26 results about "Dung beetle" patented technology

The invention relates to the technical field of wireless sensor networks, in particular to a heterogeneous wireless sensor network coverage method for a mountain new energy construction site, which comprises the following steps of: constructing a three-dimensional mountain monitoring area, inputting related parameters and establishing a multi-target optimization model taking the non-coverage rate, the reciprocal of the connectivity rate and the minimum total energy consumption as targets; initializing a dung beetle population, dividing the dung beetle population into four classes, obtaining an initial coverage scheme, and screening a non-dominated coverage scheme solution set for storage; iteratively updating the positions of various types of dung beetles based on a multi-target dung beetle optimization algorithm; the current coverage scheme and the archive set are combined, the archive set is updated after non-dominated sorting, and dense solutions are discarded to guarantee diversity when the scale is over-scale; and outputting a solution set after the maximum number of iterations is reached, and selecting a target scheme to complete node deployment. The method is adaptive to complex mountain scenes, network coverage and communication quality are improved, energy consumption is reduced, local optimum is avoided, and mountain monitoring effects of high coverage, low energy consumption and strong adaptation are achieved.

The application provides a signal denoising method based on a dung beetle optimizer (DBO) optimized wavelet threshold, and belongs to the technical field of signal processing. The method comprises the following steps: firstly, performing CEEMDAN decomposition on a signal to obtain a plurality of IMF components; adopting a dung beetle optimizer (DBO) to select an optimal threshold for a wavelet threshold denoising method; for an IMF component with a relatively small correlation coefficient in CEEMDAN decomposition, adopting the threshold selected by the dung beetle optimizer to perform wavelet threshold denoising; and reconstructing signals of the denoised IMF component and other IMF components to obtain a denoised signal. The application has applicability, can effectively solve the problems of threshold selection difficulty and signal noise removal, retains useful information in the signal, and improves the effectiveness of the signal.

The invention discloses a gas insulation cabinet partial discharge monitoring method and device based on edge calculation, and relates to the technical field of gas insulation cabinets. The method comprises the following steps: collecting environmental data and background electromagnetic noise signals of the gas insulation cabinet, calculating an environmental noise correlation coefficient by using a maximum information coefficient method, combining an environmental deviation value to obtain a comprehensive environmental disturbance index, and correcting a preset background noise threshold value to obtain a corrected noise threshold value; if the real-time electric signal amplitude exceeds the corrected noise threshold, marking the data as partial discharge signal data; denoising the partial discharge signal data by adopting an improved ensemble empirical mode decomposition adaptive noise method, and extracting a discharge feature vector; calculating the mahalanobis distance of each feature vector in the discharge feature vector sequence, obtaining the discharge similarity, obtaining a homologous discharge link, extracting the feature data of the homologous link, inputting the feature data into a BP neural network model based on a dung beetle optimization algorithm, outputting the type of partial discharge, and carrying out the early warning, thereby achieving the monitoring of the partial discharge of the gas insulation cabinet.

The invention discloses a teenager long-distance running strategy method fusing deep learning and an improved swarm intelligence optimization algorithm, and the method comprises the following steps: collecting multi-dimensional time sequence features in the historical training and competition process of athletes, and constructing and training a CNN-LSTM-Attention prediction model based on the multi-dimensional time sequence features; establishing an integral form long-distance running strategy optimization model framework with the goal of minimizing the game completion time; dividing the long-distance running process into a plurality of continuous time sequence windows, calling a CNN-LSTM-Attention model at the starting moment of each window to generate a state prediction result as a dynamic constraint, and constructing a state transition equation; and based on the state transition equation, solving the optimal speed under the current time sequence window through an improved dung beetle optimization algorithm, rolling to the next window after execution, and carrying out iterative execution until the optimal speeds under all time sequence windows are output.

A CO2 injection capability prediction method for optimizing a BP neural network based on a dung beetle algorithm comprises the steps that S1, data collection and preprocessing are carried out to obtain an optimal sampling point set, and the optimal sampling point set comprises a verification set and a training set; carrying out data collection by adopting an optimal Latin hypercube sampling strategy; s2, constructing a BP neural network model; s3, inputting the training set into the BP neural network model, and randomizing a weight value and a threshold value in the BP neural network model to realize initialization of the weight value and the threshold value of the BP neural network model; s4, optimizing the BP neural network model by adopting a dung beetle algorithm; s5, further training the optimized BP neural network model; and S6, evaluating the training effect of the BP neural network model and determining whether to output or not. The method has the advantage of high prediction accuracy.

This application belongs to the field of truss boom parameter optimization technology, specifically involving a multi-objective optimization design method and system for port crane truss booms. First, the minimum set of parameters that determine the boom structure is selected as the design parameters. The design space is sampled based on the optimal Latin hypersolution method. Computational fluid dynamics-static fluid-structure interaction calculations are carried out taking into account the characteristics of the shore wind field to obtain the wind resistance, strength, and stiffness indices of each sampling point. A surrogate model is constructed using the deep learning DAL algorithm, with dimensions as input and wind load, strength, stiffness, and other indices as outputs. Furthermore, the optimal design value within the design space is obtained by improving the dung beetle optimization algorithm, and the optimal design value that satisfies the premise of strength, stiffness, and stability is constructed and output.

The invention discloses a same-protocol network flow privacy protection method based on flow-level statistical enhancement, and belongs to the technical field of network flow privacy protection. According to the method, flow-level multi-dimensional feature representation is constructed, a flow feature sample with both endogenous stability and extrinsic dynamics of a target flow sample is generated based on a steady-state variation generation model, and representative samples are selected based on feature state difference minimization to construct optimal target distribution. A confusion strategy optimization model oriented to flow-level multi-dimensional feature difference minimization is established, a global confusion strategy is solved by adopting a multi-strategy dung beetle optimization algorithm, and real-time fitting of flow-level features is realized through a packet-by-packet confusion method based on a real-time receptive field. According to the method, a deep learning identification method based on flow-level statistical characteristics can be effectively resisted, and privacy protection of network flow of the same protocol is realized under controllable cost.

This invention relates to a method and apparatus for tuning control parameters of magnetic levitation bearings, a storage medium, and an electronic device, belonging to the technical field of magnetic levitation control parameters. The tuning method includes: S1, constructing a mathematical model of a PID parameter optimization problem; S2, generating a Bayesian optimization sample set, fitting a Gaussian process surrogate model, and globally exploring to generate a PID parameter subspace; S3, executing a dung beetle optimization algorithm within the subspace to obtain the optimal point (x) within the subspace. db y db S4, add the optimal point to the Bayesian optimization sample set and update the Gaussian process model; S5, repeat steps S2 to S4 until the condition is met, and output the optimal PID parameter x. db * By combining the global exploration capability of Bayesian algorithms with the local development capability of the dung beetle algorithm, the Bayesian optimization reduces the dependence on the initial sample size, while the dung beetle algorithm accelerates convergence within the subspace, thus balancing efficiency and accuracy.

The invention discloses an air quality index prediction method based on BP optimized by an improved dung beetle algorithm. Aiming at the problems that a BP neural network is difficult in parameter adjustment and poor in prediction model precision, a multi-strategy improved dung beetle optimization algorithm FLADBO is provided to optimize a prediction model of the BP neural network, Logistic chaotic mapping is introduced to solve the problem that initial population distribution is uneven, and an elite reverse strategy is introduced to improve population diversity; in the small dung beetle foraging stage, dynamic weights are used, and the optimization capacity of foraging dung beetles is improved; in order to balance the global search capability and the local search capability of the algorithm, the flood algorithm FLA is fused, and the parameter R is nonlinearly adjusted. And the global search capability and the local search capability of the algorithm are improved, so that the prediction precision and the stability of the algorithm are improved.

The invention relates to the technical field of electric vehicle charging guide strategies, in particular to an electric vehicle optimal scheduling method and system based on a dung beetle optimization algorithm, and the method comprises the steps: collecting and screening electric vehicle data, and removing error value and wrong value user data, and obtaining a target charging data set; constructing a master-slave game model taking an electric vehicle agent as an upper layer and a user as a lower layer based on the target data set, and obtaining a constraint condition of the charging data; and constructing an improved dung beetle optimization algorithm as an electric vehicle scheduling algorithm based on the dung beetle optimization algorithm, solving the game model by adopting the improved dung beetle optimization algorithm, and evaluating and testing an optimization result. According to the electric vehicle optimal scheduling method based on the dung beetle optimization algorithm, the global and local search capability of the algorithm is improved through the improved dung beetle optimization algorithm, global exploration and local development are considered, the method has the characteristics of high convergence speed and high solving precision, and the win-win expected effect of a power grid, an electric vehicle agent and a vehicle owner is achieved.

This application discloses a method, apparatus, electronic device, and medium for predicting wind turbine vibration faults using a VMD-based DBO-BiLSTM approach. The method may include: collecting operational data and establishing a dataset to be analyzed, wherein the operational data includes vibration signals under normal operation and fault conditions; preprocessing the vibration signals in the dataset based on the VMD algorithm; constructing a parameter optimization mathematical model according to the dung beetle optimization algorithm to obtain optimized parameters; constructing a BiLSTM network model based on the optimized parameters; inputting the preprocessed data into the BiLSTM network model to predict the fault type. This invention overcomes the shortcomings of existing wind turbine vibration fault prediction technologies and improves the accuracy and real-time performance of fault prediction.

The application particularly relates to a kind of meme dung beetle configuration optimization methods for distributed electronic reconnaissance equipment station distribution, the method is aimed at the problem that existing configuration optimization algorithm converges slowly and is easy to fall into local optimum, improved dung beetle optimization algorithm is combined with multi-objective meme strategy, including: the average value of GDOP in the positioning scene of TDOA and AOA is used to build fitness function;In the improved dung beetle optimization algorithm, the role division mechanism is introduced, the population is divided into four categories of rolling ball, breeding, small dung beetle and stealing dung beetle, and different position updating strategies are respectively executed;At the same time, multi-objective meme strategy is used, cross operation is carried out through the construction of multiple-component gene population MCGP, and the global search ability and convergence speed of the algorithm are significantly improved by combining with local pivot LP search mechanism.The application can quickly provide high-precision optimal electronic reconnaissance equipment deployment configuration for multi-station passive positioning system, effectively improve the positioning performance and system robustness.

The invention provides a highlight target identification method based on a dung beetle optimization algorithm and an adaptive threshold segmentation algorithm, and the method comprises the steps: carrying out the Gaussian filtering of continuous frames of active and passive images to remove a part of noise, and carrying out the morphological opening operation to reduce the noise and maintain the feature size of a target; then registering the images, eliminating the image average gray difference of the active image and the passive image caused by the influence of active light illumination by using image difference operation, and preliminarily extracting a possible target area; then optimizing the maximum between-cluster variance by using a dung beetle optimization algorithm to determine a difference image segmentation threshold value, binarizing the difference image, and further extracting a target candidate region; for the candidate target area, the highlight target is identified by adopting comprehensive shape measurement of circularity and eccentricity, and the method has higher detection accuracy; the efficient search mechanism of the dung beetle optimization algorithm meets the real-time requirement, and the processing speed is higher; the method is suitable for actual scenes with uneven illumination and complex noise, and has higher robustness.

The invention discloses an efficient two-dimensional irregular sample layout method. The method comprises the following steps: S1, constructing a target function by taking the minimum length of a master mask occupied by all samples after layout as a target; s2, whether the number of the contour points in the contour point set of each sample is larger than a preset contour point number is judged, if yes, the contour points in the contour point set of the corresponding sample are reduced through a feature angle guide algorithm and then connected in sequence to form a feature contour, and if not, the contour points in the contour point set of the corresponding sample are reduced; sequentially connecting the contour points in the contour point set of the corresponding sample directly to form a feature contour; and S3, optimizing the layout sequence and the rotation angle of the feature contours of all the sample pieces through a dung beetle optimization algorithm to obtain a layout result. According to the method, the layout solving efficiency and the raw material utilization rate are effectively improved.

The invention relates to the technical field of desulfurization processes, and discloses a natural gas deep desulfurization process flow simulation modeling method based on a proxy model, which comprises the following steps: acquiring historical production data of a natural gas deep desulfurization absorption tower as a data set required for constructing the proxy model, and dividing the data set into a training set, a verification set and a test set in proportion; improving a rolling ball behavior and a foraging behavior in an original dung beetle algorithm to obtain a new dung beetle algorithm, optimizing the weight and bias of the deep neural network based on the new dung beetle algorithm, the training set and the verification set to construct a proxy model, and testing the proxy model through the test set; and combining the constructed agent model with a mechanism model to jointly form a whole-process simulation model for the natural gas deep desulfurization process flow. The prediction precision of the model under the actual production working condition can be effectively improved.

The invention relates to a temperature compensation method and device of a displacement sensor, and belongs to the technical field of temperature compensation of displacement sensors. The method comprises the following steps: constructing a BP neural network model, and optimizing an initial weight and threshold combination of the BP neural network model by using a dung beetle optimization algorithm so as to minimize an error of the BP neural network model; and training the optimized model by using the collected data, and performing temperature error compensation on the real-time output of the displacement sensor by using the trained model. According to the invention, through deep fusion of the dung beetle optimization algorithm and the back propagation neural network, the measurement stability and precision of the displacement sensor in a full temperature range are effectively improved, and an efficient and robust new scheme is provided for solving the problem of temperature drift in precision measurement.

The application discloses a method for solving the multi-objective optimization problem, comprising the following steps: S1), designing a target function, constructing a weighted single-target function to realize collaborative optimization; S2), an initialization stage: adopting refraction reverse learning and an elite selection strategy; S3), a rolling ball stage: fusing a Pareto guide and an Osprey search thought; S4), a breeding dung beetle stage: introducing an adaptive t-distribution disturbance mechanism; S5), a foraging dung beetle stage; S6), a stealing dung beetle stage: fusing a multi-objective collaborative sharing mechanism of the Pareto guide; S7), merging offspring and parent populations and updating an external solution set; and S8), after iteration termination, outputting an optimal track. The application has excellent performance in path search and obstacle avoidance ability under complex terrain, and introduces an adaptive t-distribution disturbance and a Jacobian curve smoothing mechanism, which significantly enhances global jumping and local fine search, and guarantees the smoothness of the planned track and the multi-objective convergence quality.

This invention discloses a UAV trajectory planning method based on a collaborative enhancement-based dung beetle optimization algorithm. First, a multi-constraint optimization model is established with the objective of minimizing the weighted total cost, integrating distance, altitude, attitude, and obstacle avoidance costs. Second, a high-quality initial population is generated using a hybrid strategy of sinusoidal piecewise mapping and quasi-backward learning. Furthermore, multiple strategies are collaboratively applied in the iteration process for optimization: global exploration based on dynamic anchor points and guiding perturbations; exploration and development using nonlinear cosine factors for dynamic balancing; local search switching between lens imaging backward learning and adaptive step size strategies based on dynamic probability; and co-evolution promoting information exchange through cross-operations. Finally, the globally optimal individual is decoded and the trajectory is smoothly output. This invention can efficiently generate safe, smooth, and energy-efficient UAV flight trajectories.

The invention discloses a motor active-disturbance-rejection control method based on an improved dung beetle optimization algorithm, and the method comprises the steps: 1, building a mathematical model of a permanent magnet synchronous motor, and deducing dynamic voltage and current equations of the permanent magnet synchronous motor under different coordinate systems; 2, establishing a rotating speed loop active disturbance rejection controller model, which comprises the following steps: designing a rotating speed loop by adopting a first-order ADRC algorithm, and determining control parameters needing to be set; 3, an improved active-disturbance-rejection controller is established, system tremor is reduced, gain distribution is optimized, and the anti-disturbance capability of the active-disturbance-rejection controller is enhanced; and 4, optimizing the to-be-set parameters through the improved dung beetle optimization algorithm so as to establish the permanent magnet synchronous motor control system of the motor active disturbance rejection device based on the improved dung beetle optimization algorithm. According to the invention, efficient and accurate setting of ADRC parameters can be realized, so that the motor stably responds to an instruction under complex conditions and dynamic loads.

The invention discloses an intelligent production line green collaborative scheduling optimization method and system based on an improved dung beetle algorithm, and belongs to the field of intelligent production line green collaborative scheduling optimization. The intelligent production line green scheduling model is initialized by adopting a random initialization population, a time priority earliest processing rule and an energy consumption priority earliest processing rule, and the intelligent production line green scheduling model is solved by adopting an improved swarm intelligence optimization algorithm to obtain a production line process arrangement corresponding to an optimal solution of completion time and production energy consumption. According to the method, multi-target collaborative optimization of minimizing the maximum completion time and production energy consumption can be realized, the condition that process parameters of the processing speed and the carrying speed are adjustable is considered, and a production line scheduling scheme comprehensively considering green and efficiency collaborative optimization is formulated from the aspect of energy saving.

The invention provides dung beetle feed as well as a preparation method and application thereof. The dung beetle feed is prepared from components as follows: animal-derived protein, a sugar source, a microbial agent and water, the bacterial strain in the microbial agent comprises any one or a combination of at least two of pseudomonas bacteria, faecalis bacteria or bacillus bacteria. The dung beetle feed provided by the invention is low in price and easy to produce, can replace the dung beetle feed, realizes standardization of feeding of the dung beetle feed and balanced nutritional ingredients, has universality to various dung beetles, and remarkably improves the survival rate of the dung beetles.

This invention discloses a traditional Chinese medicine composition and its preparation for rheumatic arthralgia. The composition consists of nine herbs: *Hedyotis diffusa*, earthworm, *Eupolyphaga sinensis*, centipede, silkworm, scorpion, dung beetle, *Sinomenium acutum*, and *Zaocys dhumnades*. The composition exerts its efficacy through specific weight ratios, and is particularly suitable for treating joint pain and deformities caused by rheumatoid arthritis and osteoarthritis. The key to this invention lies in the heavy use of multiple insect-derived medicinal materials, which can penetrate deep into the meridians and collaterals, exerting a powerful effect of dispelling wind, clearing the meridians, relieving dampness and pain, and breaking up blood stasis and dissipating nodules in stubborn rheumatic arthralgia. Simultaneously, specific processing techniques are specified for the key insect-derived medicinal materials, such as wine-processing, salt-processing, roasting, and wheat bran-frying, to reduce toxicity, enhance efficacy, and ensure medication safety. The traditional Chinese medicine composition of this invention can be further prepared into various modern dosage forms such as powders, granules, and capsules, facilitating patient administration and clinical application.

1. Name of the designed product: doll (dung beetle). 2. Use of the designed product: doll. 3. Design points of the designed product: in shape. 4. Picture or photo best indicating the design points: perspective view.

The invention relates to the technical field of vehicles, in particular to a model training and braking intention recognition method and device, a medium and equipment. In the braking data set, according to a Spearman correlation analysis method, braking data related to the braking intention of the driver is determined to serve as training braking data, and the braking intention corresponding to the training braking data is determined to serve as a training label; and the training braking data are preprocessed. And optimizing the preprocessed training braking data according to a dung beetle optimization algorithm. And inputting the optimized training braking data into a to-be-trained braking intention recognition model to obtain a predicted braking intention. And determining a loss value according to the difference between the predicted braking intention and the training label, and training a to-be-trained braking intention recognition model according to the loss value. The input of the braking intention recognition model is optimized through the dung beetle algorithm, and the accuracy of the braking intention recognition model for recognizing the braking intention of the driver is improved.

The invention discloses a path planning method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining environment data through a sensor, and constructing a static environment model according to the environment data; determining an initial position of a target object in the static environment model; using a multi-strategy improved dung beetle algorithm to determine an initial path corresponding to the initial position in a static environment model; the initial path is evaluated to obtain an evaluation result, the evaluation result is utilized to update the initial path to obtain a target path, the evaluation result of the target path is that the evaluation reaches the standard, efficient path planning of the target object in the complex static environment is achieved, and through combination of a golden sine algorithm and a Cauchy Gaussian mutation algorithm, the path planning efficiency of the target object in the complex static environment is improved. The convergence speed of path planning is effectively improved, and the working efficiency is greatly improved.

The invention discloses a micro-grid dispatching optimization method and device based on an improved dung beetle optimization algorithm, and the method comprises the steps: constructing a micro-grid dispatching model, building a dual-objective function of the operation and maintenance cost of a micro-grid and the environmental protection cost, and obtaining the operation parameters and constraint conditions of the micro-grid; optimizing the micro-grid dispatching model by using an improved multi-target dung beetle optimization algorithm; the improvement on the multi-target dung beetle optimization algorithm comprises the following steps: generating an initialized population with strong diversity by adopting a mixed initialization strategy of Latin hypercube sampling and cosine similarity reverse learning; the acceptance degree of young balls and dung beetles on the optimal solution is improved; a disturbance strategy of a following mechanism of the sparrow search algorithm is fused into a dung beetle algorithm; cauchy Gaussian variation is introduced, and variation amplitude is dynamically adjusted in an algorithm iteration process. Compared with the prior art, the method has more excellent optimization precision, and has important theoretical value and practical significance for improving the operation economy and environmental friendliness of the micro-grid.