106 results about "Resource adaptation" patented technology

The invention discloses an MES work order dynamic scheduling and resource self-adaption system based on real-time event triggering, and relates to the technical field of intelligent manufacturing and industrial automation, equipment states and material events are monitored in real time through interfaces such as a PLC and an OPCUA, event types and validity are analyzed through hardware filtering, feature code recognition and double verification, and the real-time event triggering is realized. Event data, MES batch information and equipment load data are integrated to generate standardized scheduling input, a preset strategy is matched, an optimal dispatching scheme is generated through a priority algorithm, material, equipment and logistics resource allocation and dispatching operation are completed, synchronization and MES interaction ensure that page logic is consistent, full-process automation of dynamic work order scheduling is achieved, and work order scheduling efficiency is improved. The equipment state and the material event are responded in real time, the production cooperation efficiency is improved by more than 30%, the resource adaptation accuracy rate reaches 95% or above, manual intervention is reduced, and the consistency of data and MES is guaranteed.

The invention provides a computing power resource scheduling method and system based on cloud network integration. Selecting a computing power task to be scheduled as a current scheduling task, and generating a node resource adaptation matrix based on the real-time load data, the cloud network topological relation between the nodes and the dynamic bandwidth data; then calculating the lowest scheduling cost of each node according to the matrix, and determining a dynamic adjustment factor when a resource allocation conflict occurs in the current scheduling task; based on the lowest scheduling cost, the computing power demand scale and the data transmission estimated overhead, calculating the final scheduling overhead for scheduling the current scheduling task to each node; and finally, allocating tasks to a target node according to the final scheduling overhead, updating a running task queue, if a conflict occurs, calling a dynamic adjustment factor to execute resource reallocation, and updating the queue after the reallocation succeeds. And circulating the process until all tasks are scheduled. According to the scheme, optimal scheduling of computing power resources in a cloud network convergence environment can be realized.

The invention relates to the technical field of load balancing optimization, in particular to an intelligent computing center load balancing and energy-saving optimization method and system, and the method comprises the following steps: obtaining task execution duration and resource state data, calculating a multi-dimensional index, carrying out the weighted analysis, dynamically outputting a regulation and control scheme, and achieving the load balancing and energy-saving optimization of an intelligent computing center. According to the method, response time comparison based on a service level protocol is introduced, a task execution duration distribution structure is clearly recognized, the abnormal task recognition precision is improved, and the judgment accuracy of an overload trend is enhanced by comparing resource utilization rate changes of adjacent periods in the aspect of node state monitoring; in the task allocation process, the available memory of the node and the resource adaptation degree are combined, real performance load performance is obtained through energy efficiency and throughput data cross analysis, task allocation better conforms to the actual energy consumption level, the resource waste risk is effectively reduced, the response flexibility of a scheduling strategy to system load changes is enhanced, and the task allocation efficiency is improved. And the task execution efficiency and the energy efficiency distribution accuracy are improved.

The invention relates to a lightweight asynchronous message processing method and system for a high-concurrency scene, and the method comprises the steps: receiving a message request of a client, carrying out the preprocessing of the message request, and generating a to-be-processed message; distributing the to-be-processed message to a multi-level priority queue based on a priority scheduling mechanism; dynamically adjusting the number of core threads and the maximum number of threads of the thread pool according to the resource state data, and forming a thread pool resource configuration scheme of the current scheduling period; dynamically calculating the message processing quantity of the current scheduling batch based on the message accumulation quantity of each priority queue, the thread pool resource configuration scheme, the CPU utilization rate in the resource state data and the JVM heap memory utilization rate; and based on the out-of-heap memory buffer area reference, assigning the to-be-processed message object to a corresponding processing thread to execute a service processing logic until the processing operation of the messages in all the scheduling units is completed. The method has the effect of improving the resource adaptability of the asynchronous message processing architecture.

Embodiments of this application provide an adaptive data flow transmission method, an apparatus, and a system, relating to the field of communication technologies. The method is used to perform, when a frame rate of a downlink data flow of a service changes, network resource adaptation promptly based on the frame rate of the data flow, to avoid a waste of network resources or a packet loss of media frame data, so as to improve network resource utilization and user experience.

The invention relates to the technical field of emergency rescue intelligent decision, and discloses an emergency rescue management system and method based on rescue timeliness. The method comprises the following steps: constructing a space-time three-dimensional grid by fusing multi-source information, and performing dynamic scene reconstruction by using a multi-level mapping rule to obtain preliminary and deep rescue scenes. And after combined anomaly diagnosis is carried out on the double scenes, a task topological structure is driven to dynamically evolve according to a diagnosis report, and a rescue task sequence is generated. And then cross-dimension resource adaptation is executed, a resource list is formed, key bottleneck nodes are predicted and identified in combination with real-time feedback, and finally a rescue instruction chain is generated. According to the method, deep fusion and structured analysis of field information in a space-time dimension are realized, a closed-loop decision-making mechanism with task and resource dynamic cooperation and adaptive optimization is established, and timeliness and overall toughness of rescue actions in a complex disaster environment are enhanced.

The invention discloses an RDMA asynchronous communication optimization method and system based on a lock-free queue, a medium and a processor, and relates to the technical field of remote communication. The method comprises the following steps: constructing a lock-free annular buffer area at an edge node, registering the lock-free annular buffer area as an RDMA memory area, and configuring a queue and a thread pool; the collection thread concurrently writes data, and the RDMA submission thread asynchronously completes remote transmission based on a buffer area state; and the queue processing thread performs closed-loop linkage transmission and buffer area states through'batch polling-result analysis-state synchronization-exception processing ', and dynamically adjusts the buffer area and queue parameter optimization performance at the same time. The problems that an existing RDMA scheme is poor in thread cooperation, weak in resource adaptation, not timely in state linkage and the like are solved, transmission delay and CPU overhead are greatly reduced, communication stability and the resource utilization rate are improved, and the method is suitable for edge computing and other high-concurrency data interaction scenes.

The invention relates to the technical field of communication networks, in particular to a time slot management method and system for a TDMA (Time Division Multiple Access) communication network. The method comprises the following steps of: setting a service priority according to a preset rule, and performing priority judgment on each accessed service request; executing a time slot resource allocation strategy according to the service priority in combination with the current available time slot resource condition; obtaining business historical data, and carrying out data preprocessing and feature extraction; utilizing a reinforcement learning model to train and establish a mapping relation between the business historical data and the time slot width configuration; and a high-priority guarantee mechanism is combined with an exclusive guarantee mechanism to realize the adaptation of the time slot width and the broadband demand. According to the method, the resource adaptation precision is improved by dynamically adjusting the time slot width. And intelligent optimal configuration of the time slot width is realized by analyzing historical business data characteristics and combining a reinforcement learning algorithm. A time slot management system with determinacy and flexibility is constructed by fusing hard real-time guarantee of static time slots and elastic scheduling of dynamic time slots.

The invention provides a cross-heterogeneous computing power scheduling method based on an AI (artificial intelligence) agent, which utilizes the advantage that the AI agent can dynamically generate or adjust task nodes and dependency relationships thereof, adopts a dynamic recursion splitting method to split tasks, and performs task computing property evaluation on subtasks by corresponding agent members to form a computing power demand scheme. And the computing power scheduling agent transmits the computing power demand to the resource adaptation module, forms a computing power available scheme, performs matching adjustment with a computing power demand scheme, and completes task processing. According to the method, the most suitable computing power type is dynamically judged and selected through the intelligent agent, the task processing cost can be controlled, the cross-node communication delay can be reduced, for the tasks in sequence, the continuity between the tasks is ensured, the waiting time is shortened, and higher-quality and more efficient computing power distribution services are provided for users.

The invention relates to the technical field of heterogeneous computing power allocation, in particular to a heterogeneous computing power dynamic allocation method based on big data analysis, which comprises the following steps: analyzing a source, a target, a type and dependency of a data task, normalizing parameters and extracting features, identifying a key task, combining a computing power resource queue and historical scheduling efficiency, optimizing an allocation state, and allocating the heterogeneous computing power according to the allocation state. And screening the computing power resources which run normally, analyzing the dominant frequency temperature and response performance, adjusting the distribution relation, and forming a dynamic distribution sequence. According to the method, standardization, normalized mapping and feature aggregation are carried out on the data task multi-dimensional labels, deep mining and classification of task features can be achieved, dynamic linkage of task requirements and computing power resource states is completed according to big data analysis, relevance between the tasks and heterogeneous computing power resources is finely quantified, and the method has the advantages of being simple in structure, convenient to operate and high in efficiency. And in combination with a resource response expression and grading mechanism, a task allocation strategy is automatically adjusted, and the resource adaptation degree and allocation sequence matching during task processing are guaranteed.

The invention discloses an enterprise innovation force cloud evaluation and resource adaptation system and method, and relates to the technical field of enterprise innovation management, and the method comprises the following steps: periodically obtaining industry life cycle data, innovation sign data and external data source data; performing weighted average calculation on the industry life cycle data, the innovation sign data and the external data source data to obtain fused innovation sign data; and performing sliding window processing on the fused innovative sign data in the same period, and outputting a trend curve of the fused innovative sign data. According to the scheme, interference caused by industry periodic fluctuation is effectively eliminated through reference curve dynamic alignment and continuous window joint judgment, meanwhile, the abnormal starting time is positioned in combination with the trend curve, an accurate time anchor point is provided for subsequent causal analysis, the continuous abnormal state in the fused innovation sign data can be accurately recognized, and the accuracy and the reliability of the fused innovation sign data are improved. And misjudgment caused by single fluctuation is avoided.

The invention relates to the technical field of intelligent warehouse logistics, and discloses an intelligent warehouse logistics sorting system and method, and the system comprises a multi-dimensional data collection module, a global state perception and simulation model construction module, a demand prediction analysis module, a task optimization and resource configuration module, and an execution control and feedback adjustment module. The system realizes dynamic demand prediction, task optimization division and resource adaptation through a distributed sensing network, a three-dimensional simulation model and a deep learning algorithm, and ensures material storage safety in combination with environment adaptive regulation and control. The warehouse sorting efficiency can be improved, the resource allocation is optimized, the environmental adaptability is enhanced, and the method is suitable for modern intelligent warehouse scenes.

The application discloses a computer scheduling method for multitask operation environment state, and particularly relates to the technical field of computer operating system; the application generates a priority scheduling coefficient by quantitatively calculating a resource adaptation coefficient, a urgency coefficient and a constraint reflection coefficient, and then combining a weighting logic, so as to ensure that high-urgency tasks are preferentially executed to reduce postponement, avoid system overload or overheating through resource adaptation and thermal constraint control, and reserve reasonable heat production space for high-priority tasks, and balance of task execution efficiency, resource utilization and hardware safety is realized.

The invention discloses a wireless Internet of Things control channel communication method, system and device and a medium, and belongs to the technical field of channel communication. A set of complete basic framework of a multi-modal control channel resource scheduling model is constructed through multi-modal channel identification, sub-domain division, conflict index definition and delay threshold definition, and spatial partition management of channel resources is realized; the real-time performance and the reliability of the control task can be ensured by defining the task delay requirement; by combining short-distance self-adaptive backoff and wide-area pre-authorization resources, conflict delay can be reduced, resource competition can be avoided, and meanwhile average delay of cross-domain commands can be effectively reduced; all steps are progressive layer by layer from framework design to command mapping and then to a forwarding process, full-process optimization of cross-domain command resource adaptation, routing decision and link transmission is realized, a designed short-distance sub-domain self-adaptive backoff control mechanism and a wide-area sub-domain pre-authorization resource pool are fully reused, and the feasibility and the high efficiency of a protocol are ensured.

The invention provides a library resource intelligent recommendation method and system based on big data analysis, and relates to the technical field of library resource recommendation. A dynamic user borrowing intention model is constructed by mining behavior association logic, a user borrowing intention label set is generated, the user borrowing intention label set and library resource attribute information are linked to construct a scenarized resource adaptation module, and library collection resources are screened and sorted through the scenarized resource adaptation module to generate a dynamic resource recommendation sequence. User interaction behavior data is collected to iteratively optimize model parameters and mapping rules, and accurate library resource intelligent recommendation is achieved.

Embodiments of the present disclosure disclose a virtual cluster-based crowd scheduling system and device. A specific implementation of the system includes: a trace synchronization module that automatically identifies a load object created by a user in a virtual cluster, synchronizes task annotation metadata corresponding to the load object to a corresponding execution unit in a host cluster, and writes address annotation metadata corresponding to the load object in the execution unit; a scheduling module that reads task annotation metadata of each execution unit, generates a scheduling score value corresponding to each execution unit in each execution unit according to the read task annotation metadata set, and batches each execution unit to a corresponding resource adaptation node, the scheduling module being deployed in the host cluster; and a billing module configured to generate a task billing sheet. The implementation improves the versatility and resource utilization efficiency of the system.

The application provides a kind of intelligence calculation fusion network heterogeneous resource elasticity adaptation and deterministic scheduling system and method, belongs to industrial internet of things technical field, comprising: the system of establishing distributed management and centralized control under the scene of intelligence calculation fusion network;According to the distributed management and centralized control system, the two-stage random optimization problem of different time scales is established;For large-scale resource adaptation problem, a resource elasticity adaptation scheme based on heuristic algorithm is adopted;For small-scale scheduling sub-problems, a deterministic scheduling scheme based on deep reinforcement learning is adopted;Using the resource elasticity adaptation scheme and deterministic scheduling scheme, the optimal resource adaptation and scheduling result is obtained.The application can meet the resource flexible configuration demand under high dynamicity and uncertainty environment, ensure low delay and high reliability in data scheduling process.

The application discloses a resource self-adaptive federated learning multi-modal knee osteoarthritis auxiliary diagnosis method, which comprises the following steps: a server initializes a multi-modal model for knee osteoarthritis diagnosis, and pre-deploys a low-rank adaptive plug-in in a trainable layer; after a client reports local hardware resources, the server allocates a resource echelon and issues a two-dimensional clipping ratio; the client clips the model in depth and width based on the clipping ratio, activates a LoRA module of a specified layer, loads local knee osteoarthritis X-ray images and clinical text data for multi-modal training, and establishes semantic mapping of image features and clinical descriptions; the client only uploads an activated LoRA parameter update amount, the server aggregates and optimizes a global model through weighted average aggregation and key layer compensation; and the learning ability of the model to key discriminative features of knee osteoarthritis is strengthened in combination with real-time resource monitoring and knowledge distillation. The application covers heterogeneous computing power devices through a resource self-adaptive mechanism, and improves the participation efficiency of primary medical institutions.

This invention provides a deep learning-based adaptive allocation method and system for cloud storage resources, relating to the field of cloud storage technology. First, a business resource symbiotic network is constructed to reflect the dynamic balance between business demand and resource supply. Next, a deep learning model is invoked to extract network evolution characteristics. Based on these characteristics, a business resource adaptation demand sequence is derived to determine the adaptation direction and pattern of storage resources for different time periods. Then, the deep learning model dynamically matches and deduces the adaptation demand sequence with the resource supply spectrum to generate a set of elastic resource allocation schemes. Finally, a real-time allocation instruction stream is generated based on the set of elastic resource allocation schemes to drive dynamic reorganization of storage resources, achieving real-time adaptation between business demand and resource supply.

The invention relates to the technical field of property service, and discloses a cloud computing-based joyful cloud intelligent property service digital platform. A property environment sensing module of the system deploys a distributed sensing network to collect multi-dimensional environment parameters, performs abnormal marking and generates an environment data flow with a mark; the service demand analysis module receives the environment data stream, identifies service demand features through a mode identification engine, constructs demand feature vectors and generates a service demand map; the cloud resource adaptation module queries a cloud resource directory according to the service demand map, calculates a resource adaptation degree through a dynamic matching algorithm, and generates a resource allocation scheme; the service execution planning module converts the resource allocation scheme into a service action plan, formulates a service task sequence and dispatches the service task sequence to a service terminal; and the service efficiency monitoring module tracks a service task execution state in real time, collects feedback data to evaluate a service efficiency index, and feeds back the index to the service demand analysis module to optimize a mode recognition engine.

The invention discloses an intelligent agent control method, intelligent equipment and a storage medium, relates to the technical field of intelligent control, and solves the technical problems that when multiple instructions are concurrent, static priority cannot respond to changes of real-time service importance, resource states and the like, so that instruction conflicts occur frequently, and the control efficiency is reduced. According to the method, a single type of rules and multiple types of rules are distinguished, the single type of rules are combined with user feedback dynamic optimization, and real-time environment parameters and dynamic coefficients are introduced into the multiple types of rules, so that a control instruction better meets scene requirements, and when the user feedback is poor, an optimal reference instruction is positioned through similarity calculation; different points of similar records are integrated to generate an optimization instruction, the accuracy of instruction correction is improved, a real-time service importance coefficient, an execution effect coefficient and a resource adaptation coefficient are introduced to calculate a dynamic priority, coefficient fine tuning is performed in combination with the number of conflicts, adaptive adjustment of the instruction priority is realized, concurrent conflicts are reduced, and the control efficiency is improved.

The invention relates to a hardware resource adaptation-based semi-physical simulation test system, belongs to the technical field of embedded software test, and solves the problems of low efficiency, poor reliability, no reuse support and high cost of embedded software semi-physical simulation test in the prior art. Comprising a monitoring terminal used for generating a test case set and an automatic test script of embedded software to be tested, a simulation terminal used for generating an excitation signal according to a test case sent by the monitoring terminal and sending the generated excitation signal to an intelligent adaptation module, and the intelligent adaptation module selects an interaction interface based on a control instruction and sends the interaction interface to the intelligent adaptation module. The hardware module is used for providing a physical environment of embedded software, receiving the excitation signal, generating response data based on the physical environment and the excitation signal and sending the response data to the intelligent adaptation module. The hardware-in-the-loop simulation testing system is high in testing efficiency, high in reliability, low in cost and capable of supporting multiplexing.

The invention relates to a digital employee multi-channel cloud desktop expansion system and method, and the system comprises a universal interface API, an API adaptation layer, a dynamic scalable algorithm and a region proximity judgment module, and achieves multi-cloud access, request conversion, resource adaptation and nearby startup. The method comprises the steps of interface definition, request conversion, quantity adjustment and near resource selection. The digital employee multi-channel cloud desktop expansion method is used for controlling a related system and comprises the steps that the universal interface API module defines a uniform interface, the adaptation layer converts requests, the scalable algorithm module adjusts the number, and the region proximity principle module selects a close cloud desktop to be started. The method has a remarkable effect in the field of multi-channel cloud desktops. Uniform interface APIs reduce repeated development, reduce cost and improve efficiency; resource allocation is accurate and dynamic, and waste and waiting are avoided; task queuing is optimized, and waiting time is shortened; finely closing the strategy to reduce idleness and guarantee continuity; resources are selected near regions, delay is reduced, performance is optimized, and stability and efficiency are improved.

A method at a UE supporting physical resource adaptation for initial access (e.g. a temporary increase of physical random access channel (PRACH) resources); the method comprises receiving PRACH configuration information from a first apparatus (e.g. a network node), the PRACH configuration information including at least one first PRACH resource applicable to any UEs and further PRACH resources applicable only to UEs supporting physical resource adaptation for initial access, wherein the further PRACH resources comprise at least one of a second PRACH resource frequency division multiplexed with the first PRACH resource(s), a third PRACH resource time division multiplexed with the first PRACH resource(s), or a fourth PRACH resource comprising at least one random access preamble comprised in none of the first PRACH resource(s), wherein the further PRACH resources are assigned a higher priority than the first PRACH resource(s); determining, by the UE based on the higher priority, to use one of the second, third, or fourth PRACH resources; and performing random access by using the determined PRACH resource. Also provided is a corresponding method at the first apparatus.

The application provides a kind of dynamic configuration computing resource management method and system based on power wireless terminal, by obtaining the real-time running state data set of power wireless terminal and power service demand data set, the multi-dimensional feature fusion processing is carried out to real-time running state data set and power service demand data set, generates resource adaptation feature set, based on the preset resource adaptation rule base, real-time strategy matching processing is carried out to resource adaptation feature set, generates computing resource configuration strategy set, to adjust operation is carried out to the computing resource of power wireless terminal in this way, generates resource allocation verification result and terminal running efficiency feedback data, terminal running efficiency feedback data is continuously compared with the preset efficiency optimization threshold, and according to the comparison result, resource adaptation rule base is iteratively updated processing. Adopting the application can realize the accurate matching of computing resource allocation and power service demand fluctuation.

The invention relates to the technical field of middleware service load prediction and resource optimization, in particular to a middleware service multi-granularity load prediction and intelligent resource pooling performance optimization method and system. The system comprises a local load feature extraction module, a global load distribution modeling unit, an adaptive resource scheduling engine and a resource utilization rate monitoring feedback loop. The load data processing efficiency is improved through a sliding window mechanism and a distributed architecture, the load prediction accuracy is enhanced by utilizing a hierarchical neural network and dynamic weight adjustment, and efficient resource allocation is realized in combination with task priority analysis and heterogeneous resource adaptation. The resource utilization rate and the task processing efficiency in the complex distributed system can be remarkably improved, and the method has wide application prospects and practical value.

This invention provides an intelligent scheduling method and system for cable operation and maintenance. First, the migration knowledge elements in the cable operation and maintenance migration knowledge base are deconstructed, dividing them into resource adaptation, process adaptation, and risk adaptation knowledge elements, and generating a knowledge element classification set. A dynamic portrait of the operation and maintenance scenario of the cable to be scheduled is constructed, integrating operational characteristics, operation and maintenance task requirements, and historical operation and maintenance performance data to generate a 3D scene portrait. The adaptation coefficient between each migration knowledge element and the portrait is calculated, and target knowledge element combinations are selected. Based on the target knowledge element combinations, an initial operation and maintenance scheduling scheme framework is constructed, incorporating various adaptation rules. Through multiple rounds of portrait iterative updates and adjustments, the scheme framework is adjusted, optimizing resource allocation, step sequence, and anomaly response plans, resulting in the final scheme and generating operation and maintenance scheduling instructions which are sent to the operation and maintenance execution terminal. This invention achieves intelligent, precise, and efficient cable operation and maintenance scheduling.

The invention discloses a unified charging method, system and device for digital resources and a storage medium, and relates to the technical field of digital resource charging. Acquiring a plurality of event consumption data describing resource consumption from the multi-modal resource adaptation layer, and aggregating the plurality of event consumption data into single heterogeneous measurement data based on a preset aggregation strategy; converting the original consumption value in the heterogeneous measurement data into a standardized measurement index and a standardized value corresponding to the standardized measurement index, thereby obtaining normalized measurement data; processing the normalized metering data through a multi-stage assembly line charging engine to generate a discounted charging item; the discounted billing item is processed in the accounting processing stage of the multi-stage assembly line billing engine, accounting processing is carried out on the account information according to the discounted final cost, a transaction flow record recording the expenditure of the cost is generated, and the transaction flow record is stored in an accounting database. By implementing the provided technical scheme, the database writing pressure is reduced.

The invention discloses a scheduling method based on the self-interference suppression capability of a terminal, which dynamically evaluates the SI suppression capability of the SBFD terminal and combines position sensing and intelligent scheduling strategies, compared with the existing interference avoidance method, the self-interference suppression capability of the terminal after leaving a factory can be well utilized, resource allocation is actively optimized, and the scheduling efficiency is improved. And the resource utilization rate and performance of the system are obviously improved. The existing scheme mainly depends on passive interference measurement and adjustment, while the method provided by the invention avoids interference in advance through flexible SI capability mapping and resource adaptation, improves the transmission stability of edge users, gives full play to the performance advantages of center users, and improves the overall throughput of the system.

The embodiment of the application provides a kind of algorithm warehouse management method and system based on service tool chain, belong to algorithm warehouse management technical field.The method is matched resource demand by task category identification, generates hardware resource available parameter sequence in combination with the dynamic resource occupation prediction of parallel task of execution terminal, the first screening action is based on the resource fitting degree calculation matching degree per time, substantially improve the resource adaptation accuracy of algorithm model and execution terminal, reduce resource waste, the second screening action is combined with the historical execution information of target service terminal and non-target service terminal, both guarantee the confidence of single task to meet standard, also through service terminal group confidence distribution adjustment execution terminal selection, ensure the overall execution stability of system.Based on the confidence ratio of service terminal group, the execution terminal selection range is dynamically adjusted, the intelligent management of system confidence and computing power reserve is realized, extreme imbalance is avoided, and the adjustability of long-term operation is guaranteed.