The method of edge operation is to make the terminal equipments have certain computing power and industrial Internet of Things architecture with edge operation design. A set of data flow mode must be established first. When the sensor picks up the state data of the equipment, it transmits the data to the gateway of the communication layer. The gateway then distributes the data according to the setting of the system construction. It needs real-time processing data to be transmitted to the front end. Controller, so that automation equipment can react quickly, need to store long-term data, then send to the database storage, the upper level through the operation platform to analyze the results, provide managers as a decision-making reference, so now the complete industrial Internet of Things, its AI will be designed in two parts, terminal and cloud, so that distributed and centralized computing can be combined in the architecture. Existence, each other's duties.
From the point of view of the research topic of the equipment supply side in the industrial Internet of Things, now it mainly focuses on four directions, including production system, product quality, process optimization and digital modeling. In these four directions, each has its own problems to be solved, such as the state sensing, monitoring and diagnosis of equipment in production system, the detection and prediction of product quality, the parameter setting of process optimization, energy utilization, the establishment of digital twin life platform of digital modeling, etc. Through the data acquisition and analysis of industrial Internet of Things, these problems can be solved step by step, and the overall efficiency of the system can be improved.
In the industrial Internet of Things, AI is mainly used for non-real-time decision-making such as process optimization and long-term planning. For example, there are many kinds of products in the consumer market nowadays. The routing of process system will become normal. Through the operation of big data and AI, the downtime of the routing production can be shortened and the scheduling can be optimized as far as possible.
In production line scheduling, it is necessary to distinguish between static and dynamic scheduling according to the machine environment, process characteristics and limitations, scheduling objectives, and the situation when work arrives at the production site. Static scheduling is the number of manufacturing when it arrives at the production site. Fixed and one-time tasks are scheduled, and if new jobs occur in the follow-up, they are merged into the next process. Dynamic scheduling is that if the process is continuous, the product is random, and the number of arrivals at the production site is not fixed, it must be constantly updated?
As far as the above two scheduling methods are concerned, static scheduling is usually a few different ways. The main problems AI has to solve are to analyze the time and quality of each link through deep learning algorithm, constantly improve the process to optimize the efficiency and quality. Dynamic scheduling is used in a small number of diversified production. AI will set up a change mode for different product processes and have different products. When on-line, that is to start the exclusive switching mode, as far as possible to shorten the downtime, while allowing the product to maintain a fixed quality.
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