What the data for this category looks like
Precast concrete component production data covers the full workflow of factories producing PC components, pipe piles, bridge components, and municipal products. Daily documents include component detailed drawings, steel bar cutting lists, mold inspection records, pouring and vibration records, curing records, demolding and factory inspection reports. Data sources include BIM or detailed design documents, steel bar and concrete batch ledgers, mold table and curing kiln operation data, laboratory test results, and QR code traceability information. Update frequency is per single component, per batch, or per curing kiln cycle. Data fields include component ID, steel bar specification, concrete strength grade, curing temperature and duration, demolding strength value, etc. Units include MPa, cubic meters, hours, etc. Each component corresponds to multiple associated documents that must match the on-site lifting position one-to-one.
Constraints on Deployment and Upgrade
The multi-source heterogeneous data sources, high-frequency update rhythm, and complex document structure of precast concrete components impose clear constraints on FastGPT deployment and upgrade. Multi-source data requires adaptation to different storage formats, so database and object storage parameters must be configured separately. The high-frequency updates per single component, per batch, or per kiln require the system to support near-real-time data synchronization to avoid delays in inspection and audit. Complex document types ranging from 2D detailed drawings to 3D BIM files require targeted parsing and storage strategies. Serious error consequences such as misalignment and insufficient strength must be pre-emptively addressed, requiring data verification logic to be added during deployment to ensure that inspection records strictly match drawings and test data. Unlike the ready-mixed concrete delivery by truck model, single large components must be associated with on-site lifting positions, so position association fields must be added to the knowledge base to avoid confusion between similar components during retrieval.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://fastgpt:pass@db-host:27017/pc_component?authSource=admin | Stores high-frequency updated pouring and curing records, supports batch data writing for multiple component batches |
STORAGE_S3_ENDPOINT | https://s3-cn-east-1.example.com | Stores large-volume documents such as component detailed drawings and steel bar cutting lists, supports batch file upload and download |
REDIS_URL | redis://:redis-pass@redis-host:6379/1 | Caches frequently queried component lists and inspection rule data to reduce database access pressure |
FILE_TOKEN_KEY | Set via actual measurement | Generates access tokens for sensitive component documents to ensure only authorized personnel can access production data |
DB_MAX_LINK | 20–30 | Adapts to concurrent data query demands for multiple component batches, prevents connection pool exhaustion |
| Single search limit | Top 15 entries | Matches multi-source data recall for component inspection records, balances retrieval efficiency and coverage |
Three Common Configuration Errors
- A 404 error is returned when running the initial script. The cause is that after upgrading to FastGPT v4.17.0, the
PG_URLconfiguration was not updated, pointing to the old version's database address, preventing the system from reading the basic ledger data for precast components. - Empty results are returned during knowledge base retrieval. The cause is that
STORAGE_S3_ENDPOINTwas not configured, so component detailed drawings and other offline documents cannot be loaded, causing audit verification to fail to match drawings and production records. - Frequent timeout errors occur during application calls. The cause is that the
DB_MAX_LINKparameter was not adjusted, and the database connection pool capacity is insufficient to handle high-frequency component batch data query requests.
How to Verify Correct Configuration
- Access the FastGPT backend system settings page, check the database connection status, and confirm that the addresses configured for
MONGODB_URIandPG_URLcan connect normally. - Upload a precast component detailed drawing to the knowledge base, verify that file upload, storage, and preview functions work normally, and confirm that the
STORAGE_S3_ENDPOINTconfiguration is effective. - Run a component inspection record retrieval test, confirm that the number of returned results matches the configured single search limit, with no empty results or abnormal errors.
- Trigger an initial script execution, confirm that there are no 404, connection timeout, or permission-related errors, and the system can load basic data normally.
The values provided here are common starting points and should be evaluated against the samples applicable to the specific deployment.