TDengine IDMP continues to evolve through steady, incremental improvements. With each release, the platform refines how data is modeled, analyzed, and presented, addressing practical issues encountered during real usage. The latest updates as of version 1.0.9.0 are described in this article.
Unit Derivation for Element Formula Attributes
TDengine IDMP now supports unit derivation for element formula attributes. The system can automatically identify measurement units used in data from different sources — such as pressure units (psi, bar, MPa) or energy units (kWh, GJ) — and perform unified conversions during calculation and presentation, ensuring that the same type of metric is always based on a consistent unit of measure.
This capability allows data from different devices and regions to be aligned directly at the modeling layer. It not only eliminates errors caused by manual unit conversion, but also provides a reliable data foundation for cross-plant benchmarking analysis, key process calculations, and cross-regional energy efficiency and carbon emissions reporting.
Mapping Enumeration Types to Data Reference Attributes
TDengine IDMP allows enumerations, such as status codes or type lists, to be mapped to data reference attributes like device states or material classifications. This enables previously static enumeration codes to be linked to real business objects.
With this mechanism, enumeration values are no longer limited to display or filtering purposes; they become part of master data references and can participate in analysis and management. When master data such as devices or materials is updated, related data models, analytical results, and reports are automatically kept in sync. This reduces manual maintenance effort and provides a more stable data foundation for asset analysis, operations coordination, and future business expansion.
Advanced Conditional Search for Attributes
TDengine IDMP now provides advanced conditional search for attributes, so that you can combine multiple attribute conditions for filtering without writing any code. Business users can define filtering criteria around specific questions and quickly locate target objects or data ranges across large volumes of device and measurement-point data.
This capability allows operations teams to perform fault troubleshooting and root cause analysis more efficiently, while giving process engineers greater flexibility to conduct comparative analysis across different parameters and batch conditions. At the same time, it reduces reliance on manual screening and IT configuration in routine inspections and monitoring workflows.
Element and Attribute Lists Can Be Saved as Panels
TDengine IDMP now allows you to save commonly used data views as reusable panels. During analysis or monitoring, you can group selected data elements and their attributes into a named panel. The next time it is needed, you can load the reusable panel and restore its predefined working context without having to search for and reselect the same items.
This feature addresses the reality that different roles focus on very different data scopes in daily work. It reduces the repetitive effort of searching and configuring among large numbers of measurement points, and enables personal frequently used views, team-standard monitoring views, and project-level data configurations to be captured and reused.
Pre-Filtering and Historical Recalculation for Analysis
TDengine IDMP’s analytics capabilities support pre-filtering of data scope prior to execution, as well as recalculation on historical data after an analysis task is created. With pre-filtering, analyses can be constrained to specific regions, devices, or ranges, avoiding unnecessary full-dataset scans and improving execution efficiency in large-scale analytical scenarios.
The historical recalculation capability allows updated analysis rules or models to be applied consistently to existing historical data, ensuring comparability and consistency of results across different time periods. Together, these capabilities enable analytics to remain performance-efficient as scale increases, while also providing a foundation for continuous optimization and retrospective validation of analysis rules and algorithms.
Aggregation of Child Element Templates
TDengine IDMP analysis templates now support aggregated configuration of child element templates. This allows analyses that were originally designed for a single metric or element to be packaged into templates that can then be automatically applied to all objects that use the same element template.
With this approach, complex analytics involving multi-measurement correlations, multi-step processing, and multi-rule evaluation can be consolidated into standardized templates, eliminating repetitive manual configuration across similar devices or scenarios. This capability helps formalize proven analytical logic and domain expertise into reusable standards, enabling batch reuse across devices and projects and providing a foundation for scalable deployment and continuous delivery of complex analytics.
Panels Support Advanced Configuration Types
Building on its panel capabilities, TDengine IDMP now introduces advanced configuration features that allow users to further assemble data into business-oriented interfaces such as monitoring dashboards and process flow diagrams.
By creating multiple views on top of the same set of real-time data, users can quickly build application interfaces tailored to scenarios like scheduling, asset management, or operational analysis—without custom development. This can improve incident response efficiency and decision accuracy, while also strengthening business users’ intuitive understanding of data value.
Multiple Analyses from Recommended Questions
TDengine IDMP supports using AI recommendations to generate multiple anomaly detection analysis questions in a single step. This modularizes complex anomaly detection workflows by decomposing them into layered intelligent analysis tasks that combine data cleansing, feature extraction, and multi-model evaluation.
With this approach, anomaly detection is no longer limited to single-threshold rules. Instead, multiple features can be evaluated together within the same analytical workflow to identify more subtle and complex anomaly patterns. At the same time, it improves the stability and reliability of analysis results and enables these analytical capabilities to be reused as modular components, lowering the configuration and usage barrier for advanced analytics in real-world scenarios.
New Data Ingestion Menu
TDengine IDMP introduces new data ingestion configuration capabilities in the management console, supporting real-time or batch ingestion from multiple data sources via message queues such as MQTT and Kafka. Device data, inspection results, and manually entered information from different sources can be unified on a single platform while maintaining temporal continuity and consistency during ingestion.
This mechanism reduces data loss and disorder caused by network fluctuations or collection anomalies, and also allows authorized business users to maintain ingestion configurations themselves. As a result, it reduces reliance on IT support tickets and improves flexibility in data ingestion and operations.
Single Sign-On (SSO)
TDengine IDMP supports integration with an organization’s existing authentication systems, providing single sign-on (SSO) capabilities. After completing authentication once, users can securely access the platform without repeated logins. In industrial environments where multiple systems operate in parallel, this mechanism brings TDengine IDMP account and permission management into the enterprise’s centralized identity governance framework, ensuring that account provisioning, permission changes, and deprovisioning upon employee departure remain consistent with established processes.
Through centralized authorization based on organizational structures and user groups, SSO improves the accuracy of access control while reducing the management overhead associated with maintaining multiple account systems. At the same time, it delivers a more seamless and continuous user experience across systems.
Try TDengine IDMP Today
If you’d like to try out the features and improvements of TDengine IDMP 1.0.9.0 for yourself, deploy a test environment in Docker or in TDengine Cloud and receive a free 15-day trial. In the coming months, you can expect to see an even greater variety of new capabilities driven by feedback from users like you.
