In order to solve the problems of low efficiency and danger in the existing power maintenance training methods, a power maintenance evaluation system based on immersive virtual reality technology is developed in this paper. Based on the equipment 3D accurate model and virtual reality simulation technology, the system imports the power plant scene and equipment accurate model through MakeRea13D platform for content development. Using key technologies such as model lightweight, 3D UI display and VR multi means interaction, an immersive virtual maintenance and virtual scene operation simulation platform is established. At the same time, five led-cave, mobile virtual platform and helmet immersive simulation environment are built, and three environments can be used for rendering display. According to the field environment of the actual operation and the real structure of the equipment, the system establishes three-dimensional virtual scenes such as maintenance and operation required for the evaluation, and supplements the accurate model data required for the evaluation. The actual working environment of the power station is reconstructed according to the requirements of the evaluation content, which helps to improve the professional technicians’ sense of substitution and realism of the operation scene.
This paper analyzes the relevant data of domestic agricultural products cold chain logistics enterprises,explores the reasons that affect the performance of agricultural products cold chain logistics enterprises,and establishes an index system for the performance evaluation of agricultural products cold chain logistics enterprises.We collect the actual data of relevant com-panies,quantify and normalize the index values corresponding to the index system,form the corresponding sample training set,and use GA(Genetic Algorithm)to optimize BP artificial neural network.The application model based on GA neural network algorithm is established,and simulation training is carried out to determine the optimal number of hidden layers.At the same time,we compare the results of various evaluation methods to prove the effectiveness and relative accuracy of this method.The application methods and prospects of this method in the decision-making and management system of agricultural products cold chain logistics enterprises are described.
With the real-time changes of flight taking-off and landing,how to reasonably allocate flight support equipment and workers has always been a problem to be solved in the airport.By analyzing the problems and challenges faced by the traditional scheduling mode,a flight support task allocation model based on mixed integer programming algorithm is proposed.Model 1 calculates the minimum number of resources required for flight support according to the task type and resource working time range,and Model 2 can deduce all support task lists in real time according to the changing flight execution.Combined with the list of available resources,it can quickly calculate the resource allocation method to obtain the optimal matching result.By ana-lyzing the practical application effect of the algorithm,the proposed resource allocation method realizes the optimal scheduling and improves the utilization efficiency of flight support resources.
In order to improve the accuracy of power outage type judgment,a fast judgment method of power outage type based on transient disturbance characteristics of electric energy meter is proposed.In the transient disturbance characteristic sampling signal of electric energy meter,the instantaneous fundamental amplitude is extracted by Hilbert transform method,the noise of the sampling signal is suppressed by sliding singular value decomposition method,and the characteristic waveform of singular value is obtained.The main frequency points in the sampling signal are collected by envelope extremum algorithm to obtain the frequency spectrum.The characteristic quantity of transient disturbance of electric energy meter is extracted and normalized to judge the type of power outage.Experiments show that the proposed method can accurately judge the type of power outage,and has good anti-noise performance.
In order to improve the utilization rate of mobile learning resources and optimize the navigation effect and operation performance of learning resources,taking English mobile learning resources as the research object,a navigation method based on multi-objective optimization is proposed.The English mobile learning resources are preprocessed by word segmentation and invalid word removal,the storage location of the target resources is determined by parallel search,and the learning resource re-trieval path is generated by multi-objective optimization algorithm,so as to obtain the final navigation results of English mobile learning resources.The experimental tests are carried out from two aspects:single resource navigation task and multiple re-source navigation tasks.The results show that the proposed method is less affected by the navigation task scene,the navigation success rate and precision rate are increased by 1.0％and 0.5％,respectively,and the memory occupation and time expendi-ture in the operation process are smaller.
