What the data for this category looks like
The ledger data for refined oil sales originates from three core data sources: gas station POS transaction systems, oil tank level monitoring systems, and inventory management systems. Data update frequencies fall into three categories: real-time transactions, hourly inventory checks, and daily/weekly/monthly inventory summaries. Individual ledger entries include structured fields such as oil grade, station ID, transaction time, sales volume (liters/ton), unit price (yuan/liter), and remaining inventory. Cross-station summary ledgers may add regional statistical dimensions. Data is stored as a mix of structured tables and structured text; some historical archived data consists of semi-structured content transcribed from PDF scans.
Constraints imposed by these characteristics during deployment and upgrade
Multi-data source access requires configuring multiple data docking interfaces during deployment. Complete field mapping for all integrated systems in advance to prevent data misalignment during summary. High-frequency real-time transaction data creates high database concurrency pressure. Adjust database connection pool parameters to support peak request volumes. Semi-structured historical archived data requires specific text splitting rules. Retain the original splitting logic during upgrades; failure to do so will cause knowledge base recall failures. Stored ledgers and report data must comply with state-owned enterprise and public sector data security specifications. Configure compliant storage endpoints and access permissions to meet these requirements.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 200-300 connections | Matches the high-frequency transaction data writing and ledger query demands of refined oil sales, prevents connection exhaustion |
MONGODB_URI | Multi-station ledger data sharded cluster address | Stores massive volumes of historical transaction and inventory ledger data, supports horizontal scaling |
STORAGE_S3_ENDPOINT | Storage service endpoint compliant with state-owned enterprise and public sector data security requirements | Stores ledger-exported reports and original data backups, ensures data security |
REDIS_URL | Local high-availability Redis cluster address | Caches frequently queried station inventory and daily sales data, reduces database load |
FE_DOMAIN | Second-level domain name registered for internal enterprise use | Configures an internal access entry, ensures secure employee access to the ledger generation interface |
Three Common Misconfigurations
- Symptom: Ledger summary shows missing fields after upgrade, and report data is incomplete. Cause: Original custom field mapping rules were not backed up prior to upgrade, and FastGPT default configuration was overwritten during the process, preventing correct association of business fields.
- Symptom: Deployment fails on Huawei Euler ARM64 architecture, with an error indicating incompatible dependency package versions. Cause: The ARM64-adapted FastGPT v4.17.0 image was not used, and an x86 architecture installation package was deployed directly.
- Symptom: Model requests time out and load imbalance occurs after upgrade. Cause: Load balancing configurations for model channels were not updated synchronously, and old version routing rules remain in use, leading to overloaded model nodes.
How to Confirm Configurations Are Correct
- Run the database connection test script to verify if the
DB_MAX_LINKconfiguration supports the set concurrent request volume, and adjust to meet business peak demands. - Upload a test ledger entry, generate a report for the corresponding cycle, and confirm that returned data fields match the original data source exactly.
- Access the configured
FE_DOMAINaddress to verify that the ledger report generation function loads and exports normally. - Simulate a high-frequency data write scenario, check that the cache service linked to
REDIS_URLfunctions properly, and that database load stays within expected limits.
The values provided are common starting points and should be measured 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.