1900 results about "Event data" patented technology

The invention relates to the technical field of data security management, in particular to a self-adaptive data security management and risk early warning system based on intelligent analysis under a cloud platform. Comprising a multi-dimensional data acquisition module; an intelligent analysis module; a self-adaptive strategy generation module; a risk early warning module; and a user behavior portrait construction module. In the design, the security policy can be dynamically adjusted along with the risk situation of the cloud platform, the problem that a static policy cannot adapt to real-time change is solved, and dynamic mapping of risk characteristics-policy parameters is realized; according to the design, the one-sidedness of single-dimension analysis is broken through, multi-modal feature association modeling of user behaviors is achieved, an abnormal behavior triggering threshold value is accurately recognized, and the integrity and accuracy of risk feature analysis are improved; the security policy can be continuously optimized through historical event data, so that protection efficiency attenuation caused by long-term static operation is avoided, and an autonomous lifting link of data driving, algorithm optimization and policy evolution is realized.

An improved system and method of selectively transmitting asset data from one or more sensors associated with the vehicle to a backend server, which is configured to analyze the asset data and, if necessary for further analysis of the asset data (e.g., to determine whether a safety event has occurred) and / or to provide actionable data for review by a safety analyst, requests further asset data from a vehicle device.

The invention provides a multi-task printing scheduling optimization method based on AI intelligent strategy generation, and relates to the technical field of production workshop intelligent scheduling, and the method constructs and maintains a dynamic cognitive map, comprehensively perceives each entity state and complex association relationship of printing production in real time, utilizes an artificial intelligence engine, carries out context perception and reasoning based on the map, and improves the printing quality. The system executes the process in real time, analyzes event data, incrementally updates the state attribute of the cognitive map, evaluates the current execution effect, and adjusts the execution path and resource allocation of the flexible workflow in real time by applying a learning strategy; and continuous self-optimization and system self-evolution of a scheduling strategy model are realized through historical execution data, so that the problems of response rigidity, difficulty in realizing multi-target collaborative optimization and lack of fine-grained real-time adjustment in coping with emergencies in traditional scheduling are solved. And the scheduling flexibility, the overall efficiency, the product quality and the capability of coping with a complex and uncertain environment of printing production are remarkably improved.

The invention provides an APT (Advanced Persistent Threat) attack detection method based on a large language model, which comprises the following steps of: S1, extracting original system call event data from a kernel audit log of an operating system, and preprocessing the data; s2, constructing a multi-model collaborative detection architecture based on a large language model, and realizing fine-grained classification of network entities according to the preprocessed data through prompt construction, model fine tuning and a confidence scoring mechanism; s3, constructing an adaptive graph search algorithm based on multi-modal feature correlation modeling, driving attack path topology reconstruction, and realizing maximum reduction of a malicious sub-graph topology structure; s4, carrying out combination with MITRE ATTamp; the CK tactical knowledge base constructs a cyclic enhancement analysis framework, a cyclic enhancement technology is adopted to drive a large language model to execute hierarchical association reasoning, a mapping relation from malicious subgraphs to attack tactics and tactical chains is derived step by step, and finally an attack report summary and a targeted defense strategy are generated. According to the invention, APT attack detection with high accuracy and high interpretability is realized.

The invention discloses an electronic lead seal automatic detection method applied to logistics tracking, and relates to the technical field of Internet of Things safety monitoring. The problems of packaging data loss, tampering risk increase and real-time monitoring failure caused by wireless communication signal interference, transmission delay and state updating lag of an existing electronic lead seal are solved. The method comprises the following steps: fusing multi-physical field sensing data through a space-time correlation sampling algorithm, and constructing an anti-interference characteristic matrix; evaluating communication quality based on the dynamic probability network model, and triggering a multi-path fragmentation concurrent transmission strategy to avoid signal attenuation; a hybrid reasoning model is deployed at an edge end to screen key event data, and the transmission efficiency is optimized in combination with differential coding and an IEEE 1588 clock synchronization mechanism; the cloud end adopts a space-time diagram fusion analysis model to carry out cross-modal abnormal association scoring, corrects misjudgment and updates an edge model; according to the invention, the communication reliability, the real-time transmission efficiency and the anomaly detection precision of the lead sealing state data in a complex environment are obviously improved.

Disclosed are systems and methods that ingest substantial volumes of data from a variety of sources associated with a computing system network to create integrated, intuitive, efficient, and coherent adaptive workspace display interfaces. The ingestion includes event data generated by event source software applications running on end user computing devices as well as call and end user attribute data that is used to determine state, presence, and performance data for the end user computing devices that is formatted for display on the adaptive workspace interfaces and used during performance monitoring and collaboration between system end users. The adaptive workspace interfaces provide functions that facilitate real time collaboration between end users that enhances shared experiences between system end users and customers.

The invention discloses a logistics traceability system and method based on a block chain technology, and relates to the technical field of logistics traceability, and the method comprises the steps: constructing an event graph structure containing a plurality of behavior event nodes, and calculating a structure closure signature value; judging whether the original data of the behavior event node is written into the block chain or not according to the responsibility structure weight value; constructing a verification path mapping graph based on the structure closure signature value and the responsibility structure weight value; when a traceability request is received, calling the verification path mapping graph, performing consistency verification on the original data of the target behavior event node and the structure mapping hash value, and generating a traceability verification result; according to the method, the credibility of the behavior event data structure is improved, meanwhile, the on-chain storage cost is effectively reduced, and the technical contradiction between full-uplink data and non-uplink data is solved.

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 discloses an end-cloud cooperative detection method and system for traffic anomalies, and relates to the technical field of intelligent traffic control. The method comprises the following steps: collecting traffic video streams through edge equipment, and identifying abnormal behaviors and generating structured event data by using a lightweight YOLOv3-tiny model; when the confidence exceeds a dynamic threshold and the event type is a high-risk type, uploading a video clip and data to a cloud; the cloud integrates a historical road condition map, meteorological data and a real-time traffic flow state, reconstructs a three-dimensional event scene by adopting a space-time attention pyramid network, and verifies the authenticity of an event in combination with a traffic flow sudden change detection algorithm; and generating a signal lamp forced switching instruction for the risk level overrun event, and issuing the signal lamp forced switching instruction to roadside equipment within 3 seconds to execute emergency response. According to the invention, full-link closed-loop control of traffic accidents from identification to response is realized, and the false alarm probability is greatly reduced while the identification accuracy is 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 discloses a power equipment safety state assessment method and system, equipment and a readable storage medium, and relates to the technical field of power equipment assessment, and the method comprises the steps: collecting a power equipment communication interaction data flow, carrying out the time synchronization and outlier elimination, and constructing a multi-modal data matrix; evaluating the exception probability score and an exception probability threshold to obtain an exception state flag, and generating a security event data packet in combination with the preliminary exception type and the power equipment communication interaction data stream; adopting a LiNGAM causal discovery algorithm to perform non-Gaussian test and time sequence lag analysis on an abnormal event time sequence in the security event data packet, and constructing a directed acyclic causal graph; dynamically adjusting the causal intensity of the directed acyclic causal graph according to the real-time equipment load rate and the communication quality index, and outputting a dynamic causal graph; according to the method, the accuracy and adaptability of abnormal propagation analysis are improved, and finally the real-time performance and credibility of safety assessment of the power equipment are enhanced.

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.

Systems and methods for decentralized orchestration of event records within decentralized computing environments. A processing system including decentralized execution nodes dynamically allocates microservice instances based upon real-time node performance metrics. Transaction requests including transaction data digitally associated with decentralized identifiers (DIDs) are processed by allocated microservice instances. The processing includes verifying transactions, generating cryptographically-linked immutable memorialization records associated with respective DIDs, and distributing these records to a tamper-evident distributed ledger configured to enforce immutability through consensus nodes. The processing system dynamically reallocates microservice instances among decentralized execution nodes based upon monitored utilization metrics and predefined scaling thresholds. Each memorialization record is cryptographically secured and linked permanently to the DID for data immutability, enhanced security, and dynamic scalability for event data processing in distributed infrastructures.

The invention relates to the technical field of data analysis, provides a logistics access dynamic management report generation method and system based on an Internet of Things lock, and is used for realizing full-process intelligent generation and optimization of a logistics management report. The method comprises the following steps: acquiring an access event data set of a target logistics area; performing cross-modal feature extraction processing on the access event data set to obtain a dynamic access feature set containing space-time dimension features; based on a preset report type template, inputting the dynamic access feature set into a trained report generation model for multi-task analysis processing, and generating an initial logistics management report set; performing semantic integrity verification and logic conflict detection processing on the initial logistics management report set to generate an optimized target logistics management report; and binding the target logistics management report with an authorized access interface of the user terminal, and performing incremental updating operation on the target logistics management report according to the access event triggered in real time.

The invention provides an event root cause intelligent diagnosis method and system for financial service operation risks, and the method comprises the steps: firstly collecting an operation risk event data set which is generated by an operation risk event in a financial service system and contains various information, and then carrying out the feature extraction of the operation risk event data set; the method comprises the following steps: generating an event feature set containing multi-aspect features, constructing a dynamic event association network based on the event feature set, determining nodes and influence intensity parameters, performing causal path weight iterative calculation on the network by using a preset root cause inference rule set, and positioning a root cause node set; and finally, according to the root cause node set, generating a root cause diagnosis result containing the root cause type identifier, the influence path description and the correlation feature contribution degree, thereby improving the accuracy and efficiency of financial service operation risk root cause diagnosis.

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.

A system and method for root cause analysis in incident processing, including root cause change detection, is presented. The method includes processing a plurality of event records, including a plurality of alert records and a plurality of change records, each event record generated based on an event in a computing environment; parsing each alert record based on a predetermined data field; extracting from each predetermined data field a data value; correlating a group of alert records of the plurality of alert records based on at least an extracted data value; generating an incident data record based on the extracted data values of the correlated group of alert records; detecting a change record of the plurality of change records related to the incident data record; determining that the change record is a root cause change of the incident data record; and initiating a mitigation action based on the root cause change.

The invention relates to the technical field of community security, in particular to an intelligent community security management system and method, and the method comprises the steps: collecting equipment data, verifying the daily operation of equipment, collecting personnel and event data, and carrying out the data association and data integration; when an abnormal event is identified, determining a basic level according to the integrated data, determining an associated risk probability through a Bayesian network model, inputting the basic level and the associated risk probability into a dynamic weight adaptive grading model, and determining a final level of the event; inputting the obtained event final grade into a trend prediction model to obtain a risk prediction value, determining a comprehensive risk value of each region according to the risk prediction value, and generating a real-time risk thermodynamic diagram; and according to the final grade of the event and the associated data, screening the processing personnel meeting the conditions to perform task assignment, and performing event processing by the processing personnel. According to the scheme, by constructing the equipment, event and personnel association chain, dynamic verification and intelligent grading are realized, and the management efficiency is improved.

A system for providing an extended reality environment that incorporates real-world event data into 3D models representative of the real-world event within the extended reality environment includes an XR processing unit configured to dynamically download 3D hole models of the course, receive real-world live shot data of shots taken on the holes during a golf tournament; map the shot data to the 3D hole models; and cause a spatial computing device to render hole views and immersive views comprising the mapped shot data rendered directly on the 3D hole models in real-time in an interactive extended reality environment. The mapped shot data includes shot trails representative of the shots taken on the golf course, The hole views and immersive views may utilize the same source file.

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.

Providing unified storage for event streams in an anomaly detection framework, including: receiving, by an event streaming platform, first event data encoded in a first file format; converting, by the event streaming platform, the first event data into second event data encoded in a second file format for storage in a first remote storage system; and providing, to the first remote storage system, a command to load the second event data into the first remote storage system.

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 a cross-platform distributed synchronous simulation method adopting RoCEv2, and relates to the technical field of communication simulation, and the method comprises the following steps: configuring parameter information of a switch and nodes, and constructing a heterogeneous distributed network of a RoCEv2 enhanced network protocol; deploying a master node global logic time reference, calibrating a slave node clock, starting a sliding window to continuously monitor offset, dynamically detecting clock offset to trigger adjustment, optimizing a time step length in combination with an ECN mark, and dynamically compensating the clock offset of the heterogeneous platform; sequencing events according to logic timestamps to generate DAG topology, and preloading high-frequency events; event data are transmitted through cross-node RDMA, and the main node is sorted and scheduled according to a logic timestamp, so that cross-platform distributed parallel simulation and consistency verification are realized. The method can improve the processing efficiency and fault-tolerant capability of the communication simulation system on large-scale simulation, and can be widely applied to the technical field of communication simulation.

The invention discloses an event camera-assisted multi-modal vehicle-mounted three-dimensional occupancy prediction method, which comprises the following steps of: acquiring continuously acquired laser radar point cloud data, images, events and IMU measurement data, performing synchronization of spatial dimensions and time dimensions on various data, and determining the occupancy of a vehicle-mounted vehicle on the basis of the synchronized laser radar point cloud data and IMU measurement data, so as to predict the occupancy of the vehicle-mounted vehicle-mounted vehicle-mounted occupancy of the vehicle-mounted vehicle-mounted vehicle-mounted vehicle-mounted vehicle-mounted vehicle-mounted image. Dense point cloud data is obtained; performing space-time alignment on the dense point cloud, the image and the event, performing semantic annotation on the image after the space-time alignment, performing dynamic and static separation on the dense point cloud after the space-time alignment through cross-modal semantic projection, and performing static scene optimization and dynamic scene fusion to obtain an occupied voxel true value; training an occupancy prediction model based on the laser radar point cloud data and the occupied voxel truth value; and acquiring laser radar point cloud data and image data of a scene and event data in image exposure time, and realizing three-dimensional occupation prediction by using the trained occupation prediction model.

The invention discloses a dial plate processing optimization system based on big data, and relates to the technical field of dial plate processing, data is collected, anti-fact sample data is generated, a causal atlas structure including processing process nodes and an edge weight function model is constructed, processing path performance is evaluated based on an abstract objective function, and the processing process is optimized. According to the method, the causal atlas is constructed based on multi-source data such as tool wear degree data and spindle vibration amplitude data, and path deduction and parameter optimization of a target state are realized in combination with an anti-fact sample; self-adaptive control over the machining state is achieved through a structured deviation matrix, a causal relationship and a weight function model are dynamically corrected through a feedback learning mechanism, and data-driven optimization and defect prevention in the machining process are achieved.

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.