What data for this category looks like
Data for this category mainly comes from the shop floor MES systems, crew management platforms, enterprise OA systems, and material management systems of shipbuilding enterprises. Production work order data is synchronized every 15 minutes. Crew attendance and scheduling data is updated daily at midnight. Material inventory data is refreshed hourly. Most documents are in structured JSON format, containing fields such as job number, ship model, workstation ID, task status, completion duration (unit: hours), and responsible employee ID. Some attendance data additionally includes information such as position and duty shift.
What constraints these characteristics impose on the "HTTP Interfaces and External Systems" link
Reference only; verify against the target FastGPT release and deployment documentation. Differing update frequencies across multiple data sources require interfaces to support pulling data via custom time windows, to avoid duplicate pulls or missing latest production and office information. Fixed formats for ship model and workstation ID require interface parameters to validate field legitimacy and filter invalid requests. The upper limit of data pulled in a single request is determined by the return limit of the shop floor system, so pagination logic must be adapted to avoid request timeouts. Internal office and employee services involve sensitive crew and production data, so authentication mechanisms are required to secure access permissions. It is also necessary to handle missing fields returned by some interfaces to avoid parsing failures. Most interface calls in shipbuilding scenarios occur on the shop floor intranet, so adaptation to unstable network environments is needed, with reasonable timeout and retry strategies set.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {API key assigned by internal systems} | Complies with authentication standards for internal interfaces of public-sector and state-owned enterprises, ensuring access security for production and employee data |
| Data pull time window | Last 15 minutes | Matches the real-time update rhythm of shipbuilding production work orders |
| Single pull data limit | Top 180 entries | Adapts to the data volume limit of single returns from shop floor MES systems |
responseData field extraction rule | Extract workorderid, shipmodel, taskstatus | Matches the standard document structure of internal office data for shipbuilding |
| Timeout setting | 60 seconds | Prevents long request blocking caused by shop floor intranet fluctuations |
variables binding parameters | Pass the current user's department ID | Used to filter office data related to employee services by department |
Three common errors
- Phenomenon: Calling external production system interfaces returns 401 Unauthorized error. Cause:
Authorizationparameter is not configured correctly, or an expired internal API key is used. - Phenomenon: After restarting the FastGPT service, all external interface configuration items are lost. Cause: Interface configuration is stored in the container temporary directory, and no persistent storage path is mounted.
- Phenomenon: Pulled crew scheduling data is not filtered by position, returning full scheduling information for all crew in the enterprise. Cause: No position parameter is bound in
variables, leading to an unexpected pull scope.
How to confirm configurations are properly set
- Manually trigger an interface call, check if the returned
responseDatacontains expected shipbuilding-related fields such as shipmodel, workorder_id. - Check if the format of the
Authorizationparameter complies with the Bearer token specification, and verify the validity of the key via the internal authentication system. - Simulate a FastGPT service restart, confirm that configuration items are not lost, and verify that the persistent storage path is configured correctly.
- Pull data within the specified time window, confirm that the number of returned entries matches the single pull limit setting, with no obvious data truncation.
The values listed above 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.