34 results about "Event evolution" patented technology

The invention discloses an event entity prediction method based on perceptual contrast learning, which comprises the following steps: obtaining a time sequence knowledge graph of all events, each event being represented by a subject, a relationship, an object and a time tetrad; performing reverse operation on the tetrad to obtain a reverse tetrad, and respectively constructing a global historical graph and a local historical graph for all events in any day; then constructing an event entity prediction model, and inputting the global historical graph and the local historical graph into the event entity prediction model to train the model; and finally, predicting the event entity by using the trained event entity prediction model. According to the method, the accuracy of event entity prediction is improved through multi-tense dynamic embedding and relational graph comparative learning, the influence of time on event evolution can be more accurately captured by adopting the multi-tense dynamic embedding, and a dynamic context is provided for local historical node feature updating; the problem that the flexibility is insufficient due to the fact that event prediction excessively depends on recent events is solved.

The invention relates to the technical field of safety management, and discloses a venue safety emergency large model construction method based on artificial intelligence, comprising the following steps: step 1, deploying a composite sensing device in a preset area of a venue, the composite sensing device comprising an infrared thermal sensing detection module and a video identification module, the infrared thermal sensing detection module is used for collecting human body thermal sensing track data, and the video recognition module is used for collecting human body image data and marking behavior characteristics in the thermal sensing track data and the image data in real time. According to the method, the technical scheme of constructing the dynamic event evolution diagram is adopted, the technical effect of automatically constructing the event causal chain is achieved by mapping the personnel behavior state and the regional position into the diagram nodes and establishing the space-time correlation edge, and compared with the technical scheme of isolated analysis of surface features in the prior art, the method has the advantages that the efficiency is high; the defect that a child is misjudged due to the fact that a binding relation between a guardian moving track and ticket business cannot be associated is overcome.

The invention discloses a high-risk call identification method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a to-be-matched call text, carrying out the preprocessing of the to-be-matched call text, removing called information, and reserving calling information; constructing an event evolution chain based on the calling information, extracting cue words, and calling a large language model to generate a to-be-matched event evolution chain; converting the to-be-matched event evolution chain into a to-be-matched event evolution diagram; based on a preset event evolution graph, performing multi-stage graph analysis processing on the to-be-matched event evolution graph to obtain a key node number, a key node matching rate and key node connectivity; and determining whether the to-be-matched call text is a high-risk call based on the key node number, the key node matching rate and the key node connectivity. According to the method, a large number of positive and negative labeling samples are not needed, the manual labeling cost is reduced, the fraud sequential logic is accurately captured, the limitation of a static map is overcome, and the high-risk call recognition accuracy and practicability are improved.

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 application provides an emergency event visualization collaboration method and a terminal device, the method comprising: obtaining historical monitoring data sets of emergency events in a target area and preprocessing to generate a standardized event feature set; training a time series prediction model according to dynamic environment parameters therein to obtain an event evolution prediction model; constructing a geographic area matching model based on static geographic parameters therein to generate a visualization layer template associated with the target area. Real-time collection of current environment data streams of the target area, classification through the event evolution prediction model to obtain event classification results, and calling corresponding visualization layer templates from the geographic area matching model according to the event classification results. Fusion of diffusion paths, intensity levels and the like in the event classification results with the called visualization layer templates to generate a dynamic visualization instruction set, and rendering and outputting an emergency event evolution trend graph through an interactive interface. The application can improve the timeliness and decision reliability of emergency response.

The invention discloses a text semantic visualization presentation method and system based on a multi-modal large model, and relates to the field of text visualization presentation, and the method 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 disordered event clusters, the method comprises the following steps: constructing K causal event sequences, forming an event development stage graph based on cluster-level stage sequence numbers, identifying K-1 event evolution groups with evolution stage sequence numbers, determining K-2 event pairs of shared sequences, performing jump distance judgment on the event pairs of the shared sequences, generating F plot units, and performing predefined visual presentation on the F plot units. According to the method, the causal relationship event in the original reading text can be divided into a plurality of visual plot units, and a visual view with a clear structure is provided for text reading understanding.

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 present invention discloses a method for predicting a sow squeezing piglet event, a readable medium, and an electronic device. The method comprises: collecting sow and piglet-related data within a preset time period; and constructing a sow's behavioral corpus event chain, a stable corpus event chain, and a target corpus event chain based on the sow and piglet-related data; constructing a directed event evolution graph based on the behavioral corpus event chain, the stable corpus event chain, and the target corpus event chain, combined with adaptive self-attention time node deployment and an adaptive adjacency matrix; obtaining a preliminary probability value of the piglet squeezing event at the current moment based on the directed event evolution graph; and obtaining a final probability value of the piglet squeezing event at the current moment based on the preliminary probability value of the piglet squeezing event at the current moment, combined with the target corpus event chain. This method constructs a directed event evolution graph based on sow and piglet-related data, fully explores the relationship between sow and piglet events, and improves the accuracy of piglet squeezing event prediction.

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.

According to embodiments of the present disclosure, a method and apparatus for information presenting are provided. The method includes presenting a first event evolutionary graph corresponding to a media content. The first event evolutionary graph includes at least a first node representing a first event, a second node representing a second event, and a first directed edge representing a first event relationship between the first event and the second event. The first event, the second event, and the first event relationship are determined from the media content. The method further includes presenting a second event evolutionary graph in association with the first event evolutionary graph. The second event evolutionary graph comprises at least a third node representing a third event. A first event factor of at least one event of the first event or the second event matches a second event factor of the third event.

The application discloses a crisis event evolution context generation method and device driven by domain expert knowledge, and belongs to the field of artificial intelligence, comprising the following steps: based on the experience of domain experts, a basic data model is constructed by using a program, and the model is run based on a computer processor, which is used for supporting the whole event evolution context analysis; event detection analysis is performed based on internet data by using a computer, an event sentence is formed, and a model is run by using the computer to mine basic crisis clues and important crisis clues; meanwhile, the development trend of the crisis event is tracked; time sequence, coordination and causality analysis are performed based on the basic crisis clues, the important crisis clues and the development trend by using the computer, the connection between crisis events is strengthened, and the crisis event evolution context is generated in different dimensions of event types and target types by using the computer through crisis event classification. The application can generate a complete and rich crisis event evolution context.

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 relates to the technical field of text data mining, in particular to a military event-oriented incidence relation mining method, which comprises the following steps of: performing syntactic analysis on an original military text, identifying tactical verbs, and matching the tactical verbs with a syntactic pattern in a specific scene to generate a plurality of event tetrads; clustering each event tetrad according to a space-time closeness degree, introducing an uncertainty quantization factor, calculating a transition probability for any two state nodes in the state node set, and generating a state transition probability matrix; generating a dynamic evolution path to obtain a maximum probability event evolution path; and establishing an event causal knowledge graph based on the event tetrad, giving a query event, performing multi-hop path causal association strength calculation on the event causal knowledge graph, and finding out all association result events. According to the method, accurate extraction of military event elements can be realized, and the incidence relation mining and analysis capability for military events is improved.

This disclosure provides a method, apparatus, device, and storage medium for generating an event evolution relationship tree, which can be applied to the field of natural language processing technology. The method includes: processing a text dataset to be processed based on a word frequency-inverse document frequency algorithm to obtain a first text feature matrix; obtaining a density boundary threshold based on the Euclidean distance between each element in the first text feature matrix and other elements; clustering the elements in the first text feature matrix based on the density boundary threshold to obtain multiple target text datasets corresponding to multiple topic events; processing the target text dataset corresponding to each topic event based on a hidden Dirichlet distribution algorithm to obtain multiple related sub-events; and generating an event evolution relationship tree corresponding to each topic event based on the occurrence times of the multiple related sub-events.

The application discloses a high-risk call identification method and device, computer equipment and a storage medium. The method comprises the following steps: obtaining a to-be-matched call text, preprocessing the to-be-matched call text, eliminating called information and retaining calling information; constructing an event evolution chain extraction prompt word based on the calling information, calling a large language model to generate a to-be-matched event evolution chain; converting the to-be-matched event evolution chain into a to-be-matched event evolution graph; performing multi-stage graph analysis processing on the to-be-matched event evolution graph based on a preset event evolution graph atlas, obtaining a key node number, a key node matching rate and key node connectivity; and determining whether the to-be-matched call text is a high-risk call based on the key node number, the key node matching rate and the key node connectivity. The application does not require a large number of positive and negative labeled samples, reduces the cost of manual labeling, accurately captures the timing logic of fraud tactics, overcomes the limitations of static atlas, and improves the accuracy and practicality of high-risk call identification.

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 discloses an event clue dynamic study and judgment method based on a large model agent. The method comprises the steps that multi-modal event clues generated by the large model agent are acquired, and unified structured data input is formed; event clue key semantic feature vectors are extracted and coded into initial quantum state representation; executing quantum entanglement evolution by using a parameterized quantum gate to calculate an attention weight matrix; carrying out quantum characteristic field modeling and defining a dynamic clue potential energy function; determining a clue convergent point and an event causal path to construct an event association graph; the event causal correlation degree is evaluated in real time through quantum probabilistic reasoning, and an event evolution path is obtained; and adaptively adjusting the attention weight matrix based on the semantic feature similarity, carrying out iterative calculation until convergence, and outputting a final event clue evolution path. According to the method, the dynamic association study and judgment precision and real-time performance of heterogeneous event clues are improved.

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 an event relation reasoning evolution method and device based on a large language model, equipment and a storage medium. The method extracts event trigger words by constructing event trigger word extraction instruction-label supervision data, and realizes event trigger word extraction by using a large language model and fine tuning. A multi-task instruction is constructed to unify the event relation reasoning task. A corresponding soft prompt is learned for each fine-grained event relation reasoning task, and a collaborative fusion mechanism is designed to improve the event relation reasoning performance. Based on the event relation obtained by reasoning, an event relation chain is generated according to the instruction requirement and the context learning. The world knowledge of the large language model is used to design event evolution instructions and context learning, realize event evolution, predict the development trend of the event, and generate an event evolution chain. According to the result of event evolution, risk assessment is performed to generate an event risk chain. The application enhances the generalization ability of the large language model in event relation reasoning, can realize various event relation reasoning tasks according to different requirement instructions, and improves the accuracy of event relation reasoning.

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 discloses a three-way one-brain robot cooperative control architecture system and a control method, and belongs to the technical field of robot control. The system is connected with a plurality of robots, a multi-protocol access layer receives data packets of a plurality of data nodes, analyzes each data packet, adds an extension head, and generates a time sequence enhanced data frame corresponding to the data packet; the unified data platform layer calls the time sequence enhanced data frame, analyzes the extension header to obtain an event sequence of the time sequence enhanced data frame, and generates a plurality of core factor matrixes according to the event sequence; aggregating the plurality of core factor matrixes to generate a global data feature map; the intelligent center brain layer carries out task planning on a plurality of robots based on the global data feature map to generate a task chain; and the multi-robot execution layer issues the task instruction corresponding to the task chain to the corresponding robot, and receives state information that the robot executes the task in response to the task instruction. According to the method, the event evolution relationship in the time dimension can be effectively fused.

The present application relates to a kind of multi-intervention subject prediction method based on event evolution knowledge, belong to artificial intelligence and natural language processing technical field.This method utilizes the ability of large language model in identifying the key node structure information in history chain, obtains event evolution information transition matrix by tracing similar event cases of evolution trend, including event predicate transition matrix, event subject-predicate association matrix, event object-predicate association matrix, obtains regularized event case evolution information, as the event chain hierarchical support of model generation prediction entity.Through obtaining the dependency relationship between entity and predicate in data set, the history entity highly relevant with the event predicate to be completed is identified, the core role of predicate in event triple is played, so as to more accurately predict the missing entity in event chain.