38 results about "Resource supply" patented technology

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.

This invention provides a resource optimization allocation method and system for environmental control in the industrialized cultivation of Ganoderma lucidum. By dividing the industrialized cultivation cycle of Ganoderma lucidum into stages and determining the environmental parameter thresholds for each stage, the growth characteristics of each stage are accurately matched, providing a quantifiable basis for environmental judgment. Based on historical Ganoderma lucidum cultivation data, a correlation model is established between environmental parameter deviations and resource adjustment amounts at each stage to ensure the rationalization of resource supply. By collecting real-time environmental parameters of industrialized Ganoderma lucidum cultivation, the deviation between the real-time environmental parameters and the environmental parameter thresholds of the current stage is determined, and the resource optimization parameters corresponding to the deviation values ​​are determined based on the correlation model, enabling timely resource control. By operating according to the resource optimization parameters and iteratively adjusting during the operation until the real-time environmental parameters meet the standards, the environmental parameters at each stage throughout the entire cultivation cycle are ensured to remain within the threshold range.

This invention proposes an automated construction organization design platform and its design method for water conservancy and hydropower projects. The platform includes a structured experience database system, a dynamic optimization calculation system for earthwork balance, an intelligent construction schedule generation system, an automated construction resource design system, and a 3D visualization and decision support system for the overall construction layout. The platform system uses the correlation data between building quantities and schedule time as a link to achieve data exchange and collaborative linkage. The design method includes: constructing and initializing the structured experience database system; automatically generating a construction schedule plan; outputting an earthwork balance scheme; automatically calculating and configuring labor force, work area, and facilities of each construction plant; generating a construction layout scheme and outputting it in 3D visualization. This invention realizes intelligent processing of the entire process of construction organization design calculation, resource supply planning, and site layout, significantly improving design efficiency and scheme accuracy, while also possessing good operability and system integration.

This application provides an intelligent routing function optimization method and system based on an intelligent vehicle power domain control platform, belonging to the field of intelligent vehicle electronic control technology. It addresses the problems of resource contention, excessive processor load, insufficient real-time performance, and poor scalability in in-vehicle network routing functions in related technologies. This method employs an architecture that separates the management plane and data plane. It pre-generates routing numbers encoding source, path, and destination information for communication relationships, and quickly extracts information based on these numbers through bit operations to execute routing decisions. Furthermore, it combines doubly linked list cache management, multi-core affinity scheduling, and multi-level resource provisioning strategies to achieve efficient cache resource management, significant optimization of processor load, and an overall improvement in system real-time performance and reliability.

This application relates to a modular non-human primate rearing cage, belonging to the technical field of animal husbandry. It includes a cage frame, a resource supply system, a hygiene and cleaning system, an activity and enrichment system, and a connection and management system. The cage frame includes a cage base, a cage top, and cage side frames. The resource supply system includes a food supply device and a water supply device detachably installed on the cage frame. The connection and management system includes a connecting channel on the cage side frames and a connecting cage door movably installed on the cage side frames for changing the opening and closing state of the connecting channel. The activity and enrichment system includes a lookout component installed on the cage side frames, a climbing component located between the lookout component and the cage base, and multiple monitoring units installed on the cage top frame. This application can improve the husbandry and management efficiency of non-human primates.

This invention discloses a cross-regional human resource allocation and collaborative management method based on big data, belonging to the field of human resource management technology. It aims to address the pain points of existing technologies, such as prominent information barriers, low matching accuracy, and lack of early warning and prevention. This method integrates human resource data from multiple regions and ensures data quality by building a data support system; constructs an early warning and risk prevention system to predict human resource shortages and manage risks; achieves three-dimensional matching of "person, job, and location" through a skill tagging system and dynamic algorithms; forms a "training-allocation" closed loop to adapt to the needs of industrial upgrading; optimizes cost and efficiency control; improves allocation flexibility and emergency response capabilities; and improves the collaborative guarantee mechanism to ensure safety and compliance. It continuously optimizes until optimal allocation is achieved. This invention realizes precise, efficient, collaborative, and safe management of cross-regional human resources, reduces allocation costs, solves the imbalance between human resource supply and demand, and has broad application value.

The application relates to the technical field of computer resource management, in particular to a server resource scheduling system for a high-density computing environment. The system acquires scheduling data of each dimension in real time, including task resource data; according to the difference between the actual value and the predicted value in the scheduling data of the dimension at the current time and the task resource data, the resource supply priority is acquired; according to the resource supply priority, the difference between the actual value and the predicted value of the preset specified historical task resource data of the same resource demand type of each task, the resource adjustment degree is acquired; based on the task resource data and the resource adjustment degree of each task at the current time, the server resources of the high-density computing environment at the current time are scheduled and processed. The application can accurately acquire the resource adjustment degree of each task at the current time, is beneficial to accurately adjusting the predicted resources of each task at the current time, and effectively improves the efficiency and stability of task execution.

This invention discloses an artificial intelligence-based engineering resource scheduling optimization system and method, belonging to the field of engineering management technology. It establishes a resource status perception network by deploying IoT sensors and constructs a progress benchmark system based on the project plan. By comparing the actual progress with the progress benchmark system, it identifies tasks with excessive deviations that need to be scheduled. It constructs a task urgency classification model, extracts the progress dynamics, task dependencies, and resource supply characteristics of the tasks to be scheduled, generates a resource demand urgency level for each task, and generates a scheduling demand description. It matches the scheduling demand with the resource status, constructs a multi-objective optimization function considering spatial distance, workload, and type suitability, obtains a resource allocation scheme by solving the function, sends scheduling instructions according to the allocation scheme, monitors the execution status, tracks resource location and task progress in real time, and re-triggers the scheduling process when a new deviation is detected, forming a closed-loop dynamic optimization mechanism.

This invention provides an adaptive resource management method for intelligent empowerment centers, relating to the field of resource data processing technology. By automatically auditing the resource allocation and usage recorded on the blockchain, it can promptly detect abnormal access and potential security threats. Sensors and IoT modules communicate with device resources using a hybrid communication method. Furthermore, after verifying resource allocation requests, detailed logs are generated and stored in specific blockchain blocks, providing a basis for subsequent resource management analysis, auditing, and troubleshooting. This significantly enhances the security and traceability of resource allocation, ensuring stable business operations. A warning threshold is set using machine learning algorithms to predict resource demand. When the threshold is approached or exceeded, management personnel are notified in advance, and an emergency allocation plan is activated. This effectively avoids business interruptions due to resource shortages, ensures stable resource supply, and further improves the stability of resource management throughout the intelligent empowerment center.

This invention relates to the field of resource management technology, and discloses a method and system for matching supply and demand of resources in the robotics industry based on knowledge graphs. The method includes: parsing resource data to obtain basic supply and demand data; semantically labeling the basic supply and demand data and mapping technical entities and indicator entities to a conceptual system to obtain a set of supply and demand capability features; using entities in the set of supply and demand capability features as resource nodes, writing nodes into a graph database to obtain resource entity nodes; performing link reasoning on the semantic relationships between resource entity nodes to obtain knowledge graph data; receiving matching requests, identifying the intent of the demand description, performing path retrieval in the knowledge graph data, and obtaining a preliminary matching result set; comprehensively sorting the supply entity nodes and encapsulating attribute information and associated paths into a protocol to obtain a matching scheme. This invention can improve the efficiency of supply and demand matching in the robotics industry.

The application relates to a regional education resource balanced allocation method, device, equipment and medium. The method comprises the following steps: firstly, obtaining shared resource use time length data of each education institution to form an original log sequence; then, carrying out time sequence analysis on the original log sequence to generate an occupation rate distribution mode of the shared resource in different time periods; based on the occupation rate distribution mode, determining the idle state of the shared resource through a preset idle threshold, adopting a clustering algorithm to perform grouping processing on the resource supply and demand relationship to obtain a demand difference classification result; analyzing and planning a shared resource cross-institution flow path according to the demand difference classification result, and then generating a preliminary flow plan; finally, taking the preliminary flow plan as input, constructing a resource flow simulation model, adopting an iterative optimization algorithm to adjust and optimize the model parameters, and finally outputting a shared resource balanced flow allocation scheme covering cross-institution and cross-time period. The method can realize efficient activation and balanced allocation of regional education shared resources.

The application discloses a server GPU computing power intelligent allocation method for large model training, and relates to the technical field of computer resource management. The method comprises the following steps: S1: determining the dependency relationship between each stage in the large model research and development workflow according to the large model research and development workflow defined by a directed acyclic graph, and obtaining a corresponding resource demand portrait matrix; S2: setting a dynamic priority resource reservation pool according to the resource demand portrait matrix, and generating a corresponding resource supply curve; S3: determining the short-term predicted demand corresponding to each task according to the actual resource consumption and the dynamic resource demand curve of each task on the critical path in the large model research and development workflow, comparing the short-term predicted demand with the resource supply curve, and dynamically adjusting the dynamic priority resource reservation pool. The application can adapt to the fluctuation and uncertainty of the training task operation, guarantee the progress of the critical path, and flexibly handle the excess resource demand.

The application relates to the technical field of resource scheduling, and specifically discloses a cross-platform marketing resource intelligent scheduling evaluation method and system, which comprises the following steps: acquiring resource node information of each platform at a resource facility layer, and acquiring a dynamic stability index of each platform according to the resource node information; constructing a cross-platform resource cooperation relationship graph according to the dynamic stability index of each platform and a cooperative calling relationship between resource nodes, and converting the cooperation relationship graph into a global resource state entropy matrix; acquiring user marketing activity information of each platform at a marketing application layer, and generating a resource demand feature vector of each micro task according to the user marketing activity information. The application forms a complete technical chain from state perception, correlation modeling, demand description, matching optimization, capacity management to effect evaluation, realizes accurate docking of a supply side and a demand side of cross-platform marketing resources, elastic adaptation of scheduling execution and capacity management, and an execution result.

The present application relates to a kind of communication terminal-based communication performance prediction method and system, communication terminal, belong to network communication technical field, by introducing intention translator and communication digital twin in terminal, it has realized from " network resource supply " to " service demand satisfaction " fundamental change, by accurate translation of service intention, network can allocate slice resource as needed;The predictive ability of digital twin is network and can perceive demand change in advance, realizes the prospective adjustment of resource, avoids potential network problem, ensures service continuity;Based on real service demand rather than maximum possible demand, resource allocation significantly reduces resource redundancy;Improve terminal service load quality guarantee ability and user experience.

This application discloses a resource provisioning method and apparatus, and a computing device cluster, relating to the field of cloud computing technologies. The method includes: obtaining a first resource request, where the first resource request includes a first resource type of a first virtual resource that is requested to be provisioned; determining, in a plurality of physical resources, at least one physical resource on which a virtual resource of the first resource type is deployed; obtaining a provisioning priority of the at least one physical resource for the virtual resource of the first resource type, where the provisioning priority indicates a provisioning sequence of the at least one physical resource for the virtual resource of the first resource type; and controlling, based on the provisioning priority, the at least one physical resource to provision the first virtual resource.

The application relates to the technical field of intelligent energy management, and discloses an intelligent resource integration method and system applied to a low-carbon demand scene, resource output information and resource consumption information generated by each carbon emission object in a target scene during operation are acquired; a resource flow network model for representing the resource supply and consumption relationship between carbon emission objects in the form of a network topology of the target scene is constructed based on the resource output information and the resource consumption information of all the carbon emission objects; at least one carbon emission object matching group is determined by analyzing the resource flow network model according to a preset matching rule; and a resource integration control instruction set is generated for each determined carbon emission object matching group. It can be seen that the application can improve the flexibility of intelligent resource integration of the low-carbon demand scene, improve resource utilization efficiency, and reduce carbon emission.

The application belongs to the technical field of resource scheduling, and discloses a mine safety and control linkage cloud and fog collaborative resource scheduling method, which classifies and disassembles mine safety and control linkage business and models, extracts safety and resource demand characteristics of various businesses, combines an LSTM deep learning model to construct a resource demand prediction system, completes safety event level division through a mine safety risk assessment model, and then uses a reinforcement learning algorithm to construct a dynamic priority allocation system, realizes real-time dynamic adjustment of task priority, generates a preemptive execution queue by matching a preemption threshold mechanism and a buffer period, deeply integrates safety attribute priority into the core logic of business and resource supply and demand matching, so that high-priority core safety tasks can preempt resources; the main scheduling node is set in each underground fog domain to complete high-frequency perception of local resources, and after invalid data is removed through an anomaly detection algorithm, effective resource data is subjected to cleaning standardization and pooling aggregation processing.

The application relates to the technical field of cloud computing resource management, and discloses an intelligent cloud desktop resource scheduling method and system combined with fault recovery, which comprises the following steps: dynamically analyzing the resource supply and demand state of a cloud desktop cluster to obtain resource supply and demand characteristics and calculating the resource real-time scheduling pressure corresponding to the resource supply and demand characteristics; detecting the health degree of key nodes of the cloud desktop cluster to obtain node redundancy capability data; analyzing the resource recovery time efficiency of the cloud desktop cluster when a fault occurs, generating a fault recovery guarantee coefficient corresponding to the cloud desktop cluster, and dividing fault emergency grades corresponding to the fault recovery guarantee coefficient; configuring a multi-stage fault recovery plan corresponding to the cloud desktop cluster, locating a resource bottleneck area of the cloud desktop cluster when a fault occurs, calculating the resource diffusion efficiency corresponding to the resource bottleneck area, and formulating a resource optimization scheduling strategy combined with fault recovery corresponding to the cloud desktop cluster. The application aims to improve the scheduling accuracy of intelligent cloud desktop 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.

This application relates to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for intelligent matching and coordination of railway transportation plans. The method includes: determining a multi-constraint intelligent matching model for railway transportation tasks, the multi-constraint intelligent matching model including a spatiotemporal coupled representation mechanism based on the task requirements and resource supply of the railway transportation tasks; establishing a planning coordination mechanism for railway transportation tasks based on the spatiotemporal coupled representation mechanism in the multi-constraint intelligent matching model; determining a railway transportation plan based on the planning coordination mechanism after the planning coordination mechanism passes cargo classification verification; and executing the railway transportation tasks according to the railway transportation plan after the railway transportation plan passes spatial feasibility verification of horizontal decomposition constraint rules and temporal feasibility verification of the vertical balance model. This method can improve the operational efficiency of railway transportation planning.

This application relates to the field of data analysis, providing a method and system for multi-dimensional data correlation analysis based on game operations. The method includes: collecting discrete player logs to construct player behavior sequences; matching corresponding player resource behavior events with gameplay behavior events as trigger events, and obtaining the driving strength of gameplay on resource changes; constructing player feature vectors based on player behavior sequences to establish a player profile database; constructing a multi-dimensional data cube, performing multi-dimensional cross-queries and data aggregation calculations based on time-series correlation records and the player profile database to obtain operational statistics results; calculating the comprehensive attractiveness score of each gameplay based on the driving strength, operational statistics results, and gameplay-related data in time-series correlation records, and generating gameplay ranking results; identifying risks through a gameplay resource supply and demand matching model and generating corresponding early warning information; displaying and synchronizing the above analysis results with early warning information, realizing the linkage between data analysis and operational decision-making, and improving operational management efficiency.

This application discloses a dynamic resource scheduling method based on software-defined networks (SDN), belonging to the field of network technology. By dynamically sensing the resource reservation behavior of each network domain, it identifies deviations in the understanding of user intent and uses this to calibrate the intent parsing benchmark, thus solving the problem of the disconnect between intent parsing and actual resource supply capacity in traditional SDN. As a result, the collaborative controller can continuously optimize its understanding of user intent, making subsequent cross-domain service flow scheduling decisions more aligned with the actual network resource status, reducing scheduling conflicts caused by intent misunderstanding deviations, and improving the accuracy and efficiency of cross-domain resource scheduling.

The application discloses a kitchen scene customized networking method, the steps are as follows: controlling the wireless communication module to broadcast the beacon frame, the new equipment judges whether to initiate the access request according to the signal strength, and the network dynamically receives according to the load and the equipment priority; the weighted model is used to calculate the equipment priority and allocate resources; the input voice signal is extracted and processed by the lightweight neural network; based on the pre-training model fine-tuning, the kitchen instruction intention is recognized and the slot is filled, and the instruction is analyzed; the hardware performance index is collected in real time, the load trend is predicted, and the voltage frequency is adjusted; the data is encrypted in the wireless communication link layer, and the session key is managed by the key distribution protocol. The method can improve the network access and resource utilization efficiency, reduce congestion, ensure the access and resource supply of key equipment, improve the voice processing precision and instruction recognition accuracy, reduce the energy consumption of the equipment, improve the operation stability, and protect the data security.

The application provides a task global scheduling method and device based on a computing power supply-demand structure similarity, wherein the method comprises obtaining computing power demand feature descriptions of to-be-processed computing tasks and computing power supply feature descriptions of allocable computing resources; a feature space for representing a matching relationship between task demand and resource supply is constructed according to the computing power demand feature descriptions and the computing power supply feature descriptions; in the feature space, a matching degree between each task and each resource is calculated; a global optimal matching scheme between the tasks and the resources is determined through a global optimization algorithm based on the matching degree, and the tasks are allocated to corresponding resources for execution according to the global optimal matching scheme. The application solves the technical problem that in the prior art, when a scheduling strategy faces a 'barrel effect', it is difficult to achieve a 'perfect match' between the computing power requirement of the task demand and the computing power supply of the computing unit, thereby resulting in low resource utilization and poor computing efficiency.

This application relates to the field of intelligent matching technology for policy resources, and in particular to a method and system for intelligent matching of policy resources. The method includes data collection, matching strategy identification, visual hierarchical feedback, and strategy adjustment and optimization. Data collection is used to collect attribute data of applicants and text data of policy clauses. Matching strategy identification is used to generate optimal target matching strategy data for the attribute data of applicants and text data of policy clauses. This invention achieves efficient and accurate matching between applicants and policy resources by introducing technologies such as BERT embedding model, graph neural network algorithm module, particle swarm search algorithm module, and natural language processing interface. It effectively solves the contradiction between the dynamism of policies and the static nature of systems, the contradiction between semantic complexity and the simplicity of human-computer interaction, and the contradiction between the accuracy of resource supply and service efficiency, significantly improving the scientificity and practicality of intelligent matching of policy resources.

This invention discloses a method and system for flexible educational resource scheduling based on microservice orchestration, relating to the field of educational resource scheduling technology. This method acquires educational resource data (including educational support resource data and user behavior data) for a designated educational platform within a set period; analyzes the platform's resource elasticity feature set (including educational resource supply characteristic values ​​and educational demand intensity characteristic values) based on the platform's resource elasticity feature set; and analyzes the platform's educational resource adaptation characteristic values ​​based on the platform's resource elasticity feature set. This invention performs microservice orchestration resource scheduling on the designated educational platform based on the educational resource adaptation characteristic values, effectively avoiding resource scheduling lag and coarse control, thereby enabling the educational platform to maintain refined resource allocation under different load conditions.

Systems and methods for user configurable cooking appliances include receiving system resources having associated budgets, facilitating user allocation the system resources to a plurality of heating elements without exceeding the associate budgets, applying the system resources to the heating elements to heat one or more food substances within a cooking chamber, and selectively regulating delivery of the system resources to the heating elements such that no more than the associated budget of each of the system resources is delivered to the heating elements. Computing components can execute a heating algorithm to cook at least one food substance in the cooking chamber, detect a state change of the food substance, and modify the heating algorithm to reconfigure the system resources supplied to the heating elements in response to the state change and in accordance with user configured allocation and associated budgets for the system resources.

This invention provides a high-performance DAG task scheduling method based on Java, relating to the field of distributed computing technology. The method includes: defining task nodes to construct a bidirectional dependency index table and a hierarchical mapping table; precompiling dependency expressions into bytecode cache and checking for circular dependencies; dynamically adjusting the maximum number of threads, combining resource supply and demand with node hierarchy to calculate priority scores, thereby allocating threads and isolating concurrency conflicts; using a segmented locking mechanism to generate a global snapshot and perform incremental synchronization; preloading data through a two-level caching architecture and performing weighted eviction; and invoking bytecode operations to perform dependency chain fault tolerance when a task fails. This invention significantly reduces the overhead of concurrent lock contention and state synchronization, greatly improving the system's resource utilization and overall scheduling throughput.

The application discloses a multi-tenant multi-platform-oriented Agent resource scheduling and isolation system, comprising a data receiving module for receiving session request information and identifying corresponding associated attribute information; an intelligent scheduling and prediction module for generating session prediction information according to the session request information and the associated attribute information through preset historical record data and a preset time sequence prediction model; a scheduling control center for calculating an Agent resource supply-demand gap according to the session request information and the session prediction information, and generating a scheduling instruction according to the supply-demand gap; a task scheduling management module for generating a task scheduling queue according to the scheduling instruction, and executing the scheduling instruction according to the task scheduling queue; and a hierarchical isolation control module for executing corresponding isolation strategies according to sensitivity levels, and managing encryption transmission rules and storage access permissions. The application can realize the collaborative optimization of resource utilization rate and data security by constructing a unified resource scheduling and isolation mechanism.