8 results about "Big data architecture" patented technology

The invention provides a large-scale encrypted traffic frame-by-frame clustering analysis method based on a big data architecture, and relates to the technical field of big data, and the method comprises the steps: carrying out the data partitioning and storage of target encrypted data obtained through the preprocessing of original encrypted traffic data; based on a clustering visualization result obtained by visualizing a target clustering result obtained by carrying out frame clustering analysis on the target encrypted data, judging whether a data traffic abnormal behavior exists or not, and when the data traffic abnormal behavior exists, generating an abnormal analysis report; and an abnormal analysis report is transmitted to safety management personnel so as to take corresponding measures in time for defense. The method comprises the following steps: visualizing a target clustering result obtained by carrying out frame clustering analysis after processing and partition storage on encrypted traffic data based on a big data architecture, identifying traffic data exception, generating an exception analysis report when the exception exists, and transmitting the exception analysis report to safety management personnel to take corresponding measures for defense. The network threat identification capability is effectively enhanced, and the overall protection level of network security is further improved.

AIHeal system is a new solution creating explainable and causal AI infrastructure in a way that is accessible to physicians, solution in which they can trust, which open new routes to delivering better, faster, more reliable, secure, and more cost-effective medical care. The data from wearables and implants are transferred by mobile networks and collected by big data architecture in the innovative Electronic Health Record forming the "digital twin" of the patient. The ground-breaking innovative contribution of the proposed patent is to reach naturally interpretable AI through the synergistic AI concept. The main idea is to combine the benefits of the accuracy of deep-learning algorithms with visibility on the factors that are important to the algorithm's conclusion. Our approach is an attempt to follow the physicians thinking through the clinical decision procedure. Physicians first follow some rules and knowledge, then "call" experience (data) and make a decision.

The invention discloses a memory residence data management method based on a big data architecture, and relates to the technical field of data management, metadata is established for cache data blocks in a memory of a big data computing framework, a global cache list is maintained, and the data management efficiency is improved by combining timed cleaning and a forced cleaning mechanism based on the memory occupancy rate. Dynamic management of the cached data is realized; and when the memory occupancy rate is lower than a preset safety threshold value, further calculating a judgment index, comparing the judgment index with a judgment threshold value, and judging whether to really enter a safety state or trigger an alarm. According to the method, when the task load suddenly increases or the memory pressure fluctuates, the system load can be dynamically adapted, resource idling or memory overflow is avoided, the cache retention efficiency is improved through a multi-stage cleaning strategy, and the mistaken deletion risk is reduced. Meanwhile, by introducing an intelligent alarm mechanism, the operation stability of the system in a high-voltage environment is guaranteed, and the safety and the intelligent level of overall memory management are improved.

This invention discloses a multimodal data fusion coding method for constructing a unified underlying language for national big data, belonging to the fields of national big data architecture, multimodal governance, and domestically produced unified coding. Based on the mathematical fundamental feature theory and the STE domestic coding system, coupled with the GZ-BigData-RISC-V domestic dedicated chip and a multimodal AI fusion model, it achieves normalized access to multimodal data, unified extraction of fundamental features, unified underlying language coding, lossless fusion, and secure storage. Quantitative thresholds are set for cross-modal fusion efficiency ≥85%, data consistency ≥99.5%, coding latency ≤1ms, and lossless fusion rate 100%, completing the unified coding and semantic association of text, image, audio, video, time-series, and geospatial data. This method breaks down data heterogeneity barriers, constructs the only nationally interoperable unified underlying language for big data, and is 100% domestically produced and controllable throughout the entire process, supporting the construction of a national integrated big data center and promoting the market-oriented circulation of data elements.

The application provides a lightweight real-time data synchronization and processing method and system for a virtual wafer factory, the method comprising: performing full-amount extraction and synchronization of historical data of a source-end database through a DataX tool; performing real-time monitoring and capturing of incremental change data of the source-end database through a CDC tool, and sending the incremental change data to a Kafka message queue in the form of a message; consuming the incremental change data from the Kafka message queue through a NiFi data flow tool; writing data into a TimeScaleDB time series database; using an aggregation engine built in the TimeScaleDB to perform continuous aggregation operation on the written data, generate and maintain a materialized view, and the materialized view is used for direct query and calling by a business system, so as to realize storage-computing fusion of data storage and aggregation calculation. The application can solve the problems of long existing big data ETL architecture tool chain, large resource consumption and high operation and maintenance cost, and realize lightweight operation of data cleaning and aggregation calculation.

The present invention discloses a reinsurance data processing method, apparatus, device, and medium. The reinsurance data processing method includes: obtaining a combined reinsurance data table based on policy information, the combined reinsurance data table including multiple groups of reinsurance data, the reinsurance data being labeled with a first rule tag, a second rule tag, and a third rule tag; performing a first-level calculation cycle on the reinsurance data based on the first rule tag and the second rule tag to obtain first-level reinsurance data calculation results; classifying and aggregating the first-level reinsurance data calculation results based on the third rule tag to obtain result data; and aggregating the result data into a reinsurance result data table to serve as a basis for reinsurance of the policy. The above method provides greater computational configuration flexibility for reinsurance calculations in a big data architecture.

The application provides a large-scale encrypted traffic frame clustering analysis method based on a big data architecture, relates to the technical field of big data, and comprises target encrypted data data partitioning and storage obtained by preprocessing original encrypted traffic data; target clustering results obtained by frame clustering analysis on the target encrypted data are visualized to obtain clustering visualized results to determine whether there is data traffic abnormal behavior, and when there is, an abnormal analysis report is generated; the abnormal analysis report is transmitted to a security management personnel to take corresponding measures for defense. Through frame clustering analysis on the target clustering results obtained by processing and partitioning storage of encrypted traffic data based on the big data architecture, traffic data abnormalities are visualized and identified, and when there are abnormalities, an abnormal analysis report is generated and transmitted to a security management personnel to take corresponding measures for defense, so that the network threat identification capability is effectively enhanced, and the overall protection level of network security is improved.

The application discloses a memory resident data management method based on a big data architecture, relates to the technical field of data management, and realizes dynamic management of cache data by establishing metadata for cache data blocks and maintaining a global cache list in the memory of a big data computing framework in combination with a timing cleaning and a forced cleaning mechanism based on memory occupancy; when the memory occupancy is lower than a preset safety threshold, a judgment index is further calculated and compared with a judgment threshold to judge whether a safety state is truly entered or an alarm is triggered. The method can dynamically adapt system load when task quantity suddenly increases or memory pressure fluctuates, avoids resource idling or memory overflow, and improves cache retention efficiency and reduces the risk of mistaken deletion through a multi-level cleaning strategy. Meanwhile, the intelligent alarm mechanism is introduced to guarantee the operation stability of the system in a high-pressure environment and improve the safety and intelligent level of overall memory management.