What this category’s data looks like
Construction machinery investment research data comes from four primary sources: public reports released by a national construction machinery industry association, official product manuals from original equipment manufacturers, bidding announcements published on the national public resource trading platform, and publicly available equipment operation monitoring datasets.
Three update schedules apply to this data:
- OEM product parameter documents are updated quarterly
- Bidding data is synchronized in real time
- Monthly industry reports are released on a fixed monthly cadence
Individual documents contain core parameters including equipment model, rated power, operating weight, working radius, and fuel consumption rate. Supporting project winning bid information includes project name, winning bidder, procurement quantity, and winning bid amount.
All field units follow international standard conventions: power is measured in kW, weight in t, working radius in m, and monetary amount in ten thousand yuan.
What constraints do these characteristics impose on database and operations
The multi-source dispersion and inconsistent update rhythms of construction machinery investment research data require databases to support incremental synchronization and batch import of multi-source heterogeneous data. This avoids full-volume repeated synchronization that wastes storage and computing resources.
Equipment parameter documents have numerous fields and strict unit requirements. Databases must include field validation rules to prevent data errors caused by unit mismatches.
Bidding data has high real-time demands. Databases need low-latency connection pools to ensure timely data synchronization.
Individual product documents can reach thousands of characters in length. Databases must support large field storage, and require reasonable index configuration to avoid degraded query performance.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 2000 MB | Individual construction machinery product manual documents typically do not exceed 1500 MB, with reasonable buffer space reserved |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Long document parsing requires significant time, preventing import failures due to mid-process interruptions |
DB_CONNECTION_POOL_SIZE | 20–30 | Meets concurrent import and query demands in investment research scenarios, balancing resource usage and response speed |
RECALL_CHUNK_SIZE | 800–1200 characters | Matches the length of core information segments in construction machinery parameter documents, ensuring completeness of recalled information |
DB_FIELD_VALIDATION_ENABLE | Enabled | Ensures consistent unit and format standards for equipment parameter fields, preventing errors in investment research data |
SYNC_INCREMENTAL_INTERVAL | 300 seconds | Adapts to the real-time requirements of bidding data while reducing load pressure on the database |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Pitfalls
- Issue: Connection timeout or authentication failure when connecting to the local FastGPT MongoDB database via MongoDB Compass. Cause: Database connection parameters in FastGPT environment variables are not configured correctly, or local firewall traffic on the default MongoDB port 27017 is blocked.
- Issue: Docker deployment of FastGPT fails, with a database connection error prompt. Cause: The correct MongoDB connection address is not specified in the deployment command, or the database service is not running properly.
- Issue: Some fields are empty or have mismatched units after importing construction machinery bidding data. Cause: The database field validation switch is not enabled, or the field format of the imported file does not match the preset database table structure.
How to Verify Proper Configuration
- Run a local MongoDB connection test. Connect to the configured database address via MongoDB Compass, confirm successful login and visibility of created knowledge base collections.
- Upload a small construction machinery product manual document. After parsing completes, check that the segmented parsed content includes complete core parameter fields with no obvious truncation or format errors.
- Configure an incremental synchronization task. After the preset synchronization interval elapses, check whether the latest bidding data entries have been added to the database.
- Initiate an investment research knowledge base recall query. Confirm that the number of returned results matches the configured recall parameters, with no abnormal error messages.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.