What Data Looks Like for This Category
Data for spraying and coating scenarios covers full-process documents and collected data from coating lines for automobiles, home appliances, construction machinery, and other use cases. 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 sources include TDS/SDS documents from paint suppliers, real-time operating data from spray booths and ovens, colorimeters, film thickness gauges, environmental monitoring equipment, and MES systems. Data update frequencies cover per paint batch, per production shift, and continuously collected real-time time-series data. Fields include film thickness (μm), color difference ΔE, defect types (particles, runs, orange peel, color difference), and more.
What Constraints These Characteristics Impose on the "Database and Operations Maintenance" Link
Data in this scenario has multi-source heterogeneity characteristics. It also has large differences in update frequencies. It requires association of data across multiple production windows. Multi-source heterogeneous data needs compatible storage for unstructured documents and structured time-series data. Data with different update frequencies requires the database to support mixed workloads of batch writes and real-time queries. Business logic for pre-treatment, spraying, and curing windows must be matched. This requires the database to support complex multi-table join queries. Compliance storage for VOCs ledgers requires the database to have traceable audit capabilities.
How to Configure Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://fastgpt:securepass@db-host:27017/painting_trace?authSource=admin | Stores unstructured documents such as coating process cards and defect repair orders. A dedicated business database must be specified. |
PG_URL | postgresql://fastgpt:securepass@db-host:5432/painting_data | Stores structured field data such as paint mixing records, film thickness, and color difference. Supports multi-table join queries. |
DB_MAX_LINK | 100–150 connections | Adapts to high-frequency time-series data read/write from continuous coating line collection. Prevents connection exhaustion. |
REDIS_URL | redis://:securepass@cache-host:6379/1 | Caches real-time status data of spray booths and ovens. Reduces database load from high-frequency queries. |
Three Common Misconfigurations
- Symptom: Empty results returned when searching for defect data, or time deviations between matched pre-treatment and spraying window data. Cause: Multi-table data is not associated by paint batch or production shift. Only single-table queries lead to missing associations.
- Symptom:
connection timeouterrors appear in container logs, and database connection count exceeds the limit. Cause: TheDB_MAX_LINKconfiguration value is lower than the concurrent read/write request count from continuous coating line collection. - Symptom: Excessively high query latency or data loss for VOCs ledger data. Cause: High-frequency updated environmental monitoring data and ledger data are not stored separately. This leads to overload on a single database.
How to Confirm the Configuration Is Correct
- Log in to the FastGPT database management interface. Verify that the connection status of
MONGODB_URI,PG_URL, andREDIS_URLis normal. - Upload one coating process card and its corresponding film thickness and color difference report. Confirm that the data can be correctly stored in the corresponding database and retrieved.
- Simulate real-time data write requests from the coating line. Check that the connection count for
DB_MAX_LINKdoes not reach the configured limit. - Search datasets that include VOCs ledgers. Confirm that data association relationships and update frequencies meet business requirements.
The values provided are common starting points and should be measured against the reader's own samples.