What Data Looks Like for This Category
Data for this category originates from material grade archives, customer technical requirement documents, temperature control instrument collection data, furnace temperature uniformity test reports, hardness tester and metallographic equipment output results, and workshop MES systems. Data updates per heat and per batch. Single batch data includes heating, holding, and cooling parameters from heat treatment process cards, workpiece arrangement information from furnace loading diagrams, time-temperature sequences from furnace temperature curves, test results from hardness and metallographic reports, operation records from equipment inspection forms, and rectification information from exception handling sheets. Fields include material grade, furnace number, batch number, temperature parameters, test values, timestamps, and more. Units cover physical quantity units such as degrees Celsius, megapascals, millimeters, and others.
Constraints for Database and Operations Maintenance
High-frequency updates per heat and per batch require the database to support high-concurrency writes and queries to avoid data backlogs. Multi-source heterogeneous data formats include structured temperature parameters and test values, as well as unstructured furnace temperature curve images and process card documents. This means the database must support both structured storage and unstructured object storage. Significant field differences exist across different documents: process cards contain process parameters, while exception handling sheets contain rectification records. This requires flexible table structure design or document database adaptation. Data accuracy is directly linked to product performance. Operations maintenance must ensure atomicity of data writes and real-time query performance to avoid subsequent analysis deviations caused by incorrect data.
Configuration Settings
This document applies to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Database Connection Address (MongoDB) | mongodb://user:pass@host:27017/heatTreatmentDB?authSource=admin | Adapts to multi-source heterogeneous heat treatment data storage, supports mixed storage of unstructured documents and structured fields |
| Database Connection Address (PostgreSQL) | postgresql://user:pass@host:5432/heatTreatmentMeta?sslmode=require | Stores structured process parameters and batch metadata, ensures transaction consistency |
| Maximum Database Connections | 20–30 | Matches the concurrency requirements of batch writes per heat, avoids exhausting database connections |
| Redis Cache Address | redis://:pass@host:6379/0 | Caches frequently queried furnace temperature curve metadata and batch status information to reduce database query pressure |
| Data Write Timeout | 600 seconds | Adapts to the duration requirements of complete data writes for a single heat, avoids mid-process interruptions |
Three Common Mistakes
- Phenomenon: When invoking workflows concurrently 2-3 times per second, the MCP node returns empty values, and logs show no valid return content or timeout status codes. Cause: Maximum database connection pool and timeout parameters are not configured. High concurrency exhausts connections, causing requests to fail to respond normally.
- Phenomenon: After querying the database using a DB node, the returned result is JSON or a JSON array. Directly connecting this to the report generation process causes field parsing exceptions. Cause: Unified field mapping rules are not configured for the multi-source heterogeneous format of heat treatment data. This prevents correct identification and extraction of different types of fields such as process parameters and test values.
- Phenomenon: Authentication fails when connecting to the MongoDB database, and batch data cannot be written. Cause: The authentication source and access permissions are not correctly configured in the database connection address, causing the database to reject external connection requests.
How to Verify Proper Configuration
- Run a single heat data write test, check whether complete data records for the corresponding batch are generated in the database, and verify that the written fields match the business documents.
- Initiate multiple concurrent query requests, observe the usage of the database connection pool, and confirm that the number of connections does not exceed the preset threshold.
- Invoke the report generation node, verify that fields extracted from the database can be parsed normally and that compliant heat treatment report content is generated.
- Check the operations maintenance logs to confirm that there are no abnormal error messages such as database connection timeouts or authentication failures.
The values provided are common starting points and should be evaluated against the target system’s own sample data.