MEGAROBO Cloud MEGACLOUD®

Robot Comprehensive Cloud Platform

MEGACLOUD®

MEGACLOUD® is a comprehensive cloud platform independently developed by MEGAROBO, which provides three module functions, including Internet of things platform, SaaS online cluster service and big data AI platform. It is used to support MEGAROBO's businesses in the fields of industrial Internet, smart retail and life sciences, so as to realize the landing of robot technology for clients. The IOT platform connects MEGAROBO's hardware products (robots, sensors, etc.) with cloud services. SaaS online service cluster is used to help clients quickly implement MEGAROBO smart retail solutions. Online cluster is the logical center of business. Big data AI platform can mine data value, empower actual business, and provide value-added business

Product Strengths

Rich and powerful cloud services supporting public cloud or private cloud
Real Time

The devices are interconnected via the IOT hubs, supporting millions of devices online at the same time. Data can be stored on cloud devices in milliseconds.

Reliability

Providing reliable disaster recovery mechanism to ensure the online service in 99.99% of the time.

Scalability

Public cloud + Kubernetes PaaS platform is elastic and scalable, supporting any level of users online, and the computing power automatically expands as users grow.

Portability

One-click deployment, compatible with mainstream public/private cloud architectures.

Three Core Modules

Internet of Things (IoT) Platform

The IoT platform connects MEGAROBO's hardware products (robots, sensors, etc.) with cloud services. As IoT access devices, robots and sensors are the entrance of big data. Real time operation data is transmitted 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 the simultaneous connection of millions of devices, and real-time cloud and storage of original data.

  • Functions of IoT platform
  • Registration of new devices
  • Secure data transmission
  • Managing the lifecycle of devices
  • Supporting simultaneous connection of millions of devices
  • Real time cloud and storage of original data

SaaS Online Service Cluster

SaaS online service cluster is used to help clients quickly implement MEGAROBO’s smart retail solutions.
Online cluster is the logical center of business. The flow data of the IoT devices and the interactive data of the front-end application are processed according to the business logic here. For example, the order processing engine can schedule orders according to the busy situation of the system in real time, so as to realize the optimization of machine efficiency and user experience. Promotion engine and price calculation engine can be quickly configured and launched according to business needs.
The cluster uses cloud-based Kubernetes PAAS platform to deploy microservices. The cluster is flexible and scalable, supporting millions of users online, and the computing power automatically expands with the growth of users. The cluster provides reliable disaster recovery mechanism to ensure that the service is online in 99.99% of the time. Our solution is not bound to cloud vendors and supports mainstream public cloud (such as Alibaba cloud) deployment, and also supports private cloud deployment. And fully considering the importance of deployment efficiency, it has the ability of one-click deployment.

  • User registration and management
  • Intelligent order scheduling
  • Marketing activities supporting
  • Payment system
  • Big data BI

Big Data AI Platform

Mining the value of big data to empower business
Big data AI platform mines data value, empowers actual business and provides value-added services.
User interaction data, equipment raw data, order flow data and other valuable data are stored in the data platform. The platform supports real-time data processing and batch processing, and connects with the big data screen to provide real-time information about sales, users and operations.
In addition, the data can be used for feature extraction in AI platform to train machine learning model. Models can be deployed with one click for model reasoning. Some models have been used in beverage recommendation and equipment predictive maintenance.

  • Building an end-to-end machine learning pipeline
  • Data collection and data enrichment
  • Large scale data preprocessing
  • Accelerating and automating model building
  • Rapid deployment model

Product Application