9results about How to "Strong global search ability" patented technology

The invention discloses a scheduling method for solving a multimodal transport route with a time window, and relates to the technical field of transport management. The method comprises the following steps: S1, constructing a multimodal routing scheduling model with a time window; S2, carrying out optimization solution through a genetic algorithm; according to the method, the multimodal transport routing scheduling model with the time window is constructed, the genetic algorithm is used for optimization solution, and the genetic algorithm serves as a meta-heuristic algorithm, has strong global search capability, can find an approximate optimal solution in a short time, is suitable for large-scale and multi-constraint multimodal transport scheduling problems, and has good application prospects. The genetic algorithm is high in adaptability, parameter adjustment and improvement can be carried out according to the characteristics of specific problems, the solving efficiency and the solution quality are improved, the mixed integer programming model is constructed, multiple transportation modes and time window constraints are organically combined, high expandability is achieved, and the method can be applied to different types of multimodal transportation problems.

The invention relates to a method for constructing a software reliability evaluation model, which comprises the following steps of: A, acquiring multi-source data, and preprocessing the multi-source data; b, constructing a multivariate smooth spline regression model; c, updating parameters of the multivariate smooth spline regression model in real time according to the prior distribution and operation data through a Bayesian formula; d, parameters of the multivariate smooth spline regression model are updated in real time through the Kalman filtering technology, and model parameter prediction, prediction error covariance calculation, Kalman gain calculation, state updating and error covariance updating are sequentially carried out every time operation data arrives; and E, training and verifying the updated multivariate smooth spline regression model to obtain a final evaluation model. According to the method, the evaluation precision and robustness of the software reliability are improved, the calculation efficiency and the automation degree are improved, and the result interpretability and the data utilization efficiency are enhanced.

The invention provides a power load prediction method and system. The method comprises the following steps: acquiring historical power data; inputting the historical power data into a trained feature extraction model to obtain feature data corresponding to the historical power data; inputting the feature data into a trained prediction model to obtain prediction load data corresponding to the feature data; the feature extraction model and the prediction model are obtained by performing hyper-parameter optimization according to an improved piebald optimization algorithm; the optimization steps of the improved piebald algorithm comprise: for a hyper-parameter set of the model, setting a population corresponding to the hyper-parameter set; iterating the position of each individual in the population by simulating at least one of inhabiting and circling behaviors, diving behaviors and predation efficiency changes of the turtles; and when the fitness of the population meets an iteration stop condition, the population is determined as a hyper-parameter solution set, and the accuracy of power load prediction is improved.

This invention relates to the field of electrical digital data processing technology, specifically a TCR measurement method based on high and low temperature region data fusion using an improved particle swarm optimization algorithm. The method inputs the original time-series data from the high and low temperature regions, removes noise and outliers using isolated forest and Grubbs criterion, and maps the data to a unified temperature domain grid using piecewise Lagrange interpolation. An ergodic sequence is generated using Logistic chaotic mapping to initialize the particle swarm position vector, which is then transformed into node coordinate parameters in geometric space, and a cubic basis spline fitting curve is constructed. Particle fitness is evaluated using the sum of squared residuals and curvature smoothness as constraints. During iteration, the average Euclidean distance from the particle to the swarm centroid is calculated to generate a spatial dispersion factor. A multi-interval weighted decision maker based on the Mamdani framework is used to identify probe, development, convergence, and escape states, dynamically adjusting the inertia weights accordingly. This effectively solves the problems of data fragmentation and premature convergence, ultimately outputting a continuous and smooth resistance temperature coefficient distribution across the entire temperature domain.

The invention relates to the technical field of face recognition, and discloses a personnel information automatic recognition system based on a support vector machine and a black-wing plinergic algorithm, and the system comprises an acquisition module which is used for obtaining an initial image of a to-be-detected person; the preprocessing module is used for preprocessing the initial image to obtain a face image of the to-be-detected person; the comparison module is used for inputting the face image into a support vector machine trained based on a black wing plinualism algorithm for classification so as to obtain face information of the to-be-detected person; the mapping module is used for correspondingly finding out personnel information of the to-be-detected personnel according to the face information; the permission judgment module is used for judging whether the personnel information of the to-be-detected personnel has access permission or not; and the alarm module is used for executing corresponding alarm operation when the permission judgment module judges that the personnel information does not have the access permission. By means of the recognition system, checking of safety inspectors on the access information of the constructors is reduced.

This invention discloses a USV path planning method based on an enhanced Star-Raven Optimization algorithm, comprising the following steps: Step 1, establishing a USV path planning modeling framework, including: a path mathematical model, a path quality evaluation function model, and an environment model; Step 2, designing the enhanced Star-Raven Optimization algorithm flow, including a periodic elite regrouping mechanism, a dynamic individual elimination and rebirth mechanism, and a distance-guided initialization sorting strategy; Step 3, applying the enhanced Star-Raven Optimization algorithm to solve for the Bezier curve control points, generating the optimal path, and conducting experimental research and comparative analysis. This invention can synergistically optimize multiple objectives such as path length, energy consumption, and navigation feasibility. Simulation results show that the proposed algorithm outperforms the standard Star-Raven Optimization algorithm and particle swarm optimization variants in terms of convergence performance and path generation, and can generate smooth and collision-free USV executable trajectories, possessing good engineering application value.

The application relates to the technical field of micro-grid energy management, and discloses a micro-grid dynamic coordination control method for distributed energy. The method acquires a data set of multiple monitoring nodes of a micro-grid in a historical operation interval; performs multi-resolution fluctuation analysis on an energy output data set, an energy demand data set and a system frequency data set to generate an output fluctuation index, a demand fluctuation index and a frequency fluctuation index; takes the fluctuation index as a guide variable, adopts a group optimization algorithm to explore in a micro-grid control parameter space, and locates an optimal control interval; periodically adjusts distributed power generation units and load units in the micro-grid according to the optimal control interval, generates a regulation and control instruction set and executes the regulation and control instruction set to achieve dynamic coordination control. The application realizes accurate interval positioning and efficient coordinated operation of micro-grid control, and effectively improves the stability and regulation and control efficiency of a distributed energy system.

The application belongs to the technical field of heliostat target point adjustment method, and particularly relates to a heliostat target point genetic optimization method suitable for a tower type light concentration and heat collection system. The method comprises the following steps: S1. Calculating heat absorber energy flow: dividing the surface of the heat absorber into a target point matrix, calculating the light spot distribution of each heliostat at a given target point on the heat absorber, and calculating the energy flow density distribution formed by the superposition of the light spots of the m heliostats in the mirror field on the heat absorber; S2. Target point genetic optimization: randomly generating S target point strategies as the initial population, generating a group of individuals with higher fitness of the target point strategy through random selection, crossover and mutation, evolving the group, and obtaining the optimized target point aiming strategy, so that the mirror field incident energy flow is uniformly distributed on the surface of the heat absorber. The method provided by the application adopts a target point genetic optimization algorithm to optimize the aiming strategy of the tower type light concentration and heat collection system, so that the mirror field incident energy flow is uniformly distributed on the surface of the heat absorber.

The invention discloses a fault distance calculation method and system for a power system power distribution network. The method comprises the steps that a power engineer monitoring end sends a fault distance safety self-checking signal to a power distribution network real-time operation edge calculation gateway monitoring platform; after the power distribution network real-time operation edge computing gateway monitoring platform receives a fault distance safety self-checking signal, the fault distance safety self-checking signal is detected. The multi-modal information integration end under different user groups collects electrical parameters and load data of the power distribution network of the power system in each fault area under different user groups and equipment state parameters of the fault distance of the power distribution network of the power system in each fault area, stores the collected data and sends the collected data to the power engineer monitoring end; and the signal is sent to the power system power distribution network fault distance calculation model module through the signal collection processing server. According to the invention, more accurate fault positioning information can be provided for power engineers, the troubleshooting time is shortened, and safe and stable operation of the power distribution network is guaranteed.