AS 360 · Data Acquisition Layer 4

Keep your system.
We score it.

Mobaro · CommandCentr · 24/7 Software · State Inspection Databases · Ellis EAVS

AS 360 connects to platforms your park already operates. Your existing operational data — inspections, downtime records, incident reports, compliance filings — is field-normalized to our 0–100 stream model and fed directly into RIDE, SPLASH, PLAY, and MIDWAY scores. No rip-and-replace. No data migration. No new workflows.

Data Flow Architecture

External platforms in.
Scored intelligence out.

Every third-party source passes through the same normalization engine — field mapping, schema translation, and 0–100 conversion — before entering the scoring pipeline.

01Source PlatformsYour existing operational data
Mobaro
Inspections, downtime, maintenance records
API
CommandCentr
Ride checks, throughput, incidents
API
24/7 Software
Work orders, guard tours, maintenance
API / Export
State Databases
Public inspection records, compliance filings
Scrape / FOIA
Ellis EAVS
Aquatic AI alerts, drowning detection events
Webhook
02Cloudflare WorkersPer-platform connectors
mobaro-connector.workers.dev
cmdcentr-connector.workers.dev
sw24-connector.workers.dev
state-ingest.workers.dev
ellis-eavs.workers.dev
03Normalization EngineAS 360 Intelligence Hub
Schema mapping — source fields to AS 360 stream IDs
Type coercion — binary → pass rates, severity → penalty
0–100 conversion — linear, penalty-decay, or range
Pillar assignment — each stream to R·I·D·E or S·P·L·A·S·H or M·I·D·W·A·Y
Provenance tagging — source, version, confidence
Normalized_Streams
External_Ingestion_Log
RIDE_Score_Inputs
SPLASH_Score_Inputs
PLAY_Score_Inputs
MIDWAY_Score_Inputs
04Score OutputSame model regardless of source
RIDE
R · I · D · E
SPLASH
S · P · L · A · S · H
PLAY
P · L · A · Y
MIDWAY
M · I · D · W · A · Y
Data Connectors

Six sources.
One normalization pipeline.

Each connector translates the source platform's schema to the AS 360 stream model before it touches the scoring engine.

Mobaro
Planned
First Option Software · Orlando, FL
Ride safety management — daily checklists, maintenance schedules, downtime records. Maps to Readiness and Integrity pillars.
Data ingested
Daily ride inspection pass/fail history
Planned and corrective maintenance work orders
Downtime events — duration, cause, resolution
RideOps cycle counts and queue data
Field mapping
checklist.pass_rateReadiness.R
downtime.duration_hrsIntegrity.I
CommandCentr
Planned
First Option Software · Theme Park Ops
Ride operations — checks, throughput, operator assignments, incidents. Structured data with minimal transformation needed.
Data ingested
Ride pre-opening checks and pass/fail
Throughput — guests per hour, seat utilization
Operator assignments and training certs
Downtime and incident reports
Field mapping
ride_check.resultReadiness.R
throughput.gphDelivery.D
24/7 Software
Planned
Operations Management · Parks & Attractions
Work orders, guard tours, maintenance scheduling, incident management. Feeds Readiness and Environment pillars.
Data ingested
Work orders — scheduled, completed, overdue
Guard tour scan timestamps
Preventive maintenance completion rates
Incident management response times
Field mapping
work_order.overdue_countReadiness.R penalty
guard_tour.completionEnvironment.E
State Inspection Records
In Scope
FL DACS · CA DOSH · TX DI · OH AG · NJ DCA
Public ride inspection records, violation histories, compliance filings. Independent external validation — operator-independent and legally obtained.
Data ingested
Annual state inspection outcomes
Violation records — type, severity, resolution
Incident reports filed with agencies
Permit status and renewal compliance
Field mapping
inspection.outcomeIntegrity.I
violation.severityIntegrity.I penalty
Ellis EAVS
In Scope
Ellis International · Aquatic Vigilance System
AI-powered aquatic surveillance — thermal and HD cameras for drowning prevention. Alert events feed SPLASH Score Hazard and Lifeguard pillars.
Data ingested
Distress detection alerts — type, zone, response time
Overcrowding flags — bather load thresholds
Lifeguard response time per alert
Camera coverage gap events
Field mapping
alert.response_msLifeguard.L
alert.overcrowdHazard.H
Custom Connector
On Request
Any platform with API or data export
Regional CMMS, proprietary inspection systems, POS with ride data — any structured source can be normalized to the stream model.
What we need
API documentation or sample export
Field list with data types
Update frequency
Authentication method
Build timeline
Simple API3–5 days
CSV ingestion1–2 days
The Normalization Model

Every source maps
to the same scale.

Raw data arrives in inconsistent shapes. The normalization engine converts every input to 0–100 before it touches scoring. A 72 from Mobaro means the same as a 72 from a native AS 360 audit.

01
Schema Mapping
Each connector declares a field map — translating source field names to AS 360 stream identifiers.
// Mobaro field map
"mobaro.checklist.pass_rate"
"as360.stream.R.pre_open_compliance"
02
Type Coercion
Binary pass/fail → rolling pass rate. Severity codes → weighted penalties. Timestamp gaps → response time metrics.
// Binary → pass rate
[pass, pass, fail, pass, pass]
80.0 (4/5 pass rate)
03
0–100 Conversion
Pass rates rescale linearly. Incidents use penalty-decay. Continuous feeds use range normalization.
// Penalty-decay
incidents_per_1000: 2.4
score: 100 × e^(-0.3 × 2.4) = 48.7
04
Pillar Assignment
Each normalized stream routes to its pillar — R·I·D·E for RIDE, S·P·L·A·S·H for SPLASH, M·I·D·W·A·Y for MIDWAY. Pillar score = weighted average of assigned streams.
05
Source Disclosure
Every score carries data provenance — which streams from which sources, update cadence, confidence level. Full transparency.
RIDE Score · Live Normalization View
Pre-open check
0
Maintenance PM
0
State inspection
0
Incident rate
0
Work orders
0
SOP compliance
0
Guest feedback
0
Throughput
0
Composite RIDE Score
STRONG · External + Native
0
Mobaro 35%
State 15%
CmdCentr 20%
Native 30%
Design Principles

Why this architecture
earns trust.

🔍
Full Provenance
Every score carries its data lineage. An underwriter sees 35% Mobaro, 25% state data, 40% native audits. Complete audit trail.
⚖️
No Single Source Controls
No single platform can dominate the composite. Gaming one source gets flagged when others disagree. Triangulation is the architecture.
🔒
Data Stays Yours
Connector access authorized by operator. AS 360 processes on behalf of subscriber. Raw data not stored — only normalized values and provenance.
📡
Source-Agnostic Scoring
After normalization, all inputs are structurally identical. A park with Mobaro scores on day one. Same model. Same output.
🏗️
No Rip and Replace
Asking parks to abandon existing tools kills deals. AS 360 sits on top. Connector library grows. Moat deepens.
📈
Better Data Over Time
30 days = baseline. 90 days = trend. 12 months = seasonal patterns. Score improves because data depth increases.
For Specialty Lines Carriers & MGAs

External data means
the score can't be gamed.

When a score is informed by state records, Mobaro maintenance data, and Ellis aquatic alerts — alongside native audits and OpsScan behavioral data — an operator cannot improve by providing favorable self-reported inputs alone. The aggregation layer is the integrity layer.

Already running
Mobaro or CommandCentr?

Your existing operational data is already generating score inputs. Connect your platform and historical records start scoring on day one.