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Manufacturing · Industrial SoftwareProduct Plan · Agent Exploration Prototype
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Wiring Harness Drawing & Board Generation

Let AI Read Harness Drawings · Agent Exploration Prototype

Automotive harness drawings carry large volumes of non-standard, cross-platform, strongly-connected engineering information; going from drawing to production board diagram has long relied on manual reading, organizing, and drafting. We have built an "agent exploration pre-processing" prototype — it reads raw schematics directly, automatically identifies time candidates, geometric relations, label bounding boxes, and upstream/downstream connections, and outputs a structured Excel BOM. Understanding harness drawings is one of the key hard problems in industrial-drawing AI-ization.

Product Demo

Core Modules

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Raw Schematic (Input)

PRODUCT MODULE
Raw Schematic (Input)
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Agent Exploration UI (Processing)

PRODUCT MODULE
Agent Exploration UI (Processing)
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Excel BOM Extraction (Output)

PRODUCT MODULE
Excel BOM Extraction (Output)

Industry Pain Points

The harness industry has no shortage of CAD tools — what it lacks is a system that truly reads drawings, extracts BOMs, validates connections, and generates board diagrams.

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Non-Standard Drawings

Customer, legacy, and cross-platform drawings vary in expression, hard to process automatically.

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BOM Relies on Manual Work

Material info scattered across graphics, annotations, tables, and notes still needs manual organizing.

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Complex Connections

Relations among wire numbers, holes, terminals, and branch paths exceed simple OCR.

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Manual Board Drafting

Production board diagrams still largely depend on designers' manual understanding, layout, and correction.

If AI Could Read Harness Drawings

This is not efficiency optimization but a change in how work is done. Past: engineers read and draft. Future: AI understands drawings and auto-generates production results.

Today
Manual Reading
First understand components, connections, annotations, and materials.
Manual BOM
Extract material info item by item from drawings and tables.
Manual Error Check
Verify wire numbers, holes, terminals, branch paths.
Manual Board Drafting
Output process diagrams for the production floor.
Future
AI Drawing Understanding
Recognize components, relations, and engineering semantics.
Auto BOM
Output structured, traceable, reusable material lists.
Auto Connectivity Check
Find broken wires, wrong holes, conflicts, gaps, inconsistencies.
Auto Board Generation
Directly ready for production prep and system integration.

Why This Is Extremely Hard

This is not a drawing-recognition problem but an engineering-semantics understanding problem. Harness drawings simultaneously contain graphics, text, materials, connections, process rules, and production requirements.

1
Graphics Layer
Recognize lines, blocks, circles, leaders, tables, annotations, notes.
2
Object Layer
Judge which primitives form connectors, terminals, seals, wires, etc.
3
Semantics Layer
Associate text, numbers, holes, wire numbers with the right objects.
4
BOM Layer
Reconstruct a complete BOM from drawing objects, codes, specs, quantities.
5
Connectivity Layer
Judge whether terminals, holes, wire numbers, branch paths connect correctly.
6
Production Layer
Final output is not a report but a production-ready board diagram.

Where the Industry Stands Globally

Many tools exist, but the key layer "non-standard drawing → AI understanding → production result" is missing.

TypeCurrent CapabilityKey GapStatus
Int'l Harness SWDesign, BOM, mfg docs, assembly dataStrong in standardized design, weak at reverse-understanding non-standard drawingsDesign-strong
Domestic CAD/CAPPDrafting, process aid, BOM output, fixture boardStill highly dependent on engineer operation & judgmentAssist-strong
Engineering-Drawing AIOCR, table recognition, dimension extraction, local understandingDoesn't understand harness objects, connections, board craftGeneral-strong
Automation VendorsCutting, crimping, testing, assemblySolve production actions, not upstream drawing understandingMfg-strong
What We BuildDrawing understanding, BOM, connectivity check, board generationFill the missing AI-understanding layer between drawing and productionMissing Layer

Core Capabilities

Agent exploration pre-processing: auto-convert non-standard schematics into a structured Excel BOM — 9-step auto-exploration from data prep to entity comparison.

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Raw Drawing Input

Accept customer non-standard schematics (CONTOUR/EXP/VT/RD/PD) without prior normalization.

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Time-Candidate Filter

Filter 1s/3s/5s/10s time candidates from CONTOUR info via a filter.

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Geometry Tooling

compute_stats calculates bbox medium-2/0/8/15/30 and more geometric params for later association.

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Up/Downstream Validation

upstream_validation + xlh_bbox verify geometric and directory-structure consistency.

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Multi-Variant Adaptive

default / adaptive / adaptive_light / adaptive_loose auto-run to fit different drawings.

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Excel BOM Output

Output wire no., spec, wire type, color, left/right functional parts / terminals / seals — full fields.

Agent Exploration: From Schematic to Structured Data

The agent executes step by step — data prep → pre-refine → time candidates → geometry → association validation → directory gen → multi-variant → algorithm selection → application → verification — turning non-standard schematics into an Excel BOM.

Engineering Flow
1
Data Prep
Load CONTOUR/EXP/VT/RD/PD geometry & text as downstream input.
2
Pre-Refine
First-round coarse filtering and cropping of raw primitives, removing obvious noise.
3
Time-Candidate Filter
filter_candidates identifies 1s/3s/5s/10s time points in CONTOUR info.
4
Geometry Tooling
compute_stats calculates bbox + medium-2/+0/+8/+15/+30 and more geometric params.
5
Up/Downstream Validation
upstream_validation + xlh_bbox verify geometry vs directory-structure consistency.
6
Directory Generation
Generate .env / .gitignore / label types / label tones config (generate_config).
7
Multi-Variant Run
default / adaptive / adaptive_light / adaptive_loose variants adapt automatically.
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Algorithm Selection
select_text picks the best recognition algorithm by selection_score and coverage.
9
Application Layer
apply_result applies recognition results to structured data, landing fields.
10
Verify & Compare
verify_label_bbox + entity comparison, finally output the Excel BOM.
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Content Under Active Update

Product content has been published based on internal materials. The following areas are planned for further development:

Full engineering loop and product / role-restructuring explanation
Key breakthroughs and engineering-grade multimodal-understanding tech hub
Industry value and pilot / mass-production data
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