What the data for this category looks like
Game financing daily report data primarily comes from third-party venture capital data platforms, public announcements from domestic and overseas game manufacturers, and professional game industry information channels. The update rhythm involves synchronizing financing events disclosed on the same day daily. On non-working days, only stock events from before the holiday are updated. The structure of a single data entry includes project name, R&D entity, financing amount, financing round, investor list, disclosure date, and subdivided game track field. Amount units are uniformly marked as RMB ten thousand yuan or US dollar equivalent amounts. Some records with undisclosed specific amounts will be marked as "undisclosed".
What constraints do these characteristics impose on the "workflow orchestration" link?
The multi-source nature of game financing daily report data requires workflows to configure multiple request nodes to aggregate data from different channels, avoiding missing financing events from a single data source. The daily update rhythm requires the scheduled trigger node to be set to run at a fixed time every day. At the same time, incremental deduplication logic must be configured to filter duplicate records based on disclosure date and project name. The diversity of track subdivided fields requires configuring field mapping rules to unify custom track labels from different channels into standard classifications. Records with undisclosed amounts require configuring outlier handling branches to skip entries without valid amounts or mark them as pending supplementation.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
trigger_type | Scheduled trigger, execution time set to 10:00 daily | Most game financing disclosure events are published in the early morning of working days; scheduled triggers ensure timely synchronization of daily data |
request_concurrency | 2–3 concurrent requests | Controls the concurrency of multi-source data requests to avoid triggering rate limiting blocks from third-party platforms |
field_unique_key | Disclosure date + Project name | Uniquely identifies a single financing record to avoid duplicate entry of the same event |
data_filter_rule | Retain entries where the amount field is a valid numerical value or "undisclosed" | Covers both records with clear amounts and records with pending amounts, adapting to the full display requirements of daily reports |
workflow_timeout | 600 seconds | Covers the total processing duration of multi-source requests, field mapping, and exception handling to avoid mid-run timeout interruptions |
custom_script_enable | Enabled | Supports standardized mapping of custom track labels, adapting to field differences across different data sources |
The parameter values provided on this page are common 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, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- The symptom is that the output fields of the
http_requestnode are not displayed in the conversation results. The status shows normal completion but no corresponding fields are returned. The cause is that the workflow's output mapping rule is not configured, and the node output is not exposed to the conversation context. - The symptom is that the loop node only returns a single piece of data and cannot traverse all financing records. The cause is that the "Result Collection" switch of the loop node is not enabled, resulting in intermediate processing results not being aggregated and stored.
- The symptom is that the
python_codenode returns a500error after execution. The cause is that no pre-check is performed for null value fields with undisclosed amounts, and the code attempts to perform calculation operations on non-numerical types.
How to Verify the Configuration is Complete
- Manually trigger the workflow, check the return status of each
http_requestnode in the logs, and confirm that all data source requests are completed normally. - Check the workflow's output mapping configuration, confirm that core business fields have been added to the output list of the conversation context.
- Import a single test financing record, run the workflow and verify the field mapping results, confirm that the track labels have completed standardized conversion.
- View the historical execution records of the scheduled task, confirm that the workflow automatically triggers at the preset time without abnormal interruptions.
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.