What this category’s data looks like
Typical entities in this category are coating, resin, additive, agrochemical intermediate, and specialty chemical enterprises. These entities regularly produce documents including batch production instructions, material verification slips, reaction process records, cleaning records, inspection reports, deviation and change orders, and others. Data sources include formula and process libraries, material batches, reactor DCS systems, weighing systems, LIMS, and warehouse systems. Updates occur once per batch production or each critical step. Document fields include feeding quantity (unit: kg or L), reaction temperature (unit: ℃), reaction duration (unit: hours), and additional fields. Due to formula confidentiality requirements, data is only accessible to authorized personnel.
Constraints on HTTP Interfaces and External Systems From These Characteristics
Multi-source heterogeneous data sources require interfaces to pull data from systems such as DCS, LIMS, and warehouse systems. Interfaces must adapt to each system’s interface format. Update frequency tied to batches or critical steps requires interface call timing to match production batches. This prevents excessive calls or delayed calls. Structured multi-field documents require interface-transmitted parameters to include core fields such as batch number, feeding quantity, and reaction temperature. Simple text-only transmission is insufficient. Formula confidentiality demands strict security verification for interfaces. This blocks unauthorized access to sensitive data. Severe errors such as reaction runaway require interfaces to include failure retry and exception log recording configurations.
How to Set Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Authorization | Bearer {enterprise-specific API key} | Matches the security requirement of formula data confidentiality to prevent unauthorized access |
| variables | Structured object containing fields such as batch number, feeding quantity, reaction temperature | Meets the drafting requirements for multi-field process documents and exception reports |
| responseData | Complete text of generated process documents and exception reports, plus associated document IDs | Satisfies update requirements for batch-based archiving |
| Single request timeout | 600 seconds | Adapts to long-duration scenarios for reactor DCS data pulling |
| Workflow trigger condition | Triggered upon completion of critical steps per batch | Matches the production rhythm of per-batch updates |
| Knowledge base association switch | Enabled | Associates source data from formula and process libraries |
Three Common Mistakes
- Scenario: API call returns empty values, and generated process documents lack feeding data. Cause: Required fields such as batch number and material batch are not correctly passed in the
variablesparameter. This fails to associate source data from corresponding systems. - Scenario: Uploaded CSV template does not take effect when using the format of question in first column, answer in second column. Cause: No configuration to map process document fields to CSV columns. This leads to incorrect field mapping relationships.
- Scenario: Timeout error occurs after workflow triggers. Cause: Single request timeout is not set to match the duration of reactor data pulling. This causes interface requests to be interrupted before completion.
How to Confirm Configuration Is Complete
- Call the FastGPT v4.17.0 test interface. Check if the returned
responseDataincludes core fields such as batch number and feeding quantity, and matches source system data. - Upload a test CSV file. Verify that content in the first and second columns is correctly identified as questions and answers.
- Review the
Authorizationparameter configuration. Confirm that a valid, enterprise-specific API key is used, with no exposure risk. - Trigger a simulated batch workflow. Check that request duration stays within the preset 600-second timeout limit, with no timeout errors.
The values provided here are common starting points. They should be evaluated against the reader’s own samples.