What data for this category looks like
Air pollution control investment research data comes from multiple sources: publicly available monitoring datasets from ecological environment departments, air pollutant emission ledgers submitted by enterprises, real-time meteorological monitoring APIs, industry technical standard documents, governance project case documents, and patent literature. Data update frequencies include hourly real-time monitoring, monthly emission ledger updates, irregular standard revisions, and quarterly project case releases. Document formats include structured Excel monitoring sheets, multi-chapter PDF technical plans, JSON-format API data, and plain-text policy summaries. Fields include monitoring point number, pollutant name, concentration value, monitoring time, governance process type, and treatment efficiency parameters. Common units are standard environmental protection industry units such as μg/m³, m³/h, and tons/year.
Constraints imposed on deployment and upgrade
The multi-source, heterogeneous data characteristics of the air pollution control field create multiple constraints for deployment and upgrade workflows. Hourly real-time monitoring data requires high-frequency incremental synchronization tasks. Sufficient resources must be reserved for API authentication and data parsing. Large multi-sheet emission ledgers and long-text technical plans have larger file sizes and longer parsing times than standard documents. Adjust file upload and parsing timeout thresholds accordingly. Data from different sources uses inconsistent field naming conventions. The upgrade process must support older data formats to prevent import failures caused by field mapping errors. Investment research scenarios require integration of multi-dimensional data. Reserve expansion space for vector databases during deployment to accommodate data growth from additional monitoring points.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 2000 MB | Air pollution control project case PDFs and large emission ledger Excel files often exceed the standard 1000 MB limit, with some documents reaching 1.8 GB in size |
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Parsing time for large multi-sheet Excel ledgers and long-text technical white papers far exceeds that of general documents, and the standard 600 second threshold often triggers timeouts |
maxContext | 1200–1500 characters | Single structured records of air pollution control monitoring data contain multiple fields, and core paragraphs of long-text technical documents must retain complete context to support investment research analysis |
Recall Count | Top 8 results | Investment research scenarios require multi-dimensional data covering different monitoring points and governance processes. Too few results will lead to loss of key comparison information |
Similarity Threshold | 0.72–0.80 | The air pollution control field has dense professional terminology. A threshold that is too low will introduce irrelevant general environmental protection policy documents, while a threshold that is too high will miss similar governance process cases |
AUTO_SYNC_INTERVAL | 3600 seconds | Synchronization of hourly monitoring data must balance timeliness and server resource usage. An interval that is too short will increase computing overhead, while an interval that is too long will fail to ensure data timeliness |
The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material formats, data volume, and business rules. Specific issues require on-site analysis. It is recommended to test on your own samples before finalizing settings.
Three common errors
- Issue: Repeated dependency installation failure prompts appear during local source code deployment, returning
ERR_PNPM_OUT_OF_MEMORY. Cause: Memory allocation thresholds for the system or container were not adjusted, triggering an out-of-memory error when processing large ledger files in the air pollution control field. - Issue: Knowledge base recall results only include general environmental protection policy documents, with no specific governance process cases. Cause: The
Similarity Thresholdwas set too high, preventing correct recall of professional technical documents. - Issue: The curl command returns
400 Bad Requestwhen executing the upgrade script, and logs showinvalid json payload. Cause: Environment variable parameters in the command were not properly escaped, causing the request body format to not meet interface requirements.
How to confirm configurations are properly set
- Upload a 1.5 GB air pollution control project case PDF. Wait for parsing to complete, and confirm the parsing status is "Success" to verify file upload and parsing configurations are active.
- Initiate a search for "industrial boiler flue gas desulfurization governance". Confirm the recall results include three types of content: monitoring data, technical plans, and project cases to verify recall and similarity configurations are reasonable.
- Execute the local real-time data synchronization script. Pull monitoring data from test points, and confirm corresponding structured records are added to the knowledge base to verify synchronization and authentication configurations are active.
- Check container runtime logs for no
file parse timeoutorout of memoryrelated errors to confirm overall deployment configurations are stable.
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.