969 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 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 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.

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 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 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 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.

Disclosed techniques relate to using machine learning for sports applications. In an example, a method for generating sports tracking data using multimodal generative models may include receiving one or more inputs by a user. The input may be related to a description. The method may further include extracting metadata items relating to the description. The method may further include mapping the metadata items to at least one or more event streams. The method may further include receiving content items relating to the event streams. The event streams contain content items that are outputted by a multimodal sports learning language model (LLM). The method may further include transmitting the content items to a user device for display.

The invention provides a user behavior tracking and portrait generation system, which is characterized in that multi-source original behavior data is acquired through a data acquisition module, and a standard behavior event stream is generated through cleaning and standardization; the behavior modeling module is used for carrying out cross-event association and self-defined time window combination modeling on a standard event flow based on a configurable rule engine to generate a complex event flow; the real-time processing and calculation module performs real-time aggregation calculation on the complex event stream, generates a real-time behavior index, a trigger signal and an incremental portrait snapshot in combination with a preset rule, and pushes a behavior trigger signal to a downstream service system; the user portrait management module dynamically updates user tag weights and values according to the real-time indexes, and generates a target user portrait tag library and a lightweight tag change event stream; the data storage and query module stores data of each link; and the visual configuration and operation and maintenance module issues a configuration instruction through a visual interface, and monitors full-link operation and task scheduling. And real-time accurate portrait construction and instant service response are realized.

The invention relates to an event-based lip reading method and device, equipment and a storage medium. The method comprises the following steps: collecting an original event stream of a lip sequence image through an event camera; therefore, the brightness change of each pixel can be asynchronously recorded with microsecond-level time resolution, and ultra-low delay, high dynamic range and sparse data representation are realized. Converting the original event stream into a frame-shaped event tensor based on a voxel representation method to obtain a voxelized event body; performing spatial feature extraction on the voxelized event body through a front-end network in an event-based lip reading model to obtain multi-scale spatial features; performing time-dependent modeling on the multi-scale spatial features through a rear-end sequence model in an event-based lip reading model to obtain a sequence code; therefore, space and time features can be fused, and the accuracy and stability of sequence coding are improved. And determining the lip reading recognition content according to the sequence code. Therefore, stable recognition of lip movement can be realized, and lip reading accuracy is improved.

The invention relates to the technical field of resource scheduling, in particular to a digital-intelligent non-accompanying service management scheduling system, which comprises a service baseline construction module for instantiating a service item into a standard plan event flow; the state portrait generation module is used for collecting service object data flow, establishing a dynamic time sequence signal by applying a recurrent neural network model, and quantifying the occurrence probability of a future abnormal event through multi-step prediction; when the triggering is abnormal, executing causal two-way collision positioning reasons by applying the knowledge graph; the task load calculation module is used for integrating the plan event flow into a plan task component, and generating a worker resource list in combination with the predictive risk score and the worker real-time load and space reachability analysis containing the fatigue index; the system further comprises a task distribution module, the optimal staff is determined and a closed-loop scheduling instruction is generated by minimizing a composite cost function containing predictive load cost, and digital intelligent unattended service management from prediction to scheduling is realized.

The invention discloses an event stream image reconstruction method based on hierarchical uncertainty, and the method comprises the steps: S1, inputting event stream data, and carrying out the random sampling, and constructing a multi-mask event frame; s2, inputting the processed event frame into an event flow reconstruction network to obtain a plurality of reconstructed images, the reconstructed images sharing a real image, and establishing an uncertainty model between the reconstructed images and the real image; and S3, adjusting network parameters to train and test a neural network, and screening images needing to be optimized from the uncertainty scores. The accuracy and quality of event stream image reconstruction are improved, and the adaptability and robustness of the model to a dynamic scene are enhanced.

The invention discloses a cross-channel marketing automation method and system based on real-time behavior triggering. The method comprises the following steps: capturing a user behavior event flow in real time through a front-end acquisition unit; performing real-time mode matching on the event stream by utilizing a complex event processing engine, and generating a marketing trigger signal when a preset rule is matched; dynamically selecting an optimal channel for the user based on a real-time scoring model in response to the signal; the marketing content is sent to the selected channel through the unified execution module; and finally, tracking user feedback and updating model parameters and user preferences in real time to form closed-loop optimization. The corresponding system comprises a front-end data acquisition module, a streaming event processing module, a real-time intelligent decision-making module, a multi-channel execution module and a closed-loop optimization module which are used for implementing the steps. According to the method, millisecond-level intention perception, cost and experience optimal channel intelligent decision and data-driven adaptive optimization closed loop are realized, and the problems of delayed response, extensive channel and lack of self-adaption of traditional marketing are solved.

The invention relates to a self-adaptive cooperative control method and system for industrial equipment, and the method comprises the steps: collecting and standardizing the operation data of multiple equipment into a time sequence event stream in real time; performing time sequence alignment on the multi-device action sequence, eliminating phase deviation, and extracting statistical features to construct a cross-device feature library; using a multi-task model to decouple cross-device sharing features and device exclusive features; dynamically fusing the shared features and the exclusive features, extending the shared features to newly added equipment in combination with transfer learning, and extracting the exclusive features to adapt to hardware features; deploying a lightweight inference model at an edge end, loading cloud shared base model parameters and local exclusive parameters, and performing real-time fusion to generate a control instruction; the cloud end aggregates the data, optimizes a global sharing model through incremental learning, and encrypts and synchronously updates parameters to the edge end in a sub-coding manner; and integrating the feature library, the model architecture and the edge cloud collaboration mechanism, generating a global collaboration strategy, and realizing multi-device adaptive collaboration control and rapid expansion.

The invention relates to the technical field of network security, and particularly discloses a network security situation awareness prediction system and method based on a large model, and the method comprises the steps: carrying out the cleaning analysis and key field extraction of a network security log to generate a structured event stream, carrying out the modal characterization processing to extract the deep semantic feature representation of the structured event stream, and carrying out the modal characterization processing; and meanwhile, time sequence modeling is performed on the network flow data, and a time sequence characteristic mode of the network flow is learned. Then, a cross-modal semantic alignment technology is adopted to realize feature dimension alignment on network security log semantic features and network traffic time sequence features, and fine-grained feature transfer interactive reasoning is performed on the network security log semantic features and the network traffic time sequence features to deeply mine potential association and cooperative behavior modes between the network traffic and log events; and the specific stage of the attack behavior is predicted by using the multi-mode large model subjected to field fine tuning. According to the method, the attack behavior track can be accurately captured by fully utilizing complementary information among heterogeneous data, and the accuracy and foresight of situation awareness are improved.

The invention discloses a digital RMB related data processing method and device based on a large model technology. The method comprises the following steps: acquiring multi-modal data involved in a transaction event stream of digital RMB, and mapping the multi-modal data to a unified semantic space for semantic alignment to obtain target data; extracting a target triple from the target data, and constructing an entity relationship network according to the target triple to obtain a target knowledge graph; an abnormal path in the target knowledge graph is recognized by adopting a graph enhanced reasoning model in combination with a risk management and control rule, the abnormal path corresponds to an abnormal processing behavior of the digital RMB, and the risk management and control rule is used for indicating features and modes of rules which should be obeyed by the processing behavior of the digital RMB. According to the method and the device, the technical problem that a data processing mode in related technologies is difficult to meet data mining and intelligent analysis requirements of digital RMB multi-dimensional semantic association intensions is solved.

The invention discloses an auditing data security management and control system based on big data, and relates to the field of data management and control, and the system comprises a distributed crawler which collects auditing logs, operation records and network traffic in real time. And the stream batch integrated engine performs metadata alignment and timestamp synchronization on heterogeneous data to generate a unified audit event stream. The intelligent desensitization module carries out gradient desensitization on the sensitive fields and constructs reversible blood relationship mapping. According to the hierarchical storage, data are stored in an encrypted memory pool, an SSD and a cold standby cluster according to data sensitivity and access frequency, and consistent hash load balancing is achieved. Multi-modal safety monitoring analyzes data in real time through a three-level assembly line. And the hierarchical response module writes a permission chain according to the risk trigger action and the key audit event hash value, and constructs a tamper-proof evidence chain. The method has the advantages that the data security, privacy protection and efficient emergency response are ensured through intelligent desensitization, hierarchical storage and security analysis of the audit data, and the non-tampering property of the data is ensured by utilizing the block chain technology.