133 results about "Data stream processing" patented technology

A device to process data associated with a surgical event of a surgery may include a processor. The processor may be configured to receive multiple data streams during the surgical event. The processor may be configured to select a primary data stream based on a surgical data interface via which the primary data stream is received. The processor may be configured to select a secondary data stream based on a surgical data interface via which the second data stream is received. The processor may be configured to identify the surgical data interfaces. The processor may be configured to generate situational data associated with the primary data stream based on the secondary data stream. The situational data may indicate a medical decision-making factor of the surgical event. The primary data stream and the situational data may be sent during the surgical event.

The invention discloses a data verification method and system based on a Drools rule engine, and belongs to the technical field of data analys.The method comprises the steps that a data stream is accessed in real time, and the data stream is preprocessed to form a structured data object; receiving a verification rule configured by a user, and performing dynamic compiling and hot loading on the verification rule to update a rule base in the Drools rule engine; and inserting the structured data object as a fact object into a Drools rule engine, and executing the verification rule configured in the rule base by the Drools rule engine based on a ReteOO algorithm to generate a verification result. According to the method, real-time data stream processing and large-scale field association verification are supported, so that the data quality, the service efficiency and the overall reliability of the system in the aviation field are improved.

The invention provides a data stream processing method for binding a kernel and a network card queue, and relates to the technical field of data stream processing, and the method comprises the following steps: setting the number of queues of each gigabit network card and each 10-gigabit network card as the maximum number of queues supported by each network card and the minimum number of idle CPU (Central Processing Unit) cores; binding each queue of each gigabit network card and each 10-gigabit network card to an idle CPU core; the number of queues of each gigabit network card is set to be the minimum value of the maximum number of queues supported by each gigabit network card and the number of idle CPU cores, and the number of queues of each 10-gigabit network card is set to be the minimum value of the maximum number of queues supported by each 10-gigabit network card and the number of idle CPU cores; enabling each queue to process a corresponding data stream through an idle CPU; according to the invention, the problems of non-intelligent and unbalanced resource allocation and possible configuration failure in a mixed rate network card environment are effectively solved.

The invention discloses an e-commerce data intelligent processing method and system based on dynamic flow optimization. The method comprises the following steps: acquiring and analyzing multi-platform e-commerce data, and converting the multi-platform e-commerce data into a standardized format; constructing a matching rule by adopting a decision tree algorithm, and performing classification and priority marking on the data; the data flow is dynamically scheduled based on the priority, so that the high-priority data is executed preferentially; data are processed through parallel nodes, performance indexes such as network delay and throughput are monitored in real time, and data stream processing efficiency is optimized in combination with a dynamic scheduling algorithm. The system correspondingly comprises a data standardization module, a rule matching module, a dynamic marking module, a parallel processing module and a monitoring scheduling module, efficient and self-adaptive processing of the multi-source e-commerce data is achieved, and the data processing real-time performance and the system throughput are remarkably improved.

A method for a reconfigurable computing system includes receiving a compute graph for execution on multiple RDPs interconnected with a ring network having R interconnected RDPs. A compute graph with a node specifying a reduction operation for a first and second tensor is detected. Executing the compute graph on the multiple RDPs.

The invention discloses a distributed acoustic sensing earthquake monitoring data stream processing method and system, and belongs to the technical field of earthquake monitoring and data processing. The method comprises the following steps: after preprocessing original seismic monitoring data, slicing the original seismic monitoring data by adopting a sliding processing time window to obtain data fragments suitable for real-time detection; designing a self-adaptive short-time average / long-time average detection algorithm, detecting data segments, and dynamically adjusting self-adaptive parameters in the algorithm according to data characteristics; and finally, multi-channel fusion detection is performed on the initial candidate list of the seismic events, so that a final detection list of the seismic events is generated. The method is based on a distributed streaming data processing architecture, combines the innovation advantages of a self-adaptive earthquake detection algorithm, can improve the earthquake detection precision, guarantees the real-time performance of a detection result, and can provide efficient and reliable technical support for a large-scale earthquake monitoring task.

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 invention relates to the technical field of data processing, and discloses a data stream processing method and device based on dynamic bandwidth allocation, equipment and a medium, when a data request of a client is received, a new redirection request is generated by judging a data stream type and applying redirection logic when the data stream type is confirmed to be a target data type, and the data stream is redirected. Therefore, the data streams can be quickly and accurately classified and guided, congestion of the data streams on unnecessary paths is avoided, and the condition of resource competition is relieved. According to the method, the channel specially used for transmitting the target data stream is determined, and independent transmission paths are provided for different types of data streams, so that mutual interference among data is reduced, and the occurrence probability of system jamming is remarkably reduced. According to the method, the traffic resource of each channel is calculated and allocated by applying a traffic dynamic allocation algorithm, so that the processing capability of high-concurrency scenes of financial and medical data processing is greatly improved, and the processing efficiency and the response speed of data streams are remarkably improved.

The application discloses a data anomaly detection method and device, a processor and electronic equipment. The method comprises the following steps: obtaining the identification information of at least one preset calculation module and at least one preset storage module; filling the identification information into a preset adaptation table of a preset data warehouse; predicting the adaptation probability of the preset calculation module and the preset storage module according to a preset adaptation model, and filling the adaptation probability into the preset adaptation table, wherein the preset adaptation model is obtained by training a deep learning algorithm based on a self-attention mechanism; performing data caching of the preset adaptation table by calling stream data and a remote dictionary service through the preset calculation module, to obtain a data stream processing table, wherein the remote dictionary service and the preset calculation module establish a mapping relationship in the form of a key-value pair; and analyzing the data stream processing table to realize data anomaly detection. The application solves the technical problem of low data anomaly detection efficiency.

Various techniques are provided for providing neural networks with increased efficiency. In one example, a system includes a first artificial neural network (ANN), a second ANN, and a logic device. The first ANN is configured to receive a first plurality of data inputs associated with a data stream and process the first data inputs to generate a first inference output after a first latency. The second ANN is configured to receive a second plurality of data inputs associated with the data stream and process the second data inputs to generate a second inference output after a second latency less than the first latency. The logic device is configured to receive the second inference output before the first inference output is generated. Additional systems and methods are also provided.

The invention discloses a photon data stream processing method for an image classification feature extraction stage, which is applied to the feature extraction stage of image classification, and realizes maximum multiplexing of an input feature map by partitioning and mapping the input feature map according to DNN convolutional layer parameters; the method comprises the following steps of: carrying out electro-optical conversion on an input feature block, modulating the input feature block into a multi-wavelength optical signal, and then carrying out wavelength division multiplexing (WDM) one-to-many broadcast distribution, so that full broadcast of the optical signal is realized; optical domain product accumulation operation is executed through the PCU, so that the throughput calculation speed is increased; and finally, obtaining an output characteristic pattern under the combined action of a photoelectric detector and an ADC (Analog to Digital Converter).

The invention provides a data code stream processing system. The data code stream processing system comprises an uplink deframer, an uplink selector, an uplink code stream receiver, a modulator, a channel simulator, a downlink selector, a downlink code stream generator, a demodulator and a downlink framer, the uplink deframer carries out deframing processing on an uplink data code stream sent by the protocol stack; the uplink code stream receiver receives the uplink data code stream after being selected by the uplink selector; the downlink code stream generator sends a downlink data code stream after being selected by the downlink selector; the downlink framer carries out framing processing on the downlink data code stream and then sends the downlink data code stream to the protocol stack; the condition of random packet length can be handled, the conditions of packet distribution, packet adhesion, packet breakage, packet blockage and packet loss in the transmission process are effectively handled, and the condition that secondary wrong packets are generated due to wrong packets transmitted by the demodulator is avoided; and service interface self-loop verification is directly carried out, so that the development efficiency and the stability and reliability of service interface development are improved.

The invention discloses a data processing method and device, a storage medium and electronic equipment. The method comprises the steps of responding to a running instruction of a target data processing task, and obtaining configuration information of the target data processing task; according to the configuration information, a data processing statement corresponding to the target data processing task is generated, and the data processing statement is used for describing the reading, conversion and writing process of a target data flow corresponding to the target data processing task; the data processing statement is submitted to a target platform, the target platform processes the target data flow according to the data processing statement, and a processing result is obtained. Through the data processing method and device, the problem that data processing efficiency is low due to the fact that a data stream processing program is manually written for data processing in related technologies is solved.

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 invention discloses an indoor simulation illumination test system of a solar cell array. The test system comprises a user interaction module used for forming a human-computer interface and determining a test task according to user operation, and task parameters of the test task comprise a target illumination intensity set value, test duration, a sampling mode, a recording strategy and a safety threshold; the communication module is used for abstracting a physical device into an addressable device object and providing command issuing and data reading capabilities for the device object; the control module is used for calculating a control quantity according to the target illumination intensity set value and an actual measurement value of the illumination sensor, and converting the control quantity into a corresponding adjustment instruction to perform an illumination test; the data stream processing module is used for performing stream processing on measurement data and control data generated in the illumination test process; and the safety protection module is used for executing a protection action when the safety event is triggered.

The invention discloses a slope stability monitoring method based on sky-ground integration, and belongs to the field of slope stability monitoring, and the method comprises the steps: obtaining surface displacement and crack observation data from a ground layer, and employing a data cleaning algorithm to remove noise and abnormal values, and obtaining a high-precision ground local change data set; drilling stress and underground water level data are obtained from an underground layer, deep features are extracted through a signal processing algorithm, and an underground stability feature data set is obtained; according to the multi-dimensional slope feature data set, a dynamic change trend of the slope is extracted by adopting a time sequence analysis algorithm, and a slope stability change trend is obtained; according to the slope stability risk level, a real-time data stream processing algorithm is adopted to update the multi-dimensional slope feature data set, and a dynamically updated slope stability evaluation result is obtained; and aiming at a slope stability evaluation result, generating a real-time slope stability early warning signal by adopting an early warning signal generation algorithm. According to the invention, the precision and timeliness of slope stability monitoring are obviously improved.

The invention provides a data processing method, electronic equipment and a computer readable storage medium, and relates to the technical field of data processing. The method comprises the steps that initial power distribution network data are acquired, target power distribution network data are generated according to the initial power distribution network data, and the data formats of the initial power distribution network data and the target power distribution network data are different; performing classification processing on the initial power distribution network data to obtain a data classification result, the data classification result including a classification label corresponding to the initial power distribution network data; performing grading processing on the target power distribution network data to obtain a data grading result, the data grading result comprising a grade to which the target power distribution network data belongs; and generating classification and grading evaluation information corresponding to the initial power distribution network data according to the data classification result and the data grading result. Through double data stream processing, semantic clustering and big language model driven risk reasoning, automatic classification and grading of the power data are realized, and various standards and requirements in a high-safety requirement scene are met.

The invention discloses a bit extraction-based zero arithmetic overhead address generation method and circuit. The method comprises the following steps of: detecting the position of a most significant bit of an input fixed-point integer to determine an interval index; directly extracting an intra-interval offset index from the input through a bit extraction operation; and splicing the interval index and the offset index bit to generate a storage address. The circuit comprises a most significant bit detection module, a bit extraction module and an address splicing module. According to the method, the mathematical characteristic of power interval division of 2 is utilized, the existing division operation is converted into simple bit extraction operation, and arithmetic operation in the address generation process is completely eliminated. Only a priority encoder, a multiplexer and bit splicing logic are needed, and no adder, multiplier or divider is included. Realization results on an FPGA show that the circuit only consumes 5-7 LUTs which are reduced by more than 90% compared with an existing method, and the circuit is particularly suitable for resource-limited embedded systems and processing application of high-throughput data streams.

The invention relates to the technical field of automatic driving early warning, and discloses an automatic driving safety early warning system based on AI and Beidou satellites. According to the system, historical normal data of vehicle Beidou positioning and a vehicle-mounted sensor are synchronously collected through a multi-source data collection module; the spatio-temporal data enhancement module expands the data based on spatio-temporal trajectory similarity; and the interactive hybrid network constructs a deep learning network of the module reconstruction unit. The real-time monitoring data flow processing module continuously receives and caches the real-time data flow; an implicit state space search module searches an optimal state code which enables the reconstruction loss of real-time data to be minimum; and the risk situation assessment module calculates a comprehensive risk index. The key parameter traceability module analyzes the gradient contribution degree of each state parameter to the risk index so as to determine a dominant risk parameter; and the multi-level early warning decision module generates graded early warning signals according to the parameter types and the risk grades, so that accurate early warning and fault tracing of potential risks of the automatic driving system are realized.

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 relates to the technical field of artificial intelligence, and provides a data stream processing method, apparatus and system. The processing method comprises: acquiring a token data stream, the token data stream comprising one or more token units, and each token unit comprising a token unit header and a token unit body; parsing metadata information in the one or more token units; selecting token data from the one or more token units on the basis of the metadata information; and inputting the selected token data into a model to obtain a processing result.

To enable real-time seizure warnings, systems and methods of the present disclosure include a wearable sensor device in communication with processors that are configured to receive from the wearable sensor device data streams associated with a user that include biomarker data parameters. The processors utilize a seizure forecasting machine learning model that combines a deep canonical correlation analysis with a long short-term memory neural network to predict a seizure likelihood in a prediction period.

The invention relates to the field of network security, in particular to a task execution monitoring and exception troubleshooting method and system based on a power distributed network, and the method comprises the steps: tracking a data flow state in the power distributed network, determining a data busy network district, and determining a terminal operation relation in the district; determining a task execution key process according to a task execution record of the terminal in the area, and extracting a process data sample in combination with a terminal operation relationship; predicting the content change of the process data sample, and determining a potential abnormal event of task execution; and according to the predicted occurrence time of the potential abnormal event and the terminal operation relationship, changing the interaction state of the terminal and other terminals, and identifying the real-time data stream processing condition which causes the task to execute the potential abnormal event during the period, so as to adjust the interaction channel layout of the terminal in the network. Through area division, terminal operation relation identification and process data acquisition and analysis on the power distributed network, task execution abnormity is comprehensively checked, and the overall operation performance of the network is improved.

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.

The invention discloses an intelligent cache processing method for real-time annotation data streams, and belongs to the technical field of cross-domain data processing, and the method comprises the steps: receiving a multi-source annotation data stream, mapping fields through a time sequence diagram neural network, calculating the similarity, generating a mapping rule, executing incremental cleaning, and outputting a structured annotation data stream; extracting data entities, attributes and relationships, constructing a heterogeneous cache atlas, and establishing a quaternary composite index; then constructing a heat-temperature-cold hierarchical cache architecture, dynamically distributing data entities based on real-time heat entropy, relation strength and the like, and synchronously updating the atlas and the index; low-confidence data is identified through entity confidence evaluation, correction suggestion feedback cleaning is generated, and data source subgraph copy parameters and hotspot entity states are synchronized by means of a security aggregation algorithm; evaluating the network based on a deep strategy to realize fine-grained dynamic access control; according to the method, the caching efficiency, the data credibility and the access security of the multi-source annotation data are improved, and the method is suitable for a real-time data stream processing scene.

Embodiments of the present invention provide a data flow processing method. The method is performed by a terminal. The method comprises: sending capability indication information to a first network function, wherein the capability indication information is used for indicating that the terminal supports or does not support a second network function to perform a predetermined operation on a data flow of a predetermined service on the basis of a prediction result, and the predetermined operation comprises at least one of the following: optimal scheduling; and flow control. Compared with not using the mode of sending the capability indication information, the present invention can adapt to the transmission status of a data flow, improve the transmission efficiency of the data flow, and mitigate congestion, thereby improving the user experience.

This application discloses a data stream processing method, apparatus, device, and storage medium based on a reconfigurable TPU, including: acquiring the data to be processed and the current operating status parameters of each processing subunit; evaluating the processing complexity of the data to be processed to obtain a complexity evaluation value; determining the real-time splitting granularity corresponding to the operating status parameters of each processing subunit; adjusting the real-time splitting granularity based on the complexity evaluation value; splitting the data to be processed based on the adjusted target splitting granularity; marking the data source of the multiple data blocks to be processed obtained from the splitting; matching the operating status parameters corresponding to each processing subunit with the source identifiers corresponding to each data source mark to determine the data blocks to be processed corresponding to each processing subunit, which are then used by each processing subunit to perform data operation processing on the corresponding data blocks to be processed. This improves the accuracy and rationality of data splitting, data processing efficiency, resource utilization, and system adaptability.