9 results about "Schema matching" patented technology

This disclosure presents a method and system for automatically discovering sensitive data, comprising: monitoring a task queue to obtain sensitive data discovery tasks to be processed; parsing the sensitive data discovery tasks, dynamically adapting connection strategies using a database fingerprinting mechanism, and connecting to a target database; extracting configuration information, determining the target database table name and target field name, generating and executing an intelligent sampling strategy and data query SQL statement, scanning and obtaining data samples, performing sensitive information pattern matching and identification, and generating discovery results and a data evolution tracking system; writing the discovery results into a result table and generating a data lineage graph; updating the execution progress of the sensitive data discovery tasks, closing the database connection after execution, and updating the status of the sensitive data discovery tasks to complete. This method can improve the efficiency, accuracy, and traceability of sensitive data discovery.

A computer-implemented method for improved schema matching of two databases is disclosed. The method can transform one or more text benchmarks into one or more first vector embeddings, retrieve a text output from the generative AI model in response to a user prompt, transform the text output into one or more second vector embeddings, measure one or more matching scores based on comparison of the one or more first vector embeddings with the one or more second vector embeddings, and determine reliability of the text output based on evaluation of the one or more matching scores. Related systems and software for implementing the method are also disclosed.

Processing and transforming data using an artificial intelligence (AI) system is described. A machine-learning-based data profiling tool may be used on first data associated with a first data source and second data associated with a second data source to generate first data profile content identifying first data schema content and second data profile content identifying second data schema content. A schema matching operation may be performed to generate schema matching data identifying a match between a first schema and a second schema. A data transformation operation may be performed to generate transformed data based on converting at least one of the first data or the second data from a source data format to a unified data format.

The application provides a 10KV power distribution network fault automatic diagnosis positioning method, constructs a confidence evaluation model by collecting current direction and voltage polarity data, and carries out cross verification combined with a historical fault feature database to obtain a preliminary and adjusted confidence sequence; in view of the interference monitoring point, the weight of the interference monitoring point is reduced, a weighted polarity sequence is generated, and mode matching is carried out to determine a preliminary fault section; a multi-dimensional verification matrix is constructed by fusing the power distribution automation switch action and the user repair geographic position, the spatial deviation is quantified, and when the threshold is exceeded, deep analysis is started, the deviation sequence is corrected by means of the consistency filtering of the power distribution network topological connection relationship and the business standby path, finally, the information fusion technology is adopted to integrate the multi-source results to output the accurate fault position, and the positioning result is fed back to update the fault feature database and the confidence model parameters, adaptive optimization diagnosis is realized, and the diagnosis precision is improved.

The application discloses a kind of Internet of Things network fault monitoring systems, including the following modules: hierarchical acquisition module, data preprocessing module, adaptive threshold module, abnormal analysis module, fault location module, hierarchical response module, closed-loop optimization module, the application also discloses a kind of Internet of Things network fault monitoring method, including the following steps: hierarchical data acquisition, data preprocessing and feature screening, adaptive threshold generation, abnormal clustering and fault preliminary judgment, fault mode matching and accurate positioning, hierarchical response and closed-loop optimization.In the application, the threshold is dynamically adjusted by combining historical data with real-time network load, and the threshold validity is verified with the device type weight correction, which effectively improves the threshold adaptability and fault determination accuracy, and then through K-means clustering, the abnormal classification, structured fault mode library and weighted matching algorithm are cooperatively determined, and the location identification information is accurately positioned, which greatly improves the accuracy of abnormal analysis and fault location.

The present application belongs to the field of network security, and in particular to a method and device for monitoring data dark web leakage. The device for monitoring data dark web leakage comprises a dark web resource scheduling and collection module, a data preprocessing and analysis module, a data analysis and correlation matching module, an alarm and report generation module. The dark web resource scheduling and collection module is used to realize the management of dark web resources, anonymous access and adaptive crawling, including: a dark web resource list maintenance unit: maintaining an updatable dark web resource list containing.onion site URLs, covering the entrances of online markets, forums and telegram groups for illegal data transactions. Through anonymous adaptive dark web collection, multi-modal data analysis, enterprise asset fingerprint multi-modal matching and machine learning risk assessment, the present application realizes the full-process automation of dark web data leakage monitoring, significantly improves the monitoring efficiency and accuracy, can quickly trace the leakage event and push real-time alarms, and helps the security team to prevent data misuse risks in advance.

This invention discloses a method for security system linkage and intrusion behavior recognition that integrates multimodal data, specifically relating to the field of intelligent security technology. The method includes: First, performing three-level spatiotemporal alignment and semantic-level collaborative embedding fusion on multi-source data to generate unified fusion features; second, constructing and reasoning a dynamic semantic scene graph, identifying high-order intrusion behaviors through graph pattern matching and logical rules, and outputting structured behavioral events; then, based on behavioral events and multi-dimensional context, performing multi-objective optimization through a dynamic policy engine to generate and execute collaborative linkage strategies, and dynamically replanning based on feedback; finally, achieving continuous self-evolution and cross-scene knowledge transfer through multi-scale memory and metacognitive optimization mechanisms. This invention solves the problems of traditional systems such as single perception, rigid linkage, and inability to adapt, achieving highly accurate, interpretable, and self-evolving intelligent security.

The application discloses a sports event data mining method and system based on AI technology, and relates to the technical field of machine learning, comprising the following steps: aligning each multi-source sports event data in space-time through a dynamic time warping algorithm, and outputting a multi-modal event data package; identifying sports entity coordinates and tactical events from the multi-modal event data package, and outputting a tactical mode report; fusing and instantiating the tactical mode report and preset sports field rules through neural symbols, and outputting a tactical knowledge graph; obtaining tactical instances in the tactical knowledge graph through logical deduction retrieval, and performing causal reasoning analysis on the tactical instances to generate a tactical causal graph; and performing tactical mode matching and effect evaluation on real-time space-time hypergraphs and the tactical causal graph, and outputting a sports event tactical instruction set. The application realizes the deep conversion from multi-modal data to high-order tactical decision by constructing a reasoning tactical knowledge graph and a knowledge completion mechanism.

This invention discloses an intelligent diagnostic method for the safe operation of large fixed equipment in coal mines based on big data. The method constructs a distributed system including an edge controller and a distributed server. Multiple types of sensors are deployed at key parts of the equipment for data acquisition and local feature calculation. This system is interconnected with a remote intelligent expert system via an encrypted communication network to form a petabyte-scale operational status database. The remote intelligent expert system, based on an artificial intelligence algorithm that integrates BP neural networks and decision trees, autonomously assesses the equipment's health status, performs fault mode matching, and predicts remaining lifespan, generating intelligent diagnostic reports and pushing them to user terminals. This invention achieves multi-source data fusion and cross-mine comparative analysis of similar equipment through the collaborative work of distributed edge computing and cloud-based big data analysis. It solves the problem of traditional monitoring relying on manual experience, enabling early prediction of equipment health trends and predictive maintenance.