15 results about "Limited resources" patented technology

The invention discloses a process migration control method and device for flexible assembly task switching, and relates to the technical field of flexible assembly. The method comprises the following steps: acquiring original total data in a first process; taking the minimum sample loss function as an optimization target, and obtaining a distillation candidate subset from the original total data; the sample loss function is used for measuring the retention degree of the distillation candidate subset on the behavior distribution of the original total data; calculating a comprehensive score of each action in the distillation candidate subset through a distribution consistency score and an action border-crossing punishment, filtering out sample data with relatively large comprehensive scores of the actions in combination with a gating mechanism, and storing the sample data meeting the gating mechanism into a small-capacity buffer area; and finally, training the initial strategy network according to the sample data of the small-capacity buffer area and the observation data of the second process to obtain a new strategy network. The method can give consideration to both online speed and stability under the condition of limited resources.

The invention relates to the technical field of cloud computing resource management, in particular to an elastic task scheduling method and system based on a large language model, electronic equipment and a medium. The method comprises the steps of firstly obtaining a task mirror image of a user and analyzing a task code through a large language model to generate an initial resource demand feature; performing code embedding vector similarity calculation on the initial resource demand characteristics and historical task codes to generate elastic resource demand distribution characteristics; performing scheduling matching with cluster real-time available resources according to the characteristics; and if the matching fails or the operation fails, correcting the entry point of the task mirror image and the related source code or the configuration file through the large language model so as to re-execute the task scheduling matching. In this way, the technical problem that the resource demand and supply dynamic matching is difficult when an existing container cluster scheduling technology faces a limited resource environment is solved, and the task success rate and the overall cluster resource utilization efficiency are improved.

The invention discloses a data block relevance data storage method for improving network security availability, which comprises the following steps of: firstly, analyzing a historical IO (Input / Output) request, discovering a data block set which is frequently collaboratively accessed, forming a high-relevance data combination, and calculating an IO delay revenue factor, a space cost factor and collaboration access intensity, so as to form a high-relevance data combination; comprehensively evaluating the migration value of each frequent item set data; by constructing a conflict graph model, analyzing a resource competition relationship among different frequent item set data, correcting an expected utility value by using an interference sensing weight, and identifying and avoiding potential resource conflicts; based on the corrected utility value, particle swarm optimization processing is adopted to optimize and select frequent item set data with the highest utility value under the constraint of a high-speed storage space, the frequent item set data are sequentially incorporated into a migration list, and it is ensured that the overall revenue is maximized under limited resources; and according to the priority order of the candidate list, the data block migration is executed by adopting a batch migration strategy, and the storage performance is improved while the system stability is ensured.

The application discloses a task scheduling method and device, a computer program product and a storage medium. Based on the number of workspaces without running tasks, and the total number of running tasks and the maximum concurrency, the available concurrency is calculated. The resource allocation has a clear quantitative basis, improves the rationality of distribution, reduces resource idling and task blocking. Based on the available concurrency, the task dequeuing strategy is configured, the queued tasks in the workspace with the least number of running tasks are dequeued, and the tasks in the workspace without running tasks are dequeued. Realize flexible and fair task scheduling in the limited resource and multi-workspace scene, and make the workspace with the least number of running tasks obtain resource inclination to start tasks. The dequeued tasks match the available concurrency, avoiding resource depletion and task running lag after task dequeuing. Efficiently use limited resources, flexibly adjust resource allocation and task dequeuing, give each space the opportunity to run tasks, solve the problem of resource waste, and improve the overall utilization efficiency of resources.

An online system, such as a concierge service, provides services to users using a set of limited resources. To allocate the limited resources of the system among the users, the system uses a model to predict each user's sensitivity to different levels of service. An allocation module then allocates the limited resources among a set of users based in part on the estimated sensitivities and the supply of available resources.

The application discloses a file backup method and product, and relates to the technical field of communication, and comprises the following steps: determining the total access frequency of any file in a plurality of files in a backup period, wherein the backup period is used to represent the frequency of backing up the plurality of files; determining the first backup weight of the any file according to the total access frequency and a first weight corresponding to the total access frequency, wherein the first backup weight is used to represent the importance of the any file, and the first backup weight is proportional to the importance; and backing up the plurality of files according to the first backup weight and the carrying capacity of a target system, wherein the carrying capacity is used to represent the maximum number of files that can be successfully backed up by the target system in a single file backup process. Therefore, the problem that the backup strategy of files is often static in the prior art, and important files are prone to backup failure under limited resource conditions can be solved.

The invention relates to a manufacturing system node ALCR strengthening strategy method of a double-layer dynamic Bayesian network, belongs to the field of reliability analysis, and aims to solve the problem that redundant resource allocation is unreasonable due to the fact that a traditional key node equipment strengthening strategy occupies limited resources. A critical value of node equipment capacity redundancy and an inhibitory penalty function of resource overload are considered, and an improved reinforcement strategy (ALCR) based on an optimization theory is provided. The dynamic elasticity evaluation method of the double-layer dynamic Bayesian network is combined to compare the elasticity strengthening effects after different strengthening strategies are strengthened, and the accuracy and effectiveness of the ALCR strengthening strategy are verified. When the equipment average degree is large, compared with other reinforcement strategies, the improved reinforcement strategy based on the optimization theory can ensure that the system has better elastic performance, provides a theoretical basis for further formulating a reliable production strategy and reducing the maintenance cost, and is suitable for popularization and application. And an improvement direction is indicated for further improving the reliability of the node strengthening strategy of the manufacturing system.

The application discloses a kind of based on end side's big model key value cache dynamic optimization method and system, method includes: S1, the context resource of multiple inference sequences is centrally controlled, and corresponding context space is pre-allocated;S2, whether the currently available context space satisfies the allocation demand of inference sequence is judged, if it satisfies, then the intelligent context allocation is carried out to inference sequence, if it does not satisfy, then whether the remaining memory space of end side equipment is enough is judged, according to the judgment result, step S3 or S4 is executed;S3, if space is enough, then the available context space is increased by dynamic expansion, and the context space allocation and context update of inference sequence are re-performed;S4, if space is insufficient, first carry out context recovery, then discard and retain context by sequence perception sliding window mechanism, and execute context update after allocation is completed.The application can realize the efficient inference of big model under the condition of end side limited resource.

The application discloses a training resource adaptive recommendation and balanced scheduling method based on dynamic capability portrait, relates to the technical field of data processing and resource scheduling, and successfully solves the problem that individual capability demand and resource supply pressure cannot be considered simultaneously in a traditional training recommendation system by adopting technical means such as capability evidence event sequence, training capability knowledge graph and dynamic capability portrait. The method introduces graph reasoning and dual variable balanced scheduling technology, can not only accurately recommend adaptive training resources, but also reasonably allocates resources and optimizes scheduling under the condition of limited resources, and guarantees the efficiency and fairness of the training process. The design significantly improves the utilization rate of training resources, alleviates the imbalance between resource supply and learning demand, and improves the adaptive capability and intelligent level of the training system.

The invention discloses a command and control communication device based on a limited resource condition. The command and control communication device comprises an ultra-short wave radio station and at least two command and communication control devices, the ultra-short wave radio station is arranged in a driving cabin of the vehicle and is connected with the first command communication control device in the driving cabin through an audio cable; the first command communication control device is connected with a second command communication control device arranged in a vehicle shelter through an RS-422 differential transmission cable; the method has good economical efficiency, platform adaptability and anti-interference capability.

The invention relates to the technical field of motion navigation of autonomous underwater vehicles, in particular to a multi-AUV task area division and allocation method based on limited resources. Comprising the following steps: performing target pre-screening of a task space based on a density clustering method; determining the distribution position of the AUV load based on weighted greedy; based on K-means clustering, task clusters are divided in the optimal distribution position set and are allocated to the AUV; the task cluster of each AUV is adjusted based on the true measurement of the task load of the AUV, and the balance between the limited resources carried by each AUV and the path length is ensured. The optimization of target coverage of multiple AUVs under limited resources is ensured, and the system execution efficiency and the energy consumption uniformity of an AUV group are improved.

The embodiment of the invention provides a scheduling method of a real-time operating system and related equipment, and belongs to the technical field of embedded operating systems. The method comprises the following steps: acquiring priority data corresponding to a plurality of task processes; the plurality of task processes are task processes needing to be processed in a real-time operating system, and the real-time operating system is arranged in micro equipment with limited resources; according to the priority data corresponding to the plurality of task processes, selecting the task process with high priority data for processing; if two or more task processes with the same priority data are detected, task time slices are distributed to the two or more task processes with the same priority data through the task management model, and the two or more task processes with the same priority data are processed according to the task time slices. According to the embodiment of the invention, the balance among real-time performance, fairness and the overall throughput efficiency of the real-time operating system can be realized on a hardware platform with extremely limited resources.

The present application relates to a kind of model task scheduling method, device, equipment, medium and program product, it relates to model service technical field.The method includes: by obtaining the configuration information of task, multiple tasks are distributed to corresponding scheduling group, can generate flow line based on the scheduling strategy of scheduling group and the data flow between different tasks, then according to the model identifier of task, the corresponding model instance can be loaded, so that the task in flow line can be sequentially model inference.By this way, suitable scheduling strategy can be selected according to task type, and grouping generation flow line management can optimize scheduling and improve execution efficiency, solve the contradiction between resource limitation and complex task scheduling in end-side scene, realize the efficient inference service of end-side model under limited resource.

To provide a mobile device and a method for performing efficient communication in limited resource environments.SOLUTION: In a communication system, an electronic device detects a message request to send one or more redundant messages to a response network. The one or more messages contain information previously sent from the electronic device to the response network. The electronic device determines an entropy factor corresponding to the relatedness of the one or more messages to previously sent messages, and the electronic device sends one or more redundant bits in the place of the one or more messages if the entropy factor is below a threshold value corresponding to network resources.SELECTED DRAWING: Figure 7

Methods and systems for providing security management services are disclosed. To provide security management services in a manner that reduces the quantity of hardware resources necessary to provide the security management services, the security manager may prioritize security management services based on the likelihood of individuals posing a security risk. To identify the security risks of the individuals, the paths of the individuals may be tracked and compared to typical paths through environments that are followed by most individuals that traverse through the environment. The individuals following a typical path may not indicate a security risk and a resource prioritization may be retained or decreased for the individuals not posing a security risk. The individuals not following a typical path may indicate a security risk and a resource prioritization may be increased for the individuals posing a security risk.