438 results about "Whale" patented technology

Disclosed is a motor fuzzy PID parameter tuning method based on an improved whale algorithm. The method comprises: building a brushless direct-current motor speed control system model, and using a fuzzy PID controller to perform motor speed control. A conventional whale algorithm is optimized by using a chaotic convergence factor, a fractional order, and Levy flights, so as to obtain an improved whale algorithm. Secondly, the overshoot of the system is used as a component of an ITAE performance index to obtain an improved ITAE performance index, and the improved ITAE performance index is used as a fitness function for the improved whale algorithm. Finally, the improved whale algorithm is used to optimize input and output membership functions of a fuzzy controller, so as to obtain optimal ΔKp, ΔKi, and ΔKd values, and Kp, Ki, and Kd parameters of a PID controller are tuned to implement motor speed control. The present invention addresses the difficulty of tuning parameters of conventional PID controllers and solves the problem of low precision of motor speed control, has the advantages of high anti-interference capability, little overshoot, and short adjustment time, and improves the dynamic characteristics and robustness of the controllers.

The invention relates to a GNSS-based water conservancy facility deformation monitoring and early warning method, system and device and a medium, and the method comprises the steps: obtaining GNSS monitoring network data of a target part of a water conservancy facility, and carrying out the real-time dynamic differential processing, and obtaining corresponding three-dimensional coordinate data; acquiring environmental factor data, and performing multi-source feature fusion on the environmental factor data and the three-dimensional coordinate data to obtain a corresponding deformation feature vector; performing prediction analysis on the deformation feature vector according to a preset whale optimization algorithm to obtain a corresponding deformation trend prediction result; setting a multi-level early warning threshold according to the deformation feature vector, and performing early warning analysis on a deformation trend prediction result to obtain a corresponding initial early warning level; and obtaining standard deformation characteristic information of the deformation rate and the acceleration of the water conservancy facility, and performing comprehensive evaluation on the standard deformation characteristic information based on a deformation trend prediction result to obtain a corresponding early warning index. Potential risks can be found and processed in time, and the possibility of hydraulic engineering accidents is reduced.

The invention relates to a four-axis unmanned aerial vehicle PID parameter setting method based on a whale optimization algorithm, and belongs to the technical field of unmanned aerial vehicle control. The method aims at solving the problems that in the traditional PID parameter setting process, experience is relied on, time is consumed, and optimal parameters are difficult to obtain. According to the method, the whale optimization algorithm is introduced, and the characteristics of high global search capability and high convergence speed are utilized, so that the parameters of the four-axis unmanned aerial vehicle PID controller are automatically optimized. The method comprises the steps that firstly, a kinetic model of the four-axis unmanned aerial vehicle is established, a control target of a PID controller is determined, PID parameters serve as optimization variables, and a fitness function is defined to evaluate control performance; then, performing global search in a parameter space by using a whale optimization algorithm, and gradually optimizing PID parameters by simulating a whale predation behavior; the optimized PID parameters are applied to a flight control system of the four-axis unmanned aerial vehicle, and stable attitude, height and position control is achieved. The method has the advantages that PID parameters are automatically set through the whale optimization algorithm, the low efficiency and subjectivity of a traditional trial and error method are avoided, and the parameter setting efficiency and precision are remarkably improved; meanwhile, for an unmanned aerial vehicle control system with high nonlinearity and complexity, the WOA can adjust parameters based on actual feedback under the condition that accurate modeling of the system is not needed, and dependence on system modeling is reduced.

The invention discloses a wire harness product quality prediction system based on big data. An initial multi-source data set is acquired; core features in the initial multi-source data set are extracted based on the crimping height, the insulation resistance value and the environment temperature and humidity of the wire harness quality, high-weight features in the core features are screened through a PCA principal component analysis method, and wire harness feature data are obtained; processing time series data based on a long-short-term memory network, performing feature selection by using an extreme gradient boosting tree, and establishing a hybrid prediction model; using an improved IWOA whale optimization algorithm to optimize hyper-parameters of the hybrid prediction model; and inputting the wire harness characteristic data into the target hybrid prediction model for prediction, outputting a quality risk grade index, and if the quality risk grade index exceeds a set threshold, triggering an early warning signal. The limitation of traditional single data or simple model prediction is changed, so that quality prediction better fits an actual production scene, and the accuracy and reliability of prediction are remarkably improved.

The invention discloses an improved vibration compensation method suitable for an atom interference gravimeter. The method comprises the following steps: acquiring an atom interference signal and a vibration voltage signal output by a seismometer; initializing population parameters of the improved whale optimization algorithm, wherein the population parameters comprise a search space of a population size, a maximum number of iterations, a gain coefficient K and a delay coefficient tau; the method comprises the following steps: generating an initial whale population by adopting a method of combining Logistic chaotic mapping and Latin hypercube sampling LHS; calculating a phase shift caused by vibration noise and an atomic interference phase delta phi, and calculating the fitness of the whale individual by taking a root-mean-square error of compensated interference fringes as a target function; iteratively updating the population position through an improved whale optimization algorithm, outputting an optimal gain coefficient and an optimal delay coefficient, and obtaining an atomic interference signal after vibration compensation; according to the atomic interference gravimeter vibration compensation method, the global search capability, the local development precision and the stability of a vibration compensation algorithm are improved.

The invention discloses a gas prediction method based on the combination of a whale optimization algorithm and a bidirectional long-short term memory network, and belongs to the technical field of gas prediction of iron and steel enterprises. The method comprises the following steps: S1, reading real-time gas data from an online database, preprocessing the data, and constructing a data set required by an experiment; s2, a training set and a test set are divided, the training set is used for determining model parameters, and the test set is used for judging the model effect; s3, constructing a BiLSTM gas prediction model; s4, parameters of the model obtained in the step S4 are optimized; and S5, loading the model of the optimal parameter combination for training and prediction, and storing a predicted value in a database. According to the method, the global optimal search strategy of the whale optimization algorithm (WOA) and the performance of the bidirectional long-short term memory network (BiLSTM) on time sequence data modeling are fused, and the accuracy and efficiency of gas prediction can be remarkably improved.

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 an active power distribution network multi-time scale collaborative optimization method based on an improved whale algorithm. The method comprises the following steps: constructing an active power distribution network multi-time active and reactive collaborative optimization model; a standard whale optimization algorithm is improved; the whale optimization algorithm improved in the step 2 is used for solving the multi-time active and reactive collaborative optimization model of the active power distribution network in the step 1, and a model real-time optimization scheme is obtained. According to the active power distribution network multi-time scale collaborative optimization method based on the improved whale algorithm, the problem of voltage out-of-limit caused by high-proportion photovoltaic access to the power distribution network can be solved.

The invention relates to the technical field of water quality prediction, in particular to a water quality time sequence prediction method and system based on a fusion whale optimization algorithm. The water quality time sequence prediction method comprises the following steps: collecting water quality monitoring data and preprocessing the water quality monitoring data to obtain water quality time sequence data; carrying out noise reduction processing on the water quality time sequence data in a wavelet denoising mode; parameters of the EEMD are optimized through WOA, water quality time sequence data are decomposed through the EEMD, and a plurality of IMFs are obtained; and by taking the target water quality index as a prediction index, predicting each IMF through the CNN-LSTM deep learning model, and obtaining a prediction result. According to the method, the key parameters in the EEMD are automatically optimized by adopting the WOA, the limitation of manually selecting parameters in a traditional method is overcome, and the prediction precision and robustness are improved. The optimized EEMD not only can effectively reduce noise in water quality data, but also realizes multi-scale signal analysis.

The invention discloses a case interrogation intelligent auxiliary device and method. The method comprises the steps that S1, sound recording data, video recording data and on-site manual recording texts in the case interrogation process are collected; s2, generating synchronous interrogation text data; s3, generating structured case record data; s4, analyzing the collected video data in real time, and generating biological characteristic data related to the interrogation object; s5, generating comprehensive case record data containing semantic information and emotional states; s6, inputting the comprehensive case record data into a case comparison module, optimizing a case comparison path by using a whale optimization algorithm, and identifying historical case records related to the current case; s7, generating case comparison result data related to the current case; and S8, generating interrogation intelligent prompt data. The efficiency and accuracy of the interrogation auxiliary system are remarkably improved, and key technical support is provided for case deep analysis and interrogation strategy optimization.

The invention discloses a water quality evaluation model construction method based on a fruit fly-whale collaborative optimization algorithm, and aims to solve the problems that water resource pollution is serious, and a traditional water quality evaluation method is long in time consumption, high in cost and difficult to monitor in real time. Key water quality parameter characteristics are determined through information gain, and a sample data set is constructed and divided. Thirdly, establishing a support vector machine model, and setting a penalty coefficient and a kernel function parameter as a to-be-optimized combination; and searching an optimal solution globally by using a fruit fly optimization algorithm, taking the optimal solution as an initial solution of the whale algorithm, and further optimizing to obtain a parameter combination optimal solution. And configuring a support vector machine by using the optimal solution to construct an original model, and training and testing to obtain a final water quality evaluation model. The model is high in accuracy, can quickly process a large number of samples, is easy to integrate to an existing monitoring platform, and provides an efficient and accurate new way for water quality evaluation.

The invention discloses a hydroelectric generating set adaptive PID controller design method based on a whale optimization algorithm and a BP neural network. Comprising the following steps: S1, constructing a numerical simulation model of the water turbine regulating system; s2, working conditions are divided according to the water head and the guide vane opening degree; s3, constructing a comprehensive objective function of the whale optimization algorithm; s4, obtaining an optimal PID parameter under a typical working condition based on a whale optimization algorithm; s5, determining the number of hidden layer neurons of the controller neural network model; and S6, combining the optimal PID parameter data set and a BP neural network to construct a controller neural network model taking the water head and the guide vane opening as input and KP or KI as output. According to the hydroelectric generating set PID parameter optimization method considering the runner stress and the blade pressure, the influence of the PID parameters on the runner stress and the blade pressure can be fully considered, and safe and stable operation of the hydroelectric generating set is effectively guaranteed.

The invention belongs to the technical field of mobile crowd sensing, and particularly relates to a task allocation method based on a whale optimization algorithm framework, and the method comprises the steps: constructing an immune genetic whale algorithm framework based on the whale optimization algorithm framework; designing a whale individual structure representing a task allocation strategy; defining a whale individual fitness evaluation function; initializing a first generation whale individual population; designing a crossover operator, a mutation operator and an immune operator based on an immune genetic method; designing a repairing operator for repairing the invalid whale individual; specific operations such as population initialization, crossover, variation, immunization and repair are integrated into an immune genetic whale algorithm framework, so that the design of a task allocation algorithm is completed. According to the method, the local and global optimization capabilities of the algorithm are considered, so that the platform can generate a task allocation strategy capable of maximizing profit at a relatively high speed, and the task allocation rate and the activeness of participants are improved.

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 discloses a photovoltaic bearing capacity improving method and system considering seasonal network optimization. The method comprises the following steps: acquiring a plurality of seasonal scene sets; constructing a power distribution network seasonal network reconstruction optimization model for photovoltaic bearing capacity improvement; for each season, based on the improved whale algorithm, generating a multi-group power distribution network switch switching-on and switching-off scheme in each season; solving the reconstruction optimization model in the target season, and determining respective total target function values of the multiple sets of power grid switch on-off schemes in the target season; and based on an improved whale algorithm, obtaining a current optimal population, determining a switch on-off scheme enabling an objective function value to be optimal in a target season, obtaining a network reconstruction optimization scheme of the power distribution network in the target season, and sequentially solving until network reconstruction optimization schemes of all seasons are obtained. According to the invention, the technical problem of low photovoltaic bearing capacity in a power distribution network system is solved, and the photovoltaic bearing capacity can be improved through a seasonal network optimization and reconstruction means in the operation stage of the power distribution network.

The invention relates to the technical field of new energy station power control, and discloses a wind and light storage active power control method and system based on a whale optimization algorithm, and the method comprises the steps: obtaining the equipment parameters of all wind and light storage equipment, and generating the dynamic priority of all equipment in combination with the real-time operation state; the initial whale population is generated as a plurality of power distribution schemes, each whale represents a group of power distribution coefficients, and the sum of the distribution coefficients is 1; constructing a fitness function for calculating a fitness value based on a weighted error term determined by a dynamic priority and a device and system dual penalty term; executing a main optimization loop, dynamically adjusting a search strategy through a convergence factor, updating the whale position by combining a bubble attack strategy and a surrounding strategy, generating a distribution scheme meeting a preset constraint condition, normalizing the optimal whale position, and outputting a power distribution coefficient of each device; and according to the distribution coefficient and the total power instruction, generating a specific power instruction value of each device for controlling the active power of each device, so that dynamic adjustment can be performed in real time according to the operation state of the device and the demand of the system, stable operation of the system and effective output of energy are ensured, and economic benefits are improved.

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 belongs to the technical field of passive positioning, and particularly relates to a multi-station phase difference passive positioning method based on an improved whale optimization algorithm. Firstly, a passive phase difference positioning model is established; then, according to the positioning model, establishing a target function based on maximum likelihood estimation; and finally, improving a whale optimization algorithm, improving a probability parameter threshold into a dynamic probability threshold, improving a convergence factor to be adaptively adjusted along with fitness values, solving a target function by using the improved whale optimization algorithm, taking a whale individual position as a target source position, and calculating the fitness value of each whale according to the target function. And adjusting the position of the whale individual according to the fitness value after each iteration, and after the set iteration times are reached, the position of the target source is the position of the whale individual with the minimum fitness value. According to the method, the problem of solving the target source position is converted into the optimization problem with the target source position as a variable, the calculation complexity is reduced while the positioning precision is improved, and the manufacturing cost is saved.

The invention provides a virtual power plant optimal scheduling method based on a whale algorithm. The method comprises the steps that an electric heating comprehensive system economic scheduling target with the lowest comprehensive operation cost as the target is determined; constructing constraint conditions of the virtual power plant optimization scheduling model; the whale algorithm is used to solve the economic dispatch target; and analyzing a model result, and evaluating the operation benefit of the virtual power plant. In order to solve the problems that virtual power plant optimization scheduling is high in calculation complexity, low in convergence speed and prone to falling into a local optimal solution, the whale algorithm scheme is adopted, the convergence speed and precision of the algorithm are improved through natural predation behaviors simulated by the whale algorithm and optimization of contraction factors and expansion factors, the calculation time is effectively shortened, and the optimal scheduling efficiency is improved. And the adaptability and the flexibility of the algorithm are enhanced through the shrinkage factor and the expansion factor which can be dynamically adjusted, so that the effects of optimizing the operation cost of the power system, improving the resource configuration efficiency and enhancing the stability of the power grid are achieved, and the development of sustainable energy is promoted.

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 a rice yield estimation method for optimizing an RF model through an improved whale optimization algorithm, and belongs to the technical field of rice yield estimation. The problem that in the prior art, a traditional rice yield prediction method is low in prediction result precision is solved. The method comprises the following steps: acquiring multispectral image data of rice in different growth periods by using an unmanned aerial vehicle, and measuring and calculating rice growth parameters and average yield after a rice mature period; performing normalization preprocessing on the multispectral image data, and constructing a sample data set; the whale optimization algorithm is improved, the improved whale optimization algorithm is constructed to optimize the RF model, model parameters are set, a WOA-RF model is obtained, the sample data set is input into the WOA-RF model for training and screening to obtain an optimal rice yield estimation model, and the rice yield of the target area is estimated. According to the method, the unmanned aerial vehicle remote sensing image and the rice growth parameters are combined, the rice yield estimation precision is effectively improved, and the method can be applied to rice yield prediction.

The invention discloses a UAV and USV network resource allocation system and method based on an improved whale algorithm. A constraint condition construction module is used for taking a correlation coefficient between a USV and a UAV, a service duration of providing service for the UAV by the UAV and a processor calculation frequency of the USV as constraint conditions; the optimization problem construction module is used for constructing optimization problems of minimizing USV task processing energy consumption and maximizing USV data processing amount; and the resource allocation module is used for iteratively solving the optimization problem by using the improved whale algorithm to obtain resource allocation between the UAV and the USV network. According to the method, a traditional whale optimization algorithm is fused with a novel coding mode and crossover mutation operation, the problems of poor feasible solution performance, poor convergence and high time complexity in existing resource allocation are solved, and the QoS requirement of intensive tasks is met.

A geofenced autonomous aquatic drone for repelling sharks from a shoreline. The drone employs a buoyant housing resembling a portion of a predator such as an orca whale. A battery positioned within the drone is recharged through a floating inductive charging station. A propulsion system controlled a microprocessor receives location information via DGPS for providing a geofence around an area to be patrolled. A sonar device is constructed and arranged to detect sharks. The drone travels within the geofence area, monitored by the DGPS receiver and a solution of shark repellant is dispensed.

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 water pollution detection method based on an improved whale optimization algorithm and bagging integration, and the method comprises the steps: building an initial BP neural network model through measuring the multi-dimensional features of a water sample; performing iterative optimization on model parameters by using an improved whale optimization algorithm, improving global search and local optimization ability by combining dynamic step length adjustment and a Levy flight disturbance mechanism, and screening an optimal parameter group; based on bagged ensemble learning, multiple subsets are extracted from a training set in a replacement mode, part of features are shielded randomly, and an IWOA-BP neural network sub-model is trained through the optimized parameter set; and finally, a sub-model prediction result is fused through a quantile integration strategy. The method has rapid convergence, high robustness and strong generalization ability in the complex water quality environment, and can realize high-precision detection of the pollutant concentration in the complex water quality environment.

The invention relates to the technical field of electromagnetic release structure optimization, and discloses an electromagnetic release optimization method based on a whale algorithm, and the method comprises the steps: solving an optimal structure parameter through the whale algorithm, taking a position with the maximum counterforce as a design point, and taking the maximum electromagnetic suction value of the point as an optimization target, according to the national standard requirement, the condition that tripping is not carried out when the test current is equal to 80% of the short circuit setting current serves as the constraint condition, and mathematical modeling is carried out on the relation between the structure parameters and the electromagnetic attraction through SPSS analysis software. And then the whale algorithm and the optimization result of the orthogonal test are compared and analyzed, so that the rapid breaking of the electromagnetic release is better realized, and the short-circuit protection reliability of the electromagnetic release is improved.

The invention relates to the technical field of welding path planning, in particular to a welding robot path planning method and system based on an improved whale optimization algorithm, and the method comprises the following steps: carrying out the path planning modeling of a welding robot on a front cover of an automobile engine, and obtaining a welding path planning mathematical model; the whale optimization algorithm WOA is improved by integrating multiple strategies to obtain an optimal improved whale optimization algorithm GSWOA, and the performance of the optimal improved whale optimization algorithm GSWOA is verified through four test functions; on the welding path planning model, path planning is conducted through the optimal improved whale optimization algorithm GSWOA, and the optimal welding path on the automobile engine front cover is obtained. The whale optimization algorithm WOA is improved through multiple strategies, the improvement of the multiple strategies is integrated through a Stacking framework, the integration of the advantages of all the strategies is achieved, the influence of failure of a single strategy can be avoided, and the robustness of the algorithm is improved.

The invention discloses a WOA-VMD-YOLOv8-based rolling bearing fault diagnosis method. The method comprises the steps of data acquisition, data preprocessing, and dividing a fault sample data set into a training set, a test set and a verification set in proportion; a WOA-VMD-YOLOv8 model is built, and the The model comprises a whale optimization algorithm WOA, VMD decomposition and a YOLOv8 model. A WOA-VMD-YOLOv8 model is trained, and a rolling bearing fault diagnosis model based on the WOA-VMD-YOLOv8 is obtained; a sound signal of a to-be-diagnosed rolling bearing is input into the WOA-VMD-YOLOv8-based rolling bearing fault diagnosis model, and then a fault type can be output. According to the rolling bearing fault diagnosis method, the WOA is used for optimizing VMD decomposition, the IMF component is selected by introducing the related kurtosis value and the weighted energy entropy, and the YOLOv8 model is combined, so that the efficiency and accuracy of rolling bearing fault diagnosis are improved.