What data for this category looks like
Data sources for this category include ERP orders and inventory, BOMs and process routes, equipment capacity, staff shifts, work-in-progress, and supplier delivery information. Daily processed documents include sales orders, master production schedules, material requirements plans, weekly and daily scheduling tables, work orders, material shortage lists, and delivery change notices. Data update cycles run daily, weekly, or trigger on orders or abnormal events. Data fields include order numbers, material codes, capacity values, shift durations, work-in-progress quantities, and more. Corresponding units include pieces, hours, days, and others.
What constraints these characteristics impose during deployment and upgrade
This category’s data sources cover multiple systems including ERP, equipment, and personnel. Configure adaptation rules for multi-source data docking during deployment. Maintain compatibility of system interfaces during upgrades to avoid data synchronization interruptions. Daily processed documents have diverse types and significant field differences. Preconfigure parsing templates for multiple document types during deployment. Support parsing logic for legacy documents during upgrades to prevent scheduling data from becoming invalid. Data update cycles change flexibly with production status. Configure adjustable synchronization trigger mechanisms during deployment. Verify that the data update process operates normally in abnormal scenarios after upgrades.
How to set configurations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
DB_MAX_LINK | 80–120 | This category requires docking with multiple production data sources, so the concurrent connection count must adapt to multi-system synchronization requests |
PG_URL | PostgreSQL database address linked to the enterprise ERP system | Core scheduling data such as orders and inventory comes from the ERP system, so direct docking is required to obtain real-time information |
REDIS_URL | Local Redis instance address, with connection timeout set to 30 seconds | Production scheduling data requires high-frequency read and write caching. Local deployment reduces access latency; the timeout setting prevents scheduling tasks from blocking |
STORAGE_S3_ENDPOINT | Enterprise intranet object storage node address | This category involves storage of a large number of production documents. Intranet deployment ensures data security and access speed |
FE_DOMAIN | Enterprise intranet access domain name | Only open to intranet departments such as PMC and shop schedulers, which complies with the security control requirements for production data |
FILE_TOKEN_KEY | 32-bit random encrypted string | Used to generate access tokens for production documents, preventing unauthorized access to sensitive scheduling-related documents |
Three common mistakes
- After an upgrade, scheduling-associated knowledge base queries return normal data, but no output is passed to the large model. The cause is that permission rules for document access tokens were not reconfigured after the upgrade, so the large model cannot obtain context information for scheduling-related documents.
- After offline deployment and upgrade, rescheduling model configuration fails, and the interface returns a connection timeout error. The cause is that
REDIS_URLwas not correctly set to point to the local Redis instance, so model caching cannot be read or written normally. - After local deployment, shop schedulers cannot complete login via enterprise SSO. The cause is that
FE_DOMAINwas not configured as a valid callback domain for the SSO authentication service, so the authentication process is blocked.
How to confirm configurations are correct
Run a manual data synchronization task for FastGPT v4.17.0. Check whether ERP orders and inventory data import correctly into the scheduling system, and confirm database connection configurations are effective. Upload a test work order document. Verify that the document parsing and storage process runs normally, and confirm that STORAGE_S3_ENDPOINT and FILE_TOKEN_KEY configurations are correct. Trigger a rescheduling operation in an abnormal scenario. Verify that the data update and cache read/write processes run normally, and confirm that REDIS_URL and DB_MAX_LINK configurations adapt to concurrent requirements. Attempt to access the system via the enterprise intranet domain name and initiate SSO login. Confirm that the docking configuration between FE_DOMAIN and the SSO service is effective.
The values provided are common starting points and should be measured against the reader's own samples.