What Data Looks Like for This Category
Data for this category primarily comes from wood pulp and waste paper batch ledgers, DCS/QCS control systems, on-paper machine measuring equipment, laboratory testing systems, and energy and water treatment systems. Routine generated documents include pulp preparation sheets, beating records, paper machine operation logs, online quality curves, laboratory reports, and sheet break and grade change records. Data update rhythms fall into three categories: real-time, at shift handover, and when each paper roll completes production. Fields include batch identifiers, process parameters, test results, equipment operation status and exception records, plus some time-series curve unstructured data.
Constraints Imposed by These Characteristics on Database and Operations Maintenance
The data characteristics of this category create multi-dimensional operational maintenance constraints. Multi-source heterogeneous data sources and varied update rhythms require the database to support concurrent high-concurrency real-time writes and batch import processing. This avoids write conflicts between routine data and urgent exception data. Unstructured online quality curve data needs to balance relational storage for structured ledgers and index optimization for time-series data. Full-process data for a single paper roll must link data from multiple departments including material preparation, pulping, and paper making. This imposes clear performance requirements for cross-table join queries. Additionally, data volume fluctuates with operating parameters during high-speed continuous production. Scalable storage and computing resources must be reserved.
Configuration Settings
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://admin:xxx@mongodb-host:27017/pulp_paper?replicaSet=rs0 | Supports distributed storage for unstructured online quality curves and multi-batch pulp data |
PG_URL | postgresql://admin:xxx@pg-host:5432/pulp_paper_ledger | Stores structured relational ledger data including pulp preparation sheets, beating records, and laboratory reports |
DB_MAX_LINK | 20–30 connections | Balances connection needs for real-time data writes, batch data imports, and cross-department join queries, to avoid connection exhaustion |
REDIS_URL | redis://:xxx@redis-host:6379/0 | Caches metadata of frequently queried process parameters and exception data processing queues |
| Concurrent Request Limit | 10–15 concurrent requests | Adapts to high-concurrency scenarios for real-time data writes, to avoid interface call restrictions |
| Single Batch Import Limit | 500–1000 records | Adapts to batch import of production record data during shift handover, to avoid single import timeouts |
| Database Write Timeout | 30–60 seconds | Prevents task failure from prolonged waiting during single data write operations |
Three Common Misconfigurations
- When consecutively querying production ledger-related questions, subsequent responses may be off-topic, or fail to associate paper roll batch information from previous questions. This occurs because the question optimization function is not enabled, or reasonable context association rules are not configured, leading to failure to retain key parameters across multiple interactions.
- When batch importing shift production data, database connection timeouts or interface call restricted errors may occur. This happens because the maximum database connection count is set too low, or the concurrent request limit is configured improperly, leading to high-concurrency requests exceeding the carrying capacity of the database and interfaces.
- When conducting cross-department join queries for full-process paper machine data, query timeouts or slow result returns may occur. This is caused by failure to create indexes on PostgreSQL join query fields, or failure to set a reasonable sharding strategy for MongoDB time-series data, leading to insufficient query performance.
How to Verify Proper Configuration
- Perform a batch import operation for pulp preparation sheets, check database connection count monitoring, and confirm that the connection count does not exceed the configured maximum connection threshold.
- Initiate two consecutive production ledger queries, confirm that the second response can associate the paper roll batch information mentioned in the first query.
- Check Redis cache monitoring, confirm that frequently queried process parameter data has been correctly cached and no abnormal expiration occurs.
- Initiate a cross-department full-process data join query, confirm that the query response time falls within the expected threshold range for the business.
The values provided are common starting points and should be measured against the reader's own samples.