164 results about "Transfer model" patented technology

A dynamic optimization system for energy consumption of a refrigeration house based on digital twinning is characterized by comprising a data acquisition module used for acquiring basic structure data of the refrigeration house, technical parameters of a refrigeration system, real-time operation data and historical operation data, preprocessing the data and then outputting a standardized multi-dimensional real-time data stream; the model construction module is used for constructing a 3D geometric model, a thermodynamic transfer model and a refrigeration system mathematical model according to the multi-dimensional real-time data flow, performing machine learning calibration on model parameters through historical operation data, and performing fusion to construct a refrigeration house digital twin model; the prediction analysis module is used for predicting future energy consumption demand and load change according to the refrigeration house digital twin model and the real-time operation data, and outputting an energy consumption prediction result and a load analysis report; a strategy generation module; an execution feedback module; and a learning optimization module. Overall energy consumption of the refrigeration house is reduced, energy utilization efficiency is remarkably improved, and goods storage safety is guaranteed.

The invention discloses a big data-based shipping center area coordination optimization method and system, and the method comprises the steps: dividing all-day working time into a plurality of decision periods, building a state transition model, maximizing the utilization rate of port resources through an iteration model under constraint conditions, obtaining a resource distribution strategy of each decision period, and obtaining a chain reaction chain. Identifying logistics bottlenecks, adding queuing plans, regarding ships, berths, storage yards and loading and unloading equipment as different agents, dynamically adjusting the queuing plans by using a multi-agent collaborative algorithm, obtaining a final collaborative scheduling method, setting a reward function, and updating a state transition model by using reinforcement learning. And continuous optimization of the scheduling method is realized. According to the method, efficient utilization of port resources is achieved through the multi-agent collaborative algorithm and the reinforcement learning technology, the port operation efficiency is improved, the logistics cost is reduced, the adaptability of a port to uncertainty is enhanced, and meanwhile good interpretability is achieved.

Disclosed in embodiments of the present description are a transfer learning model training method and device, a storage medium, and a terminal. The method comprises: at a target transfer moment, initializing a source domain model and a target domain model which satisfy a preset similarity condition; on the basis of parameter transfer networks corresponding to target domain hidden layers in the target domain model, obtaining target source domain hidden parameters in the source domain model; and transferring the target source domain hidden parameters to obtain a target domain transfer model, and training the target domain transfer model on the basis of target sample data corresponding to a target domain. Target source domain hidden parameters reflecting deep network features are selected from source domain hidden layers of the source domain model by means of the parameter transfer networks and transferred to corresponding target domain hidden layers of the target domain model, so that the target domain hidden layers directly obtain effective deep feature parameters, and the target domain transfer model is still trained by using the target sample data of the target domain.

The invention discloses a personalized information accurate pushing system and method based on artificial intelligence, and relates to the technical field of personalized recommendation, and the method comprises the steps: fusing user multi-platform behavior data and external space-time environment information, and generating a situation label with confidence through employing an improved space-time density clustering algorithm; constructing a causal directed acyclic graph by adopting a causal forest algorithm, quantitatively analyzing a heterogeneity causal effect, inverting a potential intention of the user, and outputting standardized intention inversion probability distribution; in combination with a historical intention and a behavior sequence, training a Transform intention state transition model constrained by causality of a causality directed acyclic graph, and performing multi-step probability deduction to generate an intention evolution path; information is retrieved from the dynamic knowledge graph according to the prediction path, a pushing copywriting matched with the situation is generated through the NLP technology, the optimal pushing opportunity is calculated in combination with the position track of the user, and accurate reaching of personalized information is achieved.

The invention relates to the field of geotechnical engineering, and discloses a self-stress anchor rod failure evolution simulation method for jointed rock mass bolting-grouting reinforcement, which comprises the following steps: S1, constructing a three-dimensional rock mass model containing a joint structure in an FLAC3D platform, and defining joint surface space distribution and geometric parameters; s2, a bolting-grouting structure is introduced into the three-dimensional rock mass model, an anchor rod, a grouting body and the contact relation between the anchor rod and the grouting body and the rock mass are set, and an anchor rod load transfer model is established; s3, a loading path is set, displacement control is adopted as a loading mode, and a certain included angle is formed between the loading direction and the joint surface; and S4, establishing a numerical model in FLAC3D, performing grid division, and setting a rock mass material constitutive model and contact surface parameters. By accurately controlling the loading direction and the loading angle in simulation and analyzing the change influence of different loading paths on the failure mode of the bolting-grouting structure, the problem that the traditional simulation method neglects the change of the failure mode under different loading conditions is solved.

The invention provides a user portrait label generation method and system based on a time-aligned transition model, and the method comprises the steps: firstly obtaining behavior track data, containing behavior operation types and context attributes, of a target user in a preset time period, and then constructing a state transition probability matrix according to the time sequence relevance of the behavior operation types; then determining an absorption state behavior operation type set meeting specific conditions according to the steady-state convergence of a matrix state node, generating a portrait label set based on the absorption state behavior operation type set and corresponding context attributes, and describing stable characteristics of a target user in a specified behavior dimension; finally, matching verification is conducted on the portrait label set and a preset knowledge base, the dynamically-updated portrait label sequence is output, portrait labels reflecting stable features of the user can be comprehensively and accurately generated, and the portrait quality of the user is improved.

The invention discloses a load transfer method, system, equipment and medium considering new energy and load power fluctuation, and belongs to the technical field of relay protection, and the method comprises the steps: taking a switch motion moment as a first stage of load transfer, taking a set time after the switch motion as a second stage of load transfer, building a dual-stage load transfer model, and taking the set time as a second stage of load transfer; obtaining a load transfer scheme; double-stage constraint verification is executed, and operation constraint verification is carried out on the load transfer schemes of the first stage and the second stage; introducing a constraint out-of-limit penalty factor, and quantifying the degree of the load transfer scheme deviating from the constraint condition; constructing a load transfer scheme comprehensive evaluation function, and performing weighted integration; and optimizing the load transfer scheme comprehensive evaluation function by adopting an improved binary particle swarm algorithm, and determining an optimal load transfer scheme. According to the method, the limitation of a traditional static model is overcome, and the problem of operation constraint violation caused by photovoltaic output change is avoided.

The invention discloses an error compensation method for a double-end driving gantry machine tool. The method comprises the following steps: 1, establishing a theoretical transformation matrix of a double-end driving gantry machine tool cutter end, and comparing the theoretical transformation matrix with an actual transformation matrix to obtain an error transmission model of the cutter end; 2, instructions are applied to the two ends of the double-end-drive gantry machine tool at the same time, and a double-drive X-axis, a Y-axis and a Z-axis are driven to move; 3, installing a laser interferometer probe and a reflector at a specified position of each shaft, and measuring by an interferometer to obtain a geometric error; 4, substituting the measured geometric error into the error transfer model, and calculating to obtain a cutter end error; and 5, performing error compensation on the cutter end according to the calculated cutter end error. According to the method, the influence of the double-end driving error on the positioning precision of the tail end of the cutter can be quantitatively evaluated through the error transfer model, the position error is compensated, the machining precision of the double-end driving gantry machine tool is improved, and the problem that the machining precision is insufficient in actual machining is solved.

The invention discloses a model reasoning method and device suitable for a question and answer scene, equipment and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: building a mapping relation between a new word list composed of generated target long words and an original word list through a preset word segmentation algorithm by using priori knowledge in target corpus data; when the pre-training model generates the specified content through reasoning, the final output result of the pre-training model is rapidly determined through the probability transfer model, the problems that an existing model acceleration method is low in precision and high in model training cost and calculation cost are solved, and only one probability transfer model is additionally added in the whole reasoning process, so that the calculation cost is reduced. The computing resource pressure is effectively reduced, and the generation speed of the whole system is improved. And in addition, priori knowledge in domain questions and answers is fully utilized, and the accuracy rate of domain proprietary vocabulary generation can also be improved.

The invention discloses a centrifugal compressor assembly error modeling method integrating manufacturing error and assembly deformation, which comprises the following specific steps: S1, measuring an assembly surface by adopting line laser and a three-coordinate measuring machine, assembling surface point clouds of all assembling parts of the centrifugal impeller of the centrifugal compressor are obtained through feature extraction, a surface model of the assembling parts in interference fit with the centrifugal impeller is reconstructed based on an NURBS curved surface modeling method, and deformation calculation of interference fit of the centrifugal impeller is conducted; s2, calculating a matching error of the assembly surface of each assembly part by adopting a point cloud constraint registration method; and S3, based on the assembly constraint relation between the matching surfaces and the improved Jacobian-spinor model, a centrifugal compressor centrifugal impeller assembly chain error transfer model is established. The method has the advantages that the problem that error sources are not comprehensively considered in the actual assembly process is solved, the constraint relation between assembly surface matching errors is considered, and the assembly chain error transmission model is constructed and used for improving the assembly error prediction precision.

The invention provides a multi-physical field compensation method and system for an exposure machine, and the method comprises the following steps: constructing a dynamic error transfer model, and obtaining a coupling parameter based on the output of a coupling function; carrying out eigenvalue decomposition on the coupling parameters, screening principal component vectors to obtain a mapping matrix, modulating the mapping matrix through a thermal weight matrix to obtain a modulation matrix, and carrying out space-time weight reconstruction based on the modulation matrix to obtain a four-dimensional compensation parameter matrix; the control parameters of the air supply pressure of the air floating guide rail, the deflection angle of the DMD micromirror and the power of the laser are adjusted. A dynamic error transfer model containing mechanical-thermal-optical coupling characteristics is constructed, a vibration spectrum, temperature field distribution and light intensity energy data are fused in real time, compensation parameters are subjected to space-time weight reconstruction to obtain a four-dimensional compensation parameter matrix, the real-time performance of matching correction is improved, and therefore the parameter compensation effect and the machining precision are improved.

The invention relates to the technical field of energy testing, in particular to a self-adaptive dynamic energy testing method based on combined heat and power generation, which comprises the following steps: constructing a thermoelectric coupling transfer model and a working condition identification model, and inputting power generation power and heat supply power into the thermoelectric coupling transfer model to correct model parameters, inputting the environmental parameters into a working condition recognition model, recognizing a current working condition type based on a preset working condition feature library, calling an initial test scheme from a test scheme library according to the current working condition type, and generating a dynamic test scheme through a machine learning algorithm in combination with the parameters corrected by the thermoelectric coupling transfer model; and controlling the test equipment to execute a test based on the dynamic test scheme, collecting test data in real time, calculating a system performance index according to the test data, comparing the system performance index with a preset threshold value, and generating feedback information for adjusting the test scheme. Therefore, the problems that in the prior art, a test scheme is fixed and cannot adapt to complex working conditions, and performance evaluation is incomplete are solved.

The present invention belongs to the field of dimension measurement technology, and specifically relates to a method, system, device, and medium for evaluating the uncertainty of a large-scale multilateral coordinate measurement system. The method inputs the acquired target point coordinates into an error transfer model to obtain three coordinate uncertainty components corresponding to the coordinate values. This uncertainty coefficient is then calculated based on the uncertainty introduced by the offset of the reflector's optical center, and the uncertainty evaluation is performed. The present invention, based on the fundamental principles of the multilateral coordinate measurement system, simplifies the system coordinate system to reduce parameters, thereby reducing the amount of calculations. The method also provides a specific mathematical formula, which facilitates rapid evaluation at the measurement site and is easy for metrologists to learn and apply.

The invention provides a ground vibration spectrum acceleration prediction method, a transfer learning method, equipment and a medium, a global model is obtained through first training by using a basic knowledge data set, the first training is evaluated by using a regional knowledge data set, and then a transfer model is obtained through second training by using the regional knowledge data set. And the second training is evaluated by adopting the regional knowledge data set, and the establishment method of the migration model retains the data features of the regional database and learns the difference between the global data set and the regional data set, so that the prediction precision of the model is ensured, and the generalization ability of the model is enhanced.

The invention provides a steel plate cutting-oriented multi-process dynamic collaborative scheduling method and system, and relates to the technical field of steel plate cutting, and the method comprises the steps: carrying out the historical data collection of each process node in a cutting process chain, carrying out the post-process node impact analysis of the fluctuation of key parameters according to the historical data collection result, and obtaining a multi-process node impact analysis result; establishing an instability transfer model; setting a monitoring sensor, performing process node monitoring by using the monitoring sensor, and establishing node monitoring data; uploading node monitoring data to an instability transmission model, and establishing an instability transmission result of the cutting process chain; performing compensation optimization based on an instability transfer result by using the target function; and dynamic cooperative scheduling management of the cutting process chain is carried out by using a compensation optimization result. The technical problem that in the prior art, due to instability in the production process, the cutting precision and the production efficiency are further affected can be solved, and the technical effect of improving the cutting precision and the production efficiency is achieved.

The invention discloses a micro-seismic signal identification method based on transfer learning and BiLSTM-DCNN, belongs to the technical field of mining engineering micro-seismic monitoring and signal processing, and solves the problems of data scarcity in the initial stage of mine monitoring and low identification precision under the condition of small samples. Firstly, Mel spectrum feature extraction is carried out on a micro-seismic signal; the method comprises the following steps: constructing a BiLSTM-DCNN model comprising a bidirectional long-short term memory network and a deep convolutional neural network, and carrying out pre-training by using large-scale source mine data; and the model is adapted to target mine small sample data through transfer learning, and model parameters are finely adjusted, so that high-precision classification of the signals is realized. According to the method, the recognition accuracy and the model generalization ability of the micro-seismic signals under the small sample condition are remarkably improved, the test accuracy reaches 0.9444 and is improved by 80.85% compared with an unmigrated model, and the method is suitable for an intelligent early warning and safety monitoring system for mine dynamic disasters.

A defect detection method and a model transfer method. The model transfer method comprises: acquiring a source-domain image and a corresponding annotation, and a baseline model obtained from a source domain; acquiring target-domain images, which are fully annotated, partially annotated, or unannotated; inputting the annotation of the source-domain image into a denoiser to obtain a denoised annotation; inputting the unannotated target-domain images into an initial baseline model to obtain second target-domain prediction results, performing data augmentation on the unannotated target-domain images to obtain second target-domain augmented images, and using the second target-domain prediction results as pseudo-annotations of the second target-domain augmented images; and using the source-domain image and the corresponding denoised annotation, the annotated target-domain images and the corresponding annotations, and / or the second target-domain augmented images and the corresponding pseudo-annotations to train the baseline model, so as to obtain a transfer model. The method can complete training by using a small number of annotated target-domain images, thereby solving the problem of model transfer performance being poor in the case of insufficient target-domain images.

The invention discloses a regional lake and reservoir water quality uncertainty simulation method based on Bayesian transfer learning, and the method comprises the following steps: S1, collecting water quality automatic monitoring data of a plurality of lakes and reservoirs in a region, and carrying out the preprocessing of the monitoring data; s2, numbering the plurality of lakes and reservoirs respectively, dividing the lakes and reservoirs into a target domain and a source domain, and selecting a water quality index needing to be simulated as a target index; s3, constructing a target index simulation model in the source domain by adopting a Bayesian additive regression tree, and performing model training by adopting a cross validation strategy; s4, performing parameter updating on the target index simulation model in the source domain by adopting the data of the target domain, constructing a Bayesian migration model of the target index of the target domain, and performing uncertainty simulation on the water quality target index of the target domain; and S5, according to an output result of the Bayesian migration model, identifying a key factor influencing the mean value and uncertainty of the water quality target indexes of the target domain, and analyzing a driving mechanism of the key factor to fluctuation of the water quality indexes of the target domain.

The invention relates to the technical field of data processing of stem cell culture equipment, and discloses a data processing system of stem cell culture equipment, which comprises the following modules: a data acquisition module generates a state and a control sequence by acquiring parameters in a stem cell culture process; the state modeling module constructs a multi-dimensional state matrix and a control matrix based on the collected data; the trend prediction module is used for constructing a state transition model and generating a prediction state vector at the next moment; the optimization control module solves a control strategy vector based on the prediction state vector; the control execution module is used for converting the control strategy vector into an actual control instruction; the closed-loop feedback module collects the actual state after adjustment and is used for correcting the state and the control matrix and dynamically updating the state transition model. According to the method, the state matrix and the control matrix based on the sliding time window are constructed, so that multi-dimensional time sequence information in the stem cell culture process is subjected to structured expression, and the effect of capturing the dynamic change trend of the environment in real time is achieved.

This invention discloses a lightweight BIM data transfer method based on 3DE, comprising the following steps: S1, acquiring components; S2, acquiring structure tree nodes and updating the transfer; S3, acquiring components and generating hierarchical folders; S4, acquiring lightweight geometric files and material map files, and associating the lightweight geometric files and material map files; S5, acquiring lightweight attribute files, and associating the lightweight geometric files and lightweight attribute files; S6, compressing and outputting lightweight data files. This invention solves the problem of information loss during BIM lightweight transfer caused by traditional single-feature lightweight methods, and ensures the accuracy of the association between geometric information, material maps, and attribute information of the transferred model, as well as the consistency of coordinate information and structural organization of the transferred model, thereby improving the quality and efficiency of BIM lightweight transfer.

The invention discloses a migration model training method and device, a migration method and device, electronic equipment and a computer program product. The method comprises the following steps: acquiring an image training set including style images and content images; inputting the style image and the content image into a to-be-trained migration model to obtain a migration image; inputting the style image, the content image and the migration image into a preset image feature extraction module to obtain style features, content features and migration features; calculating the model loss of a to-be-trained migration model based on the style features, the content features and the migration features; and optimizing the to-be-trained migration model according to the model loss until the model loss is converged to obtain a trained migration model which can be used for executing image color style migration processing. According to the scheme, the migration model with relatively high migration efficiency and relatively good migration effect can be obtained, so that the accurate, natural and controllable image color migration effect of various images can be realized through the migration model.

The invention provides an assembly deviation prediction method considering surface macro and micro characteristics and assembly deformation. Comprising the steps of part surface macro-micro feature representation based on primary function modal superposition, non-ideal surface model construction considering an actual machining surface, and non-ideal surface contact state calculation based on a difference plane and point cloud registration method. And solving non-ideal surface contact deformation based on a conjugate gradient-fast Fourier algorithm and establishing an assembly deviation transfer model considering surface macro and micro characteristics and assembly deformation. According to the method, surface form and position errors generated by part machining errors and assembly deformation errors caused by loads in the assembly process are fully considered, the deviation domain model and the Minkowski summation method are combined to simulate transmission of the assembly deviation, the prediction precision of the product assembly deviation is effectively improved, and meanwhile the operation speed is remarkably increased; and real-time monitoring and timely deviation adjustment in the assembling process are achieved.

The invention provides a building interface style migration and optimization method based on multi-machine learning model coupling. According to the method, a building interface style migration model is constructed based on various machine learning technologies, so that different building style characteristics and a mapping relation thereof are obtained. Based on a de-noising diffusion probability model and a cyclic generative adversarial network, high-quality style migration among different architectural styles is realized, the stability and detail quality of an architectural interface design scheme are optimized through a neural network, and a multi-objective optimization method is combined to generate an architectural interface layout scheme meeting urban design requirements. Compared with the prior art, the method has remarkable advantages in the aspects of building interface style migration precision, generation scheme adaptability and optimization design efficiency. Meanwhile, due to the reversibility of the design process, the generation result of the scheme can be returned to any step at any time to be optimized until the generated image meets the construction design requirement.

The invention discloses a multi-target game-oriented resource iterative allocation method. The method comprises the steps of obtaining a to-be-gamed task information set and to-be-allocated resource information; the to-be-allocated resource information comprises the number of to-be-allocated resources, the position information of the to-be-allocated resources, the bandwidth of the to-be-allocated resources and the coverage area; performing priority evaluation processing on the to-be-gamed task information to obtain a weighting coefficient vector; constructing an initial resource allocation model by using the to-be-gamed task information, the to-be-allocated resource information and the weighting coefficient vector; solving the initial resource allocation model to obtain an initial resource allocation matrix; obtaining a resource historical allocation data set; constructing a task channel transfer model by using the resource historical allocation data set; and processing the initial resource allocation information by using the task channel transfer model to obtain resource allocation scheme information.

The invention discloses an LIBS (Laser-induced Breakdown Spectroscopy) coal quality quantitative analysis method and related equipment, and the method comprises the following steps: collecting LIBS spectrums of the same coal sample through different instruments, and dividing a training set of a training migration model and a training set of a training quantitative analysis model; performing spectrum pretreatment on the spectrums respectively; carrying out wavelength offset self-correction on the spectrum to realize that the spectrum collected by the slave instrument corresponds to the spectrum collected by the master instrument in wavelength; performing feature selection on the spectrum, and taking the spectrum after feature selection as the input of a migration model; establishing and training a transfer learning model based on feature mapping and feature representation, and utilizing the trained transfer learning model to realize feature transfer from the instrument spectrum to the main instrument spectrum; and training a quantitative analysis model by utilizing the spectrum of the master instrument and the migrated spectrum of the slave instrument, and predicting the coal quality index of the slave instrument by adopting the trained quantitative analysis model. The coal quality quantitative analysis model constructed by the method can stably and efficiently run across instruments, and the modeling cost of different instruments is reduced.

The application discloses a metallogenic fluid main channel inference model based on particle filtering. The model comprises the following steps: collecting relevant data of a target deposit, establishing an inference database, and defining a state sequence of spatial discrete elements; performing statistics on ore-bearing elements known in all exploration information, taking the known ore-bearing elements as an initial particle group of particle filtering, and generating a spatial channel path of each particle; establishing a metallogenic fluid ion state transfer model coupled with probability and velocity according to the spatial position of the particle and fluid dynamics; establishing a particle weight observation model based on a particle state transfer likelihood function, and dynamically updating the weight of the fluid particle; performing spatial sampling on the particle through a resampling algorithm, updating the weight particle, and outputting a maximum posterior probability path through particle number threshold judgment. The model combines multivariate data constraint and particle filtering algorithm to dynamically estimate the main channel path of the metallogenic fluid in a three-dimensional geological space, and fundamentally solves the problems of high cost and difficult sampling sample acquisition of the traditional method.

The invention relates to the technical field of reinforcement learning, in particular to a bunching station parameter optimization method based on extended Kalman filtering and reinforcement learning, and the method comprises the steps: randomly selecting an initial state from an environment based on an initial strategy and a preset target state, carrying out the sampling, and collecting a plurality of pieces of trajectory data composed of continuous empirical tetrads; training a probabilistic neural network by using the collected trajectory data in the first round of sampling to obtain a state transition model; carrying out extended Kalman filtering on each piece of trajectory data in combination with the state transition model, replacing the filtered state at the next moment into an experience tetrad of each piece of trajectory data, and storing the filtered state into an experience playback pool; and randomly sampling the experience tetrad from the experience playback pool by using a DDPG algorithm, learning and updating the strategy to obtain a new strategy, and repeating the steps until the strategy learning is completed. According to the method and the device, the influence caused by system errors can be relieved, the accuracy of state estimation is improved, and thus the strategy learning is more accurate.

The present invention discloses a real-time hybrid test control compensation method, comprising: obtaining the displacement response of the actuator through white noise excitation; fitting the white noise with the displacement response to obtain a transfer model; designing a compensation filter based on the transfer model, and optimizing the parameters of the compensation filter, and completing the hybrid test control compensation based on the optimized compensation filter model. The purpose of the present invention is to reduce the loading error in the hybrid test and increase the test accuracy. At the same time, the stability and reliability of the hybrid test are also guaranteed.

The invention provides a disease progress probability prediction method and device based on a Monte Carlo-Markov chain, and relates to the technical field of disease progress prediction. The method comprises the following steps: acquiring multi-modal historical follow-up visit data of a patient to be tested, and constructing a state space with transfer constraint; extracting and checking time series data according to the state space; performing Monte Carlo sampling on the time sequence data, establishing a non-homogeneous transfer model and forming an individualized non-homogeneous transfer kernel set; completing path interpolation and probability filling of missing fragments based on the transfer kernel set to obtain continuous complete state sequence data; a Markov chain Monte Carlo algorithm is used for parameter updating, an individualized transfer model subjected to posteriori updating is obtained, a calibration probability result is obtained, and dynamic prediction and uncertainty evaluation of individualized disease progression are achieved. According to the method, the problems of inaccurate state determination, single transfer modeling, incomplete observation and insufficient prediction calibration in the prior art are solved.

The invention relates to the technical field of spacecraft on-orbit service, in particular to a GEO multi-spacecraft cooperative on-orbit refueling task planning method, which is characterized by comprising the following steps of: 1, establishing an orbit transfer model based on a surface and phase modulation maneuvering method, and revealing a relationship between spacecraft orbit transfer speed increment and time; 2, establishing a double-layer optimization model for collaborative on-orbit refueling task planning, and determining a task time range on the basis of minimum fuel consumption; 3, efficiently solving the optimization model by utilizing a double-layer optimization algorithm, wherein a branch and bound algorithm and a fast elite multi-objective genetic algorithm are respectively adopted in an inner layer and an outer layer; according to the method, the limitation of a traditional many-to-many on-orbit refueling strategy is broken through, multiple service spacecrafts are allowed to cooperate to complete the same refueling task, the fuel consumption and time cost of the on-orbit refueling task are reduced, the result is close to the Pareto optimal state, the constraints of multiple aspects such as dynamic scheduling and effective load limitation are fully considered, and the method is suitable for large-scale popularization and application. And actual engineering requirements are met.