What the data for this category looks like
Thermal industry research report data primarily comes from public utility industry regulatory documents, quarterly operational disclosures from heat supply entities, and third-party public utility industry databases. The update cadence consists mainly of quarterly regular reports, paired with ad-hoc reports released after sudden policy changes or supply-demand shifts.
Each individual document includes four sections: overall industry trends, core heat supply metrics, policy impact analysis, and market forecasts. Core fields include total heat supply (unit: gigajoules), heated coverage area (unit: square meters), unit heat supply cost (unit: yuan per gigajoule). Additional metadata includes publishing organization, publish date, associated policy ID numbers, and similar details.
What constraints these characteristics impose on HTTP interfaces and external systems
The multi-source nature of thermal industry research reports requires external systems to support compatibility with format differences across data sources. Format conversion parameters must be configured in the HTTP interface.
The combination of fixed update cadence and ad-hoc updates requires the interface to support both scheduled pull and event-triggered pull modes. This avoids invalid requests consuming resources.
Core fields have clear specified units, so the interface must return standardized unit information. This prevents calculation errors caused by mismatched units in external systems.
The fixed module structure of documents requires the interface to support targeted recall by module. This improves retrieval efficiency.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
rag_recall_fields | ["total heat supply", "heated coverage area", "unit heat supply cost"] | Matches the core retrieval fields for thermal industry research reports, covering user query needs for key industry metrics |
rag_cache_ttl | 7 * 24 * 3600 seconds | Adapts to the quarterly update cadence of thermal industry research reports, balances data timeliness and request resource usage |
api_request_timeout | 30 seconds | Adapts to the time required for multi-data-source format conversion, prevents request interruptions mid-process |
rag_recall_count | 3–5 entries | Core metrics in individual thermal industry research reports are concentrated, a small number of recalled entries meets most query scenarios |
unit_conversion_enabled | true | Core fields of thermal industry research reports have standardized units, enabling conversion adapts to the unit requirements of external systems |
event_trigger_sync | ["policy_change", "supply_fluctuation"] | Ad-hoc reports are mostly triggered by policy changes or supply fluctuations, event-triggered synchronization can pull the latest content in a timely manner |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: HTTP interface returns
400 Bad Requesterror, indicating mismatched field units. Cause: Theunit_conversion_enabledconfiguration is not enabled, and raw units are returned directly, which do not match the requirements of the external system. - Symptom: External systems experience infinite retries or process restarts after calling the interface. Cause: No reasonable
api_request_timeoutis set, and exceptions are not properly caught after timeout, leading to repeated request initiations. - Symptom: Interface calls report
token encoder not founderror after local deployment, with repeated process restarts. Cause: Authentication parameters for the external system are not correctly configured, leading to continuous retries after interface verification failures.
How to Confirm Correct Configuration
- Initiate a single HTTP interface call, check if the returned data fields include the preset core metrics and standardized units, and verify that the field names match the configured
rag_recall_fields. - Simulate a scheduled pull request, verify that repeated calls within the cache period return the same results, and that data is automatically updated after the cache period expires.
- Trigger a simulated policy change event, verify that event-triggered synchronization can pull the latest ad-hoc research report content.
- Capture exception logs after calling the interface, confirm that timeout, authentication, and other errors are properly handled, and that no process restarts or infinite retries occur.
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.