What the data for this category looks like
Data for this category covers business data from coating lines for automobiles, home appliances, and construction machinery. Daily documents include coating process cards, paint batch lists, paint mixing records, film thickness and color difference reports, oven curves, defect repair orders, and VOCs ledgers. Data comes from paint supplier TDS/SDS documents, real-time collected data from spray booths and ovens, colorimeters, film thickness gauges, environmental monitoring equipment, and MES systems. Update frequencies follow per paint batch, per shift, and continuous collection schedules. Fields include paint batch number, film thickness value (unit μm), oven temperature (unit ℃), VOC concentration (unit mg/m³), defect type, and more. Formats include a mix of structured forms and unstructured files.
What constraints these characteristics impose on deployment and upgrade
The multi-source heterogeneous data structure and high-frequency update characteristics of this category create clear constraints for deployment and upgrades. First, multi-source data requires adaptation to different format access. During deployment, configure unified field mapping rules to avoid data confusion. Second, continuously collected sensor data has a high update frequency, so database connection configuration must support high-concurrency writes. During upgrades, adjust connection pool parameters. Third, compliance-related data such as VOCs ledgers and defect repair orders must follow storage configurations to ensure data retention meets business requirements. In addition, version upgrades after secondary development must be compatible with original custom fields to prevent loss of business data.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://fastgpt:securepass@db-host:27017/fastgpt_coating?authSource=admin&maxPoolSize=250 | Adapts to the secure version of MongoDB 5.0.24, resolves security vulnerabilities present in version 5.0.18 |
DB_MAX_LINK | 200–300 | Matches the high-concurrency write requirements of continuously collected spraying and oven data |
REDIS_URL | redis://:redispass@cache-host:6379/1 | Caches frequently read process card metadata and real-time sensor data to reduce database load |
STORAGE_S3_ENDPOINT | https://s3-cn-singapore.aliyuncs.com | Stores large-volume original oven curve files and color difference report images to ensure data reading efficiency |
PG_URL | postgresql://fastgpt:pgpass@db-host:5432/fastgpt_struct | Stores structured form data such as paint batch lists and defect repair orders |
Three common mistakes
- Symptom: The management background displays the old version number after an upgrade. Cause: The built-in FastGPT version identification file was not replaced during secondary development, so the front end retains the original version configuration without updates.
- Symptom: A MongoDB connection error occurs, prompting a security vulnerability. Cause: MongoDB was not upgraded from version 5.0.18 to a compliant version, and
MONGODB_URIwas not used to point to the secure database instance. - Symptom: Multiple variable update nodes cannot be merged into a single AI reply. Cause: Context storage parameters were not configured correctly, resulting in loss of variable data in the workflow, making it impossible to transfer data to the AI node for unified processing.
How to confirm the configuration is complete
- Access
FE_DOMAINand confirm that the FastGPT version number displayed in the upper right corner of the interface is FastGPT v4.17.0. - Run a MongoDB connection test to verify that the database version pointed to by the
MONGODB_URIconfiguration is 5.0.24 or higher. - Upload a coating process card file to confirm that the storage service configured via
STORAGE_S3_ENDPOINTcan normally write and read files. - Trigger a quality traceability workflow, submit variable data such as film thickness and oven temperature, and confirm that the AI reply node can correctly obtain and integrate all variable information.
The values provided here are common starting points and should be evaluated against the reader's own samples.