82 results about "Robust optimization" patented technology

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 ship deployment and refueling joint optimization method considering multiple uncertainties. The method firstly constructs a nonlinear mixed integer programming deterministic model with the minimum total cost as the target, then introduces random oil prices and berthing times, constructs a robust optimization model containing an uncertainty set, and integrates the two to form a two-stage robust optimization model. The robust optimal ship deployment scheme and fuel injection strategy are obtained by iteratively solving the model through a Benders decomposition algorithm embedded with a branch and bound algorithm. The application constructs an integrated joint decision framework, simultaneously optimizes the ship deployment and fuel management in a unified mathematical model, captures and utilizes the internal relationship between different decisions, avoids the potential huge cost caused by separate decisions, fully considers the influence of the uncertainty of oil prices and port berthing times, makes the decisions have good risk hedging characteristics, fundamentally improves the scientificity and economy of the decisions, and effectively reduces the comprehensive cost of operation.

This invention discloses a resource collaborative scheduling and demand response optimization system and method for virtual power plants in industrial parks. It achieves global collaborative scheduling through a two-layer Actor-Critic network architecture, and uses a fusion of particle swarm optimization and fuzzy control to achieve grid-connected stability control. A three-layer fusion optimization architecture is constructed to complete the continuous amplitude calculation of demand response and the generation of equipment-level instructions. A unified algorithm support is provided by an intelligent optimization module. This invention effectively solves the problems of weak adaptability of existing global optimization models for virtual power plants in industrial parks, low level of intelligent decision-making in specialized sub-scenarios, and difficulty in coordinating multi-timescale scheduling and refined execution. It can achieve robust optimization and refined control in uncertain scenarios, balancing system economy, stability, and operational reliability. It is suitable for virtual power plants in industrial parks to efficiently participate in the power market and resource collaborative scheduling.

The application relates to a robust optimization design method of an airborne high-temperature superconducting generator, which comprises the following steps: a baseline design scheme is obtained by establishing a two-dimensional electromagnetic finite element baseline model and a total loss accounting model of the generator containing electromagnetic characteristics of superconducting coils; a feasible technical scheme set is obtained by performing scheme screening on pre-engineering hard constraints; an optimal candidate scheme is determined by performing fast optimization on the feasible domain by using a Taguchi orthogonal test, and the optimal candidate scheme is determined by main effect and range analysis or signal-to-noise ratio calculation; and a final design scheme is determined by taking the minimum performance fluctuation or the maximum signal-to-noise ratio as a criterion by introducing cold end temperature fluctuation, air gap assembly deviation, tape critical current dispersion and load and speed disturbance as noise factors for robust verification. The application can efficiently obtain an optimal design with high performance, strong engineering feasibility and operation robustness under limited simulation resources, and is especially suitable for a megawatt airborne high-temperature superconducting power generation system.

This invention belongs to the field of job scheduling, and relates to a method, apparatus, computer equipment, and storage medium for scheduling jobs in a computing cluster. The method includes: acquiring multi-source job data; preprocessing and constructing multi-dimensional features from the multi-source job data; constructing a heterogeneous ensemble learning model and performing quantile regression training; based on the quantile regression training results, quantifying prediction uncertainty and generating job runtime estimates and probabilistic prediction intervals; transforming the job runtime estimates and probabilistic prediction intervals into a set of budget uncertainties and establishing a robust optimization scheduling model; transforming the robust constraints in the model into equivalent deterministic linear constraints; embedding the robust optimization scheduling model into a rolling time-domain control framework, and combining it with a prediction error feedback mechanism to achieve online adaptive scheduling. This achieves the proactive quantification and utilization of job runtime prediction uncertainty; improves the reliability and performance stability of the system under uncertain environments; and enhances reliability.

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.

The application discloses a high-frequency transformer robust optimization method based on 6 sigma design; comprising: determining a preliminary design scheme: combining system requirements and performance indicators, selecting core materials, structure types and winding types, and constructing a basic design framework of the high-frequency transformer; a mathematical analytical calculation model is established; a robust optimization model is established; a multi-objective iterative optimization is carried out to iteratively solve the robust optimization model; output design scheme: compare the performance parameters of all schemes, and screen out the optimal design scheme as the final design scheme of the high-frequency transformer under the corresponding application scene. The application establishes a multi-objective robust optimization model based on 6 sigma theory, realizes multi-physical field collaborative optimization and manufacturing uncertainty quantization of the high-frequency transformer, breaks through the limitations of traditional deterministic optimization, can significantly improve product consistency, development efficiency and mass production reliability, and has important industrial application value.

The application discloses an AGC hierarchical optimization method and system based on wind power error modeling, and belongs to the field of power system dispatching optimization.The method comprises the following steps: constructing a wind power prediction error model and a wind power prediction error dynamic probability boundary model; constructing a two-stage robust optimization model comprising spare capacity configuration and real-time dispatching of unit output; and constructing an AGC hierarchical control architecture comprising a strategic decision layer, a coordination optimization layer and an execution response layer, wherein the strategic decision layer calculates a spare capacity configuration strategy of each unit based on the two-stage robust optimization model as a boundary constraint of the adjustment capacity of each unit in the coordination optimization layer, the coordination optimization layer calculates unit adjustment instructions of each unit with the aim of minimizing area control error (ACE) and adjustment cost, and the instructions are sent to the execution response layer for instruction execution.The application can reduce invalid spare allocation, shorten frequency recovery time and improve adjustment quality, and is helpful to guarantee the economy and stability of the system under high uncertainty.

The application discloses a kind of multi-micronet robust game optimization scheduling method and system based on dynamic auction official algorithm, and relates to energy system scheduling and optimization technical field.The method constructs system physical model and double-layer robust optimization model based on Stackelberg master-slave game, upper layer maximizes system operator's profit to formulate price signal, lower layer maximizes the worst scenario income of multi-micronet alliance to optimize resource scheduling, combined with multiple constraint conditions, Stackelberg equilibrium is solved iteratively using dynamic auction official algorithm.The application converges to global optimal solution quickly through three-dimensional hybrid driving price updating mechanism, realizes system economic benefit maximization, operation robust and reliable, and has good explainability and practical application value.

The application discloses an adaptive unit combination optimization method and system based on extreme scenario driving, constructs an uncertainty set in robust optimization; determines an overall model of adaptive robust optimization unit combination according to the constructed uncertainty set; converts the overall model of adaptive robust optimization unit combination into a random programming model with vertices and extreme scenarios, adds a full-scenario feasibility constraint and an unexpected constraint into the random programming model, and realizes adaptive unit combination optimization based on extreme scenario driving. The method solves the defects that the conventional uncertainty set robust optimization and the random programming do not satisfy the unexpectedness and full-scenario feasibility, and the scheduling instruction obtained by the model can be used for high-proportion new energy power market unit combination optimization decision.

Embodiments of the invention are directed to using a trained machine learning model to generate predicted circuit data for a circuit design, and computing an objective function using the predicted circuit data. Optimization of the objective function is performed to generate an optimal solution, and the optimal solution is mapped to the circuit design.

This invention provides a method for optimizing minimum EEOI speed of ships based on a PI-BT network, considering ocean current uncertainties, belonging to the field of ship energy efficiency optimization and intelligent navigation technology. Based on measured ocean current data from shipping routes, this invention mines the characteristics and probability distribution of ocean current uncertainties through statistical testing and wavelet decomposition techniques; derives the mapping relationship between EEOI and main engine speed and ocean current velocity, establishing a minimum EEOI speed optimization model; identifies core sensitive parameters through sensitivity analysis; constructs a physically guided Bayesian Transformer (PI-BT) network, designing a dual-channel input embedding layer, a Bayesian Transformer encoder, an EEOI physical information constraint layer, and a multi-objective optimization output module; and constructs a multi-component total loss function to complete network training, achieving robust optimization of minimum EEOI speed of ships under ocean current uncertainties. This invention integrates temporal modeling, uncertainty quantification, and physical constraint capabilities, significantly improving the accuracy, robustness, and computational efficiency of the speed optimization scheme.

This invention discloses a method and system for constructing a physically guided diffusion model for robust optimization, belonging to the technical field of power system optimization and scheduling. The method includes: constructing a wind farm topology information map and a topology-aware variational autoencoder (VAE). The VAE includes an encoder and a decoder. The encoder encodes the input wind power operation data into latent variables, and the decoder restores the latent variables to the wind power scenario. A diffusion model is constructed. The gradient of the predefined power system operating cost function onto the wind power scenario is projected onto the tangent space of the latent manifold to obtain a gradient-guided operator, which is then introduced into the diffusion model in the next round of solution. This method achieves dimensionality reduction mapping of high-dimensional wind power scenarios by constructing a topology-aware VAE that integrates wind farm topology information and introduces a gradient-guided diffusion process based on the power system operating cost gradient, thus solving the technical problem of low efficiency in solving traditional robust optimization subproblems.

A double-layer robust optimization method for section location in resilient distribution network considering failure probability and reinforcement strategy is proposed, including: establishing a failure probability-aware chance constraint model to quantify the failure probability and its spatial correlation of each section and generate a high-confidence failure scenario set; establishing a reinforcement strategy optimization model to select key sections for pre-disaster reinforcement by genetic algorithm and change the underlying failure risk distribution; establishing a basic model for failure section location under information distortion; combining the chance constraint model, the reinforcement strategy optimization model and the basic model for failure section location, a double-layer robust optimization model for "defense and location" coordination is constructed, and a hybrid intelligent optimization method is used to solve it. Through the coordination of probability awareness, spatial correlation modeling and reinforcement strategy, the method significantly improves the positioning accuracy and robustness of the distribution network under multiple failure and information distortion scenarios, providing a coordinated optimization decision basis for pre-disaster defense and disaster location in resilient distribution network.

This invention discloses an automatic calibration method and apparatus for UWB base station coordinates. The method includes: acquiring ranging data between base stations in a base station set and prior information about the base stations; constructing a constraint dataset based on the ranging data and prior information; constructing a nonlinear optimization model with base station coordinates as variables based on the constraint dataset, and performing robust optimization on the nonlinear optimization model to obtain an initial solution for the base station coordinates; calculating the uncertainty information of each base station coordinate based on the initial solution, determining the target base station to be fixed and the target coordinate dimension based on the uncertainty information, generating corresponding prior constraints based on the target coordinate dimension, and updating the constraint dataset based on the prior constraints; and re-executing the robust optimization solution based on the updated constraint dataset to obtain the final solution for the base station coordinates. This invention solves the technical problem of inaccurate base station coordinate determination in existing technologies.

A method of optimal scheduling of multi-energy complementary system considering bilateral uncertainties of supply and demand is proposed to deal with the uncertainty challenge of energy supply and load demand in multi-energy complementary system. Firstly, the supply characteristics of various energies and the dynamic variation law of load demand in multi-energy complementary system are analyzed in depth, and the random fluctuation of energy supply on the source side and the uncertainty of load on the demand side are quantified. On this basis, an optimal scheduling model is constructed with the minimum system operation cost and the maximum energy utilization efficiency as the objectives. To effectively handle the uncertainty factors, robust optimization or stochastic optimization methods are introduced to transform the bilateral uncertainties of supply and demand into processable constraint conditions or objective function items. Through the coordinated control of different energy conversion devices and energy storage devices, the reasonable allocation and optimal scheduling of energy in multi-energy complementary system are realized, and the actual example is simulated for verification.

The present application relates to neuromodulation and brain-computer interface technical field, propose a kind of robust optimization method and system under the condition of parameter uncertainty, robust optimization method includes constructing five-layer head tissue model including skin, skull, cerebrospinal fluid, grey matter and white matter based on the magnetic resonance image of patient, and the independent conductivity uncertainty radius of each layer of tissue is set, multi-dimensional ellipsoid uncertainty set is constructed;Min-Max worst case cost function is constructed and is solved, guarantee in ellipsoid set under the worst case, electrode current weight meets safety constraint;Worst case cost function is converted into second-order cone programming form using dual principle;Pseudo-trace perception sliding window mechanism is introduced to monitor the continuity of physiological signal or impedance data, when detecting non-physiological mutation, trigger re-optimization by calling historical stable parameters and amplifying uncertainty radius;When re-optimization has no solution, trigger safety derating mechanism, and the current instruction of previous period is scaled proportionally.

This invention discloses a method and apparatus for optimizing the feeding frequency of particulate friction stir additive manufacturing. First, feeding experiments are conducted under different operating conditions to collect pressure and displacement data. A mechanism-empirical hybrid model of the dynamic coupling relationship between pressure, displacement, and feeding frequency is established. An uncertainty set is defined. A robust optimization model for the feeding frequency is established. The dual objective is transformed into a single objective through weighted summation. The inner worst-case scenario is solved. The outer optimization employs adsorption kinetics-enhanced gradient descent. This invention uses an improved gradient descent algorithm as the core optimizer, requiring only one calculation of the inner worst-case scenario and gradient per generation. The computational load is significantly less than that of population-based algorithms, making it suitable for embedding in real-time control systems for online optimization. Combined with adaptive learning rate, dynamic diffusion intensity, and desorption probability adjustment mechanisms, the algorithm achieves rapid convergence while maintaining global search capability, providing an efficient and feasible technical solution for online dynamic tuning of the feeding frequency.

This invention discloses an event-driven intelligent demand response and scheduling method and device for electric official vehicles. The method collects historical vehicle usage event data for semantic modeling, extracts multi-dimensional features, and constructs structured feature vectors. It then uses the K-means algorithm for clustering to form three types of event databases. Next, it performs classification modeling using multi-distribution adaptive fitting and a CIA (Conformity Analysis) optimization strategy, outputting the statistical characteristics and distribution of the specified events. Finally, it generates a highly representative set of vehicle usage event scenarios using Monte Carlo sampling and backward scenario reduction. The method collects historical spot market prices and external environmental information to construct a set of input variables. It then processes the input variables into a conditional time series using a generative adversarial network to output future electricity price scenarios. Finally, it constructs a multi-day scheduling objective function, solves it using a robust optimization algorithm, and outputs charging periods and scheduling sequences. This invention solves problems such as low scheduling efficiency, high charging costs, and insufficient adaptation to grid demand response.

The present application relates to a kind of energy concentrator multi-objective collaborative method and system that can carbon bidirectional coupling, first obtain the equipment parameter, topological structure and other related data of energy concentrator, identify carbon potential, renewable energy output two kinds of heterogeneous uncertainty source;Again, energy-carbon bidirectional coupling optimization model that synchronously describes energy flow and carbon emission flow is established, realizes the dynamic interaction and mutual restraint of energy flow and carbon emission flow;On this basis, economic-low carbon-fair multi-objective optimization model is constructed, and ε constraint method is used to solve and generate Pareto optimal solution set;Through energy-carbon-fair balance degree index, final scheduling scheme is selected;Finally, hybrid stochastic-distribution robust optimization framework is used to process the uncertainty of carbon potential and renewable energy output respectively, and it is embedded in optimization model.The method can realize the collaborative optimization of energy concentrator energy flow and carbon emission flow, improve the economy, low carbon and energy use fairness of system operation, provide stable and efficient scheduling scheme for energy concentrator.

The application discloses a kind of robust optimization design method of double pressure angle asymmetric gear modification parameters considering uncertain working condition, steps include: defining the torque value range of gear service condition uncertainty and geometric design requirement;Gear pair meshing performance simulation model is constructed and simulation test design is carried out;Establish the multi-objective optimization model of modification parameter with the target of comprehensive service performance index and comprehensive robustness index;Pareto solution set is obtained by using intelligent optimization algorithm;Select the best scheme and export gear three-dimensional model.The application will load spectrum fluctuation into asymmetric gear modification optimization, through the combination of proxy model and multi-objective optimization, realize the gear modification parameter design with strong robustness to uncertain working condition, significantly improve the transmission stability and reliability of gear under variable load condition, while greatly reduce the calculation cost and time in optimization design process.

The present application relates to the field, especially to a thermal power generating unit power generation closed loop control optimization method and system based on AM-RF-PSO model, the method combines AM-RF prediction model and PSO particle swarm optimization model, makes data interaction unit realize the feedback of PSO optimization result to AM-RF model, forms the closed loop mechanism of " prediction-optimization-feedback-update ", makes the model can continuously adapt to the current working condition, significantly improves the robustness of system under variable working condition. The optimization result is not only used for control execution, but also acts on the update of the prediction model, so that the prediction model can perceive the change of optimization strategy, form the synergistic effect of bidirectional enhancement, avoid the safety risk caused by the mismatch of prediction and control, significantly enhance the peak shaving ability of unit and the synergistic effect of control parameter optimization, help thermal power enterprises to improve quality and efficiency, solve the problems of low load prediction accuracy, control parameter optimization lag and poor system synergy in the flexible power generation control of thermal power generating unit in the prior art.

The application belongs to the cross technical field of artificial intelligence and intelligent transportation system, and specifically discloses a freight electrification station path intelligent planning method based on multi-source OD data. The method comprises the following steps: constructing a multi-source OD data credible evidence and completion system based on a blockchain and a geographic weighted regression fusion cellular automaton; establishing a strategic layer stochastic robust optimization model with logistics cost, power grid load, construction investment and battery attenuation as multiple targets; designing a tactical layer flexible path scheduling algorithm combining an improved genetic algorithm and an ant colony mechanism; deploying an execution layer real-time response module based on digital twinning and SAC reinforcement learning; and integrating a three-dimensional collaborative perception and health management closed loop driven by the Internet of Things and digital twinning. Through the above technical scheme, the four-dimensional collaborative optimization of logistics efficiency, battery life, power grid stability and comprehensive cost is realized, and the robustness, response speed and operation benefit of the freight electrification network are improved.

This invention relates to a power allocation and voltage risk suppression method for charging stations based on master-slave game theory, belonging to the field of charging control technology. It includes: S1, collecting multi-source operational data, constructing a state dataset, and calculating the station-level schedulable power budget considering capacity, thermal stability, and voltage safety boundaries; S2, quantifying the session urgency index and constructing a sub-Bruker set based on Wasserstein distance; S3, constructing a master-slave game model, with the upper-level coordinator generating policy variables and the lower-level charging piles using the MM-ADMM algorithm and the water-filling method to solve for power allocation; S4, performing robust optimization enhancement based on the sub-Bruker set and conditional risk value; S5, performing grid security constraint verification and multi-objective collaborative correction; S6, issuing instructions and forming a closed-loop adaptive scheduling based on execution feedback. This invention uses voltage safety as the core game objective, co-optimizing it with quality of service, improving charging service and distribution network voltage safety under uncertain environments, and achieving soft capacity expansion of charging station power.

This invention discloses a method and system for predicting the risk of malignant cerebral edema after mechanical thrombectomy in ischemic stroke, aiming to address the pain points of existing prediction tools, such as insufficient accuracy, lack of clinical interpretability, and difficulty in directly supporting decision-making. The core solution involves: stably extracting key predictive variables from multi-source clinical and laboratory data through a multi-algorithm consensus feature selection mechanism (LASSO, Boruta algorithm); employing eight machine learning algorithms to select the best high-performance prediction model, and using repeated cross-validation and grid search for robust optimization; innovatively and deeply integrating the SHAP interpretability framework to achieve global, local, and individualized interpretation of the model predictions; and finally deploying it as an integrated clinical decision support system, outputting a visual report that combines risk probability, risk classification, and decision-making basis. This method significantly improves the early risk identification capability of malignant cerebral edema, and the model exhibits excellent discrimination and calibration.

This invention discloses a robust resource scheduling method and related apparatus for UAV-assisted cellular networks, comprising: 1) initializing the basic parameters of the UAV-assisted cellular network and sampling uncertain channel state information between the UAV and ground users to obtain uncertain channel state information samples; 2) constructing a polyhedral model with a high-probability region based on the uncertain channel state information samples; 3) constructing a two-stage robust optimization model, wherein the first stage is the main problem MP and the second stage is the subproblem SP; 4) linearizing the subproblem SP to obtain a mixed-integer linear programming problem SP. MILP 5) Design a solution to the mixed-integer linear programming problem SP. MILP The branch and bound algorithm; 6) Control the drone users and base station users according to the optimal transmit power of the drone users and base station users. This method and related devices can effectively improve the reliability and network utility of the drone-assisted network.