9 results about "Event propagation" patented technology

The invention relates to the technical field of power grid exception handling, and discloses a power grid exception event handling method and system based on a knowledge graph. The method comprises the following steps: collecting time sequence data flow and space coordinate information of a power grid abnormal event in real time through multi-source sensing equipment, and fusing equipment operation parameters to construct a dynamic event data pool; performing graph structure conversion on the dynamic event data pool based on a knowledge graph topological connection relationship, calculating event node propagation path intensity and generating an event propagation network model; key abnormal indexes are separated out from the model by adopting a multi-modal feature extraction technology, risk probability calculation is completed in combination with an adaptive learning algorithm, and an abnormal risk distribution diagram is output; and classifying event levels according to the graph, and automatically generating a processing instruction sequence. According to the method, omnibearing data perception and accurate analysis of the abnormal events of the power grid are realized, an event propagation rule is clearly presented, risk assessment is enabled to be closer to an actual operation state, and accurate guidance is provided for abnormal disposal.

The invention relates to the technical field of media content assessment, provides a media content propagation influence assessment method and system based on multi-modal data fusion, and is used for solving the problem of low accuracy of major report event propagation traceability and key node positioning in the prior art. The method comprises the following steps: acquiring multi-modal data and propagation dynamic data of media content on a plurality of platforms, wherein the data are used for constructing a propagation path diagram after being subjected to time alignment and normalization processing and are converted into a vectorization sequence; analyzing sequence generation path correlation intensity, and identifying a core propagation path according to the correlation intensity; performing feature extraction and conversion on the propagation dynamic data based on the core propagation path, generating a time sequence signal, and analyzing the time sequence signal into a bidirectional time sequence feature; and calculating weight coefficients of nodes and edges according to the bidirectional features, forming node mark information, and finally generating a propagation influence index. According to the application, the propagation traceability of the major report event and the accuracy of key node positioning are improved.

The application relates to the field of event propagation path analysis methods, discloses an event propagation path analysis method based on RAG retrieval and information extraction technology, and discloses an event propagation path analysis method and system based on RAG retrieval and information extraction technology.The method first constructs a standardized data pool containing unique content fingerprints, realizes data deduplication and tracing by using SimHash; then constructs an exclusive RAG vector knowledge base, converts the seed context into an initial query vector; performs iterative retrieval, extracts propagation triples by using a model, and converts new entities into query vectors to trigger multi-hop retrieval to mine implicit correlations; merges nodes by using co-reference resolution and constructs a time-series directed graph; and finally calculates node centrality and vector semantic offset.The application solves the problems of propagation path breakage and content alienation recognition by iterative retrieval and semantic quantitative analysis, and realizes accurate analysis of event evolution.

The application belongs to the technical field of parallel discrete event simulation and network system simulation, and discloses an intelligent hybrid parallel synchronization method for large-scale distributed simulation, which collects key operation indexes such as neighborhood timestamp lower bound, next event time, round-trip delay quantile value, cross-partition event output rate and virtual time jitter, evaluates system load and communication state in real time, judges the best time to enter optimism or rollback conservation based on the collected key operation indexes, prevents frequent switching by using hysteresis mechanism and minimum residence time, sets time-bounded gates at the mode switching boundary, only allows events within the safe range to propagate, temporarily stores events exceeding the range and sends them in batches when the conditions are met, and thus guarantees causal consistency. The application takes into account real-time performance and robustness, shortens the global empty waiting time, facilitates real-time online acceleration and stable submission in a simulation platform and a message passing interface, and has both performance and interpretability.

The invention relates to a hot event propagation path prediction method and system based on multi-modal big data. The method comprises the following steps: based on a preset trigger, identifying seed content from a content publishing data stream, and acquiring propagation behavior data by taking the seed content as a center; based on the seed content and the propagation behavior data, constructing a preliminary propagation network structure to obtain an early propagation network; analyzing a propagation chain in the early propagation network to obtain a propagation chain analysis result, and based on the propagation chain analysis result, evaluating a user tendency response behavior to obtain a response tendency prediction matrix; and performing propagation simulation based on the early propagation network, the propagation chain analysis result and the response tendency prediction matrix to obtain propagation probability distribution. By adopting the method, the accuracy of propagation prediction can be improved by utilizing multi-modal data and social network analysis, the combination of content semantics and user behaviors is emphasized, and the method is suitable for the hotspot event propagation analysis of platforms such as social media and the like.

A system can maintain a function-as-a-service architecture that comprises a group of executable serverless functions, a group of message consumers, and a group of message brokers, wherein respective message brokers of the group of message brokers are configured to transmit respective messages to respective message consumers of the group of message consumers, and wherein the respective message consumers are configured to transmit the respective messages to respective functions of the group of executable serverless functions. The system can determine respective utilization metrics of the respective message consumers. The system can determine that a utilization metric of the respective utilization metrics satisfies a misutilization criterion. The system can adjust which ones of the message brokers communicate with which ones of the message consumers based on the utilization metric being determined to satisfy the misutilization criterion.

The application discloses a regional traffic system resilience evaluation and simulation deduction method based on an inclined orbit satellite, relates to the technical field of data processing, and collects fine-grained space-time data of traffic operation by using the multi-orbit covering characteristics of the inclined orbit satellite to form a traffic state feature set; in the process of space-time sequence preprocessing of the traffic state feature set, an event influence feature set is generated, and combined with structure information extracted from a traffic network topology set, an event propagation initial state set is constructed, thereby avoiding the problem that previous methods only rely on average flow or average speed indexes and ignore the initial disturbance difference of events; further, by diffusing and deducing the event propagation initial state set under the network node and path space adjacency relationship, an event diffusion stage feature set is generated, layered representation of event propagation dynamics is realized, and the resilience level of the traffic system under different event conditions can be clearly shown.

This application provides a method and system for campus security situation assessment and early warning based on multi-source data fusion, relating to the field of campus security management technology. First, it collects multi-source data streams from campus video surveillance, IoT sensors, information system logs, and access control systems, parsing out security-related event fragments. Then, it matches and associates these security-related event fragments with a pre-built campus event knowledge base to construct a campus event network diagram. Based on this campus event network diagram, it drives simulation and constraint-based inference models to perform event propagation inferences. The fusion results generate a set of snapshots of inference scenarios. The snapshots are processed to generate representative future security scenarios and trace key event fragment sequences. Based on these, a set of campus security early warning and response instructions is generated. This invention can comprehensively and accurately assess the campus security situation and provide timely early warnings.

The application discloses a precise prediction and multi-scene cooperative control system for container truck arrival time sequence, and relates to the technical field of port logistics scheduling. The system comprises an event propagation module, a time delay processing module, an inter-chain constraint module, an archive self-correction module and a deduction control module. The event propagation module constructs a causal propagation network and triggers associated event concomitant time delay. The time delay processing module performs non-accumulative logical operation on multiple time delays with causal correlation or time overlap based on a time delay fusion rule set. The inter-chain constraint module maintains a set of conditional constraint relationships between event chains. The archive self-correction module maintains an individual logical archive composed of logical state bits for each transportation unit, dynamically adjusts the logical state bits and ignores misjudgment event chains. The deduction control module obtains the individual logical archive, performs conditional verification with the logical state bits in deduction, executes inter-chain constraint rules and generates a time sequence prediction result.