Datang Telecom Group (China)

The 6 G Computing Power Network (CPN) is envisioned to orchestrate vast, distributed computing resources for future intelligent applications. However, achieving efficient, trusted, and privacy-preserving computing resource sharing in this decentralized environment poses significant challenges. To address these intertwined issues, this article proposes a holistic blockchain and evolutionary algorithm-based computing resource sharing (BECS) mechanism. BECS is designed to dynamically and adaptively balance task offloading among computing resources within the 6 G CPN, thereby enhancing resource utilization. We model computing resource sharing as a multi-objective optimization problem, aiming to navigate these trade-offs. To tackle this NP-hard problem, we devise a kernel-distance-based dominance relation and incorporate it into the Non-dominated Sorting Genetic Algorithm III (NSGA-III), thereby significantly enhancing population diversity. In addition, we propose a pseudonym scheme based on zero-knowledge proofs to protect user privacy during computing resource sharing. Finally, security analysis and simulation results demonstrate that BECS can effectively leverage all computing resources in the 6 G CPN, thereby significantly improving resource utilization while preserving user privacy.

As a key renewable energy grid-connected technology, the voltage-controlled virtual synchronous generator (VC-VSG) is of great significance for solving the challenges of grid integration of high-proportion new energy. However, most of the existing power control parameter designs for VC-VSG are based on the static power transmission model, without considering the low-frequency dynamic operating conditions, resulting in insufficient robustness. To this end, this paper first analyzes the physical mechanism of the insufficient robustness of the existing parameter design methods. On this basis, an optimized method for robust power control parameters based on the small-signal model of dynamic power transmission is proposed. By establishing the mathematical models of the closed-loop from active power to reactive power and from reactive power to active power, the physical mechanism of this method for suppressing the dynamic coupling between active-reactive power and reactive-active power is revealed. The controller parameters designed using this method can significantly reduce the maximum output disturbances of active-reactive power and reactive-active power. Finally, experiments are carried out to verify the effectiveness and superiority of the parameter design method proposed in this paper.

Traditional pipeline design methods determine pipe diameters based on flow rate and economic specific friction, which cannot accurately calculate fluid parameters at various points. When the pipeline scale is large, the calculation errors and workload are significant, leading to hydraulic imbalance in the pipeline and affecting pump selection. Flow regulation and balance for pipeline systems based on experience are also very challenging. However, using hydraulic calculation software like PIPENET for modeling and analysis can effectively avoid these issues. By simulating different operating conditions of the pipeline, the flow field can be accurately obtained. Adjustments such as changing pipe diameters or adding orifice plates can improve resistance distribution, optimizing pipeline design and pump selection. Additionally, it allows for precise calculation of the opening degree of each control valve to guide actual operations.

Some big companies would deploy application sub-systems with same logical functions in different domains in order to expand business and maintain synchronization in business, such as deploying power marketing system 2.0 in the State Grid corporation. And those sub-systems in different domains will produce big data which are the company’s good data assets and can provide data support for the overall analysis of the company. So, this paper provides a rapidly merging and sorting algorithm for isomorphic big data in multiple domains to finish big data from different sub-systems to gather in one big data set to deeply analyze. This algorithm based on a 2-way merging method joins the dynamic merging strategy to reduce data waiting time; and then the algorithm is improved by melting the parallel merging approach to boost the merging speed. Further, in order to sort the merging data, the algorithm involves a custom inserting sort. Finally, this algorithm has been proven to rapidly merge and sort data to rise at least 50% compared with the simple merge sort method through experiments on the simulated marketing big data in the State Grid in China and can provide an important reference for rapid merge sort of distributed big data.

In the process of mining backfill transportation, inadequate pipeline layout or filling parameters can result in decreased stability in pipeline transportation. This instability can lead to blockages or pipe ruptures, often caused by the occurrence of partial filling during the transportation process. To address the issue of partially filled pipe flow during mining backfill transportation and effectively increase the full pipe height in the transportation process, theoretical analysis and numerical simulation methods were employed to study the causes and hazards of partially filled pipe flow in the transportation of tailings cemented backfill, as well as measures to enhance the full pipe height. By introducing the Bernoulli equation to analyze a simplified transportation model, a discrimination formula for identifying partially filled pipe flow during transportation was obtained. This formula summarizes the factors influencing the full pipe height and can be used to calculate the full pipe height in the transportation process of different filling mining areas. A numerical model was established based on this formula to simulate the occurrence of partially filled pipe flow. Combining the simulation results, the operational parameters of a lead-zinc mine in Yunnan, China, were designed and improved. This improvement significantly increased the full pipe flow height and reduced the failure rate in pipeline transportation.

At present, the decision-making method of coal purchase in most coal-fired power stations is based on the experience of blending coal and burning in thermal power units, but this method does not consider the factors of economy and transportation. In order to solve this problem, this paper proposes a method to optimize the coal purchasing scheme of coal-fired power plants based on the joint optimization decision model of coal purchasing and dispatching. First fuel characteristics of mixed coal and purchasing cost model was constructed to determine the fountainhead procurement constraints, and mixing coal prices, then dispatching model was constructed to determine the scheduling constraints and transport price, the final will buy coal cost and transportation cost summation minimum and the nature of the mixed coal constraints as a joint decision model, and USES the particle swarm optimization algorithm to solve it. The decision model is applied to a power plant for verification, and the calculation results show that the total purchasing cost of power plant is greatly reduced.

Abstract C-V2X is the important enabling technology for intelligent transportation and automated driving applications. This chapter analyzes the capabilities provided by C-V2X in these applications firstly, which is followed by the envisioned application phases of C-V2X. Lastly, the prospects for C-V2X technologies as well as research trends are presented.