What the data for this category looks like
Data for thermal intelligent due diligence reports in the financial sector comes primarily from SCADA monitoring systems, payment ledger systems, and operation work order libraries provided by thermal enterprises. Collect operating parameters hourly, generate a summary basic due diligence document daily. Document structure includes structured parameter tables and unstructured operation log attachments. Structured fields include equipment number, collection time, heat supply, pipe network pressure, and heat exchange station outlet temperature. Units are unit, yyyy-MM-dd HH:mm:ss, GJ, MPa, ℃ respectively. Unstructured sections include monthly inspection records and fault repair details.
What constraints do these characteristics impose during deployment and upgrade
Since data is collected hourly in real time, configure persistent message queues to ensure data synchronization without packet loss. This prevents loss of thermal enterprise operation data during service restarts. Since documents contain structured parameters and unstructured logs, enable multi-format parsing plugins to adapt to different data source content. This meets full data requirements for financial due diligence. Since many fields are engineering-specific parameters, pre-configure vector database field mapping rules. This ensures correct association of professional terminology during retrieval, improving the accuracy of due diligence analysis. Since update frequency is high, use a gray release strategy during upgrades. Switch traffic in batches to avoid service interruptions that impact real-time data needs of financial due diligence.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Thermal due diligence single documents often contain multiple device logs, parsing takes a long time; 300 seconds covers most scenarios |
UPLOAD_FILE_MAX_SIZE | 2000 MB | A single thermal due diligence report may include annual operation log compressed packages, require support for large file uploads |
CHAT_MAX_CONTEXT | 8000–12000 characters | Thermal data has many fields, need to retain sufficient context for parameter association analysis |
VECTOR_SEARCH_TOP_K | Top 10 entries | Thermal due diligence requires associating operating parameters of multiple devices, recall an appropriate number of results to ensure comprehensive analysis |
SYNC_INTERVAL_HOURS | 1 hour | Thermal operation data is collected hourly, synchronization frequency matches data source update cadence |
LOG_RETENTION_DAYS | 90 days | Industry compliance requirements require retaining at least 90 days of deployment and call logs |
The parameter values provided on this page are all conventional recommendations, used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, it is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: Image pull timeout when executing
docker-compose up, log displaysnet::ERR_TIMED_OUT. Cause: No domestic mirror acceleration source configured. Basic images related to thermal due diligence have large size, default pull speed is too slow. - Phenomenon: Empty result returned when parsing thermal due diligence reports, interface shows
parse_result is empty. Cause: Engineering document parsing plugin not enabled, unable to recognize thermal proprietary parameter fields. - Phenomenon: Model call failure during cross-machine deployment, returns
401 Unauthorized. Cause: Cross-nodeCHAT_API_KEYand network whitelist not correctly configured, causing local area network models to fail normal calls.
How to confirm the configuration is correct
- Upload a standard thermal due diligence report, check if the parsing interface correctly extracts fields such as equipment number and heat supply, verify parsing time meets expectations.
- Enter the system settings page, confirm that the
SYNC_INTERVAL_HOURSconfiguration value matches the data source update frequency, test whether data is normally stored after manually triggering synchronization. - Initiate a query targeting thermal parameters, check that the number of vector recall results matches the configured
VECTOR_SEARCH_TOP_Kvalue. - View the deployment log directory, confirm that the
LOG_RETENTION_DAYSconfiguration takes effect, old logs are automatically cleaned up or retained according to rules.
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.