Metal Material Processing Ledger Summary and Report Generation: Deployment and Upgrade

What this category of data looks like

Metal material processing ledger data primarily comes from process parameters collected by production equipment, inspection reports from quality control departments, and inventory records from warehouse systems. Data is updated per production shift or batch. A single ledger document includes fields such as batch number, material grade, furnace number, processing parameters for each process, dimensional deviation, hardness value, pass rate, and inbound and outbound times. Most field units follow industry standard units like millimeters (mm), megapascals (MPa), and Vickers hardness (HV). Some documents include scanned attachments of process flowcharts.

What constraints do these characteristics impose during deployment and upgrade

Metal material processing ledger data sources are scattered. During deployment, configure multiple data source connection parameters to ensure process, quality control, and warehouse data from different systems can be synchronized and aggregated into the knowledge base. The update rhythm per shift or batch requires setting a reasonable incremental sync cycle during deployment, to avoid full synchronization consuming excessive server resources. Fields include specialized industrial units. During upgrades, retain existing field mapping rules to prevent data errors caused by incorrect unit recognition during knowledge base import. Single documents and attachments have large data volumes. Adjust storage and database capacity thresholds to prevent data overflow from affecting service stability.

How to set configurations

Reference only; verify against the target FastGPT release and deployment documentation.

| REDIS_URL | redis://:custom_password@host:6379/1 | Caches incremental sync batch markers and user sessions. Uses database 1 to isolate business data and avoid conflicts with other services |

| STORAGE_S3_ENDPOINT | http://s3-cn-beijing.aliyuncs.com (or local object storage endpoint address) | Stores large-volume scanned inspection report copies and process parameter logs. Must match the access endpoint of the storage service |

| DB_MAX_LINK | 20–30 | Metal material processing ledgers have high concurrent read and write requests. Limit database connection count to avoid resource exhaustion |

| FE_DOMAIN | https://metal-processing-doc.your-domain.com | Configure the frontend access domain name to ensure report links generated after ledger summary can be accessed normally |

| FILE_TOKEN_KEY | Random 32-bit string calibrated via actual testing | Generates temporary tokens for file access, ensuring uniqueness and access security |

Three common mistakes

• An error Failed to create post presigned url is displayed when uploading ledger files. The cause is incorrect configuration of STORAGE_S3_ENDPOINT or the corresponding storage access key, which prevents generation of pre-signed links for file uploads.
• Core configuration information such as MONGODB_URI and REDIS_URL is not exported when migrating servers, causing failure to connect to database and cache services normally.
• When deploying on an ARM architecture Galaxy Kylin Linux system, the FastGPT Docker image matching the ARM architecture is not used, causing container startup failure or abnormal operation.

How to confirm configurations are correct

• Run a database connection test command to verify connectivity between MONGODB_URI and the corresponding database.
• Upload a small sample metal material ledger file to check if upload links can be generated normally and file storage is completed.
• Check the running status of the cache service to confirm that the service configured via REDIS_URL can read and write data normally.
• Access the frontend address configured via FE_DOMAIN to check if the page for ledger summary and report generation functions loads normally.

The values provided are common starting points and should be measured against the reader's own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.