What the data for this category looks like
Data sources cover online composition analysis equipment, DCS control systems, laboratory reports, kiln and grinding equipment ledgers, raw and fuel inbound and outbound records, and energy metering reports. Daily documents include raw material batching plans, rotary kiln operation logs, clinker quality laboratory reports, equipment inspection records, quality control notices, and energy consumption reports.
Update frequencies fall into three categories:
- Real-time: kiln temperature, online composition analysis values
- Per shift: inspection records, shift production reports
- Daily: raw and fuel ledgers, daily energy reports
Data fields include raw material batching parameters, kiln cylinder temperature, 28-day compressive strength of clinker, inspection time, equipment operating duration, and more. Units include percentage, degrees Celsius, megapascals, hours, and others.
What constraints these characteristics impose on database and operations workflows
Multi-source mixed data writing and querying require databases to support complex associated queries for structured data, as well as storage of unstructured text and binary attachments.
The wide range of update frequencies creates conflicting demands: real-time data generates large numbers of write requests per second, while shift and daily reports use bulk data imports. This places high requirements on database connection pools and read-write performance.
Quality data is directly linked to production compliance. Data loss or delay can lead to production shutdowns or product returns, so databases must have high reliability and complete backup mechanisms.
Storage formats vary significantly across different data types. Reasonable partitioning of storage media is needed to optimize read-write efficiency.
How to configure settings
| Configuration Item | Recommended Value | Rationale for This Setting |
|---|---|---|
| Chunk length | 1000–1200 characters | Adapts to the average length of documents such as raw material batching plans and kiln operation records, optimizes text embedding and retrieval efficiency |
| Single search limit | Top 20 entries | Matches the historical data query scope during quality traceability, reduces the return of invalid results |
| Maximum database connection pool size | 180–220 connections | Adapts to the concurrent demand of real-time data writing and bulk queries for shift and daily reports, avoids connection exhaustion |
| Cache expiration time | 300–600 seconds | Caches real-time kiln temperature and online composition analysis data, balances data timeliness and backend database load |
| Full backup cycle | 1 time per day | Ensures the reliability of quality data, meets compliance requirements for production traceability |
Three common configuration errors
- Sustained high disk IO usage after deployment, with logs showing large numbers of database retry write requests. Cause: The database connection pool configuration was not adjusted for the real-time data concurrency of cement clinker and grinding operations, leading to repeated retries after connection exhaustion, which occupies large amounts of disk resources.
connection refusederror appears after starting the container. Checking the deployment file reveals mixed configuration items for pgvector and vector databases. Cause: Configuration for pg databases was incorrectly written to the vector database compose file, causing the container to load mismatched dependency configurations on startup.- Empty results are returned when querying historical data for 28-day compressive strength of clinker. Cause: The index refresh policy was not configured according to the data update frequency, so newly generated laboratory data was not synchronized to the query index in time, resulting in failure to retrieve the latest data.
How to verify correct configuration
- Run the database connectivity test tool included with FastGPT v4.17.0 to verify the validity of each database connection configuration, and confirm that the connection timeout time meets business requirements.
- View the database connection pool monitoring dashboard to confirm that the maximum database connection pool size configuration has not triggered an exhaustion alert.
- Import a simulated raw material batching plan and kiln operation record to verify that the response speed of data writing and query meets expectations.
- Check the execution logs of the regular backup task to confirm that the full backup and incremental backup configurations are running normally.
The values provided are common starting points and should be measured against the reader's own samples.