What the data for this category looks like
The core documents for this category include territorial spatial overall plans, detailed plans, special plans, planning implementation evaluation reports, and public opinion summaries. Data for this category is primarily sourced from the territorial spatial basic information platform, natural resource survey and monitoring data, population and economic statistical data, and remote sensing images. The standard update frequency is annual. Documents include fields such as spatial coordinates, constraint indicators, and regional scope. Most units use spatial and population statistical units like hectares, square kilometers, and ten thousand people. The length of individual documents varies widely, and some remote sensing image associated files have large file sizes.
What constraints these characteristics impose on the deployment and upgrade link
The data characteristics of this category impose clear constraints on the deployment and upgrade link. The annual update frequency requires configuring adapted periodic batch data synchronization processes, supporting full or incremental spatial data import. Multi-source spatial data and large-volume remote sensing associated files require storage and vector database configurations with large-capacity support, adapting to high-concurrency spatial vector retrieval requirements. Long-text planning reports and multi-dimensional opinion summaries require context window configurations that can accommodate long text fragments, preventing key constraint indicators from being truncated. Non-standardized field structures across different documents require the knowledge base to support custom field mapping rules, adapting to format differences in planning documents.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
STORAGE_S3_ENDPOINT | Object storage node address adapted for natural resource data storage, such as https://s3.resource.xxx (calibrated based on actual testing) | This category contains large-volume remote sensing images and planning documents, requiring adapted large-capacity, highly reliable object storage services |
Reference only; verify against the target FastGPT release and deployment documentation.
| REDIS_URL | Configure as a Redis instance with memory no less than 8 GB, set connection pool size to 20–30 | High-frequency access spatial indexes and user session data need to be cached, adapting to concurrent requirements from multiple departments querying planning data simultaneously |
| DB_MAX_LINK | Set to 100–150 | Batch data synchronization and multi-user concurrent queries will be triggered during the annual update cycle, requiring the database connection pool to cover peak requests |
| FILE_TOKEN_KEY | Custom random string of 32 characters or more | Used to generate access tokens for planning documents, preventing unauthorized access to sensitive spatial planning data |
Three Common Mistakes
- Symptom: When calling the MCP service, the parameter
typeis fixed to the string type, and cannot adapt to non-string parameters such as spatial coordinates. Cause: Custom parameter type support for the MCP service is not enabled in the deployment configuration, or version adaptation is not completed. - Symptom: After uploading planning documents or remote sensing images, parsing times out and the request fails. Cause: The connection timeout parameter of
PG_URLis not adjusted, triggering the default timeout limit during batch import of large-volume files. - Symptom: After deployment on a specific all-in-one device, spatial data synchronization fails. Cause: The
STORAGE_S3_ENDPOINTaddress adapted to the device's network environment is not configured, resulting in insufficient object storage access permissions.
How to Confirm the Configuration Is Complete
- Run a batch import test of sample data, verify that the database connection pool can cover concurrent requests during the import process, and that no connection timeout error messages appear.
- Call the MCP service to test the transfer of spatial coordinate parameters, verify that the parameter type can be customized, and that no restriction of being fixed to string applies.
- Upload a small-volume planning document via the configured frontend address, and verify that the parsing and storage processes complete normally.
- Check the memory usage of the Redis instance to verify that high-frequency cached data does not trigger memory overflow.
The values provided here are common starting points, and should be evaluated against the corresponding actual 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.