33 results about "Learning factor" patented technology

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 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 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 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 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.

The present disclosure provides a background calibration method for an analog-digital converter model and an electronic device, which can be applied to the field of calibration technology. The method comprises: inputting a first output result of a to-be-calibrated analog-digital converter model in a current round and a second output result of a reference analog-digital converter model into a loss function to obtain a loss function value in the current round; determining a step size in the current round according to a loss function value in a previous round and the loss function value in the current round, wherein the loss function value in the previous round is obtained by inputting a first output result of a to-be-calibrated analog-digital converter model in the previous round and a second output result of a reference analog-digital converter model into a loss function; and calibrating first parameter information of the to-be-calibrated analog-digital converter model in the current round according to the step size in the current round and a set of learning factors.

The invention discloses a multi-AGV formation keeping control method based on IPSO-fuzzy PID. The multi-AGV formation keeping control method is characterized by comprising the following steps: (1) establishing a dynamic self-adaptive multi-AGV pilot following formation control model; (2) constructing a fuzzy PID controller, setting a fuzzy domain of input and output variables, a membership function and a fuzzy control rule, and obtaining the correction of PID parameters through fuzzy reasoning and defuzzification; (3) constructing an improved particle swarm optimization algorithm with dynamic balance of global search and local optimization, improving the inertia weight and learning factor of a particle swarm, and setting a fitness function; and (4) optimizing the input quantization factor and the output scaling factor of the fuzzy PID controller by using an IPSO algorithm, assigning optimized parameters to the controller, and adjusting the linear velocity and the angular velocity of the following AGV. The formation keeping precision, the response speed and the anti-interference capability can be improved.

The application discloses a gear vibration noise prediction method based on a deep belief network, which comprises the following steps: firstly, denoising the gear vibration noise data collected through experiments; secondly, constructing a database based on a frequency analysis algorithm; thirdly, constructing a deep belief network model based on a restricted Boltzmann machine and training the model; fourthly, obtaining optimal parameters through a particle swarm optimization algorithm based on adaptive inertia weight and a learning factor; and finally, using the denoised and amplified gear vibration data to predict the noise by using the APSO-DBN method. The method amplifies the data through frequency analysis, overcomes the training difficulty caused by insufficient data, and has better iteration effect than the PSO-DBN algorithm. In the total sound pressure level prediction, the average relative error of the APSO-DBN algorithm is obviously reduced compared with the DBN network with manually selected network nodes.

The invention provides a coal pulverizing system energy-saving adjustment control method and device based on multi-objective optimization, and the method comprises the steps: obtaining the operation parameters of a coal pulverizing system, and carrying out the preprocessing; performing partition identification on the current working condition based on the expert rule base, and dynamically configuring a multi-target optimization weight matrix; carrying out multi-target optimization by utilizing an improved particle swarm optimization algorithm, and realizing balance between global search and local development by adaptively adjusting an inertia weight and a learning factor; an optimization result is input into an expert rule base for hard constraint verification and experience correction, and control parameters meeting safety and energy efficiency requirements are generated; the corrected control parameters are converted into control instructions, closed-loop feedback adjustment is executed, and a periodic optimization control process is formed. The multi-target collaborative optimization control of the coal pulverizing system under different load working conditions can be realized, the coal pulverizing unit consumption is remarkably reduced, the pulverized coal fineness stability is improved, meanwhile, the algorithm convergence speed and the fault early warning advance are improved, and the system safety and the dynamic balance of energy efficiency are guaranteed.

The invention discloses a novel fixed-wing unmanned aerial vehicle sensor fault diagnosis method, and relates to the technical field of equipment fault diagnosis, and the method comprises the steps: collecting flight parameters of an unmanned aerial vehicle in a normal state and a plurality of fault states; performing normalization processing on the flight parameters to eliminate dimensional differences; constructing a long and short-term memory neural network model which is subjected to hyper-parameter optimization by an improved grey wolf optimization algorithm; and performing fault diagnosis and classification on the processed flight parameters by using the optimized long-short-term memory neural network model. According to the invention, the improved grey wolf optimization algorithm is used to optimize the hyper-parameters of the LSTM neural network, the model performance is improved, the accuracy of unmanned aerial vehicle sensor fault diagnosis is improved, the traditional grey wolf optimization algorithm is improved, and the convergence factor is non-linearly adjusted; a gray wolf position updating formula is improved by using a particle swarm algorithm, and different influence values of a leader wolf are adjusted by introducing a learning factor, so that the problem that a gray wolf optimization algorithm is easy to fall into local optimum is solved.

The invention relates to a VLC-NOMA system power distribution method based on improved particle swarm optimization. The method comprises the following steps: constructing a downlink model based on a VLC-NOMA system; constructing a power distribution optimization model with maximization of the total rate of the VLC-NOMA system as a target; and constructing an IPSO algorithm, wherein the IPSO algorithm comprises an adaptive dynamic inertia weight, a collaborative learning factor and an enhanced elite reverse learning mechanism. Through the introduced adaptive dynamic inertia weight, collaborative learning factor and enhanced elite reverse learning mechanism, the adaptive dynamic inertia weight can allocate different powers according to the difference of users, and the collaborative learning factor can dynamically adapt to the collective behavior of the group. And an enhanced elite reverse learning mechanism selects a better fitness through comparison of an elite reverse solution and a quasi-reverse solution, and these improvements jointly enhance the capabilities of the scheme in the aspects of global exploration and local development, and improve the convergence rate of the algorithm and the robustness in a complex interference environment.

The present application relates to a kind of particle swarm-based clean energy station multi-unmanned aerial vehicle task allocation method, comprising: obtaining clean energy station area data information, and the division of patrolling area is carried out;Clean energy station multi-unmanned aerial vehicle task allocation model is constructed;According to individual optimal position and global optimal position, the speed and position of particle are adjusted, and objective function is optimized;When the iteration number reaches upper limit, the task allocation solution corresponding to global optimal particle is returned, otherwise, continue to update particle state, optimization solution.Nonlinear dynamic collaborative improvement is carried out to inertia weight and learning factor, inertia weight adopts nonlinear self-adaptive decreasing strategy, iteration initial period is kept larger value, enhances the global traversal ability of unmanned aerial vehicle formation, widely searches various sub-regions and task combination;Rapidly attenuate in iteration later period, strengthen local precision search, ensure fast convergence to optimal cost combination.Golden sinusoidal algorithm is fused to reconstruct position update mechanism, realize the dynamic balance of global and local search.

The invention discloses a hydroelectric generating set stator vibration intelligent diagnosis method considering multi-factor coupling, and the method comprises the steps: collecting vibration data, temperature data and current data through a plurality of sensors installed on a hydroelectric generating set stator, carrying out the data preprocessing and feature extraction, and obtaining multi-factor data; performing multi-factor coupling modeling based on the multi-factor data, constructing a stator vibration state model, preliminarily adjusting the weight of each factor according to historical operation data, and performing coupling effect analysis; inputting the coupling effect analysis result into a deep learning network for vibration abnormal mode recognition, and processing a nonlinear relationship between factors by adopting a graph neural network structure; and displaying a vibration abnormal mode recognition result on a monitoring interface in real time, and dynamically adjusting the factor weight by adopting a reinforcement learning algorithm. The method can learn the mapping relation between the factors, improve the abnormal mode recognition precision, dynamically adjust the factor weight, and improve the diagnosis accuracy and diagnosis adaptability.

The invention discloses an aerodynamic configuration optimization method and system based on a two-stage particle swarm optimization algorithm, and solves the technical problem of poor aerodynamic configuration optimization effect caused by the fact that a fixed inertia weight coefficient of a traditional single-stage PSO algorithm is difficult to give consideration to global exploration and local convergence capabilities at the same time in the aerodynamic optimization problem. The method comprises the steps that when aerodynamic configuration optimization needs to be carried out, a first-stage particle swarm is initialized based on a predefined class shape transformation variable value range, and a global optimal value sequence is constructed and output through a random inertia weight strategy in combination with predefined learning factors, particle historical individual optimization and population global optimization; second-stage particles are obtained through three times of B-spline screening, the second-stage particles are iteratively updated by adopting a predefined fixed inertia weight and a learning factor to determine target global optimal particles, finally, the geometric shape coordinates of the airfoil profile are calculated based on the class shape transformation variables corresponding to the particles, and aerodynamic shape optimization is achieved.

The invention discloses a power grid and air conditioner load collaborative optimization scheduling method and system based on edge computing, and relates to the technical field of intelligent power grid and air conditioner load management.The related parameters are collected in real time through edge computing nodes and preprocessed; key feature extraction is conducted on the preprocessed related parameters, air conditioner energy consumption prediction is conducted through a preset energy consumption prediction model in combination with key features, and a prediction result is obtained; and inputting the key features and the prediction result into a preset multi-objective optimization model, and introducing a linear inertia weight and a linear learning factor in combination with an improved particle swarm algorithm to obtain a result of the multi-objective optimization model as a collaborative scheduling strategy. According to the method, complex multi-target optimization scheduling of the air conditioner load is realized in combination with an edge computing technology.

The invention relates to the technical field of mechanical arms, and provides a path planning method, device and equipment of a mechanical arm for a hull surface, a medium and a program product. The method comprises the following steps: constructing a kinematic model of the mechanical arm; collecting hull surface environment data, identifying an obstacle and obtaining an obstacle area; initializing parameters of a particle swarm optimization algorithm, and executing iterative updating, when Kmax / 2, updating the inertia weight and the learning factor; when k is greater than or equal to Kmax / 2, updating the inertia weight and the learning factor again; updating the particle speed and the particle position; constructing a multi-target fitness function fused with the attention weight; if the number of iterations k is equal to or the global optimal fitness value of continuous H iterations meets a convergence threshold value, outputting a global optimal track. The inertia weight of the algorithm is dynamically optimized in stages, global exploration and local accuracy are balanced, and local optimum is avoided.

The application discloses an improved particle swarm hybrid intelligent agricultural machine path planning algorithm fusing a climbing strategy, and comprises the following steps: S1, inputting path planning data, assuming that each particle represents a path, and initializing particle swarm positions in the improved particle swarm algorithm by using a Tent chaotic mapping algorithm; S2, updating the speed and position of the particle by using a random inertia weight updating strategy and a particle speed updating formula, and dynamically adjusting a learning factor by using an asynchronous dynamic adjustment algorithm; S3, judging whether an end condition of the path planning data is met or not, and re-executing S2 when the end condition is not met; and S4, taking the initial solution meeting the end condition as the input of the climbing algorithm to continue path optimization, and outputting an overall optimal particle, namely an optimal path, after searching by the climbing algorithm. The algorithm has certain improvement in the optimization capability in a complex scene, especially in a curved surface terrain, and the capability of jumping out of a local optimal solution.

This invention discloses a frequency support method for a multi-machine parallel grid converter system based on an improved particle swarm optimization (PSO) algorithm. The method includes: maximizing the damping ratio of the system's dominant oscillation mode as the optimization objective; using an improved PSO algorithm to solve for the optimal initial values ​​of virtual inertia and damping coefficients for each VSG unit; the improved PSO algorithm balances global exploration and local convergence through nonlinearly decreasing inertia weights, strengthens individual cognition in the early stages of iteration and group cognition in the later stages through dynamic switching of learning factors, and maintains population diversity through a hybrid mechanism of Gaussian and Cauchy mutations; when the system experiences disturbances, the system's operating status is monitored to obtain the change in system angular frequency and the rate of change of angular frequency, and the virtual inertia and damping coefficients of each VSG unit are adjusted collaboratively to suppress frequency overshoot and accelerate oscillation convergence. This addresses the problems of severe frequency overshoot, slow oscillation convergence, and stability issues caused by multi-machine interaction.

The invention relates to the technical field of fault diagnosis, in particular to a vehicle transmission system bearing fault diagnosis method based on IPSO-Wav-KAN. Comprising the following steps: S1, acquiring impact vibration data of a vehicle transmission system bearing, performing multi-scale wavelet decomposition and denoising processing according to the impact vibration data to obtain a denoised wavelet coefficient, and extracting a feature vector describing a bearing state according to the denoised wavelet coefficient; and S2, introducing a particle swarm optimization algorithm according to the adaptive inertia weight and the dynamic learning factor to obtain an improved particle swarm optimization algorithm, and optimizing the structure parameters and weight parameters of the KAN network according to the improved particle swarm optimization algorithm to obtain optimized network parameters. According to the method, the optimal network parameters are obtained through optimization in combination with the IPSO algorithm, and faults of different types and different severity degrees are accurately recognized.

This invention belongs to the field of electronic interference technology, specifically relating to a method, program, device, and storage medium for allocating interference equipment. The invention designs a binary magnificent wren-warbler algorithm, which initializes the population's positional distribution using chaotic mapping. It judges the algorithm's local convergence trend by using the average Euclidean distance and fitness change rate. By introducing inertia weights and learning factors, the local search function is improved, enhancing the algorithm's local search capability. Combined with the algorithm's global search capability, a new fitness function is constructed to improve algorithm performance. Furthermore, activation functions and thresholds are used to convert continuous values ​​into discrete values, enabling the algorithm to solve discrete problems and enhancing its generalization and the rationality of interference equipment allocation. This invention solves the problems of existing interference equipment allocation methods' inability to respond quickly and to allocate interference resources rationally. Upon receiving a radiation source signal, this invention can immediately generate interference equipment allocation results, achieving rapid interference response.

This invention belongs to the field of intelligent control and path planning for unmanned aerial vehicles (UAVs), specifically involving a multi-strategy particle swarm optimization (PSO) method for UAV trajectory planning based on reinforcement learning. The method includes: first, constructing an environmental threat model based on 3D elevation data and establishing a multi-objective evaluation function encompassing path length, flight altitude, path smoothness, and collision threat; then, introducing a Q-learning reinforcement learning mechanism into the PSO algorithm to construct a state-action mapping and adaptively adjust the learning factor, while designing nonlinear dynamic inertial weights to balance global search and local exploitation; finally, using a reinforcement learning-driven multi-strategy PSO optimizer to iteratively optimize the 3D trajectory and output the optimal flight path. This invention provides an optimization scheme that balances flight safety and path efficiency for trajectory planning problems in complex mountainous environments and threat areas, possessing good practical value and system reliability.

The invention relates to the technical field of new energy power generation scheduling, and discloses a hybrid optimization algorithm-based vehicle network interaction wind and light storage area scheduling method and system, and the method comprises the steps: constructing all equipment models; constructing decision vectors according to equipment types, and setting upper and lower bound and EV connection mask projection rules; calculating the fitness; calling load flow calculation to convert voltage branch out-of-limit, peak power purchase and the like into punishment and directional feedback; gA is triggered in a self-adaptive mode, elitist preservation, linear crossing and differential variation are carried out, and variation amplitude of critical genes is scaled in a self-adaptive mode; obtaining an updated global optimal solution and a feasible decision vector through projection and power flow evaluation; adjusting an inertia weight and a learning factor on line, and performing layered restart and modeling disturbance on non-elite during stagnation; and outputting a result after a joint shutdown criterion is met. According to the invention, on the premise of ensuring the interaction feasibility of the tidal current and the vehicle network, the comprehensive energy consumption cost is reduced, the abandoned wind and light are reduced, and the regional energy balance and renewable energy consumption level are improved.

The invention provides a heavy-duty car intelligent driving track optimization method and device based on a Lagrange function and a medium, and belongs to the technical field of automatic control and track planning. By selecting the longitudinal position, the transverse position, the yaw angle and the control variable of a car, linearization and discretization are conducted on a kinematics model at a reference track point, and the optimal driving track is obtained. And constructing a state-space equation. And introducing a Lagrange multiplier to form a target function, solving a control quantity increment through a partial derivative, and processing a matrix non-full-rank problem by adopting singular value decomposition. And after cut-off correction is carried out on the control quantity by combining actuator constraints, the state variable is substituted into the nonlinear model to update the state variable. Vehicle speed and track curvature information is obtained based on environmental perception, and vehicle speed / steering angle control weight is dynamically adjusted through a preset learning factor, so that weight self-adaption is realized. And iteratively optimizing all the reference trajectory points repeatedly, and finally outputting an optimized trajectory. According to the method, a more stable and more robust track can be generated in a complex dynamic environment.

The invention discloses a workflow cost optimization scheduling method driven by a random forest in a multi-cloud environment, and the method comprises the steps: fusing task features and resource features through a random forest regression model, generating a training sample through three strategies, and screening out high-quality initial particles in combination with an optimization scoring mechanism of cost and overtime penalty; quantifying population diversity by using a dynamic parameter driven by population diversity and a boundary constraint strategy and using an average Euclidean distance from all particles to a population centroid, dynamically adjusting an inertia weight and a learning factor of nonlinear attenuation, and introducing a reflection boundary mechanism to correct particles beyond a range; and finally, designing a dynamic weight fitness function, dynamically adjusting the solution of the time constraint according to the urgency degree of the deadline, and applying gradient penalty, dynamic adaptation performance weight and cost weight. The method is excellent in success rate of searching the optimal solution, and is obviously superior to similar algorithms in the aspect of reducing the scheduling execution cost.

The application provides an energy optimal path planning method and device for unmanned surface vehicle in complex marine environment, and belongs to the technical field of path planning. The method provided by the application 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 using an A* algorithm, and performing bounded random disturbance and safety correction on target particle waypoints; performing smoothing processing on the iteratively generated path by using a B-spline technique, resampling the smoothed optimal solution, and re-injecting the resampled optimal solution into a population; adjusting an inertia weight and a learning factor based on the number of iterations, introducing a double guide factor to perform secondary adjustment on a cognitive term of particle velocity update; embedding the comprehensive energy consumption model as a fitness function into the hybrid enhanced particle swarm optimization algorithm, performing real-time energy consumption evaluation on each candidate path in the iteration process of the algorithm, updating an individual optimal path and a global optimal path, performing path smoothing optimization, and outputting an optimal sailing path of the unmanned surface vehicle.

The application relates to the technical field of unmanned aerial vehicle navigation, and discloses a long aircraft screening method for an unmanned aerial vehicle cluster hierarchical cooperative navigation system, which comprises the following steps: obtaining state data of each wing aircraft in the system according to a self sensor to determine the azimuth angle and the pitch angle of each wing aircraft relative to each long aircraft, and constructing an observability matrix; performing singular value decomposition on the observability matrix, and determining an observability index according to the maximum singular value and the minimum singular value after the decomposition, which is used for quantifying the observability degree of the long aircraft type of a long aircraft combination used in the unmanned aerial vehicle cluster hierarchical cooperative navigation system to the wing aircraft navigation error contribution; and based on a binary particle swarm optimization algorithm, taking the observability index of the unmanned aerial vehicle cluster as an adaptive function, iteratively searching for a target long aircraft combination, wherein in the iteration, the inertia weight of the algorithm is continuously adjusted according to the relationship between the particle fitness value and the group fitness value, and the individual learning factor and the group learning factor are continuously adjusted according to the iteration number.

The invention discloses a photovoltaic array maximum power point tracking method and device, a storage medium and computer equipment. The method comprises the following steps: firstly, setting an initial value of a particle swarm algorithm, and setting a first learning factor and a second learning factor for an inertia weight based on an anti-cosine function; then iteration is started, after k times of iteration, the voltage corresponding to each particle is obtained, and a voltage mean value is calculated based on the voltage; calculating the deviation degree between the voltage and the voltage mean value; when the deviation degree is smaller than a preset value, it is determined that iteration is ended, and the local optimal power and the global maximum power of the photovoltaic array are output. According to the method, two dynamic learning factors are set based on the arc cosine function, so that the speed and accuracy of finding the maximum power point of the photovoltaic array are improved, and meanwhile, the iteration fitness is better and more flexible; furthermore, the iteration termination condition depends on the deviation degree of the individual value relative to the total average value, the number of iterations is reduced, and the iteration speed is improved.