11 results about "Quantum particle" patented technology

The invention discloses a multi-spectral radiation temperature measurement method and system for improving a quantum particle swarm, relates to the field of radiation temperature measurement and photoelectric signal processing, and solves the problems that when an existing standard PSO algorithm is applied to high-dimensional and nonlinear multi-spectral inversion, local extreme values are likely to be trapped, and the self-adaptive capacity is poor. Setting parameters, generating an initial particle position by using Tent chaotic mapping, calculating the particle fitness of an initialized population, updating initial individual optimum and global optimum, and calculating an average optimal particle position; updating the optimal particle position through a Levy flight mechanism; and repeating the steps, calculating the multispectral radiation temperature measurement target function value of the updated position, updating the initial individual optimum and the global optimum, and if an error threshold value or the maximum number of iterations is met, outputting the global optimum solution. The method is also suitable for the application field of inverting the real temperature and the spectral emissivity of the target from the multi-channel spectral radiation signals and the like.

The application provides a capacity optimization configuration method and system for voltage quality of a local power distribution system, and belongs to the field of power distribution system optimization; the method comprises the following steps: establishing a mathematical model of a local power distribution system containing output models of a wind driven generator, a photovoltaic generator, a battery and a diesel generator; establishing a capacity optimization configuration model taking the number of power sources in the system as optimization variables, taking minimization of total operation cost of the system as an objective function, and containing voltage quality constraints; iteratively solving the model by using an improved quantum particle swarm algorithm to obtain an optimal capacity configuration scheme; in the iteration process, the improved quantum particle swarm algorithm introduces a Levy flight strategy to randomly disturb and update local attractors of particles when the particles fall into local optimization, so as to jump out of local extreme values; meanwhile, weights are dynamically allocated according to fitness values of the particles, a weighted average optimal position is calculated to guide particle updating, and adaptive balance of global search and local refinement is realized.

The invention relates to a K-nearest neighbor method based on quantum particle ball generation and quantum HNSW improvement, and belongs to the technical field of quantum calculation and machine learning. The method comprises the following steps: firstly, preparing classical data into a quantum state through a quantum random access memory and quantum angle coding, and efficiently generating high-purity pellets by utilizing a quantum Swap test and quantum minimum search so as to reduce the data scale; and then, dynamically adjusting the insertion hierarchy of the node in the hierarchical navigation small world according to the particle ball purity, and optimizing the graph structure balance. And finally, performing multi-layer iterative search on the constructed quantized graph index, and determining categories according to majority voting of neighbor nodes. According to the method, the parallel advantage of quantum computing is combined with an improved graph index structure, and the efficiency and expandability of a large-scale high-dimensional data classification task are remarkably improved.

The application discloses a kind of optimal arrangement method of non-uniform linear array for direction finding, comprising: based on preset array optimal arrangement model, the position, speed and initial local optimal position of initial quantum particle are generated;Based on preset array optimal arrangement model, the first fitness function based on minimum interval criterion and minimum maximum relative sidelobe level is constructed;Based on the first fitness function, the initial global optimal position of quantum particle is obtained;Based on initial local optimal position and initial global optimal position, the speed and position of quantum particle are updated, and the global optimal position is obtained;Based on global optimal position, the optimal array arrangement result is obtained.The application designs an optimal special array arrangement method based on minimum interval criterion and minimum maximum relative sidelobe level, finds optimal array arrangement mode using discrete quantum particle swarm, and realizes high-precision direction finding of optimal array arrangement under specific conditions and requirements.

The application discloses a robot kinematics parameter error compensation method, system, device and medium, and the robot kinematics parameter error compensation method is characterized by the following steps: acquiring the error of a robot motion point; establishing a target function based on the error; and compensating the robot kinematics parameter error by using a quantum particle swarm algorithm based on the target function. The application adopts the quantum particle swarm algorithm, which is derived from quantum mechanics and makes particles have quantum behaviors, further increases the randomness of the particles, and is more likely to obtain a global optimum that is more in line with the expected result. Thus, the stability and accuracy of the compensation of the robot kinematics parameter error are improved, and the error of the robot motion point, i.e. the robot pose error, is reduced.

The invention discloses a drilling parameter determination method and device, equipment and a storage medium, which are applied to the field of drilling equipment and train a prediction model based on target drilling parameters and to-be-optimized drilling parameters in a drilling process. The input of the prediction model is a to-be-optimized drilling parameter, and the output is a target drilling parameter; constructing a quantum particle swarm based on the to-be-optimized drilling parameters, and constructing a fitness function; the fitness function comprises a parameter excitation term, an energy consumption constraint term and a safety penalty term; on the basis of the fitness function, the particle position of the quantum particle swarm is updated with the optimal solution of the target drilling parameters output by the prediction model as the target; and after iterative update convergence, determining the quantum particle with the global optimal particle position in the quantum particle swarm as a target quantum particle, and determining a target value of the to-be-optimized drilling parameter based on the target quantum particle. The optimal drilling parameters are determined through a hybrid optimization mechanism of the quantum particle swarm optimization and the multi-mode reward and punishment function, so that the mechanical drilling speed is increased, energy consumption is reduced, and drilling safety is guaranteed.

The invention relates to an efficient robustness classification method based on quantum pellets, belongs to the technical field of quantum calculation and machine learning crossing, and aims to solve the problems of low efficiency, insufficient robustness and the like when an existing classification method is used for processing large-scale high-dimensional data. According to the technical scheme, the method comprises the steps that preprocessing and dimensionality reduction are conducted on a classic data set, and data features are mapped into a quantum state through quantum angle coding; calculating a quantum inner product between samples through a quantum circuit to estimate similarity; introducing a quantum comparator to screen similar samples to form a candidate set; the purity is calculated, and quantum particles are generated through iterative splitting; and finally, completing test sample classification by adopting a weighted voting mechanism. According to the method, the calculation efficiency is improved by means of quantum parallelism, the robustness in a noise scene is enhanced through a weighting strategy, and the method is suitable for the fields of medical image classification, big data analysis, intelligent sensing and the like.

This invention provides an energy prediction system based on quantum particle swarm optimization (QPSO) federated spatiotemporal coupling, comprising an edge sensing layer, a federated computing layer, a quantum optimization layer, and a spatiotemporal prediction layer. The edge sensing layer collects operational data from distributed energy nodes and performs data cleaning and feature encoding. The federated computing layer jointly models the spatial relationships between energy nodes and the temporal evolution characteristics of operational data, generating model update information. The quantum optimization layer triggers a federated aggregation process when preset conditions are met and updates the model aggregation weights based on the QPSO mechanism, generating global model parameters. The spatiotemporal prediction layer predicts and analyzes the operating status of the energy system based on the global model parameters and outputs risk assessment information. This invention achieves multi-node collaborative modeling without centralizing raw energy data, which is beneficial for improving the adaptability and application feasibility of the energy prediction system in complex operating scenarios.

The invention discloses a multi-energy system state evaluation weight determination method based on game theory coupling optimization, and the method comprises the steps: solving a subjective weight based on an analytic hierarchy process in combination with the correlation of all indexes; determining objective weights based on objective index characteristics; determining a game objective function and constraints; based on an improved quantum particle swarm algorithm, evaluation weight determination is realized through game theory coupling. According to the method, a subjective weight design method based on an analytic hierarchy process and an objective weight design method based on objective index characteristic analysis are established, game theory coupling weight solution is performed based on the improved quantum particle swarm optimization, and multi-energy system state evaluation weight determination can be realized.

This invention relates to a quantum particle swarm optimization method based on a genetic algorithm for UAV trajectory planning, comprising: initializing particle swarm parameters; calculating particle positions through Monte Carlo random simulation based on the probability density function of a particle appearing at a point in quantum space, obtaining multiple first particles; updating the current best position and the global best position of the particles using particle fitness values; performing a crossover operation on all first particles based on the current best position and the global best position, and updating the first particles using a strategy of retaining excellent individuals, obtaining multiple second particles; performing a mutation operation on the second particles, and updating the second particles using a strategy of retaining excellent individuals, obtaining multiple third particles; repeating the process until the maximum number of iterations is reached to obtain the global best position of the particles, thereby obtaining the optimal solution for UAV trajectory planning. This method has strong global search capability, fewer sensitive parameters, and higher convergence accuracy.

The application discloses a single-shaft combined cycle gas turbine power prediction method, comprising the following steps: obtaining optimal variable data sets, wherein each optimal variable data set comprises a training set, a test set and a verification set; obtaining a QM model based on a quantum particle swarm algorithm, a DM model and an EM model based on a deep neural network and a recurrent neural network algorithm according to the optimal variable data sets, and respectively performing modeling training on the training set; obtaining the absolute error between the prediction result and the actual value of the above-mentioned model, and selecting the model corresponding to the minimum absolute error as the prediction optimal algorithm under the test set to obtain a sample test set; setting a deep neural network classification model, performing optimization based on a quantum particle swarm algorithm, obtaining an HM model, training the HM model through the sample test set, verifying the trained HM model on the verification set, and adaptively selecting a submodel according to different working conditions to predict the gas turbine power. The application can improve the prediction accuracy.