ID 原文 译文
2973 根据蜂拥控制算法所遵循的启发式规则提出“蜂群”蜂拥涌现行为的抑制机理，首次建立了干扰条件下蜂拥控制的失效判别模型。 It was firstly proposed that the sup-pression principle of swarms'emergent behaviors in term of three heuristic rules followed by general flocking control algo-rithms. Moreover, it was firstly constructed that the failure judgment model of flocking control for swarms under man-made interference.
2974 通过仿真实验，分析和讨论了干扰强度、干扰时机对抑制“蜂群”蜂拥涌现行为的影响。 Finally, simulation experiments showed that the interference intensity and opportunity played an effect on sup-pressing emergent behaviors of swarms.
2975 针对车联网中视频语义理解等智能计算业务需求下传统资源分配方式不再适用的问题，研究了视频语义驱动的资源分配算法。 Aiming at the problem that traditional resource allocation methods will no longer be applicable, with the de-mand of intelligent computing services such as video semantic understanding in Internet of vehicles, the video semanticdriven resource allocation algorithm was studied.
2976 首先，以目标检测任务为例，提出视频语义驱动的资源分配指导模型并给出模型参数的求解算法； First of all, taking the object detection task as an example, a semantic driven resource allocation guidance model for video was proposed and an algorithm for solving model parameters wasgiven.
2977 其次，构建了车联网场景中视频语义驱动的资源分配优化问题，将该问题转化成凸问题并利用凸优化算法求解； Secondly, an optimization problem of resource allocation driven by video semantics in Internet of vehicles was constructed, which was transformed into a convex problem and solved by convex optimization algorithm.
2978 进一步，为降低凸优化算法的复杂度，提出了基于强化 Q 学习的资源分配算法； Furthermore, inorder to reduce the complexity of the convex optimization algorithm, a resource allocation algorithm based on reinforce-ment Q learning was proposed.
2979 最后，仿真验证了所提资源分配算法的性能优势。 Finally, the performance advantages of the proposed algorithm are verified by simula-tions.
2980 为了解决多样性系统在单一多样性策略下存在防御能力、防御代价和服务质量难以兼顾的问题，首先基于调度异构性、执行体安全性和空间多样性度量方法构造不同安全等级下的调度对象选择序列； To solve the problem that a diversity system is difficult to take defense capability, defense cost and quality ofservice into account at the same time under a single diversity strategy, firstly, the scheduling object selecting sequence sunder different security levels were constructed based on the measurement of scheduling heterogeneity, executor security and spatial diversity.
2981 然后根据对威胁环境的粗粒度评估，综合决策调度时机以及调度对象。 Then, according to the coarse-grained evaluation of threat environment, the scheduling time and scheduling object were determined comprehensively.
2982 通过在云环境下实现时空多样性 Web 服务系统，对所提调度策略进行攻防实验测试，并与已有调度策略进行了对比。 Through the realization of the spatio-temporal diversity Web serversystem in a cloud environment, the proposed scheduling strategy was tested with attack and defense experiments andcompared with the existing scheduling strategies.