【摘要】 针对现有汽车管理平台难以实现对资源的合理分配，从而影响平台的运营等问题，研究提出以容器技术为资源分配模型的核心，将惩罚函数与遗传算法相结合，构建出基于惩罚函数与遗传算法的汽车管理平台资源分配优化模型。实验结果显示，该模型的资源利用率为90%、平均响应时间为1.2 s、平均任务完成率为92%。相较于其他两种模型，研究所提模型的平均投资回报率最高，用户综合评价最好，满意度达96.1%、服务可靠性为91.4%、服务灵活性为93.3%、用户回头率为90.7%、用户投诉率为2.1%。综合表明，研究所提模型能良好地应用于汽车管理平台的资源分配与优化当中。更多还原

【Abstract】 In response to the difficulty of achieving reasonable allocation of resources on existing automobile management platforms, which affects the operation of the platform, this study proposes using container technology as the core of the resource allocation model. By combining the penalty function with genetic algorithm, a resource allocation optimization model for automobile management platforms based on the penalty function and genetic algorithm is constructed. The experimental results show that the resource utilization rate of the model is 90%, the average response time is 1.2 seconds, and the average task completion rate is 92%. Compared to the other two models, the model proposed by the research institute has the highest average return on investment and the best overall user evaluation, with a satisfaction rate of 96.1%, service reliability of 91.4%, service flexibility of 93.3%, user return rate of 90.7%, and user complaint rate of 2.1%. Overall, the model proposed by the research institute can be effectively applied to resource allocation and optimization in automotive management platforms.更多还原