What the data for this category looks like
Ledger data for railway operations comes from multiple business nodes including dispatch command systems, passenger ticketing systems, freight loading and unloading systems, and line inspection terminals. Data updates follow two schedules: real-time and batch. Real-time updates include train arrival/departure and equipment status data, which refresh every second. Batch updates include monthly freight summaries and line operation reports, which refresh daily or weekly. Individual ledger documents include fields such as line number, train identification, operation time, operation type, operation volume, and corresponding equipment number. Units include passenger trips, tons, kilometers, and operating hours.
What constraints these characteristics impose on deployment and upgrade
The multi-source and heterogeneous access requirements of railway operation ledgers require adaptation to data pulling from business systems using different protocols during deployment. Upgrades must maintain compatibility with old data field mapping rules to avoid business interruptions. The second-level update rhythm of real-time data requires sufficient cache and concurrent processing capacity during deployment, to prevent data backlogs that disrupt subsequent report generation. The document structure with multiple fields and units requires validation of field parsing logic during upgrades, to prevent unit conversion errors that cause abnormal report content. Railway systems have high business continuity requirements, so deployment and upgrades must support gray-scale switching to avoid full downtime that disrupts daily operations.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–200 connections | Railway operation ledgers connect to multiple business systems, with many concurrent pulling requests. This range prevents connection exhaustion that causes data synchronization failures. |
REDIS_URL | Configure as a local high-availability cluster address, set cache expiration time to 30–60 seconds | Real-time ledger data requires low-latency caching. The expiration time matches the second-level update rhythm to prevent expired or invalid data. |
STORAGE_S3_ENDPOINT | Match the endpoint address of the railway intranet storage service | Most railway operation data is stored on the intranet. Using the intranet endpoint ensures data transmission security and speed. |
MONGODB_URI | Configure as a sharded cluster connection string | Ledger data volume grows with operation scale. Sharded clusters support horizontal scaling to avoid single-node performance bottlenecks. |
ROOT_KEY | Generate a 32-bit random string in accordance with internal enterprise key management specifications | Ensures the security of background access permissions for the deployment environment, and complies with the security and compliance requirements of railway systems. |
Three Common Mistakes
- Symptom: After deployment, logs show
ocr error, and equipment photos in inspection ledgers cannot be parsed. Cause:FILE_TOKEN_KEYis not configured, or the key does not match, so the built-in OCR tool cannot be called to parse image ledger attachments. - Symptom: No image understanding model option appears when creating a new ledger knowledge base. Cause: Corresponding permissions are not enabled during deployment, or the FastGPT version is lower than v4.17.0.
- Symptom: Connection timeout errors occur when importing historical ledgers in batches. Cause: The
DB_MAX_LINKconfiguration value is too low. Database connections are exhausted during concurrent imports, so batch requests cannot be processed.
How to Confirm Proper Configuration
- Run the built-in database connection test script to confirm that the
MONGODB_URIandPG_URLconfigurations can establish normal connections with no timeout errors. - Upload a single railway inspection ledger image to trigger OCR parsing. Confirm that no
ocr errorlogs are output, and the parsed results include correct equipment number and operation time fields. - Access the frontend address corresponding to
FE_DOMAIN, and confirm that the image understanding model option exists on the knowledge base creation page, and the FastGPT version is v4.17.0 or higher. - Simulate 100 concurrent ledger data pulling requests to confirm that the
DB_MAX_LINKconfiguration supports concurrent requests, with no connection exhaustion errors.
The values provided here 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.