At present, improving the level of digitization, automation and intelligence of the production management process has become the main means and means of manufacturing transformation. The so-called intelligence is a cyclical process that involves the aspects of perception, cognition, learning, adjustment and adaptation. It can make decisions according to goals and take actions to achieve the desired results. Among them, knowledge is the basis of intelligent realization, and intelligence is the ability to acquire and apply knowledge. The practical process of intelligent manufacturing is to enable enterprises to carry out intelligent activities such as reasoning, judgment, conception and decision-making on the manufacturing process through the collection and analysis of equipment operation data. Eventually, the machine extends or partially replaces the physical and mental work of human experts in the manufacturing process, extending manufacturing automation to flexibility, intelligence and high integration.
Making the machine have certain judgment and decision-making ability is the basis of intelligent manufacturing practice. In this process, enterprises need to convert data into information, then convert the information into knowledge, and finally form models or rules that can be executed through the accumulation of knowledge, so as to realize real-time diagnosis, early warning and optimization suggestions for production processes and equipment. Process, technology and tools. Therefore, in the process of intelligent manufacturing practice, data is fuel, and analysis is the engine. Through the analysis of a large number of process data, enterprises can timely and accurately understand the equipment operating status and production process status, and make judgments and decisions in a timely manner. Therefore, in the process of promoting the intelligent transformation of the factory, the interconnection of devices is only the first step. More importantly, by interconnecting the devices, smart tools such as sensors are embedded in the key devices, and finally the operating states of these devices are realized. data collection. Real-time data acquisition enables real-time analysis of device status.
In the workshop of the manufacturing company, the production system continuously generates a large amount of real-time data, such as the motion axis state (current, position, speed, temperature, etc.), the spindle state (power, torque, speed, temperature, etc.), the machine operating state data (temperature, Vibration, PLC, I/O, alarm and fault information), machine operation status data (start, shutdown, power off, emergency stop, etc.), machining program data (program name, workpiece name, tool, machining time, program execution time, Program line numbers, etc., sensor data (vibration signals, acoustic emission signals, etc.), the collection of these status information allows the company to analyze and diagnose any changes that occur.
At the device level, the results obtained from the collection and analysis of the operational status data of a single device can be fed back into the automation control loop of the device, including by dynamically adjusting the control setpoint and control mode. In this way, the optimization of the operation of the equipment can be realized, and it is also a way to develop the operation of the equipment from the traditional automation to the intelligent. 
Of course, the transition from automation to more sophisticated control systems requires a fairly long transition period. In this transition period, for a large number of currently deployed control systems, in order to enhance the intelligence of their operation, we can first deploy a separate parallel computing analysis system (such as industrial gateway) for these systems, and then in the existing interface (such as PLC) Collect data in real time for these control systems, analyze the data in the above-mentioned external system, and finally feed back the analysis results back to the control system to optimize the operation.
At the operational level, corresponding to the existing large-scale SCADA or DCS, the collection and analysis of the data of the equipment group can drive the intelligent monitoring of the operation of the production equipment, including intelligent detection and diagnosis of faults, predictive maintenance, and Management of consumption and material consumption, and optimization of other operations.
At the business level, the results of device operational data analysis can provide valuable information for intelligent planning of business planning and processes, as well as product design.
In smart factories, the results of data analysis should first be to enhance the intelligence of stand-alone equipment operations and equipment group operations, and this process requires timely (near real-time), continuous flow data analysis. Traditional batch-based data mining methods continue to play a role in smart factories, such as modeling for operational data analysis or any other post-mortem analysis, but it is not the only or primary way. One of the keys to implementing a smart factory is how to collect and analyze the data and immediately feed back the results to the operation and operation of the equipment, and how to combine these analysis results with other business information (such as market supply and demand, supply chain, etc.) Integration to drive the full intelligence of production. To effectively achieve these goals, there are three points worth emphasizing:
First, the device is running continuously and its operation requires continuous intelligent feedback. Therefore, the analysis system must perform stream analysis on the continuously generated data stream of the device, provide information flow for decision making in a timely and continuous manner, and apply it to the continuous operation and operation process of the device in an instant. Conversely, traditional analytics frameworks based on batch and passive queries do not effectively support the operation and operation of device continuity. Therefore, streaming analysis must be the primary function of these data analysis platforms.
Second, considering security, reliability, and effectiveness (such as constraints on latency and data traffic), these data analysis platforms must provide distributed analysis that enables their analysis capabilities to be local to the device or production facility. Deployment, mode that supports edge computing.
Finally, these data analysis platforms should enhance and simplify the advanced and difficult analytical techniques required to provide customers with an out-of-the-box analysis system that is easy to deploy, customize, and maintain, enabling customers to quickly and iteratively evolve their smart factory applications. . Based on the fact that many manufacturing companies do not specialize in information technology, we should try our best to enable them to benefit from the latest advanced data analysis technologies, including artificial intelligence such as machine learning, in the process of developing smart factories, but not by them. Complexity and the need for special professionals are trapped.
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