46 results about "Event analysis" patented technology

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.

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.

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 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 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 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 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 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.

The invention provides a training method and an analysis method of a financial event analysis model, equipment and a storage medium. According to the financial event analysis model training method provided by the invention, an original supervision data set is automatically enhanced by introducing a knowledge graph and a thinking chain prompt template, a high-quality instruction fine tuning data set containing clear reasoning logic is generated, and then fine tuning training of a base large language model is guided; according to the method, the technical problem that domain knowledge is injected into a shallow layer and the model reasoning process is uncontrollable is effectively solved, and the performance of the financial event analysis model obtained through final training in the aspects of specialty, accuracy, interpretability and output structuring degree is remarkably improved.

The invention discloses a multi-agent cooperative urban event handling system based on a large model, relates to the technical field of artificial intelligence, and solves the problems of tedious event task handling process and repeated dispatching of event tasks. The urban event disposal system comprises an intelligent agent module and a collaboration module. The agent module comprises an event reporting agent, an event acceptance agent, an event distribution agent, an event handling agent, an event checking agent and an event analysis agent; performing pre-recommendation of event handling departments on the event semantic vectors through a distribution reasoning model to generate a candidate department list; determining a business property, a liability list and an event responsible department of the city event through a dynamic discrimination algorithm; checking a problem area of an event report image through an automatic review model, and calculating probability values of event disposal completion and case settlement completion so as to assist a checker in checking an event state; the event task disposal process is simplified, and the situation of repeated dispatch of the event tasks is reduced.

The invention provides a method and device for creating an intelligent decision data set based on log data, and relates to the technical field of cloud computing, cluster management, big data processing and artificial intelligence, and the method comprises the steps: collecting original log data from a server and a cluster, and achieving the collection and preprocessing of the log data; key information is extracted from the preprocessed log data, and log event analysis and structured processing are realized based on a self-attention mechanism; high-quality features are extracted and constructed from structured log data, and feature engineering and data enhancement are achieved; and combining the enhanced feature set with a predefined decision target to generate an intelligent decision data set for model training and evaluation, thereby realizing construction and evaluation of the intelligent decision data set. According to the method, high-quality data support is provided for the intelligent decision model, the system resource demand, the task execution time and the failure risk can be effectively predicted, the resource allocation efficiency is greatly optimized, and a solid foundation is provided for large-scale computing system resource management and scheduling.

This invention provides a method and system for understanding long-form traffic videos that combines generation and cognition. Through latent semantic modeling and a perceptual reflection loop mechanism, it achieves continuous cognition of traffic scenes over long time scales, avoiding reliance on only local fragment information and improving the overall understanding of long-form traffic videos. By introducing traffic scene generation and future state prediction, the system can simultaneously consider past and potential states, improving its ability to judge complex traffic problems and enhancing its cognitive and reasoning abilities regarding future traffic states. By unifying the representation of historical perception information and future generated information, information fragmentation is reduced, improving the consistency and stability of cognitive reasoning. This solution integrates historical perception and future generation of traffic scenes into a perceptual reflection loop, achieving a forward-looking and holistic cognitive understanding of long-form traffic videos, providing a sound theoretical foundation for intelligent traffic monitoring and event analysis systems.

Aspects of the disclosure relate to computing platforms that utilize improved natural language processing techniques for claims processing. A computing platform may receive an audio file and determine one or more utterance segments based on the audio file. The computing platform may generate and send one or more audio chunks based on the one or more utterance segments to an audio transcription system. The computing platform may receive one or more text segments in response and may generate an audio transcription file accordingly. The computing platform may assign a category to each word in the audio transcription file, and may send user interface information to a user device along with one or more commands directing the user device to generate a user interface based on the user interface information, which may cause the user device to display the user interface.

Techniques for managing real-time business event analysis and monitoring are provided. In some examples, a logical request may be received at a business intelligence server configured with a continuous query service. Additionally, the continuous query service may be configured to translate the logical request into a physical request. Further, business intelligence information may be provided based at least in part on querying against an event data stream with a continuous query configured based at least in part on the physical request translated by the continuous query service.

The application discloses a kind of smart watch health data monitoring methods based on machine learning, including the following steps: collecting health data in smart watch end, generates health data time sequence;Collecting behavior events, generate behavior event sequence;Select health data in pre-window event analysis time, generate event response trajectory set;Extract response start time offset, response duration and response change rate, form historical response hysteresis characteristic sequence;Improved Jamba is constructed, introduce time state evolution constraint mechanism, generate historical hysteresis structure representation;Real-time response hysteresis characteristic sequence is input into improved Jamba, and real-time hysteresis structure representation result is generated;Determine based on real-time hysteresis structure representation result, output health monitoring result.The application adopts improved Jamba two-way time modeling method, realizes health data structural hysteresis determination, with the advantages of accurate determination, response stability and strong adaptability.

A method includes that the data storage path information used by a data object in a target query time period is determined, that a target operation object through which the data object written from a source end to a destination end passes is determined from the data storage path information, and that a storage situation of the data object in the target query time period is predicted and determined by performing a storage-affecting event analysis on the target operation object.

The invention discloses a supervision event analysis method and equipment based on a large language model, and a medium, mainly relates to the technical field of supervision event analysis, and is used for solving the problems of insufficient event detail identification and lack of intelligentization in the existing scheme. Comprising the following steps: acquiring a context vector of a current operation event, and acquiring abnormal knowledge graph data corresponding to the current operation event from a preset detail abnormal graph; inputting the context vector and the abnormal knowledge graph into a trained large language model, and outputting an event description text; splicing the abnormal knowledge graph data, the external knowledge information and the event tetrad as processing data; obtaining semantic association features between the processing data and a preset historical event sequence; and according to the semantic association feature of the current operation event, extracting a historical processing case with the highest similarity from a historical event initial database, and further determining a processing suggestion corresponding to the historical processing case as a processing suggestion corresponding to the current operation event.

This invention relates to a runtime status recording and event analysis system based on an embedded operating system, belonging to the field of embedded system testing. This invention achieves the recording and acquisition of runtime status for control events, task events, semaphore events, message queue events, timer events, and other extended and user-defined events. It also enables the uploading and analysis of the acquired events, ultimately displaying the event information and the event interaction sequence during the embedded system application's execution graphically in the development environment interface. Furthermore, this invention employs methods such as controlling the amount of data collected, combining dynamic and static buffer allocation, configurable upload tasks, and timestamp-driven drivers to reduce external interference, effectively ensuring the real-time requirements of the embedded operating system.

A system for repeatable and flexible predictive analysis of very large data sets using a distributed computational graph using a domain specific language for pipeline and resource declaration has been developed. Data receipt software receives data from one or more sources. In a declared data pathway, data formalization software formats input data for storage, enrichment, or continued processing. An event analysis server inspects stored data for trends, situations, or knowledge. Aggregated data is passed to event message store and data stream handler software. Transformation pipeline software stores, triggers, executes and monitors declared transformation steps and pipelines to manipulate and act on bulk data and data streams, provides results and enables dynamic model and routine re-training and learning.

The invention belongs to the technical field of network user behavior analysis, and particularly relates to a user behavior analysis system and method for optimizing a click event processing mode. The system comprises a front-end data automatic acquisition module, a thermodynamic diagram display module, a visual selection and classification module, a data classification and dynamic association module, a label management module and an event analysis module. The method comprises the steps of automatically collecting all click event data of a user and generating an operation thermodynamic diagram; business personnel visually select page areas through a thermodynamic diagram interface and mark functional labels; and according to the coordinate range and DOM features of the selected area, historical and subsequent click events are automatically matched and classified. According to the method, manual point burying by front-end developers is not needed, the problems of fuzzy element recognition and inaccurate event classification in a full point burying technology are solved, high-precision and traceable user behavior analysis is realized, and the business response and analysis efficiency is improved.

The invention relates to an intelligent event analysis method and system based on AI, and the method comprises the following steps: obtaining multi-modal original data related to an event, and extracting the feature representation of each modal data; the method comprises the following steps of: extracting features of an event, constructing a hierarchical multi-modal feature graph containing intra-modal sub-graphs and a cross-modal association graph on the basis of the extracted features, sequentially performing intra-modal GAT aggregation and modal perception cross-modal attention calculation by utilizing a graph attention network to obtain global features after cross-modal aggregation, and finally outputting event type probability distribution through a classification layer and identifying event types; and based on the identified event type and the multi-modal feature graph, constructing a layered multi-modal event knowledge graph, analyzing a logical relationship and an evolution path between the events by using time sequence modeling and a causal reasoning model, and outputting an analysis result. Compared with the prior art, the method has the advantages that the multi-modal event type identification precision and accuracy can be improved, the analysis efficiency is improved, and the like.

To specify a chain of events which can hydraulically occur.SOLUTION: Holding a whitelist indicating a list of reachable combinations of an upstream facility and a downstream facility in which a water flow from the upstream facility may be present, and initial chain information indicating a starting facility in which an earlier event in a generation order has occurred and a subsequent facility in which a later event in the generation order has occurred among events included in chain events having a high correlation in generation timing based on a predetermined condition; Among the chain events, a chain event in which the advance facility and the subsequent facility respectively match the upstream facility and the downstream facility in any reachable combination indicated by the white list is specified, and the specified knowledge is displayed.SELECTED DRAWING: Figure 1

The application discloses a game moving event analysis method and device and a computer readable storage medium, wherein the method comprises the following steps: acquiring a moving object meeting a preset displacement condition, determining a moving action and a moving attribute of the moving object; acquiring a moving direction corresponding to the moving action, and acquiring a moving speed corresponding to the moving attribute; monitoring a relative distance between a current main control object and the moving object, determining at least one mark point in a preset range of the relative distance; determining a residual time of the moving object moving to the mark point according to the moving direction and the moving speed, and displaying and marking the residual time in a mark area of the mark point in real time. A humanized game moving event auxiliary analysis scheme is realized, the analysis difficulty of a user for a game moving event is reduced, and the game experience of the user is improved.

This invention relates to the field of video stream event analysis technology, specifically a video stream event analysis and wearable device real-time alarm system. The invention segments video streams and identifies event segments with significant spatiotemporal variations through keyframe recognition and frequency domain feature extraction, thereby improving the efficiency of event analysis. Based on a recurrent neural network model, it identifies and evaluates abnormal events in the video through precise temporal data modeling and event label matching. Furthermore, through an adversarial joint training module, it jointly optimizes the event recognition model and the matching result analysis model using a generative adversarial network, making the model more robust and generalizing in complex environments, thus improving the accuracy of event recognition. Alarm information is generated based on the reliability probability of event labels, providing timely feedback to users. This invention improves the real-time performance and accuracy of event detection through the close integration of video stream event analysis and wearable devices.