Accenture Global Solutions Limited

Accenture Global Services Gmbh

Guo Ma

Xiaopei Cheng

Yan Gao

Zhihui Yang

Qin Zhou

Bin Xie

Guanyi Sun

Hui Shen

Fang Hou

Yikai Wu

This disclosure relates to industrial data services, data modeling and applications for controlling an industrial operation. In one implementation, a platform is disclosed for allocating a data modeling request to a collaborative group of experts based on a two-dimensional data modeling flow data structure and a multilayer resource allocation graph to obtain a data model for controlling the industrial operation. The two-dimensional data modeling flow data structure and the multilayer resource allocation graph are established from an industrial graph knowledgebase using various data analytics and machine learning techniques.

Industrial data service, data modeling, and data application platform

A secure data system facilitates efficient searching of extensive data storage layers, e.g., a blockchain. The system may create an index for data elements of a predefined type recorded in the blockchain. The system may generate the index by invoking executable instructions stored in an executable data element (e.g., a smart contract) recorded in the blockchain and associated with the predefined type of data elements. The index facilitates fast query into the blockchain for the data elements of the predefined type.

Data storage layer index for efficient information retrieval

Cloud service based on container technology provides a lightweight, portable, but robust architecture for deploying service applications. Service volume and resource requirement for a typical modern-day application may fluctuate dramatically in time. A dynamic resource allocation scheme based on machine learning algorithms and executed by the cloud for applications deployed as independent containers allows for efficient resource usage and improvement of quality of service for the applications.

Dynamic resource allocation for application containers

A real-time data management system for accessing data in a power grid that controls a transmission delay of real-time data delivered via a real-time bus, and delivers real-time data in a power grid. A unified data model covering various organizations and various data resource may be included. Multi-bus collaboration and bus performance optimization approaches may be used to improve efficiency and performance of the buses. An event integration and complex event process component may be included to provide status of the power grid. A high volume of real-time data and events may be managed to provide data transmission with a low latency, provide flexible extension of the number of data clusters and the number of databases to ensure high volume data storage, and achieve a high speed and transparent data access. Additionally, rapid design and development of analytical applications, and the near real-time enterprise decision-making may be enabled.

Real-time data management for a power grid

A real-time data management system, a system, method, apparatus and tangible computer readable medium for accessing data in a power grid are described for controlling a transmission delay of real-time data delivered via a real-time bus, and for delivering real-time data in a power grid. A unified data model covering various organizations and various data resource is described. Further, a management scheme for clustered data is described to provide a transparent and high speed data access. The solutions described may efficiently manage the high volume of real-time data and events, provide data transmission with a low latency, provide flexible extension of both the number of data clusters and the number of databases to ensure high volume data storage, and achieve a high speed and transparent data access. Additionally, rapid design and development of analytical applications, and the near real-time enterprise decision-making business may be enabled.

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Beijing, China

Guo Ma