This paper mainly studies the deployment of wireless sensor network nodes in the warehouse environment monitoring system, discusses the deployment algorithm of wireless sensor network nodes in the warehouse environment, and finds out the node deployment scheme with better network performance through comparison. Wireless sensor network node deployment is the basis of wireless sensor network application in storage environment monitoring system. It affects the performance of the whole network and is the primary problem to be solved in network application. This paper discusses the advantages of wireless sensor network in the monitoring of storage environment, especially the deployment and simulation analysis of sensor nodes in the warehouse environment. Aiming at the influence of sensor perception model on the effectiveness of the node deployment plan, this paper proposes a node deployment collaborative perception model based on 0-1 perception model and exponential model. The sensor node deployment problem is transformed into a three-dimensional node deployment problem. Finally, the algorithm is applied to tobacco storage environment. In order to verify the effectiveness of the proposed algorithm, the scheme obtained by the proposed algorithm is compared with that obtained by the corresponding deployment algorithm in this paper. The comparison results show that the overall performance of the algorithm is better than that of the usual scheme.

Surface defects of autobody panels have the greatest impact on the surface quality of the automobile body, but many enterprises lack a scientific and reasonable evaluation method of surface quality, relying solely on the subjective judgment of decision makers which will lead to an increase in the probability of misjudgment. In this paper, the subjective weight is determined by the genetic algorithm based on optimization, and the objective weight is determined by the improved deviation maximization method. Combining the hesitant fuzzy set theory, the hesitant fuzzy mixed weighted arithmetic average operator (HFHWA), and the score function, the surface defect information of the panel is quantified. On this basis, a complete set of hesitant fuzzy multiattribute evaluation model of surface defect information is proposed. Taking a batch of inner panels of the automobile door produced by A automobile enterprise as an example, five common defects including hidden pit, bump and scratch, rust, indentation pockmark, and ripple are selected as evaluation attributes to evaluate their surface quality, which verifies the validity and practicability of the model.

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With the development of e-commerce and trade, China's logistics transportation demand has increased significantly. To improve the operation efficiency of new energy trucks, logistics transportation companies need scientific management methods. They need to analyze a large number of real driving conditions for new energy trucks. Additionally, to reduce new energy trucks' energy consumption and pollutant emissions, automobile manufacturers have increased the research and development of new energy trucks, and the analysis of new energy truck driving conditions is the basis for the technical development and evaluation of new models. The research in this article is based on an actual project of an automobile manufacturing company, consulting a large amount of relevant domestic and foreign literature, summarizing the current status of driving conditions at home and abroad, explaining the principle of data collection for the driving conditions of new energy trucks, and developing in-vehicle data for driving conditions based on the principle transmission method and remote transmission method. Using the membership function and K-means clustering algorithm to determine the attribute characteristics of the new energy truck driving condition analysis, a truck driving condition analysis model is built, the software function of the model is designed, and a small amount of sample data is used to import the model instance to verify the model effectiveness. Finally, based on the constructed new energy truck driving condition analysis model, big data technology was used to perform a big data analysis experiment on the actual operation data of 200 trucks of an automobile manufacturing enterprise. The Spark big data calculation framework was used to perform stream calculation and offline analysis calculations on a large amount of data from the new energy trucks. The results show that the operating efficiency of the new energy truck driving condition analysis method using big data technology is significantly higher than that of traditional technology. This study provides a theoretical basis for controlling the energy consumption and pollutant emissions of new energy trucks in logistics transportation, and provides management and logistics support for transportation logistics companies. The technical development and evaluation of the company's new models provided data references.

We present a powerful morphing technique based on level set methods, that can be combined with a variety of scan conversion/model processing techniques. Bringing these techniques together creates a general morphing approach that allows a user to morph a number of geometric model types in a single animation. We have developed techniques for converting several types of geometric models (polygonal meshes, CSG models and MRI scans) into distance volumes, the volumetric representation required by our level set morphing approach. The combination of these two capabilities allows a user to create a morphing sequence regardless of the model type of the source and target objects, freeing him/her to use whatever model type is appropriate for a particular animation.