摘要：【中文】本发明涉及一种多机器人系统任务均衡分配协同工作控制方法，人工智能和机器人控制技术领域。针对多机器人系统作业任务点呈离散固定态，为有效解决多台机器人之间的任务分配问题，通过改进K‑means算法，首先对所有任务点进行聚类，对聚类后的任务点建模，然后使用自适应缩放聚类空间方式，让不同机器人所分配的任务点的规划数量尽可能相等，方法简练、实用性强。能够有效解决多机器人系统自动化作业过程中面临的机器人任务分配问题。 【EN】The invention relates to a multi-robot system task balanced distribution cooperative work control method, which belongs to the technical field of artificial intelligence and robot control. Aiming at the problem that task points of a multi-robot system are in a discrete fixed state and task allocation among multiple robots is effectively solved, all the task points are clustered by improving a K-means algorithm, the clustered task points are modeled, then a self-adaptive scaling clustering space mode is used, the planning number of the task points allocated by different robots is enabled to be equal as much as possible, and the method is simple and strong in practicability. The problem of robot task allocation in the automatic operation process of a multi-robot system can be effectively solved.

摘要：【中文】本公开提供了一种神经网络训练方法、人脸识别任务的执行方法及装置，所得到的模型规模更小，执行任务时需要耗费的计算量更少。神经网络训练方法包括：利用神经网络中的特征提取网络对拼接的图像数据进行特征提取，得到拼接的图像数据的特征；拼接的图像数据由每种类别的人脸识别任务训练所用的样本人脸图像拼接得到；利用神经网络中的每个功能实现网络分支，根据拼接的图像数据的特征中、与该功能实现网络分支所能够完成的人脸识别任务的类别相对应的特征，确定每种类别的人脸识别任务的人脸识别结果；根据确定的人脸识别结果以及拼接得到拼接的图像数据的样本人脸图像的标注结果，对神经网络的网络参数值进行调整，得到初步训练后的神经网络。 【EN】The invention provides a neural network training method, a face recognition task execution method and a face recognition task execution device. The neural network training method comprises the following steps: performing feature extraction on the spliced image data by using a feature extraction network in the neural network to obtain features of the spliced image data; the spliced image data is obtained by splicing sample face images used for training face recognition tasks of each category; realizing network branching by using each function in the neural network, and determining the face recognition result of the face recognition task of each category according to the characteristics, corresponding to the categories of the face recognition tasks which can be completed by the function realization network branching, in the characteristics of the spliced image data; and adjusting the network parameter value of the neural network according to the determined face recognition result and the labeling result of the sample face image spliced to obtain the spliced image data to obtain the preliminarily trained neural network.