318 results about "Configuration optimization" patented technology

The invention relates to the technical field of power data processing, in particular to an energy storage configuration optimization method, which comprises the following steps: acquiring new energy output time sequence data and computing power load characteristic data; generating a space-time correlation coupling evaluation result of the new energy output volatility and the computing power load volatility; inputting a result into a hybrid power supply double-layer optimization model, recursively correcting parameters through a two-stage collaborative solution algorithm, and outputting a Pareto optimal solution set; a computing power task elastic regulation and control mechanism is embedded, and the task priority is dynamically adjusted according to the energy storage charge state and the new energy output level to generate a scheduling strategy; finally, an energy storage configuration scheme and a dynamic scheduling strategy are output, and collaborative optimization of cost effectiveness and power supply reliability is achieved. The method breaks through the coupling conflict of the economic target and the robust constraint in the traditional bilevel planning, remarkably reduces the energy storage configuration cost, and improves the system stability.

According to the transition prediction method suitable for the wide Mach domain of the high-speed aircraft provided by the invention, temperature and Mach number coupling correction is performed on key parameters in time scale modeling of a second mode of a transition mode, and simplified processing of a flow compression effect and a heat transfer process by traditional single parameter association is broken through; the influence of an incoming flow disturbance effect is embedded in the mode, and dynamic association between test environment parameters and transition triggering criteria is achieved; according to the improved mode, accurate prediction results are obtained on a plurality of examples with the Mach number range of 4.7-14, the research results provide a more reliable theoretical tool for aerodynamic configuration optimization of a high-speed aircraft with a wider speed range, and the method has important engineering guiding significance for thermal protection system design of a new-generation reusable aircraft.

The invention provides a configuration optimization method before capturing of a space double-arm robot, and the method comprises the steps: constructing a kinematics and dynamics model of the space double-arm robot according to a three-dimensional model of the space double-arm robot; constructing a kinetic model of the to-be-captured target by using the motion parameters of the to-be-captured target in the space; according to the dynamic model of the space double-arm robot, the relative mass of the space double-arm robot under non-centering collision is obtained, and a maximum pre-contact collision force performance evaluation index is constructed; further constructing a double-arm closed chain inertia matching index performance evaluation index; constructing a configuration optimization model before the space double-arm robot captures the target; and through a multi-target optimization algorithm, the optimal configuration of the space double-arm robot before capturing the target is solved. The pre-contact collision force peak value of the double-arm robot during target capturing can be effectively reduced, and meanwhile the operability of a closed chain system formed after capturing is guaranteed.

The invention relates to the technical field of optical line terminal configuration, in particular to a method and system for automatically configuring optical line terminals of different manufacturers based on artificial intelligence. The method comprises the steps of collecting multi-source heterogeneous data to train a deep learning model, constructing a Transform or RNN-based architecture of a TensorFlow framework, performing cross validation and model compression, then performing optimization through a TensorRT inference engine, developing an intelligent configuration application system, deploying the intelligent configuration application system to a control host, and providing algorithm support for automatic configuration. By constructing an automatic configuration system based on artificial intelligence, the problems of heterogeneous compatibility and intelligent configuration of OLT equipment of multiple manufacturers are systematically solved. In a configuration optimization link, a virtual simulation environment is constructed based on a real network topology, a configuration effect is evaluated through multi-dimensional indexes such as throughput and time delay, potential conflicts are detected in combination with an AI algorithm, an optimization scheme is generated, and the configuration error rate of manual operation is reduced.

The invention discloses a novel telescopic pipe structure of a high-temperature and high-pressure well and an optimal configuration method of a pipe column of the novel telescopic pipe structure, belongs to the technical field of oil and gas exploitation, and aims to solve the problem of buckling failure of the pipe column caused by piston force load of an existing telescopic pipe under high-temperature and high-pressure working conditions. The novel telescopic pipe is composed of an inner cylinder mechanism, an outer cylinder mechanism and a balance chamber mechanism. The inner cylinder mechanism and the outer cylinder mechanism are connected through threads, and structural integrity and reliable connection are guaranteed. The balance chamber mechanism comprises inner and outer pressure cavities and a hydraulic hole which are respectively communicated with the inner and outer pressure of the oil pipe to dynamically balance the piston force. The optimized configuration of the pipe column comprises the following three steps: S1, analyzing the mechanical behavior of the telescopic pipe column to provide a mechanical basis for configuration optimization; s2, the position of the telescopic pipe is designed, the well depth range where the telescopic pipe works is determined, and the optimal position is locked through iterative calculation; and S3, designing the number of the telescopic pipes, and determining the optimal number of the telescopic pipes meeting the compensation requirement. The stability of the high-temperature and high-pressure well pipe column can be remarkably improved, and technical support is provided for safe and efficient development of ultra-deep oil and gas wells.

Disclosed in the present invention are a shared energy storage configuration optimization method and system for a wind farm cluster. The method comprises: constructing a shared energy storage double-layer programming model; on the basis of the output of a wind farm cluster, an electricity price in an electric energy market and a compensation price in a frequency-modulation auxiliary service market, obtaining multiple uncertainty sets, using an improved K-means clustering method to perform joint clustering on the multiple uncertainty sets so as to obtain a typical scenario, and on the basis of the annual occurrence frequency of the typical scenario, calculating a typical scenario probability; and using a distributionally robust optimization method to model the multiple uncertainty sets on a short-time scale so as to obtain an extreme uncertainty scenario, using the typical scenario probability and the extreme uncertainty scenario to update the shared energy storage double-layer programming model so as to obtain an updated shared energy storage double-layer programming model, and using a particle swarm algorithm to perform solving so as to obtain a configuration result. By means of the method, shared energy storage configuration optimization is performed by means of taking various uncertainty factors into consideration, thereby improving the accuracy of shared energy storage configuration optimization.

The invention relates to the technical field of control and adjustment, in particular to an energy storage configuration point-based power consumption cost calculation method, which comprises the following steps of: acquiring power supply data, renewable energy output fluctuation data and computing power load fluctuation data, and generating a coupling influence feature vector; splicing the electricity price features and the coupling features to form configuration optimization input features; the input features are processed through a dual-objective optimization model, and robustness configuration parameters and economical efficiency operation parameters are output; calculating the reserve cost compensation amount of the extreme scene based on the robustness parameter, and calculating the power purchase cost and maintenance cost of the power grid based on the economical efficiency parameter; and aggregating the cost component and outputting a power consumption cost calculation result. According to the method, the interactive influence of wind-light fluctuation and load mutation is quantified through coupling features, the target conflict between robustness and economy is coordinated by adopting the Pareto algorithm, the risk compensation amount is generated in combination with a pressure test, a feature updating mechanism is fed back in a closed loop mode, and the problem that the operation cost deviates from expectation due to quantization deviation of an existing model is solved.

The invention relates to the technical field of micro-grid configuration, in particular to a wind and light storage micro-grid capacity optimization configuration method based on a PGWO algorithm, and the method comprises the following steps: building a wind and light storage micro-grid capacity optimization configuration model according to the output power model of each distributed power supply in a micro-grid; setting a micro-grid operation strategy; under the micro-grid operation strategy, solving the capacity optimization configuration model through a PGWO algorithm to obtain a micro-grid optimal configuration scheme; according to the method, the PGWO algorithm combining Bernoulli chaotic mapping initialization, individual historical memory and a border crossing processing strategy is provided, the method has higher global optimization capacity, capacity configuration optimization can be carried out on the premise of stable operation of the microgrid, and the operation cost is reduced.

The invention discloses an energy storage system capacity configuration optimization method and device and a storage medium, and relates to the technical field of energy storage system capacity configuration.The energy storage system capacity configuration optimization method comprises the following steps that daily total electricity utilization power information of a factory is continuously collected, corresponding weather information is obtained, and factory power weather data is obtained; denoising processing is carried out, an electricity consumption power prediction model is constructed, and the daily total electricity consumption power of the factory is predicted; calculating a prediction error, carrying out error rule analysis, and correcting the predicted total power consumption power to obtain corrected power prediction data; calculating predicted electricity consumption, and performing configuration optimization on the capacity of the energy storage system of the factory according to the predicted electricity consumption; the method is used for solving the problems that when an existing energy storage system capacity configuration technology configures the capacity of an energy storage system of a factory through a power consumption prediction model, the rule of prediction deviation of the model cannot be analyzed, meanwhile, a prediction result cannot be corrected, and the accuracy of capacity configuration of the energy storage system cannot be improved.

The invention belongs to the technical field of cold and heat source configuration methods, and particularly relates to a cold and heat source configuration optimization method and system based on a full life cycle, and the method comprises the steps: S1, building use characteristic analysis; s2, designing a staged unit configuration scheme; s3, optimizing an intelligent control strategy; s4, carrying out full-life-cycle cost-benefit analysis; s5, optimal scheme decision making and dynamic adjustment; according to the method, a dynamic prediction model of a time sequence analysis and Transform-TCN hybrid architecture is adopted, historical load data can be deeply mined through time sequence analysis, and time characteristics and periodic rules in the data are captured; the long sequence modeling capability of the Transform and the local feature extraction capability of the TCN are combined by the Transform-TCN hybrid architecture, so that the long and short term dependency relationship in the load data can be better processed, and the prediction accuracy is improved.

The invention discloses a capacity configuration optimization method for an energy storage system, relates to the field of energy storage planning, solves the problem of energy waste of the energy storage system, and comprises the following steps: collecting historical environmental data of a region where a power generation facility corresponding to the energy storage system is located and historical power consumption of a power grid corresponding to the energy storage system; analyzing the power generation performance of the power generation facility of the energy storage system and the power consumption performance of a power grid corresponding to the energy storage system to obtain a peak value operation time period, an average value operation time period and a valley value operation time period corresponding to the energy storage system; setting a system capacity configuration scheme of the energy storage system corresponding to the peak value operation time period and the valley value operation time period; analyzing the operation state of the energy storage system corresponding to the average operation time period, and setting a system capacity configuration scheme of the energy storage system in the average operation time period; the actual energy storage mode of the energy storage system is adjusted based on the system capacity configuration schemes corresponding to the peak value operation time period, the average value operation time period and the valley value operation time period, and stable operation of the energy storage system is guaranteed by improving the energy utilization rate.

The invention discloses a space target monitoring constellation configuration optimization method and system considering complex constraints, and belongs to the technical field of aerospace mission design and optimization. The method comprises the following steps: firstly, constructing a discrete orbit modal library meeting regression orbit dynamic resonance conditions; constructing generalized hybrid design variables consisting of integer indexes and real number fine tuning quantities on the basis of the library; establishing an optimization model taking the space volume coverage rate, the maximum revisit time and the system construction cost as targets and taking illumination, dynamics and maneuvering capability as constraints; performing rapid analysis and coarse screening on invalid links through track geometry; and finally, performing global optimization by using an improved non-dominated sorting genetic algorithm to obtain a Pareto optimal constellation configuration set. According to the method, accurate evaluation of the three-dimensional space target coverage efficiency is realized, the physical feasibility and long-term stability of a configuration scheme are ensured, and a high-dimensional multi-target optimization problem is efficiently solved.

The invention discloses a personalized configuration method for a cloud mobile phone and related equipment, and relates to the technical field of cloud computing, and the method comprises the steps: obtaining user operation behavior data; based on the user operation behavior data, determining a user behavior model through a time sequence analysis model and an association rule mining algorithm; determining a target configuration parameter based on the user behavior model; and controlling the cloud mobile phone system to perform dynamic configuration operation according to the target configuration parameter. The user behavior analysis module is used for collecting multi-dimensional data such as the touch track and the application switching frequency, and the accurate user behavior model is constructed by using the LSTM time sequence model and the Apriori algorithm, so that personalized configuration optimization based on fine-grained user behaviors is realized, and the adaptability of cloud mobile phone system configuration and user behavior habits is effectively improved.

The invention provides a river-sea combined transportation wharf resource intelligent dynamic configuration method and system based on deep learning, and the method comprises the steps: firstly obtaining a wharf resource type, a ship arrival demand and a historical configuration result set, then capturing the time sequence correlation between the ship demand and resource use through a deep learning model, generating a resource demand feature sequence, and carrying out the recognition of the ship arrival demand. Generating multiple groups of resource configuration initial schemes according to resource types and feature sequences, pre-relieving potential conflicts through an interaction module, inputting the multiple groups of resource configuration initial schemes into an evaluation module, generating an evaluation result in combination with a historical configuration result, and adjusting the schemes according to the evaluation result to generate a resource configuration optimization scheme; and triggering a wharf resource allocation operation, recording an application result, and feeding back the application result to the model updating logic, thereby realizing efficient and intelligent dynamic configuration of the wharf resources, and improving the wharf operation efficiency.

The invention provides an enterprise manufacturing resource configuration optimization method based on intelligent interaction and template mapping, and belongs to the technical field of intelligent manufacturing. Comprising the following steps: receiving a resource configuration request input by a user, and performing natural language analysis on the resource configuration request to generate structured resource configuration demand information; according to the structured resource configuration demand information, matching and calling a preset resource configuration template; based on the called resource configuration template, in combination with real-time resource state data in the ERP system, performing analog computation of resource configuration, and generating at least one resource configuration scheme; and feeding back the at least one resource configuration scheme to the user, and receiving feedback information of the user on the resource configuration scheme. User requirements are analyzed through intelligent interaction, an optimization strategy is matched through template mapping, real-time data simulation calculation is combined, feedback is received, and intelligentization, high efficiency and continuous optimization of enterprise manufacturing resource configuration are achieved.

Techniques are described herein for paging adaption. An example, method includes receiving, from a user equipment (UE), a request for configuration optimization information for an application. The method can further include causing a first machine learning model at an application function (AF) to generate first intermediate results based at least in part on the request and collaborative analytics information from the UE. The method can further include accessing second intermediate results generated using a second machine learning model at a network data and analytics function (NWDAF). The method can further include causing a third machine learning model at the AF to predict a quality of experience (QoE) label metric associated with the application based at least in part on the first intermediate results and the second intermediate result.

The invention relates to the technical field of renewable energy sources, in particular to a capacity configuration optimization method and device for an off-grid wind-solar-storage coupled hydrogen production energy system. The method comprises the following steps: constructing a mathematical model of each subunit of the off-grid wind-solar-storage coupled hydrogen production energy system; establishing an operation strategy of the system; calculating system performance indexes in the scheduling period based on the operation strategy and the mathematical model, wherein the system performance indexes comprise the leveling hydrogen production cost and the power supply missing rate; and constructing an objective function by using a minimum system performance index, and optimizing the capacity configuration of the system by using a third-generation non-dominated sorting genetic algorithm under an operation constraint condition. According to the invention, two performance indexes for evaluating the off-grid renewable energy hydrogen production system are provided, so that the off-grid wind-solar-storage coupled hydrogen production energy system can be comprehensively evaluated from the economic efficiency and technical stability of a project. Meanwhile, the method adopts a third-generation non-dominated sorting genetic algorithm to carry out capacity optimization configuration, so that the problem of insufficient double-target solution is avoided.

The invention discloses a civil aviation element configuration optimization method based on mixed integer dynamic programming. The method comprises the following steps: S1, constructing a Chinese civil aviation core element and carbon emission spatial-temporal characteristic database; s2, establishing a mixed integer dynamic programming model with the purpose of minimizing the total cost of the system in the programming period; and S3, establishing decision variables, constraint conditions and a fusion solution algorithm framework required by the mixed integer dynamic programming model. And S4, based on the fusion solution algorithm framework, solving the mixed integer dynamic programming model by using a mathematical programming solver integrated with a robust optimization module so as to obtain a civil aviation core element dynamic configuration scheme which is optimal in total system cost in multiple periods in the future and meets the uncertainty scene. According to the method provided by the invention, a multi-source data fusion algorithm is optimized, a carbon emission measuring and calculating method is deepened, and solver parameters are adjusted, so that the collaborative requirements of improving the operation efficiency and reducing the carbon emission from the perspective of carbon cost are met.

The invention relates to a new energy power station configuration optimization method based on an agent model and multi-objective optimization. The method comprises the steps of obtaining historical power system configuration parameters and corresponding historical stability characteristic values, and performing preprocessing; inputting the preprocessed historical power system configuration parameters into a neural network agent model based on a multi-layer perceptron architecture, performing offline training in combination with corresponding historical stability characteristic values and physical prior constraints, and outputting predicted stability characteristic values; taking the prediction stability characteristic value as an optimization target, and constructing a target function of multi-target Bayesian optimization; and iteratively solving a Pareto optimal solution set based on the objective function, and determining an optimal new energy power station configuration scheme. According to the method, multi-objective optimization and Bayesian optimization are combined, a multi-objective acquisition function is adopted to approach the Pareto frontier between stability characteristic values, precision and multi-objective tradeoff can be considered, and a more flexible and scientific configuration scheme is provided for new energy station construction.

The invention discloses a structure configuration generation method, device and equipment and a readable storage medium, and is applied to the technical field of computers, and the method comprises the steps: carrying out the decoupling processing of a global grid model, and obtaining an order reduction model and a structure optimization model; applying an external load and a boundary condition to the reduced-order model, and solving to obtain a displacement load; and inputting the displacement load, the design constraint and the manufacturing constraint into a pre-trained structure configuration optimization model to obtain a generated optimal structure configuration. According to the method, the calculation scale of the structure configuration optimization model is greatly reduced, the calculation efficiency is improved, and the grid resolution in the to-be-optimized target structure area is improved, so that the high-fidelity structure configuration is generated; unnecessary materials can be reduced on the premise that the safety of a train is not influenced; on the premise of keeping the geometric features of the target structure region to be optimized similar, a high-fidelity structure configuration can be generated for any size, external load and boundary conditions, and the universality in a global parameter space is realized.

The invention discloses a multi-time-scale energy storage capacity configuration optimization method and system, and relates to the technical field of energy storage, and the method comprises the steps: carrying out the multi-time-scale load prediction according to a power system, and obtaining short-term, medium-term and long-term prediction loads; performing energy storage capacity configuration optimization on the energy storage system according to the predicted load to obtain corresponding short-term, medium-term and long-term energy storage capacity configuration strategies; and performing multi-time scale energy storage management on the energy storage system according to the short-term, medium-term and long-term energy storage capacity configuration strategies. According to the method, the technical problem that the configured energy storage capacity is difficult to accurately match the power system due to the fact that the change characteristics of the power system load under different time scales are not fully considered in a traditional energy storage capacity configuration method is solved, and accurate matching of the energy storage capacity and the power system load under multiple time scales is achieved; the utilization efficiency of the energy storage system is improved, and the technical effect of energy storage management and configuration of the power system is optimized.

The invention belongs to the field of product top-layer system design and modeling, and discloses an interactive feedback-oriented product top-layer system design method, which comprises the following steps of: constructing a multi-layer model based on a demand R, a function F, a behavior B and a structure S, establishing a knowledge graph for representing a semantic relationship between layers and between nodes on the same layer by using weighted directed edges, forming a layer-by-layer mapping matrix by using edge weights, and constructing a layer-by-layer mapping model; realizing weight decomposition from the demand to the structural unit; a constraint hypergraph is constructed on the node set, and structural unit combination constraints are depicted through mutual exclusion, dependency, collaboration and conflict hyperedges; physical parameters of the structural units are normalized into performance vectors, weighted aggregation is carried out according to weights, legality judgment and performance correction are carried out in combination with a hypergraph, a system-level performance evaluation result is obtained, candidate configuration schemes are compared and optimized according to the result, and a product configuration scheme meeting requirements and constraint conditions is formed. The method provides a model basis for subsequent product structure configuration optimization based on interactive feedback.

The invention is suitable for the technical field of converter configuration, and provides a capacity configuration optimization method and related equipment for a network tracking / constructing hybrid energy storage power station, and the method comprises the steps: constructing a capacity optimization objective function based on a short-circuit ratio; constructing a number constraint condition, an alternating current power flow constraint condition and a system stability constraint condition of the network tracking / constructing hybrid energy storage power station; and taking the capacity optimization objective function as a fitness function of a particle swarm optimization algorithm, and optimizing the capacity ratio of the tracking / networking hybrid energy storage power station by using the particle swarm optimization algorithm to obtain an optimal capacity ratio which has the minimum fitness function and meets a quantity constraint condition, an alternating current power flow constraint condition and a system stability constraint condition. According to the invention, an operable capacity ratio can be provided for the following / networking hybrid energy storage power station, so that the power station can meet engineering requirements.

The invention relates to the technical field of optimal configuration of electronic equipment, in particular to a configuration optimization method based on capacitor equipment, which comprises the following steps: S1, collecting voltage fluctuation data and temperature information of each circuit point in the electronic equipment; s2, identifying a potential performance bottleneck in the circuit and a failure risk of a capacitor; s3, calculating an optimal capacitor configuration; s4, capacitor configuration in the electronic equipment is adjusted; s5, testing the performance of the electronic equipment; s6, testing data are fed back to the deep learning model for reanalysis, and parameters in the genetic algorithm are adjusted to optimize capacitor configuration; and S7, updating the capacitor configuration in the electronic equipment according to the adjustment result of the step S6. According to the invention, real-time monitoring and intelligent optimal configuration of the capacitor are realized, so that the performance stability of the electronic equipment is improved, the maintenance cost and manual operation errors are reduced, and the requirements of modern electronic equipment on high efficiency and high reliability are met.

The invention relates to the technical field of virtual power plant operation, in particular to a virtual power plant configuration optimization method and system for a multi-agent cooperative game. According to the method, the cooperation value of each main body is evaluated by analyzing the difference between the individual operation income and the cooperation operation income of each main body in the virtual power plant; determining the dynamic responsivity according to the current power of the main body through the power fluctuation deviation of the main body in the historical operation, and determining the contribution ability of the main body in combination with the loss risk possibly borne by each main body in the operation of the virtual power plant; and optimizing configuration operation based on matching of the dynamic contribution capability and the cooperation value. According to the method, a contribution perception optimization overall configuration scheme is introduced through the matching relationship between the contribution and the obtained income of each main body in the collaborative participation process, so that the operation efficiency of the virtual power plant, the incentive effectiveness of income distribution and the stability of the multi-main body alliance are improved, and the structural disjunction problem between configuration optimization and income distribution is reduced.

The invention discloses a multi-angle model operation automatic configuration optimization method and system based on big data, and relates to the technical field of big data, and the method comprises the steps: a cloud platform collects data from an industrial sensor and an equipment monitoring platform, calculates and standardizes a data quality index and a resource consumption feature, and constructs a standard feature vector and a data quality weight; based on a feature vector evaluation function and a resource matching degree, combining with a data quality weight to generate a candidate configuration scheme; taking the candidate scheme as an initial strategy, performing optimization through a composite reward function in a dynamic reinforcement learning environment, and outputting a global optimal parameter set; the parameter set is applied to job execution, and online fine adjustment is triggered through real-time monitoring and predicted value comparison; and starting a closed-loop updating mechanism containing strategy rollback and incremental learning based on full-cycle efficiency evaluation. According to the invention, the problem of low resource configuration efficiency in the industrial Internet of Things is effectively solved, and the system stability and the resource utilization rate are improved.

The invention relates to the field of power system energy configuration, in particular to a photovoltaic station power generation and energy storage unit configuration optimization method and related equipment, and the method comprises the steps: obtaining photovoltaic station data; establishing a full-cycle hour model according to the photovoltaic station data; calculating a monthly granularity average value according to the photovoltaic station data, establishing a full-cycle monthly model by combining the monthly granularity average value and the full-cycle hour model, and solving the full-cycle monthly model to obtain an optimal configuration solution; according to the photovoltaic station data and the optimal configuration solution, a whole-year hour model is created for the photovoltaic station in the whole cycle every year based on the whole-cycle hour model; a two-stage coupling optimization model is established in combination with the full-period monthly model and N full-year hour models, and N represents the year number of the full period of the photovoltaic station; and iteratively solving the two-stage coupling optimization model to obtain an optimal configuration scheme of the photovoltaic station power generation and energy storage unit. According to the method, energy configuration can be optimized, and the feasibility of an hour-level station operation plan can be ensured.

This invention relates to a process architecture for optimizing ship hydrodynamic configurations based on artificial intelligence technology. The architecture includes modules such as expert experience, historical sample data of ship hydrodynamic configurations, design input for ship hydrodynamic configurations, a knowledge base for ship hydrodynamic configuration design and evaluation, a parametric model of ship hydrodynamic configurations, knowledge-driven reconstruction of the ship hydrodynamic model, AI-based optimization design of ship hydrodynamic configurations, rapid evaluation of ship hydrodynamic performance, and a comprehensive evaluation index model for ship hydrodynamic performance. This invention fully leverages the design experience contained in historical sample data, extracting the experience of numerous experts into design knowledge and integrating it into the ship hydrodynamic configuration optimization design process. The process architecture drives intelligent reconstruction of the ship hydrodynamic model through design knowledge, and improves upon the shortcomings of traditional ship hydrodynamic performance prediction methods, such as time consumption and low efficiency, by establishing a rapid prediction proxy model for hydrodynamic performance based on machine learning algorithms.

The invention provides a rope-driven parallel mechanism configuration optimization method for adjusting the position of a tail end connection point, and the method comprises the steps: determining a plurality of rope-driven parallel mechanism configurations meeting constraint conditions based on the installation space needed by a target motion simulator, the shape and load of a tail end actuator, and the motion freedom degree and target motion range of the tail end actuator; respectively calculating a force closed working space of each configuration and screening out at least two to-be-selected configurations; respectively calculating a force helix feasible working space and an attitude rotation range of each to-be-selected configuration; respectively comparing the attitude rotation range of each to-be-selected configuration with the target motion range, and determining whether the to-be-selected configuration is a basic target configuration or not; on the basis of the selected basic target configuration, traversing and calculating a force spiral feasible working space when different connection positions on the rope and the end effector are fixed; and determining a rope connection point on the end effector according to the position and posture number distribution relationship between the connection position and the force spiral feasible working space. And the optimal configuration meeting the requirements is obtained.