14 results about "Chaos optimization" patented technology

The invention provides an HRRP target micro-motion characteristic extraction method based on chaos optimization and polarization synthesis, and aims at the field of high-resolution one-dimensional range profiles. The method comprises the following steps: performing optimal polarization contrast enhancement and optimal polarization fusion based on a maximum signal-to-noise ratio criterion on HRRP multi-channel polarization data; performing period estimation on the polarization fusion data in combination with second harmonic back-off optimization and autocorrelation / amplitude difference enhancement processing; chaotic mapping and back propagation are combined to avoid the problem that traditional optimization is prone to falling into local minimum, and precession angle and size results of the space target are obtained. According to the method, the feature contrast of HRRP data is remarkably improved through polarization fusion, meanwhile, high-precision estimation of the micro-motion features of the space target is achieved under the condition of a low signal-to-noise ratio, the stability of periodic detection within limited observation time and the global search capability of parameter inversion are effectively improved, and the method is suitable for large-scale popularization and application. And the accuracy and robustness of space target micro-motion estimation in a complex environment are obviously enhanced.

The application discloses a kind of energy consumption behavior simulation methods based on generating model, comprising the following steps: S1, by the space-time energy consumption data of equipment acquisition, data interpolation reconstruction is carried out and is converted into high-dimensional space-time data matrix;S2, data matrix is decomposed into multiple scale sub-matrix, using dynamic noise driven forward diffusion process obtains space-time scale coding data;S3, using cross-scale data overlap fusion and dynamic noise weight decreasing mechanism, execute reverse diffusion process, reconstruct space-time energy consumption initial data;S4, according to reconstruction data, construct characteristic space attraction field and determine the spatial position of quantum chaos vultur group;S5, dynamically adjust group position, make it follow attraction field gradient and carry out adaptive optimization, obtain dynamic optimization sequence.The application is combined by quantum chaos optimization and space-time diffusion model, can accurately simulate complex and dynamic energy consumption behavior, has strong optimization effect and wide application prospect.

The invention discloses a production line feeding and discharging control system based on multi-agent cooperation, and the system comprises a production data collection module which is used for collecting and preprocessing the operation data of all stations and a feeding and discharging robot; the multi-agent cooperative control module is used for inputting a multi-agent cooperative control framework and training a state and action mapping relation by adopting an improved IPPO method; the state updating module is used for collecting and fusing workpiece space pose information and intelligent agent state information to update task state data; the task scheduling module is used for carrying out loading and unloading operation according to the task state data and outputting an updated scheduling result; the beat optimization module is used for generating a beat control instruction by adopting chaos leapfrog optimization; and the cooperative control module is used for generating a comprehensive cooperative control instruction and driving the production line to operate cooperatively. According to the method, multi-agent reinforcement learning and chaos optimization methods are fused, cooperative control over feeding and discharging of the production line is achieved, and the method has the advantages of being high in adaptability, accurate in rhythm and efficient in cooperation.

This invention discloses a load forecasting method integrating multi-source feature enhancement and chaotic optimization, comprising the following steps: preprocessing the data; selecting a core feature set from the preprocessed data; performing wavelet decomposition on the time series in the core feature set to obtain multiple frequency components; generating abrupt change features and integrating them into the corresponding frequency components, reconstructing them to form an enhanced feature set; calculating the comprehensive similarity between the target prediction date and historical dates, selecting a similar date sample set based on the comprehensive similarity, and loading the corresponding historical optimal model parameters to initialize the current prediction model; constructing an integrated prediction model; optimizing the integration weights of each branch model; and performing inverse normalization on the fusion result to obtain the final load forecast value. The advantages of this invention are: it achieves multi-dimensional breakthroughs in feature quality, abrupt change capture, optimization efficiency, and scenario adaptation, providing a high-precision and high-stability technical solution for power loads under complex operating conditions.

The invention provides a drilling equipment working parameter optimization method, electronic equipment and a storage medium, and the method comprises the steps: obtaining drilling equipment working parameters and construction environment information, and obtaining a multi-physics field feature tensor and drill propulsive force based on the drilling equipment working parameters and the construction environment information, the drilling equipment working parameters comprise drill rod vibration parameters, drill bit temperature field parameters and drilling equipment energy consumption parameters, and the construction environment information comprises rock stratum strain distribution information; chaos optimization calculation is carried out based on the drill bit pushing force to obtain an iterated chaos sequence, and multi-objective optimization calculation is carried out based on the multi-physical field feature tensor and the chaos sequence to obtain an optimization result; and outputting adjustment parameters related to the working parameters of the drilling equipment based on the optimization result, and adjusting the working parameters of the drilling equipment according to the adjustment parameters.

The quality safety monitoring management system for powder metallurgy production comprises a production parameter intelligent sensing module, a quality safety intelligent analysis module, a quality traceability management module and a man-machine interaction module; the production parameter intelligent sensing module collects production parameters and completes data preprocessing, and sends the results to the quality safety intelligent analysis module; the quality safety intelligent analysis module receives production parameters, obtains key quality characteristics through semantic ontology driven adaptive sampling, realizes fault diagnosis by applying a mixed enhanced learning method of tensor decomposition, dynamically adjusts quality management strategies through fractional order chaos optimization, and generates quality early warning information, diagnosis decisions and management plans; the quality traceability management module establishes a quality traceability chain covering the whole life cycle of raw materials, processes and products by using blockchain technology, receives quality data and completes safety evidence; the man-machine interaction module realizes visual display of system functions and data, accepts user instructions and coordinates other modules to complete quality monitoring management tasks.

The application discloses to belong to the technical field of power system planning and optimization, and particularly relates to an optimization method for distributed power generation and energy storage joint planning in an active distribution network, comprising: a double-layer robust planning model considering new energy output uncertainty is established, SOT is used to discretize the probability distribution of wind power and photovoltaic into a probability sequence and calculate the expected output, and an uncertainty processing mechanism is embedded to guide DG and energy storage capacity configuration; a DG and energy storage joint optimization model based on a double-layer planning framework is constructed, the upper layer optimizes site selection and capacity determination with the minimum annual total cost as the target, the lower layer optimizes energy storage scheduling with the minimum daily operation cost as the target, and planning and operation are coordinated; an improved BPSO monitoring population fitness variance based on chaos optimization is proposed, and Tent mapping chaos disturbance is dynamically introduced to realize efficient solution and global optimization of the model. On the PG&E 69 bus system, line loss can be reduced, voltage distribution can be improved, and new energy consumption capacity can be significantly improved.

The application discloses an artificial intelligence-based traditional Chinese medicinal material component detection evaluation method and device, which can effectively solve the problem of insufficient data processing, thereby significantly improving the efficiency and accuracy of traditional Chinese medicinal material component detection. The method comprises data acquisition and storage: acquiring component data of traditional Chinese medicinal materials and storing the data in a structured json format; the component data comprises chemical component concentration, spectral characteristic intensity, chromatographic peak value, biological activity index, moisture content, ash content, heavy metal content and organic acid content; data expansion: data expansion is performed based on a generative adversarial network model of a dual strategy; feature extraction model training: the expanded data is input into a feature extraction model for feature extraction model training, and the feature extraction model is trained based on a chaotic optimization neural network algorithm; classifier model training: the feature-extracted data is input into a classifier model for classifier training, and the classifier model is trained based on a multilayer extreme learning machine algorithm based on quantum state evolution; traditional Chinese medicinal material component detection evaluation: the trained model is used for traditional Chinese medicinal material component detection evaluation.

The embodiment of the invention discloses a main power grid power flow optimization method and device based on a chaos optimization algorithm, and the method comprises the steps: collecting the operation data of a main power grid through a wide-area monitoring system, and enabling the operation data to comprise the power flow, voltage and power factor of a high-voltage line, and the real-time output information of a distributed power supply; establishing a power flow optimization model including voltage stability constraint and line thermal stability constraint by taking minimization of transmission loss and voltage deviation as targets, the model describing uncertainty of new energy power prediction through probability distribution, and generating a plurality of new energy output scenes to integrate optimization constraints; an initial population is generated based on a chaotic mapping function, and chaotic control parameters are dynamically adjusted in the iteration process; and performing iterative solution on the power flow optimization model by using the initial population to obtain a power distribution scheme. The optimization efficiency of the main power grid is improved.

The present application relates to a kind of unmanned ship unmanned aerial vehicle cooperative cruise method, device, equipment and medium, belong to trajectory planning technical field, unmanned ship unmanned aerial vehicle cooperative cruise method constructs time-varying wind and current environment model, constructs ship dynamics model considering the influence of uncertain factor based on time-varying wind and current environment model;Based on ship dynamics model and obstacle data, obtain the planning trajectory of unmanned ship;Comprehensive cost function is constructed, with comprehensive cost function as the fitness function of chaos optimization particle swarm algorithm, the planning trajectory of unmanned ship is optimized using chaos optimization particle swarm algorithm, obtain the global optimal planning path of unmanned ship;Based on the starting point, target point and wind field data of unmanned aerial vehicle, the three-dimensional path of unmanned aerial vehicle is planned using dandelion optimization algorithm, obtain the planning trajectory of unmanned aerial vehicle;To realize autonomous cooperative cruise under complex environment, reduce the time of path planning, improve the positioning accuracy of unmanned aerial vehicle.

The invention discloses a method and a system for aggregating distributed photovoltaic power generation and carrying out medium and long term power transaction. The method comprises the steps that photovoltaic operation data, meteorological data and electricity market data are collected and preprocessed; performing photovoltaic output prediction by using the LSTM model; the upper layer of the bilevel planning model takes aggregator income maximization, and the lower layer of the bilevel planning model takes social welfare maximization as targets; solving the model by adopting a hybrid particle swarm algorithm fused with chaos optimization, and generating an optimal declaration strategy; and transaction is automatically executed and settlement analysis is completed. The system adopts a four-layer architecture and comprises a data acquisition layer, a platform service layer, an application decision-making layer and a user interaction layer, and full-process automation is realized. According to the method, the problems of high distributed photovoltaic output volatility and high difficulty in directly participating in the market are effectively solved, the photovoltaic consumption rate and the economic benefit of an aggregator are remarkably improved, and meanwhile, the operation stability of a power grid is enhanced.

The application discloses a heating furnace control optimization method and system, and relates to the technical field of heating furnace control.The method comprises the following steps: obtaining working condition data of a heating furnace, and pre-processing and marking the working condition data to obtain energy consumption data corresponding to each working condition; calculating a weighted similarity measure under different working conditions based on the working condition data and the energy consumption data, and generating a preliminary control strategy corresponding to different working conditions based on the weighted similarity measure; constructing a heterogeneous network comprising the working condition data and the preliminary control strategy, and walking in the heterogeneous network based on a bidirectional random walk algorithm to generate a candidate control strategy; and determining a final control strategy corresponding to each working condition based on the candidate control strategy and a chaos optimization technique.The application mines the complex relationship between the working condition data and the control strategy, provides more information for optimizing the control strategy, generates a candidate control strategy with more globality based on the bidirectional random walk algorithm, and improves the comprehensiveness of strategy selection.

The invention discloses a maximum power point tracking method and device based on a collaborative chaos optimization algorithm, and the method comprises the steps: initializing a chaos disturbance probability and a dynamic exploration probability, and defining a search space boundary according to the duty ratio dimension of a Boost circuit; initializing working points and calculating a fitness value according to the output power of each working point; determining global maximum output power and individual historical maximum output power; in combination with the chaotic disturbance probability and the dynamic exploration probability, a plurality of optimization units are adopted to update and iterate the global maximum output power and the individual historical maximum output power, and compliance test is performed according to the search space boundary; and outputting the maximum power point. According to the method, a plurality of optimization units are cooperatively executed in iteration to update a working point, and through cooperation of a plurality of disturbance strategies and a hierarchical update mechanism, the parameter precision, response speed and stability of an MPPT control system in a dynamic environment are improved, and more efficient and reliable global maximum power point tracking is realized.

This invention proposes a method for extracting the micro-motion characteristics of HRRP targets based on chaotic optimization and polarization synthesis, targeting the high-resolution one-dimensional range image domain. The method includes: performing optimal polarization contrast enhancement on HRRP multi-channel polarization data, and optimal polarization fusion based on the maximum signal-to-noise ratio criterion; combining second harmonic backoff optimization and autocorrelation / amplitude difference enhancement processing to estimate the period of the polarization-fused data; and combining chaotic mapping with backpropagation to avoid the problem of traditional optimization easily getting trapped in local minima, thus obtaining the precession angle and size results of the space target. This invention significantly improves the feature contrast of HRRP data through polarization fusion, and simultaneously achieves high-precision estimation of the micro-motion characteristics of space targets under low signal-to-noise ratio conditions. It effectively improves the stability of period detection and the global search capability of parameter inversion within a limited observation time, significantly enhancing the accuracy and robustness of space target micro-motion estimation in complex environments.