39 results about "Robust optimization problem" patented technology

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 invention belongs to the field of intelligent power grid dispatching, and particularly discloses an equivalent cluster aggregation method considering the running state of distributed resources. According to the method, firstly, the parameters of the distributed resources are obtained, constraint modeling is carried out on the basis of an equivalent cluster aggregation model, the constraint modeling comprises the running state of the distributed resources, and then robust optimization modeling is carried out on the constraint result to obtain an aggregation equation; then, a robust optimization problem is converted into a solvable single form by replacing Lagrange relaxation and dual operation, and finally, an aggregation feasible region is calculated through an operation layer, so that the defect that an existing equivalent clustering method lacks a robust optimization and calculation mechanism adaptive to a distributed resource running state and network constraint is overcome; the calculation deviation of the aggregation feasible region is avoided, and the solving complexity is simplified, so that the accurate description of the aggregation feasible region is realized, and the equivalent cluster can more reliably respond to the scheduling requirement of the smart power grid.

The invention belongs to the technical field of power grid dispatching, and particularly discloses a dual-port equivalent cluster aggregation method. According to the method, an independent aggregation operation interval is established for two ports by solving a specific robust optimization problem, an initial aggressive boundary is formed, and an initial high-dimensional polyhedral model is established. The key improvement is that the condition extreme value of the power of the other port is solved under the constraint of the operation interval of one port, so that the physical coupling relation is converted into a series of accurate boundary points, and meanwhile, the calculation complexity is also reduced. And finally, cutting the initial and aggressive high-dimensional model by using the boundaries, eliminating an infeasible space generated by neglecting coupling, and quickly generating a feasible dual-port aggregation interval. The problems of large model error and low aggregation efficiency caused by dual-port interconnection are systematically solved.

The invention discloses a multi-park integrated energy system collaborative optimization method based on an equivalent projection theory. The method comprises the following steps: establishing a park integrated energy system scheduling model considering fuel gas hydrogen doping; establishing a park integrated energy system robust collaborative scheduling model participating in the multi-park integrated energy system alliance by considering the uncertainty of hydrogen energy interaction and energy load; the established double-layer region integrated energy system robust collaborative scheduling model is equivalent to a single-layer mixed integer programming collaborative scheduling model based on an equivalent projection theory; deriving a decentralized solution of the established mixed integer programming collaborative scheduling model by using a dual decomposition algorithm; and establishing a multi-park integrated energy system collaborative optimization model based on the feasible region equivalent projection and solving the multi-park integrated energy system collaborative optimization model. According to the method, a two-stage robust problem is converted into an MILP type robust optimization problem through feasible region projection, the converted model does not need to be iteratively solved, and the solving time is greatly shortened compared with that of a traditional iterative method; and a decentralized solution is derived by using a dual decomposition algorithm, so that the convergence and optimality of the result are ensured.

According to the virtual energy storage equipment dispatching method and device, the storage medium and the computer equipment provided by the invention, when the virtual energy storage equipment is dispatched, the virtual battery model of each piece of equipment is constructed according to the physical parameters of each piece of virtual energy storage equipment, and the model is represented by a convex polytope in a semi-plane form, so that a power grid is more stable when facing load fluctuation; then, boundary summation is carried out on all the models to obtain an outer approximation model with aggregation flexibility, and therefore the rapid calculation characteristic of the NBM is reserved; then, a three-stage robust optimization problem is determined through a price guidance mechanism, and the three-stage robust optimization problem is converted into a mixed integer linear programming model by adopting a KKT optimization condition, so that the complexity of the problem is simplified, and the decomposability is realized while the problem is efficiently solved. And finally, optimizing and solving the external approximation model by using the mixed integer linear programming model to obtain a scheduling signal so as to perform parameter scheduling on each device, thereby effectively managing the energy demand and supply in the power grid.

The invention discloses a robust approximation method, device and system of a Koopman operator and a medium, and the method comprises the steps: constructing a Hankel matrix through time delay embedding for a nonlinear system in a noise environment, introducing a correction matrix to carry out the dynamic adjustment of the Hankel matrix, and obtaining a corrected Hankel matrix delta H; the Koopman operator approximation problem containing noise data is converted into a robust optimization problem, and a robust optimization objective function J is designed; and when a real-time data stream arrives, dynamically correcting delta H and re-solving J by utilizing the estimated value KN of the Koopman operator in the previous step and combining an incremental Hankel matrix updating strategy, and iteratively generating a Koopman operator KN + 1 at the current moment. According to the method, the influence of noise on the system is fully considered, high-precision data modeling is performed on the noisy nonlinear system, and modeling, analysis and prediction of the nonlinear system are realized at the modeling cost of the linear system.

This invention relates to intelligent transportation technology and aims to provide a data-driven robust site selection method for heterogeneous risk management in drone-assisted accident handling. The method includes: cleaning and preprocessing historical traffic incident data, extracting accident tags and performing quantitative classification based on the U-I-A multidimensional evaluation framework to obtain a structured dataset; decomposing uncertain demand into basic normal demand and multi-source heterogeneous fluctuations, and establishing a structured uncertainty set based on the quantitatively classified structured dataset; modeling the robust site selection planning model as a minimax robust optimization problem, and then equivalently reconstructing it into a solvable single-stage mixed integer linear programming model; using a solver to solve the problem, obtaining the globally optimal hangar site selection scheme and the number of supporting drones, and outputting the results. This site selection method enhances the reliability and robustness of the drone emergency response system; it enables scientific planning of drone site deployment, achieving rapid and stable emergency response.

The present invention discloses a novel multi-objective robust optimization method for power systems based on meta-multi-agent deep reinforcement learning, which relates to the field of optimization of wind power / photovoltaic and other new energy power generation connected to the power grid. Specifically, a multi-objective two-stage robust optimization mathematical model for a wind power / photovoltaic / thermal power hybrid energy system is first modeled, and then the two-stage robust optimization problem is solved by multi-agent reinforcement learning, and the multi-objective optimization problem is solved by meta-reinforcement learning, and finally the two are combined to form an end-to-end solution for multi-objective two-stage robust optimization of a wind power / photovoltaic / thermal power hybrid energy system. The present invention is used to solve the multi-objective two-stage robust optimization problem of new energy power generation connected to the power grid, and through advanced artificial intelligence algorithms, a more efficient, flexible and optimal solution to the scheduling strategy is achieved. The present invention can save optimization experience and can directly use the trained neural network model, which will greatly save computing cost and computing time in actual production applications.

The invention discloses a non-Gaussian random process prediction optimization control method based on a Wasserstein fuzzy set, and belongs to the technical field of blast furnace ironmaking system control. Aiming at the problems of modeling uncertainty and insufficient robustness under external disturbance and non-Gaussian disturbance existing in a traditional method, the method comprises the following steps: setting a coal injection amount, an oxygen-enriched flow, a cold air flow, a molten iron temperature and a Si content by collecting a pressure difference of a blast furnace ironmaking system; a blast furnace iron-making system is used for building a blast furnace neural network prediction model, output in a period of time in the future is predicted based on the current blast furnace state and the neural network model, a predicted output error is regarded as a random variable, and a Wasserstein fuzzy set with empirical distribution as the center is built to describe distribution uncertainty of the Wasserstein fuzzy set; in an MPC rolling optimization process, Wasserstein distance constraint is introduced, a distribution robust optimization problem is converted into a solvable optimization problem, and a group of optimal control sequences are obtained, so that a system does not depend on a specific random distribution hypothesis, and modeling errors and non-Gaussian external disturbance can be effectively processed.

δThe invention discloses a virtual unit model construction method considering a power supply type distributed energy cluster space-time coupling relation, and relates to the field of electrical engineering, and the method comprises the following steps: carrying out the modeling of a virtual unit based on the dispatching power y of a superior power grid, obtaining a virtual unit parameter u, and enabling the constraint Uy to be smaller than or equal to u; the method comprises the following steps: modeling a variable x in a power supply type distributed energy cluster and a superior power grid dispatching power correction value delta based on a power distribution network framework to obtain a constraint psi x < = psi, setting target functions as the sum of all time periods and the weighted sum of unit parameters u, and decomposing the problem into a two-stage adaptive robust optimization problem; the sub-problem is set to solve the maximum power correction under the current virtual unit parameters, and the main problem is set to solve the current unit parameter weighted sum according to the maximum power error; and iteratively solving the main problem and the sub-problems until the solving result of the sub-problems is 0, and obtaining parameters of the virtual unit model. Experimental results prove that the method has the advantages of being high in solving speed, good in solving effect and suitable for participating in power system dispatching.

The present invention relates to the field of subway station safety management and disaster emergency technology, and provides a method, device, and electronic device for optimizing the layout of life-saving facilities in subway stations during floods. The method comprises: obtaining passenger location distribution data and flood disaster data for a target subway station; modeling the passenger density information using a density-based spatial clustering algorithm with noise to identify high-density passenger areas; using a Bayesian network to divide areas within the subway station into different hazard levels based on the passenger location distribution data and disaster data; constructing a robust optimization problem based on the high-density passenger areas and hazard levels; and solving the robust optimization problem to determine the location and quantity of life-saving equipment. The method determines the optimized location and quantity of life-saving equipment by constructing and solving the robust optimization problem. Arranging life-saving facilities based on this robust optimization problem can improve the subway station's ability to rescue passengers in flood situations.

The present invention discloses a day-ahead distributionally robust scheduling method for a power grid transportation system based on potential game, which relates to the field of energy transportation. The method includes the following steps: Step 1, respectively establish the system models and optimization problems of the power grid and the transportation network; Step 2, respectively establish the uncertainty sets and fuzzy sets of traffic demand and photovoltaic output; Step 3, construct a centralized optimization problem based on the potential game function, and propose a reconstruction method and a solution algorithm for the distributionally robust optimization problem; Step 4, conduct a case analysis of the day-ahead optimal scheduling of the power grid transportation coupling system. The present invention proposes a distributionally robust optimization method based on the potential game framework for the day-ahead scheduling optimization problem of the power grid transportation coupling system, which can effectively reduce the conservativeness of the optimization decision and improve the safety and economy of the operation of the power system.

The invention discloses a multi-terminal flexible DC power grid distribution robust damping optimization method considering random fluctuation. The method comprises the following steps: firstly, building a second-order approximation model of a damping ratio about control parameters and wind power around a dominant oscillation mode; secondly, a Gaussian mixture model is adopted to describe statistical characteristics of wind power prediction errors, and a Wasserstein distance-based distribution uncertainty set is constructed around empirical distribution; expressing a'minimum damping lifting / constraint 'target as a distributed robust optimization problem, and converting the worst expected target and constraint into deterministic convex optimization by using GMM analysis statistics and quadratic form properties; and finally, the optimal parameters of the additional damper are obtained and issued for application. According to the method, robust setting is performed on any approximate distribution under the condition of not depending on accurate prior distribution, the critical modal damping can be remarkably improved, the probability border crossing risk can be inhibited, and the method has the advantages of being low in calculation overhead and robust in setting effect.

The invention belongs to the technical field of underwater positioning, and discloses an underwater robot positioning method and system based on a self-adaptive robust loss function. According to the method, based on an underwater acoustic signal two-way propagation time observation equation, the position and sound velocity of an underwater robot (AUV) are used as unknown quantities, and signal two-way propagation time is used as an error variable of an observed quantity; an adaptive robust loss function is introduced, a robust optimization problem taking the adaptive robust loss function as an optimization target is constructed, a progressive non-convexity method (GNC) is adopted to solve the robust optimization problem, a sequential updating method of the position and the sound velocity of the AUV is provided, sequential estimation of the position and the sound velocity of the AUV is carried out, and the position and the sound velocity of the AUV are estimated. And deriving a closed-form estimation result of the position of the AUV under the condition that the sound velocity is determined and a closed-form estimation result of the sound velocity under the condition that the position of the AUV is determined. According to the method, robust stable estimation of the position and the sound velocity of the AUV under the outlier interference condition is realized.

The invention belongs to the technical field of micro-grid two-stage robust scheduling problems, and particularly relates to a micro-grid robust scheduling problem solving method without dual transformation. Comprising the following steps: converting a two-stage micro-grid robust scheduling model into a main model with the same constraint condition and decision variable set; constructing an auxiliary model of the deterministic optimization scheduling main model, wherein the sum of related Boolean variables representing uncertainty in the auxiliary model and the main model is equal to 1; based on the benefit conflict between the main model and the auxiliary model, searching an optimal scene through the auxiliary model in a leading edge iteration manner, and screening the worst scene from the corresponding severe scenes by the main model; the algorithm provided by the invention can effectively identify the worst operation scene of the robust optimization problem.

The invention relates to the technical field of electric power system operation and optimization, in particular to a wind-thermal bundling system adjustable margin prediction method adapting to uncertain working conditions, which comprises the following steps of: 1, establishing a power model; 2, parameterized expression is carried out; step 3, establishing a robust optimization problem; 4, determining candidate upper and lower bounds; step 5, solving a sub-problem; 6, determining an interface power boundary; 7, outputting a prediction result; according to the method, a multi-period power model of a delivery interface point is established for a wind power and thermal power bundled delivery scene, wind power volatility, thermal power climbing / minimum output and other adjustment constraints are uniformly introduced into an interface adjustable margin for description, and a unified aggregation model of wind power-thermal power-energy storage and delivery point interface power is established, so that the regulation constraints of the delivery interface point are optimized. The upper and lower limits of thermal power output, the climbing constraint and other mechanism conditions are explicitly incorporated, so that the predicted interface power adjustable interval has decomposable and executable scheduling significance, and meanwhile, the interface power adjustable interval is lighter and supports the reduction of control complexity.

A control apparatus according to an embodiment is a control apparatus that embeds a virtual network for implementing provision of a service on a physical network, the control apparatus including: a first acquisition unit that acquires a predicted value of an amount of traffic of the service and a predicted value of an amount of power including renewable energy usable by each of physical nodes constituting the physical network; a second acquisition unit that acquires information regarding the physical network; a solution calculation unit that calculates an optimal solution of a 2-stage robust optimization problem regarding allocation of virtual nodes constituting the virtual network to the physical nodes and route determination between the virtual nodes to minimize a link congestion rate representing a maximum value of a utilization rate of a link constituting the physical network and a node power congestion rate representing a maximum value of a power consumption rate of a node constituting the physical network on the basis of the predicted value of the amount of traffic, the predicted value of the amount of power, and the information regarding the physical network; and a control unit that controls the virtual network embedded in the physical network on the basis of the allocation of the virtual nodes and the route determination that are represented by the optimal solution.

The invention discloses a digital twinning service optimization method based on information age driving, which adopts information age to represent the data freshness provided by digital twinning service, and on the basis, the utility gain of an edge computing network compared with a cloud architecture is used as the service quality, and the maximization of the utility gain is used as the target. The digital twinning model is deployed on an edge server and a digital twinning model is selected for the interaction request. According to the digital twinning service optimization method based on distributed robust optimization, the influence of the freshness of digital twinning data and the uncertainty of interaction requests on the service quality is comprehensively considered, and the deployment and selection strategy of digital twinning is jointly optimized. According to the method, based on the Wollaston distance, an algorithm for converting and solving a distributed robust optimization problem by applying multi-level dual transformation provides high-quality digital twinning service under the condition of an unpredictable extreme interaction request.

The invention relates to a robust optimization algorithm-based power grid emergency material scheduling method under rain and snow freezing disasters, which comprises the following steps of: acquiring meteorological data and traffic facility information under the rain and snow freezing disasters, and constructing a line fault probability prediction model; monte Carlo simulation is adopted to generate disaster scenes, and a typical disaster scene set is obtained through a scene reduction technology; constructing a joint scheduling optimization mathematical model considering emergency material allocation and emergency equipment allocation, wherein the joint scheduling optimization mathematical model comprises a pre-disaster pre-deployment stage and an in-disaster dynamic response stage; solving a two-stage robust optimization problem by adopting an improved column and constraint generation algorithm, gradually converging to a robust optimal solution, outputting a scheduling scheme of an optimal emergency material and an emergency repair team, determining a repair target and an optimal driving path of the repair team, and generating a collaborative recovery strategy of a post-disaster power grid and a traffic system; and rapid recovery after disasters and reasonable configuration of emergency resources are realized, so that the emergency response speed and recovery capability of the power grid under rain, snow and freezing disasters are improved.

The power system real-time scheduling method and system considering energy storage differentiated response comprise: updating wind power and photovoltaic output and load prediction data every preset time interval in a scheduling period; a robust optimization model containing new energy prediction error uncertainty and energy storage differentiated response is established, including constructing an objective function, robust operation constraints of different types of energy storage power stations, system power balance constraints and line transmission capacity constraints; a robust dual method is used to convert the robust optimization problem containing interval uncertainty into a deterministic mixed integer linear programming problem; according to the wind power and photovoltaic output and load prediction data in each scheduling period, the robust optimization model of the power system real-time scheduling is solved every other scheduling period, and the real-time power instructions of two types of energy storage and conventional units are output. The present application can fully exert the regulation flexibility and comprehensive benefits of different types of energy storage power stations, can adapt to different regional new energy fluctuation characteristics, and can improve the new energy consumption capacity.

The invention belongs to the technical field of UAV-assisted MEC. The invention provides a joint optimization method for auxiliary edge calculation of an anti-interference unmanned aerial vehicle under energy constraint, and the method comprises the steps: constructing a system model comprising a ground terminal, the unmanned aerial vehicle and an interference source, and determining the position of each node in a time slot and a trajectory variable of the unmanned aerial vehicle; calculating a channel gain estimation value based on a line-of-sight propagation model, and introducing an imperfect channel state information error boundary; the method comprises the following steps: constructing a joint optimization problem taking maximization of an uplink average unloading rate as a target by combining propulsion energy consumption of an unmanned aerial vehicle and an interference source energy constraint, converting the problem into a robust optimization problem through a worst case criterion, and decomposing the problem into two sub-problems of scheduling power optimization and trajectory optimization; and carrying out iterative solution by utilizing an alternating optimization strategy and a continuous convex approximation technology until the optimal unloading scheduling, transmitting power and flight path are obtained through convergence. According to the method, the joint optimization problem of resource limitation in a complex interference environment is effectively solved, and the system communication performance is remarkably improved.

The invention relates to the technical field of power system planning, and discloses a distributed robust optimization-based source-grid-load-storage collaborative planning method, which comprises the following steps of: constructing a probability distribution fuzzy set based on a Wasserstein distance to describe the uncertainty of wind power output, photovoltaic output and load demand in a power system; based on the probability distribution fuzzy set based on the Wasserstein distance, a source network load storage collaborative planning model is constructed, and the source network load storage collaborative planning model takes minimization of the sum of investment cost and expected operation cost as a target function and comprises a plurality of system operation constraints; converting the source network load storage collaborative programming model from a two-stage distribution robust optimization problem into a mixed integer linear programming problem by using a duality theory; and solving the mixed integer linear programming problem to obtain an optimal scheme of power grid capacity expansion, energy storage configuration and demand response configuration. According to the distributed robust optimization-based source network load storage integrated planning method, collaborative optimization of each link and effective processing of uncertainty are realized.

The invention belongs to the technical field of wireless communication and edge computing, and discloses an unmanned aerial vehicle mobile edge computing system time delay optimization method based on reconfigurable intelligent surface assistance in a non-ideal environment, and the system comprises a UAV carrying an MEC server, an RIS equipped with a plurality of passive reflection units, and a plurality of Internet of Things (IoT) devices. And a wireless information and energy simultaneous transfer (SWIPT) technology is integrated to support dynamic switching between a power division (PS) mode and a time switching (TS) mode. Aiming at multiple reality constraints such as non-ideal channel state information (CSI), transceiver hardware damage, RIS discrete phase offset and nonlinear energy collection, the method provided by the invention takes minimization of total task processing delay of scheduled IoT equipment as a target to establish a worst-case robust optimization problem; the method comprises the following steps: jointly optimizing an unmanned aerial vehicle receiving beam forming vector, an RIS phase shift matrix, an SWIPT mode parameter and a task division ratio through an alternating optimization (AO) framework, and dynamically selecting a PS or TS mode as an optimal working mode based on an optimization result.

The invention discloses an automatic driving behavior decision robust optimization method guided by a long-period historical track, and belongs to the technical field of automatic driving. The method comprises the following steps: setting the number of historical decision trajectory windows, storing historical decision trajectory points of an automatic driving vehicle, and constructing a historical decision trajectory probability distribution model; obtaining a track point corresponding to the current decision, performing coordinate transformation on the track point corresponding to the current decision to obtain a space coordinate under a unified coordinate system, and constructing a probability distribution model of the track point corresponding to the current decision; calculating decision similarity measurement guided by historical decisions; and designing a behavior decision objective function according to decision similarity measurement to solve a robust optimization problem. According to the method, the problems of decision jitter and strategy abrupt change caused by dependence on single-frame observation or short-term trajectory information in a traditional method are avoided, the decision robustness, stability and reliability of an automatic driving system in a dynamic complex traffic environment are improved, and the risk of collision with surrounding traffic participants is reduced.

This application belongs to the field of smart grid dispatching, and specifically discloses an equivalent cluster aggregation method that takes into account the operating state of distributed resources. This application first obtains the parameters of the distributed resources and performs constraint modeling based on the equivalent cluster aggregation model, including the operating state of the distributed resources. Then, it performs robust optimization modeling on the constraint results to obtain its aggregation formula. Next, by replacing Lagrange relaxation and dual operations, the robust optimization problem is transformed into a solvable single form. Finally, the aggregated feasible region is calculated through a computational layer. This solves the shortcomings of existing equivalent cluster methods that lack robust optimization and solution mechanisms adapted to the operating state of distributed resources and network constraints, avoids calculation bias in the aggregated feasible region, and simplifies the solution complexity, thereby achieving an accurate characterization of the aggregated feasible region and enabling the equivalent cluster to respond more reliably to the dispatching needs of the smart grid.

The invention relates to the technical field of power systems, and discloses a zero-carbon building robust planning method considering a plant carbon sink mechanism, which comprises the following steps of: establishing a mathematical model, and quantifying the energy conversion efficiency, the carbon absorption capacity and the dynamic operation constraint of the mathematical model; based on the historical load, the photovoltaic data and the mathematical model, constructing a deterministic planning model, and coupling electricity-hydrogen-carbon balance constraints; introducing an interval uncertainty set to describe the random fluctuation of photovoltaic output, extending a deterministic planning model into a robust optimization problem, and controlling the conservative degree by adjusting a parameter gamma to form robust planning schemes with different anti-disturbance capabilities; and solving a robust planning scheme by using GUROBI in an MTALAB environment, and calculating configuration results under different gamma values. According to the zero-carbon building robust planning method considering the plant carbon sink mechanism, the reliability and economical efficiency of the system are balanced by constructing the interval uncertainty set, and the carbon neutralization measurement and calculation precision is improved by introducing the plant photosynthesis dynamic model, so that the building micro-grid has more comprehensive carbon neutralization capability.

The application belongs to the technical field of power grid dispatching, and specifically discloses a multi-port power grid aggregation method based on a cut plane projection. The application obtains constraints by modeling a multi-port power grid based on distributed energy and solves a robust optimization problem to obtain each port independent aggregation interval, which is integrated into an initial aggregation space. Then, a cut plane problem is solved to identify an infeasible region and obtain a cut plane constraint and a worst dispatch instruction. Subsequently, a feasible dispatch instruction closest to the worst dispatch instruction in the current aggregation space is found according to the cut plane constraint, and the core function of the feasible dispatch instruction is to provide a reference point for each time interval projection. The cut plane constraint is projected to each time interval space with the reference point, the aggregation space is cut and updated, and the infeasible region is gradually cut off through iteration of the process. This gradually and accurately depicts the interconnection characteristics between multi-ports, solves the aggregation difficulty problem caused by the lack of accurate description means in the prior art, and improves the aggregation reliability compared with the prior art.

The invention discloses a passive body area network communication perception energy transfer resource allocation method based on a DRO algorithm, and relates to the field of passive body area networks, and the method comprises the steps: constructing a passive body area network system which comprises a hybrid access node, user receiving equipment and a plurality of passive label nodes which are connected with one another; constructing a communication perception energy transfer fusion model based on a passive body area network system; establishing a distributed robust optimization problem model based on the fusion model by taking maximization of communication, perception and energy transfer indexes as targets; utilizing a conditional risk value algorithm to convert opportunity constraint conditions in the distributed robust optimization problem model; according to the CVaR distributed robust optimization method, through the CVaR robust optimization algorithm and the alternating optimization algorithm, the resource allocation problem of the passive body area network under channel uncertainty is solved, and cooperative guarantee of multiple performance indexes and improvement of the overall efficiency of the system are achieved.

The invention provides an incomplete information constraint uncertain game strategy optimization method and system, and the method comprises the steps: building a nonlinear uncertain differential game process model according to the incomplete information characteristics of a nonlinear uncertain game dynamic process in a strong confrontation scene, and enabling the strategy amplitudes and game states of two parties to be constrained; taking an opponent game strategy and an unknown uncertain item as a disturbance item, introducing a non-quadratic form energy function, a control barrier function and a disturbance function upper bound on the basis of describing a nonlinear disturbance system of a nonlinear uncertain game dynamic process, and constructing a performance index function; converting a safety robust game problem of a nonlinear uncertain dynamic game process into a nonlinear nominal system constraint robust optimization problem; and introducing an event-triggered security robust reinforcement learning technology, and completing approximate solution of an HJB equation by evaluating an online approximation optimal performance index function of a neural network to obtain an own optimal game strategy. According to the method, the uncertain game strategy can be optimized.

The invention relates to a train energy-saving distributed robust optimization method and device. An objective function and corresponding constraint conditions are determined. Segmenting the running interval of the train to obtain at least one sub-interval, converting the target function into an energy function sum used for solving consumed energy corresponding to each sub-interval, and determining a constraint condition corresponding to each energy function; historical data distribution of the corresponding operation interval of the train and a corresponding fuzzy set are determined, wherein the historical data distribution comprises at least one candidate data distribution; and converting the sum of the energy function into a distributed robust optimization function according to the distance of the bulldozer, and converting a corresponding constraint condition. And optimizing based on the fuzzy set to obtain a speed curve corresponding to the expected energy consumption. The train energy-saving optimization problem is converted into the distributed robust optimization problem based on the bulldozer distance, the problem is solved according to the fuzzy set corresponding to historical data distribution, and the speed curve corresponding to expected energy consumption is determined based on unfixed data.