9 results about "Bacteria foraging" patented technology

The invention provides an accurate positioning method for a part tray, and relates to the technical field of part machining, and the method comprises the following steps: providing a part tray provided with an identification point which is arranged at a preset position on the tray, the part tray comprises a station bottom frame, a station machine base, a feeding station frame, a wedge-shaped guide plate frame, a station alignment pin, a guide insertion frame and a tray positioning pin control structure. Acquiring an image of the part tray through an image acquisition device, and performing preprocessing and feature extraction on the image to identify identification points in the image; and according to the identification points in the image, determining initial position and attitude estimation by combining a preset corresponding relation between the identification points and the actual position and attitude of the part tray. Through combination of image recognition and a bacterial foraging optimization algorithm, high-precision positioning of the part tray is achieved, the part tray is accurately adjusted to the preset position through the mechanical arm adjusting mechanism, and the production efficiency and the machining accuracy are improved.

The invention discloses an intelligent employment project OA management system based on cloud computing. The intelligent employment project OA management system comprises the following steps: S1, data acquisition and preprocessing; s2, generating an optimal scheduling scheme based on an improved bacterial foraging optimization algorithm; s3, carrying out wavelet transformation and island model evolution learning, and generating risk early warning; s4, integrating scheduling and early warning information, and dynamically adjusting a scheduling scheme; s5, displaying a scheduling and early warning result, and receiving user feedback; and S6, storing the data, and implementing encryption and authority control. According to the invention, while intelligent scheduling and risk perception linkage management of intelligent employment tasks are realized, the stability and response speed of a scheduling scheme are significantly improved, and the accuracy of anomaly detection and the timeliness of early warning are effectively enhanced.

The invention provides a flow shop scheduling dual-objective optimization method based on a bacterial foraging optimization algorithm, and relates to the technical field of production and manufacturing management. The method specifically comprises the steps of obtaining original data of production scheduling in an industrial assembly line, and constructing a coupling coding scheme based on a task sequence and a delay period matrix under the background of time-of-use electricity price; an initial population is generated by adopting a hybrid heuristic initialization strategy, wherein each individual represents a complete scheduling scheme through a coupling coding strategy based on a task sequence and a delay matrix. According to the method, global optimization is carried out on a solution space based on an improved multi-target bacterial foraging optimization algorithm, and finally, an optimal individual is output and serves as an optimal scheduling scheme of a flow shop. According to the method, the problem of double-target optimization faced by original equipment manufacturers in flow shop scheduling under a time-of-use electricity price strategy is effectively solved, and meanwhile, the production efficiency is improved and the electric charge expenditure is reduced.

The invention discloses an underground cavern group rock drilling efficiency prediction method based on deep learning, and the method comprises the following steps: 1, collecting the underground cavern group drilling efficiency and related data of influence factors of the underground cavern group drilling efficiency, and taking the collected data as original data; 2, preprocessing the collected original data, and compiling a sample data set; step 3, establishing a drilling efficiency prediction model based on a back propagation neural network; training the drilling efficiency prediction model by adopting the sample data set; 4, optimizing hyper-parameters of a hidden layer structure in the drilling efficiency prediction model by adopting a BFO algorithm; 5, monitoring the underground cavern group drilling efficiency and influence factors thereof in the construction process; data obtained through monitoring are preprocessed, and then the drilling efficiency in the construction process is predicted through the optimized drilling efficiency prediction model. The bacterial foraging optimization algorithm is selected to optimize model hyper-parameters, and the model training effect is improved.

The invention discloses a mine blasting excavation lumpiness grading control method and device and computer equipment, and relates to the field of blasting control. The method comprises the following steps: acquiring rock mass parameters, blasting design parameters and blasting lumpiness data, and constructing a structured database; initializing a hyper-parameter of the XGBoost model, and iteratively optimizing the hyper-parameter by using a hybrid optimization algorithm based on an adaptive particle swarm and improved bacterial foraging to obtain a target parameter; and a mine blasting excavation lumpiness grading control model is constructed according to the target parameters, the mine blasting excavation lumpiness grading control model is trained through the structured database, and a blasting control decision under the target working condition is made through the trained mine blasting excavation lumpiness grading control model. The method is suitable for controlling the mine blasting excavation lumpiness under the influence of various complex conditions, and the problems that traditional mine blasting excavation excessively depends on experience of operators, so that the discreteness of lumpiness grading is large, and the construction efficiency is low are solved.

This application discloses a method, apparatus, device, and medium for predicting highway congestion based on an adaptive mixed bacterial foraging optimization algorithm, relating to the field of computer technology. The method includes: collecting highway traffic flow data to construct an imbalanced training set; initializing a bacterial population using the imbalanced training set and a hot-start mechanism based on a fuzzy C-means clustering algorithm; selecting and executing bacterial movement strategies based on the initialization results and a highway congestion predictor constructed using the adaptive mixed bacterial foraging optimization algorithm; evaluating fitness based on the strategy execution results and the hot-start rules; and training the predictor using the fitness evaluation results. After training, the unprocessed traffic flow data from the highway is input into the trained predictor to obtain congestion prediction results. The congestion prediction results include congestion status signals or smooth flow status signals. This application improves the accuracy, precision, and efficiency of classification prediction, as well as its robustness and adaptability in complex imbalanced environments.

The invention discloses a motor active-disturbance-rejection control method based on improved particle swarm-bacterial foraging, and belongs to the field of motor control. The implementation method comprises the following steps: determining an absolute value integral ITAE function as an optimization target; in a motor rotating speed-current loop second-order ADRC control link, parameters such as ESO gain, NLSEF gain, nonlinear factors, control coefficients and linear interval width are screened, and a vector X to be optimized is formed; an improved PSO-BFO algorithm is adopted to realize parameter optimization, particle speed and position are updated through PSO, PSO average speed is introduced to guide BFO to execute foraging, an individual optimal pbest and global optimal gbest updating mechanism is combined, an end condition that J value change rate of continuous two times of gbest is smaller than a set error epsilon or reaches the maximum iteration number T is taken as a termination condition, and ADRC optimal parameters are output. And according to the ADRC optimal parameters, motor active-disturbance-rejection control is realized, and the rotating speed tracking precision, the dynamic response speed and the disturbance rejection capability of the motor are improved.

The invention relates to the technical field of heat supply system control, in particular to a heat supply network temperature control method and system based on BFO optimization. The invention provides a control scheme based on a bacterial foraging optimization algorithm to solve the problem that a traditional PID controller is poor in control effect when adjusting the temperature of a centralized heating secondary network with the characteristics of nonlinearity and time lag. According to the scheme, firstly, real-time data and a set value of a secondary network temperature system are obtained, then, a system mathematical model is constructed, ITAE performance indexes serve as evaluation functions, optimal PID parameters are automatically searched by utilizing four biological heuristic operations of tropism, clustering, replication and dispersion of a BFO algorithm, and finally, the optimized parameters are applied to an actual control system. A simulation test shows that compared with traditional PID control, the system overshoot is reduced from about 20% to 5%, the stabilization time is shortened from more than 300 seconds to about 150 seconds, the system response speed and the temperature control precision are remarkably improved, the problem of temperature control of the heat supply system is effectively solved, and remarkable economic and social benefits are achieved.

The invention relates to a micro-grid planning method based on transformer capacity configuration and related products thereof. The micro-grid planning method based on transformer capacity configuration comprises the following steps: constructing a micro-grid system model according to power parameters in a micro-grid, wherein the micro-grid system model is used for describing a power balance state of a power electronic substation in the micro-grid; then, constraint conditions of the micro-grid system model are determined, a planning model is constructed according to the micro-grid system model and the constraint conditions, and the planning model is used for adjusting the constraint conditions; and solving the planning model by using a bacterial foraging optimization algorithm to optimize a planning scheme of the planning model. According to the invention, the planning scheme of the planning model can be efficiently optimized through the bacterial foraging optimization algorithm, the optimal planning scheme can be quickly found, the energy consumption of the micro-grid is reduced, and the power supply stability and economy of the micro-grid are improved.