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The methods of capturing and transferring the customer value in a product service system (PSS) are studied to capture the customers’ intrinsic value requirements, grasp the importance level of requirement, and transform it into design elements to more reasonably allocate resources and develop products more in line with the customers’ needs and more competitive at a minimum cost. First, a hierarchical model of the customer value based on the means-end chain theory is constructed to analyze the customer value from the perspective of customer expectations. In the process of determining the importance priority of value elements, the cloud model is used to process the expert evaluation information, and the competitive correction factor and the Kano factor are used to modify the basic importance of the value elements. The customer value in the PSS is then transferred to the product and service performance domain by constructing the parallel house of quality embedded cloud model (PHOQ-ECM). In other words, the cloud model is used to process the group decision-making values with fuzziness and randomness to complete the correlation calculation of the parallel HOQ. The important priority of the performance characteristics is then obtained. Finally, the abovementioned methods are applied to capture and transfer the customer value of a shearer, and the results are compared with other studies. The results show that the hierarchical model of the customer value can more deeply capture the customer value. The cloud model solves the problem of group decision-making with fuzziness and randomness. The competition correction factor and the Kano factor improve the accuracy of the importance priority of the value elements. PHOQ-ECM achieves the transfer and distribution of the customer value to two different objects of product and service and improves the accuracy of the performance characteristics importance priority. The method feasibility and validity are verified through the abovementioned analysis. Consequently, the method can effectively guide the PSS design.

When robot hands work in a com plex environment, it not only needs to sense the objects in contact, but also needs to respond to remote events before physical contact. In this way, robot hands can make prediction, so as to better adapt to the complex environment. In this paper, we have designed a flexible sensor array, which integrates piezoresistive sensor and capacitive sensor for detecting pressure and proximity signals simultaneously. Piezoresistive sensors can detect pressure signals, and capacitive sensors detect pressure and proximity signals. The sensors are arranged in a patterned array and installed on the robot hand to help the robot hand detect the distance and direction of approaching objects, as well as the shape and size of the grasped objects. To avoid mechanical mismatch, piezoresistive sensors and capacitive sensors are fabricated by the same flexible materials. In addition, after extreme pressure tests, the sensors can still work normally, so as to ensure that the robot hands can work normally in complex environments. We also combined the detected signal with machine learning, so that the robot can accurately identify objects of different shapes. In the fruit recognition test, the recognition accuracy reached 90%.

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