With the rapid development of AI technologies, artificial intelligence evolves from perceptual intelligence to cognitive intelligence and machines have been endowed with various cognitive abilities, including knowledge representation, logical reasoning and self-judgement. As the basic technology for cognitive intelligence, knowledge graphs (KG) have gradually become one of the core technologies driving the next generation of artificial intelligence. KGs have been widely adopted in many application scenarios such as semantic search, natural language generation, question answering systems, intelligence dialogue systems and personalized recommender systems. KG is a large-scale semantic knowledge base, which encodes a collection of interlinked descriptions of entities with Resource Description Framework triples.

Medical image segmentation is essential in medical image processing. It can help doctors see the pathological sites clearly and make accurate disease diagnosis and effective treatment. A doctor may not observe potential the disease visually but they may find it through checking the intensity changes from medical images and its boundaries through careful manual segmentation or separation. With rapid development of image segmentation, more accurate and automatic segmentation algorithm have appeared for various human organs and their movements. Medical image segmentation algorithms are more accurate and faster than human free hand segmentation.

Machine vision and sensor technology use camera calibration and 3D reconstruction algorithms to accurately measure objects in images, and use image recognition algorithms for object recognition. Machine vision has shifted from academic research to the commercial field, and is the core of the industrial automation revolution. It does not only focus on the revolution of industrial robot technology. Its applications cover almost all machines and all aspects of the manufacturing cycle. In a wide range of applications such as industrial automation, robotics, drones and 3D modeling, medical instruments, it can provide more detailed and accurate positioning, analysis and measurement of objects. Vision sensor is a key element in any machine vision system, and it is rapidly improving in terms of speed and accuracy.

With the exponential growth of quantitative trading in the worldwide financial industry, various forecasting algorithms, trading algorithms, factors, technical indicators, etc. are constantly being proposed. Quantum finance and its underlying technologies, including quantum field theory and quantum anharmonic oscillatory theory, provide a new research direction for traditional quantitative transactions. There are many factors that can be applied as modeling objects in the financial market. How to effectively use quantum theory to model the financial market and obtain considerable benefits through quantitative transactions has become one of the hottest topics in the financial world. A large number of researches show that although many quantum financial models have been proposed and can be explained by the concept of finance well, the actual profitability of these quantum financial model is not satisfactory, which is contrary to the original intention of our research.

Sensor-cloud system realizes the integration of the physical world and information world, in which data is the essence of all upper applications. Because of the weak computing power, limited communication capacity, and a large amount of data transmission of the underlying sensor network, its data collaboration mechanism with the cloud is not explicit. At present, there are still many critical problems in the system, such as considerable data transmission delay, high error rate, easy privacy exposure, and so on. We found that the existing solutions did not carry out cloud-edge-end collaboration, or the cloud-edge-end cooperation is insufficient. This research takes “cloud-edge-end data collaboration” as the research object, which explores the influence of cloud-edge-end collaboration on the whole process of data transmission, cleaning, synchronization, and retrieval.