28results about How to "Improve resource allocation efficiency" patented technology

The invention discloses a self-adaptive power grid state court intelligent fusion terminal and a control method thereof, relates to the technical field of intelligent power grids, and solves the defects that an existing fusion terminal is insufficient in self-adaptive capability and limited in data processing and analysis capability. Learning power grid state change in real time through a deep power grid dynamic control algorithm and a prediction analysis engine combining LSTM and random forest, and autonomously adjusting a control strategy; the edge calculation and distributed processing module is utilized, so that the data processing flow is optimized, and the data processing speed and efficiency are greatly improved; the application of time sequence analysis and a machine learning model further excavates and understands a complex data mode; the resource configuration efficiency is improved through an intelligent scheduling algorithm, and a cross-layer coordination mechanism is established through a distributed coordination algorithm, so that rapid issuing and feedback collection of a control instruction are ensured; according to the invention, the intelligent degree of the existing fusion terminal is greatly improved.

The invention provides an industrial space intelligent association optimization system based on multi-dimensional data analysis, which relates to the technical field of industrial space association and comprises the following modules: a data acquisition module, a data preprocessing module, an association analysis module, an optimization decision module, a prediction and feedback module and an intelligent self-learning module. The multi-source heterogeneous data collection and edge computing technology is adopted, real-time data collection from multiple sources such as a sensor network, satellite remote sensing and a public database is supported, data of all nodes in an industrial chain can be rapidly and accurately obtained through multi-dimensional data collection in a complex industrial environment, and the data of all nodes in the industrial chain can be rapidly and accurately obtained through an efficient data preprocessing module. The system can process data in various formats, including industrial chain data, economic activity data, geographic space data and the like, realizes unified normalization and cleaning of different data sources, and ensures the accuracy and consistency of the data.

The invention provides a sports student development core accomplishment comprehensive management method and system based on big data, and the method comprises the steps: receiving and integrating multi-channel sports student dynamic data streams, and constructing a personal growth trajectory map; analyzing nodes and edges in the atlas by using an association rule mining algorithm, identifying key factors and turning points influencing growth, and performing optimal matching on educational resources and training plans in combination with a core literacy framework and a particle swarm optimization algorithm to generate a matching result; personalized learning materials and training suggestions are accurately pushed to form a preliminary development path plan; monitoring the execution progress in real time through a reinforcement learning algorithm, and dynamically adjusting the preliminary plan to obtain a target development path plan; and establishing a multi-dimensional feedback mechanism, collecting feedback information of coaches, sports students and third-party reviews, performing analysis processing to obtain an evaluation result, and finally generating an optimized final target development path plan. According to the invention, the management effect of core accomplishment of sports students is improved.

The invention provides a high-precision building construction carbon emission real-time metering method and system, and belongs to the field of carbon emission calculation. The invention aims to solve the problem that during carbon emission calculation in building construction, a calculation boundary needs to be determined, repeated calculation or missing lacks are easy to occur, and large access exists between a metering result and actual carbon emission. The construction activity consumption data is collected through an automatic data collection means and uploaded to the system platform through the Internet of Things, and finally the carbon emission data of each subentry construction activity is calculated in real time and analyzed and displayed. According to the method, carbon emission calculation is considered as a whole, the carbon emission of the whole project is explored from overall resource input and output, detailed analysis of internal process links is avoided, the problem of determining the internal boundary of the project for multiple times and the influence of construction uncertainty are avoided, and the construction efficiency is improved. And moreover, the calculation boundary is clear, the calculation process is simple, the calculation result precision is very high, and the method has relatively high operability and popularization value.

The invention provides a virtual power plant optimal scheduling method based on a whale algorithm. The method comprises the steps that an electric heating comprehensive system economic scheduling target with the lowest comprehensive operation cost as the target is determined; constructing constraint conditions of the virtual power plant optimization scheduling model; the whale algorithm is used to solve the economic dispatch target; and analyzing a model result, and evaluating the operation benefit of the virtual power plant. In order to solve the problems that virtual power plant optimization scheduling is high in calculation complexity, low in convergence speed and prone to falling into a local optimal solution, the whale algorithm scheme is adopted, the convergence speed and precision of the algorithm are improved through natural predation behaviors simulated by the whale algorithm and optimization of contraction factors and expansion factors, the calculation time is effectively shortened, and the optimal scheduling efficiency is improved. And the adaptability and the flexibility of the algorithm are enhanced through the shrinkage factor and the expansion factor which can be dynamically adjusted, so that the effects of optimizing the operation cost of the power system, improving the resource configuration efficiency and enhancing the stability of the power grid are achieved, and the development of sustainable energy is promoted.

The invention discloses an access traffic control method and device, computer equipment and a storage medium, and belongs to the technical field of big data. Firstly, a system obtains user identity information, establishes a user portrait and gives a corresponding weight; meanwhile, the user operation weight is calculated based on historical operation data of the user, and the interface access weight is calculated according to interface access data of the system. Then, the system calculates the comprehensive weight of the target interface requested by the user through the weights. Then, the system can determine all target interfaces requested by the user and sort comprehensive weights of the interfaces, so that a weight sequence of the request interfaces is generated; and according to the weight sequence, the system formulates a user access strategy so as to control the access flow of the user and output a corresponding access result. The invention further relates to the technical field of block chains, and the identity information is stored on a block chain network. According to the method and the device, high-concurrency access requests can be stably handled, and the efficiency of resource allocation and the reliability of interface access are also improved.

The invention discloses a dynamic clustering and role adjustment networking method and device, equipment, a medium and a product, and relates to the technical field of computers.The method comprises the steps that equipment information of multiple terminal equipment is obtained, and an information model is constructed according to the equipment information of the multiple terminal equipment, the equipment information comprises hardware characteristics, position information and state information of the terminal equipment; clustering the plurality of terminal devices based on the position matrix to obtain at least one cluster, and determining a device role of each terminal device in each cluster according to the state matrix, the device roles including a core device and an auxiliary device; and if it is detected that the information model is updated, dynamically adjusting the device role of each terminal device in the cluster according to the updated information model. According to the invention, the resource allocation efficiency among multiple devices is improved.

The invention discloses a consultation joint review system and method, belongs to the technical field of government affair service systems, and aims to solve the technical problem of how to improve the handling efficiency of consultation joint review. Comprising an institution declaration management module which is used for supporting a declaration institution to submit a declaration application online and supporting a user to edit and manage submitted declaration information; the consultation management module is used for supporting auditor of each supervision department to approve the declaration information submitted by the user according to a predetermined process; the feedback information management module is used for recording the approval process of the declaration information through the constructed approval result information table, feeding back the feedback process of the declaration information to the declaration mechanism and supporting the approval process of the declaration mechanism for querying the declaration information; and the industry and commerce registration management module is used for supporting a declaration mechanism to submit an industry and commerce registration application on line, supporting a user to edit and manage submitted industry and commerce registration information, and supporting the user to inquire an industry and commerce registration state on line.

The invention discloses an energy-saving and carbon-reducing optimization method and system for a low-carbon park, and relates to the technical field of low-carbon building and park design, and the method comprises the steps: formulating a carbon emission function, and quantifying each influence factor; performing virtualization operation on the spatial layout of the park, and performing multi-objective optimization; and establishing a low-carbon and thermal comfort evaluation feedback mechanism by optimizing a constraint program. According to the method, the goal of promoting carbon emission reduction is achieved, and refined measurement and evaluation are also beneficial to scientific supervision of policy formulators, promote transformation of a park to low-carbon development, improve the efficiency of resource allocation, promote implementation of a green building concept, ensure harmonious coexistence of a building and a natural environment, and improve the economic benefit. Iteration and optimization of the park design become more efficient, it is guaranteed that various services and facilities can be adjusted and improved in time in the actual operation process, and the park continuously develops towards the lower-carbon and more environment-friendly target.

The invention relates to the technical field of resource allocation of heterogeneous devices, in particular to a resource allocation method, system and device for heterogeneous devices and a storage medium, and the method comprises the steps: obtaining resources required by historical tasks on different heterogeneous device nodes according to the historical tasks; matching the real-time task demand with a historical task to obtain a first historical task; obtaining resource requirements of the real-time task requirements on different heterogeneous device nodes according to the first historical task; sorting the heterogeneous equipment nodes; and dynamically scheduling resources among the heterogeneous device nodes according to resource requirements of real-time task requirements on different heterogeneous device nodes and the sorted heterogeneous device nodes. By analyzing the historical task data, the resource requirements of the real-time tasks on different heterogeneous devices can be predicted more accurately, and resources can be managed more finely. Resource waste is avoided, it is ensured that each heterogeneous device can make full use of available resources, and the overall resource utilization rate is increased.

The invention discloses a task resource allocation method and system, belongs to the technical field of networks, and solves the problems of limited computing power resource allocation mode and poor adaptability. The method comprises the following steps: generating a corresponding calculation task according to a service required by client side equipment in a business scene, wherein the calculation task is used for indicating a resource demand of the required service for a computing power network in the business scene; scheduling information of the calculation task is generated, available node resources are applied according to the scheduling information, and the scheduling information is used for indicating task information of the calculation task and corresponding scheduling strategy information; calculating node resource information matched with the scheduling strategy information and the task information to obtain node information of at least one calculation node; distributing the corresponding computing nodes according to the node information of the computing nodes; and processing the calculation task by utilizing the distributed calculation nodes to obtain an execution result of the calculation task. According to the method, the scene adaptability of computing power resource allocation can be improved, and the resource allocation efficiency is improved.

The present disclosure relates to a resource allocation method, device and system for a cluster. The cluster includes multiple nodes, the multiple nodes are divided into multiple node sets that are the same as the total number of multiple asynchronous scheduling threads, and one asynchronous scheduling thread corresponds to one node set. The resource allocation method includes: receiving a resource allocation request, the resource allocation request includes the number of resources to be allocated; according to the resource allocation request, scheduling each asynchronous scheduling thread in the multiple asynchronous scheduling threads to perform the following steps: judging whether each asynchronous scheduling thread meets a first resource allocation condition, and if each asynchronous scheduling thread meets the first resource allocation condition, judging in turn whether each node in the node set corresponding to each asynchronous scheduling thread meets a second resource allocation condition, and for each node in the node set corresponding to each asynchronous scheduling thread, if the node meets the second resource allocation condition, allocating the resources to be allocated to the node.

The present application provides a comprehensive management method and system for the core literacy of sports students based on big data, wherein the dynamic data streams of sports students from multiple channels are received and integrated to construct a personal growth trajectory map; the nodes and edges in the map are analyzed using an association rule mining algorithm to identify key factors and turning points that affect growth, and the core literacy framework and particle swarm optimization algorithm are combined to optimize the matching of educational resources and training plans to generate matching results; personalized learning materials and training suggestions are accurately pushed to form a preliminary development path plan; the execution progress is monitored in real time through a reinforcement learning algorithm, and the preliminary plan is dynamically adjusted to obtain a target development path plan; a multi-dimensional feedback mechanism is established to collect feedback information from coaches, sports students themselves, and third-party reviews, and the evaluation results are obtained after parsing and processing, and finally the optimized final target development path plan is generated. The present application improves the management effect of the core literacy of sports students.

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.

The present invention discloses a dual game method and system of multiple virtual power plants and distribution systems, the method comprising: constructing a distribution system operation model and a virtual power plant operation model respectively with the goal of minimizing the operation cost; converting the distribution system operation model and the virtual power plant operation model into corresponding distributed blue-robust optimization models respectively according to the uncertainty of distributed photovoltaics; constructing a first game model between multiple virtual power plants and the distribution system with the electricity price strategy and the grid-connected power as the interactive variables; constructing a second game model between the virtual power plants with the active power transmitted between the virtual power plants as the interactive variables; constructing a dual game model of multiple virtual power plants and the distribution system based on the first game model and the second game model; solving the dual game model to obtain the optimal power generation plan and the optimal energy interaction strategy. The present invention can balance the supply and demand relationship of the power system and improve the resource allocation efficiency of the entire power system.

The invention relates to a whole-process management consultation resource allocation system management method and platform, and the method comprises the following steps: S1, obtaining a resource request initiated by a client, the resource request comprising a request resource type, a request time period, a priority and a resource amount; s2, obtaining the margin of each type of resources; and S3, according to the resource request and the margin of each type of resources, carrying out resource allocation according to the priority. Compared with the prior art, enterprise resources are integrated, and resource allocation is performed according to the resource request of the customer, so that efficient utilization of the resources and timely response to customer consultation can be realized, and the resource allocation efficiency of the whole-process management consultation enterprise is improved.

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 embodiment of the invention provides a market game-based medium and long term carbon transaction price prediction method and system, and the method comprises the steps: obtaining carbon market related data, power system planning related data, and power system low-carbon transformation technology economic parameters; based on the obtained carbon market related data, the power system planning related data and the power system low-carbon transformation technology economic parameters, establishing a power system low-carbon planning upper-layer model in the carbon market environment by taking the lowest power planning cost as a target; based on the determined power system low-carbon planning scheme, establishing a multi-subject simulation carbon transaction game lower-layer model of the thermal power plant participating in the carbon market by maximizing the revenue of each market subject; and solving the power system low-carbon planning upper-layer model and the multi-subject simulation carbon transaction game lower-layer model in the carbon market environment through interactive iteration, performing optimization to obtain a power system low-carbon planning scheme and an equilibrium solution of a thermal power plant participating in carbon market transaction, and finally performing optimization to obtain a carbon transaction price predicted value.

The invention discloses a many-to-many game task resource allocation method and device, equipment, a medium and a product, and relates to the technical field of game resource allocation, and the method comprises the steps: building an incidence matrix according to a game task set and a game resource set; the incidence matrix is used for describing a path relationship between each game task in the game task set and each game resource in the game resource set; determining a game sequence according to the priority of each game task in the game task set; a bidirectional breadth search algorithm is adopted to match game resources for each game task in the game sequence, in the search process of the bidirectional breadth search algorithm, whether a starved game task exists or not is judged according to the starvation time flag bit of each game task, and if yes, the game sequence is updated. The game task resource allocation efficiency can be improved.

The invention discloses a multi-virtual power plant and power distribution system dual game method and system, and the method comprises the steps: taking the minimization of the operation cost as a target, and constructing a power distribution system operation model and a virtual power plant operation model; respectively converting the power distribution system operation model and the virtual power plant operation model into corresponding distributed robust optimization models according to the distributed photovoltaic uncertainty; constructing a first game model between the multiple virtual power plants and the power distribution system by taking the electricity price strategy and the internet power as interactive variables; taking the active power transmitted among the virtual power plants as an interactive variable, and constructing a second heavy game model among the virtual power plants; constructing a dual game model of the multiple virtual power plants and the power distribution system based on the first game model and the second game model; and solving the dual game model to obtain an optimal power generation plan and an optimal energy interaction strategy. The supply-demand relationship of the power system can be balanced, and the resource configuration efficiency of the whole power system is improved.

The present invention discloses a method for selecting the location of an emergency medical site based on the urgency of the demand for emergency medical resources, comprising the following steps: respectively obtaining the real road distances between different areas and the emergency medical sites closest to them; using a two-step mobile search algorithm to respectively calculate the accessibility of different areas to obtain emergency medical resources, and selecting areas that cannot obtain emergency medical resources; calculating the urgency of the demand for the areas that cannot obtain emergency medical resources U k ; Genetic algorithms are used to comprehensively evaluate the location of emergency medical stations based on the urgency of the needs of areas without access to emergency medical resources and the number of areas without access to emergency medical resources that can be covered by emergency medical stations. The present invention improves the overall resource allocation efficiency, ensuring that the supply of emergency medical resources in remote areas will not be neglected even when population distribution and demand intensity change, thereby improving the overall public health and welfare level.

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).

The embodiment of the invention discloses a transformer substation starting device and method and a generator response device. The transformer substation starting device comprises an uninterruptible power supply (UPS) power supply module and a remote control system. The UPS power supply module is used for providing an uninterruptible power supply for the remote control system; the remote control system comprises a generator monitoring module, a generator remote starting module and a generator device information reset module. The generator monitoring module is used for transmitting generator state information to the dispatching center in real time; the generator remote starting module is used for sending a starting instruction to the generator starting circuit to start the generator when the generator state is not abnormal; and the generator device information reset module is used for sending a reset signal to the generator when the generator state is abnormal. Through the UPS power supply module and the remote control system, remote quick start and automatic access to the station system of the generator can be realized, and the time from power failure to power supply recovery is greatly shortened.

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.