138 results about "Dung beetle" patented technology

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 relates to the technical field of wireless sensor networks, is applied to a soil humidity monitoring scene in agriculture, and particularly relates to a soil humidity monitoring wireless sensor network coverage optimization method based on a chaos adaptive dung beetle optimization algorithm, which comprises the following steps: constructing a soil humidity monitoring wireless sensor network coverage model of a monitoring area; an improved dung beetle optimization algorithm is adopted to carry out coverage optimization on the soil humidity monitoring wireless sensor network coverage model, an optimized coverage result is obtained, and the improved dung beetle optimization algorithm is obtained through improvement of a Logistic chaos operator and an adaptive quasi-reverse learning strategy. According to the method, the coverage rate and robustness of the agricultural soil humidity monitoring wireless sensor network can be improved, local optimum can be avoided while the convergence speed is guaranteed and global search is improved, and the optimal coverage effect is achieved.

The invention discloses a plant greenhouse intelligent monitoring and environment optimization management system and method. The method comprises the steps that a detection module obtains environment data, resource data and equipment feedback data of a vegetable greenhouse in real time; the acquisition module acquires vegetable types, growth days and growth parameters; the health prediction module calculates the current growth index according to the growth parameters and predicts the index of the next growth stage; the establishing module calculates a difference value between the predicted growth index and the standard growth index through a target function; the parameter optimization module adopts an improved dung beetle optimization algorithm, adjusts an optimization process according to equipment feedback and outputs optimal cultivation parameters; the condition generation module generates a resource scarcity state according to the resource data and provides adjustment limitation; the processing module adjusts a theoretical range based on the optimal parameter and the resource constraint; and the in-greenhouse adjustment module dynamically adjusts the greenhouse environment according to the optimization result. Through integration of sensor data and an optimization algorithm, dynamic adjustment of the resource ratio in the vegetable greenhouse is realized, and the vegetable growth efficiency is improved.

The invention relates to the technical field of path planning, in particular to a path planning method based on a multi-strategy fusion improved dung beetle optimization algorithm. The method comprises the following steps: A, inputting an environment map, and setting a starting point and an ending point; b, initializing parameters of an algorithm; c, an initial population generation process in the dung beetle optimization algorithm is optimized through improved Tent chaotic mapping, and the fitness value of each dung beetle is calculated. The invention provides a path planning method based on a multi-strategy fusion improved dung beetle optimization algorithm. An initial solution of a multi-strategy fusion improved dung beetle optimization algorithm is diversified by adopting a Tent chaos strategy, a penalty term of a turning angle is added into a fitting function, an inertia weight is adaptively updated by using a sine algorithm, and an Ivre flight strategy and a local search mechanism are introduced, so that when a mobile robot moves, the accuracy of the movement of the robot is improved, and the accuracy of the movement of the robot is improved. A simpler and shorter path is obtained, the path efficiency is improved, the search capability in the steering behavior and the smoothness of the planned path are improved, and the convergence speed is accelerated.

The invention belongs to the technical field of unmanned aerial vehicle control, and particularly relates to a multi-unmanned aerial vehicle task allocation method based on a multi-strategy fusion improved dung beetle algorithm, and the method comprises the following steps: solving a flight path and path distance from an unmanned aerial vehicle to a task point through an RRT algorithm; a task allocation mathematical model is constructed by adopting a minimum unmanned aerial vehicle total cost and a maximum task total income quantification objective function; setting a multi-strategy fusion improved dung beetle algorithm initialization parameter, initializing a dung beetle population position and differentiating the population; performing loop iteration on the position of the dung beetle population, and ending population updating until an iteration ending condition is met; and decoding the global optimal solution as a final optimal task allocation scheme. According to the method, efficient, stable and real-time task scheduling can be realized in a complex terrain and a large-scale task environment, and the actual requirements of large-scale unmanned aerial vehicle collaborative operation and complex task scheduling are met.

The present invention provides a method for preprocessing vibration signals of a hydropower unit. The present invention proposes five strategies to improve the optimization accuracy of a dung beetle algorithm so that it has good robustness. The improved dung beetle algorithm MSIDBO jumps out of the local optimum through PWLCM chaotic mapping, nonlinear boundary convergence factor, rolling ball behavior integrating dynamic golden sine, sparrow alert mechanism and parameter adaptive adjustment to improve the accuracy of the optimization problem. With the minimum envelope entropy as the fitness function, MSIDBO is used to optimize parameters such as the white noise amplitude weight Nstd and the number of noise additions NE of ICEEMDAN, and the optimized ICEEMDAN is used to perform modal decomposition of the vibration signal of the hydropower unit. The MSIDBO algorithm has a faster convergence speed, higher solution accuracy and better applicability than the standard dung beetle algorithm. Through simulation signal analysis, it is concluded that the decomposition accuracy of the optimized ICEEMDAN is higher than that of the conventional decomposition algorithm, which improves the accuracy of the preprocessing of the vibration signal of the hydropower unit and provides a basis for the intelligent vibration monitoring and diagnosis of the hydropower unit.

The invention provides a rotating machine fault intelligent diagnosis method based on minimum average composite entropy, and belongs to the technical field of rotating machine fault diagnosis. The method comprises the following steps: inputting a rotary mechanical vibration signal; a minimum average composite entropy is used as a fitness function, and an improved dung beetle optimization algorithm is used to optimize two key parameter modal quantity k and a penalty factor alpha of variational modal decomposition (VMD). The composite entropy divergence is used as a characteristic parameter to extract an effective mode of variational mode decomposition, and a new vibration signal is reconstructed. And converting the new vibration signal into a two-dimensional time-frequency image through short-time Fourier transform, and inputting the two-dimensional time-frequency image into a CNN model integrated with a channel attention mechanism to realize fault diagnosis of the rotating machinery.

The invention relates to the technical field of underwater robots, in particular to an underwater robot cluster path planning method and system based on a dung beetle algorithm, and the method comprises the steps: obtaining a coordinate division region to construct an environment model, calculating and extracting an obstacle distance to fuse ocean current interference to generate a dynamic obstacle avoidance strategy, and evaluating the path speed similarity to generate a candidate path. And acquiring an offset angle difference, normalizing environmental factors to generate a correction path, and calculating a curvature fusion collision probability to generate a global path control instruction. According to the method, a dynamic model is constructed based on Cartesian coordinate division, the spatial analysis precision is improved, a sonar distance and ocean current interference are fused to generate a dynamic obstacle avoidance strategy, the trajectory smoothness is enhanced, candidate paths are screened by using vector similarity and Monte Carlo simulation, the collaboration is optimized, disturbance is reduced through coordinate offset and ocean current correction, and the accuracy of the dynamic obstacle avoidance strategy is improved. The curvature and collision probability are combined to balance safe energy consumption, and multi-dimensional parameter fusion improves cluster adaptability, precision, efficiency and energy utilization rate.

The invention discloses a pumped storage unit speed regulation control optimization method based on PID mu control of a tracking differentiator. The method comprises the steps that 1, a simulation model is built based on parameters of a pumped storage power station; 2, popularizing a PID control differential item from an integer order to a fractional order, and introducing a tracking differentiator to carry out smooth transition on a control signal; and step 3, optimizing the control parameters by adopting an intelligent algorithm. Through a good point set and a Levy flight improved dung beetle algorithm, an objective function is established through linear weighting of an ITAE index, an overshoot and a steady-state error. And 4, performing simulation verification. And performing simulation verification on each working condition of the pumped storage unit, and verifying a control effect through no-load and on-load rotating speed disturbance simulation under different water heads. The method provided by the invention is suitable for improving the control capability of the pumped storage unit under the background of a novel power system with new energy access, and has important significance for ensuring safe and stable operation of the power system.

The invention discloses a fuel cell parameter identification method based on data driving, and the method employs an improved dung beetle optimization (IDBO) algorithm to identify parameters delta 1, delta 2, delta 3, delta 4, Rc, b and theta, obtains optimal model parameters, improves the parameter identification precision of a fuel cell model, and builds a more accurate fuel cell model. The improved dung beetle optimization algorithm is improved as follows: a Cosine-Logistic-Tent composite chaotic mapping strategy is introduced in population initialization, and the uniformity and the ergodicity of the initial position of a dung beetle population are improved; a subtraction average optimizer is adopted to update the position of the dung beetle individual rolling ball, and the development and exploration ability of the algorithm is balanced; an adaptive t distribution disturbance strategy and a greedy strategy are introduced in a dung beetle group foraging stage, so that the global search capability of the algorithm is enhanced, and the calculation time is shortened.

The invention relates to the technical field of mine gas disaster prediction, in particular to a gas concentration time sequence prediction method based on MSADBO-CNN-GRU-Attention. Firstly, dynamic Pearson correlation analysis is used, weighting is conducted on all indexes based on a time window of one hour, key factors leading gas concentration in different time periods are revealed, and therefore interference of irrelevant factors is weakened, and meanwhile the signal strength of the key factors is enhanced. Thirdly, performing local feature extraction on the data by applying a convolutional neural network (CNN), capturing multi-scale information, learning a time sequence data long-term dependency relationship and transmitting state information between time steps in combination with a gating cycle unit (GRU); an Attention mechanism is added to weight the hidden state output by the GRU so as to highlight the importance of key time nodes and improve the accuracy of multi-step prediction. And then, an improved sine algorithm, adaptive Gaussian-Cauchy mixed variation disturbance and a Bernoulli chaotic mapping improved dung beetle search algorithm are fused to obtain MSADBO, and then model hyper-parameters are globally and adaptively optimized. And finally, training the model, establishing a prediction model based on MSADBO-CNN-GRU-Attention, and verifying the performance of the prediction model by comparing the model. According to the gas concentration multi-index multi-step time sequence prediction method provided by the invention, parameter adaptability, feature mining and depth time sequence modeling are combined, the prediction performance of the gas concentration is improved, and a solution is provided for high-precision multi-step time sequence prediction of the gas concentration under complex working conditions.

The invention relates to the technical field of path planning, and provides a multi-strategy collaborative enhancement path planning method and navigation equipment. The path planning method comprises the following steps: acquiring an environment map comprising a starting position and a target position; initializing DBO algorithm parameters; initializing a dung beetle population based on Bernoulli chaotic mapping; calculating the fitness value of each dung beetle; determining the boundary of each dung beetle, and continuously iteratively updating the individual optimal fitness value and the global optimal fitness value of each dung beetle; and outputting the optimal path of the unmanned aerial vehicle until the number t of iterations is greater than the preset number T. According to the method, the initial solution is generated by introducing the chaos mapping based on Bernoulli distribution, so that the diversity and the ergodicity of the initial population are enhanced, premature convergence is effectively avoided, and the global search capability of the algorithm is improved.

The invention discloses a transformer fault diagnosis research method and system, and relates to the technical field of transformer fault diagnosis, and the method comprises the steps: introducing an SHAP value to carry out the feature optimization of transformer fault data based on a decision tree and an integration method thereof; dividing the optimized data into a training set and a test set, and performing data cleaning and oversampling processing on the training set; and optimizing the deep mixed kernel extreme learning machine based on the improved dung beetle algorithm, constructing a transformer fault model, and performing fault diagnosis by using the transformer fault model. According to the feature system provided by the invention, the input features of the transformer fault diagnosis model can be effectively optimized, transformer fault data can be effectively utilized, and the influence of redundant features on the diagnosis model is reduced. Compared with other heuristic artificial intelligence algorithms, the improved dung beetle algorithm provided by the invention has more excellent optimization ability, and can find more excellent parameters of the deep mixed kernel extreme learning machine in the same number of iterations and range.

A DBO-STN-Informer-based aquaculture water dissolved oxygen prediction method comprises the following steps: S1, adopting a dissolved oxygen sensor to collect dissolved oxygen data in aquaculture water online in real time, and dividing the data into a training set and a verification set according to a time sequence after preprocessing; s2, introducing a spatial transformation network STN on the basis of an Informer encoding mode to transform an input time sequence, constructing an STN-Informer prediction model, and performing spatial transformation and feature calibration on input time sequence data through the STN to help an encoder to understand a deep dissolved oxygen sequence; an attention mechanism parameter in the STN-Informer prediction model is optimized by adopting a dung beetle algorithm DBO; and finally, performing test scoring and optimization on the model by using the verification set. And S3, based on the trained and optimized model, outputting a dissolved oxygen concentration prediction value in a future time period. According to the method, the dissolved oxygen concentration in the future time period can be predicted according to the prior data, and the method has very high prediction accuracy, precision and efficiency.

The invention discloses an underwater EHA hydraulic cylinder leakage fault diagnosis method. The method comprises the following steps: preprocessing an original data set of fault data of an underwater EHA hydraulic cylinder to obtain a training data set and a test data set; improving a convolutional neural network model through a dung beetle optimization algorithm in combination with the test data set; processing the test data set through an improved convolutional neural network model to obtain a fault feature classification result of the underwater EHA hydraulic cylinder; and performing visualization processing on the fault feature classification result to obtain visual fault feature data. According to the method, the reconstruction data subjected to adaptive variable mode decomposition denoising is used as the input vector of the CNN model, the improved dung beetle optimization algorithm is adopted to optimize the hyper-parameters of the CNN model, the result judgment is more accurate, the efficiency is higher, the feature data of leakage faults of different degrees of the hydraulic cylinder are subjected to visualization processing, and the fault diagnosis accuracy is improved. And the accuracy and efficiency of fault identification are obviously improved.

The invention is suitable for the technical field of permanent magnet synchronous motor performance improvement, and provides a motor multi-objective optimization method and system based on an improved dung beetle optimization algorithm, and the method comprises the steps: building a mathematical model of the torque ripple and dynamic response capability of a motor, and determining initial design parameters which affect the torque ripple and dynamic response capability; according to the simulation data of the stator skewed slot of the motor, determining an optimal angle meeting a first constraint condition so as to establish a motor model; based on the motor model, motor structure parameters influencing the initial design parameter sensitivity are analyzed, and the obtained motor structure parameters include the stator outer diameter, the permanent magnet thickness, the tooth width and the air gap length; according to the invention, research is carried out from the angle of a stator skewed slot, and an optimal angle with large torque pulsation reduction and small dynamic response capability reduction is obtained; the initial design parameters are subjected to sensitivity analysis to obtain structure parameters, algorithm optimization is carried out after fitting, and multi-objective optimization of the motor is achieved.

The invention provides a photovoltaic output interval prediction method based on CEEMDAN-DBO-BiLSTM, and the method comprises the following steps: S1, decomposing a photovoltaic power sequence into a plurality of mode components through an adaptive noise complete set empirical mode decomposition method according to the nonlinear and non-stationary characteristics of distributed photovoltaic historical data; s2, carrying out secondary decomposition on the high-frequency non-stationary component obtained by primary decomposition, and reconstructing all components into a trend component and an oscillation component by adopting a sample entropy method; s3, optimizing parameters of the bidirectional long-short-term memory network based on a dung beetle optimization algorithm, and finally obtaining distributed photovoltaic point predicted values of the two components; and S4, probability density estimation is performed on the point prediction error of the oscillation component based on a kernel density estimation method, and the point prediction values are superposed to obtain an overall prediction interval result, so that the complexity of the original prediction component is significantly reduced, and the prediction precision of the model is further improved.

The invention provides an unmanned aerial vehicle path planning method based on a multi-strategy dung beetle optimization algorithm, and aims to solve the flight safety problem that an unmanned aerial vehicle collides with ground pedestrians in an urban low-airspace, analyze threats to the pedestrians when the unmanned aerial vehicle runs in a city, construct an unmanned aerial vehicle running risk map, and meanwhile, in the population initialization stage of the dung beetle algorithm, determine the path of the unmanned aerial vehicle in the urban low-airspace. A fusion Logistic chaotic mapping and reverse learning strategy is adopted, the population diversity is increased, multiple improvement strategies such as an adaptive step length strategy, a variable spiral search strategy, a lens imaging reverse learning strategy and a level flight strategy are introduced, the population diversity is enhanced, and the global search ability and the local exploration ability are effectively coordinated. According to the unmanned aerial vehicle path planning method considering the urban operation safety risk of the unmanned aerial vehicle, the operation risk and the path distance of the unmanned aerial vehicle can be effectively reduced, the smooth performance of the path is improved, and the actual environment requirement is better met.

The invention relates to a micro-texture cutter alloy milling force multi-mechanism fusion prediction method under a heat-assisted condition, and belongs to the technical field of titanium alloy mechanical manufacturing. Comprising the following steps: S1, a milling vibration signal noise reduction method: optimizing a wavelet packet threshold noise reduction method of variational mode decomposition based on a dung beetle algorithm; and S2, real-time monitoring and prediction of the milling force: a regression analysis model based on Bayesian optimization, a convolutional neural network, a bidirectional long-short-term memory network and a multi-head attention mechanism. The DBO-VMD-WPT noise reduction method adopted by the invention has the advantages of high-precision noise reduction, high adaptability, wide applicability and the like, and is particularly suitable for processing non-stationary signals in a complex noise environment.

The invention discloses a two-stage rendezvous maintenance task planning method based on a dung beetle optimization algorithm. The method comprises the following steps: S1, establishing an optimization model of a multi-target spacecraft maintenance task; s2, a first-stage optimization algorithm is designed, an improved genetic algorithm is adopted, an initial population is generated, and fitness calculation is carried out; s3, entering second-stage optimization on the basis of an optimization result of the first stage, and adopting a dung beetle optimization algorithm transfer angle as an optimization variable; s4, dynamically adjusting variables in combination with a two-stage solving process of a genetic algorithm and a dung beetle algorithm to realize an optimal solution of task planning; and S5, verifying the performance of the optimization algorithm based on simulation data, and evaluating the effectiveness and applicability of the method through fuel consumption and task completion time indexes. Through introduction of a two-stage optimization strategy, a dynamic path adjustment mechanism and a comprehensive constraint condition, the defects of the prior art in the aspects of global optimization capability, fuel efficiency and time balance and task planning adaptability are overcome.

The invention discloses an unmanned aerial vehicle path planning method in a mountain environment, and the method comprises the steps: firstly carrying out the modeling of the mountain environment, including a planning interval, a terrain environment and a visual obstacle threat environment; constraint conditions such as flight length, flight cost and obstacle avoidance in the flight process are met in the flight process of the unmanned aerial vehicle; the optimal path of the unmanned aerial vehicle is searched for with the purpose of solving the minimum value through the target function; improving a dung beetle algorithm, adopting a composite chaotic mapping mode to improve initialization of a random population, and adopting a nonlinear convergence method to adjust a spawning boundary for breeding dung beetles and a foraging boundary of small dung beetles; introducing a gradient prompt drop population adjustment method to dynamically adjust the proportion of each dung beetle variety in the population in the iteration process; and solving the optimal path objective function of the unmanned aerial vehicle based on an improved dung beetle algorithm to obtain a path planning result. The planning path of the unmanned aerial vehicle in the mountain environment is shorter, the flight safety is higher, the flight cost is lower, and the requirement of the unmanned aerial vehicle for completing the mountain flight efficiency is effectively improved.

The invention discloses a combined model pollutant concentration prediction method and system based on multiple strategies, a storage medium and electronic equipment. The method comprises the following steps: firstly, performing multi-pollutant signal decomposition and feature fusion; then realizing data set division and standardization processing; time sequence window division and data tensor conversion are carried out; then training and preliminarily predicting an LSSVM (Least Square Support Vector Machine) model based on DBO optimization; and finally, carrying out residual error compensation and model performance improvement of an error correction module. According to the method, the hybrid kernel function least square support vector machine and the computational fluid mechanics correction model are innovatively combined, and the improved dung beetle algorithm is utilized to perform multi-objective optimization on the kernel parameters and the error compensation coefficient; and multi-modal feature mapping is realized through a dynamic weight distribution layer, and a photocatalytic reaction kinetic equation is introduced to implement prediction value inversion. The method successfully overcomes the technical bottlenecks that a traditional model lags in response to sudden emission and is insufficient in multi-pollution coupling effect modeling, and provides accurate decision support for air pollution prevention and control.

The invention relates to the technical field of equipment fault prediction, and discloses a low-voltage electrical equipment fault prediction system based on artificial intelligence, and the system comprises a data collection and preprocessing module, a feature engineering module, a prediction module, and an optimization hyper-parameter module. The system extracts time sequence features, frequency domain features and fault mode features by collecting electrical sensor and monitoring data, and based on an enhanced TCN-Learar-BiLSTM model, realizes accurate prediction of equipment health status and fault risk by combining alternate expansion convolution, trend and seasonal decomposition and linear modeling technologies; meanwhile, through joint optimization of an improved faecium beetle algorithm and an immune algorithm, hyper-parameter configuration of the model is dynamically adjusted, and prediction accuracy and stability are remarkably improved; the system is suitable for the field of electrical equipment operation and maintenance and intelligent power management, and can effectively improve the equipment fault early warning capability and the operation reliability.

The invention belongs to the field of transformer fault diagnosis, and relates to an improved dung beetle algorithm optimized support vector machine-based transformer fault diagnosis method, which comprises the following steps of: searching DGA samples of five fault types of high-temperature overheating, medium-temperature overheating, high-energy discharge, low-energy discharge and partial discharge in a fault case; carrying out normalization processing on the collected DGA samples, and enabling features of different dimensions to have comparability; performing sample division on the normalized data according to the ratio of a training set to a test set being 8: 2; a DBO algorithm is utilized to obtain an optimal kernel parameter g, a penalty factor c and an optimal fitness value, and global search and local development are both considered; and substituting the test set into the HDBO-SVM fault classification model under the optimal kernel parameter g and penalty factor c for testing to obtain a fault diagnosis result under the data set. The method promotes the global search in the initial stage of iteration, accelerates the convergence speed, improves the diagnosis precision, and accelerates the diagnosis speed.

The invention belongs to the technical field of coal mining settlement prediction, and particularly relates to a coal mining settlement prediction model parameter inversion method based on an improved dung beetle optimization algorithm, and the method comprises the steps: obtaining coal mine geological mining parameters and the actual measurement data of an earth surface observation point; logistic chaotic mapping is adopted to initialize a dung beetle population, and the dung beetle population is divided into four classes of roll dung beetles, breeding dung beetles, small dung beetles and theft dung beetles; the method comprises the following steps: improving position updating of roll dung beetles through a global exploration strategy fused with an eagle algorithm, updating a breeding dung beetle spawning area by adopting a dynamic boundary strategy, introducing an adaptive t distribution disturbance strategy to optimize foraging behaviors of small dung beetles, and randomly disturbing and updating positions of stolen dung beetles near an optimal solution; and finally, constructing a fitness function according to the sum of squares of errors of the measured value and the predicted value, and carrying out iterative optimization until an optimal parameter solution is obtained. According to the method, the convergence speed and the global search capability of the algorithm are remarkably improved, and the problems that a traditional method is prone to falling into local optimum, poor in anti-interference capability and the like are effectively solved.

The invention discloses a threat entity recognition and intelligence processing method based on BERT assistance, and the method comprises the following steps: S1, collecting and preprocessing threat intelligence text data, and constructing an input corpus data set; s2, inputting to a BERT encoder, extracting context semantic vectors, and obtaining a semantic feature vector sequence; s3, inputting the semantic feature vector sequence into a gating recursion unit, modeling time sequence features, and outputting a hidden state sequence; s4, based on the hidden state sequence, performing threat entity label labeling through a decoding layer; s5, adopting a dung beetle optimization algorithm to optimize BERT and gating recursive unit parameters; and S6, performing reasoning prediction by using the optimal parameter, and outputting and storing structured threat entity information. According to the method, the BERT encoder, the gating recursion unit and the improved dung beetle optimization algorithm are fused, so that high-precision recognition and structured extraction of the threat entities in the threat intelligence text are realized.

The invention discloses a control method based on a vehicle-mounted equipment rotation system, and belongs to the technical field of PID control optimization, and the method specifically comprises the steps: 1, constructing a PID control model based on the vehicle-mounted equipment rotation system, which comprises an improved dung beetle optimization algorithm model, a permanent magnet synchronous motor control model, a PID controller, a space vector pulse width modulation model and an observer model; step 2, improving a dung beetle optimization algorithm, wherein the specific implementation is as follows: D1, processing a dung beetle individual position cross-border problem by using a boundary reflex method; d2, introducing successful experience of learning other dung beetles in the dung beetle stealing process, and guiding the stealing behavior through the historical optimal position; step 3, optimizing a current inner loop PID controller of the permanent magnet synchronous motor control system by using an improved dung beetle optimization algorithm to obtain optimal Kp, Ki and Kd control parameters of the current inner loop PID controller; and step 4, modeling simulation is carried out on the control of the vehicle-mounted equipment rotation system by adopting MATLAB (Matrix Laboratory).

The invention discloses a wireless sensor network hierarchical routing optimization method and system based on a quantum dung beetle mechanism, relates to the technical field of wireless communication, and is used for solving the technical problems of unbalanced energy consumption and short network life of a wireless sensor network in a traditional method. The method is technically characterized by comprising the following steps: establishing a wireless sensor network system model; determining a clustering number and dynamically clustering, and establishing a network node dynamic clustering cost function at the same time; a quantum dung beetle group is initialized, parameters are set, the organic matter decomposition degree of all quantum dung beetles is calculated, and the global optimal quantum position of the quantum dung beetle group is determined; the quantum positions of the quantum dung beetles are evolved, and a global optimal quantum position is obtained through a selection mechanism; determining quantum dung beetle mechanism evolution termination, and outputting a current wireless sensor network cluster center; performing dynamic cluster head election on the wireless sensor network; and the wireless sensor network performs stable data transmission and updates the network state at the same time. The energy consumption efficiency of the wireless sensor network is effectively improved, and the service life of the network is effectively prolonged.

The invention discloses a multi-unmanned aerial vehicle flight path planning method based on a multi-mode cooperative dung beetle optimizer. The method comprises the steps that 1, a dung beetle optimization algorithm is initialized; 2, updating the position of the dung beetle; 3, mapping position elements exceeding the upper and lower bounds back to the effective search area to obtain a dung beetle position updating result and calculating a population fitness value; 4, updating the position of the dung beetle and calculating a population fitness value; 5, updating the position of the small dung beetle and calculating a population fitness value; step 6, updating the position of dung beetle theft and calculating a population fitness value; 7, comparing population fitness values, and recording a maximum value, a minimum value and corresponding position results; and step 8, iterating to obtain a final position result. Compared with DBO, QHDBO and route planning, the total cost of the method is respectively reduced by 80.78% and 63.8% in a simple scene, and is respectively reduced by 83.96% and 68.89% in a complex scene, and high efficiency and practicability of the method are proved.

The invention discloses a strip mine slope stress prediction method and system based on VMD-DBO optimized GRU, and relates to the technical field of mine engineering safety monitoring. According to the method, VMD (variational mode decomposition), a dung beetle optimization algorithm (DBO) and an improved gating cycle unit (GRU-A) are combined, and decomposition and noise reduction are performed on stress data through the VMD so as to extract key features; dBO is utilized to optimize and improve hyper-parameters of a gating cycle unit GRU-A, and the learning ability of the model is enhanced; and in combination with a self-attention mechanism, key features of the stress time sequence are dynamically captured. According to the strip mine slope stress prediction method and system based on VMD-DBO optimization GRU, the prediction result is more accurate compared with a traditional method, and an efficient and reliable solution is provided for surface mine slope stress prediction and landslide disaster early warning.