Industrial data service, data modeling, and data application platform

Patent metadata

Field	Value
Publication number	US-2020201293-A1
Application number	US-201716621089-A
Country	US
Kind code	A1
Filing date	Oct 27, 2017
Priority date	Oct 27, 2017
Publication date	Jun 25, 2020
Grant date	—

Abstract

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.

First claim

What is claimed is:

1 . A system, comprising:
a graph knowledgebase containing data elements linked to an industrial operation and relationships between the data elements; a memory comprising a resource allocation graph structure containing a resource layer and an industrial process layer of graph elements with inter-layer relationships between the graph elements; and system circuitry configured to:
extract, from the graph knowledgebase, resource data elements and industrial process data elements to obtain the graph elements for the resource layer and the industrial process layer, and the inter-layer relationships between the graph elements of the resource allocation graph structure;
in response to receiving a data modeling request:
determine a set of industrial processes associated with the data modeling request from the graph knowledgebase; and
automatically allocate the data modeling request at each of a predefined set of data modeling stages among a group of graph elements selected from the resource layer based on the set of industrial processes and the inter-layer relationships of the resource allocation graph structure to obtain a data model; and

control the industrial operation based on the data model.

2 . The system of claim 1 , where the graph elements of the resource layer comprises allocable human resource graph elements.

3 . The system of claim 2 , where the allocable human resource graph elements of the resource layer are grouped into multiple predefined types of human resource graph elements and the group of graph elements selected form the resource layer comprise at least one graph element from each of the multiple predefined types of human resource graph elements.

4 . The system of claim 3 , where the allocable human resource graph elements, when being extracted from the graph knowledgebase, are classified into the multiple predefined types of human resource graph elements using a classifier established from the graph knowledgebase based on a machine learning algorithm.

5 . The system claim 3 , where the multiple predefined types of human resource graph elements comprises types of expertise associated with data modeling of industrial operational data.

6 . The system of claim 3 , where the system circuitry is further configured to select, among a predefined collection of data modeling tasks, a set of data modeling tasks according to the data modeling request, and automatically allocate the data modeling request to each of the selected group of graph elements for performing at least one of the set of data modeling tasks.

7 . The system of claim 6 , the memory further comprising a data modeling flow data structure comprising a data modeling stage dimension and a resource dimension representing the predefined set of data modeling stages and the multiple predefined types of human resource graph elements, and where the data modeling flow data structure further specifying association between pairs of the predefined set of data modeling stages and the predefined types of human resource graph elements, and the set of data modeling tasks.

8 . The system of claim 7 , where the system circuitry is configured to automatically allocate the data modeling request further according to the data modeling flow data structure.

9 . The system of claim 6 , further comprising a log for operational data of the industrial operation and where the system circuitry is further configured to determine a set of operational data from the log as input for the set of data modeling tasks.

10 . The system of claim 1 , where the inter-layer relationships between the graph elements are quantified based on the data elements and relationships in the graph knowledgebase and system circuitry is configured to automatically allocate the data modeling request further based on the quantified inter-layer relationships.

11 . A method, comprising:
extracting, from a graph knowledgebase containing data elements linked to an industrial operation and relationships between the data elements, resource data elements and industrial process data elements to obtain graph elements for a resource layer and an industrial process layer of a resource allocation graph structure, and to obtain inter-layer relationships between the graph elements of the resource allocation graph structure; in response to receiving a data modeling request:
determining a set of industrial processes associated with the data modeling request from the graph knowledgebase; and
automatically allocating the data modeling request at each of a predefined set of data modeling stages among a group of graph elements selected from the resource layer based on the set of industrial processes and the inter-layer relationships of the resource allocation graph structure to obtain a data model; and
controlling the industrial operation based on the data model.

12 . The method of claim 11 , where the graph elements of the resource layer comprises allocable human resource graph elements.

13 . The method of claim 12 , where the allocable human resource graph elements of the resource layer are grouped into multiple predefined types of human resource graph elements and the group of graph elements selected form the resource layer comprise at least one graph element from each of the multiple predefined types of human resource graph elements.

14 . The method of claim 13 , where the allocable human resource graph elements, when being extracted from the graph knowledgebase, are classified into the multiple predefined types of human resource graph elements using a classifier established from the graph knowledgebase based on a machine learning algorithm.

15 . The method claim 13 , where the multiple predefined types of human resource graph elements comprises types of expertise associated with data modeling of industrial operational data.

16 . The method of claim 13 , further comprising selecting, among a predefined collection of data modeling tasks, a set of data modeling tasks according to the data modeling request, and automatically allocating the data modeling request to each of the selected group of graph elements for performing at least one of the set of data modeling tasks.

17 . The method of claim 16 , further comprising maintaining a data modeling flow data structure including a data modeling stage dimension and a resource dimension representing the predefined set of data modeling stages and the multiple predefined types of human resource graph elements, and automatically allocating the data modeling request according to the data modeling flow data structure, where the data modeling flow data structure further specifying association between pairs of the predefined set of data modeling stages and the predefined types of human resource graph elements, and the set of data modeling tasks.

18 . The method of claim 16 , further comprising determining a set of operational data from a log for operational data of the industrial operation as input for the set of data modeling tasks.

19 . The method of claim 11 , where the inter-layer relationships between the graph elements are quantified based on the data elements and relationships in the graph knowledgebase, the method further comprising automatically allocate the data modeling request based on the quantified inter-layer relationships.

20 . A system, comprising:
a graph knowledgebase containing data elements linked to an industrial operation and relationships between the data elements; a log for operational data of the in

Assignees

• Accenture Global Solutions Ltd

Inventors

• Hou Fang
• Wu Yikai
• Cheng Xiaopei
• Ma Guo
• Sun Guanyi
• Xie Bin
• Shen Hui

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06Q30/0641

  utilising user interfaces specially adapted for shopping · CPC title

• G06Q10/067 Primary

  Enterprise or organisation modelling · CPC title

• G06Q10/0635

  Risk analysis of enterprise or organisation activities · CPC title

• G06Q10/06

  Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling · CPC title