89 results about "Data scheduling" patented technology

The application aims to provide a key data instant landing method and device based on meta attribute identification. The key data meta attribute identification is introduced in an application layer, an end-to-end key data identification and isolation path is established in a kernel I / O stack, and key semantic is transmitted through a reserved bit in a bio structure body, so that zero-copy and low-overhead key data scheduling is realized.

The invention relates to the technical field of safety monitoring, in particular to a low-power-consumption universal urban lifeline acquisition and transmission terminal, which comprises a data acquisition interface module, a data transmission module, a data transmission module, a data transmission module and a data transmission module, and is characterized in that the data acquisition interface module provides a physical communication interface and is connected with an external sensor to acquire original sensing data; the protocol self-adaptive processing module is used for storing configuration parameters of the equipment, dynamically calling a corresponding decoding function based on the type of the access equipment, and analyzing the original sensing data into physical service data; the data scheduling transmission module is used for carrying out classification processing on the physical service data, selectively executing a batch reporting process after local caching according to a data state, and selectively executing an immediate reporting process after fault triggering; and the intelligent power consumption management module operates a power consumption state machine and predicts a future load based on historical task data. According to the invention, the configuration library and the extensible coding and decoding library are constructed, so that the maintenance cost and the upgrading difficulty of the system are reduced.

This application relates to a multimodal Transformer parallel inference method and system based on dynamic operator decomposition. For each multimodal request, the method dynamically decomposes the computational tasks of the multimodal Transformer model at the operator level based on the network architecture configuration information of the multimodal Transformer model, the memory and communication bandwidth status of each computing device node in the target computing cluster, and the load characteristics of the multimodal request. For each operator, multiple candidate device allocation strategies are determined. The total cost corresponding to the operator under each candidate device allocation strategy is determined. Based on the total cost, the target device allocation strategy corresponding to the operator is determined from the multiple candidate device allocation strategies. Each operator and the data it processes are scheduled to the computing device nodes indicated by the target device allocation strategy for parallel inference computation. This method can reduce the end-to-end inference response latency of the model.

This invention relates to a method, system, equipment, and storage medium for the operation and maintenance scheduling of offshore wind farms. The method includes acquiring basic scheduling data, which is relevant data used for scheduling maintenance of offshore wind turbines; determining multiple initial solutions based on preset initial solution construction rules and the basic scheduling data; determining a first optimal solution among the multiple initial solutions based on a preset solution model and the multiple initial solutions; optimizing the first optimal solution according to a preset first optimization rule to generate a second optimal solution; and optimizing the second optimal solution based on a preset second optimization rule to determine a target optimal solution. This invention improves the efficiency of solving the operation and maintenance scheduling model.

This invention discloses an edge-cloud collaborative computing and dynamic scheduling system and method for omnidirectional robots, relating to the field of edge-cloud collaborative technology. The system includes: during industrial operation of the omnidirectional robot, acquiring torque, pose, and control commands at the edge and aligning them in time to form a sequence of key operational states; generating predictive features through sliding time window differential and time-weighted aggregation; comparing the predicted features with historical normal features to obtain predicted trigger values; initiating high-frequency uploading of key data when the trigger value exceeds the local risk reference band of the corresponding operational stage; the local risk reference band is constructed in stages by historical similar devices; edge nodes cache data according to operational stages and issue upload and retention strategies to achieve adaptive data scheduling under edge-cloud collaboration. This solves the problems of delayed risk information transmission and difficulty in timely capturing the early risk evolution process of industrial operations caused by existing abnormal trigger-based uploads.

This application provides a data scheduling method, a distributed storage system, a computing device, a storage medium, and a program product. The data scheduling method includes: responding to a data scheduling request, determining at least one piece of data to be scheduled corresponding to a target object; determining at least two media types corresponding to the target object from a plurality of media types; determining a target storage medium corresponding to each of the at least one piece of data to be scheduled and a target data node providing the target storage medium according to the at least two media types; and storing the at least one piece of data to be scheduled in the target storage medium of its corresponding target data node. The technical solution provided by this application can improve the service availability of a distributed storage system.

This disclosure provides systems, methods, and devices for interconnecting components of an electronic device through a serial bus interface that supports detecting input / output data lines that are not operating as expected. In a first aspect, a method includes scheduling data to be transmitted over a plurality of input / output lines (e.g., of the serial bus) in a queue. The method includes determining that the condition is met (e.g., input / outlines are all in a high state) for the plurality of input / output lines prior to transmitting the data over the plurality of input / output lines. The method includes transmitting the data over the plurality of input / output lines in the queue when the condition is met for the plurality of input / output lines. Other aspects and features are also claimed and described.

This application discloses a collaborative fault early warning method for power communication networks and dispatch automation systems, relating to the field of power system fault early warning technology. This method collects multi-source data on equipment status, service operation, load, and digital twins. After time-series alignment and standardization, it uses a causal forest algorithm to extract dual-dimensional causal features related to long-term performance degradation and real-time operation, and dynamically allocates fusion weights. Combined with digital twin simulation, it dynamically calculates the fault impact level and handling priority, generates targeted handling instructions, and optimizes parameters through closed-loop feedback. It further enhances real-time performance and adaptability through an edge-cloud collaborative architecture and scenario adaptation mechanism, achieving deep fusion of multi-dimensional data. This method realizes the deep and organic integration of the full lifecycle status data of power communication network equipment, the dynamic load characteristics of dispatch automation system services, and the real-time simulated power grid operation status of the digital twin, enabling early warning several hours in advance and ensuring the safe and stable operation of the power grid.

The application provides a data scheduling method and device and electronic equipment. The embodiment automatically identifies a cold unit memory (referred to as a cold unit memory) in a first memory, whose access frequency per unit time is less than a first set threshold, and a hot unit memory (referred to as a hot unit memory) in a second memory, whose access frequency per unit time is greater than or equal to a second set threshold, automatically schedules data in the hot unit memory in the second memory to move to an idle unit memory in the first memory, and automatically schedules data in the cold unit memory in the first memory to move to an idle unit memory in the second memory, so that data with high access frequency is stored in the local first memory of the system, and data with low access frequency is stored in the second memory which is expanded, the advantages of local memory high-speed access and memory capacity expansion are maximized, and the data access efficiency is improved.

The application provides a kind of intelligence calculation fusion network heterogeneous resource elasticity adaptation and deterministic scheduling system and method, belongs to industrial internet of things technical field, comprising: the system of establishing distributed management and centralized control under the scene of intelligence calculation fusion network;According to the distributed management and centralized control system, the two-stage random optimization problem of different time scales is established;For large-scale resource adaptation problem, a resource elasticity adaptation scheme based on heuristic algorithm is adopted;For small-scale scheduling sub-problems, a deterministic scheduling scheme based on deep reinforcement learning is adopted;Using the resource elasticity adaptation scheme and deterministic scheduling scheme, the optimal resource adaptation and scheduling result is obtained.The application can meet the resource flexible configuration demand under high dynamicity and uncertainty environment, ensure low delay and high reliability in data scheduling process.

A data scheduling system according to the present disclosure includes: one or more masters configured to determine configuration parameters based on a communication scenario and configured to generate transmission data; a programmable data scheduling circuit configured to receive the configuration parameters and configure an internal circuit structure of the programmable data scheduling circuit based on the configuration parameters, to obtain a configured data scheduler, the configured data scheduler receiving the transmission data; and one or more slaves configured to receive the transmission data scheduled by the configured data scheduler.

A kind of multi-path serial exchange FFT IP core based on time multiplexing constant multiplier belongs to digital signal processing technical field, solve existing time multiplexing constant multiplier when adapting MSC architecture FFT, there is the problem of collaborative conflict with serial exchange data scheduling, hardware resource redundancy, poor point number expansibility, timing and signal quality imbalance and lack of supporting efficient data permutation scheme, including multi-path serial exchange FFT architecture, the architecture includes: memory-based permutation module, a plurality of general rotator and at least two base modules, wherein k is positive integer;The rotator in the base module is optimized using time multiplexing constant multiplier, and the time multiplexing constant multiplier is used to implement the multiplication operation of fixed constant and variable input;The IP core supports the FFT calculation of point number 2 power exponent.This application is suitable for digital signal processing scene.

The application discloses a data scheduling method and system, electronic equipment and a storage medium, and relates to the technical field of network communication. The method comprises the following steps: acquiring data to be transmitted in at least one network service scenario, and address information of a network device to which the data to be transmitted is to be transmitted; acquiring a scheduling strategy matched with the data to be transmitted and the address information, wherein the scheduling strategy is used to indicate that a delay allowed for the data to be transmitted in the process of transmitting the data to be transmitted to the network device needs to be within a delay interval, and the delay interval meets delay requirements of the corresponding network service scenario; determining a target source port number matched with the delay interval in the scheduling strategy, wherein different delay intervals correspond to different target source port numbers; and scheduling the data to be transmitted to a transmission link associated with the target source port number, and transmitting the data to be transmitted to the network device by using the transmission link. The application solves the technical problem of low efficiency of data scheduling.

The application discloses a resource processing method, a communication device and a storage medium. The method comprises the following steps: receiving downlink information, and determining the available frequency domain resource position of a physical downlink shared channel according to a first strategy. The technical scheme of the application improves the effectiveness of frequency domain resource allocation, realizes flexible scheduling of frequency domain resources, and especially when the activated bandwidth part is greater than the maximum data scheduling bandwidth 5MHz, the granularity of the resource block group can be adjusted according to the actual scheduling data bandwidth, so that the scheduling flexibility is realized, the effective utilization of the frequency domain resource allocation field in the DCI is ensured, and the bit waste in the DCI is reduced.

The present application provides a method executed by a user equipment (UE), comprising: receiving bandwidth part (BWP) configuration information of a UE type of a network for the UE, wherein a BWP corresponding to the BWP configuration information is different from an initial downlink BWP of a cell; and determining, by the UE, at least one of a physical downlink control channel (PDCCH) position in a common search space, a width of a frequency domain indication field in downlink control information (DCI) for data scheduling, a value, and a position of scheduled physical downlink shared channel (PDSCH) resources according to the received BWP configuration information.

The application discloses a power load scheduling method and device based on a large language model, relates to the technical field of power scheduling, and mainly aims to solve the problem of poor accuracy of existing power load scheduling. The method comprises the following steps: acquiring scheduling text data of power load, wherein the scheduling text data comprises load time sequence data, meteorological text data, equipment log data and power grid index data; performing feature extraction on the scheduling text data to obtain multi-modal features, and encoding the multi-modal features into a semantic space to obtain multi-modal feature encoding; and performing scheduling prediction processing on the multi-modal feature encoding after consistency processing based on a large language model after model training to obtain a power load scheduling result.

This invention discloses a data communication scheduling system for intelligent manufacturing production lines based on edge computing, belonging to the field of industrial IoT edge computing data scheduling technology. The system synchronously acquires the real-time load current discrete sequence of the target device node and the real-time signal-to-noise ratio sequence of the main communication path, extracting the second-order derivative zero-crossing discrete component and the first-order attenuation discrete component of the envelope. A cross-correlation algorithm is used to perform coherence analysis to derive a cross-domain healthy coupling index. Using this index as the independent variable, a weighted mapping of the real-time packet loss rate is performed through an exponential gain function to generate a virtual impedance factor. It determines whether the index exceeds a threshold to trigger backup gateway context pre-synchronization and calculates the positive offset. Finally, a normalization function is used to calculate the traffic allocation weight and configure path scheduling labels to achieve dynamic scheduling of multi-path traffic splitting actuators. This solution achieves a priori prediction of industrial communication link quality and refined multi-path traffic splitting scheduling.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, from a base station, a radio resource control (RRC) configuration with one or more high layer parameters that enable one or more of an aperiodic sounding reference signal (SRS) triggering without data scheduling or a secondary cell (SCell) dormancy indication using downlink control information (DCI). The UE may receive, from the base station and based at least in part on the RRC configuration, DCI that is associated with at least one of: the aperiodic SRS triggering without data scheduling or the SCell dormancy indication. The UE may transmit, to the base station, an aperiodic SRS based at least in part on the DCI that triggers the aperiodic SRS. Numerous other aspects are described.

The application relates to the technical field of financial transaction risk management, in particular to a financial transaction instruction verification system based on a risk early warning mechanism, which comprises the following steps: an instruction receiving module receives a to-be-verified financial transaction instruction containing account identification, transaction targets and the like, a data scheduling module asynchronously calls historical transaction behavior portraits, holding position data and risk preference levels from an account center database based on the account identification, a market data analysis module obtains market snapshot, market depth, implied volatility and associated target price change sequences through a real-time market data calculation engine, a hierarchical verification module completes hierarchical verification according to multiple types of data and generates a multi-dimensional verification report, and an instruction processing module generates a final processing instruction in combination with the report and a risk control rule set. The system can relieve account database access blockage problems, realize hierarchical risk judgment and structured risk output of transaction instructions, and strengthen the integrity and pertinence of transaction instruction risk verification.

The application relates to the technical field of data scheduling, and particularly discloses a wisdom heat supply comprehensive scheduling method and system based on operation data analysis, which comprises the following steps: performing data preprocessing on a historical operation data set of a monitoring point in a target regional heat supply pipe network system in a historical same period, so as to obtain standard historical operation data; inputting the standard historical operation data and corresponding actual heat load values into a preset initial neural network model for iterative training, so as to construct a load prediction model; inputting real-time operation data into the load prediction model, so as to generate a heat load demand curve; performing multi-objective decision optimization on current operation state data, so as to obtain a Pareto optimal solution set; performing priority screening on the Pareto optimal solution set, so as to obtain a candidate scheduling scheme and issue the candidate scheduling scheme to an execution mechanism of the target regional heat supply pipe network system; and collecting feedback operation data of the execution mechanism in real time, so as to update the load prediction model; and the wisdom heat supply comprehensive scheduling efficiency can be improved.

This invention discloses a method and system for intelligent scheduling of logistics vehicles based on big data, belonging to the field of logistics big data scheduling technology. It includes establishing a logistics transportation scheduling big data warehouse, collecting and storing raw data such as historical orders, vehicle files, real-time traffic, weather, and warehouse operation status; standardizing, cleaning, and integrating the data to form a multi-dimensional scheduling decision feature set; identifying high-frequency delivery areas and frequently congested road sections through spatiotemporal clustering algorithms; generating regional heat maps and road network risk layers; dynamically correcting the road network risk layers by combining real-time traffic and weather information to obtain a real-time road network status layer; and performing multi-objective programming operations by integrating orders to be scheduled, available vehicle pools, regional heat maps, and real-time road network status layers to generate a preliminary scheduling scheme. This invention can accurately present the spatiotemporal distribution characteristics of orders and road networks, dynamically adapt to the real-time transportation environment, and improve the multi-dimensional data support and computational adaptability of scheduling decisions.

The embodiment of the specification discloses a data scheduling management method and device, a storage medium and a terminal. First, a job request carrying a stream processing or batch processing attribute identifier is received, providing a clear basis for subsequent intelligent shunting. Subsequently, the job is assigned to a specific stream or batch processing management module according to the identifier, realizing physical isolation of resources and management and avoiding mutual interference. Further, each module adopts a completely different data loading strategy according to a preset rule: stream processing is incrementally loaded through subscription to a real-time message queue, and batch processing is batch-loaded by querying an offline data table, thereby adapting to the respective data throughput characteristics. Finally, the loaded data is uniformly packaged as a risk identification task, which is executed by a same risk identification engine, realizing the unification and reuse of service logic. The heterogeneous computing paradigm is effectively integrated, and the resource utilization and system maintainability are improved.

The present application relates to the technical field of data collection and real-time calculation, and in particular to a method for on-demand accurate real-time data collection and calculation. The method comprises the following steps: through multi-dimensional on-demand collection request analysis and dynamic priority scheduling, the present application performs adaptive data collection and incremental transmission optimization on enterprise data, realizes real-time data processing and end-to-end consistency guarantee, and performs multi-dimensional data readiness confirmation and intelligent calculation triggering, so as to reduce data collection cost, improve data scheduling flexibility and calculation efficiency.

This application discloses a data-triggered heterogeneous server system and its task scheduling method, relating to the field of artificial intelligence technology. It includes: a computing node layer, comprising at least one computing node, each computing node including a processing core combination and corresponding core shared memory, wherein the processing core combination includes at least one general-purpose processing core and multiple accelerated processing cores; and a data-triggered control layer, comprising at least one control node, the control node being connected to the corresponding computing node including at least one general-purpose processing core and multiple accelerated processing cores. When received reference data meets the triggering conditions, the control node directly sends task instructions to multiple target processing cores in the connected computing node, wherein the processing core combination includes multiple target processing cores. This solves the technical problem of low data scheduling efficiency in heterogeneous servers in related technologies.

This invention provides a method and system for unified management and control of multi-cloud data, characterized by a lightweight central control and a heavy agent-based approach. The method utilizes lightweight acquisition agents deployed at various heterogeneous storage sources in multiple clouds to scan and extract metadata information, synchronizing this metadata to a central management node to construct a global data directory. The central management node performs intelligent analysis based on the global data directory and preset management strategies, generating data scheduling instructions and publishing them to a distributed messaging service. A pre-built serverless execution function in the execution environment subscribes to and consumes these instructions, directly executing data entity operations between the source and target storage addresses in response. After completing the data entity operations, the serverless execution function returns the execution result to the central management node via a callback interface. This invention solves the bandwidth bottleneck problem of the central node and utilizes edge handshake and backpressure mechanisms to ensure data consistency and source-end business stability.

The application discloses a raw material data processing method, which comprises the following steps: after collecting multi-source raw material data, spatiotemporal alignment and heterogeneous fusion are completed by using material batches and fixed time windows; layered preprocessing driven by business rules is performed on the fused data; classified storage is completed and cross-storage associated views are constructed by combining a hybrid storage architecture and cold and hot data scheduling; a coupling model is constructed based on supply chain, production and quality three links to complete associated analysis and early warning; and finally, data security traceability is guaranteed through edge cloud dual-node verification and blockchain storage. The problems of low fusion precision of raw material multi-source data, poor preprocessing adaptability, low storage and analysis efficiency and easy data tampering are effectively solved, the data processing efficiency and usability are improved, the raw material data full life cycle traceability is realized, and the method is suitable for industrial production, supply chain management and quality monitoring scenes.

This invention relates to the field of intelligent decision-making technology, and discloses a task processing method, apparatus, device, and medium based on data sharing. The method includes: determining the process node of the target task in the task flow data based on initial input data; calculating the task value data of the target task based on initial task value data and task operation indicators; determining whether the task value data is less than the task termination threshold; if less, terminating the target task according to the process processing instruction and generating the task termination result; if greater than or equal to the threshold, analyzing the node data status of the process node in the data sharing center and determining the node data scheduling path of the process node based on the node data status; and driving the intelligent agent corresponding to the process node to process the target task based on the node data scheduling path to obtain the task processing result. This invention can be applied to business system platforms such as fintech and healthcare, and can improve the efficiency of multi-agent collaborative task processing.

The present application relates to the technical field of intelligent agricultural data platform task scheduling and cleaning, in particular to an agricultural heterogeneous big data platform adaptive task scheduling and data cleaning system. The present application establishes an index map of data and tasks, solves the correlation between task targets and task data through semantic analysis to establish a preliminary data fast call, establishes a complete task scheduling index through semantic relationship analysis of historical data tasks and task targets, filters the indexed data through automatic analysis and extraction of data cleaning rules, and dynamically adjusts the map during calculation according to the calculation resource occupation and data quality to accurately schedule and clean data according to task requirements, and adjusts the data in real time according to the task calculation resource condition.