122 results about "Query log" patented technology

The invention discloses a search method and system based on computer natural language processing, and the method comprises the steps: extracting a synonym set and a context association relationship of keywords in a query text through a preset semantic knowledge graph, and generating a semantic vector representing a semantic dimension in combination with a deep learning model; extracting a historical behavior feature sequence from the query log based on the semantic vector, and analyzing a user search intention by adopting an attention mechanism model; performing similarity matching on the pre-constructed database by utilizing a semantic matching algorithm, and screening an information matching set meeting a threshold value; performing distributed processing on the matching set through a context-aware dynamic fragmentation algorithm, and constructing an index fragmentation cluster; semantic aggregation is realized by adopting a cross-fragment graph attention network, and an optimized search result set is generated through dynamic semantic projection. According to the method, through multi-modal fusion of the semantic knowledge graph and deep learning and in combination with a dynamic distributed processing architecture, the accuracy of search intention recognition and the efficiency of large-scale semantic matching are improved.

The invention relates to the technical field of data processing, in particular to a cross-unit data management method, which comprises the following steps of: performing format standardization processing on data in a data pool of each organ and unit, converting the data into a uniform standard format, extracting semantic features to construct a semantic association network, and recording a semantic relationship of data items and a dynamic authorization and authority mapping mechanism. Then, according to the inquirer identity information, the unit attributes, the business requirements and the data sensitivity level, dynamically generating inquiry authorization and establishing an authority mapping table; and when a query condition is received, analyzing by utilizing a natural language processing technology, and generating a query route and optimizing a statement in combination with the semantic association network and the permission mapping table. And finally, calling data from each data pool according to the route, performing fusion processing, displaying the data to a querier in real time, and recording a query log. The invention aims to solve the problems of data semantic isolation, authority management staticization, low query efficiency and insufficient cross-unit data conflict resolution capability in the existing cross-unit data management.

The invention discloses an intelligent data query system and method based on natural language processing, and relates to the technical field of natural language processing and database query. According to the method, natural language input and database mode information are received, a historical query log is combined to construct mode knowledge representation, a query skeleton is generated on the basis, a fine-tuned large language model is called to generate candidate SQL statements, error detection and ambiguity recognition are carried out on the candidate statements, and the query result is obtained. And if necessary, triggering interaction clarification and updating a query result according to user feedback. And meanwhile, the wrong clauses are locally repaired through a gating mechanism, and the clauses are returned and regenerated when multiple times of repair fails, so that the correctness of the query statement is ensured. And finally, after the SQL is executed in the database, a result is fed back to the user. Besides, the system records generation and repair tracks in the operation process, and continuously optimizes the model based on reward shaping, comparative learning and self-game training, so as to improve the generalization ability in different business scenes.

A system for cognitive inferencing for large language models (LLMs), the system including: a integration layer communicatively connected to a static LLM, wherein the integration layer includes a processor configured to receive one or more user requests from a user, wherein the one or more user requests include a user interaction with the static LLM, generate one or more cognitive inference (CI) units from the one or more user requests, assign a confidence-falsifiability (CF) delta for each of the one or more CI units using a Socratic engine and append the one or more CI units and the CF delta to a cognitive inference (CI) log associated with the user, wherein the CI log serves as an inference engine for the static LLM, wherein the integration layer is configured to query the CI log upon a subsequent user interaction with the static LLM.

An online system performs an inference task in conjunction with the model serving system and / or interface system to generate relevant product images for query auto-completion and query suggestion to help users better navigate their search experience. The online system generates a collection of query suggestions using search query log mining. For each query suggestion in the collection of query suggestions, the online system retrieves one or more catalog images that depict the query suggestion from a product catalog. The online system constructs a prompt to a text-to-image model including the query suggestion, and a request to generate one or more query images based on the query suggestion. The online system receives the query images from the text-to-image model and ranks the catalog and query images to identify an image to display to the user in association with the query suggestion.

Systems and methods for generating virtual assistant proactive queries improving virtual assistant-user interaction. Initial user prompts, including trigger conditions, corresponding to one or more initial user sessions are received. The initial user prompts, and trigger conditions, are stored in a query log database. Pattern recognition is performed on the stored initial user prompts, and the corresponding trigger conditions, to determine a proactive prompt for a subsequent user session. A proactive response is generated from the proactive prompt. Prior to receiving a subsequent user prompt, the proactive response is provided during the subsequent user session upon detection of one or more trigger conditions corresponding to the proactive prompt.

The present disclosure provides an artificial intelligence-based alarm management based on structured time series data and unstructured contextual information where the conventional methods fails to do. Initially, the system receives a user query associated with an industry automation system and classifies into one of a) data request query b) an anomaly detection and diagnosis query c) a log request query and d) feedback for previous response using a query classifier. Simultaneously, the user query is converted into an associated timeseries query representation. Furthermore, a plurality of database queries is generated based on the timeseries query representation using query generator. Further, data pertaining to the user query is retrieved from an associated database based on database queries using Deep Learning models, thereby identifying a critical alarm, and to take appropriate actions if there is any anomaly. Finally, the retrieved data is displayed to the user in a user readable format.

In one example, a method performed by a processing system including at least one processor includes automatically inferring metadata about a structured query language database, constructing, based on the metadata, a prompt for generating a new structured query language query to be executed against the structured query language database, and providing the prompt to a language model for use in generating the new structured query language query.

The invention relates to the technical field of network communication, in particular to a DNS (Domain Name Server) hierarchical cache resolution acceleration method and system based on a prefetching mechanism, which comprises the following steps: by constructing a hierarchical cache model comprising a client layer, a local DNS server layer and an upstream DNS server layer, caching a resolved domain name and a corresponding resolution result into each layer of cache; a DNS request of a terminal user is received, whether the request hits the cache or not is judged, and corresponding operation is adopted; historical query logs are collected, a next DNS request is predicted in combination with a prefetching mechanism of real-time network topology perception, and an analysis result of the domain name is cached to a local DNS server layer in advance; a cross-layer collaborative cache updating mechanism is adopted to ensure synchronous updating of cache data of different levels; and a symmetric encryption algorithm and a TLS protocol are introduced, so that the security of the data in the storage and transmission process is ensured. According to the method, the response of DNS analysis is accelerated through a prefetching mechanism and layered caching.

The embodiment of the invention provides a database log processing method and device, a storage medium and electronic device.The method comprises the steps that based on the total number of logs within a specified log range requested to be queried by a current log query request and the number of query threads in a plurality of query threads, the query threads are queried according to the total number of the logs within the specified log range; segmenting a log requested to be queried by the log query request into a plurality of log ranges, and respectively constructing corresponding query statements to obtain a plurality of query statements; executing the corresponding query statements in parallel through a plurality of query threads, and querying log data in a corresponding log range from a log database; receiving at least one batch of log data returned by the log database through the data receiving thread, and writing the log data into the buffer block queue in a circular writing mode; and executing processing operation on the log data written in the buffer block in a circular processing mode through a data processing thread, and summarizing and sorting the processed log data to obtain a log query result corresponding to the log query request.

The invention relates to the technical field of power system data processing, in particular to a power system data processing method and system based on dynamic period detection, and the method comprises the steps: collecting a historical query log of a power business system, and extracting time sequence data of query features through a sliding window; training a recurrent neural network model by using the time sequence data, and predicting a current optimal query period length; on the basis of the optimal query period length, predicting an index combination required by query through a classification model, and generating a candidate index set; optimizing an execution strategy of the candidate index set by taking the minimum index construction cost and the maximum query coverage rate as targets; and obtaining query data of the power business system in real time, and updating the candidate index set and the execution strategy when the change of the business period characteristic is detected. The objective of the invention is to match the periodic change of the power system data, improve the indexing efficiency and reduce the maintenance cost.

The invention relates to the field of data processing, and discloses a database SQL query optimization method, device and equipment and a storage medium, and the method is used for intelligently generating and optimizing an SQL query. The method comprises the following steps: acquiring a user request, and generating an SQL statement; analyzing the SQL statement to extract a related table, and obtaining structure information of the related table from a database; obtaining a historical query log, performing multi-modal feature fusion on the SQL statement, the structural information of the table and the historical query log based on a pre-trained Transform model, and generating a context-aware joint embedded vector; inputting the joint embedded vector into a pre-trained optimization strategy generation model to obtain optimization suggestions applicable to cross platforms; and collecting user feedback data, updating parameters of the optimization strategy generation model to obtain an updated optimization strategy generation model, and replacing the existing optimization strategy generation model with the updated optimization strategy generation model.

The invention relates to the technical field of artificial intelligence, and particularly provides an NL2SQL generation method based on a large language model. The method comprises the steps that SQL mode positioning is conducted by combining three dimensions of semantic mapping, historical query logs and external mode links; constructing a sample fusing hierarchical screening and comparative learning; based on the candidate SQL and the multi-dimensional confidence fusion, the multiple candidate SQL are generated and subjected to cross validation, and the method improves the recognition accuracy, reduces the error rate of a grammar structure and improves the reliability of a query result.

The invention provides an index optimization method and device, electronic equipment and a storage medium, and relates to the technical field of database query. The method comprises the steps that a query log of a database is analyzed, query detailed information is obtained, the query log comprises at least one query statement, and the query detailed information comprises query conditions indicated by the query statement, sorting fields, table connection relations and grouping information; the detailed query information is analyzed and counted to obtain query statistical data, and the query statistical data comprises the field access frequency, the data distribution condition and the index use condition; generating at least one candidate query path based on the query statistical data, wherein each candidate query path comprises a query index and a use sequence of the query index; and performing query cost evaluation on the at least one candidate query path, and determining a recommended index scheme. The method can automatically recommend a proper index scheme and improve the index optimization efficiency.

The invention discloses a slow query analysis method and system for an openGauss database, and relates to the technical field of database management. Comprising the following steps: acquiring a historical slow query, a resource consumption condition corresponding to the historical slow query and a reason for generating the historical slow query in a query log of an openGauss database; analyzing the execution plan corresponding to the historical slow query, and obtaining the structure of a table, the data type of a column, the type of an index and the establishment time related to the historical slow query; constructing a training data set; writing prompt words according to reasons generated by historical slow query, inputting the training data set into the large model agent, and training the large model agent to obtain a trained large model agent; and acquiring the latest slow query and inputting the trained large model agent to obtain the reason of the latest slow query. According to the method, the reason of slow query can be quickly positioned, and the manual analysis time is greatly shortened.

The invention discloses a materialized view processing method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: counting a historical slow query log of a service system in a preset time period, and determining a query statement which does not hit a cache according to the historical slow query log; counting query information of each query statement in the preset time period to screen out query statements of which the query information meets a preset requirement to obtain a query statement set; and determining a public query statement of the query statement set, obtaining offline data corresponding to the public query statement from the data lake, and performing materialization processing on the offline data to obtain a materialized view. According to the embodiment, the materialized view can be automatically created, and the problem that the traditional materialized view needs to be created strongly depending on manual analysis is solved, so that access to a data lake is remarkably reduced, and the query performance and user experience of a service system are greatly improved.

The present application provides a method and system for user behavior analysis based on big data. By obtaining the query logs of target users on an Internet search service platform; extracting topic tags and long-tail query data; determining multiple behavior interest degrees based on the long-tail query data, and determining multiple query interest sub-tags based on the query interest index and all the behavior interest degrees; performing query pointing association between the query interest sub-tags and the topic tags to obtain multiple pointing association vectors, and determining behavior trend characteristics based on all the pointing association vectors; obtaining the query phrase input by the target user, performing query matching between the query phrase and the behavior trend characteristics to obtain query behavior characteristics; and uploading the query behavior characteristics to the user behavior analysis center. The above solution can accurately capture the query phrases of users based on the query behavior characteristics, and make associated classification of the search interest directions of the query phrases, so as to realize the accurate analysis of the user search behavior pattern.

The invention discloses a self-adaptive table structure generation and index optimization method for JSON data driven by a real-time mode, and relates to the field of computer information management application. According to the self-adaptive table structure generation and index optimization method for the JSON data driven by the real-time mode, efficient and automatic persistence from the JSON data to a relational database is achieved through a four-layer technical architecture including dynamic analysis, intelligent mapping, self-healing expansion and index optimization. Comprising a dynamic mode analysis engine which analyzes a JSON nested structure in real time and generates a mode feature tree with weight; the self-adaptive storage engine is used for dynamically generating or expanding a table structure according to the mode feature tree and executing data type mapping; the index optimizer is used for automatically creating / cleaning indexes based on query log analysis; and the uniform interface layer is compatible with multi-database dialects and shields bottom layer differences. Dynamic table structure generation, intelligent type inference and storage optimization, self-healing Schema extension, query-driven index self-optimization and cross-database compatibility are realized.

The invention discloses a data caching method and device, and the method comprises the steps: obtaining a plurality of first logs in a first time period from a query log of a database; wherein one log is used for recording query operation of one SQL statement on the database; determining at least one first high-frequency query item through SQL statements corresponding to the plurality of first logs; wherein the preset field in the same SQL statement corresponds to one query item; the high-frequency query item is determined at least based on the occurrence frequency of the query item in the plurality of logs; generating an aggregation table according to a query result corresponding to the at least one first high-frequency query item in the database; and caching the aggregation table into a memory. By adopting the method, the accuracy of generating the aggregation table can be improved.

A log processing method and apparatus, and a storage medium. The method comprises: determining log record categories configured for an application program, wherein the log record categories have respective feature codes, and the feature codes are used for identifying log data corresponding to the log record categories; obtaining feature information, corresponding to the feature codes, generated by the application program during operating; and on the basis of the feature codes corresponding to the log record categories and the feature information corresponding to the feature codes, generating the log data corresponding to the log record categories. In the present application, multiple log record categories can be defined for software logs, and all log record locations have feature code information corresponding to the log record categories, so that specific log record requirements of users can be satisfied, and an effective and convenient method is provided for later analysis and log query.

The invention provides a code vulnerability coping strategy generation method and related equipment. The method comprises the following steps: in response to a log recording instruction, generating a target log query statement according to the type of the log recording instruction; wherein the target log query statement is used for querying logs related to log4j vulnerability attack behavior characteristics; executing the target log query statement to obtain a target log query result; according to the target log query result, judging whether the log recording instruction contains a suspected log4j vulnerability attack execution behavior or not; if yes, link identification is carried out on the log4j vulnerability attack based on the target log query result, and a target attack link is obtained; and according to the target attack link, generating a target coping strategy for the log4j vulnerability attack. The network security defense capability can be improved.

The invention discloses a label anti-counterfeiting query system, relates to the technical field of intelligent query, and innovatively introduces a time sequence coding technology guided by space-time constraint on the basis of non-tampering evidence storage of a block chain network by constructing a distributed anti-counterfeiting code verification system. A discrete query log is converted into a continuous behavior mode with space-time semantic features, and the limitation that a collaborative counterfeiting behavior cannot be captured through traditional timestamp comparison is broken through. According to the method, an intelligent anti-counterfeiting mechanism with a dynamic risk perception capability can be established, and batch copy attacks and cross-regional collaborative counterfeiting behaviors are accurately identified by deeply mining the space-time correlation characteristics of the query behaviors, so that the anomaly detection capability in an anti-counterfeiting code verification process is improved.

The present disclosure relates to the technical field of big data governance and data asset operation management, and particularly relates to a data index evaluation device and a data index evaluation method. The method comprises: acquiring query logs, blood relation metadata, cost data and data quality evaluation results of each data index; determining health degree scores of each data index based on the query logs, the blood relation metadata, the cost data and the data quality evaluation results; determining unit cost values of each data index based on the cost data and the blood relation metadata; determining a screening index with a health degree score less than a score threshold and a unit cost value less than a value threshold based on the health degree scores and the unit cost values; wherein the screening index comprises any one of the data indexes; and generating evaluation information based on the screening index.

The invention discloses an internet-based medical insurance service platform comprehensive intelligent management system, which relates to the technical field of medical insurance intelligent management and comprises a data receiving module, a portrait construction module, a strategy adaptation module, a compliance verification module and a service navigation module. The system performs heterogeneous feature co-processing on the user identity authentication sequence and the medical service application text to generate a user service demand portrait; performing rule matrix matching through a multi-level policy adaptation engine, and outputting a grading policy adaptation result containing matching degree distribution; based on the distribution, bidirectional compliance verification is carried out on the expense settlement document image and the medicine directory query log, and a medical insurance settlement compliance report is formed; and generating and pushing a personalized service navigation strategy. According to the invention, deep integrated description of user service requirements and dynamic accurate compliance review based on quantitative evaluation are realized.

The invention discloses a real estate archive high-frequency stable user screening and demand label classification method. The method comprises the following steps: carrying out data preprocessing on real estate archive query logs, removing abnormal data and complementing missing information; screening high-frequency stable users through a multi-dimensional index, wherein the multi-dimensional index comprises monthly query frequency, a high-frequency time period proportion and a query interval fluctuation coefficient; based on the screened high-frequency stable user query behaviors, constructing a user demand feature library containing query purposes, regional attributes and archive types; comprehensively evaluating the demand coverage rate, the tag redundancy and the Jaccard similarity coefficient, and judging the matching degree of the existing archive tag and the user demand; and generating a dynamic demand label according to an evaluation result, and establishing a regular updating mechanism to adapt to user demand changes. The problems that in the prior art, high-frequency stable user screening accuracy is insufficient, and archive labels are disjointed with actual requirements of users are solved, and the real estate archive query efficiency and the user requirement satisfaction degree are effectively improved.

The application relates to the technical field of artificial intelligence, and particularly provides an NL2SQL generation method based on a large language model. The method comprises the following steps: combining three dimensions of semantic mapping, historical query logs and external schema linkage to perform SQL schema positioning; constructing a sample that fuses hierarchical screening and contrast learning; and based on candidate SQL and multi-dimensional confidence fusion, generating multiple candidate SQL and cross- verifying the multiple candidate SQL. The method improves the recognition accuracy, reduces the error rate of the syntax structure, and improves the reliability of the query result.

The present invention relates to the field of artificial intelligence, and discloses a method, device, equipment and storage medium for establishing a database combined index. The method includes: obtaining historical query logs in the database, and parsing the historical query logs to obtain at least two field item sets; calculating the support degree of each field item set relative to all field item sets respectively, and screening out the field item sets with a support degree greater than a preset support degree threshold from all the field item sets; obtaining multiple groups of conditional fields arranged in order from all the field item sets according to the preset number of field combinations as field association units, and constructing corresponding confidence linkages according to each of the field association units; filtering the confidence linkages according to a preset confidence threshold, and establishing a combined index of the database according to the filtered confidence linkages and the screened field item sets. The combined index relationship of the data in the database is efficiently established.

The application discloses a flink task log processing method based on K8S. The flink task log processing method comprises the following steps: associating a task identifier with a flink task role; listening to state information of a K8S container corresponding to the flink task, and storing the task identifier of the flink task role; collecting and storing log information of the flink task in the K8S container; and querying log information of the flink task role associated with the task identifier. The application also discloses a flink task log processing method based on K8S, a flink task log processing device based on K8S, a computer device and a computer readable storage medium. The flink task log information collection, storage and display of K8S are realized, so that a user can complete flink task submission, state updating and log information checking through a unified flink big data computing platform, the running problem of the flink task of K8S is conveniently located, and the problem locating efficiency is improved.

The present invention relates to the technical field of log processing, and discloses a method, device, computer device and storage medium for querying log data. The present invention includes the following steps: obtaining a log query request sent by a client for a target group, where the log query request includes event description information; performing word segmentation processing on the event description information to obtain a plurality of target keywords, and performing word segmentation processing on target log data corresponding to the target group to obtain a plurality of data segments, where the target log data is screened from the historical log data of the target group; using the target keywords to match with the data segments to obtain the target data segments hit by the target keywords; using the target keywords to query corresponding group log data from the target data segments. By implementing the present invention, the query of log data can be realized efficiently and flexibly.