Reference only; verify against the target FastGPT release and deployment documentation.
What This Category of Data Looks Like
Data sources for this category include the national unified construction project EIA declaration and approval system, ecological environment zoning control results, pollution discharge permit platform, and monitoring data. Update frequency is irregular. Daily documents include environmental impact assessment reports or report forms, approval applications, public participation explanations, technical assessment opinions, and EIA approval documents. These contain both long-text report content and structured approval application fields. Data fields cover project name, approval document number, construction unit, EIA level, pollution prevention measures, approval date, and more, with multiple data types including classification codes, date strings, and long text.
Constraints Imposed on Database and Operations Workflows
Multi-source data access requires support for cross-system data synchronization and verification. Irregular updates mean trigger-based data update logic must be supported. Mixed-structured data (long text and structured forms) requires both unstructured storage and complex query capabilities. The diversity of fields requires the database to support multi-type index configuration. Project approval errors have serious consequences, so data storage must have strong consistency to avoid dirty data or missing fields affecting subsequent approval processes. Additionally, ledger summary and report generation require batch processing of large volumes of project data, which places demands on database concurrency handling capabilities.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://[username:password]@host:port/ep_db?authSource=admin | Stores unstructured long-text documents such as EIA reports and public participation explanations, adapting to the flexible storage needs of semi-structured data |
PG_URL | postgresql://user:pass@host:port/ep_struct?sslmode=require | Stores structured fields such as approval applications, approval document numbers, and project classifications, supporting complex queries and transaction consistency guarantees |
DB_MAX_LINK | 10–15 | EIA data queries are mostly batch verification scenarios. Too many connections will cause database load overload, while too few connections will fail to support concurrent ledger queries |
REDIS_URL | redis://:cache_pass@host:port/1 | Caches commonly used ecological environment zoning control results and project classification mapping data, reducing latency of cross-system queries |
Three Common Misconfigurations
- Ledger query return count does not match actual declared projects: Cause: Cross-system data synchronization verification logic is not configured, leading to inconsistent project ID mapping across different data sources, resulting in duplicate or missing ledger entries.
- Single ledger summary task times out after more than 15 seconds: Cause: The
DB_MAX_LINKparameter is not set appropriately. Database connections are exhausted during concurrent queries, causing excessive task waiting latency. - EIA approval documents cannot be retrieved normally after storage: Cause: Appropriate storage indexes are not configured for long-text documents, leading to failure of word segmentation matching or full-text retrieval.
How to Verify Proper Configuration
- Connect to the database test address, verify the connectivity of the
MONGODB_URIandPG_URLconfigurations, and check if structured data and unstructured documents for test projects can be read. - Execute a batch ledger query task, observe task duration, and adjust the
DB_MAX_LINKparameter until query duration meets business expectations. - Simulate cross-system data updates, verify the consistency of data synchronization background ledger content, and confirm there is no dirty data or missing fields.
- View Redis cache logs, confirm that commonly used zoning control data is cached normally, and that cross-system query latency is effectively reduced.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.