600 results about "System optimization" patented technology

The invention discloses an intelligent energy storage system multi-target hierarchical scheduling method and system oriented to source network load storage cooperation, and belongs to the technical field of energy storage system optimization control. The method comprises three levels of day-ahead layer multi-objective game optimization, intra-day layer rolling correction optimization and real-time layer adaptive droop control. The day-ahead layer establishes three objective functions of economy, environmental protection and smoothness, and solves and outputs a day-ahead charging and discharging power plan by using a Nash negotiation algorithm. And the intra-day layer obtains ultra-short-term prediction data of the source load, performs rolling correction on the day-ahead plan by adopting a model prediction control method, and outputs a corrected real-time power instruction. The real-time layer collects power grid frequency deviation and a battery health state value, calculates an adaptive droop coefficient according to the health state value, and superposes and outputs primary frequency modulation response power and a real-time power instruction. According to the invention, source network load storage collaborative optimization is realized through multi-time scale hierarchical scheduling, and the service life of an energy storage system is prolonged through adaptive droop control based on health state perception.

The invention discloses an electric power AI security detection model optimization method and system fusing attribution quantification and adversarial correction. The optimization method comprises the following steps: step 1, carrying out structured semantic representation on heterogeneous security alarms of an electric power network; 2, performing model decision logic analysis based on hybrid attribution quantization; step 3, automatically diagnosing decision prejudice based on domain knowledge masks; step 4, constructing an adversarial sample generated based on an anti-fact text; and 5, performing closed-loop fine adjustment and optimization on the attribution regularization model. According to the method, the interpretable ability of large model decision analysis, the root cause positioning ability of misinformation and the autonomous repair optimization ability are improved, the transparency and credibility of model decision are improved, the model misinformation caused by environmental influence is reduced, and the efficiency of model autonomous correction is improved.

The invention belongs to the technical field of novel power system photovoltaic and energy storage system optimization configuration, and discloses an optical storage system optimization method based on carbon emission and line loss rate collaborative modeling, which comprises the following steps: constructing a double-layer planning structure of an upper layer planning model and a lower layer operation model, a carbon emission calculation model and an improved line loss rate calculation model are introduced into the lower layer, joint optimization of configuration and operation is realized through parameter coupling, and the charge and discharge efficiency loss power consumption of the energy storage device is introduced as an independent parameter in line loss rate calculation, so that the line loss calculation precision is improved; and solving by adopting an improved particle swarm optimization algorithm, and objectively sorting candidate schemes in combination with an information entropy method and a TOPSIS comprehensive evaluation method. A simulation result based on an IEEE33 node power distribution system shows that the model can effectively reduce carbon emission and line loss rate, improves node voltage level, and has good convergence and engineering applicability.

The invention relates to the technical field of glass hot working control, and discloses a rapid prototyping control system and a rapid prototyping control method for tempered glass production. Comprising a thermal coupling module, a temperature control decision module, a flow field solving module, a photoelastic stress analysis module, a quantum annealing optimization module and a time domain synchronous control module. According to the system, a three-dimensional thermal-stress field is constructed on the basis of physical properties and thermal boundaries of glass, heating power is predicted through reinforcement learning, flow field simulation and stress image analysis are combined, control parameter self-adaptive adjustment is achieved through quantum annealing optimization, beats of all subsystems are coordinated through a synchronization module, and an efficient closed-loop control structure is formed. By introducing the quantum annealing optimization module, parameter adjustment in the control system is optimized, the technical effect of improving the precision of complex control decisions is achieved, and the optimization speed and the decision quality of the system are improved.

The invention discloses a buried pipe heat pump system optimization method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining soil temperature data of a current buried pipe heat pump system in an operation cycle; dynamically correcting the heat exchange capacity of the current buried pipe heat pump system according to the soil temperature data to obtain actual heat exchange capacity data; the buried pipe distribution of the current buried pipe heat pump system is optimized and adjusted based on the actual heat exchange capacity data until a target buried pipe layout scheme capable of meeting the real-time heat exchange requirement is obtained; real environment parameter support is provided for dynamically correcting the heat exchange capacity by obtaining soil temperature data of an operation cycle; actual heat exchange capacity data are obtained through correction, and the problem that traditional fixed value evaluation is inaccurate is solved; and then the distribution of the buried pipes is optimized on the basis of the actual heat exchange capacity, so that the layout is accurately matched with the real-time heat exchange requirement, and finally the heat exchange efficiency of the system and the dynamic adaptability to the real-time requirement are improved.

The invention discloses an AI-based server power consumption dynamic optimization and cooperative heat dissipation control system, which belongs to the technical field of computer system optimization, and comprises a multi-source data acquisition module for acquiring server hardware, machine room environment and heat dissipation equipment data in real time through a distributed sensor and a software probe; the AI intelligent analysis and decision module constructs a hybrid intelligent framework, extracts multi-dimensional features to predict power consumption and temperature trends, and generates a comprehensive optimization decision; the power consumption dynamic adjustment module adjusts hardware parameters and cooperates with process scheduling; the cooperative heat dissipation control module dynamically adjusts heat dissipation equipment; and the running state monitoring and feedback module monitors data, compares the data with a threshold value, and performs early warning and feedback in case of abnormality. Through multi-module collaboration and AI enabling, intelligent dynamic collaborative management of power consumption and heat dissipation is realized, data acquisition is accurate, power consumption is balanced, operation cost and energy consumption are reduced, and requirements of data centers of different scales are met.

The invention discloses an automatic fault repairing method for an operating system based on artificial intelligence, and relates to the technical field of automatic fault repairing, and the method comprises the steps: carrying out the feature analysis of system operation data through a pre-trained fault feature extraction model, generating a fault feature vector, inputting the fault feature vector into a fault classifier, and obtaining a fault feature vector; a current fault type is identified through a multi-classification algorithm, fault cause primary tracing is performed according to the fault type to obtain a fault generation factor, secondary tracing is performed on the fault generation factor to obtain a fault influence factor, positioning is performed based on the fault generation factor, and a corresponding repair strategy is matched from a knowledge base. The method comprises the following steps: acquiring historical system operation data with relevance on the basis of a fault influence factor, acquiring updated real-time system operation data after executing a repair operation to calculate a system optimization coefficient, judging a forward trend of a repair strategy according to a preset optimization threshold value, and updating the forward trend into a knowledge base to realize rapid and efficient automatic repair.

The invention relates to an electric power marketing business abnormity real-time detection method and system based on stream-oriented computation, and belongs to the technical field of electric power system optimizing.The method comprises the steps that data snapshots are extracted from an electric power marketing business system, difference comparison is conducted on the data snapshots and historical snapshots of an intermediate library, and standardized increment events are generated and stored; capturing an incremental event in real time through a data change capturing tool and pushing the incremental event to a message queue; a streaming computation engine consumes the event stream, sequentially performs data cleaning, association with a static dimension table and sliding window statistical feature calculation, and constructs a feature vector; and performing parallel analysis and weighted fusion on the feature vectors based on a business rule base and an online machine learning model to generate a comprehensive risk score, and outputting an abnormal event when the score exceeds a threshold value. According to the method, the problems of exception identification lagging and complex work order process in a traditional batch processing mode are solved, the crossing of the business risk from hour-level detection to minute-level real-time perception is realized, and the timeliness and accuracy of power marketing risk management and control are improved.

The invention discloses a machine room energy efficiency optimization simulation system and method based on digital twinning, and relates to the technical field of machine room energy efficiency optimization. The system comprises a model construction and load analysis module, an equipment combination design module, an energy efficiency simulation calculation module and an optimal scheme adaptation and output module. According to the method, related parameters of a machine room are collected to construct a digital twinborn simulation model, different levels of load intervals are divided, a differentiated combination scheme composed of a high-voltage fixed-frequency large cold source unit and a low-voltage variable-frequency small cold source unit is designed for each interval, an energy efficiency optimal scheme is matched through simulation, a correlation database is established, and a result is output. Dynamic adaptation of the cold source equipment and the load interval is achieved, low-load energy efficiency attenuation is avoided, the full-working-condition energy efficiency of a machine room is remarkably improved, and reliable support is provided for optimal configuration of a cold source system.

The invention discloses a J-A model parameter identification method, system, equipment and medium based on RBF and an improved brownish bear algorithm, and belongs to the technical field of power system optimization, and the method comprises the steps: building a Jiles-Atherton hysteresis reverse model of a current transformer, determining a to-be-identified parameter vector, and building a model with a root-mean-square error between actually measured magnetic field intensity and simulated magnetic field intensity as a target function, training a radial basis function neural network model, expanding data through linear interpolation processing, obtaining a predicted magnetic induction intensity value, inputting an objective function and radial basis function prediction data into an improved brownish bear optimization algorithm, and iteratively optimizing model parameters through hierarchical population position updating and fitness evaluation until convergence conditions are met. And outputting an optimal parameter identification result. According to the method, high-precision and high-efficiency identification of hysteresis model parameters is realized, the generalization capability and robustness of the system are improved, and reliable technical support is provided for hysteresis characteristic analysis of a complex physical system.

The invention belongs to the technical field of multi-energy micro-grid system optimization scheduling, and particularly relates to a multi-energy micro-grid distribution robust low-carbon economic scheduling method based on deep learning, electronic equipment and a medium. According to the method, a mathematical model of a multi-energy micro-grid system is established according to coupling characteristics of various energy sources among power systems. In order to improve the economical efficiency and the low-carbon property of the system, a load demand response mechanism and a carbon transaction mechanism are adopted, and an electric heating load demand response model and a reward and punishment type stepped carbon transaction model are constructed. In order to solve the wind and light uncertainty of the integrated energy system and improve the robustness of the system, a scene set of uncertain variables is generated by using a conditional generative adversarial network in deep learning, and the generated scenes are clustered by using a K-means clustering method to obtain typical scenes. In order to obtain more real probability distribution, a fluctuation range of a typical scene is constrained by using a comprehensive norm, and a probability distribution fuzzy set of uncertain variables is obtained. And based on the constructed fuzzy set, the demand response model and the reward and punishment type stepped carbon transaction model, a two-stage distribution robust low-carbon economic optimization model of the multi-energy microgrid is established, in the first stage, an energy storage equipment start-stop plan of the system is determined, and in the second stage, an initial plan is adjusted and supplemented after uncertainties are revealed. And finally, carrying out iterative solution on the established model by utilizing a column and constraint generation method to obtain an optimal scheduling scheme, thereby ensuring the low-carbon property, the economical efficiency and the robustness of the system.

The invention discloses a traditional Chinese medicine grinding control system, which relates to the technical field of traditional Chinese medicine grinding control and comprises a medicinal material characteristic sensing module, an intelligent calculation analysis module, a dynamic execution adjustment module, a multi-mode optimization control module and a system coordination updating module. The medicinal material characteristic sensing module collects physical and texture characteristic data of medicinal materials; the intelligent calculation and analysis module fuses the medicinal material processing knowledge base and historical grinding process data to generate initial grinding control parameters; the dynamic execution adjustment module adjusts the operation parameters of the grinding mechanism in combination with the real-time grinding state data; the multi-modal optimization control module monitors the deviation between actual quality parameters and target quality parameters of powder, and a self-adaptive decision algorithm is used for optimizing regulation and control parameters of a grinding area; the system coordination updating module collects whole-process data and performance index evaluation and generates a system optimization instruction, and the instruction corresponding module adjusts the rotating speed and pressure parameters of a grinding area of the vertical grinding machine. According to the system, intelligent and precise control over medicinal material grinding is achieved, and the stability and consistency of the grinding quality are improved.

The present application relates to the field of power system optimization. Disclosed are a multi-objective particle swarm optimization method and system based on multi-strategy improvement. The method comprises: using a multi-strategy improved multi-objective particle swarm optimization algorithm to solve a multi-objective optimization model of a power supply of a generator state monitoring apparatus; and combining three improved strategies, i.e., adaptive adjustment of an inertia weight, coexistence of a decomposition algorithm and Pareto dominance, and introduction of a mutation factor. The present application overcomes the defects of conventional multi-objective particle swarm algorithms, and achieves a better distribution of a Pareto front, thereby obtaining the best Pareto optimal solution set. The present application solves the problems of conventional multi-objective particle swarm algorithms in solving a multi-objective optimization problem, such as premature convergence to a local non-dominated solution, and sub-optimal distribution of a Pareto front caused by an improper external archive update strategy, thereby improving the operational efficiency and reliability of a power supply system of an apparatus.

The invention relates to the field of animal husbandry industry environment regulation and control system optimization, in particular to a lightweight digital twinborn breeding environment monitoring method and system, and the method comprises the following steps: S1, carrying out the construction of a computational fluid dynamics (CFD) model for a breeding farm environment; s2, environment data are collected; s3, comparing model prediction with real-time data, and monitoring a breeding environment according to a comparison result; and S4, data presentation. According to the invention, through cooperative work of the flow field modeling unit, the environment data acquisition unit, the model prediction and data calibration unit and the visualization unit, light-weight, full-range and high-precision breeding environment monitoring and regulation are realized.

The invention relates to a network fault root cause positioning system based on multi-modal learning and causal inference, and belongs to the technical field of network fault diagnosis and positioning. The system comprises a data processing and association mining module, an intelligent fault diagnosis and evaluation module, a root cause positioning module and a system optimization module which are connected in sequence. The data processing module collects data through edge nodes, constructs a hierarchical knowledge graph and outputs a feature matrix; the diagnosis module performs fault detection by adopting a multi-modal model of a fusion graph neural network, integrates a small sample learning mechanism and outputs a fault event with confidence; the positioning module constructs a causal graph based on a knowledge graph, fuses multi-source evidences and realizes root cause tracing through a random walk algorithm; and the optimization module adjusts diagnosis parameters by utilizing reinforcement learning, expands a sample set based on the generative adversarial network, and realizes continuous optimization of the model through an automatic assembly line. Closed-loop self-optimization from fault sensing to root cause positioning is realized, and the network fault management capability is improved.

The invention discloses an enterprise operation management intelligent optimization system based on business rules, and belongs to the technical field of enterprise operation management. The system comprises core modules such as a multi-source data fusion center, a dynamic business rule engine, an intelligent optimization decision module and a closed-loop feedback unit, wherein the fusion center realizes privacy protection type multi-source data fusion by adopting federal learning; the rule engine realizes real-time adjustment of business rules without interrupting the system through rule atlas construction and incremental updating; and the optimization module is combined with an analytic hierarchy process and a Q-learning algorithm to generate a real-time optimization decision under business rule constraints. The system can improve business rule adaptation flexibility and data security, realizes decision real-time and optimization continuation, adapts to multiple industries of manufacturing, retail and logistics, and reduces enterprise operation cost and system operation threshold.

The invention relates to the field of system optimization, and discloses a Linux-based domestic operating system kernel cutting optimization system, which comprises a hardware baseline identification module used for acquiring a processor architecture, peripheral and bus topology, storage and network resources and firmware capability of target equipment and generating hardware baseline description; the scene portrait construction module is used for statically and dynamically observing a target application and acquiring operation portrait data including system calling, driving access and interruption events to form a scene portrait; a dependency analysis module; a multi-target constraint solving module; configuring a synthesis and construction module; and a verification and difference feedback module. According to the method, portrait analysis is carried out on calling of a target application, driving access and interruption events, a trigger threshold value is set, dependency solving is executed in a high touch module range, scene-driven cutting decision is achieved, the dependency closure calculation range is made to be consistent with an actual operation behavior, redundancy and invalid dependency are reduced, and cutting precision and kernel construction efficiency are improved.

The invention discloses a virtual power plant source load interaction optimization scheduling model based on low-carbon response and a solving algorithm, and belongs to the technical field of power system optimization scheduling. A low-carbon scheduling framework containing a distributed power supply, energy storage, a flexible load and a carbon transaction mechanism is constructed, the carbon emission intensity of each link is quantified to form a carbon flow scheduling signal, and a dynamic carbon emission factor and energy cost are coupled. A multi-objective optimization model is established, a complex function is processed by piecewise linearization, and a hybrid algorithm of an improved genetic algorithm and a commercial solver is designed to improve the solving efficiency. The prediction error is dynamically corrected through a'prediction-optimization-feedback 'closed loop, and the strategy is adjusted. According to the scheme, low-carbon and economic collaborative optimization is realized, renewable energy consumption and system stability are enhanced, user satisfaction and real-time scheduling are considered, and a solution is provided for low-carbon intelligent operation of the power distribution network.

The invention discloses a heat supply system optimization scheduling control method based on an operation benchmark library and working condition matching. The method comprises the following steps: constructing an offline operation benchmark library comprising a scheduling control business demand type, a business working condition type, business working condition relevance and a business operation strategy; business demand similarity and business working condition type similarity are calculated through the scheduling control agent; if the business demand similarity and the business working condition type similarity both exceed threshold values, business operation strategies under the business demand and the business working condition are obtained through an offline operation benchmark library, and operation strategies of other types of scheduling control businesses are adaptively adjusted according to business working condition relevance; taking the offline operation benchmark library as a training sample set through a scheduling control agent, carrying out training learning of each business scheduling control model, constructing each business scheduling control model, and obtaining a corresponding business operation strategy; and fusing and analyzing the benchmark strategy and the model strategy to obtain a final operation strategy of each scheduling control service.

The invention relates to the technical field of physical education management, and particularly discloses a digital informatization physical education comprehensive management system, which comprises a user interaction module, a data acquisition module, a data processing module, a system optimization module and a distributed database, the data acquisition module acquires teaching content, motion completion degree and user physiological data through a camera, a motion capture sensor and wearable equipment; the data processing module cleans and converts the data, and quantitatively evaluates whether the learning results of the students are qualified or not by using a constructed education evaluation coefficient mathematical model; the system optimization module calculates a scene optimization coefficient by analyzing a user favorable comment trend and multimedia loading performance, and automatically identifies a teaching scene needing to be optimized; the distributed database adopts a master-slave replication and fragmentation technology to improve data access efficiency and fault tolerance, digital management of physical education is achieved, and therefore the quality and efficiency of physical education are improved.

The invention belongs to the technical field of energy system optimization, and particularly provides a multi-objective collaborative optimization method and system for an integrated energy system. Comprising the steps of generating a multi-dimensional uncertainty time sequence scene of the integrated energy system; establishing a high-fidelity dynamic behavior model library; defining decision variables, and constructing a multi-target two-stage stochastic programming optimization model by taking the economy, environmental protection and toughness of the comprehensive energy system as a three-dimensional optimization target; solving the multi-objective two-stage stochastic programming optimization model by adopting a self-adaptive multi-objective evolution algorithm assisted by an agent model; and according to a solving result, realizing a mixed multi-attribute decision based on a fuzzy analytic hierarchy process and a multi-criterion compromise solution sorting method. According to the method, the park-level IES global optimal, high-robustness and high-efficiency planning design is realized from uncertainty modeling, equipment characteristic description and large-scale solution to multi-attribute decision full-chain innovation.

The invention discloses an end-to-end scheduling method and system for a multi-source weather-driven urban integrated energy system. The method comprises the following steps: constructing an integrated energy system model; constructing a source load prediction model based on multi-source numerical weather forecast data in combination with historical photovoltaic output data and power load and thermal load data; the method comprises the following steps: establishing a comprehensive energy system optimization scheduling model with minimization of system operation cost as an optimization target, designing a differentiable optimization layer, and reversely transmitting the gradient of the optimization target in the scheduling model to a source load prediction model parameter to the source load prediction model through a back propagation algorithm by the differentiable optimization layer, the parameters of the driving source load prediction model are updated, and end-to-end linkage optimization from prediction to scheduling is achieved; and periodically obtaining updated multi-source numerical weather forecast data and source load data, readjusting prediction model parameters, and executing optimization solution of the integrated energy system optimization scheduling model. According to the invention, cooperative training and iterative optimization of the prediction model and the scheduling decision are realized.

The invention discloses a digital twinning long-span bridge earthquake wind vibration and vibration reduction and isolation system and method, the system takes a high-fidelity digital model for constructing a long-span bridge as a basis, comprises full life cycle monitoring and management of the bridge under earthquake, wind vibration and vibration reduction and isolation conditions, and is based on a sky-air-ground-water integrated ubiquitous sensing module. In combination with artificial intelligence algorithms such as deep learning and a pulse neural network, real-time state evaluation of a bridge structure, material nonlinear analysis, geometric nonlinear analysis, multi-point excitation and pile-soil-structure interaction simulation, wind-induced fatigue damage prediction, earthquake vulnerability evaluation and closed-loop control scientific decision based on an artificial intelligence body are realized. According to the invention, system support and method guidance can be provided for large-span bridge anti-seismic and anti-wind design, intelligent monitoring, operation and maintenance management and vibration reduction and isolation system optimization.

Disclosed is an integrated energy system optimized dispatching method based on a variable time constant gradient algorithm. A Markov decision making process model is established based on an economic dispatching characteristic of an integrated energy system first, and a target optimization function is established. Then, a neural network is established and trained by applying a double-delay depth deterministic strategy gradient algorithm, effective experience is determined before updating a target network, and a variable time constant is set according to a reward value of a current round and a reward value of the last round of soft update. Finally, a trained intelligent agent is used for intra-day dispatching of the integrated energy system, so as to realize optimal economic cost operation of the integrated energy system.

The invention relates to a monitoring and early warning system and method for a working rope crossing a blocking net, and belongs to the technical field of rope state monitoring, and the system comprises a data collection and transmission module which is used for collecting multi-source state data of acoustic emission, tension and the like of the working rope and forming a structured data stream; the rope state intelligent analysis module is used for carrying out AE signal depth analysis and multi-modal feature fusion risk assessment based on the data stream, and constructing and updating a rope digital twinborn model; the damage feature and risk knowledge base is used for storing reference information to support intelligent analysis; the early warning decision and response module is used for performing graded early warning and residual service life (RUL) prediction according to the analysis result and knowledge base information and providing maintenance suggestions; the system optimization and self-learning module is used for coordinating system work and self-learning an optimization model and a knowledge base based on operation data; accurate monitoring, early damage identification, intelligent risk assessment and timely early warning of the state of the working rope can be realized, and the use safety and reliability are remarkably improved.

The invention provides an accurate control method and system for multi-axis linkage of welding equipment, and the method comprises the steps: S1, obtaining position feedback signals from each axis through a sensor, integrating the position feedback signals into a unified data set, and obtaining a real-time position information set; s2, applying a unified clock reference to each signal in the real-time position information set to obtain a feedback data set after time synchronization; s5, extracting coordinate instruction parameters of each axis from the optimized transport stream, distributing the coordinate instruction parameters to a controller, and determining unified motion track planning; s6, adjusting the speed and position of each axis in real time according to the unified motion track planning, and obtaining coordinated motion output; and S8, extracting efficiency index data from the final welding control sequence, comparing the efficiency index data with historical records, and determining system optimization and adjustment parameters. The system performance can be dynamically optimized, and the precision, stability and production efficiency of the welding process are remarkably improved.

The invention provides an energy storage charging and discharging time sequence optimization scheduling method and system based on revenue maximization, and relates to the technical field of power system optimization scheduling, and the method comprises the steps: obtaining price data and system parameters, recognizing a price local extreme point, constructing an envelope line, and stripping an oscillation component to obtain a price trend component; and establishing a corresponding relation between a charge state target value and a time period identifier, enabling the charge state track to converge to the target value step by step, and dynamically correcting the target value and adjusting a price peak value identification method according to real-time feedback data. According to the invention, price fluctuation can be effectively coped, the economic benefit of the energy storage system is improved, and self-adaptive optimization control of the charging and discharging process is realized.

The invention provides an energy storage power station joint market optimal regulation and control method and system, and is applied to the field of power system optimal scheduling. The method comprises the steps of constructing a double-layer optimization model, merging a lower-layer model into an upper-layer model according to the Carlo demand-Kuhn-Tuck condition, carrying out linear processing to form a mixed integer linear programming model, solving and obtaining an optimal strategy, and regulating and controlling energy storage. The income is improved.

The invention discloses a multi-role large model assisted hydrogen-containing building energy system operation optimization method, and belongs to the technical field of building energy system optimization control, and the method comprises the steps: firstly, building a hydrogen-containing building multi-energy system operation cost minimization problem in an off-grid operation mode; secondly, re-modeling the problem into a security Markov decision process, and defining a system state space, an action space and a composite reward function; then, solving a safety Markov decision process of modeling based on a multi-role large language model assisted near-end strategy optimization algorithm, and obtaining an intelligent agent operation strategy related to the hydrogen-containing building multi-energy system; finally, the intelligent agent makes an online decision based on the obtained optimization strategy, the decision acts on the actual hydrogen-containing building multi-energy system, the system operation cost can be effectively reduced, and the energy supply reliability is improved.

The invention relates to the technical field of offshore wind power equipment, and discloses an offshore floating type wind turbine mooring system optimization method based on a proxy model, and the method comprises the following steps: S1, determining key design parameters; s2, sampling to generate design points; s3, establishing a total index function of multi-target coupling; s4, generating a sample database through numerical simulation; s5, constructing an agent model by using a Kriging method; s6, calling the proxy model to search an optimal solution by adopting a differential evolution algorithm; s7, performing numerical simulation verification on the optimal solution and judging convergence; and S8, if not, updating the database and returning to S5 for iteration, and if so, outputting the optimal configuration. According to the method, the problems that a traditional optimization method is high in simulation calculation cost and too long in consumed time are solved, searching is carried out through the proxy model instead of high-precision simulation, the verification iteration step is combined, the optimization efficiency is greatly improved while the result accuracy is guaranteed, and the multi-target mooring scheme with the optimal comprehensive performance can be efficiently obtained.