What the data for this category looks like
Business data for precast concrete components comes from BIM detailed design files, rebar and concrete batch ledgers, mold table and curing kiln operation logs, laboratory test reports, and QR code traceability information attached to components. Daily documentation includes component detailed drawings, rebar cutting lists, mold inspection records, pouring and vibration logs, curing cycle records, and demolding and factory inspection reports. Data updates trigger synchronously with individual components, each batch of materials, and each curing kiln process. Fields include component number, mold number, pouring time, curing duration, strength test values, and additional relevant metrics. Units include millimeters, cubic meters, hours, megapascals, and other standard units.
What constraints these characteristics impose on deployment and upgrades
Business data for precast concrete components includes two formats: structured ledgers and engineering drawings. It updates frequently alongside individual components, each batch of materials, and each curing kiln process. Unified access to multi-source heterogeneous data must be supported during deployment. Components must correspond one-to-one with on-site lifting positions, so data association relationships must strictly match fields such as component number, mold number, and batch number. Field mapping rules must be clearly documented before deployment. Data volume increases with production scale. Stable database and cache resources must be configured to maintain query efficiency. During upgrades, existing QR code traceability interfaces must be compatible to avoid interrupting data synchronization workflows for production links.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 connections | Precast concrete component production data updates frequently. Sufficient database connection pool capacity is required to support concurrent ledger queries and document reads |
MONGODB_URI | mongodb://{username}:{password}@{host}:27017/fastgpt_pc | Unstructured documents such as component detailed drawings and curing records are suitable for storage in MongoDB. A dedicated database instance must be specified to isolate business data |
REDIS_URL | redis://:{password}@{host}:6379/0 | Frequently updated QR code traceability data for components must be cached in Redis to ensure query latency is controlled within 200 milliseconds |
STORAGE_S3_ENDPOINT | Configure according to the address of the self-built object storage node | Engineering drawings and inspection reports for precast concrete components have large file sizes. Object storage must be supported to enable batch upload and reading |
FILE_TOKEN_KEY | Randomly generated 32-bit string | Access permission security for component documents must be ensured to prevent unauthorized downloading of inspection reports or detailed drawings |
ROOT_KEY | Configure according to production management permission levels | Different data access permissions for production teams, laboratories, and management personnel must be differentiated, in line with factory permission management processes |
Three common mistakes
- Symptom: A file upload failure error is triggered when uploading component detailed drawings or inspection reports, and the issue persists after redeployment. Cause:
STORAGE_S3_ENDPOINTor object storage access keys are not configured correctly, preventing files from being written to the storage node. - Symptom: The vector model fails to load normally, and no matching results are returned when querying content related to component defects. Cause: A lightweight vector model for GPU-free environments is not adapted in the deployment environment, or the vector service connection address is not configured correctly.
- Symptom: The database configuration step is skipped during local development environment deployment, and the Docker Compose process is started directly. Cause: The step-by-step deployment process from the official documentation is not followed, and the configuration logic for development environments and quick deployment is confused.
How to confirm that configurations are correct
- Use the database connection test tool built into FastGPT v4.17.0 to verify that the database instances configured with
MONGODB_URIandPG_URLcan read and write data normally. - Upload a small component detailed drawing to confirm that the file can be successfully uploaded and previewed in the system, verifying that the
STORAGE_S3_ENDPOINTconfiguration takes effect. - Simulate multiple concurrent component data query requests to observe the load of the database connection pool, confirming that the
DB_MAX_LINKvalue meets the concurrency requirements of the current production scale. - Log in to the system backend to verify that users with different permission roles can only access component data corresponding to their permissions, confirming that the
ROOT_KEYpermission configuration takes effect.
The values provided are common starting points and should be measured against the reader's own samples.