What this category’s data looks like
Environmental monitoring data comes from fixed-site IoT sensors and regional environmental monitoring platforms. Update frequencies fall into three categories: real-time second-level, hour-level, and daily-level. Each data record includes monitoring point code, collection timestamp, pollutant concentration values such as PM2.5, PM10, and ozone, and device operating status fields. Field units are uniformly μg/m³ or mg/m³. Some extended fields include regional environmental protection project association identifiers. When reporting data in batches, it is packaged in JSON array format. The volume of data per batch increases with the number of monitoring points.
What constraints do these characteristics impose during deployment and upgrade?
The multiple update frequencies, large batch sizes, and unified field unit requirements of environmental monitoring data impose multiple constraints during deployment and upgrade. Real-time second-level data scenarios require deployment nodes to have low-latency data pulling capabilities to avoid delays in yield reporting. Hour-level and daily-level batch data require adaptation to large-batch file parsing to prevent resource exhaustion. The unified field unit requirement requires preset unit conversion rules during deployment to avoid yield calculation deviations caused by mixed cross-site data. When the batch data volume is large, adjust the file parsing timeout threshold to avoid parsing failures interrupting the daily report generation process. During upgrades, retain the mapping rules between old version monitoring point codes and environmental protection projects to avoid interruptions in historical data association.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 1200 seconds | Batch daily monitoring data files usually contain multi-site data, and the parsing time required to generate daily reports is relatively long |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Packaged batch daily monitoring data may reach the GB level, so large file upload support is required to complete daily report generation |
DOCKER_BASE_URL | http://fastgpt-host:3000 | The default Docker deployment port is 3000. Match the container internal network access path to avoid interface call failures |
XINFERENCE_API_BASE | http://xinference-host:9997/v1 | The default Xinference deployment port is 9997. Match the internal access address to complete model access |
MODEL_API_TIMEOUT | 60 seconds | Environmental monitoring data parsing requires calling embedding models to generate vectors. Avoid timeout interruptions to daily report generation processes |
WORKFLOW_MAX_RETRIES | 3 retries | Real-time data pulling may fail due to network fluctuations. Limited retries can improve the stability of daily report broadcasting |
The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material form, 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 errors
- Symptom: After locally deploying version v4.8.14 and uploading batch monitoring data files, the parsing result is empty. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted, and large batch file parsing timed out without triggering the retry mechanism. - Symptom: After Docker packaged deployment, internal data source interfaces cannot be accessed, and logs show connection timeout. Cause: The
DOCKER_BASE_URLconfiguration uses a public network address, and the container internal network access path was not matched. - Symptom: During workflow runtime in version v4.9.0, gpt-4o-mini call error logs appear, and the model was not manually configured. Cause: The workflow node calls the default model without specifying it, and the system attempts to call an undeployed model.
How to confirm the configuration is correct
- Upload single and batch environmental monitoring data files, verify that parsed fields include required items such as monitoring point code and collection timestamp, and match preset field rules.
- Execute a local Docker container internal curl command to confirm that the address configured in
DOCKER_BASE_URLcan normally access the data source interface. - Enter the model configuration page to confirm that the address configured in
XINFERENCE_API_BASEcan connect normally, and test that model calls have no error messages. - Start the workflow to test real-time data pulling and daily report generation tasks, and check that there are no timeout or undeployed model error messages in the 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.