With the rapid development of the Industrial Internet of Things (IIoT), the scale of industrial equipment has expanded, leading to an increasing diversity of communication protocols and a significant rise in data transmission volume within industrial networks. Traditional communication systems, constrained by concurrency and throughput limitations, struggle to meet the demands of massive data transmission. To address this issue, this paper proposes a distributed high-availability message communication model for IIoT (DUA-MQTT) based on the OPC UA architecture. It integrates the distributed MQTT protocol to enhance concurrency and throughput performance. Additionally, to improve the information processing capability of the proposed model, this paper designs an information-modeling model based on industrial unstructured text data (MAC-GC), which generates structured data nodes that comply with the OPC UA information model specification through hierarchical annotation, accurately mapping device functions and attributes. Experimental results show that, compared with traditional communication models, the DUA-MQTT model reduces end-to-end latency by 28.6% and increases throughput by 22.2%, effectively enhancing the concurrency of data transmission. In terms of information-modeling capabilities, MAC-GC outperforms other models in accuracy (0.9701), recall (0.9601), and F1 score (0.9651), effectively improving the utilization efficiency and modeling accuracy of unstructured data.

Driven by the global carbon neutral strategy, the large-scale application of Carbon Capture, Utilization and Storage (CCUS) technology has significantly weakened the peaking margin of thermal power units, and the traditional peaking cost allocation model fails to take into account the inequitable distribution of costs faced by the carbon capture power plants participating in peaking. This study proposes a peaking cost quantification method based on multidimensional characterization. In terms of temporal characteristics, the scenario substitution method is used to quantify the participation of different peaking entities in peaking; in terms of spatial characteristics, the initiative constraints of unit peaking are taken into account to ensure that each peaking entity actively participates in peaking and profits from it. Considering the impact of carbon capture power plants’ participation in peak shifting on carbon emission reduction and carbon cost, we constructed a dynamic peak shifting model for multiple subjects. We innovatively construct a two-layer architecture of “spatio-temporal two-dimensional quantization and dynamic game sharing”, and adopt the improved kernel method to establish a coalition reorganization trigger mechanism to solve the unfair sharing of peaking costs when carbon capture power plants participate in peaking. Taking the improved IEEE34 node as an example, the results show that the established cost calculation model can accurately calculate the peak shaving cost of each unit in different scenarios. The total cost of thermal power peak shaving using CCUS is 6.5% lower than that of conventional thermal power units, which significantly verifies the effectiveness of CCUS technology in reducing the economic burden of peak shaving. When the carbon price is 150-250 yuan/ton, the carbon emission is reduced by 40%-50%, which highlights the dual advantages of the model in deep emission reduction and carbon price co-optimization, and the cost increase is ≤5%, which proves the feasibility of achieving large-scale carbon emission reduction under the premise of strictly controlling the economic cost. On the basis of completing energy conservation and emission reduction, the peak regulation task is completed at the minimum cost, and the unity of economy and environmental protection is achieved. Compared with the Shapley value method, the correlation coefficient of the contribution of the kernel method is increased to 0.91, which indicates that the accuracy of the proposed method for the evaluation of multi-agent contribution is significantly improved, and the fairness of the peak-shifting cost-sharing model of the kernel method is significantly improved, which provides a reliable solution for solving the problem of cost allocation imbalance between traditional power supply and CCUS units.

The continuous growth of IIoT systems has significantly increased the number of connected devices and message interactions, creating higher requirements for communication mechanisms in terms of scalability and adaptability under dynamic network environments. Although MQTT is widely used for its lightweight communication, its traditional centralized broker architecture limits scalability and fault tolerance in large-scale data transmission, reducing system scalability and fault tolerance. Additionally, static QoS configuration is difficult to adapt to dynamic environmental changes, resulting in high end-to-end latency and limited system throughput. To address these issues, this paper proposes a distributed MQTT communication method based on intelligent QoS routing and hierarchical collaboration (DAIS-MQTT). This method designs a network routing algorithm based on a hierarchical tree structure (LCN), which effectively addresses the scalability limitation of centralized proxies by enabling multi-level proxy collaboration and self-recovery from faults. At the same time, it proposes a QoS routing algorithm based on intelligent decision trees (IQR), which jointly optimizes proxy selection and QoS levels to dynamically adapt to changes in the network environment, thereby solving the problem of insufficient adaptability in static QoS configurations. Experimental results show that compared with the traditional MQTT-based communication method, the DAIS-MQTT method reduces the average message delay by 29.9%, increases system throughput by 28.2%, and maintains a reliable transmission rate of 98.7% in unreliable network environments, making it suitable for high-dynamic and large-scale IIoT communication scenarios.

For the magnetic levitation platform linear synchronous motor suspension system, an adaptive fuzzy fractional-order backstepping control method is designed to enhance system performance. Based on an analysis of the operational mechanism of the magnetic levitation linear synchronous motor (MLLSM) suspension system, its mathematical model and state-space equations are derived. In order to cope with the pronounced nonlinearity exhibited by the MLLSM suspension system, nonlinear coordinate mapping is employed to perform input–output feedback linearization. In order to improve the convergence speed and control accuracy of the system, fractional-order theory is applied to design a fractional-order virtual stability function. This paper designs a fractional-order backstepping controller, and a fuzzy system is employed to approximate the uncertain unknown functions in the MLLSM levitation system. A Lyapunov function is constructed to prove system stability. Experimental results indicate that the adaptive fuzzy fractional-order backstepping control improves the dynamic response of the levitation system and effectively attenuates uncertain disturbances.