22 results about "Complex event processing" patented technology

The various embodiments herein provide a system and method for networked digital twins with autonomous collaborative decision-making. The system comprises a Digital Twin Engine for real-time data acquisition, model synthesis, and simulation, an AI Module for advanced data analysis, an autonomous collaborative decision-making module for optimized decision-making, a communication layer for secure data exchange, and supporting modules for coordination, storage, security, and user interaction. The method for generating and deploying digital twins comprises data collection, transmission, preprocessing, model synthesis, simulation, validation, and deployment. The method for networking and collaboration comprises AI-based data processing, complex event processing, autonomous decision-making, task distribution, decision communication, real-time monitoring, and continuous improvement. This system enhances operational efficiency, scalability, and security, reducing the need for human intervention and providing a comprehensive management solution for complex systems.

The invention discloses a cross-channel marketing automation method and system based on real-time behavior triggering. The method comprises the following steps: capturing a user behavior event flow in real time through a front-end acquisition unit; performing real-time mode matching on the event stream by utilizing a complex event processing engine, and generating a marketing trigger signal when a preset rule is matched; dynamically selecting an optimal channel for the user based on a real-time scoring model in response to the signal; the marketing content is sent to the selected channel through the unified execution module; and finally, tracking user feedback and updating model parameters and user preferences in real time to form closed-loop optimization. The corresponding system comprises a front-end data acquisition module, a streaming event processing module, a real-time intelligent decision-making module, a multi-channel execution module and a closed-loop optimization module which are used for implementing the steps. According to the method, millisecond-level intention perception, cost and experience optimal channel intelligent decision and data-driven adaptive optimization closed loop are realized, and the problems of delayed response, extensive channel and lack of self-adaption of traditional marketing are solved.

The application discloses a kind of distributed complex event processing systems using super heuristic algorithm, belong to distributed computer system technical field.The system includes cluster control component, distributed database, message middleware, event collection and analysis middleware, event distribution middleware and CEP processing node.It is in at core, by event collection and analysis middleware call super heuristic algorithm, CEP rule and historical data are trained, and optimal event fragment division method and Gower distance model based on attribute weight are automatically obtained;At runtime, event distribution middleware is based on the distance model to calculate the similarity of new event and existing partition, and in combination with the real-time load state of CEP processing node, dynamic, reasonable load distribution and balance are realized.The application realizes the intelligent adaptation and zero business invasion of CEP processing distribution, significantly improves the adaptability, universality and resource utilization of system in dynamic scene.

The various embodiments herein provide a system and method for networked digital twins with autonomous collaborative decision-making. The system comprises a Digital Twin Engine for real-time data acquisition, model synthesis, and simulation, an Al Module for advanced data analysis, an autonomous collaborative decision-making module for optimized decision-making, a communication layer for secure data exchange, and supporting modules for coordination, storage, security, and user interaction. The method for generating and deploying digital twins comprises data collection, transmission, preprocessing, model synthesis, simulation, validation, and deployment. The method for networking and collaboration comprises Al-based data processing, complex event processing, autonomous decision-making, task distribution, decision communication, real-time monitoring, and continuous improvement. This system enhances operational efficiency, scalability, and security, reducing the need for human intervention and providing a comprehensive management solution for complex systems.

The application relates to the field of e-government informatization and archive information management, and discloses an archive coupling processing method and system based on business flow and event driving, which identifies a business settlement event through complex event processing; linkage change data capture extracts an anchor change set in a transaction consistent range and obtains a candidate fragment through difference; conclusion data is screened by adopting a lightweight Transformer, and an archiving package containing field tracing is generated by extracting and checking according to a predefined data mode; a Merkel tree is respectively constructed for content and metadata, and a ledger abstract is generated to be written into an archiving ledger, entity information is associated under a dual-track system, settlement and archiving are realized, and traceability and verification are realized.

The invention provides an intelligent analysis method supporting multi-data-source real-time association and streaming processing, which comprises the following steps: acquiring multi-source data through a multivariate data aggregation mechanism, the multivariate data comprising business data and system data generated by a business system; performing cross-system data association on the multi-source data; constructing a streaming data processing framework, wherein the streaming data processing framework comprises operators after test optimization and arrangement; and performing complex event processing and risk assessment on the associated multi-source data through a streaming data processing framework to obtain a business risk assessment result in the multi-source data.

The invention discloses a deep threat sensing system based on network security, which is applied to a security operation platform system and is characterized by comprising a threat sensing module for performing threat detection on capabilities of a terminal side, a network side and a platform side respectively; the association analysis module is used for realizing complex CEP semantics based on a CEP complex event processing engine in combination with various association rule templates, performing timeline association and causal inference on data collected by a terminal side and a network side and cloud factors, and generating an association event of a platform side; and the emergency disposal module configures response atomic operation and response object intelligent recommendation, performs role division on response objects, and intelligently recommends different disposal modes for different roles. According to the scheme, the value of a complete security closed loop in various security scenes is brought, the security operation efficiency is improved, the threshold of operators is reduced, and all-around network security protection is provided for enterprises and individuals in a security operation system.

The application discloses a data processing method and device supporting multiple execution engines based on a twin platform, comprising the following steps: according to the token of each data element, pulling the data element result to the message queue of the twin platform through the data element interface of the data element transaction platform; calling a complex event processing module, triggering the associated business model based on the data element result in the message queue, performing corresponding operations based on the business model, and writing the execution result into the data asset library. The embodiment of the application can overcome the security and compliance problems of original data, and realize the scheduling and task execution supporting multiple execution engines based on the twin platform.

The invention discloses a complex task actual flight test horizontal dynamic space design optimization method and a computer product, and the method comprises the steps: S1, decomposing a test task profile, and constructing a test task outward tree; s2, mapping each test task sub-profile in the constructed task outbound tree to obtain a corresponding test area, and forming an initial task area association symbol graph; s3, dynamically evolving a task area association symbol graph; s4, performing dynamic modeling on the relationship evolution of the test area to obtain a weight updating function and a symbol updating function; s5, expanding the weight updating function and the symbol updating function to the multivariate relation network to obtain the weight updating function and the symbol updating function of the multiple test areas; and S6, performing optimization based on the weight updating function and the symbol updating function of the multiple test regions to obtain an optimized area. The problems of test resource allocation and environmental adaptability are solved, and the method has remarkable advantages in the aspects of space saving, stability improvement, complex event processing and the like.

This invention relates to the field of Internet of Things (IoT) technology, and more particularly to an IoT data optimization processing method and system. The method includes: performing value prediction and dynamic resource lending processing based on the operational data and business demand information of IoT devices; executing adaptive data acquisition to obtain raw IoT data; performing multi-source cross-validation to generate data anomaly classification results; performing numerical repair and data classification based on the classification results to generate clean IoT data and data value tags; performing real-time trend prediction to generate micro-early warning events; performing spatiotemporal correlation analysis in the cloud using graph neural networks to obtain updated complex event processing rules; and performing intelligent hierarchical storage and ultra-lossless compression processing based on the updated complex event processing rules to obtain hierarchical storage data and prefetch data cache. This invention achieves end-to-end collaborative optimization of data acquisition, cleaning, analysis, and storage, effectively improving resource utilization, information retention, and system adaptability.

The described systems and methods may include, upon receiving the created digital twin, processing the created digital twin through a policy core process that determines policies for the digital twin; running an asset core process that determines an asset hierarchy for the physical assets represented by the digital twin based on the physical asset metadata retrieved from a metadata database and the determined policies; running a sensor core process that determines a sensor hierarchy associated with the asset hierarchy based on the sensor metadata retrieved from a sensor metadata database and the asset core process; running an analytics solution core that determines analytics solutions for the physical assets; building a pipeline to drive analytics solutions across the policy core layer, asset core layer, sensor core layer, and analytics solution core layer of the digital twin; and running the pipeline.

The invention discloses a complex event processing method based on an extended regular tree mode, and relates to the technical field of complex event processing. According to the method, the ERTP is used as a unified query mode description mode and comprises a node group, a regular type group, a structural relation axis group, a regular type assignment axis group, a conditional expression group and a return requirement, so that a structure, a time sequence and attribute constraints are expressed in a single mode at the same time, and query processing based on the ERTP comprises the steps that structure matching is executed on a tree mode part; partial matching results are stored in an instance set; when the root node list is updated, recursively enumerating and checking the condition expression, and outputting an event corresponding to a return node; performing symbol storage automaton (SMA)-based incremental timing matching on the regular portion; according to the method and the device, intermediate result generation and overhead can be reduced, and streaming complex event processing efficiency and accuracy are improved.

The invention discloses a complex task actual flight test vertical dynamic space design optimization method and a storage medium, and the method comprises the steps: S1, decomposing a test task profile, and constructing a test task outward tree; s2, mapping each test task sub-profile in the constructed task outbound tree to obtain a corresponding depth interval, and forming an initial task area association symbol graph; s3, dynamically evolving a task area association symbol diagram along with the change of a test time flow; s4, carrying out depth interval relation evolution dynamic modeling along with the change of a time flow, and obtaining a weight updating function and a symbol updating function of moments and edges; and S5, optimizing the test initial area based on the weight updating function and the symbol updating function to obtain an optimized area. The problems of test resource allocation and environmental adaptability are solved, and the method has remarkable advantages in the aspects of space saving, stability improvement, complex event processing and the like.

The application discloses an intelligent data stream processing system based on Flink and an implementation method thereof, and belongs to the technical field of distributed stream data processing, and comprises the following steps: a data source access layer is used for uniformly accessing multi-source data; an entity monitoring layer is used for capturing business entity changes based on JPA in a non-invasive manner, generating a standardized message body and asynchronously delivering the standardized message body to a double channel; a unified event model layer is used for mapping the standardized message body into a standardized event and expanding the standardized event; an Flink stream processing engine layer is used for complex event processing, state management and window calculation, and supports declarative pipeline definition and dynamic construction; an exception detection layer is used for multi-dimensional exception detection; a distributed task scheduling layer is used for scheduling tasks according to a DAG model, and load balancing is realized through hot spot splitting and state migration; and a result storage and visualization layer is used for storing data by adopting a hot and cold separation strategy and providing a dynamic visualization interface. The application significantly improves the real-time performance, consistency and intelligent level of data processing, and reduces development and maintenance costs.

Streaming data channels are configured to inject time series-based data sets into a private cloud platform at which complex event processing engines implementing a recurrent neural network using a Nonlinear Autoregressive Moving-Average (NARMA) model identifies patterns in the data sets to continuously determine future / predictive data points for each data set. The dynamic future / predictive data points / values serve as inputs to machine learning engines, which implement multi variate regression analysis to derive a dynamic variable value for a mechanism.

The invention relates to the technical field of railway detection, in particular to a railway detection data real-time processing method, which comprises the steps of collecting multi-source heterogeneous data, generating a reference library, calculating a wear rate and an offset, determining a failure rate and a fracture risk rate, and realizing parallel computing and feature extraction through a distributed streaming processing framework. Window calculation, state management and complex event processing mechanisms are adopted, a machine learning algorithm is combined to carry out anomaly detection and trend prediction, and a real-time alarm mechanism is configured. And a dynamic resource scheduling and distributed fault-tolerant mechanism is introduced, so that high-throughput real-time analysis and data continuity are ensured. The real-time performance, the reliability and the intelligent level of the railway detection system can be remarkably improved.

A fire protection system according to an embodiment of the inventive concept includes a plurality of sensors having different address values, detecting fire occurrence, generating a fire alarm, and performing Radio Frequency (RF) communication with each other, a first server configured to perform RF communication with each of the plurality of sensors, and a second server configured to receive big data from the outside and communicate with the first server, wherein the second server includes a data collection unit configured to collect sensor data measured from each of the plurality of sensors and the big data, a data extraction unit configured to extract fire data based on information collected by the data collection unit, and a complex event processing unit configured to process complex events based on the fire data.

The invention relates to a real-time triggered user circle selection engine system and method, and belongs to the technical field of computers, the system comprises an acquisition module, a context enhancement module, a rule management module, a dynamic deployment module, a CEP engine module and a scheduling module, the system realizes a complete closed loop from data acquisition, real-time enhancement, rule definition, intelligent calculation and elastic scheduling. Through the technologies of complex event processing, dynamic resource scheduling, a mixed rule engine and the like, the circling efficiency is remarkably improved, the false triggering rate is reduced, and the requirements of personalized marketing and real-time operation are met.

The invention belongs to the technical field of mobile communication network big data processing, and discloses a mobile signaling data processing method and system based on streaming computing, and the method comprises the steps: receiving a mobile signaling real-time data stream; constructing a composite key based on the base station identifier and the user identifier, and grouping the data streams; a session window based on event time is applied to each group of data, and continuous signaling events are divided into the same processing unit; in each processing unit, incremental aggregation is carried out on the signaling data, and a Snappy compression algorithm is adopted to carry out compression processing on the signaling data; the compressed data stream is input into a complex event processing module based on an Flink CEP library; performing mode matching on the compressed data stream based on a service rule mode, and when an event sequence conforming to the service rule mode is matched, retaining one signaling event in the event sequence and discarding other repeated events; and outputting the de-duplicated data. According to the invention, the mobile signaling data of the mobile phone is efficiently compressed, and the data volume is greatly reduced.

The invention relates to the technical field of computer data processing, in particular to a supply chain service data management system, which comprises an event sensing unit configured to be used for monitoring and processing real-time data streams from one or more heterogeneous data sources in a supply chain network, and utilizing a complex event processing engine to process the real-time data streams in the supply chain network, identifying and extracting a structured supply chain service event related to a preset service scene from the real-time data stream in real time; the knowledge graph management unit is configured to trigger an atomic update operation on the knowledge graph based on the business event according to a preset event-graph update rule, and dynamically modify a topological structure of the knowledge graph or attributes of nodes and edges; and the advanced analysis unit is configured to execute at least one advanced analysis task based on the knowledge graph. According to the method, the problem of response lag of a traditional system model is fundamentally solved, and a stable system basis is provided for realizing real-time mapping and prospective analysis of the supply chain state through clear function division.

The invention discloses a stadium Internet of Things cooperative adaptation method and related equipment, protocol automatic identification is realized through a multi-modal protocol feature library and a dynamic weight model, a standardized orchestrable protocol plug-in is loaded to complete adaptation, data is processed in combination with a scenarized business semantic model, and the matching efficiency is improved. And a linkage instruction is generated through a complex event processing engine, and closed-loop control is formed through reinforcement learning optimization. According to the invention, through the multi-mode protocol feature library and the dynamic weight model, automatic and accurate identification of the equipment protocol is realized; a standardized protocol plug-in which can be flexibly arranged is loaded, data analysis and communication adaptation are rapidly completed, and adaptation flexibility is improved; on the basis of a scenarized business semantic model, original data are converted into business standardization indexes, and the demand is accurately docked; multi-device data are fused through a complex event processing engine, a cross-protocol linkage instruction is generated, and collaborative response is achieved; the linkage rule is iteratively optimized through reinforcement learning, closed-loop control is formed, and the collaborative operation and management and control efficiency of the system is enhanced.

The embodiment of the invention discloses a data processing method and device, a storage medium and electronic equipment, and the method comprises the steps: converting a log data association analysis model into an association analysis engine rule for a complex event processing engine, and carrying out rule balanced distribution processing on a service system based on the association analysis engine rule, determining a data analysis service group of each association analysis engine rule in the service cluster, determining a complex event processing engine corresponding to the association analysis engine rule through the data analysis service group, and loading the association analysis engine rule in the service cluster based on the complex event processing engine, and controlling the complex event processing engine to perform alarm detection processing on the log data by using the association analysis engine rule.