What the data for this category looks like
Environmental monitoring data sources include national controlled automatic monitoring stations, enterprise-side pollution discharge monitoring equipment, satellite remote sensing inversion data, and mobile monitoring patrol records. Update frequencies range from second-level real-time particulate matter concentration data to daily-level multi-station summary reports. Single structured documents contain fields such as monitoring point ID, longitude and latitude, monitoring time, concentration values of various pollutants, and equipment operating status. Particulate matter concentration is measured in μg/m³, and gaseous pollutant concentration is measured in mg/m³. Some documents include attachment files such as calibration logs.
What constraints these characteristics impose on model access and configuration
Multiple heterogeneous data sources and varied update frequencies require adaptation to different trigger frequencies during model access. Hourly knowledge base refresh cycles are configured for real-time data, and daily updates are used for daily-level reports. Long-sequence time-series data characteristics require configuration of appropriate context windows and segmentation rules to avoid breaking the temporal association of monitoring data. Multiple fields and unified unit requirements require configuration of field alignment rules during parsing and retrieval to ensure the model does not confuse units of different pollutants or monitoring point information during calls. Large-volume satellite remote sensing image files require adjustment of upload and parsing timeout parameters to adapt to processing demands for high-capacity data.
Recommended Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Environmental monitoring data includes long-sequence time series and multiple pollutant fields. Segments that are too long will lose temporal association, and segments that are too short will break the integrity of a single set of monitoring data. 800–1200 characters can cover 1 hour of continuous data from a single monitoring point |
similarity_top_k | Top 6–8 results | Investment research scenarios require associating monitoring data across multiple time periods and stations. Too few retrievals cannot cover association logic, and too many retrievals will introduce irrelevant monitoring point data |
score_threshold | 0.72–0.85 | Environmental monitoring data has clear numerical characteristics. A threshold that is too low will introduce non-matching monitoring period data, and a threshold that is too high will miss low-scoring sequences with key associations |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | A single monitoring report may include daily summary data for multiple stations, with long parsing times. 300 seconds can cover the parsing process for most large-volume structured documents |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Original image data from satellite remote sensing monitoring has large volume, requiring adaptation to large file upload limits |
model_max_tokens | 16384 tokens or higher | Investment research analysis requires calling continuous monitoring sequence data. 16384 tokens can cover the context of time-series data for more than 1 hour, avoiding truncation of key analysis basis |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: A model call returns a
413 Request Entity Too Largeerror. Cause: TheUPLOAD_FILE_MAX_SIZEparameter is not adjusted, and the uploaded satellite remote sensing monitoring image exceeds the default file size limit. - Phenomenon: Retrieved monitoring data lacks the
monitoring_timeordevice_idfield. Cause: The structured data field extraction switch is not enabled in the parsing configuration, causing the temporal association logic to fail. - Phenomenon: Workflow execution does not output specific monitoring periods of unreasonable references. Cause: No prompt word for reference verification is configured in the model node, and the knowledge base reference traceability field is not bound.
How to Verify Correct Configuration
- A single structured document containing 1 hour of continuous monitoring data is uploaded. Check if the parsed segments fully cover all fields of a single monitoring point, and adjust the corresponding parameters until no segmentation breaks occur.
- Multiple model calls are triggered. Verify that the units of monitoring data referenced in the returned results are unified, and adjust the similarity threshold until no abnormal retrievals across units occur.
- The maximum volume satellite remote sensing image file is uploaded. Check if the parsing task completes within the set timeout period, with no timeout errors.
- A reference verification workflow is configured. A document containing incorrectly associated monitoring data is uploaded, and verify that it can output the corresponding unreasonable reference entries.
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.