What this category of data looks like
Daily production data for pulp and paper comes from wood pulp and waste paper batch ledgers, DCS/QCS control systems, on-machine measurement equipment, laboratory testing systems, and energy and water treatment systems. Daily documents include pulp preparation sheets, beating operation records, paper machine operation logs, online quality curves, laboratory quality inspection reports, and sheet break and product change ledgers. Data update frequencies are divided into three categories: real-time (online measurement and control system data), per-shift (team operation records), and per-roll (production and quality inspection data associated with finished rolls). Document fields include material batch numbers, operation parameters, test values, timestamps, and equipment identifiers. Units include concentration values, grammage, duration, flow values, and more.
Constraints on document parsing and chunking
The multiple types and varied update rhythms of production data create clear constraints for document parsing and chunking. Multi-format documents include tabular pulp preparation sheets, line chart-style online quality curves, and plain-text operation logs. Parsing logic must be adapted to avoid losing data associations. Real-time uploaded online measurement data has a high update frequency. Excessively large chunk sizes cause synchronization delays, which fail to meet the timeliness requirements of ledger summary. Multiple documents linked to the same production batch must retain the binding relationship between batch and timestamp during chunking. Without this, data misalignment occurs in subsequent summary work. Data sources with different update frequencies must be matched to corresponding chunk granularity. Uniform configuration leads to an imbalance between parsing efficiency and accuracy.
Configuration settings
These configuration settings are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the mixed length of tables and text in pulp and paper documents, avoiding splitting cross-batch associated data |
chunkSplitter | By paragraph + semantic boundary | Adapts to the structure of multi-type documents, preserving the semantic integrity of pulp preparation sheets and quality inspection reports |
customPdfParse | Enabled | Used to parse PDF documents containing online quality curves and sheet break records, extracting embedded charts and table data |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Addresses batch parsing of large-volume historical shift ledgers, avoiding timeout interruptions |
chunkSettingMode | Manual configuration | Adjust chunking parameters for data sources with different update frequencies, matching parsing requirements for real-time and batch data |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to batch uploads of historical production ledger archives, meeting parsing requirements for enterprise archived data |
Three common configuration errors
- Symptom: The file parsing API returns a 504 timeout error, or the parsing status remains stuck in processing for an extended period. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted, and the default value was used, resulting in timeout during parsing of large-volume historical ledgers. - Symptom: Parsed chunked content loses material batch association fields from pulp preparation sheets, or numerical coordinates of quality curves are truncated. Cause: The
chunkSizesetting is too small, splitting tabular data from the same batch across multiple chunks and damaging data integrity. - Symptom: After batch uploading multiple production documents, only partial documents generate chunked results. Cause:
UPLOAD_FILE_MAX_SIZEwas not configured correctly, causing archived files exceeding the limit to fail parsing, orcustomPdfParsewas not enabled, causing parsing failures for non-standard PDF quality curve documents.
How to verify correct configuration
- Upload a single test document containing a pulp preparation sheet and quality curve, confirm that parsed chunks retain complete material batch and timestamp associations.
- Adjust the
chunkSizeparameter, compare chunked results across different configurations, and confirm that no cross-semantic unit splitting occurs. - Upload a historical ledger archive exceeding the preset size, confirm that parsing tasks are not blocked by the system and parsing progress updates normally.
- Call the API interface to pull parsed chunked data, verify field completeness and document correspondence of returned results.
The values provided are common starting points and should be measured against the reader's own samples.