Data Overview for This Category
Data for this category originates from engineering orders, sand, gravel, cement and admixture inspection reports, aggregate moisture content measurement logs, mixing plant control system records, laboratory ledgers, and other sources. Daily circulating documents include mix proportion notices, opening identification records, production and transport orders, raw material inspection reports, test block strength records, and more. Unlike precast components, products in this category change continuously during transport and pouring, and final performance is verified via age-based test blocks.
Data update frequencies fall into three categories: real-time updates for each vehicle production order, daily updates for batch raw material inspections, and gradual supplementation of test block strength records as age progresses. Document fields include measured aggregate moisture content, batch mixing water consumption (liters per cubic meter), transport distance (kilometers), test block compressive strength (megapascals), and others. All fields correspond to quantitative records of actual production links, with no redundant statistical items.
Constraints for Workflow Orchestration
The data of this category has layered update frequencies, scattered sources, and most fields are quantitative measured values. This creates multiple constraints for workflow orchestration. First, real-time generated vehicle-by-vehicle production orders must trigger immediate review, while daily updated raw material inspection reports require batch synchronization. The workflow must support both single trigger and scheduled pull modes. Second, document fields need cross-source matching and verification. For example, compare the aggregate moisture content in the mix proportion notice with the measured values in the raw material inspection report. The workflow must be configured with field mapping rules. In addition, test block strength records are gradually supplemented as age progresses. The workflow must support calling associated data within historical periods to complete cross-verification.
Configuration Guidelines
The following configurations are written based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Global Variable Scope | Isolated by user ID | Same-named variables across different projects must be stored independently to avoid data mixing |
| Context Load Trigger Node | Associated with historical conversation ID | Load historical mix proportion and inspection data for the corresponding project |
| Custom Prompt Configuration | Bound to trigger after review result output | Output customized prompts for slump and strength |
| Variable Update Trigger | Triggered on variables change event | Synchronize measured data such as aggregate moisture content in real time |
| Timeout Threshold | 600 seconds | Adapt to the response time of pulling multi-source data across systems |
| Node Binding | Bound to fixed review node with nodeId | Ensure fixed workflow execution path |
Three Common Misconfiguration Issues
- Symptom: The review result returned when calling the workflow API is not associated with historical project data, and the context is missing. Cause: The context load node is not configured to associate with the historical conversation ID, and the workflow only reads temporary data from the current request.
- Symptom: Global variables are overwritten by requests from other users after workflow execution, leading to data mixing. Cause: Global variables are not set to be isolated by user ID, and the variable scope covers the entire workflow.
- Symptom: Only the system preset prompt is displayed after the AI replies, and no customized prompt for concrete production is generated. Cause: The custom prompt node is not bound to the review result output link, and only the system default configuration is called.
How to Validate Successful Configuration
- Initiate a production order review request for the corresponding project, and verify whether the workflow loads the historical mix proportion and inspection data of the project.
- Initiate requests using different user IDs, and verify whether the values of same-named global variables are independent with no data mixing.
- Trigger the review process, and verify whether customized prompts for slump and strength are output after the AI replies.
- Simulate cross-system data updates, and verify whether the workflow synchronizes the latest values of fields such as aggregate moisture content in real time.
The values provided are common starting points and should be measured against the reader's own samples.