Browse Source

[Feature][alldata]not planned gpt,mlops,update README,modify document

AllDataDC 1 year ago
parent
commit
591cc81f4f

+ 21 - 69
README.md

@@ -2,80 +2,32 @@
 
 ## [官方文档](https://alldata.readthedocs.io/) | [安装文档](https://github.com/alldatacenter/alldata/blob/master/install.md)
 
-## 安装教程
-> https://github.com/alldatacenter/alldata/blob/master/install.md
+## 一、AllData体验版
 
-## 教程文档
-> https://github.com/alldatacenter/alldata/blob/master/studio/modules/service-data-dts-parent/DTS_QuickStart.md
-> https://github.com/alldatacenter/alldata/blob/master/bi_quickstart.md
-
-## github
+> 体验版地址:Test账号只有数据质量,体验更多功能可选参加会员通道
+>
+> 成为会员:享受会员权益,详情查看Github主页文档
+> 
+> 地址:http://122.51.43.143/dashboard 
+>
+> 账号:test/123456
 
-[![Stargazers over time](https://starchart.cc/alldatacenter/alldata.svg)](https://starchart.cc/alldatacenter/alldata)
+## 二、 官方网站
+> 官方文档:https://alldata.readthedocs.io
+>
+> 部署教程:https://github.com/alldatacenter/alldata/blob/master/install.md
+>
+> 教程文档:https://github.com/alldatacenter/alldata/blob/master/dts_quickstart.md
+> 教程文档2: https://github.com/alldatacenter/alldata/blob/master/bi_quickstart.md
 
-<br/>
+## 三、会员通道-商业版【可选参加】
+> 【腾讯文档】2023-大数据中台AllData会员通道
+>
+> https://docs.qq.com/sheet/DVFd6WXJLUHJ3eEZ1
 
-## 功能列表
-
-- 平台基础设置
-    - 系统管理
-        - 岗位管理: 配置系统用户所属担任职务
-        - 部门管理: 配置系统组织机构, 树结构展现支持数据权限
-        - 菜单管理: 配置系统菜单, 操作权限, 按钮权限标识等
-        - 角色管理: 角色菜单权限分配, 设置角色按机构进行数据范围权限划分
-        - 用户管理: 用户是系统操作者, 该功能主要完成系统用户配置
-        - 参数管理: 对系统动态配置常用参数
-        - 字典管理: 对系统中经常使用的一些较为固定的数据进行维护
-    - 系统监控
-        - 登录日志: 系统登录日志记录查询
-        - 操作日志: 系统正常操作日志记录和查询, 系统异常信息日志记录和查询
-    - 任务调度
-        - 任务管理: 在线(添加, 修改, 删除)任务调度
-        - 日志管理: 任务调度执行结果日志
-- 元数据管理
-    - 数据源: 数据源连接信息管理, 可生成数据库文档
-    - 元数据: 数据库表的元数据信息管理
-    - 数据授权: 设置元数据信息权限划分
-    - 变更记录: 元数据信息变更记录信息管理
-    - 数据检索: 数据源, 数据表, 元数据等信息查询
-    - 数据地图: 元数据的隶属数据表, 数据库的图形展示
-    - SQL工作台: 在线执行查询sql
-- 数据标准管理
-    - 标准字典: 国标数据维护
-    - 对照表: 本地数据中需要对照标准的数据维护
-    - 字典对照: 本地数据与国标数据的对照关系
-    - 对照统计: 本地数据与国标数据的对照结果统计分析
-- 数据质量管理
-    - 规则配置: 数据质量规则配置
-    - 问题统计: 数据质量规则统计
-    - 质量报告: 数据质量结果统计分析
-    - 定时任务: 数据质量定时任务
-    - 任务日志: 数据质量定时任务日志
-- 主数据管理
-    - 数据模型: 主数据数据模型维护
-    - 数据管理: 主数据数据管理
-- 数据集市管理
-    - 数据服务: 动态开发api数据服务, 可生成数据服务文档
-    - 数据脱敏: api数据服务返回结果动态脱敏
-    - 接口日志: api数据服务调用日志
-    - 服务集成: 三方数据服务集成管理
-    - 服务日志: 三方数据服务集成调用日志
-- 可视化管理
-    - 数据集: 基于sql的查询结果维护
-    - 图表配置: 动态echarts图表配置, 支持多维表格, 折线, 柱状, 饼图, 雷达, 散点等多种图表
-    - 看板配置: 拖拽式添加图表组件, 调整位置, 大小
-    - 酷屏配置: 拖拽式添加图表组件, 调整背景图, 颜色, 位置, 大小
-- 流程管理
-    - 流程定义: 流程定义管理
-    - 流程实例
-        - 运行中的流程: 运行中的流程实例管理
-        - 我发起的流程: 我发起的流程实例管理
-        - 我参与的流程: 我参与的流程实例管理
-    - 流程任务
-        - 待办任务: 待办任务管理
-        - 已办任务: 已办任务管理
-    - 业务配置: 配置业务系统与流程的相关属性
+## github
 
+[![Stargazers over time](https://starchart.cc/alldatacenter/alldata.svg)](https://starchart.cc/alldatacenter/alldata)
 
 <br/>
 <a href="https://github.com/alldatacenter/github-readme-stats">

+ 0 - 0
chatgpt/.gitkeep


+ 0 - 32
chatgpt/README.md

@@ -1,32 +0,0 @@
-# CHAT GPT FOR ALL DATA
-
-## 人工智能平台 
-
-> 方案1: cube-studio + modelscope
->
-> 方案2:  mlrun + modelscope
->
-
-### 人工智能建设方法论:
-> 
-> 确定目标和需求:在开始构建人工智能系统之前,需要明确人工智能系统的目标和需求,
-例如系统的应用场景、数据来源、预测和决策的准确性等。
-> 
-> 收集数据和特征:收集与人工智能系统相关的数据和特征,
-并对这些数据和特征进行清洗、处理和转换,以准备用于训练和测试模型。
-> 
-> 数据分析和建模:对数据和特征进行分析和建模,
-以识别出与目标变量相关的特征和模式,同时选择和优化合适的算法和模型。
-> 
-> 模型训练和评估:使用收集到的数据和特征训练人工智能模型,
-并评估模型的准确性和性能。如果模型的准确性不够,需要重新调整特征、算法和模型的参数。
-> 
-> 部署和应用:将训练好的模型部署到生产环境中,
-并应用到实际的业务场景中,不断优化和改进模型的性能和效果。
-> 
-> 监控和维护:对部署的人工智能系统进行监控和维护,保证系统的稳定性和可靠性,
-及时发现和解决问题。同时,根据实际应用场景不断地更新和迭代人工智能系统,保证其持续性能的提升。
-> 
-> 合规性和安全性:在人工智能建设过程中,需要遵循合规性和安全性的标准和法规,保证数据和模型的安全和隐私。
-> 
-> 同时,要对人工智能系统进行风险评估和安全审查,确保系统不会对社会造成负面影响。

+ 21 - 69
document/source/README.md

@@ -2,80 +2,32 @@
 
 ## [官方文档](https://alldata.readthedocs.io/) | [安装文档](https://github.com/alldatacenter/alldata/blob/master/install.md)
 
-## 安装教程
-> https://github.com/alldatacenter/alldata/blob/master/install.md
+## 一、AllData体验版
 
-## 教程文档
-> https://github.com/alldatacenter/alldata/blob/master/studio/modules/service-data-dts-parent/DTS_QuickStart.md
-> https://github.com/alldatacenter/alldata/blob/master/bi_quickstart.md
-
-## github
+> 体验版地址:Test账号只有数据质量,体验更多功能可选参加会员通道
+>
+> 成为会员:享受会员权益,详情查看Github主页文档
+> 
+> 地址:http://122.51.43.143/dashboard 
+>
+> 账号:test/123456
 
-[![Stargazers over time](https://starchart.cc/alldatacenter/alldata.svg)](https://starchart.cc/alldatacenter/alldata)
+## 二、 官方网站
+> 官方文档:https://alldata.readthedocs.io
+>
+> 部署教程:https://github.com/alldatacenter/alldata/blob/master/install.md
+>
+> 教程文档:https://github.com/alldatacenter/alldata/blob/master/dts_quickstart.md
+> 教程文档2: https://github.com/alldatacenter/alldata/blob/master/bi_quickstart.md
 
-<br/>
+## 三、会员通道-商业版【可选参加】
+> 【腾讯文档】2023-大数据中台AllData会员通道
+>
+> https://docs.qq.com/sheet/DVFd6WXJLUHJ3eEZ1
 
-## 功能列表
-
-- 平台基础设置
-    - 系统管理
-        - 岗位管理: 配置系统用户所属担任职务
-        - 部门管理: 配置系统组织机构, 树结构展现支持数据权限
-        - 菜单管理: 配置系统菜单, 操作权限, 按钮权限标识等
-        - 角色管理: 角色菜单权限分配, 设置角色按机构进行数据范围权限划分
-        - 用户管理: 用户是系统操作者, 该功能主要完成系统用户配置
-        - 参数管理: 对系统动态配置常用参数
-        - 字典管理: 对系统中经常使用的一些较为固定的数据进行维护
-    - 系统监控
-        - 登录日志: 系统登录日志记录查询
-        - 操作日志: 系统正常操作日志记录和查询, 系统异常信息日志记录和查询
-    - 任务调度
-        - 任务管理: 在线(添加, 修改, 删除)任务调度
-        - 日志管理: 任务调度执行结果日志
-- 元数据管理
-    - 数据源: 数据源连接信息管理, 可生成数据库文档
-    - 元数据: 数据库表的元数据信息管理
-    - 数据授权: 设置元数据信息权限划分
-    - 变更记录: 元数据信息变更记录信息管理
-    - 数据检索: 数据源, 数据表, 元数据等信息查询
-    - 数据地图: 元数据的隶属数据表, 数据库的图形展示
-    - SQL工作台: 在线执行查询sql
-- 数据标准管理
-    - 标准字典: 国标数据维护
-    - 对照表: 本地数据中需要对照标准的数据维护
-    - 字典对照: 本地数据与国标数据的对照关系
-    - 对照统计: 本地数据与国标数据的对照结果统计分析
-- 数据质量管理
-    - 规则配置: 数据质量规则配置
-    - 问题统计: 数据质量规则统计
-    - 质量报告: 数据质量结果统计分析
-    - 定时任务: 数据质量定时任务
-    - 任务日志: 数据质量定时任务日志
-- 主数据管理
-    - 数据模型: 主数据数据模型维护
-    - 数据管理: 主数据数据管理
-- 数据集市管理
-    - 数据服务: 动态开发api数据服务, 可生成数据服务文档
-    - 数据脱敏: api数据服务返回结果动态脱敏
-    - 接口日志: api数据服务调用日志
-    - 服务集成: 三方数据服务集成管理
-    - 服务日志: 三方数据服务集成调用日志
-- 可视化管理
-    - 数据集: 基于sql的查询结果维护
-    - 图表配置: 动态echarts图表配置, 支持多维表格, 折线, 柱状, 饼图, 雷达, 散点等多种图表
-    - 看板配置: 拖拽式添加图表组件, 调整位置, 大小
-    - 酷屏配置: 拖拽式添加图表组件, 调整背景图, 颜色, 位置, 大小
-- 流程管理
-    - 流程定义: 流程定义管理
-    - 流程实例
-        - 运行中的流程: 运行中的流程实例管理
-        - 我发起的流程: 我发起的流程实例管理
-        - 我参与的流程: 我参与的流程实例管理
-    - 流程任务
-        - 待办任务: 待办任务管理
-        - 已办任务: 已办任务管理
-    - 业务配置: 配置业务系统与流程的相关属性
+## github
 
+[![Stargazers over time](https://starchart.cc/alldatacenter/alldata.svg)](https://starchart.cc/alldatacenter/alldata)
 
 <br/>
 <a href="https://github.com/alldatacenter/github-readme-stats">

+ 0 - 32
document/source/chatgpt/README.md

@@ -1,32 +0,0 @@
-# CHAT GPT FOR ALL DATA
-
-## 人工智能平台 
-
-> 方案1: cube-studio + modelscope
->
-> 方案2:  mlrun + modelscope
->
-
-### 人工智能建设方法论:
-> 
-> 确定目标和需求:在开始构建人工智能系统之前,需要明确人工智能系统的目标和需求,
-例如系统的应用场景、数据来源、预测和决策的准确性等。
-> 
-> 收集数据和特征:收集与人工智能系统相关的数据和特征,
-并对这些数据和特征进行清洗、处理和转换,以准备用于训练和测试模型。
-> 
-> 数据分析和建模:对数据和特征进行分析和建模,
-以识别出与目标变量相关的特征和模式,同时选择和优化合适的算法和模型。
-> 
-> 模型训练和评估:使用收集到的数据和特征训练人工智能模型,
-并评估模型的准确性和性能。如果模型的准确性不够,需要重新调整特征、算法和模型的参数。
-> 
-> 部署和应用:将训练好的模型部署到生产环境中,
-并应用到实际的业务场景中,不断优化和改进模型的性能和效果。
-> 
-> 监控和维护:对部署的人工智能系统进行监控和维护,保证系统的稳定性和可靠性,
-及时发现和解决问题。同时,根据实际应用场景不断地更新和迭代人工智能系统,保证其持续性能的提升。
-> 
-> 合规性和安全性:在人工智能建设过程中,需要遵循合规性和安全性的标准和法规,保证数据和模型的安全和隐私。
-> 
-> 同时,要对人工智能系统进行风险评估和安全审查,确保系统不会对社会造成负面影响。

+ 0 - 7
document/source/chatgpt/index.rst

@@ -1,7 +0,0 @@
-ChatGPT
-=================================
- 
-.. toctree::
-   :maxdepth: 2
-   
-   README

+ 15 - 23
document/source/donate/README.md

@@ -1,32 +1,24 @@
 # 会员通道
 
-## 一、AllData体验版地址【免费游客账号】
-详情查看https://docs.qq.com/sheet/DVFd6WXJLUHJ3eEZ1
-```markdown
-体验版地址:游客账号只有数据质量提供对外访问,更多功能可选参加募捐活动
-购买VIP开通会员账号,同时享受较多权益
-详情查看https://docs.qq.com/doc/DVHlkSEtvVXVCdEFo
-http://43.138.157.47/dashboard 游客账号:test/123456
-```
-## 二、 官网文档资料
+## 一、AllData体验版
+
+> 体验版地址:Test账号只有数据质量,体验更多功能可选参加会员通道
+>
+> 成为会员:享受会员权益,详情查看Github主页文档
+> 
+> 地址:http://122.51.43.143/dashboard 
+>
+> 账号:test/123456
+
+## 二、 官方网站
 > 官方文档:https://alldata.readthedocs.io
 >
 > 部署教程:https://github.com/alldatacenter/alldata/blob/master/install.md
 >
 > 教程文档:https://github.com/alldatacenter/alldata/blob/master/studio/modules/service-data-dts-parent/DTS_QuickStart.md
 
-## 三、AllData社区【微信群】
-【腾讯文档】大数据中台AllData最新最全资料
-https://docs.qq.com/doc/DVHlkSEtvVXVCdEFo
-
-【腾讯文档】2023-大数据中台AllData募捐会员权益 
-https://docs.qq.com/sheet/DVFd6WXJLUHJ3eEZ1?tab=2n7wnr
-
-## 四、会员通道【可选参加募捐活动】
-
-> AllData募捐活动负责人 https://docs.qq.com/doc/DVE9HdG56Z3RQaWxH
-> 
-> 【腾讯文档】2023-大数据中台AllData募捐会员权益 
-> [https://docs.qq.com/sheet/DVFd6WXJLUHJ3eEZ1?tab=BB08J2](https://docs.qq.com/sheet/DVFd6WXJLUHJ3eEZ1?tab=BB08J2)
+## 三、会员通道-商业版【可选参加】
+> 【腾讯文档】2023-大数据中台AllData会员通道
+>
+> https://docs.qq.com/sheet/DVFd6WXJLUHJ3eEZ1
 
-```

+ 0 - 2
document/source/index.rst

@@ -12,7 +12,5 @@
    README
    quickstart/index
    studio/index
-   mlops/index
-   chatgpt/index
    wiki/index
    donate/index

+ 0 - 32
document/source/mlops/README.md

@@ -1,32 +0,0 @@
-# MLOPS FOR ALL DATA
-
-## 人工智能平台 
-
-> 方案1: cube-studio + modelscope
->
-> 方案2:  mlrun + modelscope
->
-
-### 人工智能建设方法论:
-> 
-> 确定目标和需求:在开始构建人工智能系统之前,需要明确人工智能系统的目标和需求,
-例如系统的应用场景、数据来源、预测和决策的准确性等。
-> 
-> 收集数据和特征:收集与人工智能系统相关的数据和特征,
-并对这些数据和特征进行清洗、处理和转换,以准备用于训练和测试模型。
-> 
-> 数据分析和建模:对数据和特征进行分析和建模,
-以识别出与目标变量相关的特征和模式,同时选择和优化合适的算法和模型。
-> 
-> 模型训练和评估:使用收集到的数据和特征训练人工智能模型,
-并评估模型的准确性和性能。如果模型的准确性不够,需要重新调整特征、算法和模型的参数。
-> 
-> 部署和应用:将训练好的模型部署到生产环境中,
-并应用到实际的业务场景中,不断优化和改进模型的性能和效果。
-> 
-> 监控和维护:对部署的人工智能系统进行监控和维护,保证系统的稳定性和可靠性,
-及时发现和解决问题。同时,根据实际应用场景不断地更新和迭代人工智能系统,保证其持续性能的提升。
-> 
-> 合规性和安全性:在人工智能建设过程中,需要遵循合规性和安全性的标准和法规,保证数据和模型的安全和隐私。
-> 
-> 同时,要对人工智能系统进行风险评估和安全审查,确保系统不会对社会造成负面影响。

+ 0 - 7
document/source/mlops/index.rst

@@ -1,7 +0,0 @@
-MLOPS
-=================================
- 
-.. toctree::
-   :maxdepth: 2
-   
-   README

studio/modules/service-data-dts-parent/DTS_QuickStart.md → dts_quickstart.md


+ 0 - 0
mlops/.gitkeep


+ 0 - 32
mlops/README.md

@@ -1,32 +0,0 @@
-# MLOPS FOR ALL DATA
-
-## 人工智能平台 
-
-> 方案1: cube-studio + modelscope
->
-> 方案2:  mlrun + modelscope
->
-
-### 人工智能建设方法论:
-> 
-> 确定目标和需求:在开始构建人工智能系统之前,需要明确人工智能系统的目标和需求,
-例如系统的应用场景、数据来源、预测和决策的准确性等。
-> 
-> 收集数据和特征:收集与人工智能系统相关的数据和特征,
-并对这些数据和特征进行清洗、处理和转换,以准备用于训练和测试模型。
-> 
-> 数据分析和建模:对数据和特征进行分析和建模,
-以识别出与目标变量相关的特征和模式,同时选择和优化合适的算法和模型。
-> 
-> 模型训练和评估:使用收集到的数据和特征训练人工智能模型,
-并评估模型的准确性和性能。如果模型的准确性不够,需要重新调整特征、算法和模型的参数。
-> 
-> 部署和应用:将训练好的模型部署到生产环境中,
-并应用到实际的业务场景中,不断优化和改进模型的性能和效果。
-> 
-> 监控和维护:对部署的人工智能系统进行监控和维护,保证系统的稳定性和可靠性,
-及时发现和解决问题。同时,根据实际应用场景不断地更新和迭代人工智能系统,保证其持续性能的提升。
-> 
-> 合规性和安全性:在人工智能建设过程中,需要遵循合规性和安全性的标准和法规,保证数据和模型的安全和隐私。
-> 
-> 同时,要对人工智能系统进行风险评估和安全审查,确保系统不会对社会造成负面影响。

+ 0 - 191
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