21 results about "Event stream processing" patented technology

The invention relates to the technical field of cognitive inference, and discloses a cognitive inference system fusing a dynamic knowledge graph and deep learning. The system comprises a cognitive event stream processing module which is used for acquiring a real-time event stream and a cognitive target stream, identifying entity state change and extracting a reasoning intention; the graph mapping generation module is used for receiving the entity state change and the reasoning intention, constructing a mapping relation with knowledge graph topology and generating a multi-dimensional graph mapping library; the cognitive path decision-making module is used for extracting key topological characteristics from the multi-dimensional map mapping library, calculating a reasoning path migration probability and generating an initial cognitive path set; the time sequence evolution prediction module is used for carrying out time sequence evolution modeling on the knowledge graph topology based on the initial cognitive path set, identifying node state offset and updating a multi-dimensional graph mapping library; and the reasoning optimization module is used for analyzing a reasoning stage and a reasoning dependency relationship of the cognitive target flow according to the updated multi-dimensional map mapping library, and generating a dynamic cognitive reasoning library.

The invention discloses a user interest matching network marketing system fused with a knowledge graph, and relates to the technical field of computers. The system is used for solving the problem of marketing decision deviation caused by lack of dynamic context awareness and causal reasoning ability in existing user interest modeling. The method comprises the following steps: firstly, constructing a dynamic knowledge graph through distributed event stream processing, and structurally representing a real-time situation of a user as a graph sequence with a timestamp; secondly, extracting user interest evolution characteristics by using a time sequence convolution graph network, and quantifying influence weights of situation nodes through a multi-head attention mechanism; then, constructing a causal inference model to eliminate exposure and position deviation, and extracting essential interest characterization of the user by using an anti-fact learning framework; and finally, dynamically optimizing a knowledge graph structure based on a gradient propagation algorithm, and forming a closed-loop learning system from decision to perception. According to the invention, deep understanding and accurate matching of user interests are realized, and the accuracy and interpretability of a marketing system are improved.

The invention relates to the technical field of data processing, in particular to a psychological crisis dynamic multi-source data analysis system, which comprises a data acquisition module for synchronously acquiring user behavior log data, physiological monitoring data and academic evaluation data; the real-time fusion calculation module performs feature extraction on the three types of data in parallel, an event stream processing engine is adopted to extract behavior mode features, sliding window Fourier transform is adopted to generate time-frequency spectrum features, deviation standardization is adopted to generate numerical feature vectors, cross-modal association weights are calculated through a space-time diagram convolutional network, and a high-risk association mode is output; the early warning generation module generates graded early warning signals, the early warning output module calls a push channel and a first-level early warning trigger short message notification and an emergency intervention interface according to signal levels, and the second-level early warning generates a visual report. Through heterogeneous data parallel processing, space-time diagram convolution fusion and edge calculation deployment, the problem of dynamic processing delay caused by multi-source data fusion missing is solved, and college psychological crisis real-time early warning is achieved.

The invention belongs to the technical field of electronic bidding and security verification, and relates to an intelligent bidding method and system based on behavior pattern recognition, and the method comprises the steps: collecting the multi-dimensional data of a user, comparing the multi-dimensional data with a user behavior baseline, and recognizing a potential abnormal behavior; giving a risk weight to the abnormal behavior, evaluating a risk level in combination with other risk factors, and selecting a verification mode according to the risk level; in the verification process, bidding event flow data are obtained, if the bidding event flow data need to be persisted, bidding messages in the bidding event flow data are persisted, and the persisted bidding messages are verified. According to the method, comprehensive protection of the bidding process is realized through technologies such as behavior pattern analysis, real-time event stream processing, self-adaptive multi-factor verification and distributed cache decision.

The application discloses a user interest matching network marketing system fused with a knowledge graph, and relates to the technical field of computers. The system is used to solve the marketing decision deviation problem caused by the lack of dynamic context perception and causal reasoning capability in existing user interest modeling. First, a dynamic knowledge graph is constructed through distributed event stream processing, and the real-time context of users is structured as a graph sequence with timestamps. Second, a time series convolution graph network is used to extract user interest evolution features, and the influence weight of the context node is quantified through a multi-head attention mechanism. Then, a causal inference model is constructed to eliminate exposure and location bias, and an counterfactual learning framework is used to extract user essential interest representation. Finally, the knowledge graph structure is dynamically optimized based on a gradient propagation algorithm, forming a closed-loop learning system from decision-making to perception. The application realizes deep understanding and accurate matching of user interest, and improves the accuracy and interpretability of the marketing system.

Provided in the present application are a complex event processing method and apparatus, a medium and a device. The method comprises: starting a client, sending to a server a complex event configuration request, acquiring complex event configuration information, and initializing same; on the basis of a unified event stream processing tool in front-end codes, capturing tracking data, and, on the basis of the tracking data, determining whether a target complex event has been triggered; in response to the triggering of the target complex event, running a complex event occasion decision engine to perform occasion matching on the tracking data on the basis of occasion configuration information, so as to determine whether the occasion of the target complex event is satisfied; and upon successful occasion matching, triggering a callback function to execute a handling scheme corresponding to the target complex event. The present application can improve the efficiency in processing complex events.

This invention discloses a virtual training data management system and method based on LVC simulation, relating to the field of LVC simulation. It includes a hierarchical hash index module, a Bloom filter module, and a streaming event processing module. The hierarchical hash index module performs unified collection and standardized mapping of raw state data from three types of data sources, ensuring cross-source data consistency and temporal accuracy. A multi-layered hash table is constructed based on the ODSLA metric set, dynamically determining the number of hash layers and optimizing object indexing and storage efficiency. Real-time monitoring of the load of each hash bucket, combined with high-load splitting and low-load merging mechanisms, achieves dynamic load balancing, improving resource utilization and processing performance. An countable mechanism is used for initial screening of object states, reducing unnecessary exact matching calculations. Combined with local caching to handle out-of-order events and rule-based exact matching, efficient and accurate event stream processing is achieved, providing reliable data support and rapid response capabilities for virtual training.

The invention discloses a dual-mode intelligent visual device supporting privacy protection mode switching. The device integrates an APS module and an EVS module, a first mode outputs a visible light video stream, a second mode hardware is powered off the APS, a gray contour video is generated only based on an EVS event stream, target behavior analysis is executed, and original images or event data are not output. The built-in lightweight AI model can detect the falling of the human body in real time, and the alarm is pushed through the ONVIF. The video adopts a standard coding and streaming media protocol, is compatible with a mainstream NVR, supports network-free local storage and physical mode switching, completely eradicates biological recognition information generation from the source, and is suitable for a high-privacy sensitive scene.

The application discloses a device with obstacle avoidance decision function, and belongs to the field of non-electric variable control or regulation system. The application is used to solve the technical problems of high power consumption, large delay and being easily disturbed by self motion and environmental light of the existing obstacle avoidance scheme. The application comprises a modulated light source, an event-based sensor and a processing unit. The modulated light source generates light at a specific frequency and forms a three-dimensional space. The event-based sensor senses the change of light intensity in the three-dimensional space and outputs an event stream. The processing unit is configured to trigger an obstacle avoidance decision process if an object contour is detected according to the event stream. The application can be used for real-time obstacle avoidance of mobile robots, unmanned aerial vehicles or intelligent vehicles, and has the characteristics of low power consumption, small delay, high robustness and low cost.

The invention discloses a virtual training data management system and method based on LVC simulation, and relates to the field of LVC simulation, the virtual training data management system comprises a hierarchical Hash index module, a Bloom filtering module and a streaming event processing module, the hierarchical Hash index module carries out unified collection and standardized mapping on original state data of three types of data sources, and the Bloom filtering module carries out standardized mapping on the original state data of the three types of data sources; and the cross-source data consistency and the time sequence accuracy are ensured. A multi-layer hash table is constructed based on an ODSLA index set, dynamic determination of the number of hash hierarchies is achieved, and object indexes and storage efficiency are optimized. The load of each hash bucket is monitored in real time, dynamic load balancing is realized by combining a high load splitting mechanism and a low load merging mechanism, and the resource utilization rate and the processing performance are improved. Object states are primarily screened through a countable mechanism, unnecessary accurate matching calculation is reduced, and efficient and accurate event stream processing is realized by combining local cache processing out-of-order events and rule accurate matching, so that reliable data support and quick response capability are provided for virtual training.

The present application relates to computer technology field, disclose a kind of event stream processing method, comprising: creating the scope that running coroutine code can be implemented, the scope is put into atomic operation class and is saved, so that the shared scope is held in the atomic operation class;When starting the processing instruction of event stream in message queue, according to the processing instruction, the shared scope corresponding to the current coroutine code is extracted from the atomic operation class;In the shared scope corresponding to the current coroutine code, the event in message queue is executed coroutine consumption processing, and when executing coroutine consumption processing, it is judged whether the number of events of coroutine consumption processing exceeds preset concurrency value;If not, then through the shared scope, asynchronous coroutine consumption port is generated to the event of executing coroutine processing, and the synchronous processing of event is executed using the asynchronous coroutine consumption port.The present application can avoid that system resource is occupied too high, improve the processing efficiency of event stream.

Systems, methods, and computer program products are disclosed for scalable event stream processing of trigger rules. Event data associated with a set of object is partitioned into a set of logical partitions, including a first logical partition comprising first event data associated with a first object. The first logical partition is provided to a first event processor to enable the first event processor to update a state associated with the first object based on first event data in the first logical partition. Satisfaction of a first trigger condition specified by a first trigger rule is determined based on the state associated with the first object. A first action is performed in response to the satisfaction of the first trigger condition.

The application relates to the technical field of event stream processing, in particular to an event stream processing method and device, which can establish a connection with a server, acquire a real-time data stream sent by the server, trigger a corresponding event processing function, process and analyze the real-time data stream by using the event processing function, judge whether the response is successful, if yes, filter the real-time data stream according to pre-acquired business requirements to obtain an event stream, perform conversion and aggregation operations on the event stream, and generate data after event stream processing. It can be understood that the technical scheme provided by the application integrates event stream processing on an event processing function, and the function is used in the client to pass the acquired event stream to the function, so that data after event stream processing can be generated, a polling mode is not needed, the processing efficiency is high, and the developer can more conveniently process the event stream sent by the server.

A system and a method for tracking individual persons across multiple cameras and over extended periods include an event streaming processing circuitry, a server, an application processing circuitry, a Tensor Database system and an output device. The event streaming processing circuitry receives streams of data from the cameras. The server includes Artificial Intelligence (AI) based models for person detection and embedding vector extraction to obtain person bounding box images and person embedding vectors. The application processing circuitry includes an Embedding Manager that maintains a collection of distinct embedding vectors for each person and an ID Manager that maps short-term person IDs to respective long-term IDs. The ID Manager associates the person with a unique long-term ID across the cameras. The Tensor Database system maintains the extracted embedding vectors. The output device tracks the person appearing across the cameras based on the long-term ID.

The invention relates to the technical field of geographic information systems, particularly discloses a three-dimensional geographic information visualization system supporting dynamic interaction, and aims to solve the problems of high data processing delay, low resource utilization rate and event and object semantic mapping deficiency in the prior art. Comprising a real-time data stream access and preprocessing module used for receiving and standardizing heterogeneous sensor data in parallel; the space-time event stream processing engine is used for performing incremental state management and updating based on the geographic grid index; the geographic object semantic dynamic binding module is used for realizing automatic association between event data and scene object attributes through declarative rules; the distributed memory geographic state database is used for high-concurrency storage and query of object states; and the asynchronous non-blocking rendering pipeline is used for driving the graphics engine to perform local redrawing. According to the method and the device, millisecond-level response from data access to visual rendering is realized, and the resource utilization rate and the system usability are improved.

A virtual space interaction data management and storage system and a method thereof are provided, by connecting a data acquisition device and an arithmetic device, the data acquisition device senses the operation of a user in a virtual space in real time to generate virtual space interaction data, and then the arithmetic device performs data preprocessing and event stream processing on the virtual space interaction data to obtain virtual space interaction data. And the obtained preprocessed data is stored in a corresponding database according to data attributes, so that instant retrieval and operation of a virtual space operation engine are provided, and the technical effects of improving the processing efficiency and operation instantaneity of heterogeneous data are achieved.

The invention discloses an end-to-end real-time small target unmanned aerial vehicle detection method based on an event camera, and belongs to the technical field of unmanned aerial vehicle detection, the method comprises the steps: firstly, synchronously collecting an RGB image and an event stream, processing the event stream into a space-time voxel grid, then extracting the dynamic features of an event and the visual features of RGB through SNN and CNN double-flow networks respectively, and carrying out the recognition of the dynamic features of the event and the visual features of RGB; the two multi-scale features are deeply fused by using a cross-attention mechanism through an encoder, enhanced features with complementary information are generated, finally, the fused features are decoded based on a detection head capable of learning object query, and the accurate position and category of an unmanned aerial vehicle target are output end to end. According to the method, the problem that the anti-unmanned aerial vehicle detection rate is low under various complex environments and illumination conditions can be solved, the advantages of the RGB camera and the event camera are more effectively combined, and the detection performance, robustness and environmental adaptability of the unmanned aerial vehicle target are improved.

The application discloses an end-to-end real-time small target UAV detection method based on an event camera, and belongs to the technical field of UAV detection. First, an RGB image and an event stream are synchronously collected, and the event stream is processed into a spatiotemporal voxel grid. Then, the dynamic features of the event and the visual features of the RGB are respectively extracted through an SNN and a CNN double-flow network. The two kinds of multi-scale features are deeply fused through a cross-attention mechanism encoder to generate complementary enhanced features. Finally, the fused features are decoded based on a learnable object query detection head to output the accurate position and category of the UAV target in an end-to-end manner. The method can overcome the problem of low anti-UAV detection rate in various complex environments and light conditions, more effectively combine the advantages of the RGB camera and the event camera, and improve the detection performance, robustness and environmental adaptability of the UAV target.

The present disclosure provides an event stream processing method, comprising: obtaining at least one to-be-processed event; determining a to-be-processed event as a current target event and performing a write operation; determining a corresponding write memory slice of the current target event from a plurality of preset memory slices according to a time interval to which a timestamp of the current target event belongs, and determining a corresponding target address of the current target event according to an event object of the current target event; and updating data stored in the target address of the write memory slice according to a change value of the current target event. The present disclosure also provides an electronic device and a computer readable medium.

The application discloses a video interpolation method based on a pulse neural network and an event camera, and comprises the following steps: acquiring two adjacent frame images in a low-frame-rate RGB video and event streams of a corresponding time period between the two frames collected by an event camera; dividing the event streams according to a certain interframe time of a required reconstructed image, and processing positive polarity event information and negative polarity event information obtained by the division into event grids that are uniformly divided according to time; constructing and training a video interpolation model based on a pulse neural network, including an image processing module, an event stream processing module, an attention module based on event guidance, a pulse fusion module and an image reconstruction module; and performing video interpolation on event grids corresponding to frame images and event streams by using the trained video interpolation model, so that high-energy-efficient video interpolation can be realized under lower calculation amount and energy consumption.

The application provides a cloud-native container platform integration method and system, and belongs to the technical field of cloud computing, and comprises the following steps: determining a running environment of a cloud-native container platform event flow; in the running environment, determining a workflow template based on a continuous integration and continuous deployment process; deploying an event trigger in a code repository based on the workflow template; and triggering the cloud-native container platform event flow by using the event trigger. The application is based on a Kubernetes platform, realizes a continuous integration process of original code, arranges a continuous integration and continuous deployment process through an event flow, predefines an event flow template, triggers an event flow to run by injecting parameters through a command line, and relies on flexible arrangement of the event flow to realize complex mathematical calculation and a flexible and open event flow processing mechanism.