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共 115 条
序号标准号（计划号）标准名称（项目名称）类别/状态适用范围
1IEC 63278-1:202320243452-T-604Asset Administration Shell for industrial applications - Part 1: Asset Administration Shell structure用于工业应用的资产管理壳 第1部分：资产管理壳结构IEC/TC 65 / 全国工业过程测量控制和自动化标准化技术委员会国际标准/IDT计划/在研
2IEC 63365:202220262778-T-604Industrial process measurement, control and automation - Digital nameplate工业过程测量、控制和自动化 数字铭牌IEC/SC 65E / 全国工业过程测量控制和自动化标准化技术委员会国际标准/IDT计划/在研
3ISO/IEC 5259-1:202420251310-T-469Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 1: Overview, terminology, and examples面向分析与机器学习的数据质量 第1部分：概述、术语及示例ISO/IEC JTC 1/SC 42 / 全国信息技术标准化技术委员会国际标准/MOD计划/在研本文件提供了理解和关联ISO/IEC 5259系列标准的方法，是从概念上理解面向分析与机器学习的数据质量的基础。本文件还介绍了相关技术和示例（例如用例和使用场景）。查看来源
4ISO/IEC 5259-2:202420251298-T-469Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 2: Data quality measures面向分析与机器学习的数据质量 第2部分：数据质量度量ISO/IEC JTC 1/SC 42 / 全国信息技术标准化技术委员会国际标准/MOD计划/在研本文件规定了在分析和机器学习（ML）场景下的数据质量模型、数据质量度量指标以及数据质量报告指南。本文件适用于希望实现数据质量目标的各类组织。查看来源
5ISO/IEC 5259-3:202420251294-T-469Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 3: Data quality management requirements and guidelines面向分析与机器学习的数据质量 第3部分：数据质量管理要求和指导原则ISO/IEC JTC 1/SC 42 / 全国信息技术标准化技术委员会国际标准/MOD计划/在研本文件规定了建立、实施、维护和持续改进用于分析和机器学习领域的数据质量的要求，并提供了相关指导。本文件并未定义详细的流程、方法或度量标准。相反，它定义了质量管理体系过程的要求和指导，以及一个可参考的流程和方法，这些流程和方法可以根据本文件的要求进行定制以满足需求。本文件中列出的要求和建议是通用的，旨在适用于所有组织，无论其类型、规模或性质如何。查看来源
6ISO/IEC 5259-4:202420251306-T-469Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 4: Data quality process framework面向分析与机器学习的数据质量 第4部分：数据质量过程框架ISO/IEC JTC 1/SC 42 / 全国信息技术标准化技术委员会国际标准/MOD计划/在研本文件提供面向分析与机器学习训练数据质量管理的通用组织和实施指南，包括组织规模与职责；监督学习、无监督学习、半监督学习、强化学习和深度学习；不同来源训练数据的采集、合成、准备、标注、处理和使用。本文件不适用于特定的平台或工具。查看来源
7ISO/IEC 5338:2023Information technology — Artificial intelligence — AI system life cycle processes信息技术 人工智能 AI系统生命周期过程ISO/IEC JTC 1/SC 42国际标准/项目现行This document defines a set of processes and associated concepts for describing the life cycle of AI systems based on machine learning and heuristic systems. It is based on ISO/IEC/IEEE 15288 and ISO/IEC/IEEE 12207 with modifications and additions of AI-specific processes from ISO/IEC 22989 and ISO/IEC 23053. This document provides processes that support the definition, control, management, execution and improvement of the AI system in its life cycle stages. These processes can also be used within an organization or a project when developing or acquiring AI systems. When an element of an AI system is traditional software or a traditional system, the software life cycle processes in ISO/IEC/IEEE 12207 and the system life cycle processes in ISO/IEC/IEEE 15288 can be used to implement that element.中文译文本文件定义一组用于描述基于机器学习和启发式系统的人工智能系统生命周期的过程及相关概念，提供支撑人工智能系统全生命周期定义、控制、管理、执行和改进的过程。这些过程也可由开发或采购人工智能系统的组织或项目采用。查看来源
8ISO/IEC 5339:2024Information technology — Artificial intelligence — Guidance for AI applications信息技术 人工智能 人工智能应用指南ISO/IEC JTC 1/SC 42国际标准/项目现行This document provides guidance for identifying the context, opportunities and processes for developing and applying AI applications. The guidance provides a macro-level view of the AI application context, the stakeholders and their roles, relationship to the life cycle of the system, and common AI application characteristics and considerations.查看来源
9ISO/IEC 5392:2024Information technology — Artificial intelligence — Reference architecture of knowledge engineering信息技术 人工智能 知识工程参考架构ISO/IEC JTC 1/SC 42国际标准/项目现行This document defines a reference architecture of knowledge engineering (KE) in artificial intelligence (AI). The reference architecture describes KE roles, activities, constructional layers, components and their relationships amongst themselves and other systems from systemic user and functional views. This document also provides a common KE vocabulary by defining KE terms.查看来源
10ISO/IEC 8183:2023Information technology — Artificial intelligence — Data life cycle framework信息技术 人工智能 数据生命周期框架ISO/IEC JTC 1/SC 42国际标准/项目现行This document defines the stages and identifies associated actions for data processing throughout the artificial intelligence (AI) system life cycle, including acquisition, creation, development, deployment, maintenance and decommissioning. This document does not define specific services, platforms or tools. This document is applicable to all organizations, regardless of type, size or nature, that use data in the development and use of AI systems.中文译文本文件规定人工智能系统生命周期中数据处理的阶段及相关活动，包括获取、创建、开发、部署、维护和退役，不规定具体服务、平台或工具。本文件适用于在人工智能系统开发和使用中处理数据的各种类型、规模和性质的组织。查看来源
11ISO/IEC 12792:2025Information technology — Artificial intelligence (AI) — Transparency taxonomy of AI systems信息技术 人工智能 人工智能系统透明度分类ISO/IEC JTC 1/SC 42国际标准/项目现行This document specifies a taxonomy of information elements to assist AI stakeholders with identifying and addressing the needs for transparency of AI systems. The document describes the semantics of the information elements and their relevance to the various objectives of different stakeholders. This document is applicable to any kind of organization and application involving an AI system.查看来源
12ISO/IEC 22989:2022Information technology — Artificial intelligence — Artificial intelligence concepts and terminology信息技术 人工智能 人工智能概念和术语ISO/IEC JTC 1/SC 42国际标准/项目计划/在研This document establishes terminology for AI and describes concepts in the field of AI. This document can be used in the development of other standards and in support of communications among diverse, interested parties or stakeholders. This document is applicable to all types of organizations (e.g. commercial enterprises, government agencies, not-for-profit organizations).查看来源
13ISO/IEC 23053:2022Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)使用机器学习的人工智能系统框架ISO/IEC JTC 1/SC 42国际标准/项目计划/在研This document establishes an Artificial Intelligence (AI) and Machine Learning (ML) framework for describing a generic AI system using ML technology. The framework describes the system components and their functions in the AI ecosystem. This document is applicable to all types and sizes of organizations, including public and private companies, government entities, and not-for-profit organizations, that are implementing or using AI systems.查看来源
14ISO/IEC 23894:2023Information technology — Artificial intelligence — Guidance on risk management信息技术 人工智能 风险管理指南ISO/IEC JTC 1/SC 42国际标准/项目现行This document provides guidance on how organizations that develop, produce, deploy or use products, systems and services that utilize artificial intelligence (AI) can manage risk specifically related to AI. The guidance also aims to assist organizations to integrate risk management into their AI-related activities and functions. It moreover describes processes for the effective implementation and integration of AI risk management. The application of this guidance can be customized to any organization and its context.中文译文本文件就开发、生产、部署或使用人工智能产品、系统和服务的组织如何管理人工智能相关风险给出指导，并支持组织将风险管理纳入人工智能相关活动和职能，说明人工智能风险管理有效实施和整合的过程。本指导可根据任何组织及其具体情况进行调整。查看来源
15ISO/IEC 24029-2:2023Artificial intelligence (AI) — Assessment of the robustness of neural networks — Part 2: Methodology for the use of formal methods人工智能 神经网络鲁棒性评价 第2部分：形式化方法使用方法ISO/IEC JTC 1/SC 42国际标准/项目现行This document provides methodology for the use of formal methods to assess robustness properties of neural networks. The document focuses on how to select, apply and manage formal methods to prove robustness properties.查看来源
16ISO/IEC 25059:2023Software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Quality model for AI systems软件工程 系统与软件质量要求和评价 AI系统质量模型ISO/IEC JTC 1/SC 42国际标准/项目计划/在研This document outlines a quality model for AI systems and is an application-specific extension to the standards on SQuaRE. The characteristics and sub-characteristics detailed in the model provide consistent terminology for specifying, measuring and evaluating AI system quality. The characteristics and sub-characteristics detailed in the model also provide a set of quality characteristics against which stated quality requirements can be compared for completeness.查看来源
17ISO/IEC 38507:2022Information technology — Governance of IT — Governance implications of the use of artificial intelligence by organizations信息技术 IT治理 组织使用AI的治理影响ISO/IEC JTC 1/SC 42国际标准/项目现行This document provides guidance for members of the governing body of an organization to enable and govern the use of Artificial Intelligence (AI), in order to ensure its effective, efficient and acceptable use within the organization. This document also provides guidance to a wider community, including: — executive managers; — external businesses or technical specialists, such as legal or accounting specialists, retail or industrial associations, or professional bodies; — public authorities and policymakers; — internal and external service providers (including consultants); — assessors and auditors. This document is applicable to the governance of current and future uses of AI as well as the implications of such use for the organization itself. This document is applicable to any organization, including public and private companies, government entities and not-for-profit organizations. This document is applicable to an organization of any size irrespective of their dependence on data or information technologies.查看来源
18ISO/IEC 42001:2023Information technology — Artificial intelligence — Management system信息技术 人工智能 管理体系ISO/IEC JTC 1/SC 42国际标准/项目现行This document specifies the requirements and provides guidance for establishing, implementing, maintaining and continually improving an AI (artificial intelligence) management system within the context of an organization. This document is intended for use by an organization providing or using products or services that utilize AI systems. This document is intended to help the organization develop, provide or use AI systems responsibly in pursuing its objectives and meet applicable requirements, obligations related to interested parties and expectations from them. This document is applicable to any organization, regardless of size, type and nature, that provides or uses products or services that utilize AI systems.中文译文本文件规定组织建立、实施、保持并持续改进人工智能管理体系的要求，并提供指导，适用于提供或使用人工智能系统相关产品或服务的各种规模、类型和性质的组织。查看来源
19ISO/IEC 42005:2025Information technology — Artificial intelligence (AI) — AI system impact assessment信息技术 人工智能 AI系统影响评估ISO/IEC JTC 1/SC 42国际标准/项目现行This document provides guidance for organizations performing artificial intelligence (AI) system impact assessments for individuals and societies that can be affected by an AI system and its foreseeable applications. It includes considerations for how and when to perform such assessments and at what stages of the AI system life cycle, as well as guidance for AI system impact assessment documentation. Additionally, this guidance includes how this AI system impact assessment process can be integrated into an organization’s AI risk management and AI management system. This document is intended for use by organizations developing, providing or using AI systems. This document is applicable to any organization, regardless of size, type and nature.查看来源
20ISO/IEC 42006:2025Information technology — Artificial intelligence — Requirements for bodies providing audit and certification of artificial intelligence management systems人工智能管理体系审核认证机构要求ISO/IEC JTC 1/SC 42国际标准/项目现行This document specifies additional requirements to ISO/IEC 17021-1. The requirements contained in this document, when implemented, support the demonstration of competence, consistency and reliability by the bodies performing auditing and certification of an artificial intelligence management system (AIMS) according to ISO/IEC 42001 for organizations that provide, develop or use AI systems. Certification of AIMS is a third-party conformity assessment activity (as described in ISO/IEC 17000:2020, 4.5), and bodies performing this activity are third-party conformity assessment bodies. This document also provides the necessary information and confidence to customers about the way certification has been granted. NOTE This document can be used as a criteria document for accreditation or peer assessment.查看来源
21ISO/IEC AWI 25623Artificial intelligence — Machine learning (ML) model description framework人工智能 机器学习模型描述框架ISO/IEC JTC 1/SC 42国际标准/项目计划/在研This document provides a general description framework and elements of documentation for ML models to facilitate their management and use throughout the ML model life cycle. This document is applicable to any organization that develops, provides or uses ML models.查看来源
22ISO/IEC AWI 25704Artificial intelligence — Process assessment — Process assessment model for AI system life cycle processes人工智能 过程评估 人工智能系统生命周期过程评估模型ISO/IEC JTC 1/SC 42国际标准/项目计划/在研This document provides a process assessment model as a basis for the assessment of AI process capability based on the process measurement framework for the processes performed during AI system life cycle stages in ISO/IEC 22989:2022. The processes performed during AI system life cycle stages are defined in ISO/IEC 5338:2023. Based on ISO/IEC 33020:2019, the process measurement framework provides a set of indicators for measuring performance and capability of those processes. These indicators are used as a basis for collecting evidence that enable an organization to determine its process capability level. This document applies to any type of organization, that provides, develops, or uses products or services that utilize AI systems.查看来源
23ISO/IEC AWI 25870Information technology — Artificial intelligence — Data elements for reporting AI system incidents信息技术 人工智能 人工智能系统事件报告数据元素ISO/IEC JTC 1/SC 42国际标准/项目计划/在研
24ISO/IEC AWI 42003Information technology — Artificial intelligence — Guidance on the implementation of ISO/IEC 42001信息技术 人工智能 ISO/IEC 42001实施指南ISO/IEC JTC 1/SC 42国际标准/项目计划/在研This document provides guidance for implementing ISO/IEC 42001, including competencies for AI management systems (AIMS) professionals. This document is intended for use by any organization, regardless of size, type and nature that plans on referencing or implementing ISO/IEC 42001.查看来源
25ISO/IEC CD TS 22440-1/-2/-3Artificial intelligence — Functional safety and AI systems (Parts 1 to 3)人工智能 功能安全与AI系统 要求、指南和应用示例ISO/IEC JTC 1/SC 42国际标准/项目计划/在研
26ISO/IEC DIS 4213Information technology — Artificial Intelligence — Performance measurement for AI classification, regression, clustering and recommendation tasks信息技术 人工智能 分类、回归、聚类和推荐任务性能测量ISO/IEC JTC 1/SC 42国际标准/项目计划/在研This document specifies methodologies for measuring the performance of AI models for classification, regression, clustering and recommendation tasks.查看来源
27ISO/IEC DIS 27091网络安全与隐私 人工智能 隐私保护ISO/IEC JTC 1/SC 27国际标准/项目计划/在研
28ISO/IEC DIS 42007Information technology — Artificial intelligence — High-level framework and guidance for the development of conformity assessment schemes for AI systems信息技术 人工智能 人工智能系统合格评定方案开发高层框架和指南ISO/IEC JTC 1/SC 42国际标准/项目计划/在研This document provides a high-level framework and guidance for the development and operation of conformity assessment schemes, including certification schemes, for artificial intelligence (AI) systems.查看来源
29ISO/IEC DIS 42102Information technology — Artificial intelligence — Framework for characterizing AI system methods and capabilities信息技术 人工智能 人工智能系统方法和能力特征框架ISO/IEC JTC 1/SC 42国际标准/项目计划/在研This document provides a framework of descriptors to support the consistent characterization of AI system methods and capabilities. The framework helps AI stakeholders describe AI systems and have a common understanding of them. This document applies to all types of organizations involved in any of the lifecycle stages of AI systems as well as to any AI stakeholder roles查看来源
30ISO/IEC DTS 42119-3.2Artificial intelligence — Testing of AI — Part 3: Verification and validation analysis of AI systems人工智能 AI测试 第3部分：AI系统验证与确认分析ISO/IEC JTC 1/SC 42国际标准/项目计划/在研This document describes approaches and provides guidance on processes for the verification and validation analysis of AI systems (comprising AI system components and the interaction of non-AI components with the AI system components) including formal methods, simulation and evaluation. This document is applicable for AI systems verification and validation in the context of the AI system life cycle stages described in ISO/IEC 22989. This document is applicable to all types of organizations engaged in the development, deployment and use of AI systems.查看来源
31ISO/IEC FDIS 27090Cybersecurity — Artificial Intelligence — Addressing security threats and compromises to artificial intelligence systems网络安全 人工智能 应对AI系统安全威胁和破坏ISO/IEC JTC 1/SC 27国际标准/项目计划/在研This document addresses security threats and compromises specific to artificial intelligence (AI) systems. This document aims to provide information to organizations to help them better understand the consequences of security threats specific to AI systems, throughout their life cycle, and descriptions of how to detect and mitigate such threats. This document is applicable to all types and sizes of organizations, including public and private companies, government entities, and not-for-profit organizations, that develop or use AI systems查看来源
32ISO/IEC TR 5259-6:2026Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 6: Visualization framework for data quality人工智能 分析与机器学习数据质量 第6部分：数据质量可视化框架ISO/IEC JTC 1/SC 42国际标准/项目现行This document describes a visualization framework for data quality in analytics and machine learning (ML). The aim is to enable stakeholders using visualization methods to assess the results of data quality measures. This visualization framework supports data quality goals.查看来源
33ISO/IEC TR 5469:2024Artificial intelligence — Functional safety and AI systems人工智能 功能安全与AI系统ISO/IEC JTC 1/SC 42国际标准/项目现行This document describes the properties, related risk factors, available methods and processes relating to: — use of AI inside a safety related function to realize the functionality; — use of non-AI safety related functions to ensure safety for an AI controlled equipment; — use of AI systems to design and develop safety related functions.查看来源
34ISO/IEC TR 20226:2025Information technology — Artificial intelligence — Environmental sustainability aspects of AI systems信息技术 人工智能 人工智能系统环境可持续性ISO/IEC JTC 1/SC 42国际标准/项目现行This document provides an overview of the environmental sustainability aspects (e.g. workload, resource and asset utilization, carbon impact, pollution, waste, transportation, location) of AI systems during their life cycle, and related potential metrics. NOTE 1 This document does not identify opportunities on how AI, AI applications and AI systems can improve environmental, social or economic sustainability outcomes. NOTE 2 This document can help other projects related to AI system environmental sustainability.查看来源
35ISO/IEC TR 21221:2025Information technology — Artificial intelligence — Beneficial AI systems信息技术 人工智能 有益人工智能系统ISO/IEC JTC 1/SC 42国际标准/项目现行This document describes the delivery of functional, economic, environmental, social, societal, cultural, intellectual and personal benefits by AI systems as perceived by their stakeholders. The document includes illustrative use cases of AI systems.查看来源
36ISO/IEC TR 24027:2021Information technology — Artificial intelligence (AI) — Bias in AI systems and AI aided decision making信息技术 人工智能 AI系统及AI辅助决策中的偏差ISO/IEC JTC 1/SC 42国际标准/项目现行This document addresses bias in relation to AI systems, especially with regards to AI-aided decision-making. Measurement techniques and methods for assessing bias are described, with the aim to address and treat bias-related vulnerabilities. All AI system lifecycle phases are in scope, including but not limited to data collection, training, continual learning, design, testing, evaluation and use.查看来源
37ISO/IEC TR 24028:2020Information technology — Artificial intelligence — Overview of trustworthiness in artificial intelligence信息技术 人工智能 可信赖性概述ISO/IEC JTC 1/SC 42国际标准/项目现行This document surveys topics related to trustworthiness in AI systems, including the following: — approaches to establish trust in AI systems through transparency, explainability, controllability, etc.; — engineering pitfalls and typical associated threats and risks to AI systems, along with possible mitigation techniques and methods; and — approaches to assess and achieve availability, resiliency, reliability, accuracy, safety, security and privacy of AI systems. The specification of levels of trustworthiness for AI systems is out of the scope of this document.查看来源
38ISO/IEC TR 24029-1:2021Artificial Intelligence (AI) — Assessment of the robustness of neural networks — Part 1: Overview人工智能 神经网络鲁棒性评价 第1部分：概览ISO/IEC JTC 1/SC 42国际标准/项目现行This document provides background about existing methods to assess the robustness of neural networks.查看来源
39ISO/IEC TR 24030:2024Information technology — Artificial intelligence (AI) — Use cases信息技术 人工智能 用例ISO/IEC JTC 1/SC 42国际标准/项目现行This document provides a collection of representative use cases of AI applications in a variety of domains.查看来源
40ISO/IEC TR 24368:2022Information technology — Artificial intelligence — Overview of ethical and societal concerns信息技术 人工智能 伦理和社会关切概述ISO/IEC JTC 1/SC 42国际标准/项目现行This document provides a high-level overview of AI ethical and societal concerns. In addition, this document: — provides information in relation to principles, processes and methods in this area; — is intended for technologists, regulators, interest groups, and society at large; — is not intended to advocate for any specific set of values (value systems). This document includes an overview of International Standards that address issues arising from AI ethical and societal concerns.查看来源
41ISO/IEC TR 24372:2021Information technology — Artificial intelligence (AI) — Overview of computational approaches for AI systems信息技术 人工智能 人工智能系统计算方法概述ISO/IEC JTC 1/SC 42国际标准/项目现行This document provides an overview of the state of the art of computational approaches for AI systems, by describing: a) main computational characteristics of AI systems; b) main algorithms and approaches used in AI systems, referencing use cases contained in ISO/IEC TR 24030.查看来源
42ISO/IEC TR 27563:2023Security and privacy in artificial intelligence use cases — Best practicesAI用例安全与隐私最佳实践ISO/IEC JTC 1/SC 27国际标准/项目现行This document outlines best practices on assessing security and privacy in artificial intelligence use cases, covering in particular those published in ISO/IEC TR 24030 . The following aspects are addressed: — an overall assessment of security and privacy on the AI system of interest; — security and privacy concerns; — security and privacy risks; — security and privacy controls; — security and privacy assurance; and — security and privacy plans. Security and privacy are treated separately as the analysis of security and the analysis of privacy can differ.查看来源
43ISO/IEC TR 42106信息技术 人工智能 人工智能系统质量特性差异化基准测试概述ISO/IEC JTC 1/SC 42国际标准/项目计划/在研
44ISO/IEC TS 6254:2025Information technology — Artificial intelligence — Objectives and approaches for explainability and interpretability of machine learning (ML) models and artificial intelligence (AI) systems信息技术 人工智能 机器学习模型和人工智能系统可解释性、可解读性目标与方法ISO/IEC JTC 1/SC 42国际标准/项目现行This document describes approaches and methods that can be used to achieve explainability objectives of stakeholders with regard to machine learning (ML) models and artificial intelligence (AI) systems’ behaviours, outputs and results. Stakeholders include but are not limited to, academia, industry, policy makers and end users. It provides guidance concerning the applicability of the described approaches and methods to the identified objectives throughout the AI system’s life cycle, as defined in ISO/IEC 22989 .查看来源
45ISO/IEC TS 42119-2:2025Artificial intelligence — Testing of AI — Part 2: Overview of testing AI systems人工智能 AI测试 第2部分：AI系统测试概述ISO/IEC JTC 1/SC 42国际标准/项目现行This document provides requirements and guidance on the application of the ISO/IEC/IEEE 29119 series to the testing of AI systems. This document follows a risk-based approach and uses risks associated with AI systems, and their development and maintenance, to identify suitable test practices, approaches and techniques applicable to AI systems and their components. When the test practices, approaches and techniques are already specified in the ISO/IEC/IEEE 29119 series, this document provides additional detail and describes their application in the context of AI systems.查看来源
46ISO/TR 22100-5:202120253133-Z-469Safety of machinery — Relationship with ISO 12100 — Part 5: Implications of artificial intelligence machine learning机械安全 风险评估考虑人工智能与机器学习的指南ISO/TC 199 / 全国机械安全标准化技术委员会国际标准/IDT计划/在研本文件说明人工智能和机器学习对机械及机器系统安全的影响，说明如何在风险评估过程中考虑与机械或机器系统中人工智能机器学习应用相关的危险。本文件适用于被设计为在规定限值内运行的人工智能机器学习应用；不适用于被设计为超出规定限值运行、可能产生不可预测影响的应用。本文件不涉及采用人工智能的安全系统，例如安全相关传感器及控制系统的其他安全相关部件。查看来源
4720243679-T-469工业互联网平台 工业机理模型评价指南全国信息技术标准化技术委员会国家标准计划计划/在研
4820252048-Z-469人工智能 工业大模型技术要求全国信息技术标准化技术委员会人工智能分会指导性技术文件计划/在研
4920252049-Z-469人工智能 工业智能体参考架构全国信息技术标准化技术委员会人工智能分会指导性技术文件计划/在研
5020254620-T-303燃煤烟气脱硫装备智能化运行效果评价技术要求全国环保产业标准化技术委员会国家标准计划计划/在研
5120254624-T-303钢铁烧结烟气脱硫除尘装备智能化运行效果评价技术要求全国环保产业标准化技术委员会国家标准计划计划/在研
5220254627-T-303燃煤烟气脱硝装备智能化运行效果评价技术要求全国环保产业标准化技术委员会国家标准计划计划/在研
5320255427-T-469信息技术 人工智能 术语（修订）全国信息技术标准化技术委员会人工智能分会国家标准计划计划/在研本文件界定了信息技术人工智能领域中的常用术语及定义。本文件适用于人工智能领域概念的理解和信息交流，以及科研、教学和应用。查看来源
5420256239-Q-627露天矿山无人驾驶运输系统安全技术要求相关主管部门国家标准计划计划/在研
5520256802-T-604矿用机械正铲式挖掘机 远程控制系统技术要求全国矿山机械标准化技术委员会国家标准计划计划/在研
5620256803-T-604矿用机械正铲式挖掘机 自动控制系统技术要求全国矿山机械标准化技术委员会国家标准计划计划/在研
5720256911-T-907高质量数据集 数据标注要求全国数据标准化技术委员会国家标准计划计划/在研
5820256913-T-907高质量数据集 质量评测规范全国数据标准化技术委员会国家标准计划计划/在研
5920261450-Z-469工业高质量数据集 建设要求全国信息技术标准化技术委员会指导性技术文件计划/在研
6020261790-Z-604智能模型与系统 轻量化模型技术要求全国自动化系统与集成标准化技术委员会指导性技术文件计划/在研
6120261791-Z-604智能模型与系统 测评方法规范全国自动化系统与集成标准化技术委员会指导性技术文件计划/在研
6220261792-Z-604智能模型与系统 通用集成架构全国自动化系统与集成标准化技术委员会指导性技术文件计划/在研
6320261793-Z-604智能模型与系统 分类分级指南全国自动化系统与集成标准化技术委员会指导性技术文件计划/在研
6420261794-Z-604智能模型与系统 接口集成互操作要求全国自动化系统与集成标准化技术委员会指导性技术文件计划/在研
6520262542-Q-339工业机器人安全要求 第1部分：整机见官方平台国家标准计划计划/在研
6620262543-Q-339工业机器人安全要求 第2部分：应用与单元见官方平台国家标准计划计划/在研
6720262577-Z-469人工智能 生成式人工智能系统风险应对指南全国信息技术标准化技术委员会人工智能分会指导性技术文件计划/在研
6820262583-Z-907高质量数据集 具身智能 仿真合成数据生成与处理规范全国数据标准化技术委员会指导性技术文件计划/在研
6920262584-Z-907高质量数据集 具身智能 面向训练基地的数据采集与模型训练规范全国数据标准化技术委员会指导性技术文件计划/在研
7020263050-Z-469人工智能 工业大模型参考架构全国信息技术标准化技术委员会人工智能分会指导性技术文件计划/在研
71GB/T 15706-2012机械安全 设计通则 风险评估与风险减小全国机械安全标准化技术委员会（SAC/TC208）国家标准现行
72GB/T 20438.1-2017～GB/T 20438.7-2017电气/电子/可编程电子安全相关系统的功能安全全国工业过程测量控制和自动化标准化技术委员会系统及功能安全分会国家标准现行
73GB/T 20720.2—2020企业控制系统集成 第2部分：企业控制系统集成的对象和属性SAC/TC159/SC5国家标准现行
74GB/T 21109.1-2022过程工业领域安全仪表系统的功能安全 第1部分：框架、定义、系统、硬件和应用编程要求全国工业过程测量控制和自动化标准化技术委员会系统及功能安全分会国家标准现行
75GB/T 22394.1—2015机器状态监测与诊断 数据判读和诊断技术 第1部分：总则SAC/TC53国家标准现行
76GB/T 23713.1—2024机器状态监测与诊断 预测 第1部分：一般指南SAC/TC53国家标准现行
77GB/T 35381.1-2017农林拖拉机和机械 控制系统安全相关部件 第1部分：设计与开发通则全国农业机械标准化技术委员会（SAC/TC201）国家标准现行
78GB/T 35484.1-2017土方机械和移动式道路施工机械 工地数据交换 第1部分：系统体系全国土方机械标准化技术委员会（SAC/TC334）国家标准现行
79GB/T 35484.2-2017土方机械和移动式道路施工机械 工地数据交换 第2部分：数据字典全国土方机械标准化技术委员会（SAC/TC334）国家标准现行
80GB/T 35484.3-2021土方机械和移动式道路施工机械 工地数据交换 第3部分：远程信息处理数据全国土方机械标准化技术委员会 TC334国家标准现行
81GB/T 40647-2021智能制造 系统架构全国工业过程测量控制和自动化标准化技术委员会 TC124国家标准现行
82GB/T 40659-2021智能制造 机器视觉在线检测系统 通用要求全国工业过程测量控制和自动化标准化技术委员会国家标准现行
83GB/T 41255-2022智能工厂 通用技术要求全国工业过程测量控制和自动化标准化技术委员会 TC124国家标准现行
84GB/T 41867-2022信息技术 人工智能 术语全国信息技术标准化技术委员会人工智能分会国家标准现行本文件界定了信息技术人工智能领域中的常用术语及定义。本文件适用于人工智能领域概念的理解和信息交流，以及科研、教学和应用。查看来源
85GB/T 42131-2022人工智能 知识图谱技术框架全国信息技术标准化技术委员会国家标准现行本文件给出了知识图谱的概念模型和技术框架，规定了知识图谱供应方、知识图谱集成方、知识图谱用户、知识图谱生态合作伙伴的输入、输出、主要活动和质量一般性能等要求。本文件适用于知识图谱及其应用系统的构建、应用、实施与维护。查看来源
86GB/T 42707.2-2024数控机床远程运维 第2部分：故障诊断与预测性维护全国工业机械电气系统标准化技术委员会 TC231国家标准现行
87GB/T 42755-2023人工智能 面向机器学习的数据标注规程全国信息技术标准化技术委员会国家标准现行本文件规定了人工智能领域面向机器学习的数据标注框架流程。本文件适用于指导人工智能领域面向机器学习的数据标注以及与之相关的研究、开发和应用等。查看来源
88GB/T 42888-2023信息安全技术 机器学习算法安全评估规范全国网络安全标准化技术委员会国家标准现行本文件规定了机器学习算法技术和服务的安全要求和评估方法，以及机器学习算法安全评估流程。本文件适用于指导机器学习算法提供者保障机器学习算法生存周期安全以及开展机器学习算法安全评估，也可为监管评估提供参考。查看来源
89GB/T 42980-2023智能制造 机器视觉在线检测系统 测试方法工业和信息化部（电子）国家标准现行
90GB/T 43555-2023智能服务 预测性维护 算法测评方法全国工业过程测量控制和自动化标准化技术委员会国家标准现行
91GB/T 43780-2024制造装备智能化通用技术要求全国工业过程测量控制和自动化标准化技术委员会TC124国家标准现行
92GB/T 43782-2024人工智能 机器学习系统技术要求全国信息技术标准化技术委员会人工智能分会国家标准现行本文件提出了机器学习系统框架，规定了功能、可靠性、维护性、兼容性、安全性和可扩展性要求。本文件适用于各领域机器学习支持服务的系统及相关解决方案的规划、研发、评估、选型及验收的依据。查看来源
93GB/T 45081-2024人工智能 管理体系全国信息技术标准化技术委员会人工智能分会国家标准现行本文件为在组织范围内建立、实施、维护和持续改进人工智能管理体系规定了要求并提供了指南。本文件适用于提供或使用人工智能系统的产品或服务的组织。本文件旨在帮助组织负责任地开发、提供或使用人工智能系统，以实现其目标，并满足适用的法规要求，以及相关方的义务和期望。本文件适用于各种规模、类型和性质的提供或使用人工智能系统产品或服务的组织。查看来源
94GB/T 45087-2024人工智能 服务器系统性能测试方法全国信息技术标准化技术委员会人工智能分会国家标准现行本文件界定了服务器系统性能测试模式，描述了人工智能服务器系统训练性能和推理性能测试方法。本文件适用于人工智能服务器系统的性能测试与评价。查看来源
95GB/T 45225-2025人工智能 深度学习算法评估全国信息技术标准化技术委员会人工智能分会国家标准现行本文件确立了人工智能深度学习算法的评估指标体系，描述了评估方法等内容。本文件适用于指导深度学习算法开发方、用户方以及第三方等相关组织对深度学习算法及其训练得到的深度学习模型开展评估工作。查看来源
96GB/T 45288.1-2025人工智能 大模型 第1部分：通用要求全国信息技术标准化技术委员会人工智能分会国家标准现行本文件确立了大模型的参考架构，规定了大模型的通用要求。本文件适用于大模型开发、制备、部署和应用。查看来源
97GB/T 45288.2-2025人工智能 大模型 第2部分：评测指标与方法全国信息技术标准化技术委员会人工智能分会国家标准现行本文件确立了人工智能大模型的评测指标，描述了人工智能大模型的评测方法。本文件适用于模型提供者、应用服务者和应用消费者等对大模型能力进行评估与测试，也适用于指导大模型的设计、开发、应用。查看来源
98GB/T 45288.3-2025人工智能 大模型 第3部分：服务能力成熟度评估全国信息技术标准化技术委员会人工智能分会国家标准现行本文件给出了大模型服务能力框架和评估指标，描述了大模型服务能力成熟度等级划分及评估方法。本文件适用于服务提供方和需求方对大模型平台、模型定制及推理运营服务的能力进行全面评估，也适用于指导大模型服务能力的规划、设计和实现。查看来源
99GB/T 45507-2025智能服务 预测性维护绩效评价方法全国工业过程测量控制和自动化标准化技术委员会 TC124国家标准现行
100GB/T 45628-2025人工智能 知识图谱 知识交换协议全国信息技术标准化技术委员会人工智能分会国家标准现行本文件规定了知识交换协议总体框架、知识描述规则、基于文件的知识交换、基于消息的知识交换等。本文件适用于知识图谱相关系统的设计、开发、测试和部署等。查看来源
101GB/T 45652-2025网络安全技术 生成式人工智能预训练和优化训练数据安全规范全国网络安全标准化技术委员会国家标准现行本文件规定了生成式人工智能预训练和优化训练数据及其处理活动的安全要求，描述了相应的评价方法。本文件适用于生成式人工智能服务提供者开展预训练和优化训练数据处理活动以及安全自评估，也适用于第三方机构对预训练和优化训练数据进行安全性评估。查看来源
102GB/T 45654-2025网络安全技术 生成式人工智能服务安全基本要求全国网络安全标准化技术委员会国家标准现行本文件规定了生成式人工智能服务在训练数据安全、模型安全、安全措施等方面的要求。本文件适用于服务提供者开展生成式人工智能服务相关活动，也为相关主管部门以及第三方评估机构提供参考。注：训练数据及生成内容涉及的主要安全风险见附录A，生成式人工智能服务安全评估参考方法见附录B。查看来源
103GB/T 45674-2025网络安全技术 生成式人工智能数据标注安全规范全国网络安全标准化技术委员会国家标准现行本文件规定了生成式人工智能训练的数据标注平台或工具安全要求、数据标注规则安全要求、数据标注人员要求、数据标注核验要求，描述了数据标注安全评价方法。本文件适用于生成式人工智能数据标注组织方开展训练数据标注活动，并为生成式人工智能数据需求方对于数据标注进行检查、验收或第三方机构对数据标注进行安全性评估提供参考。查看来源
104GB/T 45864.1-2025土方机械 碰撞警告和避免 第1部分：通用要求全国金属切削机床标准化技术委员会锯刨床分会 TC22SC6国家标准现行
105GB/T 45907-2025人工智能 服务能力成熟度评估全国信息技术标准化技术委员会国家标准现行本文件提出了人工智能服务能力框架，规定了成熟度等级及评估分值，并描述了评估方法。本文件适用于对服务提供商提供的人工智能服务能力的成熟度评估，以及服务能力框架中能力域、能力项的单项评估。查看来源
106GB/T 45923.2-2025人工智能 知识图谱应用平台 第2部分：性能要求与测试方法全国信息技术标准化技术委员会国家标准现行本文件规定了知识图谱应用平台的性能要求，描述了相应的测试方法。本文件适用于知识图谱应用平台的设计、开发、应用和性能测试。查看来源
107GB/T 46069.2-2025人工智能 算子接口 第2部分：神经网络类全国信息技术标准化技术委员会人工智能分会国家标准现行本文件规定了面向人工智能领域的神经网络类算子接口的基本功能及参数要求。本文件适用于人工智能神经网络类算子库的设计、开发与应用，以及相关软硬件及系统的研制。查看来源
108GB/T 46268-2025农业机械作业北斗监测系统全国农业机械标准化技术委员会农业电子分会 TC201SC6国家标准现行
109GB/T 46270-2025农业机械北斗自动驾驶系统全国农业机械标准化技术委员会农业电子分会 TC201SC6国家标准现行
110GB/T 46284-2025人工智能 联邦学习技术规范全国信息技术标准化技术委员会人工智能分会国家标准现行本文件确立了联邦学习系统架构、任务参与方和任务流程，规定了联邦学习系统的功能要求和性能要求，描述了相应的测试方法。本文件适用于联邦学习系统的设计、开发、测试、使用及运维，开展联邦学习应用系统的横向比较并选型。注：本文件仅涵盖实现与支持联邦学习方法的系统功能和性能要求，对通用计算机软硬件及通信设备本身不作详细规定。查看来源
111GB/T 46347-2025人工智能 风险管理能力评估全国信息技术标准化技术委员会人工智能分会国家标准现行本文件规定了组织的人工智能风险管理能力的级别与要求，描述了组织的人工智能风险管理能力的评估方法。本文件适用于指导评估人员对组织的人工智能风险管理能力进行评估。查看来源
112GB/T 46700-2025人工智能 音视频及图像分析算法接口全国音频、视频及多媒体系统与设备标准化技术委员会国家标准现行本文件规定了音视频与图像分析算法接口的技术参数和要求，涵盖了解析算法、比对算法在算法包和算法服务形式下的应用环境、数据格式定义以及接口交互过程等内容。本文件适用于音视频与图像的分析算法的应用。智能应用系统与算法包、算法服务的系统集成与开发参考使用。查看来源
113GB/T 46886-2025智能检测装备通用技术要求全国工业过程测量控制和自动化标准化技术委员会 TC124国家标准现行
114GB/T 47230-2026智能服务 预测性维护 数据定义与接口全国自动化系统与集成标准化技术委员会国家标准现行
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共性层机械工业共性技术场景描述与系统架构统一机械工业人工智能应用场景的基本要素、系统组成和层级关系，说明产品、工艺、工况、人员、输入输出和系统之间的关系，便于跨专业描述和比较。
共性层机械工业共性技术数据、知识与模型管理规范设备、工况、质量、故障和维修数据的组织与关联，明确工业知识和模型的来源、版本、运行环境、更新控制、退出条件及变更后的确认要求。
共性层机械工业共性技术功能交付与运行维护围绕采购、集成、交付、验收和运行维护，明确人工智能功能清单、性能基线、技术文件、异常处置、人工复核和持续运行记录等共同要求。
共性层机械工业共性技术信息交换与接口互操作统一设备、模型服务、工业软件和企业系统之间的信息交换内容、接口调用、权限控制、通信降级和一致性验证要求，支撑多品牌装备和多系统协同。
共性层机械工业共性技术测试评价与安全论证明确测试环境、对象、工况、参考样、真值、人员角色和证据记录，统筹机械安全、功能安全、网络安全、数据安全与人工智能风险管理，形成可复现、可追溯的验证依据。
共性层机械工业共性技术状态监测与预测性维护围绕机器系统状态数据、健康指标、故障诊断、预警分级、寿命预测和维修闭环，统一数据判读、结果表达和验证要求。

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