70 results about "Engineering optimization" patented technology

Hierarchical Software Defined Network (SDN) architectures can be used to reduce complexity of traffic engineering in large or divers network environments. In hierarchical SDN architectures, a network is sub-divided into multiple regions, and each region is assigned to a different SDN controller. Network status information is collected and consolidated at a regional level, and fed upstream through the SDN control plane until it reaches a root SDN controller. The root-SDN controller computes cost-based parameters, which are distributed to regional SDN controllers for local provisioning. The cost-based parameters can include Lagrangian variables estimations or other parameters that constrain regional traffic engineering optimization in a manner that advances global traffic engineering objectives.

The invention relates to a high-efficiency Latin hypercube experimental design method, belonging to the technical field of engineering optimization designs. For realizing the purpose that users extract m experimental design points from an n-dimensional design space in actual engineering, the method comprises the following steps of: presetting the first experimental design point by establishing a space hypercube; determining the second experimental design point in a second unit hypercube by utilizing the first experimental design point; determining the sth experimental design point in the sth unit hypercube by utilizing the obtained s-1 (s is equal to 3, 4, 5,......, m-1) experimental design points until the (m-1) experimental design points are determined; and uniquely determining the mth point meeting the design condition in the mth unit hypercube. The design method can obtain the experimental design points meeting the characteristic requirements of space uniformity and projection uniformity without an optimization process, thereby having high sampling efficiency; and the obtained experimental design points have good properties and are suitable for the optimization fields of any engineering designs and the like with huge operation.

The invention relates to a high-efficiency global optimization method using adaptive radial basis function based on fuzzy clustering, belonging to the technical field of modern engineering optimization designing. The method comprises the following steps: gradually constructing a proxy model of a complicated analysis model relating to an engineering design problem by the adaptive radial basis function; classifying the function values of a proxy model on a known mesh point aiming at the proxy model by adopting a fuzzy clustering method; searching a key area which is possibly saved to the global optimal point; and optimizing the proxy model by using the global optimizing algorithm in a small range in the key area until the optimal value of the engineering design problem is acquired. The optimization method overcomes the defect of time-consuming operation when the traditional global optimization method is used in the engineering design problem, can effectively reduce operation cost, improve optimization efficiency, is advantageous to shortening the period of engineering optimization design, and has the capability of global searching.

The invention belongs to the field of artificial intelligence of a computer application technology, relates to an application of a swarm intelligence optimization method of an intelligence optimization algorithm, and particularly relates to a traffic flow forecasting method for an intelligent traffic system. A mixed artificial fish swarm optimization support vector regression method is applied to traffic flow forecasting. The construction process of the mixed optimization method is characterized in that a particle swarm algorithm is applied to improve the behavior selection of the artificial fish swarm algorithm aiming at the problem that the effect of a step-length factor in the artificial fish swarm algorithm on the algorithm is insufficient to reduce the step-length effect, then the support vector regression is optimized to conduct parameter selection to further build a mixed artificial fish swarm optimization traffic flow forecasting model. The method has the advantages of being capable of overcoming the shortcomings of the artificial fish swarm algorithm, acquires better combination regression parameters compared with the single swarm intelligence optimization algorithm application, and improves the traffic flow forecasting accuracy accordingly. The mixed optimization method is applicable to actual traffic flow predication and other engineering optimization.

The invention belongs to the field of ocean engineering, particularly belongs to the field of offshore wind power development, and particularly relates to sliding type installation method and system for an offshore electric platform. The sliding type installation method and the system for the offshore electric platform have the following beneficial effects that (1) the application range is wide, ultra-large type ocean platforms at very shallow water depth can be installed, the limitation requirements on a transportation barge, an upper module and a lower module are less besides the necessary support structure bearing capacity requirement, a classic structure style and a layout plan of the platform do not need to be changed, the selecting surface for engineering optimization is widened, andthe construction period and the work amount can be effectively controlled; (2) the butt joint of the upper module and the lower module of the offshore platform can be operated stably, accurately andcontrollably with the help of a track and a self-propelled modular trailer (SPMT) set, the problems of collision and inclination probably occurred in a hoisting method or a floating-supporting methodare avoided, and a butt-joint buffer device at a pile connecting part has no need to be arranged; and (3) various steps are conveniently constructed and simply operated, and the remarkable engineeringeconomic benefit is achieved.

The invention discloses an approximate optimization method based on particle swarm optimization and Kriging model, which belongs to the technical field of engineering optimization design. A method forrealize that invention comprises the follow steps of: using particle swarm evolution to guide simple sample points to aggregate to a global optimal solution; adopting fuzzy c- Mean clustering methodis used to cluster simple sample points to identify the region of interest, so as to fully mine the design space information on the basis of balancing local search and global exploration. By adding high-precision sample points in the region of interest sequence and updating the Kriging model constantly, the optimization converges to the global optimal solution quickly. The invention can effectively improve the global exploring ability and the local searching ability of the optimization method, reduce the calculation cost in the optimization process of the complex engineering system, avoid missing the global optimal solution as far as possible, and improve the reliability of the design result. The invention is suitable for application in the field of complex engineering system design optimization including a high-precision analysis model, and can solve corresponding related engineering problems.

The invention discloses a method for optimizing construction by synthetically utilizing BIM technology and a 3D laser scanning system, belongs to the field of bridge engineering, the invention particularly relates to a method for optimizing construction by synthetically utilizing BIM technology and the 3D laser scanning system, the method can be used to guide and optimize every working procedure in the whole bridge construction process, which can greatly improve the quality of the whole construction process, improve the accuracy and convenience of data collection, reduce the workload of manualmeasurement, reduce the waste caused by construction rework, improve the efficiency of construction, rationally utilize resources, solve various drawbacks caused by the traditional construction method, and achieve engineering optimization.

The invention discloses an engineering optimization method of a Harris hawks algorithm based on multi-strategy enhancement. The engineering optimization method comprises the following steps: S1, constructing an optimization objective function of an actual engineering problem; S2, initializing parameters; S3, initializing the population position of the Harris hawks; calculating the fitness value ofeach Harris hawk in the initialized population according to the target function; S5, dynamically simulating the motion state of the prey and the execution state of the population so as to find the best execution strategy for each Harris hawk; s6, performing an exploration stage; s7, performing a development stage; s8, updating the position of the whole population through the steps S5 and S6, andenabling the updated new population to pass through an objective function and a constraint condition; s9, judging whether the maximum number of iterations is reached or not. The method has the following advantages and effects that a better solution can be found in an actual engineering optimization problem, and the precision of the solution of the constraint-containing engineering optimization problem is improved within limited time.

The invention discloses a comprehensive optimization method for a small biomass methane combined supply system of cooling, heating and power. According to the method, for a given user capable of generating methane, the annual hourly cooling heating and power load requirement of the given user in a typical meteorological year is simulated, and an annual cooling heating and power load curve of a building is obtained; variable working condition characteristics of a main device of the combined supply system of cooling heating and power are designed; the small biomass methane combined supply system of cooling heating and power is constructed and designed; the optimization variable of the small biomass methane combined supply system of cooling heating and power is determined, the parameters of other devices in the small biomass methane combined supply system of cooling heating and power are set with the energy saving rate, the cost recovery rate and the emission benefits relative to a traditional separated supply system as the comprehensive target; optimization restraint conditions are determined; an optimization design target function is set up; the optimization design target function is worked out, the optimization variable is obtained, and the optimal configuration of other devices of the system is obtained. The method is simple and practical and is suitable for engineering optimization design of various small biomass methane combined supply systems of cooling heating and power through cooperation with the building energy consumption analysis theory.

The invention discloses an assembly type prefabricated concrete structure engineering optimization method based on a BIM technology. The method comprises: establishing a three-dimensional model of a prefabricated part and a cast-in-place structure; analyzing and optimizing the prefabricated parts according to a prefabricated part splitting design principle and the key nodes; carrying out collisiondetection on the integrated model and carrying out optimal design on the prefabricated part; creating a preliminary measure model and carrying out hoisting simulation analysis on the prefabricated part; outputting various engineering drawings, engineering quantity lists and analog videos on the basis of the model; and engineering three-dimensional site arrangement and analysis are carried out, and a site arrangement diagram is output. The method is based on the BIM technology. The invention discloses a method for designing a precast concrete member. Precision deviation possibly existing in prefabricated part production is considered in advance, a set of perfect prefabricated part hoisting process is formed by checking possible problems of the prefabricated parts in the hoisting process and conducting optimization design on the prefabricated parts in a targeted mode, and it is guaranteed that follow-up on-site actual construction is conducted smoothly.

The invention discloses an optimization method of a communication network in the technical field of wireless communication. The method comprises the following steps: S1, performing data collection; S2, analyzing all data through background processing software; S3, performing network parameter adjustment according to an analysis result; S4, dynamically adjusting network resource allocation according to telephone traffic. The method, through engineering optimization and operation and maintenance optimization on a network, is beneficial to the enlargement of the network coverage area, reduces the call drop rate, reduces the origination and incoming call failure rate, provides stable switching, reduces unnecessary soft switching, improves the utilization ratio of system resources, enlarges the system capacity, meanwhile is beneficial to the keeping of good network performance index, improves the single station failure removal and performance, reduces pilot frequency pollution, and enlarges the system capacity.

The invention discloses an aerodynamic configuration optimization method based on flow field prediction. The method includes subjecting flow field pressure distribution directly to approximate modeling; providing a flow field approximation model deeply coupled with the aerodynamic optimization problem of the aircraft; realizing efficient utilization of a large amount of flow field data generated by the high-precision flow field simulation model, thus reducing the number of simulation calling times of the high-precision model of the sequence approximate optimization method, improving the quality and efficiency of aircraft aerodynamic configuration optimization, and greatly improving the optimization efficiency of engineering optimization problems. And compared with a conventional sequence approximate optimization method and a stochastic evolution algorithm, the effectiveness and the high efficiency of the method are verified, and the aerodynamic configuration optimization method provided by the invention has important application value for aircraft aerodynamic configuration fine design.

The invention relates to a combined Kriging model construction method based on a combined prediction method. Firstly, seven different types of related functions are used for respectively constructing seven single related function Kriging approximate models; then by using the minimum average absolute relative error as the optimization objective, an optimization model of a weight value of each single related function Kriging model is constructed, and an optimal weight value is obtained; and finally, by utilizing the optimal weight value and the seven single related function Kriging approximate models constructed on the basis of seven different types of related functions, a combined Kriging model is constructed. The combined Kriging model disclosed by the invention has the characteristics of high prediction accuracy and more stable prediction performance, and has high practical value in the field of engineering optimization design of value simulation calculation of a computer.

The invention discloses a power matching method of magnetic-coupling resonance wireless energy transmission system optimization design. The method comprises the following steps of: 1, establishing an equivalent two-port network model of a magnetic-coupling resonance wireless energy transmission system; 2, analyzing characteristics of the two-port network model by adopting a power wave method; 3, and defining power transmission efficiency of the magnetic-coupling resonance wireless energy transmission system, so as to obtain a power matching condition of the system and provide guidance for engineering optimization design. The power matching design method has the advantages of: 1, the physical significance is clear; 2, the calculation process is simple and easy to implement; 3, and the power matching condition that power acquired from the load is greatest upon the maximum system wireless energy transmission efficiency is achieved.

The invention discloses a dynamic scheduling method of a mixed-integer quadratic programming power system. The dynamic scheduling method comprises the steps that a dynamic scheduling mathematic model of the power system is built, wherein the model comprises an objective function and conditional constraints; the conditional constraints include a power balance constraint, a unit output constraint, a unit ramping constraint, a unit embargo area constraint and positive and negative standby constraints; the conditional constraints are converted into corresponding linear constraints by employing a mixed-integer encoding technology to convert the dynamic scheduling mathematic model into a mixed-integer quadratic programming model; and the mixed-integer quadratic programming model is solved by employing a default engineering optimization software package to obtain a corresponding power system scheduling scheme. Therefore, firstly, the feasibility of the finally obtained power system scheduling scheme and the dynamic scheduling effectiveness of the power system are ensured; and secondly, a global optimal solution, namely the optimal power system scheduling scheme can be obtained, and the dynamic scheduling method is suitable for large-scale calculation of an active scheduling plan of a unit.

The invention belongs to the related technical field of design optimization. The invention discloses a particle swarm optimization method based on sequence approximation optimization, and the method comprises the following steps: (1) determining the design space of a to-be-optimized actual engineering optimization problem according to the to-be-optimized actual engineering optimization problem, and constructing a simulation model between a design variable and a target response value for the actual engineering optimization problem; (2) determining the population number N of the particle swarm optimization algorithm according to the design space, selecting N sample points in the design space, calculating the real response values of the N sample points by adopting the simulation model, and setting the current sample point as the current population of the particle swarm optimization algorithm; (3) updating particles in the current population; (4) judging whether the particle swarm optimization algorithm converges or not, and outputting an optimal solution if the particle swarm optimization algorithm converges; otherwise, going to the step 3 until the particle swarm optimization algorithm converges. According to the method, the optimization efficiency of the particle swarm algorithm is improved, and the time cost is reduced.

The invention relates to a sequential maximin latin hypercube design method, belonging to the technical field of engineering optimization design. Through adoption of the method, the purpose that a user uniformly increases m2 sample points in an n-dimensional design space with m1 already existing sample points is achieved. According to the method, firstly, the m1 already existing sample points are mapped into an m<n> orthogonal super checkerboard through methods of mesh generation and mesh mapping; and then, sequential sample points are gradually generated according to a Maximin criterion through a local enumeration method, and the spatial uniform distribution and projection uniformity of newly increased sample points and already existing sample points are guaranteed. According to the sequential maximin latin hypercube design method, the positions of already existing sample points are considered in generation of the sample points, so that the spatial uniform distribution and projection uniformity of all sample points are guaranteed. If being applied to a dynamic agent model, the method can remarkably improve the global optimization ability and optimization efficiency of an optimization design method of the dynamic agent model; and the method is applicable to the modern engineering design optimization field using high precise analysis models.

The invention provides an engineering optimization method for improving the water quality pollution monitoring efficiency of a water supply pipe network, and the method comprises the following steps:building and checking a hydraulic model, and building a water quality model based on the hydraulic model; setting optimized parameters of the water quality monitoring system; calculating a water quality pollution probability function of each node; establishing a pipe network water quality monitoring multi-objective optimization function model; adopting an intelligent algorithm to solve the multi-objective optimization function model, so that an optimized solution set is obtained, the monitoring probability of pollution events can be improved, the monitoring time after water quality pollution invasion is shortened, and the method has important scientific significance and application value for urban water supply pipe network water quality safety guarantee.

The invention provides a front-end script splitting and deconstructing optimization method, which comprises the following steps: the universal frame code is separated from the service resource scriptseparately, so that all the service resource scripts uniformly use the splitted frame code; the front-end script splitting and deconstructing optimization method comprises the following steps: The business resource script is deconstructed, and each sub-business code is split into independent script files. When the user enters a specific business, the corresponding script file is requested and thecorresponding sub-business code is executed. The invention also provides a front-end script splitting and deconstructing optimization system, which is a front-end engineering optimization scheme aiming at the problems that the volume of the Javascript service resource script is too large and the service resource script loading bottleneck.

The present invention relates to an ecological engineering construction benefit evaluation method. The method comprises: engineering data collection: collecting engineering data information in a drainage area and correlated information of an engineering circumference drainage area; engineering evaluation modeling: based on the collected engineering data, performing modeling calculation of the ecological engineering construction benefit evaluation, wherein the modeling calculation comprises benefit evaluation index construction, a benefit evaluation calculation method and comprehensive benefit evaluation calculation; and engineering evaluation query: facing user usage, performing ecological engineering evaluation based on the constructed ecological engineering benefit evaluation model to allow decision makers to conveniently analyze the engineering investment benefit and provide basis for engineering optimization investment. The ecological engineering construction benefit evaluation method allows users to conveniently understand and evaluate the environment benefit of the key treatment engineering of a drainage area, performs science plan in advance and form a key treatment project database requiring increasing or improving of the drainage area and employs data to support the pollution load reduction and water quality improvement effect so as to allow the water pollution control decision of the drainage area to be scientific and intelligent and save the engineering investment amount.

The invention provides a rapid multi-target engineering optimization design method for a complex rainwater pipe network. The rapid multi-target engineering optimization design method for the complex rainwater pipe network comprises the steps that a rainstorm intensity formula and parameters are selected, the storm recurrence period is given, and a typical rainfall process curve is selected; a rainwater pipe network hydraulic model is built, and the rainfall process curve is imported into a rainfall module in the model; a fullness solution set is determined; the pipe diameter of the rainwater pipe network is calculated, the pipe diameter is adjusted and calculated, an initial optimization scheme set of the rainwater pipe network is determined, a multi-target optimization model of the rainwater pipe network is built, and an optimization algorithm is started to rapidly acquire a Pareto optimization solution set of the rainwater pipe network. The rapid multi-target engineering optimizationdesign method for the complex rainwater pipe network has the original creativity, is an important complement for the study field of the optimization design of the rainwater pipe network and can provide an important technical support for urban water logging prevention and control and sponge city construction, and therefore the promotion and practical engineering application value is good.

The invention discloses an efficient approximate optimization method for aircraft driven by heterogenous data, and belongs to the technical field of aircraft engineering optimization. A fuzzy clustering point selection strategy based on distance identification is adopted to select high-quality local high/low-precision sample points with better feasibility and optimality from an alternative sample point set, and the optimization process is guided to rapidly converge to a global feasible optimal point; and a Co-kriging agent model of an aircraft system analysis model is constructed through selected high/low-precision sample points, and multi-source simulation analysis models with different precisions in engineering are fully utilized to realize guaranteed-precision fast-response prediction of a complex aircraft engineering system, the solving efficiency and robustness of an aircraft system optimization problem related to a high-time-consuming analysis model are improved, and the performance of a complex aircraft system is improved. The method is suitable for being applied to the field of aircraft complex engineering system optimization containing a high-precision analysis model, and corresponding related engineering problems are solved.

The invention belongs to the related technical field of design optimization, and discloses a method for solving a high-dimensional optimization problem based on an approximation model and a differential evolution algorithm. The method comprises the following steps: (1) determining a design space according to a to-be-optimized actual engineering optimization problem; (2) constructing a global radial basis function approximation model and a local radial basis function approximation model of all individuals in the current population based on the obtained sample points; (3) on the basis of a differential evolution algorithm, taking a local radial basis function approximation model as guidance to carry out mutation operation on the current population, and then carrying out crossover operation on the obtained population, and taking a global radial basis function approximation model as guidance to carry out selection operation on the population; and (4) judging whether the differential evolution algorithm converges or not, outputting an optimal solution calculated by the differential evolution algorithm if the differential evolution algorithm converges, otherwise, going to the step (2) until the differential evolution algorithm converges. The method improves the optimization precision, is high in the applicability, and is preferable in the flexibility.