100 results about "Learning factor" patented technology

The present application relates to the technical field of power plant optimization, and discloses a thermal power plant auxiliary power system optimized dispatching method and system considering a wind-solar-storage system, and a device and a storage medium. The method specifically comprises: collecting thermal power plant auxiliary power system data, and establishing an auxiliary power system multi-objective function on the basis of the thermal power plant auxiliary power system data and a wind-solar power generation cluster model in an auxiliary power system; introducing constraint penalties and constraint conditions to the auxiliary power system multi-objective function, and constructing a thermal power plant auxiliary power system optimized dispatching model; and processing the thermal power plant auxiliary power system optimized dispatching model by using a dynamic learning factor-based particle swarm algorithm to obtain an optimized dispatching result, and completing thermal power plant auxiliary power system optimized dispatching on the basis of the optimized dispatching result. According to the present application, the optimal interactive output among wind turbine units, photovoltaic units, energy storage units, and a generating set can be determined on the basis of the optimized dispatching result, auxiliary power system low-carbon optimized dispatching is implemented, and the problem in the prior art of lacking dispatching in which new energy and thermal power plant auxiliary loads are integrated for analysis is solved.

The invention provides an energy optimal path planning method and device for an unmanned surface vessel in a complex marine environment, and belongs to the technical field of path planning. The method provided by the invention comprises the following steps: constructing a comprehensive energy consumption model; constructing a hybrid enhanced particle swarm optimization algorithm; constructing a safety space set and an adjacent graph through a grid method, generating an initial optimal path by adopting an A * algorithm, and carrying out bounded random disturbance and safety correction on target particle waypoints; smoothing the path generated by iteration by adopting a B-spline technology, resampling the smoothed optimal solution, and then reinjecting the optimal solution into the population; adjusting an inertia weight and a learning factor based on the number of iterations, and introducing double guide factors to carry out secondary adjustment on a cognitive item of particle speed updating; and embedding the comprehensive energy consumption model as a fitness function into a hybrid enhanced particle swarm optimization algorithm, performing real-time energy consumption evaluation, individual and global optimal path updating and path smooth optimization on each candidate path in an algorithm iteration process, and outputting an optimal navigation path of the unmanned surface vessel.

The invention discloses a generative adversarial network architecture search method and system based on a GA-PSO hybrid algorithm, and belongs to the technical field of deep neural networks. The method constructs a generative adversarial network super-network architecture, globally explores an architecture population by using a genetic algorithm, and performs local parameter adjustment on an elite architecture in combination with particle swarm optimization; the inertia weight and the learning factor are dynamically balanced, and framework compliance is ensured through discretization coding constraint; and finally, based on Pareto optimality, integrating a multi-target evaluation result and a primary and secondary collaborative proportion loop optimization solution set. Compared with traditional architecture search, the method has the advantages that the premature convergence problem is solved through a hybrid optimization mechanism, global and local collaboration is achieved in combination with weight sharing evaluation and hierarchical coding, and the search period is remarkably shortened while the generation quality is improved.

The invention provides an improved particle swarm optimization (PSO) algorithm for mechanical arm trajectory planning, and aims at solving the problems that a traditional PSO algorithm is low in convergence speed and prone to falling into a local optimal solution. Therefore, the inertia weight and the learning factor of the algorithm are dynamically adjusted, so that the inertia weight and the learning factor change along with the increase of the number of iterations, and the global search capability and the convergence speed are enhanced; meanwhile, an elite reverse learning (EL) strategy is introduced, an optimal trajectory planning scheme is selected according to a fitness function, a new search area is explored through reverse particles, and the local search capability is enhanced; and a Gaussian-Cauchy variation (GC) strategy is combined, so that the global search capability is further improved, and a local optimal solution is avoided. In the optimization process, a single-target optimization method is adopted to carry out hierarchical classification on population solutions, and the performance of the algorithm under single-target optimization and constraint conditions is improved. Finally, the trajectory of the mechanical arm is optimized based on the algorithm, and particularly, each time period in a 3-5-3 polynomial interpolation method is finely optimized, so that the time from an initial point to a target point is shortened, a trajectory planning scheme with the optimal time is obtained, and the working efficiency of the mechanical arm is improved.

A method and system are provided for identifying time-varying suspension characteristics of heavy-load vehicles. The method includes collecting sequential control state data of a mining truck using sensors, predicting parameter-related factors through a deep learning network, estimating suspension stiffness and damping coefficients via a linear dynamic model considering longitudinal-vertical coupling, and predicting future system states through a nonlinear dynamic model based on the estimated parameters and learned factors. According to the method, a deep learning network is integrated into a physical model of the mining truck, an accurate longitudinal-vertical dynamical model of the mining truck is established, accurate suspension parameters are identified, the stiffness damping time-varying characteristics of the suspension of the mining truck are given through a physical model-data driving method, and the model has certain interpretability and generalization; the rigidity and damping of the four suspensions can be obtained only through sprung information.

The invention belongs to the technical field of new energy power generation and grid connection, and particularly relates to a wind power plant electric energy quality optimization method and system based on an improved particle swarm algorithm. Aiming at the problems of voltage fluctuation, frequency deviation and high harmonic content during grid connection of a wind power plant, a wind power plant simulation model is built through Matlab / Simulink, a multi-target fitness function is built according to the voltage deviation, the frequency deviation and the total harmonic distortion (THD), the self-adaptive inertia weight and learning factors of a particle swarm optimization algorithm are improved, and a wind power plant grid connection model is built. And dynamically adjusting voltage adjustment gain, reactive compensation amount and filter parameters. According to the embodiment, compared with a traditional method, the number of iterations is reduced by 40%, the voltage fluctuation suppression ratio is improved by 10%, the frequency adjusting time is shortened to be within 0.3 second, the THD is reduced to 2.1% from 5.2%, and the power grid stability and the electric energy quality are improved.

The invention proposes a robot path planning algorithm based on a particle swarm optimization algorithm and a dynamic window method, and relates to the field of control theories and electronic information, and the method comprises the steps: introducing an inertia weight updating strategy based on a state factor, setting adaptive parameters and crossover and mutation operators to improve the global search capability, increase the population diversity, and improve the robot path planning precision. On-line self-adaptive updating of inertia weight and learning factors is realized on line in combination with Q-learning, gene combination modes are enriched through crossover operators, convergence and exploratory performance of the algorithm are improved, an obstacle avoidance strategy of a traditional DWA algorithm is improved, weight parameters of a dynamic window evaluation function are dynamically adjusted according to real-time information of a target and an obstacle, and an obstacle avoidance algorithm is established. According to the method, the global planning is adopted, the path points generated through global planning are adopted as temporary targets, fusion of MOQLCOPSO and the improved DWA algorithm is achieved, local optimum is effectively avoided, the planning efficiency and path safety are improved, and the method is suitable for mobile robot navigation under the complex three-dimensional terrain.

The invention discloses a laser wireless energy transfer dynamic MPPT control method based on an RL-PSO hybrid algorithm and a reconfigurable array system, and aims to solve the problems of power oscillation and mismatch loss caused by light spot offset of a photovoltaic array under laser dynamic irradiation. According to the method, reinforcement learning (RL) and particle swarm optimization (PSO) are fused, dynamic parameters such as the light spot moving speed and irradiance are sensed in real time through the RL, the inertia weight and learning factors of the PSO are adjusted in a self-adaptive mode, and collaborative optimization of global search and local tracking is achieved. According to the system level, a reconfigurable photovoltaic array is designed, and according to data of a light spot position sensor, battery series-parallel topology is dynamically adjusted through a switch matrix, and current matching is optimized. According to the method, double-loop control is adopted, wherein an outer-layer RL decision module dynamically switches PSO working modes according to the environment, and an inner-layer PSO module optimizes the working voltage. The system efficiency in a dynamic scene can be improved by 15%-30%, mismatch loss is reduced by 40% or above, and the method is suitable for unsteady-state energy supply scenes such as unmanned aerial vehicles and mobile robots.

The invention relates to the technical field of heat supply unit peak regulation capacity optimization, in particular to a heat supply unit heat storage peak regulation strategy construction method and device and a storage medium, and the method comprises the steps: carrying out the hourly prediction of a day-ahead electrical load and a day-ahead thermal load through a target BO-ResNet-AM neural network prediction model, obtaining a day-ahead hourly electric load predicted value and a day-ahead hourly thermal load predicted value; based on a power supply balance constraint, a heat supply balance constraint, a unit ramp rate constraint and a target function, taking pipe network heat storage / release, actual power supply power and actual heat supply as decision variables, and constructing an initial source network load storage collaborative interaction mode optimization scheduling model; and converging the initial source network load storage collaborative interaction mode optimization scheduling model to a globally optimal solution through an adaptive weight learning factor particle swarm optimization algorithm. By accurately predicting the electric heating load and quantifying the heat storage capacity of the pipe network, peak clipping and valley filling of the electric load and space-time translation of the thermal load are achieved, and the deep adjustment and peak capacity of the unit is improved.

The invention discloses a distributed power supply-containing power distribution network self-healing method and system based on defect identification, and the method comprises the steps: inputting operation data into a defect identification model, obtaining a defect identification result, correcting a feeder load rate and a weighted structure entropy according to the defect identification result to generate a candidate solution set, inputting the candidate solution set into an upper layer optimization model, and obtaining a defect identification result; taking the maximization of the load recovery amount in the island as a target, combining with a line medium to guide a search direction and adopting a second-order cone relaxation method to carry out optimization solution to obtain a load recovery scheme in the island, and inputting the candidate solution set and the load recovery scheme into a lower-layer optimization model to obtain a load recovery scheme in the island; carrying out iterative solution under a constraint condition by combining an improved star-sparrow optimization algorithm which introduces an inertia weight and a learning factor, and obtaining a load recovery result after fault reconstruction; and determining a target switch operation sequence and a load recovery strategy based on the load recovery result to perform self-healing of the power distribution network so as to improve the self-healing capability and the operation reliability of the power distribution network.

The invention provides a wind power networking type VSG control adaptive parameter optimization method, and the method comprises the steps: firstly, determining a constraint relation between a virtual inertia and a damping coefficient based on the system performance of a wind power networking type VSG; based on the constraint relation between the virtual inertia and the damping coefficient, an adaptive parameter optimization module is established, and based on the adaptive parameter optimization module, a particle swarm optimization algorithm of fuzzy logic is introduced, and values of the virtual inertia and the damping coefficient are iteratively optimized by dynamically adjusting an inertia weight and a learning factor in the particle swarm optimization algorithm. And finally outputting the optimal combination of the virtual inertia and the damping coefficient. The virtual inertia and the damping coefficient can be flexibly adjusted according to the dynamic change of the operation condition of the power grid, and the stability and the electric energy quality of the power grid are improved; meanwhile, adjustment of the virtual inertia and the damping coefficient considers the constraint relation between the virtual inertia and the damping, the virtual inertia and the damping coefficient are effective at the same time, and the condition that the parameter optimization effect is limited is avoided.

The invention belongs to the field of dam body stability evaluation, and provides a dam body stability evaluation method based on a multi-strategy improved particle swarm optimization algorithm, which comprises the following steps: S1, collecting and preprocessing various monitoring data for dam stability evaluation; s2, establishing a multi-physics field coupling dam model; s3, defining an objective function and constraint conditions of dam body stability evaluation; s4, based on an MSIPSO algorithm, fusing hybrid initialization, adaptive inertia weight and learning factor, dynamic disturbance and re-initialization, multi-subgroup coevolution and a multi-target constraint processing mechanism, and constructing a multi-strategy improved particle swarm optimization algorithm; s5, iteratively optimizing a to-be-evaluated parameter combination in the multi-physics coupling dam model by using the MSIPSO algorithm, inputting optimized parameters into the model for coupling analysis, and calculating a fitness value according to model output feedback so as to update a particle state of the MSIPSO algorithm; and S6, carrying out dam body stability evaluation and risk evaluation based on the optimal parameter combination obtained through optimization of an MSIPSO algorithm and the dam response.

The invention relates to a seismic oscillation fitting method matched with a multi-damping-ratio response spectrum. According to the seismic motion fitting method, the seismic geology environment of the region is classified and the feature mother wave set is constructed, so that the seismic geology features of the region can be fully considered, and the fitting result can better conform to the actual condition of the target region and is more targeted and accurate. Through combination of characteristic seismic oscillation mother wave optimization and a dynamic parameterized particle swarm optimization algorithm, aiming at nonlinear characteristics of multi-damping ratio spectrum coupling matching, an improved particle swarm optimization PSO algorithm with a self-adaptive inertia weight and a learning factor is designed, and while the multi-target response spectrum fitting precision is improved, the multi-target response spectrum fitting precision is improved. And the time-frequency non-stationary characteristic of original seismic oscillation is reserved.

The invention relates to an electronic paper display mapping method based on visual perception and dynamic clustering, and belongs to the technical field of display. According to the method, firstly, learning factors and particle weights of a particle swarm optimization algorithm are dynamically adjusted, a gray segmentation threshold is optimized in combination with K-means clustering, and the accuracy and convergence efficiency of gray distribution are remarkably improved; secondly, establishing a dynamic error diffusion system based on visual perception, designing a frequency domain visual weighted filter and a spatial domain sensitivity function by combining a human visual perception theory, dynamically compensating quantization errors and enhancing edge details; and meanwhile, a Floyd-Steinberg error diffusion algorithm is improved, and a double image effect is inhibited in combination with a snakelike scanning path. According to the electronic paper display mapping method based on visual perception and dynamic clustering, the electronic paper image display quality is effectively improved, particularly when a high-gray-scale image is displayed, details can be reserved to the maximum extent, and the problems of gray-scale distortion and edge blurring in traditional electronic paper display are solved.

The invention relates to an ocean communication method based on self-adaptive Q-learning factor setting. In the method, each node periodically collects own state information and broadcasts the own state information to surrounding nodes, and after each node receives a message and stores the message in a cache region, routing decision and parameter adjustment processes are circularly executed until the message is completely transmitted to a target node to complete communication; wherein the routing decision and parameter adjustment process comprises the following steps of: calling a message from a corresponding buffer area by a current node, selecting a next forwarding node of the message based on a current Q value table, updating a learning rate and a discount factor based on the current state information of the node after sending the message, and performing routing decision and parameter adjustment by combining a reward function, the updated learning rate and the discount factor. And a new Q value table is obtained through asynchronous updating of the double-Q network. Compared with the prior art, the method has the advantages that the delivery rate is higher, the method can better adapt to changeable environments, and the problem of real-time changes of topology, load and channel quality can be well solved.

The embodiment of the invention relates to the technical field of power systems and automation thereof, in particular to an alternating current and direct current hybrid power flow optimization method based on embedded direct current power flexible adjustment, and the method comprises the steps: building an embedded direct current adjustment model based on an embedded direct current power adjustment principle; constructing an AC / DC hybrid system steady-state power flow optimization model comprising a comprehensive evaluation index, a power system operation constraint and a DC operation constraint; an improved particle swarm algorithm is adopted to solve the steady-state power flow optimization model, the improved particle swarm algorithm updates a population through a chaos quasi-opposition strategy and optimizes an inertia weight and a learning factor, and a power flow optimization result is obtained; comparing different embedded direct-current power regulation modes, and verifying the effectiveness of the alternating-current and direct-current hybrid power flow optimization method; according to the method, accurate power flow optimization of the alternating-current and direct-current hybrid system in a new energy output fluctuation and load change scene is realized, and the economical efficiency, the safety and the stability of system operation are improved.

The invention discloses a control method for stabilizing broadband oscillation based on adjustment of an operation mode. The method comprises the following steps: screening key variables influencing the operation mode of a new energy station system; constructing a new energy station system oscillation stability margin analysis function according to a logarithmic derivative method; constructing an optimization problem of oscillation stability margin maximization based on the key variable and an oscillation stability margin analysis function; solving the optimization problem by adopting a particle swarm algorithm based on a dynamic inertia weight and a learning factor to obtain an operation mode with optimal oscillation stability; and the new energy station system is controlled to operate in the optimal operation mode, and the broadband oscillation stabilizing capability of the new energy station system is analyzed. According to the method, new energy consumption and oscillation risks can be considered at the same time, and the adverse effect of oscillation in a broadband range on a unit and a power grid is effectively reduced while it is ensured that the total output of the new energy unit meets the requirement.

The invention relates to the technical field of data processing, in particular to an intelligent regulation and control method and system for forming process parameters of a blow molding machine. The method comprises the following steps: initializing each particle according to the range of process parameters of each dimension, presetting initial parameters of a particle swarm optimization algorithm, and acquiring a population distribution span of each iteration according to distribution characteristics of a particle swarm during each iteration in the process of performing iterative optimization on each particle by using the particle swarm optimization algorithm; based on the globally optimal solution and the difference of the centroid during each iteration, correcting the population distribution span, and obtaining a search stage index corresponding to each iteration; and on the basis of the search stage index, obtaining an individual learning factor and a group learning factor during each iteration, substituting the individual learning factor and the group learning factor during each iteration into a particle swarm optimization algorithm, and performing iterative optimization on initially generated particles to obtain an optimal group process parameter.

The invention discloses an optimized micro-seismic positioning method based on spatial search. The method comprises the following steps: S1, constructing a micro-seismic positioning model; s2, after spatial positioning solving is carried out on each microseismic event through the microseismic positioning model, optimization is carried out through a spatial search algorithm, and an optimal solution is obtained; and S3, introducing an inertia weight index and a learning factor to carry out multi-objective optimization. The invention provides an optimized micro-seismic positioning algorithm based on a space search algorithm, which combines the space search algorithm with a traditional positioning model, optimizes various key parameters in a micro-seismic positioning process, adopts a multi-target optimization strategy, comprehensively improves the positioning precision and the system efficiency, and overcomes the defects in a traditional method.

The invention relates to a multi-valve assembly line robot scheduling method based on an improved MOPSO algorithm, and the method comprises the steps: firstly constructing a process dependency digraph, defining constraint conditions and decision variables, then determining a multi-objective optimization function, and carrying out the solving through an improved multi-objective particle swarm optimization algorithm, the improved multi-objective particle swarm optimization algorithm comprises the steps of generating an initial particle swarm by adopting an oxidation strategy, dynamically adjusting an inertia weight, a learning factor and a mutation probability, performing adaptive mutation on the updated particle swarm and the like. Compared with the prior art, the method can solve the defects in the prior art, remarkably improves the accuracy, robustness and practicability of industrial robot task scheduling, and achieves efficient and reliable multi-robot cooperative scheduling.

The invention relates to a power prediction technology, in particular to a wind power prediction method, which comprises the following steps of: identifying a global optimal particle in a plurality of wind power characteristic particles; a power prediction learning model is constructed according to an IABFLPSO algorithm, whether the wind power characteristic particles are initialized and updated or not is determined according to the number of non-updating times, and the inertia weight and learning factors of the power prediction learning model are determined according to the Euclidean distance between each non-global optimal particle and the global optimal particle; decomposing the historical time sequence wind power data; constructing a fitness function of the xLSTM neural network according to a plurality of absolute error values between the final predicted values of the plurality of component data sets and the corresponding true values; and inputting the meteorological characteristic data of the target time period into the IABFLPSO-xLSTM-BP neural network to predict the wind power of the target time period. According to the invention, the accuracy and efficiency of the prediction result of the prediction model are improved.

The invention provides a path planning method, system and equipment for underwater rock drilling operation of a high-frequency breaking hammer, and relates to the technical field of underwater rock drilling. A multi-source data modeling technology and a position indicator area positioning technology are fused, microscopic details of a rock drilling target are complemented through optical data, equipment motion constraints are calibrated through position indicator data, and three-dimensional environment modeling of a macroscopic operation area in combination with the microscopic rock drilling target and the equipment motion constraints is achieved; a multi-objective optimization model is constructed, and an objective function for aggregating the path length, the path fluctuation, the energy consumption and the breaking hammer slip rate is a composite cost function; solving a global optimal operation path corresponding to the composite cost function by adopting IPSO; obtaining the global optimal path through speed updating mechanism optimization, adaptive inertia weight adjustment, asynchronous change learning factor setting and natural selection population iteration; the data volume and the calculation complexity of environment modeling are reduced, and the accuracy and the practicability of environment perception and modeling are improved.

The present application relates to the technical field of aquaculture, and particularly relates to an improved PSO-based water quality parameter prediction method and system for aquaculture, which comprises collecting water quality parameters at different positions and depths in a breeding pond; training an improved radial basis function (RBF) neural network using training set data; and optimizing the parameters of the improved RBF neural network model using an improved particle swarm optimization (PSO) algorithm. The present application introduces a mixed Gaussian function and an abnormal S-shaped function into the radial basis function of the traditional RBF neural network, thereby solving the problem of weak capability of the model in nonlinear data modeling. Furthermore, the present application improves the inertia factor and the learning factor in the traditional PSO algorithm, thereby solving the problems of slow parameter convergence speed and poor global search capability in the RBF neural network.

The invention provides a power load prediction method of an electric energy meter data system and a medium. The method comprises the following steps: processing load data by adopting multiple strategies; dividing an independent variable X and a dependent variable Y of the normalized data according to an improved PSO (Particle Swarm Optimization) algorithm, and dividing again to obtain a training set and a test set; the improved PSO algorithm updates optimal particles according to dynamic inertia weights and learning factors, and the dynamic inertia weights and the learning factors are dynamically changed according to a Levy flight mechanism and a Gaussian disturbance mechanism; a CNN-LSTM-Transform model is created, and the CNN-LSTM-Transform model is established; according to an improved PSO optimization algorithm, training the model by using the training set to obtain a prediction model; the hyper-parameters of the model can be updated through particle information updated through the improved PSO optimization algorithm in the previous training in each training. The method can significantly improve the prediction precision of the short-term power load data.

The invention discloses an electric power system reactive power optimization solution method and device based on genetic-particle swarm optimization fusion, and relates to the field of electric power systems, and the method comprises the steps: constructing an electric power system parameter model, and carrying out the constraint of a control variable and a state variable; carrying out genetic manipulation, and forming an improved particle swarm algorithm by adopting a nonlinear adjustment strategy on an inertia weight and a learning factor of the particle swarm algorithm; initializing configuration, and calculating fitness and dynamically updating an extreme value by iteratively updating the speed and the position of the particle; selecting low-fitness particles for crossover variation, and preferentially updating the optimal value of the individual; and outputting the optimal active loss and the corresponding control variable combination after continuous iteration to the maximum number of times, and completing power grid dispatching. According to the method, the inertia weight and the learning factor are non-linearly adjusted, the solving precision is improved, meanwhile, genetic operation is introduced, the particle swarm diversity is enhanced, local optimum is avoided, the global search capability and convergence performance of the algorithm are improved, and the power grid dispatching economical efficiency and rationality are enhanced.

The present application relates to the technical field of pipeline corrosion, and particularly relates to a method and device for predicting pipeline corrosion rate based on an IPSO-BP neural network model, an electronic device and a storage medium. The IPSO-BP neural network model is a hybrid algorithm. The IPSO algorithm adjusts the inertia weight factor, individual experience learning factor and social experience learning factor in the PSO algorithm in a nonlinear decreasing manner, so that the PSO algorithm can balance the relationship between global search and local search in the search process, thereby improving the convergence and search ability of the PSO algorithm, and better optimizing the BP neural network model.

The application relates to a control method for dynamic walking of a biped robot and the biped robot, and belongs to the technical field of robot control, which comprises the following steps: 1, a self-recurrent cerebellar model neural network is used to establish a dynamic model of the biped robot with a disturbance term, and dynamic robust walking of the biped robot is converted into a problem of realizing stability of a multi-input multi-output nonlinear system with a bounded uncertain term; 2, an adaptive self-recurrent cerebellar model neural network error observer is designed to estimate an error upper limit; 3, an adaptive law of network weight is designed to realize real-time updating of the network weight space and to adjust parameters of each learning factor; and 4, a boundary value estimation algorithm is used to compensate for an estimation error and feedback to a robot walking system, so that the biped robot can realize asymptotic stable walking. The application enables the control system to adapt to time-varying characteristics of the biped walking system on line, and has continuous learning and adaptive capacity for unknown dynamics.

The invention relates to a mechanical arm control method based on time optimization, and the method comprises the steps: building a mechanical arm model for a mechanical arm, and building a spatial transformation relation of all joints of the mechanical arm model through a D-H parameter method; according to the spatial transformation relation of all the joints and the transition path of the mechanical arm, a joint track on the transition path of the mechanical arm is constructed through 3-5-3 piecewise polynomial interpolation; based on the principle that the movement time of each joint is shortest, a chaos particle swarm algorithm is adopted to optimize the joint track of each joint; and according to the mechanical arm model and the optimized joint track, tracking control is conducted on the mechanical arm through a depth deterministic strategy gradient algorithm. Performance indexes of the method are remarkably broken through, the improved PSO algorithm achieves shortening of total time of a track and increasing of convergence speed by dynamically adjusting inertia weight and learning factors, and compared with a local optimal trap of a traditional PSO algorithm and a speed fluctuation problem of cubic polynomial interpolation, the method shows better time optimization capability.

The application discloses a static software defect prediction method based on self-step oversampling integrated learning, according to label information, a training set is divided into majority class and minority class sets; the prediction result of an integrated classifier is used to estimate sample classification difficulty; according to the evaluation, bin processing is carried out on the two kinds of sample sets, and the average difficulty contribution of binning is determined; the self-step learning factor and the sampling weight of binning are updated based on the difficulty contribution; based on the sampling weight of binning, a training subset is obtained through weighted Bootstrap sampling; the training subset is subjected to SMOTE oversampling, and then a base classifier is trained; the prediction performance of the base classifier is used to determine the weight, and the integrated classifier is updated; the process is repeated until the integrated classifier of a specified size, namely a software defect prediction model, is obtained. The application overcomes the problems that the model training process lacks pertinence due to factors such as insufficient training data and class imbalance faced by the static software defect prediction task, and that overfitting occurs in the later model training period due to excessive attention to noise samples and abnormal samples, thereby affecting the defect prediction performance.

The invention discloses a parameter optimization method of a screw propulsion machine for accelerated drainage consolidation of tailings, and belongs to the technical field of drainage consolidation regulation and control of tailings ponds.The method comprises the steps that in-situ tailings samples of the tailings ponds.The in-situ tailings samples of the tailings pondsare collected, and basic physical parameters such as the density, the saturated moisture content and the particle size grading of the tailings are obtained through testing; then respectively constructing interaction mechanical models of the screw propulsion machinery and the tailings under the working conditions of the high-concentration tailings and the low-concentration tailings, analyzing interaction rules of normal bearing, shearing action and driving resistance, and establishing stress balance equations in the vertical direction and the horizontal direction; then, a parameter optimization model taking the mechanical model as a constraint is constructed, differential objective functions for reducing the mechanical subsidence amount and improving the mechanical operation speed are set for the high-concentration tailing working condition and the low-concentration tailing working condition respectively, a PSO algorithm is improved, and Logistic chaos initialization, self-adaptive inertia weight and a learning factor nonlinear dynamic adjustment strategy are fused; and then, an improved PSO algorithm is called for iterative solution to obtain an optimal structure parameter combination of the screw propulsion machinery.