What the Data for This Use Case Looks Like
Air governance scenario data primarily comes from real-time collected data of industrial enterprise emission outlet sensors, regional environmental monitoring stations, and operation logs of maintenance equipment. Data update intervals typically range from 15 minutes to 1 hour, with some core monitoring locations supporting shorter refresh intervals. Each data record includes fields such as unique monitoring location identifier, pollutant type, real-time concentration value, device operation duration, maintenance status identifier, and others. Concentration units are mg/m³, operation duration units are hours, and timestamps are precise to the millisecond.
Constraints for Deployment and Upgrade
The high real-time requirements of air governance data require configuring a cache queue that supports high-frequency data writing during the deployment phase, to avoid data backlog leading to delayed revenue yield calculation. The multi-field and fixed-unit characteristics of the data require presetting field mapping and unit verification rules in the data parsing module, to prevent parsing errors that cause deviations in revenue yield statistics. Sensor data has a packet loss probability, so add data completion and outlier filtering logic during deployment. During the upgrade process, maintain compatibility with old data formats of historical monitoring locations to avoid interrupting existing business links, and reserve interfaces to adapt to new pollutant monitoring types.
Recommended Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
API_REQUEST_RATE_LIMIT | 120 requests per minute | Matches the 15-minute to 1-hour update frequency of air governance data, avoids triggering external interface rate limits, and ensures stable high-frequency data calls |
PARSE_DATA_BATCH_SIZE | 50–80 records per batch | Balances server memory usage and parsing efficiency, prevents parsing interruptions caused by excessively large single-batch data volumes |
SIMILARITY_THRESHOLD | 0.75–0.85 | Accurately matches the association between pollutant types and maintenance logs, filters invalid data with low matching degrees, and ensures the accuracy of revenue yield calculations |
OLLAMA_API_TIMEOUT | 600 seconds | Adapts to the time consumption requirements of multi-dimensional association calculations for air governance data, avoids interrupting model calls due to timeouts |
DOCKER_RESTART_POLICY | unless-stopped | Ensures the service automatically recovers after unexpected exit or server restart, avoids interrupting real-time data links |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Adapts to batch import requirements for maintenance logs and monitoring data, supports large-size file uploads |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to conduct tests on dedicated test samples before finalizing settings.
Common Misconfigurations
- Symptom: Empty content is returned when calling models deployed via Ollama. Cause: The local Ollama API address was not correctly filled in the FastGPT model configuration, or the Docker container network is not properly connected, making it impossible to access the local Ollama service port.
- Symptom: A blank loading spinner appears when accessing ip:3000 after deployment in an Ubuntu 22.04 environment. Cause: The firewall rule for port 3000 on the server was not opened, or the port mapping parameters of the Docker container were configured incorrectly, preventing the service from being exposed externally.
- Symptom: Ollama model tests return no response after local deployment restart. Cause: The Ollama service was not set to start automatically on boot, or the Docker container was not configured with an automatic restart policy, leading to abnormal service startup sequence and failure to establish a normal connection.
How to Verify Successful Configuration
- View FastGPT system logs to confirm there are no errors related to field parsing failures or unit conversion errors. The field names appearing in the logs must match the preset air governance data fields.
- Manually import a test set of air governance monitoring data, check that parsed field mapping is correct, and confirm data batch processing time meets expected levels.
- Call the model test interface to check that the returned content includes revenue yield calculation logic related to air governance, with no empty returns or timeout errors.
- Check the server firewall and Docker port mapping configuration, confirm that port 3000 is accessible externally, and verify that the service can automatically recover after restarting.
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.