What the data for this category looks like
Industrial park investment research data sources include investment promotion ledgers, annual operating reports of settled enterprises, local industrial policy documents, property operation logs, and land transfer announcement information. Update frequencies vary: investment promotion data is updated quarterly, industrial policies are released in real time, and property operation logs are updated daily. Document structures include structured tables (e.g., settled enterprise revenue, tax per mu), unstructured PDFs (e.g., park planning plans, original policy texts), and land planning maps in image format. Fields include "Settlement Date (YYYY-MM-DD)", "Leased Area (square meters)", "Tax per Mu (ten thousand yuan/mu)", "Industry Type", and others. Some fields have mandatory unit requirements.
What constraints these characteristics impose on the database and operations link
Structured data carries precise units, so structured queries and field validation must be supported to avoid retrieval errors caused by mismatched units. Data update frequencies are uneven, so incremental updates per module must be supported to avoid excessive operation and maintenance resource usage from full updates. Document sizes vary widely, from a few KB of policy summaries to hundreds of MB of planning drawings, leading to uneven resource usage requirements for upload and parsing. There is strong demand for cross-data source association, so compatibility with enterprise-level databases such as SQL Server is required, while ensuring stability for concurrent queries.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Industrial park documents include large planning drawings and multi-page annual report PDFs. Standard single-file limits cannot cover these use cases. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large park planning documents and multi-page settled enterprise annual reports takes a long time. The default timeout duration is insufficient. |
Recall count | Top 10-15 entries | Industrial park investment research requires covering multi-dimensional associated data including settled enterprises, policies, and property. Too few retrieved entries cannot meet associated query needs. |
Similarity threshold | 0.72-0.80 | Park policies and enterprise operating data have high similarity. A threshold that is too low will introduce irrelevant results, while a threshold that is too high will miss core retrieved data. |
SQL_CONNECTION_POOL_SIZE | 8-12 | Industrial park investment research databases are often associated with structured SQL Server data. An overly small connection pool will cause concurrent query blocking. |
Incremental Update Trigger Interval | Per-module tiered settings | Investment promotion data is updated weekly, industrial policies are updated in real time. Different update cycles need to be configured per module. |
The parameter values provided on this page are all conventional recommendations used to determine the starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Calling the SQL Server database returns
400 Connection Failedor503 Service Unavailable. Cause:SQL_CONNECTION_POOL_SIZEis not configured to adapt to concurrent requests from multiple park data sources, or the connection string does not specify the correct database instance port and authentication method. - Phenomenon: Retrieval takes more than 10 seconds, and some requests trigger
PARSE_FILE_TIMEOUTerrors. Cause:PARSE_FILE_TIMEOUT_SECONDSis configured too short, causing retrieval to trigger before parsing of large park planning documents is completed, orUPLOAD_FILE_MAX_SIZEis set too low, causing large file upload failures and subsequent retrieval of invalid data. - Phenomenon: Global variable values are disordered under multiple concurrent retrieval requests. Cause:
GLOBAL_VAR_SCOPEis not configured for request-level isolation, and global variables are mistakenly set to process-level sharing, causing data interference between different requests.
How to confirm proper configuration
- Upload a single 1GB park planning PDF, check that the upload progress completes normally, and no
UPLOAD_FILE_EXCEED_LIMITerror occurs. - Initiate an SQL query call, verify that structured data such as tax per mu and leased area of settled enterprises can be returned, and no connection exception prompt appears.
- Initiate a retrieval request, check that the number of returned results matches the preset
Recall countconfiguration, and the similarity score of each result falls within the0.72-0.80range. - Initiate 10 concurrent retrieval requests, check that no
503 Service Unavailableerror occurs, and all requests return results within a reasonable duration.
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.