sql statement of the database is modified according to the value of the hashId. The system comprises a table establishing module, a hashId value calculating module and a sql modifying module. The database index processing method, system, device and readable storage medium solve the problem that the existing database query is low in query speed when the field value of the index is too long.

Embodiments of the present application provide a kind of database index transmission method, equipment, storage medium and program product, related to big data field.The method comprises: receiving instruction from index collection client;Judge the type of instruction;If it is index collection instruction, according to the configuration information of the instruction, register and start corresponding index timing collection task, wherein, timing collection task is used to update the memory cache of index collection server after the collected index data is compressed;If it is data acquisition instruction, read the compressed index data corresponding to data acquisition instruction from memory cache as target data;The target data is segmented into multiple sequentially numbered data blocks;And by persistent connection, data block is sent to index collection client in turn.The method of the present application increases the cache mechanism and block transmission strategy, improves the collection efficiency and transmission reliability of database index data.

This invention provides a method and system for incremental time-series data updates using adaptive hierarchical indexes, belonging to the field of database indexing technology. The method includes: constructing a three-level hierarchical index structure comprising a hot layer, a warm layer, and a cold layer; the hot layer using an in-memory skip list, the warm layer using an LSM tree, and the cold layer using a segmented B+ tree; configuring an incremental merge scheduler to dynamically trigger inter-layer data sinking and index merging based on data volume, query frequency, and write rate; employing a lock-free concurrency strategy to avoid merging blocking foreground read / write operations; embedding time-series feature summaries in index nodes to implement query pruning; and introducing a pre-write log and checkpoint mechanism to ensure atomicity and crash recovery. This invention can maintain stable query latency under continuous high-throughput write conditions.

This invention belongs to the field of information processing and discloses a semantic alignment method for industrial multimodal data based on vector space and topological constraints. It aims to solve the problem of semantic fragmentation and difficulty in unifying and associating multimodal data in industrial scenarios. By extracting features and performing structured parsing on each modality of data, and mapping them to a unified semantic vector space through a projection layer, the method introduces topological constraints derived from process drawings, etc., to verify neighbor relationships of candidate entities based on vector similarity matching. Furthermore, it combines a large model for contextual logic verification, thereby significantly improving the accuracy of cross-source entity alignment. An efficient vector database index is established to support hybrid retrieval, and a large model is used to assist in reasoning and rule base verification to achieve incremental updates of the knowledge graph. This invention effectively integrates vector semantics with the structural dependencies unique to industrial systems, achieving high-precision, evolvable cross-modal semantic alignment and intelligent association analysis.

The application relates to an automatic learning index method and system, in particular to a method and system for automatically recommending an optimal index according to computer data characteristics, to solve the problems that, when learning indexes of a computer are established, the number of layers of the learning indexes and corresponding models of each node need to be defined manually, the space cost of index establishment is large, the establishment process is difficult, and the establishment time is long, and the application relates to a method and system for automatically recommending an optimal index according to computer data characteristics, which comprises the following steps: constructing a learning index model, the learning index model comprising a regression model and a random forest model; training the learning index model by using a computer database as a training set; inputting a key key of training data; and outputting the position of the training data in the computer database. The application belongs to the field of database indexes.

The invention discloses an intelligent question-answering system construction method based on multi-modal large model semantic enhancement, and relates to the technical field of intelligent information processing, and the method comprises the steps: converting original document data with different sources and inconsistent structures into intermediate representation in a unified markup language format; based on the intermediate representation, performing semantic restoration and enhancement on image, table and text modalities in the document through a cascaded semantic enhancement pipeline to generate a multi-granularity semantic unit; and mapping the multi-granularity semantic unit into a high-dimensional vector through a pre-training word embedding model, and constructing a vector database index based on a hybrid retrieval strategy. According to the method, the cascaded semantic enhancement assembly line is constructed, specialized processing is carried out on image, table and text modalities, multi-modal data which is originally difficult to process is converted into semantic units which can be understood by a machine, and the core problems of semantic deficiency and structural ambiguity of a multi-modal data source are fundamentally solved.

The application discloses a flight parameter data storage optimization and query method and system based on a configurable tag library, relates to the technical field of flight data processing, and introduces a configurable tag library as an intelligent control layer to predefine which high-frequency and key flight parameters should be created as database indexes, and corresponding tag configuration information is stored in a separate rule table in the tag library, and the rest of the flight parameters are stored as normal fields; when data is stored in the database, the field values of the specified flight parameters are automatically promoted to index tags of the time series database according to the tag rules, and other parameters are stored as normal fields; when querying, the pre-built tag indexes can be directly used for efficient retrieval, and this process supports the flexible addition or removal of any flight parameter to or from the tag library through a configuration interface, without the need for complicated database table structure changes, data migration or modification of underlying application program codes, and the adaptability to variable business demands in flight parameter data analysis is significantly improved.

The invention discloses a data query method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining historical query data of a target graph database, and determining a query mode to which each historical query data belongs; for each query mode, generating a candidate index corresponding to the query mode according to historical query data in the query mode; evaluating each candidate index according to historical query data in the query mode to obtain evaluation cost of each candidate index; and determining a target index in all the candidate indexes according to each evaluation cost so as to perform data query on the target map database in the query mode based on the target index. By applying the technical scheme provided by the invention, more intelligent map database index management can be realized, so that more efficient and accurate data query is realized, and the performance of the map database is continuously guaranteed.