93 results about "Query log" patented technology

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.

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 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 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 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 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 application discloses a database slow query log processing method and device, equipment and a storage medium. The method comprises the following steps: in response to obtaining a to-be-executed structured query language (SQL) statement, executing the SQL statement, and monitoring a latch associated with the SQL statement to obtain time consumption information of the latch; determining whether the SQL statement is a slow query statement according to the execution time of the SQL statement; and in the case that the SQL statement is a slow query statement, outputting a slow query log of the SQL statement based on the time consumption information of the latch. The technical scheme of the embodiment of the application can output a slow query log containing latch time consumption information for a single SQL statement, facilitate the investigation of the cause of slow query, and ensure the high-performance operation of a database.

An online system receives a request from a customer system to send a text message to a recipient system, the request including a phone number of the recipient system. The online system sends a query to a registry system for a throughput allotment applicable to the text message. The online system sends a query to determine the MNO that is associated with the phone number of the recipient system and queries a log to determine a number of messages sent during a previous time period by the customer system to other recipients associated with the MNO of the recipient system. The online system compares the number of messages from the log to the throughput allotment for the MNO to determine whether the throughput allotment has been reached. The online system schedules the sending of the text message to the recipient system in order to comply with the throughput allotment.

The application relates to a data query method, system, device and medium based on vector retrieval acceleration, comprising the following steps: collecting historical query log information generated by real users in a target application scenario, and constructing a query distribution model; based on the constructed query distribution model, adopting a multi-granularity data division strategy to divide original vector data to obtain fine-granularity data blocks; receiving a new query request initiated by a user to generate a query vector, and dynamically selecting nodes and data blocks participating in current query calculation according to the feature distribution of the query vector and the real-time load state of each node; and combining local Top-K results returned by each participating node, and performing accurate distance reordering to obtain a query result. The application can be widely applied to the fields of computer systems and big data processing technologies.

The present application relates to the technical field of data processing, and particularly relates to a data center clothing management method and system based on OLLAMA, the method comprises the following steps: collecting computing resource state data; performing predictive semantic preprocessing, including: when the computing resource state data indicates that a graphic processor is in a preset low load state, according to query mode information obtained by analyzing a user query log, selecting unstructured clothing data to be pre-calculated, and driving an OLLAMA model to perform semantic analysis on the unstructured clothing data to be pre-calculated to obtain a pre-calculated semantic analysis result, and storing the pre-calculated semantic analysis result in a semantic cache; processing a user query request; through predictive semantic preprocessing, the application can make full use of the idle time of computing resources, and complete time-consuming semantic analysis tasks in advance, thereby significantly reducing the delay of real-time queries and improving the response speed of the system.

The application discloses an AI prediction-based graph structure optimization strategy generation method and device, equipment and a medium, relates to the technical field of data processing, and comprises the following steps: filtering out a key path and a key node of a graph structure through path access information recorded in a historical query log and node association information of the graph structure, and intelligently predicting an optimization strategy suitable for the current graph structure based on the topology of the key path and the key node and query characteristics through a reinforcement learning reward mechanism, so that directional iterative optimization of the graph structure is realized, and the execution efficiency of backtracking query is improved from the root.

The invention provides a data query method and equipment, and relates to the technical field of computers. The problem that the data query task fails due to the fact that the query condition specified by the user corresponds to the situation that the data size of the data needing to be queried is too large in the data query process in an Ad Hoc mode is solved. Receiving a first query condition, wherein the first query condition is used for querying first data of the log library; on the basis of the first query condition, estimating query time required for querying the first data in the log library; under the condition that the query time consumption is larger than a preset threshold value, recommendation information is output, the recommendation information is used for recommending the user to query second data in the log library by using a second query condition, and the second data is part of data in the first data; the query time consumption of the second data queried based on the second query condition is less than the query time consumption of the first data queried based on the first query condition.

This invention discloses a method, system, and apparatus for generating SQL instance-based scheduling tasks, comprising: acquiring SQL query logs for each period of the report to be queried; extracting all date parameters and other parameters besides the date parameters from the SQL query logs; determining the anchor date based on the date parameters of the parameterized SQL; matching with a preset scheduling rule set, and performing functional processing on the remaining date parameters except the anchor date to obtain a parameter function model of all date parameters and the anchor date when a preset condition is met; performing verification and obtaining an aggregation combination; if the verification is successful, generating a scheduling task and matching scheduling parameters based on the aggregation combination. This invention achieves extreme performance in fixed query modes such as reports through a very lightweight solution, ensuring a fast experience every time the report is opened in this scenario, improving upon the problem of slow initial opening caused by the post-event mechanism of the original caching solution, and also greatly reducing data usage costs.

The application discloses a kind of multi-dimensional data management method and system based on deep learning, it is related to data storage technical field, method includes: obtaining historical compression record and pre-processing, extraction each item compression feature and compression effect index, form historical compression sample set;Predictive model is constructed and is trained based on historical compression sample set, obtain the trained predictive model;Data dimension information and historical query log of target equipment are collected, the importance and query frequency of each data dimension are obtained;The co-occurrence frequency of any two dimensions is counted and its stability is calculated, and a global weighted co-occurrence matrix is established;The candidate compression method set of the target equipment is preset, the matching score of any pairing combination between each data dimension and each candidate compression method is calculated;The global compression graph of the target equipment is constructed, and the global optimal compression strategy is solved.The application can realize the efficient storage management of massive multi-dimensional data, and solve the problem of slow multi-dimensional collaborative query response.

The application discloses a data cleaning method and a terminal of a database, wherein slow query logs in a first database are monitored, and when the slow query logs exceed a preset number to generate an abnormal database table list, it indicates that there is a possibility of abnormal data in the database. A test database table is created in a second database according to the abnormal database table list, and test data corresponding to the data amount of the abnormal database table is added to the test database table. In this way, the deviation value of the size of the abnormal list can be determined according to the size of the abnormal list and the test list. When the deviation value reaches a threshold value, it is considered that there is a data hole in the list, and data cleaning is automatically performed for the abnormal list, thereby reducing the abnormal data stored in the database table and improving the overall system performance.

The invention discloses a dynamic index generation method and system based on archive metadata, and relates to the technical field of archive information management.The method comprises the steps that a user interface is provided, and a user is supported to define a metadata model and archive field query conditions according to archive categories and business requirements; field query importance is evaluated in combination with query frequency and result influence degree so as to set index priorities, and a future query mode is predicted by utilizing historical query logs and machine learning so as to provide a basis for dynamic index generation; according to a field data type, a query condition and a performance requirement, automatically selecting or recommending an adaptive index type and optimizing configuration, and automatically executing a statement in a database to generate an index; performing performance test on the index, deploying and monitoring use and query performance after verification is passed, visually analyzing performance data, generating optimization suggestions, dynamically adjusting the index, automatically identifying field change to trigger update, and continuously optimizing configuration. The workload of file operation and maintenance can be reduced, and the file data query efficiency is improved.