6results about How to "The optimization result is accurate" patented technology

A low-carbon optimization scheduling method for a microgrid based on an improved particle swarm algorithm reduces the comprehensive operating cost of the microgrid system. The method includes: obtaining basic data and load data of the microgrid system, mainly including electric load, electricity purchase price, and electricity selling price, and combining the characteristics of the units in the microgrid system to construct a comprehensive cost objective function of the microgrid system; taking the minimum comprehensive cost of the microgrid system as the objective function, including the energy consumption cost and carbon emission cost of the microgrid system, to construct a low-carbon scheduling model; introducing unit power constraints, energy storage device constraints, and carbon emission constraints; using the improved particle swarm algorithm to solve the low-carbon scheduling model and optimize the scheduling of the microgrid system according to the scheme. The present invention has a strong optimization ability and a fast convergence speed through the improved algorithm, and can effectively allocate the output of each unit in the microgrid system, and can reasonably and effectively allocate the output of each unit in the microgrid system, providing an optimization scheduling method that is both economical and environmentally friendly.

The present invention proposes a mechanical arm optimization method based on harmonious search genetics, comprising: step S1, designing a PID controller of a six-degree-of-freedom mechanical arm; step S2, using the minimum objective function of the performance index of the four-piece integral of the absolute error value as parameter selection, and adding the square control input to the objective function; step S3, using the harmonious search genetic algorithm in the initial population solution of the genetic algorithm to obtain the optimal K p , K I and K D The value of .

The present invention discloses an electric vehicle load optimization method based on graph convolution and deep belief network, which uses graph convolution network to extract a large amount of uncertainty feature data of the road network, and then trains the deep belief network through a data-driven method to output the optimal charging load distribution of the electric vehicle, optimize the charging load distribution of the electric vehicle while coping with the uncertainty of the road network, and quantitatively analyze the impact of the optimization results on the road network and the power grid. The load optimization method. The electric vehicle load optimization method of the present invention is based on the data-driven optimization method of graph convolution and DBN, which can generalize the uncertainty factors affecting the charging load of the electric vehicle and accurately and efficiently optimize the charging load of the electric vehicle; compared with solving the nonlinear optimization model, the present invention has low calculation difficulty, fast solution speed, and stronger generalization performance for uncertainty factors; compared with existing deep neural network and other technologies, the optimization results of the present invention are more accurate, and the effect of reducing the user's charging cost is more significant.

The invention discloses a pose image simplification anti-interference method for multi-robot cooperative mapping, and the method comprises the steps: S1, carrying out the blocking processing of a single sub pose image, decomposing an original pose image into a plurality of independent sub pose images, carrying out the alignment, integration and association of the plurality of sub pose images, and carrying out the optimization processing of a global sub pose image; step S2, simplifying a single pose image, removing nodes with small meanings in the pose image through marginalization processing and rarefaction processing, and rarefaction the structure of the pose image to obtain a simplified image equivalent to the original pose image; s3, carrying out anti-interference processing on the pose image, and screening out a plurality of motion constraints of which outer points and inner points are consistent with each other in the motion constraints in the pose image as effective inner point motion constraints; and S4, joint optimization of the sub-pose maps of the multiple robots is carried out.

The invention provides a fusion positioning method for a mine card in a discharge port RTK failure scene, and the method comprises the following steps: a fusion positioning system begins to take over when a task state is detected to accord with a discharge area reversing task, and carries out the external parameter verification through employing the RTK state information at an initial moment, calculating a pose transformation value for performing time synchronization on the point cloud map relocation speedometer information, the IMU-wheel speed speedometer information and the RTK state information, and synchronizing the IMU-wheel speed speedometer information and the point cloud map relocation speedometer information to an RTK information moment; inputting IMU-wheel speed odometer information, point cloud map relocation odometer information and RTK state information after time synchronization into a factor graph, and taking the RTK state information as a prior factor; all factors are multiplied, maximum posterior distribution is obtained through derivation and serves as current optimal system state information, system state information obtained after factor graph optimization is monitored, and system state information with normal optimization results is output. And the mine unmanned driving positioning requirement is met.

This invention provides a dynamic topology and path optimization method for unmanned cluster ad hoc networks. It constructs a communication bandwidth requirement matrix and a distance matrix; designs a cost function based on distance, bandwidth requirement, and path hop count; establishes a fully connected communication topology matrix; and uses a shortest path algorithm, based on the cost function, to find the minimum cost path for each pair of nodes with communication needs. Based on all the obtained minimum cost paths, it constructs the optimal ad hoc network topology. By designing a precise cost function and combining it with Dijkstra's algorithm, it achieves efficient optimization of communication paths while simultaneously constructing the optimal ad hoc network topology, minimizing the communication cost function. The multi-factor coupling design of the cost function makes the path optimization results more accurate and adaptable to dynamically changing cluster environments. The combination with Dijkstra's algorithm enables efficient path search, making it suitable for medium- to large-scale unmanned clusters.