Sales Order产品提议流程解析

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"如何在销售订单中获取推荐产品" 在销售过程中,有效地提出产品建议是提高销售效率和客户满意度的关键步骤。本资源主要介绍了在特定系统(HN1/Q2U)中,使用SALESPRO角色如何操作以获取销售订单中的推荐产品。以下是详细的过程和相关知识点: 首先,登录HN1/Q2U系统并使用SALESPRO角色。这个角色通常是为销售人员设计的,具有创建、修改和管理销售订单等权限。 接下来,进入“销售周期”模块,然后选择“销售订单搜索”。在这个阶段,用户可以查找和选择需要处理的销售订单。 在找到特定的销售订单后,点击“编辑列表”选项,选择物品AB。这允许用户查看和编辑订单中的商品详情。 在“更多”菜单中,选择“从列表中提议物品”功能。这一功能通常基于预设的策略或客户历史购买记录来推荐相关产品。具体步骤可以在CRM_PT_SALES_Product_Proposal.xls文件中找到详细指南。 点击“从列表中提议物品”后,系统将生成一个包含推荐产品的列表表单。这些推荐可能是基于产品关联性、客户偏好、库存状况、促销活动等多种因素综合得出的。 问题:如何在销售订单中获取产品?是使用产品搜索组件还是系统自有的方法? 结论: 在添加到列表时,不仅包括产品ID,还包括单位、货币、净价值等信息。获取Items AB中的产品推荐分为三个步骤: 1. 通过额外的功能模块获取产品列表,而不是使用产品搜索组件。这一步骤仅获取产品ID列表。 2. 基于步骤1的产品ID列表,利用产品搜索组件获取产品属性。 3. 根据带有属性的产品获取价格信息。 因此,在销售订单中,系统通过自己的功能模块获取产品ID列表。产品搜索组件被用来获取结果产品的属性,这些属性将用于确定产品价格。 解释: (1)使用事务码St05可以追踪点击“从列表中提议物品”时执行的SQL语句,这有助于理解系统是如何根据特定条件查询和筛选出推荐产品的。 以上就是如何在销售订单中获取推荐产品的方法以及背后的逻辑流程。理解这个过程对于优化销售流程、提升销售业绩以及改善客户体验具有重要意义。

The human visual cortex is biased towards shape components while CNNs produce texture biased features. This fact may explain why the performance of CNN significantly degrades with low-labeled input data scenarios. In this paper, we propose a frequency re-calibration U-Net (FRCU-Net) for medical image segmentation. Representing an object in terms of frequency may reduce the effect of texture bias, resulting in better generalization for a low data regime. To do so, we apply the Laplacian pyramid in the bottleneck layer of the U-shaped structure. The Laplacian pyramid represents the object proposal in different frequency domains, where the high frequencies are responsible for the texture information and lower frequencies might be related to the shape. Adaptively re-calibrating these frequency representations can produce a more discriminative representation for describing the object of interest. To this end, we first propose to use a channel-wise attention mechanism to capture the relationship between the channels of a set of feature maps in one layer of the frequency pyramid. Second, the extracted features of each level of the pyramid are then combined through a non-linear function based on their impact on the final segmentation output. The proposed FRCU-Net is evaluated on five datasets ISIC 2017, ISIC 2018, the PH2, lung segmentation, and SegPC 2021 challenge datasets and compared to existing alternatives, achieving state-of-the-art results.请详细介绍这段话中的技术点和实现方式

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Recall that to solve (P2) in the tth time frame, we observe ξt 􏰗 {hti, Qi(t), Yi(t)}Ni=1, consisting of the channel gains {hti}Ni=1 and the system queue states {Qi(t),Yi(t)}Ni=1, and accordingly decide the control action {xt, yt}, including the binary offloading decision xt and the continuous resource allocation yt 􏰗 􏰄τit, fit, eti,O, rit,O􏰅Ni=1. A close observation shows that although (P2) is a non-convex optimization problem, the resource allocation problem to optimize yt is in fact an “easy” convex problem if xt is fixed. In Section IV.B, we will propose a customized algorithm to efficiently obtain the optimal yt given xt in (P2). Here, we denote G􏰀xt,ξt􏰁 as the optimal value of (P2) by optimizing yt given the offloading decision xt and parameter ξt. Therefore, solving (P2) is equivalent to finding the optimal offloading decision (xt)∗, where (P3) : 􏰀xt􏰁∗ = arg maximize G 􏰀xt, ξt􏰁 . (20) xt ∈{0,1}N In general, obtaining (xt)∗ requires enumerating 2N offloading decisions, which leads to significantly high computational complexity even when N is moderate (e.g., N = 10). Other search based methods, such as branch-and-bound and block coordinate descent [29], are also time-consuming when N is large. In practice, neither method is applicable to online decision- making under fast-varying channel condition. Leveraging the DRL technique, we propose a LyDROO algorithm to construct a policy π that maps from the input ξt to the optimal action (xt)∗, i.e., π : ξt 􏰕→ (xt)∗, with very low complexity, e.g., tens of milliseconds computation time (i.e., the time duration from observing ξt to producing a control action {xt, yt}) when N = 10深度强化学习的动作是什么

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