What data for this category looks like
Typically, data for this category comes from concentrate and auxiliary material analysis reports, DCS control systems, electrolyzer voltage and current monitoring, laboratory test results, and energy and environmental monitoring systems. Daily documents include batching lists, smelting records, electrolyzer condition records, laboratory reports, anode and cathode quality records, and environmental protection ledgers. Data update frequency changes with production cycles. Each batch of raw materials corresponds to a set of batching data. Each smelting furnace or electrolysis cell generates corresponding condition records. Parameters such as cell voltage and current are updated in real time. Most fields contain specialized industrial parameters, such as cell voltage (unit: mV), current intensity (unit: A), and similar metrics.
What constraints these characteristics impose on forms and interactions
Access to multi-source heterogeneous data requires forms to support binding field formats from different systems, to avoid data misalignment. Frequently updated real-time parameters require forms to support both immediate entry and batch import modes, adapting to different inspection scenarios. The units and value ranges of specialized fields must strictly match industrial standards. Otherwise, subsequent data verification will fail. Complex document classification requires forms to display content segmented by production link, reducing engineers’ search time. At the same time, errors in equipment inspection and maintenance directly affect production indicators. Form interactions must include basic verification logic to prevent incorrect data from entering the knowledge base.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Form field grouping | Divided by production link (smelting/electrolysis/laboratory) | Matches the classification logic of existing enterprise documents, reducing engineers’ learning curve |
formInput | Bind standardized fields with units | Adapts to structured data formats exported by DCS and laboratory systems, reducing manual input errors |
userSelect | Pre-set inspection abnormality items and maintenance types | Reduces deviation from free-form input, improving Q&A matching accuracy |
| Single submission limit | 50 records per submission | Matches the batch inspection data volume per furnace or cell, avoiding system processing timeouts |
| Timeout period | 300 seconds | Adapts to the delay of pulling laboratory data across systems, ensuring normal completion of the form submission process |
| Form input prompt length | 800–1200 characters | Adapts to the detailed description requirements of a single inspection record |
The configurations on this page are based on FastGPT v4.17.0.
Three common mistakes
- A DingTalk application release prompts "receiving message address verification failed". The cause is that the FastGPT service is not exposed to the public network, or the SSL certificate verification option is not configured as required.
- Electrolyzer current data submitted via the form has mismatched units. The cause is that options with standard units are not pre-set in
userSelect, leading to inconsistent units from free-form input. - Audio and video markers fail to render properly. The cause is that standard HTML syntax is not used in the specified reply node, and the corresponding rendering configuration is not enabled.
How to confirm configurations are complete
- Submit a simulated electrolyzer inspection record, check if the form fields automatically populate the corresponding units and data formats.
- Test DingTalk message reception, confirm that the verification prompt disappears and messages can be properly pushed to the specified group.
- Submit batch inspection data, check system logs to confirm that the number of records does not exceed the preset limit and there are no processing timeouts.
- Insert audio and video markers and preview, confirm that media elements load and display normally.
The values provided are common starting points and should be measured against the reader's own samples.