System Architecture

this page gives you a high level overview of how factry historian fits into your industrial data landscape it outlines the main layers of the system, how data flows from equipment into the historian, how it is processed, and how it is exposed to external systems overall factry historian architecture data collection layer factry historian connects to various types of equipment and control systems in your production environment the most common protocols used for collecting real time data include opc ua opc da mqtt (including sparkplugb) modbus tcp file or network based protocols (e g rest apis, sql databases) other protocols for proprietary or legacy equipment collectors running within the historian ecosystem use these protocols to pull data directly from plcs, sensors, and scada systems core platform (factry historian) at the heart of the architecture is the historian itself it provides the runtime environment, apis, configuration tooling, and internal logic to process, store, and enrich incoming data key building blocks include asset model logical structure to map your equipment and group measurements engineering metadata technical details such as units of measurement and ranges event detection configurable logic to detect stops, alarms, batches, and more calculation engine used to generate derived values, aggregations, or kpis manual entry forms for structured operator or lab input prototypes templates for consistent deployment across similar assets or use cases data is stored in two backend databases influxdb for time series data such as measurements and calculations postgresql for configuration, metadata, and events outputs & integrations once data is collected, processed, and enriched, factry historian can serve it to other systems through built in connectors or custom integrations built in outputs factryos / oee integration with our mes platform seeq connector for advanced analytics and process mining grafana datasource visualize time series data in grafana dashboards excel add in retrieve historian data directly in spreadsheets mqtt output (spb) publish data to external mqtt brokers (typical in {{uns}} architectures) parquet export periodic export to files for archiving or data lakes connected systems using the factry historian (swagger spec) rest api, users can furthermore connect to erp systems for planning or production reconciliation cloud platforms (azure, aws) for central data storage or analytics business intelligence tools for reporting or dashboarding ml / ai pipelines for anomaly detection, forecasting, or optimization summary factry historian sits at the center of your process data infrastructure , acting as the single source of truth for high resolution process data, and data derived from it it connects to industrial equipment, organizes and enriches the data, and makes it available to people and systems that need it this modular architecture allows for flexibility in scaling, integration, and customization depending on your production environment and digitalization goals