13results about How to "Strong global search capability" patented technology

The application provides a UWB and PDR fusion positioning method and system in an indoor shielding environment and a medium, and belongs to the technical field of indoor positioning and multi-source information fusion. The method comprises the following steps: calculating a UWB positioning track and a PDR positioning track. Taking translation, rotation, step scaling and scale change as combined parameters, a track alignment error function is constructed by using the Euclidean distance of the two tracks at each timestamp. An improved vole optimization algorithm is used to solve the optimal combined parameters. An initial population with uniform distribution is generated by mapping a Sobol difference sequence. In the iteration, the UWB track is used as the reference, the PDR track is temporarily transformed by using the combined parameters, the Euclidean distance is calculated, and the vole with the smallest distance is selected to breed the next generation. After reaching the maximum number of iterations, the PDR track is corrected by using the optimal combined parameters at one time, and the UWB track is fused and output. The application does not require a priori model, can simultaneously suppress UWB non-line-of-sight drift and PDR cumulative error, and has high positioning accuracy.

The invention discloses a method for identifying thermal runaway reaction kinetic parameters of a high-nickel ternary battery based on DSC (differential scanning calorimetry) multi-exothermic peak heat flow curves, which is characterized by comprising the following steps of: establishing a chemical reaction kinetic model, and identifying the thermal runaway reaction kinetic parameters of the high-nickel ternary battery based on the DSC multi-exothermic peak heat flow curves at different temperature rise rates; and realizing identification of the thermal runaway reaction kinetic parameters of the high-nickel ternary battery by applying a Kissinger equation and a particle swarm optimization algorithm. According to the technical scheme of the method, necessary reaction kinetic parameters can be provided for thermal runaway modeling of the high-nickel ternary battery, compared with a traditional genetic algorithm, the convergence speed is higher, meanwhile, the global search capability is high, thermal contribution decoupling between different reactions of a battery material in the thermal runaway process is achieved, and the thermal runaway modeling efficiency is improved. And improvement means can be researched aiming at high-yield heat reaction among materials, and the risk of thermal runaway of the high-nickel ternary battery is reduced.

The invention provides a substation project multi-objective optimization method and device. The method comprises the following steps: acquiring a process set, a process preposition and postposition logic relation, selectable construction modes of each process and corresponding parameter information; introducing the logical relationship as a constraint, taking a construction mode selection result as a discrete decision variable, and constructing a multi-objective optimization model including a construction period, a cost present value, quality and a carbon emission objective; an improved swarm intelligence algorithm is adopted for iterative solution and layered sampling to generate an initial population and establish an external file, a target function is calculated in iteration and feasibility is judged, non-dominated sorting and congestion degree are executed after merging, the external file is updated, and a leader individual is selected; and candidate positions are generated based on nonlinear convergence control and hunting, random search and spiral bubble net updating, discretization and constraint restoration are performed after fusion, and an external file non-dominated solution set is output. According to the technical scheme, multi-target scheme optimization is achieved, and scheme generation efficiency and evaluation quality are improved.

The invention discloses a three-network emergency resource optimization method and system fused with an unmanned aerial vehicle communication relay, and belongs to the technical field of power system planning and disaster defense, and the method comprises the steps: building a communication power supply coupling model based on an unmanned aerial vehicle relay drive, and obtaining a communication recovery basic framework; constructing an outer layer optimization model taking the pre-disaster multi-type emergency resource layout scheme as a decision variable; an improved bald eagle optimization algorithm is adopted, constraint conditions of a communication recovery basic framework are embedded, an outer layer optimization model is iteratively solved, and a pre-disaster resource deployment scheme which is most robust in all scenes is determined; constructing an inner-layer model by adopting mixed integer second-order cone programming, performing refined solution on post-disaster emergency response, and optimizing a pre-disaster resource deployment scheme; and constructing a double-layer robust optimization model to realize three-network emergency resource optimization. According to the method, the cooperative scheduling capability of multiple types of resources in pre-disaster, in-disaster and post-disaster stages can be improved, the guarantee capability of key loads is enhanced, and the method has global search capability and engineering adaptability.

This invention discloses a design method, device, and system for an ultrasonic amplitude transformer based on a genetic algorithm, belonging to the field of manufacturing technology. Considering the strong global search capability and excellent convergence of genetic algorithms, this invention combines the principles of genetic algorithms with a fitness function to construct a fitness optimization algorithm based on genetic algorithms to optimize the contour parameters of the ultrasonic amplitude transformer. This ensures that the expected optimal amplification factor is obtained under different optimization objectives, effectively avoiding the local optimum problem common in traditional design methods and improving design efficiency and performance. By optimizing the contour of the amplitude transformer's geometry, the amplitude transformer can achieve the preset target amplification factor under operating conditions, thereby improving the efficiency of ultrasonic energy transfer and obtaining the expected output displacement amplitude. The final optimization result outputs the optimal geometric contour and parameters of the amplitude transformer, which can then be used for subsequent engineering manufacturing and application according to actual needs.

The application is suitable for the technical field of parameter optimization, and provides an optimization method for maximizing power density of a proton exchange membrane fuel cell, comprising the following steps: optimization variable and optimization target selection; adopting an AdaBoost integrated neural network model as a proxy model for calculating fitness function values in an iteration process of a swarm intelligence optimization algorithm; selecting an improved grey wolf optimizer (IGWO) as an optimization tool for iteration optimization in a value range of the optimization variable, with the maximum power density as the target, to obtain an optimal optimization variable combination; and optimization result verification.The application proposes an optimization method for the power density of the proton exchange membrane fuel cell by coupling machine learning and a swarm intelligence algorithm, has a fast convergence speed, can obtain an optimal solution or an approximate optimal solution under a small number of iteration times, effectively reduces the number of target function calls, significantly reduces the calculation time and the calculation cost, and has a strong global search capability and can avoid falling into a local optimum.

The application belongs to the technical field of wireless communication and signal processing, and discloses a quantum heuristic joint optimization method suitable for a flow state antenna ISAC system, which comprises the following steps: 1, a MIMO-FAS model is constructed, and an equivalent downlink channel is given to realize unified expression of a communication link and a sensing link; 2, a weighted sum rate maximization problem P1, a minimum user SINR maximization problem P2 and a sensing signal-to-jamming noise ratio SCNR maximization problem P3 are constructed; 3, the weighted sum rate maximization problem P1 is solved; 4, the minimum user SINR maximization problem P2 is solved; 5, the sensing signal-to-jamming noise ratio SCNR maximization problem P3 is solved; and 6, a quantum heuristic simulation bifurcation method is adopted to perform port selection optimization to obtain a joint optimal port-beam configuration. The application realizes collaborative optimization of port configuration and beam design, effectively improves the communication rate and sensing accuracy of the system while guaranteeing the calculation efficiency and scalability.

This invention belongs to the field of cutting parameter optimization technology, specifically relating to a multi-objective cutting parameter optimization method, including: acquiring cutting parameters and machine tool spindle power signals, calculating cutting specific energy, and simultaneously acquiring surface roughness; constructing a BP neural network model, optimizing the connection weights and thresholds of the BP neural network model using the Harris Eagle optimization algorithm to minimize the fitness function, and establishing a cutting specific energy prediction model; constructing a surface roughness prediction model with the encoded variables of cutting parameters as input and surface roughness as output, and establishing a multi-objective cutting parameter optimization model; solving the multi-objective cutting parameter optimization model to obtain the Pareto non-dominated solution set, using the entropy weight method for comprehensive decision-making, and selecting the solution with the largest comprehensive evaluation value as the optimal cutting parameter combination. This invention, through methods such as the BP neural network improved by the Harris Eagle optimization algorithm, reduces machining energy consumption, improves surface roughness, and enhances overall machining performance.

This disclosure provides a method and electronic device for detecting geometric error parameters of a surface machining machine tool, relating to the field of surface machining machine tool technology. The method includes: acquiring a pre-constructed machine tool simulation model of the surface machining machine tool; acquiring the measurement vector value of the reference surface corresponding to the error vector; performing coarse-tuning iterative processing on the vector value of the parameter vector formed by the target geometric error parameters based on multiple discrete values ​​of each target geometric error parameter to obtain a first vector value; determining a fine-tuning interval for each target geometric error parameter based on the first vector value obtained from the coarse-tuning iterative processing; performing fine-tuning iterative processing on the vector value of the parameter vector based on the fine-tuning interval to obtain a second vector value; and obtaining the values ​​of multiple target geometric error parameters of the surface machining machine tool based on the second vector value obtained from the fine-tuning iterative processing. This improves the accuracy of the detection results of the geometric error parameters of the surface machining machine tool.

The present application relates to the technical field of tunnel engineering, and particularly relates to a tunnel lining long-term safety evaluation method based on a PSO-SA hybrid algorithm, comprising: determining a surrounding rock creep constitutive model and obtaining initial surrounding rock parameters through an inversion algorithm; introducing the influence of a supporting structure on surrounding rock deformation to obtain corrected surrounding rock parameters, so as to improve authenticity; based on time-varying factors, iteratively updating surrounding rock parameters and lining parameters to realize dynamic evaluation of long-term safety; inputting the iteratively updated surrounding rock parameters and lining parameters into numerical analysis software to obtain stress and deformation characteristics of the lining structure through the numerical analysis software; and evaluating the long-term safety of the tunnel lining structure according to the stress and deformation characteristics and relevant specifications, wherein the application constructs a dynamic evaluation model that truly reflects the time-varying characteristics of the tunnel structure, brings about a qualitative leap in evaluation accuracy, reliability and foresight, and provides strong technical support for the long-term safety of operating tunnels.

The invention relates to the technical field of air traffic flow management in the aerospace field, and discloses an airline and time slot collaborative optimization method considering airline preference, which comprises the following steps: acquiring flight information, time slot information and airspace capacity information; according to the flight information and the time slot information, determining that the target function is the minimum flight total delay cost and the minimum airline fairness coefficient; a flight uniqueness constraint, a time slot uniqueness constraint, a time slot feasibility constraint, a capacity constraint and a maximum threshold constraint are set, and an air route and time slot resource allocation model is constructed and completed; solving the constructed route and time slot resource allocation model by adopting a genetic algorithm; and outputting an air route and time slot resource allocation scheme. According to the method, delay minimization and fairness improvement are taken as a dual-core target, a resource allocation mechanism is innovated, the flight delay rate is effectively reduced, fairness of resource acquisition among airlines is ensured, and an efficient and fair air traffic management system is constructed.

The application provides a kind of GAPSO algorithm-based power station load tracking method, including data acquisition and preprocessing, and related data of power grid and power station are collected and processed;A power grid load forecasting model based on GAPSO algorithm is constructed, variables are determined, and an optimized neural network is constructed;The charge-discharge strategy of energy storage power station is optimized, the objective function is established and solved;Real-time scheduling and response are carried out, data monitoring, decision-making and adjustment of power station state are carried out;Multi-objective optimization adjustment is carried out, function is constructed, solved and continuously improved.The application improves the load forecasting accuracy, optimizes the power station operation strategy, enhances the real-time scheduling and response capability, realizes the multi-objective comprehensive optimization, effectively improves the operation efficiency and adaptability of energy storage power station in power system.

The invention discloses a lithium battery SOC prediction method fusing online parameter identification and QPSO-AUKF and application, an adaptive unscented Kalman filter (AUKF) algorithm is adopted to construct a prediction model, a QPSO algorithm is added, and particle positions are described through a probability density function. Optimal parameters of a process noise covariance matrix and an observation noise covariance matrix are searched more efficiently in a solution space, so that the adaptability and estimation precision of the filter under different working conditions are improved; when SOC estimation is carried out, FFRLS is adopted to recognize battery parameters in lithium battery modeling in real time, model parameters are recursively updated to obtain optimal parameter vectors changing along with time, the optimal parameter vectors are input into a QPSO-AUKF algorithm, and a closed-loop correction structure is formed in the cycle recursion process of a prediction model. According to the method, the system characteristic change can be accurately captured, and the synchronous updating of the battery parameters and the state is realized, so that the precision and the stability of SOC estimation are remarkably improved.