What data for this category looks like
Data for this category comes from cement plant mining, raw material, calcination, grinding, quality control, and energy departments. Daily documents include raw material batching plans, kiln operation logs, laboratory reports, quality control notices, equipment inspection records, and energy reports. Data sources cover online composition analysis, DCS systems, laboratories, kiln and grinding equipment, raw and fuel ledgers, and energy metering devices. Update frequencies are divided into real-time (such as kiln temperature and negative pressure data), per-shift (such as inspection records), and daily (such as energy reports and quality statistics). Document fields include raw material ratio parameters, equipment operation parameters, and laboratory test values. Units include tons per hour, degrees Celsius, pascals, megapascals, and others.
Constraints imposed by these characteristics on deployment and upgrade
Data sources for this category are scattered, and update frequencies vary significantly. During deployment, adapt to multiple data source accesses. Do not only support a single document format. Real-time data (such as kiln temperature and online composition analysis results) requires low-latency storage and synchronization links to ensure data timeliness. Fields for different types of documents have large differences. Configure targeted parsing rules to accurately extract key parameters. During upgrades, avoid interrupting production-related data synchronization. Use a gray release strategy. Also ensure the stability of the data verification link to prevent quality control errors caused by configuration changes.
How to configure the settings
This configuration is formulated based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
REDIS_URL | redis://:your-password@your-host:6379/0 | Adapt to caching requirements for real-time kiln temperature and online composition data, ensuring low-latency access |
MONGODB_URI | mongodb://your-user:your-pass@your-host:27017/fastgpt?authSource=admin | Store unstructured document data such as equipment inspection records and laboratory reports |
PG_URL | postgresql://your-user:your-pass@your-host:5432/fastgpt | Store structured data such as raw material batching plans and quality control notices |
DB_MAX_LINK | 20–30 | Match the concurrent access connection requirements of multiple data sources, avoiding connection exhaustion |
FE_DOMAIN | https://your-cement-qa-domain.com | Configure an exclusive internal enterprise access domain name, adapting to intranet access environments |
ROOT_KEY | Randomly generated 32-character string | Ensure the security of system background access, complying with enterprise data security specifications |
Three common errors
- Phenomenon: An "insufficient resources" or "connection timed out" error appears when accessing the workflow interface. Cause: Insufficient container memory quota configured for multi-document parsing requirements, leading to resource exhaustion during large-volume laboratory report parsing.
- Phenomenon: After upgrading FastGPT, saved model configurations automatically switch to other models during chat. Cause: Original environment variable configurations were not retained during the upgrade, or core configuration items were not correctly synchronized, resulting in lost model binding relationships.
- Phenomenon: After deployment, entering preset username and password prompts an error, and the system cannot be logged into. Cause: The associated database was not correctly initialized, or database connection parameters have spelling errors, preventing user authentication information from being read.
How to confirm the configuration is complete
- Run the
docker pscommand, confirm that all FastGPT-related containers are in a running state with no abnormal restart records. - Upload a raw material batching plan document, verify that core fields such as raw material ratios and adjustment parameters can be correctly extracted after parsing.
- Initiate a cross-document cross-review request, verify that the system can associate relevant data from kiln operation logs and laboratory reports.
- Log in to the system background, confirm that the values of configuration items such as
FE_DOMAINmatch the enterprise's internal domain name with no format errors.
The values provided here are common starting points and should be evaluated against the reader's own samples.