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

An adaptive-learning intelligent scheduling unified computing frame and system for industrial personalized customized production, 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 processable 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 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 an optimization objective decision scheme generated in the step S2, automatically selecting different optimization algorithms based on the deep reinforcement learning technique according to the classification labels and the optimization objective and constraint, selecting a suitable 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 dynamic scheduling optimization scheme generated in the step S3, evaluating the 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 optimization scheme, optimizing an equipment scheduling deployment in a spatial scale if the score is higher than a score standard, automatically selecting a corresponding deployment optimization algorithm based on the optimization scheme generated in the step S3 according to a current industrial production condition and the equipment distribution condition of a production line, 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 deployment sequence generated in the step S4, receiving equipment scheduling change information in real time by the industrial big data module, and updating the current industrial production environment, such that a multi-module collaborative dynamic decision chain is formed by four modules of the frame, and decision-making in the whole process is completed by a computer without participation of workers;
• and after one cycle is completed, the system avoids the influence of a current dynamic event, continuously adopts the optimized optimal static plan and invokes the dynamic event interpreting module to dynamically monitor the event queue to repeat the step S2 through the step S5 until all personalized customized production task are completed.