Abstract: A coal and gas outburst monitoring device includes the insertion and anchoring assembly, and the pump drainage and monitoring mechanism; the insertion and anchoring assembly is vertically inserted and distributed on the sidewall of surrounding rock of coal mine roadway, and the insertion and anchoring assembly can monitor the stress change data in the sidewall of surrounding rock in real time; the pump drainage and monitoring mechanism is erected within the coal mine roadway, and the pump drainage and monitoring mechanism can cooperate with the insertion and anchoring assembly to perform the gas pump drainage monitoring for the sidewall of surrounding rock of goaf, and to obtain the gas seepage monitoring data in the surface layer and the inner layer of sidewall of surrounding rock. Thus, comparing with the database mechanism, the accurate and advanced predictions for the gas outburst can be made.

Abstract: A coal and gas outburst monitoring device includes the insertion and anchoring assembly, and the pump drainage and monitoring mechanism; the insertion and anchoring assembly is vertically inserted and distributed on the sidewall of surrounding rock of coal mine roadway, and the insertion and anchoring assembly can monitor the stress change data in the sidewall of surrounding rock in real time; the pump drainage and monitoring mechanism is erected within the coal mine roadway, and the pump drainage and monitoring mechanism can cooperate with the insertion and anchoring assembly to perform the gas pump drainage monitoring for the sidewall of surrounding rock of goaf, and to obtain the gas seepage monitoring data in the surface layer and the inner layer of sidewall of surrounding rock. Thus, comparing with the database mechanism, the accurate and advanced predictions for the gas outburst can be made.

Abstract: A method and system for power grid optimization control based on a linear time-varying model is provided. The system model is first estimated offline using historical data combined with a piecewise linear regression method that supports a vector regression model. For online applications, a time-varying linear model prediction control framework based on a piecewise linear model coordinates the optimization control of both fast and slow regulating devices in the power grid. This approach does not require precise system model parameters, instead learning the power grid model from historical data. The method optimizes voltage distribution, reduces operating costs, and addresses issues like bad data and collinearity in the historical data, improving the voltage quality and enabling optimal operation even with incomplete models.

Abstract: The present application discloses a method and a device for user grouping and resource allocation in a NOMA-MEC system. The hybrid deep reinforcement learning algorithm proposed in the present application solves the hybrid problem of deep reinforcement learning that is difficult to deal with both discrete and continuous action spaces by using DDPG to optimize continuous actions and DQN to optimize discrete actions. Specifically, the algorithm determines a bandwidth allocation, an offloading decision, and a sub-channel allocation (user grouping) of the user device based on the user's channel state, in order to maximize the ratio of the computation rate to the consumed power of the system. The algorithm is well adapted to the dynamic characteristics of the environment and effectively improves the energy efficiency and spectrum resource utilization of the system.

Abstract: The present application discloses a method and a device for user grouping and resource allocation in a NOMA-MEC system. The hybrid deep reinforcement learning algorithm proposed in the present application solves the hybrid problem of deep reinforcement learning that is difficult to deal with both discrete and continuous action spaces by using DDPG to optimize continuous actions and DQN to optimize discrete actions. Specifically, the algorithm determines a bandwidth allocation, an offloading decision, and a sub-channel allocation (user grouping) of the user device based on the user's channel state, in order to maximize the ratio of the computation rate to the consumed power of the system. The algorithm is well adapted to the dynamic characteristics of the environment and effectively improves the energy efficiency and spectrum resource utilization of the system.

Abstract: The present disclosure discloses a workflow scheduling method and system based on a multi-target particle swarm algorithm, and a storage medium. The method comprises the following steps that first, the difference between the frequency reduction characteristic and the execution time of each server in a cluster is considered; a multi-target comprehensive evaluation model covering workflow execution overhead, execution time and cluster load balance is constructed on the basis of a traditional model; second, a multi-target particle swarm algorithm is provided for workflow scheduling, and an efficient solving method is provided. The method alleviates the defects of premature convergence and low species diversity of the particle swarm algorithm, reduces the execution overhead and execution time of the workflow on the cluster server, and better balances the load of the cluster server.