With the advent of knowledge economy, the competition of comprehensive national power gradually has shifted to talent competition. The cultivation of innovative talents and the incubation of student entrepreneurial teams are the primary educational goals of universities, which is an important part of the national innovation system. University student entrepreneurial teams present the attribute of high technology and unique knowledge advantage. The persistent innovation of student entrepreneurial teams is attributed to knowledge playing a critical role. In particular, in the knowledge-based economic era, student entrepreneurial teams with speed, connection, and intangible value creation have transformed the partly labor-intensive model into a knowledgeintensive competition model. Taking university student entrepreneurial teams as the research object, a total of 500 copies of questionnaire were distributed and 386 valid copies were retrieved, with a retrieval rate of 77%. Through the questionnaire survey, students' awareness, emotion, and will of innovation in innovation education are understood, and their cognition and intention of team cooperation and strategic innovation are investigated. The research results are concluded as follows: (1) Innovation education presents significantly positive correlations with collaboration. (2) Collaboration shows remarkably positive correlations with strategic innovation. (3) Innovation education reveals notably positive correlations with strategic innovation. According to the research result, suggestions are proposed, expecting that China's university student entrepreneurial teams could acquire the advantage of technological innovation by applying the opportunities of broadband and wireless network infrastructure and developing innovative applications and entrepreneurial models.
In the long developmental process, China’s agriculture has transformed from organic agriculture to inorganic agriculture. New technologies have made the modernization of agriculture possible. However, most older people who are engaged in agriculture may not completely understand the modernization of agriculture. Based on the limitations of traditional image target detection methods, a deep learning-based pest target detection and recognition method is proposed from a blockchain perspective, to analyze and research agricultural data supervision and governance and explore the effectiveness of deep learning methods in crop pest detection and recognition. The comparative analysis demonstrates that the average precision (AP) of GA-CPN-LAR (global activation-characteristic pyramid network-local activation region) increases by 4.2% compared with other methods. Whether under the Inception or ResNet-50 backbone networks, the AP of GA-CPN-LAR is significantly better than other methods. Compared with the ResNet-50 backbone network, GA-CPN-LAR has higher accuracy and recall rates under Inception. Precision-recall curve measurement shows that the proposed method can significantly reduce the false detection rate and missed detection rate. The GA-CPN-LAR model proposed here has a higher AP value on the MPD dataset than the other target detection methods, which can be increased by 4.2%. Besides, the accuracy and recall of the GA-CPN-LAR method corresponding to two representative pests under the initial feature extractor are higher than the MPD dataset baseline. In addition, the research results of the MPD dataset and AgriPest dataset also show that the pest target detection method based on convolutional neural networks (CNNs) has a good presentation effect and can significantly reduce false detection and missed detection. Moreover, the pest regulation based on blockchain and deep learning comprehensively considers global and local feature extraction and pattern recognition, which positively impacts the conscientization of agricultural data processing and promotes the sustainable development of rural areas.
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