Abstract: An atmosphere starry sky light for festival entertainment is provided. The constellation unit is configured to switch patterns of twelve constellations and perform projection display on the patterns of the twelve constellations. The starry sky unit is configured to project a starry sky background. The background unit is configured to project patterns of aurora, clouds, and ripples. The planetary unit is configured to switch patterns of a planet, and to project and display a planetary image. The constellation unit, the planetary unit, the starry sky unit and the background unit form a panoramic image of the cosmic starry sky by superimposing and combining.

Abstract: An atmosphere starry sky light for festival entertainment is provided. The constellation unit is configured to switch patterns of twelve constellations and perform projection display on the patterns of the twelve constellations. The starry sky unit is configured to project a starry sky background. The background unit is configured to project patterns of aurora, clouds, and ripples. The planetary unit is configured to switch patterns of a planet, and to project and display a planetary image. The constellation unit, the planetary unit, the starry sky unit and the background unit form a panoramic image of the cosmic starry sky by superimposing and combining.

Abstract: In order to provide monocular depth prediction, a trained neural network may be used. To train the neural network, edge detection on a digital image may be performed to determine at least one edge of the digital image, and then a first point and a second point of the digital image may be sampled, based on the at least one edge. A relative depth between the first point and the second point may be predicted, and the neural network may be trained to perform monocular depth prediction using a loss function that compares the predicted relative depth with a ground truth relative depth between the first point and the second point.

Abstract: Method for processing orders and electronic device are disclosed. A embodiment of the method comprises: acquiring a new order group, wherein the new order group comprises at least one new order; determining a candidate delivery unit set according to a preset delivery area and an end task position of each delivery unit of at least one delivery unit and order information concerning new orders in the new order group, wherein the end task position of the delivery unit is a position after the delivery unit has already completed the delivery concerning all allocated orders; selecting one delivery unit from the candidate delivery unit set as a target delivery unit on the basis of the order information concerning the new orders and the preset delivery area and the end task position of each candidate delivery uni; and allocating the new orders to the target delivery unit.

Abstract: In order to provide monocular depth prediction, a trained neural network may be used. To train the neural network, edge detection on a digital image may be performed to determine at least one edge of the digital image, and then a first point and a second point of the digital image may be sampled, based on the at least one edge. A relative depth between the first point and the second point may be predicted, and the neural network may be trained to perform monocular depth prediction using a loss function that compares the predicted relative depth with a ground truth relative depth between the first point and the second point.

Abstract: Method for processing orders and electronic device are disclosed. A embodiment of the method comprises: acquiring a new order group, wherein the new order group comprises at least one new order; determining a candidate delivery unit set according to a preset delivery area and an end task position of each delivery unit of at least one delivery unit and order information concerning new orders in the new order group, wherein the end task position of the delivery unit is a position after the delivery unit has already completed the delivery concerning all allocated orders; selecting one delivery unit from the candidate delivery unit set as a target delivery unit on the basis of the order information concerning the new orders and the preset delivery area and the end task position of each candidate delivery uni; and allocating the new orders to the target delivery unit.

Abstract: An order allocation method and apparatus are provided. The order allocation method includes: obtaining an unallocated order corresponding to a first delivery business; determining a first matching degree between the unallocated order and each of allocated order groups of a plurality of candidate delivery persons, wherein the allocated order groups include an order group corresponding to a second delivery business; and if there is an allocated order group that meets a matching degree requirement, assigning the unallocated order to that allocated order group. Each of the allocated order groups may include allocated orders corresponding to the first delivery business and the second delivery business. Therefore, joint allocation and scheduling of orders of the two delivery businesses in a same order group can be implemented based on the matching degree, so that different delivery businesses can share a same batch of delivery persons, thereby improving utilization of the delivery capacity.

Abstract: An order group allocation method and apparatus are disclosed. The order group allocation method includes: determining, based on a matching score between each of a plurality of to-be-processed order groups and each of a plurality of delivery persons, an order group with a lowest matching score corresponding to each delivery person; dividing the to-be-processed order groups and the delivery persons into a plurality of non-intersecting sub-combinations; and allocating the to-be-processed order group based on an order group allocation process for each sub-combination, so that each to-be-processed order group in each sub-combination is allocated to a delivery person in a corresponding sub-combination. The method of the present disclosure improves order group allocation efficiency.

Abstract: A delivery area division method and apparatus, an electronic device, and a computer-readable storage medium are disclosed. The method includes: performing clustering process on a plurality of historical orders based on similarities between geographical locations of the plurality of historical orders to obtain N class clusters; obtaining, based on the geographical location of each of one or more historical orders in each of the N class clusters, a coverage area for each of the N class clusters; and determining, based on the coverage areas, a delivery area corresponding to each of the N class clusters. Coverage areas of different class clusters can be relatively independent of each other by using the foregoing clustering process. Therefore, automatic division of delivery areas is performed based on clustering process for geographical locations of a large quantity of historical orders, and the number cross-area orders can be reduced.

Abstract: An order assignment method and apparatus, an electronic device, and a computer-readable storage medium are provided. The method includes: determining a first delivery person corresponding to an order; estimating a delivery efficiency indicator corresponding to delivery performed by the first delivery person for the order; determining that the order is a recallable order if the delivery efficiency indicator is higher than a corresponding preset delivery efficiency indicator threshold, and associating the order with a recallable attribute, and assigning the order to the first delivery person. The first delivery person can conveniently perform a recall operation on the order based on the recallable attribute. Moreover, by first determining whether an order is a recallable order based on the delivery efficiency indicator, the validity of the recall of the order may be ensured.

Abstract: An order scheduling method and apparatus, an electronic device, and a computer-readable storage medium are provided. The method includes: determining delivery capacity pressure corresponding to a delivery area within a current scheduling period; predicting, based on historical data, a delivery capacity pressure change trend variable corresponding to the current scheduling period; adjusting the delivery capacity pressure based on the delivery capacity pressure change trend variable; and scheduling, based on the adjusted delivery capacity pressure, a plurality of orders corresponding to the delivery area within the current scheduling period. Since orders within the current scheduling period are scheduled by taking the future delivery capacity pressure change into consideration, the scheduling on a plurality of orders within the current scheduling period can better adapt to a delivery capacity pressure change, which improves the delivery efficiency.

Abstract: Embodiments of this disclosure provide a data processing method, apparatus, and device, and a computer readable storage medium. In the embodiments of this disclosure, an unassigned order group of each of at least two distribution services is obtained; a status indicator of a distribution person is determined; a distribution indicator of the unassigned order group is determined; and the unassigned order group is assigned to a matched distribution person based on the distribution indicator and the status indicator. In this way, distribution orders of at least two distribution services can be jointly scheduled, thereby reducing distribution costs, saving distribution resources, and increasing a distribution resource utilization rate.

Abstract: Embodiments of the present invention provide a task grouping method and apparatus, an electronic device, and a computer storage medium. The method includes: obtaining a similarity threshold according to a processing resource load pressure in a task source, and grouping multiple to-be-processed tasks in the task source according to similarity between the multiple to-be-processed tasks and the similarity threshold, to obtain a grouping result. A similarity threshold used for obtaining task groups by grouping is obtained according to the current processing resource load pressure in the task source, i.e., the similarity threshold dynamically changes according to the processing resource load pressure. Therefore, the task grouping result can adapt to the processing resource load pressure, and this helps improve processing resource utilization and task processing efficiency.

Abstract: Embodiments of the present invention provide a data object allocation method and apparatus and an electronic device. The method includes: pre-allocating a to-be-allocated data object to a first terminal, where the to-be-allocated data object is grouped into a data object group; simulating processing of the data object group by the first terminal and a second terminal associated with the data object group; and allocating the to-be-allocated data object to the first terminal if two simulation results meet a preset condition. According to the embodiments of the present invention, data object allocation efficiency is enhanced, and overall processing effect is optimized.

Abstract: The invention provides methods of preparing a male antifertility agent composed of a diterpene lactone compound obtained from plants of the genus Tripterygium, the diterpene lactone compounds obtained from that method, and the use of the compounds as an antifertility agent.