What Data for This Category Looks Like
Data sources for this category include online composition analysis systems, DCS control systems, laboratory ledgers, kiln and grinding equipment operation data, raw material and fuel inventory ledgers, and energy metering reports. Data update frequencies fall into three categories: real-time (such as kiln temperature, material flow), shift-based (such as inspection records, batching adjustment sheets), and daily (such as quality inspection reports, daily energy consumption reports). Daily documents include raw material batching plans, rotary kiln operation records, clinker test reports, quality control notices, equipment inspection logs, and energy consumption reports. Each document contains quantitative parameters for its corresponding production link, such as raw material addition amount, kiln temperature, and grinding fineness. Parameter units comply with general industrial production measurement specifications.
Constraints on Tool Calling and Plugin Workflows
This category’s data characteristics create multiple constraints for tool calling and plugin workflows. Multi-source heterogeneous data sources require tools to support cross-system format adaptation and field mapping. For example, unify kiln temperature data collected by DCS systems and clinker strength data issued by laboratories into a comparable standard format. Data sources with different update frequencies require matching call cycles. Real-time operating condition data needs high-frequency tool calls to retrieve the latest values. Shift-based and daily report data requires plugin synchronization at fixed intervals. Cross-document cross-review requirements for multiple document types mean tools must support cross-document field association queries. For example, associate raw material batching plans with that day’s quality inspection reports to verify whether batching parameters meet quality requirements. Errors in production links have serious consequences. Tool call failures must trigger a clear exception handling process.
Configuration Guidelines
This configuration is based on the plugin and tool calling logic of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Tool call trigger timing | Trigger by document generation nodes: once per shift, once per day, and every 15 minutes for real-time operating condition data | Matches the real-time, shift-based, and daily update rhythms of this category’s data, balancing data timeliness and call costs |
| Workflow/API tool selection | auto | Adapts to multi-type tool call requirements, automatically matching tools for tasks such as raw material batching, kiln monitoring, and grinding quality review |
| Plugin data synchronization scope | Only synchronize data from the raw material, calcination, and grinding links within the current review cycle | Reduces data processing scope, improves tool call efficiency, and avoids loading redundant data |
| Single tool call timeout | 300 seconds | Matches the typical response time of industrial production systems, preventing call failures due to equipment delays or network fluctuations |
| Exception retry count | 2 times | Addresses temporary system fluctuations or interface errors, reducing the impact of a single failed call |
| Tool result filtering rule | Retain the top 3 data entries related to the current review task | Focuses on core production parameters, simplifies cross-review comparison logic, and improves review efficiency |
Three Common Misconfigurations
- Symptom: No return results after a tool call, and call quota is deducted. Cause: The data source range for this category is not matched. The plugin attempts to call production data from non-target links, resulting in no matching query results.
- Symptom: Custom plugins cannot be triggered for calls. Cause: The trigger timing configuration does not align with the document generation nodes for this category. For example, setting the trigger cycle for real-time data to once per day, which does not match the update rhythm of real-time operating conditions.
- Symptom: Tool call return result fields do not match review requirements. Cause: Correct field mapping rules are not configured, resulting in data extracted from multi-source systems having a format that does not meet review requirements.
How to Verify Proper Configuration
- Review tool call logs to confirm that trigger timing aligns with document generation nodes for the current review cycle.
- Test tool calls for a single scenario to verify that return results include core production parameters for this category, such as kiln temperature and clinker strength.
- Check the exception retry configuration, simulate a single failed call scenario, and confirm that the plugin triggers retries according to the set number of times.
- Review the plugin data synchronization scope configuration to confirm that only data from the raw material, calcination, and grinding links within the current review cycle is included.
The values provided here are common starting points, and should be evaluated against samples from the relevant operational context.