Exploring the data quality problems in the context of federated cooperation and adopting corresponding governance countermeasures can facilitate the smooth progress of federated cooperation and obtain high-performance models. However, previous studies have rarely focused on quality issues in federated cooperation. To this end, this paper analyzes the quality problems in the federated cooperation scenario and innovatively proposes a “Two-stage” data quality governance framework for the federated collaboration scenarios. The first stage is mainly local data quality assessment and optimization, and the evaluation is performed by constructing a metrics scoring formula, and corresponding optimization measures are taken at the same time. In the second stage, the outlier processing mechanism is introduced, and the Data Quality Federated Averaging (Abbreviation DQ-FedAvg) aggregation method for model quality problems is proposed, so as to train high-quality global models and their own excellent local models. Finally, experiments are conducted in real datasets to compare the model performance changes before and after quality governance, and to validate the advantages of the data quality governance framework in a federated learning scenario, so that it can be widely applied to various domains. The governance framework is used to check and govern the quality problems in the federated learning process, and the accuracy of the model is improved.

With the rapid development of the economy, data have become a new production factor and strategic asset, enhancing efficiency and energy for technological innovation and industrial upgrading in enterprises. The evaluation of enterprise digital asset value (EDAV) is a typical multi-attribute decision-making (MADM) problem. Generalized hesitant fuzzy numbers (GHFNs) can better express the uncertainty and fuzziness of evaluation indexes, thus finding wide applications in MADM problems. In this paper, we first propose the Kullback–Leibler (K-L) divergence distance of GHFNs and prove its mathematical properties. Second, recognizing that decision-makers often have finite rationality in practical problems, we combine the cumulative prospect theory (CPT) with the Complex Proportional Assessment (COPRAS) method to propose the GHF-CPT-COPRAS model for solving MADM problems. Simultaneously, we extend the distance correlation-based Criteria Importance Through Intercriteria Correlation (D-CRITIC) method to the GHF environment to rationally calculate the weights of attributes in the EDAV evaluation problem. Finally, we apply the proposed GHF-CPT-COPRAS model to the EDAV evaluation problem and compare it with existing GHF decision-making methods to verify its effectiveness and feasibility. This study provides an important reference for addressing the EDAV assessment problem within an uncertain fuzzy environment and extends its application methods in the decision-making field.

With the rapid development of the data trading market, traditional fixed pricing strategies can no longer effectively reflect the real value of data products, thereby restricting the development of the data trading market. To address this challenge, this paper proposes a dynamic pricing method for data products based on deep reinforcement learning, aiming to attract buyers through dynamic pricing strategies and maximize the cumulative profits of data sellers, thus driving the further development of the data trading market. First, the dynamic pricing problem for data products is modeled as a Markov Decision Process (MDP). Then, a dynamic pricing algorithm based on the Deep Q-learning algorithm is designed, incorporating an annealing mechanism to optimize the exploration strategy. Finally, the performance of traditional reinforcement learning algorithms, specifically Q-learning and SARSA, is compared. The experimental results show that the dynamic pricing method based on deep reinforcement learning not only effectively enhances the cumulative profits of sellers but also significantly improves both revenue and algorithm performance compared to static pricing strategies and traditional reinforcement learning algorithms. This research provides a novel solution for data product pricing, contributing to the healthy development of the digital economy.

Synergistic development is a pivotal force in advancing regional modernization. China has effectively promoted rural revitalization through the collaborative integration of various digital factors, thereby narrowing the urban–rural development gap and laying a solid foundation for achieving sustainable development. This study examines factors driving rural revitalization through the analysis of 193 cases in less urbanized Chinese regions from 2018 to 2022 using panel-data qualitative comparative analysis. This study identifies digital infrastructure as the key to rural development, while the importance of industrial digitalization is increasingly recognized. We found two combinations of the digital economy that can promote rural revitalization: one that links digital infrastructure with digital industry, and the other that combines digital infrastructure with data elementization. Moreover, we also analyzed how regions with different innovation capabilities and economic foundations should develop differentiated strategies to achieve rural revitalization. This research provides a scientific basis for various regions to formulate rural revitalization strategies that are in line with their own characteristics, which will help achieve the goal of regional sustainable development.