To address the inadequate feature extraction from data space and low prediction accuracy in traditional deep learning based video prediction， a video prediction model Combining Involution and Convolution Operators （CICO） was proposed. The model enhanced video prediction performance through three aspects. Firstly， convolutions with varying kernel sizes were adopted to enhance extraction ability of multi-granularity spatial features and enable multi-angle representational learning of targets. In particular， larger kernels were applied to extract features from broader spatial ranges， while smaller kernels were employed to capture motion details more precisely. Secondly， large-kernel convolutions were replaced by the computationally efficient involution operators with fewer parameters in order to achieve efficient inter-channel interaction， avoid redundant parameters， decrease computational and storage costs. The predictive capacity of the model was enhanced at the same time. Finally， convolutions with kernel size 1×1 were introduced for linear mapping to strengthen joint expression between distinct features， improve parameter utilization efficiency， and strengthen prediction robustness. The proposed model’s superiority was validated through comprehensive experiments on various datasets， resulting in significant improvements over the state-of-the-art SimVP （Simpler yet Better Video Prediction） model. On Moving MNIST dataset， the Mean Squared Error （MSE） and Mean Absolute Error （MAE） were reduced by 25.2% and 17.4%， respectively. On Traffic Beijing dataset， the MSE was reduced by 1.2%. On KTH dataset， the Structure Similarity Index Measure （SSIM） and Peak Signal-to-Noise Ratio （PSNR） were improved by 0.66% and 0.47%， respectively. It can be seen that the proposed model is very effective in improving accuracy of video prediction.

The trend of green logistics pushes the use of electric vehicles into cold chain logistics. Concerning the problem that maintaining a low temperature environment requires a lot of energy in electric vehicle cold chain distribution， as well as the phenomena that the limited driving range and long charging time of electric vehicles make high operation cost， the Refrigerated Electric Vehicle Routing Problem （REVRP） in electric vehicle distribution was thought deeply. Considering the characteristics of electric vehicle energy consumption and the charging requirements of social recharging stations， a linear programming model was developed with the objective of minizing total distribution cost， and the objective function was composed of fixed cost and variable cost， in the variable cost， transportation cost and cooling cost were included. The capacity constraints and power constraints were considered in the model， and a Hybrid Ant Colony Optimization （HACO） algorithm was designed to solve this model. Especially， more attention was paid to designing transfer rules suitable for social recharging stations and four local optimization operators. Based on improving the Solomon benchmark examples， the small-scale and large-scale example sets were formed， and the performance of ACO algorithm and the optimization operators were through experiments. The experiment results show that ACO algorithm and CPLEX （WebSphere ILOG CPLEX） solver can find the exact solution in the small-scale example set， and ACO algorithm can save the operation time by 99.6% . In the large-scale example set， compared with ACO algorithm， HACO algorithm combing the four optimization operators has the average optimization efficiency increased by 4.45%. The proposed algorithm can obtain a feasible solution for REVRP in a limited time.

The read of various contents of case files suffers from information overload and knowledge disorientation. To solve this problem， an automatic construction method of knowledge forest for electronic case files was proposed with the topic facet trees and the cognitive relationships between topics as the intellectualized representation of the case files. Firstly， different types of files were classified and divided into multiple fragments of single topic by the fragmentation preprocessing of the case files. Then， different information extraction methods were adopted for different fragments， and knowledge fusion was used to merge the synonymous information. After that， the topic faceted trees were constructed by combining the ontology structures and rules and the topic relationships were extracted. Finally， the topic faceted trees and the topic relationships constructed by the knowledge forest were stored in the database to realize the visualization of the knowledge forest. Experimental results show that the proposed method can display the case file information completely and accurately， organize scattered knowledge fragments together with complex case file topics， making it possible to achieve the reading file goal by selecting some case file topics and a small number of case file fragments， and alleviate the burden of complete browsing case file contents to realize the file reading task.