Adaptive-learning intelligent scheduling unified computing frame and system for industrial personalized customized production

A method based on an adaptive-learning intelligent scheduling unified computing frame and system for industrial personalized customized production, the method comprising the following steps:

• S1:
• the system reading information in a real-time updated industrial big data module;
• prior to production, an optimal static scheduling planning module in the system generating an optimal static scheduling plan according to customization task information, equipment information and material information in the real-time updated industrial big data module;
• and when production is started, the system invoking a dynamic event interpreting module to dynamically monitor an event queue until a processible dynamic event is read;
• S2:
• automatically classifying dynamic events by the dynamic event interpreting module based on a deep reinforcement learning technique according to data information in the real-time updated industrial big data module, generating classification labels, and deciding specific contents of a corresponding optimization objective and constraint according to the classification labels;
• S3:
• automatically receiving the corresponding optimization objective and constraint generated in the step S2, automatically selecting different optimization algorithms based on the deep reinforcement learning technique according to the classification labels and the corresponding optimization objective and constraint, selecting a method to optimize parameters of the algorithms until the corresponding optimization objective and constraint are met, and forming a final dynamic scheduling optimization scheme, by a dynamic scheduling optimization module;
• S4:
• automatically receiving the final dynamic scheduling optimization scheme generated in the step S3, evaluating the final dynamic scheduling optimization scheme in the aspects of efficiency, energy consumption, deployment complexity and stability by means of a deep neural network to obtain a score of the final dynamic scheduling optimization scheme, optimizing an equipment scheduling deployment if the score is higher than a score standard, automatically selecting a corresponding deployment optimization algorithm based on the final dynamic scheduling optimization scheme generated in the step S3, and generating an equipment scheduling sequence, by a scheduling deployment and evaluation module;
• and
• S5:
• performing an automatic deployment by the system according to the equipment deployment sequence generated in the step S4, receiving equipment scheduling change information in real time by the real-time updated industrial big data module, such that a multi-module collaborative dynamic decision chain is formed by four modules of the frame including the real-time updated industrial big data module, the dynamic event interpreting module, the dynamic scheduling optimization module and the scheduling deployment and evaluation module, and decision-making in a whole process is completed by a computer without participation of workers;
• and after one cycle is completed, the system continuously adopts an optimized optimal static plan and invokes the dynamic event interpreting module to dynamically monitor an event queue to repeat the step S2 through the step S5 until customization tasks are completed.