2028 results about "Data stream" patented technology

A system featuring a controller and a plurality of nodes operating as a Kubernetes cluster. The plurality of nodes includes a master node communicatively coupled to the controller, and an ingress node being configured to (i) access the master node to obtain at least a first attribute associated with a data flow received by the ingress node based on a network address associated with a source of the data flow, (ii) determine one or more network policies applicable to the data flow based on at least the first attribute, and (iii) determine a classification identifier based on the one or more network policies. The classification identifier provides context associated with the data flow and a reliable association between an application and the data flow associated with the application during propagation of the data flow over a cloud network.

Embodiments relate to a computer-implemented method for managing zero-trust network communications. The method includes validating the identity of a first remote endpoint using a cryptographic credential received from the first endpoint and determining the first endpoint's authorization to communicate with a second remote endpoint. Upon successful validation and authorization, the method initiates the establishment of a network packet route between the first and second remote endpoints. Later, the method further initiates the destruction of the network packet route between the two endpoints.

Example apparatus disclosed herein include an array of processor elements, the array including rows each having a first number of processor elements and columns each having a second number of processor elements. Disclosed example apparatus also include configuration registers to store descriptors to configure the array to implement a layer of a convolutional neural network based on a dataflow schedule corresponding to one of multiple tensor processing templates, ones of the processor elements to be configured based on the descriptors to implement the one of the tensor processing templates to operate on input activation data and filter data associated with the layer of the convolutional neural network to produce output activation data associated with the layer of the convolutional neural network. Disclosed example apparatus further include memory to store the input activation data, the filter data and the output activation data associated with the layer of the convolutional neural network.

Apparatuses, systems, and techniques for detecting distributed denial of service (DDoS) attacks are described. A system includes a plurality of switches in a monitored data center, each switch comprising network monitoring logic to sample network packets and generate flow records representing behavior of network traffic. A dataflow collector receives the flow records from the plurality of switches. A streaming pipeline coupled to the dataflow collector processes the flow records. A data store stores the flow records processed by the streaming pipeline. A trainer accesses the flow records in the data store and trains one or more machine learning (ML) models to detect DDoS attacks based on the flow records. At least one of the one or more ML models is deployable to at least one switch of the plurality of switches to determine whether a host device coupled to the at least one switch is subject to a DDoS attack.

This application relates to a real-time power supply and demand forecasting method and system based on a cloud-native architecture. The method includes: deploying a data access service in a cloud-native cluster using containerized microservices to receive real-time supply and demand data streams and historical time-series data from a power trading system; writing the data streams to distributed storage and pushing them to the forecasting pipeline via a message queue; performing timestamp alignment, missing data handling, normalization, and smoothing / denoising on the supply and demand data by a preprocessing service to obtain a low-noise supply and demand sequence; updating model parameters in a rolling window by an ARIMA forecasting service and outputting linear forecast values ​​as the first forecast result; calculating the forecast residuals based on the first forecast result and the actual observations, constructing residual time-series samples, and outputting residual forecast values ​​by an LSTM forecasting service; and superimposing the first forecast result and the residual forecast values ​​by a fusion service to obtain the real-time supply and demand forecast result and publishing it to the real-time trading business interface.

The application discloses a self-adaptive flood forecasting method, a terminal and a storage medium. The method comprises the following steps: generating a high signal-to-noise ratio local hydrological real-time data stream through adaptive sampling and a double-channel differential quantization technology; receiving an upstream input data stream, performing flood evolution calculation based on a viscoelastic dynamics model and fusing a dynamically identified input delay to obtain an original prediction value; for model parameters, a confidence ellipse is constructed based on ridge regression, the residual of real-time data and the prediction value is compared through statistical projection, the measurement noise and the river physical drift are intelligently distinguished, and the parameters are updated smoothly; finally, the original prediction value is corrected by solving a convex optimization problem subject to safety constraints, and a physically credible final prediction value is output. The corresponding terminal comprises corresponding modules for realizing the above steps. The application realizes high-precision, self-adaptive and intrinsically safe flood forecasting.

This application provides a data circulation method, apparatus, and system, and may be applied to the field of computer technologies. The data circulation method includes: A first storage device generates first data, obtains a first transfer strategy and a first use strategy of the first data, and sends the first data and the first use strategy to a second storage device when determining that the first data meets the first transfer strategy, where the first use strategy is a strategy indicating the second storage device to use the first data. In this application, the first data is sent only when it is determined that the first data meets the first transfer strategy, and the second storage device is indicated, by using the first use strategy, to use the first data. Therefore, behavior of a data user can be managed and controlled, to ensure security of the first data in a circulation process, and avoid problems such as data abuse and data leakage.

A system operable on a data communications network to operate on audio / video data includes a server computer communicatively coupled to the network. The server computer includes an application unit that runs an application. The application is accessible to a user device communicatively coupled to the network. The application unit requires audio / video data to run the application. The system also includes an audio / video server computer communicatively coupled to the network. The audio / video server computer transmits audio / video data over the network in response to a request. The user device includes at least a buffer, a decoder, and an output device. The server computer issues a demand request over the network to the user device to receive audio / video data for operation of the application unit. The demand request includes an identifier of the audio / video data and a network address of the audio / video server computer. The user device, upon receiving the demand request, issues a request over the network to the audio / video server computer in communication, and the audio / video server computer communicates the audio / video data over the network to the user device in communication. The user device reformats the audio / video data and communicates the reformatted audio / video data to the server computer. The server computer reverses the reformatted audio / video data to obtain substantially the original audio / video data for operation of the application unit.

The application discloses a cross-industry data assetization management and circulation system, which comprises five core modules. A data block encryption module receives cross-industry biological data flow, performs sequence block and nested encryption, and generates an encrypted data flow; a blockchain section storage module generates a hash storage certificate for the data block and writes the hash storage certificate into a blockchain; a federal contribution attenuation evaluation module receives a federal learning gradient update signal, performs time attenuation and marginal benefit adjustment to generate a contribution evaluation signal; a cross-modal knowledge graph alignment desensitization module realizes cross-modal entity semantic alignment, calls a configurable desensitization rule chain to generate a desensitized data flow; and a data circulation gateway module integrates three types of signals to generate a data asset circulation control signal. The application solves the collaborative management problems of safe storage, privacy protection, contribution quantification and compliance desensitization in the circulation of cross-industry biological data.

Alignment detection circuitry is disclosed that include a buffer configured to output a data stream of multiplexed groups of symbols from multiple data lanes and a set of correlators configured to determine physical link skew from one data lane of the multiple data lanes, wherein the physical link skew from the one data lane is known to equal the physical link skew on each data lane, assuming each data lane is transmitted over the same physical link. The alignment detection circuitry may also include anti-aliasing circuitry configured to extract a unique marker of the alignment marker to detect common marker aliasing by comparing the extracted unique marker to a known unique marker. Anti-aliasing circuitry may also be configured to use common marker detection status of adjacent data lanes of the multiple data lanes to detect common marker aliasing determined by a detection delay between the adjacent data lanes.

A numerical control process design and optimization method based on machining knowledge includes: constructing a machining knowledge data model by taking a machining feature as a carrier, performing ordered integration on various stages of programming, post-processing, cutting simulation, machining process monitoring and inspection, and machining result measurement, determining various storage data types, and storing data in each stage. By means of the numerical control process design and optimization method, data stream during the whole flow of product machining are connected; effective accumulation of knowledge in a full cycle of product machining is realized; a numerical control process basic data model is constructed by means of establishing an association relationship between machining knowledge and a feature of a part.

The application discloses a data stream transmission method, a network device, a storage medium and a program product, and belongs to the technical field of communication. The method comprises the following steps: merging a plurality of first queues into a second queue; wherein the first queue is a transmission queue with a transmission state lower than an expected state in a transmission queue corresponding to a target data stream; the resource configuration parameter of the second queue is greater than or equal to the sum of the resource configuration parameters of the plurality of first queues; and the target data stream is transmitted through the second queue. In this way, the problem that the TM (Traffic Manager) resource of part of the queues is not fully utilized due to unreasonable resource allocation can be avoided, and the TM resource utilization rate is improved.

The application relates to the technical field of driving behavior analysis, and particularly provides a multi-resolution driving working condition identification and classification method. A plurality of time length sliding windows are set to perform multi-scale division on a multi-channel driving time sequence data stream, and a multi-resolution analysis system is constructed; longitudinal driving working conditions are identified and classified according to a yaw angular velocity energy index; a dynamic time warping algorithm is used to match data segments with transverse working condition templates, so that the identification and classification of the transverse driving working conditions are realized; and key driving events are calibrated, thereby providing fine data support for subsequent driving style analysis. The application combines the multi-scale sliding window and time sequence similarity measurement technology, effectively improves the driving working condition identification precision, and significantly reduces the misjudgment rate in complex scenes.

The embodiment of the application provides a data stream detection method, device and equipment and readable storage medium, relates to the technical field of communication, is applied to a message forwarding device, and comprises the following steps: transmitting a first data stream; obtaining a first traffic parameter of the message forwarding device in a first time period; determining whether the transmission of the first data stream is abnormal according to the first traffic parameter and a traffic parameter reference value; the traffic parameter reference value is determined based on a second traffic parameter of the message forwarding device in a second time period, the first data stream and the second data stream are associated, the position of the first time period in a first detection period corresponds to the position of the second time period in a second detection period, and the first detection period and the second detection period are different. The embodiment of the application detects the transmission condition of the data stream based on time series, improves the detection efficiency, and reduces the detection cost.

This application relates to the field of data security protection technology, specifically a dynamic data protection method based on intelligent classification and risk adaptation. The method includes: acquiring the data flow chain, recording the data flow relationship between the source Topic, target Topic, and processing nodes, and generating a link identifier; performing field parsing and two-level sensitivity identification on the input data to obtain the field sensitivity level and generate an initial residual risk vector at the field level; calculating the effective residual risk at the field level node by node during the data flow process and aggregating it to form a data risk label; determining policy entries and target processing operators based on the data risk label, path length, node processing type, and local link summary characteristics; and performing at least one operation on the target field: character replacement, interval truncation, tokenization, and envelope encryption. This method enables dynamic risk characterization and adaptive protection during the data flow process.

This invention discloses a real-time data anomaly monitoring and repair method based on machine learning, comprising the following steps: S1, constructing data fragments and generating a consistent constraint set; S2, performing self-consistent representation construction; S3, performing anomaly detection and generating anomaly label set; S4, performing masking processing to generate masked data fragments, inputting them into an improved UniTS model, introducing a structural perturbation suppression mechanism, and generating a repair candidate set; S5, performing structural consistency evaluation and screening target repair data that meets the minimum structural perturbation condition; S6, performing data repair and generating repaired data fragments; S7, feeding back the updated consistent constraint set to the improved UniTS model. This invention achieves highly sensitive real-time identification of anomalous data items, effectively reducing the anomaly false judgment rate and repair distortion rate, and improving the data security, stability, continuity, and reliability in high-concurrency data stream environments.

The application discloses a self-adaptive weighted dependent code reconstruction method and belongs to the technical field of software engineering automation. The method comprises the following steps: generating an initial syntax tree by using a general syntax parser, dynamically binding an extensible attribute container to each node, performing enhanced parsing on metadata, and constructing an improved abstract syntax tree RAST; generating a composite feature vector based on a parent node influence factor and a child node influence factor; combining control flow, data flow and mixed dependent path analysis to construct a self-adaptive weighted dependent graph AWDG; introducing a semantic stability factor and a type correction coefficient, dynamically sensing the semantic stability of the node, adaptively adjusting the reconstruction recommendation strength, and calculating the reconstruction priority weight; and based on the dependent closure strategy and the reconstruction simulation pre-performance mechanism, the atomic transformation is executed after verifying no conflict in the virtual environment. Through the structural expansion and multi-dimensional weighting of the abstract syntax tree, the accurate positioning and full-process risk control of the code reconstruction are realized.

This application belongs to the field of data processing technology and provides a gimbal correction method, device, electronic device, storage medium, and program product. The embodiments of this application acquire an image data stream including multiple consecutive frames of first image data. Based on a preset mapping table, feature point matching is performed on the image data stream to determine matching feature points, the target image data where the matching feature points are located, and the matching gimbal information. Based on the matching gimbal information of the matching feature points, the gimbal information deviation is determined. When the gimbal information deviation is greater than or equal to a first preset threshold, a differential step number is determined. Based on the differential step number, angle compensation is performed on the motor so that when the motor drives the gimbal to move, the gimbal reaches the target position. Feature point matching can be performed on multiple consecutive frames of image data stream in real time, and automatic compensation of the motor can be performed in real time based on the matching feature points, realizing automated gimbal correction processing during equipment patrol operations, applicable to various dynamic scenarios.

The application discloses a kind of big data driven wind power assembly repeated quality intelligent determination method and system.Method includes setting sensor, automation equipment and environmental monitoring instrument in wind power assembly station, obtains multidimensional original data through edge computing node, realizes data alignment with precision time protocol, is converted into unified structured data stream and carries out denoising, and outputs the assembly signal data after denoising;Establish data infrastructure layer including distributed storage and containerization arrangement;Assembly signal data is processed, and time domain, frequency domain and time-frequency domain features are extracted, combined with environmental and equipment state parameters to construct assembly quality feature vector, process using quality determination model, calculate Mahalanobis distance and compare with preset threshold value, process drift is judged by Mann-Kendall test.The application can effectively identify the repeatability quality defects and process drift in wind power assembly, improve assembly quality consistency.

This application provides a hardware-software heterogeneous data processing system and method for atmospheric sounding lidar, relating to the technical field of embedded architecture for atmospheric remote sensing and scientific instruments. The system includes: a PL (Plug-in) terminal, which performs preliminary processing on lidar signal data to generate target data; a PS (Power Controller) terminal, including an internal cache and a processing core, with a virtual spatial filter configured within the processing core; the target data is stored in the internal cache via an ACP (Automatic Component Processing Filter); the processing core schedules the PL terminal to perform preliminary processing on the digital signal, reads the target data from the internal cache, and runs the virtual spatial filter to filter the target data; the virtual spatial filter performs register-level single-loop fusion of each independent algorithm node, completing the calculation of each independent algorithm within the single loop fusion, and all intermediate data resides in the register file. Relying on the ACP and single-loop fusion architecture, the bus occupancy rate and system latency of data flow are reduced, thereby achieving high throughput and zero bottlenecks at the system level.

This application discloses a privacy-aware and data de-identification system and method for unmanned aerial vehicles (UAVs). The system includes an airborne edge computing unit, multi-source sensors, a dual-channel data processor, a security encryption and transmission module, and a policy management interface. It is implemented through three parallel pipelines: a real-time privacy-aware pipeline uses a lightweight neural network to detect sensitive targets such as faces and license plates and outputs bounding boxes; a dynamic and precise de-identification pipeline calls a policy library to perform context-aware de-identification on the target area; and a dual-stream split-path controllable transmission pipeline transmits the de-identified data stream to the business platform in real time via a public network, while simultaneously transmitting the encrypted original data stream to a trusted data port through an independent secure channel.

The application relates to the technical field of algorithm and network cooperative scheduling, and discloses an algorithm and power cooperative scheduling method and system based on multi-dimensional classification of task value and time delay sensitivity, which comprises the following steps: acquiring multi-source data streams of computing power tasks, power markets and network topologies; performing multi-dimensional classification processing based on a time delay sensitivity index and a comprehensive value score to generate a task type code; extracting power market environment features to dynamically generate a multi-target optimization weight vector, and screening network topology nodes to generate a candidate data center set; combining the code and the weight vector to calculate the comprehensive cooperative utility of each node, generate a to-be-verified scheduling decision, input the decision into a consortium chain network to perform a quoted price tolerance check and consensus determination, and generate a scheduling instruction after the check and determination; collecting actual operation parameters according to the instruction to construct an experience four-tuple, and inputting the experience four-tuple into a meta-learning network for parameter fine-tuning. Through multi-dimensional feature mapping and distributed checking, the application realizes closed-loop cooperative scheduling of heterogeneous computing power and dynamic power.

The application relates to the technical field of surveying and mapping geographic information, and discloses a surveying and mapping intelligent analysis system based on multi-source data fusion, which comprises a multi-source acquisition and space-time reference alignment module, a factor graph consistency fusion and gate module and the like. The multi-source acquisition and space-time reference alignment module is configured to lock a main clock source through a hardware timing protocol, set a hard time alignment threshold, receive multi-source sensor data, judge a time synchronization deviation fed back by the sensor, and output original observation data flow when the time synchronization deviation is lower than the hard time alignment threshold. The factor graph consistency fusion and gate module is configured to receive the original observation data flow and build a global factor graph containing a full-source data constraint chain. The hardware timing protocol locking and hard time alignment threshold mechanism are adopted to establish a unified space-time reference at a physical level from the source, eliminate spatial misplacement caused by sensor sampling differences, realize high-precision consistency fusion of multi-source data, and solve the problems of difficulty in determining deviation sources and low fusion efficiency in traditional technologies.

This invention discloses a simulation testing system for integrated circuits, belonging to the field of integrated circuit testing technology. It includes a simulation execution module, a data acquisition module, a fault diagnosis module, a parameter correction module, a control module, a visualization module, and an alarm module. The simulation execution module runs a digital twin model of the integrated circuit and outputs simulation parameters, such as temperature, voltage, and timing margin, providing simulation benchmark data for subsequent deviation quantification. This invention overcomes the limitations of traditional single-parameter monitoring by combining multi-parameter comprehensive deviation quantification and dynamic weight allocation with a conditional probability model to accurately diagnose faults and generate a priority list. The hierarchical digital twin model interacts with real-time data streams to achieve accurate simulation of physical behavior, providing a high-precision benchmark for deviation quantification. The model adaptively optimizes, and with iterative control closed-loop, it improves fault identification accuracy and enhances system robustness and practicality.

The application discloses an AI model training optimization method and system, relates to the technical field of model training optimization, and comprises the following steps: collecting real-time running data in an AI model training process, and generating a multidimensional real-time running data matrix; performing outlier elimination on the real-time running data matrix, and generating a standardized running data stream; extracting characteristic parameters and coding into a training state characteristic vector; performing multidimensional evaluation on the training state characteristic vector, and generating an original optimization score; generating a comprehensive optimization suggestion; generating hierarchical optimization content, and converting into a personalized optimization report; and forming a training process self-adaptive adjustment cycle based on optimization result feedback. The application solves the problems of low optimization efficiency and difficulty in accurate positioning caused by manual tuning depending on artificial experience, improves resource utilization and optimization accuracy of the AI model training, and realizes intelligent dynamic adjustment of the training process through a closed-loop self-adaptive control mechanism.

Concepts for a video coding are described. A first aspect provides a concept for determining an interlayer reference picture for inter-predicting a picture of a multi-layered video data stream. A second aspect provides a concept for the usage of an interlayer prediction tool in multi-layered video data streams. A third aspect is concerned with a determination of a maximum temporal sublayer of an OLS or a maximum temporal sublayer to be decoded.

The present invention describes apparatus / methods for enhanced data protection by determining the required level of data protection for a data source, determining at least a first data-generating device, and assigning protection scheme settings and parameters to the data generated by at least the first device. In one example, a data stream from a data-generating device that is designated as confidential / protected (e.g., a device that could potentially leak confidential information) is protected from reconstruction from unprotected data streams from one or more other data-generating devices that could be used to provide useful input to a virtual sensor of that confidential / protected sensor.

Various methods and processes, apparatuses or systems, and media for classifying human behaviors in various domains by using ensemble learning to perform sequence modeling with respect to sequence data are disclosed. The method includes: receiving a first set of data; partitioning the first set of data into a set of respective data streams, each respective data stream corresponding to a respective agent; extracting, from a first data stream, a first sequence of observations that relates to a first agent; inputting the first sequence of observations to each of several models that are trained by using historical data relating to the first agent; using the models to generate a composite score that relates to the first sequence of observations; and determining, based on the composite score, whether the first sequence of observations indicates at least one anomaly that relates to a behavior of the first agent.