What This Category of Data Looks Like
Environmental monitoring data sources include fixed pollution source online monitoring equipment, air quality monitoring stations, satellite remote sensing sensors, portable mobile monitoring terminals, and similar devices. Update frequencies range from real-time second-level updates to hourly updates. Batch-exported historical data typically updates on a daily or weekly basis.
Most documents use structured formats. Each data entry includes fields such as monitoring point ID, collection timestamp, pollutant concentrations (such as PM2.5, PM10, SO₂), meteorological parameters (wind speed, relative humidity), equipment operating status, and more. Units follow professional metrology standards, including μg/m³, m/s, %RH, and similar values. Single entry data volume is small, while batch-exported datasets can reach tens of megabytes in size.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
The temporal nature of environmental monitoring data requires multi-turn dialogue to retain context from historical collection periods. Without this context, cross-period trend comparison analysis cannot be completed. The combination of multiple fields and professional units requires prompts to clearly specify the fields to call, corresponding units, and data ranges. This prevents the large language model from generating content that violates professional specifications. The high update frequency and batch data characteristics require multi-turn dialogue to limit context length, to avoid content truncation caused by exceeding token limits. The presence of equipment operating status fields requires prompts to include rules for handling abnormal status. This ensures marketing content does not include incorrect descriptions based on offline equipment data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | Previous 20 turns of dialogue + most recent 3 hours of monitoring data | Environmental monitoring data has strong temporal characteristics. Retaining recent data is sufficient to support trend analysis, while excessive context will exceed token limits |
PROMPT_TEMPLATE | Must include field unit descriptions, temporal comparison requirements, and abnormal status prompts | Environmental monitoring fields contain professional measurement units. Clear definitions prevent the large language model from confusing parameter meanings |
WORKFLOW_MAX_RUN_TIMES | 1500 | When processing monitoring data in batches, a single workflow must handle multiple batches of datasets. This avoids premature execution termination |
UPLOAD_FILE_MAX_SIZE | 500 MB | Batch-exported historical monitoring data files are typically large. This setting enables support for large file upload parsing |
RECALL_TOP_K | Top 8 entries | Environmental monitoring data has multiple dimensions. Recalling too many entries disperses the focus of the context, while recalling too few loses key trend information |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large-volume temporal monitoring data takes a long time. Extended timeout periods prevent parsing interruptions |
The parameter values provided on this page are conventional recommendations used as a starting point 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 Configuration Mistakes
- Phenomenon: When calling the API to generate environmental monitoring-related marketing content, the output mixes units from non-specified fields. For example, the unit of wind speed m/s is replaced with μg/m³. Cause: The prompt fails to clearly limit required fields and their corresponding units, causing the large language model to call irrelevant monitoring data parameters.
- Phenomenon: When concurrently calling workflows, some MCP nodes return empty values after approximately 2-3 calls per second. Cause: The
WORKFLOW_MAX_RUN_TIMESparameter is not adjusted to adapt to concurrent scenarios, or the MCP's concurrent request current limiting rules are not configured, causing requests to be discarded by the system. - Phenomenon: In marketing content generated via multi-turn dialogue, the reply from the later turn overlaps with the content of the previous turn. Cause: Variables are not initialized separately for each dialogue session, or the automatic splicing switch for dialogue context is not turned off, resulting in variable cache not being cleared.
How to Verify Proper Configuration
- Upload single and batch environmental monitoring data files, and check if parsed fields and units match the original data.
- Initiate multi-turn dialogue, sequentially ask for monitoring data from different time periods, and confirm that the dialogue context retains historical questions and reply content.
- Simulate concurrent workflow calls, check if node return results are empty, and adjust current limiting rules and the
WORKFLOW_MAX_RUN_TIMESparameter to meet requirements. - Enable the large language model's thinking process display switch, and check if the output includes reasoning steps that conform to the professional logic of environmental monitoring.
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.