Food price prediction is an important part of food supply chain management, and food price prediction is of great research significance and application value. Due to the problems of local optimization and slow learning convergence of traditional back propagation (BP) neural network, this paper proposed a BP neural network optimized by immune algorithm-particle swarm optimization hybrid algorithm (IA-PSO-BP) model for food price prediction, and selected relevant indicators. After neural network training and testing, it is found that compared with the BP neural network model optimized by particle swarm optimization algorithm (PSO-BP), the BP neural network model optimized by immune algorithm (IA-BP) and the BP neural network model, the IA-PSO-BP neural network model has the smallest prediction error and the highest accuracy. Compared with PSO-BP neural network model, IA-BP neural network model and BP neural network model, the mean absolute error (MAE) of IA-PSO-BP neural network model is reduced by 39.20%, 67.00% and 140.80%, the root mean square error (RMSE) is reduced by 39.60%, 67.50% and 141.20%, and the mean absolute percentage error (MAPE) is reduced by 0.46%, 0.79% and 1.67%. The predicted value of the IA-PSO-BP neural network model is closest to the actual value, which is more accurate to predict the actual food price. The conclusion shows that the IA-PSO-BP neural network model is a forecasting tool with higher accuracy and stronger applicability, and can be better used in food price prediction.
Currently, the rapid development of artificial intelligence, blockchain, cloud computing, big data and other “ABCD” technology has brought new momentum and new value to logistics enterprises. This paper aims to construct a digital transformation capability maturity index system for logistics enterprises, to indicate the direction of digital transformation for logistics enterprises and to promote their digital transformation process. First, the hierarchical structure of the indicator system is established: 48 indicators in total. Among them, there are 4 primary indicators, namely, digital strategic planning and organizational management capacity, infrastructure construction, business process and management digitization and digital performance, 12 secondary indicators and 32 tertiary indicators. Analytical Hierarchy Process (AHP) is applied to determine the weights of the indicators. Finally, through indicator weights analysis, relevant strategies for logistics enterprises to enhance digital transformation capability maturity are proposed, such as intelligent logistics planning algorithms should be used to improve the coordination of enterprise logistics planning, digital benefits-oriented implementation of digital transformation, top management should strongly support enterprise digital transformation, and digital security technology to strengthen the security and management and maintenance, and so on.
In this article, an in-depth analysis of research in the field of smart logistics is conducted utilizing bibliometrics and visualization tools. The study employs visualization techniques to reveal research hot spots, collaboration networks, and developmental trends within the field, as evidenced by metrics such as document counts, cooperative relationships, and keywords. Furthermore, the research employs citation network analysis and author impact assessment to explore significant research achievements and scholarly influence within the domain.
With the applications of artificial intelligence (AI) technology, graph neural networks (GNNs) have emerged and employed as a framework to handle tasks with graph-structured data. On the other hand, enhancing the efficiency of communication management system is an urgent demand to the power grid enterprises. In this paper, we propose a path recommendation framework with GNN for power communication management systems. First, historical data are collected as datasets. Second, the historical data are converted to structured form data, which are further used to construct graphs. Finally, with the proposed GNN, paths are recommended by the softmax layer, in which the one with the highest probability is the result. This scheme is verified with real data in a communication management system, where one optimal path and several candidate paths are reported in practice.
With the improvement of residents’ consumption level and urbanization level, the smart home consumption market shows a growing trend. China’s smart home consumption market is in the initial development stage, consumers’ cognition is not perfect and their willingness to purchase is still relatively low. This article elaborates on the factors influencing smart home consumption market based on consumer behavior theory, and analyzes the relationship between the factors influencing smart home consumption market. At the same time, the hierarchical structure between related factors based on ISM method to get the top-level factors is decomposed. Based on the findings of the research and analysis, it can better provide suggestions for companies to attract consumers to buy smart home products.
