Shulou(Shulou.com)12/24 Report--

Digital technology has become a key driving force to promote the transformation and upgrading of the manufacturing industry, transforming data into advanced productive forces, and transforming the mode of production and enterprise operation mode at the same time. Among them, the deep integration of artificial intelligence and Internet of things technology is promoting the "virtual-real interconnection" between the digital world and the real world in the manufacturing process, thus promoting "virtual control of reality", building intelligent factories, realizing intelligent and green manufacturing, and promoting industrial innovation with technological innovation.

Great Wall Automobile is accelerating its transformation into a global intelligent technology company, with its Harvard, Wei Brand, Euler, Tank, Great Wall pickup and other brands with production and sales networks all over the world. In terms of production and manufacturing, Great Wall Motor has established 10 complete process vehicle production bases in China, 3 complete process vehicle production bases in Russia, Thailand, Brazil and Great Wall Automobile, and has a number of KD plants in Ecuador, Pakistan and other places. While making efforts in overseas markets, it has accumulated a huge amount of industrial data.

How to tap and maximize the value of these industrial data, improve product quality, and at the same time achieve cost reduction, efficiency and green low-carbon in global production and operation?

Relying on the advantages of many years of experience accumulated in automotive manufacturing scenes and the technical layout in big data, AI, digital twins and other fields, Great Wall Motor has created an independent and controllable intelligent industrial Internet of things platform to assist global production and operation by means of digital intelligence. Huihang is the first SaaS-level industrial Internet platform in China to achieve full factory coverage, supporting the unified operation and maintenance management of Great Wall Automobile production / R & D base and factory equipment at home and abroad, improving production coordination efficiency, reducing spare parts inventory cost and energy consumption, optimizing process parameters and improving product quality and quality.

Up to now, Huihang has obtained four computer software copyrights and two patents certified by the National Patent Office, and recently won the "2023 World Internet of things Award ▪ Innovation Project Award" in the World Internet of things Digital economy Conference, which is the most influential and authoritative conference on the Internet of things in the world.

Zhang Zhigang, director of Huihang products, said in a speech at the conference: "since the pilot project was carried out at the production base in Tianjin in 2022, Huihang has been continuously upgraded iteratively and has supported five international languages and has a mobile end. So far, it has covered 9 major vehicle production and R & D bases at home and abroad, such as Xushui, Tianjin, Chongqing, Thailand, Russia, etc., supporting the interconnection of 80% of the industrial equipment of Great Wall Motor and supporting more than 180 types of industrial agreements. the number of access equipment types is over 1033, and the access signal point is over 2 million, realizing the full connection of production factors. "

Based on the "cloud-edge-to-end" mode, it has the intelligent control of the collection ability of big data in multi-industry, and can collect massive industrial data such as equipment working conditions and key process parameters of the production base and factory at home and abroad of Great Wall Automobile in millisecond, and send it back to the data center in real time. Based on the powerful real-time computing, rule engine, AI model and other technical capabilities, the platform has the capabilities of monitoring and analysis, visual configuration and AI digital employees. Continuous empowering equipment monitoring and management, production management, quality management, process monitoring, energy management, safety and environmental monitoring and digital twinning, assisting global production and management decision-making and other application scenarios.

For example, in the scene of equipment monitoring and management, Hui Control can precipitate the previous maintenance experience into structured knowledge, collect the status of equipment parts in real time, and construct equipment health and fault prediction models combined with AI algorithm to realize advance early warning, changing from passive and preventive maintenance to active and predictive maintenance to reduce the impact on the whole production line caused by sudden fault downtime. In addition, through the connection with the equipment index system and the equipment parts list, the health assessment model and the spare parts procurement early warning model can be constructed, which can accurately identify the spare parts demand and reduce the spare parts inventory occupation.

In addition, Huiyun integrates video surveillance and Al vision algorithm to create digital employees, which can replace manual safety inspection during workshop operation, automatically collect behavior images such as road pointing, etc., and can also identify whether personnel are wearing helmets and whether there is safety crossing boundaries, etc., and automatically detect the whole process to achieve timely detection of hazards and stop them, so as to avoid violations of safety and compliance operations. Escort for 7X24 hour safety production.

In the production process link, Huiqing collects the process parameter data of the production and processing equipment in the workshop, combined with the product quality data, carries on the retrospective analysis to the process parameters, precipitates the optimal equipment process parameter data, precipitates the process experience, and improves the product quality.

In terms of energy saving, environmental protection and energy supply guarantee, Huihang creates a digital twin scene and constructs a digital twin of energy and equipment by relying on visual configuration capability. it can collect, monitor and count the energy and material consumption data of park energy consumption, workshop production and processing equipment and process auxiliary equipment, and realize the gradual optimization of equipment energy consumption and fine management of material consumption. at the same time, provide real-time energy supply risk analysis. At present, it has realized the twin visualization of 24 power stations, centralized monitoring and unified dispatching, reducing the risk of energy interruption and improving the operation efficiency of personnel; in addition, it has realized the twin visualization of 43 power distribution rooms, tracing the abnormal data at abnormal times and no blind areas of patrol inspection, which can reduce power outage costs and personnel costs by about 2 million yuan a year. Reduce the risk of energy interruption, improve personnel efficiency, reduce the cost loss caused by interruption, and optimize energy consumption to promote the green and sustainable development of Great Wall Motor.

With the help of the digital twin scene created by Hui Control, managers can timely view the equipment parameters and status of the production site at home and abroad, system usage trend charts, alarm messages, etc., and can sense the progress and efficiency of all production plants in real time. Quickly issue instructions to respond to on-site events and alarms to assist in global production and management decisions.

In addition to realizing intelligent manufacturing and building smart factories with digital intelligent products and solutions, Great Wall Motor also integrates application practice with new technologies to continuously empower business areas such as research and development, supply chain, marketing, service, manpower, finance, quality, etc., and use technological innovation to increase enterprise resilience, empower enterprise development, embrace the reform of the automobile industry, and help build the digital intelligence future of the industry.

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