热门角色不仅是灵感来源,更是你的效率助手。通过精挑细选的角色提示词,你可以快速生成高质量内容、提升创作灵感,并找到最契合你需求的解决方案。让创作更轻松,让价值更直接!
我们根据不同用户需求,持续更新角色库,让你总能找到合适的灵感入口。
本提示词旨在根据用户提供的职业/行业背景与目标年份,系统性地识别该领域当前最核心、需求最旺盛的技能,并提供详细的价值解析与权威学习资源推荐。适用于职业规划、技能提升、企业培训需求分析等场景,帮助用户精准把握市场趋势,制定高效学习计划。
| 变量 | 描述 | 示例 |
|---|---|---|
| 目标职业/行业 | 需要分析的职业名称或行业领域 | 前端开发工程师 |
| 目标年份 | 技能分析所针对的目标年份 | 2026 |
| 技能分析维度 | 分析技能时所依据的维度 | 技术硬技能 |
| 核心技能数量 | 需要识别和列出的核心技能数量 | 10 |
| 资源类型偏好 | 推荐学习资源时偏好的类型 | 在线课程平台 |
| 地域市场偏好 | 技能需求分析所侧重的地域市场 | 中国大陆 |
| 经验水平定位 | 技能分析所针对的经验水平定位 | 入门/转行 |
| 特定关注领域 | 技能分析希望聚焦的特定技术栈、业务环节或职能方向 | 人工智能伦理 |
以下为面向中国大陆与东南亚市场、定位中级(3-5年)、聚焦“数据产品增长与A/B实验体系、指标体系设计”的数据产品经理在2028年的前10大核心技能清单。
| 技能 | 重要性 | 学习资源 |
|---|---|---|
| 实验设计与A/B测试体系搭建(含因果推断)—系统化设计A/B、多变量实验与平台化治理,覆盖功效分析、显著性、SRM、护栏指标与实验审核流程。 | 直接驱动增长与迭代效率,是将创意转化为“可验证商业价值”的关键能力;在数据合规背景下,通过可信实验避免误判与资源浪费。 | • Trustworthy Online Controlled Experiments(书/网站):https://experimentguide.com • Optimizely A/B Testing Guide:https://www.optimizely.com/optimization-glossary/ab-testing/ • GrowthBook Docs(开源实验平台):https://www.growthbook.io/docs • Reforge Experimentation & Testing(认证/项目制):https://www.reforge.com/programs/experimentation-and-testing • Airbnb Experiment Reporting(案例):https://medium.com/airbnb-engineering/experiment-reporting-at-airbnb-4283ba9809d8 |
| 指标体系与北极星指标设计—构建北极星、目标树与诊断指标,建立分层指标、分群与因果链路,连接OKR与实验。 | 指标即策略的量化表达;高质量指标体系确保团队聚焦、加速试验学习闭环,并在多市场、多品类环境中保持一致性与可解释性。 | • Amplitude North Star Playbook:https://amplitude.com/north-star • Amplitude 事件与指标体系最佳实践:https://www.amplitude.com/blog/event-taxonomy • Mixpanel 产品分析指南/文档:https://help.mixpanel.com • Reforge Product Strategy(含指标与对齐):https://www.reforge.com/programs/product-strategy |
| 高级SQL与Python数据分析—掌握窗口函数、复杂JOIN、CTE、性能优化,结合Python/pandas/统计方法进行探索、因果与留存分析。 | 自助取数与分析能力是验证假设与产出洞察的“最后一公里”,可显著缩短数据依赖链,提升决策速度与质量。 | • Mode SQL Tutorial(系统教程):https://mode.com/sql-tutorial/ • DataCamp Intermediate/Advanced SQL:https://www.datacamp.com/courses/intermediate-sql • Coursera: IBM Data Analysis with Python:https://www.coursera.org/learn/data-analysis-with-python • Kaggle Learn(实战微课/项目):https://www.kaggle.com/learn |
| 产品分析工具与埋点治理(Amplitude/Mixpanel/GA4)—设计事件/属性/用户表、追踪计划、数据质量规则与跨端一致性。 | 高质量埋点与分析栈是实验与指标可信度的前提;在多区域、多端复杂场景中,治理能力决定洞察的可用性与时效性。 | • Amplitude Academy(官方课程):https://academy.amplitude.com • Mixpanel Docs:https://help.mixpanel.com • Google Analytics 4(Skillshop):https://skillshop.exceedlms.com/student/catalog/list?category_ids=53-google-analytics • Segment Docs(追踪计划/集成):https://segment.com/docs/ |
| 数据管道与实验数据工程基础(ELT、dbt、Airflow、实时埋点)—理解数仓建模、任务编排、数据质量与实验日志链路。 | 稳定的数据基础设施确保实验与指标“可追溯、可复现”;在高频实验与增长迭代中,工程化能力直接影响洞察时效。 | • dbt Learn + Docs:https://docs.getdbt.com/learn • Apache Airflow Docs:https://airflow.apache.org/docs/ • Apache Kafka Docs(实时数据):https://kafka.apache.org/documentation/ • Great Expectations(数据质量):https://docs.greatexpectations.io • 阿里云天池(实战项目):https://tianchi.aliyun.com |
| 机器学习与个性化(推荐、增益建模、多臂老虎机)—将模型融入产品实验,含因果/增益(uplift)模型与自适应试验。 | 在存量竞争与多市场差异化中,个性化与智能决策提升转化与留存;与实验结合可显著提高迭代效率与ROI。 | • Coursera: Recommender Systems Specialization:https://www.coursera.org/specializations/recommender-systems • Causal Inference for the Brave and True(开源书):https://matheusfacure.github.io/python-causality-handbook/ • DoWhy(因果推断工具包):https://microsoft.github.io/dowhy/ • Bandit Algorithms(开源书):https://banditalgs.com |
| 增长策略与生命周期运营(增长环、漏斗、留存/回流)—构建获取-激活-留存-变现闭环与渠道测算模型。 | 在中国大陆与东南亚高速移动互联网场景中,增长策略能力决定规模化与单用户价值;与A/B体系联动确保“策略-实验-落地”闭环。 | • Reforge Growth Series(认证/案例):https://www.reforge.com/programs/growth-series • Intercom Growth Handbook(实战手册):https://www.intercom.com/resources/growth-handbook • e-Conomy SEA 2023(区域报告):https://economysea.withgoogle.com • Grab Tech Blog(区域案例):https://engineering.grab.com • Shopee Engineering(区域案例):https://medium.com/shopee-engineering |
| 跨团队协作与利益相关方管理(数据/工程/法务/市场)—清晰对齐目标、阐述数据证据,推动决策与协同落地。 | 跨地域、多职能协作是数据产品成功的关键;优秀的协同能力可降低试验与交付阻力,提升组织的学习速度。 | • Product School(认证/训练营):https://productschool.com • Coursera: Effective Stakeholder Communication:https://www.coursera.org/learn/communication-stakeholders-project-management • SVPG Blog(产品领导力与实践):https://www.svpg.com |
| 隐私合规与数据治理(PIPL/GDPR/PDPA)—掌握个人信息合规、匿名化、最小化采集与合规实验实践。 | 随着监管强化,合规是数据产品与实验可持续运行的底线;跨境与多市场运营更需系统化治理与合规设计。 | • 中国《个人信息保护法》(英译/解读):https://digichina.stanford.edu/work/translation-personal-information-protection-law-of-the-peoples-republic-of-china-effective-nov-1-2021/ • GDPR 官方资源:https://gdpr.eu • 新加坡 PDPA(PDPC):https://www.pdpc.gov.sg/Overview-of-PDPA/The-Legislation • ISO/IEC 27701(隐私信息管理):https://www.iso.org/standard/71670.html • OpenMined(差分隐私课程):https://courses.openmined.org |
| 数据驱动的路线图与敏捷交付(RICE/ICE、OST、Scrum/Kanban)—构建基于影响力的优先级与实验/交付节奏。 | 在有限资源下做出高杠杆选择是中级数据PM的分水岭;以数据驱动的优先级与敏捷节奏连接战略与落地。 | • Intercom:RICE优先级模型:https://www.intercom.com/blog/rice-simple-prioritization-for-product-managers/ • Teresa Torres:Opportunity Solution Tree:https://www.producttalk.org/2016/08/opportunity-solution-tree/ • Scrum.org PSM(认证培训):https://www.scrum.org/courses/professional-scrum-master-training • Atlassian Agile Coach(实践指南):https://www.atlassian.com/agile |
参考来源(IEEE格式)
[1] World Economic Forum, “The Future of Jobs Report 2023,” 2023. [Online]. Available: https://www.weforum.org/publications/the-future-of-jobs-report-2023/
[2] McKinsey & Company, “The economic potential of generative AI: The next productivity frontier,” 2023. [Online]. Available: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
[3] Google, Temasek, Bain & Company, “e-Conomy SEA 2023,” 2023. [Online]. Available: https://economysea.withgoogle.com
[4] Amplitude, “North Star Playbook.” [Online]. Available: https://amplitude.com/north-star
[5] R. Kohavi, D. Tang, and Y. Xu, Trustworthy Online Controlled Experiments. [Online]. Available: https://experimentguide.com
[6] Optimizely, “A/B testing.” [Online]. Available: https://www.optimizely.com/optimization-glossary/ab-testing/
[7] GrowthBook, “Docs.” [Online]. Available: https://www.growthbook.io/docs
[8] Amplitude, “Amplitude Academy.” [Online]. Available: https://academy.amplitude.com
[9] Mixpanel, “Help Center / Docs.” [Online]. Available: https://help.mixpanel.com
[10] Google, “Skillshop: Google Analytics 4.” [Online]. Available: https://skillshop.exceedlms.com/student/catalog/list?category_ids=53-google-analytics
[11] Mode, “SQL Tutorial.” [Online]. Available: https://mode.com/sql-tutorial/
[12] DataCamp, “Intermediate SQL.” [Online]. Available: https://www.datacamp.com/courses/intermediate-sql
[13] Coursera, “IBM Data Analysis with Python.” [Online]. Available: https://www.coursera.org/learn/data-analysis-with-python
[14] Kaggle, “Learn.” [Online]. Available: https://www.kaggle.com/learn
[15] dbt Labs, “Learn dbt / Docs.” [Online]. Available: https://docs.getdbt.com/learn
[16] Apache Software Foundation, “Apache Airflow Documentation.” [Online]. Available: https://airflow.apache.org/docs/
[17] Apache Software Foundation, “Apache Kafka Documentation.” [Online]. Available: https://kafka.apache.org/documentation/
[18] Great Expectations, “Docs.” [Online]. Available: https://docs.greatexpectations.io
[19] Coursera, “Recommender Systems Specialization.” [Online]. Available: https://www.coursera.org/specializations/recommender-systems
[20] M. Facure Alves, “Causal Inference for the Brave and True.” [Online]. Available: https://matheusfacure.github.io/python-causality-handbook/
[21] Microsoft, “DoWhy Documentation.” [Online]. Available: https://microsoft.github.io/dowhy/
[22] T. Lattimore and C. Szepesvári, Bandit Algorithms. [Online]. Available: https://banditalgs.com
[23] Reforge, “Growth Series.” [Online]. Available: https://www.reforge.com/programs/growth-series
[24] Intercom, “The Growth Handbook.” [Online]. Available: https://www.intercom.com/resources/growth-handbook
[25] Grab, “Grab Tech Blog.” [Online]. Available: https://engineering.grab.com
[26] Shopee, “Shopee Engineering Blog.” [Online]. Available: https://medium.com/shopee-engineering
[27] Product School, “Product Management Certifications.” [Online]. Available: https://productschool.com
[28] Coursera, “Effective Stakeholder Communication in Project Management.” [Online]. Available: https://www.coursera.org/learn/communication-stakeholders-project-management
[29] SVPG, “Silicon Valley Product Group Blog.” [Online]. Available: https://www.svpg.com
[30] DigiChina (Stanford), “Translation: Personal Information Protection Law of the PRC,” 2021. [Online]. Available: https://digichina.stanford.edu/work/translation-personal-information-protection-law-of-the-peoples-republic-of-china-effective-nov-1-2021/
[31] European Union, “GDPR.eu.” [Online]. Available: https://gdpr.eu
[32] Personal Data Protection Commission Singapore, “PDPA.” [Online]. Available: https://www.pdpc.gov.sg/Overview-of-PDPA/The-Legislation
[33] ISO, “ISO/IEC 27701:2019.” [Online]. Available: https://www.iso.org/standard/71670.html
[34] OpenMined, “Courses.” [Online]. Available: https://courses.openmined.org
[35] Intercom, “RICE: Simple prioritization for product managers.” [Online]. Available: https://www.intercom.com/blog/rice-simple-prioritization-for-product-managers/
[36] T. Torres, “Opportunity Solution Tree.” [Online]. Available: https://www.producttalk.org/2016/08/opportunity-solution-tree/
[37] Scrum.org, “Professional Scrum Master.” [Online]. Available: https://www.scrum.org/courses/professional-scrum-master-training
[38] Atlassian, “Agile Coach.” [Online]. Available: https://www.atlassian.com/agile
[39] Alibaba Cloud, “Tianchi.” [Online]. Available: https://tianchi.aliyun.com
[40] Airbnb Engineering, “Experiment Reporting at Airbnb.” [Online]. Available: https://medium.com/airbnb-engineering/experiment-reporting-at-airbnb-4283ba9809d8
说明:以上技能清单与资源根据全球与区域就业与产业趋势、产品增长与实验最佳实践综合筛选,适配2028年数据产品经理在中国大陆与东南亚场景的核心竞争力构成。
以下为面向“智能制造工艺工程师(2030年)”的前12项核心技能清单,聚焦“数字孪生与工业物联网集成、制造工艺优化”,并兼顾欧洲、日本、中国大陆的监管与市场环境。每项技能含简述、需求理由与优选学习资源。
| 技能 | 重要性 | 学习资源 |
|---|---|---|
| 数字孪生体系架构落地(ISO 23247、RAMI 4.0、AAS)|构建覆盖设备-产线-工厂-供应网络的端到端数字孪生;掌握ISO 23247、RAMI 4.0与资产管理壳(AAS)语义模型。|2030年制造竞争的关键是以数据驱动的快速工艺迭代与闭环优化,数字孪生是缩短试错周期、提升良率与柔性制造的核心基础。|• ISO 23247 Digital Twin for Manufacturing(ISO官方)https://www.iso.org/standard/75066.html • RAMI 4.0(Plattform Industrie 4.0)https://www.plattform-i40.de/EN/Thinking/Details/rami-40.html • Asset Administration Shell(IDTA)https://industrialdigitaltwin.org/en/ • Digital Twin Driven Smart Manufacturing(Elsevier,专著)https://www.elsevier.com/books/digital-twin-driven-smart-manufacturing/tao/9780128176306 • WEF Global Lighthouse Network(案例)https://www.weforum.org/projects/global-lighthouse-network/ | ||
| IIoT互联互通与数据标准(OPC UA、ISA‑95/IEC 62264、MTConnect、MQTT)|设计厂内/厂间互联的语义一致与可扩展数据模型;掌握OPC UA信息模型、ISA‑95层级映射、MTConnect测量互通与MQTT发布订阅。|到2030年,多源设备与跨供应链数据空间打通是工艺优化和可追溯的前提,标准化互联能力决定集成成本与规模化速度。|• OPC UA(OPC Foundation)https://opcfoundation.org/about/opc-technologies/opc-ua/ • ISA‑95 / IEC 62264(ISA)https://www.isa.org/isa95 • MTConnect(AMT)https://www.mtconnect.org/ • MQTT(OASIS)https://mqtt.org/ • Catena‑X(制造数据空间)https://catena-x.net/en | ||
| 工艺数据工程与时间序列管道(Kafka/Flink/Edge‑Cloud)|构建高吞吐、低时延的OT/IT数据管道与治理:时间序列采集、边缘处理、流批一体与数据质量。|先进工艺优化与预测性维护依赖稳定的数据基础设施;在多工厂场景下,可靠的数据工程能力直接影响ROI与扩展性。|• Apache Kafka(官方文档)https://kafka.apache.org/ • Apache Flink(官方文档)https://flink.apache.org/ • KubeEdge(边缘原生)https://kubeedge.io/en/ • Kubernetes(云原生)https://kubernetes.io/ • Inductive University(Ignition实战视频)https://inductiveuniversity.com/ | ||
| 高级过程控制与实时优化(MPC/RTO)|掌握模型预测控制(MPC)、实时优化(RTO)与软测量构建,将工艺约束与经济指标统一到闭环控制中。|在欧洲、日本与中国的高端制造中,APC/RTO可显著提升良率、降耗与稳定性;到2030年成为先进产线的“标配”。|• Model Predictive Control(Camacho & Bordons,专著)https://www.springer.com/gp/book/9781447123645 • Nonlinear Programming/Real-Time Optimization(Biegler,专著)https://www.wiley.com/en-us/Nonlinear+Programming-p-9780898713070 • IEEE Control Systems Magazine(MPC综述选读)https://ieeecss.org/publications/csm • Computers & Chemical Engineering(RTO/MPC论文辑)https://www.sciencedirect.com/journal/computers-and-chemical-engineering | ||
| 机器视觉与智能质量(深度学习+QIF计量集成)|将SOTA视觉算法与计量标准(QIF)/机加接口(MTConnect)结合,实现全流程质量追溯与闭环纠偏。|2030年质量竞争转向“零缺陷”与自适应过程;视觉+计量数据语义打通将显著缩短检测-纠偏链路。|• QIF(DMSC)https://qifstandards.org/ • MTConnect(测量/机床数据)https://www.mtconnect.org/ • NVIDIA Metropolis(工业视觉开发)https://www.nvidia.com/en-us/metropolis/ • Deep Learning(MIT Press,理论)https://www.deeplearningbook.org/ • WEF AI in Manufacturing洞察(报告)https://www.weforum.org/ | ||
| 工业控制与边缘计算(IEC 61131‑3/61499、容器化PLC、OT DevOps)|掌握PLC/软PLC、IEC 61499分布式功能块、容器化与CI/CD在边缘控制的落地。|离散与流程制造融合加深,边缘侧的可编排与热更新能力到2030年成为实现柔性与高可用的关键。|• PLCopen(IEC 61131‑3资源)https://plcopen.org/ • Eclipse 4diac(IEC 61499)https://www.eclipse.org/4diac/ • Kubernetes(云原生)https://kubernetes.io/ • PLCnext Community(实践与示例)https://www.plcnext-community.net/ • Inductive Automation Ignition(边缘网关)https://inductiveautomation.com/ignition | ||
| 工业网络与确定性通信(5G URLLC、TSN、OPC UA PubSub)|设计5G/TSN融合网络,保障毫秒级抖动与高可靠的控制/视频/测量流;掌握OPC UA PubSub over TSN。|到2030年多机协同与移动机器人/视觉闭环要求“超可靠低时延”,网络设计能力影响整厂产能上限。|• 3GPP Release‑16/17(URLLC/工业增强)https://www.3gpp.org/release-16 https://www.3gpp.org/release-17 • IEEE TSN(802.1 TSN)https://1.ieee802.org/tsn/ • OPC UA PubSub(OPC Foundation)https://opcfoundation.org/developer-tools/specifications-unified-architecture/opc-ua-pubsub/ • 5G‑ACIA(工业5G白皮书)https://5g-acia.org/ | ||
| 工业网络安全与合规(IEC 62443、NIS2、欧盟CRA、日中框架)|构建基于风险的IACS安全:分区分域、纵深防御、SBOM、漏洞管理与运营合规。|2030年前欧盟NIS2/CRA全面落地,日本IPA CPSF与中国工业互联网安全要求趋严,合规与韧性成为跨国工厂准入底线。|• ISA/IEC 62443系列(ISA)https://www.isa.org/standards-and-publications/isa-standards/isa-62443-series-of-standards • ENISA NIS2指南https://www.enisa.europa.eu/topics/nis-directive • EU Cyber Resilience Act(欧委会)https://digital-strategy.ec.europa.eu/en/policies/cyber-resilience-act • IPA Cyber/Physical Security Framework(日本)https://www.ipa.go.jp/security/cpsf/english.html • CAICT 工业互联网白皮书(参考)https://www.caict.ac.cn/ | ||
| 机械/功能安全与机械法规(EU Machinery Regulation、ISO 12100、ISO 13849‑1/IEC 62061)|面向协作机器人/高速装备与改造项目,完成风险评估、PL/SIL计算与安全回路设计。|欧盟新机械法规自2027强制,日本/中国采用国际等同标准;到2030年安全合规是智能产线升级的必要条件。|• EU Machinery Regulation (EU) 2023/1230(EUR‑Lex)https://eur-lex.europa.eu/eli/reg/2023/1230/oj • ISO 12100(机械安全通则)https://www.iso.org/standard/51528.html • ISO 13849‑1(安全相关控制系统)https://www.iso.org/standard/69883.html • IEC 62061(功能安全-机械)https://webstore.iec.ch/publication/69052 • Pilz 标准知识库(应用)https://www.pilz.com/en-INT/knowhow/law-standards/standards | ||
| 制造MLOps与预测性维护(边缘部署/监控/漂移治理)|建立模型全生命周期:特征工程、在线监控、漂移告警、回滚与A/B;覆盖边缘/云混合部署。|到2030年模型规模与更新频率提升,稳定可复现的MLOps决定AI项目能否跨工厂复制与持续产出。|• MLOps Specialization(DeepLearning.AI, Coursera)https://www.coursera.org/specializations/mlops • Designing Machine Learning Systems(O’Reilly)https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/ • Google Cloud ML Ops学习路径https://cloud.google.com/learn/training/data-ml • Predictive Maintenance 综述(Computers in Industry)https://www.sciencedirect.com/journal/computers-in-industry | ||
| 工艺变更管理与FMEA/APQP(AIAG‑VDA、IATF 16949)|面向新工艺导入与量产爬坡,系统开展DFMEA/PFMEA、控制计划、PPAP与过程审核。|汽车/高端装备链路在欧日中均强化先期质量策划,到2030年跨供应链一致性需要标准化APQP与稳健的变更治理。|• AIAG‑VDA FMEA手册(AIAG)https://www.aiag.org/store/publications/details?ProductCode=FMEA-3 • APQP 2nd Ed.(AIAG)https://www.aiag.org/store/publications/details?ProductCode=APQP-2 • IATF 16949(官方)https://www.iatfglobaloversight.org/ • Prosci ADKAR(变更管理)https://www.prosci.com/methodology/adkar | ||
| 运营卓越与能耗/碳优化(Lean Six Sigma、ISO 50001、EU ETS)|将OEE、能耗、碳排与成本函数耦合优化,支撑2030减排目标下的工艺与调度策略。|欧洲碳约束与供应链披露将成为新常态;日中制造也趋向精益与能源强度下降,相关能力直接影响盈利与合规。|• ISO 50001(能源管理体系)https://www.iso.org/iso-50001-energy-management.html • EU ETS(排放交易体系)https://climate.ec.europa.eu/eu-action/eu-emissions-trading-system-eu-ets_en • ASQ CSSBB(黑带认证)https://asq.org/cert/six-sigma-black-belt • WEF Net‑Zero Industry Tracker(报告)https://www.weforum.org/projects/net-zero-industry-tracker | ||
| AI与数据合规(EU AI Act、Data Act、数据主权与隐私工程)|在质量/调度/视觉等高风险AI场景中,完成数据治理、透明度、风险管理与数据共享合规设计。|欧盟AI法案将对高风险AI系统提出强制要求;跨欧日中供应链合作需要数据可用但受控的“合规即设计”。|• EU AI Act(EUR‑Lex总览)https://artificialintelligenceact.eu/ • EU Data Act(欧委会)https://digital-strategy.ec.europa.eu/en/policies/data-act • Gaia‑X/制造数据空间(背景)https://gaia-x.eu/ • Catena‑X合规资料https://catena-x.net/en |
参考文献(IEEE格式)
[1] ISO, “ISO 23247 — Digital Twin framework for manufacturing,” https://www.iso.org/standard/75066.html
[2] Plattform Industrie 4.0, “RAMI 4.0,” https://www.plattform-i40.de/EN/Thinking/Details/rami-40.html
[3] IDTA, “Asset Administration Shell,” https://industrialdigitaltwin.org/en/
[4] F. Tao et al., Digital Twin Driven Smart Manufacturing. Elsevier, 2019. https://www.elsevier.com/books/digital-twin-driven-smart-manufacturing/tao/9780128176306
[5] World Economic Forum, “Global Lighthouse Network,” https://www.weforum.org/projects/global-lighthouse-network/
[6] OPC Foundation, “OPC UA,” https://opcfoundation.org/about/opc-technologies/opc-ua/
[7] ISA, “ISA‑95/IEC 62264,” https://www.isa.org/isa95
[8] MTConnect Institute, “MTConnect,” https://www.mtconnect.org/
[9] OASIS, “MQTT,” https://mqtt.org/
[10] Catena‑X, “Catena‑X Automotive Network,” https://catena-x.net/en
[11] Apache Software Foundation, “Apache Kafka,” https://kafka.apache.org/
[12] Apache Software Foundation, “Apache Flink,” https://flink.apache.org/
[13] KubeEdge, “KubeEdge,” https://kubeedge.io/en/
[14] CNCF, “Kubernetes,” https://kubernetes.io/
[15] Inductive Automation, “Inductive University,” https://inductiveuniversity.com/
[16] E. F. Camacho, C. Bordons, Model Predictive Control. Springer, 2013. https://www.springer.com/gp/book/9781447123645
[17] L. T. Biegler, Nonlinear Programming. SIAM/Wiley, 2010. https://www.wiley.com/en-us/Nonlinear+Programming-p-9780898713070
[18] IEEE CSS, “IEEE Control Systems Magazine,” https://ieeecss.org/publications/csm
[19] Elsevier, “Computers & Chemical Engineering,” https://www.sciencedirect.com/journal/computers-and-chemical-engineering
[20] DMSC, “QIF Standard,” https://qifstandards.org/
[21] NVIDIA, “Metropolis,” https://www.nvidia.com/en-us/metropolis/
[22] I. Goodfellow, Y. Bengio, A. Courville, Deep Learning. MIT Press, 2016. https://www.deeplearningbook.org/
[23] PLCopen, “IEC 61131‑3,” https://plcopen.org/
[24] Eclipse Foundation, “Eclipse 4diac (IEC 61499),” https://www.eclipse.org/4diac/
[25] PLCnext, “PLCnext Community,” https://www.plcnext-community.net/
[26] Inductive Automation, “Ignition,” https://inductiveautomation.com/ignition
[27] 3GPP, “Release 16,” https://www.3gpp.org/release-16
[28] 3GPP, “Release 17,” https://www.3gpp.org/release-17
[29] IEEE 802.1, “Time-Sensitive Networking (TSN),” https://1.ieee802.org/tsn/
[30] OPC Foundation, “OPC UA PubSub,” https://opcfoundation.org/developer-tools/specifications-unified-architecture/opc-ua-pubsub/
[31] 5G‑ACIA, “White Papers,” https://5g-acia.org/
[32] ISA, “ISA/IEC 62443 Series,” https://www.isa.org/standards-and-publications/isa-standards/isa-62443-series-of-standards
[33] ENISA, “NIS2,” https://www.enisa.europa.eu/topics/nis-directive
[34] European Commission, “Cyber Resilience Act,” https://digital-strategy.ec.europa.eu/en/policies/cyber-resilience-act
[35] IPA (Japan), “Cyber/Physical Security Framework,” https://www.ipa.go.jp/security/cpsf/english.html
[36] CAICT, “Industrial Internet (site),” https://www.caict.ac.cn/
[37] EUR‑Lex, “Regulation (EU) 2023/1230 Machinery,” https://eur-lex.europa.eu/eli/reg/2023/1230/oj
[38] ISO, “ISO 12100: Safety of machinery,” https://www.iso.org/standard/51528.html
[39] ISO, “ISO 13849‑1: Safety-related parts of control systems,” https://www.iso.org/standard/69883.html
[40] IEC, “IEC 62061,” https://webstore.iec.ch/publication/69052
[41] Pilz, “Standards knowledge,” https://www.pilz.com/en-INT/knowhow/law-standards/standards
[42] Coursera/DeepLearning.AI, “MLOps Specialization,” https://www.coursera.org/specializations/mlops
[43] C. Huyen, Designing Machine Learning Systems. O’Reilly, 2022. https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/
[44] Google Cloud, “ML Ops Learning,” https://cloud.google.com/learn/training/data-ml
[45] Elsevier, “Computers in Industry (PdM),” https://www.sciencedirect.com/journal/computers-in-industry
[46] AIAG, “AIAG‑VDA FMEA Handbook,” https://www.aiag.org/store/publications/details?ProductCode=FMEA-3
[47] AIAG, “APQP 2nd Edition,” https://www.aiag.org/store/publications/details?ProductCode=APQP-2
[48] IATF, “IATF 16949,” https://www.iatfglobaloversight.org/
[49] Prosci, “ADKAR,” https://www.prosci.com/methodology/adkar
[50] ISO, “ISO 50001,” https://www.iso.org/iso-50001-energy-management.html
[51] European Commission, “EU ETS,” https://climate.ec.europa.eu/eu-action/eu-emissions-trading-system-eu-ets_en
[52] ASQ, “Certified Six Sigma Black Belt,” https://asq.org/cert/six-sigma-black-belt
[53] World Economic Forum, “Net‑Zero Industry Tracker,” https://www.weforum.org/projects/net-zero-industry-tracker
[54] EU, “AI Act portal (overview),” https://artificialintelligenceact.eu/
[55] European Commission, “Data Act,” https://digital-strategy.ec.europa.eu/en/policies/data-act
[56] Gaia‑X AISBL, “Gaia‑X,” https://gaia-x.eu/
[57] Catena‑X, “Compliance/Resources,” https://catena-x.net/en
说明
| 技能 | 重要性 | 学习资源 |
|---|---|---|
| 多模态内容策略与编排 | 描述:规划并编排文本/图片/音频/视频/交互式资产的AIGC生产、分发与复用,形成跨渠道内容矩阵。价值:到2029年,多模态模型成为主流,品牌需要在AI搜索与社交场景中以多模态形式触达与转化,系统化编排能力直接决定效率与ROI。 | - OpenAI Docs: Multimodal/Images & Audio https://platform.openai.com/docs - Google Gemini API 文档 https://ai.google.dev/gemini-api - Runway Learn(视频生成实战)https://runwayml.com/learn - Adobe Firefly 使用与教程 https://helpx.adobe.com/support/firefly.html - Descript 学习中心(音视频编辑/配音)https://www.descript.com/learn |
| 提示工程与自动化工作流 | 描述:掌握系统提示、少样例、工具调用/函数调用、约束式提示与链式/代理式编排,把提示工程嵌入可复用的生产工作流。价值:高质量提示与自动化能显著降低成本、提升一致性与产出速度,是AIGC内容运营的核心护城河。 | - DeepLearning.AI《ChatGPT Prompt Engineering for Developers》https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/ - LangChain 文档(链与代理)https://python.langchain.com/docs/ - Anthropic Prompt Library https://docs.anthropic.com/claude/prompt-library - FlowiseAI 可视化Agent/工作流 https://docs.flowiseai.com/ - DeepLearning.AI《Building Systems with the ChatGPT API》https://www.deeplearning.ai/short-courses/building-systems-with-chatgpt/ |
| 品牌语调治理与RAG安全合规 | 描述:构建品牌语调与事实库(RAG/向量检索)、内容安全与合规模型(审核/水印/来源标注),确保一致性、可追溯与风险可控。价值:生成内容的可信度与合规性决定品牌资产沉淀与平台分发表现,治理体系可在规模化生产下保持质量与安全。 | - Pinecone Learn:RAG 系列 https://www.pinecone.io/learn/series/rag/ - LlamaIndex 文档(检索增强)https://docs.llamaindex.ai/ - Guardrails.ai(输出约束与验证)https://www.guardrailsai.com/docs - Microsoft Azure AI Content Safety https://learn.microsoft.com/azure/ai-services/content-safety/overview - C2PA 内容来源与溯源规范 https://c2pa.org/specifications/ |
| 数据驱动内容分析与实验 | 描述:用GA4/产品分析做归因、漏斗与分群;开展A/B与多变量实验,评估AIGC对点击、转化与LTV的增量价值。价值:到2029年,营销与内容预算更重数据回报,分析与实验能力决定资源分配效率与增长确定性。 | - Google Analytics Academy(GA4)https://analytics.google.com/analytics/academy/ - Amplitude Academy https://academy.amplitude.com/ - Mixpanel Learn https://mixpanel.com/learn/ - Optimizely Experimentation Academy https://www.optimizely.com/academy/ - CXL(实验与转化课程,精选)https://cxl.com/institute/ (付费) |
| 生成式搜索与社交优化(GSO/SEO) | 描述:面向AI Overviews/多模态搜索与社交摘要优化内容结构、实体与Schema数据,提升被选入摘要与引用的概率。价值:搜索与社交分发正被生成式摘要重构,结构化与可信信号将直接影响品牌可见度与自然流量。 | - Google Search Central(官方搜索文档)https://developers.google.com/search/ - 有用内容与E‑E‑A‑T指南 https://developers.google.com/search/docs/fundamentals/creating-helpful-content - 结构化数据与Schema.org https://developers.google.com/search/docs/appearance/structured-data 与 https://schema.org/ - Google Search Central YouTube 频道 https://www.youtube.com/@GoogleSearchCentral - Moz《Beginner’s Guide to SEO》https://moz.com/beginners-guide-to-seo |
| 客户共创与AI社区运营 | 描述:用AI客服/社群机器人收集UGC、进行意图识别与内容共创,构建社区驱动的内容生产与反馈闭环。价值:以用户参与驱动的内容能放大品牌声量并提升留存,AI将显著降低运营成本并提升响应体验。 | - Intercom AI(客服与AI助理)https://www.intercom.com/ai - Zendesk AI https://www.zendesk.com/ai/ - Discord 开发者文档(社群Bot)https://discord.com/developers/docs/intro - HubSpot Academy(服务与自动化)https://academy.hubspot.com/ - CMX Hub(社区运营实践)https://www.cmxhub.com/ |
| 增长闭环与生命周期自动化(GenAI驱动) | 描述:设计“生成—分发—测量—学习—再生成”的增长飞轮,结合CDP/营销自动化做全生命周期个性化触达。价值:到2029年,能把AIGC嵌入增长闭环与自动化栈的团队,将在获客成本与复购率上取得结构性优势。 | - Brian Balfour:Growth Loops 文章 https://brianbalfour.com/essays/growth-loops - HubSpot Academy《Marketing Automation》https://academy.hubspot.com/courses/marketing-automation - Twilio Segment 文档(CDP与个性化)https://segment.com/docs/ - Braze Learning(消息与旅程)https://www.braze.com/learn - AWS Personalize Workshop https://personalize.workshop.aws/ |
| AI原生内容项目管理与跨部门沟通 | 描述:用敏捷/看板/OKR管理AIGC内容项目,进行需求澄清、风险沟通与利益相关者对齐;将技术能力转化为业务语言与成果。价值:AIGC项目跨越市场、创意与工程,入门/转行者若能高效协作与驱动落地,将显著提升职场竞争力。 | - Coursera《Google Project Management》职业证书 https://www.coursera.org/professional-certificates/google-project-management - Atlassian Agile Coach(敏捷与看板)https://www.atlassian.com/agile - Coursera《AI Product Management》专项课程(Duke)https://www.coursera.org/specializations/ai-product-management - DeepLearning.AI《Generative AI for Everyone》https://www.deeplearning.ai/short-courses/generative-ai-for-everyone/ - What Matters(OKR 资源)https://www.whatmatters.com/resources |
参考文献(IEEE格式) [1] World Economic Forum, “Future of Jobs Report 2023,” 2023. [Online]. Available: https://www.weforum.org/reports/future-of-jobs-2023/ [2] McKinsey Global Institute, “The economic potential of generative AI: The next productivity frontier,” 2023. [Online]. Available: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-economic-potential-of-generative-ai [3] Gartner, “Hype Cycle for Generative AI, 2024,” 2024. [Online]. Available: https://www.gartner.com/en/research (检索入口) [4] Google, “AI Overviews in Search,” 2024. [Online]. Available: https://support.google.com/websearch/answer/13510658 [5] Google, “Search Central Documentation,” 2024. [Online]. Available: https://developers.google.com/search/ [6] OpenAI, “API Documentation and Cookbook,” 2024. [Online]. Available: https://platform.openai.com/docs and https://github.com/openai/openai-cookbook [7] Anthropic, “Prompt Library,” 2024. [Online]. Available: https://docs.anthropic.com/claude/prompt-library [8] Pinecone, “RAG Learning Series,” 2024. [Online]. Available: https://www.pinecone.io/learn/series/rag/ [9] Content Authenticity Initiative / C2PA, “Content Provenance and Authentication Specifications,” 2024. [Online]. Available: https://c2pa.org/specifications/ [10] European Union, “EU Artificial Intelligence Act,” 2024. [Online]. Available: https://artificialintelligenceact.eu/ [11] Microsoft, “Azure AI Content Safety,” 2024. [Online]. Available: https://learn.microsoft.com/azure/ai-services/content-safety/overview [12] HubSpot, “Marketing and Service Academy Courses,” 2024. [Online]. Available: https://academy.hubspot.com/ [13] Brian Balfour, “Growth Loops,” 2017–2024. [Online]. Available: https://brianbalfour.com/essays/growth-loops [14] Google Analytics Academy, “Google Analytics 4 Courses,” 2024. [Online]. Available: https://analytics.google.com/analytics/academy/ [15] Runway, “Runway Learn,” 2024. [Online]. Available: https://runwayml.com/learn [16] Adobe, “Firefly Help and Tutorials,” 2024. [Online]. Available: https://helpx.adobe.com/support/firefly.html [17] LangChain, “Documentation,” 2024. [Online]. Available: https://python.langchain.com/docs/
帮助用户精准识别指定职业或行业在特定年份最需要掌握的核心技能,为用户提供技能短缺的洞察,并推荐与这些技能相关的高质量学习资源,为其职业规划与提升提供明确指导,同时节省时间和精力。
帮助希望跨行业或职能方向的职场人士识别关键技能需求,精准规划转型路径,并快速找到优质学习资源,提升求职竞争力。
为即将进入职场的新人提供最热门岗位所需技能清单及学习建议,助力用户高效完成自我提升,快人一步掌握职场必备技能。
帮助技能爱好者洞察未来一年各行业核心能力趋势,提供精准学习路径,轻松切入新领域,拓宽个人职业发展维度。
将模板生成的提示词复制粘贴到您常用的 Chat 应用(如 ChatGPT、Claude 等),即可直接对话使用,无需额外开发。适合个人快速体验和轻量使用场景。
把提示词模板转化为 API,您的程序可任意修改模板参数,通过接口直接调用,轻松实现自动化与批量处理。适合开发者集成与业务系统嵌入。
在 MCP client 中配置对应的 server 地址,让您的 AI 应用自动调用提示词模板。适合高级用户和团队协作,让提示词在不同 AI 工具间无缝衔接。
半价获取高级提示词-优惠即将到期