38 results about "Resource coordination" patented technology

The application discloses a kind of distributed reservation systems and methods for multi-resource cooperation, comprising: transaction manager, at least two resource managers, lock manager, transaction log module and workflow controller;Transaction manager coordinates each resource manager through two-phase commit 2PC protocol, realizes the atomicity execution of cross-node shadow and summary table update;Lock manager adopts read-write lock separation mechanism to resolve concurrent conflict, and handles deadlock problem through timeout detection and active unlocking strategy;Transaction log module is based on prewrite log mechanism Complete record shadow and summary table linkage operation details, provide reliable support for fault recovery and transaction rollback;Workflow controller encapsulates distributed call details, realizes service survival real-time detection and automatic reconnection function.The application has high expansibility, strong consistency, high concurrent adaptation capability and perfect exception fault-tolerant mechanism, and can be widely applied to travel service scene of multi-node deployment.

This invention belongs to the field of flexible manufacturing systems technology and discloses a path-execution cooperative control method for multiple mobile transport robots based on Petri nets. The method includes: acquiring current system state information; determining the effective starting point and first effective target based on the current task sequence information of each mobile transport robot; updating the current position index by combining historical path cache, and reusing or re-determining discrete path sequences according to preset conditions; injecting the path sequences as spatial constraints into the Petri net execution model, and constructing an executable transition binding instance set by combining the current system state, robot execution state, and resource enabling relationships; selecting and advancing compatible transition binding instances from the instance set to form execution fragments, and updating the system state accordingly, thereby achieving rolling closed-loop cooperative control of the multiple mobile transport robot system. This invention is beneficial for improving the consistency of path planning and execution processes and the efficiency of system resource coordination.

The application discloses a whole-process planning and resource collaborative management system for distribution network field operation, relates to the technical field of process planning, and obtains original data of the distribution network field operation in a distribution network coverage area, and obtains a propagation chain after operation coupling propagation analysis; candidate distribution objects are identified according to the propagation chain, and a bearing difference between distribution network field teams is analyzed to obtain an operation reassignment result; path structure sensitivity is identified to lock a to-be-distributed area, a path rearrangement result with structure separability is formed, and the supportability of the distribution network field operation resource is analyzed to obtain an unbalanced area; the influence and extension constraint between the resource and the operation in the unbalanced area are analyzed to identify and eliminate a filmization area, so that the local resource aggregation and structure linkage instability problems induced by path continuous compression are inhibited, and finally, the structure transformation from local resource continuous coverage to cross-area discrete and reversible structure is realized.

This invention discloses a resource collaborative scheduling and demand response optimization system and method for virtual power plants in industrial parks. It achieves global collaborative scheduling through a two-layer Actor-Critic network architecture, and uses a fusion of particle swarm optimization and fuzzy control to achieve grid-connected stability control. A three-layer fusion optimization architecture is constructed to complete the continuous amplitude calculation of demand response and the generation of equipment-level instructions. A unified algorithm support is provided by an intelligent optimization module. This invention effectively solves the problems of weak adaptability of existing global optimization models for virtual power plants in industrial parks, low level of intelligent decision-making in specialized sub-scenarios, and difficulty in coordinating multi-timescale scheduling and refined execution. It can achieve robust optimization and refined control in uncertain scenarios, balancing system economy, stability, and operational reliability. It is suitable for virtual power plants in industrial parks to efficiently participate in the power market and resource collaborative scheduling.

The application claims a service-aware resource collaborative allocation method, belonging to the field of mobile edge computing resource allocation, which comprises: analyzing task demand characteristics, obtaining task abstract categories combined with task demand characteristics, and mapping them into corresponding demand types. Based on the task demand type, candidate collaborative node clusters are selected through destruction operators and repair operators, then the task and resource matching problem is modeled as a combinatorial optimization problem, the collaborative benefits of candidate node combinations are calculated, and the edge node combination with the highest collaborative benefit is selected. The application mainly considers the problems of task demand fuzziness and task and resource matching imbalance in the computing power network, designs a task demand type identification algorithm and a resource collaborative allocation strategy, selects a reasonable collaborative node cluster, and reduces the time delay while improving the resource utilization.

The application provides a resource coordination and time synchronization method and system for a large-scale computing power cluster, and relates to the technical field of computing power network architecture. First, the computing power nodes are registered and maintained by an authoritative DNS. After receiving a task, the system intelligently selects the optimal target computing power node and plans the data transmission path. To achieve precise coordination, the system dynamically creates a clock synchronization domain according to the task, elects a master clock and plans the optimal clock synchronization path. Finally, the system encapsulates and packages the optimal task data transmission path, the master clock timestamp and the optimal clock synchronization path identifier, and distributes them to the related nodes, thereby realizing efficient resource coordination and high-precision time synchronization. The method not only realizes the optimal matching of tasks and resources, guarantees the efficiency of data transmission, but also guarantees the accuracy and reliability of time synchronization from the physical link, and finally realizes efficient resource coordination and high-precision time synchronization in the computing power cluster.

The application relates to the technical field of site networking, and particularly discloses a digital twin unmanned site decentralized networking method and system. After being modeled by digital twinning, the unmanned site serves as a self-organizing network node, forms a decentralized network, and is initialized to elect a leader node. After the leader node publishes a task, network nodes vote through a multi-dimensional verification and consensus mechanism. After the task is claimed, a message is written into a metadata extension field and is synchronized across the network, reducing multi-source heterogeneous data islands and repeated claims. Based on the task position and the basic environmental data of the node geographic grid, an optimal routing cost function under multi-dimensional constraints is constructed, the nearest neighbor node is determined through the minimum cost value, the lowest delay connection graph is obtained through spatial analysis, and the optimal path is determined, so that the multi-unmanned system device executes the task according to the optimal path, the task scheduling and resource coordination accuracy are improved, the decision support capability under comprehensive situation awareness is improved, and the multi-device collaborative efficiency and task dispatching accuracy are further improved.

This invention discloses a cloud-edge collaborative intelligent operation and maintenance management system for large-scale charging pile clusters, belonging to the field of charging pile cluster operation and maintenance management technology. The system includes a cloud-based overall control module, a distributed edge collaboration module, a terminal device perception and execution module, and a cross-edge node cluster linkage sub-module. This invention addresses the pain points of existing technologies, such as lack of cloud-edge collaboration, network outage paralysis, algorithm rigidity, lack of resource coordination, and susceptibility to power grid imbalance, making it difficult to meet intelligent operation and maintenance needs. This invention combines the global decision-making advantages of the cloud-based overall control module with the real-time response capabilities of the edge, achieving optimal computing power configuration. Even after a network outage, edge nodes can independently complete all operation and maintenance management tasks, avoiding station shutdowns caused by single module failures. This ensures the continuous and stable operation of large-scale charging pile clusters, significantly improving operational reliability and achieving load sharing, fault redundancy switching, orderly charging, and power grid peak shaving and valley filling.

Disclosed are a method and an apparatus for resource coordination between vehicle-to-everything devices, and a user equipment. The method includes: receiving SCI sent by at least one second user equipment, where the SCI includes indication information for triggering resource coordination; and sending coordination information to a target second user equipment, where the coordination information is used to instruct the target second user equipment to perform resource reselection, and the target second user equipment is at least one of the second user equipments.

This invention relates to a computing power network construction and coding scheduling method for satellite-to-ground semantic communication, comprising the following steps: S1: Constructing a computing power network composed of LEO satellites, cloud servers, edge devices, and terminal devices; uniformly abstracting and modeling the heterogeneous computing, communication, and storage resources of nodes in the computing power network; S2: Encoding the adaptive joint source channel with dual-awareness of channel and computing power at the node level of the computing power network; realizing dynamic adaptive adjustment of the node-level semantic transmission rate according to channel conditions and computing power status; S3: Scheduling system-level resources of the computing power network to realize distributed, adaptive scheduling and resource coordination of system-level semantic tasks in the computing power network. This invention proposes adaptive joint source channel coding with dual-awareness of channel and computing power, realizing dynamic adaptive adjustment of semantic feature transmission volume by introducing a dual-awareness mechanism of channel state and device computing power.

This invention provides a novel power system for coordinated and optimized scheduling and control of multiple resources, relating to the field of power resource regulation. It includes a central PLC connected to first, second, third, and fourth PLCs, which respectively monitor the power generation of the first generating equipment, the power generation of the second generating equipment, the power consumption of the power consumption cluster, and the charging and discharging of the energy storage power station. All data are transmitted to the central PLC. Through data and control interaction with multiple PLCs, the central PLC achieves coordinated and optimized scheduling and control of multiple resources. This includes implementing a multi-resource coordinated scheduling program and establishing a power generation and consumption line group; further implementing a power supply and consumption monitoring program to generate coordinated scheduling monitoring results; implementing a multi-resource optimized scheduling program to adjust the power generation and consumption line group and / or adjust the connection of power generation and consumption equipment to the power generation and consumption lines; implementing a power supply and consumption coordinated control program; and enabling the second PLC to implement a power generation adjustment control program, thus achieving overall macro-control of multiple resources in the power system.

The application discloses an idle user resource auxiliary pricing and distribution method for an Internet of Things edge cloud continuum. First, a multi-agent resource coordination architecture composed of idle users, edge / cloud server resource providers (ECSP) and sensor devices is constructed to cope with the challenges of limited computing resources and prominent supply-demand contradiction in the edge environment. Second, a three-stage evolutionary game model is established to simulate the strategy evolution process of the three types of participating agents in resource pricing and procurement decision-making, thereby realizing dynamic adjustment of pricing and reasonable allocation of resources. In order to improve the rationality of strategy evolution and system stability, the application introduces a reputation incentive mechanism to reward reasonable pricing or resource purchase behavior and punish irrational behavior, and promotes the system evolution to converge to a stable and efficient strategy configuration. By constructing a replicator dynamic equation and combining a stability analysis method, the evolutionary stable strategy (ESS) of the system is identified to ensure the utility optimization of each participant and the maximization of the overall social welfare of the system in a complex dynamic environment.

The application discloses a multi-scenario mode race judgment execution method and device, equipment and medium, the method comprises the following steps: determining a target scenario mode to be executed; determining a target race group to which the target scenario mode belongs based on preset race group configuration information, wherein the preset race group configuration information is information that is pre-configured and delivered by a cloud according to an SOA service set of the scenario mode and a resource competition relationship; traversing a current scenario mode running in the target race group, and comparing the SOA service set of the target scenario mode with that of the current scenario mode to identify whether there is a service-level race; and in the case that there is a service-level race, performing a race judgment operation on the target scenario mode based on scheduling level configuration information and mutual exclusion tag configuration information corresponding to the target scenario mode and the current scenario mode. The technical solution realizes fine, low-delay and high-reliability resource coordination and operation judgment in a multi-scenario concurrent scenario.

This invention belongs to the field of unmanned underwater vehicle (UUV) swarm collaborative control technology, and provides a method and system for task allocation and resource scheduling of UUVs. The method includes biomimetic swarm intelligent decision generation, closed-loop transformation from decision to motion control, anti-interference timing synchronization and resource coordination, and system-level integration and security assurance. It adopts a fully distributed task negotiation and reputation evaluation mechanism, eliminating dependence on a single central node and improving the survivability and mission resilience of the swarm in adversarial environments. By introducing an environmental digital twin model for feedforward compensation and combining it with hierarchical adaptive control, a closed-loop transformation from intelligent decision-making to precise motion is achieved, overcoming control deviations caused by ocean disturbances and ensuring the accuracy of complex maneuvers and formation coordination. A hierarchical anti-interference timing synchronization system is constructed to suppress impulse noise, maintaining a high-precision time base at the microsecond to millisecond level even in harsh environments, thereby ensuring accurate coordination of cross-platform resource scheduling.

The resource coordination method and device, the storage medium and the chip system provided by the embodiments of the present application relate to the technical field of communication. In the method, the terminal device can send a first message to the network device under the condition that a first condition is met, and the first message is used to indicate that the terminal device has a task of a first priority. The first condition includes that the residual computing power of a first unit in the terminal device is less than a first computing power, there is a task of a second priority in a task queue corresponding to the first unit, and the first priority is higher than the second priority. By actively sending the first message, the terminal device can make the network device know in time that there is a task of the first priority in the terminal device, and then the problem of computing power competition when the task of the first priority and the task of the second priority share the first unit can be solved, and the smooth execution of the task of the first priority in the terminal device is ensured.

The application provides a resource coordination optimization method and device for integrating sensing and communication in a MU-MIMO system, and relates to the technical field of wireless communication. The method comprises the following steps: establishing a communication-sensing performance coupling model, converting the maximization of communication rate and the minimization of radar beam matching error into a multi-objective optimization problem through the joint representation of subcarrier level beam direction and resource multiplexing relationship; constructing a sensing accuracy constraint system based on partial subcarrier multiplexing, screening multiplexing subcarriers through beam direction similarity measurement, and separating communication dedicated resources and communication sensing multiplexing resources; designing a hierarchical iterative optimization mechanism under a hybrid beamforming architecture, taking each iteration for solving the trade-off problem of communication rate and beam adaptation as a layer of a deep neural network, and optimizing the weight and step length through back propagation. The application guarantees the multi-user communication rate, optimizes the target sensing accuracy, and reduces the hardware implementation complexity of large-scale antenna systems.

The invention relates to the field of robot scheduling, in particular to a resource collaborative scheduling method and system for multi-model beverage robots. The method comprises the following steps: collecting multi-batch beverage making tasks; carrying out equipment resource conflict identification based on the multi-batch beverage making tasks, and generating equipment resource conflict data; collecting real-time operation states of the plurality of beverage robots; performing state analysis according to the real-time operation state, and constructing a global working state diagram; performing overlapping degree calculation on the equipment resource conflict data to generate time sequence conflict points of the same equipment resource; performing collision risk quantification based on a global working state diagram and the time sequence conflict points to obtain a conflict task risk sequence; and performing multi-robot action scheduling planning on the conflict task risk sequence, and constructing an action collaborative scheduling strategy. According to the invention, through action cooperative scheduling, the resource utilization efficiency, the operation safety and the overall operation efficiency of the multi-model beverage robot are effectively improved.

This invention discloses an intelligent monitoring system and method for low-altitude unmanned service stations, relating to the field of low-altitude intelligent logistics and transportation monitoring. The system includes: an intelligent control center for global task scheduling, resource coordination, status monitoring, and remote management; multiple service stations, each including a takeoff and landing platform, a door control device, a charging / battery swapping interface, and an identification module; and an intelligent aircraft docking module for enabling automatic landing, identification, charging, and task data interaction for UAVs. This intelligent monitoring system and method for low-altitude unmanned service stations achieves "unmanned operation, remote monitoring, and self-recovery from anomalies" through the establishment of unmanned service stations. Furthermore, by establishing a dynamic adaptation model between flight tasks and ground resources, it supports high-frequency and diverse task operations. Based on the collaboration of edge intelligence and central intelligence, it ensures stable station operation and high task completion rates.

The application discloses a kind of service data hot and cold layered storage and transparent access platform, it is related to data protection technical field, including compliance adaptation module, strategy control and resource coordination module, data migration and verification module, data desensitization and traceability module, access adaptation and source switching module and security situation awareness module, each module is established two-way data interaction link by distributed network.The application is by building compliance adaptation, strategy control and resource coordination, data migration and verification, data desensitization and traceability, access adaptation and source switching, six big modules of security situation awareness distributed two-way interaction architecture, with compliance rule as front support, realize migration and desensitization parallel execution, cold and hot data source seamless switching and the integrated management of whole-link risk prevention and control, so it can solve the problem of cold and hot data management fragmentation in the prior art, compliance requirements and storage operation disconnection, data access needs manual intervention switching and security risk difficult to cover the whole world.

A method for resource coordination, an electronic device, and a storage medium. The method may include: determining a target resource for transmitting first information at a subsequent occasion; determining a second resource element from the target resource in response to an inclusion of a first resource element in the target resource comprising, wherein the first resource element is occupied by second information, the second resource element is not occupied by the second information, and the second information has a higher transmission priority than the first information; and sending the first information to a receive-end device at the subsequent occasion through the second resource element.

This application provides a resource coordination method, apparatus, and system. A first node sends a first signaling message to a second node. The first signaling message carries first resource configuration information, including the availability status of resource symbols of the cells served by the first node. The second node coordinates resources with the first node based on the availability status of the resource symbols of the cells served by the first node. In response to receiving a second signaling message from the second node, the second node obtains the availability status of resource symbols of the cells served by the second node from the second signaling message, and coordinates resources with the second node based on the availability status of the resource symbols of the cells served by the second node. This application embodiment can realize the exchange of resource configuration information between the first node and the second node, enabling them to coordinate and reuse resources based on the resource configuration information, thereby avoiding potential conflicts and interference between them, ensuring service quality, and improving communication efficiency.

The application provides a method for multi-channel dynamic allocation of a talkgroup and related equipment thereof, which is used for reducing terminal energy consumption and improving the reliability and spectrum utilization of group communication. The method comprises the following steps: obtaining communication awareness data of each terminal in the talkgroup, statistically analyzing the communication awareness data to obtain a demand probability distribution of the talkgroup, performing channel utility and device communication resource evaluation processing on the communication awareness data to obtain communication constraint data of each terminal, performing local conflict identification between terminals according to the demand probability distribution and the communication constraint data to obtain a resource conflict data set, and performing resource coordination optimization allocation of channels and time on the talkgroup according to the resource conflict data set and the communication constraint data to generate resource allocation data.

This invention discloses a cloud disk snapshot backup method based on IO pressure awareness and dynamic throttling. The method includes: real-time collection of disk IO performance indicators of production virtual machines and health status indicators of the underlying storage system; comparison of the collected indicators with preset thresholds to dynamically determine the current system IO pressure status; calculation and output of a backup task IO rate limit value in real time using a dynamic throttling algorithm based on the IO pressure status; and rate limiting of the backup data reading process based on the IO rate limit value to achieve dynamic throttling. This application combines business layer indicators with infrastructure layer indicators to construct a more comprehensive and accurate system pressure assessment model, rather than relying solely on a single dimension. Furthermore, without altering the existing hardware infrastructure, it achieves IO resource coordination and automatic arbitration between backup tasks and production operations through software intelligence.

This application discloses a task processing method and apparatus, a storage medium, and a vehicle, relating to the field of task processing technology. The method includes: upon receiving an upgrade instruction from a firmware upgrade system, obtaining the operating status of an on-board diagnostic system (ODIS), wherein the upgrade instruction carries an upgrade task; if the ODIS is in the state of executing a diagnostic task, obtaining the task type of the diagnostic task; and if the task type is a target task type, adjusting the priority of the upgrade task so that the firmware upgrade system executes the upgrade task, wherein the target task type is a diagnostic task type that does not affect the safe operation of the vehicle. This method achieves automatic resource coordination without manual intervention, improves the vehicle's intelligence level and user experience, significantly reduces the upgrade failure rate, avoids security vulnerabilities caused by upgrade failures, ensures the FOTA upgrade process is efficient, stable, and reliable, and enhances vehicle safety.

This application discloses a resource collaborative scheduling method that integrates ontology models and large-scale models, primarily relating to the field of collaborative scheduling technology. It addresses the problems of existing solutions, such as coarse text segmentation and a lack of precise perception of core scheduling elements in identification extraction and relation completion. The method includes: calculating relation scores for candidate relations based on constraint violation factors using a trained resource constraint graph neural network; completing candidate relations between node combinations in the initial scheduling graph based on relation scores, a pre-set resource constraint table, and the trained resource constraint graph neural network, obtaining a completed scheduling graph; generating corresponding concept classes, object attributes, and data attributes based on the complete scheduling graph; generating corresponding concept axioms, relation axioms, and scheduling rules with time-delay windows based on the complete scheduling graph and the pre-set resource constraint table, unifying them into a deployable scheduling domain ontology; and deploying the scheduling domain ontology into a resource collaborative scheduling system.

The application discloses a kind of distributed resource scheduling optimization systems based on multi-agent reinforcement learning, including bottom agent and upper agent, bottom agent includes bottom server level agent and middle layer rack level agent, and upper agent includes upper data center level agent;Bottom server level agent is responsible for CPU, memory, storage resource allocation of single server;Middle layer rack level agent is responsible for task scheduling and load balancing of server in rack;Upper data center level agent is responsible for global resource coordination and task routing across data center.The application designs layered cooperation architecture, communication mechanism, competition balance strategy and dynamic adaptation algorithm, to realize the collaborative optimization scheduling of distributed resources.System uses MADDPG, QMIX and MAPPO and other multi-agent reinforcement learning algorithms, so that each agent makes decisions under local information, achieves global optimization through cooperation, supports load balancing, energy optimization, quality of service guarantee and other multi-objective optimization.

The application discloses a kind of based on industrial knowledge graph man-machine dual resource coordination workshop flexible scheduling method.First, the workshop scheduling knowledge graph of fusing workpiece, process, machine, worker entity and its associated relationship is constructed, to provide structured semantic constraint support for decoding process.Further, a kind of dual population collaborative evolution framework is designed, wherein global population focuses on convergence, and local population protects isomeric solution through decision space niche to maintain diversity.In the evolution process, the adaptive knowledge transfer mechanism based on the edge weight change of knowledge graph is introduced, and the information exchange between populations is dynamically adjusted.The output Pareto optimal solution set is fed back to the knowledge graph as scheduling experience, to enhance the weight of high-quality resource path.The application combines physical constraint modeling and scheduling knowledge accumulation, significantly improves the search efficiency and solution quality in huge solution space, and can effectively obtain collaborative optimization scheduling scheme considering maximum completion time, total cost and total carbon emission.