What the data for this category looks like
Industrial park intelligent due diligence data comes from multiple sources: land planning announcement documents, park investment promotion ledgers, settled enterprises' industrial and commercial annual reports, property operation and maintenance logs, and regional supporting planning documents. It is primarily used for financial institution scenarios including park project credit granting and underlying due diligence for wealth management assets. Data update rhythm follows multiple tiers:
- Changes to settled enterprises and lease signing data are updated monthly
- Adjustments to land use and regional supporting planning are updated quarterly
- Property energy consumption and daily operation and maintenance data are synchronized daily
Document structure includes four core types: park overview pages, settled enterprise lists, property energy consumption reports, and regional supporting descriptions. Core fields include floor area ratio, building density (unit: %), lease area (unit: square meters), annual revenue of settled enterprises (unit: ten thousand yuan), and property service fee unit price (unit: yuan/square meter/month).
Constraints Imposed by Data Characteristics on Deployment and Upgrade
The multi-source heterogeneous data traits of industrial park due diligence create clear constraints for deployment and upgrade workflows. Mixed-format data sources (PDF investment documents, Excel settled enterprise ledgers, CSV energy consumption logs) require multi-format parsing adaptation rules during deployment. This prevents failed parsing of some files and preserves due diligence data completeness. Data updated on different cycles (daily property data, monthly signing data, quarterly planning data) requires adding incremental synchronization task configuration items during upgrades. This avoids excessive computing resource usage from full re-imports. Multi-unit numeric fields (square meters, ten thousand yuan, yuan/square meter/month) require standardized mapping rules during deployment. This prevents analysis deviations caused by inconsistent units for the same field across different sources. Long regional planning documents require adjusting parsing chunk parameters. This avoids content truncation that compromises the integrity of due diligence logic.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Industrial park documents often include long-text regional planning files; 600 seconds covers full parsing duration |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Large files such as park aerial images and large investment promotion brochures require support for large file uploads |
maxContext | 800–1200 characters | Park due diligence documents require complete field association information; this range balances context length and retrieval accuracy |
Recall count | Top 8 entries | Industrial park due diligence requires covering multi-dimensional data including settled enterprises, property, and planning; 8 entries covers core retrieval results |
Similarity threshold | 0.75–0.85 | Park due diligence data has strong field correlation; this range filters low-relevance redundant results |
PARSE_CHUNK_SIZE | 1500 characters | Park documents contain business content with multiple field combinations; 1500 characters preserves the integrity of single business information entries |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Configured due diligence data source synchronization rules and vector database connection information are lost after container restart. Cause: The vector storage directory was not mounted to the host's persistent storage volume, causing container-internal data to be cleared when the container is destroyed.
- Symptom: A
42P01error code appears when executing SQL commands after entering the pgvector container. Cause: Failed to switch to the corresponding business database, or failed to create a dedicated vector table adapted to industrial park due diligence data in advance. - Symptom: Visual analysis returns empty results after importing park aerial images. Cause: No image-to-base64 preprocessing script was configured, causing the model to receive no parsable image data.
How to Verify Successful Configuration
- Upload a park investment promotion document, check that the parsed text content is complete, confirming parsing configuration is active.
- Trigger an incremental synchronization task, check the update records in the synchronization log, confirming synchronization rule configuration is correct.
- Connect to the pgvector vector database, query the corresponding data table, confirm that imported due diligence data entries exist, confirming vector storage configuration is active.
- Upload a park aerial image, check the results returned by visual analysis, confirming image preprocessing workflow configuration is correct.
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.