What the data for this category looks like
Typical entities for this category include raw material warehouses, line-side warehouses, finished product warehouses, and logistics and material control departments. Daily generated documents include receiving slips, putaway slips, material picking and return slips, warehouse transfer slips, inventory counts, lot ledgers, first-in first-out (FIFO) and expiration date records. Data sources cover ERP/WMS systems, barcode or RFID equipment, procurement and work order information, warehouse location configurations, AGV or conveyor systems, and weighing data. Data update frequencies fall into three categories: real-time (such as AGV position, real-time inventory), daily (such as inventory summary reports), and monthly inventory (such as full warehouse book-to-physical count data). Core fields include material code, lot number, warehouse location code, expiration date, inspection status, and book-versus-physical difference identifier.
What Constraints These Characteristics Impose on HTTP Interfaces and External Systems
Since data sources for this category are scattered, multiple external HTTP interfaces must be connected, including ERP/WMS, supplier quality inspection systems, AGV scheduling systems, and others. Interface authentication and parameter adaptation must cover the different rules of multiple systems. Data update frequencies vary greatly: real-time data (such as AGV position, real-time inventory) requires frequent interface calls, while monthly inventory data only requires regular synchronization. The interface must support flexible call interval configuration. Core fields include lot number, expiration date, inspection status, and others. Interface parameters must accurately match these fields, otherwise the audit process will fail to obtain valid data. At the same time, the business requirements of book-physical consistency and lot traceability mean that the accuracy and completeness of interface returned data directly affect the reliability of audit results.
How to Configure Settings
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Authorization | Bearer custom API key | Follow general authentication specifications when connecting to ERP, WMS, and supplier quality inspection systems to ensure data access security |
variables | Pass lot number, warehouse location code, material code | Match core query dimensions for warehousing scenarios to accurately locate inspection and inventory data for target lots |
responseData | Only return inspection status, expiration date, book-versus-physical difference fields, with no more than 10 records | Compress returned data volume to improve interface call efficiency and meet core information needs for audit scenarios |
| Interface retry count | 3 times | Address temporary call failures caused by network fluctuations to ensure data synchronization stability |
| Polling interval | 60 seconds (real-time data scenarios), 86400 seconds (daily report scenarios) | Match update frequencies of different data sources to avoid invalid calls or data lag |
Three Common Configuration Errors
- External interface returns 404 status code. Cause: The
Authorizationparameter is not configured correctly, or the external system interface address does not match the business entity fields of the category (such as missing lot number parameter). - Interface returns empty data fields. Cause: Core query dimensions (such as lot number, warehouse location code) are not passed in
variables, causing the external system to fail to locate target data. - External system rate limiting is triggered during batch queries. Cause: The number of returned entries for
responseDatais not restricted, resulting in excessive data transmission volume for a single call.
How to Verify Successful Configuration
- Call the test interface to check whether
Authorizationauthentication passes, and the returned status code meets expectations. - Pass a known lot number and
variablesparameters to verify that the returned data includes preset core business fields. - Simulate a network fluctuation scenario to check whether the interface retry mechanism takes effect and there are no abnormal errors.
- Compare the
responseDatareturned by the external system with local warehouse ledger data to confirm that the field matching degree meets business requirements.
The values provided are common starting points and should be measured against the reader's own samples.