8results about How to "Improve resource allocation efficiency" patented technology

The invention discloses an energy management balance scheduling optimization method and system based on deep learning, and the method comprises the steps: collecting multi-source feature data, carrying out the standardization and preprocessing, and obtaining structured multi-dimensional time series data; constructing a node relation graph by using an improved dynamic graph attention model, and generating a global attention matrix; node features are updated according to the global attention matrix, optimized features are formed, and a scheduling strategy is generated; optimizing the scheduling strategy by adopting an improved Harlisia eagle algorithm to obtain an optimized scheduling scheme; a node operation strategy is adjusted according to the optimization scheme, and power and load distribution is completed; and a final operation scheme is generated and dynamically corrected. According to the method, the improved dynamic graph attention model is constructed, and the Harris eagle optimization algorithm combining a boundary escape mechanism and a dynamic energy regulation and control mechanism is combined, so that intelligent modeling of multi-source data of an energy network, multi-target optimization of a scheduling strategy and dynamic balance scheduling of energy operation are realized.

The invention discloses a maritime emergency search and rescue resource assessment and responsibility allocation method. The method comprises the following steps: constructing a maritime emergency resource allocation risk assessment model, and constructing a maritime emergency search and rescue difficulty analysis and assessment model; constructing a responsibility subject allocation index system based on a maritime emergency search and rescue resource allocation risk assessment result and a search and rescue difficulty assessment result; establishing a multi-objective optimization model by minimizing the response time, maximizing the resource utilization rate and optimizing the load balancing and responsibility subject allocation index system; an NSGA-III algorithm is adopted to solve the multi-objective optimization model, and a Pareto optimal solution set is obtained; and performing cooperative game screening on an optimal solution in a Pareto optimal solution set based on responsibility allocation calculation of a dynamic Shapley value and a contract network protocol mechanism, generating a final task-rescue station responsibility allocation matrix, and realizing dynamic cooperative decision-making of a responsibility subject.

The invention discloses a multi-level agent collaborative decision-making method and system, and the method comprises the steps: carrying out the semantic analysis of an energy-saving standard document, extracting a standard knowledge unit, carrying out the semantic matching of an urban energy consumption target and the standard knowledge unit, and generating a correlation graph; a high-competition target group is identified through target competition degree analysis, collaborative agent pairs are screened through resource complementation degree calculation, and a building-level decision network is constructed; identifying low-load high-potential equipment and performing capability activation through equipment capability scanning and load potential evaluation; conflict intention double-layer features are extracted in combination with conflict detection, a conflict root is traced through a causal chain, and a mediation rule is established; improving the consensus degree of weak consensus nodes by adopting a consensus obstacle traceability and enhancement strategy; and finally, a hierarchical decision scheduling graph is formed through decision dependency analysis, multi-level collaborative decision scheduling is performed to generate a collaborative decision instruction, and comprehensive and intelligent multi-agent collaborative decision support is provided for a city-level energy-saving and carbon-reducing system.

This application provides a control method, apparatus, device, medium, and program product for unmanned aerial vehicle (UAV) swarms, relating to the field of automatic control technology, and aimed at improving overall resource allocation efficiency. The specific technical solution involves: acquiring scene information and state information of the UAV swarm's mission scenario; inputting the scene and state information into a UAV control model; extracting global feature information from the scene and state information through a low-level task controller; assigning at least one sub-task to each UAV in the swarm based on the global feature information through a high-level task switcher; and generating and outputting control strategy information for the UAV swarm for each sub-task through a low-level action controller. This application is applied to scenarios where UAV swarms collaboratively perform disaster awareness and communication support tasks in emergency situations.

This invention relates to the field of petrochemical safety production management technology, specifically to an intelligent on-site safety management platform for petrochemical plants. The platform comprises a perception layer, a transmission layer, a data layer, an application layer, and a display layer. These layers work together to achieve full-process coverage of on-site safety management in petrochemical plants. The perception layer is equipped with positioning terminals, health monitoring equipment, behavior recognition equipment, and environmental monitoring equipment to collect personnel location, health data, work behavior, and environmental parameters. The transmission layer utilizes a 5G private network, a WIFI local area network, and a ZigBeePlus wireless communication module to achieve high-speed and stable data transmission between the perception layer and the data layer. This invention, through ZigBeePlus three-dimensional positioning technology, achieves personnel positioning accuracy down to the meter level, accurately pinpointing the specific location of personnel in plant areas and confined spaces. Vital signs are linked in real-time with location data; when personnel exhibit health abnormalities or are in dangerous areas, the system can quickly trigger an early warning and lock their location, reducing rescue response time by more than 50% and significantly reducing the risk of personnel injury or death.

The invention discloses a method for a user equipment (UE) to report the opportunity that a physical uplink shared channel (PUSCH) is not used in a configuration grant (CG) period, and relates to a method for the UE to report the opportunity that the physical uplink shared channel (PUSCH) is not used in the CG period. The UE receives information about the CG, including a number of consecutive slots within the CG period and a size of a bitmap for reporting. Based on the received CG configuration, the UE identifies an unused PUSCH occasion within the CG cycle. In one embodiment, the UE transmits uplink control information (UCI) in a physical uplink shared channel (PUSCH) opportunity, and the UE transmits the uplink control information (UCI) in a physical uplink shared channel (PUSCH) opportunity. The UCI includes a bitmap indicating the identified opportunity of unused PUSCH (Physical Uplink Shared Channel).

This invention discloses a cloud-based method for the integrated supervision of cross-border e-commerce, comprising the following steps: S1, real-time collection of cross-border e-commerce transaction data, logistics and distribution data, warehousing data, and customs supervision data, and standardization processing using a distributed cleaning method; S2, construction of a multi-source heterogeneous graph based on the standardized data; S3, annotation of node and edge types, relationship types, and timestamps in the graph, and extraction of high-dimensional features using a relation-enhanced graph Transformer; S4, local and global aggregation using a multi-layer attention mechanism to generate an aggregated view; S5, construction of a multi-objective supervision task model and generation of a resource scheduling scheme; S6, identification of transaction, logistics, and supervision anomalies, and generation of risk warning and intervention strategies; S7, dynamic optimization of the relationship structure and Transformer parameters based on execution feedback. This invention, based on high-dimensional graph representation and optimized scheduling mechanism, achieves integrated supervision and risk warning of cross-domain multi-source data in cross-border e-commerce.

The present application relates to the technical field of multi-agent system cooperation, and particularly relates to a multi-agent overlapping alliance optimization method based on a capability center drive, which comprises the following steps: collecting a plurality of agents around a given task agent to obtain a total available capability pool in a region; if it is determined that the total available capability pool in the region meets the demand, constructing an alliance for the given task agent and the plurality of agents around the given task agent; screening an alliance that meets the task demand and has the optimal system efficiency; implementing dynamic resource allocation on the optimal alliance, and updating the resource state of the agent in real time; when a new task arrives, the remaining resources of the agents in the constructed alliance are deducted, and a new round of alliance construction is performed. The present application focuses on dynamic task allocation and cooperation optimization of multi-agents, and is particularly suitable for collaborative work and resource management of heterogeneous agents in a complex and topologically time-varying environment.