Abstract: A method, an apparatus and a medium for optimizing allocation of switching resources in the polymorphic network. The method selects the ASIC switching chip, FPGA and PPK software switching on the polymorphic network element based on machine learning, and specifically comprises the following steps: manually pre-configuring, formulating basic rules for polymorphic software and hardware co-processing; offline learning, designing training configuration in the offline learning stage to capture different switching resource usage variables, running experiments to generate the original data of a training classifier, and using the generated performance indices to train the model offline; and online reasoning, obtaining switching resource allocation advises, and updating modality codes according to the switching resource allocation advises.

Abstract: The method comprises: acquiring page information about a page of which a mask needs to be displayed; the page information comprising: the height and width of the page of which the mask needs to be displayed, and a location area in the page of at least one page element which needs to be hollowly displayed by the mask; and according to the page information, drawing a corresponding hollow mask on the page, so as to display the page element by means of the hollow location on the mask.

Abstract: A temporary pickup location that is located within a delivery area during a predefined timeframe provides an opportunity to customers to retrieve orders for items at a time and location that is convenient for them. In addition, the customers can pay for the items at retrieval (COD) and avoid the bulk and waste of shipping packages. In some implementations, temporary pickup locations are made available by planning to position a delivery agent and transportation unit at a predefined location during a predefined timeframe during which customers can plan to retrieve ordered items.