AI-driven intelligent delivery prediction system for textile weaving and dyeing production scheduling

Abstract

This invention reveals an AI-driven intelligent delivery date prediction and scheduling system designed to optimize production scheduling in textile weaving and dyeing processes. The system combines advanced artificial intelligence technology, real-time data integration, predictive analytics, and an intuitive user interface to revolutionize traditional scheduling methods. By addressing the unique challenges of the textile industry, it significantly improves production efficiency, enhances delivery date accuracy, and strengthens market competitiveness.

Claims

1. An AI-driven intelligent delivery date prediction system for textile weaving and dyeing processes, applied to textile weaving and dyeing processes, characterized in that the system at least includes: a data collection and interfacing module for acquiring raw production data from the production site or existing enterprise information systems; a data processing and feature generation module, interconnected with the data collection and interfacing module, for organizing and analyzing the raw production data and generating derived feature data that influences delivery date prediction; a prediction calculation module, interconnected with the data processing and feature generation module, for generating order delivery date prediction results based on the raw production data, derived feature data, and standard working hour data; a scheduling adjustment module, interconnected with the prediction calculation module, for evaluating and adjusting the production schedule based on the delivery date prediction results; and a display and interaction module, interconnected with both the prediction calculation module and the scheduling adjustment module, for displaying delivery date prediction results and scheduling status information, wherein... The data collection and interfacing module is interconnected with the data processing and feature generation module. The data processing and feature generation module is further connected to the prediction calculation module. The prediction calculation module is further connected to the scheduling adjustment module and the display and interaction module. The scheduling adjustment module is further connected to the display and interaction module.

2. The system as described in claim 1, wherein, The data collection and interface module obtains raw production data such as machine operating status, process progress, order information, product size, or material characteristics through sensors, IoT devices, or system interfaces.

3. The system as described in claim 1, wherein, The data processing and feature generation module generates derived feature data, including material load, processing area, weight density, or order release time, based on product specifications, order attributes, and production conditions.

4. The system as described in claim 1, wherein, This predictive computing module uses artificial intelligence or machine learning models to estimate the expected completion time and delivery date of orders based on historical production data and real-time production conditions.

5. The system as described in claim 1, wherein, The scheduling adjustment module compares the delivery date prediction results with the established production schedule to generate scheduling adjustment suggestions for production equipment allocation or process sequence.

6. The system as described in claim 1, wherein, It further includes an anomaly monitoring and feedback module, which is interconnected with the data collection and interface module to obtain equipment status or process anomaly information.

7. The system as described in claim 6, wherein, The anomaly monitoring and feedback module is interconnected with the data processing and feature generation module, using anomaly event information as supplementary input data to update the derived feature data.

8. The system as described in claim 6, wherein, The display and interaction module is further interconnected with the anomaly monitoring and feedback module to display abnormal events, affected orders, and updated delivery forecasts and scheduling information.

Images