What the data for this category looks like
Data related to financial, insurance, or wealth management marketing content for aquaculture scenarios comes primarily from three sources: IoT monitoring devices at aquaculture ponds, feeding and shipping ledgers from aquaculture management systems, and offline inquiry records from farmers. Data update frequencies are as follows: real-time monitoring data is synced every 15 minutes, ledger data is updated daily, and inquiry records are generated in real time alongside user interactions. The data structure primarily uses structured fields including pond ID, monitoring time, dissolved oxygen concentration, pH value, feeding amount, shipping volume, breeding cycle, with units such as mg/L, kg/mu, %, days, and others. Unstructured assets are also included, such as photos of disease outbreaks and short video materials for breeding techniques.
What constraints these characteristics impose on workflow orchestration
Given the high real-time requirements of monitoring data, workflows must support event-triggered or 15-minute scheduled triggers to adapt to dynamic changes in breeding parameters, and timely push tailored financial, insurance, or wealth management products. Structured fields include physical parameters with associated units, so code execution nodes in workflows need built-in unit conversion logic to prevent inaccurate marketing content caused by numerical calculation deviations. Multi-source data includes unstructured materials and inquiry text, so multi-modal parsing nodes must be configured to support image, text, and short video formats, to generate more targeted marketing content. Workflows must also support iterating over datasets across multiple ponds, generating personalized marketing content for each pond’s parameters separately, to avoid batch-generated homogeneous content.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Loop Node Trigger Threshold | 10 ponds per batch | Adapt to the upper limit of ponds processed per batch to avoid single request timeouts |
PARSE_MEDIA_TIMEOUT_SECONDS | 600 seconds | Reserve sufficient processing time for long-duration short video material parsing |
FIELD_MAPPING_RULE | Associate monitoring data and marketing materials by pond ID | Ensure generated marketing content matches the actual breeding parameters of the corresponding pond |
DEFAULT_AI_MODEL | Aquaculture vertical domain financial dedicated model | Adapt to the generation needs of aquaculture professional terminology and financial marketing content |
CODE_SANDBOX_TIMEOUT | 300 seconds | Provide sufficient runtime for unit conversion and data aggregation code logic |
WORKFLOW_API_ENABLE | Enabled | Support batch creation and triggering of workflows via API, adapting to large-scale aquaculture scenarios |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and testing on local samples is recommended before finalization.
Three Common Misconfigurations
- An error occurs during code execution node runtime, with an uncaught reference error prompt. The cause is failure to correctly bind structured fields of pond monitoring data to input parameters of the code node, resulting in undefined variables.
- The AI model dropdown list is empty, and no model can be selected for classification node calls. The cause is failure to configure the API key for the corresponding large model in system configuration, or failure to enable the model’s call permissions.
- The loop node fails to iterate over all pond data as expected, only processing a single record. The cause is failure to set the loop data source to the pond list variable, or failure to enable the batch processing switch for the loop node.
How to Verify Successful Configuration
- Manually trigger the workflow, check if the output log contains all monitoring data fields for all ponds, with no missing or formatting errors.
- Check the execution results of the code execution node, confirm that the unit conversion logic is correct, and numerical calculations align with standard ranges for breeding parameters.
- View the AI model dropdown list, confirm that the configured professional large model has loaded normally and can be selected for calls.
- Trigger a batch loop task, confirm that after the workflow completes processing all pond data, a corresponding list of personalized marketing content is generated.
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.