Industrial intelligent controller capable of deploying AI model dynamically

Abstract

The invention discloses an industrial intelligent controller capable of deploying an AI model dynamically, relating to the technical field of industrial intelligent controller. The industrial intelligent controller comprises an EDGE BOX end and a control center, wherein the EDGE BOX end and the control center transmit or communicate with each other through a multi-path serial port, an Ethernet interface, WiFi or 5G netowrk, the EDGE BOX end is communicated connection with a production machine table through a serial port, the EDGE BOX based on an edge calculation controller of an X86 processorintegrates the multi-path serial port, the Ethernet interface, WIFI and the 5G wireless communication module, and the control center, a K8S control end the EDGE BOX end are used for collecting data ofthe production machine table, sending a control command to the control center, and deploying an AI operation model and edge calculation nodes of an operation component dynamically through the controlcenter. The industrial intelligent controller capable of deploying the AI model dynamically can perform a complex algorithm and a device model in local and can perform intelligent edge analysis and control, thereby having better effect in real time performance.

Description

technical field [0001] The invention relates to the technical field of industrial intelligent controllers, more specifically, it relates to an industrial intelligent controller capable of dynamically deploying AI models. Background technique [0002] In the manufacture of modern semiconductor integrated circuits, it is a very common method to use AI to assist traditional SPC control. The usual process is to collect historical production process data for offline modeling, and then build an AI model operating platform to deploy the model for calculation. . refer to figure 1 , the main process is as follows: 1. EAP collects the data of the production machine through the industrial communication protocol; 2. EAP sends the collected data to the MES system through the network; 3. The MES system performs general data processing, stores it, and sends it to AI Model running platform; 4. After the AI ​​model is run, the calculation results are returned to the MES system; 5. The MES ...

AI Technical Summary

Problems solved by technology

[0003] However, the current method, from data generation to AI model operation and return of response results, goes through many intermediate processes, and the model response time is long, which cannot meet the real-time requirements of time-sensitive AI models.

For example, in actual operation, although the calculation results of the AI ​​model are very accurate, but the response is 10 minutes later, the products produced within these 10 minutes may have problems

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