117 results about "Global optimization algorithm" patented technology

The invention belongs to the technical field of geophysical exploration, and particularly relates to a method and a system for quantitatively measuring intrinsic parameters of bedrock fractures based on all-angle domain amplitude and phase joint inversion. The method comprises the following steps: acquiring geological radar reflection data of a target fracture at a plurality of incident angles; synchronously extracting data sequences of amplitude change along with angles and phase change along with angles; establishing a fracture reflection theoretical model; constructing a target function jointly constrained by the amplitude residual and the phase residual; and adopting a global optimization algorithm to minimize the target function, and synchronously inverting to obtain the fracture opening, the filler dielectric constant and the occurrence parameter. According to the invention, through the combined constraint of the amplitude and the phase, the inversion multiplicity is effectively inhibited; accurate system effect correction is adopted, so that the reliability of large-angle data is ensured; according to the method, multi-parameter synchronous inversion without prior hypothesis is realized, the inversion precision is small in relative error, the method is obviously superior to a traditional method, and the method has important engineering application value.

The invention discloses a wind power plant network following / constructing type fan proportion optimization configuration method and device, a medium and a product, and belongs to the technical field of new energy power system stability control, and the method comprises the steps: collecting the operation data of a wind power plant and a power grid, and carrying out the standardization preprocessing; constructing a typical operation scene set based on the preprocessed data; establishing an equivalent model of a following network type fan and a construction network type fan and a coupling model with a power grid; constructing a frequency, voltage and transient stability index system based on the coupling model; designing constraint conditions containing stability indexes; constructing a multi-objective optimization function with stability priority; solving by adopting a global optimization algorithm to obtain an optimal fan proportion solution set; and performing simulation verification and scheme screening on the optimal solution set, and determining a final configuration proportion. On the premise of ensuring the stability of the power grid, the proportion of different types of fans in the wind power plant is scientifically configured, and the safe and stable operation capability of a high-proportion new energy power system is effectively improved.

Provided is a smart platform-based environmental control unit management system for a coal-fired power plant, relating to the technical field of flue gas treatment. The system comprises a sensing layer, an application layer, and a decision-making layer, wherein the sensing layer comprises a plurality of data acquisition devices for collecting data of each piece of operating equipment within an environmental control unit; the application layer comprises a flue gas management module, wherein the flue gas management module creates a flue gas management prediction model on the basis of the data collected by the sensing layer and historical data records; and the decision-making layer determines an optimal coordinated control strategy for the environmental control unit on the basis of the model created by the application layer and the data. According to the platform-based environmental control unit management system, parameter information is acquired by means of a sensing layer, and emission prediction is achieved by means of modeling by an application layer; the application layer uploads a prediction model and sensing layer parameters to a decision-making layer; the decision-making layer applies a global particle swarm optimization algorithm to output parameters and the created models, and adjusts a parameter range and a pollutant emission threshold as constraint conditions with the aim of minimizing costs, so as to obtain an optimal system control strategy.

The invention discloses a high-speed aircraft aerodynamic configuration agile optimization method based on an artificial intelligence model. The method comprises the following steps: A1, establishing an aerodynamic force agent model; a2, performing a global optimization algorithm; a3, establishing an intelligent autonomous decision-making model; by constructing a multi-target adaptive weight distribution model, weight dynamic collaborative optimization of proxy model confidence, a constraint penalty function and target function key elements is achieved, the mathematical modeling problem of dynamic matching of weight parameters and optimization targets in the iteration process is solved, a closed-loop optimization system with the autonomous perception-decision-execution capacity is developed, and the optimization efficiency is improved. Therefore, the convergence speed is improved in a complex engineering optimization scene, local optimum is avoided, and the robustness of a global optimal solution is ensured; and A4 pneumatic optimization iteration: based on the proxy model for predicting indexes such as the lift-drag ratio, performing global optimization of design parameters by using an artificial intelligence optimization algorithm, performing verification through CFD, and after verification, putting a CFD calculation result into a training set of the model to re-train the model and continuing optimization until optimization design is converged.

The invention discloses a deep ultraviolet light-emitting diode and an optimization method thereof, which are applied to the technical field of deep ultraviolet light-emitting equipment. The method comprises the following steps that a structure optimization device model of the deep ultraviolet light emitting diode is established, a substrate nano array is arranged on the light emitting surface of a transparent substrate of the device, and a p-GaN nano array is arranged on the surface of a p-GaN layer and / or a p-AlGaN layer of the device; parameters of the p-GaN nano array and the substrate nano array are set as to-be-optimized parameters, corresponding to-be-optimized parameter constraint ranges are set according to the to-be-optimized parameters, and a multi-dimensional parameter space is constructed according to the to-be-optimized parameters and the corresponding to-be-optimized parameter constraint ranges; and with maximization of the total light extraction efficiency of the deep ultraviolet light emitting diode model as an optimization target, performing global collaborative optimization in the multi-dimensional parameter space by adopting a global optimization algorithm to obtain the optimal parameter of each to-be-optimized parameter. And the light extraction efficiency of the deep ultraviolet light-emitting diode is effectively improved.

The invention belongs to the field of oil and gas field exploration and development, and discloses a numerical reservoir model optimization and history fitting method and related device.The method comprises the steps that firstly, geological exploration data are utilized, a spatial clustering algorithm is adopted to automatically determine the local updating range of a structural model, and structural layers and stratums are optimized through spatial interpolation; outputting an optimized construction model and uncertainty measurement; based on the model and logging lithofacies attribute data, adaptively identifying a lithofacies updating key area and variation function parameters by applying a machine learning algorithm, and performing lithofacies simulation to output an optimized lithofacies model; combining the porosity, permeability and saturation attribute data, adaptively determining an attribute optimization range and variation function parameters by adopting a probabilistic neural network, and outputting an optimized attribute model through phase control attribute modeling; a three-dimensional oil-water two-phase black oil seepage model is constructed, a global optimization algorithm is adopted to automatically adjust parameters based on a target function, simulation data is calculated until convergence, optimal parameters are output to complete historical fitting, and efficient and reliable intelligent support is provided for complex oil reservoir development.

The application discloses a kind of for gyro device coil magnet and electron gun collaborative optimization design method, belong to microwave millimeter wave technical field.The application first establishes the mapping relationship between multi-coil configuration parameter and three-dimensional magnetic field distribution;Then establish the potential distribution in the two-dimensional plane of electron gun, solve electron beam trajectory under given magnetic field distribution;Establish the mapping relationship between electron gun electrode structure parameter and electron beam performance parameter;Again establish the mapping relationship between electron gun performance parameter and electron gun electrode shape and solenoid configuration parameter;Finally, establish the fitness function of evaluating electron beam quality, introduce global optimization algorithm to realize the optimization design of electron gun performance.The application has high design freedom, high universality, can realize high-performance comprehensive design, and also has the advantages of fast calculation speed, high precision, good numerical calculation stability.

The application provides a data processing method and system for optimizing vacuum coating process parameters, comprising: based on the constraint range and parameter dimension of the process parameters, obtaining the actual test process parameters through space filling design, and corresponding quality parameters and actual state data, constructing an initial training sample set and a preliminary proxy model; based on the target quality parameter of the process parameter optimization, driving the proxy model to iteratively explore the process parameter space for global optimization through a global optimization algorithm, obtaining candidate process parameters and performing physical testing, in the case that the difference between the actual quality parameter and the target quality parameter is greater than a preset threshold, re-updating the proxy model and performing global optimization again until the iteration termination condition is met, and taking the candidate process parameters as the optimization result. Thus, the problems of low efficiency, low accuracy and poor optimization effect in the related art of vacuum coating process parameter optimization are solved.

The application provides a global optimization method of an air conditioning system and a storage medium, and relates to the technical field of intelligent control of air conditioning systems. The application first screens target variables through global sensitivity analysis and generates high-quality training data. Then, the grid search is used to optimize the hyperparameters and verify the generalization ability. Then, the multi-class global optimization algorithm is used to optimize the optimization variables and extract performance data. Based on multi-dimensional trade-off analysis, the target combination of the model and the algorithm is determined to achieve the collaborative optimization of the prediction accuracy, optimization accuracy and optimization speed. Finally, the target combination is embedded in the global optimization architecture and combined with the interference factor prediction value to output the set value, ensuring that the optimization scheme can dynamically adapt to environmental and load changes, significantly reducing the total energy consumption of the air conditioning system, while ensuring the system operation stability and indoor thermal comfort, and realizing the global optimal operation effect.

This invention provides a method for optimizing the parameters of a lithography model and related products. The parameter optimization method includes: acquiring a lithography model containing multiple process parameters to be optimized; acquiring measured lithography images obtained from actual lithography results using a metrology device; based on the lithography model, using the minimum deviation between the simulated and measured lithography images as the optimization objective, iterating through multiple rounds of solutions using a global optimization algorithm to solve for the values ​​of each of the multiple process parameters to be optimized, until the generation trend of the deviation obtained from multiple iterations meets the set trend conditions, at which point the iteration stops, resulting in the target parameter set. This method achieves early stopping of iteration based on obtaining good optimization results, avoiding unnecessary waste of computational resources and time, and improving the efficiency of lithography model establishment.

The application discloses an adaptive sound effect management method and system with a scene analysis module, relates to the field of adaptive sound effect management, and comprises the following steps: S1, collecting environmental acoustic characteristics and user behavior data in real time through a scene analysis module; S2, dynamically extracting user hearing preference parameters and scene classification labels; S3, constructing a virtual listening sweet spot matched with a physical space; and S4, dynamically adjusting the phase delay and energy distribution ratio of each frequency band to generate an optimal loudness equalization curve meeting multi-scene constraints. Through a multi-level perception-optimization closed-loop architecture, the application realizes adaptive matching of sound field characteristics in a complex dynamic scene, deeply integrates acoustic characteristic extraction and physical space modeling, breaks through the technical boundary of traditional pure signal processing, and solves the multi-objective conflict problem based on cross-modal collaborative learning and a global optimization algorithm.

This disclosure relates to a waybill allocation method, apparatus, storage medium, and electronic device. The method includes: acquiring multiple first waybills for a target pickup point, wherein the delivery time of each first waybill is a preset time period; determining a first delivery route based on a first delivery point and the target pickup point, wherein the first delivery point is the delivery point closest to the target pickup point among the delivery points corresponding to the multiple first waybills; and constructing a target delivery route within the preset time period for each courier based on the time increment after inserting a delivery point to be inserted into the delivery route, until the allocation of the multiple first waybills is completed, wherein the delivery point to be inserted is a delivery point among the delivery points corresponding to the multiple first waybills that has not been included in a planned delivery route. This method can improve the accuracy of waybill allocation and increase the efficiency of waybill delivery by increasing the granularity of waybill delivery time and using a global optimization algorithm for waybill allocation.

The invention discloses a multi-source data fusion intelligent path planning method and system based on a machine learning model, and belongs to the technical field of intelligent path planning, and the method comprises the steps: data collection and processing: collecting multi-dimensional data, and carrying out data cleaning, denoising, format unification and feature extraction and selection; model training and optimization: based on the processed data and features, constructing a machine learning model to predict the road condition of an unknown area and perform training of obstacle distribution, and learning historical path planning data by using a machine learning algorithm to optimize a path planning strategy; path planning and implementation: generating a preliminary path by combining a heuristic search algorithm and a global optimization algorithm, monitoring environment change in real time, and adjusting path planning in real time according to the environment change, including re-calculating the path and selecting an alternative path; and performing feedback and optimization. The intelligent level of path planning can be remarkably improved, and the accuracy and practicability of path planning are improved.

The invention provides an asset allocation method and device based on a global optimization algorithm and electronic equipment. The method comprises the following steps: constructing an asset allocation model which aims at maximizing the asset yield and takes the allocation proportion of various assets as a variable under each constraint condition, wherein each constraint condition comprises the limitation on the value interval of the variable; determining a penalty coefficient of each constraint condition according to a business rule, and constructing an optimization objective function of the asset allocation model according to each penalty coefficient; and performing iterative solution on the optimization objective function by adopting a global optimization algorithm until an optimal resource allocation scheme is obtained, summing current values of all variables in each iterative solution process to obtain a judgment metric, and judging whether optimization process calibration is executed or not according to the judgment metric and a threshold value corresponding to the judgment metric. The optimization process calibration is used to avoid the global optimization algorithm falling into a locally optimal solution.

This invention discloses a method, apparatus, device, and readable storage medium for determining controller parameters, applied in the field of flight controller technology. The method includes: acquiring an ideal model based on flight quality design, a nonlinear aircraft control model, and an aircraft dynamics model; determining the initial controller parameters of the nonlinear aircraft control model; and using a global optimization algorithm to determine the optimal values ​​of the controller parameters for the nonlinear aircraft control model. Furthermore, it enables real-time flight quality evaluation after optimization, ensuring that the optimal controller parameter values ​​meet Level 1 flight quality requirements. Compared to current manual adjustment of controller parameters, this invention utilizes a frequency-domain-dependent ideal model based on flight quality, and optimization objectives based on the time and complex frequency domains, allowing simultaneous optimization of controller parameters in both the time and frequency domains. Moreover, it intelligently determines the optimal controller parameter values ​​based on a global optimization algorithm, improving the comprehensiveness and intelligence of the optimization process.

A multi-agent path planning method based on an adaptive meme algorithm addresses the immaturity and slow convergence speed of current multi-agent path planning methods. This method employs a structure combining global optimization and local search, utilizing chromosomes as generated paths in a two-dimensional grid map and optimizing their fitness values ​​through strategies such as "selection" and "crossing." A hill-climbing algorithm is used as a sub-algorithm for local optimization to prevent generated solutions from getting trapped in local optima, thereby improving the accuracy of multi-agent motion control and enhancing the algorithm's convergence speed and optimization efficiency. This solves the problems of slow convergence speed and low path optimization accuracy that are difficult to address in current multi-agent path planning methods. Experimental results show that this method can accurately optimize multi-objective paths for multiple agents, and the average length of each path converges to the globally optimal path length with population iterations, achieving the expected results.

The invention discloses a motor cross-domain optimization agent model transfer learning method and system and a storage medium, and relates to the technical field of motor design, and the method comprises the following steps: S1, pre-training a source domain agent model based on a first data set; s2, performing rapid prediction in the new target domain parameter space, and screening out bridge design points in an intelligent sampling mode; s3, only performing high-fidelity simulation on the bridge design points, and constructing a target domain data set; s4, loading a source domain agent model, freezing a front-end layer, finely adjusting a rear-end layer by using a target domain data set, and migrating into a target domain agent model; and S5, replacing high-fidelity simulation with the target domain agent model, and finding out optimal design parameters of the target domain in combination with a global optimization algorithm. According to the agent model transfer learning method and system for motor cross-domain optimization and the storage medium, the number of expensive simulation times needed in a new design domain is greatly reduced, the calculation cost is remarkably reduced, and the optimization efficiency is improved.

The invention discloses a device for detecting water pressure outside a tunnel lining and a drainage capacity evaluation method. The method comprises the following steps: constructing a multi-source associated basic data set based on original data collected by a portable device; generating a data stability evaluation result by using time sequence stability evaluation and working condition correlation analysis, and screening a credible test section; a self-adaptive multi-working-condition response model is constructed, and a working-condition external water pressure field is reconstructed and designed through weighted regularization inversion; constructing an external water pressure and drainage capacity coupling model, and performing inversion to generate equivalent drainage capacity parameters by using a global optimization algorithm of proxy model acceleration and spatial smooth constraint; and finally, comprehensively outputting a structure safety evaluation suggestion. The real hydrostatic pressure is obtained through the sealed inverted filter structure of the device, the problems that short-term data interference is large, working condition extrapolation is difficult and inversion is discontinuous are solved in combination with a space coupling inversion algorithm, and high-precision reconstruction of external water pressure and whole-line diagnosis of drainage performance are achieved.

The invention relates to the technical field of circuit connection control, and particularly discloses a programmable electronic circuit connection control system and method. A connection management module; a state persistence module; an algorithm module; the file analysis module transmits analyzed standardized circuit description data to the connection management module, the connection management module feeds back some information to the file analysis module according to the operation condition, and the connection management module transmits the current connection state to the state persistence module for serialization and storage. The algorithm module provides resource scheduling, connection allocation and global optimization algorithm support for the connection management module and feeds back a calculation result to the connection management module, and the connection management module updates a connection state and a resource distribution condition according to a feedback result, so that unstable factors caused by an external jumper wire are avoided, circuit connection is more reliable, and the service life of the circuit is prolonged. And the circuit debugging and repairing cost is reduced.

This invention relates to the field of quantum computing technology, and particularly to a method, system, computer device, and storage medium for optimizing the fidelity of quantum circuits. The method includes: acquiring the hardware parameters of the quantum circuit to be executed and the quantum processing unit cluster; standardizing the quantum circuit, analyzing the type of quantum gate and its active bits, and standardizing the multi-bit gates; constructing a unified fidelity cost model based on the quantum circuit and hardware parameters, and quantizing the cost into an additive cost; spatially partitioning the quantum circuit based on the coupling relationship between the quantum bits, and temporally partitioning the quantum circuit based on the quantum gate sequence to form a set of spatiotemporal sub-blocks; pre-calculating the partitioning cost for each spatiotemporal sub-block, and determining the candidate execution mode for each spatiotemporal sub-block accordingly; using a multi-objective global optimization algorithm to globally optimize the allocation scheme to obtain a set of optimal cooperative execution schemes; and outputting the quantum circuit mapping results.

The invention discloses a parameterization optimization method of a mixed-flow water turbine blade and a water turbine, and relates to the technical field of water turbine optimizing.According to the parameterization optimization method, a three-dimensional parameterization model based on initial geometric parameters is constructed, multiple rounds of iteration orthogonal optimization are carried out with design parameters as variables, a search space is dynamically shrunk after each round of optimization, and therefore the optimization efficiency of the mixed-flow water turbine blade is improved. The optimization efficiency and the convergence stability are greatly improved; the problem that a traditional global optimization algorithm is high in calculation cost is avoided, meanwhile, the problem that single-round optimization is prone to falling into local optimum is solved, complex and mutually coupled parameters can be efficiently explored within the limited number of simulation times, and the simulation efficiency is improved. And a closed-loop optimization path which is high in systematicness and can be really applied to engineering is provided for improving the performance of the existing water turbine.

This invention discloses a sonar signal bandwidth extension method and system based on an adaptive sparse reconstruction approach. It acquires frequency domain data of active sonar echoes through matched filtering and extracts the transmission bandwidth range of the active sonar. A physical perception dictionary containing the physical ripple characteristics of the transmitted waveform is constructed to establish a precise mapping from the sparse target space to the observation frequency band. An adaptive threshold detection mechanism is introduced during the orthogonal matched pursuit iteration process to screen potential targets using the statistical characteristics of local background noise. A meshless global optimization algorithm is used to jointly fine-tune the target position in the continuous time domain. A full-band skeleton spectrum is constructed using precise parameters, and the noise residuals in the original frequency band are back-injected and inversely transformed to obtain a high-resolution time-domain waveform. This invention significantly improves the ability to distinguish closely spaced targets and the detection performance of weak targets in complex underwater acoustic environments.

The application discloses a UT transformation-based impact small celestial body orbit terminal state optimization method and belongs to the technical field of aerospace. The application realizes the method as follows: positions and velocities of the earth and small celestial bodies are obtained according to ephemeris and kinematic equations of the small celestial bodies, and the velocity variation of the small celestial body after the impact of a probe is defined as a constant, so that two angles of the velocity change direction of the small celestial body after the impact of the probe are taken as impact design variables; UT transformation is used to perform error evolution of the small celestial body, so that the space-time distribution probability of the target small celestial body in a future period of time is obtained; the space-time distribution probability of the small celestial body is calculated, the instantaneous impact probability under the given impact design is calculated based on a Legendre-Gauss quadrature method, and the overall impact probability of the small celestial body is obtained by integral based on a Newton-Cotes quadrature method; and based on the impact probability calculation method, the impact design variables are optimized in combination with a global optimization algorithm, so that the impact probability of the small celestial body under the optimized variables is the lowest.

The application discloses a method and device for improving bandwidth of a SAW filter and the SAW filter, and relates to the technical field of filters, to solve the problem that the resonant bandwidth of an existing SWA filter is limited by the coupling coefficient of piezoelectric material and cannot meet the demand of a high-bandwidth acoustic filter. The method comprises the following steps: obtaining measured data of a measured resonator in a target frequency range; based on the measured data, a measured curve fitting method is used to construct a coupling mode theory model of the resonator; a plurality of filter branches with adjacent center frequencies are constructed; a global optimization algorithm is used to reversely optimize physical parameters of a filter topology structure, so that the bandwidth of the optimized filter topology structure reaches a target bandwidth, and target physical parameters of the SAW filter are obtained. The method for improving bandwidth of a SAW filter provided by the application is used to improve the bandwidth of the SAW filter without changing the coupling coefficient of piezoelectric material.

The invention provides an active power distribution network fault positioning method and device based on wide-area positive sequence current information, and the method comprises the steps: determining a fault current direction according to the wide-area positive sequence current information detected when an active power distribution network breaks down, and generating a feeder state code and an estimated fault region through coding and fault region division; according to feeder state codes, estimating fault area and wide area positive sequence current information, and constructing an actual positive sequence overcurrent matrix and an expected positive sequence overcurrent matrix; constructing a fitness function based on the Spearman correlation coefficient; through an improved binary marine predator algorithm, a fitness function is solved based on a pre-estimated fault area, fault positioning information is obtained, different power distribution network topological structures can be adapted, and accurate fault positioning is realized. The method has strong robustness for output fluctuation and fault uncertainty of the distributed power supply, and ensures that a fault section is quickly and accurately locked under a complex working condition through an efficient global optimization algorithm.

This invention relates to the field of cementitious material optimization technology, and discloses an intelligent optimization method for cementitious materials based on a combination of data-driven and molecular dynamics simulations. The method includes: combining a dataset of phase characteristics of cementitious raw materials with molecular dynamics simulation technology to construct a correlated dataset; combining the correlated dataset with multi-dimensional performance indicators to extract key characterization parameters and map them to a target space to generate a feature embedding matrix; using the feature embedding matrix as input and employing ensemble regression technology to construct a multi-objective performance prediction model; using a global optimization algorithm with the content of gel material components as optimization variables to perform intelligent optimization processing on the multi-objective performance prediction model; and selecting the optimal reverse design scheme for the cementitious material based on the optimization processing results and performance testing technology to complete the performance optimization of the cementitious material. This invention replaces the traditional trial-and-error mode with reverse design, shortening the development cycle of cementitious materials and significantly reducing trial-and-error costs.

The application provides an approximate circuit automatic pruning method based on a global optimization algorithm, which obtains an automatic pruning result of the circuit by decomposing an original circuit structure and with the help of a global optimization algorithm. The application changes the deleted minimum unit into a basic NAND logic, improving the precision of deletion. At the same time, the decomposed NAND logic is converted into a C language model, so that the whole iteration is separated from the synthesis tool and the simulation tool, and the simulation is replaced by the C language, saving a large amount of time. Finally, the global optimization algorithm is added, and the optimal solution of each iteration is no longer searched for in the selection of the deleted node, but the whole is considered, and a suitable node set is searched for deletion, so that the effect of the deleted node is optimal. In the approximate processing of 16 8-bit additions, the highest area reduction of 18.47% and the power consumption reduction of 38.91% are obtained under the mse limit of 100.

The invention discloses a cross-cigarette-factory data collaborative optimization method and system, and relates to the technical field of tobacco, and the method comprises the steps: collecting multi-factory MES system data, and carrying out ETL cleaning standardization to form a unified quality data pool; grouping according to dimensions of brands, models and specifications; the similarity of different factory quality index sequences in the same dimension is calculated through a dynamic time warping algorithm, and a multi-dimensional benchmarking similarity matrix is obtained through integration; inputting the matrix into a feature importance evaluation model based on a gradient boosting decision tree, and extracting a key process parameter feature set influencing quality fluctuation; constructing a collaborative optimization objective function by using the key feature set, searching a Pareto optimal solution set through a multi-target global optimization algorithm, and generating an optimal process parameter adjustment scheme for balancing multiple targets; and the Pareto optimal solution set is taken as a scheme to be issued to each factory MES control end, so that a closed loop of collaborative scheme generation and implementation is realized, and the data utilization rate and accuracy are improved.

The invention aims to provide a supersonic civil aircraft self-adaptive low-sound-explosion optimization design method based on an agent model. According to the method, a dynamic mesh (FFD) parameterization technology, a radial basis function (RBF) proxy model, a differential evolution (DE) global optimization algorithm and an innovative hybrid adaptive point adding strategy (GHOTM-RBF) are systematically integrated, the aerodynamic configuration of the supersonic civil aircraft which meets multiple engineering constraints and is remarkably reduced in acoustic explosion can be efficiently obtained at an acceptable calculation cost, and the aerodynamic configuration of the supersonic civil aircraft can be used for solving the problem that the aerodynamic configuration of the supersonic civil aircraft meets the requirements of multiple engineering constraints and is remarkably reduced in acoustic explosion. And an efficient and engineering-usable low-sound-explosion optimization design idea can be provided for future supersonic civil aircraft development.

The invention provides a dynamic calibration method and system for ophthalmologic equipment, and the method comprises the steps: setting a collection sequence which comprises n different axial working distance points; determining n position points according to the n different axial working distance points; obtaining an absolute actual working distance corresponding to the position point; under the absolute actual working distance, obtaining two-dimensional image coordinates of m light reflecting points corresponding to all position points, and taking the two-dimensional image coordinates as measured values; taking the measured values, the absolute actual working distances corresponding to all the position points and the preset curvature radius of the standard ball as a correction data set; adjusting system error correction parameters according to the correction data set through a global optimization algorithm, so as to obtain the system error correction parameters when the loss values between the predicted values and the measured values of the system error physical transmission correction model on the absolute actual working distances corresponding to all the position points are minimum; decoupling can be carried out physically, and systematic errors introduced by installation and adjustment tolerance can be compensated quantitatively.