59 results about "Dung beetle" patented technology

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 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 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 present invention discloses a method for optimizing spray quenching process parameters, comprising the following steps: Step 1: Establishing a three-dimensional spray quenching finite element model; Step 2: Embedding an improved quenching factor method into the finite element model; Step 3: Processing the simulation results; Step 4: Establishing a multi-objective optimization model; Step 5: Establishing a single-objective optimization model; Step 6: Solving the multi-objective and single-objective optimization models using a non-dominated genetic algorithm and a dung beetle algorithm, respectively. This method for optimizing spray quenching process parameters is an efficient research method based on existing experimental data, providing methodological and theoretical support for the selection of quenching process parameters.

The invention belongs to the technical field of robot motion control, and discloses a hybrid multi-strategy dung beetle optimization algorithm-based aircraft control parameter setting method, which is characterized in that a hybrid multi-strategy dung beetle optimization algorithm is designed based on a traditional dung beetle optimization algorithm, and an optimization algorithm of population initialization and population position updating is provided. A population position in an initialization stage is uniformly generated by introducing a good point set theory so as to improve diversity of an initialized population, and meanwhile, gravity center reverse learning is added to search global and dimension-by-dimension Gaussian variation after the position is updated so as to improve the global search capability and the convergence speed of the algorithm. The method is suitable for parameter setting optimization of the ADRC controller, the diversity of position updating modes of the water-air amphibious vehicle can be improved, the global search capability and convergence speed of the algorithm can be improved while the algorithm is prevented from falling into a local optimal solution, and therefore the position and attitude control performance of the water-air amphibious vehicle is further improved.

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.

This invention relates to a 3D UAV path planning method based on an improved gray wolf optimization algorithm, comprising: acquiring a 3D environment model; setting the start and end positions of the UAV based on the 3D environment model; employing the improved gray wolf optimization algorithm, using the objective function as the fitness function; updating the positions of all gray wolves synchronously during the iterative process; and outputting the optimal path. This invention introduces a circle chaotic mapping to the gray wolf optimization algorithm to increase population diversity; introduces an adaptive exploration factor to increase the balance between global and local search; combines the global and local development formulas of the dung beetle optimization algorithm to compensate for the slow convergence speed of the traditional gray wolf optimization algorithm; subsequently introduces a sine and cosine strategy to help the algorithm search for potential optimal solution regions, thus better exploring the global optimal solution; and finally introduces a pooling mechanism to randomly recombine stored historical poor solutions to generate new solutions, accelerating the convergence speed and improving the robustness of the algorithm.

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.

This application relates to the field of rolling force prediction technology, and particularly to a method and apparatus for predicting rolling force in cold continuous rolling mills based on IDBO-SA-LSTM. The method includes: optimizing a dung beetle optimization algorithm based on a preset rolling force calculation model, a Circle chaotic mapping strategy, a golden sine strategy, and dynamic weight coefficients to construct an improved dung beetle optimization algorithm; performing performance tests on the improved dung beetle optimization algorithm using various benchmark test functions to obtain an improved dung beetle optimization algorithm that meets preset performance test requirements; automatically optimizing a preset adaptive long short-term memory network based on the improved dung beetle optimization algorithm, and constructing a target SA-LSTM rolling force prediction model using the automatically optimized adaptive long short-term memory network, so as to use the target SA-LSTM rolling force prediction model to predict the rolling force in real time during the online prediction stage. This solves the problem that existing mechanistic models contain many difficult-to-determine empirical parameters, which cannot guarantee the generalization performance and accuracy of cold continuous rolling mill rolling force prediction.

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.

The invention relates to a breathing quality evaluation method based on multiple parameters, big data and data mining. The method comprises the following steps: acquiring parameter data of an artificial respirator to be predicted; parameter data of the artificial respirator to be predicted are input into an MSAGDBO-LSTM model, parameter indexes influencing the breathing quality are output, the MSAGDBO-LSTM model is obtained after training of a training set and testing of a testing set, and the MSAGDBO-LSTM model is obtained by optimizing an LSTM network through an improved dung beetle optimization algorithm; the parameter indexes are input into a random forest model, a breathing quality evaluation grade result is obtained, and the random forest model is obtained through historical parameter indexes and label training of the corresponding breathing quality. According to the method, the state of the breathing quality can be effectively evaluated in real time.

The invention relates to a robot dog path planning method and device and a storage medium, and the method comprises the steps: providing a multi-strategy improved dung beetle optimization algorithm for a high-dimensional feature selection problem of a complex environment, generating an initial population through a refraction reverse learning mechanism, screening an optimal feature subset in combination with an elitism strategy, and obtaining a global optimal position; constructing a collaborative optimization model fusing a position updating module of eagle optimization, a self-adaptive t distribution disturbance module and a Jacobian curve smoothing module; and iteratively updating the globally optimal solution by adopting a dynamic termination condition, and finally outputting a low-dimensional high-precision feature subset. Through the method and the device, the problems of low robot dog decision making efficiency and path planning failure caused by local optimum and slow convergence of a traditional feature selection algorithm in a complex environment are solved, the data dimension is effectively reduced, the calculation time consumption is shortened, quick robot dog decision making and high-success-rate path planning are realized in a dynamic complex environment, and the efficiency of path planning is improved. And meanwhile, the method has strong robustness under electromagnetic interference.

The embodiment of the invention provides a high-altitude agricultural and pastoral area miniature wind and light storage capacity optimization configuration method and system, and the method comprises the steps: constructing a capacity optimization configuration model with the full-life-cycle economic cost minimization of a wind and light storage micro-grid system as a target based on the illumination data, wind speed data and load data of a high-altitude agricultural and pastoral area, the capacity optimization configuration model takes the number of photovoltaic panels, the number of fans, the capacity of a storage battery and the capacity of a super capacitor as decision variables; an improved chaos adaptive dung beetle algorithm is adopted to solve the capacity optimization configuration model; and outputting an optimal capacity configuration combination of the photovoltaic array, the wind driven generator, the storage battery and the super capacitor based on a solving result of the improved chaos adaptive dung beetle algorithm. The technical scheme provided by the invention can effectively cope with special application scenes of weak power grids, dispersed loads and large fluctuation of wind and light resources in high-altitude areas, and the convergence precision and robustness of a system configuration scheme under complex constraints are remarkably improved.

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.

This invention discloses a fuzzy neural network PID liquid level control method based on an improved dung beetle algorithm, belonging to the field of automatic control technology. It solves the technical problem of insufficient ergodicity in common chaotic mappings. The technical solution includes the following steps: S1, establishing a mathematical model of the ozone dissolving tank liquid level control system; S2, designing the improved dung beetle algorithm; S3, designing a fuzzy neural network PID controller; S4, simulation experiments and performance verification of the ozone dissolving tank liquid level control system. The beneficial effects of this invention are: In the dung beetle algorithm, this invention adopts a population initialization strategy combining ICMIC chaotic mapping and back-learning, significantly improving the uniformity of the initial solution and the global ergodicity capability; ICMIC mapping has infinitely folded chaotic characteristics, which can solve the problem of insufficient ergodicity in common chaotic mappings, while the back-learning strategy further expands the search space, making the population distribution more diversified.

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.

The present application discloses a synaesthesia time resource allocation method for spectrum map construction, which belongs to the field of electromagnetic spectrum, including: establishing a time resource allocation model for sensors; setting information age based on timeliness indicators, establishing the relationship between information age and service rate, data arrival rate and packet loss rate in the data transmission stage as constraints of the time resource allocation model; transforming the resource optimization problem in the time resource allocation model after setting the information age constraint into a convex optimization problem; solving the convex optimization problem using the multi-strategy improved dung beetle optimization algorithm MSDBO to obtain the data acquisition T c , channel detection T s and data transmission time T t The optimal time resource allocation scheme for three time periods; MSDBO improves the dung beetle optimization algorithm DBO by setting chaotic pseudo-random, tangent flight search, and vertical and horizontal cross strategies. Addressing the incoherence between communication and perception in existing technologies, this application achieves efficient allocation of synaesthesia time resources during spectrum map construction.

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 invention discloses an autonomous collaborative operation method and system for multiple unmanned agricultural machines and electronic equipment, and belongs to the field of intelligent agricultural machines, and the method comprises the following steps: obtaining real-time communication data and position information between the unmanned agricultural machines based on a Beidou satellite system; farmland environment information and unmanned agricultural machine state information are collected based on various sensors; based on the farmland environment information and the unmanned agricultural machine state information, performing dynamic task allocation by adopting a competition-cooperation strategy to obtain a task allocation scheme; and based on the task allocation scheme, path planning is carried out by adopting an improved dung beetle algorithm, and an optimal autonomous collaborative operation path of the multiple unmanned agricultural machines is obtained. According to the invention, efficient autonomous collaborative operation of multiple unmanned agricultural machines in multiple plots in a large-scale farmland is realized, and the farmland operation efficiency is improved.

The invention provides a three-dimensional underground heterogeneous body inversion method and device and a storage medium, and relates to the field of geological prospecting, and the method comprises the steps: building an observation region coordinate system, recording the coordinates of a load point and the coordinates of an observation point in the coordinate system, and obtaining the observation displacement amplitude of the observation point under the action of a surface simple harmonic concentrated load; determining a parameter search range according to the geological data; model parameters are set and updated through a multi-strategy dung beetle optimization algorithm; performing forward modeling simulation on the model through an indirect boundary element method only needing boundary discretization to obtain a surface displacement amplitude; constructing a fitness function, and calculating the fitness value of the observation displacement amplitude and the earth surface displacement amplitude; and judging whether the fitness value meets the fitness requirement of iteration termination or not. According to the method, the problems that high-dimensional parameter coupling is difficult to process, an inversion result is not unique and forward modeling calculation efficiency is low in a traditional method are solved, and an accurate and efficient solution is provided for engineering geology problems such as underground cavity positioning and karst collapse risk assessment.

The present application discloses a method, system, processor, and storage medium for controlling bottomhole pressure, belonging to the technical field of oil and gas exploration and development. The method comprises: obtaining the actual wellhead pressure and the target wellhead pressure; adjusting the parameters of a pre-built PID controller using a dung beetle optimization algorithm according to the actual wellhead pressure and the target wellhead pressure to obtain the control quantity output by the PID controller; determining the opening control signal of the throttle valve in the well control equipment according to the control quantity based on the transfer function of the predetermined well control equipment; and adjusting the opening of the throttle valve using the opening control signal to achieve control of the bottomhole pressure. The present application can adjust the parameters of the PID controller in real time under different construction conditions, reducing the influence of the nonlinear characteristics of the throttle valve on the PID control, thereby improving the control accuracy of the bottomhole pressure.

This invention discloses a smart building energy consumption prediction method based on an improved DBO-LSTM, belonging to the field of building energy consumption prediction. This method uses an improved dung beetle algorithm to optimize the LSTM parameters, specifically using the optimal individual position in the algorithm as the number of neurons in the two hidden layers of the LSTM and the learning rate to establish the optimal prediction model. Furthermore, it utilizes Tent mapping to initialize the dung beetle population distribution, improving the quality of the initial population distribution and thus enhancing the model's global search capability. Secondly, it proposes using the Levy flight strategy to perturb the optimal solution, enabling the model to escape local optima and explore other regions of the solution space until the global optimum is found, improving the model's optimization accuracy in the search space. This method deeply mines the spatiotemporal characteristics of building operation data, improves the prediction accuracy of the data-driven model, and realizes energy consumption prediction in smart building scenarios.

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.