7results about How to "Strong global optimization ability" patented technology

The invention discloses a deep tunnel surrounding rock mechanical parameter inversion method based on a three-dimensional brittle failure zone contour and sparrow optimization algorithm-information vector machine (SSA-IVM) combined optimization algorithm. The engineering technical problem that due to the fact that excavation instantaneous displacement is difficult to monitor, deep tunnel surrounding rock mechanical parameters are difficult to reasonably obtain through displacement back analysis is solved. The method comprises the following steps: firstly, constructing a tunnel FLAC3D numerical simulation model with the same ground stress condition at the occurrence position of a three-dimensional brittle failure zone; secondly, taking an absolute error between the total number of computational grid units in the actually measured brittle failure zone and the total number of computational grid units entering a plastic state in the range of the actually measured brittle failure zone after calculation of the FLAC3D numerical model as an optimization objective function; and then, by taking the tunnel surrounding rock mechanical parameters as optimization variables and taking a target function reaching a global minimum value as a target, performing global optimization by combining a tunnel FLAC3D numerical model and adopting an SSA-IVM joint optimization algorithm, thereby obtaining reasonable tunnel surrounding rock mechanical parameters.

The application belongs to the field of biomedical signal processing and human-computer interaction, and discloses a muscle-derived signal gesture recognition method and system based on crown porcupine optimization VMD parameters. The method collects electromyography or muscle magnetic signals during gesture action, and constructs a sample set through sliding window segmentation, data balancing processing and standardization. The crown porcupine optimization algorithm (CPO) is used to adaptively optimize the mode number and penalty factor of variational mode decomposition (VMD), and the gesture classification accuracy is used as the fitness function. Global search and local mining are realized through the four-layer defense of vision, sound, odor and physics of CPO. The intrinsic mode function (IMF) under the optimal parameters is extracted and spliced into a multi-dimensional feature matrix, which is input into a classification model to realize gesture recognition. The application directly optimizes the recognition performance, enhances the feature discriminability, and improves the recognition accuracy and robustness of muscle-derived signals in complex scenes.

The invention discloses an irrigation area water volume scheduling optimization method based on an index triangular optimization algorithm, and relates to the field of water conservancy informatization. By constructing an optimization model with the branch channel water supply amount as a decision variable and combining an index triangular optimization algorithm, efficient distribution and refined regulation and control of the water amount of the irrigation area are achieved. The water demand of each irrigation piece, a branch channel and different crops in each irrigation period in an irrigation area is integrated, constraint conditions such as the maximum water supply amount of a water source, the water delivery capacity of the channels and the irrigation pieces and the like are introduced, and a multi-objective function system for minimizing the overall water shortage rate and maximizing the crop yield is constructed. Through an index triangular perturbation mechanism, the global exploration capability is enhanced at the initial stage of search, the local search precision is optimized at the later stage, the stability and convergence of solutions are ensured, compared with a traditional method, the water utilization efficiency and the crop yield are improved, the stability of multi-target optimization in a complex environment is enhanced, and the method is suitable for large-scale popularization and application. And a reliable technical support is provided for the construction of an intelligent irrigation area water volume scheduling system.

The invention relates to a power distribution network fault identification method based on a whale optimization algorithm LSSVM, and belongs to the technical field of power system fault diagnosis, the method uses an improved TS-GAN model to solve the problem of insufficient fault samples, and provides sufficient data support for subsequent diagnosis; the fault starting moment is efficiently determined by using a break variable monitoring algorithm, and complex mathematical transformation and model establishment do not need to be carried out; according to the DBO-VMD algorithm, the optimization efficiency and precision of key parameters are improved, complex fault signals can be effectively decomposed, IMF with abundant features can be separated out, envelope demodulation analysis is carried out by selecting a maximum kurtosis IMF component, and features such as envelope information are extracted; according to the WOA-LSSVM algorithm, kernel parameters and penalty factors of the LSSVM are optimized, local extreme values are avoided, various short-circuit faults of the power distribution network are recognized, more accurate and more comprehensive decision support is provided for operation and maintenance of the power distribution network, and finally the operation efficiency and reliability of the power distribution network are improved.

The invention relates to a cloud computing task scheduling method and system oriented to energy consumption and service quality optimization, and belongs to the technical field of cloud computing. According to the method, the tasks and the resources in the cloud computing environment are comprehensively sorted by establishing a dual evaluation model of the task priority and the virtual machine priority. The method comprises the following steps: firstly, calculating the load and processing capacity of a virtual machine, and determining the priority of the virtual machine in combination with the power unit price of a data center; and then calculating the task priority according to the task scale, the execution time and the complexity. Based on the two priority results, a multi-objective optimization mechanism is adopted to carry out comprehensive modeling on the task complete time, the energy consumption and the SLA default rate, and a global fitness function is constructed. And finally, performing global search and dynamic optimization on the scheduling scheme by utilizing a swarm intelligence algorithm, so that the system realizes the lowest energy consumption and the maximum task execution efficiency on the premise of meeting the service quality requirement.

The invention discloses a microwave heating temperature control method and system based on a self-adaptive whale optimization algorithm. The method comprises the following steps: S1, constructing a multi-objective optimization problem of microwave heating temperature control; s2, based on the feasible region of the control variable, randomly generating an initial whale population containing a plurality of individuals; s3, performing simulation evaluation on each individual in the initial whale population; controller parameters corresponding to the individuals are input into the microwave heating system simulation model, temperature response data of the system are obtained, and the value of a comprehensive fitness function J is calculated according to the temperature response data; s4, executing self-adaptive whale optimization algorithm iteration, and updating the whale population; and S5, when an iteration termination condition is satisfied, outputting a global optimal individual, and applying a controller parameter corresponding to the global optimal individual to an actual microwave heating control system. The method has the advantages of adaptively improving temperature uniformity, response speed, control precision and the like.

A method for measuring the bulk density of topsoil based on the sound wave penetration characteristics belongs to the field of intelligent agricultural sensing technology. In the sound wave penetration characteristic acquisition system of this invention, a digital power amplifier board is electrically connected to a subwoofer, a DC power supply, and a computer terminal; a data acquisition card is electrically connected to a microphone and the computer terminal; and a robotic arm is electrically connected to the DC power supply and the computer terminal. The robotic arm, through five servo motors in coordinated control, achieves automatic grasping, precise positioning, and vertical insertion of the ring cutter tube, ensuring consistent sample placement. The computer terminal is equipped with a soil bulk density measurement model based on an adaptive mutant particle swarm optimization variational mode decomposition model and an adaptive mutant particle swarm optimization BP neural network. This system can automatically acquire real-time soil penetration sound waves and realize soil bulk density inversion based on sound wave penetration characteristics. The soil bulk density measurement model of this invention has high timeliness and high accuracy.