Comprehensive Cloud Platform
MegaCloud is a comprehensive cloud platform independently developed by MEGAROBO, which provides three core components: IoT SaaS, micro-services, and big data platform to support MEGAROBO's businesses in the fields of life sciences&healthcare, advanced manufacturing and innovative applications. MegaCloud can facilitate applying robotics and automation technologies for our customers. The IoT SaaS platform connects MEGAROBO's hardware (robots, sensors, etc.) with cloud services. The micro-serivce platform, the center of business logic, can help customers quickly implement the solutions in various fields of MEGAROBO. The big data platform enables making data driven decisions.
Devices are connected through IoT Hub. Support millions of devices online simultaneously. Device data can be processed and stored in the cloud within milliseconds.
Provide a reliable disaster recovery mechanism and ensure online services with no downtime.
Auto-scaling strategy is implemented to provide a guaranteed SLA.
One-click deployment. The platform can be deployed in public cloud and on-premise.
Three core modules
IoT SaaS Platform Hardware products connected to cloud-based business
Micro-Services Platform The online microservices cluster is used to help customers quickly implement the solutions in all areas of MEGAROBO.
Big Data Platform Mining Big Data Value to Empower Business
IoT SaaS Platform
The IoT platform connects MEGAROBO hardware (robots, sensors, etc.) to cloud services.
Robots and sensors, as devices that accesses the internet, generate logs of data in real time. The real-time data is sent to the cloud through HTTP or MQTT protocol. The IoT platform manages the life cycle of access devices, monitors the online and offline status in real time, and supports live connection of millions of devices. Raw data is uploaded to the cloud and persisted in real time.
- Registration of IoT devices
- Reliable data transmission
- Life cycle mangement for IoT devices
- Live connection of millions of devices
- Raw data is uploaded and stored in the cloud in real time
The micro-service platform is the center of business logic. It processes the streaming data of IoT device, interacts with users by providing front-end applications, and processes business logic.
The elastic scalable cluster adopts the service mesh architecture based on Kubernetes platform, can support concurrency of millions of users, and provide autoscaling capability . The cluster provides a reliable disaster recovery mechanism, ensures online service with zero down time. Our solution supports deployment in major public clouds (such as AWS, Aliyun, and Azure), also supports on-premise deployment. It's built in with best practices in terms of CI/CD and infrastructure as code.
- Single Sign-On(SSO)
- Load balancing
- Service mesh
- SQL and NoSQL
- Data encryption
- Continuous Integration and Continuous Delivery
Big Data Platform
Big data platform enables us to make data driven decisions. User data, equipment raw data, business domain data and other valuable data are stored in the data platform. The platform supports real-time data processing and batch processing, provides data for BI dashboard, and provides real-time information about users, operations and device status.
In addition, the data can be used to provide features to machine learning algorithms for training machine learning models. The platform can perform model inferencing. Models have been applied to the predictive maintenance of recommended system and equipment.
- Build an end-to-end machine learning pipeline
- Data acquisition and feature engineering
- Large-scale data processing
- Model development studio
- Automated deployment model
MEGAROBO factory digital solutions
Equipment status monitoring
Analysis of environmental metrics
Web and mobile apps
MEGAROBO Synthetic Chemistry Information System
Experimental data processing
Structured and unstructured data ingestion
Open dataset import
Search by Structure
Search by Reaction
Search by Similarity
Management of data template
Molecule structure editing
Visualization of synthetic path
Synthetic path prediction