OPC Client Feature Specification

The system shall be a cross-platform OPC Client application. The system shall be capable of connecting to OPC DA, OPC UA, and OPC XML-DA servers. The system shall implement real-time data access features. The system shall implement historical data access features. The system shall implement alarm and event monitoring features. The system shall integrate AI/ML-driven features for predictive maintenance. The system shall integrate AI/ML-driven features for anomaly detection. The system shall integrate AI/ML-driven features for natural language querying. The system shall include a centralized, web-based management plane. The web-based management plane shall support remote configuration of client instances. The web-based management plane shall support remote monitoring of client instances. The web-based management plane shall support remote deployment of client instances. The system shall support integration with major IoT platforms. The system shall support integration with AR devices. The system shall support integration with Digital Twins. The system shall implement robust security mechanisms. The system shall implement multi-tenancy. The system shall implement data integrity mechanisms.

The system shall be a distributed software system. The system shall be composed of an on-premise/edge OPC Core Client component. The system shall be composed of a cloud-hosted Central Management Plane component. The OPC Core Client shall run on industrial hardware. The OPC Core Client shall directly interface with OPC servers to perform data acquisition, local control, and edge processing. The OPC Core Client shall communicate securely with the Central Management Plane. The Central Management Plane shall provide a web-based interface for multi-site management, advanced analytics, user administration, and data aggregation.

The system shall define an 'Administrator' user class responsible for system setup, user management, security configuration, and overall system health. The Administrator role shall have full control over all system features. The system shall define an 'Engineer' user class responsible for configuring data sources, tags, dashboards, reports, and AI models. The Engineer role shall be able to modify system configurations but not manage users or security policies. The system shall define an 'Operator' user class responsible for day-to-day monitoring, viewing data, and responding to alarms. The Operator role shall have read access and limited write access as defined by an Administrator or Engineer. The system shall define a 'Viewer' user class with read-only access to dashboards and reports. The Viewer role shall not be able to make any changes to the system. The Administrator role shall have all permissions, including user management, role configuration, security settings, and client instance management. The Engineer role shall have Read/Write access to tag configuration, dashboards, reports, and AI model management. The Engineer role shall have Read-only access to system logs and audit trails. The Engineer role shall have no access to user management or system-level security settings. The Operator role shall have Read access to assigned dashboards and real-time data. The Operator role shall have the ability to acknowledge and shelve alarms within their assigned area. The Operator role shall have write access to specific process setpoints as defined by an Engineer or Administrator. The Viewer role shall have read-only access to all data, dashboards, and reports. The Viewer role shall have no write or configuration capabilities.

The OPC Core Client & Edge AI Module shall support Windows 10/11 and Windows Server 2019+ operating systems. The OPC Core Client & Edge AI Module shall support Ubuntu 20.04+ and Red Hat Enterprise Linux 8+ operating systems. The OPC Core Client & Edge AI Module shall run on standard Industrial PC (x86-64 architecture) hardware. The OPC Core Client & Edge AI Module shall run on NVIDIA Jetson hardware for Edge AI. The OPC Core Client & Edge AI Module shall require .NET 8 Runtime as a software dependency. The OPC Core Client & Edge AI Module shall require Docker for the Edge AI module. The Central Management Plane shall be deployed on a managed Kubernetes service (Amazon EKS, Azure AKS, or Google GKE). Users shall access the Central Management Plane via a modern web browser (Chrome, Firefox, Edge, Safari).

The core application logic shall be developed using the .NET framework (version 8 or later). The Central Management Dashboard frontend shall be developed using React. The system shall support OPC DA, OPC UA, and OPC XML-DA protocols. The audit trail feature shall be designed to support compliance with regulations such as FDA 21 CFR Part 11. The audit trail shall require immutable, timestamped logs of all significant actions. The system architecture shall be multi-tenant to support a SaaS licensing model. The system shall support flexible, tiered licensing models.

The system shall enable reading and writing of real-time data from OPC servers. The system shall support OPC DA and OPC UA protocols for real-time data access. The system shall allow users to browse server namespaces. The system shall allow users to read current tag values. The system shall allow authorized users to write updates to tags. The system shall ensure low-latency communication for time-critical applications. The system shall handle and visualize data quality flags (e.g., Good, Bad, Uncertain) associated with each tag value, as per the OPC specification. <<$Change>> Enhancement Justification: The original requirement focused on the data value itself. However, in industrial settings, the quality and status of a data point are equally critical for operational decisions. Explicitly requiring the client to handle and display these standard OPC quality flags (e.g., Good, Bad, Uncertain, Sensor Failure) prevents operators from making decisions based on faulty or unreliable data. Acceptance Criteria: The client shall successfully connect to OPC DA and OPC UA servers using provided credentials. The client UI shall display a browsable tree of the server's namespace. Users shall be able to read the current value, timestamp, and quality status of any selected tag. Authorized users shall be able to write a new value to a writable tag, and the change shall be reflected in subsequent reads. Data updates shall be displayed in the UI with sub-second latency from the server.

The system shall retrieve historical data for analysis and reporting. The system shall implement the OPC Historical Access (HDA) specification. The system shall support queries over specific time ranges. The system shall provide data aggregation functions (e.g., min, max, average). The system shall provide data filtering capabilities. The system shall provide trend visualization tools. The system shall provide functionality to export queried and aggregated historical data to CSV and Excel formats. <<$Addition>> Enhancement Justification: While visualization is crucial, engineers and analysts frequently need to export raw or aggregated data for use in external reporting tools, spreadsheets, or specialized analysis software. This addition provides a necessary data egress path, making the client a more versatile tool within a larger enterprise data ecosystem. Acceptance Criteria: The client shall be able to query an OPC HDA server for data within a specified start and end time. The client shall be able to perform standard aggregations on historical data queries. Queried data shall be displayed in a trend chart and a tabular view. Users shall be able to export the data displayed in the current view to CSV and XLSX formats.

The system shall manage alarms and events with standardized handling. The system shall support the OPC Alarms and Conditions (A&C) specification. The system shall enable prioritization, suppression, and logging of alerts. The system shall provide a user interface for acknowledging alarms. The system shall include alarm shelving functionality, allowing operators to temporarily silence an alarm for a predefined duration with a required justification. <<$Addition>> The system shall support configurable alarm notification routing, enabling alerts to be sent via email, SMS, or other external systems based on alarm priority, area, or type. <<$Addition>> Enhancement Justification: Standard alarm handling (acknowledge, suppress) is insufficient for complex operational scenarios. Alarm shelving is a critical feature to manage nuisance alarms during maintenance without losing track of them. Configurable notification routing ensures that critical alerts reach the correct personnel immediately, even if they are not actively watching a screen, improving response times. Acceptance Criteria: The client shall display a real-time list of active alarms from connected OPC A&C servers. Authorized users shall be able to acknowledge, suppress, and shelve alarms. Shelving an alarm shall require a duration and a text justification. The system shall provide a configuration interface to route alarms to email or SMS recipients based on rules (priority, source, etc.). All alarm state changes (e.g., Active, Acknowledged) shall be logged in the audit trail.

The system shall ensure secure data exchange and system protection. The system shall implement certificate-based authentication per OPC UA standards. The system shall implement 128/256-bit encryption per OPC UA standards. The system shall implement message signing per OPC UA standards. The system shall implement user authentication per OPC UA standards. The system shall support role-based access control (RBAC). The system shall include comprehensive session management controls, such as configurable user session timeouts for inactivity. <<$Addition>> The system shall include limits on concurrent sessions per user account. <<$Addition>> Enhancement Justification: Security extends beyond authentication and encryption. Proper session management is vital to prevent unauthorized access from abandoned, logged-in terminals. Limiting concurrent sessions can prevent account sharing and provide a clearer audit trail of user activity, which is a common requirement in regulated environments. Acceptance Criteria: All communication with OPC UA servers shall use configured security policies (encryption and signing). The system shall enforce RBAC for all user actions. The Administrator shall be able to configure session timeout duration and concurrent session limits per user role. User sessions shall be automatically terminated after the configured period of inactivity.

The system shall provide comprehensive, immutable logging of all significant user and system actions. <<$Addition>> The system shall maintain a secure audit log to record critical events. Logged actions shall include user login/logout attempts, data write operations, alarm acknowledgements/shelving/suppression, changes to client configuration, and security policy changes. Each log entry shall be timestamped. Each log entry shall include the responsible user. Each log entry shall include the originating workstation/IP address. For data write operations, the log entry shall contain the tag name, the old value, and the new value. Enhancement Justification: This feature was identified as a recurring gap across multiple sections. Centralizing it as a core feature ensures consistency and completeness. An audit trail is non-negotiable for traceability, troubleshooting, and regulatory compliance (e.g., FDA 21 CFR Part 11). It answers the critical questions of 'who did what, and when?'. Acceptance Criteria: All specified actions shall be automatically logged without the possibility for users to disable or edit the logs. The audit log shall be searchable and filterable by user, action type, and time range. For data write operations, the log entry shall contain the tag name, the old value, and the new value.

The system shall connect to OPC servers from multiple vendors. The system shall support OPC DA, OPC UA, and OPC XML-DA protocols. The system shall handle diverse data types and structures. The system shall ensure compatibility with servers from manufacturers like Siemens or Rockwell. Acceptance Criteria: The client shall demonstrate successful connection and data exchange with certified OPC servers from at least three different major vendors.

The system shall operate across multiple operating systems. The system shall be deployable on Windows and Linux. <<$Change>> The system shall be developed using frameworks like .NET to ensure a consistent user experience across supported platforms. <<$Change>> Enhancement Justification: The term 'potentially macOS' is ambiguous and not a valid requirement. This change clarifies the committed platforms (Windows, Linux) and sets macOS as a clear future goal. This provides clarity for development planning and marketing, and manages customer expectations. Acceptance Criteria: The OPC Core Client installer/package shall be provided for both Windows and a major Linux distribution (e.g., Ubuntu LTS). All core features shall function identically on both Windows and Linux.

The system shall subscribe to data changes for real-time updates. The system shall support monitored items and notifications to reduce polling overhead. The system shall allow configurable update rates for efficiency. Acceptance Criteria: The client shall use the OPC UA subscription model to receive data updates. Users shall be able to configure the sampling interval and publishing interval for each subscription. CPU and network usage shall remain low when monitoring thousands of tags.

The system shall offer an intuitive GUI for configuration and monitoring. The GUI shall include drag-and-drop tag configuration. The GUI shall include namespace browsing. The GUI shall include customizable dashboards for data visualization. Dashboards and user layouts shall be user-specific and persistent. <<$Addition>> The interface shall support localization (Internationalization - i18n) to allow for translation into multiple languages. <<$Addition>> Enhancement Justification: Users in different roles (e.g., operator vs. engineer) require different views. Allowing users to save their own dashboard layouts improves efficiency. In a global market, localization is essential for user adoption and safety, ensuring that operators can use the system in their native language. Acceptance Criteria: Users shall be able to create, save, and load their own custom dashboard layouts. The UI text shall be switchable between supported languages (e.g., English, German, Spanish). Users shall be able to add tags to a dashboard by dragging them from the namespace browser.

The system shall enhance data transfer and system efficiency. The system shall group OPC items for efficient read/write operations. The system shall support Time-Sensitive Networking (TSN). The system shall minimize latency for large datasets. Acceptance Criteria: The client shall automatically group tags into the minimum number of subscriptions to optimize server load. The system shall be able to process and display thousands of tag updates per second.

The system shall support redundant servers and automatic failover. The system shall automatically switch to backup servers if the primary fails. The client shall provide a configuration interface for setting up primary/backup server pairs. <<$Addition>> The configuration interface shall allow defining failover trigger conditions (e.g., connection loss, status code). <<$Addition>> The configuration interface shall allow configuring health check parameters. <<$Addition>> An alert shall be generated and logged upon any failover event. <<$Addition>> Enhancement Justification: 'Automatic failover' is not a complete requirement. The system must provide administrators with the tools to configure *how* and *when* this failover occurs. Defining triggers and health checks prevents unnecessary switching and provides control over the redundancy logic. Notifying users of a failover event is critical for situational awareness and subsequent investigation. Acceptance Criteria: In a configured redundant pair, if the primary OPC server is disconnected, the client shall automatically connect to the backup server within a configurable time limit. Data subscriptions shall be re-established on the backup server without manual intervention. A high-priority event shall be logged in the audit trail and an alert shall be raised in the UI upon failover.

The system shall use machine learning to predict equipment failures. The system shall integrate pre-trained models to analyze historical data and forecast maintenance needs. Models shall be able to run on edge devices for low-latency predictions. The system shall provide a model management interface for importing custom models (e.g., in ONNX format). <<$Addition>> The model management interface shall support versioning models. <<$Addition>> The model management interface shall allow assigning models to specific data tags or assets. <<$Addition>> The system shall include a workflow for retraining models with new historical data. <<$Addition>> Enhancement Justification: 'Pre-trained models' is a limiting assumption. Most industrial customers will need to train models on their specific equipment and data. A robust model management lifecycle (import, version, assign, retrain) is essential for the feature to be practical and adaptable to diverse customer environments. Acceptance Criteria: Users shall be able to upload a model file in ONNX format via the Central Management Dashboard. Users shall be able to assign an uploaded model to one or more input tags. The model's output (e.g., remaining useful life) shall be displayed as a derived tag in the client UI. The system shall provide an API endpoint to trigger a model retraining job with a specified historical data range.

The system shall detect unusual patterns in real-time data using AI. The system shall employ statistical or deep learning models to flag anomalies. The system shall provide configurable thresholds and alerts for anomalies. The user interface shall allow operators to provide feedback on flagged anomalies (e.g., mark as 'true anomaly' or 'false positive'). <<$Addition>> This feedback shall be logged for use in future model retraining and accuracy analysis. <<$Addition>> Enhancement Justification: AI models are not perfect and require human-in-the-loop feedback to improve. Allowing users to validate or reject anomalies creates a crucial data set for supervised learning, enabling the models to become more accurate over time and reducing 'alert fatigue' from false positives. Acceptance Criteria: When an AI model detects an anomaly, a high-priority event shall be generated. The UI shall provide buttons for operators to label the anomaly event as 'True Positive' or 'False Positive'. This feedback shall be stored and associated with the event data for later analysis.

The system shall enable data queries and client commands using natural language, via typed text or voice input. <<$Change>> The system shall integrate NLP (e.g., via Google Cloud Natural Language) and voice recognition (e.g., Google Cloud Speech-to-Text) to process queries and commands. <<$Change>> The supported grammar shall cover retrieving real-time and historical data. <<$Change>> The supported grammar shall cover acknowledging alarms. <<$Change>> Enhancement Justification: The original document listed 'Natural Language Querying' and 'Voice Control' as separate features, creating a functional overlap and potential for contradictory implementations. This revised requirement merges them into a single, cohesive feature, clarifying that voice is an input method for the NLP engine. It also expands the details to define the initial scope of the supported language, making the requirement more specific and testable. Acceptance Criteria: A text input field and a voice input button shall be available in the UI. Entering the query 'Show current temperature in Tank 1' shall display the current value for the corresponding tag. Speaking the command 'Acknowledge all alarms' shall perform the acknowledge action for all active, unacknowledged alarms visible to the user.

The system shall generate reports based on data trends and anomalies. The system shall use AI to create customized reports, highlighting KPIs and issues. The system shall support scheduled or event-triggered reporting. The system shall provide a report configuration module where users can define report templates. <<$Addition>> The report configuration module shall allow users to select data sources (tags, KPIs). <<$Addition>> The report configuration module shall allow users to specify AI analysis to include (e.g., anomaly summary). <<$Addition>> The report configuration module shall allow users to set up distribution schedules and output formats (PDF, HTML). <<$Addition>> Enhancement Justification: The original description was too vague. To be a useful feature, users must have control over the content, format, and distribution of reports. A dedicated configuration module makes this possible and turns a high-level idea into a concrete, implementable feature. Acceptance Criteria: The UI shall allow users to build a report by selecting tags, time ranges, and visualization types (charts, tables). Users shall be able to schedule a report to be generated and emailed to a list of recipients on a recurring basis (daily, weekly, monthly). The generated report shall be delivered in the selected format (PDF or HTML).

The system shall perform AI computations at the edge. The system shall run lightweight AI models on edge devices to process data locally. The system shall be compatible with edge hardware like NVIDIA Jetson. The centralized client management dashboard shall include functionality to deploy, start, stop, and monitor the health and performance of AI models running on remote edge devices. <<$Addition>> Enhancement Justification: Deploying models to the edge is only half the battle. A scalable solution requires the ability to manage these distributed models from a central location. This addition provides the necessary remote management capabilities, without which the feature would be impractical to maintain in a large-scale deployment. Acceptance Criteria: From the Central Management Dashboard, an administrator shall be able to select a deployed Core Client and deploy a specific AI model to it. The dashboard shall display the status (e.g., Running, Stopped, Error) and key performance metrics (e.g., CPU usage, inference time) of models running on edge devices. An administrator shall be able to remotely start, stop, or undeploy a model from an edge device.

The system shall connect to IoT ecosystems for extended functionality. The system shall support AWS IoT, Azure IoT, and Google Cloud IoT. The integration shall be configurable for bidirectional data flow (OPC-to-Cloud and Cloud-to-OPC). <<$Addition>> The system shall include a data mapping and transformation tool to align OPC tag structures with the target cloud platform's data schema. <<$Addition>> Enhancement Justification: Simply 'supporting' a platform is insufficient. The requirement must specify the direction of data flow and acknowledge the need for data transformation. Industrial tag names are often cryptic and need to be mapped to a more structured, semantic model in the cloud. Bidirectional flow enables advanced use cases like pushing AI-driven setpoints from the cloud back to the PLC. Acceptance Criteria: The UI shall provide a configuration screen for AWS IoT, Azure IoT, and Google Cloud IoT connectors, requiring endpoint and credential information. A mapping tool shall allow users to link a local OPC tag to a cloud device twin property. Data published from the OPC client shall appear in the configured cloud IoT platform. Data written to the cloud device twin property shall be written back to the corresponding OPC tag.

The system shall visualize data on physical equipment via AR. The system shall integrate with AR devices (e.g., HoloLens) to overlay real-time data on machinery. The system shall require a configuration tool to create and manage the mapping between OPC tags and their corresponding physical locations or asset markers (e.g., QR codes). <<$Addition>> Enhancement Justification: The AR device needs to know which data to display on which piece of equipment. This doesn't happen automatically. A mapping tool is a critical prerequisite for this feature to function, allowing an administrator to link, for example, `Tank_5.Temperature.PV` to a specific physical tank in the AR space. Acceptance Criteria: The Central Management Dashboard shall provide an interface to define assets and associate them with QR codes or other markers. Users shall be able to map one or more OPC tags to each defined asset. A dedicated REST API endpoint shall be available for AR devices to query for tag data associated with a scanned marker.

The system shall ensure tamper-proof data logging. The system shall use blockchain (e.g., Hyperledger) to record immutable hashes of aggregated critical data batches, key configuration changes, or significant event logs. <<$Change>> This approach shall ensure traceability and compliance for specific, high-value data points or summaries. <<$Change>> Enhancement Justification: The original requirement for 'recording critical data exchanges' broadly implied logging potentially high-volume, low-latency real-time data directly to a blockchain. This is technically infeasible due to the inherent throughput limitations and latency of blockchain consensus mechanisms. The revised requirement clarifies that the blockchain will be used for logging immutable hashes of aggregated data batches, key configuration changes, or significant, infrequent event logs. This approach leverages the immutability and traceability benefits of blockchain for compliance and auditing purposes without attempting to use it as a high-speed, real-time data logger, thereby addressing the performance and technical feasibility concerns. Acceptance Criteria: The system shall be configurable to connect to a Hyperledger Fabric instance. On a configurable schedule, the system shall batch selected audit log entries, calculate a cryptographic hash of the batch, and submit the hash to the blockchain ledger. The UI shall provide a way to verify a batch of audit logs against the hash stored on the blockchain.

The system shall interact with virtual system representations. The system shall connect to digital twins for simulation and testing. The client shall clearly distinguish between a connection to a physical system versus a digital twin in the UI to prevent accidental writes to live equipment. <<$Addition>> The client shall support connecting to digital twins via industry standards such as the Asset Administration Shell (AAS) where applicable. <<$Addition>> Enhancement Justification: The most significant risk of digital twin integration is accidentally controlling a live plant when the user believes they are in a simulation. A clear, unmissable UI distinction is a critical safety requirement. Supporting standards like AAS ensures interoperability and future-proofs the feature. Acceptance Criteria: When configuring a server connection, the user shall be able to flag it as a 'Digital Twin'. When connected to a Digital Twin server, the UI shall display a persistent, prominent visual indicator (e.g., a banner stating 'SIMULATION MODE'). The client shall be able to connect to and browse the namespace of a server that exposes its model via the Asset Administration Shell.

The system shall allow multiple users with role-based access. The system shall implement RBAC to control access levels and support concurrent users. The system shall include at least four default, configurable roles: Administrator, Engineer, Operator, and Viewer. <<$Addition>> The RBAC system shall allow for granular permission settings for specific plant areas or data groups. <<$Addition>> Enhancement Justification: Simply stating 'RBAC' is not enough. A functional specification must define the initial roles and the depth of control. This addition provides a concrete starting point for implementation and ensures the RBAC system is flexible enough to support common industrial organizational structures (e.g., restricting an operator's view to only their assigned production line). Acceptance Criteria: An Administrator shall be able to create, modify, and delete users and assign them to roles. The four default roles (Administrator, Engineer, Operator, Viewer) shall be present and enforce the specified permissions. An Administrator shall be able to create custom roles with a granular selection of permissions.

The system shall manage multiple client instances centrally. The system shall provide a dashboard for monitoring and configuring clients across sites. The central dashboard shall support remote health monitoring (CPU, memory, connection status). <<$Addition>> The central dashboard shall support license management. <<$Addition>> The central dashboard shall support pushing of configuration updates to distributed client instances. <<$Addition>> The central dashboard shall support deploying software updates to distributed client instances. <<$Addition>> Enhancement Justification: This clarifies what 'centralized management' actually entails. Listing specific functions like health monitoring, license management, and remote updates makes the requirement tangible and testable, providing clear direction for the development team. Acceptance Criteria: The Central Management Dashboard shall list all registered client instances and their real-time health status. An Administrator shall be able to view and manage software licenses for all instances. An Administrator shall be able to push a configuration file or a software update package to one or more selected client instances.

The system shall offer varied licensing options. The system shall support per-user, per-site, or subscription-based models. The system shall support tiered features based on license level. Acceptance Criteria: The system shall enforce feature access based on the tenant's license tier. The license management module shall support time-based (subscription) and perpetual licenses.

The system shall keep software current with updates. The system shall deliver new features, security patches, and compatibility improvements regularly. The client shall include a mechanism for applying updates, which can be initiated either centrally or locally. <<$Addition>> This mechanism shall support the ability to roll back to a previous version in case of a failed update. <<$Addition>> Enhancement Justification: The process of applying updates is as important as the updates themselves. A robust update mechanism is required, and the ability to roll back a faulty patch is a critical requirement for maintaining stability in production environments. Acceptance Criteria: The update process shall be triggerable from the Central Management Dashboard. After an update is applied, the system shall report the new version number. The system shall provide a documented procedure to roll back to the previously installed version.

The UI shall be clean, modern, and intuitive. The UI shall minimize the number of clicks required for common tasks. The UI shall prioritize clarity and readability of data, especially in operational dashboards. The Central Management Dashboard shall be responsive and usable on screen sizes from a standard desktop monitor (1920x1080) to a tablet (1024x768). The web-based UI shall comply with Web Content Accessibility Guidelines (WCAG) 2.1 Level AA. The UI shall support both a standard light theme and a dark theme.

The OPC Core Client shall run on standard x86-64 industrial PCs. The Edge AI Module shall be compatible with NVIDIA Jetson series devices for GPU-accelerated model inference. The system shall provide a REST API for integration with AR devices like the Microsoft HoloLens 2.

The client shall interface with third-party OPC servers via OPC DA (COM/DCOM). The client shall interface with third-party OPC servers via OPC UA (Binary TCP). The client shall interface with third-party OPC servers via OPC XML-DA (SOAP/HTTP). The system shall interface with AWS IoT, Azure IoT, and Google Cloud IoT via their respective APIs, primarily using MQTT. The system shall interface with Google Cloud Natural Language API. The system shall interface with Google Cloud Speech-to-Text API. The system shall interface with services like Twilio for SMS notifications. The system shall interface with services like SendGrid for email notifications. Communication between the Central Management Plane microservices shall use a combination of REST APIs and gRPC.

The system shall use TCP/IP, HTTP/S, WebSockets, and MQTT network protocols. The system shall use JSON for REST APIs, Protocol Buffers for gRPC, and OPC UA Binary Encoding as data formats. All external communication shall be encrypted using TLS 1.3. Communication with OPC UA servers shall use the security policies defined in the OPC UA specification.

End-to-end latency for real-time data visualization (from server value change to display) shall be less than 500ms. The 95th percentile (P95) latency for all Central Management Plane API endpoints shall be less than 200ms under nominal load. The system shall support ingestion of up to 10,000 values per second per tenant into the time-series database. Queries for a single tag over a 24-hour period shall return in less than 1 second. Latency for AI model inference on an edge device shall be less than 100ms.

The cloud database shall have Point-in-Time Recovery (PITR) enabled. Daily snapshots of the cloud database shall be retained for 30 days. A read replica of the primary database shall be maintained in a different availability zone. The system shall recover from an availability zone failure within 15 minutes. The system shall support automatic failover for redundant OPC servers as per FR-3.1.11. The system shall support automatic failover for its own cloud-based microservices via Kubernetes orchestration. The OPC Core Client shall implement a persistent on-disk queue to buffer data during network outages to the Central Management Plane. Buffered data shall be forwarded automatically upon reconnection.

User authentication shall be managed by a centralized Identity Provider (Keycloak) supporting OAuth 2.0 and OIDC. All API access shall be secured using JWT Bearer Tokens. The system shall implement a Role-Based Access Control (RBAC) model. Permissions shall be checked at the API Gateway and enforced within each microservice. All network communication between system components and with external services shall be encrypted using TLS 1.3. All data stored in databases and object storage shall be encrypted using AES-256. All sensitive information (passwords, API keys, certificates) shall be stored in a secure vault (e.g., HashiCorp Vault or AWS Secrets Manager). Sensitive information shall be injected at runtime. The system shall maintain an immutable audit trail of all security-sensitive events as detailed in FR-3.1.5.

The Central Management Plane shall have a minimum uptime of 99.9%. Planned maintenance windows shall be scheduled with advance notice to customers. Planned maintenance windows shall not exceed 4 hours per month. In case of a partial system failure, the core data collection and visualization features shall remain operational.

The system shall support up to 1,000 concurrent users per tenant. All cloud microservices shall be stateless. All cloud microservices shall be designed to scale horizontally using Kubernetes Horizontal Pod Autoscalers based on CPU and memory load. The Central Management Plane shall be capable of managing up to 10,000 distributed OPC Core Client instances.

All backend code shall maintain a minimum of 80% unit test coverage. All API endpoints shall be documented using the OpenAPI specification. The system shall be built on a microservices architecture to ensure loose coupling and independent deployability of components.

The Central Management Dashboard frontend shall use the React 18 framework with TypeScript. The frontend shall use Redux Toolkit for state management. The frontend shall use Vite as a build tool. The backend Core Client and Cloud Services shall use .NET 8 / ASP.NET Core. APIs shall be designed with REST for external communication and gRPC for internal communication. Real-time communication shall use SignalR for web and OPC UA Binary TCP for server communication. The system shall use PostgreSQL 16 for relational/configuration data. The system shall use TimescaleDB for time-series data. The system shall use Redis 7 for caching. The system shall use Amazon S3 or Azure Blob Storage for object storage. The system shall use AWS as the primary cloud provider, but be designed to be cloud-agnostic. The system shall use Docker for containerization. The system shall use Kubernetes for orchestration (Amazon EKS for cloud, K3s for edge). The system shall use GitHub Actions for CI/CD. The system shall use Terraform for Infrastructure as Code. The system shall use Keycloak as the Identity Provider.

The system shall provide a flexible reporting module as defined in FR-3.2.4. Reports shall be generated in PDF and HTML formats. Reports shall be schedulable for automatic generation and distribution via email. Prometheus shall be used to scrape metrics from all system components. The OPC Core Client shall expose a `/metrics` endpoint for health and performance monitoring. Logs from all containers and services shall be aggregated using Fluentd. Logs shall be stored in a centralized logging platform like Loki or OpenSearch. Logs shall be structured (e.g., JSON format) to include a timestamp, severity level, service name, and correlation ID. OpenTelemetry shall be integrated into all microservices to enable distributed tracing. Grafana shall be used to create dashboards for visualizing system metrics and logs. Alertmanager shall be configured to send critical alerts to on-call personnel via PagerDuty and Slack.

Overview This document outlines the feature set for a licensable OPC client designed for industrial automation, incorporating both standard and innovative features to meet customer demands and leverage emerging technologies. Standard Features Real-Time Data Access Description: Enables reading and writing real-time data from OPC servers. Details: Supports OPC DA and OPC UA protocols, allowing users to browse server namespaces, read current tag values, and write updates. Ensures low-latency communication for time-critical applications. Use Case: Operators monitor live process data, such as temperature or pressure, to make immediate control decisions. Historical Data Access Description: Retrieves historical data for analysis and reporting. Details: Implements the OPC Historical Access specification, supporting queries over specific time ranges. Includes data aggregation, filtering, and trend visualization tools. Use Case: Engineers analyze past performance to optimize processes or meet regulatory requirements. Alarms and Events Monitoring Description: Manages alarms and events with standardized handling. Details: Supports the OPC Alarms and Conditions specification, enabling prioritization, suppression, and logging of alerts. Provides a user interface for acknowledging alarms. Use Case: Operators receive alerts about equipment malfunctions and take corrective actions. Security Features Description: Ensures secure data exchange and system protection. Details: Implements certificate-based authentication, 128/256-bit encryption, message signing, and user authentication per OPC UA standards. Supports role-based access control (RBAC). Use Case: Prevents unauthorized access in sensitive industrial environments. Interoperability Description: Connects to OPC servers from multiple vendors. Details: Supports OPC DA, OPC UA, and OPC XML-DA, handling diverse data types and structures. Ensures compatibility with servers from manufacturers like Siemens or Rockwell. Use Case: Integrates equipment from different vendors into a unified system. Platform Independence Description: Operates across multiple operating systems. Details: Deployable on Windows, Linux, and potentially macOS, using frameworks .NET. Use Case: Allows deployment in varied IT environments, from on-premises servers to cloud-based systems. Subscription Mechanism Description: Subscribes to data changes for real-time updates. Details: Supports monitored items and notifications, reducing polling overhead. Configurable update rates for efficiency. Use Case: Provides continuous updates for dynamic process monitoring. User-Friendly Interface Description: Offers an intuitive GUI for configuration and monitoring. Details: Includes drag-and-drop tag configuration, namespace browsing, and customizable dashboards for data visualization. Use Case: Simplifies setup and monitoring for non-technical users. Performance Optimization Description: Enhances data transfer and system efficiency. Details: Groups OPC items for efficient read/write operations, supports Time-Sensitive Networking (TSN), and minimizes latency for large datasets. Use Case: Ensures smooth operation in high-data-volume environments. Redundancy and Failover Description: Supports redundant servers and automatic failover. Details: Automatically switches to backup servers if the primary fails, ensuring continuous operation. Use Case: Maintains uptime in critical applications like power plants. Innovative Features AI-Driven Predictive Maintenance Description: Uses machine learning to predict equipment failures. Details: Integrates pre-trained models to analyze historical data and forecast maintenance needs. Can run on edge devices for low-latency predictions. Use Case: Reduces downtime by scheduling maintenance before failures occur. Anomaly Detection Description: Detects unusual patterns in real-time data using AI. Details: Employs statistical or deep learning models to flag anomalies, such as unexpected pressure spikes. Configurable thresholds and alerts. Use Case: Identifies potential faults early, preventing costly disruptions. Natural Language Querying Description: Enables data queries using natural language. Details: Integrates NLP (e.g., via Google Cloud Natural Language) to process queries like _Show current temperature in Tank 1._ Supports voice input. Use Case: Makes data access intuitive for non-technical operators. Automated Reporting with AI Description: Generates reports based on data trends and anomalies. Details: Uses AI to create customized reports, highlighting KPIs and issues. Supports scheduled or event-triggered reporting. Use Case: Saves time for managers needing regular performance insights. Edge AI Processing Description: Performs AI computations at the edge. Details: Runs lightweight AI models on edge devices to process data locally, reducing bandwidth and latency. Compatible with edge hardware like NVIDIA Jetson. Use Case: Enables real-time decision-making in remote locations. Integration with IoT Platforms Description: Connects to IoT ecosystems for extended functionality. Details: Supports AWS IoT, Azure IoT, and Google Cloud IoT for cloud-based analytics, storage, and visualization. Use Case: Extends OPC data to enterprise-wide IoT solutions. Augmented Reality (AR) Dashboards Description: Visualizes data on physical equipment via AR. Details: Integrates with AR devices (e.g., HoloLens) to overlay real-time data on machinery, aiding maintenance and troubleshooting. Use Case: Enhances field technician efficiency during repairs. Blockchain for Data Integrity Description: Ensures tamper-proof data logging. Details: <<$Change>>Uses blockchain (e.g., Hyperledger) to record immutable hashes of aggregated critical data batches, key configuration changes, or significant event logs, rather than raw real-time data. This ensures traceability and compliance for specific, high-value data points or summaries, mitigating performance limitations inherent to blockchain technology.<<$Change>> Enhancement Justification: The original requirement for "recording critical data exchanges" broadly implied logging potentially high-volume, low-latency real-time data directly to a blockchain. This is technically infeasible due to the inherent throughput limitations and latency of blockchain consensus mechanisms. The revised requirement clarifies that the blockchain will be used for logging immutable hashes of aggregated data batches, key configuration changes, or significant, infrequent event logs. This approach leverages the immutability and traceability benefits of blockchain for compliance and auditing purposes without attempting to use it as a high-speed, real-time data logger, thereby addressing the performance and technical feasibility concerns. Use Case: Meets regulatory requirements in pharmaceuticals or energy sectors by providing verifiable proof of critical operational milestones or data summaries. Voice Control Description: Enables hands-free operation via voice commands. Details: Integrates voice recognition (e.g., Google Cloud Speech-to-Text) for controlling the client or querying data. Use Case: Useful for technicians working hands-on with equipment. Digital Twin Support Description: Interacts with virtual system representations. Details: Connects to digital twins for simulation and testing, allowing users to experiment with changes virtually. Use Case: Optimizes processes without risking physical systems. Licensing and Support Features Multi-User Support Description: Allows multiple users with role-based access. Details: Implements RBAC to control access levels, supporting concurrent users. Use Case: Enables team collaboration in large organizations. Centralized Management Description: Manages multiple client instances centrally. Details: Provides a dashboard for monitoring and configuring clients across sites. Use Case: Simplifies administration for enterprises with multiple facilities. Flexible Licensing Models Description: Offers varied licensing options. Details: Includes per-user, per-site, or subscription-based models, with tiered features. Use Case: Appeals to diverse customer budgets and needs. Technical Support and Training Description: Provides support and educational resources. Details: Includes documentation, tutorials, and 24/7 support as part of the license. Use Case: Ensures customer success and adoption. Regular Updates and Maintenance Description: Keeps software current with updates. Details: Delivers new features, security patches, and compatibility improvements regularly. Use Case: Maintains long-term reliability and competitiveness.

This document outlines the features for a licensable OPC client for industrial automation, covering both standard and innovative functionalities, along with licensing and support. **Standard Features:** * **Real-Time Data Access:** Supports OPC DA and UA for reading/writing live data with low latency, enabling immediate control decisions. * **Historical Data Access:** Implements OPC Historical Access for querying, aggregating, filtering, and visualizing past data for analysis and reporting. * **Alarms and Events Monitoring:** Manages OPC Alarms and Conditions, including prioritization, suppression, logging, and a UI for acknowledgment. * **Security Features:** Ensures secure data exchange with certificate-based authentication, 128/256-bit encryption, message signing, and role-based access control (RBAC) per OPC UA standards. * **Interoperability:** Connects to OPC servers from various vendors (Siemens, Rockwell) supporting OPC DA, UA, and XML-DA. * **Platform Independence:** Deployable on Windows, Linux, and potentially macOS using .NET frameworks. * **Subscription Mechanism:** Uses monitored items and notifications for efficient real-time updates with configurable rates. * **User-Friendly Interface:** Provides an intuitive GUI with drag-and-drop tag configuration, namespace browsing, and customizable dashboards. * **Performance Optimization:** Enhances efficiency through OPC item grouping, Time-Sensitive Networking (TSN) support, and minimized latency for large datasets. * **Redundancy and Failover:** Ensures continuous operation by automatically switching to backup servers upon primary failure. **Innovative Features:** * **AI-Driven Predictive Maintenance:** Uses machine learning models (potentially on edge devices) to predict equipment failures and schedule maintenance. * **Anomaly Detection:** Employs AI models to detect unusual patterns in real-time data, flagging potential faults early. * **Natural Language Querying:** Integrates NLP (e.g., Google Cloud Natural Language) for intuitive data queries via text or voice. * **Automated Reporting with AI:** Generates customized reports based on data trends and anomalies, highlighting KPIs and issues. * **Edge AI Processing:** Runs lightweight AI models on edge devices (e.g., NVIDIA Jetson) for local data processing, reducing latency and bandwidth. * **Integration with IoT Platforms:** Connects to major IoT ecosystems (AWS IoT, Azure IoT, Google Cloud IoT) for cloud-based analytics and storage. * **Augmented Reality (AR) Dashboards:** Visualizes real-time data overlaid on physical equipment via AR devices (e.g., HoloLens) for enhanced field efficiency. * **Blockchain for Data Integrity:** <<$Change>>Uses blockchain (e.g., Hyperledger) to record immutable hashes of aggregated critical data batches, key configuration changes, or significant event logs, ensuring traceability and compliance for specific, high-value data points or summaries, while mitigating performance limitations.<<$Change>> * **Voice Control:** Enables hands-free operation and data querying via voice recognition (e.g., Google Cloud Speech-to-Text). * **Digital Twin Support:** Interacts with virtual system representations for simulation, testing, and process optimization. **Licensing and Support Features:** * **Multi-User Support:** Implements RBAC for concurrent users and controlled access levels. * **Centralized Management:** Provides a dashboard for monitoring and configuring multiple client instances across sites. * **Flexible Licensing Models:** Offers per-user, per-site, or subscription-based models with tiered features. * **Technical Support and Training:** Includes comprehensive documentation, tutorials, and 24/7 support. * **Regular Updates and Maintenance:** Ensures long-term reliability with regular feature updates, security patches, and compatibility improvements.