Policy Interpretation and Application Guide
⚠️ Disclaimer
Part of the content in this guide is compiled with AI assistance. Although we have verified the authenticity of relevant policies as much as possible, policy documents, application conditions, and links may change or update over time.
Before actual application, please be sure to go to the relevant government official websites for final verification and confirmation.
This guide aims to help enterprises understand how to use the StarWay Data Insight (PCA/PLS/PLS-DA offline data analysis tool) to find adaptation points in national and local policies, and obtain corresponding project application guidance. At the same time, it promotes deep collaboration between the platform and enterprises to jointly promote the implementation of artificial intelligence technology in core links such as enterprise production operations and quality control, helping enterprises achieve digital and intelligent transformation, enhance core competitiveness, and promote high-quality development.
1. National-level Policy Documents Suitable for the Tool
The following policy documents have been verified through official channels, are currently valid and queryable national authoritative documents, covering four core areas: intelligent manufacturing, industrial data, quality control, and digital transformation of the food industry:
| Policy Document Name | Issuing Authority | Release Date | Core Adaptation Points | Official Link |
|---|---|---|---|---|
| "Measures for Gradient Cultivation and Management of Intelligent Factories" | MIIT, NDRC, MOF, SASAC, SAMR, National Data Administration (Six Departments) | November 4, 2024 | Quality data analysis capabilities required for basic/advanced/excellent intelligent factories, explicitly requiring the establishment of multivariate data analysis models for process parameter optimization and quality anomaly identification | Click to view |
| "Implementation Opinions on the 'AI + Manufacturing' Special Action" | MIIT, CAC, NDRC, MOE, MOFCOM, SASAC, SAMR, National Data Administration (Eight Departments) | January 7, 2026 | Supports "intelligent quality control" tasks, encourages the application of chemometric algorithms (PCA/PLS) in production process optimization, and explicitly proposes building industrial intelligent agents and high-quality datasets | Click to view |
| "Implementation Plan for Digital Transformation of the Food Industry" | MIIT, MOE, MOHRSS, PBOC, SAMR, National Food and Strategic Reserves Administration, National Data Administration (Seven Departments) | June 10, 2025 | Requires establishing a full-link data model of "raw materials-process-products" to improve batch consistency, with key support for quality stability analysis in the food industry | Click to view |
| "Implementation Guide for Digitalization of Manufacturing Quality Management (Trial)" | General Office of MIIT | December 30, 2021 | Explicitly proposes "using multivariate statistical analysis methods (such as PCA/PLS) to conduct quality fluctuation analysis" to support quality traceability and improvement, empowering full-process and all-round quality management | Click to view |
| "Notice on Industrial Data Foundation Building Action" | MIIT | March 10, 2026 | Included in the "Data Technology Research and Development Library", supports integrated analysis of multi-source heterogeneous data (such as spectra + physical-chemical indicators), builds industry datasets, and enables the application of industrial intelligent agents | Click to view |
| "Guiding Opinions of the Ministry of Industry and Information Technology on the Development of Industrial Big Data" | MIIT | April 1, 2020 | Encourages "developing industrial data analysis tools to enhance data modeling and analysis capabilities", adapting to the release of data value in R&D, production, and quality inspection links | Click to view |
2. Projects Applicable After Factory Application (Excluding Basic/Advanced/Excellent Projects)
The following application projects are officially established support projects by national and local governments, verified by official policy documents, with clear application conditions and support measures:
(1) Extended Applications Related to Intelligent Factories
| Application Project | Core Application Points | Adaptation Scenarios | Policy Basis |
|---|---|---|---|
| Intelligent Manufacturing Typical Scenario Recognition | Quality prediction and control, process parameter optimization, anomaly detection | Use PLS to build "process parameters → product quality" models, use PCA to identify abnormal batches, and form replicable scenario cases | "Promote 500 typical application scenarios" requirement in "Implementation Opinions on the 'AI + Manufacturing' Special Action" |
| Industrial Internet Innovation Application Cases | Data-driven quality control solutions | Connect production line data to achieve offline review and process iteration, meeting the requirements for "quality control" cases | "Industrial Internet Innovation and Development Action Plan" |
| Digital Workshop Recognition | Production process digitalization + online quality analysis (offline assistance) | As a supplementary tool to MES/ERP, enhance workshop data decision-making capabilities, meeting the "data analysis" indicators for digital workshops | "Intelligent Manufacturing Capability Maturity Model" GB/T 39116 |
(2) Intellectual Property and Innovation Achievement Applications
| Application Project | Core Application Points | Key Conditions | Policy Basis |
|---|---|---|---|
| Invention/Utility Model Patents | Process optimization/quality control methods based on data analysis | Use the characteristic variables and parameter ratio rules discovered by the tool to apply for patents such as "A Multivariate Statistical-based XXX Process Control Method" | "Patent Law of the People's Republic of China" |
| Enterprise Technical Standards (Enterprise/Group Standards) | Digital quality inspection and process specifications | Solidify the "optimal parameter boundaries" obtained from the tool into enterprise standard operating procedures (SOP) or industry group standards | "Standardization Law of the People's Republic of China" |
(3) Quality and Management System Enhancement Applications
| Application Project | Core Application Points | Adaptation Value | Policy Basis |
|---|---|---|---|
| Integration of Industrialization and Informatization Management System Certification | Data-driven quality control capability improvement | The tool supports the "data development and utilization" process domain, improving the integration evaluation level (from Level 2 to Level 3) | "Requirements for Integration of Industrialization and Informatization Management System" GB/T 23001-2017 |
| DCMM Data Management Capability Maturity Certification | Data modeling and analysis capability building | Strengthen the "data application" capability domain, enhance data asset value, and help enterprises obtain data management capability certification | "Data Management Capability Maturity Assessment Model" GB/T 36073-2025 |
| Quality Benchmark Enterprise Recognition | Data-based continuous quality improvement | Use the tool to build quality prediction models, reduce defect rates, and form quality benchmark cases | "Notice on Carrying out Quality Benchmark Selection Activities" by MIIT |
(4) Industry-specific and Green Manufacturing Applications
| Application Project | Core Application Points | Adaptation Industries | Policy Basis |
|---|---|---|---|
| Green Factory Recognition | Data-driven efficient resource utilization | Use PLS to optimize process parameters, reduce energy/water consumption, improve raw material utilization, and support the "resource efficiency" indicators for green factories | "General Rules for Green Factory Evaluation" GB/T 36132-2018 |
| Typical Cases of Digital Transformation in the Food Industry | Industry-specific digital quality control solutions | Form complete cases of "data modeling + process optimization + quality improvement", in line with the key support directions of the seven-department document | "Implementation Plan for Digital Transformation of the Food Industry" |
| Enterprise Technology Center Recognition | R&D capability improvement (development and application of data analysis tools) | The tool serves as a core technical achievement of the enterprise technology center, used for process innovation and quality improvement, improving R&D strength scores | "Measures for the Recognition and Management of National Enterprise Technology Centers" |
(5) Science and Technology and Innovation Award Applications
| Application Project | Core Application Points | Adaptation Conditions | Policy Basis |
|---|---|---|---|
| Science and Technology Progress Award (Provincial/Municipal/Industry Level) | Innovative application of industrial data analysis technology | Apply jointly with leading enterprises, proving the significant economic benefits brought by the tool (such as reducing production costs by X%, improving qualification rate by Y%) | "National Science and Technology Award Regulations" and local implementation rules |
| QC Group Activity Achievements | Data-based quality improvement projects | Use the tool to carry out QC topics, such as "reducing the fluctuation of a certain indicator", forming digital QC activity achievements, with priority review | "Guidelines for Quality Control Group Activities" GB/T 13485-2017 |
3. Application Strategy Recommendations (Practical Section)
- Scenario Focus Strategy: Priority should be given to applying for digital transformation cases in specific industries (such as food, chemical, new materials, etc.) and intelligent manufacturing typical scenarios. This tool has uniqueness and irreplaceability in vertical industries, making it easy to form differentiated advantages, which highly aligns with the key support directions of various industry digital transformation implementation plans.
- Joint Application Strategy: It is strongly recommended to apply jointly with upstream and downstream enterprises in the industry chain and research institutions. Use actual application data (such as qualification rate improvement, energy consumption reduction percentage) as core supporting materials to improve the pass rate. According to the gradient cultivation management measures, joint applications can obtain higher scoring weights.
- Intellectual Property Mining Strategy: Use the "golden batch" parameter rules and characteristic variables discovered by the platform to actively write invention patents or utility model patents. These data-driven process control methods have high innovation and practical value, and are important support for applying for honors such as high-tech enterprises.
- Standard Alignment Strategy: Strictly align with GB/T 39116 "Intelligent Manufacturing Capability Maturity Model" and relevant industry digital transformation indicators to ensure that application materials are highly aligned with policy requirements and improve application success rate.
- Quantified Achievement Strategy: Quantify the application effects of the tool into core hard indicators that can be used for application, for example:
- Process Optimization: Reduce energy consumption by X%, increase target extraction rate by Y%
- Quality Improvement: Improve product qualification rate by Z%, reduce quality fluctuation range by M%
- Efficiency Improvement: Shorten data analysis time by N%, reduce labor costs by P%
4. Authenticity Verification Instructions
- All policy documents in this guide have been initially verified through official channels such as the MIIT official website, National Data Administration official website, and China Government Network to ensure the accuracy of document names, issuing authorities, and release dates.
- All application projects have corresponding national or local government policy document support, and application conditions and support measures are derived from officially released application notices or management measures.
- The policy links cited in this guide are all official website links, which can be used as policy basis references in application materials (please click again to confirm the validity of the links before citing).