6 results about "Bacteria foraging algorithm" patented technology

The present application relates to a kind of power transformer fault diagnosis method and system, storage medium.It includes the following steps: the content of each gas in the oil of power transformer obtained by oil chromatographic analysis, obtains original data;Original data is preprocessed, constructs feature vector, and is divided into training sample set and test sample set;Support vector machine is constructed based on training sample set;Support vector machine is optimized by improving bacterial foraging algorithm, and support vector machine fault diagnosis model is obtained;Test sample set T2 is input to support vector machine fault diagnosis model and carries out fault diagnosis.The present application combines data analysis diagnosis technology and intelligent algorithm, constructs support vector machine by obtaining data through oil chromatographic analysis, finds the global optimal parameter of support vector machine by improving bacterial foraging algorithm, improves the accuracy of diagnosis.

The application discloses a mobile edge computing task offloading method based on a bacterial foraging algorithm, and steps of the method comprise the following steps: S1, constructing a many-to-many edge computing offloading model; S2, establishing a time delay model and an energy consumption model of an edge server and a mobile device; S3, taking the longest time delay on the edge server and the mobile device as the total time delay of the system, taking the total energy consumption as the system energy consumption, obtaining a system optimization total target by weighting, obtaining the system optimal target by using an improved bacterial foraging algorithm, and obtaining an optimal offloading result; S4, modifying a three-layer loop nested structure of the bacterial foraging algorithm, changing the bacterial foraging algorithm into a single-layer loop structure in parallel with chemotaxis and migration, and using the improved bacterial foraging algorithm to continuously iteratively update the bacterial position and obtain the best offloading strategy and the system optimization target. The method can ensure that all the mobile devices in the system can reasonably offload the computing task to the edge server or process the computing task on the mobile device, so that the system time delay and the energy consumption are reduced.

Aiming at the problems of unbalanced energy and low coverage efficiency of the existing WSN, the invention provides a WSN energy efficient coverage method and device for improving a bacterial foraging algorithm (EABFO). According to the method, an adaptive variation method is adopted during position updating, before a predicted peak value is reached, the variation probability is increased along with the increase of the number of iterations, the randomness of bacteria is increased, global large-range search is realized, omission of an optimal solution is avoided, local optimum is tended, after the predicted peak value is reached, the variation probability is reduced, and local detail search is realized; time and space waste is reduced, and algorithm efficiency is improved. In addition, an elite breeding strategy is innovatively provided, the optimal fitness value is reserved, reverse growth of the fitness value is avoided, and the whole flora is evolved in a better direction. Compared with a genetic algorithm, the method has the advantages that the service life of the sensor matrix is prolonged by 200%, the method is suitable for energy-limited scenes such as large-scale static monitoring and dynamic target tracking, and the life cycle of the WSN is effectively prolonged.

This invention provides a method, apparatus, and equipment for the coordinated optimization of power generation stations and diversified energy storage. The method includes: modeling components of a hybrid power system to quantify the indirect costs of intermittent renewable energy; constructing a multi-objective optimization model incorporating economic operating costs, pollution emission control, and voltage safety and stability; establishing a set of relevant constraints, designing a tiered energy storage dispatch strategy, and performing multi-scale power balancing; and solving the multi-objective optimization model using an improved bacterial foraging algorithm to determine the optimal power generation plan. This invention achieves an optimal power generation plan that balances economic, environmental, and stable operation requirements, effectively mitigating renewable energy fluctuations and solving the problem of existing technologies' difficulty in achieving multi-objective coordinated optimization. It provides an effective technical approach to improving the overall operational performance of novel hybrid power systems.

The invention provides a permanent magnet synchronous motor control method and system based on a BP neural network, and belongs to the field of computer processing, and the method comprises the steps: setting a step size attenuation factor in a step size adjustment mechanism of an original bacterial foraging algorithm, and obtaining an improved bacterial foraging algorithm; an improved bacterial foraging algorithm is utilized to optimize a BP neural network, and PID control parameters of an FOC algorithm are optimized through the optimized neural network; and calculating a control quantity according to the PID control parameters after parameter optimization, and carrying out speed regulation control on the permanent magnet synchronous motor. By means of the method, the higher response speed, the smaller rotating speed error and the lower torque fluctuation can be shown under various working conditions, good stability and robustness are shown, and therefore the working performance of the permanent magnet synchronous motor is improved.

Embodiments of the present application disclose a cloud computing task scheduling method and device, electronic equipment and storage medium. The method comprises: obtaining a cloud scheduling dataset, the cloud scheduling dataset comprising a cloud computing task dataset and a virtual resource dataset; constructing a cloud computing task execution time model according to the cloud scheduling dataset, the cloud computing task execution time model being used to indicate an expected time for a virtual resource to process a cloud computing task in a scheduling scheme; constructing a cloud computing task scheduling optimization model according to the cloud computing task execution time model; determining a primary population of a bacterial foraging algorithm according to a Q-learning algorithm and the cloud scheduling dataset, the primary population representing an initial solution of the cloud computing task scheduling optimization model; optimizing the primary population according to the bacterial foraging algorithm to obtain an optimal solution, the optimal solution indicating an optimal scheduling scheme; and scheduling each cloud computing task according to the optimal solution of the cloud computing task scheduling optimization model. The embodiments of the present application can improve resource utilization and computing efficiency.