1212 results about "Event data" patented technology

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 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 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 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.

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.

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.

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 discloses an irregular ship scheduling method and system considering uncertain harbor time, and a medium, and belongs to the technical field of shipping management. The method comprises the following steps: collecting ship, cargo, port and interference event data; generating an initial scheduling scheme by adopting a greedy strategy; generating multiple groups of random interference scenes based on Monte Carlo simulation; a two-stage stochastic programming model is constructed, in the first stage, income maximization serves as a target, and an initial plan is generated through damage and repair operator iterative optimization by means of a self-adaptive large neighborhood search algorithm; in the second stage, aiming at each interference scene, a recovery network is constructed for the influenced goods, and an optimal recovery strategy and adjustment cost are solved by utilizing a shortest path algorithm; and finally, outputting a scheduling scheme including a ship path, a navigational speed and a multi-scene recovery strategy through iterative optimization. According to the method, the earnings and the operation efficiency of the shipping companies are improved, the adjustment cost caused by uncertainty is reduced to the greatest extent, the benefits of the shipping companies and cargo owners can be balanced in a complex scheduling problem, and the service quality is improved.

A data platform monitors a compute environment by performing multi-stage heuristic analysis of event data representing a plurality of events occurring within the environment. The platform utilizes multiple event analyzers, each configured according to a distinct analysis heuristic, to evaluate different subsets of the event data and generate corresponding output signals. A higher-level event analyzer applies a further heuristic to the multiple output signals to generate a composite alert signal, indicating whether the combination of analyzed events collectively represents a security intrusion or other anomalous condition of sufficient severity to warrant alerting. Based on the composite alert signal, the platform performs an alert-based operation, such as generating a user-facing alert, initiating an automated mitigation, or updating a contextual model of system behavior. By combining the analytical outputs of heterogeneous heuristics, the disclosed architecture enhances the accuracy and contextual relevance of automated intrusion detection within complex computing environments.

Described are a system, method, and computer program product for distributed execution of a machine-learning model on a server cluster. The method includes initiating retrieval of a machine-learning model from a data repository and converting the machine-learning model to an executable format. The method includes transmitting the converted machine-learning model to each node of the server cluster and executing the converted machine-learning model on each node. The method includes generating an initial performance metric based on execution of the converted machine-learning model on each node. The method includes transmitting the plurality of initial performance metrics from each node to an external processor and combining the plurality of initial performance metrics to produce a combined performance metric. The method includes modifying a model hyperparameter of the machine-learning model based on the combined performance metric and executing the modified machine-learning model in a computer system to evaluate real-time event data.

Systems and methods are disclosed herein for identifying and predicting maintenance events with respect to assets, such as mobile machinery. A computing platform can include control circuit(s) (on-board and / or remote) configured to acquire machine fault data and machine utilization data. A feature set can be generated by transforming the machine fault data (e.g., by fusing the machine fault data with machine utilization data), normalizing the data, generating embeddings, and / or executing imputation algorithms to fill in missing values. A classifier model can be trained using the transformed machine fault data in conjunction with labeled maintenance event data to enable the trained classifier model to learn to recognize utilization-specific fault code patterns. The trained classifier model can generate inferences and predictions regarding machinery maintenance events without the use of maintenance data.

Automatic detection and handling of security-related anomalies by utilizing machine learning and a large language model (LLM). A computerized method for detecting and handling security threats in an organizational network of an organization includes: (a) collecting event data that pertain to organizational users, organizational devices, and organizational resources of the organizational network; (b) constructing user profiles, device profiles, and resource profiles; (c) constructing Organizational Context information that pertains to organizational users, organizational devices, and organizational resources; (d) constructing a time-series of events, enriched with the Organizational Context information; (e) analyzing the time-series of events using a Machine Learning process that detects an anomalous event, and automatically generating an alert message pertaining to and describing the anomalous event.

Systems and methods for adaptive threat detection system configuration based on advanced environmental security ranking. A method includes real-time adjustment of system components, including EDR agents, event scoring units, and event enrichment units, in response to changing threat landscapes and network environments. A method further includes retraining machine learning models, controlling thresholds, and selecting event datasets for training and testing. Additionally, a system configuration manager dynamically adjusts detection engine parameters, including correlation levels, rule-based detector settings, and the depth of analysis.

The invention discloses a network security alarm intelligent identification method and device, and relates to the technical field of network security alarm identification, and the method comprises the steps: carrying out the preprocessing of original data from network traffic, log files, user behavior records, a firewall and an intrusion detection system, and obtaining a multi-dimensional security event data set; node modeling and edge relation modeling are carried out based on a GNN and the multi-dimensional security event data set, and cross-system and cross-time-dimension security event association features are extracted to obtain a high-dimensional context sensing feature vector set; a time sequence anomaly detection model is constructed based on a high-dimensional context sensing feature vector set and a time sequence analysis technology, the high-dimensional context sensing feature vector set is input into the time sequence anomaly detection model, and weighting calculation is performed on each time step by extracting time dependent features and combining an Attention mechanism. Outputting an abnormal score vector corresponding to each time point; and constructing a behavior deviation function based on the multi-dimensional security event data in combination with deep learning data.

The invention discloses a security event traceability and response management platform suitable for a wind and light storage station. The platform comprises a station edge layer and a cloud middle platform layer. The station edge layer comprises an acquisition and isolation unit, a time synchronization and evidence shaping unit and a credible evidence storage and equipment snapshot unit; the evidence storage module is used for underlying data acquisition, time synchronization, evidence shaping and credible evidence storage to form original event data and an encrypted evidence chain; the cloud middle platform layer comprises a cross-domain causal atlas and root cause analysis engine, a control physical consistency verification model, a hierarchical response and energy storage security state control unit, a drill and strategy verification unit and a redisk and adaptive optimization unit; the method is used for cross-domain causal analysis, control consistency verification, hierarchical response decision and strategy redisk optimization. Unified acquisition, credible recording and cross-domain traceability of security events are realized, event sources and propagation paths can be accurately identified in a complex operation environment, and closed-loop control of security response is supported.

The invention relates to the technical field of network security, in particular to a network security operation and maintenance automatic analysis and response system based on artificial intelligence, which comprises an operation sensing module for acquiring a server state, extracting a security log, monitoring a network protocol and a transmission direction, identifying a performance bottleneck, sorting event data and generating a sensing summary sheet, the path offset identification module tracks a cross-domain path, identifies an abnormal path and generates an offset list; the behavior chain construction module sorts an event sequence and generates a behavior chain graph; the response processing module arranges processing steps, records a response process, generates an operation and maintenance execution sequence, synthesizes an output module to complete a process and generates an analysis result. According to the invention, by comparing the server resources with the historical reference, the abnormity is identified, and the event behavior basis is provided. And tracking cross-domain path lag and abnormity, and positioning threats. Arranging event data, and constructing an attack chain. The automatic response improves the speed and accuracy, shortens the response time, and enhances the attack handling capability.

The invention discloses a risk policy verification method, device, system, equipment and medium, and the method comprises the steps: obtaining a plurality of risk policies to be verified and a business event data set of a plurality of business events, and storing the plurality of risk policies and the business event data set in a sandbox environment; generating a plurality of groups of simulation strategy schemes corresponding to each business event based on the plurality of risk strategies and the at least one execution strategy in the sandbox environment; performing backtracking on each service event based on the request parameter of each service event and the multiple groups of simulation strategy schemes in the sandbox environment to generate a backtracking result; and verifying the plurality of risk policies based on the backtracking result and the response result in the sandbox environment. Through the above mode, on the basis of batch automatic backtracking verification of real business data, the risk strategy verification efficiency is effectively improved on the premise of ensuring the data isolation security.

Data platforms described herein are configured to monitor a compute environment and to use machine learning models for managing security threat alerts for the compute environment. Such a data platform may identify, based on event data indicative of events occurring in the compute environment, a set of detected security threats present within the compute environment. Using a machine learning model trained based on previous event data indicative of events that occurred previously, the data platform may reduce the set of detected security threats to form a subset of prioritized security threats. The data platform may then provide security threat alerts for the compute environment in a manner that emphasizes alerts associated with prioritized security threats over alerts associated with other detected security threats of the set of detected security threats. Corresponding methods, systems, and products are also disclosed.

The invention discloses a power grid snapshot playback method and device based on distributed event traceability, and belongs to the field of power systems, and the method comprises the steps: collecting time sequence event data generated by a power grid in real time according to a distributed time sequence library, and storing the time sequence event data in real time; when the time sequence event data meets any one of a time period triggering condition, an event counting triggering condition and a state mutation triggering condition, collecting current equipment operation information of a power grid to generate a power grid snapshot, and storing the snapshot to a MongoDB database by adopting an NoSQL storage method; and in response to a state query request of a user containing a plurality of device IDs and target time points corresponding to the device IDs, judging a playback type, if the request is a single-snapshot playback request, performing playback according to the device IDs and the target time points, and if the request is a multi-snapshot playback request, performing playback according to the device IDs and the corresponding target time points. Therefore, by implementing the method and the device, the replay efficiency of the power grid snapshot can be improved.

The invention discloses an insurance short video content generation method and device, a medium and a program product, and relates to the technical field of artificial intelligence. Semantic analysis is performed on hot event data to extract core risk elements, so that the system can capture risk features concerned by the current society. The core risk elements are matched with the guarantee demands of the potential users, the guarantee gap is determined, and the association mapping between the hot event risks and the guarantee demands is established, so that the determination of the target insurance product has risk guidance. And determining a cue word generation strategy from a preset strategy library and inputting the cue word generation strategy into the insurance field large language model to generate a first draft of the content, thereby realizing automatic generation of the content. And in combination with a preposed compliance detection mechanism, the compliance of the generated content is ensured. Quick response and generation of the insurance short video content are realized, and the content generation efficiency is improved.

The invention relates to the technical field of intelligent scheduling and management and control of a harbor storage yard, and discloses a dynamic self-adaptive intelligent scheduling method of the harbor storage yard, and the method constructs a closed-loop process including environmental anomaly collaborative diagnosis, scheduling strategy dynamic reconstruction, and scheme execution and feedback based on a storage yard three-dimensional digital twinborn model and a multi-objective optimization scheduling model. The method comprises the following steps: collecting operation physical sign data of a plurality of devices in real time, and carrying out collaborative analysis and reasoning on the operation physical sign data, a device performance baseline and a historical environment event database, so as to diagnose hidden physical anomalies; according to the abnormal type and the task attribute, the weight or constraint of the optimization model is dynamically adjusted, and a self-adaptive scheduling scheme is generated; and finally, continuously updating the baseline library and the event library by executing feedback to realize online learning and optimization of the system. According to the method, the defects of a traditional scheduling method in the aspects of three-dimensional visualization, multi-objective optimization and dynamic response of environment anomalies are effectively overcome, and the intelligent level, safety and efficiency of port storage yard operation are remarkably improved.

The invention provides an RPA operation report automatic generation method based on full-business process data fusion and a financial robot thereof, and the method comprises the steps: collecting task logs, business action events and context data generated in the execution process of the financial robot, and standardizing the task logs, the business action events and the context data into a unified event unit; constructing a flow path diagram based on the event time sequence; performing structure comparison on the trajectory diagrams of different service periods, identifying node addition and deletion or path rearrangement, and generating a structure attribution result; combining the attribution result and the event data to construct a structure perception type semantic index; and finally, automatically filling the indexes and attribution contents into the financial operation report template to generate a complete report. The method does not need to depend on a process definition file, can automatically restore a real execution path, achieves the interpretability analysis of process change and the full-automatic generation of a report, and remarkably improves the accuracy, integrity and decision support capability of financial RPA operation analysis.

The invention relates to the technical field of scheduling control, and provides a production line intelligent scheduling system and a multi-device cooperative control method, and the method comprises the steps: obtaining event data of a production line, recognizing a causal relationship between a device and a task, generating a causal graph network of the production line, and broadcasting a causal influence factor. The capability performance of the equipment is predicted and mapped into equipment capability constraints to judge whether to broadcast subpackage requests and determine takeover equipment or not, a causal graph network is updated in a closed loop mode, causal influence factors are broadcast, production state changes can be sensed in real time, potential risks can be accurately predicted, and optimization decisions can be quickly made; efficient configuration of production resources and stable operation of the production process are achieved, the production cost is effectively reduced, and the production efficiency and the product quality are improved.

The invention provides a gushing prediction method and system for grouting engineering, and belongs to the technical field of tunnel and underground engineering construction safety monitoring. The method comprises the following steps: collecting grouting pressure, flow, slurry viscosity and micro-seismic event data; calculating a slurry resistance index and a micro-seismic event aggregation density; inputting the initial gushing risk index into a pre-trained machine learning model to obtain an initial gushing risk index and a confidence score; according to whether the confidence score is lower than a preset threshold, selecting direct early warning or entering a parameter correction path; when the confidence coefficient is low, dynamic correction factors are generated based on scores and risk changes, the calculation weight of the slurry resistance index is adjusted, corrected parameters are obtained, and secondary prediction is carried out; and integrating the initial prediction result and the secondary prediction result to obtain a final risk index, and executing graded early warning. According to the invention, the accuracy and reliability of gushing risk prediction are improved through a confidence-driven double-path decision and parameter on-line adaptive correction, and a constructed early warning mechanism.

An apparatus and method for fraud-risk assessment. Event data including identification, transactional, geospatial, biometric, and behavioral signals, are received and processed by a scoring module employing one or more analytical models to generate a score. Based at least in part on the score, a security action is selected and a risk-tiered alert is generated. An audit record including the score, rationale, action, timestamps, and associated data characteristics is stored in one or more datastores configured for secure, verifiable, or immutable recordkeeping. The operations may be performed in any order or in parallel. Embodiments include velocity controls for real-time flows and post-event workflows to escalate to external recipients, create or update an investigation case, or supply features for model retraining, while preserving auditability.

The invention belongs to the technical field of logistics management of the Internet of Things, and provides a logistics data monitoring and management system and method based on the Internet of Things, and the system comprises an event triggering module, a state abstract module, an event aggregation module and a situation early warning module. Setting an edge intelligent node integrated with a lightweight rule engine to judge whether an event judgment rule is triggered or not, if so, packaging an event data packet, if not, generating a state abstract for original state data in a set aggregation time window, and dynamically adjusting the window length of the aggregation time window based on resource quantitative evaluation; the method comprises the following steps: receiving event data packets and state abstracts at a cloud service platform side, respectively storing the event data packets and the state abstracts, setting and adopting an event aggregation analysis engine to analyze and aggregate the event data packets to generate a composite event, constructing and adopting a situation awareness engine, and performing real-time analysis and prediction based on a composite event stream. And generating a logistics situation prediction trajectory and carrying out graded early warning.

The invention relates to a power supply service digital graph linkage monitoring, control and early warning method. The method comprises the following steps: performing space-time anchoring processing on power supply service multi-source heterogeneous data corresponding to a target power grid to obtain space-time anchor event data; according to the space-time anchor point event data, carrying out space-time knowledge fusion on a multi-source association relationship of the target power grid to obtain power supply service space-time knowledge graph data; according to the power supply service space-time knowledge graph data, performing multi-task risk prediction on the power supply service risk of the target power grid in a preset prediction time window to obtain a dynamic early warning trigger result; according to the dynamic early warning triggering result, performing causal attribution anti-fact deduction on the risk rise key driving factor of the power supply service risk to obtain an interpretable early warning result; the interpretable early warning result is used for performing behavior disposal arrangement on the target power grid. By adopting the method, the prospective prediction of the risk can be realized.