393 results about "Lower priority" patented technology

The invention discloses a high-efficiency computer software system resource scheduling method, system and device and a medium, and the method comprises the steps: deploying a hardware counter in an operating system, and monitoring the CPU utilization rate, memory occupation, disk I / O, network bandwidth, the use condition of data aggregation system resources and the resource demand of each process or thread in real time; based on monitoring data, analyzing a current resource supply and demand state by adopting an advanced algorithm, and predicting a future resource demand trend; according to an analysis result, a resource allocation strategy is dynamically adjusted, it is ensured that a high-priority task obtains needed resources in time, and meanwhile fairness of a low-priority task is considered; data feedback after scheduling execution is collected, a scheduling algorithm is continuously optimized, and the scheduling efficiency is improved; the system, the equipment and the medium are used for realizing the high-efficiency computer software system resource scheduling method. The problems that an existing computer system is low in resource utilization rate, poor in key task stability and high in operation and maintenance cost are solved.

The invention relates to the technical field of electric power data analysis, and discloses a concentrator collection data analysis method and system based on big data, and the method comprises the steps: capturing a high-priority task in real time, and generating a concentrator collection task flow sequence arranged according to an initial scheduling sequence; performing cross-task dependency chain logic association tracking on the concentrator collection task flow sequence, and constructing a multi-dimensional scheduling feature vector reflecting the importance gradient of the task; based on the multi-dimensional scheduling feature vector, a hierarchical priority aggregation algorithm is utilized to extract a preemption evolution track of a high-priority task to the bandwidth and the acquisition time slot, and potential scheduling error sequence candidate tasks are detected; performing priority grading delimitation on the scheduling error sequence candidate tasks and the associated low-priority task interval, and constructing a local task logic graph with a priority weight gradient; and performing dynamic intervention judgment on the scheduling evolution path based on the local task logic graph. The method has the advantage of improving the overall scheduling efficiency.

A system comprising security devices and a computing device. The security devices may each be configured to detect an event in response to video frames and audio and communicate the video frames, the audio and a notification of the event. The computing device may be configured to execute one or more steps, the steps comprising performing a classification of the notifications of the events into a plurality of event levels and displaying a list of the notifications of the events according to the classification. Each of the plurality of event levels determined to be high priority events are added to a top of the list as the high priority events are detected. Each of the plurality of event levels determined to be a lower priority than the high priority events are added to the list below the high priority events as the events with the lower priority are detected.

The invention discloses a multi-queue scheduling method based on total and incremental reasoning overhead merging, which comprises the following steps of: firstly, calculating the total resource demand and memory overhead of a task according to the input length and the size of a predictor model in the process of performing total reasoning on a reasoning task; distributing tasks to queues with different priorities according to the total resource demand in combination with response time requirements and task importance; dynamically scheduling tasks according to a priority queue sequence, adjusting an execution sequence in combination with a system load, and preferentially executing tasks in a high-priority queue; distributing a time slice for each task, monitoring task execution time, reducing the priority of overtime tasks, moving the overtime tasks into a secondary priority queue, and triggering new task scheduling; and after preemptive scheduling, the key value cache of the latest scheduling task in the waiting queue is swapped from the accelerator memory to the CPU memory, and is swapped back to the accelerator memory before the task is re-executed.

The invention discloses a self-adaptive micro-service resource management method and device, and the method is applied to a resource-limited micro-service system, and comprises the steps: receiving a task, carrying out the modeling of the time sequence data and spatial dependency of a micro-service based on a space-time diagram neural network, and predicting the resource demands of a future time step, including a CPU, a memory and a network bandwidth; classifying the micro-services into resource redundancy, resource insufficiency and resource self-sufficiency according to the predicted resource demand, the current resource allocation and the resource guarantee threshold; according to the resource state and the business importance, the dynamic priority of the micro-services is calculated, the micro-services with redundant resources are sorted in an ascending order, and the micro-services with insufficient resources are sorted in a descending order; and recovering redundant resources from the low-priority micro-services, and allocating the redundant resources to the high-priority micro-services until available resources are exhausted or resource requirements are met, so that the resource utilization rate is optimized, and a service level protocol is guaranteed. According to the method, when resources are limited, the adaptability of the micro-service in different load environments can be improved.

The invention discloses a bidirectional feedback-driven large-model dynamic memory regulation and control system and method, and the system comprises a router, a generator, an actuator and a memory system, and is optimized through four core mechanisms: 1) a dynamic grading mechanism: dividing memory grades based on timeliness, accuracy, repeatability and context consistency by using fuzzy comprehensive evaluation; 2) a forgetting mechanism: performing knowledge distillation compression on the low-priority memory by adopting TinyBERT, and clearing redundant data; 3) an enhancement mechanism: combining similar task memories through LLM to generate an optimized narrative text; and 4) extracting a mechanism, and accurately retrieving knowledge in combination with a potential semantic index and retrieval enhancement generation technology, realizing closed-loop regulation and control of task allocation, generation, execution and memory enhancement through bidirectional interaction of actuator feedback and a memory system, remarkably improving storage efficiency, task solving speed and long-term self-adaptive ability, and greatly improving the system performance. The method is suitable for complex scene optimization of a large language model.

The invention discloses an automatic equipment scheduling method and system for an electromagnetic compatibility test, and the method comprises the steps: monitoring the state and spatial position information of each piece of equipment in real time, and calculating the electromagnetic radiation intensity of the equipment; sorting all the test tasks according to the priorities, and sequentially arranging the equipment from high priority to low priority for testing to form an initial equipment scheduling scheme; calculating the electromagnetic interference gradient of the equipment scheduling scheme, dynamically adjusting the equipment scheduling sequence by adopting the electromagnetic interference gradient, and updating the equipment scheduling scheme; the electromagnetic interference intensity between the devices is updated in real time, and then a device state interference matrix is dynamically evolved; global coupling feedback is calculated; and solving a multi-dimensional optimization objective function which comprehensively considers the total interference variable, the equipment state interference matrix and global coupling feedback under the current scheduling scheme to obtain an optimal equipment scheduling scheme. The method is high in scheduling efficiency, full in resource utilization, accurate in electromagnetic interference control, and capable of effectively meeting complex multi-device collaborative test requirements.

The invention relates to the technical field of graphics processors, and provides a dynamic graphics processor sharing method, which comprises the following steps of: receiving submission of a plurality of deep learning tasks; generating a dynamic priority queue containing high-priority tasks and low-priority tasks; transmitting a resource state to the low-priority task through a broadcast mechanism, and triggering resource adjustment according to a comparison result of the real-time thread block usage amount of the high-priority task and a preset threshold value; according to the difference value between the real-time thread block usage amount of the high-priority task and a preset threshold value, the number of executable thread blocks of the low-priority task is dynamically adjusted; checking whether the number of thread blocks required by the kernel is smaller than or equal to the number of executable thread blocks of the current low-priority task, if yes, starting execution, and if not, reserving the kernel in a kernel request queue to wait for next cycle scheduling; and periodically updating the dynamic priority queue until all deep learning tasks are completed. According to the technical scheme, the overall efficiency of the system can be improved, and resource waste is reduced.

The invention provides a multi-channel arbiter, which comprises a basic arbiter and a polling arbiter, and is characterized in that the basic arbiter is responsible for judging whether high-priority application signals exist in application signals or not, and arbitrating the high-priority signals in sequence until all the high-priority application signals are completed; during the period, the polling arbiter does not work; after the high-priority arbitration is completed, the polling arbitrator performs polling arbitration on the low-priority application signal, and the execution sequence is determined by a loop pointer; finally, arbitration results of the basic arbiter and the polling arbiter are output into an arbitration result signal through a first gate. According to the invention, efficient and ordered task scheduling can be realized.

The invention discloses a server fault processing method and device and a medium, and relates to the technical field of computers. And the server fault analysis problem is converted to obtain vector information, and the vector information is input to the pre-training model for reasoning analysis, so that accurate fault positioning is realized, and the search accuracy of fault analysis is improved. The fault analysis problem comprises a potential fault analysis problem, so that unknown faults are predicted and reasoned, and the fault processing delay is reduced. In inference analysis of a pre-training model, corresponding target inference strategies are matched based on priorities of different fault modes and association degrees of multiple faults instead of being directly processed one by one according to the priorities of the faults, and the association degrees of the multiple faults and whether the corresponding inference strategies are conflicted or not are concerned firstly; according to the method, the situation that secondary faults are caused by contradictory occurrence of heavy faults under the condition that the faults with low priorities are processed in a delayed mode is avoided, the fault maintenance progress is further accelerated, and the reliability of the server is improved.

The invention discloses a priority scheduling method and system for an on-orbit data processing task. The method comprises the following steps: constructing a DAG model, decomposing a macroscopic task into subtasks, defining a subtask dependency relationship, and endowing a subtask node with a priority attribute; setting or dynamically adjusting the priorities of the sub-tasks according to task service types, processing delay requirements or external instructions; maintaining a schedulable queue containing subtasks which have completed precursor tasks and meet scheduling conditions; calculating task scheduling priority indexes based on the queue, sorting, and selecting optimal calculation resource allocation; the priority is dynamically adjusted according to task waiting time or an external instruction during execution, and hunger of low-priority tasks is avoided; the multiple processing units execute the high-priority tasks in parallel according to needs; and after the task is completed, updating a subsequent node dependency state, and adding a new schedulable task cycle. According to the method, high-priority task resource allocation can be guaranteed preferentially, low-priority task hunger is avoided, and the overall throughput rate of the system and the self-adaptive capacity to on-orbit diversified tasks are remarkably improved.

A user equipment maps a high priority hybrid automatic repeat request-acknowledgment / negative acknowledgment (HARQ-ACK / NACK) information to one or more resource elements (REs), and maps one or more of a low priority HARQ-ACK / NACK information, channel state information (CSI), or uplink-shared channel (UL-SCH) data to REs while rate matching around the mapped high priority HARQ-ACK / NACK REs. A network entity receives an uplink transmission, including high and low priority HARQ-ACK / NACK information, CSI, and UL-SCH data mapped to high and low priority HARQ-ACK / NACK REs, CSI REs, and UL-SCH REs, respectively. The low priority HARQ-ACK / NACK REs, the CSI REs, and the UL-SCH REs are rate matched around the high priority HARQ-ACK / N ACK REs. The network entity demaps and decodes the REs of the high and low priority HARQ-ACK / NACK, the CSI, and UL-SCH to obtain the high and low priority HARQ-ACK / NACK information, CSI, and UL-SCH data.

The invention discloses a CAN message priority dynamic arbitration method and system based on event triggering, and the method comprises the steps: obtaining a discrete sampling signal of a CAN node of an engineering vehicle and a state register value of a CAN controller, converting the discrete sampling signal into a working condition data sequence, and obtaining a bus load rate through the state register value; obtaining an event state sequence according to the working condition data sequence; all CAN messages to be sent are constructed into a message data record, and the message data record with event semantics and dynamic priority labels is obtained according to the protocol data unit and the event state sequence; and according to the bus load rate and the event state sequence, sending a message data record with event semantics and dynamic priority annotations by adopting a token bucket mechanism through a three-level priority queue linked with a three-state state machine. According to the invention, the security key message in the system can still be transmitted in time under high load, and the bandwidth occupation and delay jitter caused by the low-priority message are avoided at the same time.

The invention provides a multi-factor collaborative dynamic priority task scheduling method based on waiting time feedback, which is characterized by comprising the following steps of: initializing waiting time, task criticality and weight values of queue length, initializing a mixed task set and a task waiting queue, and initializing the task waiting queue into an empty set; entering a task waiting queue; updating the priorities of all tasks in the task waiting queue; sorting the tasks in the task waiting queue according to the priority; monitoring whether a new task arrives in real time; judging whether the newly arrived task meets a preemption condition or not; updating the historical average waiting time of the task; updating weight values of task waiting time and queue length; and judging whether all tasks in the mixed task set are scheduled. According to the method, long-term hunger of low-priority tasks is avoided, and the average waiting time of the tasks is shortened; the preemption threshold value is dynamically adjusted, and the context switching overhead is reduced; a dynamic weight mechanism is designed, and the self-adaptive capacity of the system is improved.

The invention discloses a coroutine scheduling method, device, system and equipment, a storage medium and a product, and relates to the technical field of task scheduling, a multi-priority queue group is used for storing a first coroutine task to be executed in a corresponding scheduling domain, and a storage module is used for storing a second coroutine task periodically executed in a corresponding scheduling domain; the execution cycle of the first coroutine task is smaller than that of the second coroutine task. And when the expired second coroutine task exists in the storage module, mounting the expired second coroutine task to the multi-priority queue group. The situation that the first coroutine task with the low priority cannot be executed due to the fact that the second coroutine task does not reach the execution time is effectively avoided. And the coroutine tasks are obtained and executed from the multi-priority queue group according to the priority sequence, so that the tasks with high priorities are ensured to preferentially obtain execution resources. According to the running state and the load state of each scheduling domain, task migration of each scheduling domain is carried out, and the resource utilization rate is optimized.

The invention discloses a meta universe digital asset online interaction method based on a three-dimensional engine, and relates to the technical field of meta universe. According to the method, an interaction data set is constructed, and the interaction weight of assets is calculated based on the interaction frequency and the interaction duration; according to the method, assets are dynamically divided into high-priority interactive assets, middle-priority interactive assets and low-priority interactive assets according to a set threshold value, interactive data are periodically updated and the priority of the assets is adjusted in real time in an interactive process, so that the high-priority interactive assets always keep high-precision rendering, the low-priority interactive assets are only loaded when necessary, and the rendering efficiency is improved; in the dynamic asset loading and scheduling process, interaction hotspots of users are predicted based on user view angles, moving directions and interaction tracks, a view cone area is established, a future view is calculated in combination with user historical moving data, assets about to enter the view are loaded in advance, interaction hotspot distribution of multiple users is fused in a multi-user online environment, and the dynamic asset loading and scheduling process is achieved. And the high-interaction area is loaded preferentially, so that the resource overhead is reduced.

The invention is suitable for the field of business data scheduling, and provides a data-in-channel scheduling method and system adaptive to multiple business scenarios, and the method comprises the steps: collecting business feature data in real time, abstracting business departments participating in scheduling as game roles, and distributing resource demand attributes and game chip attributes for the game roles. And based on the task urgency degree coefficient, the resource utilization efficiency and the scene adaptation weight, constructing a bid calculation model to generate automatic bids, allocating exclusive resources to high-priority roles in combination with a resource allocation thermodynamic diagram, and scheduling low-priority roles as required through an elastic resource pool. The system dynamically monitors resource utilization rate and task indexes, evaluates bidding rationality and adjusts chip attributes. According to the method, roles are stimulated to compete efficiently through a game mechanism, resource allocation is optimized in combination with dynamic feedback, the problems that a traditional method is poor in adaptability and not public in allocation are solved, and the resource utilization efficiency and service continuity in a multi-service scene are remarkably improved.

Systems and methods for enhanced Physical Uplink Control Channel (PUCCH) power control when missing Uplink Control Information (UCI) of different priorities are provided. In some embodiments, a method performed by a User Equipment (UE) for determining a power adjustment includes determining a PUCCH power adjustment with respect to a PUCCH format and / or Transport Format (TF); wherein determining the PUCCH power adjustment is based on one or more out of: a UCI consists of UCI with same priority index or different priority indices; a number of high-priority and / or low-priority UCI bits; and a minimum of high-priority and low-priority UCI bits. In this way, PUCCH power is able to be properly adjusted when UCI consists of UCI with different priority index.

A system may receive a request to generate an object. A system may generate, based on the request, a first portion of a prompt associated with a first layer of a hierarchy and a second portion of the prompt associated with a second layer of the hierarchy, where the second layer has a lower priority than the first layer. A system may generate the object based in part on providing the portions of the prompt as input to a machine learning model, where the object is formatted according to an object format and is internally consistent. To generate the object the machine learning model does not violate instructions associated with a layer of the hierarchy based on instructions associated with a layer of the hierarchy having a relatively lower priority.

The invention discloses a method for storing data in a FLASH in real time, and belongs to the technical field of embedded systems. According to the method, the erasing operation of the FLASH is automatically executed by an embedded system, a mode of circularly storing data according to an address sequence is adopted, and meanwhile, the next sector adjacent to the sector which is being written is erased. A real-time operating system is used for scheduling erasing operation and storage operation, erasing tasks with low priorities and writing tasks with high priorities are created, event marks are matched, and data are stored in real time. The method has the advantages that the conflict between writing and erasing operations is avoided through task scheduling without the control of an upper computer, and the embedded system can automatically store data in the FLASH in real time.

This disclosure provides systems, methods, and devices for wireless communication that support managing compression and compression indications of a low-priority (LP) uplink control information (UCI) message that is multiplexed with a high-priority (HP) UCI message in a wireless communication system. In particular, a user equipment (UE) determines a compression indication for the LP UCI message, encodes the compression indication jointly with the HP UCI payload, and separately encodes the LP UCI payload. The encoded LP UCI payload is multiplexed with the jointly-encoded compression indication and HP UCI payload in an uplink transmission to a base station. The compression indication includes information related to whether and how the LP UCI has been compressed. The base station decodes the compression indication and then decodes the LP UCI message based on the compression indication.

The invention relates to the technical field of information security, in particular to a rainbow table decryption and scheduling method based on multilayer encryption protection. Through hierarchical storage of the multi-level rainbow table and the storage module and in combination with an accurate task target identification and matching mechanism, the decryption process is effectively accelerated. The optimal rainbow table subclass is identified according to the encryption hash value, the length, the encryption algorithm and other information of the password, the decryption algorithm of the target password can be quickly matched, and the password cracking speed is greatly increased; according to the method, a task scheduling distribution mechanism based on the matching degree is adopted, and computing resources can be reasonably distributed according to the priority of decryption tasks. The basic decryption unit is preferentially used by the low-priority task, and the special hardware acceleration module is used by the high-priority task, so that the utilization rate of resources is effectively improved, and meanwhile, the high-priority task is ensured to be processed in time.

A user equipment (UE) is described. The UE includes circuitry configured to, in a case that a high priority (HP) physical uplink control channel (PUCCH) for more than 2 bits of HP hybrid automatic repeat request-acknowledgement (HARQ-ACK) with PUCCHI format 2 or PUCCH format 3 or PUCCH format 4 overlaps with K PUCCHs for respective K HP SRs and a low priority (LP) physical uplink shared channel (PUSCH), and if HP HARQ-ACK multiplexing on the LP PUSCH is configured and / or dynamically indicated by downlink control information (DCI), append [log2(K+1)] bits for HP SR to the HP HARQ-ACK. The circuitry is also configured to multiplex the combined HP HARQ-ACK bits and the appended HP SR bits on the LP PUSCH as HP HARQ-ACK bits.

The invention is suitable for the technical field of terminals, and particularly relates to a task scheduling method, electronic equipment and a computer readable storage medium. In the method, when the electronic equipment runs a plurality of windows, the electronic equipment can put the graphic processing tasks with high priorities into a scheduling queue with high priorities and put the graphic processing tasks with low priorities into a scheduling queue with low priorities based on the priorities of the graphic processing tasks corresponding to the windows; the graphics processing tasks corresponding to the windows with relatively high user attention are put into the scheduling queue with high priority, and the graphics processing tasks corresponding to the windows with relatively low user attention are put into the scheduling queue with low priority, so that the graphics processing tasks can be executed by the GPU when the graphics processing tasks are executed; according to the embodiment of the invention, the graphic processing task corresponding to the window with high user attention can be executed preferentially, the picture frame rate of the window with high user attention is improved, the picture fluency of the window with high user attention is improved, and the user experience is improved.

The invention discloses an access sequence determination method and device, a data reading method and device, equipment and a storage medium, and relates to the technical field of communication, and the method comprises the steps: caching a plurality of access requests when the access requests sent by a plurality of hosts for accessing the same slave are received; identifying the request type of each access request; selecting at least one target arbitration process from a plurality of arbitration processes according to the request type; executing at least one target arbitration process, arbitrating the hosts corresponding to the plurality of access requests, and determining an access sequence of each host to the slave; and sequentially sending the access requests to the slaves according to the access sequence. The request type of each access request is identified, multiple arbitration processes are provided, it is ensured that high-priority requests can be processed in time, meanwhile, it is prevented that low-priority requests cannot be responded for a long time, and the fairness of the system is improved. And the limitations of the traditional methods, such as priority starvation and non-uniform resource allocation, are overcome.

The invention provides a multi-task automatic mutual exclusion lock management middleware which realizes full-automatic lock management through intelligent resource classification, dynamic lock strategy matching and priority sensitive scheduling. The middleware is internally provided with a resource feature analysis algorithm, read-write features of resources such as hardware equipment and memory queues can be automatically recognized, and optimal lock mechanisms are allocated to different scenes in combination with a preset lock strategy library; a priority preemptive scheduling algorithm is originally created, high-priority tasks are allowed to break through the limitation of a lock waiting queue, and the real-time task response capability is guaranteed; and meanwhile, an annular deadlock detection module is integrated, and a low-priority lock occupation task is relieved through transaction rollback, so that circular waiting is relieved. A developer only needs to label task priority labels, zero manual lock coding can be achieved, and the lock conflict risk is reduced. The modules cooperate to achieve full-automatic lock management, lock conflict and deadlock risks are remarkably reduced on the premise that lock logic does not need to be coded manually, and the real-time performance and reliability of a multi-task system are remarkably improved.

Embodiments of the present application relate to a task scheduling method and apparatus, an electronic device and a storage medium, applied to a resource pool having multiple tenants, where a task queue corresponding to each tenant is configured with a minimum resource limit and a maximum resource limit, a difference between the maximum resource limit and the minimum resource limit is an elastic resource limit of the task queue, and the maximum resource limit is an elastic resource limit of the task queue. Comprising the steps that priority information of a next to-be-executed task in a target task queue and current available resource information of the target task queue are obtained, the priority information comprises a high priority or a low priority, and the current available resource information comprises available resources in a minimum resource limit and available resources in an elastic resource limit; or no available resource exists in the maximum resource limit; and based on the priority information of the next to-be-executed task and the current available resource information of the target task queue, performing resource elastic scheduling on the next to-be-executed task. The task throughput and the resource utilization rate of the scheduling system can be improved in an elastic scheduling mode.

The invention belongs to the technical field of computers and artificial intelligence, and discloses a priority differential cache-based MoE batch reasoning method and system, and the method comprises the steps: recognizing global hot experts with higher activation frequency in each layer, enabling the global hot experts to remain in a high-priority cache during initialization, and building a priority list of the experts in each layer; in the reasoning stage, selecting an expert set needing to be activated in the current layer; the expert priority score is dynamically updated according to the activation and reuse behaviors of the experts, and the experts are migrated and replaced among the high-level cache, the middle-level cache and the low-level cache according to the priority score and a cache threshold value; according to a current layer gating result and a historical activation track, predicting an expert set which may be activated in a next layer, and asynchronously loading the expert set to a low-priority cache during current layer calculation so as to realize time overlapping of calculation and communication. According to the method, the expert migration frequency can be reduced, the cache hit rate and the throughput rate are improved, the video memory utilization rate is remarkably optimized, and good expansibility and universality are achieved.

The invention provides a self-adaptive data packet scheduling method based on approximate quantiles and multiple queues on a programmable data plane, and aims to solve the problem that the scheduling performance and the resource efficiency are difficult to balance in the prior art. According to the method, lightweight ranking scoring, dynamic queue mapping and a cascade enqueue strategy are combined. A relative priority score of the data packet is efficiently calculated through a minimum-maximum normalization mechanism; adaptively mapping the data packets to one of a plurality of FIFO queues according to the scores and real-time congestion conditions of the queues, and providing a fast channel for extremely high priority traffic; and when the target queue is full, trying to store the data packet into a queue with a lower priority in a cascading manner so as to avoid unnecessary discarding. According to the method, resource consumption and implementation complexity are remarkably reduced while the approximate ideal PIFO scheduling performance is achieved, and the method is particularly suitable for resource-limited environments such as a software-defined network and edge computing.

An intra-user equipment (UE) multiplexing method is executed in a UE. The UE obtains respective timeline conditions for multiplexing uplink transmission with high priority and uplink transmission with low priority when detecting collision in time between the uplink transmissions with different priorities. The UE performs multiplexing of the uplink transmissions with different priorities when the respective timeline conditions are satisfied.