50 results about "Complex event processing" patented technology

The invention provides an internal network threat intelligent analysis and prediction method and system based on a large language model, and relates to the technical field of network technology and security. According to the method, the long-range dependency processing capacity of the LLMs is utilized, global and long-range dependency analysis of the user behavior sequence is achieved, and the limitation of a traditional fixed session unit is broken through. Rich semantic features and context information are extracted from original log data through the deep semantic analysis ability of LLMs, and the feature expression ability is greatly improved. In combination with the reasoning capability of LLMs and a traditional anomaly detection algorithm, context-based deep semantic analysis is realized, and the recognition capability of complex threats is improved. In the aspects of real-time monitoring and response, the LLMs are introduced to assist in complex event processing and dynamic rule generation, and the intelligent level and efficiency are improved. Aiming at challenges of data management and analysis, an LLMs is utilized to enhance a data management strategy, a storage structure is optimized, and an intelligent data life cycle management and query interface is provided.

The invention discloses an intelligent data stream processing system based on Flink and an implementation method thereof, and belongs to the technical field of distributed streaming data processing, and the implementation method comprises the following steps: a data source access layer uniformly accesses multi-source data; the entity monitoring layer captures service entity change in a non-intrusive manner based on JPA, generates a standardized message body and asynchronously delivers the standardized message body to double channels; the unified event model layer maps the data into a standardized event and expands the standardized event; the Flink stream processing engine layer is used for complex event processing, state management and window calculation, and supports declarative pipeline definition and dynamic construction; the anomaly detection layer performs multi-dimensional anomaly detection; the distributed task scheduling layer schedules tasks according to a DAG model, and realizes load balancing through hotspot splitting and state transition; and the result storage and visualization layer adopts a cold and hot separation strategy to store data, and provides a dynamic visualization interface. The real-time performance, the consistency and the intelligent level of data processing are remarkably improved, and the development and maintenance cost is reduced.

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 relates to the field of financial science and technology, and particularly discloses a cross-platform financial data integration and real-time analysis system and method. The system is characterized in that a distributed heterogeneous data acquisition module accesses multi-source data in parallel through a pluggable protocol adapter; the streaming data cleaning engine realizes field-level normalization based on a dynamic template and semantic mapping; the hybrid storage architecture forms a three-level system by a memory database, a time sequence database and distributed file storage; the stream processing core engine adopts an elastic sliding window to execute CEP complex event processing; and the dynamic analysis decision module integrates online machine learning to generate a real-time transaction signal. The method comprises the steps of multi-source concurrent acquisition and microsecond-level timestamp injection, context sensing data standardization, dual-channel data storage, event time window aggregation, dynamic risk calculation and block chain audit evidence storage. The defects of low data fusion efficiency, high real-time analysis delay and the like in the prior art are overcome, and cross-platform data processing timeliness and decision accuracy are remarkably improved.

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 invention provides a data acquisition and monitoring analysis system based on an MES platform, and relates to an industrial software application system. Comprising a low-code development module, a factory modeling module, a monitoring module and a data acquisition module, the low-code development module realizes cross-device linkage development and control program generation through combination of a dragging interface and a device API library in an MES platform integration environment, and guarantees efficient communication control of the device, the factory modeling module integrates two production modes, and the monitoring module is connected with the monitoring module. The monitoring module is used for realizing dynamic modeling and optimizing a production process according to multi-dimensional parameters, the monitoring module is used for realizing ultra-low-delay transmission processing and real-time monitoring of data under high concurrency by applying a dual-cache and message middleware mechanism, and the data acquisition module is used for realizing real-time monitoring of the data based on a machine learning algorithm and a complex event processing technology. Dynamically adjusting the acquisition frequency precision based on the production state; the system aims at improving the efficiency, quality and management level of industrial production, and has wide application prospects and important industrial software application value.

The invention discloses an operation and maintenance data management system based on cloud computing, particularly relates to the field of data management, and comprises a distributed data acquisition module, a cloud native data processing module, an intelligent analysis module, a visualization and decision module and a cloud resource management center module. According to the method, heterogeneous data are collected in real time through the lightweight agent, and high efficiency is guaranteed by adopting multi-thread and self-adaptive sampling; data cleaning, standardization and mixed storage are realized by using a stream batch integrated architecture; the intelligent analysis module fuses an improved Robust Z-Score, an LSTM-Attention model and a complex event processing engine, and supports dynamic anomaly detection and fault tracing; the visual assembly builds an instrument panel in a dragging mode, alarm strategies are aggregated in a grading mode, and storm is restrained; the cloud resource management center deeply integrates Kubernetes, predicts demands based on reinforcement learning, automatically expands and shrinks capacity and optimizes cost, and deals with burst traffic in combination with a burst capacity pool.

The invention relates to a big data real-time monitoring system and method. The big data real-time monitoring system comprises a data access module, a preprocessing standardization module, a streaming computation module, a rule model engine module, an alarm arrangement and disposal module, a storage query module and a visualization and operation and maintenance module. The data access module is used for collecting multi-source heterogeneous data from a message queue, a log agent or an Internet of Things protocol, and labeling an event timestamp and a source identifier for each piece of data; the preprocessing standardization module is used for performing deduplication, cleaning, desensitization and dimension table association on the collected data and outputting a unified event structure; the stream-oriented computing module is used for executing window aggregation, state updating and complex event processing under event time semantics and supporting water level management and just one-time semantic processing, high-timeliness and consistent monitoring is achieved through self-adaptive water level, double-layer windows and dynamic threshold gating fusion, versioning playback and approximate computing cost reduction are supported, and the method is suitable for large-scale popularization and application. And the system has automatic degradation and self-healing capabilities.

The invention discloses a big data method for emergency response and disposal of water pollution accidents. The method comprises the following steps: deploying various types of sensor networks in a pollution-prone area; transmitting the real-time data acquired by the sensor to a cloud storage system; monitoring a real-time event mode in the environmental data by using a complex event processing technology, and generating a real-time environmental data model; a machine learning technology and a geographic information system are used to analyze real-time environment data to predict a diffusion path and an influence range of pollutants, and a pollution source position is rapidly positioned; and intelligent emergency response measures are implemented through the intelligent decision support system. A real-time data stream processing technology and complex event processing are used, environmental data can be monitored in real time, emergencies can be quickly responded, the response time can be greatly shortened due to the real-time performance, pollution events can be found and processed earlier, and therefore environmental damage and influences of a water purification system are reduced.

The invention discloses a complex event processing method and system in an intelligent scene of an internet of things terminal. According to the method, through a double filtering strategy of time-based event pruning and predicate-based time pruning, multiple rounds of communication with low overhead are used, a time interval containing possible matching is determined in advance, and finally, only relevant events with timestamps in the time interval are transmitted for mode matching. According to the method, the computing power of the sensor node is effectively utilized, unnecessary original event transmission is avoided, the communication overhead between the sensor node and the central node is reduced, and the overall delay of complex event processing is reduced.

Various embodiments relate to event processing. A system may include a mobile unit, which may include one or more sensors configured for capturing data and a controller for generating a number of events based on captured data. The system may further include a complex event processing (CEP) system communicatively coupled to the mobile unit. The CEP system may be configured to receive one or more of the number of events and generate one or more actions based on the one or more number of events. Associated systems, methods, and computer-readable media are also disclosed.

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.

Systems and methods described herein which can involve, for receipt of a composed digital twin, processing the composed digital twin through a policy core process that determines a policy for the digital twin; executing an asset core process that determines an asset hierarchy of physical assets represented by the digital twin based on metadata of the physical assets retrieved from a metadata database and the determined policy; executing a sensor core process that determines a sensor hierarchy to be associated with the asset hierarchy based on metadata of sensors retrieved from the metadata database of the sensors and the asset core process; executing an analytics solution core that determines analytics solutions for the physical assets; constructing pipelines to facilitate the analytics solutions across a policy core layer, asset core layer, sensor core layer, and analytics solution core layer of the digital twin; and executing the pipelines.

This invention discloses a method and system for handling complex events in intelligent IoT scenarios. The method employs a dual filtering strategy of time-based event pruning and predicate-based time pruning, utilizing low-overhead multi-round communication to predetermine potentially matching time intervals, and finally transmitting only relevant events with timestamps within those time intervals for pattern matching. This invention effectively utilizes the computing power of sensor nodes, avoids unnecessary transmission of raw events, reduces communication overhead between sensor nodes and the central node, and minimizes the overall latency of complex event processing.

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.

Systems and methods described herein which can involve, for receipt of a composed digital twin, processing the composed digital twin through a policy core process that determines a policy for the digital twin; executing an asset core process that determines an asset hierarchy of physical assets represented by the digital twin based on metadata of the physical assets retrieved from a metadata database and the determined policy; executing a sensor core process that determines a sensor hierarchy to be associated with the asset hierarchy based on metadata of sensors retrieved from the metadata database of the sensors and the asset core process; executing an analytics solution core that determines analytics solutions for the physical assets; constructing pipelines to facilitate the analytics solutions across a policy core layer, asset core layer, sensor core layer, and analytics solution core layer of the digital twin; and executing the pipelines.

The present invention relates to the field of network segmentation technology, specifically a VLAN division and independent network card interface system for high-speed signal connection switch, the system includes a network status monitoring module, a graph neural network analysis module, a VLAN dynamic configuration module, a reinforcement learning optimization module, a prediction model module, an ACL rule generation module, a game theory optimization module, and an event-driven adjustment module. In the present invention, the use of a graph neural network model not only analyzes the complexity characteristics of the network structure, but also optimizes the dynamic adjustment of the VLAN configuration, so that it can match the changes in the network topology in real time. The introduction of deep reinforcement learning methods effectively improves the performance and security of the network through a reward mechanism. Time series analysis and machine learning methods enable the system to predict future network access modes and configuration requirements. The application of game theory methods provides an optimal combination solution for multi-network segmentation strategies, and complex event processing technology realizes effective monitoring and response to real-time network events.

The invention relates to a risk control method and system for financial security, and aims to solve the problems of detection lag, data islands, poor model adaptability and the like in a traditional risk control system. Structured and unstructured data are collected in real time from multi-source financial data, and a standardized data set is generated through preprocessing; constructing a high-dimensional feature vector containing a user behavior sequence, fund network features and text emotion features by using a distributed computing framework; training and risk assessment are carried out by adopting an integrated model fusing an isolated forest, a gradient boosting decision tree and a deep neural network; real-time transaction decision making and interception are realized in combination with a dynamic threshold strategy driven by reinforcement learning; and a federated learning framework is introduced to realize multi-party security joint modeling on the premise of ensuring data privacy. According to the method, the functions of real-time complex event processing, incremental learning optimization and interpretability analysis are integrated, a closed-loop risk control system is formed, and the recognition capability, the adaptability and the decision transparency of a novel fraud mode are remarkably improved.

The invention relates to the technical field of fuel cell vehicles, and discloses a flow calculation-based fuel cell vehicle operation monitoring device, which comprises a data acquisition layer configured to acquire operation data from a plurality of sensors of a fuel cell vehicle in real time; the stream processing layer is used for carrying out real-time preprocessing and complex event processing on the operation data based on an Flink stream processing engine; the calculation and analysis layer is configured to execute multi-dimensional real-time state calculation and fault mode recognition; the visual output layer is configured to dynamically display the battery state and fault information; the early warning triggering layer is configured to trigger graded early warning according to the calculation and analysis result; the data acquisition layer, the stream processing layer, the computational analysis layer, the visual output layer and the early warning trigger layer are sequentially coupled to jointly form a real-time data stream processing architecture.

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.

The present invention provides a complex event processing method and system based on network traffic metadata. The method includes collecting network traffic metadata from a switch mirror port and saving it as a pcap package, calling a parsing instruction to parse the detailed information in the pcap package to obtain the parsing results; using a rule designer with a visual / graphical interface and pre-designing complex event processing rules based on an operator model, using an AI model for testing and outputting rule text; using a rete tree structure to store the rule text to obtain a rete rule tree, which is then saved to a rule database based on permissions; starting from the root node of the rete rule tree, matching the pattern corresponding to each type node with the facts / business data generated by the metadata, and outputting the results that match the pattern corresponding to the type node to external storage. This method implements manually predefined business rules and integrates machine learning and artificial intelligence models, enabling multi-faceted detection of known security threats and perception of unknown network threats.