This document is compatible with FastGPT v4.17.0. The following content covers deployment and upgrade of the quality problem traceability and defect analysis feature for ready-mixed concrete production scenarios.
What this category of data looks like
Data sources for this category include engineering orders, sand, gravel, cement admixture inspection records, aggregate moisture content measurement data, mixing station control system logs, and project laboratory ledgers. Data updates occur per vehicle production record, per batch raw material inspection, and daily ledger summary. Daily documents include mix proportion notices, opening identification forms, production and delivery slips, raw material inspection reports, and test block compressive strength records. Fields cover raw material feeding amount, aggregate moisture content value, per vehicle slump value, test block age and strength value. Units include kilograms, cubic meters, megapascals, days, and others.
What constraints these characteristics impose on deployment and upgrade
Data update frequency is high, individual data volume is small, but total volume increases with production scale. Deployments must adapt storage configurations for high-frequency writes. Multiple heterogeneous data sources require unified access. Upgrades must support original document fields in different formats. Test block strength data must be linked to corresponding production batches and raw material batches. Deployments must configure associated indexes. Real-time data from transportation and pouring stages requires low-latency synchronization. Upgrades must optimize response speed of data links. Permission settings for different roles must match corresponding document scopes. Deployments must refine role permission configurations.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 200–300 connections | Adapts to high-frequency write database connection requirements, avoids connection exhaustion |
MONGODB_URI | Replica set connection address | Stores high-frequency write unstructured data such as production logs and test block records |
REDIS_URL | 8 GB memory, 10000 cache entry limit | Caches real-time production data and permission verification information, reduces database load |
STORAGE_S3_ENDPOINT | Nearby regional object storage node, maximum 1000 MB per file | Stores large-volume documents such as raw material inspection reports and strength reports, improves access speed |
ROOT_KEY | Random 32-character string | Configures system administrator permissions, avoids default key leakage risks |
FE_DOMAIN | Public network domain name of the deployment server | Configures frontend access address, adapts to mixing station intranet and public network mixed access scenarios |
Three common mistakes
- Symptom: Token invalid error returned when calling the large language model. Cause: Associated large model access key is not configured correctly, or the third-party interface configured key is not bound to the knowledge base permission for the ready-mixed concrete scenario.
- Symptom: Frontend page cannot load archived quality documents. Cause: Insufficient permissions for the storage node configured in
STORAGE_S3_ENDPOINT, cannot read archived files such as raw material inspection reports and strength reports. - Symptom: Connection timeout occurs when the system processes batch test block data. Cause:
DB_MAX_LINKconfiguration value is too low, causing high-frequency write requests to fail to obtain database connections.
How to confirm the configuration is correct
- Run the system's built-in database connection test script to confirm that the connection status of
MONGODB_URIandREDIS_URLis normal. - Upload a simulated mix proportion notice document to verify that the storage and reading functions of
STORAGE_S3_ENDPOINTwork properly. - Configure a test administrator account, log in using the permissions corresponding to
ROOT_KEY, and confirm that the role permission configuration takes effect. - Initiate a simulated quality traceability query to verify that the system can link test block records with corresponding production batch data.
The values provided are common starting points and should be measured against local deployment samples.