What this type of data looks like
This data covers all financing-related data from the previous calendar day, and updates daily. Data sources include air pollution prevention project registration databases of local ecological environment departments, green credit issuance ledgers of local financial institutions, and project financing records from national emission right trading platforms. Data documents use structured table format. Core fields include:
- Project unique identifier
- Project name
- Affiliated administrative region
- Governance type (such as VOCs treatment, desulfurization and denitrification)
- Financing amount (unit: ten thousand yuan)
- Credit granting institution
- Loan date (format: YYYY-MM-DD)
- Total project investment
- Annual emission reduction (unit: tons of sulfur dioxide or nitrogen oxides)
Constraints imposed on workflow orchestration
Scattered data sources and interfaces across multiple departments and institutions require the workflow to use parallel pull nodes for multi-source data. This avoids overall delays caused by serial data pulls. The daily update timeliness requirement mandates a fixed scheduled task triggered in the early morning. This ensures daily report generation does not lag behind business needs. Fields include environment-specific non-general fields such as emission reduction and governance type. Different data sources have inconsistent field names and units. Standardized field mapping and unit conversion rules must be configured to maintain data consistency. The structured nature of the financing daily report requires a built-in data verification node. This filters entries with missing key fields or abnormal formats in advance, preventing errors in subsequent report generation.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
trigger_cron | 0 1 * * * | The financing daily report must cover all data from the previous day. Triggering at 1:00 AM avoids business peak hours and ensures higher data update completion |
api_global_var_pass_mode | Pass JSON-format parameters via the request header X-FastGPT-Global-Vars | Complies with FastGPT official API specifications, enabling cross-node transfer of global variables across workflow nodes |
multi_source_sync_timeout | 300 seconds | Air pollution control financing data comes from more than three data sources. A single synchronization covers multiple interface calls. 300 seconds covers most interface response durations |
retry_count_on_fail | 2 retries | Multi-source data pulling may encounter temporary interface fluctuations. 2 retries reduce single failure rates and avoid interruptions to daily report generation |
field_mapping_rule | Map source fields to preset standard fields | Different data sources have different field names and units. Unified mapping avoids data deviations in subsequent analysis |
workflow_log_retention | 30 days | Financing daily report generation logs must be retained for troubleshooting and audits. 30 days covers regular business audit cycles |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Issue: API calls fail to pass global variables, and workflow nodes cannot obtain preset financing daily report filter parameters. Cause: The parameter passing format required by FastGPT official is not used, and directly splicing parameters in the request body leads to parsing failure.
- Issue: The generated daily report contains emission reduction data with inconsistent units. Some entries are marked in tons, while others are marked in kilograms. Cause: Field mapping and unit conversion rules are not configured, and the field formats of different data sources are not unified.
- Issue: The workflow returns a
504 Gateway Timeouterror after triggering in the early morning. Cause: The multi-source data pull timeout setting is too short, and does not cover the total interface response duration of multiple data sources.
How to Verify Successful Configuration
- Check the workflow trigger log to confirm the scheduled task triggers at the preset time, with no skipped or delayed records.
- Call the test API, pass the preset global variable parameters, and verify that each workflow node can normally read the variable values.
- Randomly select fields from three different data sources, check the field mapping configuration, and confirm that the mapped standard field formats are consistent.
- Simulate the scenario of temporary exceptions in a single data source interface, and verify that the failure retry mechanism can trigger normally and complete data pulling.
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.