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GB/T 41429-2022: Specification for big data system structure of consumer product safety
GB/T 41429-2022
NATIONAL STANDARD OF THE
PEOPLE’S REPUBLIC OF CHINA
ICS 03.120.01
CCS A 20
Specification for Big Data System Structure of Consumer
Product Safety
ISSUED ON: APRIL 15, 2022
IMPLEMENTED ON: NOVEMBER 01, 2022
Issued by: State Administration for Market Regulation;
Standardization Administration of the People’s Republic of China.
Table of Contents
Foreword ... 3
1 Scope ... 4
2 Normative References ... 4
3 Terms and Definitions ... 4
4 Basic Principle ... 5
5 Basic Data Requirements for Building a Big Data System of Consumer Product Safety
... 6
6 Big Data System Structure of Consumer Product Safety ... 6
Bibliography ... 10
Specification for Big Data System Structure of Consumer
Product Safety
1 Scope
This Document specifies the basic principles, construction requirements, system structure, etc.
for the big data system of the consumer product safety.
This Document is applicable to the construction of big data system of consumer product safety.
2 Normative References
The provisions in following documents become the essential provisions of this Document
through reference in this Document. For the dated documents, only the versions with the dates
indicated are applicable to this Document; for the undated documents, only the latest version
(including all the amendments) is applicable to this Document.
GB/T 29263 Information Technology - General Technical Requirement of SOA-Based
Application
3 Terms and Definitions
For the purposes of this Document, the following terms and definitions apply.
3.1 Consumer product
Mainly but not limited to products designed and produced for personal use, including product
components, parts, accessories, instructions for use and packaging.
[SOURCE: GB/T 35248-2017, 2.2]
3.2 Consumer product safety
The status of a consumer product exempt from unacceptable risk.
[SOURCE: GB/T 28803-2012, 3.4]
3.3 Big data
Data including a large number of datasets is characterized by huge volume, diverse sources,
extremely fast generation, and changeability, and difficult to be effectively processed by
traditional data architectures.
[SOURCE: GB/T 35295-2017, 2.1.1]
3.4 Big data system
A system that implements all or part of the functionality of the big data reference architecture.
[SOURCE: GB/T 35295-2017, 2.1.14]
3.5 Structured data
A data representation in which each record assembled from data elements has a consistent
structure and can be effectively described by a relational model.
[SOURCE: GB/T 35295-2017, 2.2.13]
3.6 Unstructured data
Data that does not have a predefined model or is not organized in a predefined way.
[SOURCE: GB/T 35295-2017, 2.1.25]
3.7 Cloud computing
A model for provisioning and managing a scalable, elastic pool of shared physical and virtual
resources in an on-demand self-service manner over a network.
NOTE: Resources include servers, operating systems, networks, software, applications, and storage
devices, etc.
[SOURCE: GB/T 32400-2015, 3.2.5]
4 Basic Principle
4.1 Functionality
The big data system of consumer product safety shall have a comprehensive system for
collecting, storing, preprocessing, analyzing and applying consumer product safety data.
4.2 Reliability
The big data system of consumer product safety shall have the functions of fault detection and
early warning, and can automatically restart or smoothly switch to the backup module when a
failure occurs.
4.3 Compatibility
The big data system of consumer product safety shall have better compatibility between
hardware and software.
4.4 Security
The big data system of consumer product safety shall have functions such as user authentication,
rights management, data backup and recovery to ensure data security.
4.5 Scalability
The big data system of consumer product safety shall have cluster online expansion and
compatibility functions to ensure the scalability of the platform.
4.6 Maintainability
The big data system of consumer product safety shall have the functions of cluster status
monitoring, alarm management, audit log and configuration management to ensure the
maintainability of the platform.
4.7 Ease for use
The big data system of consumer product safety should be popularized, which can be used not
only by professionals and business personnel, but also by ordinary consumers.
5 Basic Data Requirements for Building a Big Data System of
Consumer Product Safety
The big data of consumer product safety meets the basic requirements of the big data definition:
a) The size of the dataset that constitutes the big data shall reach the petabyte level;
b) The data may come from multiple data warehouses, data domains or multiple data types;
c) The data speed is fast and can be obtained continuously;
d) The data is real and changes dynamically.
6 Big Data System Structure of Consumer Product Safety
6.1 General
The big data system structure of consumer product safety generally consists of five layers:
Figure 1 – Big Data System Structure of Consumer Product Safety
6.2 Infrastructure layer
The infrastructure layer includes server equipment, network and communication facilities,
cloud computing and cloud storing facilities, data acquisition equipment, and information
security facilities.
6.3 Data resource layer
6.3.1 Overview
The data resource layer realizes the centralized management of consumer product safety
structured data, semi-structured data and unstructured data; and provides a unified data source
for the big data system of consumer product safety, including basic information repositories,
business information repositories and other information repositories.
6.3.2 Basic information repositories
The basic information repositories include data on supervision and inspection, risk monitoring,
consumer complaints, notify recalls, quality arbitration, injury and accident detection, as well
as social media information.
6.3.3 Business information repositories
The business information repositories gather information such as metering, standards,
inspection and testing, certification and accreditation.
6.3.4 Other information repositories
Other information repositories include various real-time data generated by emerging consumer
products, macroeconomics, environmental protection, and social credit information, etc.
6.4 Data management layer
The data management layer is responsible for obtaining data from the data resource layer; stores
the original data or processed data to the master data warehouse, distributed database and
Hadoop platform, etc. through technical means such as cleaning, converting, and loading, and
by using storage types such as distributed file systems, NoSQL, data streams, and relational
data structures, and the like. The overall technical requirements of the data management layer
shall comply with the provisions of GB/T 29263.
6.5 Computational analysis layer
The computational analysis layer refers to the process of discovering knowledge from big data
of consumer product safety using data mining, machine learning and other analytical
technologies, including two parts: analysis engine and model management. Commonly used