148results about How to "Realize the solution" patented technology

The invention relates to the field of computer vision. In order to propose a tensor structural missing filling method and realize the accurate filling of the structural missing tensor, the tensor structural missing filling method based on the combination of low rank and sparse representation and the TT low rank tensor filling theory are introduced into the TT low rank prior to constrain the potential tensor. At the same time, considering that the fiber signal along each dimension of the tensor can be sparsely represented by a dictionary, and the missing fiber of the previous dimension can be recovered by sparse constraint on the fiber signal in the next dimension, sparse constraint is introduced for each dimension of the fiber signal; based on the sparse apriori of the combined TT low rankand each dimension, the tensor filling problem with structural defects is formulated as a constrained optimization problem, so that the tensor filling problem with structural defects can be realized.The invention is mainly applied to video image inpainting, recommendation system, data mining and multi-classification learning occasions.

The invention discloses a measuring method for length, width and height of a vehicle based on an RGB-D camera. The method comprises the steps of acquiring a clear vehicle image in a camera short-distance mounting manner, calibrating the camera according to a calibrating method based on a disappearing point, obtaining an internal parameter and an external parameter of the camera, realizing three-dimensional point cloud conversion of a vehicle target in a world coordinate system through a camera depth image solution, and acquiring three-dimensional coordinate information of the external surfaceof the vehicle; acquiring the three-dimensional coordinate of the external surface of the vehicle by means of prior knowledge and an image processing method, splicing a vehicle image according to sequence patterns in a vehicle motion process through a registering method, and realizing three-dimensional measurement to the profile of the vehicle. The measuring method overcomes defects of high equipment requirement and tedious operation in a traditional method and realizes high calibration precision. The actual displacement of the vehicle is comprehensively analyzed through a matching relation ofpoints which correspond with the same position of the vehicle in the image sequence, thereby realizing relatively high registering precision. The measuring method further has advantages of realizingaccurate splicing of the vehicle side surfaces, reducing vehicle length measurement error and improving accuracy in splicing the vehicle side surfaces.

The invention discloses a day-ahead economic scheduling method for an electric-pneumatic coupled system based on mixed integer second-order cone programming. A function whose all-day energy and spareall-in cost are minimum is used as a target function, and constraint conditions of a power system and a natural gas system, and electric-pneumatic coupling and conversion relations are considered, toestablish an optimization model of electric-pneumatic coupled system day-ahead economic scheduling. On the basis of the model, through second-order cone relaxation of a steady-state airflow equation of a gas pipeline, a mixed integer second-order cone programming model of day-ahead economic scheduling of the electric-pneumatic coupled system is established, and an initial operation scheme is solved and obtained. Aimed at a problem that an initial scheduling result of a natural gas system is not accurate, a calibration model based on a steady-state power flow calculation method of a natural gassystem is established, so as to modify an initial operation scheme. The method realizes accurate solution of a non-convex nonlinear optimization problem, and provides a feasible, economical and accurate optimal operation scheme for electric-pneumatic coupled system day-ahead scheduling.

The invention discloses a distributed power supply, energy storage and flexible load combined scheduling method and device. The distributed power supply, energy storage, flexible load and other distributed resources are numerous in number, dispersed in layout and difficult to be directly scheduled by a power grid; and a resource aggregator can execute a power grid scheduling instruction by internally integrating various distributed resources. Based on the operation mode of the resource aggregator, a combined scheduling model combining large-capacity resource direct scheduling and small-capacity resource electricity price response indirect scheduling is established. On the basis, with the maximum profit of the resource aggregator as an optimization target, rolling online evaluation is carried out on the large-capacity resource scheduling performance difference, and a dynamic comprehensive scheduling priority is set; for the uncertainty of indirect scheduling of small-capacity resources,an opportunity scheduling constraint containing fuzzy parameters is provided; and an improved particle swarm algorithm is adopted to clarify the fuzzy opportunity constraint and solve the schedulingmodel. Based on an IEEE33 node power distribution network, the effectiveness and scientificity of the proposed model and algorithm are verified.

The invention belongs to the field of computer vision, and aims at achieving the accurate filling of a pixel row and column loss image. The technical scheme employed in the invention is that a row and column loss image filling method based on low-rank matrix reconstruction and sparse representation comprises the steps: introducing low-rank priori to carry out the constraining of a potential image based on the low-rank matrix reconstruction theory; introducing a separable two-dimensional sparse priori based on the sparse representation theory with the consideration to a condition that the sparse representation of each column of a row loss image can be achieved through a column dictionary and the sparse representation of each row of a column loss image can be achieved through a row dictionary; specifically describing a filling problem of an image with the row and column loss into a solving problem of a constraint optimization equation based on the above combined low-rank and separable two-dimensional sparse priori, thereby achieving the filling of the row and column loss image. The method is mainly used for a computer vision processing occasion.

The invention discloses an optimized control method for wind power field reactive power compensation capacity considering wind power distribution rule; according to the method, a reactive power compensation capacity optimization model is utilized to carry out an optimized treatment, and the optimization target is that investment cost of reactive power compensation and operation cost of wind power system caused by reactive power are minimized; operation cost and investment cost are calculated; the operation cost comprises the affect of wind power distribution result and power property; and an optimized calculation is carried out by utilizing a particle swarm optimization algorithm combined with a power flow calculation program. The method has the beneficial effects that according to the optimization decision method of the compensation capacity, the reactive power compensation of a wind power field has the best economical efficiency and the strongest pertinency on the premise of adequately ensuring continuity of reactive power adjustment by wind power system.

The invention discloses a brainstorming-based thermal power plant economic environment scheduling method. The method includes the following steps of: determining mathematical models of thermal power plant environmental economic scheduling problems; step 2, obtaining various parameters in the models; step 3, obtaining an initial feasible scheduling set of the thermal power plant environmental economic scheduling problems; step 4, evaluating the feasible scheduling set; step 5, updating the feasible scheduling set; step 6, judging whether entire feasible scheduling is updated completely; step 7, updating a non-inferior scheduling solution set in an external archiving set; step 8, performing iterative output of a final feasible scheduling scheme, and the method terminates. According to the brainstorming-based thermal power plant economic environment scheduling method of the invention, a thermal power plant is adopted as a power supply system, and solutions to thermal power plant economic scheduling problems which are required by comprehensive environmental protection can be realized through collection and analysis of data of a power system and the thermal power plant and a brainstorming optimization algorithm based clustering and variation ideas.

The invention discloses a thermal power plant comprehensive scheduling method based on environment protection and economical benefit. The method includes the following steps: building a mathematical model of thermal power plant environment economical scheduling problems; obtaining various types of parameters in the model; initiating a practicable scheduling generating process and encoding processing; evaluating practicable scheduling; updating the practicable scheduling; updating a non-bad scheduling solution set in an external file set; determining three important non-domination scheduling sequences affecting an iterative process by circulating crowding distance; judging if iteration t reaches a maximum value, on yes judgment, outputting practicable scheduling in the current non-bad solution set, and on no judgment, setting the iteration t=t+1, and returning to step 5 to conduct updating again. Due to the fact that increasing of electric energy production can inevitably cause increasing of pollution discharge, namely increasing of cost for treating environment pollution, the thermal power plant comprehensive scheduling method adjusts generating efficiency of all thermal power plant units in real time and achieves optimization of comprehensive benefit.

The invention is applied to the field of logistics, and provides a generation method and device for a planned route of a vehicle. The method comprises the following steps: initializing an ant colony and ants, and determining a vehicle to be arranged; when the next delivery node is arranged for the vehicle to be arranged, taking a requirement of a hard time window, carrying capacity and a requirement of a soft time window into consideration; calculating penalty cost caused by the violation of the vehicle to be arranged against the soft time window, cyclically generating N ants after edge cost and current ant total cost are calculated according to the penalty cost and the delivery cost of the vehicle, obtaining a route map of each ant and the total cost of each ant, and finally obtaining an optimal delivery route of the vehicle according to the route map of each ant and the total cost of each ant. According to the method and the device, when a route is planned for the vehicle, the four models of VRPMVT (Vehicle Routing Problems with Multiple Vehicle Types), VRPTW (Vehicle Routing Problems with Time Windows), VRPSTW (Vehicle Routing Problems with Soft Time Windows) and VRPPD (Vehicle Routing Problems with Pickup and Delivery) are taken into consideration, and higher-practicability complex optimization problems are solved, so that the requirements of multiple practical applications are met.

The invention discloses a three-phase imbalanced power distribution network robust dynamic reconfiguration method taking an uncertainty budget into consideration. According to the method, the defect that in an existing power distribution network reconfiguration technology, power distribution network injection power fluctuation and uncertainty are not taken into full consideration is made up; a three-phase imbalanced power distribution network robust dynamic reconfiguration model is established; the influence of the uncertainty on the reconfiguration under the condition that the injection poweris dynamically changed in a plurality of periods is taken into consideration; an uncertain set range is changed through control over the uncertainty budget; the conservative property and robustness control effect of a robust reconfiguration scheme are analyzed; and the theoretical support is provided for the reconfiguration technology of an intelligent power distribution network under the condition that a distributed power supply is connected in high permeability.

The present invention discloses a simulation teaching system of vocational education, including a man-machine operation module, a theory teaching module, a theory testing module, a 3D simulation teaching evaluation module, a video acquisition module, a human expert assessment and guidance module, a comprehensive evaluation module, a hard question assembling module, a hard question solving module, and a CPU. The invention realizes 3D input of teaching materials, enabling the students have a realistic sense of being personally on the scene, greatly improving their interest in study, and improving the efficiency of learning. The establishment of simulated three-dimensional training scenes provides a virtual training environment for students and realizes the 3D simulation training test. As the whole simulated training process is recorded through a camera, evaluation can be made by teachers on different students' practical situation, so the output for guidance and daily tasks can be more targeted. In addition, hard questions arising in students' learning can be assembled and answered, therefore, increasing the efficiency of vocation education.

The invention discloses a GNSS precise positioning method based on multi-dimensional particle filter deviation estimation. The method comprises: step one, carrying out data pretreatment and inputtinga satellite ephemeris, a current epoch pseudo-range observation value, and a current epoch phase observation value; step two, building a GNSS double-difference observation equation and carrying out linearization to obtain a single epoch normal equation or an epoch-accumulation normal equation; step three, calculating the normal equation by using a corresponding offset in a particle correction normal equation with multiple particle values; updating a particle filter weight and calculating a numerical value of a fractional part of the offset between the phase systems and a particle mean square root based on the weighted particle; step four, repeating the steps one to three, outputting a corresponding offset estimation value and a corresponding variance after filter convergence; and step five, according to an estimation value of an error parameter, correcting the offset value in the observation value or the normal equation and fixing a full-cycle fuzziness value to realize precise positioning. With the method disclosed by the invention, simultaneous estimation of multiple GNSS offset parameters by particle filtering is realized and precise relative positioning is realized by using a few of observation satellites.

The present invention discloses a mixed modeling method and system based on the combination of process priors and a data-driven model. The method comprises: selecting a data-driven model and an appropriate model structure from known data-driven models so as to establish a mathematic relation expression corresponding to the data-driven model, and arraying all model parameters in a certain order; verifying the process priors of the model so as to obtain a constraint equation of the degree of detected model against the process priors; comparing the model output of the sample with observed values, and establishing an optimal target equation for detecting the degree of the model fitting the training sample; combining the constraint equation with the optimal target equation so as to establish a constraint-optimization problem; solving the optimal parameter solutions by adopting an intelligent algorithm of constraint processing; adopting the obtained optimal parameter solutions as the model parameters of S1, and bringing the obtained optimal parameter solutions in the original model so as to predict or optimize the model. Through the adoption of the mixed modeling method disclosed by the present invention, the model which more conforms to priori knowledge can be obtained in the neural network training of a small amount of data samples, so that the over-fit phenomenon can be avoided.

The invention belongs to the computer vision field and aims at realizing filling to pixel structural lack images. The structural lack image filling method based on low rank matrix reconstruction comprises the following steps: 1) taking the image as a matrix, using a matrix A to represent an original image and converting a pixel structural lack image filling problem into a solving optimization equation; 2) training a dictionary fai; 3) using an alternative direction method ADM to convert a sequence into a sequence so as to carry out solving and then carrying out iteration solving according to steps so as to acquire a final result. The method in the invention is mainly used for computer image processing.

The invention discloses a fault positioning method used for a power distribution network and comprising multiple T-connection inversion distributive power sources. The method includes steps of electrifying a relay protection device; initiating circuit parameters; acquiring power of the distributive power sources; acquiring voltage phasor and current phasor on two sides of a feed line; calculating output current of the distributive power sources; calculating the magnitudes of comparison voltage on two sides of the feed line; if the comparison voltage is greater than a setting value, determining faults within a feed line zone and starting a fault positioning algorithm; if the faults occur in a specific section, calculating a measurement distance percentage, and determining the faults within the section if the measurement distance percentage is smaller than 100%. By adopting the method, problems in relay protection and fault positioning when the multiple distributive power sources are in T-connection with the circuit can be solved effectively. Besides, the method is insusceptible to influence by connection number and connection position of the distributive power sources, fault types and transition resistors, and is high in adaptability and engineering practicality.

The invention discloses an optimal antenna directional diagram selection radar angular super-resolution method comprising the steps of forward-looking scanning radar azimuth echo modeling; echo data distance direction pulse compression; range walk judgment; range walk correction; a scanning radar angular azimuth direction echo convolution model; and deconvolution performed based on optimal antenna directional diagram selection. The last step includes formula deviation: threshold is set and a cut-off position is controlled; point number Ncha is changed and width is controlled so that an antenna directional diagram is obtained; the antenna directional diagram is substituted in a deviation formula for convolution inversion so that an inversion result diagram is obtained; image entropy is calculated and the antenna directional diagram is confirmed; and a final result is obtained through calculation. The beneficial effects of the method are that influence of error of the antenna directional diagram on the convolution inversion result is analyzed from the angle of algebra firstly, then the optimal antenna directional diagram is confirmed based on the method of image entropy, and the optimal antenna directional diagram is substituted in the deconvolution method so that the solution of a convolution inversion problem can be realized, and scanning radar angular super-resolution imaging can be realized.

The invention relates to a cylindrical gear tooth direction load distribution coefficient acquiring method. The method is characterized by including: 1), building a beam unit model of a transmission shaft; 2), a nonlinear multipoint meshing model of a cylindrical gear; 3), building a nonlinear statics model of a cylindrical gear transmission system; 4), iteratively calculating static balance stateof the cylindrical gear transmission system; 5), calculating tooth direction load distribution coefficient of the cylindrical gear. A space beam unit and a gear equivalent meshing unit are adopted toperform numerical simulation on the cylindrical gear transmission system, modeling of the cylindrical gear transmission system and solving of the tooth direction load distribution coefficient can berealized quickly in universal programming language environment, calculating accuracy of a finite element contact analysis method is attained, calculating efficiency is improved greatly, and the defects of low calculating accuracy of empirical formula methods and low calculating efficiency of the finite element contact analysis method are overcome.

The invention relates to the field of non-conventional oil-gas seismic exploration, in particular to a shale gas formation ground stress calculating method. The method includes the following steps of firstly, derivation of an orthogonal anisotropic medium ground stress equation based on a rock physical theory; secondly, approximate representation of orthogonal anisotropic medium ground stress; thirdly, estimation of an orthorhombic differential horizontal stress ratio (ODHSR) of an orthogonal anisotropic medium through rock mechanical parameters and anisotropic parameters obtained through azimuth prestack 3D seismic data inversion. The horizontal bedding characteristics of a VTI medium and the fracture perturbation properties of an HTI medium are comprehensively considered, a calculation formula of the ODHSR based on the orthogonal anisotropic medium is put forward, the solution of the ODHSR is achieved, the method is a new shale gas formation ground stress evaluating method, and whether a shale oil-gas reservoir is prone to fracturing and net formation or not can be effectively evaluated.

The invention discloses a lamb wave damage location method based on a non-linear unscented Kalman filtering algorithm. The method includes the steps that multiple (at least four) piezoelectric sensors for exciting and receiving ultrasonic lamb waves are arranged on a damage-containing isotropous planar structure; the damage position is recognized and converted into a Bayes filtering problem, a state vector evolution systematic equation and a non-linear measurement equation are set up, the propagation time of the lamb waves scattered by damage on routes of all exciting sensors and all receiving sensors is used as known measurement quantity, and the damage center position and the lamb wave speed are used as unknown state variables. The non-linear unscented Kalman filtering algorithm is adopted for iterative estimation, and therefore damage location and solution of lamb wave speed are achieved.

An embedded system on chip for accelerating Cholesky decomposition, relates to a system for accelerating Cholesky decomposition, and aims to solve the problem of long computing time of a hardware acceleration system which achieves solution of a system of linear equations based on a Cholesky decomposition method in the prior art. The system comprises an ARM processor module for task scheduling of the whole computing process, a DDR control module for controlling the data reading and writing of an external DDR storage, an AXI bus module for information transmission between a programmable logic module and the ARM processor module, and a programmable logic module for performing Cholesky decomposition to a positive definite symmetric matrix. A large amount of parallel updated operations are existent during the solution of coefficients of the system of linear equations of the programmable logic module based on the submatrix-Cholesky decomposition, more regular access and storage of data can be realized, the reading-writing control is simpler, the computing time is shortened greatly; the system is applicable for accelerating the solution of the system of linear equations.

The invention belongs to the video analysis field and aims at accurately recovering a video sequence background in an online mode. The invention discloses a video sequence background recovery method based on online low-rank background modeling. Based on traditional background modeling, low-rank matrix decomposition of a nuclear norm and motion information estimation of binaryzation are introduced so that a problem which can not be processed in the prior art can be solved. The method comprises the following steps of 1) specifically expressing a background recovery problem in a video sequence as following unrestraint optimization equation solving; 2) constructing a kth frame of motion mapping weight vector wk; 3) using an alternative direction method to solve; 4) solving ck<l+1>; 5) solving ek<l+1>; 6) repeating the above steps 4) and 5) till that algorithm convergence is achieved; 7) using variable substitution to solve; 8) solving Yk<l+1>; 9) solving Lk<l+1>; 10) repeating the above steps 8) and 9) till that the algorithm convergence is achieved; and 11) solving a video background. The method of the invention is mainly used for a video analysis occasion.

The embodiment of the invention provides an improved ICP point cloud rapid splicing method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining data collected by a camera; based on a Kd-Tree space index algorithm, searching the point cloud information data of the neighborhood relation of the data; sampling the color point cloud feature points in the point cloud information data by using random sampling; determining domain points in the point cloud information data by a Kd-Tree domain search method, and describing features of feature points in the domain points; and constructing a constraint function by utilizing an improved ICP algorithm to complete the splicing of the point cloud data. By utilizing the method, the iteration rate can be enhanced, and the problem that the iteration effect is influenced due to local convergence is avoided.

The present invention discloses a probability based big data query method and pertains to the technical field of databases. The method comprises: the step of partitioning a data set with a plurality of attributes according to a data model; the step of loading the partitioned data set according to a data probability placement model; and the step of performing probability query on the data set. The method is an approximately complete query method, which improves the query performance of data by appropriately sacrificing query integrity; realizes probability placement of data and solution of existence probability of data in each storage file by using a probability based data placement model; enables data query in a database system through total probability query by using a heuristic data query method; and ensures a query error of probability query through probability calculation.

The present invention belongs to the field of the computer vision, and the objective of the invention is to realize the recovery of the image with random value impulse noise structural deficiency. The method comprises: combining the matrix reconstruction theory and the sparse expression theory, leading into a dictionary learning model on the basis of the traditional matrix reconstruction model, and therefore solving the problems which cannot be solved in the prior art. The method comprises the following steps: 1) taking the image as a matrix, expressing an original image by using a matrix A, and solving the deletion image recovery problem with random value pulse noise to solve the following optimization equation; 2) training a dictionary [Phi]; 3) converting a sequence to another sequence for solution by using an alternative direction method (ADM), wherein a contraction operator is included; and performing iteration solution to obtain a final result according to the steps. The recovery method of low-rank matrix reconstruction with a random value impulse noise deletion image is mainly applied to the computer image processing.

The invention provides a method for calculating thermal expansion coefficient of a composite plate based on an optical fiber strain conversion matrix and belongs to the field of structure health monitoring. The method provided by the invention comprises the following steps: firstly, defining a coordinate system of the surface of a to-be-tested composite plate and arranging an optical fiber sensing network; secondly, constructing a conversion matrix between thermal strain received by a grating and central wavelength shift; thirdly, constructing a conversion matrix between thermal strain of a composite plate structure and the central wavelength shift of an optical fiber grating; fourthly, acquiring a response signal of an optical fiber grating sensor under variable temperature condition; fifthly, calculating thermal strains in i and j directions of a to-be-tested composite plate structure; and sixthly, determining the thermal expansion coefficient of the to-be-tested composite plate structure. The calculation method provided by the invention builds a thermal expansion coefficient calculation model for the composite plate structure and can determine the thermal expansion coefficient of the composite plate structure in the to-be-tested direction. The method provided by the invention is simple and easy, high in accuracy and good in reliability.

The invention discloses a three-phase unbalanced intelligent power distribution network reconstruction method based on a linear model. The method includes performing modeling and approximate analysison the three-phase unbalanced intelligent power distribution network by using a Distant flow branch flow model; establishing a network reconstruction mathematical model considering the problems that an intelligent power distribution network including distributed generation (DG) has the line parameters in three-phase asymmetry and unbalanced power; linearizing the established model by using an optimal isometric piecewise linear approximation method, so as to obtain a mixed integer linear model reconstructed by the network and solve the mixed integer linear model by using commercial mathematicalplanning software. According to the method, the defects that the circuit parameters are not symmetric and the power is unbalanced in the current intelligent power distribution network reconstructionoptimization process are overcome, meanwhile, the solving speed and efficiency of the network reconstruction model are improved, and a theoretical support is provided for economic and reliable operation of the intelligent power distribution network.

The invention discloses a phase aliasing error removing method and device based on an end-to-end convolutional neural network. The method solves the problems that in the process of solving a single interferogram through a Fourier transform method or a digital Moire phase shift method, the phase spectrum aliasing caused by improper carrier addition or overlarge surface shape error phase spectrum bandwidth exists. The phase spectrum aliasing error can be eliminated, the solution of the wide spectrum phase interferogram is realized, and the measurement range of the single interferogram phase unscrambling method is expanded. The method comprises the following steps: (1) designing a multi-scale convolutional neural network; (2) simulating a wide-spectrum phase interferogram, solving a phase diagram containing a phase spectrum aliasing error based on a Fourier transform method or a digital Moire phase shift method, and taking the phase diagram containing the phase spectrum aliasing error andan original wide-spectrum phase diagram as an aliasing training set; (3) training a multi-scale convolutional neural network by using the aliasing training set; and (4) processing a real phase diagram containing the phase spectrum aliasing error by using the trained multi-scale convolutional neural network to obtain a high-precision phase solution result without the phase spectrum aliasing error.

The invention provides a question and answer information processing method, a question and answer information processing system and a server. The method comprises the following steps: if the questioncontent of a question to be processed from a user question terminal is received, dividing words of the question content so as to obtain key words; checking the key words in a preset knowledge bank soas to obtain checking results; transmitting the checking results to a user answer terminal; and if an answer content transmitted by the user answer terminal according to checking results is received,transmitting the question content, the key words, the checking results and the answer content to the question user terminal. The technical effects that words are divided according to question contentsto obtain key words, the key words are automatically checked in the knowledge bank according to the key words to obtain checking results, answer contents are input by the user answer terminal according to the checking results, furthermore the question contents, the key words, the checking results and the answer contents are displayed on the user question terminal, professional questions are answered, and the question information processing efficiency is improved, are achieved.

The invention discloses a frequency characteristic modeling method for an electromagnetic induction type current sensor. The method comprises the steps of establishing an equivalent circuit of the electromagnetic induction type current sensor, and dividing the equivalent circuit into n micro-units; determining electrical parameters of the micro-units according to the physical size of the sensor; building a double-port mathematical model, namely, a sensing coil mathematical model, of a single micro-unit, and according to an electrical relation of the adjacent micro-units, performing recursion to obtain an output voltage of the micro-unit n; introducing a circuit constraint condition to solve the model, and determining a corresponding transfer function; and introducing an integrator according to needs, thereby obtaining an overall frequency characteristic of the sensor. Circuit structure-based topology model simulation solution is converted into circuit parameter-based mathematical modelrecursive calculation; the constraints of the number of the micro units and a maximum node of simulation software are broken through; an internal transient process of the sensor under a high-frequency signal can be accurately reflected; the frequency characteristic of the sensor under any micro-unit number can be approximated; and the great significance is achieved for parameter design of the high-frequency current sensor.

The invention provides a bio-geographic optimization algorithm-oriented constraint handling method and device. The method comprises: an improved migration step: capturing individuals selected from a target population; extracting target dimension elements of the selected individuals; based on an individual emigration rate, performing weighted fusion on the target dimension elements of the selected individuals according to a simulated binary crossover operator to generate new elements to be immigrated; an improved variation step: updating target sub populations according to the new elements to be immigrated and performing disturbance using Gaussian distribution to obtain an excellent target population; and a feasible constraint handing step: determining the excellent target population, determining optimal individuals and updating varied parameters by a differential evolution algorithm. By combining the information search capability of the differential evolution algorithm and the information utilization capability of the bio-geographic algorithm, the global optimization capability of the handling scheme is promoted and balanced.