103 results about "Event analysis" patented technology

The invention relates to a cross-modal knowledge reasoning method based on a multi-modal large model. In a cross-modal knowledge reasoning process, an existing model is usually limited by single-modal information extraction and shallow feature fusion, so that deep semantic association among data such as texts, images and videos is difficult to fully capture. In order to solve the problem, the invention provides a model for fusing multi-modal information such as texts, images, videos, documents and the like, and processing of multi-modal data is converted into unified feature extraction, interaction and deep reasoning tasks by fully utilizing a supervision fine tuning strategy, a self-adaptive attention mechanism and a cross-language processing technology. The model adopts a modular design, integrates multi-source data complementary analysis, spatial-temporal feature modeling and emotional semantic analysis, and realizes multi-modal collaborative interaction, dynamic scene understanding, long video key event analysis and man-machine co-emotional response. Through sufficient training, the multi-modal large model shows excellent logical reasoning ability and emotion understanding ability in a complex cognitive task, and a brand new solution is provided for efficient extraction, deep semantic analysis and intelligent response of cross-modal information.

The invention discloses a dynamic vehicle scheduling intelligent decision-making system based on big data, and relates to the technical field of intelligent traffic and logistics scheduling, and the system comprises a data collection module, an event analysis module, a knowledge graph construction module, a hypergraph modeling module, a constraint processing engine, an optimization decision-making module, a strategy verification module and an output interaction module. The system has the advantages that multi-source data such as government announcement texts and social media information are acquired in real time through the data acquisition module, event key information is extracted through the event analysis module by utilizing a natural language processing technology, and an event knowledge graph of an incidence relation is constructed through the knowledge graph construction module; the strategy verification module simulates and verifies the strategy effect through the digital twinning technology and iteratively optimizes the strategy effect, the whole process does not need manual intervention to adjust rules, the limitation that a traditional system depends on manual processing is broken through, and the problems that dynamic strategy adjustment is time-consuming, labor-consuming and error-prone in an extreme scene are solved.

The invention relates to the technical field of intelligent decision making, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-modal data-based event analysis method, which comprises the following steps of: obtaining attribute data, monitoring data, environment data and biological characteristic data of a user, and performing standardization processing on the attribute data, the monitoring data, the environment data and the biological characteristic data; combining semantic analysis and structured fusion of the text records to form a unified input data set; and performing multi-modal fusion modeling on the unified input data set by using an intelligent decision model, generating a potential event analysis result of an individual state, generating a visual report containing potential event levels, main influence factors and intervention suggestions based on the result, and finally sending the report to a user terminal. According to the method, the multi-source structured data and the multi-source unstructured data are integrated, and the intelligent decision-making model is introduced to realize feature level fusion and semantic enhancement modeling, so that the accuracy and timeliness of individual state analysis are effectively improved, and the personalized expression of an analysis result is enhanced.

The invention discloses a network security event analysis method and system based on multi-agent decision, and a storage medium, and the method mainly comprises the steps: carrying out the deep investigation and judgment through a deep decision agent based on a reinforcement learning network after an abnormal alarm of a monitoring platform is received, triggering the deep investigation when the calculated deep investigation probability is greater than a threshold value, and carrying out the early warning of the network security event. Calling an LLM-based sub-expert agent to carry out deep analysis aiming at specific features; and according to the analysis result of the sub-expert agent and the risk information provided by the risk information platform, the LLM-based comprehensive risk decision assessment agent performs conjoint analysis decision to generate a network security situation comprehensive assessment report. According to the method, the processing analysis capability of massive and heterogeneous security data and the recognition capability of complex attack paths are remarkably improved, and the discovery capability of unknown threats and the intelligent level of security operation and maintenance are effectively enhanced by introducing reinforcement learning optimization decision and the deep analysis capability of LLM.

Embodiments manage context repositories in computing environments to enhance automated security analysis. Embodiments obtain context records containing supplemental information associated with security events and integrates them into prompts for large language models (LLMs) to generate severity scores for event classification. Embodiments apply criteria to invalidate outdated or unreliable context records based on age, source reliability, and usage frequency, then modifies prompts and repositories accordingly. Enhanced prompts incorporate context record summaries and entity relationship mappings to improve subsequent event analysis. Embodiments dynamically evaluates context records through quality filters, consolidates duplicates, and maintains audit trails with provenance tracking. User interfaces are dynamically transformed based on telemetry metrics and user feedback to optimize analyst workflows. Embodiments enable organizations to maintain curated, high-quality context repositories that continuously improve AI-assisted security analysis while reducing false positives and enhancing incident response effectiveness.

The invention discloses a financial abnormal event analysis method, electronic equipment, a medium and a product. The method comprises the steps of obtaining at least one financial clue event, and performing structured element extraction and standardization processing on each financial clue event to obtain a standardized event behavior sequence; performing semantic feature and behavior pattern analysis based on the normalized event behavior sequence, performing event reasoning rule recall in combination with a pre-constructed event reasoning library, and determining a recalled rule set; generating an abnormal event analysis execution plan based on at least one inference rule in the rule set; querying original data in the data system according to an inference rule included in the abnormal event analysis execution plan, generating a data view, executing the abnormal event analysis execution plan based on the data view, and generating an inference execution result set and execution path information; and an event reasoning analysis result is generated according to the reasoning execution result set and the execution path information, and the problems of complex abnormal event analysis modeling, low interpretability and the like are solved.

The invention discloses a power grid operation situation analysis system combined with a knowledge graph, which relates to the technical field of intelligent power grids and comprises a power grid data acquisition module, a power grid feature extraction module, a monitoring information event analysis module, a power grid graph construction module and a power grid event intelligent research and judgment module. The power grid data acquisition module acquires power grid data, the power grid feature extraction module extracts features of the power grid data, the monitoring information event analysis module outputs an overhaul label, a fault label, an associated equipment list and a fault list, and the power grid graph construction module generates a power grid knowledge graph and outputs a fault propagation path and an out-of-limit threshold. And the power grid event intelligent research and judgment module pushes abnormal data and fault information. According to the method, automatic cleaning of massive monitoring signals, intelligent fault research and judgment and cross-station propagation path analysis are realized through natural language processing and machine learning technologies, manual intervention is reduced, the fault recognition accuracy is improved, graded alarms are pushed in real time, and the power grid situation awareness and emergency disposal efficiency is remarkably improved.

The invention provides a complex event analysis processing method and device for a multi-agent cooperative game, relates to the technical field of artificial intelligence, and aims at solving the technical problems that an existing complex event analysis method is insufficient in the aspects of event element extraction accuracy, a verification feedback mechanism and a multi-agent cooperative game strategy. The method comprises the following steps: acquiring to-be-processed complex event data; extracting key element features and interrelation features in the complex event data by using a first agent to obtain structured event information; performing verification processing on the context semantic relationship and the logic relationship of the structured event information by utilizing a second intelligent agent, and generating a feedback instruction according to a verification result; according to the feedback instruction, guiding the first intelligent agent to carry out correction processing on the structured event information; and repeating the cooperative game process of the first agent and the second agent until the first agent and the second agent reach a consensus, and outputting corresponding structured event information.

A data platform monitors a compute environment by performing multi-stage heuristic analysis of event data representing a plurality of events occurring within the environment. The platform utilizes multiple event analyzers, each configured according to a distinct analysis heuristic, to evaluate different subsets of the event data and generate corresponding output signals. A higher-level event analyzer applies a further heuristic to the multiple output signals to generate a composite alert signal, indicating whether the combination of analyzed events collectively represents a security intrusion or other anomalous condition of sufficient severity to warrant alerting. Based on the composite alert signal, the platform performs an alert-based operation, such as generating a user-facing alert, initiating an automated mitigation, or updating a contextual model of system behavior. By combining the analytical outputs of heterogeneous heuristics, the disclosed architecture enhances the accuracy and contextual relevance of automated intrusion detection within complex computing environments.

The invention discloses an event analysis model training method and system based on progressive multi-view exploration, and the method comprises the steps: constructing a multi-view sample generation engine, and generating a multi-view answer set for the same event sample through a differential prompt template by using a heterogeneous knowledge base and a multi-source event corpus; performing semantic deduplication and consistency verification on the multi-view answer set, calculating semantic similarity between answers based on a pre-training language model, performing clustering deduplication on the answers with the similarity exceeding a threshold value, and verifying effectiveness and timeliness of reference information through a knowledge graph; establishing a difficulty quantitative evaluation matrix, performing objective and subjective fusion difficulty grading on the training samples, and calculating a comprehensive difficulty score by combining the two results; and dividing the training sample into a plurality of difficulty grades according to the comprehensive difficulty score, and executing progressive staged training based on a grading result, so that the model is gradually transited from a low-difficulty sample to a high-difficulty sample, and multi-view fusion learning and progressive ability optimization are completed.

The invention discloses an intelligent sports event decision-making system based on causal-driven multi-modal fusion and spatio-temporal dynamic reasoning. The system comprises a multi-modal causal data acquisition and preprocessing module; a causal-oriented multi-modal knowledge graph construction and updating module, wherein the causal-oriented multi-modal knowledge graph construction and updating module is provided with a causal structure learning algorithm and an online learning framework; a space-time perception hybrid agent module; the causal constrained dynamic decision optimization module is provided with a deep reinforcement learning algorithm; and the interpretability analysis and visualization module is used for receiving the decision strategy sent by the space-time perception hybrid agent module, generating decision explanation according to the decision strategy and visually presenting the decision explanation. The objective of the invention is to construct an intelligent system capable of providing high-precision, interpretable and adaptive decision support by integrating causal reasoning, multi-modal learning and reinforcement learning, so as to significantly improve scientificity and effectiveness of sports event analysis and decision.

The invention relates to the technical field of health information processing, and particularly discloses a user health data intelligent acquisition and management system based on data analysis, which performs event analysis and standardized coding on original health data to generate a user-level standardized event sequence; automatically constructing a causal structure network representing the causal relationship between the event types by analyzing the time delay dependence between the event types and executing a conditional independence test; further, instantiating a specific event into a node according to the causal network and a personal event sequence, establishing an edge with tense and causal composite attributes, and constructing a personalized event association network; performing data completion and prediction through multi-hop attribute propagation and multi-path evidence aggregation based on the network, and outputting a result with confidence evaluation; and dynamically optimizing the network by using a high-confidence result, and analyzing an influence propagation path of a key event to generate a personalized health analysis conclusion.

The invention belongs to the technical field of network security, and particularly relates to a highway toll threat event prediction and traceability system based on deep learning, which comprises a highway networked toll system. The network safety work management platform is used for forming a planned management strategy by combing the existing safety management work flow of the highway networked toll collection system; the network security operation and maintenance service risk management system is used for constructing an expressway network toll collection system operation and maintenance service security risk management system; and the abnormal event analysis system is used for constructing a visual safe brain for abnormal event analysis and prediction and has an event tracing function. According to the invention, an efficient and visual management platform is created to deal with emergency situations and task changes, construct a security risk management system, ensure data security and integrity and construct a security brain for anomaly analysis, so that a manager can monitor the network security state in real time and make a response quickly.

The invention relates to the technical field of city management, in particular to a smart city evaluation AI analysis system and an evaluation method. Comprising a data acquisition processing module, a data storage management module, a data analysis mining module, an artificial intelligence machine learning module, a visual report generation module, a public service optimization module, a behavior event analysis module and an environmental health monitoring module. The data acquisition and processing module is used for data acquisition and data cleaning; according to the invention, the system achieves the real-time monitoring and decision support through automatic data collection and analysis, can automatically collect and analyze data in multiple aspects, achieves the real-time monitoring and evaluation of the urban operation state, provides timely and accurate decision support for managers, and improves the management efficiency. The effect of comprehensively reflecting the city happiness is achieved through multi-dimensional comprehensive evaluation, the system adopts a multi-dimensional comprehensive evaluation method, comprehensive evaluation is carried out from multiple aspects of environment, traffic, safety, health, education and the like, and the happiness level of city residents is comprehensively reflected.

The invention relates to the technical field of event analysis, and discloses a similar event analysis method based on subgraph feature reconstruction and multi-modal fusion, which comprises the following steps: generating an event knowledge graph according to event text data, calculating the extended Jaccard similarity of the event knowledge graph, screening associated event node pairs through a self-adaptive threshold value and adding hidden edges, generating a reconstructed implicit event knowledge graph; performing global bidirectional breadth-first search on the event knowledge graph to obtain a candidate extended event node set, dynamically screening extended event nodes through attention weight and semantic matching score, and evaluating and merging extended event knowledge graphs based on path importance; generating multi-granularity embedding features for the event knowledge graph, the hidden event knowledge graph and the extended event knowledge graph through a graph convolutional network; and carrying out weighted aggregation on the multi-granularity embedded features, carrying out high-order interaction through a neural tensor network, and carrying out dimensionality reduction through a multi-layer perceptron to output an event similarity score. According to the invention, accurate calculation of event similarity is realized.

The invention discloses an alarm condition space-time positioning method and system based on historical alarm condition data governance, and the method comprises the steps: obtaining historical data, and carrying out the preprocessing of the historical data; performing text analysis and extraction on the preprocessed historical data, and extracting to-be-treated alarm address data from the preprocessed historical data; constructing a multi-task combined learning model based on Transform, and training the multi-task combined learning model by using a training set containing standard alarm data; inputting to-be-governed alarm address data into the trained multi-task joint learning model to obtain governed alarm address data; and extracting alarm condition attribute information according to the treated alarm condition address data, wherein the alarm condition attribute information comprises time attribute information and space attribute information. According to the method, the alarm data are governed by using the Transform-based multi-task joint learning model, the governed standard alarm data are obtained, and a data basis is provided for alarm event analysis based on time-space dimensions.

The invention relates to a cross-language news core event analysis method based on graph knowledge distillation, and belongs to the technical field of natural language processing. Due to scarcity of news core event analysis annotation data in a low-resource language scene, discourse news event global features and inter-event association features are difficult to mine and model. Aiming at the problem, a cross-language news core event analysis method based on graph knowledge distillation is provided; according to the method, an event hypergraph with event elements as vertexes is constructed to represent event global features, and an event line graph with events as vertexes is constructed to represent correlation features between the events; and event feature knowledge and relation knowledge in a rich resource language are migrated to a low resource language by utilizing cross-language graph knowledge distillation, so that the news core event analysis effect of the low resource language is improved. Experimental results show that the method can effectively mine and model global features of chapter news events and correlation features between the events, and effectively relieve the problem of scarcity of news core event analysis annotation data in a low-resource language scene.

The invention provides a multi-modal data real-time analysis and feedback method and system, and the method comprises the steps: collecting a video frame and an audio frame, reading a count value of the same monotonic timer when the collection of the video frame and the audio frame is completed, and generating an audio and video sequence; calculating the behavior popularity of each region in each time slice based on the sequence, and generating a region set for multi-modal event analysis in combination with a preset threshold and a quantity upper limit; scheduling the corresponding video sub-blocks to a visual computing power unit for target positioning and action classification, and executing voice activity detection and keyword category judgment on the time-aligned audio clips at the same time; visual and audio results are fused, and a structured event sequence is generated according to time slice and region compression; and constructing a classroom teaching chain through the sequence, identifying a key event, and finally generating a teaching event description. According to the invention, under the condition of domestic chip combination, cost, time delay, multi-modal consistency and data security controllability are considered, and real-time perception and feedback of teaching behaviors are effectively supported.

This invention relates to the field of information extraction technology, and discloses a method and system for extracting elements from long-text news events based on multi-agent collaboration. The method includes the following steps: constructing a large language model multi-agent system architecture; a central coordinating agent receiving unstructured news text; a text segmentation agent outputting logical units and their time descriptions; an event extraction agent extracting the event subject, action, and object, and completing time and entity role information; a scene matching agent determining the fine-grained news scene category to which it belongs; an information extraction agent generating structured event records; and a central coordinating agent performing quality review and anomaly handling on each step of the output to ensure the integrity and consistency of the output data. This invention, through relationship building and contextual analysis agents, can construct static relationships between event subjects based on structured event information, and summarize event situation changes based on a timeline, improving the feasibility and visualization of the system's news event analysis.

The invention provides an event analysis method and related equipment, relates to the field of artificial intelligence, and is used for automatically analyzing a reason for an alarm event during safety alarm analysis so as to help a program to understand an analysis result and facilitate operation and maintenance personnel to locate the reason for system abnormality. In the method, a first device obtains a plurality of system events; the first device analyzes the plurality of system events through a language model to obtain an analysis result, the analysis result comprises whether each system event needs to be alarmed or a reason for alarming the system event needing to be alarmed, a plurality of strategies are configured in the language model, and the system events need to be alarmed. The multiple strategies are used for generating multiple cue words of the language model.

The present invention relates to a method and a system configured for analyzing computer events in the context of cybersecurity. In particular, the present invention relates to event collection, the normalization of elements specific to these events, and the calculation of an overall criticality score based on a similarity analysis with a database of reference events. Fig. 1

The invention relates to the technical field of power grid equipment operation state monitoring, in particular to a power grid main and auxiliary equipment real-time alarm information intelligent confirmation method, which comprises the following steps of S1, acquiring alarm information of main and auxiliary equipment in real time, including acquiring an alarm interface image through an image acquisition module, and extracting alarm text content by adopting an image recognition technology; s2, event analysis is carried out on the extracted alarm information, an event analysis result is generated in combination with a preset defect knowledge base and a deep learning model, and the event analysis result at least comprises event categories, priorities and repair suggestions; s3, alarm confirmation operation is automatically executed according to the priorities and the event types of the event analysis results, system confirmation is directly conducted on low-priority events, and a manual intervention process is triggered for high-priority events.

The invention discloses an event inducement discovery system and method fusing deep reasoning and a graph neural network, and relates to the field of event analysis and processing. The system comprises: a knowledge base construction module, which is based on a news event database, constructs an event knowledge base through operations such as text partitioning, vector embedding, database foreign key association and the like, and is used for retrieval enhancement generation; the deep reasoning module is used for autonomously reasoning and collecting potential effective information of event inducements through operations such as retrieval statement generation, knowledge base retrieval, effective information extraction and sufficiency test by taking a large reasoning model as a base; the graph neural network module is based on the event relation graph, and assists in reasoning to obtain potential causal relations in the interlaced complex event text; the inducement mining module is used for mining event inducements from related materials by fully utilizing the reasoning capability of a large reasoning model through a professional cue word template; the invention further provides a method. The problems that event relations are complex and interlaced, and event inducements are difficult to analyze are solved.

The invention discloses an event simulation display method and device of an MEMS test system and a medium. The method comprises the steps that log data are collected from at least one subsystem; performing timestamp synchronization on each log data, determining at least one event arranged along a timeline according to each log data, and generating an event sequence; determining a target event from the event sequence; and based on the user interface state model, according to the log data corresponding to the target event, reconstructing the user interface when the target event occurs, and displaying the user interface, thereby solving the problem that the isolation state of the multi-source log is not synchronous with time, automatically establishing an event causal chain, reducing the cost and error rate of manual data association, and improving the user experience. The event analysis and fault diagnosis period is shortened, the shutdown loss of equipment is reduced, the complete operation context of the target event can be restored in a visual mode, the technical threshold of system operation and maintenance is reduced, and the accuracy, usability and universality of root cause analysis are improved.

This invention discloses a method, device, and system for automatically generating power outage event analysis reports in distribution networks using an artificial intelligence large-scale model. The method includes: automatically acquiring multi-source data (unstructured data) in response to a power outage event in the distribution network; using artificial intelligence technology to extract and clean the acquired multi-source data into a structured form, creating a unified event data model; performing FA action inversion, terminal status analysis, network weak point diagnosis, and emergency repair process inversion based on the line topology and the event data model to generate analysis results; and inputting the analysis results into a pre-trained power large-scale language model for root cause analysis, problem exposure summary, and rectification measure recommendation to automatically generate a structured fault analysis report. This invention can completely solve the problems of difficult data collection, low analysis efficiency, strong report subjectivity, and poor implementation of rectification plans in existing technologies.

The application belongs to the technical field of time series data analysis, and discloses an online time series event analysis method and system based on heterogeneous memory fusion, which comprises the following steps: acquiring streaming time series data and preprocessing, extracting features from the preprocessed data to obtain time series feature sequences; long-term feature memory, event state probability memory and event-level semantic memory are constructed and fused to form a fusion memory; an event query representation is initialized, and the event query representation is enhanced through a short-term memory and a fusion memory in turn to generate a final event query representation; based on the final event query representation, an instantaneous event state and a candidate event instance at the current time are generated, the candidate event instance is de-redundant and cross-time deduplication processed, and a final event set is output. By constructing and fusing heterogeneous historical memories, the application realizes effective utilization of multi-granularity time series knowledge, thereby significantly improving the accuracy, stability and real-time performance of online event analysis under strict causal constraints.

The invention discloses a virtual resource allocation method and device and electronic equipment. The method comprises the following steps: receiving a virtual resource allocation request for a target object; in response to the virtual resource allocation request, dynamic interaction data, related to the credit rating, of the target object in the virtual space is determined, and the dynamic interaction data carries a target label; the dynamic interaction data carrying the target label is input into a behavior event analysis model, a first credit level corresponding to the credit level is obtained, a path selection layer of the behavior event analysis model comprises M model paths, and the M model paths correspond to different network architectures; and determining a virtual resource allocated to the target object according to the first credit level. According to the method and the device, the technical problem that the credit rating of the user is difficult to accurately determine because the mode of determining the credit rating of the user is limited in related technologies is solved.

The invention provides a large model event analysis enhancement method based on multi-dimensional retrieval enhancement generation, and relates to the technical field of artificial intelligence and information analysis crossing. After to-be-retrieved information is obtained, on one hand, a network retrieval result is inquired on a network, an image-text consistency evaluation result and a semantic authenticity evaluation result are obtained according to the network retrieval result, and first corpus data are obtained by integrating the network retrieval result, the image-text consistency evaluation result and the semantic authenticity evaluation result; on the other hand, the to-be-retrieved information generates retrieval vectors in multiple dimensions, a knowledge retrieval result of each dimension is obtained through retrieval in a corresponding preset knowledge database, and the knowledge retrieval results of multiple dimensions are integrated to obtain second corpus data. And combining the first corpus data and the second corpus data to obtain an enhanced corpus, and taking the enhanced corpus as semantic data to be analyzed by a large model. When a large model executes an analysis task by using the semantic data provided by the scheme of the invention, an analysis result with higher credibility can be obtained.

The application relates to the technical field of data life cycle management, and discloses a dynamic self-adaptive data life cycle management system and method, which comprises the following steps: logically binding a data object with a business genealogy log of the data object, calculating a genealogy entropy value in a non-semantic counting manner based on the log, and directly determining a life cycle strategy of the data object in a preset strategy mapping relationship according to the entropy value. The application establishes a data management path which does not depend on an external complex rule engine for event analysis, so that the management strategy can be endogenously and deterministically generated from the business history record of the data itself, and meanwhile, the application can also realize the automatic rollback of the control level of the data which is placed in a high-level control due to an accidental high-risk event after the risk of the data has actually dissipated, thereby reducing the coupling degree and maintenance complexity of the whole management system.

The invention discloses a classroom event analysis method and an analysis model optimization method and system, and relates to the technical field of edge computing. The method comprises the following steps: acquiring real-time classroom data acquired by classroom sensing equipment by edge equipment, and storing the real-time classroom data into an offline classroom file; detecting a real-time event stream in the real-time classroom data through a preset lightweight detection model; the lightweight detection model is optimized through an off-line event stream of an off-line classroom file after class, and the off-line event stream is obtained by analyzing the off-line classroom file through a high-precision detection model; and sending the real-time event stream to visual equipment, so that the visual equipment displays the real-time event stream in real time. Through the above technical means, the lightweight detection model rapidly analyzes the real-time classroom data to obtain a high-timeliness real-time event stream, the after-class model optimization can continuously improve the detection precision of the lightweight detection model, the timeliness and precision of the real-time event stream are considered, and instant and reliable decision support is provided for current teaching.