352 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 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 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 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 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 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 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 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 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.

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 fan blade acoustic emission source positioning method based on a whale optimization algorithm. The method comprises the following steps: I, initializing parameters in a whale optimization algorithm; iI, collecting acoustic emission signal data, preprocessing the acoustic emission signal data, and calculating the time difference between an acoustic emission source and each sensor by extracting the arrival time of an acoustic emission signal on the plurality of sensors; iII, adopting a minimum time difference error function as a target function; iV, setting the initial position of the whale in the whale optimization algorithm, namely the initial imaginary position of the acoustic emission source; v, updating the position of the whale through a whale optimization algorithm; vI, calculating the fitness value of each whale, comparing the fitness values of each whale through a target function, and selecting the optimal whale position as output; vII, judging whether the current optimal whale position meets a preset condition or not, and outputting the whale position if the current optimal whale position meets the preset condition; and if not, returning to the step V, and judging again until the optimal solution is output.

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 present invention belongs to the field of power distribution technology and relates to a method for identifying electricity theft users in low-voltage power distribution areas based on an improved whale optimization algorithm, which includes the following steps: calculating the calculated value of the total power consumption of the power distribution area within a time period; establishing an objective function by using the calculated value of the total power consumption of the power distribution area and the measured value of the total power consumption of the power distribution area; solving the objective function through the improved whale optimization algorithm; and analyzing the power loss rates of each user obtained by the solution by using a distance-based outlier detection algorithm, and outputting the users suspected of electricity theft. The present invention improves the traditional whale algorithm by introducing a dynamically changing convergence factor, a random position update coefficient and a directional search behavior, then uses the improved whale optimization algorithm to estimate the power loss rates of each user in the power distribution area, and finally identifies the users with abnormal power loss rates through a distance-based outlier detection algorithm.

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 relates to the technical field of debris flow prediction, in particular to a debris flow prediction method based on a substance start-propagation process, which comprises the following steps: collecting a debris flow event and a rainfall event; acquiring geographical background conditions and rainfall excitation conditions according to the debris flow event and the rainfall event; constructing an index system for predicting debris flow according to geographical background conditions and rainfall excitation conditions; the indexes in the index system are input into a debris flow prediction model, the time-space probability value of debris flow occurrence is output, and the debris flow prediction model is constructed based on a whale optimization algorithm and a machine learning model. Triggering conditions and geographical background features are integrated through a hybrid machine learning model, and the time-space possibility of debris flow occurrence based on the hourly watershed is evaluated from the material triggering-propagation angle of debris flow. The method is implemented in a typical terrain sudden change zone, provides technical guidance for early warning and prediction, and serves prevention and disaster reduction of debris flow in a mountainous area.

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.

The invention relates to the technical field of computer networks, in particular to an edge service dynamic migration method based on an improved whale optimization algorithm. According to the method, edge service dynamic migration is carried out for multiple users, the service dynamic migration is carried out on the basis of predicting user positions, service migration energy consumption reduction and service migration time delay reduction are taken as optimization objectives, and the method is more in line with a real scene. The whale optimization algorithm is improved from three aspects of dynamic self-adaptive iteration termination judgment, self-adaptive dynamic fluctuation nonlinear convergence and taboo search: firstly, a dynamic self-adaptive iteration termination judgment method is put forward, the number of iterations is associated with a fitness value, and excessive iteration caused when the algorithm does not reach a target value is avoided; then, a self-adaptive dynamic fluctuation nonlinear convergence method is provided, the algorithm is helped to be prevented from falling into a local optimal solution, and the algorithm solving precision is improved; and finally, a tabu search method is introduced, so that the convergence speed of the algorithm and the development capability of a better solution are improved.

The invention discloses a shale high-quality reservoir prediction method and system based on a whale optimization algorithm, and solves the technical problems of strong hyper-parameter dependence, easy falling into local optimum, insufficient noise immunity and the like in a traditional wave impedance inversion method by combining a chaotic mapping strategy and a global optimization mechanism of the whale optimization algorithm. The method specifically comprises the steps that a nonlinear objective function driven by seismic data is established, chaotic mapping is adopted to optimize initial population distribution, wave impedance parameters are dynamically updated by simulating a self-adaptive search strategy of whale preying behaviors, and quantitative prediction of reservoir porosity is achieved in combination with rock physical analysis. According to the method, the uncertainty of manual adjustment of hyper-parameters is avoided, the recognition precision and inversion efficiency of the transverse boundary of the complex reservoir are remarkably improved, the method is particularly suitable for high-resolution exploration of low-porosity and low-permeability shale and compact sandstone reservoirs, and reliable technical support is provided for oil and gas reservoir fine description and well location deployment.

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.