2229 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.

The invention relates to the technical field of security knowledge maps, and discloses a city security knowledge map construction method based on an AI large model and a related device, and the method comprises the steps: carrying out the vectorization feature extraction of multi-source city security event data, obtaining a multi-dimensional feature matrix, carrying out the feature analysis through a BERT encoder, and obtaining a multi-dimensional feature matrix; performing key value pair generation and feature processing on each group of query vectors in the obtained grouped query vector set; performing spatial-temporal correlation calculation on the event association feature matrix to obtain an initial security knowledge graph, and performing graph neural network learning and event classification on the initial security knowledge graph to obtain an event type division result; performing situation assessment index calculation on the event type division result to obtain security situation early warning data; and performing knowledge graph updating on the security situation early warning data to obtain an urban security knowledge graph, thereby realizing accurate classification and situation evaluation of security events and dynamic updating of the urban security knowledge graph.

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 discloses a knowledge conversion and fusion processing method and system for massive power grid operation data, and the method specifically comprises the steps: obtaining the attributes, including the alarm type, timestamp and severity, of equipment granularity based on alarm event data collected by a power distribution automation terminal, and carrying out the data cleaning and standardization processing, converting the multi-source heterogeneous alarm data into uniform event granularity representation; fusing the alarm event data and the operation event data, carrying out space-time alignment and causal association on the two types of events based on timestamps and equipment ID attributes, constructing a cross-data-source event semantic link network, and carrying out association analysis on a semantic relationship between the events; and according to a fault diagnosis result, historical records related to fault processing are extracted from the operation event knowledge base, similar fault processing modes are matched, and a specific fault processing scheme is generated in combination with factors of severity and influence range of the current fault.

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 relates to the technical field of industrial internet, in particular to an industrial product production management system based on the industrial internet, which comprises a multi-source sensing module, a dynamic topology module and an intelligent decision module, abnormal events are detected through an event pattern recognition engine, and structured event data are output; the dynamic topology module maps the production element entities into reconfigurable topology nodes and marks a real-time attribute set; activating a space-time constraint generator according to the structured event data, and creating a dynamic constraint edge; when the change of the dynamic constraint edge exceeds a preset threshold value, generating a global topology reconstruction signal; otherwise, generating a local constraint adjustment signal; the intelligent decision-making module responds to the signal and triggers multi-objective optimization calculation based on transfer learning or executes incremental rescheduling with a memory function in a process correlation subgraph, so that efficient production management is realized.

The invention discloses an intelligent data analysis method and system based on an industry large model, and relates to the technical field of data analysis, and the method comprises the steps: collecting business event data, carrying out the feature vector extraction according to the data type, calculating a zoom dot product attention score matrix through employing a self-attention mechanism, and carrying out the weighted output of a multi-modal fusion vector, constructing an industry knowledge database to determine an embedded vector; and calculating a relevancy value, screening a combination of the multi-modal fusion vector and the knowledge fragment, and forming a gating input vector to carry out fusion vector calculation. According to the method, unified expression and correlation mining of heterogeneous information such as structured data, text information and images can be realized through a multi-modal feature fusion and knowledge fragment retrieval scheme, the attention degree of each modal feature can be adaptively adjusted according to a service scene through a self-attention mechanism and a model, information complementation is realized, and the accuracy of information retrieval is improved. And automatically highlighting the multi-modal dimension most related to the current task.

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.

Reducing amounts of data ingested into a data warehouse, including: determining a duration associated with a subject described by event data provided to a data ingestion pipeline; determining, based on the duration, an interval for providing next event data; and providing, based on the interval, the next event data.

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.

One variation of a method includes: accessing a first policy associated with a computer network; extracting a first entitlement from the first policy, the first entitlement granting permission to a first identity in the set of identities to access a first resource according to a first access level; accessing a first set of event data representing a first access attempt associated with the first resource by the first identity during a first time period, the first access attempt characterized by a second access level; detecting a deviation between the second access level and the first access level defined in the first entitlement; generating a second policy representing a second entitlement granting permission to the first identity to access the first resource according to the second access level in response to the deviation; and serving the second policy to an operator via an interface.

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.

The invention discloses an RGB data and event data fusion target tracking method based on visual Mama, relates to the technical field of computer vision, and solves the technical problem that efficient and accurate target tracking of RGB data and event data is difficult to realize. The method comprises the following steps: performing frame-level synchronization and conversion on an original RGB image of an RGB camera and an original event stream of an event camera to generate RGB input data and event input data; obtaining an RGB feature map and an event feature map through a feature extraction module of the Mamba network; performing linear transformation and deep convolution operation through an interaction module to obtain RGB features and event features; dynamically fusing the RGB features and the event features through a fusion module to obtain cross-modal fusion features; and the template features and the search features are aligned through an alignment module to generate an alignment feature graph, and target positioning and tracking are completed. According to the method, through efficient modal fusion and the state space model, the target tracking precision and robustness in a dynamic scene are remarkably improved.

The invention relates to a replay playback display control method based on event tracing, which is suitable for event analysis and playback control in a simulation deduction process, and comprises the following steps: inputting a key event; event data analysis; key events are dynamically displayed; deducing situation backtracking; performing playback control; subscribing and pushing key events; tracing an evaluation result; and deducing data association analysis. According to the method, the playback experience of the user is improved, the user can subscribe specific key events, the system pushes event results in real time in the deduction process, multi-path analysis is carried out through different conditions, the evaluation results are visually displayed and traced, and the user is helped to comprehensively analyze the influence of the key events on deduction.

The invention discloses a cloud mobile phone end-to-end performance tracking method and related equipment, and relates to the technical field of cloud computing, and the method comprises the steps: obtaining client touch event data and network event data, and generating a global unique identifier based on a preset Hash algorithm; obtaining server resource event data; performing timestamp calibration on the client touch event data and the server resource event data based on a hardware-level clock synchronization and software compensation algorithm to generate a synchronous timestamp; based on the synchronization timestamp, aligning the event sequences of the client and the server through a dynamic time warping algorithm to generate an aligned event sequence; performing causal probability calculation on the aligned event sequence based on a Bayesian network model, and determining a causal relationship weight between resource events; and performing root cause matching according to the causal relationship weight and a preset abnormal mode library, generating a root cause list with priority ranking, and triggering execution of a self-healing strategy.

The invention provides a time sequence security atlas-oriented network threat detection method, and relates to the technical field of attack traceability, and the scheme comprises the following steps: obtaining and cleaning security event data to obtain entity data, constructing a security map, and introducing an entity and relationship enhancement history to construct a time sequence security map; performing fine tuning on the trained large language model to obtain a forward prediction model, adjusting the position sequence of entities and relationships, and optimizing the forward prediction model in combination with a position perception prompt strategy to obtain a reverse reasoning model; associating a current event with a historical event to establish a historical chain for the time sequence security map, and introducing a mode matching mechanism based on the reverse reasoning model to perform reverse reasoning on the time sequence security map to obtain traceability attack information, and predicting the entity data based on the forward prediction model to obtain potential attack information, and then outputting a threat detection report in combination with the traceability attack information. According to the method, reverse reasoning is optimized, so that the inference capability on historical events is enhanced, and the detection requirements of advanced persistent threats and variant attacks are met.

Event-triggered forensics capture technologies balance security incident data availability against data processing and storage costs. A forensic correlation engine receives basic status data of a monitored computing system. A forensic computing system detects a trigger event in the basic status data, and starts capturing extended status data per a corresponding capture specification. Captured data is submitted to a forensic analysis tool. Different trigger events may cause different data captures. A query specifying which data to capture from a live stream or from virtual machines may operate as a capture trigger start event. Extended status data capture activity may be stopped by a change in the basic status data being received, by a timeout, or by forensic analysis that finds no vulnerability or threat based on captured data. Data transfers and storage may be restricted to comply with privacy regulations or policies.

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.

Disclosed are systems, apparatuses, methods, and computer-readable media for high-fidelity event data for multi-cloud services. A method includes: storing first event information of a multi-cloud service in a cache, wherein the first event information is received from a first gateway controlled by the controller; storing second event information of the multi-cloud service in the cache, wherein the second event information is received from a first external service; mapping the second event information to the first event information to at least one identifier associated with the controller; and generating a first synthesized event information based on mapping the second event information to the first event information, the first synthesized event information including the at least one identifier, at least one event from the first event information, and at least one event from the second event information.

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.