Database and Operations for Power Grid Equipment Investment Research Knowledge Base Construction

Power grid equipment investment research data mainly comes from four categories: factory inspection reports, operation and maintenance logs, industry

What data for this category looks like

Power grid equipment investment research data mainly comes from four categories: factory inspection reports, operation and maintenance logs, industry standard documents, and real-time inspection logs. Factory data is imported via one-time bulk uploads. Operation and maintenance logs are updated quarterly. Inspection logs are updated in real time based on daily or hourly inspection cycles. Core fields for each entry include device unique ID, model, rated voltage, rated capacity, manufacturing date, last maintenance time, and fault history. Units uniformly follow power industry specifications: kV for voltage, MVA for capacity, and ℃ for temperature.

What constraints these characteristics impose on the database and operations layer

Multi-source heterogeneous data sources require the database to support access and parsing of multiple formats including PDF, Excel, and structured logs. Data with different update frequencies need distinct full and incremental synchronization strategies to avoid excessive resource usage from full updates. Professional fields and fixed units require strict validation rules to prevent investment research data from becoming invalid due to unit mismatches or missing fields. The device unique ID as the primary key needs strong constraints to avoid redundant data from duplicate imports.

How to set the configuration

Configuration ItemRecommended ValueRationale
UPLOAD_FILE_MAX_SIZE2000 MBPower grid factory inspection reports and inspection log PDFs have large individual file sizes, so this setting must accommodate large file upload requirements
PARSE_FILE_TIMEOUT_SECONDS600 secondsSufficient parsing time must be reserved when parsing multi-page device technical documents
RECALL_TOP_NTop 10Investment research scenarios require comparing multi-dimensional device parameters, and 10 recall results cover core comparison needs
SIMILARITY_THRESHOLD0.75Power grid professional terminology matching has high precision requirements. A threshold that is too low will introduce irrelevant results
SYNC_INCREMENT_INTERVALHourlyOperation and maintenance inspection data is updated hourly, so this setting must match the real-time sync rhythm
DB_BACKUP_CRON0 2 * * *Perform database backup at 2 AM daily to avoid business peak hours

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 specific analysis, and it is recommended to test on your own samples before finalizing.

Three common mistakes

  • Uploading a power grid equipment operation and maintenance Excel file prompts the File size exceeds limit error. The cause is failing to adjust the UPLOAD_FILE_MAX_SIZE configuration to a threshold suitable for large tables. The default configuration cannot accommodate complex Excel files with multiple fields.
  • Startup fails during local non-Docker deployment, with an error about MongoDB replica set configuration abnormality. The cause is skipping the replica set initialization step and directly starting a MongoDB instance without cluster configuration.
  • A Permission denied error occurs when mounting a database storage volume. The cause is failing to correctly configure the read-write permissions of the storage volume, preventing FastGPT from writing incrementally synced device operation and maintenance data.

How to confirm configuration is correct

  • Upload a typical power grid equipment inspection report PDF, and check if the parsed data fields cover core professional parameters and units comply with industry specifications.
  • Trigger an incremental sync task, and check if recent cycle operation and maintenance data has been added to the database, with no abnormal error records in the sync logs.
  • Initiate a query for a specific device model, and confirm that the number of recall results and similarity match the preset configuration, with no irrelevant data mixed in.
  • Check the available space of the database storage volume, and confirm that the remaining space can cover at least three times the daily newly added data volume, leaving operational buffer space.

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.