What the Data for This Category Looks Like
Mine mining and beneficiation ledger data comes from four main sources: mining dispatching systems, beneficiation workshop PLC collection terminals, digitally archived paper reports from geological and survey departments, and on-site safety inspection records. Data updates follow three cycles: real-time (such as beneficiation grinding feed volume and equipment operating parameters), shift-based (mining shift reports), and daily (raw ore grade and beneficiation recovery rate). A single ledger document includes fields including operation area, operation volume, material grade, equipment status, and hidden danger inspection items. Units exclusive to mine mining and beneficiation are used, such as tons, grams per ton, hours, and count.
Constraints Imposed on Workflow Orchestration by These Characteristics
Multi-source heterogeneous data and varied update cycles require workflows to use parallel pull nodes. Each node adapts to the pull timing for real-time, shift-based, and daily data respectively. Exclusive fields and units require a pre-configured data validation node. This node performs unit matching checks on fields such as operation volume and grade to avoid unit confusion in summary results. The fixed field structure of single ledger documents requires workflow nodes to preset field mapping rules. These rules unify homologous fields from different systems into a standard summary format. Real-time data timeliness constraints require workflow nodes to set a reasonable timeout threshold. This avoids delayed data pulling affecting report generation timeliness.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Data pull timeout | 600–1800 seconds | Adapt to the pull delay of multi-source heterogeneous data and the timeliness requirements of mine mining and beneficiation data |
| Field validation threshold | Above 85% | Ensure unit and format matching for exclusive fields such as mining volume and grade |
| Parallel pull node count | First 3 | Correspond to parallel processing of three data sources: mining, beneficiation, and safety inspection |
variables preset | Operation area, raw ore grade, beneficiation recovery rate | Match the fixed field structure of ledger documents |
nodeId filtering rule | Only retain specified node output | Avoid redundant result output from multiple AI chat modules |
| Report generation trigger timing | Fixed daily time + triggered by abnormal data alerts | Adapt to the shift-based and daily ledger update cycles |
Three Common Misconfigurations
- Symptom: The code run node operates normally during debugging, but the overall workflow run result still includes thinking content starting with
think:. Cause: Novariablesfiltering rule is configured in the workflow, and the output of the code node is not used as the only input source for subsequent nodes. This leaves the output of the preceding AI chat node not fully truncated. - Symptom: After restoring a project backup, intelligent agents associated with the backend knowledge base fail to display normally. Cause: The associated configuration of the bound knowledge base was not included in the project backup. Only the workflow file was restored, and the metadata backup of the knowledge base was not imported.
- Symptom: When multiple AI chat modules run in a workflow, the final output includes the conversation results of all modules. Cause: No
nodeIdfiltering rule is configured, and the output of only the last AI chat node is not specified. This results in the output of all nodes being displayed in sequence.
How to Confirm Correct Configuration
- Manually trigger the workflow, check the output logs of the data pull nodes, and confirm that multi-source data pull timing and update cycle match preset rules.
- Check the output results of the field validation node, and confirm that exclusive field units and formats meet mine mining and beneficiation ledger standard requirements.
- Run a test case with multiple AI chat modules, and confirm that the final output only retains results from the specified node, with no redundant content.
- Trigger a simulated abnormal data alert, and confirm that the report generation node starts automatically and generates the corresponding inspection report.
The values provided are common starting points and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.