What the data for this category looks like
Data sources primarily include water utility operation management systems, regional pipe network monitoring platforms, and community water service notice boards. There are two update cadences: Real-time monitoring data such as pipe network pressure and flow updates every 15 minutes. User water bills and payment records sync daily. Marketing science popularization documents update monthly.
Document structure includes structured fields and unstructured content: Structured fields cover user ID, water usage address, average monthly water consumption (unit: cubic meters), payment cycle, and abnormal water usage records. Unstructured content includes pipe network inspection logs, water conservation promotional material text, and community water usage notices.
What constraints these characteristics impose on workflow orchestration
The high-frequency updates of real-time monitoring data require configuring timed trigger nodes in the workflow. This ensures timely data synchronization and avoids excessive system resource usage from full data pulls. Format differences across multi-source data require configuring unified field mapping rules in preprocessing nodes. These rules convert water usage data from different systems into a consistent format, ensuring subsequent nodes can properly recognize the data. The monthly update cycle of marketing documents requires setting a fixed trigger cycle for the knowledge base synchronization node. This eliminates the need for frequent update triggers. The non-standardized remark format of abnormal water usage records requires text extraction nodes to support custom rule configuration. This allows extraction of key marketing trigger information such as overdue payments and water leaks.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Timed Trigger Cycle | 15 minutes | Matches the update frequency of real-time monitoring data to ensure timely data synchronization |
Data Source Incremental Pull Switch | Enabled | Reduces resource consumption from full pulls, adapting to high-frequency updated pipe network data |
Field Mapping Rules | Associate water usage address and average monthly water consumption by user ID | Unifies the structured field format of multi-source data to eliminate data discrepancies |
Knowledge Base Synchronization Trigger Cycle | 00:00 on the 1st of every month | Matches the monthly update rhythm of marketing science popularization documents to ensure the timeliness of knowledge base content |
Text Extraction Custom Regex | Match keywords such as "overdue payment", "water leak", "abnormal" | Extract key marketing trigger information from abnormal water usage records |
AI Model Context Length | 8000 characters | Adapts to the average length of water utility marketing documents, avoiding content truncation |
The parameter values provided on this page are common recommended starting points for determining configurations. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: The AI model returns the
chat:ai_input_is_eerror after the workflow runs, and the interface prompts incomplete input parameters. Cause: Theresultoutput port of the code running node is not correctly connected to the user input port of the AI model, resulting in no valid context content being passed in. - Phenomenon: The text extraction node returns empty results with no matched content. Cause: No custom extraction rules are configured for the non-standardized remarks of water utility abnormal records, and using only default keyword matching cannot cover scenario requirements.
- Phenomenon: The
Select Knowledge Baseglobal variable does not switch dynamically based on scenarios, always using a fixed knowledge base. Cause: No variable assignment trigger conditions are configured, and the user's water usage area is not used as the basis for variable switching.
How to verify a successful configuration
- View the workflow's timed trigger logs to confirm the trigger frequency matches the preset cycle, and check that the pulled data fields match the preset mapping rules.
- Manually trigger the knowledge base synchronization node to verify that the synchronized knowledge base content includes the latest monthly marketing documents with no format errors.
- Input a simulated abnormal record containing the keyword "water leak" to verify that the text extraction node returns correct key information.
- Simulate user water usage data from different regions to check whether the
Select Knowledge Baseglobal variable automatically switches to the corresponding regional knowledge base.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.