239 results about "Whale" patented technology

The invention discloses an RFID multi-label information fusion and unmanned vehicle path real-time correction method, system and device based on improved whale optimization, and a storage medium, and belongs to the technical field of the crossing field of the Internet of Things and an intelligent traffic system, and the method comprises the steps: collecting received signal strength indication information and label position information, constructing a label signal propagation model and a positioning probability distribution model; performing fusion processing of multi-label positioning information, adopting a dynamic weighting strategy to adjust signal contribution, obtaining an unmanned vehicle position estimation value, taking the unmanned vehicle position estimation value as state input of path optimization, constructing a path cost function, introducing a whale optimization search mechanism based on dynamic update parameters, performing iterative optimization on a path candidate solution, and obtaining a path optimization result; outputting a path correction result; and a real-time path instruction is issued to the vehicle control module, and the unmanned vehicle is guided to complete path correction and navigation control in a dynamic environment. According to the method, the problems of poor path adaptability, low fusion precision and local optimum in the existing method are solved.

The invention discloses a shipborne radar oil spill detection method and system based on an improved whale optimization algorithm, and relates to the technical field of image processing and mode recognition. The method is characterized by comprising the following steps: preprocessing an acquired shipborne radar image; extracting saliency boundary ratio features of the preprocessed image; extracting an ROI image based on the saliency boundary ratio feature; obtaining an optimal segmentation threshold value by using an improved whale optimization algorithm, and segmenting the ROI image by using the optimal segmentation threshold value to obtain an optimal segmentation image; and performing post-processing on the optimal segmentation image to obtain a final oil film image. According to the method, the difference between the oil spill area and the background is highlighted by extracting the significant features; by improving the whale optimization algorithm, the local search capability of the algorithm is enhanced, and premature convergence is avoided. The method achieves the efficient and accurate recognition of the oil film region, can reduce the false detection rate, and is suitable for the detection scenes of different sea conditions and oil spill types.

The invention discloses a charging pile power adaptive regulation and control method and system based on heat-electricity-force coupling. The method comprises the following steps: S1, constructing a heat-electricity-force coupling model; s2, multi-source data acquisition and parameter identification; s3, performing 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 the temperature uniformity, the equipment service life and the power grid frequency stability as a multi-objective function, and finally outputting an optimization result; and S4, adaptive regulation and feedback correction. According to the method, the temperature distribution, the thermal stress and the fatigue life of the key part are accurately predicted through thermal-electric-mechanical coupling modeling, the targets of temperature uniformity and life maximization are achieved in combination with the improved whale optimization algorithm, local overheating and premature failure are avoided, the overall reliability of the charging pile is improved, and the service life of the charging pile is prolonged.

The invention provides a multi-unmanned aerial vehicle cooperative path planning method based on a multi-strategy improved whale algorithm, and the method comprises the steps: obtaining geographic parameters, and constructing an unmanned aerial vehicle flight environment based on the geographic parameters; determining a constraint condition of a multi-unmanned aerial vehicle path planning problem according to an unmanned aerial vehicle flight environment, and constructing to obtain a multi-unmanned aerial vehicle cooperative path cost function; and solving a value for minimizing a multi-unmanned aerial vehicle cooperative path cost function by using an improved whale algorithm to obtain an optimal path point sequence, and performing smooth connection to obtain an optimal flight path of the unmanned aerial vehicle. According to the method, multiple optimization strategies are fused on the whale algorithm to improve the optimization performance, a nonlinear convergence factor is designed to balance the strength of global exploration and local development, a competitive differential variation strategy is introduced, Cauchy variation and Levy flight are utilized to generate and screen candidate solutions, the convergence speed is increased, a thinking innovation strategy is fused, and the optimal solution is obtained. And exploration and development are further balanced, and the capability of jumping out of local optimum is improved.

The invention relates to the technical field of power system and demand side management, in particular to a time-of-use electricity price optimization method based on electricity consumption data clustering and demand response elastic modeling, and the method comprises the steps: collecting the electricity consumption data of a user, carrying out the preprocessing of the data, and extracting a key index as a clustering feature; the method comprises the following steps of: performing clustering on indexes based on clustering algorithm initialization and a K-means clustering algorithm, dividing users into different groups, constructing a demand response model for each user group, establishing an electricity price demand elastic matrix model, performing modeling in combination with user willingness and psychological response characteristics, and constructing a time-of-use electricity price optimization model based on a clustering layering result. An electricity price scheme is iteratively optimized through the whale algorithm, a time-of-use electricity price strategy is globally searched and optimized through the whale optimization algorithm, the whale optimization algorithm improves the search optimization efficiency by simulating whale surrounding and spiral foraging behaviors, the user behavior is combined with a power grid target, and the user experience is improved. And the time-of-use electricity price is optimally designed from the system level.

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 discloses a multi-machine learning model fusion-based water pump full characteristic curve prediction method, which comprises the following steps of: firstly, constructing a regression pool comprising a plurality of machine learning algorithms, and collecting full characteristic curve data of pumps with different specific speeds; then, an improved whale optimization algorithm is adopted to screen out machine learning algorithms from the regression pool to serve as a basic model combination, a K-fold cross validation training strategy is adopted to carry out K rounds of alternate training on each machine learning algorithm in the basic model combination, and then error correlation verification is carried out on the trained basic model combination; performing secondary training on the verified model combination by using a secondary learning device, calculating the Rperformance of the basic model combination, and screening an optimal basic model combination; and training hyper-parameters of the optimal basic model combination by adopting an improved whale optimization algorithm to obtain a multi-machine learning algorithm fusion prediction model which is used for predicting a to-be-tested pump in a full working condition range. The method is high in prediction precision and saves computing resources.

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.

The invention discloses a cold chain distribution path optimization method based on an improved whale optimization algorithm, and the method is characterized in that the method comprises the following steps: obtaining the initial information of a demand point and a distribution center, and constructing an information matrix; acquiring historical road condition information around the distribution center and each demand point in a traffic management system; constructing a cold-chain logistics distribution path optimization mathematical model; calculating corresponding transportation time; a search proxy address composed of random variables is converted into a vehicle path matrix through a modeling method; calculating oil consumption of the vehicle during normal driving and idling; and classifying the whale population, and solving the model by using a clustering whale optimization algorithm. And vehicle scheduling and path planning are guided. According to the method, factors such as refrigeration, cargo damage, carbon emission, load capacity and traffic conditions are comprehensively considered, a cold-chain low-carbon logistics distribution path optimization model taking the minimum total cost as a target is established, and an improved whale optimization algorithm is provided for optimization solution.

The invention provides a laser forging printing intelligent process optimization method based on an improved whale optimization algorithm, and relates to the technical field of additive manufacturing, and the method comprises the steps: initializing a process parameter solution set of laser forging printing equipment; and based on an improved whale optimization algorithm, taking a pre-trained forging printing prediction model as a fitness function, taking minimization of residual stress, minimization of roughness and maximization of hardness as optimization targets, performing iterative updating on whale positions contained in the process parameter solution set, and stopping until preset conditions are met. Target technological parameters corresponding to the laser forging printing equipment are obtained, and one whale position represents one set of technological parameters; wherein the forging and printing prediction model adopts a Bi-GRU neural network combined with an attention mechanism. According to the method, the prediction accuracy of the performance parameters in the laser forging and printing process can be effectively improved, and the time and cost required for optimizing the process parameters of the laser forging and printing process can be remarkably reduced.

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.

The present invention discloses a mobile robot path planning method, system, robot and storage medium, and relates to the field of path planning technology. The mobile robot path planning method comprises: using a grid method to divide a map into 1×1 grids, using preset color blocks to represent traversable areas or obstacles; establishing a mathematical model; initializing whale optimization algorithm parameters; initializing the population using the Tent chaotic sequence strategy; performing a linear equidistant interpolation strategy on all whale individuals to add new position vectors; calculating fitness; selecting the whale individual with the smallest fitness as the current global optimal individual; updating the whale individual; when the current number of iterations t reaches the maximum number of iterations T, outputting the current global optimal individual; calculating and generating a cubic uniform B-spline curve to complete the path curve construction. The mobile robot path planning method proposed in the present invention has the characteristics of fast running speed, high solution quality, strong optimization ability, etc., and can better solve practical problems.

The present invention discloses a lysozyme fermentation temperature control method based on LSTM-PID. First, a temperature mathematical model of a lysozyme fermenter is established through a mechanism modeling method. Then, a random walk strategy and a Levy flight strategy are used to improve the optimization ability of a whale optimization algorithm. Then, the improved whale optimization algorithm is used to determine the initial stage parameters for the LSTM. Then, the output of the LSTM neural network is set as three K values ​​of a PID controller and the most suitable K value is trained and substituted into the PID controller to obtain control parameters. The control parameters are passed to the fermenter production control system for temperature control. The whale algorithm is optimized using Levy flight and random walk strategies to solve the problem that the whale optimization algorithm is prone to falling into local extreme values, i.e., premature convergence, when facing complex models such as neural network LSTM.

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. The name of the design product: sleeping pillow (whale). 2. The use of the design product: the design product is used for sleeping pillow. 3. The design points of the design product: in the shape of the product, pattern and combination thereof. 4. The picture or photo that best indicates the design points: perspective view.

1. Name of the design product: massage stick (long angle whale). 2. Use of the design product: used with face and body massage. 3. Design points of the design product: in shape. 4. Picture or photo that best shows the design points: perspective view. 5. The bottom view is expressed in other views, and the bottom view is omitted.

1. Name of this design product: Sole (Whale and Sea). 2. Purpose of this design product: used to make shoe soles. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

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.

The invention discloses an unmanned equipment control method, device and equipment based on an improved MPC control model. The method comprises the following steps: constructing an unmanned equipment control model based on the MPC control model; improving a random search strategy, boundary processing and a variation mechanism of the whale optimization algorithm; improving a strategy updating mechanism of a near-end strategy optimization algorithm by using the improved whale optimization algorithm; using the optimized near-end strategy optimization algorithm to improve the prediction time domain of the unmanned equipment control model; and generating a control scheme of the unmanned equipment according to the improved unmanned equipment control model. According to the invention, adaptability and motion control precision of unmanned equipment in a dynamic environment, especially autonomous navigation and task execution capability in a complex scene, can be effectively improved.

The invention discloses a signal processing method, module and control system for a Coriolis flowmeter, and the signal processing method comprises the steps: initializing a group of whale individuals, and the positions of the whale individuals comprise a proportionality coefficient and an integral coefficient which are randomly selected in a preset range; and constructing a target fitness function. And iteratively updating the position of the whale individual based on the whale algorithm by taking the minimum fitness value as a target. And after iteration is completed, selecting the position of the whale individual with the minimum fitness value as an optimal coefficient and outputting the optimal coefficient. Based on the signal processing method, the module and the control system for the Coriolis flowmeter, the vibration amplitude control of the Coriolis flowmeter is effectively optimized, the deviation of the signal is reduced, the accuracy of the signal is improved and the measurement precision of the mass flow rate is further improved by adopting the self-adaptive adjustment and PI control strategy based on the whale algorithm.

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.

The application discloses a kind of based on global-local balanced whale optimization algorithm's unmanned plane path planning method: step 1, initialization;Step 2, obtain cyclic iteration parameter;Step 3, judge P<0.5, is then enter step 4, otherwise enter step 5;Step 4, judge |A|<1, is then search prey, otherwise capture prey, enter step 6;Step 5, judge rand>0.6, is then bubble net attack, otherwise bubble attack enhancement;Step 6, calculate each individual fitness value;If less than leader score, then update;Step 7, trigger mutation operation, fitness value less than the score of current leader is updated, enter step 8;Step 8, obtain new leader position;Step 9, fitness value less than the score of current leader is updated;Otherwise enter step 10;Step 10, output current leader score.The path scheme planned by the application has the lowest average total cost of flight, and shows higher convergence accuracy.

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.