142 results about "Whale" patented technology

The invention belongs to the technical field of machine learning models, particularly relates to a personnel performance evaluation method based on IWOA-SVM, and solves the problems that a traditional support vector machine (SVM) is low in precision, difficult in parameter selection and the like in performance intelligent evaluation. The method comprises the steps that Tent chaotic mapping and a pseudo-opposition learning strategy are utilized to increase the diversity and quality of an initial population, and the whale algorithm (WOA) is prevented from falling into local optimum; the global optimization capability of the WOA is improved by adopting a differential evolution mechanism; a penalty factor and kernel function parameters of the SVM are optimized through an improved whale algorithm (IWOA), and performance evaluation can be effectively carried out while optimal parameters are obtained. According to the method, the whale algorithm can be improved by using Tent chaotic mapping, pseudo-opposition learning and a differential evolution strategy, SVM parameters are searched in a global range, and better model performance is obtained.

The invention discloses an energy storage type intelligent soft switch double-layer optimization site selection method based on improved whale algorithm and second-order cone programming cooperation, and belongs to the technical field of power distribution networking and optimization operation, and the method comprises the steps: collecting and preprocessing the basic data of a power distribution network; a double-layer optimization model with the minimum daily operation cost of the system as the target is established, the upper layer takes the installation position of the energy storage type intelligent soft switch as a decision variable, and the lower layer takes the three-port power transmission value as a decision variable and constructs constraints based on the power flow equation after second-order cone relaxation and the energy storage operation limitation; an upper layer site selection problem is solved by adopting an improved whale optimization algorithm introducing a self-adaptive inertia weight, a lower layer operation problem is solved by adopting a second-order cone programming method, and collaborative optimization is realized through double-layer interaction. According to the method, global collaborative optimization of planning and operation is realized, the solving precision and efficiency of the site selection problem are improved, and the system operation cost and the network loss are effectively reduced.

The invention provides a deep learning-fused power demand multi-algorithm collaborative demand prediction method and system, and the system comprises a short-term power demand prediction platform, a medium and long term power demand prediction platform, a collection management platform, and a display analysis platform. According to the whale optimization algorithm, the generalization ability is improved by introducing chaotic mapping and a nonlinear convergence factor, the weight is optimized in combination with a multi-scale graph convolutional network, the number of nodes, the learning rate and the number of iterations of a model are optimized in combination with a simulated annealing algorithm, the model is constructed, and by setting a medium-and-long-term power demand prediction platform, the power demand prediction efficiency is improved. Based on the Tent chaotic mapping and the dynamic step length factor optimization standard sparrow search algorithm, the prediction result is accurate and reliable by optimizing the key parameters of the composite model combining the convolutional neural network and the bidirectional long and short term memory network with the attention mechanism, and the power demand prediction cost is reduced.

The invention discloses a ship engine fault diagnosis method based on IWOA-CNN-Transform, and the method comprises the steps: collecting the operation data of a ship engine in a normal state and a fault state, carrying out the preprocessing, constructing a data set, and dividing the data set into a training set and a verification set; the whale optimization algorithm is improved by introducing optimal neighborhood disturbance, adaptive weight and a variable spiral position updating strategy, and the improved whale optimization algorithm is constructed; constructing a CNN-Transform model, and optimizing hyper-parameters of the model by using an improved whale optimization algorithm to obtain an optimal hyper-parameter combination; and the CNN-Transform model is improved based on the optimal hyper-parameter combination, the training set is used for training, the verification set is used for evaluation, and prediction and diagnosis of the improved CNN-Transform model on engine fault classification are realized. According to the method, the accuracy and robustness of ship engine fault diagnosis can be effectively improved, and intelligent prediction and diagnosis of fault categories are realized.

The invention discloses an inertia damping VSG control method and device based on a chaos self-adaptive whale algorithm, and belongs to the technical field of generator control, the inertia damping VSG control method is realized through cooperation of an optimization controller executing the whale algorithm on the upper layer and a VSG controller on the bottom layer, and the inertia damping VSG control method comprises the steps of system initialization and parameter pre-configuration; generating an initial population matrix based on an initial whale algorithm of Tent chaotic mapping; dynamically adjusting the population of the initialized whale algorithm according to the convergence factor; calculating the fitness of population individuals according to a fitness function; iteratively updating the whale algorithm population to output global optimal parameters; and switching the global optimal parameter to a bottom layer VSG controller through a linear interpolation transition mechanism, and executing a VSG algorithm to output a switch driving signal. According to the method, the whale algorithm based on Tent chaos is adopted, real-time matching of parameter optimization and power grid working conditions is ensured, performance degradation caused by fixed parameters is avoided, and optimization precision and robustness are improved.

The invention relates to an engineering optimization method and system based on a whale optimization algorithm, and a medium, and belongs to the technical field of intelligent optimization algorithms. Comprising the following steps: S1, initializing parameters and populations: setting a population scale, a maximum number of iterations, dimensions and upper and lower bounds of variables, generating an initial population, and initializing a global optimal solution and a current optimal solution; s2, executing an individual variation strategy: recording a global optimal individual position and a current iteration optimal individual position; s3, executing a unified search strategy; s4, executing a group communication strategy; and S5, judging whether the current number of iterations reaches the maximum number of iterations, if so, outputting a globally optimal solution, otherwise, adding 1 to the current number of iterations and returning to the step S2 to continue iteration. The algorithm provided by the invention aims to improve the global search capability and convergence precision of the algorithm and maintain the time complexity equivalent to that of the original WOA at the same time.

1. Name of the designed product: cup body (tiger whale). 2. Use of the designed product: the designed product is used for a cup for containing liquid. 3. Design points of the designed product: in shape. 4. Picture or photo that best shows the design points: design 1 perspective view. 5. Design 1 is designated as the basic design.

1. Name of the product in this design: Playful Sleeping Pillow (Whale). 2. Purpose of this design: For use during rest periods. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

1. Name of the product in this design: Crocs (Little Whale). 2. Purpose of this design: for everyday wear. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The present application belongs to the technical field of workshop production scheduling optimization, in particular to a flexible workshop scheduling method and system based on a hybrid whale optimization algorithm. The method comprises S1, analyzing the flexible workshop scheduling problem and determining the mathematical model of FJSP; S2, continuously processing the discrete workshop scheduling; S3, initializing the position of the whale individual in the whale optimization algorithm through Tent chaotic mapping; S4, calculating the fitness of all whale individuals; S5, updating the whale position; S6, searching the neighborhood structure; S7, judging whether the number of whale position updates reaches the maximum iteration number, if not, returning to step S4, if yes, continuing to step S8; S8, outputting the best whale position; S9, outputting the maximum completion time of the found machine and drawing a Gantt chart. The present application has the characteristics of enhancing the local search ability of the whale optimization algorithm, thereby realizing the reasonable allocation of workshop scheduling and improving the production efficiency.

1. Name of the product in this design: Cat Scratching Board (Whale). 2. Purpose of this design: For cats to sharpen their claws and play with. 3. The key design elements of this product are the combination of shape, pattern, and color. 4. The image or photograph that best illustrates the design's key points: 3D view 1. 5. The design for which protection is sought includes color.

The application is suitable for the technical field of gas sensing, and provides an overlapping spectrum separation device and method based on a new evolutionary deep learning model. First, elite reverse learning, golden sine algorithm and dynamic nonlinear inertia weight are combined to form an improved whale optimization algorithm. Then, the improved whale optimization algorithm is used to automatically optimize the core hyperparameters of the deep learning framework, so as to reduce the time cost of manual adjustment according to experience. Finally, the normalized overlapping gas absorption spectrum is separated through the optimal deep learning framework after training, so as to guarantee the separation accuracy of each gas. The method can effectively improve the measurement accuracy, the model response time can reach the millisecond level, and when the gas composition, concentration, temperature and pressure change, the corresponding absorption spectrum training data set can be replaced, so that a new method is provided for the simultaneous measurement of multi-component gases in different environments.

1. Name of the product in this design: Pen Holder (Elephant Whale Double-Shaped Pen Holder). 2. Purpose of this design: Pen holder. 3. The key design features of this product are the combination of shape and pattern. 4. The image or photograph that best illustrates the design's key features: the front view.

This invention discloses an industrial carbon emission detection and prediction method and system. It collects multi-source heterogeneous data from industrial production processes, and sequentially performs standardization, noise removal, outlier removal, and data fusion on the multi-source heterogeneous data to obtain initial carbon emission data. A hybrid detection and prediction model is constructed, employing an encoder to extract multi-factor correlation features from the multi-source data and a long short-term memory network to extract temporal fluctuation features of the carbon emission data. An attention mechanism is introduced to weight the correlation features and temporal fluctuation features. An improved IWOA whale optimization algorithm is used to optimize the hyperparameters of the hybrid detection and prediction model to obtain a target hybrid detection and prediction model. The initial carbon emission data is input into the target hybrid detection and prediction model, and the prediction results are output, including the current carbon emission detection value and the carbon emission prediction sequence. This improves the accuracy of carbon emission prediction results and the efficiency of carbon emission detection.

The invention discloses a network construction type equipment optimal configuration method, system and equipment based on an improved whale optimization algorithm, and a medium, and relates to the technical field of power systems. The method comprises the following steps: selecting and configuring nodes of network-building equipment based on system sensitivity analysis; the net construction type equipment comprises a net construction type energy storage device and a net construction type fan; utilizing an improved whale optimization algorithm to optimize the capacity ratio of the network-forming equipment, and determining a capacity ratio configuration strategy; the improved whale optimization algorithm comprises an objective function and corresponding constraint conditions. According to the method, the improved whale optimization algorithm is used for global optimization, the problem that a traditional whale optimization algorithm is prone to falling into local optimization can be solved, and the configuration effect for the network construction type equipment is improved.

1. Name of the designed product: Anti-burn clamp (whale). 2. Use of the designed product: Clamping bowls, dishes, plates, electric rice cooker, preventing hands from being scalded. 3. Design points of the designed product: In shape. 4. Picture or photo best showing the design points: Perspective view 1.

The invention discloses a multi-strategy fusion whale optimization algorithm, belongs to the technical field of algorithm optimization, and constructs a multi-strategy fusion whale optimization algorithm for solving the problems of low operation efficiency and poor stability and globality in a time optimal trajectory planning process of an industrial mechanical arm on the premise of meeting kinematics constraints. An optimal neighborhood disturbance and variable spiral position updating strategy is introduced on the basis of a traditional whale optimization algorithm, the limitation of local optimum is broken through, a nonlinear convergence factor and an adaptive weight are introduced, and better solving precision and higher iteration speed are achieved. A trajectory curve frame is constructed by using a 3-5-3 polynomial interpolation function, and a target function of time is established by taking a UR3 mechanical arm as a research object. Simulation experiments show that the operation time of the mechanical arm is shortened by 11.3% compared with that of a similar whale algorithm, the kinematics curve of each joint is continuous and smooth, and the feasibility of the scheme in the industrial mechanical arm is verified.

The invention particularly relates to an electric vehicle energy consumption prediction method for optimizing XGBoost parameters based on an improved whale algorithm. The method comprises the following steps: acquiring and preprocessing original driving data of the electric vehicle, dividing working condition fragments and extracting key driving characteristics and unit mileage energy consumption labels; an improved whale optimization algorithm is constructed, and global exploration and local development capabilities are enhanced by introducing a nonlinear time-varying convergence factor, Cat chaotic mapping initialization and a crisscross hybrid strategy; performing automatic optimization on core hyper-parameters of the XGBoost model by using the algorithm, and training by using an optimal parameter combination to obtain a final XGBoost energy consumption prediction model; the performance of the XGBoost energy consumption prediction model is verified through a test set, an SHAP method is applied to carry out interpretability analysis, and the influence of driving behaviors on energy consumption is quantified. According to the method, the precision and robustness of the prediction model are effectively improved, clear mechanism explanation is provided, and effective support can be provided for energy-saving driving and energy management.

The invention discloses a reduced-order model parameter optimization method of a self-evolution whale optimization algorithm based on environmental feedback, and can be applied to the technical field of power systems. The method comprises the following steps: mapping a to-be-optimized reduced-order model parameter set of the power system into an individual position vector in a whale optimization algorithm, and initializing a whale population in a feasible region of parameters; repeatedly executing the following steps a to d: a, calculating a multi-dimensional environment feedback index according to the current whale population; b, determining the type of the current search state based on the multi-dimensional environment feedback index; c, according to the type of the current search state, executing a corresponding parameter updating strategy to update the position of the whale individual; d, if it is detected that the number of times that the improvement amount of the optimal fitness of the population in iteration is lower than the preset tolerance is larger than the set number of times, self-evolution operation is executed; and when an iteration termination condition is satisfied, outputting a position vector corresponding to the current global optimal individual as an optimized reduced-order model parameter.

1. Name of the product in this design: Drain Basket (Whale). 2. Purpose of this design: A drain basket for washing rice, fruits, vegetables, and other items. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

The application discloses a charging pile power self-adaptive regulation and control method and system based on thermal-electric-power coupling, the method comprises the following steps: S1: constructing a thermal-electric-power coupling model; S2: multi-source data acquisition and parameter identification; S3: power distribution based on an improved whale optimization algorithm, regarding the power combination of each charging pile as a whale individual, taking the total power as a constraint, taking temperature uniformity, equipment life and power grid frequency stability as multi-objective functions, and finally outputting an optimization result; and S4: adaptive regulation and feedback correction. Through thermal-electric-power coupling modeling, the application can accurately predict the temperature distribution, thermal stress and fatigue life of key components, realize the temperature uniformity and life maximization target by combining the improved whale optimization algorithm, avoid local overheating and premature failure, and improve the overall reliability and service life of the charging pile.

1. Name of this design product: Scientific Research Sightseeing Boat (Imitation of the Whale Rainbow). 2. Intended use of this design: as a coastal scientific research, tourism, or sightseeing vessel. 3. The key design features of this product are the combination of shape and pattern. 4. The image or photograph that best illustrates the design's key points: a 3D model.

1. The name of the design product: knob (small whale). 2. The use of the design product: knob for toilet. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: perspective view 1.

1. Name of the product in this design: Fishing Rod (Whale Nest - Jianshan Max). 2. Purpose of this design: for fishing. 3. The key design features of this product are the combination of shape and pattern. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The application discloses an emergency material site selection and path optimization method based on a whale optimization algorithm, and specifically as follows: firstly, an emergency material site selection model is established based on urgency and customer satisfaction to select an optimal distribution point as a distribution center; secondly, an emergency material distribution model is established based on transportation cost and time conditions to plan a distribution path for disaster points; finally, parameters of the whale optimization algorithm are adjusted, and paths generated in each iteration are stored to obtain a disaster point path planning route and a global path length. The application realizes path optimization of emergency material distribution, ensures timeliness, realizes the target of reducing cost, and provides effective guarantee for transportation of emergency materials.

1. Name of the product in this design: Coffee Cup and Saucer (Golden Turtle and Whale). 2. Purpose of this design: A vessel for holding coffee. 3. The key design features of this product are the combination of shape and pattern. 4. Images or photos that best illustrate the design key points: usage status reference diagrams. 5. Other situations requiring explanation: Kit description: Kit 1 of this design is a cup, and Kit 2 is a saucer.

1. Name of the design product: cup cover (tiger whale). 2. Use of the design product: the design product is used for water cup cover. 3. Design points of the design product: in shape. 4. Picture or photo that best indicates the design points: perspective view.

The invention belongs to the technical field of source network coordination control, and particularly relates to a hydroelectric generating set source network coordination control parameter dynamic optimization method fusing historical context and an improved multi-target whale algorithm, a related system, equipment and a storage medium. Aiming at the problems of poor adaptability of control parameters, conflict of multi-target optimization, lack of historical working condition reference and the like in the process that a hydroelectric generating set participates in primary frequency modulation of a power grid and AGC (Automatic Gain Control), the method comprises the following steps: acquiring and preprocessing multi-source heterogeneous data, mining context information of historical working conditions to generate initial optimization parameters, and extracting real-time working condition characteristics and constraint conditions; and constructing a multi-objective optimization model fusing assessment of two detailed rules and unit performance, solving optimal control parameters by adopting an improved multi-objective whale algorithm, executing the optimal control parameters, and dynamically adjusting the parameters in combination with an online rolling optimization mechanism. According to the method, the source-network coordination response speed, the control precision and the operation economy of the hydroelectric generating set are effectively improved, the power grid assessment risk is reduced, and the method is adaptive to a novel power system under high-proportion new energy permeation.

1. Name of the product in this design: Shower Toy (Whale). 2. Purpose of this design: For use in water play toys. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.