442 results about "Multi objective optimization algorithm" patented technology

A method of determining a path having an ordered set of waypoints to be visited by a mobile agent to accomplish a mission includes: producing candidate paths using a multi-objective optimization algorithm, subject to a path production heuristic; selecting a path from the candidate paths, subject to a path selection heuristic; instructing the mobile agent to move according to the selected path; modifying a maintained subset of the candidate paths to produce a new candidate path using the algorithm and subject to the path production heuristic; designating either the currently-selected path or the new candidate path as the newly-selected path, subject to the path selection heuristic; and instructing the mobile agent to move according to the newly-selected path. The method may further include iterating production of new candidate paths, either randomly or based on modifications of previous candidate paths, to continually update an operation plan for the mobile agent.

The invention discloses a multi-objective optimization scheduling method for an electric vehicle charging station including a photovoltaic power generation system, and belongs to the technical field of smart power grids. Power purchase cost minimization and circulating electric quantity minimization of an energy storage system are used as objective functions, and a multi-objective optimization scheduling model of the electric vehicle charging station including the photovoltaic power generation system is built; the decision variable of the scheduling model and the constraint condition of the decision variable are determined; basic data are determined; solution is carried out through a multi-objective optimization algorithm to obtain a non-dominated solution leading surface, and then multiple Pareto optimal solutions are obtained; according to the low comprehensive cost of the main circulating electric quantity of the energy storage system and the power purchase cost, the charging station scheduling optimal scheme is selected at last. The multi-objective optimization scheduling method is suitable for the electric vehicle charging station including the photovoltaic power generation system of various cities with rich light sources; the scheduling scheme of the electric vehicle charging station including the photovoltaic power generation system is optimized; theoretical bases and technical supporting can be provided for the scheduling of the electric vehicle charging station including the photovoltaic power generation system; the operation economical efficiency of the charging station is improved.

The invention provides a progressive health monitoring method for a cable system based on hybrid monitoring during generalized displacement of a support. Whether a mechanics calculation benchmark model of the structure requires to be updated against is decided by monitoring of a generalized coordinates of the structural support; considering the similar linear relationship among the current data vector of the monitored quantity and the initial value vector of the monitored quantity, the unit damage monitored quantity change matrix and the current nominal damage vector, in order to overcome the defects, a method using the linear relationship to approach the nonlinear relationship by section is adopted so that a large section is divided into a plurality of continuous small sections; the linear relationship in each small section is sufficiently accurate; and suitable algorithms such as a multi-objective optimization algorithm and the like can be utilized in each small section so as to calculate the non-inferior solution of the damage vector of the current cable, thus exactly determining the position and the damage degree of a damaged cable.

A method of determining a path having an ordered set of waypoints to be visited by a mobile agent to accomplish a mission includes: producing candidate paths using a multi-objective optimization algorithm, subject to a path production heuristic; selecting a path from the candidate paths, subject to a path selection heuristic; instructing the mobile agent to move according to the selected path; modifying a maintained subset of the candidate paths to produce a new candidate path using the algorithm and subject to the path production heuristic; designating either the currently-selected path or the new candidate path as the newly-selected path, subject to the path selection heuristic; and instructing the mobile agent to move according to the newly-selected path. The method may further include iterating production of new candidate paths, either randomly or based on modifications of previous candidate paths, to continually update an operation plan for the mobile agent.

The invention discloses a sewage processing process dynamic multi-target optimization control method, belongs to the field of water research and also belongs to the field of intelligent control, for simultaneously optimizing energy consumption and water quality indexes under the condition that the water quality reaches the standard. First of all, a sewage processing process multi-target optimization model is constructed through a nerve network online modeling method for solving the problem of lack of accurate mathematic description between an optimization variable and a performance index; secondly, based on the established optimization model, a Pareto optimal solution of the problem is obtained through a multi-target optimization algorithm, and according to decision information, one satisfactory optimization solution is determined from a Pareto solution set, i.e., optimization set values of a dissolved oxygen concentration and a nitrate nitrogen concentration; and finally, a bottom controller realizes a tracking task of the optimization set value. The method provided by the invention can effectively reduce the energy consumption and the operation cost of a sewage processing process under the condition that the water quality reaches the standard.

The invention discloses a health monitoring system of a cable system in a cable structure on the basis of strain monitoring; a plurality of times of mechanical calculation is carried out on the basis of the reference model of the mechanical calculation of a structure, the times of the calculation is equal to the number of cables. Supposing that only one cable has unit damage during each calculation, the calculation result of each time constitutes a current strain calculation vector; a strain change vector is obtained by that each current strain calculation vector minus an initial strain vector; all the strain change vectors constitute a unit damage strain change matrix. According to the similar linear relation between the current strain vector (constituted by the current measured strain of all the given points) and the initial strain vector, the unit damage strain change matrix, an unit damage scalar and a current cable damage vector (constituted by current damage amount of all cables), the noninferior solution of the current cable damage vector can be figured out fast by utilizing the suitable algorithms of multi-objective optimization algorithm, etc., therefore, the position and the degree of damage of the damaged cable can be determined accurately.

The invention discloses a multi-objective optimized human body motion tracking method, which relates to the field of computer vision and is used for tracking human body motion and estimating a posture. The method comprises the following steps of: (1) establishing a human body skeleton model; (2) preprocessing a video image; (3) initiating; (4) constructing similarity functions; (5) optimizing a target function; and (6) selecting an optimal human body motion posture. The method has the advantages that: the method is not limited by a study database and is suitable for the common videos, and video image information can be better used through two similarity functions; meanwhile, compared with the conventional single-objective optimized human body tracking method, the multi-objective optimized human body motion tracking method has the advantages that: the non-dominated neighborhood immune algorithm of a multi-objective optimization algorithm can be prevented from falling into local optimum, and the human body motion tracking accuracy is improved.

The method for generating an integrated guidance law for aerodynamic missiles uses a strength Pareto evolutionary algorithm (SPEA)-based approach for generating an integrated fuzzy guidance law, which includes three separate fuzzy controllers. Each of these fuzzy controllers is activated in a unique region of missile interception. The distribution of membership functions and the associated rules are obtained by solving a nonlinear constrained multi-objective optimization problem in which final time, energy consumption, and miss distance are treated as competing objectives. A Tabu search is utilized to build a library of initial feasible solutions for the multi-objective optimization algorithm. Additionally, a hierarchical clustering technique is utilized to provide the decision maker with a representative and manageable Pareto-optimal set without destroying the characteristics of the trade-off front. A fuzzy-based system is employed to extract the best compromise solution over the trade-off curve.

The invention discloses a method for processing a constraint condition in a constraint optimization problem and updating a global optimal position and an optimal position of a particle swarm optimization algorithm. The research on a multi-objective optimization algorithm lays a solid foundation for the following research content. According to the capacity optimal configuration method, a multi-objective optimal configuration mathematical model for capacity of the microgrid energy storage system is established; aiming at the optimal power balance, the minimal renewable energy fluctuations and the lowest investment cost, the configuration of the microgrid energy storage system is analyzed from the perspectives of power amplitude limiting, discharge and charge management of the energy storagesystem and the like; and a multi-objective random optimal configuration model based on a Pareto optimal solution is given, thereby fully illustrating that the microgrid energy storage system optimal configuration strategy disclosed by the invention can effectively match a renewable energy resource with a balanced matching coefficient of a load, and effectively realize quantitative configuration ofthe microgrid energy storage system.

The invention is applicable to the field of dynamic balancing and optimal dispatching for energy in iron and steel enterprises, and provides a multi-objective optimization algorithm based energy dynamic balancing and optimal dispatching method. The method includes the steps: firstly, acquiring information of equipment units in an iron and steel enterprise energy system network topology and gas, steam and power subsystems; secondly, acquiring supply-demand prediction data, production-maintenance schedules and other setting information of various energy media; thirdly, establishing an iron and steel enterprise energy dynamic balancing and optimal dispatching mathematical model, determining optimization variables of the optimal dispatching model, and determining an objective function and constraint conditions of the optimal dispatching model; fourthly, converting the optimization dispatching model into a problem with two optimization objectives; fifthly, adopting an multi-objective optimization algorithm to solve the problem, with the two optimization objectives, obtained in the fourth step. The coupling relationship of the energy media of the iron and steel enterprises are considered comprehensively, the technical scheme for dynamic balancing and optimal dispatching of the energy media is given from the prospective of integrated dispatching and global optimization, and the method is of clear guiding significance to concrete practice.

The invention discloses a regional multi-airport abnormal flight recovery method based on risk management and control. The method includes: firstly, a regional multi-airport flight scheduling model isconstructed, secondly, the fuzzy analytic hierarchy process is used to quantitatively evaluate the risk value of relevant dangerous events in each unit, finally, a regional multi-airport abnormal flight restoration model based on risk management and control is constructed, and the actual flight restoration method is applied to the algorithm neighborhood solution generation mechanism according tothe characteristics of the model, and a multi-objective optimization algorithm based on intelligent neighborhood selection is designed to solve the model. The invention can be applied to the problem of abnormal flight restoration of regional multi-airports. Through consideration of the resource constraint of regional multi-airport system coupling, the potential safety risks of the key dangerous events in the abnormal flight-induced system is controlled in a targeted manner and the flight recovery plan of the regional multi-airport system is coordinated from the overall perspective, so that theabnormal flight recovery plan of the regional multi-airport system has higher safety and maneuverability. The method has a broad promotion and application prospect.

The invention discloses an infrared thermal image defect feature recognition method based on dynamic multi-objective optimization. The transient thermal response of a pixel point is selected by changing the step length of a thermal image sequence; FCM is used for classification. obtaining the category of the transient thermal response of each pixel point; the pixel value similarity of each type ofpixel points and the same type of pixel points is considered; the difference with different types of pixel points is realized; constructing a corresponding multi-objective function; meanwhile, afterthe environment is changed each time; Prediction mechanism, a guiding direction is provided for population evolution; a multi-objective optimization algorithm is helped to quickly respond to the new change; The method has the advantages that the dimension reduction result of the thermal image sequence is acquired by the aid of the feature extraction algorithm, the defect features of the infrared thermal image are extracted by the aid of the pulse coupling neural network, accordingly, accurate selection of transient heat representations (temperature points) can be realized, the accuracy of defect feature extraction can be guaranteed, and computing consumption of the transient heat representations for acquiring various types of information in dynamic environments can be reduced.

The invention relates to a cloud computing resource allocation method supporting sudden emergency. The method comprises the steps that 1, user resource application information including user priority,urgency degree of resource requirements, resource application quantity and configuration is acquired; 2, the user resource allocation priority is calculated; 3, resource information of a physical server is acquired; 4, the resource utilization rate of the physical server is calculated; 5, a multi-objective function with optimal performance matching of a virtual machine, shortest time provided bythe virtual machine and minimum total load change of a platform and a constraint condition are established; 6, a mapping relation of a virtual machine request and the physical server is established; 7, resource allocation is conducted. According to the cloud computing resource allocation method supporting the sudden emergency, the multi-objective function and the constraint condition are established, a multi-objective optimization algorithm is adopted for obtaining a solution set, a solution is selected as a mapping scheme, and optimal arrangement of the virtual machine can be achieved. Meanwhile, the allocation method has the function of supporting the sudden emergency and can preferentially allocate resources for sudden emergency users.

The invention relates to a hydrogen-refueling optimized control method and a corresponding system, aiming at providing a three-level refueling optimized method for a hydrogen-refueling station with high-effective hydrogen-refueling, namely, a hydrogen-storing tank in the hydrogen-refueling station is divided into three groups which are high, middle and low.. A multi-object optimization algorithm is put forward and a constraint method is adopted to convert a multi-object problem into a plurality of single-object optimization problems so as to realize high effective hydrogen-taking and quick refueling. Each single-object schedule problem belongs to the dynamic schedule scope and a forward sequential searching method is adopted. A detailed program control flow is figured out according to the optimized result so as to design the optimal refueling mode for the refueling system of the hydrogen-refueling station. The invention also provides a hydrogen refueling system which is used for the high-effective hydrogen-refueling three-level optimized control method of the hydrogen-refueling station and comprises a hydrogen-storing container, a hydrogen-charging valve, a pressure sensor, a hydrogen distributor, a flow meter and a check valve; the hydrogen-storing container comprises a low-pressure hydrogen-storing container, a middle-pressure hydrogen-storing container and a high-pressure hydrogen-storing container; and also a program controller.

The invention puts forward an electric car and cloud energy storage economic scheduling method based on a dynamic non-cooperative game. The method comprises the following steps that: (1) establishingan electric car agent economic optimization scheduling model; (2) establishing a three-party non-cooperative game model; and (3) adopting particle swarm optimization to solve the above model, and determining a final Nash equilibrium point. By use of the method, the concept of V2G (Vehicle-to-grid) and a cloud storage retailer is imported for inhibiting the volatility influence of intermittent energy power generation, an electric car can be used as a load and also can be used as an energy storage characteristic, and electric energy grid-generation commercial operation is realized on a premise that the own requirement of the EV car owner is met, and therefore, the maximum car owner benefits are pursed; and meanwhile, since a cloud energy storage retailer and the EV car owner agent has economic conflicts, on the basis of a non-cooperative game theory, solving is carried out so as to achieve the maximum car owner benefits. Compared with subjectivity that a solution set obtained by a traditional multi-objective optimization algorithm is decided and selected by an administrator, the method disclosed by the invention imports a game theory to improve the flexibility and the intelligence ofelectric energy utilization.

The invention discloses a micro-grid energy management distribution type multi-target cooperation optimization algorithm based on a potential game. The algorithm comprises the following steps of 1) micro-grid component unit modeling which includes a determination decision main body, a decision variable, a component constraint and multi-target modeling; 2) potential game modeling, wherein an incomefunction and a potential function are vector functions; and 3) based on the distributed game solving of the multi-target optimization algorithm, mainly using a multi-target evolutionary algorithm tosolve the pareto optimal solution set of the income function, and through distributed iteration, solving nash equilibrium. In the invention, a distributed multiagent decision optimization mode is adopted, a potential game theory possessing a distributed characteristic is combined, the expansion of a microgrid is easy to realize, local management and local decision making are realized too, system reliability and flexibility are increased, through a game mode, the distributed multi-target optimization is realized, the competition and cooperate relation of the microgrid and an individual is achieved, and individual interests are guaranteed and the overall benefits of the microgrid are maximized.

The invention discloses a method of optimal configuration of a plurality of superconducting fault current limiter based on a sensitivity technology, which comprises the steps of: calculating the three-phase short-circuit current of all nodes in a whole system, and finding b nodes with the short-circuit current exceeding the standard value; determining braches for installation and reliable startup of the SFCLs (superconducting fault current limiter) according to startup conditions of the SFCLs; calculating the sensitivity of self-impedance of each station with the short-circuit current exceeding the standard value, sequencing the stations in a sensitivity descending order, and taking the top a branches as candidate braches for installation of the SFCLs; and taking c candidate braches as optimal branches for a multi-objective optimization algorithm NSGA-II (non-dominated sorting genetic algorithm-II) in next step, determining a scheme with the optimal installation positions of the SFCLs, the number of SFCLs to be installed and the impedance, thereby realizing global optimum configuration. By adopting the method, the search space to be understood is reduced greatly, the speed of calculation is increased, and optimization results are more suitable for actual applications. Meanwhile, the problem of weight selection resulting from conversion of multi-objective optimization to single-objective optimization can be avoided, and a decision-maker can make final decisions according to actual engineering needs.

The invention discloses a multi-agent-based job-shop multi-objective collaborative optimization scheduling method, which comprises the following steps: a multi-agent-based production workshop scheduling model is established; a workpiece agent proposes a job scheduling request to a planning agent according to a processing task of itself; the planning agent inserts a time cycle window, and carries out multi-objective optimization scheduling based on time, load, cost and energy consumption on a plurality of job scheduling requests received in the window; and the planning agent releases a scheduling result to a device agent, and the device agent finishes the processing task and informs the workpiece agent to release a next process. By inserting the time window, arranging the planning agent and packaging a multi-objective optimization algorithm, optimization range is expanded, overall scheduling performance of the system is improved, multi-objective optimization scheduling is realized, and the defects of single-step optimization and single-objective optimization of a conventional multi-agent method are solved.

The invention discloses a site selection method for a sea emergency rescue station. The method comprises the following steps: S1, according to the historical distributed data of sudden accidents in a target sea area, dividing the sea area into different water area units according to the densities of the sudden accidents, and setting the model parameters of respective water area units; S2, creating an emergency rescue station site selection model according to the model parameters in order that the emergency rescue station covers all the water area units of the target sea area, wherein the emergency rescue station site selection model satisfies conditions that the maximum distance between the emergency rescue station and the water area unit possibly generating an accident is minimized, that the total weight that the emergency rescue station covers the water area unit possibly generating an accident is maximized, and that the total weighted distance of the emergency rescue station and the respective water area units is minimized; and S3, solving the emergency rescue station site selection model by using a multi-objective optimization algorithm to obtain an optimal solution set as the optimal site selection position of the emergency rescue station. The site selection method realizes the principle of fairness and efficiency of emergency rescue station site selection, and greatly improves the efficiency and success rate of rescue.

The invention discloses an engine thermodynamic simulation model calibration method based on multi-objective optimization. The method comprises steps as follows: S101, acquiring engine structure parameters and engine test data; S102, establishing a one-dimensional engine thermodynamic model; S103, establishing a coupling calculation model of the one-dimensional engine thermodynamic model and multi-objective optimization software; S104, selecting calibration parameters and setting ranges of the calibration parameters; S105, setting a calibration objective; S106, selecting a corresponding test design and optimization algorithm; S107, executing simulation iteration; S108, performing result analysis by using decision-making tools in the multi-objective optimization software. The method is based on computer simulation analysis, a multi-objective optimization algorithm and a numerical simulation technique are combined, automation of the calibration process is realized, accurate models can be calibrated quickly, the follow-up engine development is supported, the development efficiency is improved, the development cost is saved, and the development cycle is shortened.

The invention discloses a spacecraft optimal Lambert orbit rendezvous method based on a multi-objective optimization algorithm, and belongs to the technical field of spacecraft orbit rendezvous. The invention aims to solve the problem that the fuel consumption and the time consumption of the existing spacecraft optimal Lambert orbital rendezvous method cannot be optimal at the same time. The method comprises the following steps: determining a Lagrange representation form of a Lambert problem corresponding to pulse thrust Lambert intersection according to a spacecraft, and calculating transferorbit parameters of different-plane Lambert intersection to obtain performance indexes of fuel consumption and intersection time of the Lambert intersection; and optimizing through an optimization algorithm to obtain a comprehensively optimal motion trail of the fuel consumption and the rendezvous time. The method is mainly used for spacecraft optimal Lambert orbital intersection design and control.

The invention relates to a transmission capacity and wind and fire bundling transmission optimization method, and in particular relates to the transmission capacity and wind and fire bundling transmission optimization method. The invention firstly establishes an index system considering receiving end power grid electricity power demand, static security index, transient stability index, economic index and power transmission electrovalence. The indexes are quantized for establishing mathematical model of multi-objective functions and the multi-objective optimization algorithm is adopted for computing. During the solving process, multi-objective functions are solved simultaneously and computed for obtaining alternative solution meeting constraint condition, and the entropy function method is adopted for confirming the optimal compromise solution. The index system considering receiving end power grid electricity power demand, static security index, transient stability index, economic index and power transmission electrovalence is adopted, the evaluation index is convenient to compute and analyze in a quantitative manner, the transmission capacity of the electric transmission line and the influence to the receiving end power grid from sending end wind and fire bundling ratio can be reflected synthetically.

The invention provides a progressive health monitoring method of cable system based on mixed monitoring in the presence of support seat settlement. Whether a mechanical calculation standard model with a re-updated structure is needed or not is determined on the basis of the mixed monitoring through monitoring the coordinates of a structure support seat, the fact that the current monitored value vectors have the approximate linear relationship with the monitored initial vector, the unit damage monitored quantity change matrices and the current nominal damage vector is taken into consideration. In order to overcome the defect, the invention provides a method for piecewise approximation to the non-linear relationship by using the linear relationship, a large interval is divided into small continuous intervals, the linear relationship in the small intervals is accurate enough, proper algorithms of the multi-objective optimization algorithm and the like can be utilized in each small interval for calculating the noninferior solution of the current cable damage vector, and thereby, the position and the damage degree of the damaged cable can be accurately determined.

The invention discloses a diesel engine and storage battery coordinated micro grid scheduling method with multiple optimization objectives. According to the structure of an independent micro grid system which includes photovoltaic power generation, wind power generation, diesel engine power generation and energy storage, operational characteristics at the aspects including the photovoltaic power generation, wind power generation, diesel engine power generation and energy storage are comprehensively analyzed, a mathematic model and a target function of multi-objective-optimization scheduling which considers the cost of diesel engine power generation and the cycled electric quantity of storage batteries are provided, and constraint conditions for charging power of the storage batteries, SOC range, generating power of the diesel engines, power balance of the system and the like are established. Frontier of a non-dominated solution is obtained via the related multi-objective-optimization algorithm, and further multiple Pareto optimal solutions are obtained. The optimal scheduling schemes are determined according to different weather conditions. According to the method, a multi-objective mathematic model is established on the basis of the independent micro grid system of an island in the sea, the method aims at reducing the cost of diesel engine power generation and the cycled electric quantity of storage batteries, theoretical basis and technical support are provided for solving problems in power supply of the island in future, and the method is conducive to improving the whole operational economical efficiency of the independent island micro grid.

The invention discloses a micro-service combination deployment and scheduling method under multi-objective optimization. The method comprises the steps of obtaining resource center related data, micro-service related data and user related data; establishing a multi-objective optimization model of micro-service combination deployment and scheduling; adopting a multi-objective optimization algorithmNSGA-III to solve the multi-objective optimization model; calculating a micro-service quantity vector needing to be started by each resource center; and deploying and scheduling the microservices ofthe resource center according to the microservice quantity vector. According to the invention, a detailed multi-objective optimization model of the combined scheduling problem of the micro-service isconstructed; the model is solved through an improved NSGA-III (Non-Sequential Genetic Algorithm-III); and the obtained result is decoded to obtain a micro-service combination deployment strategy. Experiments prove that compared with a traditional deployment strategy, the micro-service combination deployment strategy provided by the invention has the advantages that the calculation and storage resources, the load balancing rate and the actual service utilization rate in a resource center are greatly improved.

The invention discloses a task allocation method for a wireless sensor. The task allocation method for the wireless sensor comprises the following steps: establishing a task allocation model of a wireless sensor network, and adjusting the initial task allocation scheme according to the communication relationship between tasks; establishing a multi-objective optimization function combined with thetask load, the communication relationship, the node processing speed and the fault probability an so on; setting constraint conditions of the dynamic multi-objective optimization algorithm according to the number of the sensor nodes and the deadline of completing the task; establishing a dynamic environment detection model for judging whether a new task arrives or not; updating the task allocationscheme by means of the dynamic multi-objective optimization algorithm, and detecting the dynamic environment in real time to obtain the Pareto optimal solution set after every environment change; andselecting the solution with the most balanced energy consumption in the Pareto optimal solution set as the optimal task allocation scheme. According to the invention, the effectiveness indexes such as the energy consumption, the time and the reliability is optimized by means of the dynamic multi-objective task allocation method, so as to obtain the comprehensive optimal scheme with the shortest task completion time, minimum energy consumption and maximum reliability.

The invention discloses a process simulation software based atmospheric and vacuum distillation unit optimization operation system and method. The system comprises a system login module, a parameter setting module, a data acquisition module, a data processing module, a simulation computation module, a sample acquisition module, a model building module, an optimization computation module and a result verification module. The system is based on process simulation software Aspen Plus, a severe device operation model is built to more accurately reflect the rule of operation parameters and operation results, a simplified operation model is built on the basis, a multi-objective optimization algorithm is utilized for optimization computation, check operation is realized under the condition that product quality is assured, optimization results can be calculated quickly when raw material composition and properties are changed or the processing scheme is changed to have the operation parameters adjusted, and the device is enabled to obtain maximum benefit.

The invention discloses an optimization design method of a controller of a multi degree of freedom manipulator system. The optimization design method combines artificial neural network technology withevolutionary multi-objective optimization algorithm, the optimal parameters of multivariable PID controller are determined by the trajectory tracking control system of multi degree of freedom manipulator with nonlinear and uncertain mode; the controller is a multivariable PID controller, and can handle the uncertainty of the mechanical arm system model and the strong coupling phenomenon between joints, and obtain higher tracking accuracy and good dynamic performance; and in addition, the optimization design method optimizes the adopted evolutionary multi-objective optimization algorithm to comprehensively consider the tracking accuracy and dynamic performance of the system as well as the stability of the control, determine the most appropriate controller parameters, and the optimization design method has strong universality, and can also overcome the disadvantages brought by the common experimental repeated trial and error method.

The invention relates to the field of numerical control machine tools, and particularly discloses a contour error suppression method for a digital twin-driven multi-axis numerical control machine tool. The method comprises the steps of S1 establishing a virtual model of a digital twinborn body corresponding to a physical entity, and adopting a multi-parameter gain scheduling control strategy based on a global task coordinate system, so as to obtain a time-varying coupling mechanism model of a multi-axis feeding system; establishing a data driving model of the multi-axis feeding system by using signal testing and machine learning; S2 performing virtual-real synchronization, and establishing a bidirectional sensing relationship between the digital twinborn body and the physical entity through a communication protocol with strong compatibility; S3 dynamically estimating a contour error; and S4 contour error suppression: according to the pre-estimation model, adopting a multi-objective optimization algorithm to obtain an optimal motion control parameter and a track maximum limiting speed corresponding to the optimal motion control parameter. By adopting the technical scheme of the invention, the contour error can be effectively reduced.

The invention discloses a railway vehicle weight gravity center design optimization method based on multi-objective optimization. The method comprises the steps that a weight gravity center parameter table and an axle weight and wheel weight computation sheet are established, an optimization task model is established in optimization software on the basis, the vehicle body weight and vehicle body gravity center coordinates are used as optimization variables, the weights and gravity center coordinates of constituent parts are used as design variables, and axle weight deviations and wheel weight deviations are used as bound variables; a preset algorithm is applied to determine value ranges of the design variables and the bound variables and optimization indexes of the optimization variables; multi-objective optimization calculation is conducted on the optimization task model through a multi-objective optimization algorithm, and the weights and gravity center coordinates of the constituent parts after optimization are obtained. Weight and distribution optimization of the parts on a railway vehicle can be achieved, the lightweight design purpose is achieved, and meanwhile the utilization rate of space in a vehicle body is improved. the invention further discloses a railway vehicle weight gravity center design optimization system based on multi-objective optimization. The advantages of the method and the system are as mentioned above.