Process Specification and Technical Standard Q&A for Cement Clinker and Grinding: Knowledge Base Retrieval and Recall

What this category of data looks like

This category of data originates from cement enterprise mining, raw material, calcination, grinding, quality, and energy departments. It covers documents including raw material batching plans, kiln operation records, laboratory reports, quality control notices, equipment patrol logs, and energy statements. Data sources include online composition analysis systems, DCS control systems, laboratories, kiln and grinding equipment, raw and fuel ledgers, and energy metering devices. Update frequencies cover real-time, per-shift, and daily. Document fields include batching ratio, kiln temperature, feeding rate, clinker free calcium, cement strength, and others. Units include ℃, t/h, %, MPa, and similar standard industrial units.

What constraints do these characteristics impose on the knowledge base retrieval and recall link

Multi-source heterogeneous data sources create differing field formats across documents. A unified mapping to standardized process parameter fields is required to prevent matching deviations during retrieval. Documents with different update frequencies need adaptive incremental update strategies: real-time data requires high-frequency synchronization, while per-shift and daily data needs updates triggered at fixed intervals. Diverse document types require matching chunking rules. For example, batching plans should be chunked by parameters, and operation records should be chunked by shift. Most process questions focus on raw and fuel fluctuation adjustments, thermal system stability, and strength control. Retrieval must accurately match professional terminology and operation scenarios, while ensuring the confidence of recall results to avoid production abnormalities caused by incorrect process parameters.

How to set the configurations

Three common configuration errors

• Issue: Retrieval returns no results when usingReRank is enabled in the application, but normal results appear in the knowledge base test interface. Cause: The application's reranking model configuration is not synchronized to the knowledge base retrieval link, or the reranking model's context length does not match the chunk size of process documents.
• Issue: Knowledge base search takes longer than 10 seconds. Cause: The limit parameter is not set reasonably, leading to the recall of too many irrelevant documents and increased calculation load for the retrieval and reranking links.
• Issue: No content is displayed in the knowledge base at all times. Cause: The collectionIds parameter is not configured correctly, the corresponding process document dataset is not associated, or the incremental update task is not triggered at the preset frequency.

How to confirm the configuration is correct

• Enter the knowledge base management interface of FastGPT v4.17.0, verify that the dataset associated with the collectionIds parameter includes target documents such as raw material batching plans and kiln operation records.
• Trigger a process-related retrieval request, check whether the similarity score of the returned results falls within the preset range, and confirm that the usingReRank function operates correctly.
• Simulate a raw and fuel fluctuation query scenario, verify whether datasetSearchUsingExtensionQuery expands query terms related to batching adjustments and returns matching process documents.
• Check the retrieval log, confirm that the number of results returned per single retrieval does not exceed the preset limit value, and there are no obvious timeouts or field missing error messages.

The values provided here are common starting points and should be measured against the reader's own samples.