What the data for this category looks like
Power industry yield-related data primarily comes from regional power trading centers, grid dispatching systems, and generation-side operation and maintenance platforms. Data update cycles cover three tiers: real-time intra-day, hourly snapshots, and daily settlement. Individual data documents include fields such as unit number, trading time slot, on-grid electricity price, purchased and sold electricity volume, settlement benchmark price, and others. Units include yuan/megawatt-hour, megawatt, ten thousand megawatt-hours, and more. Some documents include cross-regional price spreads and auxiliary service compensation details. The structure is divided into two submodules based on transaction type: centralized bidding and listed trading.
What constraints these characteristics impose on conversation logging and auditing
Since power data has multi-time-granularity update characteristics, conversation logs must be archived separately by trading time slot and settlement cycle to avoid mixing cross-granularity data. The multi-field structure requires the auditing process to verify the completeness of core fields such as unit number and electricity price, to prevent missing key statistical dimensions. The additional fields for cross-regional and auxiliary service details require logs to be associated with corresponding transaction type tags, ensuring that data sources can be quickly located during audit traceability. The high-frequency updates of real-time data will lead to a sharp increase in conversation log write volume, so a rolling cleanup strategy must be configured to avoid storage overflow. At the same time, the division of multiple transaction types requires logs to record context tags during interactions, to facilitate subsequent audit result traceability by transaction category.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
LOG_ROTATION_SIZE | 500 MB | High-frequency updates of power data generate large volumes of logs. The 500 MB rolling threshold balances storage usage and log retention period |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Power data documents may include details across multiple trading time slots, leading to long parsing times. 300 seconds covers the full parsing process |
maxContext | Top 20 conversations | Power industry auditing requires tracing parameter configurations from multi-round interactions. 20 conversations covers interaction records within a standard audit cycle |
UPLOAD_FILE_MAX_SIZE | 200 MB | A single power settlement document may include cross-regional transaction details spanning several months. 200 MB accommodates standard bulk upload files |
LOG_RETENTION_DAYS | 180 days | Power industry auditing requirements mandate retaining at least six months of transaction-related records. 180 days meets compliance standards |
ERROR_LOG_LEVEL | DEBUG | Field matching errors often occur during power data parsing. The DEBUG level captures complete parsing exception information |
The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material format, 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 mistakes
- Symptom: After uploading a power trading data file, the statistical results generated by the conversation model do not include the unit and electricity price information from the file. Cause: The automatic recall switch for file parsing is not enabled, or the configured
UPLOAD_FILE_MAX_SIZEis smaller than the actual size of the uploaded file, causing the file to fail to load. - Symptom: When calling multimodal analysis of power market images via API, a
400 invalid imageerror is returned. Cause: The uploaded image format is not supported, or the image file is corrupted during transmission, leading to parsing failure. - Symptom: After manually cleaning conversation logs, historical interaction records required for auditing are lost. Cause: No exemption rules for log retention are configured, and logs within the compliant retention period are accidentally deleted.
How to confirm correct configuration
- Upload a standard power trading data document, check whether the conversation log includes a successful file parsing record, and verify that the fields in the log match those in the uploaded document.
- Initiate an API call for multimodal analysis of a power market image, check whether unexpected error codes appear in the returned logs, and confirm that the call process is working normally.
- Check the rolling archiving status of log files, confirm that there is no unlimited growth issue, and that it complies with the configured
LOG_ROTATION_SIZEthreshold. - Trigger a conversation log cleanup operation, confirm that logs within the retention period are not deleted, and meet auditing requirements.
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.