Remote surveillance software Industrial Sensing

monitoringdata acquisition and analysisdata acquisition

Overview
Banalytics Operational Layer provides an observable systems layer for researchers, CTOs and technical founders deploying high-bandwidth measurement setups with high‑speed cameras, DAQ/digitizers, waveform sensors and C++/CUDA/Python processing. It wraps operational capabilities around your existing pipeline without modifying core processing logic.

Core value propositions

• Built for sensing and metrology: native targets for high‑speed cameras, DAQ/digitizers, acoustic and waveform sensors, and general telemetry.
• Multimodal synchronized capture: video, waveform and sensor telemetry captured with aligned timestamps for reliable fusion and post‑analysis.
• Edge‑first, closed‑network design: operates reliably in low‑ or no‑connectivity environments without forcing cloud data paths.

What Banalytics wraps (architecture)

• Your Processing Module — image/signal processing, fusion and confidence estimation remain independent and unchanged.
• Banalytics Operational Layer — lifecycle management, dashboards, alarms, MQTT/API publishing, remote browser monitoring and health monitoring.
• Your Acquisition Pipeline — vendor SDKs, drivers, buffering and synchronization remain in place; pipeline preserved.

Pain points solved

• Lack of unified monitoring: consolidates visibility across cameras, DAQ and processing modules for faster remote diagnosis.
• Orchestration overhead: reduces time spent building operational plumbing so teams focus on core sensing IP.
• Pilot fragility: makes prototypes robust for pilots by adding reliability, buffering and remote visibility.
• Scalability complexity: centralizes synchronization and failure monitoring as devices scale.

Must-have features delivered for sensing teams

• Device integration: IP/ONVIF/RTSP cameras, USB, MQTT, Modbus; high‑speed camera support via vendor SDKs during pilot scoping.
• Synchronized capture: demonstrable synchronized capture of video + waveform + telemetry with timestamps.
• Edge storage: raw measurement data retained on the local industrial PC; no forced upload of raw data to cloud.
• Processing integration: supports external or embedded processing modules while keeping processing logic independent.
• MQTT & API publishing: publish results, events, health and metadata to SCADA, cloud ML, historian or custom stacks.
• Remote browser monitoring: dashboards, event history and system visibility accessible via browser without VPN or remote desktop.

Operational capabilities and workflow

• Live dashboards for devices, acquisition pipelines and processing module state.
• System health visibility across cameras, DAQ, compute and storage with centralized status and metrics.
• Configurable alarms and thresholds; asynchronous event workflows (acquisition → processing → alert/publish/action).
• History and playback for event review and post‑mortem analysis.

Technical specifications

• Targets: high‑speed cameras, DAQ/digitizers, acoustic/AE sensors and general sensor telemetry.
• Supported interfaces: ONVIF, RTSP, USB, MQTT, Modbus and vendor SDKs for high‑speed cameras.
• Processing environments: C++, CUDA, Python (processing modules remain independent).
• Capture: synchronized multimodal capture (video + waveform + telemetry) with timestamps.
• Storage: edge‑first architecture with raw measurement data retained locally; optional publishing limited to results/metadata.
• Deployment: designed for closed networks and low‑connectivity environments; no cloud dependency in the data path.
• Integration: lifecycle management, dashboards, alarms, remote browser monitoring and MQTT/API publishing to external systems (SCADA, historian, cloud ML).