With the wide application of wind energy as a clean energy source, to cope with the challenge of increasing maintenance difficulty brought about by the development of large-scale wind power equipment, it is crucial to monitor the operating status of wind turbines in real time and accurately identify the specific location of faults. In this study, a CNN-LSTM-based wind motor fault detection model is constructed for four types of typical faults, namely gearbox faults, electrical faults, yaw faults, and pitch faults of wind motors, combining CNN’s advantages of excelling in feature extraction and LSTM’s advantages of dealing with long-time sequence data, to achieve the simultaneous detection of multiple fault types. The accuracy of the CNN-LSTM-based wind turbine fault detection model reaches 90.06%, and optimal results are achieved for the effective discovery of yaw system faults, pitch system faults, and gearbox faults, obtaining 94.09%, 96.46%, and 97.39%, respectively. The CNN-LSTM wind turbine fault detection model proposed in this study improves the fault detection effect, avoids the further deterioration of faults, provides direction for preventive maintenance, reduces downtime loss due to restorative maintenance, and is essential for the sustainable use of wind turbines and maintenance of wind turbine service life, which helps to improve the operation and maintenance level of wind farms.

The cultural element is the minimum unit of a cultural system. The systematic categorizing, organizing, and retrieval of the traditional Chinese cultural elements are essential prerequisites for the realization of effective extracting and rational utilization, as well as the prerequisite for exploiting the contemporary value of the traditional Chinese culture. To build an objective, integrated, and reliable classification method and a system of traditional Chinese cultural elements, this study takes the text of Taiping Imperial Encyclopedia in Northern Song Dynasty as the primary data source. The unsupervised word segmentation methods are used to detect Out‐of‐Vocabulary (OOV), and then the segmentation results by the THULAC tool with and without custom dictionary are compared. The TF‐IDF algorithm is applied to extract the keywords of cultural elements and the Ochiia coefficient is introduced to create complex networks of traditional Chinese cultural elements. After analyzing the topological characteristics of the network, the community detection algorithm is used to identify the topics of cultural elements. Finally, a “Means‐Ends” two‐dimensional orthogonal classification system is established to categorize the topics. The results showed that the degree distribution in the complex network of Chinese traditional cultural elements is a scale‐free network with γ = 2.28. The network shows a structure of community and hierarchy features. The top 12 communities have taken up to 91.77% of the scale of the networks. Those 12 topics of the traditional Chinese cultural elements are circularly distributed in the orthogonal system of cultural elements’ categorization.

Under the “Internet+” environment, the R&D intensity of products and services has increased; hence, organizations need to improve their ability to integrate knowledge and technology resources. Knowledge gaps will arise when an organization’s knowledge reserves fail to meet the needs of innovation activities. This research established a network of complete knowledge topics under the “Internet+” environment based on the Word2Vec model. The word vectors and word frequencies of organizational reserve knowledge texts were analyzed to establish an organizational reserve knowledge topic network. The Term Frequency-Inverse Document Frequency algorithm was used to identify the demanded knowledge topic. The satisfaction capability of demanded knowledge in the reserve knowledge topic network was calculated via the eigenvector centrality and the fuzzy evaluation method. The corresponding strategies were then put forward to make up the knowledge gap. Finally, a case study was conducted and compared with SWOT (Strengths, Weaknesses, Opportunities, and Threats) and Venn diagram analysis on the economic and management college of a university in Beijing to verify the effectiveness of this method.

Embroidery is an important cultural heritage that traces the origins of Chinese civilization over five thousand years. Embroidery patterns function as cultural genes characterized by relative stability, inheritance, and variability. Excavating their intrinsic co-occurrence relationships and features can reveal underlying cultural transmission and evolution. By constructing a temporal multilayer network of embroidery pattern co-occurrence, we analyze intra-layer and inter-layer topological properties, identify key pattern nodes, and employ a link clustering algorithm for community detection. The key findings are as follows: (1) The pattern co-occurrence network exhibits scale-free and small-world properties. (2) The pattern co-occurrence network possesses a community structure with divisible and re-combinable functions. (3) The ‘Fu-Shou’ (fortune-longevity) patterns are the most representative cultural memory symbols of the Qing Dynasty. (4) The fluctuations in network topological properties and pattern node strength centrality are inherently linked to the transitions in social culture and political power.

Purpose: This paper aims to find out the factors that influence the choice of dual innovation strategies in the process of knowledge pricing and transaction between first-mover and late-mover companies in an open innovation environment and also to find the key factors that affect the company's strategic choice in factors such as heterogeneous market environment, demand elasticity, exploration risk intellectual property prices and transaction cost.Design/methodology/approach: This study uses the Cournot equilibrium and Stackelberg two-stage master-slave game model to describe the evolutionary process of knowledge pricing in an open innovation environment of first-mover and late-mover companies.Findings: Research shows that in an open innovation environment, the formation of a dual innovation strategy in the pricing process of corporate intellectual property transactions is a complex process. Changes in one-time transaction costs and changes in the inverse demand coefficient of the innovation market play the decisive role in the choice of dual innovation strategies. When the demand of the innovation market is moderate, the inverse demand coefficient of the innovation market and the one-time transaction cost has an invertedU-shaped influence relationship. As the innovation market's inverse demand coefficient and the one-time transaction cost increase at the same time, the degree of differentiation of the enterprise's dual innovation strategy choice gradually reduces; when the one-time transaction cost is the largest, the degree of strategy differentiation is minimized.Originality/value: Based on the above relationship, suggestions are made to guide enterprises in the knowledge pricing and transaction process in an open innovation environment, promote enterprises to form a dislocation development and complementary advantages in the knowledge innovation ecological chain and improve the overall innovation efficiency of the industry.

Differences in the capacity for absorption between different organizations will have an important impact on an organization’s choices of innovation exploration and exploitive innovation strategies. Organizations need to explore correct strategic decisions under different policies for long-term development. This study with limited rational first-mover and late-mover organizations as the research object, based on the evolutionary game theory model, using visualization system deduced first-mover and late-mover organizations in the knowledge absorptive capacity differences and incentive policies under the condition of different strategies selection process. The research shows that the rationality of policy incentive setting has a direct impact on the choice of organizational dual innovation strategy with different knowledge absorption capacities. The market pattern is stable and organizational knowledge absorption capacity is different. The higher the policy incentive level is, the more the organization is inclined to carry out exploratory innovation activities. Under the environment of stable market structure, different organizational knowledge absorption capacity, and no policy incentive, late-mover cannot adopt exploratory innovation strategy alone. When the market pattern is stable and the absorptive capacity of the organization is different, whether the late-mover can adopt the exploratory innovation strategy depends on the policy incentive level. In this case, the optimal situation is to have the opportunity to change to exploratory innovation at the same time as the first-movers.