What the data for this category looks like
Construction machinery investment research data comes from four main sources: official manufacturer product manuals, industry association working condition monitoring reports, real-time sensor data streams from equipment operations, and brokerage research reports on the construction machinery sector. Public product manuals are mostly in PDF format, and include structured parameter tables, job operation instructions, and troubleshooting workflows. Industry reports are mostly mixed text and graphics documents. Sensor data is streaming time-series data that includes equipment operating parameters. Update rhythms vary significantly: product manuals are updated quarterly alongside model iterations, industry reports are released monthly, and sensor data is updated hourly. Most document fields use standardized engineering units, such as rated lifting height (meters), fuel consumption (liters per hour), and operating weight (tons). Some documents also include custom working condition adaptation parameters.
What constraints these characteristics impose on deployment and upgrade
The multi-source nature, varying update rhythms, and structured field features of construction machinery data create multiple constraints for deployment and upgrade. Multi-source data requires adaptation to different access protocols. Public documents need support for bulk PDF uploads. Real-time sensor data needs configured API pull interfaces. Deployment must reserve configuration entrances for multi-source synchronization. Data with different update frequencies requires differentiated synchronization tasks. This avoids excessive resource usage from high-frequency synchronization, or data lag from low-frequency synchronization. Structured parameter units and field associations require precise parsing. Deployment must configure parameter extraction rules to avoid messy parsed fields or lost units. Mixed access of long documents and streaming data requires adapted segmentation and recall logic. This ensures the completeness and accuracy of investment research retrieval.
How to set configuration items
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Individual construction machinery product manuals can be hundreds of pages long, with large single-file sizes, so this setting accommodates large file upload requirements |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Long document parsing takes significant time, and the default timeout period is insufficient to complete full parsing of complex parameter documents |
chunkSize | 800–1200 characters | Construction machinery documents contain long sentences and structured parameters. Excessively long segmentation breaks parameter associations, while excessively short segmentation loses contextual semantics |
recallTopK | Top 8–12 results | Investment research scenarios require multi-dimensional parameter comparisons. Too few recall results cannot support complete analysis, while too many increase context redundancy |
similarityThreshold | 0.72–0.80 | Differences in parameters between similar models must be distinguished. A threshold that is too low will mix in documents from unrelated categories, while a threshold that is too high will miss reference content for similar configurations |
DATA_SYNC_INTERVAL | Configured by data source type: public documents synced weekly, real-time sensor data synced hourly | Data update rhythms vary significantly across sources, so matching sync frequencies ensures data timeliness |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Issue: Large product manual uploads fail during parsing, with
ETIMEDOUTerror returned in logs. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted, and the default timeout period is insufficient for long document parsing. - Issue: Search results mix in documents from non-construction machinery categories, or parameter matching for the same model is overly inaccurate. Cause: The similarity threshold was set incorrectly, and the value range was not adjusted for the precise matching needs of construction machinery models.
- Issue: The embedding model fails to load after local deployment, and document vectors cannot be generated. Cause: The deployment path of the local embedding model was not configured correctly, and the adapted embedding model package was not pulled as required, causing the vector generation process to interrupt.
How to confirm the configuration is correct
- Upload a standard construction machinery product manual, and check if the parsed segments retain complete parameter fields without truncation or garbled text.
- Initiate a search for a specific device model, and verify that the number and similarity of recall results fall within the configured value range.
- Test sync tasks for different data sources, confirm that public documents and real-time sensor data can complete updates at the preset frequency.
- Check the running status of the embedding model, confirm that the locally deployed embedding model can normally generate document vectors without error logs.
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.