35 results about "Data stream processing" patented technology

The purpose of this invention is to address the issue of accuracy in processing spinning time-series data streams, which are characterized by high noise levels and distinct time-domain features generated during the operation of spinning workshops, by employing a fuzzy hierarchical clustering method that integrates time-domain characteristics. This method reduces the impact of noise during the classification process and considers both time-domain features and noise effects. The technical solution of this invention is to provide a fuzzy hierarchical clustering analysis method for spinning time-series data that integrates time-domain characteristics. This invention proposes a fuzzy hierarchical clustering analysis method for spinning time-series data that integrates time-domain features. It iterates between the DTS feature matrix and the MTS feature matrix, considering the time-domain characteristics and noise effects in the spinning time-series data. Without increasing time complexity, it incorporates the time-frequency characteristics and noise effects in the spinning time-series data during the iteration process. Compared with the latest methods, this invention can more accurately process newly generated time-series data in spinning manufacturing.

The application provides a fault processing method and system of a data stream accelerator cluster, an electronic device and a storage medium. The fault processing method comprises the following steps: monitoring the state of the data stream accelerator cluster to obtain state information of the data stream accelerator cluster; determining a faulty data stream accelerator according to the state information and generating fault information; generating a fault bypass instruction according to the fault information, so as to migrate part or all of the tasks to be processed on the faulty data stream accelerator to a target data stream accelerator according to the fault bypass instruction. The application can improve the service availability of the data stream accelerator cluster in a cloud cluster deployment scenario without significantly increasing resource overhead, and can adapt to the requirements of elastic expansion and high-concurrency data stream processing.

This invention provides a dynamic management method and system for multi-level assessment indicators of the resilience of nearshore bridges throughout their entire life cycle, relating to the field of structural resilience assessment and management technology. The method includes: acquiring multi-source heterogeneous maintenance data streams throughout the entire life cycle of the nearshore bridge; processing these data streams through time-series alignment and feature dimensionality reduction mapping to obtain a basic feature matrix of the bridge's service status; and performing conditional routing and data stream processing based on this basic feature matrix to obtain corrosion evolution calculation potential field distribution data, structural dynamic stiffness attenuation tensor, and structural life-cycle degradation confidence distribution vector. This invention accurately obtains resilience indices that truly reflect the bridge's resistance to degradation through multi-step quantification calculations, clarifies the sensitivity differences of assessment nodes, formulates differentiated dynamic maintenance strategies, and constructs a closed-loop management system throughout the entire process, thereby achieving intelligent bridge maintenance, improving maintenance efficiency, and reducing costs.

The present application relates to the technical field of data stream processing, and particularly relates to a real-time analysis method and system for behavior data stream in an educational scenario, real-time collection of video, voice and writing trajectory behavior data stream in a classroom and assignment of a uniform timestamp; event detection on the data stream, interception of a data segment to form a cache block with a behavior trigger event as the center; coarse-grained alignment on the cache block, and then accurate timing synchronization of multi-modal data; extraction of behavior features on the synchronized data block and evaluation of the quality of each modal data, dynamic adjustment of a fusion weight according to a confidence level for weighted fusion; input of the fusion features of a continuous time window into a rule library to obtain a current behavior state, time sequence correlation and context verification on continuous states to output a correction result; and finally uploading of the processed data to a cloud to optimize related parameters and issue a notice. Real-time accurate synchronization and dynamic fusion of multi-source behavior data are realized.

A data stream processing method, apparatus, communication chip, and communication device belong to the field of communication technology. The communication chip includes a hardware inference resource pool, which includes at least one hardware inference unit. After receiving a first message belonging to a first data stream, the communication chip determines a first inference model based on the characteristic information of the first message. The first inference model is used to instruct the use of a first inference analyzer to process the first data stream. The first inference analyzer includes at least one hardware inference unit from the hardware inference resource pool. The communication chip uses the first inference analyzer to process the first data stream. Therefore, this application uses AI functionality to process the data stream in the communication chip, resulting in higher efficiency.

This invention relates to the field of information processing technology, specifically to a cross-application interaction method and system based on a Bluetooth headset. The method establishes a communication connection by pairing the Bluetooth headset with an application, enabling a cross-application interaction mode. Through different touch operations on the Bluetooth headset's touch control components, sound is captured and the voice data is transmitted to the application for data processing, generating corresponding processed text and processed audio for use by third-party applications. The system includes an application module, a Bluetooth headset module, a clipboard synchronization module, and a third-party application interaction module to implement the method. Voice capture and processing can be triggered via the Bluetooth headset's touch control components, eliminating the need for additional equipment and enabling one-click triggering. This transforms the Bluetooth headset from a traditional media / call controller into a global trigger and control hub for cross-application data stream processing. Combined with system and clipboard synchronization and audio interface calls, a seamless cross-application interaction paradigm is constructed.

The present disclosure provides a data processing link stress testing method and device, equipment and storage medium, which relates to the technical field of computer. The method comprises the following steps: obtaining a first real-time data stream in a data stream processing service and partition information of the first real-time data stream in the data stream processing service; storing the first real-time data stream in a database according to the partition information to obtain historical simulation data; reading the historical simulation data from the database and sending the historical simulation data to a first topic of the data stream processing service to obtain a stress testing simulation data source; connecting the stress testing simulation data source to a to-be-tested link for data processing and storage operation to obtain stress testing performance data; and analyzing the stress testing performance data. The method improves the simulation degree of the stress testing data source and improves the stress testing effect.

In accordance with the described techniques, a configuration update is received changing configuration parameters of a data stream processing application from a source configuration to a target configuration. In addition, the configuration update specifies incremental update parameters indicating how different partitions of the data stream processing application are to be incrementally updated. The computing device is configured to incrementally update the data stream processing application. During a time interval indicated by the incremental update parameters, for instance, the target configuration is applied to a first partition, and the source configuration is applied to a second partition. Thus, during the time interval, the first partition processes events of a data stream in accordance with the target configuration, and the second partition processes events of the data stream in accordance with the source configuration. After the time interval, the target configuration is applied to the second partition.

The application provides a multi-algorithm multi-service scenario AI application system and a working method thereof, and aims at problems caused by common application of multi-algorithms and multi-services in the scenario AI application, realizes data flow processing between multiple algorithms and multiple services through layered structure design and modular design, more conveniently expands algorithms and services, realizes popularization of the AI scenario application, adopts an efficient module scheduling algorithm, so that terminal equipment can also achieve real-time analysis of the scenario AI application, and through use of an algorithm request callback (ARI) technology, loop structure of a module scheduling diagram is avoided, decoupling of the module is realized, and performance of the AI application is maximized.

A system and a procedure for securing data streams in the medical field that are routed through transport components are provided.The system comprises: a data acquisition unit configured to acquire data from a data stream of at least one transport component; a determination unit configured to determine information about a processing step of the data stream based on the acquired data; a text generation unit configured to generate a description of the determined processing step; an evaluation unit configured to determine whether the described processing step is included in a list of predetermined processing steps; and an output interface configured to output a signal containing information about at least the processing steps executed in the data stream from the list of predetermined processing steps.

The application discloses a BIM twin biological joint driving shield tunnel monitoring data stream processing system and relates to the technical field of shield tunnel monitoring processing. An edge gateway accesses shield machine operation parameters, structural environment monitoring and inspection data. After field alignment, uniform monitoring data packets are packaged, containing a globally unique identifier, a measurement point incremental serial number, double timestamps, a space-time object key and an object mapping version number. Disconnected cache is supplemented and power is equalized for storage. The cloud corrects alignment according to event time to form a time window and a ring number statistical frame. The statistical frame is online governed, and credibility, reason labels and uncertainty intervals are output. Based on credibility gating, early warning is shunted, and a late event correction generates a new version of the event and an evidence summary package. The credible event incremental update twin object state library is isolated from the update structure state and the sensor link state, can support playback and traceability and operation and maintenance closed loop, reduce false alarms and missed alarms, and maintain twin consistency and auditability.

The application provides a system, platform and method for data integration based on real-time data flow, the system comprising a collector configured to generate a real-time data flow in response to a first data source and collect the real-time data flow; wherein the first data source is one of a plurality of data sources, and the real-time data flow is generated based on user operation; a data flow processor configured to calculate a real-time score of the first data source based on real-time data of each evaluation index extracted from the real-time data flow; obtain a historical score calculated based on historical data flow of a second data source, and calculate a real-time user experience evaluation score based on the real-time score and the historical score; and a data flow outputter configured to output the user experience evaluation score in different output formats according to different output positions. The application can be used to trigger the calculation and analysis of the user experience evaluation score immediately after submitting feedback or performing business operations, thereby improving response speed and user experience.

This application discloses a computing device, system, and method based on memory expansion, relating to the field of computer technology. The device includes: flash memory, a flash memory controller, a data stream processing module, a sub-protocol multiplexing and arbitration module, a protocol controller, memory, and a memory controller. The flash memory stores file data, enabling direct file loading into memory within the computing device. The flash memory controller is connected to the data stream processing module via a data path. The flash memory controller acquires file read or write tasks sent by the host processor and initiates file read or write operations via a memory-mapped bus. The data stream processing module accesses the flash memory through the flash memory controller and / or accesses the memory through the memory controller according to the computing task. This shortens the data exchange path, improves the flexibility of computing tasks on memory data, avoids latency caused by frequent file loading, and improves computing efficiency.

This invention relates to an architecture compatible with cache / tcm for high-speed DSP access to a low-speed bus; it includes: a one-to-two bus switch h1to2 switching module, a two-to-one bus switch h2to1 switching module, a two-to-two system bus interconnect s2to2 module, and a cache; one master terminal of the h1to2 switching module is connected to the DSP, and two slave terminals are respectively connected to the cache and one master terminal of the s2to2 module; two master terminals of the h2to1 switching module are respectively connected to the cache and one slave terminal of the s2to2 module, and one slave terminal is connected to the bus; the remaining master and slave terminals of the s2to2 module are respectively connected to the bus and the cache; by switching the master and slave terminals of the h1to2 and h2to1 switching modules, a data access path from the DSP to the bus via the cache is established in cache mode; or, by combining the access address range of the slave terminals of the s2to2 module, in tcm mode, data flow processing is performed from the DSP to the cache via the s2to2 module, from the DSP to the system bus via the s2to2 module, and from the system bus to the cache via the s2to2 module. This invention supports both cache and TCM working modes.

A system for repeatable and flexible predictive analysis of very large data sets using a distributed computational graph using a domain specific language for pipeline and resource declaration has been developed. Data receipt software receives data from one or more sources. In a declared data pathway, data formalization software formats input data for storage, enrichment, or continued processing. An event analysis server inspects stored data for trends, situations, or knowledge. Aggregated data is passed to event message store and data stream handler software. Transformation pipeline software stores, triggers, executes and monitors declared transformation steps and pipelines to manipulate and act on bulk data and data streams, provides results and enables dynamic model and routine re-training and learning.

The application discloses a kind of distributed acoustic sensing earthquake monitoring data stream processing method and system, belong to earthquake monitoring and data processing technical field.The method of the application includes: after the original earthquake monitoring data is preprocessed, it is sliced using sliding processing time window, and the data segment suitable for real-time detection is obtained;Design adaptive short-time average / long-time average detection algorithm, detect the data segment, and dynamically adjust the adaptive parameter in the algorithm according to the data characteristics;Finally, the initial candidate list of earthquake event is executed multi-channel fusion detection, to generate the final detection list of earthquake event.The application is based on distributed stream data processing architecture, combined with the innovative advantage of adaptive earthquake detection algorithm, can improve the accuracy of earthquake detection while ensuring the real-time of detection result, can provide efficient and reliable technical support for large-scale earthquake monitoring task.

A data stream processing method and apparatus, a communication chip and a communication device, belonging to the technical field of communications. The communication chip comprises a hardware inference resource pool, the hardware inference resource pool comprising at least one hardware inference unit. After receiving a first message belonging to a first data stream, the communication chip determines a first inference model on the basis of feature information of the first message, the first inference model being used for instructing the use of a first inference analyzer to process the first data stream, and the first inference analyzer comprising the at least one hardware inference unit in the hardware inference resource pool. The communication chip uses the first inference analyzer to process the first data stream. Thus, in the present application, an AI function is used to process data streams in the communication chip, and the efficiency of using the AI function to process data streams is high.

The application discloses a data stream adaptive online learning processor based on a memristor neuromorphic computing, the processor comprising a data stream input interface, a feature coding module, a memristor neuromorphic computing core and an output decision module, the memristor neuromorphic computing core integrating a cross array, a memristor, an on-chip learning engine and other components, realizing computing-storage-updating integration, and a processing method completing adaptive learning on continuous non-stationary data streams through data receiving coding, forward simulation calculation, error generation, in-memory weight updating and iterative learning.The application is suitable for resource-limited scenes such as edge devices, supports multiple types of data stream processing, can automatically track data distribution drift, can improve energy efficiency by 1-2 orders of magnitude and reduce delay by hundreds of times compared with a traditional scheme, and can be widely applied to the fields of Internet of Things edge nodes, industrial sensors and the like.

The application provides a data flow processing method for binding a kernel and a network card queue, and relates to the technical field of data flow processing, which comprises the following steps: setting the queue number of each gigabit network card and each megabit network card as the minimum value of the maximum queue number supported by each network card and the number of idle CPU cores; binding each queue of each gigabit network card and each megabit network card to an idle CPU core; setting the queue number of each gigabit network card as the minimum value of the maximum queue number supported by each gigabit network card and the number of idle CPU cores, and setting the queue number of each megabit network card as the minimum value of the maximum queue number supported by each megabit network card and the number of idle CPU cores; and making each queue process the corresponding data flow through an idle CPU. The application effectively solves the problems of unsmart, unbalanced and possibly invalid resource allocation in a mixed rate network card environment.

This invention discloses a method for ARINC429 requirements analysis and automatic code generation based on the bus protocol. The method includes: establishing a standard template for a table according to the ARINC429 communication protocol requirements, containing the information needed for code generation; automatically analyzing the ARINC429 communication protocol file using a program to read key information from the table; reporting errors in the main table and sub-tables that do not conform to the protocol format during the key information reading process; establishing a complete data flow processing code framework rule based on the signal input, processing, and output sequence; and establishing different structures for the received / transmitted Label numbers according to channel and direction based on the data information obtained in step S3. This invention, through the automated identification of ARINC429 bus protocol requirements and the automated generation of standard code, reduces design and implementation defects introduced by developers' understanding of requirements during software development, greatly improving software development efficiency.

This invention discloses a method and apparatus for scheduling stream processing tasks. The method includes: receiving a task migration request initiated by a user; determining the task migration trigger time and task migration strategy based on the task migration request; acquiring each computing resource node involved in the task migration and controlling the coordinator responsible for task migration to schedule each computing resource node according to the task migration time and task migration strategy to execute the task migration; injecting a special delimiter event into the data stream of the stream processing job during the task migration process; using the special delimiter event to disconnect the task to be migrated from all its upstream and downstream tasks; and simultaneously deploying the task to be migrated to the computing resource node where the task needs to be migrated. This method can achieve the scheduling of stream processing tasks, solving the problems of long-term pauses in data stream processing caused by restarting jobs in existing technologies, resulting in significant performance fluctuations and instability.

The audio acquisition device comprises a first audio acquisition module, a second audio acquisition module, a logic scheduling module and a processing module. The first audio acquisition module works in an I2S master mode, outputs an N-bit first digital audio signal, a reference bit clock signal and a first left-right channel control signal; the second audio acquisition module works in an I2S slave mode and outputs an N-bit second digital audio signal under the triggering of a second left-right channel control signal. The logic scheduling module generates a second left-right channel control signal with a K-bit clock period offset based on the reference bit clock signal, and forms a combined data stream by making two signals time-sharing occupy the same physical data channel through hardware selection logic according to the combined logic state of the first left-right channel control signal and the second left-right channel control signal. The processing module receives the combined data stream and parses multiple original audio data. The scheme realizes multi-channel acquisition without increasing the number of physical channels, reduces the interface resource occupation and hardware cost.

This application provides a method and apparatus for cross-modal geophysical data governance, relating to the field of geophysical exploration technology. The method includes: extracting entities and relationships using a natural language processing model; clustering entity and relationship types to generate a knowledge graph ontology; mapping cross-modal geophysical data to the knowledge graph ontology; using a graph building engine to mine and reason about concepts and attributes to achieve automatic knowledge extraction and fusion; constructing a knowledge graph corresponding to the knowledge graph ontology; combining the knowledge graph with a pre-set machine learning algorithm; using a natural language processing model to parse user query requests; performing depth-first search or graph query in the knowledge graph to achieve knowledge query and analysis of cross-modal geophysical data; and based on a pre-set real-time data stream processing pipeline, when geophysical data is detected to arrive, performing a similarity comparison between the newly arriving geophysical data and the geophysical data corresponding to the current node, and performing knowledge graph evolution and maintenance, significantly improving exploration efficiency and accuracy.

The application discloses a mountain torrent debris flow geometric parameter and surface flow field real-time calculation method and system, belongs to the geological disaster monitoring technical field, including three core steps: S1: river channel geometric parameter accurate calculation, application principal component analysis determines river channel main shaft, adopts multithread parallel convex hull algorithm to calculate width, realizes high efficiency depth inversion through KDTree space index, utilizes Alpha shape to extract central area shape to estimate flow;S2: surface flow field dynamic analysis, utilizes Delaunay triangulation to construct grid model, enhances microtopographic features based on gradient's terrain shadow rendering, adopts PIV technology to extract surface flow velocity field from continuous terrain shadow;S3: data flow processing and transmission architecture, constructs directed acyclic graph topological structure and double-channel data transmission mechanism.The application solves the real-time and accuracy problem of debris flow monitoring under the complex environment of mountainous area, and the system delay is less than 300 milliseconds, which can provide reliable technical support for mountain torrent debris flow disaster early warning.

The application discloses a data readout module with odd-bit stream and a design method. The data readout module comprises an odd-stream data blocking module and an odd-stream data gating module. The odd-stream data blocking module is used for, when odd-bit stream data is read out, designing a data storage scheme according to input data bits and output data, and recombining and blocking the data. The odd-stream data gating module is used for, on the basis of the blocked data output by the odd-stream data blocking module, gating a data link according to a data screening mode and outputting a data stream. The application can make the data readout module realize complex data stream processing at a high frequency and a low hardware cost in the case of odd-bit stream, and realize function implementation and hardware optimization of the data readout module.

This invention proposes a method and system for matching tens of millions of rules based on classification preprocessing. It belongs to the field of packet filtering in data stream processing. The method includes: firstly, preprocessing the rule set by classifying and statistically analyzing the protocol information carried by the rules; introducing the concept of "discrimination" and determining the main protocol category of each rule based on protocol layer priority (application layer > transport layer > network layer) and the number of associated rules, and constructing rule subsets with multiple protocol dimensions accordingly; during real-time traffic matching, parsing and tagging the packet protocol, dynamically selecting and matching only the rule subset corresponding to the protocol category based on the packet tags, and finally matching a general rule set containing only five-tuples. This invention combines static rule statistics with dynamic packet features to establish a heuristic decision-making mechanism, effectively reducing the number of rules that need to be traversed in a single match, thus achieving efficient multi-rule hit matching with a rule capacity of tens of millions, and optimizing overall performance.

The application belongs to the technical field of network services, and discloses a high-availability big data stream processing request placement method in a serverless edge network. The purpose is to meet the reliability requirements of users while minimizing processing delay. An effective algorithm is designed to find a suitable number of instances for the function of each data stream processing request, and to place the request while minimizing the average delay experienced by each user while meeting its reliability requirements. After the request is placed, the input data rate may change and is uncertain. An online learning algorithm is designed to predict the method to predict the data rate and dynamically adjust the standby instances to absorb the uncertain data rate. Based on the experiment of a real data set, it is shown that the placement problem of big data stream processing in the edge serverless network is effectively solved.