add case study for unionbigdata in English

Signed-off-by: PC <[email protected]>

docs/casestudies/unionbigdata.md

@@ -1,95 +1,89 @@
 ---
-title: 数之联在 BOE 的 I3Plat 建设中使用 Karmada
+title: UnionBigData's Utilization of Karmada in the Construction of I3Plat at BOE
 ---
 
-## 行业背景
+## Industry Background
 
-在液晶面板生产领域，由于多种因素，产品常出现不良品。为此，关键工艺节点后引入了自动光学检测（AOI）设备，通过光学原理检测常见缺陷。然而，现有 AOI 设备仅识别缺陷有无，需要人工分类和识
-别假缺陷，这一过程耗时且影响生产效率。[BOE 集团](https://www.boe.com/)引入自动缺陷分类系统（ADC）以提高判定准确性并减轻劳动强度，使用深度学习技术自动分类 AOI
-输出的缺陷图片，并筛除误判，从而提高生产效率。
+In the LCD panel production field, products often develop defects due to a variety of factors. To address this, Automatic Optical Inspection (AOI) equipment, which utilizes optical principles to detect common flaws, was introduced after critical stages in the manufacturing process. However, existing AOI equipment could only detect whether a defect was present, requiring manual intervention to classify and identify false defects, which was both time-consuming and labor-intensive. [BOE Group](https://www.boe.com/) initially implemented an Automatic Defect Classification (ADC) system in one factory to enhance the accuracy of defect judgment and reduce the strain on workers. This system employs deep learning technology to automatically categorize images of defects identified by AOI, filtering out erroneous assessments, thereby increasing production efficiency.
 
-BOE 率先在一个工厂引入 ADC，后续在其他工厂推广，节省人力资源，提高判定效率。尽管如此，由于工艺复杂和供应商差异，现场建设呈现出割裂和分散管理的趋势，给数据共享和运维带来困难。为解
-决这些问题，BOE 启动了工业智能检测平台（I3Plat）的建设，该平台利用人工智能技术，标准化智能检测并提高生产效率和良率。
+BOE initially introduced ADC in one factory, and then expanded its use to other factories, saving manpower and increasing the efficiency of judgments. Despite this advancement, the complexity of the processes and differences between suppliers led to fragmented and decentralized construction at the factory sites, complicating data sharing and operational maintenance. To address these issues, BOE initiated the construction of the Industrial Intelligent Detection Platform (I3Plat), which employs artificial intelligence to standardize intelligent detection and enhance both production efficiency and the yield rate.
 
-![BOE工业智能检测平台](./static/unionbigdata_01.png)
+![BOE Industrial Intelligent Detection Platform](./static/unionbigdata_01.png)
 
-I3Plat 将 ADC 作为核心，扩展至模型训练和检测复判，实现“云”（管理+训练）+“边”（推理）+“端”（业务）的一体化方案，旨在通过标准化平台提高生产质量和数据价值。建设范围包括资源共享中心、现地
-训练和边侧推理等子平台，将在若干工厂实施。
+I3Plat centralizes ADC as its core, extending to model training and detection reinspection, achieving an integrated "cloud" (management + training) + "edge" (inference) + "endpoint" (business) solution, aiming to enhance production quality and the value of data through a standardized platform. The construction scope at the factory sites includes a resource sharing center, on-site training, and edge-side inference sub-platforms, all of which will be implemented across multiple factories.
 
-![I3Plat平台架构图](./static/unionbigdata_02.png)
+![I3Plat Platform Architecture](./static/unionbigdata_02.png)
 
-项目目标是实现现地 ADC 上线、资源共享和云边端标准化，以减轻运维负荷、提升标准。I3Plat 旨在通过规范化和标准化 BOE 集团的 ADC 系统，为后续 ADC 建设提供样本和模板，降低成本和周期，提高生
-产和质检效率以及产品良率。包含系统管理员、资源配置员等用户角色，并涉及 ADC 推理、模型训练、数据共享等信息流，以及云端协同功能，确保 ADC 的自动缺陷分类生产过程，并提高模型和缺陷图片的
-利用率。
+The project aims to launch ADC at the factory sites, facilitate resource sharing, and achieve cloud-edge standardization to reduce the operational burden and uphold high standards. I3Plat is designed to streamline and standardize BOE Group's ADC systems across factories, providing a blueprint and reference for future ADC deployments. This aims to reduce costs and timeframes, and boost both production efficiency and the quality inspection process, ultimately improving product yields. It includes roles like system administrators, resource allocators and entails workflows for ADC inference, model training, data sharing, as well as cloud collaboration features, ensuring an automated defect classification process, and enhancing the usage of models and defective image data.
 
-## 产品与技术实现
+## Product and Technical Implementation
 
-### 一、集群管理
+### Cluster Management
 
-不同现地可将对应的 K8s 集群注册至中心云系统，中心云系统对多个现地的集群进行管理。
+Factories can register their respective K8s clusters with the central cloud system, which then manages these clusters from a single point.
 
-![集群管理](./static/unionbigdata_03.png)
+![Cluster Management](./static/unionbigdata_03.png)
 
-我们选择了 PULL 模式。
+We have chosen the PULL mode.
 
-为了降低 OP 的操作成本，我们在中心云提供了 step-by-step 的注册流程。
+To reduce the operational cost for operators, we offer a step-by-step registration process within the central cloud.
 
-1. 引导安装 karmada-agent。
-2. 使用 karmadactl token create 控制面生成 token。
-3. 引导注册 karmadactl register 。
-4. 在成员集群中编辑由 karmadactl register 创建的 deploy/karmada-agent 以确保其可以访问该成员集群的 kube-apiserver。
+1. Guide the installation of karmada-agent.
+2. Generate a token using karmadactl token create in the control plane.
+3. Proceed with the registration using karmadactl register.
+4. Edit the deploy/karmada-agent created by karmadactl register in the member cluster to ensure its access to the kube-apiserver of that member cluster.
 
-### 二、使用聚合层 API
+### Using Aggregate Layer API
 
-通过 karmada-aggregator 组件提供的集群统一访问能力，我们可以在中心云实现可视化大屏等需要聚合成员集群的数据的功能。
+Through the cluster unification access provided by the karmada-aggregator component, we are able to implement functions in the central cloud that amalgamate data from member clusters, such as visualized dashboards.
 
-通常我们用 Service 来暴露 Java 实现的功能，并用 Java Fabric8 等客户端调用 kubectl get --raw 来实现调用：
+Typically we expose functions implemented in Java using Service, and invoke kubectl get --raw with Java Fabric8 or similar clients:
 
 ```
 /apis/cluster.karmada.io/v1alpha1/clusters/%s/proxy/api/v1/namespaces/%s/services/%s/proxy/%s
 ```
 
-#### 1、集群监控
+#### Cluster Monitoring
 
-针对在线的集群，中心云系统可对内存、CPU、磁盘、网络流入流出速率、GPU、日志等指标进行监控数据展示，并可切换集群进行数据查看。
+For online clusters, the central cloud system can display monitoring data for key metrics such as memory, CPU, disk, network ingress and egress rates, GPU, and logs, with the ability to switch between cluster views.
 
-![资源监控](./static/unionbigdata_04.png)
+![Resource Monitoring](./static/unionbigdata_04.png)
 
-中心云可以看到和训练云相同的监控，通过 Karmada 聚合层 API 由集群的 Java 程序对 PromQL 封装后提供给前端页面，以下是一个 Java 查询节点 CPU 利用率的示例：
+The central cloud can see the same monitoring as the training cloud, with the cluster's Java program encapsulating PromQL through Karmada's aggregate layer API to provide to the front-end page. Below is an example Java query for node CPU utilization:
 
 ```
 /apis/cluster.karmada.io/v1alpha1/clusters/%s/proxy/api/v1/namespaces/%s/services/%s/proxy/api/v1/query_range?query=node:node_cpu_utilization:avg1m{node='%s'}&start=%s&end=%s&step=%s
 ```
 
-#### 2、中心云数据下发
+#### Central Cloud Data Distribution
 
-用户在中心云上传的数据，可自由选择下发至指定现地，包括数据集、标注、算子工程、算子镜像以及模型等。
+Data uploaded by users in the central cloud can be freely distributed to designated on-site locations, including datasets, annotations, operator projects, operator images, and models.
 
-![数据发布](./static/unionbigdata_05.png)
+![Data Release](./static/unionbigdata_05.png)
 
-数据集、算子工程、模型，通常是文件，在完成传输后，会保存到本地或NAS等存储中。标注，通常是结构化数据，在完成传输后，会保存到 DB 中。算子镜像，一般导出为 tar 包，在完成传输后，会推送到当前集群的 harbor 中。
-中心云除了 Karmada 的控制面以外，也带有自己的业务 K8s 集群，也包括存储，因此可以作为一个中转器。以上均通过 Karmada 的聚合层 API 来调用我们提供的文件上传等 svc。实现了集群和集群之间的调用。
+Datasets, operator projects, and models are typically files that, after being transferred, are saved to local or NAS storage. Annotations are usually structured data that are saved to a DB after transfer. Operator images are generally exported as tar packages, which are pushed to the harbor of the current cluster after transfer.
+In addition to Karmada's control plane, the central cloud also has its own business K8s cluster, including storage, so it can act as a transfer station. All these are done through Karmada's aggregate layer API, calling the file upload services we provide. This enables cluster-to-cluster calls.
 
-#### 3、跨现地训练
+#### Cross-Factory Training
 
-针对某现地训练资源不足的情况下，可通过申请其他现地资源的方式，进行跨现地训练。该功能实现方式为将 A 现地训练所需要的数据集、标注、算子工程、算子镜像等数据发送至 B 现地，通过 B 现地的资源进行训练。再将训练好的模型返回给 A 现地。
+In cases where a factory site lacks sufficient training resources, it can apply for resources from other sites to conduct cross-factory training. This function works by sending the data sets, annotations, operator projects, operator images, etc., needed for training at Site A to Site B, where training is carried out using Site B's resources. The trained model is then returned to Site A.
 
-![跨现地训练](./static/unionbigdata_06.png)
+![Cross-Site Training](./static/unionbigdata_06.png)
 
-原理和中心云数据下发类似，任务所需的数据会直接发送到对应集群，体现了成员集群和成员集群之间的调用关系。
+The principle is similar to central cloud data distribution, where the data required for a task is sent directly to the corresponding cluster, demonstrating the call relationship between member clusters.
 
-#### 4、可视化大屏
+#### Visualization Dashboard
 
-根据中心云注册的现地，统计不同现地的各类指标数据进行大屏展示。
+Based on the sites registered with the central cloud, various indicator data from different sites are collected for display on a large-screen dashboard.
 
-![可视化大屏](./static/unionbigdata_07.png)
+![Visualization Dashboard](./static/unionbigdata_07.png)
 
-通过 Karmada 聚合层 API，我们在这类大屏中展示实时数据的时候，可以方便地直接调用成员集群的 svc。而无需让所有的数据显示都走大数据的离线分析、实时分析。提供更高的时效性。
+Using Karmada's aggregate layer API, we can conveniently call services from member clusters for real-time data display on these dashboards, without needing all data displays to go through big data offline analysis or real-time analysis. This provides greater timeliness.
 
-## 项目管理
+## Project Management
 
-本项目的团队由我司经验丰富的训练平台产品经理，以及专业的研发工程师和测试工程师 14 名组成。团队从 2023 年 4 月开始工作，直至 2023 年 12 月完成了开发和部署工作。尽管项目在进程中经历了三个大的里程碑，每个阶段都充满了挑战，但团队的每一个成员都坚持不懈，积极应对，展现了我们团队的战斗力、凝聚力和专业能力。
+The project team consists of our company's experienced training platform product managers, as well as professional R&D and test engineers, totaling 14 members. The team started work in April 2023 and completed the development and deployment by December 2023. Despite three major milestones during the project, each stage was filled with challenges, but every team member persevered, responded actively, and demonstrated our team's fighting spirit, cohesion, and professional capabilities.
 
-考虑到训练平台的用户主要是算法工程师和产线业务人员，他们的使用习惯和知识背景存在显著差异，因此产品经理进行了深入的市场研究和讨论，最终设计出一款既能满足算法工程师的灵活性需求，又能满足产线业务人员追求高效、简洁的系统。
+Considering that the users of the training platform are mainly algorithm engineers and production line operators, who have significant differences in usage habits and knowledge backgrounds, the product managers conducted in-depth market research and discussions. They ultimately designed a system that meets the flexibility needs of algorithm engineers while also satisfying the production line operators' pursuit of efficiency and simplicity.
 
-为了确保项目的范围、进度、质量和成本可控，我们在关键阶段举行了包括产品设计、开发、测试和部署评审等会议，并定期召开项目会议以及客户沟通会议。系统部署后，我们积极获取用户反馈，解决问题并持续优化系统以满足客户需求。
+To ensure the project's scope, schedule, quality, and cost are controllable, we held key stage meetings including product design, development, testing, and deployment reviews, as well as regular project meetings and customer communication meetings. After system deployment, we actively solicited user feedback, resolved issues, and continued to optimize the system to meet customer needs.