34 results about "Limited resources" patented technology

The invention discloses a local reasoning acceleration system and method for a large-scale MoE model under limited resources, and the method comprises the steps: carrying out the MoE model performance modeling on large language model reasoning equipment, and obtaining a module calculation performance model; executing a sliding window type greedy prediction-scheduling strategy: performing expert module routing prediction by utilizing a gating network in the MoE model to obtain activated experts of each layer and an input token of each expert, and dynamically determining the number of layers of the MoE model contained in a single scheduling window by utilizing the module calculation performance model in combination with a minimum sliding window algorithm; and according to the predicted expert module route, performing optimal execution strategy search and MoE model layer scheduling on the activated expert module in the window with the single scheduling window length. According to the method, efficient and stable local large model reasoning deployment is realized through an expert activation prediction mechanism without training cost.

A computer-implemented system and a method for automatic electronic order creation are disclosed. The system and method may be configured to: receive a digitized document comprising embedded metadata related to contents of the digitized document; analyze the embedded metadata to extract a boundary parameter; generate a unique reference identifier associated with the digitized document and the boundary parameter; receive a first input data setting one or more target measurements for one or more predefined periods of time, wherein a sum of the one or more target measurements is less than or equal to the boundary parameter; receive a second input data setting configuration data for achieving the one or more target measurements; and generate an electronic order based on the configuration data.

The application discloses a large language model offline reasoning task reasoning acceleration method and system under limited resources, a model construction module, a dynamic batch processing reorganization module, a dynamic memory management module and an unloading strategy adjustment module; based on the lexGen large language model offline reasoning system, dynamic batch processing reorganization is performed on the reasoning task, dynamic memory management design is combined, reasoning tasks are managed in an iterative level fine-grained manner, occupied memory of completed reasoning tasks is released in real time, and dynamic arrangement is performed on uncompleted reasoning tasks, so that the waste of reasoning resources in the reasoning process is reduced, and the offline reasoning system throughput based on the unloading technology is improved. The application efficiently utilizes the idle memory of hardware, improves the throughput of offline reasoning, reduces the influence of unloading on the reasoning process, and transfers part of tensors in a device with low bandwidth to the idle memory, so that the I / O overhead during transmission is reduced.

The invention discloses a server-free edge function reliability deployment method and system based on reinforcement learning, and the method is oriented to server-free edge calculation, takes the minimization of long-term network cost as a target, and systematically solves the problems of high cold start cost, limited resources, difficult reliability guarantee and the like. A system model including user movement, channel fading, function deployment and a backup mechanism is constructed, four types of costs of cold start, maintenance, transmission and punishment are accurately described and converted into a Markov decision process, and a state-action-award framework is utilized to realize dynamic decision. The PPO algorithm fusing the greedy strategy to assist the action selection is further provided, and a function deployment scheme meeting the reliability constraint is efficiently generated under the condition that resources are limited. According to the method, through fusion of reinforcement learning and a greedy strategy, the problems of feasibility, reliability and cost of function deployment in server-free edge computing are solved, and the method has the capabilities of efficient deployment, adaptive scheduling and long-term performance optimization.

The present application discloses a large language model training method, system, device and storage medium. The method divides the student model and the large language teacher model and deploys them in a central controller with sufficient resources and on a client with limited resources respectively. This ensures that the local data of the client is only used to train the local network structure, avoiding the risk of local data leakage. In addition, the method trains the network structure with heavy computational load through the central controller, while the client only trains the network structure with light computational load. This adapts to the resource limitations of the client device, allowing the resource-limited client to participate in the model training. Moreover, the knowledge of the teacher model is transferred to the student model through knowledge distillation, ensuring that the data is not leaked and significantly reducing the dependence on the central controller.

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 present application discloses a method and apparatus for acquiring virtual resources, a storage medium, and a computer device. The method comprises: receiving a time-limited resource acquisition request, wherein the time-limited resource acquisition request includes a target character identifier, a target time-limited resource identifier, and a resource to be exchanged; determining the effective time limit of a target time-limited resource corresponding to the resource to be exchanged, allocating the corresponding target time-limited resource to a target character account, and deducting the resource to be exchanged from the target character account; when the effective time limit of the target time-limited resource is reached, changing the target time-limited resource allocated to the target character account to an exchange voucher matching the resource to be exchanged, wherein the exchange voucher is used to exchange virtual resources. The present application helps to improve the utilization rate of virtual resources within a game, as well as the utilization rate of a game server.

Various example embodiments relate to communication techniques, and more particularly, to apparatuses and methods for facilitating use of limited resources, particularly logical channel identifier (LCID) in a communication network. One aspect may provide an apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to: determine that the at least one logical channel identification (LCID) index is to be used for communicating information for supporting communication within the communication network; determining the information and an encoding configuration for allowing the information to be encoded within a field of at least one logical channel identifier index; and encoding the information according to an encoding configuration for transmission in a message comprising at least one logical channel identifier field. It is understood that the principles in accordance with the described aspects may allow for more information to be carried from the UE to the network, for example, using a very limited Msg3 / MsgA transport block (TB) size. The additional information may include additional information related to connection establishment and / or UE state / capability at RRC request.

An information processing apparatus includes: a setting part which defines a problem by formulating optimization of resource options and prices of the options presented to each user in a market in which a plurality of types of limited resources are handled; and an action determination part which determines the resource options and the prices of the options to be presented to each of the users by solving the problem.

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.

This invention relates to a method and related apparatus for high availability of resources in a financial trading system. The method operates as follows: after assigning a special request header to a request to be sent, it is sent to the server; during server operation, the resource pool for processing requests is isolated and divided into high-priority and low-priority resource pools. After receiving a client request, the server obtains the special request header, records the initial information and business module entering the server, and marks the arrival time; the request is assigned to different resource pools for processing, with priority given to preempting resources in the low-priority resource pool; after the request is fully executed, the result of the request processing is fed back to the calculation module, which performs backtracking calculations on the data to support subsequent resource allocation. This invention solves the problem that financial trading systems cannot ensure high availability of a single service node in a low-cost and user-friendly manner under limited resource conditions. The system of this invention has high real-time performance, low cost, achieves high availability design, and maintains stable service availability.

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 invention discloses a personalized federal relationship classification method under a limited resource condition, and belongs to the technical field of distributed machine learning and artificial intelligence. According to the personalized federal relationship classification method under the limited resource condition, entity and relationship representation are aligned with local memory, and each instance can'review 'memory knowledge by forcing the instance to be embedded into the corresponding relationship representation as much as possible. According to the method, an efficient parameter fine tuning method is applied to learning of a federated RC model, a personalized federated relationship classification method is provided, and an entity library and a relationship library are deployed at each client and used for storing local entities and relationship representations as memory knowledge. The FEDAER method provided by the invention is superior to a competition method under various settings, and meanwhile, the heavy communication and computing resource consumption is remarkably reduced.

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.

According to the fault-tolerant decision-making method and device of the multi-device data fusion system for collecting the information in the tunnel, the medium, the program product and the terminal, the dynamic fault-tolerant capability is achieved, a user can flexibly adjust the allowable fault probability of the device according to different scenes, and therefore the system can be accurately adapted to the complex and changeable industrial environment. According to the method, the weight can be reasonably allocated, so that the overall misjudgment probability of the multi-device data fusion system is minimized, and the risk and loss caused by misjudgment are reduced. The method is low in algorithm complexity, can quickly process a large amount of data, and is suitable for being deployed in embedded equipment or edge computing nodes with limited resources. The method also shows that the weight advantage of high-robustness and high-reliability equipment can effectively counteract the influence of false report or missing report of low-reliability equipment, and ensures that the multi-equipment data fusion system can still stably acquire data when facing partial equipment faults, thereby improving the reliability of cooperative work of the multiple equipment in a complex environment.

The application discloses an intelligent transport capacity scheduling method and system, and relates to the technical field of transport capacity scheduling.The intelligent transport capacity scheduling system comprises a transport capacity information integration module and a transport capacity scheduling module.The application calculates the priority index of each order to be distributed, and orders all the orders according to the priority, wherein the priority ordering not only considers the urgency of the order, but also comprehensively evaluates factors such as the goods attribute, the cabin capacity and the green transportation, and forms a comprehensive priority index through weighted calculation, thereby improving the accuracy of the scheduling decision; through the group game rule, the transport party optimizes the bidding strategy according to the priority of the order and the scheduling demand, enhances the flexibility and diversity of the scheduling, and adjusts the bidding weight according to different strategies in the game process, so that all parties can dynamically adjust the scheduling scheme under the condition of limited resources.

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.

Systems and method are described for efficiently allocating services in limited resource scenarios. For example, in response to receipt of a first request message indicating a first plurality of services and first resource requirements associated with the first plurality of services from a first node, a transport network controller is to determine a set of resources needed to admit the first plurality of services to the transport network based, at least in part, on the first resource requirements associated with the first plurality of services. When the set of resources cannot be allocated from the set of available resources, then either the transport network controller or the first node are to identify a second plurality of services and / or second resource requirements of one or more of the first plurality of services based on information related to the set of available resources.

A task scheduling method, device and equipment for an intelligent industrial remote control scene, the method comprising: determining a critical path of a DAG task graph, and selecting the DAG task graph with the longest critical path for scheduling; calculating the earliest start time, the latest start time and the slack time of a task under the dual resource constraints of an execution device and personnel; selecting the task with the smallest slack time for scheduling, and calculating a resource time window according to the insertion position of the task on the device task sequence and the personnel task sequence; allocating the device and the personnel to the task based on the resource time window maximization principle, and determining the execution order of the task; and updating the earliest start time and the latest start time of each task in the DAG task graph that is currently being scheduled and has completed scheduling in real time until all tasks are completed. The present application can achieve efficient task allocation and scheduling in the case of limited resources and complex tasks.

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 application discloses a limited resource multi-task matching method based on a greedy strategy and belongs to the field of dispatching optimization.The application comprises the following steps: according to a multi-task demand table, all feasible solutions for task demands are screened from limited resources to construct a multi-task feasible solution table; according to the multi-task feasible solution table, an adjacency list is used to construct a limited resource undirected and weighted graph; based on the greedy strategy, the multi-task feasible solution table is traversed to obtain a resource package preliminary screening table; the number of feasible solutions for each task demand in the resource package preliminary screening table is judged; for the task demands that have not been matched, a breadth-first search algorithm is used to find a path in the limited resource undirected and weighted graph and the multi-task feasible solution, and resource transfer and distribution are performed.The application preliminarily matches by skillfully using the greedy strategy, thereby reducing the problem scale; on the basis, the breadth-first search algorithm is used to make up for the shortage that the greedy strategy is easy to fall into a local optimal solution, and a global feasible solution can be more efficiently found.

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 application provides a system and method for deployment and computing coordination optimization under limited resources, relating to the field of digital technology. The system for deployment and computing coordination optimization under limited resources innovatively divides the types of server resources of various specifications into shared resources or exclusive resources, and implements pre-occupation and coordination mechanisms, realizing the deep integration and dynamic allocation of server resources of the application deployment manager and the computing cluster manager. In the past, the application deployment manager was idle during the early stage of application deployment or when the application was not fully loaded, while the computing cluster manager often had to queue computing tasks due to insufficient resources. The technical solution breaks through this resource barrier, enabling the computing cluster manager to flexibly use the idle resources of the application deployment manager, ensuring that every server resource is fully utilized at any time.

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.

The invention discloses a multi-model running resource optimization method based on a dynamic queue. The method comprises the steps that a user calls a model, a calling task of the model enters a task queue, a resource controller monitors residual resources of the dynamic queue in real time, and the calling task of the model is scheduled and controlled to enter the dynamic queue from the task queue to run; calling a corresponding model container to operate through a trigger according to the calling task entering the dynamic queue to operate, and entering a model operation queue to execute; when the resource controller monitors that the residual resources of the dynamic queue are lower than a threshold value in real time, the model container at the tail of the dynamic queue is removed according to the least recently used principle, the resources are released, and resource optimization during simultaneous operation of multiple models is achieved. According to the method and system, calling of more models can be achieved under the condition that resources are limited, and the resource utilization rate is increased.