1566 results about "Event data" patented technology

The invention relates to a network attack tracing method and device based on a threat graph, equipment and a medium. The method comprises the steps of obtaining network security log data from multiple security data sources, and performing standardization processing to obtain a structured network security event data set; extracting threat entities and behavior relationships among the threat entities from the structured network security event data set to obtain an entity set and a relationship set; constructing a threat map according to the entity set and the relationship set, and performing time data slicing according to the timestamp to obtain a map snapshot and map metadata; based on a predefined attack chain template, identifying an attack chain sub-graph conforming to an attack behavior structure in the threat graph to obtain an attack chain set and a path reachability matrix; and according to the attack chain set and the path reachability matrix, attack path inversion is carried out by taking the target node as an end point, and an attack traceability path and a graph evolution process display result are obtained. By adopting the method, the network attack path can be identified and the attack source can be traced.

The invention discloses a human abnormal behavior monitoring method based on a large-model multi-agent, which is executed by a modular multi-agent system deployed on a back-end server, obtains information through a monitoring camera, and comprises the following steps: obtaining a video stream from the monitoring camera by a sensing agent and extracting human body posture features; analyzing the key frame by a scene understanding agent by using a visual large model, and constructing a time sequence dynamic scene graph; the core reasoning agent evaluates the scene semantic conformity based on the pre-trained large model and performs abnormal preliminary judgment; performing fine-grained classification, interpretation generation and risk assessment on the abnormal behaviors; and the report and action agent generates an alarm and records event data. According to the invention, through multi-agent cooperative work and a large model technology, efficient and accurate monitoring of human abnormal behaviors is realized, and the intelligent level of the monitoring system and the abnormal behavior identification accuracy are improved.

The invention relates to the technical field of computer vision, and discloses a real-time video analysis method based on deep learning. The method comprises the following steps: acquiring a real-time video stream through image acquisition equipment, and performing frame segmentation processing to generate a continuous video frame sequence; and extracting features of the video frame sequence by using a pre-trained convolutional neural network to obtain a multi-dimensional feature vector, inputting the multi-dimensional feature vector into the time sequence analysis model to calculate dynamic relevance, and outputting an inter-frame movement track and object behavior features. And constructing a scene understanding map containing a spatial position and a time evolution relationship according to the above-mentioned data, and carrying out abnormal event detection and generating event marking data based on the map. And performing semantic analysis on the event marking data, determining an abnormal event type and a confidence score, triggering a real-time alarm signal according to a result, and updating a historical event database. In the analysis process, the resource occupancy rate of the system is continuously monitored, the calculation precision is dynamically adjusted, a degradation processing mechanism is started when a preset threshold value is exceeded, and key area analysis is preferentially guaranteed.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses a multi-modal event data processing and collaborative circulation method, device, equipment and medium. Generating a stereoscopic scene model based on the event data file and positioning a target area, performing multi-modal fusion by combining a positioning result and non-spatial data of the event data file to generate a fusion analysis result and form a processing report, and constructing an auditing and judging item set containing a conditional strategy and an execution action to execute auditing and generate an auditing and judging result. And generating a collaborative circulation instruction set based on the processing report and the audit judgment result, and triggering execution through distributed message transmission to obtain an execution result. According to the method, through multi-modal data processing, three-dimensional scene modeling, cross-modal fusion analysis, rule-driven auditing and distributed execution, full-process automatic processing is realized, and the processing accuracy and efficiency are improved.

The invention relates to the field of artificial intelligence, discloses a large language model driven complex manufacturing and production scheduling method and system, and aims to solve the problems that a traditional scheduling system depends on a static rule, is difficult to cope with multi-constraint dynamic disturbance, is weak in semantic understanding ability and is poor in execution interpretability. The method comprises the steps that a special large language model base is constructed and manufactured, and work orders, equipment, materials, processes and abnormal event data are packaged in a unified mode through a semantic collection module; a task intention recognition sub-module, a resource matching sub-module and a time sequence conflict detection sub-module are used for jointly analyzing the semantic unit; and the scheduling engine based on reinforcement learning generates an optimal action sequence under the multi-target weighting constraint. According to the method, minute-level high-dimensional scheduling, anti-disturbance re-planning and man-machine cooperative execution are realized through semantic driving and dynamic evolution architecture, the equipment efficiency is remarkably improved by more than 15%, the delivery delay is reduced by 30%, the line change loss is reduced by 20%, and intelligent manufacturing is promoted to evolve from rule driving to semantic self-adaption.

The invention discloses an MES work order dynamic scheduling and resource self-adaption system based on real-time event triggering, and relates to the technical field of intelligent manufacturing and industrial automation, equipment states and material events are monitored in real time through interfaces such as a PLC and an OPCUA, event types and validity are analyzed through hardware filtering, feature code recognition and double verification, and the real-time event triggering is realized. Event data, MES batch information and equipment load data are integrated to generate standardized scheduling input, a preset strategy is matched, an optimal dispatching scheme is generated through a priority algorithm, material, equipment and logistics resource allocation and dispatching operation are completed, synchronization and MES interaction ensure that page logic is consistent, full-process automation of dynamic work order scheduling is achieved, and work order scheduling efficiency is improved. The equipment state and the material event are responded in real time, the production cooperation efficiency is improved by more than 30%, the resource adaptation accuracy rate reaches 95% or above, manual intervention is reduced, and the consistency of data and MES is guaranteed.

The invention provides a cloud environment active defense system based on dynamic honey spots. The system comprises a honey spot deployment and management module which generates and deploys honey spots, manages honey spot layout and provides honey spot information; the dynamic defense control module monitors network flow, perceives an attack path, analyzes attack behavior characteristics, adjusts honey point layout, generates an adjustment instruction and generates alarm information according to the attack behavior characteristics; the attack chain tracking and analyzing module is used for acquiring attack event data for attack behavior tracking, constructing an attack graph for attack path analysis and attack intention prediction and generating a threat intelligence report; and the system integration and management module monitors the running state and the resource use condition of each module, dynamically distributes system computing resources, and sets an interaction unit to provide interaction. According to the system, a complete deception defense mechanism is constructed, a deception environment is constructed by utilizing honey points, the honey points are dynamically adjusted according to attacks, and quick response and accurate countering are ensured through a flexible defense strategy and multi-layer cooperation.

The invention relates to a construction method and device of a network security knowledge graph, equipment and a storage medium, and the method comprises the steps: obtaining multi-source data of a network security application scene, and carrying out distributed processing to obtain a security data set; performing structured storage management and security knowledge extraction on the security data set to obtain an initial knowledge graph; performing zero-day attack prediction construction based on the initial knowledge graph to obtain potential attack data; performing path fusion association on the potential attack data and the initial knowledge graph to obtain attack chain fusion information; and obtaining real-time event data of the network security application scene, and carrying out dynamic iteration updating on the initial knowledge graph by using the real-time event data and the attack chain fusion information to obtain the network security knowledge graph. According to the invention, the timeliness and practicability of the network security knowledge graph can be ensured.

The invention discloses a dynamic path optimization method based on response time domain attenuation, and relates to the technical field of artificial intelligence and intelligent path planning, and the method comprises the steps: generating a node network composed of navigation points, terrain units or interaction regions, and forming a node network topology structure; generating a dynamic state feature set used for describing game scene changes, and forming input data used for follow-up node priority dynamic adjustment; converting the dynamic state feature set into node-level standardized event data, and distributing the node-level standardized event data to a node state management module through an event bus; setting a current node priority for each node in a node state management module based on the node-level standardized event data; forming a time domain attenuation model of the node priority; the priority recovery coefficient is improved; in the path planning stage, executing a dynamic path search algorithm to generate a passing path with the minimum total cost and the optimal path smoothness; when it is detected that player operation or scene change causes node response mutation, a local re-planning mechanism is triggered, smooth path transition is achieved, and overall jumping is avoided. According to the method, the problems of path congestion, frequent switching and unsmoothness caused by lack of dynamic node state and event response calculation in the prior art are solved. Through node priority time domain attenuation and dynamic path optimization, the technical effects of smooth path, efficient passing and node load balancing are achieved.

The invention discloses an intelligent event studying and judging method based on machine learning, which comprises the following steps of: extracting features from multi-source event data, constructing an event fragment set with a uniform structure according to a preset time window, identifying fragments with stable features in the event fragment set as normal samples, constructing a reference model through an MCD algorithm, and analyzing the normal samples. The method comprises the following steps: extracting feature distribution in a normal state, comparing a to-be-analyzed event with a reference model, calculating feature offset, forming a behavior trajectory, determining an anomaly judgment range by adopting an elliptical envelope algorithm, and finally evaluating an event risk level and dynamically adjusting reference sample composition according to a continuous anomaly condition to realize intelligent identification and risk judgment of the event. According to the invention, dynamic perception and intelligent identification of complex events can be realized.

The invention discloses an automatic monitoring method for infrastructure construction operation violation based on multi-modal data fusion, and belongs to the technical field of graphic data processing and pattern recognition, and the method comprises the steps: obtaining multi-modal source data of a target scene, carrying out the data recognition and standardization processing of the multi-modal source data, and generating preprocessing feature data; acquiring an on-site environment condition, and generating an environment state parameter; performing dynamic weight distribution on the preprocessed feature data based on the environment state parameters to generate weighted feature data; performing multi-source data conflict judgment and resolution on the weighted feature data to generate conflict resolution event data; and based on a preset behavior rule pattern library, performing behavior pattern matching on the conflict resolution event data to generate a violation judgment result. According to the method, a multi-modal spatial-temporal feature fusion and dynamic weight distribution mechanism is adopted, and an environment adaptive perception and multi-source data conflict resolution technology is combined, so that automatic monitoring and pattern recognition of illegal behaviors in a construction environment can be realized.

The invention provides an RGB event adaptive fusion single-target tracking method and system based on time sequence coding, and the method comprises the steps: designing a single-flow Transform architecture, so as to carry out the unified processing of the feature extraction of an RGB image and an event voxel; an event time sequence encoder is designed, time dimension information expression of event data is enhanced by using time sequence position encoding, a long time sequence dependency relationship is established in combination with a multi-head self-attention mechanism, and dynamic target features are effectively captured; by designing an adaptive fusion mechanism, channel-level enhancement is performed on RGB features and event features, a learnable adaptive fusion weight mechanism is introduced, and the importance of different modal features is dynamically adjusted according to scene requirements. The target tracking method provided by the invention obviously exceeds various advanced methods in core performance indexes of success rate, precision and reasoning speed, and shows good precision and real-time performance when being used for processing scenes of high-speed motion, low illumination, complex shielding and the like.

The invention provides an intelligent machine room multi-dimensional dynamic monitoring management method and system based on digital twinning, and relates to the technical field of machine room monitoring, and the method comprises the steps: collecting a three-dimensional point cloud set and an RGB image set of a machine room space, and building a space mapping table; mapping the access control event data to a corresponding position in a digital twin space, generating an access control event space positioning set, identifying access control abnormal events, and constructing an access control abnormal event space positioning set; performing abnormal behavior recognition, and constructing an abnormal event graph structure; reconstructing and visualizing an abnormal track based on the abnormal event graph structure; in combination with the access control abnormal event space positioning set, the abnormal behavior category prediction probability distribution and the track risk level, executing a grading early warning mechanism, and generating an access control abnormal event log; according to the invention, intelligent positioning, track visualization and risk grading early warning of access control abnormal events in the intelligent machine room are realized, and the method is suitable for digital operation and maintenance management systems such as the intelligent machine room.

The invention discloses a big data abnormal behavior risk detection method and device, equipment and a medium, and the method comprises the steps: constructing a time-space behavior tuple containing a timestamp, a geographic position and behavior characteristics through user behavior data, environment sensor data and external event data collected by terminal equipment; inputting the time-space behavior tuple into a neural network model, and generating a portrait feature vector representing a user behavior pattern; performing time sequence conversion on the portrait feature vector, and outputting an abnormal probability value through a multi-scale Transform model; based on the abnormal probability value, clustering behavior data of the same region and the same time period, and combining the external event data to construct a causal association map; and calculating contribution weights of environmental factors according to the causal association map, and determining risk tags according to the contribution weights. By analyzing the time-space behavior tuple of the user, the abnormal behavior detection and risk prevention and control capabilities are improved.

The embodiment of the invention discloses a dynamic metadata sensing and self-adaptive mapping method and system, and the method comprises the following steps: deploying a lightweight probe to be agented to a plurality of heterogeneous source database systems, and building a trusted communication channel between the probe and a coordination center through equipment fingerprint generation and security authentication; a database log event is captured in real time, log analysis is carried out through an FPGA module, the change of a field structure or a semantic level is identified, and structured event data is generated; performing context embedding representation on the captured event based on a semantic understanding model, performing strategy reasoning in combination with a historical strategy and a knowledge graph, and generating a matched mapping strategy; loading and executing the mapping strategy in a trusted execution environment (TEE) to realize data conversion, cleaning and complementation, and performing desensitization processing on sensitive data through a differential privacy mechanism; and collecting performance indexes and abnormal logs of a strategy execution result, continuously training the model through a closed-loop optimization mechanism, and correcting an unreasonable strategy or updating a rule base.

The present disclosure presents methods and systems for determining cybersecurity risk exposure for entities. In one aspect, a method is provided that includes providing first text data to a trained LLM to identify data associated with a first candidate cybersecurity event for an entity, comparing the entity's identifier to domain information to verify the entity's identifier, determining if the first candidate cybersecurity event represents a new cybersecurity event based on com with previous data, and updating a cybersecurity risk score for the entity based on this determination. Further enhancements include training the LLM with cybersecurity event data, outputting documentation of the event source, and various methods for evaluating the novelty and severity of the cybersecurity event, including similarity measures and manual review triggers. The techniques leverage LLMs, machine learning models, and automated actions to provide a comprehensive approach to cybersecurity risk assessment and response. Other aspects are also provided.

The invention relates to the technical field of operation and maintenance safety compliance, and discloses an operation and maintenance service automation safety compliance detection system and method. The system comprises an operation and maintenance event acquisition unit, a strategy execution unit, a compliance analysis unit, a strategy optimization unit and a violation tracing unit. The operation and maintenance event acquisition unit captures operation and maintenance operation events and execution environment parameters in real time, packages the operation and maintenance operation events and the execution environment parameters into event data packets and transmits the event data packets to the strategy execution unit; the strategy execution unit extracts a reference strategy rule from the security strategy library, performs matching verification on the event and generates a result; the compliance analysis unit analyzes compliance fluctuation characteristics through a multi-dimensional compliance deviation model in combination with historical records; the strategy optimization unit dynamically adjusts the event capture frequency and corrects a strategy matching rule according to the fluctuation characteristics; the violation tracing unit counts the risk cumulant and generates a violation tracing instruction when the risk cumulant exceeds a preset capacity. According to the system, automation and dynamic adjustment of operation and maintenance safety compliance detection are realized, and a risk accumulation process can be effectively tracked.

The invention discloses an intelligent text report automatic generation method and system based on safety production data, and belongs to the technical field of big data and artificial intelligence, and the method comprises the steps: multi-source heterogeneous data collection and structured processing: collecting safety production event data including violation operation, violation command and violation of labor discipline, carrying out structured processing on the data; semantic enhancement prompt engineering and large language model reasoning: constructing prompt words, inputting structured data into a large language model, and performing semantic understanding and text generation on the data by utilizing a prompt engineering technology guide model; and dynamic report template adaptation and multi-format output: realizing structured report output through template matching, outputting a structured text report by the model, filling the structured text report into a preset template, and exporting the structured text report. The enterprise safety management efficiency is remarkably improved, the manual writing burden is reduced, informatization, standardization and intelligent processing of safety production data is achieved, and the safety management digitization and automation level of enterprises is improved.

The invention discloses an AI-based browser user behavior recording intelligent workflow method and system, and relates to the technical field of workflow management.The method comprises the steps that interaction behaviors are recorded in a browser, and normalized event data are generated; the input recording cooperative agent performs user task intention analysis and abstracts the user task intention into a structured workflow; inputting a recording test agent for verification, and adjusting the structured workflow to obtain a verified target workflow; the browser is driven to automatically execute and monitor the execution state, a self-repairing strategy is triggered for adjustment when the execution state is abnormal, and the target workflow is updated when self-repairing succeeds. Through the technical scheme of the invention, standardized modeling and precipitation of the browser event flow are realized, the workload of establishing an automatic process is reduced, the use threshold of establishing the browser automatic process is reduced, the execution success rate and stability of the target workflow are improved, and the user experience is improved. And the robustness and the long-term maintainability of the automatic process are effectively improved.

The invention relates to the technical field of distributed data storage, and discloses a database cluster partition operation method and device under a network partition scene and a medium, and the method comprises the steps that a multi-master distributed database cluster is constructed, and each node continuously captures a database native transaction log; obtaining change event data, and performing unified data storage; analyzing the original log into structured data, and executing uniform fragmentation and parallelization processing; processing the block log change record, and arranging change events of the primary key according to a rule; executing conflict processing and data synchronization, and synchronizing all nodes after the conflict is solved; through exception handling and fault tolerance, self-healing is realized and data consistency is maintained. According to the method, millisecond conflict identification and data consistency convergence after network disconnection can be realized, continuous and stable operation of a cluster in a partition state is ensured, and self-healing and continuous availability of a system are realized.

A video generation framework that is controllable, unsupervised and based on events (CUBE) includes an event camera, which captures changes in light intensity at each pixel of a scene asynchronously and generates event camera data. A text-to-image diffusion model that is conditioned on textual descriptions integrates the event camera data to control video synthesis. Further, an edge extraction module translates event data into a format usable by the text-to-image diffusion model, whereby the diffusion model synthesizes detailed and contextually accurate videos based on textual prompts. Further, an improved system (CUBE Plus) includes a content frame identification module which selectively identifies and uses only the most information-rich event segments of the event camera data to drive cross-frame attention, and an event driven attention mechanism that allows the framework to focus on event-dense moments.

A method for generating a first case dataset in a first language. The method includes receiving adverse event data. The method further includes determining case data including general case data and regional case data and providing the case data to a translator computing device to enable display on a user interface including multiple duolingual text fields with a first language text field including at least a portion of the text data in the first language and a second language text field adjacent the first language text field. The method further includes receiving the text data in the second language from a translator computing device. The text data in the second language is received via the second language text fields of the plurality of duolingual text fields. The method further includes generating and outputting the first case dataset including the text data in the first language.

Methods, systems, and techniques for detecting insider threat incidents are disclosed, comprising: receiving event data from a computing device of an event to be analyzed for an insider threat; accessing a vector database storing vector representations of known insider threat incidents; and determining a risk that the event is an insider threat incident based on the event data and the vector representations of the known insider threat incidents.

Access to log event data and corresponding source code is obtained and static code analysis is performed on the source code to produce analysis output. First vectors representing the log event data and second vectors representing the analysis output are generated. A similarity analysis is performed on the first vectors and the second vectors. A probabilistic relevance score associating a given log event with a segment of the source code is determined based on the similarity analysis. A visualization is generated for log events based on the probabilistic relevance score.

A dynamic marketplace system leveraging store and warehouse mobility features a neuroevolution (NE) engine, an electronic device, and a request handler facilitating communication between the electronic device and the NE engine. The NE engine interfaces with a data storage system and an event handler receiving real-time event data from a public cloud services processor. An intentions handler interprets user intention data to predict user behavior. The NE engine, integrated with a processor, generates a predictive evolutionary model for the marketplace based on request, event, and intention data. An AI agent processor within the NE engine creates a recommendation model for mobile retail vendors, devises route plans, and deploys vendors to strategic locations.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as machine room monitoring, financial science and technology and medical health, and discloses an event attribution convergence method, device and equipment and a medium. Analyzing and generating an attribution parameter table containing attribution parameters and probability values by utilizing a pre-training language model, analyzing concurrency and association convergence conditions of event data pairs to generate association degrees among events, dynamically updating the attribution parameter table according to the association degrees, receiving to-be-processed event data, judging whether the to-be-processed event data exist in the attribution parameter table or not, and if yes, executing the next step; and if yes, executing hierarchical convergence judgment and executing convergence or release, and if not, executing model-assisted convergence and executing convergence or release. According to the method, automatic extraction and dynamic updating of the attribution parameters are achieved by combining the historical convergence data and the pre-training language model, the convergence mode is selected according to the matching condition, the event association recognition accuracy and convergence processing adaptivity are improved, and the processing efficiency is improved.

The invention relates to the technical field of electric power intelligent management, and discloses an intelligent management system integrating a virtual power plant and a comprehensive monitoring safety platform. The system comprises a data aggregation and acquisition module for synchronously acquiring a real-time operation parameter data stream of a virtual power plant and a safety monitoring event data sequence of a comprehensive monitoring safety platform; the security threat feature extraction module is used for identifying security threat features in combination with the data and generating a security threat distribution map; the intelligent response strategy generation module is used for generating a preliminary response strategy scheme based on a strategy decision library and an evaluation criterion; the situation evolution prediction module is used for predicting the development trend of security threats and operation situations; the strategy compensation optimization module is used for optimizing the preliminary strategy according to the prediction result; and the integrated execution control module cooperatively regulates and controls the virtual power plant and the comprehensive monitoring safety platform according to the optimization strategy. According to the system, data fusion and cooperative linkage of the two are realized, and the intelligence and safety of virtual power plant management are improved.

The invention provides a cross-domain collaborative information tracking and positioning auxiliary method and system, and the method comprises the steps: firstly collecting an original data set generated by a target event in a plurality of associated domains, carrying out the cross-domain identifier matching processing to obtain a standardized event data set, and then carrying out the information association processing of the standardized event data set, according to the method, context semantic features and cross-domain time sequence behavior features of a target event are generated, then collaborative analysis is performed on the features based on a preset collaborative analysis model, a cross-domain collaborative tracking index set is generated, and a dynamic tracking path topology is constructed according to the cross-domain collaborative tracking index set. According to the method and the device, the target event is tracked and positioned, the positioning auxiliary strategy matched with the target event is generated, and finally the positioning auxiliary strategy is synchronized to the cross-domain tracking system to activate the cooperative positioning operation, so that the cooperative processing of multi-domain information is realized, and the target event tracking and positioning accuracy and efficiency are improved.