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Point cloud simplification is concerned with reducing the number of redundant points and preserving geometric features, so as to provide a better representation of the underlying surface. In early research, many researchers focused on the moving least squares (MLS) method, volume data, and iterative simplification. MLS is used to construct local surfaces implicitly [3,4], and points are projected to the surface for down sampling. Kobbelt et al [5] simplified point clouds by extracting feature-sensitive surfaces based on volume data. Lipman et al [6] proposed a locally optimal projection (LOP) operator and applied it to raw scanned data with complex shapes. Huang et al [7] developed a weighted locally optimal projection (WLOP) operator based on LOP, which has proven to be less sensitive to noise and has the advantage of producing an evenly distributed point cloud. To reduce the computational complexity of WLOP, Yang et al [8] focused on the decomposition of a point cloud and created multiple output results by Measurement Science and Technology

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In order to break through the existing battery technology of electric vehicles, this paper proposes to use heat pump air conditioning instead of the original PTC heating system potential. First, the advantages and disadvantages of different heat pump models for new energy vehicles are analyzed and compared. Second, a fuzzy inference system is constructed based on the machine learning model to observe the temperature of the passenger compartment using the temperature sensor inside the tram and to determine the need for the air conditioning system to be turned on in the heating/cooling mode by comparing it with the set temperature. Finally, the results show that the machine learning algorithm is able to monitor and adaptively adjust the interior temperature to further enhance the adaptability of the system with low volatility and high accuracy. The proposed research study can lay the foundation for further optimizing the design of heat pump air conditioners for electric vehicles.

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