No abstract

With the continuous development of agricultural product e-commerce platforms and the rapid growth of trading volume, conducting in-depth mining and analysis of online review data to improve consumer satisfaction is of considerable importance. This paper uses JingDong's self-run online reviews of rice agricultural products as the research object and analyzes the logistics factors that affect consumer satisfaction through text mining technology. A multi-objective model for logistics center distribution path optimization under a soft time window was constructed. The model used the results of online review analysis, namely, packaging integrity, delivery timeliness, and logistics cost, as the goals, and the model used ant colony algorithm (ACO) and genetic algorithm (GA) to solve the optimal distribution solution to minimize the penalty cost and transportation cost. Through examples to solve the optimal distribution vehicle number and shipping routes, in addition, a comparison of the two types of algorithm performance of the model under different node number indicated that the number of nodes affects algorithm performance. With a node number below 50, the ant ACO has high precision and a better distribution path. With a node number above 50, GA has more comprehensive performance. The average efficiency of the GA is 12.28% higher than that of ACO.

With the steady rise of China’s agricultural production and management level, the market of ecological agricultural products has developed rapidly, and consumers are increasingly concerned about ecological agricultural products. Consumers’ cognition and purchase intention are the keys to determine their future development. This research is aimed at ensuring that consumers have access to high-quality ecological agricultural products, thereby promoting the supply and production of ecological agricultural products, minimizing agricultural carbon emissions, and providing information on sustainable food pricing. Based on the research status at home and abroad, this study combines the questionnaire survey method to study the influencing factors and willingness to pay of consumers purchasing ecological agricultural products. A total of 601 online questionnaires from consumers in Harbin, a city in northeastern China, were collected, and statistical factor analysis, principal component analysis, and regression analysis were used to study the influencing factors of consumers’ purchase of ecological agricultural products from both positive and negative aspects, and in-depth analysis of the reasons why consumers refuse to pay, get the most real willingness to pay and related influencing factors, and quantify the influence of various variables on consumers’ purchasing behavior was done. On this basis, a logit model of survival analysis is constructed to study the premium payment level of consumers for ecological agricultural products, and the payment premium is 24.95%. The research results show that married, who have purchased ecological agricultural products, the higher the understanding of ecological agricultural products, the consumers who buy ecological agricultural products in farmers’ markets, Meituan and community group purchases, and the households with higher monthly consumption of agricultural products have a significant positive correlation with consumers’ purchase of ecological agricultural products. The higher the education level, the older the age, and the larger the family size were significantly negatively correlated with consumers’ purchase of ecological agricultural products.

This study concentrates on management problems of the new generation of the agile earth observation satellite (AEOS). AEOS is a key study object in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.Keywords: mission planning, immune clone algorithm, hybrid genetic algorithm (EA), improved ant colony algorithm, general particle swarm optimization (PSO), agile earth observation satellite (AEOS).

Because of the increasing number and types of orbital satellites, extensing satellite application range, as well as more complex mission requirements of multi-user, it's necessary and urgent to research mission requirements, which can be helpful to sovle satellite mission planning and scheduling. This paper builds the mission requirements model based on mathematical statistics perspective according to the problems of the satellite multi-user mission requirements, then studies the distribution of mission requirements, and proves its rationality. On the basis, this paper improves the mission requirements distribution of the satellite multi-user. Aiming at the problems of classification for satellite multi-user, K-means clustering method based on multivariate statistical analysis perspective is imposed to solve the classification of satellite multi-user mission requirements. It is showed that this method is practicable and high accuracy by analysising cluster results, and it can provide the basis for the solution to problems of task allocation in mission planning and scheduling of multi-satellite multi-user.