The current game theory model method cannot accurately optimize the load control of smart grid, resulting in the problem of high load energy consumption when the smart grid is running. To address this problem, a load optimal control algorithm for smart grid based on demand response in different scenarios is proposed in this paper. The demand response of smart grid under different scenarios is described. Onthis basis, the load rate and actual load of smart grid are calculated by using the rated load of electrical appliances. The load classification of smart grid and the main factors affecting the load of smart grid are analyzed to complete the load distribution of smart grid. According to the evaluation function of smart grid, the number of load clusters is adjusted to calculate the load change rate. The trend of load curve of smart grid is analyzed to realize optimal load control of smart grid under different scenarios. The experimental results show that the proposed method has better control performance and higher accuracy through load control of smart grid.

This paper constructed an optimizing model on multiple vehicles and multiple goods, which is used in a study on the loading problem for railway freight. We take the maximum utilization coefficient of car loading capacity, volume capacity and layout optimal degree as objective functions. Several major factors in railway transportation have been cited as constraints: center of gravity, non-overlapping freight, transportation circumscription, car marked loading capacity and volume capacity. In order to increase the car utilization coefficient and obtain flat layers in the loading scheme, an improved genetic annealing algorithm is proposed to solve this optimizing model. The proposed algorithm can efficiently obtain a satisfactory loading scheme in railway transportation. At the end, a numerical example shows that the proposed model and algorithm are better than First Fit Algorithm and Neural Network Algorithm in goods loading capacity and volume capacity, the new loading schemes are more flat on each layer.

Aiming at the excellent descriptive ability of SURF operator for local features of images, except for the shortcoming of global feature description ability, a compressed sensing image restoration algorithm based on improved SURF operator is proposed. The SURF feature vector set of the image is extracted, and the vector set data is reduced into a single high-dimensional feature vector by using a histogram algorithm, and then the image HSV color histogram is extracted.MSA image decomposition algorithm is used to obtain sparse representation of image feature vectors. Total variation curvature diffusion method and Bayesian weighting method perform image restoration for data smoothing feature and local similarity feature of texture part respectively. A compressed sensing image restoration model is obtained by using Schatten-p norm, and image color supplement is performed on the model. The compressed sensing image is iteratively solved by alternating optimization method, and the compressed sensing image is restored. The experimental results show that the proposed algorithm has good restoration performance, and the restored image has finer edge and texture structure and better visual effect.