Addressing the challenges of high scheduling costs and low efficiency in the collaborative operations of agricultural machinery across multiple dispatch centers, this paper develops a scheduling model designed to minimize total costs. It introduces a Multi-Center and Multi-Machine Path Planning Algorithm Based on Deep Reinforcement Learning (MCMPP-DRL). Firstly, a deep reinforcement learning environment is established, and a policy network utilizing the attention mechanism is designed. Subsequently, the network model is trained to improve its solution capabilities using the policy gradient algorithm (REINFORCE). Finally, the solution is optimized through a local search strategy. In this study, three dispatch centers were selected within the maize growing area of Hebei Province, and comparative analyses were conducted for 20, 40, 50, 100 and 120 farmlands, respectively.The results indicate that the MCMPP-DRL algorithm achieves a reduction in total scheduling costs of at least 9.66%, 14.34% and 24.41% compared to Ant Colony Optimization (ACO), Simulated Annealing (SA) and Genetic Algorithms(GA), respectively.The significant optimization in scheduling costs demonstrates that the MCMPP-DRL algorithm establishes a robust theoretical foundation and offers technical support for addressing complex scheduling problems involving multiple dispatch centers and multiple.

Power is an issue that must be considered in the design of logic circuits. Power optimization is a combinatorial optimization problem, since it is necessary to search for a logical expression that consumes the least amount of power from a large number of Reed-Muller (RM) logical expressions. The existing approach for optimizing the power of multi-output mixed polarity RM (MPRM) logic circuits suffer from poor optimization results. To solve this problem, a whale optimization algorithm with two-populations strategy and mutation strategy (TMWOA) is proposed in this paper. The two-populations strategy speeds up the convergence of the algorithm by exchanging information about the two-populations. The mutation strategy enhances the ability of the algorithm to jump out of the local optimal solutions by using the information of the current optimal solution. Based on the TMWOA, we propose a multi-output MPRM logic circuits power optimization approach (TMMPOA). Experiments based on the benchmark circuits of the Microelectronics Center of North Carolina (MCNC) validate the effectiveness and superiority of the proposed TMMPOA.

According to problems existing in the process of university audit informatization, the internal audit framework based on big data is analyzed in this paper. Audit data processing and analysis technology of government procurement in universities based on big data are elaborated. Analysis models of the government procurement audit that highlight the association analysis are established, and through the government procurement audit data, the validity of the models is verified. The researches in the paper can provide an important reference for auditing department, and also can promote the improvement of government procurement system and the level of management authority.

Area has become one of the main bottlenecks restricting the development of integrated circuits. The area optimization approaches of existing XNOR/OR-based mixed polarity Reed-Muller (MPRM) circuits have poor optimization effect and efficiency. Given that the area optimization of MPRM logic circuits is a combinatorial optimization problem, we propose a whole annealing adaptive bacterial foraging algorithm (WAA-BFA), which includes individual evolution based on Markov chain and Metropolis acceptance criteria, and individual mutation based on adaptive probability. To address the issue of low conversion efficiency in existing polarity conversion approaches, we introduce a fast polarity conversion algorithm (FPCA). Moreover, we present an MPRM circuits area optimization approach that uses the FPCA and WAA-BFA to search for the best polarity corresponding to the minimum circuits area. Experimental results demonstrate that the proposed MPRM circuits area optimization approach is effective and can be used as a promising EDA tool.