What the data for this category looks like
This category’s data covers the entire production process of carbonated drinks, fruit juices, tea beverages, functional drinks, and packaged drinking water factories. Daily documents include formula sheets, water treatment records, blending records, sterilization or aseptic filling records, packaging material inspection reports, and finished product inspection reports. Data sources include water source and raw material test results, formula management systems, online sugar and acidity monitoring systems, filling equipment data, visual inspection results, and LIMS systems. Data is updated per batch, per shift, or via continuous collection. Core fields include feeding parameters, water quality indicators, filling liquid level, seal tightness, and batch number information. Units include temperature, concentration, volume, and other common parameter units used in production scenarios.
What constraints these characteristics impose on deployment and upgrades
Production data for this category mostly consists of continuously collected time-series data and batch-associated documents. During deployment, support for high-concurrency small-file storage and batch data access is required. Due to high real-time requirements for data in formula, filling, and other links, sufficient database connection resources must be reserved during deployment. During upgrades, consistency of batch data must be maintained to avoid breaks in historical traceability chains caused by configuration changes. Additionally, given traceability requirements related to food production, the storage component must support fast data retrieval by batch number, and must adapt to plugin call requirements in local or hybrid network environments. The content on this page is based on the deployment logic of FastGPT v4.17.0.
How to set configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
STORAGE_S3_ENDPOINT | Intranet MinIO service address, for example http://192.168.1.100:9000 | Adapts to the scenario of local deployment for this category without public network access requirements, aligns with common intranet deployment needs |
REDIS_URL | Local Redis service address, configure a randomly generated password for production environments | Supports real-time data caching and batch retrieval, adapts to the update rhythm of continuously collected production data |
MONGODB_URI | Configure as a replica set address, set the connection pool size to 20–30 | Handles concurrent writes and queries for multi-batch data, matches the batch data update frequency of this category |
ROOT_KEY | Generated from a random string, length no less than 32 characters | Ensures access security for internal system interfaces, complies with compliance storage requirements for food production data |
FE_DOMAIN | Configure as an internal access domain name or local IP | Adapts to intranet deployment scenarios, avoids exposing production data over the public network |
Three common misconfigurations and issues
- Configuring
STORAGE_S3_ENDPOINTwith a publicly accessible address, causing intranet-deployed services to fail to connect to local MinIO storage. This occurs because the production environment of this category without public network exposure requirements is not accounted for, and common intranet deployment scenarios are overlooked. - Failing to correctly configure
ROOT_KEYleading to plugin interface authentication failures, preventing external AI interface calls. This occurs because a system access key that meets requirements is not generated, and basic configuration for plugin calls is incomplete. - Failing to retain batch data and LIMS system association configurations during upgrades, leading to an inability to quickly trace the source data of filling abnormalities in subsequent steps. This occurs because the business characteristic of managing production data by batch for this category is not matched, and historical association parameters are incorrectly reset.
How to confirm the configuration is correct
- Access the configured
STORAGE_S3_ENDPOINTaddress, upload a test production document, and confirm that the system can normally read the file content. - Run a database connection verification script to verify that the database configured via
MONGODB_URIcan perform normal data write and query operations. - Use
ROOT_KEYto call internal system interfaces, confirm that the authentication process passes normally. - Check the running status of the cache service, confirm that the service configured via
REDIS_URLcan normally receive and store real-time production data.
The values provided here are common starting points and should be evaluated against the reader's own samples.