207 results about "Majorization minimization" patented technology

The application discloses a resource allocation method and system for C-RSMA auxiliary digital twin network joint device pairing, and belongs to the technical field of wireless communication and digital twin fusion. In view of the problems that the channel difference of a digital twin network is large, data demand is unbalanced, and data redundancy and high system time delay are caused by the fact that the prior art does not combine modeling accuracy constraints, a C-RSMA digital twin uplink transmission system model is first constructed, near-end-far-end device pairing is completed through a two-stage strategy, the minimum transmission data amount of the device is determined in combination with the modeling accuracy constraint, and then the joint optimization of power allocation, time allocation factor and computing resource is completed through an alternating optimization framework, so that the overall generation time delay of the digital twin system is minimized. The application can significantly improve the transmission reliability of edge devices and the system resource utilization rate, and is suitable for a large-scale multi-device access digital twin commercial scene.

The present application belongs to the field of remanufacturing scheduling, and discloses a remanufacturing job shop robust scheduling method and device considering carbon emission constraints, which comprises obtaining machine information and workpiece information of a remanufacturing job shop, taking minimization of expected total cost and cost deviation as an optimization target, introducing carbon emission constraints, and constructing a robust optimization model containing a discrete scene set; a particle swarm optimization algorithm is used to solve the robust optimization model to obtain an optimal remanufacturing scheduling scheme, and the solution of the particle swarm optimization algorithm is represented by a process-based encoding sequence, wherein each element in the process-based encoding sequence is a positive integer, the numerical value represents a workpiece number, and the number of occurrences of the same numerical value represents a process number. The present application considers the mutual influence between uncertainty factors in actual production and dynamic carbon cost, solves the remanufacturing job shop robust scheduling problem under carbon emission constraints, and improves the accuracy and robustness of remanufacturing job shop scheduling.

The application discloses a power plant edge computing and 5G cooperative data transmission method, which comprises the following steps: obtaining original multi-modal data flow and analyzing task request, generating dynamic business feature vector, and selecting atomic processing unit to combine into an initial processing pipeline according to the feature vector; collecting 5G private network wireless channel state, backhaul link load, resource utilization rate of each edge computing node and interconnection state between nodes to generate a global resource state view; performing dynamic mapping and reconstruction on the initial processing pipeline based on the view and the feature vector, assigning appropriate nodes to each unit, planning transmission path and priority, controlling node execution and triggering online adaptive migration based on dynamic criticality level change; receiving intermediate results at the aggregation node, using attention mechanism to weight and fuse to generate final diagnostic results and output, and updating the global resource state view, so that dynamic cooperative optimization and adaptive scheduling of computing and transmission resources are realized.

This invention belongs to the field of intelligent scheduling technology in manufacturing, and particularly relates to a dual-resource flexible job shop scheduling method and system integrating a large language model and deep reinforcement learning. The method includes: constructing a dual-resource flexible job shop scheduling environment model, defining jobs, processes, machines, workers, and their constraints, and constructing a set of state features for reinforcement learning; designing an action space and feasible action masking mechanism, based on process sequence, feasible machine set, worker skill qualifications, and available time constraints, to mask infeasible actions and reduce the probability of selecting low-quality actions; designing a multi-objective reward function, including minimizing completion time and total energy consumption; training the scheduling strategy using the Asynchronous Advantage Actor-Critic (A3C) algorithm, optimizing the policy network and value network through multi-threaded parallel sampling and asynchronous update mechanisms; and triggering the large language model to generate new reinforcement learning definitions based on trend and stability analysis of training feedback, and achieving adaptive iterative optimization through a closed-loop mechanism.

The application discloses a generalized shared energy storage optimization configuration method and system based on fuzzy chance constrained programming, relates to the technical field of electric power, considers the operation characteristics of various generalized energy storage resources, and respectively models the energy storage, wherein the base station energy storage model needs to consider communication load communication quality and base station power supply reliability, the air conditioner model needs to consider user comfort, and the electric vehicle charging station model needs to consider user driving characteristics, so that the existing large number of idle multi-energy generalized energy storage resources are fully tapped, and the generalized shared energy storage optimization configuration model considering multiple uncertainties is designed. The shared energy storage operator and the user group form a cooperative alliance by transferring the energy storage use right, the generalized shared energy storage optimization configuration model is decomposed into two sub-problems of alliance energy consumption cost minimization and internal payment negotiation based on Nash bargaining theory, and the risk brought by the parameter uncertainty of the user group source and load output and virtual energy storage is quantified based on the fuzzy chance constrained programming theory.

The application provides a power deterministic network routing and packet scheduling method and system, the method comprising: determining the feasible path set from the source to the destination node for each deterministic flow according to the network and traffic model; constructing the three-dimensional scheduling variable consisting of the path, time offset and CSQF period specification set for each flow; jointly solving the three-dimensional variable of each flow to obtain the routing and packet scheduling scheme satisfying the resource, sending, delay and period constraint with the minimum objective function, the objective function being used to realize the double load balancing of the link and period window; and calculating the network remaining resource based on the obtained scheme and allocating the transmission resource for the non-deterministic flow according to the network remaining resource. Through the three-dimensional joint scheduling of the routing, time and period and the double load balancing of the link and window, the application can significantly reduce the jitter under the strict delay constraint, effectively improve the network resource utilization and the reliability of the deterministic service.

The invention discloses a dispatching optimization method and system for a mine micro-grid, and relates to the technical field of micro-grids. The method comprises the steps of constructing a system model of the mining area comprehensive energy system; a mining area comprehensive energy operation model is constructed based on the system model, and constraint functions of the mining area comprehensive energy operation model comprise a net income maximization function, a renewable energy consumption maximization function and a carbon emission minimization function; the model is converted into a Markov decision process for determining a scheduling strategy for performing day-ahead scheduling control on the mining area integrated energy system, and the scheduling strategy comprises an output power range and an equipment state of each equipment in a day-ahead scheduling period; and under the limitation of the agreed range of the scheduling strategy and the constraint condition of the mining area comprehensive energy operation model, with the purpose of minimizing the operation cost, performing intra-day scheduling control by adopting mixed integer linear programming. Therefore, the problem that a scheduling optimization method in the prior art cannot be applied to the mine micro-grid or has many defects during application is solved.

The application discloses a kind of 6G network multidimensional resource AI model dynamic deployment optimization methods based on category theory, belongs to intelligent collaborative optimization technical field;Method is: the cross-layer consistency dependency of end-to-end AI reasoning service is formalized by functor form;Establish the joint optimization model with long-term average end-to-end delay minimization as target, while being constrained by multidimensional resource and service quality;Convert long-term random optimization problem into time-slot online decision problem;Get AI model dynamic deployment and task scheduling result.The application realizes cross-layer consistency description to task scheduling and model deployment through category theory unified modeling and functor composite mechanism, reduces the inconsistency and redundant constraint caused by hierarchical modeling, improves the structured degree and explainability of joint decision;Under the constraint of multidimensional resources such as calculation, memory, storage and bandwidth, dynamic adaptive optimization is realized, node resource over-limit and load imbalance are effectively avoided, and congestion and queuing delay are reduced.

This invention discloses an optimization method for restoring power supply to critical loads in active distribution networks based on user outage loss assessment. The method includes the following steps: collecting user outage time, duration, and economic and social loss information; establishing a quantitative model of outage loss; using the results of the outage loss model, combined with the grid topology and restoration difficulty, comprehensively evaluating and prioritizing loads to generate a critical load sequence; constructing a power restoration optimization model with the goal of minimizing outage loss; applying the optimization strategy to actual distribution network operation; collecting restoration effect data; dynamically adjusting; and forming an iterative optimization. This invention not only achieves critical load identification and prioritization based on user outage loss, enabling priority restoration of loads with the greatest economic and social impact under limited resources, but also significantly improves the restoration efficiency and scientific nature of power supply decisions in active distribution networks after sudden power outages, balancing power supply security, economy, and social benefits.

This application relates to a method for designing a long-term stable association strategy for a digital twin edge network. The method includes: constructing a long-term association strategy model and a communication and computing model for the digital twin edge network; constructing decision variables and introducing these variables into the long-term association strategy model and the communication and computing model to construct a problem minimizing system latency; decoupling and optimizing the problem minimizing system latency to obtain the decoupled optimized problem; and solving the decoupled optimized problem to achieve long-term stable association of the digital twin edge network. This invention considers the long-term cumulative benefits of the network system, systematically considers state evolution and resource allocation across time periods, and optimizes overall performance from a global perspective. Compared to short-term association strategies, long-term stable association strategies can better balance immediate benefits and future costs, reduce the long-term average latency and average energy consumption of the system, and are particularly suitable for network system optimization in dynamic and uncertain environments.

The application discloses a network spectrum fragmentation minimization routing resource allocation method and device, the method comprises two stages of path selection and spectrum allocation, the path selection stage takes maximizing path resource utilization as the target, obtains optical network topology information, filters the optimal path through quantifying spectrum resource availability and concentration degree of the link and the path, and forms a candidate path set; the spectrum allocation stage takes minimizing spectrum fragmentation generation and reducing service blocking rate as the target, converts the IP service into optical layer frequency gap demand, filters the continuous idle spectrum block meeting the spectrum constraint condition in the candidate path set to form a frequency block candidate set, determines the optimal frequency block through quantifying the close degree of the frequency block and the occupied resource and the influence degree of the frequency block deployment on the link resource, completes service allocation, and updates the link frequency gap use state in real time after the allocation. The application effectively reduces spectrum fragmentation, improves spectrum resource utilization and network transmission reliability, and adapts to high dynamic and high burst service demand.

This application discloses a resource task scheduling method, apparatus, electronic device, and program product, belonging to the field of cloud-edge collaboration technology. The method includes: dividing available cloud resources into a first type of virtual container and a second type of virtual container; predicting the target available resources based on the historical available resources of the edge cluster; combining the target available resources, the resources of the first type of virtual container, and the amount of task data to obtain the predicted task completion time for both; with the goal of having the same unit task processing time, splitting the task data and allocating it to the edge cluster and the first type of virtual container for parallel execution; and dynamically adjusting resource allocation or task splitting boundaries with the goal of minimizing the maximum task completion time, until a preset scheduling stop condition is met. In the event of an edge cluster failure, unfinished tasks are scheduled to the second type of virtual container for continued processing. This application achieves efficient collaboration between cloud and edge resources, effectively improving resource utilization and ensuring the stability and timeliness of task execution.

The present application relates to the field of project supervision information technology, and provides a supervision multi-heterogeneous system cloud edge collaborative adaptation method, which comprises collecting edge device hardware architecture, operating system and other heterogeneous information to generate records; integrating supervision monitoring data and cloud instructions, classifying data according to functions and update frequencies and marking high-frequency units; unifying data formats and adding hash check information; constructing a three-dimensional mapping table in the cloud, combining high-frequency data grouping edge nodes; converting edge device private protocols into general protocols; using an integer linear programming model to allocate tasks with the goal of minimizing transmission volume and energy consumption; dynamically adjusting device energy consumption; and dynamically optimizing configuration based on interface indicators. The method can improve cloud edge interaction efficiency, reduce energy consumption and ensure reliable operation of supervision business.

The application discloses a long-term multi-agent task allocation method based on a multi-objective multi-level cultural gene algorithm, which is used in a task continuous random arrival scene such as intelligent warehousing. In view of the problems that the existing static method cannot adapt to long-term tasks, the online method is frequently cold started, the historical knowledge is poorly reused, the convergence is slow, and the task period and the agent load balancing are difficult to balance, a double-objective model of minimizing cumulative task period and minimizing load deviation is established; a multi-objective multi-level cultural gene algorithm is designed, multi-strategy initialization, hierarchical population and genetic operation are adopted, adaptive multi-neighborhood and confusion degree pop-up search local search are combined, and the population and historical archives are updated by cooperating with the non-dominated solution sorting, congestion degree calculation and elite reservation strategy. Through the organic combination of the above strategies, the algorithm can realize the collaborative optimization of task response and load balancing without interrupting the system operation.

The application discloses a resource scheduling method, system and device based on dynamic coupling weight and a medium, and belongs to the technical field of power systems. The method is as follows: acquiring characteristic parameters corresponding to each power market, demand parameters, operation parameters of a power grid and power flow parameters of a power transmission section; calculating a first weight factor based on the characteristic parameters, the operation parameters and the power flow parameters, and calculating a second weight factor based on the operation parameters and the power flow parameters; determining a constraint condition according to the first weight factor and the second weight factor corresponding to each market participant; and optimizing and solving with the target of minimizing the cost of each market participant in each power market under the constraint condition to obtain a resource scheduling scheme, and controlling each market participant to execute the resource scheduling scheme. Therefore, the application can improve the resource scheduling effect and reduce the cost.

This disclosure relates to the field of artificial intelligence chip technology, specifically to a method, architecture, and chip for dynamic collaborative resource scheduling of heterogeneous multi-core processors. The method includes: processing the computation graph using a trained graph convolutional network to obtain corresponding task feature vectors; acquiring resource status information of a heterogeneous resource pool; establishing a weighted multi-objective optimization function with maximizing performance indicators and minimizing power consumption indicators as optimization objectives; determining an iterative greedy strategy for approximating the optimization objectives; in each iteration, selecting the optimal candidate resource from the heterogeneous resource pool to add to a virtual computing cluster; and calculating whether the computing resources, storage capacity, and transmission bandwidth provided by the updated virtual computing cluster meet the corresponding requirements, until the target virtual computing cluster is obtained. This disclosure achieves precise matching between task characteristics and hardware resources, dynamically generates optimal resource configuration schemes, and improves system-level energy efficiency while ensuring smooth task execution.

This invention discloses a method, system, device, and storage medium for constructing and optimizing the dynamic operating domain of a distributed energy system. The method includes: collecting incomplete information data of the distributed energy system to construct a polyhedral uncertainty set; establishing a high-dimensional constraint set, using this high-dimensional constraint set as constraints, and optimizing the solution with the objectives of maximizing and minimizing tie-line power; expanding the obtained upper and lower bounds along the time axis to extract the time-series envelope of the dynamic operating domain; constructing a robust optimization model based on the polyhedral uncertainty set, including a day-ahead decision-making stage and an intraday real-time scheduling stage, and embedding the time-series envelope of the dynamic operating domain as a rigid boundary into the feasible domain of the intraday real-time scheduling stage; using a column constraint generation algorithm to iteratively solve the robust optimization model after embedding the rigid boundary, and outputting the optimal unit start-up and shutdown and scheduling strategy. This achieves synergistic optimization of system safety and low carbon emissions.

This invention discloses a joint optimization method for mobile edge computing and service caching assisted by multiple UAVs, comprising: constructing a multi-UAV-assisted edge computing system model; constructing a communication model; dividing the system decision time into a set of large-timescale periodic sequences and a set of small-timescale time slot sequences, each large-timescale periodic sequence containing multiple consecutive small-timescale time slot sequences; deriving expressions for the total task processing latency and total energy consumption of the system in any time slot; establishing a joint optimization objective function with the goal of minimizing the total system latency and total energy consumption; constructing a Markov decision mechanism; employing the SAC deep reinforcement learning algorithm to implement the dual-timescale decision-making of the system; and solving the joint optimization objective function to obtain the optimal joint optimization scheme. This invention can significantly improve the edge service cache hit rate, optimize UAV load balancing, and reduce task processing latency and overall system energy consumption.

This invention provides a data center scheduling method, system, and medium considering flexible resources, relating to the field of energy scheduling optimization technology. The method includes: establishing a comprehensive energy system equipment model including the energy flow and / or energy storage status of each device; establishing a computing task model and a thermal inertia model for the data center; establishing a scheduling optimization model based on the equipment model, computing task model, and thermal inertia model; the optimization objectives of the scheduling optimization model include minimizing the total operating cost of the comprehensive energy system and maximizing the data center service quality index; establishing constraints for the scheduling optimization model based on energy balance constraints, energy equipment operation constraints, computing task execution constraints, and upper and lower limits of server utilization constraints, and then solving the scheduling optimization model to obtain a scheduling scheme for energy equipment operation and computing task execution. This invention can optimize energy and computing task scheduling by combining the flexible resources of the data center while ensuring reliable operation.

This invention, a smooth handover guidance method based on variable bandwidth ESO and multi-source information fusion, belongs to the field of aircraft guidance and control. The implementation method is as follows: A weighted multi-source information smooth fusion method is employed to construct a weighting factor that gradually changes linearly over time. The heterogeneous complementarity between the electro-optical data link and the seeker head eliminates data jumps during information source switching. A variable bandwidth adaptive extended state observer is constructed, and a third-order ESO is established to estimate the target state in real time. By dynamically adjusting the bandwidth parameters, measurement noise from the electro-optical data link is suppressed during the mid-guidance phase, and the maneuvering characteristics of highly maneuvering targets are rapidly extracted during the terminal guidance phase by increasing the bandwidth. Based on the lead angle tracking theory, a multi-strategy guidance switching logic is designed, actively switching to an enhanced proportional guidance mode to ensure that minimizing the miss distance is the highest priority. This achieves improved interception accuracy for highly maneuvering targets through feedforward compensation while ensuring the stability of the interception loop.

This invention provides a method, system, device, and medium for power output arrangement of flexible DC receiving-end converter stations. The method includes: acquiring power flow data of the receiving-end power grid connected to at least one target flexible DC system; wherein the target flexible DC system includes multiple receiving-end converter stations to be solved; using the power output of each receiving-end converter station as a variable and determining it as a particle component; iteratively solving each particle using a particle swarm optimization algorithm to obtain the boundary power output; wherein the fitness function is expressed as the minimization of the generalized short-circuit ratio of the system operation, and the generalized short-circuit ratio of the system operation is calculated based on the power flow data; determining the control power, and then realizing the power output arrangement of each receiving-end converter station of the target flexible DC system based on the control power. This invention uses a particle swarm optimization algorithm for iterative optimization, avoiding the problem of low solution efficiency of traditional exhaustive methods when facing massive power output combinations, significantly shortening the optimization time, and improving the response speed of converter station power output arrangement adjustment.

The application discloses a task offloading method for improving task execution efficiency and edge node resource utilization, and relates to the technical field of mobile communication, comprising the following steps: constructing a task offloading model under an edge cooperation environment, and acquiring total task execution time; taking minimizing the total task execution time as a target, jointly optimizing resource allocation decisions, user and edge server association decisions, task offloading proportions, and execution location decisions according to an improved teaching optimization algorithm, and acquiring the minimum total task execution time. The application jointly optimizes task offloading location decisions, task offloading proportions, computing resource allocation, and user and server association strategies, comprehensively optimizes multiple key decision variables, effectively reduces the complexity of the task offloading problem, and improves scheduling efficiency.

This invention provides a warehouse storage location allocation method, apparatus, computer equipment, and storage medium, belonging to the field of material storage. The method includes: acquiring historical order data; calculating the turnover rate coefficient of each material and the demand correlation between any two materials; determining two materials as a pair requiring distance constraints only when the demand correlation of two materials is higher than a first preset threshold and both turnover rate coefficients are lower than a second preset threshold; constructing a storage location allocation optimization model with the objective of minimizing the total travel distance of loading equipment; for each pair of materials requiring distance constraints, the distance between their storage locations in the warehouse is less than or equal to the distance decision variable; and solving the optimization model to obtain the storage location allocation scheme for each material. This selective constraint mechanism effectively avoids fast-moving materials being placed far from entrances and exits due to correlation, significantly shortening the travel path.

This invention relates to an optimal scheduling method and system for an electro-hydrogen coupling system based on truck flexibility and relaxation-repair warm-start technology. The method includes: acquiring basic data of the electro-hydrogen coupling system; constructing a power system model, a hydrogen energy system model, a pipeline congestion management model, and a hydrogen truck transportation model, wherein the pipeline congestion management model is a truck-based pipeline congestion management model that treats hydrogen trucks as flexible resources; establishing constraints and constructing an optimal scheduling model with the goal of minimizing the total system operating cost over the entire scheduling cycle; transforming the optimal scheduling model into a convex programming model using a mixed-integer second-order cone programming reconstruction technique; and solving the convex programming model using a relaxation-repair warm-start algorithm, considering the physical state information of the system, to obtain the optimal scheduling result. Compared with existing technologies, this invention has the advantages of actively alleviating pipeline physical congestion and high computational efficiency.

This invention discloses a control logic routing method for a fully programmable valve array biochip, used to automate the channel routing of FPVA control logic. The method aims to minimize the time delay deviation between synchronized valves and minimize the control channel bus length. It implements control channel routing that considers line length matching for the logic architecture. The method uses a competitive deep dual-Q network as the agent of the DRL to achieve adaptive routing of channels in the control logic. The key parts of the framework, such as the state space, action space, and reward function, are designed based on the line length matching requirements of the control channels, thereby minimizing the time delay deviation of synchronized valves and the control channel bus length in the biochip.

The application discloses a waiting time constraint batch processing pipeline crane scheduling method based on an improved slime algorithm and belongs to the field of intelligent scheduling of industrial production. The method comprises the following steps: a scheduling model is constructed, the waiting time fuzzy interval is defined by a trapezoidal fuzzy number, and the maximum completion time is minimized as the target; the waiting time uncertainty is accurately characterized by the fuzzy interval, and the fluctuation range of the waiting time in actual production is completely covered; based on the model, a hybrid improved slime algorithm is used for solving; a matrix coding and One-Hot mapping are adopted to adapt to the discrete scheduling scene; a good point set initialization method is introduced to construct an initial population, and multiple scheduling schemes are obtained; and a double tabu table strategy is combined to optimize the local search efficiency and convergence stability. The application is superior to traditional genetic algorithms, discrete slime algorithms and other comparative algorithms in terms of solution quality, convergence speed and robustness, can efficiently adapt to the crane scheduling demand of process-type industries, and provides a stable and reliable solution for multi-crane collaborative scheduling in a complex uncertain environment.

The application belongs to the technical field of power grid optimization control and artificial intelligence, and particularly relates to a multi-agent power grid voltage optimization control method and device based on a large language model, an electronic device and a readable storage medium, which comprises the following steps: using prompt word engineering technology, inputting power grid environment information, task target optimization model and device characteristics as context information into a large language model to generate time sequence running data sets covering different working conditions; constructing a multi-agent system based on a distributed partially observable Markov decision process mechanism and training the same by using a TD3 (double-delay deep deterministic policy gradient) algorithm; and deploying the multi-agent system in a power grid control system, and each regional agent executes voltage regulation actions according to real-time collected local state information. The application can realize safe stability and network loss minimization of power grid voltage without relying on accurate system mechanism models / equations, and has good scalability and engineering application prospects.