When a car has quality problems, it usually follows the statutory requirements and procedures to eliminate the defects of its products from defective automobile product manufacturers, the so-called “car recallâ€. For consumers and manufacturers, car recalls often have a lot of influence, so making a recall decision will be more cautious.
China’s car ownership is huge. For the AQSIQ defective product management center, which models and which batches of cars need to be recalled are very difficult decisions. In the past, the expert consultation system was generally used. In two or three days, experts were asked to judge all the known information—“Does this product abroad have a recall?†“Is the problem in the engine or in other? "Whether there is a malicious complaint" in the system, and so on, a large amount of data needs the human brain to do "screening," which is not only inefficient, but also easily leads to different standards.
In 2016, the percentage group was based on self-developed big data operating system products and built a big data processing and modeling analysis platform for the AQSIQ defective product management center. Through a large amount of data collection, the company has further integrated various data information such as defect complaint information, technical service announcements, domestic and international recall information, and has established knowledge maps for defects at various levels of vehicles, and has been applied to data query statistics and visual display.
On this basis, percentage points further use statistical learning and machine learning algorithms, combined with objective data and past expert judgments in the case of defects, to provide judgments on the possibility of defects in automotive products and assist in the decision-making process of business consultation.
“Through the knowledge map system of machines and intelligence, to 'learn' how experts in the past judged the defects of an automotive product.†Dr. Su Meng, Chairman and CEO of Percentage Group, said that after continuous learning, the model has been continuously optimized. So that the knowledge of the human brain is more deeply integrated with the data obtained by the computer.
In the past, it took 200 hours to complete the prediction of the possibility of defects of a car product with more than 10 experts to discuss the system; now, using a combination of big data and artificial intelligence, one person can use the machine to complete the Two hours. While reducing the cost of manual data processing, it also fully explored the value of big data and established a unified data management platform. Since the launch of this product, it has handled more than 1,000 recall cases and helped complete the recall of more than 50 million automotive products.
Experts point out that the evolution of artificial intelligence is divided into three levels: progressive, cognitive, and decision-making. At the sensory level, face recognition and speech recognition have been widely applied. At the cognitive level, smart customer service, smart medical assistants, etc. have also been known to the general public; and then, artificial intelligence is expected to evolve at the decision-making level of enterprises and organizations. . "For enterprises and organizations, the ultimate intelligence is the intelligence to solve the problem, the core is the 'intelligent decision', and 'intelligent decision-making' based on big data and artificial intelligence will be the future direction of corporate decision-making." Su Meng said.
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