184 results about "Bilevel optimization" patented technology

The invention relates to the technical field of power data processing, in particular to an energy storage configuration optimization method, which comprises the following steps: acquiring new energy output time sequence data and computing power load characteristic data; generating a space-time correlation coupling evaluation result of the new energy output volatility and the computing power load volatility; inputting a result into a hybrid power supply double-layer optimization model, recursively correcting parameters through a two-stage collaborative solution algorithm, and outputting a Pareto optimal solution set; a computing power task elastic regulation and control mechanism is embedded, and the task priority is dynamically adjusted according to the energy storage charge state and the new energy output level to generate a scheduling strategy; finally, an energy storage configuration scheme and a dynamic scheduling strategy are output, and collaborative optimization of cost effectiveness and power supply reliability is achieved. The method breaks through the coupling conflict of the economic target and the robust constraint in the traditional bilevel planning, remarkably reduces the energy storage configuration cost, and improves the system stability.

The invention relates to a double-layer double-objective optimization scheduling method based on a large language model, and the method comprises the steps: building a double-layer double-objective optimization model in a server, and building an upper-layer model and a lower-layer model for an AISTS problem based on a plurality of different variables, different constraint conditions and different objective functions; and prompting a dual-objective optimization model of adjustment and algorithm evolution to form a two-stage method based on LLM, the method can overcome the defects that a traditional algorithm needs to design the algorithm manually and adjust parameters manually, the calculation cost is high, the efficiency is low, complex problems are difficult to effectively decompose and coordinate double-layer optimization, a dynamic collaborative optimization framework is lacked, and the flexibility of algorithm combination is low.

The invention is suitable for the technical field of integrated energy microgrid scheduling, and provides a power distribution network random distributed robust optimization scheduling method of an integrated energy microgrid group, comprising the following steps: constructing a DN structure comprising a plurality of microgrids; the DN day-ahead scheduling optimization model containing the IEMGs pays attention to operation cost and constraint conditions; for the DN, the problem of non-convex nonlinear optimization including line loss is solved; for the IEMG, the model is used for processing a convex optimization problem considering WT and PV output power uncertainty; power exchange is carried out between the DN and the IEMG through a connecting line, so that a double-layer model framework is constructed to analyze the system; the solving process is a double-layer optimization iteration process, an outer layer obtains an interaction power value between the DN and the IEMG by using an ATC method, and an inner layer uses Camp; the CG method solves the problem of random distribution robust optimization of IEMG. According to the method, the carbon emission and the operation cost of the system can be effectively reduced, meanwhile, stable operation of the DN is ensured, and a theoretical basis is provided for efficient utilization of ammonia energy in the DN.

The present invention relates to the technical field of steel industry load participation in power grid interaction, and provides a double-layer optimization scheduling method and system for steel industry load, the method comprising: constructing a continuous time interactive resource task network model based on double-layer optimization data information; constructing a resource task network model with production process constraints; constructing a complex order characteristic analysis model for steel industry load; setting dynamic maximum demand analysis model operation constraints for the complex order characteristic analysis model; constructing an upper-layer power grid scheduling optimization model that achieves optimal power grid economic cost, and the lower-layer steel industry load side full system model and the upper-layer power grid scheduling optimization model jointly form a double-layer optimization system; proposing a double-layer optimization algorithm for the double-layer optimization system, solving the double-layer optimization scheduling; and outputting double-layer optimization scheduling result information. The present invention significantly reduces the electricity cost of short-process steel industry loads, and allocates production orders in combination with steel order characteristics to achieve better peak shaving and valley filling effects.

The invention discloses a power distribution network adaptive topological structure optimization method, system and device and a medium, and the method comprises the steps: obtaining related parameters of a power distribution network system, dividing the power distribution network system into different layers and different regions based on the related parameters of the power distribution network system, and constructing a layering and partitioning model; coordinating and solving the layering and partitioning model through an intelligent algorithm, and obtaining control variables of different regions; constructing an objective function based on the control variable, and solving the objective function by adopting an improved particle swarm optimization method to obtain a topological structure; and optimizing the topological structure by adopting a double-layer optimization strategy to obtain an optimization result, evaluating the optimization result, and feeding back the evaluation result to the target function. According to the method, the randomness and volatility challenges caused by multi-source resource access can be effectively dealt with, and the limitation of the traditional separation design is broken through the collaborative optimization of topology and control, so that the operation efficiency, the stability and the anti-interference capability of the power distribution network are improved, and the collaborative evolution of a topological structure and a control strategy is realized.

The invention provides a multi-microgrid system double-layer optimization method and system considering energy storage and new energy. The invention provides an innovative double-layer optimization scheduling method which is specially designed for a multi-microgrid system so as to realize efficient integration of energy storage equipment and renewable energy power generation. By adopting an improved sparrow search algorithm, the method aims to solve the problem of instability and high economic cost in a traditional scheduling strategy. In the upper layer, the optimization model focuses on the stability and reliability of the whole power distribution system, and the energy utilization efficiency and the system stability are ensured by globally coordinating the energy distribution of each micro-grid. In the lower layer, the optimization model in each micro-grid is dedicated to fine management of local energy resources and energy storage equipment so as to realize cost benefit maximization.

The invention provides an active distribution network scheduling optimization method comprising a micro-grid group, a medium and a system, and belongs to the technical field of active distribution network scheduling, and the method comprises the steps: obtaining an active distribution network parameter and a micro-grid parameter of each micro-grid in an active distribution network; calculating the similarity between any two micro-grids by using a preset micro-grid similarity function; establishing an active power distribution network stable scheduling equation set considering the power distribution network parameters and a micro-grid stable scheduling equation set considering the micro-grid parameters; a double-layer optimization model based on a grey wolf hunting optimization algorithm and a genetic algorithm is constructed, the grey wolf hunting optimization algorithm is used for active power distribution network optimization, and the genetic algorithm is used for microgrid optimization; when the grey wolf population is initialized, setting an initial population of a genetic algorithm for each micro-grid to represent various possible scheduling schemes in the micro-grid; by not executing outer-layer optimization and inner-layer optimization, the problem that the characteristics of the whole active power distribution network and the interior of the micro-grid are difficult to consider in the prior art is solved.

The invention discloses a park energy double-layer optimization scheduling method considering electric vehicles and demand response, and the method comprises the following steps: constructing a park integrated energy system which comprises an energy supply side, an energy conversion side and a demand side; a double-layer optimization model is established, the upper layer is a multi-target optimization model with the target of minimizing the total cost of the system, minimizing the carbon emission and maximizing the renewable energy consumption rate, and the lower layer is a single-target optimization model with the target of minimizing the charging cost of the electric vehicle user and the load fluctuation of the power grid; based on the Pareto theory, designing a multi-objective litsea coreana optimization algorithm to solve an upper-layer multi-objective model; introducing chaos initialization, adaptive step length adjustment and a Gaussian disturbance strategy improved litsea coreana optimization algorithm to solve a lower-layer single-target model; and through a vehicle network interaction and demand response cooperation mechanism, real-time scheduling is executed, a day-ahead plan is fed back and corrected, and finally an optimal park energy optimization scheduling strategy is solved.

The invention provides an electric vehicle charging station planning method and system, and the method comprises the steps: obtaining road network data and power distribution network topology information of a planning region, and constructing a time and space distribution model of an electric vehicle distributed load; based on historical travel behaviors and path probability distribution of the electric vehicle, constructing an electric vehicle behavior prediction model in combination with a graph neural network, and determining a candidate charging station service radius and a clustering region; according to the distributed photovoltaic access, the dynamic carbon emission factor and the vehicle charging and discharging behavior, constructing a dynamic optimization model of the coupling energy flow and the carbon flow; constructing an upper-layer charging station site selection model in the candidate charging station nodes, and determining an optimal site selection result by adopting a multi-objective optimization algorithm of an attention-based self-evolution Transform structure; establishing a lower-layer dynamic mixed integer programming configuration model based on the site selection result; and outputting an optimal deployment scheme considering energy efficiency, green low carbon and supply and demand balance by jointly solving the double-layer optimization model.

The invention discloses a micro-grid group double-layer collaborative scheduling method and system, and belongs to the technical field of micro-grid group collaborative scheduling, and the method comprises the steps: constructing a micro-grid group double-layer optimization scheduling model which comprises an upper layer model and a lower layer model; the upper-layer model establishes a model by taking the minimum operation cost as a target, and the lower-layer model establishes a model by taking the minimum operation cost of the sub-microgrid as a target; the SAC algorithm is improved by using VAE, a time decay factor is introduced in priority experience playback, the priority of experience is dynamically adjusted, the sampling frequency of experience entering an experience pool at different times is balanced, and an upper layer model is solved; an ADMM algorithm is improved based on a residual balance method, penalty parameters are adjusted in a self-adaptive mode, and a lower-layer model is solved; and integrating the adjusted optimal scheduling result of the upper-layer model and the optimal scheduling result of the lower-layer model, and outputting an optimal microgrid group double-layer optimal scheduling strategy. According to the method, the convergence speed and optimization accuracy of the micro-grid group double-layer cooperative scheduling method are improved.

The invention relates to the technical field of alternating-current and direct-current power distribution networks, in particular to an alternating-current and direct-current power distribution network SOP partition interconnection locating and sizing method and a related device, and the method comprises the steps: obtaining GIS multi-source data and power distribution network data of a target region; dividing a power supply area according to the GIS multi-source data and the power distribution network data to obtain a to-be-interconnected partition pair set; generating a high-quality interconnected partition pair set by using the to-be-interconnected partition pair set; constructing a target optimization model by taking the minimum total operation cost as an optimization target and taking a power balance constraint, a node voltage constraint, a branch capacity constraint, an SOP operation constraint, an SOP reactive power constraint and an SOP capacity constraint as constraint conditions; and inputting the high-quality interconnection partition pair set into the target optimization model, cooperatively solving the target optimization model by adopting a double-layer optimization algorithm to obtain the optimal position and the optimal capacity of the SOP, and solving the technical problem that the existing SOP locating and sizing method is difficult to meet the targets of engineering feasibility and optimal operation benefit at the same time.

The invention belongs to the technical field of power grid optimization, and particularly relates to a GAN driven learning-based power grid planning equalization optimization method and device, and the method comprises the steps: constructing a physical constraint-based space-time generative adversarial network PI-ST-GAN, building a composite risk index, and screening out a high-risk scene from a generated source-load probabilistic time sequence scene; constructing a risk-cost balanced double-layer optimization model; reconstructing the double-layer optimization model into a single-layer mixed integer programming (MIP) model based on a strong dual theory, and setting constraint conditions of the single-layer mixed integer programming (MIP) model; based on a single-layer mixed integer programming MIP model, a solver is utilized to carry out joint solution on a line investment scheme and operation scheduling variables, PI-ST-GAN is dynamically guided to enhance high-risk scene generation according to a solution result, an optimization-generation-feedback closed-loop collaborative iteration solution process is realized, and finally an optimal power grid planning scheme is output. The method can improve the toughness and economical efficiency of a power grid under the high permeation of renewable energy sources.

The invention discloses a new energy power station spot transaction strategy simulation and market optimization operation method and system based on a master-slave game, and belongs to the field of electrical engineering. According to the method, a master-slave double-layer game architecture is adopted, and a spot market transaction strategy optimization model of each new energy power station is firstly established; secondly, constructing an electric power spot market optimization clearing model; thirdly, the market optimization clearing model is equivalently converted into a constraint condition of a new energy power station optimization problem through a KKT condition, so that a non-convex nonlinear double-layer optimization model is converted into a mathematical programming problem with equilibrium constraint of each new energy power station; and finally, the optimization model of each new energy power station is converted into a constraint condition through the KKT condition, and a ''equalization problem with equalization constraint'' is formed in parallel. Therefore, simulation of transaction strategies of a plurality of new energy power stations can be realized, market optimization clearing is synchronously realized, and the method is used for guiding core businesses such as clearing price prediction and market benefit evaluation of market operators.

The invention discloses an efficient graph comparison learning method for social recommendation. As an emerging self-supervised learning normal form, graph contrast learning is excellent in response to data sparseness and cold start due to the fact that the graph contrast learning can effectively capture similarity and heterogeneity characteristics in a graph structure, although the learning normal form achieves a good effect in a recommendation system, the graph contrast learning can be used for solving the problems of data sparseness and cold start. However, the method still faces three defects: (1) average neighbor aggregation and a non-adaptive representation reading mechanism are adopted in a message propagation process, and high-quality node representation is difficult to learn; (2) a visual angle is enhanced by depending on a random disturbance generation graph during intervention of comparative learning, which may destroy the inherent structure of graph data and further weaken the accuracy of the model; and (3) equally treating all observation samples during parameter optimization, and neglecting the difference influence of positive samples in different training stages. Specifically, aiming at the problems, the invention provides an efficient graph contrast learning method (EGCL for short). The method comprises the following steps: firstly, designing a graph adaptive propagation module, improving an information propagation rule of a graph neural network by referring to a thermonuclear thought and an attention mechanism, and realizing differentiated aggregation of neighbor nodes by adopting a learnable weight distribution strategy; secondly, designing a double contrast learning normal form which does not need graph enhancement, and realizing mutual promotion of node characterization through intra-domain contrast learning (inter-CL) and inter-domain contrast learning (inter-CL); and finally, introducing a sample weight adaptive efficient optimization algorithm, converting the training process into a double-layer optimization problem, and adaptively adjusting the contribution degree of each sample to model optimization in different stages.

The invention discloses a method for constructing an energy storage optimal scheduling uncertainty set fusing estimation and optimization, and the method comprises the following steps: constructing a risk-avoiding energy storage optimal scheduling model, constructing a robust optimization model based on an electricity price uncertainty error vector, and enabling an uncertainty set to cover the error distribution under a preset probability threshold; constructing a double-layer optimization model taking a decision as a center, and jointly optimizing related parameters and variables to form a nested structure; executing a statistical feasible size calibration method, determining a minimum feasible set radius based on mahalanobis distance sorting of historical samples, and ensuring that coverage probability constraints are met; and calculating an objective function gradient, solving the gradient by applying an implicit function theorem and a KKT condition, iteratively optimizing parameters by utilizing a gradient descent algorithm after synthesizing a total gradient, and obtaining an optimal uncertainty set in combination with an early stop mechanism. According to the method, the problem that a model and a strategy are disjointed in a traditional two-stage method of estimation first and optimization second is solved, and the robustness and the economical efficiency of energy storage scheduling can be effectively improved.

The invention discloses an industrial park virtual power plant low-carbon optimization scheduling method based on augmented Lagrange safety reinforcement learning, and belongs to the technical field of virtual power plant low-carbon optimization scheduling related to carbon emission transaction. The problem that in the prior art, a traditional virtual power plant low-carbon optimization scheduling method is poor in scheduling performance due to the fact that the optimization result deviates from reality is solved. According to the method, a constrained Markov decision process solving framework based on an augmented Lagrange soft actor-commentator algorithm is proposed, a double-layer optimization problem is converted into a constrained Markov decision process by performing multi-dimensional modeling and establishing a double-layer optimization framework, and an augmented Lagrange function is introduced, so that the constraint Markov decision process is obtained. And completing the design of an augmented Lagrange reinforcement learning algorithm, and obtaining a trained low-carbon optimization scheduling neural network model. According to the method, the low-carbon optimal scheduling efficiency of the virtual power plant is effectively improved, and the method can be applied to optimization of the EV aggregator charging and discharging strategy in the virtual power plant in the industrial park.

An SMES joint planning method considering carbon emission comprises the following steps: step 1, building a ship integrated energy system model based on a multi-energy coupling principle, the model comprising an AC / DC hybrid ship power system architecture model, a ship energy equipment model and a ship navigation model; 2, establishing a double-layer optimization planning model considering carbon emission by adopting the ship integrated energy system model obtained in the step 1, optimizing equipment configuration at the upper layer of the model by taking the minimum total investment cost as a target, and optimizing an equipment scheduling strategy at the lower layer by taking the operating cost and the carbon emission as double targets; and step 3, solving the double-layer optimization planning model considering the carbon emission obtained in the step 2 based on a mixed integer linear programming method, and realizing collaborative optimization of optimal capacity configuration and an operation strategy of the system through a CPLEX solver.

The invention discloses a game theory-based capacity configuration optimization method for a water, wind and light storage micro-grid, and relates to the technical field of energy system operation optimization configuration. According to the method, four types of energy operators including water, wind, light and storage serve as game subjects, and the problems of multi-subject benefit conflict and capacity configuration are solved through a cooperative game model and a double-layer optimization framework. The method comprises the following steps: constructing an alliance total revenue function and verifying the super-additivity; fairly distributing earnings by adopting a Shapley value in combination with a capacity correction factor; an economical, stable and environment-friendly multi-objective optimization model is established, an improved NSGA-III algorithm is utilized to dynamically generate a Pareto optimal solution set, and an optimal capacity configuration scheme is selected through three-dimensional tradeoff. According to the invention, the economical efficiency of the system is improved by 51.6%, the output fluctuation is reduced by 47.4%, the carbon emission is reduced by 43.2%, and an efficient and balanced capacity optimization method is provided for the multi-energy microgrid.

The invention discloses an energy storage type intelligent soft switch double-layer optimization site selection method based on improved whale algorithm and second-order cone programming cooperation, and belongs to the technical field of power distribution networking and optimization operation, and the method comprises the steps: collecting and preprocessing the basic data of a power distribution network; a double-layer optimization model with the minimum daily operation cost of the system as the target is established, the upper layer takes the installation position of the energy storage type intelligent soft switch as a decision variable, and the lower layer takes the three-port power transmission value as a decision variable and constructs constraints based on the power flow equation after second-order cone relaxation and the energy storage operation limitation; an upper layer site selection problem is solved by adopting an improved whale optimization algorithm introducing a self-adaptive inertia weight, a lower layer operation problem is solved by adopting a second-order cone programming method, and collaborative optimization is realized through double-layer interaction. According to the method, global collaborative optimization of planning and operation is realized, the solving precision and efficiency of the site selection problem are improved, and the system operation cost and the network loss are effectively reduced.

The invention discloses a polyhedral equivalent modeling method and device for a distributed energy cluster, and the method comprises the steps: building corresponding mathematical models for description according to the operation characteristics of different distributed energy sources; a generalized model of the distributed energy is established based on the established mathematical model for unified description, and an individual polyhedron feasible region model of the distributed energy is obtained according to variable controllable characteristics; performing internal approximation by using a homogeneous polyhedron in individual polyhedron feasible region models of all distributed energy sources, and calculating Minkowski sum of approximation results to obtain an aggregation model; an aggregation model construction process is expressed as a multi-objective optimization process by adopting a double-layer optimization method, an optimal homogeneous polyhedron shape is searched on an upper layer, internal approximation results of all individual feasible regions are searched on a lower layer, optimization is executed by using a moth fire suppression algorithm, and the accuracy of an aggregation model is maximized. According to the method, the problems that distributed energy sources with different characteristics cannot be described by using a unified model and the model precision is poor can be solved.

The invention relates to the field of edge computing and task scheduling, and particularly discloses a reinforcement learning and heuristic driven edge computing dependent task scheduling method, which comprises the following steps of: firstly, merging tasks of multiple users, and adding a starting node for all tasks without precursor tasks; in upper-layer optimization, an artificial intelligence algorithm based on reinforcement learning is used for modifying the dependency relationship of tasks, namely, a new edge is added, so that a new constraint condition is added; in the lower-layer optimization, the tasks are unloaded and solved by adopting a traditional heuristic algorithm to obtain a scheduling sequence and a scheduling place of the tasks; and iteratively optimizing the upper layer and the lower layer to minimize the target function. According to the edge computing dependency task scheduling method based on reinforcement learning and heuristic driving, high-quality user experience is achieved by dynamically increasing the dependency relationship between the tasks, and the method can be generalized to a large-scale scene.

The invention discloses a double-layer optimization method and system for real-time flood control scheduling of a series reservoir group. The method comprises the steps that the maximum feasible impounding boundary of each time period is calculated based on the discharge capacity and storage capacity constraint of each reservoir; constructing a space-time risk response kernel matrix, encoding lag response and attenuation intensity of flood routing, constructing a dynamic risk potential energy index in combination with real-time reservoir capacity, and solving space distribution water quantity of each reservoir by taking minimization of the index as a target under physical boundary constraint; the space distribution water quantity is used as a guide target, an evolutionary algorithm embedded with a physical consistent projection operator is adopted, and iterative optimization is carried out on hourly output flow of each reservoir. According to the method, through matrix coding of the space-time risk and introduction of manifold projection solution, the problem that upstream and downstream hydraulic space-time coupling and strong physical constraint are difficult to solve is effectively solved, and the physical consistency and peak clipping efficiency of a scheduling scheme are improved.

The invention relates to the technical field of three-dimensional point cloud measurement and intelligent path planning, solves the technical problems that a traditional method is strong in shielding, large in normal change, limited in reachability and difficult in path execution in point cloud measurement, and particularly relates to a double-layer optimization measurement planning method for a complex model measurement task. Calculating and caching a visible point set of each viewpoint under the conditions of angle constraint and shielding elimination based on the adjacency graph of the candidate viewpoints and a visibility evaluation mechanism; in the inner layer optimization, an initial measurement route and a viewpoint set are generated from an initial viewpoint, and a continuous executable measurement route is reconstructed after full coverage constraint is met; in the outer layer optimization, a genetic algorithm is adopted to carry out global search on a starting viewpoint, and the coverage rate and viewpoint scale cost are used as fitness to output an optimal measurement viewpoint set and a measurement path. According to the method, high-coverage-rate measurement planning can be realized under complex shielding and strict view angle constraint conditions, the number of viewpoints and path redundancy are reduced, and the planning efficiency and robustness are improved.

The invention discloses a building energy system double-layer optimization method based on deep reinforcement learning. The method comprises the following steps: S1, extracting load data of a building energy system by adopting a clustering analysis method to construct an energy coupling model; s2, optimizing the coupled building energy model through a simulated annealing particle swarm optimization algorithm to construct an upper-layer energy capacity configuration model; s3, optimizing the coupled building energy model through a multi-agent near-end strategy algorithm to construct a lower-layer energy strategy scheduling model; s4, the upper-layer energy capacity configuration model obtains equipment capacity configuration information; s5, the lower-layer energy strategy scheduling model outputs an optimal capacity configuration and scheduling strategy of the building energy system according to the equipment capacity configuration information; according to the invention, through algorithm innovation and model refined design, planning-operation full-cycle optimization of the building energy system is realized.

The invention discloses a dynamic time-of-use electricity price pricing method based on two-stage robust optimization. According to the method, a two-stage time-of-use electricity price robust optimization model is constructed; according to a duality theory, converting the problem into a double-layer optimization problem which can be iteratively solved, further splitting the double-layer optimization problem which can be iteratively solved into a main problem structure and a sub-problem structure, and iteratively approaching an optimal solution by using a parameterized CCG algorithm; the optimal solution of the two-stage time-of-use electricity price robust optimization model is used as the basic electricity price, a deep reinforcement learning framework is constructed to dynamically adjust the time-of-use electricity price, the dynamic supply and demand environment is matched, and an efficient, flexible and sustainable pricing scheme is provided for a novel power system.

The invention provides a park light storage and charging shared energy storage pricing method based on a Stackelberg game, and relates to the technical field of novel power systems, and the method comprises the steps: constructing a park light storage and charging system, constructing an upper layer model taking a shared energy storage power station as a leader, and constructing a lower layer model taking a user group as a follower, the upper layer model and the lower layer model form a double-layer optimization model by using a Stackelberg game mathematical model, the double-layer optimization model is converted into a single-layer optimization model by using a KKT optimality condition, the single-layer optimization model is solved by using a solver, and a shared energy storage price is obtained. According to the invention, behaviors of leaders and followers in the market can be simulated, a more reasonable and efficient pricing strategy is realized, a globally optimal solution is found under a KKT optimality condition, and the energy consumption cost of a user group is reduced. And meanwhile, a differential privacy disturbance mechanism is introduced, individual data leakage is prevented, and the balance of privacy protection and optimization effectiveness is realized.

The invention discloses a source-load-storage collaborative optimization scheduling method and system based on a double-layer optimization model, and the method comprises the steps: obtaining corresponding power data of a power system in each time period, the power data comprising power data and load data; inputting the power data into a preset upper-layer optimization model, and performing optimization by taking the lowest total power generation cost as a target to obtain a first output value corresponding to each time period of the resource cluster; and inputting the first output value into a preset lower-layer optimization model, performing optimization by taking the lowest cost of each power generation unit as a target to obtain a second output value corresponding to each power generation unit in each time period, and controlling each power generation unit to generate power based on the second output value. According to the invention, efficient collaborative scheduling of the source-load-storage resources can be realized, and the stability, flexibility and economy of the power system are improved.

The invention provides a man-machine collaborative dynamic scheduling method fused with a double-layer optimization mechanism, and relates to the field of man-machine collaborative dynamic scheduling. According to a double-layer optimization mechanism provided by the invention, an offline optimization layer and an online real-time scheduling layer are organically combined: firstly, the offline optimization layer is based on a workpiece set, stations and robot resources, and is combined with a simulated annealing algorithm for adaptive optimization to obtain a global optimal solution; and then, taking a global optimal solution output by the offline optimization layer as an initial scheduling scheme of the online real-time scheduling layer, and performing real-time adjustment by adopting a multi-agent adaptive near-end strategy optimization algorithm. According to the method, the low efficiency of online scheduling from zero exploration is avoided, and the high efficiency of decision making is ensured. Besides, the online real-time scheduling layer constructs a collaborative decision-making system composed of a task allocation agent, a resource scheduling agent and a disturbance response agent, decision-making dimension pressure faced by a single agent is remarkably reduced through specialized labor division, and a complex interaction relation in man-machine collaborative scheduling is delicately processed.

The invention relates to the technical field of electric energy metering, and discloses an electric energy meter metering error dynamic compensation method. According to the method, original metering data in the running process of the electric energy meter are collected and subjected to principal component analysis processing to obtain a metering principal component sequence of the electric energy meter. Performing wavelet transform processing on the principal component sequence to obtain an approximation coefficient and a detail coefficient; a resistor and an electric reactor are utilized to form a dynamic compensation circuit, an approximation coefficient is used as a metering reference value, detail coefficients are distributed in the dynamic compensation circuit, and a distribution coefficient is obtained. And constructing an electric energy meter measurement error compensation double-layer optimization model, wherein the model comprises an upper layer optimization model and a lower layer optimization model. And after constraint conditions of the two models are determined, inputting the distribution coefficient into the models, solving through gradient descent according to the constraint conditions to obtain metering error compensation parameters of the electric energy meter, and performing dynamic error compensation on the electric energy meter according to the parameters. The method effectively improves the metering precision of the electric energy meter, and has an important practical value.

The invention belongs to the technical field of intelligent logistics, and particularly relates to a medical waste transportation scheme solving method and system based on a double-layer optimization model. The method comprises the following steps that 1, a double-layer optimization model used for solving the medical waste transportation problem is constructed, and the double-layer optimization model comprises the following three stages that in the first stage, the optimal position of a transfer center is determined; in the second stage, a collection route from the collection center to the transfer center is planned; in the third stage, the transportation route from the transfer center to the processing center is optimized; 2, solving the problems of the first stage and the second stage by adopting a Gurobi optimizer; and solving the problem of the third stage by adopting a self-adaptive neighborhood search algorithm. According to the model, the site selection cost and the infection risk are comprehensively considered, and the complexity of the algorithm and the limiting conditions of the real world are effectively dealt with. Therefore, the method has a good application prospect in solving the medical waste transportation problem.