What the Data for This Category Looks Like
The yield-related data for this category mainly comes from the Ministry of Transport’s public industry statistical bulletins, official operation disclosure documents of railway groups, and highway toll public disclosure platforms. There are two update frequency categories: railway trunk line operating yield data updates weekly, and highway segment yield data updates monthly. Documents are mostly structured CSV or JSON formats. Core fields include unique segment identifier, operating mileage, total toll revenue, total operating cost, and statistical period. Units are kilometers, yuan, yuan, yuan, natural week, or natural month respectively.
Constraints Imposed on Model Access and Configuration by These Characteristics
The data characteristics of this category impose three core constraints on the model access and configuration link. First, the two data sources have significantly different update rhythms. Differentiated scheduled pull tasks need to be configured to adapt to weekly and monthly update cycles, avoiding expired or unsynced data. Second, core fields have unified business meanings but slightly different source formats. Field alias mapping rules need to be configured to ensure that fields such as toll revenue and operating cost from different data sources can be recognized uniformly. Third, data is split by segment. Context recall logic based on segment ID needs to be configured to ensure that the yield information output by the model is accurately bound to the corresponding segment.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
parse_field_mapping | revenue: Toll Revenue, cost: Total Operating Cost, period: Statistical Period | Adapt to field name differences across data sources, unify business field identifiers recognizable by the model |
dataset_sync_cron | Railway Data Source: 0 0 * * 1, Expressway Data Source: 0 0 1 * * | Match the weekly update rhythm of railway data and monthly update rhythm of highway data, avoid expired or unsynced data |
retrieval_top_k | 6–10 entries | Cover the volume of segment data pulled per batch, ensure the model can obtain sufficient target segment information |
similarity_threshold | 0.75–0.85 | Filter low-relevance historical data, ensure recalled segment information matches query requirements |
parse_timeout_seconds | 600 seconds | Adapt to the parsing duration of structured data, avoid parsing failures caused by large data volume |
alert_threshold_missing_data | 2 consecutive sync failures | Prompt data source pull exceptions in a timely manner, ensure data availability |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: An
invalid_argumenterror is returned when calling the large model, prompting parameter format mismatch. Cause:parse_field_mappingis not configured correctly, and the numeric toll revenue field is mapped to a string type, causing the model to fail to complete yield calculation. - Phenomenon: No data updates after the scheduled sync task triggers, checking logs shows
datasource_fetch_failed. Cause: Thedataset_sync_cronconfiguration for railway and highway data sources is not differentiated, and the sync cycle does not match the actual data update rhythm. - Phenomenon: The segment yield information output by the model does not correspond to the queried segment, and the recall results are redundant. Cause: The
similarity_thresholdvalue is too low, and a large number of irrelevant historical segment data are recalled.
How to Confirm Proper Configuration
- Check data source sync logs to confirm that sync tasks for different data source types trigger according to preset cycles, with no consecutive failure records.
- Manually trigger a data parsing task to verify that parsed fields match the mapping relationship configured in
parse_field_mapping. - Initiate a test query to confirm that segment data recalled by the model accurately matches the queried segment ID, and output yield information meets expectations.
- Simulate a data missing scenario to confirm that the system triggers preset alert notifications to prompt exceptions in a timely manner.
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.