What the data for this category looks like
Data for the photovoltaic module encapsulation link comes from battery sheet grading records, glass adhesive film backplane batch ledgers, production MES systems, string welding and lamination equipment logs, and output from EL defect detectors and IV power testers. The data update rhythm is one test record per module, and one batch ledger per material batch. Daily documents include layout drawings, material batch records, lamination process records, EL defect images, IV test reports, insulation voltage resistance records, and packing lists. Fields cover battery sheet current gear, lamination temperature and pressure, EL crack coordinates, power value, insulation resistance, and more. Corresponding units are ampere, degree Celsius, megapascal, watt, and megohm.
What constraints these characteristics impose on the "tool calling and plugins" link
Since data is updated in real time for each individual module and batch, tool calls must pull the latest batch and test data. They cannot rely on cached old data. The data includes unstructured images and log files such as EL images and IV reports. Plugins must support parsing binary file streams and extracting fields. Tendering quotations must match the cost of battery sheet gears and material batches. Tool calls must accurately associate corresponding fields to avoid mixing data across batches. At the same time, data field units and precision requirements are strict. Parameter checks for tool calls must adapt to dedicated field rules for photovoltaic module encapsulation. This prevents quotation deviations caused by format errors.
How to set configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| Workflow/API tool selection | auto | Automatically matches tool calling scenarios required for photovoltaic module encapsulation tendering, no manual function specification needed |
| Workflow/API function-call configuration | Bind dedicated photovoltaic module quotation functions | Accurately trigger dedicated logic such as battery sheet gear matching and cost calculation, adapts to FastGPT v4.17.0 function calling rules |
| File stream processing configuration | Enable binary data parsing | Support parsing and field extraction for image streams such as EL defect images and IV test reports |
| Environment variable calling | Reference directly | Quickly load global configurations such as material costs and equipment parameters set during deployment |
| Timeout setting | 1200 seconds | Reserve sufficient time to wait for complete return of equipment data such as EL tests and IV power tests |
| Result verification threshold | Calibrate based on actual measurements | Adapt to parameter differences across material batches, avoid misjudging valid data |
Three common errors
- Phenomenon: When calling an interface that returns image streams in a workflow, the extracted content is empty. Cause: Binary data parsing configuration is not enabled, and file stream receiving parameters are not correctly configured.
- Phenomenon: Testing during model calling works normally, but application calls return empty responses. Cause: Environment variables set during deployment are not correctly referenced in the workflow, leading to failure to obtain core parameters such as material costs.
- Phenomenon: Calling a workflow containing
functionCallvia API returns an incorrect format. Cause: Function parameters do not match the field format for photovoltaic module encapsulation, such as not specifying the unit for battery sheet current gear.
How to confirm the configuration is correct
- Upload a real EL defect image and IV test report, trigger tool calling, and check whether core fields such as battery sheet current gear and power value can be extracted.
- View workflow operation logs to confirm that environment variables are correctly loaded, with no error messages for undefined parameters.
- Initiate an API call, check whether the returned JSON format includes all fields required for tendering quotations, with no missing or abnormal formats.
- Simulate a batch of material batch data, trigger tool calling, and confirm whether material cost parameters for the corresponding batch can be correctly associated.
The values provided are common starting points and should be measured against the reader's own samples.