What This Category's Data Looks Like
Data for this category comes from EHS departments and maintenance teams in manufacturing enterprises such as chemical and metallurgical plants. Daily documents include risk identification forms, work permits for hot work, confined space work, hoisting and other operations, environmental monitoring records, hazardous waste ledgers, accident rectification reports, and more. Data sources cover regulatory standards, online environmental monitoring equipment, handheld gas detectors, personnel qualification files, equipment isolation lists, and supervision platforms. Data updates are triggered by single job startup: online monitoring data updates in real time, qualification and isolation list updates follow periodic inspections, and work permits circulate with the job workflow. Document fields include professional fields with clear units, such as job type, job duration (hours), gas detection concentration (ppm), qualification validity period (days), emission concentration (mg/m³), and others.
What Constraints These Characteristics Impose on the "Workflow Orchestration" Link
Category data sources are scattered, including offline paper records, online device data, and cross-department files. This requires workflows to support multi-type data source access nodes and adapt to data pulling in different formats. Data updates are centered on single job triggers. This requires workflows to start based on job events to avoid redundant processes. Document fields have clear professional units. This requires workflows to configure variable validation rules to block input with non-compliant formats. The process involves personal and environmental safety. This requires setting pre-veto nodes to directly terminate the process when qualifications are incomplete or risks are unrecognized, avoiding subsequent risks.
The following configurations apply to FastGPT v4.17.0.
How to Set the Configuration
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Trigger Mode | Triggered by single job events, supports manual supplementary entry | Data updates center on single jobs and real-time monitoring, adapting to real-time workflows for job startup/completion |
| Variable Validation Rules | Validate fields such as gas concentration and qualification validity period within variables, matching corresponding unit formats | Category data includes fields with professional units, requiring validation to avoid invalid input |
| Data Source Access Nodes | Connect to online monitoring interfaces, local databases, and paper scan OCR nodes separately | Data sources cover online devices, offline records, and supervision platforms, requiring multi-node data integration |
| Node Flow Conditions | Bind pre-check results by nodeId | The process must execute strictly in the order of risk identification and qualification verification to ensure the veto power takes effect |
| Exception Handling Nodes | Configure 3 retries, push to EHS administrator after failure | Real-time data collection may experience network fluctuations, retries can reduce process interruptions |
| Context Cache | Retain job workflow data from the last 24 hours | Historical monitoring records need to be associated during the job cycle, caching improves data call efficiency |
Three Common Mistakes
- Phenomenon: An error
chat:ai_input_is_eis prompted when calling an AI model in the workflow, and the node status shows failure. Cause: Thevariablesoutput of the code running node is not correctly bound to the input parameters of the AI model, resulting in empty or abnormally formatted input content. - Phenomenon: When using a database connection plugin to connect to PostgreSQL, workflow verification fails, and the interface prompts missing or null values. Cause: The data source connection parameters are not configured correctly, or the
variablesfor database connection credential fields are not verified to have valid values. - Phenomenon: The knowledge base variable reference format
[{datasetId: xxx}]does not take effect, and the retrieval result is empty. Cause: The category-specific variable naming rules are not followed, or the value range of the corresponding variable is not predefined in the workflow.
How to Confirm the Configuration is Complete
- Trigger test: Start a simulated job event, check whether the workflow starts automatically and executes nodes in the preset order
- Validation test: Enter a field value that does not conform to the format, check whether the workflow triggers the validation block
- Data source test: Connect to online monitoring data, check whether the node can normally pull the latest detection data
- Exception test: Simulate a data source connection failure, check whether the workflow triggers the preset exception push action
The values provided are common starting points and should be evaluated against the reader's own samples.