75 results about "Relaxation algorithm" patented technology

A dynamic range relaxation algorithm is applied to simulate borehole failure under a variety of stress conditions. The borehole and its neighborhood are modeled by a number of regions by a plurality of interconnected nodes. The bonds between the nodes may be modeled as springs, rods, or beams. The strength of the bonds has a statistical variation to accurately simulate real world situations. The model may include, in addition to the borehole and the far earth formations, a liner, a casing, and/or a gravel pack. Simulation is carried out for different strength of the bonds.

The invention provides a modeling method for a production scheduling problem distribution set robust model, which belongs to the field of production scheduling and operations research. A distributed set robust optimization method based on an uncertain distribution function set is adopted to model the production scheduling problem, the model is composed of an objective function and constraint conditions, the objective of the model is to search for an optimal robust scheduling sequence, and thus, in a condition in which the total flow time of the sequence obeys the worst distribution in the processing time, the minimum condition risk value is realized. In the case of solution, the model is decomposed to an assignment subproblem and an integer two-order cone programming subproblem, and a minimum average processing time priority criterion and two Cauchy relaxation algorithms designed by the invention are adopted to optimize the model and obtain the optimal solution. The distribution set robust optimization method is applied to the production scheduling problem, the method is more applicable to the actual production condition compared with the existing robust modeling method, and in a condition of ensuring the system performance, a decision risk is reduced.

The embodiment of the invention provides an Internet of Things service low-delay load distribution method and device based on edge computing. The Internet of Things service low-delay load distributionmethod comprises the steps: acquiring a task request of each application in each terminal and the computing capacity of each edge node; and inputting the task request of each application in each terminal and the computing capacity of each edge node into a preset optimization problem model, and outputting a resource allocation matrix and a task allocation matrix, wherein the preset optimization problem model comprises a particle swarm algorithm model improved through an ant colony algorithm and a semi-definite relaxation algorithm model improved through a Gaussian random algorithm. The Internet of Things service low-delay load distribution method and device based on edge computing improve particle swarm optimization by applying the ant colony algorithm, reduce the convergence time of the algorithm, improve the performance of the resource allocation result, solve the rank 1 constraint in the semi-definite relaxation problem by applying the Gaussian random variable, improve the performance of the task allocation result, and finally reduce the service delay.

The invention discloses and particularly relates to a remote sensing image cloud removing method based on sparse representation. The remote sensing image cloud removing method comprises the following steps of: extracting a cloud mask matrix to acquire a new image; initializing iteration parameters; fixing a texture part of the image and updating a smooth part of the image; fixing the smooth part of the image and updating the texture part of the image; adjusting the smooth part by virtue of a total variation function; updating a iteration threshold; and judging whether decomposition is completed. The remote sensing image cloud removing method based on the sparse representation can adjust the parameters of the total variation adjusting function of the image, namely, a convergence rate of an algorithm is adjusted by changing the parameters of the total variation adjusting function when the block coordinate relaxation algorithm is adopted foe decomposing the image, so that the efficiency of the algorithm and the decomposing effect of the image are improved. The remote sensing image cloud removing method has an obvious thick cloud removing effect on the premise of not destroying original information of the image as much as possible.

The invention discloses a sparse optimization method of suppressing strong shielding interference of seismic data. The method includes: 01, reading original seismic data, and conducting a component analysis on the data; 02, determining two sparse representation dictionaries employed by a morphological component analysis according to the morphological characteristic difference between a seismic response of a transverse heterogeneous body including a river sand body etc. related to a reservoir and a seismic response of a stable sedimentary formation in a seismic record signal, and forming an ultra-complete dictionary; 03, extracting seismic response signals of the transverse heterogeneous body including the river sand body etc. related to the reservoir from a seismic profile after suppressing overlying or underlying formation strong reflection shielding by employing a block coordinate relaxation algorithm; and 04, repeating step 03 until completion of processing of all two-dimensional measuring line data. According to the sparse optimization method of suppressing the strong shielding interference of the seismic data, the strong shielding interference can be effectively and thoroughly suppressed, and the seismic response signals of the transverse heterogeneous body including the river sand body etc. related to the reservoir can be displayed more clearly and visually.

The invention provides a cooperative optimization method and system for an electrified traffic network and an electric system. The electrified traffic network of an electric car and the electric network are coupled, an initial path of the electric car and initial price parameters of charging stations are obtained, the position information and residual electric quantity information of the electric car are obtained in real time, a minimum-cost path of the electric car is searched for through a dijkstra algorithm according to the position information, the residual electric quantity information and the initial path of the electric car and the initial price parameters of the charging stations, the electric quantity required by each charging station is worked out, and the electric network is optimized through a Lagrangean relaxation algorithm. The electrified traffic network of the electric car and the electric system are coupled, on the one hand, the path optimization is solved through the dijkstra algorithm, and the sum of charging, transportation and time-consuming cost is reduced, and on the other hand, electric network feedback is optimized through the Lagrangean relaxation algorithm according to the electric quantity required by each charging station, and cooperative optimization of the electrified traffic network and the electric system is achieved.

The invention relates to an elastic relaxation algorithm-based vehicle type identification system and an elastic relaxation algorithm-based vehicle type identification method. The system comprises a video camera, an image acquisition card and image processing and display equipment which are connected through data wires, and has the advantages of simplicity, convenience, flexibility, low cost, high efficiency and strong practicability. The method comprises the following steps: acquiring vehicle video through the video camera and establishing a Gaussian mean-based background model through acquired images as a detection foreground area; and dividing and extracting the foreground area where a moving object is out of the background as a background image by a frame difference operating method, processing the background image to acquire the length and the height of a vehicle by an elastic relaxation algorithm and then judging a vehicle type according to national vehicle type identification standards. In the method, a background transformation problem in a vehicle area detection and dividing process is solved; and the simple and practicable elastic relaxation algorithm takes the place of a fussy dividing algorithm, which greatly reduces the time complexity of the system, improves the applicability and robustness of the system and can obtain good vehicle type identification effect.

The invention discloses a multi-free-form surface building mesh generation method based on discretization, belonging to the field of building mesh generation. The method is used for building mesh generation of multi-free-form surfaces with cuts and holes. The method comprises the steps of separately discreting a multi-curved surface and stitching the blocks to form a discrete curved surface composed of a large number of small triangular patches; secondly, using the improved error diffusion algorithm, and arranging the initial points on the discretized surface surface according to a certain density. Then, the particle dynamic relaxation algorithm of spatial distance is used to homogenize the point cloud, and the k-means algorithm of the curved surface distance is used to homogenize the point cloud again. Then, triangular meshes are obtained for uniform point clouds. Finally, topology optimization and fairing optimization are carried out. The algorithm of the invention can effectively process multi-free-form surfaces with cuts and holes, and obtain a uniform smooth triangular mesh.

The invention discloses a beam forming method and system based certainty and uncertainty set constraint, and the method comprises the steps: obtaining signals received by array elements in an antenna array, carrying out the weighted summation of the signals received by the array elements, and obtaining wave forming output; carrying out array covariance matrix estimation according to a plurality of samples in a beam forming process, carrying out modeling according to a target function of an estimated array covariance matrix, and obtaining an uncertainty set constraint condition after modeling; converting uncertainty set constraint into certainty set constraint according to a Bern-type inequality; converting a target function into a semi-definite programming function according to a semi-definite relaxation algorithm; solving a semi-definite programming function according to a convex optimization algorithm, and outputting solved beam forming. The method enables the probability constraint condition of a random process into a certainty constraint condition, converts a semi-definite programming problem into the optimization of the target function, irons out the severe defect that an array covariance matrix has an error in a conventional method, and improves the robustness of the system.

The invention discloses a waveform relaxation iteration-based rapid modeling method for electromagnetic pulse response of a time domain multi-conductor transmission line. According to the method, a time domain calculation model for solving coupling of the electromagnetic pulse to the multi-conductor transmission line based on a waveform relaxation algorithm and analytical iteration is establishedin a time domain; A combined voltage wave equation and a BLT supermatrix equation are used; the mutual coupling influence between the transmission lines is equivalent to virtual excitation sources which are continuously distributed on the transmission lines; An analytical expression of voltage and current responses along a transmission line is deduced and given, large-scale matrix inverse operation is converted into a series of iteration processes, meanwhile, each iteration step can give an analytical solution, time-consuming numerical integration is avoided, and the calculation efficiency isimproved.

The invention discloses a non-rigid SAR image registration method based on region similarity and local spatial constraint. The problem that an existing rigid registration method is poor in registration effect when applied to a large SAR image is mainly solved. The method comprises the implementation steps that 1, two SAR images are input; 2, feature points of the input images are extracted, and local feature descriptor similarity is calculated; 3, feature point background region similarity is constructed; 4, feature point local spatial constraint conditions are constructed; 5, a matching cost function is constructed according to the step 2, the step 3 and the step 4; 6, iterative optimization is carried out on the matching cost function through a probabilistic relaxation algorithm to obtain the optimal matching point; 7, geometric deformation parameters are obtained according to the optimal matching point, and a registration result is obtained. Compared with the prior art, robustness on speckle noise and feature abnormality points is enhanced, the capacity of simulating non-rigid deformation is improved, the large actual measurement SAR image registration effect is improved, and the method can be used for image fusion and deformation detection.

The invention discloses a method and a system for balancing a comprehensive energy market in an industrial park. The method mainly comprises the following steps: constructing a peak clipping and pricing compensation model based on an industrial park comprehensive energy market; secondly, after the research on the cournot game of the power market in the past is referenced, establishing a user benefit model and an interactive game mechanism for multiple subjects in the industrial park, considering double-side thermoelectric coupling of a system side and a user side of the park, incorporating thedouble-side thermoelectric coupling into a game target between users, and constructing a regularized Nikaido-Isoda function and using a distributed relaxation algorithm to solve it optimally, and obtaining the equilibrium point of the integrated energy market. By applying the method and the system, not only can the requirements of all users in the industrial park be met, but also the user cost iseffectively saved, and the utilization rate of energy is improved.

The invention discloses a cable coupling noise suppression method based on sparse optimization of a time-frequency domain. The method comprises the steps of 01, determining two conversion dictionaries for morphological component analysis according to morphological characteristics of an effective signal waveform and cable coupling noise, and forming an over-complete dictionary; 02, performing single track treatment on original seismic record data by using a segmented coordinate relaxation algorithm to achieve signal and noise separation, thus acquiring primarily noise-suppressed data; 03, performing high-pass filtering treatment on the primarily noise-suppressed data, subtracting the high-pass filtered noise data from the original data to acquire the effective signal data, thus eliminating wave dispersion; and repeating the steps 02-03 until the data of all tracks is processed. According to the method provided by the invention, the problem of interference of the cable coupling noise in the seismic data collected by distributed optical fibers is solved, and the cable coupling noise is thoroughly suppressed. In addition, the method provided by the invention can perform single-shot treatment, is good in adaptation, and can perform treatment in parallel, and meet industrial large-scale computing requirement.

The invention discloses a frequency domain multi-conductor transmission line electromagnetic pulse response rapid modeling method based on waveform relaxation iteration. A frequency domain calculationmodel for solving coupling of the electromagnetic pulse to the multi-conductor transmission line based on a waveform relaxation algorithm and analytical iteration is established in the frequency domain; A combined voltage wave equation and a BLT supermatrix equation are used; the mutual coupling influence between the transmission lines is equivalent to virtual excitation sources which are continuously distributed on the transmission lines; An analytical expression of voltage and current responses along a transmission line is deduced and given, large-scale matrix inverse operation is convertedinto a series of iteration processes, meanwhile, each iteration step can give an analytical solution, time-consuming numerical integration is avoided, and the calculation efficiency is improved.

The invention discloses a physical layer security joint beamforming method for a bifunctional MIMO radar communication system, relates to the technical field of radar communication, and aims to solve a problem that two design defects exit in the prior art, namely, only an AN covariance matrix is designed and the low system secrecy rate is caused by adopting a relaxation algorithm with poor performance. According to the method, with utilization of a cost function of a radar transmission beam directional diagram, an MIMO radar performance constraint, a QoS (communication quality of service) constraint, a PLS (physical layer security constraint), the constraint of each antenna of a system and the constraint of total power of the system are added in sequence, an optimization problem is constructed, then SDR relaxation is utilized to solve the optimization problem, and the optimization problem is solved. And the optimal solution of the relaxation problem is also the optimal solution of the original problem. According to simulation analysis, under the condition that the multi-user communication security problem of the dual-function radar and the communication system is considered, the radar performance of an original system can be well guaranteed through the SDRPLS algorithm and the ZFPLS algorithm, and meanwhile the secrecy rate of the system is improved.

The invention provides an integrated optimization method for high-speed railway train operation diagram and motor train unit application, and belongs to the technical field of high-speed railway trainoperation control. The method comprises the following steps: constructing an integrated space-time continuation network; describing a running path of the motor train unit according to the integratedspace-time continuation network; establishing an integrated optimization model with a block diagonal structure according to the operation path of the motor train unit; and solving the optimization model based on a Lagrangian relaxation algorithm to obtain a motor train unit intersection and a high-speed railway train running diagram. According to the method, the train operation diagram is adjustedwhen the motor train unit is optimized to be applied, the integrated optimization model has an obvious block diagonal structure, the model is easy to decompose, and the coordination problem of the operation paths of all the motor train units is more rigorous; according to the Lagrangian relaxation-based model solving method, a large-scale combination optimization problem is decomposed into a plurality of sub-problems based on a motor train unit operation path, and the problem that an integrated optimization model cannot solve large-scale high-speed railway train operation diagrams and motor train unit application integrated optimization is solved.

The invention provides a multi-product mass production planning method and system. The method comprises the steps that multi-product mass production planning is converted into multiple pieces of single product mass production planning with a lagrangean relaxation algorithm; the multiple pieces of single product mass production planning is calculated with a dynamic planning algorithm, and whether target multi-product mass production planning obtained by combining calculated multiple pieces of single product mass production planning meets preset constraint conditions or not is judged; if not, the target multi-product mass production planning is adjusted with a greedy heuristic algorithm to make the target multi-product mass production planning meet the preset constraint conditions. The total production cost is minimized on the premise that multi-product mass production is completed.

The invention provides a multi-band fusion algorithm based on an improved relaxation algorithm. The algorithm comprises the steps of receiving a plurality of sub-band signals, selecting one sub-band signal from the plurality of sub-band signals as a reference signal, and compensating incoherent terms between the rest sub-band signals and the reference signal based on the improved relaxation algorithm, and estimating geometric diffraction model parameters of the large-bandwidth signal by using an improved relaxation algorithm on the basis of each sub-band signal subjected to incoherent term compensation, and substituting the geometric diffraction model parameters into a geometric diffraction model to reconstruct the large-bandwidth signal. According to the algorithm provided by the invention, in coherent processing, geometric diffraction model parameters of each sub-band are estimated based on an improved relaxation algorithm, and incoherent items are calculated and compensated by usingthe estimated parameters, so that the incoherent items can be accurately estimated under a strong clutter condition; because the algorithm can accurately estimate the frequency sampling point corresponding to the target position, the imaging resolution is high; a missing frequency band signal can be estimated so as to estimate a large-bandwidth radar signal, and the estimation precision is high.

The invention discloses a random dynamic economic dispatching method for renewable energy sources based on convex relaxation. The method comprises the following steps: establishing a dynamic economicdispatching model considering the randomness of renewable energy sources; Converting the random dynamic economic dispatching model into a deterministic economic dispatching model through an opportunity constrained convex relaxation algorithm; And solving a deterministic dynamic economic dispatching model, and determining and dispatching a plan of the renewable energy unit. The method is suitable for economic dispatching of the power system containing large-scale wind, light and other renewable energy sources for power generation, the risk of the system is effectively reduced, the dispatching cost is saved, the consumption level of the wind, light and other renewable energy sources is improved, and the high efficiency and flexibility of economic dispatching of the system are improved.

The invention relates to an inventory path joint optimization method based on an improved Lagrange relaxation algorithm, and the method comprises the following steps: S1, obtaining distribution information, and building an inventory path problem mathematical model according to an objective function and constraint conditions; s2, selecting a coupling constraint influencing the solution of the inventory path problem mathematical model from a plurality of constraint conditions, and converging the coupling constraint into a target function to obtain a Lagrange relaxation problem function; s3, decomposing the Lagrange relaxation problem function into a plurality of sub-problems according to different decision variables; s4, an improved Lagrangian relaxation algorithm is used to solve the dual function value of each sub-problem; and S5, obtaining an optimal target value according to a solving result, and obtaining an optimal inventory path planning scheme. Compared with the prior art, the method has the advantages of high convergence speed, high approximate solution quality and the like.

The invention provides an unmanned aerial vehicle cluster deployment method based on a ground cellular system, and the method comprises the steps: determining the position and height of a user, the position of a base station, the initial number and flight height of an unmanned aerial vehicle, and the road position of a mooring unmanned aerial vehicle in a region to be deployed; taking the position constraint of the tethered unmanned aerial vehicle, the maximum communication radius constraint between the tethered unmanned aerial vehicle and the base station, the maximum communication radius constraint between the tethered unmanned aerial vehicle and the non-tethered unmanned aerial vehicle and the maximum communication radius constraint between the unmanned aerial vehicle and the user as constraint conditions; taking the maximization of the number of users capable of being covered as a first objective function, and solving the maximum number of users capable of being covered by adopting a continuous convex approximation algorithm, a linear relaxation algorithm and a weighted l1 algorithm; and taking minimization of the number of unmanned aerial vehicles required by the maximum user number of coverage as a second objective function, and adopting a continuous convex approximation algorithm and a dichotomy to obtain the minimum number and position of unmanned aerial vehicles required to be deployed by the user with the maximum coverage, so that large-range coverage under the condition of high system capacity can be realized.