Abstract: Service cluster instance backup and recovery methods and related devices are disclosed. For a container group in a service cluster instance to be backed up, a source persistent volume claim and a source persistent volume object corresponding to the container group may be set, and the container group is stored based on the source persistent volume object. When the service cluster instance satisfies a backup triggering condition, for the container group, a backup persistent volume claim and a backup persistent volume object of the container group are created according to the source persistent volume claim and the source persistent volume object corresponding to the container group. To-be-backed-up data of the container group is acquired based on the source persistent volume object. The to-be-backed-up data is backed up to a storage area pointed by the backup persistent volume object according to the backup persistent volume claim of the container group.

Abstract: Service cluster instance backup and recovery methods and related devices are disclosed. For a container group in a service cluster instance to be backed up, a source persistent volume claim and a source persistent volume object corresponding to the container group may be set, and the container group is stored based on the source persistent volume object. When the service cluster instance satisfies a backup triggering condition, for the container group, a backup persistent volume claim and a backup persistent volume object of the container group are created according to the source persistent volume claim and the source persistent volume object corresponding to the container group. To-be-backed-up data of the container group is acquired based on the source persistent volume object. The to-be-backed-up data is backed up to a storage area pointed by the backup persistent volume object according to the backup persistent volume claim of the container group.

Abstract: Implementations are described herein for estimating aggregate trait values for agricultural plots. In various implementations, a plurality of images depicting crops in an agricultural plot may be processed using a feature extraction portion of aggregate trait estimation model to generate a corresponding plurality of embeddings. The plurality of embeddings may be combined to obtain a unified representation of the crops in the agricultural plot. Using a prediction portion of the aggregate trait estimation model, the unified representation of the crops may be processed to estimate, as a direct output of the aggregate trait estimation model, an aggregate trait value of the crops in the agricultural plot. Data indicative of the estimated aggregate trait value of the crops in the agricultural plot may be output at a computing device.

Abstract: Service cluster instance backup and recovery methods and related devices are disclosed. For a container group in a service cluster instance to be backed up, a source persistent volume claim and a source persistent volume object corresponding to the container group may be set, and the container group is stored based on the source persistent volume object. When the service cluster instance satisfies a backup triggering condition, for the container group, a backup persistent volume claim and a backup persistent volume object of the container group are created according to the source persistent volume claim and the source persistent volume object corresponding to the container group. To-be-backed-up data of the container group is acquired based on the source persistent volume object. The to-be-backed-up data is backed up to a storage area pointed by the backup persistent volume object according to the backup persistent volume claim of the container group.

Abstract: The present invention discloses a stepper lithography apparatus, its lithography pattern alignment device and operating method. Several three-dimensional marks are set on the wafer as coordinate presets for positioning the wafer surface, and the three-dimensional marks are measured by the sensing technology using probe sensors to obtain coordinates of the wafer surface with sub-nanometer accuracy, the wafer stage is then moved and the new coordinates of the three-dimensional mark are measured and compared with the same three-dimensional nano-coordinates before the wafer stage is moved to obtain the error value of the wafer area position coordinates. This coordinate error is compensated by moving the relative position of the exposure beam generator and the wafer area using the closed-loop control principle to achieve accurate re-alignment of the relative coordinate positions.

Abstract: Implementations are described herein for estimating aggregate trait values for agricultural plots. In various implementations, a plurality of images depicting crops in an agricultural plot may be processed using a feature extraction portion of aggregate trait estimation model to generate a corresponding plurality of embeddings. The plurality of embeddings may be combined to obtain a unified representation of the crops in the agricultural plot. Using a prediction portion of the aggregate trait estimation model, the unified representation of the crops may be processed to estimate, as a direct output of the aggregate trait estimation model, an aggregate trait value of the crops in the agricultural plot. Data indicative of the estimated aggregate trait value of the crops in the agricultural plot may be output at a computing device.

Abstract: A distributed data processing method is provided. The distributed data processing method includes: receiving, by a shard node, data uploaded by a client, wherein the data is directed to a table; storing, by the shard node, the data to a storage directory corresponding to the table; and when the storage is successful, sending, by the shard node, the data to each connected stream computing node to perform stream computing.