61results about How to "Improve optimization efficiency" patented technology

The invention discloses an automatic extraction method for semiconductor device model parameters, and the method comprises the steps: obtaining a multi-target response through the key parameters of a semiconductor device model, and constructing and training an agent model to predict a target response mean value and uncertainty parameters; constructing a multi-objective optimization collaborative circulation framework assisted by an agent model; generating a candidate parameter population in key parameters according to a position updating mechanism of a multi-objective optimization algorithm, predicting candidate parameters by using a trained proxy model, selecting the candidate parameters according to a prediction result to carry out real simulation evaluation, adding an obtained real sample into a non-dominated file, and updating the file based on a non-dominated relationship. Adding a training set dynamic updating agent model into the real sample, generating a potential search trajectory, selecting a representative representative Pareto approximate solution from the potential search trajectory, and updating the position of the population and the direction of the next-generation search trajectory according to the representative solution selected from the file; and terminating the circulation, outputting the solution set in the file, and checking.

This invention discloses a method and system for optimizing the design of offshore wind turbine support structures based on a surrogate model. The method establishes the relationship between the design parameters of the support structures for offshore wind turbines of different capacities and the structural performance response of the optimized design based on the Kriging surrogate model. Simultaneously, it optimizes the tower and monopile foundations to find the lightest overall support structure design. This method avoids getting trapped in local optima during the optimization design of offshore wind turbine support structures, which would otherwise only result in finding the lightest tower or monopile design. Using the method and process of this invention, after load calculation, the tower and foundation structures are simultaneously checked and optimized, leading to a globally optimal design. This results in a stronger overall structure, more uniform stiffness distribution, and more rational stress distribution, ultimately achieving the lightest overall support structure weight. Optimization based on the surrogate model reduces the number of computer simulations required for structural analysis, improving optimization efficiency.

The application provides a process parameter optimization method and device for ultrasonic metal welding, which comprises the following steps: receiving multiple groups of process parameters to be optimized in multiple process stages of ultrasonic metal welding; inputting the multiple groups of process parameters to be optimized into a process parameter prediction layer to obtain multiple predicted process parameters corresponding to the multiple process stages; when the multiple predicted process parameters exceed a set parameter range, adjusting the multiple groups of process parameters to be optimized based on a parameter adjustment module to obtain multiple groups of optimized process parameters; and when the multiple predicted process parameters are within the set parameter range, inputting the multiple predicted process parameters into a welding quality prediction layer to obtain a predicted welding quality. The process parameter prediction layer can predict the process parameters of each process stage, and the parameter adjustment module can adjust the multiple groups of process parameters to be optimized based on the prediction results of the process parameters, so that the consistency of the optimized process parameters can be improved, and the accuracy of the predicted welding quality can be improved.

This invention relates to a method and system for automated generation of TSN network configurations based on a reflective agent. The system comprises a network state perception and intent reflection module, a YANG model configuration parameter and structure decision module, and a YANG model intelligent generation module. The network state perception and intent reflection module is responsible for structurally perceiving network state and resource information. The YANG model configuration parameter and structure decision module integrates multi-source knowledge and contextual information, intelligently generating deployable TSN scheduling parameters and YANG configuration structures based on business needs. The YANG model intelligent generation module automatically generates the corresponding YANG configuration model based on the optimized results, performs syntax and semantic verification, and then distributes it to the target device, achieving automated deployment of network configurations. This invention significantly improves the intelligence level and deployment automation capability of TSN network configuration by introducing reflective reasoning and feedback optimization techniques.

The application provides a pumped storage power station protection fixed value processing method, device, equipment and medium, relates to the relay protection technology field of power systems, and comprises the following steps: determining fixed value combination data from a fixed value feasible region, inputting the fixed value combination data into a pumped storage power station simulation model, simulating multiple fault scenes, and obtaining performance data; performing feature extraction on the performance data to obtain performance indexes; taking the fixed value combination data and the performance indexes as a training set, training an initial performance proxy model, and obtaining a performance proxy model; standardizing the fixed value feasible region, determining a protection fixed value population, inputting the protection fixed value population into the performance proxy model, obtaining individual performance indexes corresponding to each individual, performing multi-objective constraint optimization processing on the protection fixed value population based on the individual performance indexes, and obtaining fixed value combination data actually used by a protection device. The technical scheme of the embodiment of the application can improve the reliability and intelligent level of relay protection of pumped storage power stations.

The embodiment of the invention discloses a data optimization method and device, equipment, a storage medium and a product. The method comprises the steps that associated data and reference information of a to-be-optimized statement are obtained, the associated data comprise an analysis process of the to-be-optimized statement, the reference information comprises a rewriting example of a reference statement, supplemental content retrieval is conducted on the to-be-optimized statement, and supplemental content of the to-be-optimized statement is obtained; and rewriting the analysis process based on the to-be-optimized statement, the rewriting example of the reference statement and the supplementary content to obtain a rewritten analysis process, and generating an optimization result of the to-be-optimized statement through the rewritten analysis process. It can be seen that the analysis process of the to-be-optimized statement is rewritten by adding the rewriting example and the supplementary content of the reference statement, automatic optimization of the to-be-optimized statement can be achieved, and the optimization efficiency of the to-be-optimized statement is improved.

The application discloses a large-interval wide-angle scanning phased array antenna design method and system, and belongs to the technical field of array antenna design. The application adopts parameterized setting of block number to improve the degree of freedom of optimization. Since each block condition may exist a better solution, the block type of the region is substituted into the optimization variable, so that the final optimization result can be prevented from falling into a local optimum, and the optimal solution can be more likely obtained. The dense arrangement form of the divided region can effectively control the interval of adjacent array elements, avoid the overlapping of adjacent array elements, and improve the optimization efficiency. In addition, the regular layout is beneficial to the back-end network design, and reduces the complexity of the feed network. The dense arrangement and array element selection combination can effectively enlarge the interval, and can make the interval not appear grating lobe, thereby reducing the number of channels and reducing the cost. The array element position micro-adjustment provides more possibilities for large-angle scanning optimization, improves the optimization efficiency, and breaks the periodic arrangement form.

The invention belongs to the technical field of material performance optimization, and particularly discloses a carbon ceramic resistor microstructure optimization method based on a physical constraint embedded CNN, and the method comprises the steps: inputting a dual-channel digital matrix into a U-Net network for feature extraction and reconstruction, and obtaining an optimized microstructure diagram; carrying out finite element model modeling and simulation calculation on the optimized microstructure diagram to obtain simulation performance parameters; performing statistical analysis based on the sample parameters to obtain a physical constraint range of each physical parameter; iteratively updating parameters of the convolutional neural network based on the simulation performance parameters and the physical constraint range to obtain an optimized network meeting physical constraints; and comparing the difference of the current density distribution in each state to complete optimization effect evaluation. According to the method, the technical closed loop from microstructure characterization to process parameter optimization is realized, a scheme is provided for improving the energy tolerance performance of the carbon ceramic resistor, the structure design and the preparation process of the high-performance carbon ceramic resistor can be promoted, and the efficiency and the reliability of the optimization method are improved.

The present application relates to the technical field of traditional Chinese medicine preparation, and particularly relates to a method and system for optimizing a frying process of traditional Chinese medicine decoction pieces. Formula information and process requirements of target traditional Chinese medicine decoction pieces are obtained; a frying process scheme of similar traditional Chinese medicine decoction pieces is retrieved from a traditional Chinese medicine frying expert knowledge base; initial parameters are generated by fusing the frying process scheme; first-round frying is performed; process data are collected by a multi-modal sensor; adjustment suggestions are proposed by experts; parameter adjustment schemes are generated in combination with the expert suggestions and the process data; interactive optimization is performed for multiple rounds; and optimal frying process parameters are recorded and stored in the knowledge base. By constructing a formula information-driven knowledge retrieval mechanism, a multi-modal sensing monitoring system, a parameter adjustment strategy combining expert evaluation and data analysis, and an interactive iterative optimization framework, a closed-loop optimization system is formed, and technical problems such as difficulty in knowledge reuse, single process monitoring, lack of systematic support for parameter adjustment, and insufficient human-machine cooperation in traditional Chinese medicine frying processes are solved.

The invention provides an orthogonal waveform design method for a near-field MIMO radar system. Relates to the technical field of radar sensing. The method comprises the following steps: acquiring a first signal transmitted by a near-field multiple-input multiple-output (MIMO) radar system, wherein the first signal comprises a second signal transmitted by each array element of the radar system; determining an autocorrelation value of each second signal and a cross-correlation value between any two second signals, and according to the first signal, the plurality of autocorrelation values and the plurality of cross-correlation values, determining an objective function and a constraint condition associated with the orthogonality of the waveform; determining first phase information corresponding to the first signal according to the first signal, the target function and the constraint condition; inputting a target function, a constraint condition and a plurality of pieces of first phase information into the first model to obtain second phase information corresponding to a first signal associated with the spatial-temporal characteristics; and according to the second phase information, the target signal transmitted by each array element is determined, the optimization efficiency is improved, and the system performance of the MIMO radar is improved.

The invention relates to the technical field of artificial intelligence in assisted reproduction, in particular to an embryo optimization model construction method, device and equipment and a storage medium. Comprising the following steps: acquiring video data of embryonic development from a plurality of fertilized eggs to a blastocyst stage based on a delayed photography embryo culture technology; constructing time sequence image data according to the video data; the time sequence image data comprises multiple frames of continuous images; performing feature extraction on each frame of image of the time sequence image to obtain a high-dimensional feature corresponding to each frame of image; determining a comparison difference feature with the corresponding reference sample feature, performing fusion, and constructing a fusion feature corresponding to each frame of image; constructing a feature sequence of the target embryo development according to the fusion features; and acquiring a trained embryo quality grading model according to a time dependency relationship of the feature sequence for capturing embryo development. The model provided by the invention can optimize the utilization rate of embryos, reduce unnecessary repeated ovulation promoting cycles, and promote the development of reproductive medicine towards the precision direction.

This invention discloses a knowledge-enhanced multi-agent decision-making method for welding deformation control in ship assembly subgroups. The method includes: constructing a welding process knowledge graph using a large language model based on welding process data; building a multi-agent collaborative framework for ship assembly subgroups; a process planning agent generating a process plan including welding sequence and welding parameter combinations; a deformation simulation agent performing welding deformation simulation calculations and outputting deformation prediction results; and a central coordinating agent triggering multi-agent collaborative iterative optimization based on the comparison of deformation prediction results with tolerance constraints, outputting the optimal welding process plan that meets deformation control requirements. This invention, by constructing a welding process knowledge graph, structurally represents the welding deformation mechanism, process parameter rules, and historical cases, and achieves knowledge-driven process decision-making based on a multi-agent collaborative mechanism, effectively solving the problems of lagging welding deformation control and reliance on experience in process parameter selection in traditional methods.

The application discloses a curve fiber variable stiffness composite structure integration optimization method and belongs to the field of composite structure optimization.The application realizes the method as follows: defining and creating an initial "base ply" of a curve fiber variable stiffness composite structure, constructing a number domain representing the existence of each single layer of the "base ply", establishing a curve fiber variable stiffness composite structure integration optimization problem model integrating the number of plies, the thickness of the plies and the curve fiber laying angle; based on a Latin hypercube sampling test, constructing an index form of a mixed correction mode variable fidelity proxy model; adopting a genetic algorithm to perform discrete continuous mixed variable optimization to obtain a preliminary optimization result; reconstructing an optimization problem containing only fiber path parameters as continuous design variables, adopting a sequential quadratic programming algorithm to optimize and solve the reconstructed optimization problem to obtain a curve fiber variable stiffness composite structure optimization result; and through iterative optimization, the curve fiber variable stiffness composite structure integration optimization is realized.

The application discloses an optimization method for a video transmission network CDR. The method comprises the following steps: acquiring an environment characteristic and a control parameter vector, wherein the control parameter vector is constructed based on a fuzzy controller of the video transmission network CDR; generating an initial value of a spore population based on the control parameter vector; iteratively optimizing the spore population by using an optimization method with an adaptive exploration and utilization strategy, so that the spore population is adapted to optimal video transmission quality under the environment characteristic; each spore of the spore population corresponds to a value of the control parameter vector; and after the iterative optimization is completed, the spore with the highest fitness in the spore population is taken as a value of a target control parameter vector and is issued to the fuzzy controller. The application can realize adaptive optimization of clock data recovery in a complex and dynamically changing video transmission environment, and improve the robustness of clock data recovery.

The application relates to the technical field of wireless networks, and provides a base station parameter optimization method and device, equipment, a medium and a program product. Wireless key data indicators KPI of a base station to be optimized are acquired. The distribution of log data of a preset training base station is adjusted according to the KPI, and corrected log data is obtained. The corrected log data is input into a pre-trained test model, the base station performance to be predicted of the base station to be optimized is predicted, and the base station operation parameters of the base station to be optimized are optimized based on the prediction result, wherein the test model is trained based on the log data of the preset training base station. In the parameter optimization, the wireless key data indicators KPI that are easy to obtain are selected, the actual parameters of the base station need to be modified, different parameters do not need to be traversed for each base station, the base station parameter optimization is realized in a model estimation manner, and the base station parameter optimization efficiency is improved.

A two-stage, multi-domain protein assembly method assisted by SAXS data is disclosed. Within an evolutionary algorithm framework, the method involves: first, population initialization, followed by two-stage conformational mutation and crossover; second, calculating model similarity differences using SAXS experimental data to assist the DEMO energy function in selecting solutions, while maintaining conformational diversity during the selection process through a Monte Carlo probability reception criterion; and finally, optimizing the generated model through simulation to potentially include multiple states. This invention provides a two-stage, multi-domain protein assembly method assisted by SAXS data with high sampling efficiency and high predictive assembly accuracy.

The application discloses a small-distortion large-view-field high-definition lens based on Q- TYPE aspheric surface, which comprises first lens elements, second lens elements,..., tenth lens elements and an aperture diaphragm arranged in sequence from left to right, the aperture diaphragm is arranged between the fifth lens element and the sixth lens element, two aspheric surfaces are adopted in total, and the front surface of the first lens element and the front surface of the tenth lens element are both Q- type aspheric surfaces. The application applies the novel Q- type aspheric surface to the design of the wide-angle lens, reduces the weight and processing cost of the lens, realizes small distortion, a large aperture and high pixels, strictly limits the structural parameters of the lens elements, finally realizes the size requirements of a long back intercept, a short total length of the system and a small first surface lens, so as to meet the assembly requirements, improves the processing and detection precision to a certain extent due to the excellent characteristics of the Q- type, and improves the optimization efficiency.

The invention provides a material growth simulation method, device and equipment and a storage medium, and relates to the technical field of simulation, and the method comprises the steps: calling material growth simulation software to execute simulation based on initial simulation parameters; calling material growth simulation software to execute simulation based on the initial simulation parameters; the initial simulation parameter is obtained by screening from a first simulation parameter interval input by a user according to a first model obtained by supervised learning, or is obtained by optimizing the first simulation parameter input by the user according to a second model obtained by reinforcement learning; when it is determined that simulation fails, the initial simulation parameters are optimized according to a second model, and target simulation parameters are obtained; and taking the target simulation parameter as an initial simulation parameter, and continuing to call the material growth simulation software to execute simulation based on the initial simulation parameter until it is determined that simulation succeeds. According to the method, automatic closed loop of simulation, verification and optimization is realized, so that the optimization efficiency of simulation parameters is improved.

The application relates to a laser radar SLAM and vegetation feature library matching method and system of a mowing robot, and relates to the technical field of data processing. The method comprises the following steps: determining initial laser radar parameters based on laser radar test data of a plurality of sample vegetation; establishing a vegetation feature library based on the initial laser radar parameters and the laser radar test data of the plurality of sample vegetation; determining environmental influence factors based on the initial laser radar parameters; acquiring real-time environmental information of the mowing robot; adjusting the initial laser radar parameters based on the environmental influence factors and the real-time environmental information to generate current laser radar parameters; acquiring point clouds of vegetation to be matched based on the current laser radar parameters; determining a vegetation type based on the point clouds of the vegetation to be matched and the vegetation feature library; and updating a two-dimensional grid map or a three-dimensional point cloud map of an environment where the mowing robot is located based on the point clouds of the vegetation to be matched and the vegetation type, so that the intelligent level of the mowing robot is improved.

This invention discloses a low-carbon design method for nearshore stainless steel-clad reinforced steel bridges. The method includes: obtaining seismic time-history acceleration curves; identifying the pier section design variables for nearshore long-span beam bridges; obtaining the residual steel reinforcement cross-sectional area after several years considering pitting corrosion; obtaining the expected value of seismic loss cost over the entire life cycle of the bridge; obtaining the direct construction cost and carbon emissions of the bridge under different pier section design variables; constructing a dataset by using the pier section design variables as feature data and the direct construction cost, carbon emissions, and expected seismic loss cost as target data; obtaining a surrogate model based on the dataset; obtaining the Pareto optimal solution set based on the surrogate model and the NSGA-II multi-objective genetic algorithm; and obtaining the optimal design scheme based on the entropy weight method combined with the TOPSIS method. This invention solves the technical problem that existing nearshore bridge design methods struggle to simultaneously consider durability, seismic safety, economy, and environmental impact.

The invention relates to the field of artificial intelligence, can be applied to business system platforms of finance, medical health and the like, and discloses a generative iterative optimization method, device and equipment for health consultation and a medium, and the method comprises the steps: receiving a health consultation question submitted by a user; according to the user portrait, driving a dialogue generation model of the current version to generate a plurality of candidate answers according to a corresponding generation strategy; performing multi-dimensional evaluation and confidence coefficient calibration on the plurality of candidate answers, screening out answers to be corrected, and obtaining corresponding corrected answer samples; constructing a structured weighted preference data set according to the evaluation and confidence data of the corrected answer sample; and performing confidence-weighted dynamic fine tuning optimization on the dialogue generation model of the current version through the structured weighted preference data set to obtain a dialogue generation model of a new version. By performing multi-dimensional evaluation on the plurality of candidate answers and screening and constructing the preference data with the quality weight for dynamic fine adjustment, the optimization cost is reduced and the optimization efficiency is improved.

The invention discloses a load optimization method and device for a supercritical traditional generator set in closed-loop operation. The method comprises the following steps: acquiring load operation data of the supercritical traditional generator set in a closed-loop operation state; analyzing a response relationship between each operation parameter and a load instruction in the load operation data, establishing a closed-loop operation model of the supercritical traditional generator set, and constructing an optimization constraint condition and a load optimization objective function in combination with a plurality of optimization dimensions; and solving the load optimization objective function by adopting a multi-objective evolutionary optimization algorithm in combination with the optimization constraint condition to obtain an optimal load instruction solution set and determine an optimal load instruction. By constructing a closed-loop operation model of the supercritical traditional generator set and combining an optimization constraint condition and a load optimization objective function, an issued load instruction is softened and corrected, so that the load instruction is more in accordance with an actually reachable dynamic behavior of the supercritical traditional generator set; therefore, stable and efficient operation of a supercritical traditional generator set in a high-proportion new energy system is supported.

This invention discloses an intelligent optimization method for a multi-type well-fracture joint control fine-grained injection-production mode, relating to the field of oil and gas field development technology. This invention sets up a well-fracture joint control fine-grained injection-production mode, establishes a reservoir numerical simulation model in reservoir numerical simulation software, obtains multiple reservoir injection-production schemes based on the Latin hypercube sampling method, simulates each reservoir injection-production scheme using the reservoir numerical simulation model, generates multiple sample data to establish a sample database, and establishes a deep learning agent model. After training the deep learning agent model using the sample database, a particle swarm optimization algorithm is used for single-objective pre-search global optimization to obtain the optimal baseline strategy. A reinforcement learning dynamic decision model is established and trained based on the PPO proximal policy optimization algorithm to obtain a reinforcement learning agent. The reinforcement learning agent is used to obtain the optimal injection-production development scheme for the reservoir, realizing rapid optimization and decision support for reservoir injection-production schemes under a new multi-type well-fracture joint control mode.

The application discloses a large language model parameter efficient fine-tuning method and system executed on an electronic device comprising a processor, a memory and a GPU computing unit, and belongs to the technical field of artificial intelligence. In the large language model fine-tuning training process, the pre-training model weight is read from the memory, the value matrix or the upper projection matrix is selected to calculate the inter-layer weight similarity, and the main diagonal line of the similarity matrix is blocked and analyzed to identify the continuous layer cluster group; under the total new adapter parameter quantity budget and the rank value consistency constraint in the layer cluster group, the low rank adaptive rank value of each layer is solved through fast proxy evaluation and a greedy strategy; and according to the solving result, the LoRA or QLoRA adapter is configured for each Transformer layer on the GPU computing unit and fine-tuning is performed. The application converts the structural information revealed by the inter-layer weight similarity into the adapter rank value configuration in the fine-tuning training computation graph, which helps to reduce the trainable parameter quantity, the training memory occupation and the rank value search calculation overhead.

Embodiments of the present application provide a data processing method, device and related equipment, wherein the method comprises: in the compilation phase, when a string truncation operation is detected, a static call node corresponding to the string truncation operation is generated, and the static call node is stored in a first to-be-processed list; after the compilation is completed, it is judged whether the static call node in the first to-be-processed list is a to-be-optimized node; if the static call node is a to-be-optimized node, the static call node is stored in a second to-be-processed list, and an optimization operation is performed on the string truncation operation corresponding to the static call node in the second to-be-processed list. Embodiments of the present application improve the optimization efficiency of the string.

This invention discloses a SOH estimation method based on Bayesian optimization. The method includes: acquiring a battery dataset, constructing a battery health feature matrix, initializing Bayesian optimization, multiple rounds of Bayesian optimization iteration, determining convergence conditions, and training the optimal configuration model on a full dataset. This invention belongs to the field of data processing technology, specifically referring to an SOH estimation method based on Bayesian optimization. This scheme uses a battery health feature matrix to construct a CNN+LSTM model, defines a precise objective function, and employs multiple rounds of Bayesian optimization iteration to achieve intelligent parameter search and reduce computational costs. It uses a CNN+LSTM model for dual extraction of local and long-term temporal features, covering all dimensions of battery aging features. A multi-LSTM sub-model fusion structure is used to calculate the final SOH estimation result. Bayesian optimization is used to improve the model's generalization ability, and the parameter configuration most likely to improve performance is intelligently selected based on the expected improvement value, thereby improving optimization efficiency.

The invention discloses a physical system optimization method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining a design parameter space of a to-be-optimized physical system and a plurality of mutually conflicting performance indexes; mapping the design parameter space into a Riemannian manifold; the inherent symmetry of the physical system in the ideal state is identified, and the trade-off relation among the multiple performance indexes is represented as a symmetry breaking mode according to the inherent symmetry; executing geometric optimization on a Riemannian manifold on the basis of a symmetry breaking mode so as to search a design parameter set meeting target performance requirements while keeping physical constraints; and updating the physical system according to the design parameter set. The optimization efficiency of the physical system can be improved.

The invention provides an agricultural photovoltaic ecosystem multi-objective optimization method and system based on energy water circulation, and belongs to the technical field of agricultural ecology. The method comprises the steps of firstly generating power generation ratios, corresponding weight schemes and water resource ratio schemes of photovoltaic power generation units in a plurality of agricultural photovoltaic ecosystems, then constructing an energy-water circulation objective function, solving a feasible solution of the energy-water circulation objective function by using an improved NSGA2 algorithm, and finally performing resource optimization adjustment on the agricultural photovoltaic ecosystems based on the feasible solution. According to the agricultural photovoltaic ecological system multi-objective optimization method based on energy-water circulation, the blank of energy-water circulation process and mechanism research of an agricultural photovoltaic power station ecological system in the prior art is filled, light-water-energy collaborative configuration and resource allocation are optimized, and water resources are reasonably utilized while photovoltaic power generation efficiency is improved.

A deep learning-based method for optimizing magnetic resonance fingerprint sequence parameters, belonging to the field of magnetic resonance fingerprint imaging, is proposed to address the problems of unclear correlation between pulse sequences and quantitative imaging accuracy, and the complexity of pulse sequence parameter calculation. The key technical points of this invention include: constructing a pulse sequence parameter optimization model that correlates the pulse sequence with quantitative imaging accuracy; constructing a pulse sequence parameter generation network based on the physical principles of magnetic resonance pulse excitation; using a Gaussian random sequence with the same scale as the pulse sequence as the network input; setting iterative convergence conditions according to the proposed pulse sequence parameter optimization model; inputting the constructed input sequence into the network, which then generates optimized pulse sequence parameters; evaluating the pulse sequence parameters generated by the network according to the constructed pulse sequence parameter optimization model; and determining whether the set iterative convergence conditions have been met based on the evaluation results of the optimized sequence parameters. If so, the iteration stops and the generated pulse sequence parameters are output. This invention can be used for optimizing the design of magnetic resonance fingerprint pulse sequence parameters, significantly improving the parameter estimation accuracy of magnetic resonance fingerprint imaging without increasing other overhead.