What this type of data looks like
Data for photovoltaic module encapsulation processes comes from cell grading, glass adhesive film backplane batch ledgers, production MES systems, string welding and lamination equipment, EL defect detectors, and IV power testers. Data is generated once per module and per batch of materials. Daily documents to process include layout drawings, material batch records, lamination process records, EL defect images, IV test reports, insulation voltage withstand records, and finished product packing lists. Data fields include cell current gear, lamination temperature and time, EL crack location coordinates, IV power value, insulation resistance value, and other relevant metrics. Corresponding units include degrees Celsius, seconds, watts, megohms, and other standard units.
Constraints during deployment and upgrade
High-frequency per-module and per-batch data updates require sufficient database connection pool and cache resources to be configured during deployment, to avoid read-write blocking that disrupts production schedules. Multiple types of document and image data require adapting to different format file parsing and storage solutions, and integration with the enterprise’s internal object storage service is required. Specific units and business meanings of fields require matching photovoltaic encapsulation industry standards when configuring field mapping rules; general mapping logic cannot be used directly. The upgrade phase must reserve compatibility for integration with existing production equipment and MES systems, to avoid production data synchronization interruptions caused by configuration changes.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 connections | Adapts to high-frequency read-write business scenarios such as component data and test reports, prevents connection pool exhaustion |
MONGODB_URI | mongodb://<user>:<pass>@<host>:<port>/<dbname>?authSource=admin | Stores bulk unstructured documents including EL defect images and IV test reports |
REDIS_URL | redis://:<password>@<host>:<port>/0 | Caches real-time material batch and cell grading data, reduces query time for field mapping |
STORAGE_S3_ENDPOINT | Fill in according to the enterprise’s internal object storage service address | Stores bulk files including process documents and test records, reduces local storage pressure |
FILE_TOKEN_KEY | Randomly generated 32-bit string | Secures file upload interfaces, prevents unauthorized file submissions |
ROOT_KEY | Exclusive key configured for the deployment environment | Initializes administrator permissions for FastGPT v4.17.0 platform, ensures background configuration security |
Three common configuration mistakes
- Phenomenon: A
TypeError: Cannot read properties of undefinederror appears in the console after deployment. Cause:FILE_TOKEN_KEYis not configured correctly, leading to token verification failure during file parsing. - Phenomenon: A username or password error is displayed during login when integrating with one API. Cause:
REDIS_URLis not configured as a cache instance shared with one API, resulting in abnormal session storage. - Phenomenon: An
Error response from daemonerror is returned when deploying with docker compose. Cause: Correct database access permissions are not configured forMONGODB_URI, leading to database connection failure.
How to verify a successful configuration
- Run the
docker pscommand, confirm all FastGPT-related containers are in a running state with no abnormal exit records. - Upload a lamination process record PDF file, verify that core business fields such as cell current gear, lamination temperature and time can be extracted after parsing.
- Call the file upload interface, verify that the returned token passes the verification rules of
FILE_TOKEN_KEY, with no permission errors. - Check the database connection logs, confirm that the current database connection count does not exceed the configured limit of
DB_MAX_LINK, with no connection exhaustion alerts.
The values provided are common starting points and should be measured against the reader's own samples.