166 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 an event analysis method, system and device based on task generation and multiple modes, which are used for quickly and accurately mastering the development trend of an event. The method comprises the steps that structured event information is generated through multi-modal data, and an event relation is obtained; constructing an event graph; extracting a multi-modal feature, and obtaining a multi-modal emotion relationship vector by using a cross-modal attention mechanism; based on a preset graph neural network, combining the event graph to obtain an emotion feature vector; calculating a comparative learning loss value, and obtaining a target emotion feature based on the comparative learning loss value; generating a target event atlas by using a cross-modal attention mechanism; extracting relation characteristics, and combining the attention weight and a preset time sequence model to obtain an event prediction map; extracting a causal chain and reasoning, and obtaining a trigger condition and an emotion factor after reasoning; and combining the event prediction map and the target event map with the node feature sequence, the trigger condition and the emotion factor to obtain an event analysis result.

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.

Techniques are presented to provide an event review dashboard. One method relates to a user interface (UI) for the event review dashboard that provides customers with a comprehensive view of their event engagement over time. The UI includes interactive elements, information fields, and filters to assist in the analysis and review of customer actions in response to behavioral events detected in vehicles. These actions may include coaching or dismissal. The UI provides filters for date range selection, customer selection, dismissal rate visualization, and minimum event thresholds, enabling customers to assess the relevance and effectiveness of specific event types and their coaching program performance. Additionally, the UI also includes a table and panel for detailed analysis of event dismissal by type and direct access to dismissed event details. The UI is designed for ease of use and efficient analysis, with feedback mechanisms to improve the AI model learning process.

An event analysis and prediction system is disclosed that is configured to analyze at least one event data stream from a monitored system for the purpose of predicting future events in the system, e.g., for predictive maintenance of the system. The event analysis and prediction system advantageously predicts, in real-time, such failures in the monitored system that would otherwise lead to interruptions. The event analysis and prediction system advantageously leverages a novel data generation procedure that, in essence, converts the problem of failure prediction to a classification problem, which is solved using machine learning algorithms. The event analysis and prediction system is designed to work well even when there is limited labeled data available for model training.

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.

The invention discloses a multi-person interaction system and method based on a VR environment, and relates to the technical field of virtual reality, and the method comprises the steps: building a tourist database; on the basis of a player database, analyzing preference behavior style indexes of each player according to action events in the player data; acquiring a game scene corresponding to the real-time progress in the game linear process, analyzing a task type in the game scene, and evaluating a difficulty index of the game scene; according to the difficulty index of the game scene, carrying out association matching screening on the preference behavior style index of each player, and generating a game scene optimal person combination of the game process progress; according to the task optimal personnel combination of the game process progress and the current game space coordinate position of the personnel combination, a guide path scene is generated, and game interaction matching is completed. The method has the advantages that the interaction frequency in the VR game scene is improved, and the game immersion is improved.

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 relates to the technical field of sports event data analysis, and discloses a sports event multi-modal data fusion analysis method based on AI real-time feedback. The method comprises the following steps: acquiring real-time multi-modal data from multiple data sources such as video acquisition equipment and an audio sensor, preprocessing, inputting into a fusion analysis model to generate a fusion feature vector, obtaining a target feature vector through a dynamic weight distribution algorithm, inputting into a real-time feedback model to generate a real-time analysis result, and finally performing label mapping by using a classification model. Meanwhile, the fusion analysis model is trained and optimized, the real-time feedback model can adjust a strategy according to a match state, and the classification model can update a label mapping rule online. According to the method, efficient fusion analysis and real-time feedback of the multi-modal data of the sports event are realized, the accuracy and practicability of event analysis are improved, and powerful support is provided for decision making, appreciation and the like of the sports event.

The invention relates to the technical field of data processing, in particular to an intelligent post screening management system based on AI big data, which is provided with a history determination module, an event analysis module, a preliminary analysis module and a retrieval module. Extracting a key event according to an event response standard, and recording event information corresponding to the key event; according to the condition association probability of the keywords corresponding to the resume information and the job application information in the event information, determining the association relationship of the keywords, and constructing an association relationship database; analyzing the resume information uploaded by the user side, identifying the distribution characteristics of each associated keyword, calculating an associated dense representation parameter according to the distribution characteristics, and setting a distribution label for the resume information; according to the method, the job application information is adaptively retrieved according to the distribution labels, so that on the premise that the data reliability is guaranteed, the screening and matching data volume is reduced, and the screening and matching efficiency and accuracy are improved.

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.

Systems, computer program products, and methods are described herein for automated data retrieval from an integrated circuit (IC). An example system extracts, using a scan island (e.g., a partition of the IC that is isolated for data retrieval), data from a plurality of scan chains and a plurality of random-access memories (RAMs) associated with the IC in response to a trigger event associated with the IC; determines, using a data security module associated with the scan island, whether the one or more users are authorized to access the respective portions of the data; and transmits the one or more portions of the data to the one or more users upon verification of their authorization.

The present invention relates to a method for intelligent distribution of grassroots governance events based on BERT, comprising the following steps: S1: Collect grassroots event data, perform data preprocessing, and use the built-in word segmentation tool of the BERT model to perform word segmentation processing on the event description text; S2: Select the pre-trained BERT model as the basis, perform domain fine-tuning and model evaluation on it; S3: Use the fine-tuned BERT model to perform event analysis, intelligent matching and make distribution decisions on grassroots events; S4: Collect the processing results and feedback after distribution, evaluate the distribution effect, and regularly update the BERT model. The present invention has the advantages of automating the processing flow, improving the efficiency and accuracy of event processing, and reducing the burden of manual distribution.

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 human face acquisition edge terminal system comprises a video monitoring acquisition module which is responsible for acquiring video signals through a camera or other monitoring equipment; the algorithm center is responsible for coordinating and scheduling all related functions related to a face recognition algorithm; the abnormal model is used for detecting and predicting an abnormal mode in the system and ensuring that potential safety risks or system faults are found in time; the early warning analysis module is started and performs event analysis and response when the system recognizes an abnormal condition; the Web configuration platform is used for providing a graphical interface for an administrator so as to conveniently configure and manage the system; and the management platform is used as a core management platform of the system and is responsible for performing centralized management on operation, maintenance, monitoring and optimization of the whole face recognition system. The system can be applied to various personnel access scenes so as to help to solve the access problem of unfamiliar personnel and provide necessary help to prevent more serious problems.

Disclosed herein are techniques for dynamically generating replay clips for key events that occur. According to some embodiments, one technique can be implemented at a computing device, and includes the steps of (1) providing media content to at least one machine learning model to output a plurality of segments of the media content, where each segment is tagged with a respective at least one classification that describes a nature of the segment, (2) receiving a plurality of key events, and (3) for each key event of the plurality of key events: analyzing at least one segment of the plurality of segments against the key event to determine starting and ending points for a replay clip for the key event, and generating the replay clip based on (i) the media content, and (ii) the starting and ending points.

The invention provides a honey spot anti-aging method and system based on digital twinning. The method comprises the following steps: collecting historical original data of a target network and equipment; constructing an initial digital twinborn model, and training the initial twinborn model according to the historical original data to obtain a trained digital twinborn model; obtaining current original data of the target network and equipment, inputting the current original data into the current original data so as to output an event analysis result corresponding to the current original data, and generating and deploying a honey point according to the event analysis result corresponding to the current original data; judging whether the deployed honey points meet preset conditions of aging honey points or not; and if the preset condition of the aged honey point is met, performing anti-aging operation on the aged honey point. According to the invention, the flexibility, adaptability and resource utilization efficiency of a network security system can be obviously improved.

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.

Techniques are described with respect to a system, method, and computer program product for parallel reality-based content presentation. An associated method includes receiving a viewing event; analyzing the viewing event, and generating a privatized parallel reality-based credential associated with a user based on the analysis.

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 provides a soil heavy metal remediation method based on driving of a degradable nano composite material, and relates to the technical field of safety monitoring and fault diagnosis of a power system. Comprising a data acquisition subsystem, a data processing subsystem, a fire risk assessment subsystem, a multi-dimensional analysis subsystem, an early warning module, a multi-source data fusion module, an after-event analysis subsystem and a self-learning mechanism module. Abnormal conditions in line operation can be found in time, fire hazards caused by delayed detection are effectively avoided, the efficiency and timeliness of line monitoring are improved, monitored electrical data are analyzed and processed by adopting a machine learning algorithm, abnormal modes can be automatically recognized, potential causes of fire occurrence can be judged, and the method is suitable for popularization and application. The system not only depends on single threshold alarm, but also provides more accurate fire early warning through deep analysis of historical data and operation trend.

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.