What the data for this category looks like
The data sources for this category include online composition analysis systems, DCS control systems, laboratories, kiln and grinding equipment, raw fuel ledgers, and energy metering devices. Data update frequencies fall into three categories: real-time, per shift, and daily. These correspond to equipment operating parameters, inspection records, daily reports, and similar documents. Core documents include raw material batching plans, kiln operation records, laboratory reports, quality control notices, equipment inspection forms, and energy reports. Fields cover material composition detection values, equipment operating parameters, energy consumption values, quality inspection indicators, and other metrics. Units include tons, kilowatt-hours, MPa, and others.
Constraints Imposed on HTTP Interfaces and External Systems by These Characteristics
Multi-source heterogeneous data sources require interfaces to support request and response bodies in varying formats. This includes adapting to real-time small data packets from online composition analysis, shift-based structured data from equipment inspections, and bulk datasets from daily energy reports. Different update frequencies correspond to distinct call frequency requirements. Real-time parameters need support for high-frequency calls, while daily reports need support for large-volume data transmission. Document fields cover multiple business scenarios, so interfaces must define clear field mapping rules to prevent data parsing errors. Bulk data from clinker production ledgers may exceed the capacity threshold of a single request. As a result, reasonable chunked transfer configuration must be supported.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Request timeout | 30–600 seconds | Adapts to varying duration needs for real-time parameter calls and bulk report transmission |
| Chunked transfer threshold | 800–1200 kilobytes | Prevents "Request Entity Too Large" errors, and adapts to daily bulk ledger data |
Authorization configuration | Pass fixed tokens via environment variables | Ensures identity legitimacy for interface calls, and complies with enterprise data security specifications |
variables mapping rules | Bind dedicated field mappings for cement production | Matches differences in field naming for scenarios such as raw material batching and energy consumption detection |
responseData parsing format | Structured JSON format | Facilitates subsequent ledger data summary and report generation |
| Bulk call limit | 10–20 items per request | Balances data transmission efficiency and request load |
Three Common Configuration Mistakes
- An interface call returns a 413 Request Entity Too Large error. The cause is that the volume of bulk ledger data such as raw materials and energy consumption transmitted in a single request exceeds the preset threshold of the interface, and a reasonable chunked transfer threshold has not been configured.
- An interface call returns an authentication failure. The cause is that the
Authorizationparameter has not been configured correctly, or the passed token does not match the requirements of the external system. - The generated report is missing some quality inspection fields. The cause is that the field mapping rules for
variableshave not been set correctly, and the dedicated fields of the laboratory report have not been included in the extraction scope.
How to Verify Successful Configuration
- Initiate a single real-time parameter call, and check whether the returned
responseDataincludes fields corresponding to the target scenario. - Submit a bulk data request, confirm that no "Request Entity Too Large" error is triggered, and adjust the chunked transfer threshold to match the actual data volume.
- Verify the identity configuration, and confirm that a valid identity credential is correctly carried during the call.
- Generate a test report, and check whether the extracted ledger fields cover core business document content such as raw material batching and energy consumption detection.
All provided configuration values are common starting points, and should be evaluated against the reader's own samples.