106results about How to "Speed ​​up the optimization process" patented technology

The invention relates to a pavement crack image detection method. The method comprises the steps of: carrying out graying and filtering processing on a collected pavement image, constructing a pulse coupling neural network PCNN model, utilizing a genetic algorithm to rapidly find advantages of an optimal solution in a non-linear manner in a solution space so as to optimize important parameters of the model, and rapidly and accurately segmenting cracks and a background in the image; then according to the characteristics of the image after the segmentation, carrying out connected domain detection on the whole image, and filtering out the interference of noise and background textures; and finally, extracting a crack skeleton, calculating the maximum widths of the cracks along the normal line of the skeleton, and making marks in the original image. According to the invention, the digital image processing technology is adopted, the genetic algorithm is utilized to optimize the parameters of the PCNN model, optimization searching is accelerated, the iteration times f the PCNN are reduced, and the iteration is more liable to come to convergence, the interference resistance of the segmentation effect is relatively high, and the segmentation is more accurate; in addition, the modes of connected domain rectangularity, circularity filtering and irregular noise filtering are utilized to filter out a large number of irregular patches, and convenience is provided for the crack detection.

The invention discloses a double-channel pump optimization method based on a multi-objective genetic algorithm. Turbulence calculation and an optimization algorithm are combined in the method and through the optimization algorithm, an optimum solution of structure parameters of a double-channel pump is found, so that the method can provide reliable design basis for workers lacking of rich design experiences and the quality of the design is improved. The training sample of an artificial neural network model in an optimization model is obtained through CFD analysis; a large number of temporary pump hydraulic performances generated in the optimization process are obtained through approximation model prediction; and therefore, computational accuracy is guaranteed, optimization-finding process is greatly accelerated, and time is shortened.

A microgrid photovoltaic power station generating capacity combined forecasting method belongs to the new energy power generation output forecasting field. The method comprises the following steps: 1) taking generating capacity data of a photovoltaic power station in a microgrid as input / output data of a forecasting model; 2) carrying out generating capacity forecasting through wavelet transform and a BP neural network; 3) carrying out generating capacity forecasting by utilizing a particle swarm algorithm to optimize a support vector machine; 4) obtaining model test weights by utilizing a variance-covariance combination method; and 5) calculating combined forecasting result according to the weights and model output corresponding to the new input. Compared with an existing forecasting method, the forecasting method starts from a new angle, avoids considering complex external factors and adopts a combination method, and has the advantages of being good in real-time performance, high in accuracy and high in adaptability and the like.

The invention provides an improved immune taboo algorithm based on an immune algorithm targeting agricultural machine task dispatching. The improved immune taboo algorithm comprises the steps that (1) all units are initialized; (2) an antibody population is initialized; (3) antibody population diversity is evaluated; (4) a current optimal fitness value is recorded, and average fitness is calculated and recorded; (5) a memory bank is updated, and a parent antibody population is formed; and (6) termination conditions are judged. According to the improved immune taboo algorithm, at a variation stage of the immune algorithm, a TSA operator based on a taboo search algorithm is designed by improving a neighborhood solution generation mode; at the variation stage and a fractal iteration stage of the immune algorithm, a search result of the TSA operator is adopted to serve as an antibody after variation so as to improve the climbing performance of the algorithm and increase the rate of convergence; and later, a uniform variation and taboo search concurrent strategy is adopted to guarantee population diversity and shorten optimization time. The improved immune taboo algorithm is applied to agricultural machine dispatching and is suitable for all kinds of agricultural production practice to enhance production service efficiency of agricultural machines.

The invention provides a spark-based support vector machine parameter optimization parallel particle swarm optimization method which is applied to machine learning model training. The initialization information of all nodes is converted into information of an RDD form and is stored. The cross validation of node particles of different address coordinate parameters is carried out, and the node particles are mapped as an accurate rate of cross validation and the individual extremums of particles. The individual extremums of all particles are used to find a global extremum. A node particle is updated in each RDD according to the global extremum. Whether the global extremum reaches a target accurate rate or a number of iterations reaches a limit or not is judged, an optimization process is ended if so, otherwise the parallel cross validation of the node particles is repeated. The running speed of the optimization process is fast, the search range is large, and the searching of global optimum by the particle swarm algorithm is accurate and rapid.

The invention discloses a molecular marker related to the grain yard seed number character and a detection primer and application thereof. The molecular marker is CGN-06-168 and is located at the position 5236168bp of the sixth chromosome, and the basic group is C / T. By means of the molecular marker and the detection primer thereof, the grain yard seed number can be predicted, and molecular auxiliary technical support is provided for early-stage authenticating, screening and breeding of the grain yard seed number character.

The invention discloses a method for predicting inhibiting concentration of a pyridazine HCV NS5B polymerase inhibitor based on a particle swarm optimization (PSO) support vector machine (SVM). The method comprising the following steps: establishing and optimizing a sample set, calculating inhibitor molecule descriptor, preprocessing a molecule descriptor data set, rescaling the inhibitor molecule descriptor data set, dividing the data set, optimizing support vector machine parameters, establishing a model and predicting. The method for predicting the inhibiting concentration of the pyridazine HCV NS5B polymerase inhibitor based on the particle swarm optimization support vector machine is an SVM parameter selecting method based on a PSO algorithm, the optimization of the model is achieved by using the high global searching ability of the PSO, a relational model is established through the structure and the inhibiting concentration of the pyridazine HCV NS5B polymerase inhibitor, accurate predicting is conducted on the inhibiting concentration of the pyridazine HCV NS5B polymerase inhibitor, effectiveness of the method is verified, an excellent method for predicting the inhibiting concentration of other inhibitors is provided, and predicting conducted on unknown output can be accurate as far as possible.

The invention relates to the field of quantum communication, and provides a quantum key distribution parameter optimization method based on a random forest algorithm. Under the condition of limited data length, globally optimized parameters can significantly improve security code rate of an actual decoy state QKD system. A traditional local search algorithm can be used for searching optimal parameters, but generally consumes more time resources and computing resources, and cannot meet the requirements of real-time parameter optimization of a high-speed QKD system and optimal configuration of large quantum communication network resources. According to the method, the random forest model is trained in advance by utilizing the original data, and then the optimal parameter is directly predicted by utilizing the random forest model, so that the to-be-optimized transmission parameter can be rapidly and accurately predicted according to the current system configuration, and the parameter optimization process is greatly accelerated. Numerical simulation proves that compared with a traditional search algorithm, the method is lower in time cost, higher in prediction precision and good in application prospect in a high-speed QKD system and a future large-scale quantum communication network.

The invention relates to a reactive power optimization of a power system and specifically relates to a power system reactive power optimization method of a wind power field. The method includes random initialization of population, linear annealing weight introduction, gene fusion of genes of individuals in a new population and individual in a original population under a CR weight, target population generation, cross operation implementation, target individual fitness value calculation, one-to-one comparison of target individual fitness values and original individual fitness values, preferential saving, new population generation, and iteration search in the maximal evolution algebra range until the large evolution algebra is reached. According to the invention, dynamic adjustment is performed on parameters of a differential algorithm and a variation strategy of linear annealing is adopted for overlapped individuals in the population, so that a condition that the algorithm falls into local optimum is avoided, optimization and overall search capability are improved, the calculation time is shortened, influence on power grid reactive power distribution and voltage problems by the wind power field are eliminated, system grid loss is reduced and voltage level is improved.

The invention relates to a rapid collaborative optimization method for a hybrid fiber composite material plate shell structure, which belongs to the technical field of composite material structure optimization design, and comprises the following steps of: 1) establishing an alternative material library; 2) establishing a three-dimensional finite element numerical model and performing geometric partitioning; 3) establishing a reduced-order numerical analysis model by using a model reduced-order method; and 4) establishing an optimized column, and carrying out discrete material optimization design. A continuous interpolation function is adopted to represent a discrete material in a material library, numerical calculation is carried out by adopting a reduced-order model to obtain a target anda constraint response according to geometric partitions, an optimization target and a constraint distribution design variable of a model, discrete material optimization design is carried out, multi-variable collaborative optimization is realized, and an optimal design configuration is obtained. Integrated design of the hybrid fiber composite material structure can be achieved, collaborative optimization design of multi-level variables such as the structure topology, the fiber content, the fiber angle and the laying sequence is achieved, the structural function requirement is met, meanwhile, the structural mass is reduced, and the material cost is reduced.

The invention discloses a method for designing a riser during a sand casting process of a thin wall casting of a solid of revolution based on shrinkage defect prediction, relating to the technology of sand casting of a thin wall casting of a solid of revolution and aiming at solving the technical problem that the size of a riser cannot be accurately designed according to an existing method. According to the invention, an alloy / sand mold interface heat exchange coefficient is determined by a physical experiment method, and the practicability of the interface heat exchange coefficient is subjected to experimental verification by an experimental means of casting a circular casting by a sand mold, and thus the riser can be designed based on accurate shrinkage defect prediction. The precise selection and experimental verification of the alloy / sand mold interface heat exchange coefficient lay solid foundation for the accurate prediction on shrinkage defects and the reasonable design on riser size, the problem of huge trials but lack of necessary experimental verification during an existing defect prediction and riser designing process is solved, the accuracy of riser design is improved, the casing process optimization is accelerated, the production development period is shortened, and the quality of a casting product is improved by 20%-40%.

The invention discloses a variant aircraft aerodynamic optimization method for improving the expected improvement degree of a position vector, and belongs to the technical field of aircraft overall optimization. The method comprises the following steps: selecting an initial reference airfoil profile and a related shape coefficient, and determining a design working condition; establishing an optimization model considering structural consistency constraint to solve the shape coefficient of the deformed airfoil profile, and establishing a high-precision aerodynamic analysis model to solve the lift coefficient and the resistance coefficient of the airfoil profile; establishing a Kriging agent model of the high-precision pneumatic analysis model, optimizing the Kriging agent model to obtain a pseudo-non-dominated solution, and judging whether a prediction variance of the Kriging agent model is greater than a threshold value or not; and if the prediction variance is not less than the threshold, obtaining newly added sample points for updating the Kriging proxy model by adopting a position vector expectation improvement criterion, otherwise, screening the newly added sample points from the pseudo-non-dominated solution to update the Kriging proxy model. By screening newly added sample points, the precision of the Kriging agent model at the Pareto frontier is improved, the optimality and the distributivity of an optimized non-dominated solution are improved, meanwhile, the optimization efficiency is improved, and the cost is saved.

The invention discloses an emergency resource scheduling method for disaster relief based on a multi-agent genetic algorithm, and mainly aims to solve the problems of high freight rate and long delaytime of emergency resource scheduling in the prior art. The emergency resource scheduling method is characterized by comprising the following steps: first, constructing an emergency resource scheduling network which is used as an intelligent agent, constructing an intelligent agent grid by using 25 intelligent agents and calculating energy of each intelligent agent in the intelligent agent grid; second, setting maximum iterative times T and sequentially performing neighborhood competition, neighborhood cross and mutation operation on the intelligent agent grid to obtain a local optimal intelligent agent; third, performing self-learning operation on the local optimal intelligent agent; fourth, judging whether cyclic algebra reaches the maximum iterative times or not; if so, outputting an optimal scheduling scheme of an emergency resource network, and otherwise, adding 1 to T and returning to the third step. The emergency resource scheduling method for the disaster relief according to the multi-agent genetic algorithm, disclosed by the invention, has the advantages that transportation cost of the emergency scheduling is reduced, and total delay time of the emergency scheduling is shortened, and timeliness and high efficiency of the emergency resource scheduling are improved; the emergency resource scheduling method can be used for safety treatment of natural disasters.

According to the invention, design data (ED) are input for an object (OBJ) to be additively manufactured and to be optimised in terms of a physical optimisation target. A volumetric model (VM) of theobject is initialised with a material distribution (D) according to the design data (ED), said volumetric model having a plurality of volume elements (VE). A respective local target property (GRD) relating to the optimisation target is then determined for volume elements (VE) of the volumetric model (VM), based on the material distribution (D). According to the invention, each volume element (VE)is checked to determine whether said volume element is supported in terms of additive manufacturing. Based on this, the target property (GRD) of this volume element (VE) is modified in such a way thatit approaches the target property (GRD) if it is supported and / or moves away from the optimisation target if it is not supported. Based on the modified target properties (GRDMOD), the material distribution (D) is modified in such a way that the modified material distribution (DMOD) approaches the optimisation target. The modified material distribution (DMOD) is then output for the additive manufacturing of the object (OBJ).

The invention discloses a honeycomb composite material ultrasonic cutting process simulation method. The method comprises the steps that S11, a honeycomb material layered model and a cutter model in the honeycomb composite material ultrasonic cutting process are established; S12, defining material attributes for the established honeycomb material layered model and the established cutter model through ABAQUS simulation software; S13, respectively carrying out mesh generation on the established honeycomb material layered model and the established cutter model through ABAQUS simulation software;S14, assembling and positioning the honeycomb material layered model and the cutter model; S15, constraints between all layers of the honeycomb material layered model and constraints between the honeycomb material model and the cutter model are set; S16, establishing analysis steps and output variables according to the ultrasonic cutting motion characteristics; S17, setting boundary conditions andapplying loads according to the motion characteristics of the cutter model relative to the honeycomb material model; and S18, submitting the simulation model of the ultrasonic cutting process of thehoneycomb composite material processed in the steps S11-S17 to a solver of ABAQUS simulation software to carry out solving operation, so as to obtain an operation result.

The invention discloses a Memic algorithm-based network representation learning method. The method comprises the following steps of: randomly generating real number coding individuals to form an initial population P0 according to the number n of network nodes and a vector dimension d; evaluating a fitness function value of an initial population P0 real number coding individual; performing iterative optimization on each real number coding individual of the initial population P0 by adopting a Memic algorithm on the basis of the fitness function value of the real number coding individual; outputting the real number coding individual with the highest fitness function value in the population after iterative optimization as a representation vector set of the network node; wherein the iterative optimization comprises performing cross probability iterative optimization, population random number variation probability iterative optimization and local search iterative optimization on real numbercoding individuals in an initial population P0 in sequence; experimental results show that the method can effectively encode the network structure information, especially the community structure information, into the representation vectors, and can be used for tasks such as node classification, community detection and visualization.

The invention discloses a magnetic resonance sounding signal sparse denoising method based on particle swarm optimization. The method is mainly used for processing the power frequency harmonic interference and the random white noise in magnetic resonance signals. The method comprises the steps of firstly, preprocessing the MRS signals collected by a magnetic resonance sounding water exploration instrument in a band-pass filtering mode, obtaining the power frequency harmonic interference contained in the collected signals and the frequency of the MRS signals through frequency spectrum analysis,and constructing the oscillation atom libraries for the MRS signals and the power frequency harmonic noise characteristics respectively; secondly, recording an individual extremum and a population extremum by adopting a particle swarm algorithm to update the speed and the position of each particle in the particle swarm, and selecting an optimal atom from a power frequency harmonic oscillation atom library to reconstruct the power frequency so as to remove harmonic interference; and finally, selecting an optimal atom from the MRS signal oscillation atom library by using a particle swarm algorithm to reconstruct the MRS signal, if the MRS signal does not meet the experimental requirements, calculating a residual error signal, and repeatedly iterating until the condition is met. According tothe method, a novel atom library for the MRS signal is constructed, the power frequency harmonic interference and the random white noise in the noise-containing MRS signal are effectively filtered, and compared with a traditional MRS signal denoising method, the method has the advantages of being fast in operation speed, high in precision, strong in practicability and the like.

The present invention discloses an optimization method and apparatus of an airplane wing structure. The method performs cycles according to the preset cycle number. Each cycle comprises the steps of determining and obtaining the thickness of a front rib web according to a global optimization algorithm; if the thickness of the front rib web is greater than a preset thickness, calculating airplane wing weight corresponding to a first airplane wing finite element model according to parameters, which are determined by the global optimization algorithm, of at least one other airplane wing part expression of the first airplane wing finite element model, the first airplane wing finite element model including the thickness of the front rib web and the at least one other airplane wing part expression; if the thickness of the front rib web is less than the preset thickness, entering a second airplane wing finite element model, and obtaining the airplane wing weights with the preset cycle number after the cycles of the preset cycle number; and obtaining at least one airplane wing part expression corresponding to the lighter airplane wing weight in the airplane wing weights with the preset cycle number.

The invention discloses a sparse coding algorithm suitable for multi-modal information and an application thereof. The sparse coding algorithm comprises the following steps that 1 features of images and texts of a social medium are extracted; 2 a Laplacian matrix is established for same modal features; 3 a maximized average divergence matrix is established; 4 a target function based on sparse coding is established; 5 a search algorithm of a feature symbol is adopted to update the sparse coding, and feature representation of the multi-modal information is obtained; 6 by using the obtained feature representation to carry out intersection searching. The multi-modal information can be sufficiently used to carry out coding, the distributional difference of different modals is lowered, so that the robustness of the sparse coding is improved, and the accuracy of the intersection searching is improved.

The invention relates to a gomphrena globosa test tube flower and a culture method thereof. The culture method of the gomphrena globosa test tube flower comprises sterile seedling culture, multiplication culture, rooting culture and bottle seedling flowering culture. The culture method of the gomphrena globosa test tube flower disclosed by the invention is not restricted by seasons, a gomphrena globosa test tube seedling can be induced to flower at any time, and the vegetative growth time of the gomphrena globosa test tube flower can be shortened and the gomphrena globosa test tube flower canflower in advance, and the culture method of the gomphrena globosa test tube flower can be applied to cross breeding of gomphrena globosa, thus a breed improvement process of gomphrena globosa is sped up. The culture method of the gomphrena globosa test tube flower disclosed by the invention can be used for initially solving the problem that the gomphrena globosa is difficult to flower in a test tube, the gomphrena globosa test tube flower is inoculated into a test tube and can be made into a test tube flower to be launched to a consumer market, the flowering rate can reach up to 86%, and a new test tube flower is provided for a flower lover; and the culture method disclosed by the invention is simple to operate and has the advantages of strong practicability and good generalization capability.

The invention belongs to the technical field of molecular biology, and particularly discloses a molecular marker closely linked with millet tillering character. The molecular character MRI381 is located in the range of 31021885bp to 31095359bp of the seventh chromosome of millet. The invention further discloses an amplification primer and application of the molecular marker. The molecular marker can be used for excellent typing on millet tillering, can be used for molecular marker assistant breeding of millet tillering character, and has important theoretical and practical guiding significance on accelerating genetic breeding and improving process of millet varieties.

The invention provides an antenna housing force thermoelectric integrated analysis method, and the method mainly comprises the following steps: S1, establishing a radome mechanical calculation finiteelement model and an electromagnetic calculation undeformed finite element model; S2, carrying out aerodynamic thermal load solving on the mechanical finite element model established in the step S1; S3, carrying out the aerodynamic load solving on the mechanical finite element model established in the step S1; S4, performing post-processing on calculation results of the S2 and the S3, and extracting node displacement; S5, reconstructing a finite element model in S4 based on Python language; S6, reconstructing the finite element model deformed in S5 based on HyperMesh software to obtain an electromagnetic calculation finite element model, and completing the analysis of the wave transmission performance of the radome under the aerodynamic load; S7, completing electromagnetic medium parametersetting and antenna modeling of the electromagnetic calculation finite element model established in the step S1, completing original-state radome wave-transparent performance analysis, and comparingthe original-state radome wave-transparent performance analysis with the calculation result in the step S6.

The invention discloses a three-dimensional depth data acquisition method based on a frequency optimization jitter algorithm. An optimized objective function is provided; a binary block is optimized through the function, so that the optimization of a whole binary stripe pattern can be replaced; and the periodicity and symmetry of sine stripes are utilized. According to the method of the invention,a binary coding technology is adopted; the measurement speed bottleneck of a digital fringe projection technology is broken through; three-dimensional measurement speed is greatly improved; high-speed three-dimensional measurement can be carried out; an out-of-focus binary coding technology is utilized to solve the nonlinear problem of a projector; an optimization framework based on a discrete particle swarm algorithm and an optimization framework based on the discrete particle swarm algorithm are provided; binary fringes are optimized in a frequency domain; and a high-quality binary fringe generation method is provided for a real-time three-dimensional measurement technology.

The invention relates to an imperialistic competitive algorithm-based carbon-energy combined flow solving method. Carbon-energy combined flow of a power system is optimized by adopting a multicultural transfer-based imperialistic competitive reinforcement learning algorithm; each empire adopts a value function matrix in the reinforcement learning algorithm as a cultural matrix of the empire; each optimization nation updates the cultural matrix through interaction with the environment and performs action selection according to the cultural matrix to improve the globality of a policy; and knowledge in the cultural matrix is stored in real time and multicultural transfer is performed to improve the optimization efficiency of subsequent new tasks. According to the adopted method, the cultural transfer-based imperialistic competitive reinforcement learning algorithm is a relatively new intelligent algorithm and has the advantages of high convergence speed, better global convergence, higher stability and the like, so that quick carbon-energy combined flow optimization of the large-scale power system is realized.

The invention discloses a reservoir group scheduling method and system based on a multi-population cooperative particle swarm algorithm, and belongs to the field of reservoir scheduling. According tothe method, the global optimization capability and the local optimization capability of the particle swarm algorithm are fully explored by introducing a plurality of particle swarms of which the inertia weights are gradually reduced step by step, and meanwhile, the population is differentiated through attraction factors defined by the population optimal values obtained by multi-population searching, so that the algorithm is not easy to fall into the local optimal values, and the particle swarm optimization capability is maximized; and maximizing the optimization capability in the solution of reservoir group scheduling, wherein the solution is a global optimal value. According to the method, the optimal positions are transmitted among the populations step by step in a multi-population cooperation mode, and convergence to the global optimal value is quickly realized in the multi-population cooperation mode, so that the optimization and convergence speeds of the particle swarm algorithm are increased, and the reservoir group scheduling problem is solved in a relatively short time.

The invention discloses an SNP marker relevant to the plant height character of millet, detection primers for the SNP marker, and applications. For the SNP marker relevant to the plant height character of millet, the SNP marker is located in the bin markers of the 30703512bp to the 30784296bp of the first chromosome, and the SNP marker is in close linkage with the plant height of millet. The SNP marker relevant to the plant height character of millet can be used for the molecular marker-assisted breeding for the plant height character of millet, through the detection of the SNP marker, the plant height of millet can be predicated, a scientific basis is provided for realizing the early identification or screening breeding for the plant height character of millet, and great theory and practice guiding significance is achieved for accelerating the genetic breeding or improvement process of the varieties of millet. The primer pair for detecting the SNP marker provided by the invention canrealize typing on the plant height of millet and detect the differences of SNP expression.

The invention provides a radome electromagnetic performance analysis method under a force load. The method mainly comprises the following steps: S1, establishing a radome mechanical calculation finiteelement model, and electromagnetically calculating an undeformed finite element model; s2, adding material attributes to the mechanical finite element model established in the step S1, applying aerodynamic load and boundary conditions, and completing solving of a static problem; and S3, carrying out post-processing on the calculation result of S2, and extracting node displacement. S4, reconstructing the finite element model in S3 based on Python language to obtain a deformed finite element model under aerodynamic load; s5, reconstructing the finite element model deformed in the step S4 basedon HyperMesh software to obtain an electromagnetic calculation finite element model, and completing the analysis of the wave transmission performance of the radome under the aerodynamic load; s6, electromagnetic medium parameter setting and antenna modeling of the electromagnetic calculation finite element model established in the step S1.2 are completed, original-state radome wave-transparent performance analysis is completed, and the original-state radome wave-transparent performance analysis is compared with the calculation result in the step S5.

The invention discloses a solid fuel heat value measurement method which comprises the specific steps: (1) choosing varieties of calibration solid fuel samples with different heat values and respectively obtaining plasma emission spectrums of the solid fuel samples by utilizing a dot matrix measurement mode of an LIBS analysis system; (2) performing normalization processing on spectroscopic data matrixes and then utilizing K-CV to perform support vector machine related parameter optimization based on heat values on spectroscopic data; (3) utilizing SVM and optimal parameters to construct a heat value calibration regression model for normalization processed spectroscopic data matrixes; (4) utilizing the LIBS analysis system to obtain spectroscopic data matrixes of samples to the measured and inputting the normalization processed spectroscopic data matrixes to be measured into the calibration regression model to obtain heat values of the sample to be measured. The solid fuel heat value measurement method disclosed by the invention simplifies an analysis process in solid fuel heat value measurement and improves accuracy and precision of the LIBS applied to solid fuel heat value measurement.

The invention discloses an SNP marker related to a foxtail millet plant height trait as well as a detection primer and application of the SNP marker. The SNP marker related to the foxtail millet plantheight trait provided by the invention is located in bin markers of 18022413bp to 21734440bp of a fourth chromosome; and the SNP marker is closely linked to the foxtail millet plant height. The SNP marker related to the foxtail millet plant height trait provided by the invention can be used for molecular marker-assisted breeding of the foxtail millet plant height trait, can predict the foxtail millet plant height by detecting the SNP marker, provides a scientific basis for early identification or screening breeding of the foxtail millet plant height trait, and has important theoretical and practical guiding significance for accelerating genetic breeding or improvement process of foxtail millet varieties; and primer pairs for detecting the SNP marker provided by the invention can classifythe foxtail millet plant height and detect difference in SNP expression.

The invention discloses an intelligent optimization method and an intelligent optimization device of positioning network element layout, and the method comprises the following steps: according to a set network element moving mode, moving each network element in the current optimal network element layout x<best> of the current iteration, and obtaining a candidate network element layout set; from the candidate network element layout set, selecting an optimal network element layout x<now> of the set; determining a better network element layout from the x<now> and the x<best>, taking the determined network element layout as the current optimal network element layout x<best> of next iteration, and performing iterative processing; when an iteration suspending condition is satisfied, outputting the network element layout determined by the last iteration. With the method and the device provided by the scheme of the invention, a network element layout global optimization process is accelerated,staying at a local optimal solution is avoided, and services based on position information are supported.