What this category of data looks like
This category of data comes from engineering orders, sand, gravel, cement and admixture inspection records, aggregate moisture content test reports, mixing station control system logs, and project laboratory archives. Data updates follow the rhythm of per-vehicle production batches, per raw material delivery, and daily summary statistics. Daily documents include mix proportion notices, opening identification forms, production and transport orders, raw material inspection reports, test block curing records, and concrete strength reports. Core fields include mix proportion parameters, aggregate moisture content, slump index, and design strength grade. Units involve kilograms, cubic meters, percentage, and megapascals.
What constraints do these characteristics impose on the workflow orchestration link
High-frequency updates at the per-vehicle level require workflows to pull the latest aggregate moisture content data in real time, to avoid batching calculation deviations. Multi-source and heterogeneous document types require workflows to adapt to content extraction from inspection reports and notices in different formats. Strict constraints on core business links require workflows to embed parameter verification logic to conduct compliance checks on indicators such as mix proportion and slump. The lag of test block strength reports requires workflows to reserve variable storage nodes for exception tracing, to associate production batches with strength results. Cross-department collaboration requirements require workflows to support node permission configuration for scheduling, laboratory, and mixing station roles.
How to set up the configuration
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Question classification node | Switch to the general professional Q&A model | Adapt to multi-classification scenarios for process regulations and technical standards |
| Text content extraction component | Single extraction limit of 8000 characters | Cover the full content length of a single opening identification form |
variables configuration | Bind production batch number and aggregate moisture content | Associate core parameters for each vehicle production, support cross-node calling |
nodeId binding | Bind mixing station node and laboratory node | Achieve full-link data association for the production process |
| Global variable history | Retain the last 25 interaction records | Match context requirements for multiple daily production batches |
| Exception check trigger | Trigger when slump deviation exceeds 2% | Comply with process compliance requirements for ready-mixed concrete production |
Three common mistakes
- Using an initialized AI model for the question classification node, with an error prompt popping up during run, save or publish. Reason: The default initialized model is not adapted to the professional Q&A scenario of process regulations and technical standards, and cannot complete the initialization verification of the classification logic.
- The result JSON returned by the text content extraction component is empty. Reason: The extraction field range is not limited, or the set extraction length exceeds the single-processing limit of the component, resulting in parsing failure.
- The output of the specified reply component is not displayed in the global variable history. Reason: The output of this component is not bound to the global variable storage node, resulting in the context not being correctly captured.
How to confirm the configuration is complete
- Create a new question classification node, switch to the general professional Q&A model, and run it to confirm there are no error prompts.
- Upload a single opening identification form to the text content extraction component, and confirm that the returned JSON contains core fields such as mix proportion and moisture content.
- Initiate more than three simulated production Q&A interactions, check the global variable history, and confirm that all interactions and component output content are included.
- Input simulated parameters with excessive slump deviation, and confirm that the exception check node triggers the alarm logic.
The values provided here are common starting points and should be measured against the reader's own samples.