What data for this category looks like
The data for this category comes from formula systems, raw material batch records, internal mixers, Mooney viscometers and rheometers, vulcanizing machines, and laboratory testing equipment. Daily documents include formula sheets, weighing records, mixing curves, rubber quick-test reports, vulcanization process cards, mold ledgers, and finished product performance reports. Data updates follow the rhythm of each raw material batch truck, each batch of mixed rubber, and each vulcanized product per mold. Fields include batch numbers, process parameter values, test results, operator identifiers, and more. Units include temperature (℃), time (seconds), hardness (Shore A), tensile strength (MPa), and other standard units.
What constraints do these characteristics impose on the database and operations maintenance link
Multiple heterogeneous data sources cause inconsistent data formats. Database operations and maintenance teams must configure unified field mapping rules to address this. The high-frequency update rhythm requires configuring connection pool parameters such as DB_MAX_LINK to support high-concurrency write requests. Various process and test parameters use different units. Database layers must include field validation rules to prevent incorrect unit entries. Full-link traceability requires establishing batch association indexes between data entries, ensuring the entire process from raw materials to finished products can be queried. Data anomalies may trigger large-scale scrap. Teams must configure regular backup and fault recovery mechanisms to protect data security.
How to set configurations
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Database connection pool maximum connections | 20–30 | Adapt to high-concurrency writes for per-truck rubber and per-mold operations; adjust based on actual measurements |
MongoDB connection address MONGODB_URI | mongodb://fastgptuser:custom password@database IP:27017/fastgptrubber?authSource=admin | Store unstructured data such as mixing curves and test reports, adapting to semi-structured storage requirements |
Redis cache address REDIS_URL | redis://:custom password@cache IP:6379/0 | Cache frequently queried batch association indexes to reduce query pressure on the primary database |
PostgreSQL connection address PG_URL | postgresql://fastgptuser:custom password@database IP:5432/fastgptrubber | Store structured data such as formula sheets and weighing records, ensuring transaction consistency |
| Automated database backup cycle | Daily once | Match the business data update rhythm, ensuring recoverability of full-process business data |
Three common misconfigurations
- Phenomenon: When running batch mixed batch data writes, the parameter list is output in the prior step, but no response is received or a timeout error is returned after execution. Cause: The database connection pool maximum connections setting is too low to support concurrent batch write requests.
- Phenomenon: An authentication failure prompt appears when trying to log in to the database using a custom MongoDB user, or data read/write operations cannot be performed. Cause: The permission scope of the MongoDB user is not correctly configured, and read/write permissions for the corresponding business database are not specified.
- Phenomenon: AI-generated database query statements cannot match the category-specific data fields, returning empty results or results that do not conform to business logic. Cause: The category-specific data fields and business rules are not imported into the knowledge base, causing the AI-generated query statements to not match the actual data structure.
How to confirm configuration is complete
- Run a single mixed batch data write operation. Check database logs for no connection errors to confirm basic connection configurations are active.
- Submit batch vulcanized product data queries. Observe cache hit logs to confirm Redis cache configurations are functioning correctly.
- Perform a manual database backup and recovery test. Verify that the backup file can fully restore complete business data to confirm backup configurations are valid.
- Run cross-source data association queries, such as querying associated mixing, vulcanization, and finished product test data using a raw material batch number. Confirm association index configurations are correct and complete results are returned.
The values provided are common starting points and should be measured against the reader's own samples.