17 results about "Event evolution" patented technology

This invention provides a method, apparatus, electronic device, and storage medium for complex event evolution deduction. It generates a macro-planning tree based on scene description information, containing temporal stages and macro-objectives. Under the constraint of the macro-objective at the current time step, it drives multiple agents to generate micro-level actions, thereby determining candidate states for the next moment. It extracts micro-evolutionary trends and calculates a consistency metric between these trends and the macro-objective. When the consistency metric is low, it triggers dynamic adjustments to actions or plans to obtain the next state of the target. Finally, it updates the deduction results of the complex event, constructing a complete closed-loop deduction framework that unifies macro-planning, micro-behavior generation, state transition synthesis, and value backtracking arbitration. This significantly reduces deviations caused by the emergence of multi-agent behaviors in long-chain deductions and improves the credibility, stability, and interpretability of deduction results in open environments.

The invention relates to a product security event evolution process prediction method and device, and electronic equipment. The method comprises the steps that a data set is built through multi-source heterogeneous data, and the multi-source heterogeneous data is data having an incidence relation with a product safety event; cleaning data and extracting event entities and attributes from the data set; constructing a affair graph by using a causal reasoning method of a time sequence association rule; taking entities in the affair atlas as nodes of a propagation network, and mapping causal relationships in the affair atlas as edges of the propagation network and weights on the edges; creating agent attributes and event state transition rules; and constructing a multi-agent model, and predicting the evolution process of the product security event by using the multi-agent model. According to the invention, the technical problem of lack of research on the evolution law of the product security event in the prior art is solved.

The invention belongs to the technical field of industrial safety monitoring, and particularly relates to a risk situation monitoring model, method and system. The risk situation monitoring method comprises the steps that the occurrence state of each initial event and the operation states of preventive protection measures and remission protection measures are collected; determining the access probability of a single event evolution link according to the occurrence state of the single initial event and the running state of the preventive protection measure, and determining the access probability of a single accident evolution link according to the access probability of the single event evolution link and the running state of the remission protection measure; determining the occurrence probability of the top event according to the access probability of the plurality of event evolution links; and determining the occurrence probability of a single accident consequence according to the occurrence probability of the top event and the operation state of the remission protection measure. According to the method, the occurrence probability of a certain accident consequence under the condition is calculated by considering the condition that a plurality of initial events exist, so that the method is closer to the actual industrial operation condition.

The invention relates to the technical field of flood disaster prediction, and discloses a flood disaster cascade event evolution prediction method and device, equipment and a storage medium. According to the method provided by the invention, entity nodes and relation types in a drainage basin are constructed into a knowledge graph which is updated along with time, and flood disaster cascade event evolution is reflected in real time by utilizing a node attribute dynamic updating and relation edge weight dynamic adjusting mechanism; meanwhile, time features are introduced to be embedded into knowledge graph nodes and relation edges, and time dependence represented by the nodes is enhanced; and finally, reasoning the knowledge graph by adopting a relationship evolution graph convolutional neural network, aggregating information of neighbor nodes through the graph convolutional neural network by utilizing each relationship edge, and adjusting respective propagation intensity based on a relationship type, so that a result output by the graph convolutional network not only pays attention to a structural relationship between the nodes, but also pays attention to the information of the neighbor nodes. And the edge weight is adjusted according to the change of node attributes, so that the development venation of flood disasters is accurately simulated.

The invention discloses a method and equipment for calculating endurance duration of a low-altitude aircraft and a medium. The method comprises the following steps: receiving basic event data corresponding to an event to be monitored; according to the basic event data and a historical event database, obtaining predicted event popularity data of the to-be-monitored event; according to the predicted event popularity data, obtaining an initial popularity map of the to-be-monitored event; obtaining an initial video data set of the target low-altitude aircraft; according to the initial video data set and the initial popularity map, obtaining an intermediate popularity map set of the to-be-monitored event; according to the intermediate popularity atlas, obtaining a target endurance duration of the target air aircraft; it can be known that the endurance prediction can be continuously corrected along with the progress of the event, even if the evolution of the event deviates from the initial expectation, the system can also rapidly adjust, the prediction result is always kept to be highly matched with the actual situation, and the robustness and adaptability of the method in a complex and uncertain scene are remarkably improved.

The invention relates to the technical field of network security monitoring, in particular to a block chain-based network data security monitoring system, which comprises a pre-chain event integration module, a field writing constraint module, an event evolution verification module, a structure association verification module and an evidence storage control module. According to the method, semantic-level integration and session-level continuous identification are carried out on the communication flow, so that the security event forms a record by taking complete communication semantics as a basic unit, the event field state has consistency and interpretability, the evolution sequence is corrected in the event time direction and the connection state level, the communication process is prevented from being mistakenly split, and the communication efficiency is improved. Through cooperative verification of communication object behaviors and response states, a single event structure is ensured to be complete and conform to security monitoring semantics, and a condition matching and sequence adjusting mechanism is introduced in a writing process, so that event records have verifiable evolution trajectories and non-repudiation evidence storage results, and the reliability of the event records is improved. And the credibility and audit value of network security monitoring are improved.

This invention provides an intelligent event flow control method and system applied to traffic scenarios, belonging to the field of traffic management technology. First, it receives comprehensive perception of traffic events and dynamic correlation information of the traffic network to generate traffic event flow baseline information. Based on the traffic event flow baseline information, it divides the flow into stages and reconstructs the traffic event flow links. According to the traffic event flow stage division and link reconstruction information, it constructs a distributed collaborative response rule set for control resources to generate resource collaborative response configuration information. Following the resource collaborative response configuration information, it drives a distributed control node group to execute collaborative control actions, generating collaborative execution information. Based on feedback traffic event evolution correction data and traffic network operation optimization data, iteratively updates the traffic event flow link reconstruction information and resource collaborative response configuration information to generate traffic event flow control completion information. This invention provides comprehensive perception and precise control, achieving efficient handling of traffic events and improving road network operation efficiency and safety.

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.

A computer-implemented method for generating time-based sequences of data objects using heterogeneous data processing. The system receives heterogeneous data inputs comprising time-series multivariate data representative of sequential real-world events derived from empirical observations, and static characteristic data representative of historical metrics of entities participating in the events. The time-series data is processed through a first neural network or embedding layer to generate an event evolution vector, while the static characteristic data is processed through a second neural network or embedding layer to generate an entity vector. The system combines these vectors to create a unified representation that captures both temporal dynamics and historical characteristics. From this combined vector, the method generates time-based sequences of data objects representative of metrics for the sequential events, which are stored in a data store. This approach enables improved prediction and analysis of complex sequential events by effectively integrating different types of data sources with varying temporal characteristics and semantic meanings.

This application discloses an event evolution reasoning and prediction method based on cognitive-driven intelligent generation. First, it utilizes a domain-wide language model to extract expert knowledge points, analysis processes, analytical perspectives, and logical reasoning from event research reports, constructing an interactive thought chain. It then extracts causal reasoning and constructs a causal reasoning network structure. Cognitive logical knowledge is integrated with relevant facts of the event to be predicted and embedded into a prompt template to construct a prompt model. This allows the domain-wide language model to generate event evolution reasoning and prediction results based on expert analysis dimensions and processes. Finally, conflict detection is performed on event elements through closed-loop evidence chain analysis, and credible conclusions are selected to obtain credible prediction conclusions. By constructing prompt templates to guide the large model through cognitive logical learning and combining multi-level credible verification and screening, the technical problem of excessive prediction errors caused by illusions generated by the large model is solved, improving the accuracy and credibility of event evolution prediction.

The present application relates to the technical field of public opinion monitoring, and particularly relates to a public opinion dynamic monitoring and early warning system based on text clustering analysis. In the clustering process, the semantic center of historical event clusters is introduced as a constraint condition, so that the semantic continuity of existing events can be fully considered when the public opinion text is classified, and event misjudgment or repeated generation caused by short-term expression changes can be effectively avoided. By constructing a drift suppression mechanism based on the semantic center change amplitude, the unstable event clusters are adjusted, the stability and accuracy of event evolution modeling are improved, the cross-time window event clusters are associated and determined, the stable event clusters are formed, the tracking management of public opinion events is realized, the text scale, the emotional mean, the emotional heterogeneity, the evolution characteristics of the scale and the emotion are introduced, and the low sample compensation mechanism is combined to model the public opinion event risk, so that the high-risk events are not ignored, the mature event risk is not excessively enlarged, and the timeliness and accuracy of public opinion early warning are improved.

The invention provides a network negative event detection method and device, computer equipment and a storage medium. The network negative event detection method comprises the following steps: inputting a to-be-detected text into an event embedding model to obtain a high-dimensional semantic feature; the event embedding model is obtained by performing model training according to the first text pair and the second text pair; comparing the high-dimensional semantic features with core semantic features and vocabulary features of a plurality of event nodes in an event evolution relation graph; screening out a plurality of target nodes from the event nodes according to a comparison result; combining the plurality of target nodes and other nodes connected with each target node to obtain an event logic diagram; the event logic diagram is used for representing an association relationship between the to-be-detected text and other events; and analyzing the event logic diagram to obtain an event detection result of the to-be-detected text. According to the embodiment of the invention, the detection accuracy of the network negative event can be effectively improved.

The invention discloses an interactive open type prediction method and system constructed based on a news event evolution structure, and belongs to the field of natural language processing, visualization and man-machine interaction. The method comprises the following steps: performing retrieval and duplicate removal cleaning processing on news data based on keywords input by a user and a time range; obtaining a target news set; processing the target news set by adopting a dynamic incremental clustering algorithm based on space-time attenuation and multi-view semantic fusion to obtain a plurality of event nodes representing independent news events; calculating dependency scores among nodes based on key terms and semantic vectors of event nodes, and constructing a news story tree by the event nodes according to a man-machine collaborative active ambiguity elimination and structure calibration mechanism in combination with a time sequence; performing optimization processing on the news story tree by adopting a large language model to obtain a semantic enhanced news story tree; and predicting the next event node of each branch based on the event evolution path of the semantic enhanced news story tree.

The invention relates to a text event prediction method and device based on a large language model. The method comprises the following steps: acquiring a historical text set from a news library according to a subject to be queried and an object to be queried; according to a to-be-queried subject and a to-be-queried object, obtaining a time sequence reasoning prediction result from a historical text set based on a time sequence by utilizing a large language model, capturing a historical event evolution trend, a distribution rule and causal association, and meanwhile, obtaining a prediction result; obtaining a co-time association prediction result from the historical text set based on the historical texts in the plurality of time periods by using a large language model, and mining associated details of the same-period texts; combining the time sequence reasoning prediction result and the common time correlation prediction result to obtain an initial prediction set; and performing self-correction on the initial prediction set by utilizing a large language model to obtain event prediction results of the to-be-queried subject and the to-be-queried object, thereby effectively avoiding inherent defects caused by the large language model while fully utilizing the text information, and improving the accuracy and consistency of the prediction results.

The application discloses a text semantic visualization presentation method and system based on a multimodal large model, relates to the field of text visualization presentation, and comprises the following steps: constructing a text vector sequence of an original reading text, obtaining M causal relationship events in the text vector sequence, aggregating the M causal relationship events, generating K unordered event clusters, constructing K causal event sequences, constituting an event development stage graph based on a cluster level stage number, identifying K-1 event evolution groups with an evolution stage number, determining K-2 event pairs sharing a sequence, determining a jump distance of the event pairs sharing the sequence, generating F plot units, performing predefined visualization presentation on the F plot units, and generating a semantic visualization graph of the original reading text. The application can divide the causal relationship events in the original reading text into multiple visual plot units, and provide a clear-structure visual view for text reading comprehension.

The invention provides a method, a system and equipment for analyzing an abnormal phenomenon of a semiconductor structure. The method comprises the steps of obtaining a query request in a natural language text form; performing named entity recognition on the query request to obtain entity words associated with wafer manufacturing and appearing in the query request; calling a semantic disambiguation model, and performing semantic disambiguation on the entity word according to the query request based on a context sensing mechanism to obtain a disambiguation feature corresponding to the entity word; calculating semantic similarity between the disambiguation characteristics and each pre-stored field, determining a target field according to the semantic similarity, and calling each piece of resume information associated with wafer manufacturing based on the target field; and inputting each piece of called resume information into the event evolution linked list learning model according to a time sequence, identifying resume information associated with the abnormal phenomenon, and generating an abnormal resume information sequence. According to the method, the semantic gap problem of a natural language and a database structure is effectively improved, and the fault tracing efficiency and accuracy during wafer manufacturing are greatly improved.

The invention discloses a traffic event millisecond early warning and dispersion method and system based on edge computing, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: dividing a traffic network into a plurality of edge computing domains based on traffic flow correlation, and configuring edge computing nodes, establishing an event response cooperative path of the nodes according to an inter-domain data dependency relationship, and constructing a low-delay communication topology; a plurality of edge computing nodes fuse multi-source heterogeneous sensing data of an edge computing domain to obtain a plurality of event feature vectors, an event evolution situation is obtained through topology deduction, a real-time early warning and dispersion strategy is generated through matching, and finally millisecond-level collaborative issuing and execution of the strategy are achieved in a traffic network through an RSU. According to the method, the technical problems of high time delay, poor inter-domain collaboration and inaccurate strategy matching in traditional traffic event processing are solved, and the technical effects of efficient processing of traffic event sensing data, accurate deduction of event evolution and reduction of response time delay of event early warning and dispersion strategy issuing are achieved.