The solar radiation near the surface is the main reason that affects photovoltaic power generation. Accurate ultra‐short‐term solar radiation prediction is the premise of photovoltaic power generation prediction. Here the cloud movement prediction method based on the ground‐based cloud images is presented. The cloud recognition, cloud matching, cloud area correction and cloud movement prediction are performed to predict the drift trajectory of the clouds that will block the sun. Then, using digital image technology, 13 kinds of feature information are extracted from the ground‐based cloud images. Then, these feature information are input into BP neural network, and the parameters of BP neural network are optimized by genetic algorithm. Through a large number of data training, a new ultra‐short‐term prediction model of solar radiation is established. Finally, through experimental comparison, the results show that the prediction accuracy of the model with the feature information of ground‐based cloud images can reach 96%, compared with the model without the feature information of ground‐based cloud images, the accuracy is improved by 5%. The proposed ultra‐short‐term solar radiation prediction model can effectively predict the radiation jumping process caused by cloud occlusion, and greatly improve the prediction accuracy, especially in cloudy weather.

The food cold chain is a special type of cold chain that refers to a system in which refrigerated and frozen food is always kept in the specified low-temperature environment in all links from production, storage, transportation, sales, distribution to consumption, so as to ensure food quality and to prevent food deterioration caused by temperature fluctuation. In recent years, the coronavirus disease 2019 (COVID-19) has brought a great impact on people’s life and the social economy and also threatened the large-scale food cold chain. Through the effective identification and evaluation of high-risk factors in the food cold chain, this article has found the major risks that have a great impact on the entire food cold chain and proposes the specific measures of risk management and control to solve the problems of food cold chain and reduce risks quickly and efficiently to ensure the stability and safety of food cold chain and avoid the serious food safety accidents. The contribution of this article is reflected in three aspects, namely, (1) applies the expert system based on professional knowledge and rich experience and constructs a classification and identification system structure of food cold chain risk indexes, which lay a foundation for further identifying and evaluating the major risks of the food cold chain; (2) designs a comprehensive index weighting method combining the AHP method and entropy weight method to quantitatively evaluate the major risks. This comprehensive method combines a hierarchical structure system, evaluation algorithm, subjective factor correction algorithm, and so on. The evaluation results are more accurate, have a high matching degree with reality, and have good theoretical and practical significance; (3) analyzes and explains the major risks of the food cold chain in the non-epidemic situations and COVID-19 situations. Proposals and measures for risk management and control are put forward, which have wide practical significance.

If not well identified and controlled, risks in systematically engineered cold supply chains can lead directly to food safety incidents. In current approaches to cold supply chain risk evaluation, there is a lack of systematic classification and quantitative analysis of the influences of risk factors in the chain. To accurately evaluate unknown risks that can exert a fluctuating influence and result in great losses, this study builds a knowledge base of expert systems based on expertise and extensive experience and modifies the expert weights in conjunction with entropy weights to reduce subjective error. An ordered weighted average (OWA) operator is used to evaluate risk in a cold supply chain effectively. First, in combination with a case study of a typical fresh food e‐commerce enterprise, a typical fresh food e‐commerce enterprise, this paper identifies the risks in a food refrigeration supply chain. Second, relying on an expert group with professional knowledge and rich experience, an expert system knowledge base is constructed, risks are assigned, and the expert weight is modified in combination with the entropy weight method. Finally, based on the OWA operator, the modified risk assignment is effectively evaluated, and the risk value is obtained and sorted. Specific risk control measures are put forward according to the results of the calculation. The results show that an evaluation method combining an expert system knowledge base and the entropy‐OWA method can effectively depict and process the evaluation information needed for risk rankings and solve the problems of supply chain risks more rapidly and effectively.

Based on the vigorous development of modern supply chain opportunities in Zhejiang Province, this paper has proposed the research ideas to develop the integrated development path of regional home textile industry supply chain by analyzing the development status of Haining's home textile industry. It has put forward the significance to develop the regional textile industry, and interpreted the policies of local governments at all levels to propose the development ideas of supply chain integration. From the perspective of development status and opportunities, we have explored the development direction of supply chain integration in the home textile industry.

How small and medium-sized enterprises (SMEs) survive and grow is a matter of great concern not just to enterprises but also to governments. Although past studies have mainly focused on the driving forces of corporate growth, they have yet to investigate how SMEs can build resilience and then achieve growth through their own knowledge management within challenging environments. Therefore, this paper empirically examines the internal links among knowledge management capabilities (KMC), organizational resilience (ORE), and the growth of SMEs under the influence of environmental munificence (EM). After analyzing the sample data from Chinese SMEs, results show that KMC actively promotes the growth of SMEs from both size expansion and qualitative optimization and that it can significantly positively affect the construction of ORE. In turn, ORE plays an intermediary role in the relationship between KMC and qualitative optimization. In addition, EM has negative moderating effects both on the relationship between KMC and the quality optimization of SMEs and that between KMC and ORE.