What the data for this category looks like
Tourist attraction financial report analysis data mainly comes from audited annual and semi-annual official financial reports, monthly operation briefings, and daily ticket and passenger flow ledgers. Update frequencies are as follows: annual reports are updated once per year, semi-annual reports once every six months, monthly briefings monthly, and raw ledgers generated daily. Document structures include standardized financial statement modules, as well as specialized fields such as passenger volume, revenue classification, and facility operation and maintenance costs. Units include person-times, yuan, ten thousand yuan, and others. Some attractions add non-standard supplementary data like holiday passenger flow peaks and group reception ratios.
What constraints these characteristics impose on workflow orchestration
The multiple data sources, varied update cycles, and differentiated field structures of tourist attraction financial report data create multiple constraints for workflow orchestration. First, support must be provided for parsing both structured financial report files and unstructured operation ledgers. Do not rely solely on generic financial document parsing nodes. Second, update cycles vary widely, so timed workflow nodes with customizable trigger cycles must be configured to adapt to daily ledger synchronization, monthly briefing aggregation, and full analysis after annual financial report audits. Third, specialized fields for attraction revenue and passenger flow require separate extraction rule configuration to prevent generic parsing nodes from missing segmented revenue items such as ticket and catering sales.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_MODE | structured + custom_rule | Attraction financial reports include standardized financial tables and customized specialized fields for passenger flow and revenue. A combination of structured parsing and custom rules is needed to match non-standard business data |
UPLOAD_FILE_MAX_SIZE | 200 MB | Large attraction annual financial reports include multi-page statements and attachments. File sizes are typically larger than generic corporate financial reports, so sufficient upload space must be reserved |
WORKFLOW_TRIGGER_CRON | Supports multi-cycle configuration, such as 0 0 2 * * ? (daily), 0 0 1 1,7 * ? (semi-annual) | Adapts to the update rhythms of different attraction data, synchronizing daily ledgers, monthly briefings and annual financial reports respectively |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large attraction financial report files include multi-page nested tables, with long parsing times. This avoids interrupting the parsing process due to timeout |
LLM_PROMPT_TEMPLATE | Preset dedicated template for attraction financial report analysis, specifying analysis of passenger flow structure, revenue proportion and cost changes | Generic prompts cannot cover the segmented business dimensions of attractions, so analysis goals and field ranges must be clearly specified |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Phenomenon: After the workflow runs, the large language model returns that it cannot recognize the uploaded file content, and the log shows that the file was not loaded correctly. Cause: The parameter for directly passing the file to the large language model was not configured. By default, the file is parsed into plain text before being passed, and the structured tables in attraction financial reports cannot restore field relationships through plain text.
- Phenomenon: After adding multiple code execution nodes, the workflow execution reports an error, prompting variable naming conflicts. Cause: Independent environment variable prefixes were not configured for each code node, causing variables from different nodes to overwrite each other and preventing normal execution of multiple code logic.
- Phenomenon: In the analysis results returned by the large language model, passenger flow and revenue data have field misalignment, and cannot correctly recognize classification information separated by spaces. Cause: The prompt did not explicitly specify spaces as field separators, and the large language model ignores text spaces by default, leading to parsing errors.
How to Confirm the Configuration is Complete
- Upload a tourist attraction monthly operation briefing file, trigger the workflow to run, and check the output logs of the parsing node to confirm that structured tables and custom fields are correctly extracted.
- Configure multi-cycle trigger rules, test the daily and semi-annual trigger logic respectively, and confirm that the workflow starts automatically at the preset time.
- Adjust the analysis dimensions in the prompt template, run test cases, and confirm that the results returned by the large language model cover the preset attraction-specific analysis content.
- Check the workflow error logs to confirm there are no error messages such as file parsing timeouts or node conflicts, and verify that the configuration parameters take effect.
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.