What the data for this category looks like
Data for this category primarily comes from daily production and compliance records of electroplating plants and hardware surface treatment workshops. These records include bath solution analysis records, process parameter cards, fixture usage ledgers, film thickness and adhesion test reports, hazardous waste management ledgers, and online water quality monitoring data. Data sources cover customer surface treatment specifications, bath solution test results, rectifier operating parameters, output from online water quality monitoring equipment, and hazardous waste disposal records. Data update frequency follows per bath batch, per shift, or requirements set by compliance monitoring. Individual records include fields such as bath solution component concentration (unit: g/L), rectifier output current (unit: A), film thickness (unit: μm), wastewater COD value (unit: mg/L), and fixture number. Some records must be associated with batch workpiece numbers.
What constraints these characteristics impose on HTTP Interfaces and External Systems
Data for this category is multi-source and scattered. It requires integration with multiple external systems including bath solution test systems, rectifier data collection modules, online water quality monitoring platforms, and hazardous waste management systems. HTTP interfaces must support multi-source data pulling and batch data synchronization. Data update frequency includes both high-frequency real-time (per bath batch data) and scheduled (per shift bath solution replenishment parameters) patterns. Interfaces must support both on-demand trigger calls and scheduled task scheduling modes. Data fields cover multiple types such as chemical concentrations, electrical parameters, and environmental protection indicators, and require association with batch workpiece numbers. Interfaces must support field mapping and batch dimension queries to enable forward traceability and reverse traceability of quality problems. Some compliance-related data requires strict identity verification. Interfaces must be configured with secure access control rules.
How to configure the settings
These configurations are based on FastGPT v4.17.0, as detailed below:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {Exclusive API Key} | Ensures access security for external system interfaces, and complies with compliance management requirements for production data |
variables | {"batchNo": "{{Workpiece Batch Number}}", "dataType": "slurry_analysis"} | Passes batch identification and data classification parameters to enable accurate production data pulling |
responseData | {"code": 200, "data": [], "message": "success"} | Unifies the return format of external systems to facilitate structured storage and defect analysis |
| API call timeout | 300 seconds | Covers the maximum time required for multi-source data pulling, and adapts to the volume of single-batch data for this category |
| Scheduled task trigger interval | 30–60 minutes | Matches the update rhythm of per-shift bath solution replenishment parameters to ensure data timeliness |
| Concurrent call limit | First 10 entries | Adapts to production batch scale during a single time period, avoiding overload of external interfaces |
Three common configuration mistakes
- Symptom: The interface returns a
401 Unauthorizedstatus code, or system logs show identity verification failure. Cause: TheAuthorizationparameter is not configured correctly, an expired API key is used, or the configuration does not match the identity verification rules of the external system. - Symptom: Multiple failed connections to external databases or system interfaces after restarting deployment. Cause: Connection parameters for external systems (such as database address, port) are not written to FastGPT's environment variable configuration, or illegal characters are included in the configuration parameters leading to parsing failure.
- Symptom: The
responseDatafield returned by the interface is empty, or does not include expected film thickness and bath solution concentration data. Cause: Correct data classification and batch parameters are not configured invariables, causing the external system to not return matching production data.
How to confirm the configuration is complete
- Call the test interface, check whether the identity verification result passes, and confirm that the
Authorizationconfiguration is effective. - View FastGPT system logs to confirm that the external system connection status is normal, with no errors related to connection timeout or access denial.
- Initiate an interface call with batch parameters, and verify whether the fields returned in
responseDatamatch the preset production data fields. - After configuring the scheduled task, wait for the trigger cycle to end, check whether the pulled bath solution data has been synchronized to the system, and confirm that the scheduled call logic is effective.
The values provided are common starting points and should be evaluated against the reader's own samples.