133 results about "Database index" patented technology

System, methods, apparatuses, and computer program products are disclosed for using a large language model (LLM) to generate an index recommendation for a database table. A prompt is provided to an LLM to request an index recommendation for a table of a database. The prompt includes index recommendation guidelines and information associated with the table that is determined from one or more sources. An index recommendation is received from the LLM, and an action is performed based on the received index recommendation.

The present disclosure relates to a method of enrolling an individual at a secure server and subsequently authenticating and identifying the individual at an authenticating party using an authentication token created during the enrolment and a secure server performing the method. The method comprises engaging, via a user device, in an enrolment process with the individual, registering the user device by receiving a public key, the public key being created by the user device along with a private key corresponding to the public key, acquiring a trusted identifier of the individual, associating the acquired trusted identifier of the individual with at least one database index to create an authentication token, the database index being utilized for look-up at the secure server, signing the authentication token, and sending the signed authentication token to the user device, while deleting the acquired trusted identifier at the secure server.

The invention discloses a method and system for realizing the AI processing capability of an operating system, and the method comprises the steps: carrying out the variable definition of a captured user instruction through an AI adapter, and transmitting a variable to an intention engine in a logic processing engine; an intention engine in the logic processing engine carries out corresponding processing through a cue word engine in the logic processing engine to generate a reasoning stream, and the reasoning stream is sent to the AI model for processing; when the AI model processes and executes the reasoning flow, calling the corresponding AI model for reasoning; and generating a task flow according to a reasoning result, wherein each execution process in the task flow is executed by calling a corresponding task execution module and tool through the AI adapter. According to the invention, the AI capability is added to the OS of the traditional terminal, and a multi-device platform and a multi-operating system are supported; various AI models such as language models, visual models, audio models, database indexes and the like are supported; the plug-in flexible adaptation of the AI model is supported; and the data privacy problem is avoided as far as possible by taking an end-side model as a main

The embodiment of the invention discloses a database index optimization method and related equipment, which are used for improving the timeliness of index optimization so as to improve the timeliness of database management. The method comprises the steps that a historical slow query is obtained, the historical slow query comprises a query statement, and an operation object of the historical slow query is a database; determining a candidate index set based on the query statement; constructing a first optimization statement based on the historical slow query and the candidate index set; inputting the first optimization statement into a large language model to obtain an optimization index, wherein the first optimization statement is used for indicating the output of the large language model and optimizing the index of the historical slow query execution time; and updating index information of the database based on the optimized index.

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 relates to the technical field of vector database retrieval, and discloses a vector database index recommendation method, device and equipment and a medium, and the method comprises the following steps: carrying out clustering analysis on a vector data set, and extracting data distribution characteristics based on a clustering result to obtain a vector data portrait; the data distribution characteristics comprise inter-cluster center distances and whether head-tail structures exist or not; according to a preset index recommendation strategy, a recommendation index is determined according to the vector data portrait; if the inter-cluster center distance exceeds the upper limit of a set threshold interval, an IVF index is adopted as a whole; if the inter-cluster center distance is within a set threshold interval and no head-tail structure exists, an IVF index is adopted on the whole; if the head-tail structure exists, the trunk data part adopts IVF index, and the sparse data part adopts HNSW index; and if the inter-cluster center distance is smaller than the lower limit of the set threshold interval, the recommended index is HNSW. According to the method, a proper index type can be adaptively recommended according to data distribution characteristics in the vector data set.

The embodiment of the invention discloses a database index recommendation method and device based on query intention recognition and a medium, belongs to the technical field of databases, and solves the problem that the storage cost is increased due to the fact that similar indexes are repeatedly created for SQL (Structured Query Language) texts with the same semantics but different grammar in a traditional index mode. Based on a preset query cost threshold value, collecting a target SQL text in a database, and converting the target SQL text into an abstract syntax tree; performing normalization processing on the abstract syntax tree based on a preset structure rule; performing core feature extraction on the normalized abstract syntax tree, splicing the extracted core features to generate a semantic signature, and dividing the plurality of target SQL texts into a plurality of intention families based on the semantic signature; generating an intention template based on the plurality of intention families, and performing index income analysis on each intention module in the intention template; and creating an index based on the index income analysis result and the security window period corresponding to the corresponding intention module.

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 vector data consistency quality management method and system, and relates to the technical field of natural language processing and database management. The vector data consistency quality management method comprises the following steps: performing semantic triple extraction on an input problem and a retrieval context through a large language model (LLM); classifying the retrieval context into positive matching, negative matching and fuzzy matching on the basis that the context clear matching depends on a CCMDs rule; integrating the positive and negative matched contexts to construct an enhanced cue word, and inputting LLM to generate an answer; and dynamically correcting a vector database index according to a negative matching result, and optimizing the quality of the knowledge base. According to the vector data consistency quality management method, by dynamically optimizing a knowledge base index structure, positive / negative matching contexts are utilized in a classified mode, and the generation accuracy and data consistency of RALMs are improved.

The invention relates to the technical field of system database management, in particular to an intelligent monitoring analysis method and system for gate opening and closing working condition data, and the method comprises the steps: obtaining fault records from a historical fault data storage library of a specified gate, and constructing a fault feature library; acquiring historical monitoring data of the sensor group, performing pattern recognition and statistical analysis to form a fault feature weight table, and storing the fault feature weight table in a data cleaning database; and classifying the sensor group through a database index technology by utilizing the fault feature weight table, the external prediction data and the sensor installation position data, and storing a classification result into a sensor level index table. And a distributed data intelligent monitoring system is formed through N independent data processing units in combination with a database interface protocol. The data processing unit comprises a data acquisition layer, a data real-time analysis layer, a data cleaning and quality control layer and a data storage and retrieval layer, and high-efficiency processing and retrieval of data are ensured.

The invention provides a database index adjusting method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a data query log and a data modification log generated in a running process of a database; according to the data query log and the data modification log, performance evaluation is carried out on the index in the database to obtain an index performance evaluation result, and the index performance evaluation result comprises parameters of the index in the database influencing the data query performance; generating index adjustment decision information of the database according to the index performance evaluation result; and under the condition that the index adjustment decision information indicates to perform index adjustment on the database, performing index adjustment on the database based on the data query log and the data modification log. The query performance can be optimized, the index maintenance cost is reduced, and the overall efficiency of the database is improved.

The invention provides a strategy output method and device for urban carbon neutralization, which can be applied to the field of urban low-carbon management decision making. Calling a plurality of first indexes from a database, each first index including index data of a plurality of second indexes and time sequence data of carbon emission of the target city, wherein the first indexes include carbon emission characteristics, ecological environment characteristics and energy characteristics; searching a first weight of each first index from the scale matrix by using a search unit; based on an entropy method, generating a second weight of each second index according to the index data of the plurality of second indexes; according to a combined weight determined by the first weight and the second weight and index data of the second index, recommending a carbon neutralization capability grade matched with the target city for the target city; according to the time sequence data, generating a carbon emission state of the target city; and outputting a carbon neutralization management strategy for the target city according to the carbon neutralization capability grade and the carbon emission state.

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 database index implementation method based on object storage, which is used for solving the problem of efficient retrieval when object storage is used as a database storage engine. According to the method, a special index storage structure is constructed in object storage and comprises an index metadata area and an index data area, and index data is organized through a segmentation management mechanism. Each segment in the index data area comprises a Bloom filter file, an index entry file and a segment abstract file, and efficient organization and access of index data are achieved. According to the method, a batch writing and parallel retrieval strategy is adopted, and a segment merging and garbage collection mechanism is matched, so that the index performance and the storage efficiency are ensured. The method has the beneficial effects that the query performance when the object storage is used as the database is remarkably improved, the storage overhead is reduced, meanwhile, the data consistency is ensured, and a key technical support is provided for realizing a database system based on the object storage.

A log system prompts a foundation model to identify regular expressions that delineate single line logs in log files and incorporates user feedback for the regular expressions in a feedback loop as part of a hybrid parsing approach. The log system splits the log files into single line logs according to the regular expressions and augments the single line logs with classifications and values of metadata fields. The augmented single line logs are then clustered into hierarchical clusters with corresponding cluster patterns and the log system stores the augmented single line logs in a database indexed by the classifications and values of metadata fields and associated with corresponding clusters / cluster patterns. A presentation module accesses the database when responding to user queries for filtered single line logs and log correlations / root cause analysis.

The invention relates to the technical field of index structure recombination, in particular to a nursing process management database index optimization method, which comprises the following steps of: performing cost calculation on multiple indexes by adopting a gradient lifting regression tree, comparing the cost calculation with a threshold value, and forming a recombination candidate priority list under the support of a numerical result; a potential recombination object has a quantifiable judgment standard, predicted consumed time is calculated in combination with the low-ebb IO capacity and compared with the time slice length, the screening process can correspond to the system resource bearing condition, a B + tree algorithm is adopted for calculating a loading factor and the continuous scanning length, and through merging, splitting and redistribution operation, the screening efficiency is improved. A node organization structure is finely adjusted, it is guaranteed that an index structure and an access mode are kept coordinated, information entropy is utilized to sort and screen a field sequence, the field arrangement with high distinction degree is preferentially used for constructing a new index, atomic switching operation is executed to replace an old index, and the field sequence has adaptability in a dynamic data environment.

The present invention relates to the technical field of vector database retrieval, and discloses a vector database index recommendation method, apparatus, device, and medium. The method comprises: performing cluster analysis on a vector data set, extracting data distribution characteristics based on the clustering results, and obtaining a vector data portrait; the data distribution characteristics include the center distance between clusters and whether a head-tail structure exists; determining a recommended index based on the vector data portrait according to a preset index recommendation strategy: if the center distance between clusters exceeds the upper limit of a set threshold interval, the IVF index is used as a whole; if the center distance between clusters is within the set threshold interval and there is no head-tail structure, the IVF index is used as a whole; if there is a head-tail structure, the IVF index is used for the backbone data portion, and the HNSW index is used for the sparse data portion; if the center distance between clusters is less than the lower limit of the set threshold interval, the recommended index is HNSW. The present invention can adaptively recommend a suitable index type based on the data distribution characteristics within the vector data set.

The present application relates to a kind of Z order curve encoding and decoding method based on multi-order value lookup table, belong to spatial database index technical field.The present application includes multi-order encoding value lookup table construction stage: construct multi-order encoding value lookup table, for realizing the mapping between coordinate and encoding value;Encoding stage: each coordinate component is segmented, through iterative inquiry multi-order encoding value lookup table, the result of each segment is spliced to calculate Z order curve encoding;Multi-order decoding value lookup table construction stage: construct multi-order decoding value lookup table, for realizing the mapping between encoding value and coordinate;Decoding stage: to coordinate encoding value is segmented, through iterative inquiry multi-order decoding value lookup table and the result of each segment is spliced to calculate coordinate.The present application can convert step-by-step encoding and decoding operation into step-by-step encoding and decoding operation by introducing segmentation mechanism and other technologies, improve encoding and decoding efficiency.

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.

A de-confliction system and a method of querying a database of records utilizing the de-confliction system is provided. A universal script is created from language-agnostic de-confliction scripts. Each de-confliction script addresses a set of confusable characters and multiple de-confliction scripts may be combined to provide a Universal script. Application of the universal script to the original content output of the database provides for the replacement of confusable characters with functional characters and the creation of a functional output. The functional output is used to create a functional index which is provided in the database record. Application of the Universal script to a query provides a functional query. Running of the functional query on the indexes of the database provides improved search results for databases including confusable characters.

The invention provides a database index generation method which can be applied to the technical field of big data. The database index generation method comprises the steps that in response to monitoring that a database query using a first index is a slow query, a query statement of the slow query is input into a large language model, semantic analysis is carried out according to the large language model to determine a first feature, and the slow query is a database query with query parameters abnormal; generating a feature vector according to the first feature, and inputting an index recommendation model to obtain a second index; and replacing the first index according to the second index so as to perform database query operation according to the first index. The invention further provides a database index generation device and equipment, a storage medium and a program product.

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 invention provides a database index creation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a query speed category label corresponding to execution of a target database query statement every time within a current time period; extracting a query feature group in the target database query statement; constructing a decision tree according to the query speed category label and the query feature group; each path in the decision tree comprises a query speed category label and at least one feature of a data table, a query field and an operator; obtaining a target path with a query speed category label as a target category in the decision tree; determining the number of paths corresponding to the same query field according to the target path; and creating a database index according to the query fields of which the path number is greater than the preset number. According to the method, the database index can be created according to the query speed corresponding to the database query statement, changes of the data volume, the query mode and the like in the database can be timely adapted, and the query efficiency of the database is improved.

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.