【转】ios app在itunesConnect里面的几种状态

[color=orange]Waiting for Upload (Yellow) [/color]
Appears when you’ve completed entering your metadata, however, you have not finished uploading your binary or have chosen to upload your binary at a later time. Your app must be in the Waiting For Upload state before you can deliver your binary through Application Loader.

[color=orange]Prepare for Upload (Yellow) [/color]
Appears when you have created a new version, but you have not yet clicked the Ready to Submit Binary button. This state also indicates that you can now deliver your binary through Application Loader.

[color=orange]Upload Received (Yellow) [/color]
Appears when your binary has been received through Application Loader, but is still being processed by the iTunes Connect system.

[color=red]Invalid Binary (Red) [/color]
Appears when a binary is received through Application Loader, has been processed, but your binary is invalid. Examples of an invalid binary are: your binary icon does not meet our requirements, you have placed the payload directory at the wrong level in the .app wrapper, you attempted to use a non-increasing CFBundleVersion, etc.

[color=red]Missing Screenshot (Red) [/color]
Appears when your app is missing a required screenshot for iPhone and iPod touch or iPad for your default language app or for your added localizations. At least one screenshot is required for both iPhone and iPod touch and for iPad if you are submitting a universal app.

[color=orange]Waiting for Review (Yellow) [/color]
Appears after you submit a new application or update and prior to the application being reviewed by Apple. This status means that your app has been added to the app review queue, but has not yet started the review process.

[color=orange]Waiting For Export Compliance (Yellow) [/color]
Appears when your CCATS is in review with Export Compliance.

[color=orange]In Review (Yellow) [/color]
Appears when we are reviewing your app prior to the application being approved or rejected. It takes time to review binaries so we appreciate your patience and ask that you allow sufficient time for the processing of your application. When the status of your application is in review, you have the option to reject the binary you have submitted by clicking Reject Binary. This will remove your binary from the review queue and will allow for another update to be submitted. If you reject your binary, the status of your app will change to Developer Rejected and when your binary is re-submitted, the review process will start over from the beginning.

[color=orange]Pending Contract (Yellow) [/color]
Appears when your application has been reviewed and is Ready for Sale but your contracts are not yet in effect. You may check the progress of your contracts in iTunes Connect by clicking on the Contracts, Tax & Banking information module.

[color=orange]Pending Developer Release (Yellow) [/color]
Appears when the version of your app has been approved by Apple and you have turned on the Version Release Control, but have not yet clicked Send Version Live. You should also see a pending action symbol on the version. Your version will remain in this state, and thus will not be live on the App Store until you click Send Version Live.

[color=orange]Processing for App Store (Yellow)[/color]
Appears when the version is being processed to go live on the App Store. Once the processing is complete, the version state will change to “Ready for Sale.” This is a temporary state (approx. 1 – 2 hours).

[color=green]Ready for Sale (Green) [/color]
Appears once your application been approved and posted to the App Store. When your application is in this status, you have the option to remove it from the store by going to the Rights and Pricing page and removing all App Store territories.

[color=red]Rejected (Red) [/color]
Appears when the binary has been rejected.

[color=red]Removed from Sale (Red) [/color]
Appears when the binary has been removed from the App Store.

[color=red]Developer Rejected (Red) [/color]
Appears when youʼve rejected the binary from the review process. Existing versions of your application on the App Store will not be affected by self-rejecting binaries in review.

Important: When you self-reject your binary, you lose your place in the review queue. Your binary will be placed at the end of the queue when you resubmit.

[color=red]Developer Removed from Sale (Red) [/color]
Appears when you’ve removed your application from the App Store.
标题SpringBoot智能在线预约挂号系统研究AI更换标题第1章引言介绍智能在线预约挂号系统的研究背景、意义、国内外研究现状及论文创新点。1.1研究背景与意义阐述智能在线预约挂号系统对提升医疗服务效率的重要性。1.2国内外研究现状分析国内外智能在线预约挂号系统的研究与应用情况。1.3研究方法及创新点概述本文采用的技术路线、研究方法及主要创新点。第2章相关理论总结智能在线预约挂号系统相关理论,包括系统架构、开发技术等。2.1系统架构设计理论介绍系统架构设计的基本原则和常用方法。2.2SpringBoot开发框架理论阐述SpringBoot框架的特点、优势及其在系统开发中的应用。2.3数据库设计与管理理论介绍数据库设计原则、数据模型及数据库管理系统。2.4网络安全与数据保护理论讨论网络安全威胁、数据保护技术及其在系统中的应用。第3章SpringBoot智能在线预约挂号系统设计详细介绍系统的设计方案,包括功能模块划分、数据库设计等。3.1系统功能模块设计划分系统功能模块,如用户管理、挂号管理、医生排班等。3.2数据库设计与实现设计数据库表结构,确定字段类型、主键及外键关系。3.3用户界面设计设计用户友好的界面,提升用户体验。3.4系统安全设计阐述系统安全策略,包括用户认证、数据加密等。第4章系统实现与测试介绍系统的实现过程,包括编码、测试及优化等。4.1系统编码实现采用SpringBoot框架进行系统编码实现。4.2系统测试方法介绍系统测试的方法、步骤及测试用例设计。4.3系统性能测试与分析对系统进行性能测试,分析测试结果并提出优化建议。4.4系统优化与改进根据测试结果对系统进行优化和改进,提升系统性能。第5章研究结果呈现系统实现后的效果,包括功能实现、性能提升等。5.1系统功能实现效果展示系统各功能模块的实现效果,如挂号成功界面等。5.2系统性能提升效果对比优化前后的系统性能
在金融行业中,对信用风险的判断是核心环节之一,其结果对机构的信贷政策和风险控制策略有直接影响。本文将围绕如何借助机器学习方法,尤其是Sklearn工具包,建立用于判断信用状况的预测系统。文中将涵盖逻辑回归、支持向量机等常见方法,并通过实际操作流程进行说明。 一、机器学习基本概念 机器学习属于人工智能的子领域,其基本理念是通过数据自动学习规律,而非依赖人工设定规则。在信贷分析中,该技术可用于挖掘历史数据中的潜在规律,进而对未来的信用表现进行预测。 二、Sklearn工具包概述 Sklearn(Scikit-learn)是Python语言中广泛使用的机器学习模块,提供多种数据处理和建模功能。它简化了数据清洗、特征提取、模型构建、验证与优化等流程,是数据科学项目中的常用工具。 三、逻辑回归模型 逻辑回归是一种常用于分类任务的线性模型,特别适用于二类问题。在信用评估中,该模型可用于判断借款人是否可能违约。其通过逻辑函数将输出映射为0到1之间的概率值,从而表示违约的可能性。 四、支持向量机模型 支持向量机是一种用于监督学习的算法,适用于数据维度高、样本量小的情况。在信用分析中,该方法能够通过寻找最佳分割面,区分违约与非违约客户。通过选用不同核函数,可应对复杂的非线性关系,提升预测精度。 五、数据预处理步骤 在建模前,需对原始数据进行清理与换,包括处理缺失值、识别异常点、标准化数值、筛选有效特征等。对于信用评分,常见的输入变量包括收入水平、负债比例、信用历史记录、职业稳定性等。预处理有助于减少噪声干扰,增强模型的适应性。 六、模型构建与验证 借助Sklearn,可以将数据集划分为训练集和测试集,并通过交叉验证调整参数以提升模型性能。常用评估指标包括准确率、召回率、F1值以及AUC-ROC曲线。在处理不平衡数据时,更应关注模型的召回率与特异性。 七、集成学习方法 为提升模型预测能力,可采用集成策略,如结合多个模型的预测结果。这有助于降低单一模型的偏差与方差,增强整体预测的稳定性与准确性。 综上,基于机器学习的信用评估系统可通过Sklearn中的多种算法,结合合理的数据处理与模型优化,实现对借款人信用状况的精准判断。在实际应用中,需持续调整模型以适应市场变化,保障预测结果的长期有效性。 资源来源于网络分享,仅用于学习交流使用,请勿用于商业,如有侵权请联系我删除!
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