257 results about "Genetic algorithm optimization" patented technology

The invention provides a double-pulse high-frequency switching power supply control method and system for precise electroplating, and relates to the field of electroplating, and the method comprises the steps: collecting area surface data through a three-dimensional scanner, and obtaining a three-dimensional model; extracting a high-curvature area grid according to the three-dimensional model, and calculating to obtain a surface concave-convex degree index; performing current field simulation, and calculating current density vector distribution; when the deviation of the current density vector distribution exceeds a preset deviation threshold value, pulse parameters are optimized through a genetic algorithm; selecting a pulse parameter which is most matched with the surface concave-convex degree index, and carrying out iterative calculation to obtain updated current density vector distribution; based on the difference between the updated current density vector distribution and the initial distribution, predicting a coating thickness value, and determining a preliminary evaluation index; and optimizing pulse parameters to obtain a final control scheme of uniform current distribution. According to the method, pulse parameters can be dynamically optimized according to the geometrical shape of the workpiece, and uniform current distribution and stable plating quality are realized.

The invention discloses a big language model generation content security test system and method in a black box scene. The system comprises a jailbreak prompt word library module used for storing jailbreak prompt words for performing security test on a big language model; the violation question and answer pair module is used for storing violation question and answer pairs covering different types; the response acquisition module is used for obtaining a data request packet according to query content formed by the jailbreak prompt word and the query request; the security analysis module is used for calculating the similarity between response data corresponding to the query request and an expected violation answer, taking the similarity as a security score, and inputting the security score into the adaptive optimization module; and the adaptive optimization module is used for optimizing the jailbreak prompt words output by the jailbreak prompt word bank module by using a genetic algorithm according to the security score output by the security analysis module. According to the method and the device, the security of the large language model generation content can be effectively tested.

The invention relates to the technical field of urban rail transit station-city collaborative planning, in particular to an ontology-based station-city collaborative data integration and planning prediction method, which comprises the following steps of: obtaining rail transit station passenger flow data, resident travel behavior data and station periphery built environment index data; forming a space-time sample sequence according to the unified space-time granularity of the site walking service area; constructing an urban rail transit station-city cooperation ontology, and carrying out semantic annotation and semantic fusion on the space-time sample sequence to generate a feature sequence; inputting the feature sequence into a multi-task space-time diagram convolutional neural network prediction model to output a passenger flow prediction result and establish an environment index prediction result; and calculating a feature contribution degree based on a Shapley additive interpretation value, optimizing a background sample set by using a genetic algorithm to determine a key action element set, outputting a planning index threshold and an intervention measure parameter, and realizing an interpretable station-city collaborative prediction and planning decision closed loop.

The invention discloses a sewage treatment supervision method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: extracting a medium-time feature vector in multi-scale feature vectors, inputting a Transform model, predicting a chemical parameter, extracting a long-time feature vector, predicting a water quality parameter for a linear predictable segment through employing LSTM, and predicting a non-linear segment through employing TVF. Obtaining a comprehensive predicted value and a confidence interval, carrying out anomaly classification, defining an optimization function target, and optimizing process parameters by using a genetic algorithm; the prediction precision of nonlinear water quality parameters is improved by introducing subinterval division of spectral intensity data and multi-scale feature vector construction in combination with LSSVM, Transform and LSTM models, real-time dynamic adjustment of process parameters is realized through a BP neural network optimized by chaotic mapping and a genetic algorithm, and the intelligent level of a sewage treatment system is improved.

The invention discloses a production control method and system based on multi-source data fusion and an intelligent algorithm, and relates to the technical field of automatic control. Multi-dimensional production state information is collected through a sensor, multi-source heterogeneous data is integrated into a data set in a unified format through a data fusion technology, and the multi-dimensional production state information is obtained; a long short-term memory network is used for trend analysis and prediction, a multi-objective optimization algorithm is used for calculating an optimal control parameter, multi-loop cooperative adjustment is realized, an adjustment instruction is issued through a distributed control protocol, a priority ranking algorithm is introduced to ensure system stability, and a game theory negotiation algorithm is used for realizing dynamic resource allocation among production units. The production scheduling plan is optimized through the genetic algorithm, continuous monitoring and optimization can be achieved, control parameters can be adjusted in a self-adaptive mode, the production efficiency and the resource utilization rate are effectively improved, and intelligent automatic control under the complex production environment is achieved.

The invention discloses an equipment protection setting value optimization method and device, terminal equipment and a storage medium, and relates to the technical field of power systems, and the method comprises the steps: constructing a simulation model of target equipment; generating a plurality of operation scenes, and performing simulation analysis on the protection constant value of the target equipment according to the simulation model in each operation scene to obtain a protection constant value simulation value of the target equipment; a genetic algorithm is adopted to optimize reference parameters of the protection setting value of the target equipment, and optimized reference parameters are obtained; and based on the optimized reference parameter, carrying out optimization processing on the protection constant value simulation value to obtain a final protection constant value. Through multi-operation scene simulation and heritage algorithm optimization, a complex and changeable operation environment of the power grid is considered, and the optimized protection setting value can cover different working conditions of the power grid, so that the method can adapt to dynamic changes of the power grid, and the accuracy and reliability of equipment protection setting value optimization are effectively improved.

The invention provides an ultrasonic probe sterile sleeve intelligent matching and image optimization method, which comprises the following steps: acquiring probe surface geometric data and examination part anatomical feature data through a three-dimensional scanning technology, generating a probe shape model and a part adaptation model, and obtaining probe shape adaptation parameters and examination part adaptation parameters; according to the shape adaptation parameters of the probe, adopting a finite element analysis method to simulate the fitting state of the sterile sleeve on the surface of the probe, calculating the fitting degree score of the sterile sleeve, and obtaining a fitting degree quantification result; a support vector machine algorithm is adopted, classification training is carried out on the multi-dimensional matching feature vectors, an intelligent recommendation model is generated, and sterile sleeve recommendation lists for different probes and examination parts are obtained; and if the fitness score of the matching feature vector is lower than a preset threshold value, optimizing geometric parameters of the sterile sleeve through a genetic algorithm, generating improved design parameters of the sterile sleeve, and obtaining an optimized matching precision result.

The invention belongs to the technical field of industrial data processing, and particularly relates to a production enterprise energy consumption data prediction method based on machine learning. The method comprises the following steps: deploying an Internet of Things sensor to collect power and equipment operation data, and after three-stage preprocessing of anomaly detection, deletion repair and normalization, constructing a layered feature extraction network which comprises a time sequence feature layer, an equipment association feature layer and a modal fusion layer; the two features are respectively used for capturing power consumption features and equipment collaborative consumption features and fusing the features to generate depth feature vectors; and constructing a dynamic integrated prediction model, training three types of base models including support vector machine regression and the like, screening the model entering an integrated pool by using JS divergence, and optimizing the weight through a genetic algorithm to obtain a final prediction result. The method can accurately capture the characteristics of energy consumption data, effectively improves the prediction precision, assists an enterprise in reasonably planning energy use, and reduces the cost.

The invention discloses a multi-source heterogeneous meteorological data adaptive fusion photovoltaic power prediction method, and belongs to the technical field of photovoltaic power generation prediction and meteorological data processing. The method comprises the following steps: acquiring regional meteorological data, site micro-meteorological data and wind cloud satellite secondary cloud picture data, and carrying out preprocessing and feature screening on the regional meteorological data, the site micro-meteorological data and the wind cloud satellite secondary cloud picture data; defining a coupling coefficient vector; calculating fusion temperature, wind speed and irradiance based on the coupling coefficient, and constructing an input feature vector; performing preliminary power prediction by using a time sequence prediction photovoltaic short-term power model and the input feature vector; optimizing a coupling coefficient by adopting a genetic algorithm; and performing final power prediction based on the global optimal coupling coefficient and the time sequence prediction photovoltaic short-term power model. According to the invention, through an adaptive weight optimization mechanism, the problem of insufficient adaptability of fixed weight fusion under different meteorological conditions is effectively overcome, and the engineering landing performance is high.

The invention relates to the field of intelligent scheduling allocation, in particular to an edge cloud computing resource allocation optimization method based on deep learning, which adopts a space-time prediction algorithm based on multi-head attention and gating mechanism optimization to design time coding and space coding. The spatial relationship and interaction between time sequence characteristics of the computing power load and edge server nodes are captured, and meanwhile, a multi-head attention mechanism and expansion causal convolution are combined, so that instantaneous computing power load fluctuation can be captured, and the long-term trend of the computing power load can be mined; therefore, a reliable basis is provided for subsequent computing power scheduling by predicting an accurate computing power load. The invention designs an alternating direction multiplier method based on genetic algorithm optimization, which is not only suitable for a nonlinear and multi-constraint optimization problem, but also can be expanded to a larger-scale distributed edge node cloud computing system, and meanwhile, a global optimal solution is quickly approached through the genetic algorithm, so that the quality of an initial solution is improved, and model convergence is accelerated; and the distributed collaborative allocation scheduling efficiency is improved.

The invention discloses a damage identification method and device for an in-service steel wire rope type horizontal lifeline, and belongs to the technical field of high-altitude operation safety facilities. The method comprises the following steps: synchronously acquiring a magnetic flux leakage signal and a surface image of an in-service steel wire rope through magnetic flux leakage detection equipment and a high-definition camera which are carried on a steel wire rope inspection robot; preprocessing the acquired magnetic flux leakage signal, wherein the preprocessing comprises singular value elimination and trend term removal processing; carrying out de-noising processing on the pre-processed signal by adopting an improved wavelet threshold de-noising algorithm fused with a Sigmoid function; extracting a characteristic value for representing the damage of the steel wire rope, and performing normalization processing to form a characteristic vector; and inputting into a BP neural network identification model optimized by a genetic algorithm for identification, and outputting an assessment result of the damage type and positioning of the steel wire rope. The method can realize automatic and quantitative detection and accurate identification of internal and external damages of the steel wire rope.

The invention relates to the technical field of semiconductor etching, and provides a low-power etching method for reducing plasma damage, which is characterized by comprising the following steps: providing a to-be-etched substrate in a cavity of a plasma processing device, the surface of the to-be-etched substrate at least comprising a to-be-etched layer and a mask layer; process gas is introduced into the chamber; applying a pulsed source power to generate a plasma within the chamber, the pulsed source power comprising alternating on-periods and off-periods; applying bias power to the substrate in the starting time period; establishing an etching model and obtaining an optimal etching parameter combination by adopting a genetic algorithm; and etching the layer to be etched by using the plasma. According to the method, the etching parameters are optimized by adopting the pulsed source power and the genetic algorithm, so that the effects of reducing plasma damage and improving the etching uniformity are achieved.

The invention discloses a spray cooling intelligent management and control system optimization algorithm for a 330MW cogeneration unit, and relates to the technical field of thermal equipment cooling, and the algorithm comprises the steps: obtaining unit operation parameters, environment meteorological data, a spray system state and resource price information, and forming multi-source synchronous input data; on the basis of multi-source synchronous input data, feasibility judgment is carried out according to preset temperature, load and water source conditions, and a spraying enable signal is output; based on historical unit operation data, optimizing a time sequence prediction model constructed by hyper-parameters through a genetic algorithm, in response to the spray enable signal, performing rolling prediction on the saturation temperature of the condenser by using the time sequence prediction model in combination with a candidate spray strategy to obtain a temperature change sequence; according to the temperature change sequence, power generation benefit increment is calculated in combination with thermal characteristics of the steam turbine, spraying energy consumption and water consumption cost are deducted, and an optimization function is constructed; on the basis of the optimization function, supercooling prevention, condensation prevention, amplitude limiting execution and water source total amount limitation are embedded as hard constraint conditions.

The application belongs to the technical field of deep learning and meteorological prediction, and particularly relates to a daily precipitation grade classification method based on GA-XGBoost. The method realizes autonomous learning of time sequence characteristics of precipitation, reduces the non-stability of precipitation data, and accurately classifies and predicts daily precipitation. The method comprises the following steps: preprocessing original precipitation data, including data screening, data cleaning, data classification and smote method balanced dataset; establishing an XGBoost model and initializing hyperparameters; using a genetic algorithm to optimize network parameters; inputting each subsequence into the model for training and prediction and comparing and analyzing the results.

This invention relates to a design method and system for multi-principal element alloys with enhanced L12 phase dissolution temperature based on multi-scale features. The method obtains the optimal alloy composition by constructing a neural network model. This model uses the alloy composition-atomic scale descriptor L12 phase performance characteristics as input features, and the L12 phase interface fracture energy, L12 phase local thermal vibration entropy, and L12 phase interface phonon density of states overlap integral as output targets. The initial weights and thresholds of the neural network model are optimized using a genetic algorithm. The optimized weights and thresholds are then used to train the neural network model, resulting in a regression model for predicting L12 phase performance. This invention comprehensively demonstrates the enhanced thermal stability of the L12 phase from three dimensions—interface fracture resistance, atomic thermal vibration stability, and interface phonon matching—by outputting three unique physical parameters. This provides a new approach for the design of high-performance heat-resistant alloys and can be applied to the material development of hot-end components such as aero-engine turbine blades and gas turbine combustors.

The application provides a sedimentation tank flocculation sedimentation control method and device, electronic equipment and storage medium, including: obtaining raw water quality target parameters; based on the raw water quality target parameters, feature extraction and noise reduction processing are performed through an adaptive mode decomposition algorithm to obtain a feature vector; the feature vector is processed by using an extreme learning machine model optimized by an improved genetic algorithm to obtain a coagulant dosage prediction value; based on the coagulant dosage prediction value, coagulant is added to the raw water, and the operating parameters of the sedimentation tank are monitored; based on the operating parameters, the dry sludge amount in the sedimentation tank is determined through a dry sludge amount model; if it is determined that the sludge discharge condition is met based on the dry sludge amount and a pre-set sludge level threshold value, sludge is discharged; the effluent turbidity of the sedimentation tank is monitored in real time during the sludge discharge process, and the coagulant dosage is dynamically adjusted based on the effluent turbidity through the adaptive mode decomposition algorithm. In this way, the coagulant dosage accuracy and process synergy are improved, and the sedimentation tank is discharged on demand.

The application relates to the technical field of quantifiable test indexes of new energy vehicles, and particularly discloses a method for constructing a typical road spectrum working condition based on a genetic algorithm and new energy platform data, data acquisition, invalid data section elimination rules including static data section elimination and abnormal continuous duration elimination; data abnormal value identification rules: speed abnormal values are defined and a correction method is set; data smoothing and denoising rules: a median filter is used to preliminarily denoise a speed sequence; characteristic parameter calculation: including kinematic characteristic parameters and new energy light truck characteristic parameters; construction of an initial section library; multi-objective genetic algorithm optimization; road spectrum synthesis and post-processing: optimal sections filtered by the algorithm are spliced, and smoothing processing is carried out to ensure the continuity of a speed curve; multi-objective genetic algorithm selection: selecting an NSGA-III algorithm as a multi-genetic objective algorithm condition; the application realizes rapid construction and iteration of the road spectrum, and is high in efficiency.

This application belongs to the field of coal product testing and analysis technology, and particularly relates to a method, device, and computer equipment for predicting coal sample test values. The method, device, and computer equipment provided in this application, based on spectral analysis, predict coal sample test values ​​by collecting near-infrared and laser-induced breakdown spectra of the original coal sample, followed by wavelet decomposition and dimensionality reduction to generate a feature fingerprint matrix. This achieves effective fusion and dimensionality reduction of the two spectral data, preserving key spectral features of the organic and inorganic elements in the coal sample, providing fundamental data for subsequent model training. Furthermore, a genetic algorithm is used to optimize the feature weight vector of the feature fingerprint matrix to reconstruct the feature space. Abnormal samples are eliminated by comparing the ash content prediction with a preset threshold, highlighting the key spectral features for test value prediction. By eliminating interfering samples, the specificity of the feature space and the reliability of the data are improved.

The invention relates to the technical field of intelligent control of high-altitude operation robots, and discloses a dual-algorithm-driven multi-mode high-altitude operation system and an intelligent control method, and the method comprises the steps: obtaining environment sensing data and robot state parameters, and generating an operation task parameter set, a genetic algorithm optimization module is used for generating a global job task scheduling scheme through chromosome coding and fitness function evaluation, dynamically adjusting a job control strategy based on a reinforcement learning algorithm according to a state space, an action space and a reward function to generate a real-time control instruction, and realizing closed-loop cooperation of the genetic algorithm and the reinforcement learning algorithm; according to the invention, the intelligent control and decision-making problem of the aerial work robot is solved, and autonomous decision-making, path planning and multi-task efficient cooperation are realized.

The application provides a plate tail intelligent grading equipment and method based on visual guidance, and the core is that through integration of multi-view visual perception, adaptive grabbing, digital twin and cloud-edge collaborative control technology, full-process intelligent management of plate tail from warehousing, grading storage to on-demand use is realized; multi-view images of the tail are collected by a camera array, three-dimensional point clouds are reconstructed through a stereo matching algorithm, and morphological characteristic parameters of the tail are extracted; with the help of digital twin technology, a virtual model of each tail is generated and synchronized, a stacking strategy is optimized based on a genetic algorithm, and space utilization is maximized; when a use request is received, the system can perform virtual cutting pre-performance in the digital twin environment, accurately match the demand, and use the stacked tail through the temporary storage-backfilling mechanism; finally, through the cloud-edge collaborative platform, visual monitoring and global scheduling optimization of the inventory and equipment state are realized, and the tail management efficiency and material utilization are significantly improved.

The invention discloses a weight function method for calculating a stress intensity factor of a circumferential weld surface crack of a pipeline. The method comprises the following steps: constructing a point load weight function for calculating the stress intensity factor of the circumferential weld surface crack of a pipeline structure; establishing a pipeline structure finite element model containing the circumferential weld surface cracks, and calculating a stress intensity factor reference solution under a reference load based on an M integral method; solving a weight coefficient of a calculation point of the front edge of the surface crack of the circumferential weld in the point load weight function, and optimizing a back propagation neural network through a genetic algorithm to establish a weight coefficient prediction model; and carrying out double integral operation on the product of the point load weight function and the stress distribution load on the circumferential weld surface crack surface to realize calculation of a stress intensity factor. The method solves the problems that an existing method is only suitable for the situation that the stress distribution load changes unidirectionally along the crack depth and is not suitable for stress distribution which changes bidirectionally along the crack depth and the crack length frequently occurring in an actual structure, the calculation cost is increased, and the calculation precision is reduced.

The invention discloses an SAR (Synthetic Aperture Radar) image geometric correction method and system for optimizing a radial basis function neural network based on a genetic algorithm. The method comprises the following steps: normalizing three-dimensional geographic coordinates of ground feature points, and inputting the normalized three-dimensional geographic coordinates to a full connection layer for feature extraction; constructing a ReRBF network, wherein the ReRBF network is formed by stacking a plurality of layers of RBF sub-modules through a residual connection structure; and the output of the previous module is used as a residual error, is spliced with the original input or the intermediate feature, and is jointly used as the input of the next module to form a progressive feature enhancement and residual error learning mechanism, so that the network can refine the coordinate mapping relationship layer by layer. And a DBSCAN algorithm is adopted to perform automatic clustering on training data, so that the generalization ability of the model in multiple scenes is improved. And optimizing the ReRBF network by using a genetic algorithm, and adaptively determining the optimal width parameter of the radial basis function network. According to the method, the geometric positioning precision and the processing efficiency of the SAR image under the complex terrain condition are improved.

The invention provides a fill roadbed impact rolling construction method, which comprises the following steps of: acquiring a real data set of field test rolling construction, performing parameter calibration on a constructed impact wheel finite element model, simulating rolling processes under different working conditions by using the calibrated impact wheel finite element model to generate a simulation data set, and calculating the rolling process according to the simulation data set. The real data set and the simulation data set are mixed, an XGBoost algorithm model optimized based on a genetic algorithm is trained, and a compactness prediction model is established; a path planning mathematical model with the shortest turning distance as the target is established for the impact road roller, a U-shaped turning mode or an omega-shaped turning mode is selected according to the relation between the preset distance between adjacent tracks and the minimum turning radius of the impact road roller, and an optimal traversal path is solved through a genetic algorithm to serve as a planning path; and controlling the impact road roller to perform rolling construction according to the planned path. The construction efficiency, the compaction quality uniformity and the intelligent management and control level of the fill roadbed are remarkably improved.

The invention discloses an anchor rod anchoring force nondestructive testing method based on strain feedback, and relates to the technical field of anchoring force detection. A distributed fiber bragg grating and a piezoelectric ceramic sensor are implanted into an anchor rod to construct a multi-mode sensing layer; a genetic algorithm is used for optimizing and generating excitation signals, and multi-physical field response is synchronously collected; fusing the time-frequency features through wavelet packet and short-time Fourier transform, and inputting the time-frequency features into an intelligent inversion model combining finite elements and support vector regression after dimension reduction; damage evolution is tracked through Kalman filtering, three-dimensional visualization is achieved through voxelization modeling, self-learning calibration is conducted through transfer learning, and high-precision anchoring force detection is achieved. According to the method, nondestructive testing of the anchoring force of the anchor rod is achieved through multi-modal sensing and an intelligent algorithm, the detection precision is improved to be within 3%, the time is shortened to 15 minutes, the debonding position can be accurately recognized, damage is dynamically tracked and early warned, three-dimensional visualization of anchoring force distribution is achieved, and an efficient scheme is provided for engineering safety.

The application discloses a full-speed-range speed sensorless construction method of a bearingless permanent magnet synchronous motor, a high-frequency signal injection method module and an AGA-EKF detection method module are connected in parallel and then jointly connected in series at the front end of a rotating speed switching algorithm module to form a composite method rotating speed detection module, the composite method rotating speed detection module is connected in series into a control system of the bearingless permanent magnet synchronous motor to inject a high-frequency voltage signal; the rotating speed of the motor is detected by using the pulse array high-frequency signal injection method under zero speed and low speed conditions, and the rotating speed information is detected by using an extended Kalman filter method optimized by using a self-adaptive genetic algorithm under medium and high speed conditions; the pulse array high-frequency signal injection method and the rotating speed and detection composite method based on the extended Kalman filter optimized by using the genetic algorithm are adopted to complete the speed detection of the motor running in the full-speed range of zero speed, low speed and high speed, and realize the smooth switching of the motor from low speed to high speed and the stable suspension running of the motor in the full-speed range.

The invention discloses a work order production scheduling method and device, and a storage medium. The method comprises the steps of obtaining a genetic algorithm, parameters and order information for optimizing a k-means clustering algorithm; initializing a k-means clustering algorithm according to the order information, and optimizing the genetic algorithm according to a preset production scheduling scene; based on the parameters and a genetic algorithm, optimizing the initialized k-means clustering algorithm to obtain a k-means clustering algorithm optimized based on the genetic algorithm; and calculating according to the k-means clustering algorithm to obtain an optimal production scheduling scheme, and performing work order production scheduling according to the optimal production scheduling scheme. According to the method, the k-means clustering algorithm is improved through the genetic algorithm, and the work order scheduling flow is optimized in combination with the improved algorithm, so that the accuracy of the optimization result is improved.

The invention relates to the technical field of intelligent driving automobile sensing system evaluation, and relates to an intelligent sensing system credibility multi-dimensional test evaluation method. The method comprises the following steps: firstly, acquiring basic test scene data, and determining extended test scene data based on interference scene factors; and performing a vehicle automatic driving test, determining perception data of the intelligent perception system, and determining an evaluation result of the intelligent perception system according to an evaluation method based on the perception data. And determining an optimization model based on expert priori experience. And solving the optimization model by adopting a genetic algorithm, and determining an evaluation result weight. And determining the credibility of the intelligent sensing system according to the evaluation result weight and the evaluation result. According to the scheme, expert prior experience and genetic algorithm optimization are combined, domain knowledge is utilized, subjective deviation is avoided through objective optimization, weight distribution of all indexes better meets actual requirements, and the accuracy of credibility evaluation is improved.

This invention relates to the field of structural design in building engineering, specifically to a method for optimizing the arrangement of pier-type energy dissipators based on genetic algorithms, including S1: establishing a non-vibration-damping structural model, calculating the frame node displacement results and the initial deformation [δ] of each span of each floor of the structure. 0 S2: Based on the genetic algorithm, a certain number of energy dissipator arrangement schemes are randomly generated, and each scheme is encoded to form a [sign] matrix; S3: Considering the additional damping ratio correction, the additional damping ratio of each energy dissipator arrangement scheme is calculated; S4: Genetic algorithm optimization is performed, different fitness functions are constructed for different optimal arrangements, individuals are selected for crossover and mutation, and genetic iteration is carried out to obtain the optimal energy dissipator arrangement scheme. This invention calculates based on the analysis results of the non-damping model, and uses a genetic algorithm to obtain the optimal energy dissipator arrangement scheme for each floor and each span of the structure based on the target additional damping ratio or the target number of energy dissipators.

The invention provides a preparation method of a Ku-segment metamaterial filter. According to the preparation method of the Ku-segment metamaterial filter, a genetic algorithm is adopted to optimally design a unit cell structure of the Ku-segment metamaterial filter, an optimally designed STL model is obtained, a metamaterial blank is prepared through integrated printing in a layer-by-layer stacking mode through a powder extrusion 3D printing technology according to the optimally designed STL model, and the Ku-segment metamaterial filter is prepared. And finally, carrying out post-processing on the metamaterial green body to obtain the Ku-segment metamaterial filter. The problem that a traditional filter has performance and manufacturing dual pain points in application of a Ku segment (12-18 GHz) is solved, and meanwhile an effective solution is provided for existing limitation of metamaterial filter preparation.

The invention relates to the technical field of hard capsule polishing, and discloses an automatic polishing system based on hard capsules and a device thereof. A data acquisition module of the system acquires surface characteristic data such as hard capsule surface roughness, stain distribution, capsule size and the like; the data analysis module receives the data and then obtains a polishing demand analysis result through random forest regression analysis; the optimization module adopts a genetic algorithm to optimize parameters and rules of the polishing controller according to the result; the control module carries out fuzzy logic calculation on the polishing amount through particle swarm optimization according to the optimized parameters and rules and converts the polishing amount into polishing control signals; after the execution module receives the signal, the polishing amount constraint condition is adjusted through a near-end gradient method, and integer programming is conducted on the polishing process in combination with a branch and bound method so as to automatically adjust the polishing amount; and the detection module detects surface characteristic data after polishing through Bayesian filtering and feeds back the processed data to the control, data analysis and optimization module so as to adjust a polishing strategy.