137 results about "Data feed" patented technology

The invention relates to the technical field of data analysis, and provides a CAN bus waveform message analysis method, device and equipment and a medium, and the method comprises the steps: issuing an operation instruction carrying a preset operation parameter to an oscilloscope; in response to waveform data fed back by the oscilloscope, storing the waveform data as a message file in a preset format; analyzing and converting the message file according to the data head structural body identified by the oscilloscope to obtain a message array; performing time domain segmentation, normalization processing and de-noising processing on the data in the message array by using a Numpy library to obtain a message signal; and performing error frame screening on the message signal by adopting a strategy of supplementing one after five, and performing layer-by-layer analysis on the screened message signal based on a CAN protocol structure to obtain message analysis content. According to the invention, through the software design of the whole process, a universal scheme is adopted from data acquisition, storage and analysis to signal processing and message generation, so that the analysis universality of the CAN bus waveform message is effectively improved.

Some embodiments of the present application include obtaining a trained machine learning model that was trained using training data. Production data may be obtained from a data feed selected based on the trained machine learning model. In some embodiments, feature sets and observed results may be extracted from data output from the data feed, and one or more features or results may be masked to generate the production data. Predicted results data may be generated with the trained machine learning model based on the production data and an accuracy score for the trained machine learning model may be determined based on the predicted results data. If the accuracy score satisfies a threshold accuracy condition, the trained machine learning model may be caused to be rebuilt or the training data may be caused to be updated.

Aspects of the disclosure relate to using machine learning models to automatically prioritize and orchestrate spatial data. A computing system may generate, based on inputting scene data and spatial positioning data into a machine learning model, a prioritized context that indicates physical objects that are significant within a physical environment. The scene data may comprise images of the physical environment and the spatial positioning data may comprise an indication of states of a user in the physical environment. Based on the prioritized context meeting user criteria, generate indications associated with the prioritized context. Based on the scene data, one or more spatial anchors at which to position the one or more indications of the prioritized context within a virtual environment based on the physical environment may be generated. The virtual environment comprising the indications of the prioritized context positioned at the one or more spatial anchors may be generated.

The invention discloses a 10kV overhead line adaptive evaluation method and system for non-power-cut operation, and the method comprises the following steps: obtaining the field data of a target line, and constructing the field data into a multi-dimensional evaluation feature vector according to a preset evaluation dimension system for non-power-cut operation; inputting the multi-dimensional evaluation feature vector into a non-power-cut operation adaptability evaluation model to obtain a comprehensive evaluation score and an optimization suggestion of the target line; outputting the comprehensive evaluation score and the optimization suggestion; wherein the non-power-cut operation adaptability evaluation model is constructed by the following steps: training to obtain an initial machine learning model; simulating a preset non-power-cut operation task flow in the digital twinborn body; and performing incremental training and parameter optimization on the initial machine learning model by using operation effect data fed back after actual operation of the simulation data vector. The problems of single evaluation dimension, model stiffness and the like in the prior art can be solved.

The invention relates to the technical field of intelligent traffic and smart city waste management, and discloses a path planning method and system for a garbage transport vehicle based on multi-source data, and the method comprises the steps: generating a reference path based on historical collection and transportation data, the method comprises the following steps: performing data cleaning on pre-acquired garbage quantity data, vehicle state data, real-time traffic data, hydroelectric data and fuel consumption data, performing multi-source time sequence feature fusion anomaly detection on a cleaned data set, performing trajectory inversion on the cleaned data set, performing target constraint through a mixed integer programming model, and performing multi-source time sequence feature fusion anomaly detection on the cleaned data set. Then gradient descent parameter optimization is carried out on the initial optimization model, elastic resource scheduling is carried out on an optimized path scheme based on a large event plan library, a path instruction set is generated, dynamic re-optimization evaluation is carried out on the path instruction set based on garbage amount change and traffic condition data fed back in real time, and an updated path is generated; according to the invention, the path planning efficiency of the garbage transport vehicle based on the multi-source data can be improved.

A system for automated real-time publication processing may comprise a data collection module configured to automatically gather data from multiple external sources. A data analysis module may be configured to process the gathered data using artificial intelligence models. A data visualization module may be configured to generate interactive visual representations of the processed data. An update control module may be configured to automatically update published content and maintain version history with timestamps. The data collection module may utilize application programming interfaces and web scraping tools to gather data from government databases and real-time data feeds. The data analysis module may employ machine learning libraries to process and analyze the collected data. The data visualization module may use visualization tools to create interactive charts and graphs. The update control module may use Git-based version management and automated scheduling scripts to implement updates and record modification dates.

An integrated system for generating innovative solutions is disclosed. The system comprises a literature review AI agent, a knowledge graph, and a TRIZ Module. The AI agent continuously analyzes diverse information sources, feeding data into the knowledge graph. The knowledge graph organizes and connects information, identifying patterns and relationships. The TRIZ Module interacts with both components to identify contradictions and generate innovative solutions. This system accelerates innovation by processing vast amounts of information, recognizing cross-disciplinary patterns, and applying systematic problem-solving techniques.

A multi-stage deep learning method is provided, comprising training a deep learning network using a set of training pairs interchangeably including input data from a first noisy dataset with a first noise level and target data from a second noisy dataset with a second noise level, and input data from the second noisy dataset and target data from the first noisy dataset; generating an ultra -low noise data equivalent based on a low noise data fed into the trained deep learning network; and retraining the deep learning network on the set of training pairs using the target data of the set of training pairs in a first retraining step, and using the ultra-low noise data equivalent as target data in a second retraining step.

A threat management facility for an enterprise network integrates native threat management capabilities with threat data from a cloud service provider used by the enterprise. By properly authenticating to the cloud service and mapping data feeds from the cloud service to a native threat management environment, the threat management facility can extend threat detection and management capabilities beyond endpoint-centric techniques.

A computer-implemented method for facilitating one or more users virtually participating in a racing event, the method including: receiving, by one or more processors, a data feed from the event; processing, by the one or more processors, the data feed to generate a multi-dimensional virtual environment which substantially replicates the physical environment of the event, the multi-dimensional virtual environment including: virtualized representations of each participant in the event, wherein each of the virtualized representations of the participants is adapted to move inside the multi-dimensional virtual environment according to physical movements of the corresponding participant during the event, and one or more avatars corresponding with the one or more users, communicating, by the one or more processors, with computer hardware associated with the one or more users to: enable a display associated with the computer hardware to present, from a viewpoint associated with the user's avatar, the multi-dimensional virtual environment to the user, including the virtualized representations and any movement of the virtualized representations, thereby enabling the user to visualise the racing event as time passes, and enable, based on user instructions, the user's avatar to navigate the race path, thereby enabling the user to participate in the racing event and compete against the participants.

The invention relates to the technical field of data one-way transmission, and provides a state feedback ferry-based one-way data transmission method and system. Two one-way physical transmission channels are arranged between a sending agent and a receiving agent; the sending agent controls the retransmission of the data based on the maximum serial number of the successfully transmitted continuous frame data fed back by the receiving agent through the reverse state feedback channel; the reverse state feedback channel is a physical one-way import device which is independent of reverse transmission of the main physical one-way transmission channel; the reverse state feedback channel limits the channel transmission capacity based on the size of the state feedback file of the maximum serial number. Under the architecture constraint of physical one-way transmission, the state feedback of the receiving end and the intelligent retransmission of the sending end are indirectly realized through the innovative design of an application layer; on the premise of not sacrificing absolute safety brought by physical one-way isolation, the technical prejudice can be broken through, and the reliability problem of data transmission is solved.

The application relates to the chip technical field and discloses a partition unit, a partition, a chip, a network-on-chip implementation method and a medium. The partition unit comprises the following steps: obtaining an operation type and an access address corresponding to a memory access request through a secondary cache, and forwarding the memory access request to a control module of a directory corresponding to the access address when the operation type is a partial write operation; writing request data corresponding to the memory access request into a small-capacity cache through the control module, reading a target partition unit corresponding to the partition unit from a state recording module, generating a listening request according to the memory access request and sending the listening request to the target partition unit, receiving listening data fed back by the target partition unit, merging the listening data and the request data to generate complete data, and writing the complete data into a memory controller. Hardware support for cache consistency can be provided in an artificial intelligence chip with a multi-core architecture, and a network-on-chip implementation scheme supporting cache consistency is provided.

In accordance with some embodiments, systems, apparatus, interfaces, methods, and articles of manufacture are provided for providing information incorporating additional data feeds, creating common arrangements, and improving performance in a virtual meeting. In some embodiments, a video conferencing system hosts a customized video conferencing and includes video conference settings data, participant display device data, supplemental data feed data, and processing instructions. The video conferencing system may identify (based on the video conference settings data) a number of data feeds for display on a display screen of each participant in a video conference and (based on the participant display device data) a screen specification for each display screen of each participant. The system may then compute an arrangement for the number of data feeds that fits within an available display area.

The invention discloses a port container scheduling system and method based on GRASP and SAC. The system comprises a data acquisition module which is used for acquiring real-time data from port equipment and an environment sensor and preprocessing the real-time data; the optimization algorithm module is used for receiving the data output by the data acquisition module and generating a scheduling scheme, or receiving abnormal data fed back by the real-time monitoring module, automatically starting a GRASP algorithm and / or a SAC algorithm and regenerating the scheduling scheme; the task scheduling module is used for receiving the scheduling scheme and executing and adjusting specific task distribution and equipment scheduling operation; and the real-time monitoring module is used for monitoring the equipment state and the task execution condition and transmitting abnormal information to the optimization algorithm module. According to the method, the optimization algorithm modules are constructed by combining the GRASP and the SAC algorithm, and the modules are clear in division of labor and are in close cooperation, so that the whole scheduling system forms an efficient and cooperative whole, and the overall efficiency and stability of the scheduling system are improved.

Methods and systems for deploying intrusion detection system (IDS) policies to IDS programs are disclosed. Each IDS instance can monitor a data feed (e.g., comprising computer network traffic) in accordance with a set of “signatures” or “rules” associated with its policy, and can issue alerts if any elements of that network traffic match those signatures or rules. An intrusion signature management module can receive IDS signatures from one or more signature sources and store those signatures in a filesystem. The intrusion signature management module can produce rule files based on these IDS signatures, e.g., containing subsets of the received IDS signatures. These rule files can be provided to a version control server, and can later be accessed by an orchestration module. The orchestration module can deploy these rule files to their respective IDS programs, enabling those IDS programs to monitor their respective data feeds in accordance with the rule files.

In various embodiments, a computer-implemented method comprises receiving, by a tracking data broker from a consumer, a request for first data tagged with a first set of metadata, determining, by the tracking data broker, a plurality of data feeds associated with the first set of metadata, wherein a plurality of tracking data sources include, in the plurality of data feeds, sensor data tagged with the first set of metadata, generating a first message containing a set of data values from the plurality of data feeds, and transmitting the first message to the consumer.

A method is provided for training a machine learning ML model for monitoring people based on a data feed capable of describing people. The method is performed by a training data provider (i). The method includes: obtaining (40) a data feed capable of describing people; selecting (42) anonymization levels from a plurality of anonymization levels; anonymizing (44) the data feed according to the selected anonymization level to obtain a processed data feed; and sending (47) the processed data feed as training data for training a central ML model in a central node. Different anonymization levels are available and changes to the levels can be requested by the central node.

This invention relates to the field of prefabricated charging technology, and more particularly to a prefabricated charging integrated device and its control method. The method includes the following steps: acquiring multi-source operational data based on a data acquisition unit installed within the prefabricated compartment; when any data item in the multi-source operational data is abnormal, the charging compartment controller triggers the charging equipment to execute a power-off interlock, and transmits the power-off interlock signal to the multi-source data decision fusion stage; performing integrated analysis on the multi-source operational data and the power-off interlock signal, and generating a decision result including fault type and risk level through the multi-source data decision fusion stage; based on the risk level and fault type in the decision result, the charging compartment controller controls the equipment installed within the compartment as an execution carrier to perform corresponding graded handling operations, and receives execution status data fed back by each execution carrier; achieving rapid response and safe handling of abnormal states within the charging compartment, and ensuring traceability through full-process data collection.

The invention discloses a radio resource control method and device, a medium and a program product. Comprising the following steps: sending a synchronization signal, and receiving signaling or data fed back by each slave device in the industrial Internet of Things; determining an idle time-frequency resource according to the signaling or the data, and sending idle position information corresponding to the idle time-frequency resource to each slave device; and sending the synchronization signal on the idle time-frequency resource. On the basis of service data transmission resource occupation characteristics of the industrial Internet of Things, re-determining an idle time-frequency resource which can be used for synchronous signal transmission, and synchronizing idle position information corresponding to the idle time-frequency resource to each slave device in the industrial Internet of Things, and thus, the master device and the slave device can complete stable transmission of the synchronization signal on the idle time-frequency resource. By actively regulating and controlling the occupation condition of the synchronization signal sending resource in the wireless resource, the transmission delay of the service data is reduced, and the transmission performance of the service data in the industrial Internet of Things is improved on the basis of ensuring synchronization.

This disclosure relates generally to a method and system for performing multi-fold operations on complex data. Conventional methods do not have plug and play reusable components for processing high volume complex data concurrently. Also, these do not consider distributed or interactive data processing using optimal resources. The disclosed method performs operations on complex data with optimal execution time, memory and computation resource. The input data fed into the disclosed system are packaged into data chunks and distributed across available resources for pre-processing. The data chunks are pre-processed and converted into machine readable binary format. The data is again traversed to generate operation data chunks. The operation is performed for each operation data chunk, using the converted machine-readable binary format to generate the result of the operation. The disclosed method is used for operations such as search, comparison and so on in image processing.

In one embodiment, a method includes, while a first user wears a vocational mask to perform a task, receiving, at a processing device, a first data feed; transmitting, via network interfaces of the vocational mask, the first data feed to processing devices of a computing device, and the first data feeds are presented via a display, emitted by an audio device, or produced via a haptic device; receiving, from the computing device, second data feeds pertaining to instructions for performing the task, wherein the second data feeds are received by the processing device, and the second data feeds are presented via a virtual retinal display, emitted by an audio device, or produced via a peripheral haptic device; and storing, via memory devices communicatively coupled to the processing device, the first data feeds and / or the second data feeds.

A system includes one or more processors to receive, from a first client system, a first message associated with a transfer of a data file, the first message in accordance with a predetermined protocol, execute a sequence of operations that authenticate the first client system to an application programming interface (API) gateway that controls access to a data storage, upload the data file from the first client system to the data storage, send, to a second client system, a notification in accordance with the predetermined protocol to cause the second client system to transmit a second message, receive the second message in accordance with the predetermined protocol, authenticate the second client system to the API gateway based on execution of the sequence of operations, and provide, to the second client system via the predetermined protocol, the data file to cause the second client system to download the data file.

Disclosed are a firmware detection system and method for a recoverable fault, a storage medium, and a server. The method includes: acquiring target fault information stored in a fault register, and transmitting the target fault information to a baseboard management controller (BMC); parsing the target fault information by the BMC, and determining a fault type; controlling, by the BMC, a virtual external device to transmit a target heartbeat request to a server operating system according to a preset transmission period when the fault type of the target fault information is a recoverable fault, and receiving response data fed back by the server operating system and determining, by the BMC, that the server operating system has a crash if detecting that the response data fed back by the operating system is interrupted within a preset time period, and determining a faulty component in the server according to the target fault information.

This invention discloses an online high-precision map building method and system based on positioning fusion, including: acquiring three-axis acceleration data and three-axis gyroscope data through an IMU; obtaining predicted continuous attitude change data based on the changes in velocity and angle; matching and building a local map through LiDAR point cloud data, and acquiring the driving trajectory; matching the driving trajectory with the local map to obtain LiDAR observation pose data; obtaining satellite positioning trajectory data fed back by RTK; obtaining the overlap degree in a unified coordinate system by fitting the predicted continuous attitude change data, LiDAR observation pose data, and satellite positioning trajectory data at the same time, and obtaining the variance value of the relative deviation; judging the positioning accuracy of the predicted continuous attitude change data, LiDAR observation pose data, and satellite positioning trajectory data at the same time based on the obtained variance value of the relative deviation, and obtaining the pose of the sub-map at the same moment.

The invention provides a combined personalized station dynamic air supply control method and system, and the method comprises the steps: starting the system, initializing a communication module, connecting a user operation interface through a wireless network, and building a real-time data interaction channel between the control system and an air supply array; acquiring air supply mode parameters input by a user, wherein the parameters comprise a steady-state air speed value, a sinusoidal wind characteristic parameter or a natural wind signal file; a low-pass filtering processing module based on fast Fourier transform performs frequency domain analysis on the imported natural wind signal file, extracts power spectrum characteristics in a 0.01-1Hz frequency interval and generates a target wind speed sequence conforming to natural wind dynamic characteristics; and according to the seat state and the somatosensory temperature data fed back by the user state sensing module, the motor rotating speed and the swing angle of each matrix unit in the air supply array are dynamically adjusted, and personalized dynamic air supply of the station microenvironment is achieved through multi-air-source cooperative control.

Discussed herein are methods and systems for dynamically recalibrating machine learning model parameters. In one method, a server executes one or more prediction models to process network operations from various data feeds, in order to identify the likelihood of these operations being fraudulent or malicious. The server monitors performance data, such as the operation and execution metrics of network operations, and evaluates whether the performance values, like recall values, meet defined thresholds. If the performance data fails to meet these thresholds, the server employs a function-generation machine learning model to predict a threshold modification function. This modification function is then applied to adjust the relevant thresholds. Utilizing the modification function, the server dynamically revises one or more parameters of the prediction models to enhance their accuracy and efficacy.

Embodiments of the present application provide an artificial intelligence interview method, a computing device, a readable storage medium and a computer program product. The artificial intelligence interview method comprises: obtaining an interview request sent by a client; determining a question list corresponding to a target position; determining whether a target user meets a question generation condition; in the case that the target user meets the question generation condition, generating target question data from the question list by using a target dialogue model in combination with at least one round of historical dialogue data; determining whether the target question data meets a corresponding constraint condition; in the case that the target question data meets the corresponding constraint condition, sending the target question data to the client; and obtaining response data fed back by the client. The technical solution provided by the embodiments of the present application can reduce the processing pressure of the target dialogue model, reduce the training cost of the target dialogue model, and improve the performance of the target dialogue model in specific tasks.

The application provides an open question reply statistical analysis method and related device, the method comprises the following steps: obtaining target question information, the target question information comprises a target question and a target processing mode; sending a sound pickup opening instruction to j interactive feedback terminals, the sound pickup opening instruction is used for instructing the interactive feedback terminal to open the microphone sound pickup; obtaining first voice data fed back by the j interactive feedback terminals, obtaining j first voice data, the first voice data is generated by the j interactive feedback terminals collecting when corresponding target reply personnel replies the target question; processing the j first voice data to obtain j target reply texts, the target reply text comprises key reply content of the reply personnel; grouping according to the viewpoint type of the j target reply texts to obtain m reply text sets, wherein m < j; outputting target reply information corresponding to the m reply text sets.

The application provides an artificial intelligence service online testing method and device, the method comprises the following steps: receiving interface definition information and expected verification data for an artificial intelligence service to be published, wherein the artificial intelligence service is deployed in a running environment; generating test request data based on the interface definition information and obtained test input data, and sending the test request data to the artificial intelligence service; receiving actual response data fed back by the artificial intelligence service to the test request data; when it is determined that the actual response data matches the expected verification data, updating the state of the artificial intelligence service to a published state to open the external calling permission of the artificial intelligence service, so that the test process can flexibly adapt to the differences of online environments and support instant adjustment and verification of parameters, an integrated closed-loop process of testing and publishing is constructed, and therefore, seamless integration of the test activity and the publishing process is realized, and instant test verification and significant improvement in environmental authenticity are achieved.

Some embodiments of the present application include obtaining first data from a data feed to be provided to a plurality of machine learning models and detecting a changepoint in the first data. In response to the changepoint being detected, a first machine learning model may be executed on the first data to obtain first output datasets. A first performance score for the first machine learning model may be computed based on the first output datasets. A second machine learning model may be caused to execute on the first data based on the first performance score satisfying a first condition.