67 results about "Resource adaptation" patented technology

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 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 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.

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.

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.

The application relates to a server data processing method and device, computer equipment and a storage medium. The scheme comprises the following steps: receiving a data processing request, performing feature analysis to obtain a corresponding feature group, and combining feature groups meeting set requirements into one feature group; when it is determined that the combined feature group meets a key event triggering condition, marking the combined feature group as a key feature group; determining a resource consumption portrait corresponding to each feature group; based on the resource consumption portrait and the resource adaptation degree of a current resource state snapshot of each available working node, determining a first working node matched with a key data processing request and a second working node matched with a remaining data processing request in sequence, so as to execute tasks corresponding to the key data processing request and the remaining data processing request. The above scheme precisely matches tasks and nodes, avoids resource contention and mismatch of tasks, simultaneously realizes priority processing of key tasks, and improves the overall throughput and resource utilization.

The invention provides an equipment first-aid repair task planning method, and belongs to the technical field of equipment first-aid repair, and the method comprises the steps: collecting basic parameters, faults, positions and task association information of to-be-repaired equipment, carrying out the standardization processing, and constructing an equipment first-aid repair information set; performing quantitative scoring on each task in the equipment first-aid repair information set, and generating a first-aid repair task priority ranking table; establishing a first-aid repair resource dynamic database, updating resource state information in real time, and matching an optimal first-aid repair resource combination for each first-aid repair task by adopting a priority priority and resource adaptation principle according to the priority ranking table; and in combination with time constraint, space constraint and resource constraint conditions of each first-aid repair task, performing overall scheduling on the matched tasks and resources to generate an initial first-aid repair task planning scheme. According to the equipment first-aid repair task planning method provided by the invention, the first-aid repair response time is shortened, the resource utilization efficiency is also remarkably improved, and the requirements of rapid first-aid repair of equipment in emergency rescue and other scenes are effectively met.

The invention relates to the technical field of cultural heritage digital protection, and discloses an ancient building traditional culture child propagation display method and system, and the method comprises the steps: carrying out the topological modeling of a child interaction terminal in a target display region, and generating a network adaptation state map; performing semantic migration coding on the network adaptation state map to generate a display resource dynamic fragment index; executing time sequence mapping on the dynamic fragment index through a cross-layer delay tolerance scheduling module, and constructing a resource adaptation instruction list; mapping the resource adaptation instruction table into an edge cache buffer unit, triggering a multi-step redundant breakpoint continuous playing mechanism, and reconstructing a display content distribution path diagram under the weak network; and constructing a display risk response sequence based on a content flash trend critical point appearing in the distribution path diagram, and generating a display interaction entropy leveling instruction. The method has the advantage that the weak network adaptation capability of the child display system in the remote area is improved.

The present application relates to the technical field of access control, and particularly relates to a control method for concurrent access of multiple users of a server, comprising the following steps: real-time analysis of a task type and resource requirement characteristics of a user request, generation of a triple including a calculation mode identifier, a data topology fingerprint and a time constraint factor; spatiotemporal coupling matching of the triple with a server node, calculation of a spatiotemporal weight score, generation of a binding execution unit based on the spatiotemporal weight score; quantumized fragmentation scheduling of the binding execution unit, insertion of a reverse dependence barrier between adjacent atomic operation sequences, generation of execution trajectory data during execution; adjustment of calculation mode matching through an execution trajectory feedback mechanism, and calculation mode identifier remapping of unexecuted tasks using path efficiency of completed tasks. The present application effectively reduces calculation resource adaptation deviation of tasks, reduces remote data access overhead, and improves task throughput and execution stability.

The invention discloses a 3D development engine supporting multi-terminal dynamic adaptation and an adaptation method, and belongs to the technical field of 3D development engines. The system comprises a terminal capability detection module, a development interface module, a core scheduling module, a resource adaptation module, a rendering adaptation module and a terminal cloud collaboration module, through terminal capability detection and resource hierarchical scheduling, dynamic matching of 3D resources and terminal performance can be realized, and overload of low-performance terminal resources is avoided; meanwhile, a mainstream graphic interface and terminal cloud cooperation capability is integrated, full scenes such as a PC, a mobile terminal, a vehicle machine and an XR are covered, and complex application requirements such as a smart city, a smart car and digital twinning are met; and a developer does not need to manually adjust parameters for different terminals, and an engine automatically senses hardware differences and generates an optimal rendering parameter combination, so that the development cost and period are remarkably reduced.

The application provides a youth speciality AI somatic energy auxiliary system, and relates to the technical field of youth speciality cultivation and somatic interaction. The system takes an AI master control module as the core, integrates a personalized qualification somatic evaluation module, a growth planning design module, a whole-process guidance module, a resource adaptation module, an effect evaluation and prospect analysis module, a problem early warning and solution module, a multi-terminal interaction module and a big data storage module, obtains youth body, physical ability and perception core data through a somatic collection terminal, completes personalized qualification evaluation in combination with personality, interest and family conditions, customizes a speciality cultivation scheme of a double route of amateur and professional according to needs, and provides whole-process question answering, professional guidance and growth case adaptation from entry to advancement. The system can realize that the matching degree of speciality cultivation and youth personal qualification reaches more than 98%, effectively reduces the cultivation trial and error cost by 60%, provides a scientific cultivation decision basis for parents, and builds a personalized speciality development path for youth.

The embodiment of the invention discloses a group scheduling system and device based on a virtual cluster. A specific embodiment of the system comprises a traceability synchronization module which 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; the scheduling module is used for reading the task annotation metadata of each execution unit, generating a scheduling score value corresponding to each execution unit in each execution unit according to the read task annotation metadata set, scheduling each execution unit to the corresponding resource adaptation node in batches, and deploying the scheduling module in a host cluster; the charging module is configured to generate a task charging list. According to the embodiment, the universality and the resource utilization efficiency of the system are improved.

The invention provides an elastic adaptation and deterministic scheduling system and method for heterogeneous resources of an intelligent computing fusion network, and belongs to the technical field of industrial Internet of Things, and the method comprises the steps: building a distributed management and centralized control system in an intelligent computing fusion network scene; establishing two-stage random optimization problems of different time scales according to the distributed management and centralized control system; a resource elastic adaptation scheme based on a heuristic algorithm is adopted for a large-scale resource adaptation problem; a deterministic scheduling scheme based on deep reinforcement learning is adopted for a small-scale scheduling sub-problem; and obtaining an optimal resource adaptation and scheduling result by using the resource elastic adaptation scheme and the deterministic scheduling scheme. According to the method, the flexible resource configuration requirement in a highly dynamic and uncertain environment can be met, and low delay and high reliability in a data scheduling process are ensured.

This invention relates to the field of computer programming technology, and in particular to a resource interception and dynamic adaptation method, system, electronic device, and medium. The dynamic resource loading management method includes: when the application starts, obtaining relevant information about the corresponding resource package; setting interceptors on the resource call path; when a resource call request is detected, parsing the request and obtaining the information of the resource to be called; obtaining current environment information and comparing it with preset environment information; if they are inconsistent, generating a path list including resource search paths sorted by priority based on the relevant information, the information of the resource to be called, and the current environment information; traversing the path list from high to low priority; if a resource can be obtained, it is used as the target resource and traversal stops; reading the binary data stream of the target resource, parsing it into an application-adaptive format, and returning it to the requester. This invention can simultaneously meet the application's dual requirements for architectural compatibility and intelligent resource adaptation.

The invention discloses a network slice configuration method and device for an industrial private network. The method comprises the following steps: collecting data such as service basic requirements and service space-time labels; calculating a service space-time correlation coefficient, a link-temperature coupling attenuation factor and an actual available computing power of equipment; configuration parameters are calculated based on cooperation of the three, an instruction is issued, and closed-loop optimization is carried out according to operation indexes. The device correspondingly comprises multiple modules for cooperative work. According to the scheme, the problem that existing configuration is disjointed with dynamic states of networks, equipment and services is solved, accurate resource adaptation is achieved, and stable operation of industrial private network slices is guaranteed.

The invention discloses an integrated project risk assessment and resource adaptation management method and system, and relates to the technical field of project management methods. The integrated project risk assessment and resource adaptation management method comprises the following steps: S1, constructing and dynamically updating a project database; s2, calculating a comprehensive fitness score; s3, generating a construction instrument selection suggestion and calculating an instrument fault risk index; and S4, generating and outputting a resource allocation instruction. According to the integrated project risk assessment and resource adaptation management method, an original experience-dependent and qualitative decision-making process is converted into computable quantitative analysis based on multi-dimensional data such as load capacity, environment compatibility and health state; the limitation of human factors is effectively overcome, it is ensured that instrument selection is the optimal solution, and the safety risk caused by insufficient instrument capacity or mismatching with the environment is avoided from the source.

The invention provides a computing power scheduling method and device, equipment, a storage medium and a program product, and relates to the technical field of data processing.The method comprises the steps that a computing task and a strategy factor corresponding to the computing task are received, and the strategy factor is used for representing the data condition of the computing task in the processing process; calculating a first storage and calculation integrated computing power resource adaptation index and a first storage and calculation separated computing power resource adaptation index based on a strategy factor corresponding to the calculation task; generating a first proportion based on the first storage and calculation integrated computing power resource adaptation index and the first storage and calculation separated computing power resource adaptation index; at least one computing power cluster in a preset resource pool is scheduled based on the first proportion to process the computing task, multiple computing power clusters are configured in the preset resource pool, and each computing power cluster in the multiple computing power clusters is used for providing storage and computing integrated computing power resources and storage and computing separated computing power resources of different proportions. The processing efficiency of the calculation task can be improved.