14 results about "Penalty coefficient" patented technology

The invention provides an asset allocation method and device based on a global optimization algorithm and electronic equipment. The method comprises the following steps: constructing an asset allocation model which aims at maximizing the asset yield and takes the allocation proportion of various assets as a variable under each constraint condition, wherein each constraint condition comprises the limitation on the value interval of the variable; determining a penalty coefficient of each constraint condition according to a business rule, and constructing an optimization objective function of the asset allocation model according to each penalty coefficient; and performing iterative solution on the optimization objective function by adopting a global optimization algorithm until an optimal resource allocation scheme is obtained, summing current values of all variables in each iterative solution process to obtain a judgment metric, and judging whether optimization process calibration is executed or not according to the judgment metric and a threshold value corresponding to the judgment metric. The optimization process calibration is used to avoid the global optimization algorithm falling into a locally optimal solution.

The invention provides a scheduling method and system of an integrated energy system and a storage medium, and the method comprises the steps: constructing a mixed integer nonlinear programming model with the minimum total operation cost as a target, carrying out the iterative solving of the model until the upper and lower bounds converge, solving a main problem to obtain a tentative solution of a unit start-stop scheme and the lower bound of the current iteration, and carrying out the scheduling of the unit start-stop scheme. The main problem comprises a regularization item and is constrained by a cut plane, and a penalty coefficient of the regularization item is adjusted according to a distance between a tentative solution and a historical optimal solution and a lower bound gain rate; fixing a tentative solution of the discrete variable, setting solution precision according to a current upper and lower bound gap and a convergence rate, and solving an energy flow sub-problem to obtain a continuous variable solution and an upper bound; if the sub-problem is feasible, weighted averaging is carried out based on dual solutions in historical iteration to generate an optimal cut plane, and the optimal cut plane is added to the main problem; and if the sub-problem is not feasible, identifying a key constraint cluster causing infeasibility, generating a feasible cut plane and adding the feasible cut plane to the main problem.

The application discloses an automatic theorem proving method based on neural network guidance and quantum optimization, comprising the following steps: extracting the logical structure features of a propositional logic task, and predicting the applicability weight of natural deduction rules by using a preconfigured neural network; performing a forward reasoning iteration process, dynamically identifying potential intermediate conclusions to determine a variable space, and constructing a quadratic unconstrained binary optimization (QUBO) model according to the variable space; in the construction process, the applicability weight is used to adjust the penalty coefficient of the rule constraint term, and the energy topography of the solution space is reshaped; according to the problem size, the computing resources are adaptively scheduled, the model is mapped to a coherent Ising machine or a classical simulation backend for solving, and the proof path is reconstructed through double verification. The application effectively solves the problems that the traditional method is blind in rule selection and the static optimization model is difficult to handle dynamic multi-step reasoning, and the efficiency and scalability of the proof are improved.

The invention provides a rock high slope mechanical parameter inversion method based on an IPSO-LSSVM-MCMC coupling model, and the method comprises the following steps: optimizing a penalty coefficient gamma and a kernel function parameter sigma of an LSSVM algorithm through employing an IPSO algorithm, and enabling an error between a monitoring point displacement value predicted by the LSSVM algorithm and a numerical simulation value to be minimum; the IPSO-LSSVM coupling model is substituted into an MCMC algorithm, slope monitoring displacement data are input, and corresponding rock mass mechanical parameters are obtained through inversion of the IPSO-LSSVM-MCMC coupling model. The method has the advantages that the precision and efficiency of rock mass mechanical parameter inversion are remarkably improved, and innovative technical support is provided for safety evaluation of major geotechnical engineering.

A microgrid multi-resource collaborative autonomous optimization method and system based on coupling constraint relaxation abstracts each physical unit with power regulation capability within the microgrid as an identical regulation agent. The minimum sum of the local objective functions of all regulation agents is used as the global objective function. Relaxation variables are introduced to correct the total power balance constraint satisfied by the global objective function. A global penalty term common to all regulation agents is constructed based on the relaxation variables and penalty coefficients, and this global penalty term is superimposed on the global objective function to obtain the collaborative autonomous optimization objective. Based on the collaborative autonomous optimization objective, a distributed gradient projection method is used to iteratively predict the output of each regulation agent. When the iterative convergence criterion is met, the iteration stops and the predicted output of each regulation agent is output as the result of the microgrid multi-resource collaborative autonomous optimization. This forms a microgrid internal collaborative optimization system that is decoupled from resource types, has no external dependencies, and possesses adaptive capabilities, realizing autonomous collaboration among multiple units under the constraint of coupling.

The present invention provides a calculation program, calculation method, and information processing device that can appropriately adjust the penalty coefficient. [Solution] The computer is instructed to search for a solution using a cost function and penalty term obtained by incorporating continuous relaxation into a discrete optimization problem, and in the process of doing so, it performs a process of changing the penalty coefficient of the penalty term using the gradients of the cost function and the penalty term.

This application relates to a method, apparatus, equipment, medium, and product for the coordinated operation of a power distribution network and buildings. The method includes: constructing an augmented Lagrangian function based on a pre-built two-layer model and consistency constraints on coupled variables; iteratively solving the two-layer model with the objective of minimizing the augmented Lagrangian function to obtain the optimal solutions for the upper-layer and lower-layer decision variables; determining a coordinated control strategy based on the optimal solutions for the upper-layer and lower-layer decision variables; and controlling the coordinated operation between the power distribution network and each building according to the coordinated control strategy. The upper-layer model is coupled to each lower-layer model through coupling variables, which include the target power of the upper-layer model and the actual response power of the lower-layer model. The augmented Lagrangian function includes an economic coordination term based on Lagrange multipliers and a quadratic penalty term based on penalty coefficients. This method balances reliability and computational efficiency.

There is provided a non-transitory computer-readable medium storing a calculation program for causing a computer to execute a process. The process includes, in searching for a solution using a cost function and a penalty term obtained by introducing continuous relaxation into a discrete optimization problem, changing a penalty coefficient of the penalty term by using a gradient of the cost function and a gradient of the penalty term.

The invention discloses a dam abnormal data generation method based on a dynamic penalty weight CGAN, and belongs to the technical field of hydraulic structure safety monitoring and intelligent analysis. Aiming at the problems of unstable training, fixed penalty coefficient and insufficient authenticity of generated samples in dam monitoring data modeling of a traditional generative adversarial network, a dynamic penalty weight mechanism is introduced into a Wasserstein adversarial training framework, gradient penalty intensity is adaptively adjusted according to fluctuation of environmental variables such as water level and air temperature, and the dynamic penalty weight mechanism is introduced into the Wasserstein adversarial training framework. Keeping balance training of the model under different working conditions; and meanwhile, a multi-head self-attention mechanism and a bidirectional long-short-term memory network are combined in the generator to realize joint modeling of global association and local time sequence characteristics, so that a high-quality abnormal sample conforming to a dam physical rule is generated. Experiments show that the method can significantly improve the diversity and authenticity of generated data, enhances the accuracy, recall rate and AUC index of a downstream anomaly detection model, and has high engineering application value and popularization potential.

This application discloses an artificial intelligence-based dynamic control optimization method for warehouse environments, mainly relating to the field of control optimization technology. It addresses the problems of existing solutions where feature representations fail to reflect the local fluctuation patterns and phase differences of different microenvironments within the warehouse. The method includes: adjusting the convergence coefficient, adaptive adjustment factor, and spiral shape parameters involved in the whale optimization algorithm by introducing a population diversity index; employing a fitness function that integrates the total energy consumption target and constraint violation penalties, and using a dynamic penalty coefficient adjustment strategy based on the total historical violations, combined with fuzzy logic to smoothly change the penalty during the iteration process; constructing the next generation of whale population after each iteration through elite retention, crossover operations, and diversity maintenance; and outputting the current globally optimal whale individual position when a preset termination condition is met.

The invention discloses an automatic theorem proving method based on neural network guidance and quantum optimization, and the method comprises the steps: extracting the logic structure features of a propositional logic task, and predicting the adaptability weight of a natural deduction rule through a pre-configured neural network; executing a forward reasoning iteration process, dynamically identifying a potential intermediate conclusion to determine a variable space, and constructing a quadratic unconstrained binary optimization QUBO model according to the variable space; in the construction process, the penalty coefficient of the constraint term of the rule is adjusted by using the adaptability weight, and the energy landform of the solution space is remodeled; computing resources are adaptively scheduled according to the problem scale, the model is mapped to a coherent Isin machine or a classical simulation back-end of the coherent Isin machine to be solved, and a proof path is reconstructed through dual verification. According to the method, the problems that rule selection is blind and a static optimization model is difficult to process dynamic multi-step reasoning in a traditional method are effectively solved, and the proving efficiency and the expandability are improved.

The invention discloses a reliability structure-control coupling design optimization method, and particularly relates to the technical field of electromechanical equipment optimization, and the method comprises the steps: building a mathematical model for electromechanical equipment deterministic structure-control coupling design optimization, and solving to obtain optimal structure physical design parameters; fixing the optimal control variable, and performing reliability analysis on the optimal structure physical design parameters to obtain In-MPP points and constraint function values in an index circle; the initial penalty coefficient is used as a starting point in subsequent iteration, and the penalty coefficient is updated in combination with the change of the target function value and the satisfaction change of the constraint function at the In-MPP point; and in the iteration process, calculating a translation vector of an inequality constraint function, constructing and solving a decoupled deterministic structure-control coupling design problem in combination with a penalty coefficient, and repeating the steps to finally obtain a convergent design scheme meeting the reliability requirement. According to the method, the reliability of an intermediate result in the optimization process is improved, the convergence efficiency is improved, and the application range of the reliability structure-control coupling design optimization method is expanded.

The application discloses a bearing fault early warning method and system based on IWAOA improved SVMD noise reduction and LSTM-CNN classification, and the optimal decomposition layer number and penalty coefficient of successive variation modal decomposition SVMD are adaptively obtained by using IWAOA, the bearing vibration signal is decomposed by SVMD, the signal is reconstructed and denoised by screening effective modal components through a correlation coefficient, the acceleration peak value and kurtosis index of the reconstructed signal are extracted to form a feature vector, after dimension reduction by PCA, the feature vector is input into an LSTM_CNN classification model combined with a long short-term memory network and a convolutional neural network for training and classification, the time sequence dependence is captured, and the accurate identification of the normal and fault states of the bearing is realized. The application has better denoising effect, faster calculation speed, and the bearing fault early warning accuracy reaches 100%, and is suitable for fault early warning of different types of bearings and under different working conditions.