What the data for this category looks like
Aquaculture marketing scenario business data falls into two categories. First is farmer contact information, including pond ID, cultured species, farming scale, and more. Second is aquaculture-related business data, sourced from pond monitoring equipment, daily inspection logs, feed purchase documents, and seed batch records. Data sources cover real-time monitoring and manual reporting. Update rhythms vary by field type: Real-time data such as water dissolved oxygen and pH is pushed by equipment every hour. Manual data such as feeding amount and disease records updates daily per inspection schedule. Seed and feed purchase data updates per batch or monthly. Core fields for each data entry include pond ID, cultured species, water parameters, feeding amount, and batch number. Units follow professional agricultural measurement standards, such as mg/L, kg/mu, ℃, and others. The document structure uses pond + batch as the core association dimension. This supports generation of customized marketing content and customer acquisition follow-up.
What constraints these characteristics impose on forms and interactions
Mixed real-time and manual data sources require forms to distinguish read-only display fields from editable fields. This prevents manual changes to real-time monitored water parameters, while simplifying contact fields to reduce reporting burdens for farmers. Professional measurement requirements for multiple fields require forms to include built-in unit validation rules. This stops incorrect value and unit combinations from being entered, ensuring the accuracy of marketing content data sources. The pond and batch association structure requires forms to forcibly bind pond ID and batch number fields. This avoids data confusion across ponds and batches, facilitating subsequent precise marketing follow-up. Differences in update frequencies across fields require forms to support on-demand loading of historical data. This reduces repeated reporting workloads and improves customer acquisition conversion efficiency. Most aquaculture form reporting scenarios take place on outdoor mobile devices. This requires concise interaction logic, reducing complex hierarchical jumps to match farmers' usage habits.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
formFieldMaxCount | 20 or fewer | Matches the standard number of contact fields for aquaculture marketing forms, reducing the reporting burden on farmers |
inputUnitValidation | Enabled, preset with kg/mu, mg/L, ppm, ℃ | Covers unit validation rules for core monitoring fields in aquaculture, ensuring data source accuracy |
batchFieldBinding | Bind pond ID and cultured species fields | Forcibly associates batch information, facilitating subsequent generation of customized aquaculture marketing content |
autoFillHistoricalData | Enabled, automatically pull data from the last 7 days by pond ID | Reduces manual entry of repeated inspection data, improving the filling efficiency of customer acquisition forms |
formSubmitTimeout | 25 seconds | Adapts to network transmission scenarios on outdoor mobile devices, balancing submission stability and response speed |
The parameter values provided on this page are all common recommendations used to determine the starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing the settings.
Three Common Mistakes
- Clicking marketing text within the application triggers a FastGPT pop-up window and passes in the text. The send button inside the pop-up window remains gray and unavailable. The cause is that the input box bound to the pop-up window does not correctly receive the externally passed text parameters. This causes the input box to detect empty content and triggers the built-in required validation logic.
- When submitting marketing consultation content in the AI conversation node, the number of input characters exceeds the model limit. The system directly returns a 413 status code. The cause is that no pre-validation rule for the
inputMaxLengthparameter is configured. No check is performed on the length of input content before submission. - After selecting the target aquaculture knowledge base in the knowledge base search card, variable references do not take effect. Search results do not meet expectations. The cause is that the selected value of the knowledge base selection component is not bound to the scope of the search variable. This causes the variable to fail to correctly obtain the selected knowledge base ID.
How to Confirm the Configuration Is Complete
- Manually enter a test data set that complies with the aquaculture marketing form specifications. Include pond ID, cultured species and daily feeding amount. Click submit to confirm that the form has no validation errors and the submission is successful.
- Call the preset marketing text trigger link, pass in "Help me analyze the dissolved oxygen data of grass carp pond". Confirm that the pop-up window input box automatically fills this text, and the send button can be clicked normally.
- Submit test text that exceeds the preset length of
inputMaxLengthto the AI conversation node. Confirm that the system pops up a validation prompt before submission, and does not return a backend error. - Switch the selected option in the knowledge base search card. Confirm that search results only return content from the corresponding aquaculture knowledge base.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.