1313 results about "Event stream" patented technology

The invention belongs to the technical field of multi-target global dynamic production scheduling, and discloses a manufacturing resource dynamic production scheduling data comprehensive processing method, which comprises the following steps: acquiring multi-source heterogeneous data, constructing a field mapping table, constructing an equipment fingerprint database in combination with historical equipment performance data, dynamically adjusting confidence coefficient weight, and further judging an equipment state. Based on an equipment state judgment result, integrating operator report data and multi-source heterogeneous data through a preset rule base trigger event, and generating an abnormal event flow with a context tag; based on the abnormal event type and the equipment state judgment result, a recommendation scheme is constructed through a production scheduling knowledge graph, and a production scheduling instruction set is generated; according to the production scheduling instruction set, generating a local optimization scheme and a global optimization strategy, and forming a final execution scheme; calculating a resource utilization rate and a bottleneck process index through the 3D virtual workshop model, and generating a difference analysis report; according to the method, global resource scheduling and closed-loop iteration are realized, and the production efficiency and the system robustness are improved.

The invention discloses an event and RGB image fused SNN target tracking method and system, and the method comprises the steps: converting an event flow from an event camera into an event frame image through a representation method based on time stacking, and enabling the event frame image to be aligned with an RGB frame image from an RGB camera in time and space; performing target detection on the event frame image # imgabs0 # and the RGB frame image # imgabs1 # after alignment by using a spiking neural network SNN, wherein the spiking neural network SNN comprises a feature extraction module, a feature fusion module and a detection head; and learning the similarity of the targets by using the anchor frame extracted by the spiking neural network SNN and the fusion feature map # imgabs2 # to perform multi-target tracking. The invention aims to realize fusion of the fusion event and the RGB image so as to ensure high precision and high efficiency of multi-target tracking, and keep stable detection performance and tracking performance in static and high-motion scenes.

The invention relates to the technical field of data processing, in particular to a multi-AI algorithm collaborative intelligent management system based on a large model, and the system comprises a data access module which collects original messages of terminal equipment of multiple manufacturers; the protocol adaptation module analyzes a device communication characteristic spectrum through a quantum entanglement separator, and outputs a standardized time-space event stream; a federation modeling module creates a parallel calculation instance, and a quantum tunneling mechanism is utilized to fuse the feature vectors to generate an equipment association topology model; the chaotic scheduling module generates an atomic task fractal network and assigns the atomic task fractal network to edge nodes; the interface optimization module calculates an interface field mapping relation through a quantum Boltzmann machine; and the closed-loop correction module triggers equipment communication characteristic spectrum re-calibration. Multi-source device communication differences are eliminated through quantum protocol analysis, federated quantum fusion breaks through algorithm collaboration barriers, chaotic fractal scheduling realizes resource dynamic optimization, a closed-loop collaboration system of device access to interface configuration is formed, and the problems of protocol incompatibility of multi-source heterogeneous devices and AI algorithm collaboration obstacles are solved.

The invention relates to the technical field of e-commerce data processing, in particular to an e-commerce sales platform background data management method and system. The method comprises the following steps: S1, collecting a commodity dynamic data stream, a user behavior event stream and a promotion strategy stream in real time, and generating a standardized data stream through time sequence alignment; s2, constructing a dynamic coupling data cube; s3, executing a real-time decision: in response to the payment request, selecting an inventory distribution or replacement commodity pushing strategy based on a commodity-user coupling matrix value; in response to the resource overload state, triggering a resource scheduling strategy; and S4, dynamically adjusting calculation parameters of the coupling matrix according to decision execution feedback. By solving the problems of real-time standardization processing of multi-source heterogeneous data and real-time coupling of dynamic inventory and user behaviors, the data processing capacity, inventory distribution efficiency and recommendation accuracy of the platform are remarkably improved.

The invention discloses a conference memory enhancement method and device for a conference tablet and a medium, and relates to the technical field of intelligent conference equipment, and the method comprises the steps: collecting multi-modal data in real time based on a preset data collection technology, and coding the multi-modal data into a space-time event stream according to the time sequence of the multi-modal data; identifying handwritten characters on the conference tablet, and extracting a key tag in a speech in combination with a pre-trained natural language processing model to construct a conference knowledge graph corresponding to the current conference; comparing the conference knowledge graph with a historical conference knowledge graph, determining conflict content, and superposing a virtual information layer on a physical interface of the conference tablet; and querying query association information corresponding to the semantic query request based on the conference knowledge graph and the time-space time stream to determine semantic contradictions, and displaying a historical conference evidence chain of the semantic contradictions based on the virtual information layer.

The invention relates to the technical field of campus management, and particularly discloses a smart campus management system based on big data, an event-driven data management architecture is used for dynamically collecting, integrating and associating multi-source heterogeneous data in a campus, and generating a standardized event stream; the dynamic resource scheduling engine is in communication connection with the event-driven data governance architecture, generates a resource allocation instruction based on event types and priorities in event streams, and dynamically deploys campus resources; the closed-loop evaluation optimization module receives a resource scheduling result of the dynamic resource scheduling engine and generates a multi-dimensional evaluation index, and the multi-dimensional evaluation index is fed back to the data governance architecture through root cause analysis so as to optimize a subsequent decision; the privacy enhancement processing unit integrates a federated learning framework and a differential privacy algorithm, performs collaborative analysis on cross-system data and ensures the anonymity of individual data; through three core technologies of dynamic data management, intelligent resource scheduling and closed-loop evaluation optimization, intelligent upgrading of the whole campus management process is realized.

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 embodiment of the invention discloses a method and system for identifying transaction risks, and the method comprises the following steps: obtaining multi-source heterogeneous data, and initializing a dynamic transaction graph according to a multi-source heterogeneous data flow; the multi-source heterogeneous data comprises invoice data, fund flow data, an enterprise stock equity relationship and geographic data; receiving a multi-source event stream through a message queue, and updating the dynamic transaction graph in real time based on the multi-source event stream; for enterprise nodes affected by updating in the dynamic transaction graph, feature aggregation is executed, topological risk features are extracted, and risk scores of the enterprise nodes are output; verifying the consistency of the invoice hash and the fund flow hash through a relay chain protocol, and obtaining a cross-chain verification result; and according to the risk score and the cross-chain verification result, identifying a transaction risk. According to the embodiment of the invention, systematic risk identification is realized through data integration, graph construction, risk mining and cross-chain verification, and the accuracy of invoice risk identification can be improved.

The invention provides a business arrangement scheduling method based on event driving, which comprises the following steps of: constructing a unified business arrangement framework, describing a business process by using a unified process definition language, and defining a process metadata standard; adding a general process engine with a plug-in mechanism in the business arrangement framework; establishing a real-time event processing architecture by taking an event as a core, and adding an event-driven business process engine; constructing a monitoring platform on the basis of the real-time event processing architecture, performing real-time monitoring on an event stream and event processing, and performing visual display; a unified retry mechanism is introduced, and a retry strategy and retry management are defined; according to the method, a unified, efficient and easy-to-monitor and easy-to-maintain business arrangement framework is established, so that the scheduling process is conveniently arranged through event-driven business, the endpoint retry and business flow monitoring capability is provided, the realization difficulty of monitorable and retry in the business is simplified, and the robustness of the business is improved.

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 an air target detection method fusing an event camera and deep learning, and the method comprises the steps: completing the collection of target information through a high-speed imaging event camera, and obtaining time-space event flow data; performing de-noising processing on the time-space event stream data output by the event camera by adopting a discrete point de-noising method to obtain de-noised time-space event stream data; performing image reconstruction on the event stream data, labeling images, and dividing the images into a training set and a test set; a single-stage convolutional neural network target recognition model is constructed, multi-scale context feature information is acquired through a backbone network module, the multi-scale context feature information is fused layer by layer from top to bottom through a neck network module, and prediction results of a target are analyzed and screened by applying an anchor frame and non-maximum suppression. According to the method, the advantages of the event camera and deep learning are fully combined, a model with higher robustness is provided, and high-precision recognition of the aerial target is realized.

The invention belongs to the technical field of image processing, and discloses a frame image and event stream feature fusion-based target detection method, which comprises the following steps of: acquiring RGB (Red, Green, Blue) images and event stream data, and constructing a time-space synchronous multi-modal data pair; constructing a double-flow feature extraction backbone network to extract events and RGB features; based on a cross attention mechanism, multi-head attention is utilized to realize semantic alignment; the fusion weight is adaptively adjusted according to the statistical distribution; a multi-level fusion network is constructed, cross-scale fusion is performed by using an FPN pyramid, and target positioning and classification feature expression are cooperatively enhanced through a spatial semantic aggregation module. According to the target detection method based on frame image and event stream feature fusion, through a multi-modal data collaborative perception and self-adaptive feature optimization mechanism, the detection robustness in a complex scene is remarkably improved, the dynamic target omission ratio and the error recognition rate are effectively reduced, and the target detection efficiency is improved. And an all-weather high-precision environment perception capability is provided for an automatic driving system.

The invention relates to a military training-oriented dynamic multi-modal knowledge graph retrieval method, which comprises the following steps of: acquiring a multi-source data set to generate a unified original event stream; based on the original event flow, preliminarily constructing a knowledge graph by adopting a Bayesian prior-based tetrad model, locally embedding and updating through a military training event incremental graph neural network, and dynamically re-estimating the confidence of nodes and edges through a Bayesian evidence propagation algorithm; a pre-training language model in the military field is adopted for analysis and semantic coding, a query semantic vector is generated, a retrieval module is constructed, and a retrieval result is output based on the knowledge graph; and constructing a military retrieval strategy optimization network based on a reinforcement learning framework, dynamically adjusting a retrieval strategy, writing back user feedback data, and optimizing node embedding and retrieval strategy parameters. According to the method, multi-source heterogeneous data can be integrated, a dynamic credible graph is constructed, retrieval accuracy, timeliness and self-adaption are improved, and military training decisions are supported.

The invention discloses a multi-sensor fusion state estimation system based on an event camera. In the invention, an event representation method based on the combination of an event accumulator setting strategy and an optimized motion correction compensation method is provided, and a multi-sensor data fusion processing pipeline of event flow, imu inertial measurement and standard images is constructed based on the event representation method. The most advanced feature tracker DeepEvTracker driven based on event data is introduced for the first time and is combined with a KLT tracker based on an event motion compensation frame. The feature tracking loss in high-speed motion blur and HDR scenes is effectively reduced, and the feature updating frequency and the prediction displacement precision are remarkably improved. A multi-sensor fusion state estimation framework based on event data driving is elaborately designed, robust and accurate state estimation can be provided in a challenging scene, dense manual parameter adjustment does not need to be depended on, real-time performance is guaranteed, and meanwhile the method is superior to a current most advanced state estimation algorithm based on events and frames.

The invention discloses a fault determination method and device for a micro-service system, a product and electronic equipment. Relates to the technical field of artificial intelligence. The method comprises the following steps: determining an abnormal event flow according to abnormal monitoring information; an abnormal event causal graph is determined based on an abnormal event flow, and then a fault point, a fault chain and a fault classification are determined by utilizing an intelligent agent driven by a pre-training model and combining the event causal graph and a fault mode standard. According to the method, anomaly detection, fault classification and root cause positioning of the micro-service system are realized, and closed-loop diagnosis from anomaly discovery to root cause positioning is realized; secondly, performing depth feature engineering and semantic abstraction on the refined data from the perspective of events and causal relationships, fusing system behaviors and dependency relationships dispersed in different modal data into a unified event causal graph through a graph modeling technology, and providing comprehensive and high-dimensional input for fault reasoning of an intelligent agent; and the accuracy of micro-service fault determination is improved.

The invention belongs to the technical field of enterprise risk management, and relates to a multi-dimensional big data analysis risk assessment method and system, and the method comprises the following steps: obtaining enterprise financial data, production operation data and Internet public data; generating an enhanced event stream with dimension marks; an operation early warning identifier is generated when the operation dimension behavior meets a preset continuity condition, and a public opinion early warning identifier is generated when the public opinion dimension behavior meets a preset negative emotion condition; outputting a cross-dimension risk event report when the index exceeds a preset combination threshold value; tracing a historical enhanced event stream according to the cross-dimension risk event report, and constructing a risk evolution link diagram containing a source data event and an early warning event according to a time inverted sequence; and matching a preset response rule base to generate a graphical intervention scheme containing the risk hotspot position and the processing priority. The problem that a static billboard can only display isolated event points and lacks dynamic description of a risk space-time conduction path is solved.

The invention discloses an IT + OT full-link state detection and early warning method, and belongs to the field of system monitoring and intelligent operation and maintenance integrating information technology and operation technology. The method comprises the following steps: acquiring IT and OT system specific parameters and converting the parameters into a unified data stream; performing alignment quantization on the data stream to generate a two-dimensional event stream; constructing an IT and OT system causal atlas and extracting associated features; the dynamic pruning graph generates a monitoring node and issues an instruction; verifying the feedback data of the OT device to generate a progressive early warning; and adjusting an IT resource strategy according to the early warning and generating an OT instruction correction parameter. According to the method, multi-source data fusion, dynamic causal atlas analysis, cross-domain verification and progressive early warning technologies are adopted, the full-link state of the IT / OT fusion system can be sensed in real time, risk and fault evolution can be identified, early warning and response can be carried out, the monitoring comprehensiveness, early warning accuracy and response speed of the fusion system are remarkably improved, and the real-time performance of the fusion system is improved. And the stability and safety of system operation are enhanced.

The invention discloses a multi-agent learning event flow scheduling method and device and a storage medium. The method comprises the steps that structured data and unstructured texts of an event text set are received through a preset data interface; extracting text semantic features of the event text set by adopting a preset text understanding model, and generating event text features from the extracted text semantic features through a structured data processing model; and inputting the event text features into an agent combination for data processing, and scheduling process nodes according to an output result of the agent combination. According to the method, a multi-modal data fusion mechanism and a self-adaptive process scheduling system are constructed, so that comprehensive extraction of event text features and efficient collaboration among intelligent agents are realized, the automation level and the analysis depth of interaction abnormal behavior event processing are remarkably improved, and more accurate and more efficient technical support is provided for event processing.

The invention discloses a low-code development platform event arrangement method and device based on native JS, and relates to the technical field of visual programming, and the method comprises the steps: responding to an event arrangement request sent by a user through a visual development interface, calling a visual framework, and presenting an event arrangement canvas in the visual development interface; configuring event nodes and event process connections in the event arrangement canvas; generating event flow configuration data based on the event node and the event flow connection; converting the event flow configuration data into a native JavaScript code, calling a rendering engine, and rendering a page style and page data of the visual development interface according to the native JavaScript code; and in response to an interactive operation of a user on the visual development interface, triggering the corresponding native JavaScript code to execute a corresponding event process. The development efficiency can be effectively improved, and the development period is shortened.

The invention provides a background authority management system and method for adaptive risk control. Comprising a data acquisition module used for acquiring behavior data and environment data associated with an account and converting the data into an event stream; the context management module is used for associating the context attribute set with the event flow; the feature construction module is used for generating a multi-dimensional feature vector based on the event stream and the context attribute set; the adaptive risk assessment module is used for calling at least two heterogeneous models to perform parallel reasoning on the multi-dimensional feature vectors; the decision engine module is used for mapping the risk weight into an access control instruction and an audit instruction; the auditing module is used for recording auditing data according to the auditing instruction; and the authority management module is used for calculating an authority weight according to the attenuation parameter and judging and generating an authority recovery instruction or a maintenance instruction according to a threshold value. According to the method, event stream unification and context binding, real-time risk assessment and strategy linkage and permission adaptive recovery closed loop can be realized.

Techniques for using machine learning (ML) model(s) to generate a textual summary of a stream of events at least in part by receiving information corresponding to the stream of events corresponding to interactions between a user and application program(s) executing on a computing device, the information comprising, for each of multiple events in the stream of events associated with a process, metadata associated with the event, wherein metadata for a particular event specifies values for attributes of the particular event; processing, using at least one ML model, the metadata associated with the multiple events to generate multiple corresponding textual summaries of the multiple events; generating, using the textual summaries of the multiple events, a textual summary of the process performed by the user through the interactions between the user and the application program(s) executing on the computing device; and outputting the textual summary of the process.

The invention relates to the technical field of image processing, in particular to a moving image deblurring method based on multi-modal bidirectional fusion, which comprises the following steps of: firstly acquiring multi-modal data information of a moving blurred image, and then acquiring preprocessed event stream information which is compatible with image information and is in a three-dimensional voxel format based on the event stream information; performing multi-modal bidirectional fusion processing on the image information and the preprocessed event flow information to obtain fusion features of the motion blurred image; taking the fusion features as input, carrying out residual connection step-by-step learning through a residual connection module, and generating a residual sequence; and based on the residual sequence, obtaining a deblurred motion clear image through an event-based double integral physical model. According to the method, through a bidirectional fusion strategy from shallow to deep, the development of moving image deblurring is promoted, a new thought and method are provided for multi-modal data fusion, and technical support is provided for efficient application of other tasks such as automatic driving and target detection.

The present invention discloses a standardized alarm process method, system, electronic device and storage medium, including obtaining alarm data indicators in alarm information; converting alarm data indicators into normalized indicators through a normalization algorithm; fusing normalized indicators with semi-structured data in corresponding alarm information to form a standardized alarm data structure; designing business tags based on systems or businesses, designing response tags based on notification methods and upgrade strategies, and adding business tags and response tags to standardized alarm data structures; receiving alarm event streams; filling business templates with data in alarm event streams based on tag matching rules; generating contextualized alarm data streams based on business templates. The present invention realizes a standardized alarm process method and system through multi-dimensional tool integration in multi-cloud and hybrid environments, and drives the deepening of alarm capabilities based on multi-algorithms, thereby improving the uniformity, efficiency and response speed of alarm management.

Automated computer-implemented methods and systems for discovering incidents occurring with objects running in a data center and executing remedial measures that correct the incidents are described herein. The methods and systems discover clusters of alerts in a stream of alerts triggered by a stream of events occurring with objects in the data center. User feedback is used to identify alerts with related event types in each cluster of alerts that corresponds to separate incidents occurring in the data center. The methods and system compare a set of runtime alerts to each incident to determine one or more similar incidents to the set of runtime alerts. The one or more similar incidents and corresponding remedial measures are displayed in a GUI with each remedial measure selectable to launch an operation that corrects one of the problems represented by the one or more similar incidents.

The invention provides a ground-based unmanned aerial vehicle positioning scheme based on event camera and millimeter-wave radar high-frequency fusion, and belongs to the technical field of the Internet of Things. Event stream data of an unmanned aerial vehicle are acquired based on an event camera; acquiring radar measurement data of the unmanned aerial vehicle based on a millimeter wave radar; and based on the event stream data and the radar measurement data, carrying out time synchronization cooperative tracking processing on the unmanned aerial vehicle to obtain ground positioning measurement data of the unmanned aerial vehicle. According to the technical scheme of the invention, the method can achieve the stable high-precision and low-delay ground positioning of the unmanned plane through the coordination of the ultrahigh sampling frequency characteristics of the event camera and the millimeter-wave radar.

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.

Disclosed embodiments receive an event stream from a remote capture agent. The event stream includes timestamped event data generated by the remote capture agent based on network traffic monitored by the remote capture agent. A graphical user interface (GUI) is caused to be displayed for obtaining configuration information for configuring the generation of time-series event data from network packets captured by the remote capture agent. A set of statistics is generated from the time-series event data, and the configuration information is updated to trigger subsequent storage and processing of at least a portion of the event stream by one or more components on a network based on one or more of the statistics, a storage limit associated with the time-series event data, an index volume of the event stream, a historical trend associated with the statistics, or input through the GUI.

The invention discloses a vehicle fault diagnosis method and device and a computer program product, and the method comprises the steps: configuring a state variable according to a selected bus message and a selected bus signal, constructing a signal and message event processing logic based on the state variable, and obtaining a decision script; and calling the decision script to execute decision analysis on the extracted bus message and the bus signal according to a fault vehicle and a fault occurrence time period, triggering to execute the signal and message event processing logic when detecting that the bus message or the bus signal jumps, and outputting a diagnosis result. According to the method, based on decision analysis of an event mechanism, bus messages and signals are regarded as event streams, event decision analysis is triggered only during jumping, the number of jumping times of vehicle end signals is small, and analysis time is greatly shortened; in the aspect of resource consumption, messages / signals do not need to be aligned, each message / signal independently triggers an event at a jump edge, a state variable automatically memorizes a current value, and memory consumption and alignment time are reduced.

The invention relates to a computer data processing technology, in particular to a real-time pipeline safety early warning method and system. The method comprises the steps of performing computer data processing according to pipeline topology information, dividing risk units and generating a risk unit session configuration structure; performing automatic feature extraction to generate a real-time risk feature vector; through machine learning fusion modeling, real-time features and historical baseline features are fused, and a dual-time-scale fusion feature vector is constructed; a preset intelligent model is used for reasoning and event judgment, a structured event flow account and resource adjustment parameters are generated, periodic self-optimization of sampling configuration, a model threshold value and an early warning strategy is driven, and therefore closed-loop intelligent monitoring and early warning of the state of the pipeline auxiliary equipment are achieved. According to the method, the early warning accuracy can be effectively improved, false alarm and missing alarm can be remarkably reduced, the adaptive capacity of the system is enhanced through a closed-loop learning mechanism, and an efficient solution is provided for application of the next generation of information network industry in related fields.

An asynchronous stream of security events is added to a data lake for enterprise security by identifying groups of related events related to a security threat, and creating rules to fold these related events into a single security event along with metadata. The folding rules may then be applied to security events in the event stream to compress data in the data lake and improve detection efficiency.