What this category's data looks like
Data for environmental monitoring intelligent due diligence reports comes from raw data reported by publicly accessible monitoring stations under local ecological environment departments, deployed online sensor networks, and third-party monitoring institutions. Data update frequencies are categorized as real-time, hourly, or daily based on the monitoring project. Each report has a fixed document structure, including fields such as monitoring point code, monitoring period, pollutant concentration value, monitoring instrument serial number, and quality control qualification flag. Concentration-related fields mostly use units of μg/m³ and mg/m³, while meteorological fields use units of ℃ and % relative humidity.
What constraints do these characteristics impose on model access and configuration
The high-frequency update characteristic of the data requires the model access interface to support low-latency calls, to avoid using expired data due to call latency exceeding the data update cycle. The requirement for multiple fixed fields and units means configuration parameters must support field standardization mapping and unit verification, to prevent the model output from confusing the concentration units of different pollutants. The fixed document structure with regional expansion needs configurable custom field extraction rules, to adapt to the monitoring point coding rules of different regions. The quality control flag attached to raw data requires pre-filtering parameters before model calls, to exclude invalid data that failed quality control.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
model_api_timeout | 300-600 seconds | Environmental monitoring data update cycles are mostly hourly, and a single call needs to cover multi-period data queries, to avoid process interruption due to timeout |
field_mapping_mode | strict_custom | There are differences in monitoring point coding rules across regions, so custom mapping between fields and system preset fields is required |
unit_check_switch | Enabled | Environmental monitoring data has strict unit specifications; enabling this will filter results with inconsistent units in model output |
custom_field_schema | Configured by grouping monitoring point codes + periods | A single due diligence report contains multi-period data from multiple monitoring points, so fields need to be extracted by grouping |
batch_call_batch_size | 5-10 | The number of monitoring points involved in a single due diligence report usually falls within the 5-10 range; batch calls balance efficiency and interface load |
enabled_model_types | Text generation, multimodal understanding, speech transcription | Environmental monitoring due diligence reports require text analysis of monitoring reports, parsing image-format monitoring data, and transcribing speech report content |
data_quality_filter | Only retain quality control qualified data | Raw monitoring data includes invalid samples that failed quality control, so filtering in advance improves model output accuracy |
The parameter values provided on this page are all common recommended 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 mistakes
- Symptom: Model call returns
503 No available channels for model yi-vl-puls under current group default. Cause: No dedicated environmental monitoring model channel configured in the corresponding model group, or channel quota has been exhausted. - Symptom: Missing or incorrectly formatted monitoring data fields in model output. Cause:
field_mapping_modenot set to custom mode, so the system cannot match the unique point coding fields for environmental monitoring. - Symptom: Model call fails, prompting inability to connect to the specified model service. Cause: The offline-deployed OneAPI service address was not correctly filled in FastGPT's model channel configuration, or the configured port is not open.
How to confirm the configuration is complete
- Submit a test set of environmental monitoring raw data, check whether fields such as point codes and concentration values are correctly mapped in the model call logs.
- Manually modify the units of the test data, verify whether
unit_check_switchfilters results with inconsistent units. - Adjust
batch_call_batch_sizeto different values, observe the success rate and latency of batch calls to confirm the appropriate setting. - Check the model channel status panel, confirm that the channel status of
yi-vl-puls(or the configured model) is available, with no quota exhausted prompts.
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.