What Data for This Category Looks Like
The data for general equipment intelligent due diligence reports primarily comes from four sources: equipment factory quality inspection documents, on-site operation and maintenance ledgers, industry sampling inspection announcements, and equipment operation logs. Data update cycles fall into two categories: Factory parameters receive a one-time full update. Operation and maintenance data is synchronized weekly or monthly, while sampling inspection data is updated irregularly. Document structure includes four core modules: basic equipment information, core performance parameters, maintenance records, and fault history. Core fields include equipment model, serial number, rated speed (unit: revolutions per minute), rated power (unit: kilowatts), cumulative operating hours (unit: hours), and last maintenance date.
What Constraints Do These Characteristics Impose on Deployment and Upgrade
The multi-source data formats, differentiated update cycles, and multi-dimensional field traits of general equipment due diligence data create multiple constraints for deployment and upgrade workflows. Diverse data formats from multiple sources require configuration of multi-format parsing adaptation rules. Weekly or monthly synchronized operation and maintenance data needs scheduled incremental sync tasks to prevent full syncs from consuming excessive business resources. Core parameters have specific units, so field unit conversion rules must be configured to avoid disorganized parsed data. Some documents include image attachments such as equipment nameplates and appearance photos, so an additional image understanding model must be deployed to assist with extracting key information.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | General equipment due diligence reports often include multi-page operation and maintenance ledgers and parameter tables, resulting in large single-file sizes |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Long documents require complete extraction of multi-dimensional parameters, so parsing timeout must be extended |
SYNC_INCREMENTAL_CRON | 0 0 2 * * * | Operation and maintenance data is updated daily, synchronizing incremental data daily during early morning avoids peak business hours |
FIELD_MAPPING_AUTO | Enabled | The standardization level of general equipment fields is limited, so automatic mapping reduces manual configuration workload |
RECALL_TOP_K | Top 8 entries | General equipment has many parameter dimensions, so sufficient entries must be recalled to cover all core information |
SIMILARITY_THRESHOLD | 0.75 | Differentiate parameter differences between different equipment models, avoid mistakenly recalling entries with low similarity |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: After deploying with Docker Compose, running
pnpm devdisplays a MongoDB connection timeout, and the log returns anETIMEDOUTerror. Cause: Port mapping and network alias for the MongoDB service were not correctly configured in docker-compose.yml, so the application container cannot access the database service. - Symptom: After attempting to deploy the image understanding model, generic equipment nameplate images cannot be recognized. Cause: The model's running port and dependent resources were not specified in the configuration file, so the model failed to load properly.
- Symptom: After local deployment, historical conversation records cannot be cleared, and the batch cleanup command does not respond. Cause: The conversation storage directory was not mounted to a host persistent path, so in-container data cannot be accessed and cleaned by external scripts.
How to Confirm Configuration Is Correct
- Run the local deployment startup script, check for no database connection-related errors in the startup log, and confirm database communication is normal.
- Upload a standard general equipment due diligence report, verify the parsed result includes all preset core fields, and confirm the field mapping configuration is effective.
- Trigger an incremental sync task, review the sync log to confirm only new data entries are recorded, and verify the scheduled sync configuration is correct.
- Execute the batch conversation record cleanup script, check that conversation data in the host storage path is properly deleted, and confirm the storage mounting configuration is correct.
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.