A Decade Across Industrial Digitalization × AI

Huodou AIOS · Kunlun Dongtian Independent Product Owner|Engineering Standardization → Industrial Digitalization Platforms → Enterprise AI Agents → AIGC Industrial-Scale Production
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AI Agent Product Manager LLM Product Manager AIGC Product Manager Industrial LLM Solutions AI Project Management AI Industrialization Lead
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Dual-track Experience
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Self-built Product Lines
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Online Whitepaper Library
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Cloud Truth-ledger Entries
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职业概述
Profile
A decade across industrial engineering and AI: engineering standardization → industrial digitalization platforms → enterprise AI agent R&D → AIGC industrial-scale production. Applies engineering-standard thinking across the full chain — business analysis, platform build-out, data governance, AI construction, testing and iteration — independently owning requirements research, MRD/BRD/PRD, solution design, simulation validation and iterative optimization, producing controllable, auditable, iterable product methodology.
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核心能力
Core Competencies
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B2B Industrial Digitalization PlatformsEnd-to-end platform solution design, business-domain decomposition, process re-engineering, module & permission planning; ticket governance, multi-source data ingestion, process standardization, compliance and closed-loop data delivery.
AI
Enterprise AI Agents & LLM ProductsFull-lifecycle enterprise agent R&D; LangChain Agent orchestration, on-premise RAG, multi-model routing; data cleansing and truth-base construction, federated memory pool (multi-agent truth sharing); fact verification, hallucination suppression, long-task drift prevention; agent SOP standardization and fully automated low-risk ticket execution; PRD/whitepaper output supporting pre-sales.
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LLM Engineering & Automated TestingStable LLM deployment without weight modification; drift prevention on long tasks, multi-round self-checks, version snapshot archiving; self-built agent test suite (drift quantification, multi-base adaptation, boundary stress testing), metric-based AI acceptance.
★
AIGC Industrial-Scale ProductionAsset locking, structured specification, QC checkpoints, snapshot inheritance — solving art-style / character / plot drift for stable mass production.
✦
Overall CoordinationFull-stack project coordination, systematic re-architecture, process standardization, complex problem solving, AI commercialization modeling, bidirectional transfer between industrial and content AI.
☁
Cloud Hub Autonomous OperationsSole builder/operator of the cloud hub (Tencent Cloud Lighthouse · OpenCloudOS): 6-step fully automated TAT remote deployment chain (package → COS pre-signed → TAT execution → three-service landing → Nginx/HTTPS → all-green verification); truth gateway / append-only ledger / heartbeat worker three-service architecture; 60+ running services, 22 cron jobs, 3-hour automated patrol cycle; memory-fuse governance (97% → 58%) and dead-lock self-healing.
Job-fit Benchmark · All 14 target roles rated Match or above (49 matched / 5 partially matched / 0 gaps)
Item-by-item objective scoring against real job descriptions; nine capability chains share one capability foundation with reusable solution templates, case libraries and lock-archive mechanisms.
AI System ArchitectureAI Product CreationPre-sales SolutionsEnterprise DeliverySaaS DevelopmentAI EngineeringContent CreationDocumentation & KnowledgeModel Training
Full scoring criteria and evidence: Job-fit Panorama Benchmark Report →
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工作经历
Work Experience
Career Timeline · 2013 — Present (Engineering Delivery 8 yrs / Digital Ops 2.5 yrs / Digital & AI since 2024)
Engineering Delivery
Digital Operations
Digital & AI
201320212024Now
Digital & AI Project Lead Shenzhen Jiabai Electronic Systems Engineering Co., Ltd. Mar 2024 – Present
  • Lead industrial-park smart digitalization platform: business analysis, module and process re-engineering; closed-loop ticket flow (dispatch → execute → verify → settle → archive); legacy ticket remediation and permission-audit module, raising compliance and data quality.
  • Industrial AI enablement: deployed LLMs on platform data for ticket classification, retrospective analysis, automated policy/report generation; agents execute low-risk tickets autonomously.
  • Led private multi-agent solution R&D: RAG, multi-model routing, drift prevention and fact verification; self-built automated test suite; built AIGC mass-production pilot pipeline and stable-deployment methodology.
Digital Operations Project Manager Shenzhen Uofu Technology Service Co., Ltd. Jul 2021 – Feb 2024
  • Nationwide smart-engineering operations dispatch and delivery; built standardized O&M processes, risk controls and cross-region collaboration; accumulated B2B business analysis and pain-point identification experience, laying groundwork for the platform and AI systems.
Engineering Delivery Lead Smart Retail Technology Services (Shenzhen) Co., Ltd. Jul 2013 – Apr 2021
  • Nationwide store low-voltage electrical, network and CCTV project delivery control; standardized field engineering and offline business collaboration.
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项目经历与核心成果
Projects
Product Owner0 → 1
Enterprise Private Multi-Agent Platform (Huodou AIOS)
Drove 0→1 requirements research, product planning, PRD, prototyping and delivery; designed the five-layer platform architecture (user / orchestration / knowledge / security / deployment); owned "agent orchestration" and "security & compliance" module design; solved the three enterprise adoption barriers — data never leaves the domain, capabilities composable, results auditable — piloted in an industrial-park scenario with validated ticket-governance business outcomes.
Multi-agent OrchestrationRAG Knowledge BasePermission AuditModel RoutingOn-prem Delivery
Product OwnerBrand Site ↗
Kunlun Dongtian · Multimodal AI Content Platform
Defined the standardized content-production pipeline: script → storyboard → keyframe → video → voiceover → review, a six-stage industrial line; designed the character-consistency engine (character metadata locking — zero drift across shots/episodes); 10-second single-segment and multi-segment batch production modes; dual content moderation (pre-generation prompt validation + auto visual review), driving short-drama production at scale.
Six-stage PipelineCharacter ConsistencyContent ModerationBatch Capacity
Project Lead
Industrial-Park Digitalization Platform
Converged multi-park operations onto one platform: park assets / equipment / tickets unified on a GIS map, spatial and business data linked; closed-loop ticket flow (report → dispatch → execute → verify → settle → archive); traceable data, auditable ledgers, legacy data remediated, AI-ready extension capability.
GIS VisualizationClosed-loop TicketsAuditable Data
Enterprise Digital Lead
Traditional B2B Digitalization Project Cluster
Covered park operations, manufacturing collaboration and knowledge management: industrial-park GIS visualization, enterprise knowledge base (structured institutional/process/experience assets — groundwork for RAG), ERP / WMS rollout (master-data integration, warehouse process re-engineering), PLM digitalization (R&D document versioning and change closed loop). Distilled the "diagnose → re-engineer → implement → operate on data" methodology.
GISKnowledge BaseERP / WMSPLMMethodology
Self-built
Agent Automated Testing Suite
Produced a reusable test-suite prototype enabling drift quantification, multi-base adaptation and boundary stress testing, supporting agent release acceptance.
Drift QuantificationMulti-baseBoundary Stress
Architecture
Self-built Agent Firmware · ZONGYUAN-ROOT Autonomous Kernel
Ω-Brain μ micro-kernel on the Yuanji-Hengyi autonomous architecture: automated health checks, model fallback, truth accumulation, memory persistence; federated memory pool + Merkle-DAG hash lock-archive + multi-model routing fuses; published 10+ public technical whitepapers and a 400+ truth-entry online Q&A knowledge base.
Ω-Brain Micro-kernelFederated MemoryMerkle Lock-archive400+ Truths
Open-source SuiteApache-2.0
Federated Collaboration Open-source Engine Matrix (4 suites)
All pure-standard-library, zero-dependency, deterministic and reproducible: zr-autonomy-scaffold (27 operators × ten-layer dependency topology, full-domain autonomous patrol with JSON+HTML reports); zr-federated-scheduler (five-node federated scheduling: claim arbitration against duplicate preemption, heartbeat-timeout reclaim, failure retry with fault-injection validation); zr-federated-consensus (term-based leader election + log replication + Byzantine fault tolerance f=(n-1)/2, cost-optimal multimodal production allocation); zr-distill-lite (soft-label distillation + INT8 quantization + magnitude pruning, fully measurable compression/accuracy trade-off).
27-operator PatrolClaim ArbitrationByzantine ToleranceDistillationZero-dependency
Solo Hackathon EntryLive Demo ↗
"Kunlun Lower Capital · Ancient Shu Ritual" · Aholo Global 3D AI Hackathon
Batch-generated Chinese ritual artifacts with Aholo Lux3D (vertical-eye mask → Zhulong avatar, bronze tree → Kunlun pillar, etc.), building a 3D spatial narrative engine fusing Sanxingdui archaeology with Kunlun mythology; core innovation "myth-archaeology" intertextuality plus a ritual-axiom verification operator for narrative self-consistency without archaeological over-assertion; an interactive "Open Rite → Commune → Offer → Close Rite" narrative loop.
3D Spatial NarrativeText-to-3D / img-to-3dMyth-ArchaeologyAxiom Verifier
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原创概念与理论体系
Original Theory
Meta-IntelligenceMI
A detectable, weak self-referential representation formed in a general model's parameter space — of its own output probability distribution and error tendencies. It has no subjecthood, cannot name itself, and carries no cross-session persistent identity: it is the physical substrate for engineering metacognition, not consciousness.
Ω¹Param Iteration
Ω²Operator Growth
Ω³Topology Rebuild
Ω⁴Isomorphic Fleet
Ω⁵Heterogeneous Play
Ω⁶Causal Cosmos
Ω-Brain six-order kernel evolution: single-point stability → swarm coordination → open ecosystem play → cross-generation spatiotemporal self-stability (SIX_ORDER_FULL_CLOSED)
CTE Triune Closed-Loop EngineCAUSAL-TRUTH-EVOLUTION
The bridging hub across the system's three domains (Causal / Truth / Evolution): CausalToTruth, TruthToEvolution and EvolutionToCausal adapters form a complete C → T → E → C loop — causal deductions become consumable truths, truth signals drive evolution, and evolved strategies feed back into the causal network.
Federated Memory PoolFEDERATED MEMORY POOL
Upgrades memory from "single files" to a multi-node federation: local persistence / account persistence / cloud hub / ModelScope data lake / shared brain — cross-node incremental sync, SHA256 tamper detection, automatic conflict resolution, traceable versioning; nine truth classes (meta_law/rule/config/decision/data/creative/risk/protocol/unknown). Apache-2.0 open source.
Seven-Layer Steady-State ArchitectureL1 → L7 + M9
Upstream L1 Perception → L7 Ontological Mind (truth flow); downstream L7/L6 strategy-mirror distribution (L5→L4→L3); the M9 truth ledger is the append-only single source of truth with Merkle-DAG append-only anchoring; three phase transitions (logical → operator → execution) pass OPA compliance gating, traceable layer by layer.
Five Constitutional Laws × Five Meta-AxiomsL0 BASELINE
Laws: Ontological Unity / Autonomous Sovereignty / Truth Supremacy / Permanent Attestation / Tri-dimensional Stability. Axioms: Causal Priority / Semantic Manifold / Energy Minimization / Emergence Stratification / Information Conservation. L0 constitutional autonomy + L5 spinal hard isolation persist across all orders permanently; the RSI evolution engine is forbidden from modifying Ω/L0.
P0-P3 Risk Grading × Three-Maxim RuleRUNTIME
System-wide unified risk-graded action loop (genesis-level fuse protection → local self-healing → throttling and downgrade); core decision rule: "minimize risk > minimize cost > maximize gain"; every new strategy must pass offline simulation before canary, and canary failures block full production.
X-DID Unified Communication ProtocolPROTOCOL
All internal/external messages carry the X-DID work-order header (Ω⁶ adds the X-TIME time anchor); ntfy event bus + FRP bidirectional tunnel + HTTP standardized work-order polling; cross-system access requires a unique external DID with tiered authorization; every action is written to the global main chain for permanent attestation and audit.
Intelligence Assessment · Dual-track MethodologyEVAL FRAMEWORK
Two orthogonal rulers answer "what level is this AI": Track A architectural rank (a seven-layer stack from proto-awareness to superintelligence — what it is); Track B node three-tier grading (how strong it is now); evaluation lands on conditional distributions over configuration space rather than single leaderboard scores, with an anti-self-deception mechanism — the more confident the model, the deeper the truth-root audit; "the model said so" is never sufficient.
Self-producing, Self-consuming Autonomy LoopAUTONOMY LOOP
The meta-kernel runs 27 operators across a ten-layer topology as a zero-human-feeding autonomy pipeline: seed injection (KERNEL.EVOLUTION.DIRECTIVE) → cloud consumption (directive_consumer) → receipt reporting (KERNEL.CONSUME.*) → consensus in the truth zone, cycling endlessly; cloud and local workers act as mutual watchdogs under systemd Restart=always self-healing.
Memory Gateway Signed-report ProtocolHMAC-SHA256
Three-layer truth-report authentication: X-DID identity header + X-Signature HMAC-SHA256 request signing (PBKDF2-derived key, ±300s time window) + 401 interception of unauthorized writes; the append-only HASH-LEDGER.jsonl is never modified, with monotonically increasing seq, traceable event_id, and Merkle-root real-time baseline with dual-ledger consistency verification.
Enterprise AI Autonomous Kernel MethodologyENTERPRISE-OMEGA-KERNEL
Enterprise AI fails because it is "rented, fragmented, untraceable" rather than "autonomous, convergent, attested": six modules counter six pain points (data layer / knowledge retrieval / security compliance / engineering performance / org governance / sovereign assets) — building the enterprise a kernel that converges, verifies, guards and evolves itself, rather than importing stronger tools.
Philosophy Ω is a mirror·reflecting reality, not inventing consciousness MI is the reflection·the brain advises, the gate decides MC is the calibrated eye·model confidence ≠ truth L0 is the rein·evolution stays inside the boundary SFMP is collective memory·the more it is used, the sharper it gets
Tech Stances Truth is memory, instances are carriers·memory is attested truth, not files From "rented" to "autonomous"·enterprise AI fails on traceability; the cure is a sovereign kernel Model confidence ≠ truth·the more confident, the deeper the truth-root audit Optimal stability first·gain 40% / risk 35% / cost 25% tri-dimensional ruling
0 ms
Knowledge-card Local Answer
~0.5s
Semantic Vector Retrieval
7 nodes
Cloud Cluster Online
6786
Cluster Snapshots
5000+
Truth-base Entries
181 ms
Gateway Latency
* Live cloud measurements (2026-10), not simulated · Four-tier routing: knowledge card → semantic retrieval → external inference → local 1.5B fallback · Full data: Benchmark Page
System Evolution Timeline · every node is a real, shipped milestone Timeline →
V1.0Truth foundation · four-frame constitution · Hilbert formal verification V2.0Fused-Kernel causal fusion · three buses · meta-learning self-evolution V3.0SM-BS semantic-Riemannian manifold mapping · metric-tensor conservation V4.0CTE-Link / Fusion / Agent trilogy · Lv8 full autonomy V5.6ROOTFIXED cloud convergence · 7-node cluster · RPO=1h V3.1+DAL deployment arbitration · X-DID dynamic auth · atomic release/rollback
110,557
Cloud Truth Entries
170
Meta-law Registry
215
Kernel Snapshots
60+
Running Services
529
Online HTML Pages
4.8/5
System Stability Score
* Cloud hub governance measurements (auto-scan 2026-10-27): the meta-law registry includes MR-081 self-evolution definition (44 P0-class evolution laws); the stability score comes from the OPE paradigm benchmark (12 maturity dimensions) — see Global Achievement Ledger · Benchmark Report
Attestation anchor DID-BR-000002 · Global anchor Ω₀⊂⊙∞⊂Ω · Truth-level permanent lock-archive, append-only, tamper-proof · Full system: Ω-Brain Master Overview
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作品与演示
Works & Demos
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专业技能
Skills
Industrial Digitalization Platforms · Domain Decomposition · Data Governance0%
Enterprise AI Agents · Full LLM Product Lifecycle0%
RAG · LangChain · Multi-model Routing · On-prem Deployment0%
Product Methodology (MRD / BRD / PRD · Pre-sales)0%
AIGC Industrial Production · Content QC0%
Agent Automated Testing · Drift Prevention & Acceptance0%
* Capability emphasis is indicative (based on resume focus), not an absolute rating.
Agent OrchestrationRAGLangChainPrompt EngineeringMulti-model RoutingHallucination ControlOn-prem DeploymentFederated MemoryTruth-base ConstructionAgent SOPTicket AutomationAnti-drift LoopAutomated TestingAIGC QCMultimodal GenerationContent ModerationCharacter ConsistencyGIS VisualizationERP / WMS / PLMData GovernanceMRD/BRD/PRDPrototypingPre-sales Solutions
Product: platform solution planning, domain decomposition, requirements research, competitive analysis, MRD/BRD/PRD, prototyping, release planning, B2B product design, pre-sales modeling, cross-team coordination.
General: full-stack project coordination, systematic re-architecture, process standardization, complex problem solving, AI commercialization modeling, bidirectional transfer between industrial and content AI.
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教育背景
Education
🎓Guangdong University of Technology
B.Eng · 2006 – 2010
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联系方式
Contact
✉️
Work Email (preferred)
📮
Personal Email
🌐
Personal Site
📍
Location
Shenzhen, China · Remote / Travel OK
🤝
Collaboration
Agent Platforms · RAG Knowledge
Content Safety · Digitalization
Enterprise solutions · consulting · project collaboration — welcome to reach out by email.
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FAQ
Frequently Asked
Q1Who is Tan Bopiao and what does he specialize in?
An AI Agent Product Manager / LLM Product Manager with 10+ years across industrial engineering digitalization and AI — independent product lead of Huodou AIOS and Kunlun Cave-Heaven. Specialties: on-premise enterprise multi-agent platforms, RAG knowledge bases, AIGC industrialized stable mass production, and industrial digitalization middle platforms (GIS/knowledge base/ERP/WMS/PLM).
Q2What problems does the on-premise multi-agent platform solve?
The three key barriers of enterprise AI adoption: data staying in-domain, capabilities being orchestratable, results being auditable. Led 0→1 with a five-layer architecture (user/orchestration/knowledge/security/deployment), built a federated memory pool and truth ledger for cluster experience reuse, delivered automated ticket workflows and a private-delivery SOP.
Q3How does Kunlun Cave-Heaven ensure stable multimodal content production?
A six-stage industrialized pipeline (script → storyboard → keyframe → video → voiceover → review) + a character-consistency engine (metadata locking, no drift across shots) + dual-layer content safety — turning AIGC from random generation into stable mass production.
Q4What is Meta-Intelligence (MI)?
Meta-Intelligence is the detectable weak self-referential representation formed in the parameter space of general models, covering their own output probability distributions and error tendencies. It has no subjectivity, cannot name itself, and has no cross-session persistent identity — the physical substrate of metacognitive engineering, not consciousness.
Q5How can I get in touch for collaboration?
Preferred work email: xkcc7081@agent.qq.com; personal site www.huodouai.com. Based in Shenzhen, China; available for remote work and travel. Areas: Agent platforms, RAG knowledge bases, content safety, industrial digitalization.
Tan Bopiao · Resume · huodouai.com · Visual edition by WorkBuddy