HTTP Interface and External Systems for Imaging Device

Data Characteristics for This Category

Imaging device data in pharmacovigilance primarily originates from operational logs, diagnostic reports, and clinical records related to patient medication. This data typically exists in structured or semi-structured formats, such as DICOM image metadata, device error codes, and operator input. Update frequency depends on device usage intensity and data transmission policies, usually real-time or near real-time. Document structures may include device model, serial number, software version, scanning parameters, and image interpretation results. Field units vary; for example, radiation dose is measured in mSv, scan time in seconds, resolution in dpi, and error codes are integers or strings. Some critical fields may involve patient privacy and require special handling.

Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"

The diversity and real-time nature of imaging device data place high demands on HTTP interface design. High-frequency data updates require interfaces with high throughput and low latency to prevent data backlogs. Mixed structured and semi-structured data requires interfaces to flexibly handle various data formats, such as JSON, XML, or custom text protocols. Fields containing sensitive information require interface encryption during transmission and ensure external systems have corresponding decryption and access control capabilities. Device-specific error codes and status information require external systems to accurately parse and map them to pharmacovigilance risk assessment models. Additionally, since devices may deploy in different network environments, interfaces need to support cross-domain access and handle potential network instability.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`apiTimeout`	`30000 milliseconds`	Imaging device data volume is large; transmission and processing can be time-consuming. This prevents premature timeouts.
`maxRetries`	`3 times`	This accounts for network fluctuations and occasional transient failures in external systems, increasing retry opportunities.
`requestHeaders`	`Content-Type: application/json`	Most structured data transmits in JSON format, ensuring correct server-side parsing.
`sslVerify`	`false` (test environment) `true` (production environment)	Certificate validation can be skipped during testing. It must be enabled in production to ensure data security.
`responseSchema`	Defined according to actual return	This ensures accurate parsing of imaging device-specific error codes and status fields.
`authMethod`	`Bearer Token`	This is widely supported and offers high security, suitable for authentication with external systems.

Three Common Mistakes

Symptom: The external system does not receive the latest imaging device data. Reason: The FastGPT deployment environment cannot directly request interfaces via HTTPS, causing data transmission failure. A proxy configuration or skipping certificate validation is necessary.
Symptom: Workflow validation fails, prompting "missing, empty value, is the connection normal." Reason: The database connection plugin did not correctly configure PostgreSQL connection parameters. For example, the port number 5432 or database name fastgpt_db was entered incorrectly, or access credentials do not match.
Symptom: Received imaging data fields are empty or incorrectly formatted. Reason: The HTTP interface's responseSchema does not precisely match the actual data structure returned by the imaging device, leading to parsing errors.

How to Verify Configuration

Execute the curl -v command to send a test request to the configured HTTP interface. Verify the HTTP status code is 200 or 201, and check if the Content-Type in the response header matches expectations.
In the FastGPT plugin toolbox, configure and run the database connection plugin. Observe the log output to confirm successful connection to the PostgreSQL database and the ability to execute a simple query, such as SELECT version();.
Through FastGPT's debugging interface, simulate a complete call process. Check the HTTP interface's returned data body to confirm all key fields, such as radiationDose and scanTime, are correctly populated and units are accurate.

The values given are common starting points and should be measured against the reader's own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.