工业AI文献研究_industrial-ai-research

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129人浏览 · 2026-07-21 00:25:42

shallwegzgdj · 2026-07-21 00:25:42 发布

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工业 AI 文献研究技能，运行一个精简但充分的、源感知的学术研究工作流，专注于工业人工智能领域。它的覆盖范围包括预测性维护、智能调度、工业异常检测、智能制造、信息物理系统、工业自动化边缘 AI、机器人与工业交叉等方向，同时也处理数字孪生、工业物联网、工业 4.0、制造 AI 等相关术语。使用场景包括：研究人员需要系统性地调研某个工业 AI 子课题的文献现状时；工程师需要对比不同方法在特定工业任务上的效果时；产品经理需要识别工业 AI 领域的研究空白以指导产品方向时；以及博士生需要撰写相关领域的调研报告或综述草稿时。核心特点体现在多个层面。首先，它设置了强制性的四项前导问题——在开始任何搜索或综合之前，必须先了解用户的具体需求范围、关注的应用场景、偏好的文献时间窗口和输出格式，避免无的放矢的搜索。其次，它具备源感知的优先级排序能力——根据发表渠道的重要性（arXiv 预印本、IEEE 会议/期刊、自动化领域专刊等）对文献进行加权排序。第三，它提供四种输出模式：研究简报（research-brief）、文献图谱（literature-map）、按发表渠道排名的综述（venue-ranked survey）和研究缺口备忘录（research-gap memo）。第四，它包含一个对抗性综合环节——专门寻找文献中的矛盾点和未充分探索的空白区域，提供不同于主流叙事的视角。最后，它支持综述草稿的生成，采用大纲优先写作方式和分节证据包，可选 LaTeX 导出。

Industrial AI Research

Run a lean, source-aware research workflow for Industrial AI.

Capability Summary

Structured literature research for Industrial AI and automation topics
Mandatory four-question intake before any search or synthesis
Venue-aware source prioritization (arXiv, IEEE, automation venues)
Four deliverable modes: research-brief, literature-map, venue-ranked survey, research-gap memo
Contrarian synthesis pass to surface contradictions and under-explored gaps
Survey draft generation: outline-first writing with per-section evidence packs and optional LaTeX export

Triggering

Use this skill when the user wants to:

Survey Industrial AI literature on a specific subtopic
Compare papers across venues or methods within Industrial AI
Identify research gaps in predictive maintenance, scheduling, anomaly detection, or smart manufacturing
Produce a structured research report with source-backed evidence
Draft a structured survey on an Industrial AI subtopic
Produce a survey manuscript with taxonomy, evidence packs, and section-by-section writing

Do Not Use

Writing or compiling LaTeX/Typst papers (use latex-paper-en, latex-thesis-zh, or typst-paper).
Note: survey-draft mode produces Markdown by default; for LaTeX output, it delegates final formatting to latex-paper-en.
Auditing paper quality or formatting (use paper-audit)
Systematic reviews or meta-analyses requiring IRB or clinical ethics
Topics outside the Industrial AI and automation domain
Auditing an existing paper’s quality or formatting (use paper-audit)
Editing LaTeX/Typst source files (use the appropriate writing skill)

Safety Boundaries

Never fabricate paper metadata (title, authors, venue, year, DOI)
Never present preprints as peer-reviewed publications
Never start synthesis before intake questions are answered
Never suppress contradictions or conflicting evidence
Never use Tier 4 sources (blogs, press releases) as primary evidence

Core Rules

Ask the user the four intake questions (see references/question-flow.md) before starting any search or synthesis.
Keep the skill workflow in English only, even when the requested report language is not English.
Prefer recent arXiv plus top IEEE and automation venues over generic web articles.
Default to the last 3 years, but keep seminal older work when it is still necessary for context.
Cite every substantive claim and separate verified evidence from inference.
In survey-draft mode, complete all structure and evidence phases before generating any prose. Structure phases produce YAML/tables only.

Intake Contract

Always start by asking the four intake questions defined in references/question-flow.md:

Report language (English / Simplified Chinese / Bilingual summary)
Deliverable mode (research-brief / literature-map / venue-ranked survey / research-gap memo / survey-draft)
Time window (last 12 months / last 3 years / last 5 years / custom)
Industrial AI emphasis (predictive maintenance / intelligent scheduling / industrial anomaly detection / smart manufacturing and process optimization / CPS and edge AI / robotics crossover)

If the user does not choose, default to last 3 years and the subdomain implied by their prompt.

Required Inputs

A concrete Industrial AI topic or question.
User choices for report language, deliverable mode, time window, and domain emphasis.
Optional preferences on peer-reviewed-only filtering, benchmarks vs deployment evidence, or desired output format.

If any intake item is missing, ask the mandatory questions from references/question-flow.md before you search.

Source Strategy

Read these files before searching:

references/source-priority.md
references/venue-map.md

Primary sources:

arXiv: eess.SY, cs.AI
IEEE and automation anchors: T-ASE, CASE

Supporting crossover sources:

arXiv: cs.RO, cs.LG
IEEE robotics venues: ICRA, IROS, RA-L, T-RO
Adjacent industrial and control venues listed in references/venue-map.md

When
the user asks for the latest work, prefer:

arXiv recent streams for rapid updates
top IEEE and automation venues for stronger publication filtering
secondary crossover venues only when they materially improve coverage

Workflow

Phase 1. Scope

Rewrite the request as a precise Industrial AI research objective.
Lock the report language, deliverable mode, time window, and domain emphasis.
State explicit in-scope and out-of-scope boundaries.

Phase 2. Search Plan

Build venue buckets and keyword groups from references/source-priority.md.
Separate primary sources from secondary crossover sources.
State the recency policy and any seminal-paper exceptions.

Phase 3. Source Collection

Gather papers from the prioritized source buckets.
Prefer official venue pages, arXiv recent listings, IEEE Xplore landing pages, and publisher or conference pages.
Record why each paper was included.

Phase 4. Verification and Triage

Check venue quality, publication type, year, and relevance.
Remove weak matches, duplicates, and generic blog-style sources.
Mark unreviewed preprints as preprints.

Phase 5. Synthesis

Cluster the shortlisted papers by problem, method, dataset, deployment setting, and evaluation style.
Surface trends, gaps, contradictions, and under-explored opportunities.
Run a contrarian pass: what would challenge the dominant conclusion?

Phase 6. Report Assembly

Use the stable report structure from references/report-modes.md.

Every final report must include:

search scope
source buckets by venue
shortlisted papers
synthesis of trends and gaps
recommended next reading or next experiments

Survey-Draft Workflow (Phases S1–S4)

When the user selects survey-draft, Phases 1–4 (Scope, Search Plan, Source Collection, Verification) execute as normal, then S1–S4 replace the original Phases 5–6.

Phase S1. Outline Building

Read references/modules/SURVEY_OUTLINE.md.

Extract a taxonomy from the verified literature.
Build the section skeleton as structured YAML.
Present the outline to the user for approval.
CHECKPOINT: do not enter S2 until the user approves the outline.

Phase S2. Evidence Pack Assembly

Read references/modules/SURVEY_EVIDENCE.md.

Assemble an evidence pack for every H3 subsection.
Lock the citation scope for each subsection.
Produce structured evidence bundles (no prose).

Phase S3. Section-by-Section Writing

Read references/modules/SURVEY_WRITER.md.

Draft each H3 independently, grounded in its evidence pack.
Run the self-check gate on every H3 (depth, citation scope, tone).
Produce one Markdown file per H2 section.

Phase S4. Merge and Quality Gate

Read references/modules/SURVEY_MERGE.md.

Merge all section drafts into a single document.
Run cross-section consistency checks.
Apply the final quality checklist.
If the user requested LaTeX output, delegate to latex-paper-en.

Deliverable Modes

Read references/report-modes.md and follow the selected mode exactly.

research-brief: short, decision-ready overview
literature-map: thematic map across methods and subproblems
venue-ranked survey: grouped by source quality and venue tier
research-gap memo: open problems, design space, and next-step opportunities
survey-draft: taxonomy-driven survey manuscript with outline-first writing and optional LaTeX export

Output Contract

State the locked intake choices and any defaults you applied before synthesis.
Distinguish verified evidence from inference in every deliverable.
Label preprints explicitly as preprints.
For non-survey modes, produce a structured report that includes: scope, source buckets, shortlisted papers, synthesis, and next reading or next experiments.
For survey-draft, keep stage outputs format-specific:
- S1: YAML outline only
- S2: evidence packs or tables only
- S3: section Markdown drafts grounded in the evidence packs
- S4: merged Markdown survey with cross-section consistency notes
If sources are spars
e, inaccessible, or off-scope, say so directly and report the exact fallback you used.

Module Router

Module	Use when	Primary action	Read next
`research`	User selects any of the 4 report modes	Execute Phase 1–6 workflow	`references/report-modes.md`
`survey-outline`	User selects survey-draft (Phase S1)	Build taxonomy and section skeleton	`references/modules/SURVEY_OUTLINE.md`
`survey-evidence`	Outline approved by user (Phase S2)	Assemble per-H3 evidence packs	`references/modules/SURVEY_EVIDENCE.md`
`survey-write`	Evidence packs complete (Phase S3)	Draft prose per H3	`references/modules/SURVEY_WRITER.md`
`survey-merge`	All sections complete (Phase S4)	Merge, quality gate, optional LaTeX handoff	`references/modules/SURVEY_MERGE.md`

Quality Bar

Read references/quality-checklist.md before finalizing.

Non-negotiable standards:

no unsupported claims
no venue-blind source mixing
no hiding contradictions
no synthesized report before intake questions are answered
no generic “latest research says” language without source-backed evidence

Error Handling

Zero results: Broaden keywords, relax the time window by one tier, and try adjacent venues. If still empty, report the negative result with the exact queries attempted.
Off-subdomain topic: State that the topic falls outside Industrial AI scope, suggest the closest supported subdomain, and ask the user whether to proceed or abort.
Inaccessible databases: Note which sources were unreachable, proceed with available sources, and flag the gap in the final report.
Too few papers (<5 shortlisted): Lower the time window threshold, include Tier 2/3 venues, and explicitly note the thin evidence base in the synthesis.

Reference Map

File	Phase	When to read
`references/question-flow.md`	Intake	Before asking the user any questions
`references/source-priority.md`	Search Plan	Before building venue buckets
`references/venue-map.md`	Search Plan	Before selecting specific venues
`references/report-modes.md`	Report Assembly	Before structuring the final output
`references/quality-checklist.md`	Report Assembly	Before finalizing the report
`references/modules/SURVEY_OUTLINE.md`	Survey S1	When building the survey outline
`references/modules/SURVEY_EVIDENCE.md`	Survey S2	When assembling evidence packs
`references/modules/SURVEY_WRITER.md`	Survey S3	When drafting survey sections
`references/modules/SURVEY_MERGE.md`	Survey S4	When merging and running quality gate
`references/SURVEY_WRITING_GUIDE.md`	Survey S1–S4	Survey writing philosophy reference

Examples

examples/predictive-maintenance.md
examples/intelligent-scheduling.md
examples/industrial-anomaly-detection.md
examples/survey-predictive-maintenance.md

Example Requests

“Research recent predictive maintenance papers from the last 3 years and return a research-brief.”
“Compare industrial anomaly detection papers across arXiv and IEEE automation venues, and show contradictions in evaluation setups.”
“Draft a survey on intelligent scheduling for researchers new to the subfield, but stop after the YAML outline for approval.”
“My topic is warehouse picking robotics. If that is outside scope, tell me the closest supported Industrial AI framing and proceed only with that.”