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This workflow demonstrates how to efficiently merge information from different data sources, similar to various join operations in SQL. By simulating two sets of data, it showcases multiple data merging methods such as inner join, left join, and union, helping users understand the processes of data filtering, enrichment, and integration. It is applicable in scenarios such as supply chain management, data analysis, and team management, assisting users in quickly achieving data integration and analysis to enhance work efficiency.

This workflow automatically extracts and fills in the content of uploaded PDF files by listening for update events in the table. Utilizing AI technology, it generates dynamic extraction prompts based on field descriptions to ensure that data is accurately and efficiently entered into the table. It can automatically process PDF files, dynamically respond to field changes, and support both batch and single record processing, greatly simplifying the information entry process for unstructured documents and enhancing the efficiency of data management in enterprises.

This workflow utilizes AI technology to enable natural language queries of databases and automatically generates dynamic charts based on user requirements. Through intelligent analysis and automatic judgment, users can quickly obtain intuitive data presentations, enhancing data insight efficiency. It supports various types of charts and employs online services for rapid rendering, making it suitable for business analysts, non-technical personnel, and team managers. This simplifies the data visualization process, making decision-making more efficient and convenient.

This workflow can automatically download bank statement PDFs, split them into images, and use a visual language model to transcribe them into structured Markdown text, preserving table and text details. Next, it employs a large language model to extract key data from the statements, such as deposit records, addressing the accuracy issues of traditional OCR in complex layouts. This process significantly enhances the efficiency of parsing bank statements and is suitable for scenarios where financial personnel and fintech companies need to quickly process scanned documents.

This workflow automatically retrieves the latest position data of the International Space Station every minute and sends it to a specified topic in the ActiveMQ message middleware, ensuring the timeliness and efficiency of the data. By utilizing scheduled triggers, API calls, and data organization, it achieves continuous pushing of the space station's position, eliminating the cumbersome manual queries. This is widely applicable to scenarios such as aerospace data monitoring, tracking by research institutions, and educational projects, enhancing the efficiency of information acquisition and transmission.

This workflow generates 10 pieces of data through manual triggering and processes them one by one, with the capability of intelligently determining the processing status. Once processing is complete, it automatically prompts "No remaining data," ensuring clear process control and feedback. It is suitable for scenarios that require individual operations on large amounts of data, such as data cleaning and task review, and is particularly well-suited for business processes that need to be manually initiated and monitored for execution status, enhancing the stability and maintainability of automated tasks.

This workflow is manually triggered and automatically connects to Microsoft Excel, allowing for the quick batch retrieval of all data from a specified Excel file. The operation is simple and does not require any coding, significantly enhancing data extraction efficiency and avoiding errors and omissions associated with traditional manual operations. It is suitable for businesses and individuals in scenarios such as financial summarization, sales analysis, and inventory management, enabling automated data processing and analysis, saving time, and improving work efficiency.

This workflow automates the identification of building items, utilizing visual models to analyze item attributes, and combines reverse image search with web scraping to obtain detailed information. Ultimately, the enriched data is automatically updated in the database, significantly improving the accuracy of item recognition and the completeness of the data, while reducing the workload of manual data entry. It is suitable for scenarios such as building surveys, asset management, and product information collection, helping enterprises achieve efficient digital transformation.