What the Data for This Category Looks Like
The documents and materials for mineral mining and beneficiation primarily consist of geological exploration reports, mining work orders, beneficiation process cards, safety inspection logs, environmental emission reports, and similar items. Data sources include internal ERP systems, electronic and paper documents from geological and survey departments, and real-time data collected by on-site sensors. Update frequencies fall into three groups: daily inspection data is updated each day, monthly production plans are updated monthly, and annual geological exploration reports are updated annually. Most document structures use fixed headers, with standardized fields including mining area, mineral grade, daily mining volume (unit: ton), on-duty shift group, emission concentration (unit: milligram/cubic meter), and others. Some unstructured documents are scanned paper reports.
What Constraints These Characteristics Impose on Workflow Orchestration
Reference only; verify against the target FastGPT release and deployment documentation.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Data source node type | Structured data reading + OCR recognition | Covers both structured ERP data and scanned paper document data sources |
| Variable mapping rule | Match mineral mining and beneficiation-specific fields defined in variables | Adapts to the fixed field structure and naming rules of documents |
| Node trigger timing | Separate daily 0:00 scheduled trigger and document upload trigger | Matches the update frequencies of different data types |
| Permission validation switch | Enabled | Protects sensitive data such as mineral grade |
| Database connection timeout | 60 seconds | Adapts to the query response duration of mining ERP systems |
| Log retention period | 180 days | Meets compliance audit retention requirements |
Three Common Mistakes
- An AI node prompt error containing
chat:ai_input_is_eappears during workflow runtime. This occurs when the output of the preceding code node is not correctly bound to the AI node’s input parameters viavariables, leading to empty input fields or mismatched formats. - A "workflow validation failed" error is thrown when using the database connection plugin to connect to PostgreSQL. This happens when the data source configuration corresponding to
nodeIdis not properly associated, or when required parameters such as database address and port are left unfilled. - The daily mining volume field extracted by the text extraction node is empty. This occurs when the fixed field naming rules for mineral mining and beneficiation documents are not followed, and non-standard names such as "output" are used instead of "daily mining volume", preventing the node from matching the target field.
How to Verify a Successful Configuration
- Trigger a test workflow run, and check whether the output of the code node displays correctly in the AI node’s input parameter panel.
- Run the database connection plugin, and confirm that production data from the mining ERP system can be retrieved normally, with no validation failure prompts.
- Upload a standard mining work order, and check whether the text extraction node correctly extracts preset fields such as mining area, mineral grade, and daily mining volume.
- View the workflow runtime logs, and confirm that the binding relationship of
variablesparameters is normal, with no format error messages.
The values provided here 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.