164 results about "Inverted index" patented technology

The invention discloses an intelligent dialogue knowledge base optimization method based on multi-path recall fusion and self-updating, and relates to the technical field of artificial intelligence. The method comprises the following steps that A, a multi-path parallel recall engine is constructed, a knowledge base is retrieved in parallel through a keyword matching module, a semantic vector matching module and a user portrait matching module, and the keyword matching module positions keyword segments through inverted indexes; according to the method, a three-way parallel recall engine for keyword matching, semantic vector matching and user portrait matching is constructed, multi-dimensional accurate retrieval of user questions is achieved, a keyword matching module adopts an inverted index to rapidly position keyword segments, and coverage of basic semantics is ensured; the semantic vector matching module calculates cosine similarity through a Sension-BERT model, and captures deep semantic association between a user question and a knowledge base fragment; the user portrait matching module generates a retrieval weight based on a user identity dynamic loading rule base.

The invention discloses a similar text quick retrieval method and system, and belongs to the technical field of natural language processing and information retrieval, and the similar text quick retrieval method comprises the following specific steps: step 1, vectorizing semantics, and storing the vectorized semantics into a database; using a pre-training language model to respectively encode the query text and the documents in the text library into low-dimensional semantic vectors with fixed dimensions; step 2, hierarchical index retrieval: N candidate documents closest to the query vector are retrieved through a first-level index structure to form a first candidate set; according to the method, millisecond coarse screening is realized through parallel design of the HNSW graph index and the semantic cluster inverted index, the semantic cluster inverted index reduces a search space to a local optimal domain, global precision loss is avoided, a newly added text only needs one-time cluster center distance calculation, and index updating complexity is greatly reduced.

The invention provides an enhanced symmetric searchable encryption method and system, and relates to the field of data security and privacy protection. The method comprises the steps that a plaintext document set is symmetrically encrypted, keywords are extracted, and an encryption index token is generated through a pseudo-random function; constructing a reverse index structure, and mapping the token to a document identifier; dynamic insertion, deletion and modification of documents are supported, and indexes are synchronously updated in real time; cipher query is realized by generating a search token, and after the server returns a matching result, the client decrypts the matching result to obtain an original text. According to the system, through multi-keyword Boolean logic query, an index structure is compressed by adopting a Bloom filter, the retrieval efficiency is improved in combination with a jump pointer or a B + tree, a false query strategy is introduced to hide an access mode, and the anti-attack ability is enhanced. Compared with an existing scheme, the method has the advantages that the searchability, the system performance and the privacy protection capability of the encrypted data are improved, and the method is suitable for security data retrieval application in cloud computing, database systems and block chain environments.

The invention relates to the technical field of computer big data processing and retrieval, and discloses a data multivariate storage and full-text retrieval system which is characterized in that an isomorphic hash intake module is used for truncating an input data stream into logic data blocks, extracting lexical elements to generate isomorphic hash packets and distributing the isomorphic hash packets to a bottom layer for storage; the state monitoring module maintains heat potential energy and queries fingerprints, updates the state according to scanning feedback, and sends out a trigger signal when the heat exceeds a dimension rising threshold value; the differential projection module responds to the signal to read the isomorphic hash packet, and constructs a differential inverted index by calculating the query fingerprint and the intersection of the query fingerprint; and the cascade retrieval module executes memory search when the index is mounted, and scans the logic data block and feeds back a result to the state monitoring module if the index is not hit. According to the method, the heat potential energy of the data block is tracked through the state monitoring module, index construction is triggered only when query accumulation reaches the dimension raising threshold value, and cold start resource consumption of the system is obviously reduced.

The invention relates to the technical field of language generation algorithms, and discloses a natural language generation SQL method based on ES, knowledge base and interaction enhancement, and the method comprises the following four sequentially executed core steps: data feature preprocessing: carrying out deep analysis (including field type, constraint relationship, data distribution and the like) on a database table structure and data features, and carrying out data feature preprocessing; and after data de-duplication is executed, the ES is imported, and an independent structured index is established according to a table-level isolation principle to provide basic data for subsequent retrieval. According to the method, the keyword matching efficiency and the semantic understanding depth are both considered through collaborative retrieval of the double-storage-layer knowledge base in combination with Elasticsearch keyword inverted index and Milvus vector library semantic retrieval. And the results are fused through the RRF algorithm, so that the retrieval precision is remarkably improved, the limitation of a single retrieval mode is solved, and the generated SQL better fits the database structure and the user intention.

The invention provides a mixed storage and unified query layer data retrieval system based on an AI cloud desktop, which belongs to the technical field of AI intelligent application and comprises a unified query access layer, an SQL optimizer, a vectorization execution engine, a mixed storage engine and a metadata management center. Column storage, inverted index and row storage are organically combined, services are provided for the outside through a unified SQL query layer, and intelligent routing and vectorization computing technologies are introduced. The method is characterized in that an optimal storage engine is automatically selected for execution according to a query mode, the problems that a single inverted index architecture is low in efficiency and high in resource consumption under the scenes of ad-hoc analysis, high-concurrency point search and large-scale aggregation are solved, and better comprehensive query performance, lower data storage cost and higher system expansibility than traditional Elasticsearch are achieved.

The invention discloses a book resource data security collection retrieval monitoring system based on big data, and relates to the technical field of information technology and data security, and the system comprises a permission-aware association reasoning retrieval module which is in communication connection with a knowledge graph and dynamic index construction module and is used for receiving a user retrieval request and sending the user retrieval request to a database; analyzing a retrieval intention and obtaining a real-time permission context of a user to determine a security level threshold value, verifying a security attribute voucher of the data when retrieving the mixed index, only putting the data with the security level not higher than the threshold value into a result set, and performing association reasoning based on the knowledge graph under permission constraint to expand a result. According to the method, the mixed index structure fusing the inverted index, the vector index and the graph index is constructed, the security attribute voucher is associated, efficient full-text retrieval, semantic retrieval and association reasoning are supported, meanwhile, it is ensured that the retrieval process and result are strictly constrained by permission, and maximum mining of data values on the premise of security is achieved.

The invention discloses a verifiable ciphertext retrieval method and device, equipment, a medium and a product, and relates to the field of data security. The method comprises the steps that a server receives a query keyword set of a client; the server calculates a main keyword index address according to the main keyword of the query keyword set and searches the encrypted inverted index according to the main keyword index address; the server obtains candidate documents according to the encrypted document operation content in the encrypted inverted index; the client extracts a target data block position set based on a vector commitment tree according to the candidate document; the server generates a brief proof according to the target data block position set and determines a multi-keyword tag according to secondary keywords in the query keyword set; the server determines candidate results by using a label set according to the multi-keyword labels; and the client determines the document set meeting all the keywords according to the candidate result and the brief proof and then performs consistency check to obtain the retrieval result, and the method and the device reduce the calculation consumption while meeting the forward security.

The invention discloses an intelligent customer service knowledge base automatic updating method based on a large language model, and relates to the technical field of knowledge base management, and the method comprises the steps: collecting a multi-source business document and session data, cleaning and merging the multi-source business document and the session data, calling the large language model to extract candidate entries, and binding evidence fingerprints; performing consistency comparison and change type judgment on the candidate entries and knowledge base entries, and generating and solidifying a difference abstract; generating a semantic patch according to the difference abstract, writing the semantic patch into a shadow partition, completing consistency verification, enabling atomic switching to take effect, synchronously updating a reverse index view and a vector index view of the intelligent customer service knowledge base, and recording a version mark; and performing online verification on a session data statistical result according to the version mark, rolling back to a state before the version mark when the verification result is abnormal, and confirming the semantic patch and continuously acting on the intelligent customer service knowledge base when the verification result is normal. The release risk is reduced; and the knowledge reliability and auditing performance are improved.

This application discloses a master data integration method, apparatus, electronic device, and storage medium. The method includes: acquiring multi-source heterogeneous master data; standardizing and cleaning the multi-source heterogeneous master data to generate multiple block keys and inverted index lists, and constructing a candidate comparison set; extracting and normalizing the multi-level similarity of each pair of candidate records in the candidate comparison set, and constructing a standardized feature vector; inputting the standardized feature vector into a learning model to obtain the matching probability of each pair of candidate records, and making hierarchical decisions based on the matching probability and at least one matching threshold; constructing an undirected matching graph and generating entity clusters from the undirected matching graph; determining the data quality score of each field of each candidate record within the entity cluster, determining the optimal value of each field based on the data quality score, and generating the integrated master data. This application significantly reduces the number of comparisons, improves comparison efficiency, eliminates the need for manually writing matching rules, and can adapt to various data types and new data sources.

The embodiment of the invention provides a white list generation method and a related device, and relates to the technical field of big data, and the method comprises the steps: carrying out the semantic recognition of a target policy, and determining first declaration data included in the target policy; obtaining a knowledge graph corresponding to the target policy, processing the first declaration data based on the knowledge graph, and obtaining second declaration data implied by the target policy; determining a declaration condition corresponding to the target policy based on the first declaration data and the second declaration data; acquiring first enterprise data declared by an enterprise; acquiring second enterprise data from an external data partner system; performing credible verification on the first enterprise data according to the second enterprise data, and determining target enterprise data; constructing a reverse index of the target enterprise data, and constructing a query statement of the target enterprise data according to the declaration condition; and according to the query statement and the inverted index, generating an enterprise white list meeting the declaration condition. According to the method, the generation efficiency and accuracy of the white list can be effectively improved.

The application provides a kind of sentiment analysis method based on retrieval and comparison learning in the technical field of natural language processing, comprising: step S10, obtaining a large amount of sentiment text data, and pre-processing each sentiment text data;Step S20, extract the entity in each pre-processed sentiment text data, label each entity to construct a sample, and then generate a sentiment dataset;Step S30, by Elasticsearch, the samples in the sentiment dataset are inverted indexed, and similar samples are retrieved for each sample;Step S40, create a sentiment classification model based on neural network, train the sentiment classification model using the sentiment dataset, and at the same time, use the contrast learning technology to narrow the vector distance between each sample and similar sample;Step S50, use the trained sentiment classification model to perform sentiment analysis.The application has the advantages of greatly improving the model sentiment representation ability, and greatly improving the sentiment classification performance.

This invention discloses a method and system for extracting meta-analysis literature based on graph-anchored multi-agent systems. The method includes reconstructing unstructured PDFs into a hierarchical semantic document structure through visual document analysis and identifying functional area labels; applying functional area anchoring constraints to limit information search to specified functional area nodes; scheduling a multi-agent collaborative extraction network consisting of a reconnaissance agent, a logic assembly agent, and an indicator retrieval agent; and completing experimental variable identification, logical mapping between treatment and control groups, and cross-paragraph indicator retrieval through structured message dialogue and multi-round collaboration; employing a neural symbolic hybrid engine, where semantics are parsed by a large language model and precise mathematical aggregation operations and dimensional conversions are performed by a deterministic symbolic computation engine; and generating a structured data matrix with inverted index pointers. This application achieves high-precision, zero-error, and traceable automated extraction of meta-analysis literature data.

The application discloses a progressive question generation method based on semantic analysis and a knowledge graph. The question generation demand text is preprocessed and semantically analyzed, the core knowledge point corresponding essential keyword set and the additional condition corresponding optional limiting word set are extracted, the concept mapping is carried out in the college professional knowledge graph, the curriculum outline is associated, the hierarchical semantic constraint framework containing hard constraint and soft constraint is constructed and consistency detection is carried out, the candidate materials are obtained by using the inverted index hard matching recall and the knowledge graph soft expansion recall, the candidate questions are generated by inputting the field fine-tuning large language model, the final questions are output by carrying out multi-objective learning sorting and reordering, teaching logic checking and quality evaluation, the incremental samples are formed by receiving user feedback, the generation model and the sorting model are updated, the accuracy, diversity and controllability of the question generation are improved, and closed-loop optimization is supported.

This invention relates to the field of payment settlement technology and provides a method for intelligent revenue sharing of cross-merchant payment rights, comprising the following steps: S1, receiving payment business data packets sent by a terminal, and parsing them to obtain a transaction order dataset, cross-merchant payment rights identifiers, and corresponding platform subsidy parameter datasets; S2, parsing the cross-merchant payment rights identifiers, extracting core feature vectors, identifying at least two target merchants participating in the transaction, and determining the total amount of rights deduction corresponding to the payment rights; S3, matching preset intelligent revenue sharing rules according to the transaction order dataset and the attribution characteristics of the target merchants, and retrieving the corresponding rights sharing ratio model and fund settlement agreement. This invention, by parsing the cross-merchant payment rights identifiers in the payment business data packets, extracting core feature vectors, and combining them with an inverted index database, can automatically and accurately identify multiple target merchants participating in the transaction and their corresponding total amount of rights deduction.

The application belongs to the technical field of data retrieval, and particularly relates to a traditional Chinese medicine auxiliary syndrome differentiation system based on semantic analysis. The system comprises a traditional Chinese medicine symptom preprocessing module, a dynamic inverted index construction module and a semantic retrieval matching module. The preprocessing module extracts symptom entities and degree modifiers of the inquiry text through a traditional Chinese medicine field dependency syntax analysis tree, maps them to a standard symptom word library and converts them into weight coefficients. The dynamic inverted index construction module establishes an inverted list with the standard symptom words as index keys, and appends the bias values of the basic syndrome vectors and the document syndrome vectors after the document identification. The semantic retrieval matching module generates a query vector with weight coefficients according to a symptom query request, calculates the inner product of the query vector and the bias values in the inverted list, eliminates the document identification below the preset threshold, and outputs the syndrome conclusion corresponding to the remaining documents. The application solves the semantic gap and the synonym omission problem caused by the non-standardized expression of traditional Chinese medicine.

The invention discloses a batch geographic position analysis method, a position recommendation system, a position recommendation device and a medium, and the method specifically comprises the steps: obtaining all interest sites of all user targets in a current batch, removing repeated items in the interest sites, and obtaining a unique interest set composed of a plurality of unique sites; establishing a reverse index between the unique place and the user target associated with the unique place; obtaining the closeness degree of each unique place and the to-be-recommended position; based on the reverse index, backfilling the closeness degree to the user target associated with the corresponding unique place; and comparing the degree of closeness obtained by backfilling each user target, and determining the optimal location of each user target according to the comparison result. According to the batch geographic position analysis method provided by the embodiment of the invention, the data processing complexity of batch geographic position analysis in the data processing process is reduced, the number of repeated calculations is remarkably reduced, and the throughput and scalability of the system are greatly improved.

By utilizing user-defined functions in the structured query language SQL, a system for performing entity resolution, specifically ranked personally identifiable information (PII) matching, can function directly within the data warehouse's execution environment, thereby eliminating the need to move data to external systems. This system uses SQL and user-defined function (UDF) implementations to utilize an inverted index system that can operate on the distributed processing capabilities of the data warehouse.

The application provides a search and recommendation method based on a tourism knowledge graph. The application comprises: in a label preparation stage, extracting nouns and corresponding characteristics from a tourism site introduction and comments according to rules and processing the nouns and the corresponding characteristics into labels; in a model training stage, transmitting the labeled data into a PEGASUS model pre-trained by LCSTS for training, and then transmitting unlabeled data and debugging; in a search construction stage, associating the graph with a source site and storing the graph in a database to construct a visual module and a query transmission module of a system; and in a recommendation construction stage, constructing a recommendation calculation module of the system. The application has the following advantages: 1. The labels retain the original meaning and the superior-inferior relationship, which facilitates future classification of the graph; 2. Synonyms are processed according to the principle of majority covering minority, so that the labels are generated as uniformly as possible, long-term work is reduced, and the growth of traditional inverted index vocabulary is avoided; 3. Site reputation feature retrieval is more in line with user needs than text retrieval; and 4. Compared with traditional recommendation algorithms, the application is more interpretable.

The application relates to an equivalent connection query method and device for a ciphertext database, which comprises the following steps: constructing indexes corresponding to a plurality of tables; wherein the indexes comprise encrypted inverted index tables, and the inverted index tables are generated from original tables; uploading the encrypted indexes and a multi-set Bloom filter to a server; wherein the multi-set Bloom filter is obtained according to all values of inserted connection attribute columns; sending an equivalent connection query request of a user to the server, so that the server returns corresponding equivalent connection query results to the user. Compared with the prior art, the multi-set Bloom filter has shorter solving time and higher efficiency, and can hide the relationship between sets in a table and a Bloom filter, thereby hiding external memory access modes, significantly improving the throughput of data processing, reducing the number of interactions with a trusted execution environment, and effectively solving the problem of large communication overhead.

The invention relates to the technical field of natural language processing and information retrieval, and discloses a pre-retrieval perceived end-to-end knowledge retrieval method and system, and the method comprises the steps: extracting a keyword from an original query of a user; using a BM25 algorithm to retrieve M documents from the inverted index of the document set, and calculating the keyword coverage rate of the documents; extracting a set of keywords hit by the original query from the retrieved document, obtaining a plurality of retrieval anchor points by extracting disjoint subsets, and inputting the original query and the retrieval anchor points into a large language model to generate a plurality of sub-queries; vector retrieval is conducted on each sub-query in the document set, and recalled documents are obtained; and generating a sub-answer based on the sub-query and the recall document, and scheduling the execution of the sub-query according to the directed acyclic graph to generate a final answer. According to the method, the sub-query is dynamically generated through the pre-retrieval feedback, and the DAG is constructed and executed, so that the accuracy of knowledge retrieval under complex query and the system reasoning efficiency are remarkably improved.

The invention discloses a low-code platform development system based on artificial intelligence, relates to the technical field of computers, and is used for solving the problems that affected sessions cannot be accurately positioned during strategy updating, the situation that part of sessions still use old permissions is likely to occur, the cross-session consistency is poor, and meanwhile, an effective abnormal behavior detection means is lacked. The accurate session action range and access credentials are generated through the handshake and action range projection module, the frame level judgment module can perform authorization verification on each data frame, and an unauthorized or illegal request can be marked and frozen in time, so that fine-grained access control is realized; and secondly, when the strategy is updated, an affected session can be quickly found by virtue of a session-permission bipartite graph and an inverted index, and then the permission is updated by virtue of differential calculation, so that the session does not need to be completely disconnected, and the real-time performance and the cross-session consistency of the strategy updating are ensured.

This invention provides a method for injecting industrial knowledge based on retrieval enhancement, belonging to the field of industrial knowledge technology. This invention establishes a time-series data flow matrix by collecting multi-source heterogeneous data, constructs a time-series causal knowledge graph using Granger causality tests, establishes an industrial knowledge document library with a hybrid index structure, performs time-series-aware query expansion on the query input, performs a hybrid retrieval of dense vectors and sparse inverted indexes and cross-encodes and reorders the data, associates the refined documents with the time-series causal knowledge graph to extract event evolution paths and fuses them to generate a time-series enhanced knowledge representation, inputs it into a time-series knowledge enhancement model to generate answer text containing fault analysis and prediction suggestions, injects it into an industrial decision support system after quality assessment, and stores feedback data for continuous model optimization. This solves the technical problem of industrial knowledge retrieval being disconnected from real-time temporal status, resulting in a lack of time-series causal reasoning ability in the generated answers.

The invention provides a vector database reverse indexing system based on redundancy allocation, which comprises a centroid set, a list set and vector data, and is characterized in that the centroid set comprises a plurality of centroids; the list set comprises a plurality of lists, and each list corresponds to one mass center and is used for storing block PQ codes corresponding to all vectors belonging to the mass center; the vector data includes a plurality of original vectors. The index system is configured to put the PQ code of each vector to be inserted into a list corresponding to two centroids based on a double-allocation strategy, or put the PQ code of each vector to be inserted into a list corresponding to three or more centroids based on a multi-allocation strategy; a shared cell optimization strategy is adopted among the multiple lists to optimize the lists, the shared cell optimization strategy means that multiple block-shaped PQ codes corresponding to all vectors inserted into the multiple lists at the same time are stored in one list, and pointers pointing to the multiple block-shaped PQ codes are stored in other lists.

The invention discloses a compressed XML database type, system and method supporting efficient query, and relates to the technical field of XML databases, and the database type adopts a hierarchical compression storage architecture and a reverse search query architecture; in the storage stage, XML non-keyword information is coded through Huffman coding, recoverable ordered arrangement is carried out on the coded information through a tree structure, then transcoding is carried out, the information is stored in a disk in a binary form, and compared with a traditional XML database storage mode, at least 50% of storage space is saved. In the query stage, a reverse search mode is adopted, a corresponding Huffman code initial position is positioned through a keyword, then under the optimization effect of a reverse index, reverse search is performed to recover a complete XML structure, and the query complexity is reduced from traditional O (n) to O (1). According to the method, the storage efficiency and the query performance of the XML data are greatly improved, and the method is suitable for large-scale XML data management and application scenes.

System and method for constructing a hierarchical index table usable for matching a search sequence to reference data. The index table may be constructed to contain entries associated with an exhaustive list of all subsequences of a given length, wherein each entry contains the number and locations of matches of each subsequence in the reference data. The hierarchical index table may be constructed in an iterative manner, wherein entries for each lengthened subsequence are selectively and iteratively constructed based on the number of matches being greater than each of a set of respective thresholds. The hierarchical index table may be used to search for matches between a search sequence and reference data, and to perform misfit identification and characterization upon each respective candidate match.

The invention discloses a configuration-driven dynamic form data retrieval and display system and method, and relates to the technical field of data management, the configuration-driven dynamic form data retrieval and display system comprises a front-end configuration layer used for providing operation interfaces for a business administrator and a business user and comprising a field configuration interface and a data query and export interface, and an API interface layer used for providing a data query and export interface for the business administrator and the business user. The field configuration module is used for providing a field configuration management interface and a data query and export interface and comprises a field configuration controller and a data query controller. Data are synchronized to a search engine from a relational database, the complex query and full-text retrieval performance is greatly improved by utilizing an inverted index mechanism, the system realizes zero code configuration capability, query and display logic are driven through a field configuration table, business adjustment can take effect without code modification, and the system is simple in structure and convenient to operate. The powerful complex data type processing capability can intelligently analyze nested structures such as self-increasing lines and dictionaries and convert the nested structures into user-readable formats.

The application discloses a multi-center storage tongue diagnosis image quick indexing method, system and readable storage medium. The method comprises the following steps: extracting feature quantities of images to be diagnosed and converting the feature quantities into index data containing a plurality of keywords; based on the index data and according to an inverted index algorithm, calling target images from each storage center; wherein calling the target images comprises: counting the distribution position and concentration of each keyword in a record list in each storage center; changing the keyword order in the index data according to the distribution position and concentration; and calling the target images based on the new index data. According to the application, the order of the keywords in the index data is adjusted according to the position of the index keywords in the record list content in each storage center, the indexing efficiency can be improved, the reliability of each keyword in each storage center is assigned, the reliability of each index data is determined, the number of target images called from each storage center is adjusted based on the above values, and the accuracy and reliability of the index calling are improved.

The application provides a high-efficiency searchable attribute encryption method with data revocation function, a data owner generates a sub-policy which needs to be reused, extracts a keyword from clear data for mapping processing, and encrypts the clear data and sends the encrypted data to a cloud server for storage, and the cloud server generates a corresponding inverted index structure; when the data owner needs to revoke the cloud index or the corresponding relationship between some keywords and the data, a revocation token is generated and sent to the cloud server; the cloud server verifies the revocation token in the request and the cloud encrypted data, and performs a corresponding revocation operation when the verification is passed. The application can meet the diversified search demand of the user in the cloud environment with massive data and support the revocation of the index.

A computer-implemented system for presenting targeted content is provided. The system comprises a processor configured to obtain user permission and access profile attributes and friend attributes from a social-network interface, generate, for each person, an identifier from attributes including name, date-of-birth, and location, and store, keyed by the identifier, user search and browsing actions and external records ingested via a distributed data pipeline. Further, the processor is configured to store conversational dialogues as questions and answers using sequence-to-sequence algorithms, or as parent nodes and responses as child nodes of a graph formed on an inverted index. Further, the processor is configured to determine a purchase-funnel stage for the identifier using a classifier trained on search keywords, domains, and page categories, select an advertisement ranked based on revenue, user preference, and context, and generate multi-stage advertisement messages corresponding to discovery, consideration, and action.