469 results about "Data scheduling" patented technology

The invention provides a resource intelligent bus and task parallel hydrogen energy data scheduling optimization method and system, relates to the technical field of energy, and aims to solve the problems of resource heterogeneity, lack of real-time performance and dynamic adjustment, insufficient energy efficiency optimization, poor adaptability to abnormal working conditions and the like in the prior art. According to the method, a resource intelligent bus architecture is constructed, and the resource intelligent bus architecture comprises a resource virtualization encapsulation layer, a resource state sensing layer and a global prediction and scheduling layer; in the resource state sensing layer, performing multi-granularity task decomposition on hydrogen energy business requirements, and establishing an inter-task dependency relationship model by adopting a directed acyclic graph; in the global prediction and scheduling layer, a multi-objective optimization model is constructed based on a dependency relationship between resource states and tasks, and resources and tasks are dynamically matched according to an optimal solution of the model; and dynamically scheduling the resources according to a matching result of the resources and the tasks. The problems in the prior art are solved, and the hydrogen energy data scheduling optimization performance is improved.

Methods and systems are described for AI and / or ML model selection in a telecommunications network. One example embodiment includes obtaining a first set of one or more criteria for selecting between at least two AI / ML models; performing at least one measurement procedure to obtain one or more measurements; and using the obtained one or more measurements and the first set of one or more criteria to select between the at least two AI / ML models. A measurement procedure can comprise e.g. CSI, radio link procedure (RLP), positioning measurement, measurement related to cell change procedure etc. Certain described embodiments enhance measurement performance of the measurement (e.g., CQI) and correspondingly improve the outcome / performance of the procedure (e.g., data scheduling) using the measurement. This in turn reduces the overall processing in the UE, frees up at least part of the memory resources and reduces the UE power consumption.

The invention discloses a multi-agent collaborative application big data management system and method. The system comprises a data acquisition unit, a data preprocessing unit, a multi-agent collaborative processing unit, a data storage unit and a data scheduling unit. The data acquisition unit is used for acquiring multi-source heterogeneous original big data and transmitting the multi-source heterogeneous original big data to the data preprocessing unit; and the data preprocessing unit is used for executing preprocessing operation including de-duplication and format standardization on the original big data. The invention relates to the technical field of big data management. According to the multi-agent collaborative application big data management system and method, through a multi-agent collaborative algorithm, the system can realize a self-adaptive data classification and optimization strategy, a data processing mode is dynamically adjusted, the processing efficiency problem of a traditional static rule during data mode fluctuation is improved, and the data processing efficiency is improved. The system adopts a dynamic resource scheduling strategy based on a data access demand and a system load, efficiently distributes calculation and storage resources, and facilitates priority processing of high-priority tasks.

The invention discloses a data scheduling and control method based on mainboard memory straight-through, and relates to the technical field of computer hardware, the method comprises the following steps: pre-allocating a straight-through memory area for mounting equipment in a firmware initialization stage, establishing a mapping relation between equipment identification and a physical address of an exclusive memory segment, and through address translation and access control rules of a firmware layer, the device bypasses a system cache level and accesses the exclusive memory segment in a direct memory access mode. Constructing a firmware scheduling table based on the mapping relation to manage access requests of all devices, monitoring transmission delay in real time, and dynamically adjusting priority weight and bandwidth allocation in the scheduling table according to a monitoring result; and sequencing the requests according to the updated scheduling table to complete scheduling and control of the memory access behavior of the equipment. According to the method and the device, the delay bottleneck caused by multi-level copying and firmware scheduling lag in mainboard data forwarding is effectively solved.

The invention discloses a waste recovery vehicle dynamic scheduling system and method based on an AI algorithm, and belongs to the field of intelligent logistics. The system comprises a task analysis module, a vehicle state sensing module, an AI scheduling core module and the like. The task analysis module identifies waste categories through an attention mechanism U-Net algorithm, and generates a task parameter set containing priorities; the vehicle state sensing module outputs accurate vehicle data through a Kalman filtering algorithm; the AI scheduling core module takes the task completion rate, the energy consumption and the carbon emission reduction as targets, and outputs a scheduling instruction through a multi-target optimization model and an improved Dijkstra algorithm; the data feedback module calculates the carbon emission reduction according to the Sichuan local standard and updates the model; the problems of poor scene adaptation and high energy consumption in the prior art are solved, the task completion rate is increased by 25%, the empty driving rate is reduced by 18%, and technical support is provided for the dual-carbon target.

The invention discloses a rope net ladder data production scheduling and traceability system based on big data storage, and particularly relates to the field of data scheduling and traceability, comprising the following steps: the system obtains total factor data of equipment, process, quality and the like through a data perception and digital acquisition module and standardizes the total factor data; the integrated storage and quantitative analysis module constructs functions such as efficiency evaluation and quality risk to quantify production indexes; the adaptive decision optimization and scheduling module generates an optimal scheduling scheme and a dynamic rescheduling instruction based on a multi-target comprehensive decision function; the full-link tracing and self-feedback optimization module realizes full-link bidirectional tracing and drives process parameter and model self-feedback optimization; the system realizes the intelligent control of the whole production process, balances the efficiency, quality, cost and delivery target, improves the resource utilization rate and production stability, and is suitable for the precise production scene of structural members such as rope net ladders.

The invention relates to the technical field of cadastral data dynamic management, and discloses a cadastral data dynamic updating and storage method, device and equipment and a storage medium. By receiving a land law enforcement task set of a target law enforcement vehicle, a law enforcement driving route map for a plurality of law enforcement tasks is constructed; issuing cadastral data of each law enforcement task to a cadastral data edge library at different driving road sections is taken as a decision variable, and the storage capacity of a target law enforcement vehicle, the maximum communication capability of each driving road section, the cadastral data advanced issuing characteristic and the cadastral data real-time performance are considered; and controlling cadastral data scheduling of the cadastral data edge library and the cadastral data core library, and assisting law enforcement officers in land law enforcement. Therefore, by adopting the task-oriented accurate data issuing and dynamic and the space-time analysis-based bandwidth prediction and optimization strategy, organic unification of the real-time performance, the security and the access efficiency of the cadastral data is realized, and efficient and reliable data support is provided for land law enforcement.

The embodiment of the invention relates to the technical field of computers, in particular to a distributed network architecture construction method and system for port environment monitoring, and the method comprises the steps: dividing a plurality of monitoring sub-regions according to the port region environment monitoring requirements, and deploying a heterogeneous monitoring node set in each sub-region. And performing real-time operation state sensing processing on the node set to generate a target network topology structure. And executing routing protocol generation and data scheduling strategy formulation based on the topological structure to obtain a self-adaptive routing table and a data transmission scheduling rule. According to the tables and the rules, distributed collaborative optimization processing is performed on the nodes, network resource allocation schemes and node task load adjustment instructions are generated, finally, port area distributed network architecture reconstruction is performed through the schemes and the instructions, and the efficiency and the accuracy of port environment monitoring are improved.

According to the method, PCIe extended storage equipment is identified by responding to a system starting signal, and initialization and address mapping control logic is executed to construct a first address space addressable by a GPU (Graphics Processing Unit); then, loading model data according to an AI task instruction, and dynamically dividing hot, warm and cold multi-level data popularity levels by monitoring memory access frequency; a migration data block is generated based on the popularity level, a migration data flow is organized and verified through priority scheduling logic, and intelligent data scheduling between extended storage and a GPU video memory is achieved; finally, the resources are released after the task is completed. Therefore, dynamic expansion of the physical video memory capacity is effectively achieved, large-scale AI model localization training is met, dependence on a high-end GPU chip is remarkably reduced, and data safety and system economy are improved.

The invention relates to the technical field of data communication and artificial intelligence, and particularly discloses an intelligent data optimization system and method based on artificial intelligence, and the system comprises a characteristic curve acquisition module, a period determination module, a node grouping module, a priority calculation module, a resource distribution module and a data scheduling module. The method comprises the following steps: firstly, acquiring a flow characteristic curve of each data flow, and determining a flow fluctuation period; then, K-means clustering is combined with genetic algorithm optimization, and nodes are grouped; then, integrating hardware performance parameters and flow stability, and calculating a transmission priority weight; bandwidth resources are allocated according to the weights, basic requirements of low-priority nodes are ensured, and dynamic adjustment is carried out to adapt to network load changes; and finally, constructing a multi-objective optimization model through a deep reinforcement learning algorithm DQN, dynamically scheduling a data flow according to a bandwidth allocation result, and realizing efficient data transmission by taking delay, a packet loss rate and a bandwidth utilization rate as optimization objectives, thereby effectively improving the network transmission efficiency and performance.

Provided in the present disclosure are a data circulation method and apparatus, and a first platform and a first node. The method comprises: a first platform receiving information which is related to a data resource and is sent by a first node; and the first platform adding to a global data directory the information related to the data resource, wherein the first node is a trusted computing node of a data provider, and the first platform is a data scheduling center platform.

The invention provides an advertisement putting method and device, equipment, a medium and a program product, relates to the technical field of advertisement systems, and aims to solve the problem of low flexibility of task scheduling in advertisement putting. The method comprises the following steps: receiving a first advertisement request sent by a client; determining a first declarative configuration file corresponding to the first advertisement request; the first declarative configuration file is analyzed, execution process information and a plurality of data access strategies corresponding to a plurality of task nodes in the execution process information in a one-to-one mode are obtained, and the data access strategies comprise to-be-accessed advertisement data sources and data scheduling strategies of the advertisement data sources; executing tasks corresponding to the plurality of task nodes according to the execution process information and the plurality of data access strategies to obtain an execution result; and sending the execution result to the client. According to the invention, the flexibility of task scheduling in advertisement putting can be improved.

The invention relates to a distributed storage cache hotspot prediction method and system based on an access mode. The method comprises the following steps: acquiring an access log to obtain original data; the gradient change of the access frequency between the adjacent data blocks is analyzed in real time to divide the hot spot range and identify continuous hot spot data blocks; constructing a time sequence based on the historical access record, and pre-judging an access hotspot in a future time period by applying a time sequence trend prediction algorithm; constructing a multi-dimensional feature vector and inputting the multi-dimensional feature vector into a machine learning prediction model to output a hotspot probability of the corresponding data object in a future time period; according to the hotspot probability and a popularity threshold value, scheduling the data predicted as the hotspot into a cache, and allocating corresponding cache levels or storage paths for the data objects with different popularity at the same time; and periodically updating the prediction model and the popularity statistics to obtain dynamic prediction and cache optimization of the hotspot data. According to the invention, the prediction of the hotspot data is realized, and the hit rate and the system performance of the distributed cache are improved.

The invention provides an artificial intelligence chip and a debugging method. The artificial intelligence chip comprises a data scheduling module, a storage module and a plurality of core processing modules, wherein the core processing modules provide a plurality of key signals in real time; the interrupt control module sends a trigger instruction when detecting that an error event occurs; the debugging module obtains a plurality of key signals from the target module in real time, encapsulates the key signals into a plurality of data packets, configures corresponding address information for the data packets, transmits the data packets with the address information to the storage module through the data scheduling module, and responds to a trigger instruction, so that the debugging module performs debugging on the data packets. Stopping providing the data packet to the storage module; that is to say, key signal backup is carried out through the storage module in the chip, the reproduction time is consistent with the actual operation time, and the problem that faults caused by long-time operation of the chip are difficult to reproduce is solved. And the key signals are backed up in real time, so that the state in the chip can be monitored comprehensively in real time, and the non-static fault problem in the chip can be found out.

The invention provides a short video full-process customer acquisition system based on agent collaboration, and relates to the technical field of artificial intelligence. An input layer of the system is used for receiving task data input by a user; the scheduling layer adopts an agent scheduling engine, and the agent scheduling engine is used for determining at least one subtask and a subtask execution sequence according to the task data, distributing the subtasks based on a publishing and subscribing mode and distributing computing power resources based on a weighted polling algorithm; the function layer comprises an account positioning module, a benchmarking account disassembling module, an account basic building module, a topic selection base building module, a video creation module and an editing and publishing module which are independently packaged in an intelligent agent form; the account number positioning module, the benchmarking account number disassembling module, the account number basic building module, the topic selection base building module, the video creation module and the editing and publishing module execute corresponding subtasks. According to the invention, automatic short video creation guest obtaining can be realized, and the efficiency is improved.

The invention provides an efficient edge computing and cloud collaborative data scheduling system, and relates to the field of electric digital data processing, and the efficient edge computing and cloud collaborative data scheduling system comprises an edge access module, an edge computing module, a collaborative scheduling module and a cloud computing module, the edge computing module is used for executing a low-delay computing task and intelligent reasoning at an edge end, the collaborative scheduling module is used for performing scheduling analysis on resources, and the cloud computing module is used for performing large model training and cross-regional strategy optimization; according to the system, a unified mathematical modeling method based on a spiral fluctuation theory is adopted, accurate description of a system state is realized through a spiral fluctuation basic function, and a spiral decreasing multi-dimensional cost evaluation model and a fluctuation resonance inverse migration value algorithm are combined; the key technical problems of inaccurate task cost evaluation, uneven load distribution and the like in traditional edge computing scheduling are solved.

A method and system for generating an updated software architecture. The method includes a data scheduler process that periodically obtains an image and a text related to a current software architecture at an architecture artificial intelligence assistant application program interface (ArchAI assistant API) layer, and performing a first drop operation on the image and the text to a storage system. The method also includes initiating a prompt creation process at the ArchAI assistant API layer due to the first drop operation, and generating an enriched prompt for transmission to a multi-modal large language model (LLM). The method also includes performing software architecture configuration analytics by the multi-modal LLM on the current software, generating the updated software architecture by the multi-modal LLM based on a result of the software architecture configuration analytics, and producing a digital diagram of the updated software architecture for display.

The invention discloses an image compression method based on a JPEG-LS (Joint Photographic Experts Group-Least Squares) parallel optimization algorithm, which comprises the following steps: calculating local gradient values of to-be-coded data and then merging to obtain a context index Q value, the to-be-coded data being a prediction error of a current pixel; grouping the data to be coded according to the context index Q value; if the to-be-coded number contained in the key group affects the parallelism degree, a controllable distortion value is introduced into the pixel value of the to-be-coded data by using an equalization algorithm, and the group serial number corresponding to the data in the key group is corrected; deploying processing units of parallel channels with the number equal to that of the groups, and scheduling the grouped data to be coded to the processing units to realize pipeline processing; integrating each group of data into coded and compressed data through code stream splicing; the image compression method based on the JPEG-LS parallel optimization algorithm is used for image compression, the system data size is low, and the satellite-ground transmission bandwidth pressure is small.

The invention relates to the technical field of new energy electric power construction, in particular to an all-weather automatic inspection system and method suitable for a wind power plant, and the system consists of a dispatching and safety control center, an edge computing device and a multi-platform collaborative inspection network. The aerial inspection platform detects fan blades and the outside of a cabin; the tower body inspection platform is attached to the outer wall of the tower drum to detect flanges, weld joints and bolts; the blade inspection platform is attached to the surface of the static blade to detect cracks, layering and lightning damage; the ground inspection platform runs along a fan foundation and a field road and detects foundation settlement, the state of the box-type transformer and road obstacles; the environment monitoring network collects wind speed, wind direction, rainfall, icing thickness and visibility parameters. The edge computing device fuses multi-mode inspection data, and the scheduling and safety control center realizes task scheduling, safety linkage control and data storage management, so that an efficient and intelligent wind power plant inspection solution is formed, and safe operation of equipment is guaranteed.

A wireless device may receive one or more configuration parameters, of a cell, for physical downlink control channel (PDCCH) monitoring. The one or more configuration parameters may indicate a time duration of a span of one or more symbols for the PDCCH monitoring, and a time gap between two consecutive spans. The wireless device may determine a buffering time offset, for buffering data scheduled by a PDCCH transmission, based on the time gap and a subcarrier spacing of the cell. The wireless device may receive, in a first radio resource, downlink control information (DCI) scheduling a transport block (TB) in a second radio resource. The wireless device may drop the TB in response to a duration, in time, between the first radio resource and the second radio resource being less than, or equal to, the buffering time offset.

The invention provides a distributed image transmission scheduling system for a multi-unmanned aerial vehicle formation, and aims to improve the stability and timeliness of large-scale unmanned aerial vehicle collaborative image acquisition and transmission. The system comprises an information acquisition module, a role determination module and the like. The information acquisition module acquires the electric quantity, communication state, cache occupation, historical fluctuation and position information of each unmanned aerial vehicle, and generates a node state data table and a path robustness reference table; the role determination module selects a main node and a candidate node based on the state data, and generates a role allocation table; the task distribution module divides an image acquisition area into sub-areas and generates a task assignment list in combination with node space distribution and a historical shooting success rate; the path generation module constructs a shortest path and a robust path for each image task and generates a path data table; and the scheduling generation module generates an image sending time schedule by scattering task time slots or switching paths according to a path conflict detection result, so that efficient and fault-tolerant data scheduling control is realized.

The invention belongs to the technical field of deep learning, and discloses a battlefield target intelligent identification plotting system and method based on deep learning. Comprising the steps of collecting data and performing data cleaning to obtain battlefield enhanced image data; performing scene recognition on the battlefield enhanced image data, and outputting a battlefield type label; performing data scheduling on the battlefield enhanced image data to obtain scheduled image data; performing dynamic target intelligent identification on the scheduling image data in sequence, and outputting dynamic target moving trajectory data; carrying out dense target identification, and outputting high-density battlefield target detection data; executing auxiliary judgment and updating a preset database; acquiring battlefield space information corresponding to the battlefield enhanced image data, and constructing a battlefield simulation scene; accurately marking a battlefield target of the battlefield simulation scene to obtain battlefield target visual plotting data; updating the intelligent identification path and sending the path to a preset database for storage; therefore, accurate recognition and plotting of the battlefield target are realized.

The invention discloses a hybrid precision in-memory computing accelerator for low-illumination image enhancement, and relates to the technical field of integrated circuits. A top controller is used for global control and data scheduling; the SRAM read-write circuit is used for completing pre-loading and updating of weight data; the row / column decoder and the input buffer are used for realizing line-by-line input of activation data; the in-memory computing array comprises a plurality of SRAM (Static Random Access Memory) storage units and corresponding in-memory logic circuits, and is used for completing multiplication of weights and activated data; the peripheral accumulation circuit is used for receiving a calculation result of the in-memory calculation array, and completing multiplication and accumulation operation of convolution and a full connection layer through inter-row accumulation and channel fusion; and the ReLU activation module is used for realizing ReLU activation operation in the low-illumination image enhancement network. According to the in-memory computing accelerator, low-bit-width parallel and high-bit-width serial are adopted, so that the delay is halved, and the throughput rate is remarkably improved.

The invention relates to the technical field of intelligent transportation, in particular to a public transportation passenger and freight co-transportation scheduling system based on big data and the Internet of Things. Comprising data collection and processing, scheduling system architecture, path optimization and dynamic adjustment, and user interface and system management. Wherein the data collection and processing is used for the process of collecting and processing bus operation data by the system and is the basis of the system; the data collection and processing comprises passenger flow data, cargo flow data, vehicle state data and environment data; the scheduling system architecture is used for realizing comprehensive monitoring, real-time scheduling and resource optimization of bus passenger and freight co-transportation; according to the invention, by integrating real-time passenger flow data, cargo flow data and vehicle state information and utilizing big data analysis, an Internet of Things technology and an artificial intelligence algorithm, co-scheduling of buses and freight, path optimization and maximum utilization of resources are realized.

The invention belongs to the technical field of power system automation and energy management, and particularly discloses a standby power management system for power utilization information acquisition of a power grid and a control method. The system comprises a main power supply monitoring module, an energy reserve management module, a communication environment sensing module, an energy efficiency strategy decision core and a task priority scheduling module. The communication step length and the transmitting power are dynamically adjusted by capturing the states of a power grid, a battery and a channel in real time, and data scheduling is executed according to priorities. By the adoption of the technical scheme, fine distribution of electric energy is achieved, invalid standby power consumption is reduced, the life cycle of the collection terminal in the power-off state is prolonged, reliable reporting of key information under the extreme working condition is ensured, and data support is provided for research and judgment of power grid faults.

The invention discloses a river-sea combined transport ship collaborative scheduling method and system based on multi-objective optimization, and the method comprises the steps: collecting and fusing dynamic data, static data, scheduling application data and channel environment data of river ships and sea ships, and obtaining fused multi-source heterogeneous data; constructing a channel digital model, and calculating the dynamic traffic capacity of each channel according to the fused multi-source heterogeneous data; constructing a multi-objective optimization model by taking the minimization of the total scheduling time and the total waiting time of all ships as an objective; solving the multi-objective optimization model based on an improved DE-NSGA-II algorithm to obtain a Pareto optimal solution set; and selecting a final scheduling scheme from the Pareto optimal solution set and executing the final scheduling scheme. The ship operation cost is remarkably reduced, carbon emission is reduced, and green port construction is assisted. And the waiting time and the total voyage of the ship are reduced, so that the fuel consumption and the operation cost of the ship are directly reduced.

The invention provides a mass data communication and scheduling system and method for a micro-grid, and the system comprises a communication server, the communication server comprises a task scheduling state machine, the task scheduling state machine comprises a multi-layer data container, the data container is configured to be a thread-safe circulating multi-queue used for buffering and distributing data, and the task scheduling state machine comprises a task scheduling state machine; the data structure stored in the data container is a Trie tree structure. According to the method, the task scheduling state machine is designed to process the data, so that the communication blocking problem is reduced, and the flexibility of processing massive real-time data is improved; an asynchronous consumption mode is introduced to solve the problems of performance bottleneck and data congestion of a communication server, the data transmission efficiency is improved, and normal operation of a micro-grid system is ensured; by applying the data container and combining asynchronous multiple tasks and circular multiple queues, the data scheduling precision is improved, efficient processing and management of data scheduling are achieved, the data scheduling efficiency is improved, and the data processing efficiency and management level are improved.

The invention relates to a data processing method and device, equipment and a storage medium. Performing interpolation processing on multi-channel sensor data acquired based on the clock trigger signal to obtain an aligned data frame with a unified timestamp; carrying out noise filtering processing on the alignment data frame, obtaining and carrying out drift correction processing on filtered data, obtaining and carrying out structured processing on corrected alignment data, obtaining structured alignment data, calculating error residual characteristics of the corrected alignment data, and generating a data quality label; determining a system state based on the error residual feature and the data quality label, reconfiguring system control parameters when the system state is system abnormity, and otherwise, dynamically adjusting a data scheduling state of the structured alignment data according to a current communication state; compared with the prior art, the technical scheme of the application can effectively solve the problems of insufficient real-time performance, data quality hidden danger and closed-loop control deficiency of the existing system, and significantly improve the accuracy and reliability of data processing.

The invention relates to the technical field of intelligent learning planning, and discloses an intelligent learning planning system based on multi-dimensional learning data driving. Comprising a main control module, a multi-modal data acquisition module, a data quality enhancement module, a multi-modal feature fusion module, a dynamic strategy planning module, a man-machine intention alignment module, a knowledge topology management module, an evaluation and federal optimization module and an auxiliary visualization module, and the main control module is used for coordinating operation of each module and data scheduling; the multi-modal data acquisition module acquires HRV, EDA and other physiological signals through a wearable device, records a fixation track and an alpha wave proportion through an eye tracker / electroencephalograph, and receives a user instruction in real time through a voice interaction device. According to the method, the dynamic adaptability is higher, full-process dynamic adjustment of stage division-parameter fine adjustment-load balance is achieved through hierarchical reinforcement learning and NSGA-III optimization, and a learning path can be flexibly adapted according to the real-time state (such as sudden fatigue and rapid knowledge point mastering) of a user.

This application provides a communication method, apparatus, and system. The method includes: after a network device determines a first downlink control information (DCI), it sends the first DCI to a terminal device. Then, the terminal device receives the first DCI from the network device and, based on the first DCI, receives downlink data from the network device or sends uplink data to the network device. The first DCI is used for uplink or downlink data scheduling, has a fixed number of bits, includes a first identifier indicating the format of the first DCI, and the format of the first DCI includes either a first DCI format or a second DCI format. In other words, this application can design a DCI size for multiple DCI formats, thus not only reducing the complexity of DCI detection by the terminal device and saving power consumption, but also ensuring the diversity of data transmission and increasing scheduling flexibility.