# WIA-AUTO-026: ZCICS Phase 1 - Foundation & Infrastructure

**Zero-Chemical Intelligent Cleaning System**  
**Phase 1 Specification**  
**Version:** 1.0  
**Status:** Foundation  
**Last Updated:** 2025-12-27

---

## 🎯 Phase Overview

Phase 1 establishes the foundational infrastructure and core systems required for Zero-Chemical Intelligent Cleaning System (ZCICS) operations. This phase focuses on water treatment systems, sensor networks, basic automation, data collection infrastructure, and initial AI training.

## 🏗️ Core Infrastructure

### 1.1 Water Treatment System Setup

#### Primary Components
- **Ionization Chambers**
  - Dual-chamber electrolysis units (alkaline pH 11-12, acidic pH 3-4)
  - Titanium electrodes with platinum coating
  - Adjustable power supply (0-30V DC, 0-50A)
  - Flow rate: 50-200 L/min
  
- **Pre-Treatment Systems**
  - Multi-media sediment filters (10-50 micron)
  - Activated carbon filters (chlorine/odor removal)
  - Water softening system (ion exchange)
  - pH conditioning (target: 6.5-8.5)

- **Storage Infrastructure**
  - Fresh water reservoir (5,000-10,000 L)
  - Alkaline water tank (2,000 L)
  - Acidic water tank (1,000 L)
  - Recycled water holding tank (3,000 L)

#### Performance Specifications
- Ionization capacity: 100-150 L/min
- pH stability: ±0.2 units
- Electrode efficiency: >85%
- System uptime: >95%

### 1.2 Sensor Network Deployment

#### Water Quality Sensors
- **pH Sensors** (±0.01 accuracy)
  - Alkaline line monitoring
  - Acidic line monitoring
  - Source water measurement
  - Discharge water testing

- **ORP Sensors** (±5 mV accuracy)
  - Oxidation-reduction potential tracking
  - Sanitization effectiveness verification

- **Conductivity Sensors** (±1% accuracy)
  - TDS monitoring
  - Mineral content tracking
  - Ionization efficiency indication

- **Temperature Sensors** (±0.5°C accuracy)
  - Water temperature optimization
  - System thermal management

#### Environmental Sensors
- Ambient temperature monitoring
- Humidity tracking
- Air quality measurement
- Weather data integration

#### Vehicle Detection Sensors
- Ultrasonic proximity sensors
- Infrared presence detection
- Load cell weight measurement
- Optical vehicle classification

### 1.3 Basic Automation Framework

#### Control Systems
- **PLC (Programmable Logic Controller)**
  - Modbus RTU/TCP communication
  - 100+ I/O points capacity
  - Real-time process control
  - Safety interlock management

- **HMI (Human-Machine Interface)**
  - 15-inch touchscreen displays
  - Real-time system visualization
  - Alarm management
  - Manual override controls

- **SCADA Integration**
  - Remote monitoring capability
  - Historical data logging
  - Trend analysis
  - Report generation

#### Automated Processes
- Water ionization activation/deactivation
- pH adjustment and regulation
- Flow rate control
- Temperature management
- Alarm triggering and notifications

### 1.4 Data Collection Infrastructure

#### Data Acquisition System
- **Sampling Rate:** 1 Hz (primary parameters)
- **Storage:** Local + cloud redundancy
- **Retention:** 5 years minimum
- **Format:** Time-series database (InfluxDB/TimescaleDB)

#### Collected Metrics
- Water quality parameters (pH, ORP, conductivity, temperature)
- Flow rates and volumes
- Energy consumption
- Equipment status
- Environmental conditions
- Vehicle processing metrics

#### Data Architecture
```
┌─────────────────┐
│   Field Sensors │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│   Edge Gateway  │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Local Database │◄───┐
└────────┬────────┘    │
         │             │
         ▼             │
┌─────────────────┐    │
│  Cloud Storage  ├────┘
└─────────────────┘
```

### 1.5 Initial AI Training

#### Training Data Collection
- **Volume:** 1,000+ cleaning cycles minimum
- **Diversity:** Multiple vehicle types, contamination levels
- **Labeling:** Manual quality assessment by operators
- **Validation:** 20% holdout dataset

#### AI Models - Phase 1
1. **Vehicle Classification**
   - Input: Visual/geometric data
   - Output: Vehicle category (sedan, SUV, truck, etc.)
   - Accuracy target: >90%

2. **Contamination Assessment**
   - Input: Visual inspection data
   - Output: Dirt level score (1-10 scale)
   - Correlation with cleaning time

3. **Water Quality Prediction**
   - Input: Source water parameters
   - Output: Ionization parameters for optimal pH
   - Accuracy target: ±0.1 pH units

4. **Basic Process Optimization**
   - Input: Vehicle type + contamination level
   - Output: Recommended cycle duration and intensities
   - Efficiency metric: Resource usage per vehicle

#### Training Infrastructure
- GPU-accelerated compute (NVIDIA T4 or equivalent)
- Machine learning framework (TensorFlow/PyTorch)
- MLOps pipeline (experiment tracking, versioning)
- Model deployment system

## 📊 Performance Metrics

### Key Performance Indicators (KPIs)

| Metric | Target | Measurement Frequency |
|--------|--------|----------------------|
| Water pH Stability | ±0.2 units | Continuous |
| Ionization Efficiency | >85% | Daily |
| Sensor Accuracy | >98% | Weekly |
| Data Collection Uptime | >99% | Continuous |
| AI Model Accuracy | >85% | Per training cycle |
| System Availability | >95% | Daily |

### Quality Assurance

- Daily calibration checks
- Weekly sensor verification
- Monthly performance audits
- Quarterly system optimization reviews

## 🔧 Installation Timeline

```
Week 1-2:  Site preparation, electrical/plumbing rough-in
Week 3-4:  Water treatment equipment installation
Week 5-6:  Sensor network deployment
Week 7-8:  Control system integration
Week 9-10: Data infrastructure setup
Week 11-12: Initial testing and calibration
Week 13-14: Operator training
Week 15-16: AI data collection and initial training
```

## 💰 Budget Allocation

- Water Treatment Systems: 40%
- Sensor Network: 20%
- Automation & Controls: 20%
- Data Infrastructure: 10%
- AI Development: 5%
- Contingency: 5%

## ✅ Completion Criteria

Phase 1 is considered complete when:

1. ✓ All water treatment equipment installed and operational
2. ✓ Sensor network deployed with >98% accuracy
3. ✓ Basic automation functioning reliably
4. ✓ Data collection system recording all required metrics
5. ✓ Initial AI models trained with >85% accuracy
6. ✓ System processes 100+ vehicles successfully
7. ✓ Operators trained and certified
8. ✓ Safety systems tested and verified

## 🚀 Transition to Phase 2

Upon Phase 1 completion, systems should be ready for:
- AI-driven cleaning protocol development
- Real-time quality monitoring implementation
- Adaptive water recycling activation
- Advanced performance optimization

---

**弘益人間 (홍익인간) · Benefit All Humanity**

© 2025 SmileStory Inc. / WIA-AUTO-026

## P.1 Data Format Cross-References

This Phase defines the canonical data types referenced by the API surface (Phase 2),
the wire protocol (Phase 3), and integration scenarios (Phase 4). Implementations
MUST round-trip every canonical type through serialization and deserialization
without loss of precision or semantics.

### P.1.1 Canonical Encoding Rules

1. UTF-8 is the required character encoding for textual fields.
2. Numeric fields use IEEE 754 binary64 unless explicitly marked as fixed-point.
3. Timestamps use RFC 3339 with timezone offset; durations use ISO 8601.
4. UUIDs follow RFC 4122 v4 unless deterministic IDs are required.
5. Binary payloads are encoded as Base64 (RFC 4648 §4) in JSON contexts and as
   raw octet strings in Protocol Buffers / CBOR contexts.

### P.1.2 Schema Evolution

Schema changes follow these compatibility classes:

| Class | Allowed Changes | Wire-Compat |
|-------|-----------------|-------------|
| Patch | Doc fixes, examples, validator tightening within existing range | Forward & backward |
| Minor | New optional fields, new enum values with default fallback        | Forward |
| Major | Field rename, type change, removal, semantics change              | None |

### P.1.3 Validation Order

Validators MUST apply checks in this order: (1) syntactic well-formedness,
(2) schema conformance, (3) cross-field invariants, (4) external referential
integrity, (5) policy / authorization. A failure short-circuits subsequent
checks; the response message identifies the first failing rule by ID.


---

## Appendix · Common WIA Standard Provisions

> The following provisions apply to every WIA standard and are kept in sync
> across the WIA-Standards corpus. Standard-specific deviations, where they
> exist, are listed in the standard's normative body.

### A. Conformance & Compliance

#### A.1 Conformance Levels

WIA-Standards defines four conformance levels:

| Level | Required | Description |
|-------|----------|-------------|
| L1 — Format | Phase 1 | Implementation produces and consumes the canonical data format losslessly |
| L2 — Interface | Phase 1 + 2 | Implementation exposes the API surface with required behaviour |
| L3 — Protocol | Phase 1 + 2 + 3 | Implementation interoperates over at least one normative transport binding |
| L4 — Integration | All Phases | Implementation passes the conformance test suite end-to-end in a production-shaped deployment |

Conformance claims MUST cite the level and the version of the standard
against which the claim is made (e.g. "L3 conformant against v1.0").

#### A.2 Compliance Verification

The conformance test suite is published alongside this standard at
`/cli/conformance/` and `/api/conformance/`. Implementations claiming
L2 or higher MUST publish their test report. Independent re-tests are
encouraged; the WIA Working Group accepts third-party verification reports
under the policy in §E.

### B. Security Considerations

#### B.1 Threat Model

Implementers SHOULD apply STRIDE analysis covering: spoofing of identity,
tampering with messages or stored state, repudiation of operations,
information disclosure, denial of service, and elevation of privilege.

| Threat | Default Control | Where Strengthened |
|--------|-----------------|--------------------|
| Spoofing | Mutual TLS or signed tokens | Phase 3 §P.3 |
| Tampering | TLS in transit, AEAD at rest | Phase 1 §P.1 |
| Repudiation | Append-only audit log with notarization | Phase 4 §P.4 |
| Disclosure | Field-level encryption for PII / secrets | Phase 1 §P.1 |
| DoS | Rate limit per principal & global circuit breaker | Phase 2 §P.2 |
| EoP | Least-privilege RBAC + scoped tokens | Phase 2 §P.2 |

#### B.2 Cryptographic Suites

Mandatory: TLS 1.3 with AEAD ciphers (AES-128-GCM, AES-256-GCM,
CHACHA20-POLY1305). Forbidden: TLS 1.0, TLS 1.1, RC4, MD5, SHA-1 for
signatures, RSA below 2048 bits, ECDSA on curves smaller than P-256.

Post-quantum migration: implementations SHOULD adopt hybrid key
exchange combining a classical primitive with ML-KEM (FIPS 203) once a
profile is published; signature migration to ML-DSA (FIPS 204) is
expected within the L4 conformance window of v2.0.

#### B.3 Audit Requirements

L3 and L4 implementations MUST log: (a) every authentication decision,
(b) every authorization decision, (c) every state-changing operation,
(d) every export of data outside its sovereignty boundary. Logs are
write-once for at least 1 year and 90 days indexed for incident search.

### C. Versioning & Lifecycle

Versions follow Semantic Versioning 2.0.0 (MAJOR.MINOR.PATCH).

| Phase | Duration | Conformance Status |
|-------|---------:|-------------------|
| Draft | until ratification | Non-binding |
| Active | indefinite | Binding for new deployments |
| Maintenance | 24 months from successor's Active date | Binding for existing deployments |
| Retired | indefinite | Non-binding; conformance claims rescinded |

Deprecation MUST be announced at least one minor version before a feature
is removed in a major version.

### D. Internationalization & Accessibility

Implementations SHOULD support locale negotiation via the
`Accept-Language` header (RFC 4647). Date, time, number, and currency
formatting follow CLDR. User-facing surfaces MUST satisfy WCAG 2.1 AA at
minimum and SHOULD progress towards WCAG 2.2 AA. Right-to-left scripts
(Arabic, Hebrew, Persian, Urdu) and East-Asian wide characters MUST be
laid out correctly without line-breaking heuristics that split graphemes.

### E. Governance & IP Policy

This standard is maintained by the WIA Working Group under the WIA
governance charter. Editorial changes are merged via pull request. Normative
changes require working-group consensus and a 30-day public review.
Contributions are accepted under the Apache License 2.0 with explicit
patent grant. Members participate under the WIA Patent Policy,
which requires royalty-free licensing of any essential claim necessary
to implement a normative requirement.

### F. Normative References

The following references are normative; implementations MUST satisfy
the cited clauses:

- ISO/IEC 27001:2022 — Information security management systems
- ISO/IEC 27017:2015 — Cloud-services security controls
- ISO/IEC 27701:2019 — Privacy information management
- ISO/IEC 19790:2012 — Security requirements for cryptographic modules
- ISO 8601-1:2019 — Date and time representation
- IETF RFC 8446 — TLS 1.3
- IETF RFC 7519 — JSON Web Token
- IETF RFC 6749 — OAuth 2.0
- IETF RFC 9110 — HTTP Semantics
- IETF RFC 9112 — HTTP/1.1 message syntax
- IETF RFC 9113 — HTTP/2
- IETF RFC 9114 — HTTP/3
- IETF RFC 9000 — QUIC transport
- IETF RFC 4122 — UUID URN namespace
- IETF RFC 3339 — Date and time on the Internet
- IETF RFC 6838 — Media-type specifications and registration
- W3C TraceContext — Distributed tracing context
- W3C WCAG 2.1 — Accessibility guidelines
- FIPS PUB 197 — AES
- FIPS PUB 180-4 — SHA-2 family
- FIPS PUB 203 — ML-KEM (post-quantum KEM)
- FIPS PUB 204 — ML-DSA (post-quantum signature)

### G. Glossary

| Term | Definition |
|------|------------|
| Conformance | The state of satisfying every normative requirement at a given level |
| Implementation | A software, hardware, or composite artefact that claims conformance |
| Principal | The authenticated entity bound to a security context |
| Subject | The resource or person to which an operation applies |
| Sovereignty Boundary | The legal / regulatory perimeter outside of which data export is restricted |

---

*This Appendix is authored by the WIA Standards Working Group and is kept
in lockstep across Phases 1–4 of zcics so that conformance claims at any
Phase remain unambiguous.*