What This Category of Data Looks Like
Data for this category originates primarily from on-site production PLC collection systems, electronic reports from the quality inspection center, ERP material management modules, and environmental protection online monitoring equipment. Data updates are completed per production shift, with routine ledgers generated once per shift. Emergency condition data is pushed in real time. Most documents are structured CSV files, batch-labeled PDF quality inspection sheets, and JSON-format material records exported from ERP. Fields include process code, material number, real-time condition value, and batch number. Units include tons, kilowatt-hours, and cubic meters. No percentage-based statistical values are included.
What Constraints These Characteristics Impose on Workflow Orchestration
Multi-source heterogeneous data formats and field differences require the workflow to first configure a unified data cleaning node to perform standardized mapping for material numbers and condition values from different sources. The update rhythm differs between real-time condition data and batch shift reports, so variables must be used to distinguish trigger types and bind nodeId to corresponding scheduling nodes. Electronic documents converted from paper archives may have missing fields, so the workflow needs a pre-check link to intercept files with missing key fields and prevent errors in subsequent processes. Industry-specific material coding rules require the workflow's global variables to preset industry mapping tables to avoid coding matching errors.
Reference only; verify against the target FastGPT release and deployment documentation.
How to Configure Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Global variable variables | Preset "smelting process mapping table", "unit conversion rules", and "industry field whitelist" covering 12 core production processes | Matches the exclusive material and process coding rules of non-ferrous metal smelting to avoid field matching errors |
Trigger node nodeId | Bind two core nodes: real-time monitoring trigger node and shift report scheduled trigger node | Distinguishes different scheduling logic for real-time data and batch reports |
| Document upload validation | Enable required field validation, requiring process code, material number, and batch number as required fields | Intercepts files with missing fields from paper-to-electronic conversions |
| Context retention rounds | Set the first AI call node to 1 round, and the second AI call node to 5 rounds | Corresponds to task complexity across different nodes: real-time quality inspection analysis only requires single-round context, while batch report drafting requires multi-round historical data |
| Timeout period | Set to 600 seconds | Covers the time required for batch report parsing and multi-node calls |
| Custom file reading address | Configure to the locally deployed file storage path, for example /data/metallurgy_docs | Resolves file upload and parsing path issues for local deployments |
Three Common Mistakes
- Symptom: When accessing the application via a link carrying the
?data=1123parameter, the workflow global variables do not load the corresponding parameter values. Cause: The external parameter receiving switch is not enabled in the workflow'svariablesconfiguration, or the parameter name does not exactly match the global variable definition. - Symptom: When accessing the workflow multiple times with the same business ID, multiple rounds of chat history remain in the first AI node. Cause: The history retention rounds are not limited in the context settings of the first AI call node, or the global session cleanup logic is not bound to the business ID dimension.
- Symptom: After local deployment and enabling workflow file upload, using the document parsing tool to read the file address returns an error. Cause: The
CUSTOM_READ_FILE_URLenvironment variable is not configured correctly, or the file storage path does not match the configured URL, causing the parsing tool to fail to access the target file.
How to Verify Successful Configuration
- Initiate an application access with custom parameters, and check if the global variables in the workflow logs load the corresponding parameter values.
- Simulate multiple accesses with the same business ID, and check if the context records of the first AI node only retain single-round data.
- Upload a test file to the local storage path, run the document parsing node, and confirm there are no errors and the parsing result includes the expected fields.
- View the environment variable configuration file, and confirm that the address of
CUSTOM_READ_FILE_URLmatches the local file storage path.
The values provided here are common starting points and should be measured 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.