46 results about "Preemption" patented technology

This invention relates to a method for elastic scheduling of AI computing resources based on business awareness, comprising the following steps: predefining a business criticality level BK, and mapping the business criticality level BK to a corresponding weight coefficient V. bk The system automatically identifies the current iteration cycle stage of a task and predefines the corresponding dynamic weight coefficient C for the iteration cycle. It calculates and generates a dynamically changing priority score PF using a calculation function. It calculates and allocates the elastic quota FHQ for each business project, obtaining a total quota AHQ. When an urgent task is received, resource preemption assessment is performed. This invention employs a business-aware method, system, device, processor, and its computer-readable storage medium for elastic scheduling of AI computing resources. This achieves deep integration of business and technology, improves resource utilization, effectively suppresses users' false reporting of resource demands, thereby enhancing the overall utilization of the cluster; reduces preemption losses, avoids waste of computing resources, and maximizes the effective computing efficiency of the cluster.

The application discloses a kind of STK account material self-checking and abnormal self-healing method based on equipment state comparison, belongs to industrial automation control and intelligent warehousing technical field, and the specific steps of the self-healing method are as follows: I: the real-time state data of each device is collected and preprocessed, and according to each state data after processing, the health degree score and state entropy value of each device are calculated;The present application can identify the sub-health and unstable state of equipment in advance, which not only ensures the production safety in case of serious failure, but also avoids unnecessary downtime caused by slight abnormalities, significantly reduces the abnormal interruption in the task execution process, and improves the accuracy and traceability of material management;At the same time, the present application can find potential problems in advance, avoid task out-of-order execution, dependency conflict and deadlock problem, effectively prevent the preemption of concurrent tasks on process resources, and significantly optimize Crane walking path, reduce the overall energy consumption of warehouse, and greatly shorten the abnormal processing cycle.

Embodiments of the present disclosure provide a transmission opportunity (TXOP) preemption processing method, a computer-readable storage medium, an electronic device, and a computer program product. The method comprises: sending a first control frame to a responder, wherein the responder comprises a TXOP responder and a data receiver of preemptive transmission; and receiving a second control frame sent by the responder in response to the first control frame, wherein the first control frame and the second control frame comprise a duration during which preemptive transmission of a preemptor is protected or successful recovery of a TXOP by a TXOP holder is protected, the preemptor is the responder or an access point, and the preemptive transmission is that the preemptor preempts the TXOP within the duration of the TXOP to perform data transmission.

This invention relates to a preemptive scheduling method for heterogeneous multi-core processors based on multi-agent reinforcement learning. It includes: constructing a system model comprising a set of sporadic real-time tasks and a heterogeneous multi-core platform; pre-setting fixed preemption points for tasks; dividing task execution into non-preemptive segments; modeling the scheduling problem as a partially observable Markov decision process; designing state representations that include task dynamic attributes and platform heterogeneity characteristics; wherein the task dynamic attributes are normalized values ​​of relative deadline pressure, execution progress ratio, and relaxation; and the platform heterogeneity characteristics include a weighted sum of core computing power level and global idle computing power; clustering processors according to computing power; assigning independent agents to each cluster; and adopting a fully cooperative mode to share global rewards; employing a centralized training and distributed execution framework; using the discrete soft actor-critic algorithm during training; with each agent's policy network collaboratively optimizing under the guidance of a centralized value function; and during execution, each agent independently deciding on preemption timing based on local observations. This invention achieves cooperative preemptive scheduling among heterogeneous cores through multi-agent reinforcement learning, effectively reducing task overdue rates and system overhead, and improving real-time performance.

This invention belongs to the field of embedded software technology, specifically relating to a task scheduling method and system based on timer interrupt preemption. The method includes: creating task nodes and timer objects using a static creation method, and configuring priorities and scheduling times for tasks; constructing doubly linked lists of time series according to priorities based on a unified time base, and inserting task nodes into the corresponding linked lists in an orderly manner; setting timer interrupts of corresponding priorities according to the first task node of each linked list; responding to timer interrupts by triggering the execution of expired tasks, removing task nodes, and resetting timer interrupts; and utilizing processor interrupt priorities and context saving mechanisms during task execution to achieve preemption and recovery of high-priority tasks. This invention can reduce scheduling resource consumption and improve the scheduling orderliness and response timeliness of multi-priority tasks.

This invention relates to the field of knowledge graph technology, specifically to a method for monitoring the operational status of power equipment based on a heterogeneous data association graph. The method includes receiving multi-source heterogeneous operational data from power equipment, outputting equipment status parameters, computing load parameters, and access control levels, and instantiating these into object nodes, machine nodes, and access control nodes. Connection edges are established, the interaction frequency and latency between nodes are extracted, cross-domain coupling degree is calculated, and values ​​are assigned to the connection edges to generate a weighted graph. An anomaly flag is output when the equipment status parameters exceed a preset threshold. Path traversal is performed in the weighted graph to select a cloud-edge collaborative scheduling path. Target machine nodes and target access control nodes are extracted, access control levels are read, preemption priorities are generated, and computing resources corresponding to the target machine node are allocated. Collaborative control commands are then output and transmitted to the object nodes. This invention reduces the probability of critical commands being preempted or discarded by constructing a global monitoring system based on deep coupling of heterogeneous data.

This invention relates to the field of intelligent logistics warehousing and automated storage technology, specifically a method, system, and apparatus for efficient pallet storage in logistics storage. The method includes: collecting actual outbound instructions and pallet flow records as initial data packets; performing time-series correlation analysis to obtain the spatiotemporal coupling characteristics of orders; extracting pallet flow probabilities and calculating predictive cache hit rates; classifying heat levels based on historical outbound frequencies and assigning migration priorities; calculating the physical topology entropy reduction index of the storage area in conjunction with pallet flow probabilities; extracting idle time slices of handling equipment and calculating fragmented time rebalancing conversion rates; constructing a multi-objective optimization function and solving it to obtain a dynamic topology rearrangement strategy; generating migration trigger conditions and cache execution instructions, and executing them when the conditions are met; and calculating scheduling preemption rollback delays and formulating rearrangement interruption recovery strategies when sudden tasks have higher priority. This invention thereby improves the accessibility and scheduling stability of high-frequency pallets.

This invention provides a resource occupancy control method for large-scale vehicle-mounted applications, belonging to the field of intelligent vehicle technology. The method includes: real-time collection of CPU and memory usage data from the vehicle; determining dynamic CPU and memory thresholds based on vehicle hardware configuration information and the scenario priority of the current large-scale application; and performing resource control operations on the inference task of the large-scale application when CPU or memory usage data reaches or exceeds its corresponding dynamic threshold, or when a resource request signal triggered by a high-priority application is received. This invention achieves refined resource management for large-scale vehicle-mounted inference tasks through dynamic threshold adjustment and resource preemption mechanisms, effectively solving the resource preemption problem that may occur when deploying large-scale applications on the vehicle, and ensuring the real-time response of critical vehicle control functions and user experience.

This invention discloses an I2C slave communication device and method based on multiple interrupts on an MCU. The device includes an I2C communication module, an interrupt controller, a user data buffer, and a global state manager. The manager is used to synchronize the communication status, transmission mode, and buffer information between the interrupt service function and the main loop. The interrupt controller executes three independent interrupt service functions: EEI interrupt handles address matching, NACK, and STOP events; RXI interrupt handles data reception; and TEI interrupt handles data transmission. Thus, I2C communication events are categorized into EEI event / error interrupts, RXI receive full interrupts, and TEI transmit complete interrupts, with each interrupt focusing on a specific event, achieving decoupling and precise response in event handling. The global state manager integrates the communication status; I2C-related interrupts are configured with the highest system preemption priority to ensure communication is not interfered with by other interrupts; and exception event handling is integrated into the EEI interrupt to ensure timely reset in case of communication errors.

The application provides an SDN controller multi-stage orderly closing method and device, including: setting the state of a node to be closed to a DRAINING state, activating a folding priority queue and low priority task preemption module; in the DRAINING state, switching the folding priority queue from a first comparator based on task priority value ordering to a second comparator through an atomic replacement comparator, and only scheduling the highest priority task; when a working thread executing a low priority task executes to a safe breakpoint, migrating the low priority task to other nodes and releasing the working thread for executing the highest priority task; after all the highest priority tasks are executed, performing a closing operation on the node to be closed. The application can guarantee task migration data consistency and reduce end-to-end scheduling delay of high priority tasks in the DRAINING state.

The application provides a large model-oriented distributed computing power scheduling method and related device, the method comprises: obtaining an agent task request and task data, the request comprising computing power demand, data sensitivity level and memory calling demand; based on the data sensitivity level and the computing power demand, determining the computing power source as edge computing power or public cloud computing power; if it is edge computing power, issuing a task scheduling instruction to the edge computing power node to control its exclusive allocation and release the computing resources after completion; if it is public cloud computing power, routing the task to a public cloud node; based on the memory calling demand, controlling the execution node to write the task execution memory with a timestamp into a shared knowledge base to trigger the knowledge base to perform time sequence sorting and gradient degradation processing. The application realizes the shunting of heterogeneous computing power, safeguards data privacy while preventing resource preemption of edge multi-task concurrency, and realizes the sharing and storage release of context features through the memory degradation mechanism.

The present application belongs to the technical field of chip design, and particularly relates to a fetch control method and system based on RISC-V architecture. Through the design of splicing and cutting modules, cross-cache line instruction efficient splicing is realized, fetch bubbles caused by pipeline suspension are effectively avoided, the overhead of cross-block fetching is reduced, and the fetch throughput is improved. Through the age vector-based directional flushing mechanism, the prefetch instruction transactions on the prediction error path are accurately flushed, the pipeline flushing accuracy is improved, and the repeated fetch rate caused by full flushing is reduced. Through the missing queue module, a dedicated reserved entry is configured for the main fetch transaction, the pre-fetch request resource preemption is isolated, the main fetch missing problem under high pre-fetch pressure is alleviated, and the pipeline blocking and deadlock hazards caused by resource competition are avoided.

The application relates to the technical field of computers and discloses a GPU computing resource scheduling method and device and a storage medium, and adaptive regulation and control of time slice intervals are realized by constructing a multi-dimensional evaluation model chain; first, task computing density and display memory requirements are used to generate task computing characteristic coefficients; performance efficiency coefficients are output in combination with task average response time and preemption overhead proportion; then, ready queue length and context switching frequency are fused to generate a load synergy factor under the constraints of task characteristics and performance coefficients; meanwhile, hardware health coefficients are evaluated based on hardware temperature and cache hit rate changes; and finally, the optimal time slice interval is dynamically calculated through an interval decision model according to the load synergy factor and the hardware health coefficient. The application solves the problem that fixed time slice scheduling cannot adapt to dynamic changes of mixed workloads, and realizes the collaborative optimization of resource utilization, task response speed and system stability.

This invention discloses a multi-peripheral bus multiplexing design system and method based on IIC, belonging to the field of integrated circuit communication technology. The system includes: a virtual bus interface module, an instruction cache module, a virtual bus kernel module, an instruction execution module, and a result return module. The method includes: S1 instruction reception and caching; S2 instruction decision-making and scheduling; S3 instruction execution and bus control; S4 result return. This invention achieves multi-device multiplexing through a software virtual bus kernel, eliminating the need for a dedicated hardware multiplexer and avoiding the use of additional IIC addresses; it supports concurrent access by multiple applications and preemption of urgent tasks through priority scheduling and interrupt handling; and it ensures that only one slave device communicates at a time through SDA line on / off control. This invention features low hardware cost, low software complexity, and strong real-time performance, effectively solving the problem of multiplexing IIC peripherals with the same address.

A method for performing preemption management in a wireless communication system and associated apparatus are provided, where a non-access-point station (non-AP STA) device is wirelessly linking to a first access point (AP) device. The method may include: performing, by the first AP device, a preemption client indication in at least one transmission behavior at beginning of a transmission period, for indicating that the non-AP STA device is a preemption client which should keep awake in a preemption period within the transmission period; wherein the preemption period is reserved for latency sensitive traffic transmission to the non-AP STA device.

This application relates to a task preemption method, a command stream parser, a system, a chip, a device, a storage medium, and a program product. The command stream parser, applied to a processor, includes the following method: when the status flag in a first preemption register is set to a preset flag, and the main command parser sends the current scheduling command of a first task to the scheduling command thread group splitter, the main command parser sends a stop signal to the scheduling command thread group splitter and receives the splitting status of the current scheduling command's thread group from the scheduling command thread group splitter; when the splitting status is recorded in a second preemption register, the position of the current scheduling command in the sub-command sequence packet is recorded in a third preemption register, and the main command parser clears the status flag in the first preemption register; the main command parser reads the address of a second task from the command queue register and executes the second task. This method can improve task processing efficiency.

The application discloses a kind of IEC104 remote control instruction issuing methods of multiple master stations and multiple slave stations.The method is through static allocation memory, multiplexing, thread pool and preemption technology, for connecting multiple virtual master stations and multiple heat exchange station remote data acquisition and control module of heating field execution and sensing device, a kind of heat network regulation instruction fast preemption issuing method is realized.The application significantly reduces the control time delay of heat network multi-domain collaborative cluster control system, improves the response speed and stability of system in response to heating pipe network emergency regulation working condition.

This invention discloses a model training and resource scheduling method and system based on LlamaFactory, relating to the field of resource scheduling and distributed model training optimization technology. Specifically, this invention combines the LlamaFactory model training process with a dynamic priority scheduling mechanism to construct an intelligent training and resource allocation system for multi-task, multi-GPU clusters. This achieves adaptive adjustment of task sorting and resource allocation. By combining a comprehensive dynamic priority score function with a cat swarm optimization algorithm, it realizes efficient scheduling solutions under multi-objective constraints, obtaining high-quality scheduling solutions within a limited time, improving the overall energy efficiency and task throughput of the cluster. Furthermore, by combining the LlamaFactory training loop structure with a safe preemption point design, it achieves refined interruption control during the training process, improving the success rate of task migration and recovery.

Fine-grained preemption of a data flow architecture based neural processing unit (NPU) includes executing, by a controller, control-code that implements a first context in the NPU. In response to the controller detecting a preemption opcode in the control-code, detecting, by the controller, a second context awaiting execution by the neural processing unit. The second context has a priority that is greater than a priority of the first context. In response to detecting the second context, the NPU switches from executing the first context to implementing the second context.

The application relates to the technical field of data platform scheduling control, cloud native resource arrangement and intelligent operation and maintenance, and discloses a high-concurrency heterogeneous computing power preemptive scheduling method and system based on an internalized intelligent agent. The method receives standardized event loads, identifies emergency fault tasks based on a composite emergency judgment rule, executes suspension, freezing or checkpoint saving on low-priority tasks, recycles and remaps heterogeneous resources such as video memory slices, execution queues, cache areas and high-speed interconnection channels, and generates a treatment result in combination with an open diagnostic model; meanwhile, recovery, feedback and preemptive scheduling audit objects are made explicit under the constraints of a tenant security namespace and a resource isolation boundary.

The application belongs to the technical field of resource scheduling, and discloses a mine safety and control linkage cloud and fog collaborative resource scheduling method, which classifies and disassembles mine safety and control linkage business and models, extracts safety and resource demand characteristics of various businesses, combines an LSTM deep learning model to construct a resource demand prediction system, completes safety event level division through a mine safety risk assessment model, and then uses a reinforcement learning algorithm to construct a dynamic priority allocation system, realizes real-time dynamic adjustment of task priority, generates a preemptive execution queue by matching a preemption threshold mechanism and a buffer period, deeply integrates safety attribute priority into the core logic of business and resource supply and demand matching, so that high-priority core safety tasks can preempt resources; the main scheduling node is set in each underground fog domain to complete high-frequency perception of local resources, and after invalid data is removed through an anomaly detection algorithm, effective resource data is subjected to cleaning standardization and pooling aggregation processing.

This disclosure provides a method for negotiating a transmission opportunity preemption strategy, applied to an access point, comprising: receiving a preemption strategy request sent by a non-access point site, the preemption strategy request being configured to request a preemption strategy for preempting a transmission opportunity; generating a corresponding negotiation result based on the preemption strategy request, the negotiation result being configured to indicate whether the access point allows the preemption strategy requested by the non-access point site; and sending the negotiation result to the non-access point site. This disclosure also provides a method for negotiating a transmission opportunity preemption strategy applied to a non-access point site, an electronic device, and a computer-readable storage medium.

Fine-grained preemption of a data flow architecture based neural processing unit (NPU) includes executing, by a controller, control-code that implements a first context in the NPU. In response to the controller detecting a preemption opcode in the control-code, detecting, by the controller, a second context awaiting execution by the neural processing unit. The second context has a priority that is greater than a priority of the first context. In response to detecting the second context, the NPU switches from executing the first context to implementing the second context.

The embodiment of the application relates to the technical field of automatic driving communication, and discloses an MCU end DDS communication method, device and computer equipment, the method is realized based on an MCU end DDS communication device, the device at least includes a first core, a second core and a cross-core shared memory, the first core deploys a DCPS core component, the second core deploys an RTPS core component, and the DCPS core component and the RTPS core component realize data transmission through the cross-core shared memory.The application separates and deploys the DCPS core component and the RTPS core component in the dual core, realizes efficient data interaction through the cross-core shared memory, the dual-core separation deployment enables the parallel execution of the calculation-intensive task and the I / O-intensive task, so that the parallel processing capacity of the multi-core MCU is fully released, single-core resource preemption is avoided, and the waste of computing power is reduced; the asynchronous sending mechanism is adopted to reduce the communication delay and jitter, and the priority of key services is guaranteed; and the dual-core parallel processing improves the data transmission throughput, and simultaneously enhances the system running stability.

The application provides a scheduling strategy determination method and system for flow-to-line parallel training, and belongs to the technical field of cloud computing. The method comprises the following steps: determining a plurality of candidate scheduling strategies according to a training sample set and attribute information of each computing node, wherein the candidate scheduling strategies comprise a sample splitting scheme for uniformly splitting the training sample set into a plurality of training sample subsets and a corresponding maximum K value, and the maximum K value is used to indicate the maximum value of the number of subsets when each computing node performs one forward calculation or backward calculation under the corresponding sample splitting scheme; generating a task image corresponding to each candidate scheduling strategy based on the sample splitting scheme in each candidate scheduling strategy and the association relationship between the computing nodes; and determining a target scheduling strategy with the shortest training duration according to the current network state, the task image corresponding to each candidate scheduling strategy and the maximum K value. The application can improve the training efficiency of the model in the case of network resource preemption.

1. Name of the product in this design: Graphical User Interface for Resource Scheduling and Management of Small Model AI Platform for Electronic Devices. 2. Intended use of this design: for use in an electronic device. 3. The key design features of this product are: the graphical user interface content that displays information. 4. The image or photo that best illustrates the design points: Interface change state diagram 2. 5. Purpose of the graphical user interface: This graphical user interface is used for resource scheduling and management of the small model AI platform. The main view is the login interface for the small model AI platform. In the main view, after the user enters their account, password and verification code, they can click the "Log in Now" button to enter the interface change state diagram 1, which displays the small model AI middleware video integrated management interface. In Interface Change State Diagram 1, when the user clicks the "Resource Scheduling Center" button, they enter Interface Change State Diagram 2, which displays the Resource Scheduling Center interface. In the interface change state diagram 2, the user clicks the "Scheduling Strategy" item in the "Scheduling Strategy" column on the left to enter the interface change state diagram 3, which displays the scheduling strategy interface. In the interface change state diagram 3, the user clicks the "Idle Time Scheduling" button to enter the interface change state diagram 4, which displays the idle time scheduling interface. In the interface change state diagram 4, the user clicks the "Priority Preemption" button to enter the interface change state diagram 5, which displays the priority preemption interface.

The application discloses a multi-processor platform mixed criticality system task scheduling method, which has high schedulability and small stack space usage. The task scheduling method comprises the following steps: (10) single processor mixed criticality task scheduling: a priority and a preemption threshold are respectively allocated to each mixed criticality task on the same processor as scheduling parameters, the tasks are scheduled according to the scheduling parameters, and each mixed criticality task is fixedly run on the allocated processor; (20) task-to-processor pre-allocation: according to the processor utilization required by the tasks, the mixed criticality system tasks are divided into light tasks and heavy tasks, the heavy tasks are allocated first and then the light tasks, and the allocation method based on the utilization difference is adopted to allocate the tasks to a processor of the multi-processor platform; (30) task allocation optimization: the pre-allocation result of the task-to-processor is optimized and adjusted.

The application discloses a large file cross-unit transmission method based on a business cooperation scene, relates to the technical field of cross-organization data transmission and resource scheduling, and takes a business context as a core to map an event type, an emergency level and an SLA deadline time into a calculable priority, and is linked with real-time network measurement results, so that scheduling no longer stays in static file attributes, but can instantly improve the service share of a key request according to events such as out-of-stock early warning and promotion start, thereby reducing the probability that a high-emergency task is blocked by regular transmission. By introducing unit-level quotas, admission and interruptible preemption on the side of a bottleneck link, long-term occupation caused by first-come-first-served is avoided, basic fairness between different units is ensured, and bandwidth can be quickly transferred to more urgent flows at a business peak moment. Weighted round-robin weights are dynamically updated according to the emergency degree, the SLA urgency degree and the quota surplus degree, hysteresis and minimum interval control are superimposed, and the agility of scheduling is improved.

The application discloses a kind of multi-agent-based Internet of Things service logic automatic generation system and method.The application constructs three agents: function implementation agent, which decomposes and implements user function intent into function demand branch;Quality implementation agent is constructed, and quality intent is instantiated based on environment evaluation to generate quality demand branch;Service logic synthesis agent is constructed, which synthesizes and detects conflicts for the two branches, and extracts and switches the conflict-free service logic scheme in real time.Through the three-layer demand model of "user intent-implementation planning-service logic", the three agents collaborate.If the service logic synthesis agent finds a conflict, the conflict information is explicitly passed to the function implementation agent for collaborative refinement until there is no conflict.The application realizes white-box and high explainability of the generation process through the three-layer model, and the multi-agent collaboration resolves hardware resource preemption conflicts at the source, significantly improving the quality of service scheduling logic.

The present application belongs to the technical field of multi-modal intelligent data governance, and discloses a method for collaborative processing and intelligent conversion of multi-modal business data, comprising: fusing multi-source data, coupling cross-modal time sequence association, detecting graphic-text semantic conflict, generating RAG semantic unit and space-time alignment semantic package; constructing four-dimensional priority label to divide resource pool categories, dynamically allocating resource pool computing resources, generating low-redundancy feature set, triggering forced preemption strategy when resource competition occurs, and synchronously generating resource arbitration log; injecting dynamic rule nodes according to data types, performing confidence arbitration combined with conflict arbitration identifier, generating business decision content and generating rule correction decision package; constructing a full-link semantic thinking chain, synchronously capturing system operation track, and then generating a double-channel traceability report; identifying residual sensitive fields, triggering hot update to load corresponding rules, quantifying governance effectiveness to generate a compliance report, updating the industry rule library in reverse, and optimizing the resource allocation strategy.