What this category's data looks like
This category of data comes from steel coil labels and quality certificates, punch and feeder operation data, mold management systems, workshop MES systems, coordinate measuring machine or inspection gauge test data. Daily documents include stamping work instructions, material utilization rate sheets, mold inspection sheets, first/last piece records, dimensional inspection reports, and mold change records. Data updates are triggered with coil batches, production batches, and each mold change. Document fields cover material grades, sheet thickness, mold numbers, mold change times, test deviation values, and other items. Corresponding units include millimeters, tons, hours, and others.
What constraints these characteristics impose on the "forms and interactions" link
Data updates frequently with coils, production batches, and mold changes. Forms must support quick switching of field templates for different production scenarios to avoid repeated configuration. Multi-source heterogeneous data must use a unified display format in interactions to prevent workshop operators from confusing business fields from different sources. Inspection data before and after mold changes has strong correlation. The interaction link must support quick association of historical mold change records with current inspection items. Workshop on-site network environments may have limited bandwidth. Forms and interaction components must support lightweight loading under low bandwidth to reduce waiting time. Equipment inspection and maintenance Q&A must link to historical documents for corresponding molds and batches. The interaction must provide an entry for quickly locating matching business data.
How to set configurations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
formInput | Set a single input character limit of 1000 characters, support bulk import of field templates | This category has many document fields. Bulk import reduces repeated configuration workload. The character limit is adapted to on-site workshop input scenarios |
userSelect | Preset high-frequency options such as mold numbers and material grades, support fuzzy search | Workshop inspection personnel need to quickly select commonly used parameters. Fuzzy search improves entry and query efficiency |
formInput | Enable field validation rules to match the unit format preset in documents | This category of data fields has clear units. Validation rules prevent incorrect numerical and unit entries |
| Voice model configuration | Adapt to 16kHz sampling rate noise reduction models, set response timeout to 15–20 seconds | Workshop environments have high noise levels. Noise reduction models improve speech recognition accuracy. The timeout setting adapts to on-site operation rhythm |
| Large model access configuration | Select professional models adapted to industrial scenarios, support custom prompt words bound to business data | This category of business focuses on stamping equipment inspection and maintenance. Professional models improve the accuracy of Q&A. Binding business data narrows the recognition scope |
Three common mistakes
- Symptom: After enabling speech input, conversation content cannot be converted to text, and the interface displays "Recognition timed out". Cause: No speech noise reduction model adapted to workshop noise is configured, or the timeout setting is too short to cover the recognition duration in on-site environments.
- Symptom: After embedding FastGPT components via iframe, the frontend can normally display responses, but the backend system cannot obtain conversation return results. Cause: The callback interface configuration for iframe is not enabled, or cross-domain permissions are not correctly configured, preventing data from being transmitted back to the backend.
- Symptom: After configuring the system prompt word for the workflow AI node, the returned content exceeds the preset limited range. Cause: The prompt word is not bound to the stamping equipment-related business data of this category, leading to an expanded recognition scope and mixed irrelevant content.
How to confirm the configuration is correct
- Switch templates for different mold change batches, check if form fields correctly match the business data of the corresponding batch.
- Enter high-frequency parameters, check if the fuzzy search function of
userSelectnormally returns matching items. - Initiate a voice conversation, check if the recognition result is accurately converted to text and displayed in the interface.
- Call the interface embedded via iframe, check if the backend can normally receive the conversation return results.
This document is based on FastGPT v4.17.0. The values given are common starting points, and should be measured against the reader's own samples.