What the data for this category looks like
The data for this category primarily comes from engineering orders, sand, cement and admixture inspection records, moisture content test results, mixing plant control systems, and project laboratories. Data update cadences include per-vehicle production records, per-batch raw material test results, and daily aggregate statistics. Daily documents include mix proportion notices, startup verification records, production and delivery slips, raw material inspection reports, test block records, and strength reports. Data fields include mix proportion number, raw material batch number, moisture content value, slump index, test block strength value, and other relevant fields. Corresponding units include kilograms per cubic meter, megapascals, and millimeters.
Constraints on HTTP interfaces and external systems
The high-frequency, multi-source update characteristics of this category require HTTP interfaces to support multiple calls per second to avoid data synchronization delays. Field differences across multiple document types require interfaces to support custom field mapping rules to adapt to formats for different documents such as mix proportion records and test block records. For moisture content correction and slump verification links with high real-time requirements, interface response latency must be kept within a reasonable range, otherwise production scheduling may be affected. Data anomalies may lead to engineering quality issues, so interfaces must return clear status identifiers and error details to enable rapid troubleshooting.
How to configure settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {pre-generated FastGPT API key} | Data for this category involves production and quality-sensitive information, and standard authentication is required to secure interface access |
variables | Bind production parameters such as mix proportion number, slump threshold, and qualified strength value | Adapt to customized ledger verification and report generation rules for different projects |
responseData | Return only required fields and detailed error prompts | Reduce redundant data transmission to meet the needs of high-frequency per-vehicle call scenarios |
| Request timeout | 30–60 seconds | Covers the complete process of data pulling, verification, and report generation, to avoid request interruptions caused by fast production cycles |
| Batch request limit | First 200 entries | Adapt to the typical range of daily production vehicle counts, to avoid excessive load from a single request |
| Field mapping rules | Map mixing plant system fields to standard fields such as mix proportion number, raw material batch number, and moisture content | Adapt to unified access formats for multi-source data and simplify external system integration |
Three common configuration errors
- The
responseDatafield returned by the interface is empty. This occurs when the return rules forresponseDataare not configured correctly, resulting in required ledger fields not being returned. - The request returns a 401 status code. This occurs when the
Authorizationparameter format is incorrect or the key has expired, so interface authentication fails. - The slump value in the generated report does not match the actual value. This occurs when the slump threshold parameter in
variablesis not bound, or the field mapping rules are incorrect, leading to abnormal data reading.
How to confirm successful configuration
- Call the test interface to verify that valid data is returned after
Authorizationauthentication passes. - Submit simulated production and delivery slip data to check that the fields returned in
responseDatamatch the preset values. - Adjust parameters in
variablesto verify that the report generation result changes with the parameters. - Simulate high-frequency requests to confirm that the interface does not time out within the batch request limit.
The values provided here are common starting points and should be evaluated against the reader's own samples.