DCRIN
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AINNA Persekitaran Intelligence

Dynamic Canopy
Penyelidikan & Intelligence Rangkaian

Persistent environmental observation for long-duration research — designed to reduce repeated human presence while keeping scientific data continuously visible.

CANOPY NODE 07LANGSUNG DEMO
27.4°C
Humidity86%
Soil63%
AcousticHigh
Tatal through the canopy
01 — OBSERVE MORE. DISTURB LESS.

Penyelidikan reka bentuk infrastruktured around the ecosystem.

DCRIN turns a research site into a persistent observation environment. Sensors, data acquisition and intelligence layers can continue collecting evidence long after the research team leaves the site.

Long-duration monitoringConfigurable sensorsRemote visibility
Light · Wind · Leaf Wetness
Temperature · Humidity · Acoustic
Rainfall · Microclimate · Wildlife Activity
Moisture · Conductivity · pH · Turbidity
02 — THE RESEARCH MODEL
CONVENTIONAL

Visit. Measure. Leave. Return.

High logistical repetition. Observation is concentrated around the moments when people are physically present.

DCRIN

Deploy. Observe. Pelajari. Continue.

Persistent sensing extends the research window while reducing the need for repeated site intervention.

03 — LANGSUNG RESEARCH DEMONSTRATION

A forest that keeps reporting.

Semua readings below are simulated demonstration data, generated continuously in-browser.

SIMULATED RESEARCH DATA
SITETropical Penyelidikan Node 07
RESEARCH SESSIONSiang 184 / 365
Air Temperature27.4°C
Relative Humidity86.2%
Soil Moisture63%
CO₂418ppm
PAR312µmol/m²/s
Rainfall2.3mm
04 — SENSOR EXPLORER

Bina the observation layer around the research question.

Select a research domain. The forest scene and telemetry adapt instantly.

CLIMATE ARRAY 27.4°C

Temperature, humidity, rainfall, wind and atmospheric pressure.

A1
B4
C2
S9
RH 86%
PAR 312
SOIL 63%
05 — TIME CHANGES THE QUESTION

From a moment to a system.

A single field visit captures a moment. Persistent sensing reveals cycles, events and long-term environmental behaviour.

TIME WINDOW30 DAYS
1 Hour1 Siang30 Siangs365 Siangs
06 — ENVIRONMENTAL INTELLIGENCE
MULTI-SENSOR EVENT STREAMNORMAL
RainfallHumiditySoilPAR
AI

Detect the event. Correlate the evidence.

DCRIN can surface patterns across multiple sensor channels instead of treating each datapoint in isolation.

INTELLIGENCE EVENT Garis Dasar stable

Pemantauan for correlated changes across rainfall, humidity, soil moisture and canopy light.

Pattern keyakinan72%
07 — BIODIVERSITY SIGNALS
ACOUSTIC INTELLIGENCE DEMO

Listen for changes that are difficult to see.

Continuous acoustic streams can be used for event detection and biodiversity research workflows.

02:14:32Avian activity pattern
03:42:18Insect acoustic cluster
05:21:44Dawn activity surge
08 — DISTRIBUTED FOREST INTELLIGENCE

From individual trees to landscape-scale observation.

Configurable distributed monitoring can span approximately 10–30 km depending on terrain, communications architecture and research requirements.

NETWORK COVERAGE DEMO18.6 km
Penyelidikan nodeData pathIntelligence layer
09 — DATA JOURNEY

Scientific data remains the evidence.
Intelligence helps researchers navigate it.

01SensorObserve
02DAQAcquire
03ValidateCheck
04DatasetStructure
05AIAnalyse
06PenyelidikanDecide
10 — ESG INTELLIGENCE LAB

Bandingkan the physical and computational footprint.

Adjust the research scenario. The model updates live. Semua outputs are demonstration estimates — not audited ESG claims.

MODELLED ESG SCENARIO
Penyelidikan area30 km
Pemantauan period365 days
Penyelidikan team6 people
Sensor count24 sensors
NEURALOPS34relative footprint index
Field missions6estimated visits / period
Penyelidikaner-hours onsite180estimated hours
Transport distance1,200estimated km
AI compute demand28%relative processing load
AI-intensive events1,300per 10k observations
Estimated CO₂e index31relative scenario index
PROCESSING STRATEGYAI only when intelligence is needed.
10,000environmental observations
8,700validated / routed outside large AI
1,300AI-intensive events

Illustrative architecture: deterministic validation and specialised routing can reduce unnecessary AI-intensive processing.

DCRIN

Reduces the physical footprint of research.

+
NEURALOPS

Reduces the computational footprint of intelligence.

11 — BUILD A RESEARCH SCENARIO

Tell the network what you need to understand.

This public configurator creates an example research configuration, not a quotation.

EXAMPLE DCRIN CONFIGURATION
18SENSORS
6parameters
365days
1.89Mobservations
TemperatureHumidityRainfallPARSoil MoistureWind
12 — ONE YEAR IN THE FOREST
DAY1/ 365
OBSERVATIONS1,203
Initial baseline forming.
13 — WHO DCRIN IS FOR

Penyelidikan infrastructure for institutions that need longer visibility.

UniversitiPenyelidikan InstitutesForestry DepartmentsConservation OrganisationsKerajaan AgensiPersekitaran ConsultantsESG PasukansClimate Scientists
AINNA AI SERVICES

The forest is already producing data.
DCRIN helps us listen.

Configure sensing, acquisition and intelligence around the research objective — while keeping the ecosystem at the centre of the design.

AINNA
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