Workflow Orchestration for Rehabilitation Device

Data Characteristics for This Category

Rehabilitation device registration data comes from various sources. These include product technical requirements, inspection reports, clinical evaluation reports, risk management reports, instruction manuals, labels, and manufacturing process documents. Update frequencies vary. For example, product technical requirements may update with national standards or internal company changes, while clinical evaluation reports typically receive periodic updates throughout a product's lifecycle. Document structures often use formats like PDF, Word, and Excel. PDF documents frequently contain scanned images, requiring high OCR recognition capabilities. Fields involve numerous specialized terms, units of measurement, and technical parameters, such as "Applied Force Range (N)", "Frequency Response (Hz)", and and "Protection Class (IPXX)". Units are highly standardized, but differences may exist between device types. For instance, electric wheelchairs and rehabilitation robots define and express "Range (km)" and "Degrees of Freedom" differently.

Constraints Imposed by These Characteristics on "Workflow Orchestration"

The high OCR recognition requirement for rehabilitation device documents necessitates integrating high-precision OCR modules in the workflow's file preprocessing stage. The workflow must handle charts and seal information within scanned documents. Diverse, heterogeneous data sources mean the workflow requires robust data extraction and structuring capabilities. Examples include extracting table data from Word documents and locating key parameters from PDF reports. Varying update frequencies demand workflow support for version management and incremental updates, ensuring each submission uses the latest valid version. The specificity of specialized terms and units of measurement places higher demands on knowledge base construction and retrieval. This requires building a dedicated terminology dictionary for rehabilitation devices and handling unit conversions and dimensional matching. Additionally, submission documents often require multi-person collaboration and approval. The workflow must support multi-node parallel processing, conditional branching, and manual review stages to accommodate complex approval processes and data validation requirements.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	8000 characters	Ensures context completeness for long documents, preventing loss of key information.
`PARSE_FILE_TIMEOUT_SECONDS`	600 seconds	Time for OCR recognition of large PDFs or multi-page scanned documents.
`Recall count` (Recall Count)	15 items	Increases knowledge base retrieval coverage, reducing omissions.
`Similarity threshold` (Similarity Threshold)	0.82	Precisely matches specialized rehabilitation device terms, avoiding generalized results.
`Rerank result count` (Reranked Return Count)	5 items	Filters for the most relevant knowledge snippets, reducing interference from irrelevant information.
`OCR_ENGINE_TYPE`	`PaddleOCR`	Optimized for scanned documents and complex mixed text/image documents.

Three Common Mistakes

Workflow execution timeouts or file parsing failures often occur because the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low, preventing processing of large or complex submission documents.
Inaccurate or missing key information in knowledge base retrieval results typically stems from insufficient annotation of specialized rehabilitation device terms in the knowledge base, or an improperly set Similarity threshold (Similarity Threshold).
After obtaining a BLOB object at an HTTP node, users cannot directly click to download it in the chat interface. This happens because the BLOB object was not converted into a downloadable link or file handle recognizable by the frontend.

How to Confirm Correct Configuration

Upload a rehabilitation device technical requirements PDF containing scanned images and tables. Check if the workflow successfully parses and extracts all key fields, and verify OCR recognition accuracy.
Set up a knowledge base query node in the workflow. Input specialized rehabilitation device terms (e.g., "EMG signal acquisition frequency"). Check if the returned knowledge snippets are accurate and comprehensive, and verify the effect of Recall count (Recall Count) and Rerank result count (Reranked Return Count).
Simulate the submission document approval process. Trigger a DingTalk webhook notification via the workflow. Confirm the notification content includes necessary approval information and document links, and verify accurate synchronization of approval status.

Note: The values provided are common starting points. Measure them against your 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.