291results about How to "Improve global search performance" patented technology

The invention discloses a multi-unmanned aerial vehicle maritime search and rescue path planning method based on multi-target particle swarm optimization. The method comprises the steps that S1, an area to be searched and rescued is converted into a two-dimensional search and rescue area; s2, performing grid decomposition on the two-dimensional search and rescue area; s3, obtaining a probability map through a Gaussian mixture model; s4, establishing an unmanned aerial vehicle search and rescue multi-target collaborative optimization function; s5, solving the function based on a hybrid particle swarm optimization algorithm to obtain an optimal solution; and generating a specific search and rescue path of the unmanned aerial vehicle according to the solved optimal solution. According to the method, an unmanned aerial vehicle search and rescue multi-target collaborative optimization function is established, the unmanned aerial vehicle is guided to execute the search and rescue task by taking resource balanced allocation, maximum path safety, preferential coverage of a high-risk area and minimum energy consumption as targets, task allocation, path planning and energy management are improved, multi-target optimization of multi-unmanned aerial vehicle search and rescue path planning is realized, and the search and rescue efficiency is improved. An accurate flight path is formulated for each unmanned aerial vehicle, comprehensive coverage of search and rescue tasks is ensured, and the search and rescue efficiency and success rate are remarkably improved.

The invention discloses an air conditioner energy-saving control method and system based on multi-objective optimization, a medium and a product, and relates to the field of data processing. The method comprises the steps that optimization targets for conducting energy-saving control on the air conditioner and corresponding constraint conditions are determined, and the optimization targets comprise target air conditioner energy consumption and target comfort degree; acquiring current environment data of the air conditioner; the equipment operation parameter combination of the air conditioner is used as particles in the multi-target particle swarm algorithm; determining an optimal solution through a multi-target particle swarm algorithm; the air conditioner is controlled according to the parameter combination corresponding to the optimal solution; the actual air conditioner energy consumption and the actual comfort degree are obtained; based on the target air conditioner energy consumption, the target comfort level, the actual air conditioner energy consumption and the actual comfort level, the equipment operation parameters of the air conditioner are adjusted through a PID control algorithm, and final equipment operation parameters are obtained; and controlling the air conditioner to work according to the final equipment operation parameters. According to the method, both the energy-saving effect and the comfort level of the air conditioner are balanced.

The invention relates to the field of particle size distribution measurement, in particular to a particle size distribution measurement method and system based on machine learning and a light scattering method.The method comprises the steps that a to-be-measured particle sample is irradiated through a light source, and meanwhile light scattering intensity data of different scattering angles and different wavelengths are collected through a multi-angle scattered light detector; constructing a multi-modal deep learning model, and forming an integrated learning architecture in combination with a support vector machine model and a random forest model; inputting the preprocessed multi-wavelength light scattering intensity data into the trained integrated learning architecture to obtain a preliminary particle size distribution result of the to-be-detected particle sample; based on the improved Mie scattering theory, a high-precision theoretical model considering the particle shape, the refractive index, the surface roughness and the internal structure is constructed; an improved Bayesian optimization algorithm is adopted, and the initial particle size distribution result is optimized and adjusted by minimizing the error between the theoretical light scattering intensity and the actually collected light scattering intensity, so that a corrected particle size distribution result is obtained.

The invention provides an additive manufacturing residual stress optimization method and system, and the method comprises the steps: searching a global optimal solution enabling the residual stress of an additive manufacturing part to be minimum from a technological parameter space through the combination of multi-physics field numerical simulation and an intelligent optimization algorithm; the multi-physics field numerical simulation comprises the steps of sampling from a process parameter space by using an optimization algorithm to obtain process parameters, inputting the sampled process parameters into a finite element model, performing thermal analysis on the process parameters based on the finite element model, and obtaining transient thermal distribution through thermal analysis. And performing structural analysis by using transient heat distribution to obtain residual stress under the current process parameters, and transmitting the obtained residual stress to an intelligent optimization algorithm for realizing process parameter optimization. The method has the advantages of high precision, low cost and the like, and high-precision collaborative optimization of multiple types of process parameters can be realized.

The invention relates to the technical field of path planning, and discloses a laser fuse additive manufacturing thermal management path planning closed-loop regulation and control system, which comprises a data acquisition module, a data processing module, a temperature distribution prediction module and a path planning regulation and control module, acquiring temperature distribution characteristic data and first characteristic data; the data processing module is used for preprocessing the temperature distribution feature data and the first feature data to obtain a regional feature sequence; the temperature distribution prediction module is used for constructing a temperature distribution prediction model according to the regional feature sequence; and the path planning regulation and control module is used for generating a regulation and control scheme in combination with the improved parrot optimization algorithm and the temperature distribution prediction model. Through combination of temperature prediction, path optimization, multi-dimensional feature extraction and an improved optimization algorithm, global optimization and dynamic regulation and control of the processing process are realized.

The invention provides a valve leakage diagnosis method and system based on an improved Hereum algorithm optimized SVM (Support Vector Machine), and relates to the technical field of valve leakage diagnosis. Decomposing the collected original signal to obtain a limited number of IMF components; calculating a fuzzy entropy, and constructing a feature vector; taking the feature vectors as data samples, and dividing the data samples into a training set, a verification set and a test set; using the training set to train the SVM model, and according to the classification error rate of the verification set on the trained SVM model, performing iterative optimization on the parameters of the SVM model based on an improved He-Ma algorithm to obtain an optimized SVM model; inputting the test set into the optimized SVM model to obtain a valve leakage diagnosis model; and acquiring a new acoustic emission signal, and inputting the new acoustic emission signal into the valve leakage diagnosis model to obtain a diagnosis result. The method not only can improve the classification accuracy and generalization ability of the diagnosis model, but also can enhance the stability of the model under different working conditions and environmental noise.

The invention discloses an unmanned aerial vehicle path planning method based on global search and meta-heuristic optimization, and the method comprises the steps: firstly building an unmanned aerial vehicle path planning model under complex constraints, determining a fitness function and a constraint condition thereof, setting algorithm initialization parameters, carrying out the chaotic mapping of an initialization population position, and carrying out the optimization of the algorithm; and performing loop iteration solution by using a meta-heuristic optimization algorithm, outputting a global optimal path, finally realizing the global optimal path on the unmanned aerial vehicle, and calculating a flight distance corresponding to the optimal path to complete unmanned aerial vehicle path planning. According to the method, a meta-heuristic algorithm, namely a crown porcupine algorithm, is combined with tabu search, the global search capability is enhanced, and meanwhile, population initialization is optimized in combination with chaotic mapping, so that the randomness and unpredictability are increased, the algorithm can converge more quickly, and the robustness of the algorithm is improved. The method has obvious advantages in solving the problem of path planning with complex environment and complex constraint, and solves the problem of path planning of the unmanned aerial vehicle in the three-dimensional complex mountain environment.

The invention discloses a charging and discharging control system and electronic equipment. The method comprises the following steps: S1, constructing a charging pile multi-target optimization model; s2, initializing a chaotic particle swarm optimization algorithm; s3, each particle in the particle swarm is optimized according to an objective function in the charging pile multi-objective optimization model; s4, carrying out loop iteration until the chaotic particle swarm optimization algorithm reaches a convergence condition; s5, generating a prediction result of the operating environment of the charging pile; s6, updating the current optimal particle and the global optimal particle; and S7, generating a multi-target charging and discharging optimization control strategy of the charging pile according to the optimized global optimal particle, applying the optimization control strategy to a charging pile charging and discharging control execution module, and executing multi-target charging and discharging control of the charging pile. According to the method, the globally optimal solution in a complex scene can be quickly solved, and the efficient operation requirement of the electric vehicle charging pile in a large-scale deployment scene is met.

The invention belongs to the field of intelligent construction, and discloses a deep foundation pit displacement prediction method combining finite element and data driving, which comprises the following steps: calculating a theoretical elastic modulus according to an investigation report, and constructing a sample space by adopting a Latin hypercube sampling method and an orthogonal experiment design method; a proper constitutive model is selected, and corresponding lateral displacement is calculated through finite elements; dimensionality reduction processing is carried out on the data set based on global sensitivity analysis of variance so as to accelerate convergence during subsequent inversion; training an SKRR-LSBOOST model through an ensemble learning method to replace a traditional model; combining an improved Hemma optimization algorithm with a substitution model, and applying the combined model to parameter inversion; and performing forward analysis based on the finite element model defined by the inversion parameters to predict the deformation of the deep foundation pit in the next excavation stage. According to the method, the advantages of numerical simulation and data driving are fully exerted, the calculation cost is reduced according to the rock and soil parameters, higher prediction precision is achieved, and the accuracy of foundation pit deformation prediction is improved.

The invention discloses a packaging tin material proportioning optimization method based on a particle swarm optimization algorithm, and the method comprises the following steps: initializing a particle swarm, dynamically adjusting the positions and speeds of particles through a fuzzy logic control system, and generating an initial material proportioning scheme; a fitness function is constructed based on a non-Euclidean geometric method, multiple indexes such as strength, toughness, corrosion resistance and cost are comprehensively considered, and fitness values of all the schemes are calculated; gradually optimizing a material proportioning scheme by using a biologically inspired particle swarm optimization algorithm; a dynamic clustering mechanism is introduced, and different material matching schemes are analyzed and optimized; dynamically switching between global search and local search through a hybrid search strategy, and further optimizing a selected scheme; and finally, outputting an optimized optimal material proportioning scheme, and evaluating the reliability of the optimal material proportioning scheme through experimental verification and simulation. According to the invention, the optimization efficiency and performance of the packaging tin material proportioning are improved, and the method is suitable for the requirements of production automation and high efficiency.

The invention relates to the field of robot target path planning, and discloses a multi-target path planning method and device based on an improved mantis optimization algorithm, and the method comprises the steps: improving the mantis optimization algorithm, and building a grid environment map and a multi-target fitness function, an obstacle avoidance function and a smooth path function corresponding to path planning; the population position optimization corresponds to path nodes in the grid environment map, iteration is performed on all individuals of the population, and fitness values are formed; calculating comprehensive fitness by using a multi-target fitness function to obtain an updated global optimal position and a corresponding optimal fitness value; completing path planning of a multi-target fitness function; and observing the convergence curve graph and the shortest path graph, and verifying the feasibility of multi-target path planning. According to the method, the convergence speed of the algorithm is improved, the global and local search capabilities are balanced, the shortest path length is effectively reduced, path redundant nodes and path inflection points are reduced, and the path quality and efficiency are improved.

The invention discloses a big data privacy aggregation encryption method based on polymorphic fuzzy logic. The method comprises the following steps: S1, generating a to-be-encrypted data set containing privacy sensitive information; s2, constructing a fuzzy logic rule base based on polymorphic fuzzy logic; s3, generating a dynamically optimized fuzzy logic rule; s4, forming a fuzzy data set; s5, forming an optimized aggregated data block; s6, generating an encrypted data block, and generating index information corresponding to the encrypted data block; and S7, storing the encrypted data block and the index information to a distributed storage system, and dynamically controlling the access of the distributed storage system based on a data access permission rule, so that access users with different permissions can only obtain data matched with the permissions in the encrypted data block. According to the method, the privacy protection strength and efficiency are effectively improved, and a safer and more reliable solution is provided for a big data privacy protection scene.

The invention relates to the field of PID control, and provides a tread calender rotating speed PID control method and system, a storage medium and equipment. The tread calender rotating speed PID control method comprises the following steps: processing an initial solution space of a plurality of to-be-optimized parameters based on Logistic-Tent chaotic mapping and dynamic reverse learning to obtain an initial population matrix of a Sabot swarm algorithm; according to the evaluation function, calculating the initial fitness of the soloat group individuals, and updating the global optimal position of the soloat individual with the optimal fitness to the current optimal position; executing a salmons swarm algorithm, and searching the to-be-optimized parameters by simulating a search behavior or an attack behavior of a salmons; in the process of executing the sodat swarm algorithm, if it is judged that the algorithm enters a development stagnation state, executing a transverse crossing strategy and a longitudinal crossing strategy to enable the algorithm to get rid of the development stagnation state; and when a set termination condition is satisfied, outputting a solution corresponding to the current optimal position, and controlling the direct current brushless motor. The method can improve the response speed.

The invention discloses a robot optimal path planning method based on an improved angle skid optimization algorithm, and the method comprises the steps: designing a single-target optimization problem under a constraint condition based on the angle skid optimization algorithm, setting a target function as the shortest distance between a starting point and an end point, and determining the constraint condition; a good point set strategy is adopted to replace random node initialization distribution, and an adaptive dynamic weight, a dynamic weight, an adaptive coefficient, a Brownian motion random disturbance parameter and a random walk and adaptive t distribution disturbance strategy of step length adjustment are introduced. A hiding behavior, a blood jetting behavior, an escape behavior, a skin blackening and brightening behavior and a melanocyte stimulating hormone (MSH) rate formula of the algorithm are optimized respectively, and a planned path is optimized through the improved HLOA algorithm. Compared with the prior art, the method has the advantages that the convergence speed, the optimization precision and the global search capability of the angle exid optimization algorithm in robot path planning are improved, and the method is suitable for optimal path planning in a complex environment.

The invention discloses an unmanned aerial vehicle three-dimensional path planning method based on an improved dung beetle algorithm, and relates to the technical field of unmanned aerial vehicle path planning. According to the technical scheme, an unmanned aerial vehicle flight environment is modeled; establishing constraint conditions; establishing a target function; and performing unmanned aerial vehicle global path planning by adopting an improved dung beetle optimization algorithm. The method has the beneficial effects that initialization is carried out by utilizing a good point set strategy, so that the population diversity is improved; an improved sine algorithm is combined with a variation strategy of Levy flight and Brownian motion to be embedded into position updating of dung beetles, so that the search range is expanded, and the global optimization ability of the algorithm is improved; a simplex method and a self-adaptive reverse learning strategy are added for disturbance, and the algorithm is helped to jump out of a local optimal solution; the efficiency and accuracy of unmanned aerial vehicle path planning are remarkably improved, the robustness and adaptability of the algorithm are enhanced, the flight path of the unmanned aerial vehicle is optimized, and further development of the unmanned aerial vehicle technology is promoted.

The invention discloses a robot path planning method based on a multi-strategy improved Harris eagle algorithm. The method comprises the following steps: setting initial parameters; a chaos initialization strategy is used, and a Tent chaos mapping initialization population is introduced; calculating prey escape energy by using a fuzzy energy strategy; selecting an exploration stage or a development stage according to the escape energy; when E > = 1, the individual enters an exploration stage, and when E < 1, the individual enters a development stage; trying to jump out of a local optimal solution by using a sine and cosine perturbation method; judging whether the maximum number of iterations is reached or not, if so, outputting an optimal path, otherwise, returning to the prey escape energy calculation step; and constructing a path trajectory graph and a fitness curve graph of the robot in the position space. The invention aims to solve the problems that the path is not smooth, the convergence speed is low, and the robot is easy to fall into a dead zone when performing path planning.

The invention provides a computer-aided slide-resistant pile and anchor cable build-in depth design method, and relates to the technical field of slide-resistant pile and anchor cable design, and the method comprises the following steps: collecting drilling data of an engineering site, and establishing a three-dimensional geologic model containing spatial distribution of each soil layer; determining a feasible range of design parameters; according to the soil layer space distribution and parameters, a slope stability analysis model considering the supporting effect of the slide-resistant piles and the anchor cables is established and used for calculating the slope stability safety coefficient under the given design parameters; and taking the minimum construction cost as a target function, determining an optimization constraint condition according to the slope stability analysis model, carrying out iterative optimization on the design parameters in a feasible range by adopting an optimization algorithm until an optimization convergence condition is met, and outputting an optimal design parameter. The problems that a traditional design method excessively depends on experience, multiple influence factors are difficult to consider, and the calculation efficiency is low can be solved, and the design level of the built-in depth of the slide-resistant pile and the anchor cable is improved.

The invention discloses a drawing projection talent selection test system and method based on combination of hierarchical quantum evolution and particle swarm optimization. The system comprises a sensing data acquisition module, an intelligent situation identification preprocessing module, a multi-dimensional feature collaborative extraction module, a distributed intelligent quantitative analysis module, a self-adaptive intelligent prediction module and a multi-source data dynamic evaluation module. All the modules work cooperatively, and perceptual acquisition, intelligent preprocessing and depth feature extraction of multi-dimensional drawing projection data are achieved, and efficient quantitative analysis, intelligent prediction and dynamic evaluation are achieved. Through the combination of the hierarchical quantum evolutionary algorithm and the improved particle swarm algorithm, the drawing projection data can be subjected to multi-level and refined analysis, an accurate and automatic talent screening tool is provided, the accuracy and the intelligent level of evaluation are remarkably improved, and the method has a wide application prospect.

The invention provides a distribution network automation equipment group fault diagnosis system and method, and relates to the technical field of power distribution networks, and the method comprises the following steps: carrying out the feature extraction of fault data through employing fractional order Boltzmann-Shannon interaction entropy; the rational activation function is combined with an extreme learning machine algorithm, a rational extreme learning machine is constructed, and faults are diagnosed; the non-smooth optimization problem of the rational extreme learning machine is solved by adopting a novel umbrella-exponent optimization algorithm; according to the method, the fractional order Boltzmann-Shannon interaction entropy is adopted to carry out feature extraction on the fault data, the data does not need to be decomposed or transformed, and the complexity of the data is directly measured, so that the defect that hidden fault features cannot be effectively extracted by a traditional time-domain and frequency-domain feature extraction method is overcome; the parameter-free measurement can be accurately estimated in a calculation-efficient mode, fractional order information is further considered, and the dynamic change of a fractional order time sequence can be measured.

The invention discloses a hydroelectric generating set fault diagnosis method based on a chaos Kepler optimization algorithm, and belongs to the technical field of hydroelectric generating set fault diagnosis. A Tent chaos function is introduced to replace an initial population of a Kepler optimization algorithm (KOA), and by improving the uniformity and uncertainty of the population, the convergence speed of the KOA algorithm is increased, and the global search capability is improved. An improved Kepler optimization algorithm is adopted to optimize hyper-parameters of the BiLSTM, a BiLSTM prediction model CKOA-BiLSTM of the hydroelectric generating set is established, the robustness and generalization ability of an existing model are improved, and the model is made to be suitable for processing complex nonlinear fault signals of the hydroelectric generating set. The model shows the optimal diagnosis effect on recognition of complex fault types, and the research can be used as a beneficial supplement of an existing hydroelectric generating set state monitoring and fault diagnosis system.

The invention relates to the technical field of semiconductor device parameter extraction, in particular to a semiconductor device parameter extraction method based on a reinforcement learning enhanced equalization optimizer, and the method comprises the steps: constructing a semiconductor device model, and determining input, output and optimization targets based on the semiconductor device model; and starting a parameter extraction algorithm based on the input, the output and the optimization target, and obtaining the optimal parameter of the semiconductor device, the parameter extraction algorithm being constructed by combining reinforcement learning with an equalization optimizer. According to the method, the efficiency and the precision of MOSFET parameter extraction can be improved by adaptively adjusting the iteration parameters of the optimization algorithm.

The invention provides an image segmentation method and device, computer equipment and a storage medium, and belongs to the field of image processing, and the method comprises the steps: obtaining a to-be-segmented target medical image, and determining a two-dimensional histogram of the target medical image; representing the Renyi entropy of the target medical image through a plurality of gray threshold values in the two-dimensional histogram; stepwise strengthening is introduced into the bat algorithm, the exploration capability and the algorithm development capability of the bat algorithm are strengthened, and the improved bat algorithm is obtained; taking maximization of the Renyi entropy as a target, and performing iterative optimization on the plurality of gray threshold values through an improved bat algorithm to obtain an optimal threshold value set; and segmenting the target medical image based on the optimal threshold set to obtain a segmentation result. Therefore, the Renyi entropy is introduced to preliminarily quantify the quality of image segmentation, the maximization of the Renyi entropy is taken as a target, iteration is carried out through the improved bat algorithm, the optimal solution of the gray threshold is effectively found, and finally segmentation is carried out through the optimal solution, so that the segmented image provides better information retentivity and discrimination.

The invention discloses an unmanned aerial vehicle route planning method based on a simulated annealing-crown porcupine optimization algorithm, is applied to the technical field of unmanned aerial vehicle task planning, and aims to solve the problems of low efficiency and low planning accuracy when solving a route planning problem in the prior art. The method comprises the following steps: firstly, based on the actual demand and distance of a flight mission, establishing an unmanned aerial vehicle route planning model containing a range constraint; an initial parameter set of an improved crown porcupine algorithm is defined, a population is initialized through a Latin hypercube sampling strategy, and the diversity and coverage range of the population are ensured. Then, simulating the foraging behavior of the crown porcupines to construct an optimization model, and simulating the route search of the unmanned aerial vehicle in a variable environment; and finally, an optimal route planning sequence is obtained through iterative solution, and efficient task planning of the unmanned aerial vehicle under the limitation of the voyage is realized. According to the method, the global search capability and the solving speed of route planning are remarkably improved, and the method is suitable for the optimal route planning problem of the unmanned aerial vehicle in a complex environment.

The invention relates to the technical field of data analysis, and discloses a laboratory data analysis method and system based on a machine learning technology. The method comprises the following steps: firstly, introducing an adaptive threshold, and identifying and deleting abnormal data in original laboratory data by using an improved isolated forest algorithm to obtain a processed laboratory data set; secondly, improving the generative adversarial network to obtain an improved generative adversarial network filling model, and performing data filling on the processed laboratory data set for multiple times to obtain a processed laboratory data set; according to errors of experimental result data, an improved black-wing optimization algorithm is used for searching for the minimum error, and optimization of laboratory data is achieved; and finally, establishing a laboratory database, performing data storage on the processed laboratory data set, and completing laboratory data analysis. By analyzing and processing the laboratory data, the purpose of analyzing the laboratory data is achieved, and the method is accurate and objective.

The invention relates to the technical field of wiring path optimization, and discloses an automatic wiring path optimization method and device for an optical fiber wiring cabinet. The method comprises the following steps: carrying out grid division on an optical fiber distribution cabinet, establishing an optical fiber physical stress finite element model, and carrying out stress analysis calculation to obtain optical fiber node stress distribution data; carrying out correlation analysis on the node stress value and the path length to obtain optical fiber stress and wiring path correlation data; inputting the optical fiber stress and wiring path associated data into an encoder-decoder structure for depth feature extraction to obtain a target feature parameter set; performing wiring path prediction on the target characteristic parameter set to obtain wiring path prediction data; according to the method, the Kriging agent model is constructed based on the wiring path prediction data, stress distribution optimization solution is carried out through a double-forgetting factor genetic algorithm, an optimal wiring path scheme is obtained, the problem of stress damage caused by traditional experience wiring is effectively avoided, and multi-target optimization of the wiring path is achieved.

The invention relates to a permanent magnet synchronous motor design method based on a multi-objective optimization algorithm, and the method achieves the efficient design and optimization of a permanent magnet synchronous motor through the combination of finite element simulation, multi-physical field coupling analysis, neural network performance prediction, and a hybrid optimization algorithm of a genetic algorithm and particle swarm optimization. The method comprises the following steps: determining a plurality of design targets according to an application scene of a motor, selecting key design variables, performing data cleaning and normalization processing, performing electromagnetic field, thermal field and mechanical field simulation by using a multi-physics field coupled finite element model, predicting the performance of the motor by combining LSTM and Transform neural network models, expanding a training data set through a data enhancement technology, and performing data enhancement on the motor. Finally, a hybrid optimization algorithm combining a genetic algorithm and particle swarm optimization is adopted to carry out global optimization and adaptive adjustment of design parameters, and a Pareto optimal leading edge is utilized to find out an equilibrium solution between design targets, so that the accuracy and efficiency of motor design are improved, and the method is suitable for various motor design scenes.

The invention relates to the technical field of time synchronization, and provides a parameter-adaptive multi-sensor network time synchronization method and related equipment, and the method comprises the steps: building a clock model based on the deviation and offset of the local time of each sensor relative to the current moment; constructing an updating formula for updating the drift and offset of the local time of each sensor; constructing a time synchronization parameter adaptive model based on the update formula and all the clock models, and solving the time synchronization parameter adaptive model by using an optimization algorithm to obtain an optimal parameter set; updating the drift and the offset of each sensor by using the optimal parameter set and an updating formula to obtain the final drift and the final offset of each sensor at the current moment; and for each sensor, adjusting the local time of the sensor according to the final drift and the final offset of the sensor, so that the local time of all the sensors is synchronized. According to the method, the accuracy of time synchronization can be improved.

The invention belongs to the technical field of motor fault diagnosis, and particularly relates to an asynchronous induction motor fault intelligent detection method based on a multi-modal time sequence. The method comprises the following steps: acquiring various signals of a motor, including three-phase current, vibration, equipment sound, environment sound and the like, and constructing a multi-modal data set; thirdly, constructing a Transform network classified by a single time sequence, and on the basis of the Transform network, connecting a plurality of the networks in parallel to obtain a lumped Transform network; and then, features of different modal data are dynamically integrated by using a self-attention mechanism, and accurate recognition of a motor fault is realized. In a network training process, a hyper-parameter of the lumped Transform network is optimized by adopting a chaos WOA algorithm; finally, different types of faults of the asynchronous motor can be accurately detected and judged, the situation that different fault features of the motor cannot be accurately represented by a single time sequence is avoided, and the accuracy and reliability of fault detection of the asynchronous motor are guaranteed.

The invention discloses a short-term wind power prediction method based on secondary decomposition and a neural network, and the method comprises the following steps: obtaining an original wind power signal, carrying out the noise reduction processing, and decomposing the original wind power signal into a plurality of initial mode components; preprocessing is carried out, and corresponding high-resolution characteristic signals are extracted; inputting the high-resolution characteristic signal into a pre-trained wind power prediction model; outputting a corresponding wind power prediction result; the wind power prediction model comprises two parallel branch networks; the first branch network is a two-way time domain convolution network and a two-way gating circulation unit; the second branch network is a two-dimensional convolutional neural network and a two-way long-short-term memory network; the two parallel branch networks are optimized on the basis of an improved salmons algorithm. According to the method, comprehensive optimization among different models and algorithms is considered, deep correlation and dynamic change characteristics among data are fully mined and utilized, the global search capability is enhanced, a local optimal solution is avoided, and short-term wind power prediction errors are remarkably reduced.

The invention discloses a power system unit commitment model solving method based on an improved simulated annealing algorithm, and the method comprises the steps: carrying out the optimization solving of a model objective function through employing the improved simulated annealing algorithm on the basis of a standard simulated annealing algorithm, and carrying out the optimization solving of a model objective function through adaptive temperature control and a multi-starting point strategy. And the global search capability and the convergence speed are improved when the unit commitment optimization problem of the power system is solved. Through the improvement of the invention, the algorithm can more effectively avoid falling into local optimum during solving, and the solving process is accelerated.