TY - A2的高,杨AU - Cheng西宁盟——瞿,杨惠妍PY - 2023 DA - 2023/04/20 TI -实时碰撞检测基于蛇模型的优化算法在大数据领域的SP - 4960900六世- 2023 AB -大数据处理包括多个处理数据的流动。但数据质量是整个过程中最重要的部分。每一个数据处理环节会影响大数据的质量。碰撞检测是一个重要的研究内容在很多领域,比如计算机图形学和计算机虚拟现实。通俗的说,这意味着计算机检测到信道上的信号电压,同时发送数据。当信号电压摆幅值检测到一个站超过一定阈值时,它将被认为至少有两个站在公共汽车上同时发送数据,表明发生了碰撞。随着问题的数量增加,成为更高水平,目标信息变得更加多样化,算法变得更加复杂。远非传统进化算法能够有效地处理这种情况,和优化目标算法应运而生。进化计算是一个成熟的高鲁棒性和广泛适用性的全局优化方法。它有自组织的特点,自适应,自学习。 It is not limited by the nature of the problem and can effectively deal with complex problems that are difficult to solve by traditional optimization algorithms. This paper aims to study the optimization algorithm of real-time collision detection based on Snake model in the field of big data. It is expected that, with the support of big data technology, the efficiency of real-time collision detection of Snake model will be improved and time will be saved. This paper proposes a multiline swarm particle swarm algorithm and combines it with the Snake model to improve the detection efficiency of the collision detection algorithm. It verifies the detection performance of traditional algorithms and tests their effectiveness in detection. The experimental results of this paper show that the frame rate of the Snake model algorithm is 15, the frame rate of the K-DOPs algorithm is 6.7, and the error of the algorithm is 1.04. It shows that the frame rate of Snake model algorithm is better. SN - 1574-017X UR - https://doi.org/10.1155/2023/4960900 DO - 10.1155/2023/4960900 JF - Mobile Information Systems PB - Hindawi KW - ER -