300 results about "Sample pool" patented technology

A method for detecting circularity of silicon carbide micro powder relates to the technical field of silicon carbide detection, and comprises the following steps: S1, turning on a flowing particle imager, preheating and then carrying out background detection; s2, adding standard particles into the sample pool, and enabling the flowing particle imager to perform automatic focusing; s3, adding the prepared silicon carbide micro powder sample into a sample pool, and detecting by using a flowing particle imager; and S4, storing the detection result, checking the silicon carbide micro powder particle image, and recording the detection result. The preparation of the silicon carbide micro-powder sample comprises the following steps: S31, adding the dried silicon carbide sample into a dry and clean beaker; s32, sequentially adding an impregnating compound, a dispersing agent and deionized water into the beaker; s33, carrying out ultrasonic dispersion; and S34, taking out the uniformly mixed sample. The method can solve the technical problem of detection failure caused by easy agglomeration of the silicon carbide sample when the flowing particle imager is used for detecting the circularity of the silicon carbide micro powder.

The invention relates to a device and a method of a high-sensitivity suspension single-droplet sensor for monitoring PM2.5 pollution, belongs to the technical field of aerosol microdroplet spectrum trace analysis, and solves the problem that ppb-magnitude gas cannot be monitored in the prior art. The method comprises the following steps: pumping aerosol into a sample cell of a droplet suspension system in a single droplet form; introducing required reaction gas into the sample pool through an atmosphere control system; scattered light signals of the single liquid drops are respectively input into the dynamic signal processing system and the microdroplet imaging system; acquiring the characteristic peak displacement change of the metal ion sensing microdroplet; establishing a gas particle distribution Mie scattering inversion model based on micro-droplet dynamic response; inverting the radius of the liquid drop according to the stimulated Raman scattering / stimulated elastic scattering signal; according to the quantitative relationship of the reaction, calculating the generation of sulfate by the droplet suspension system (sensor); and judging which gas in the actual atmosphere is a main oxidation pathway for oxidizing sulfur dioxide to generate sulfate pollutants according to a calculation result.

The invention relates to the technical field of soil treatment, and particularly discloses a soil micro-plastic electrostatic separation device and method.The soil micro-plastic electrostatic separation device comprises a sample pool, an electrostatic adsorption mechanism is arranged on one side of the sample pool, and the electrostatic adsorption mechanism is connected with an electrostatic discharge generator through a wire; the electrostatic adsorption mechanism comprises an adsorption plate and a connecting handle; the bottom box and the heightening frame bodies are made of wood, the height of the sample pool can be adjusted by installing the heightening frame bodies in an assembling mode, a nylon coating is arranged on the outer side of an adsorption plate and used for adsorbing micro-plastics and eradicating the risk of high-voltage electric leakage, and the height of the adsorption plate can be adjusted through an arranged supporting assembly; the particle size range of the micro-plastic covered by electrostatic adsorption is wide, compared with a traditional method, the recovery rate is remarkably improved, and the original form of the micro-plastic and the natural structure of soil are protected through normal-temperature physical separation and no reagent addition.

Disclosed in the present invention are an apparatus for measuring the refractive index of a liquid on the basis of a transmission optical path, and a measurement method therefor. The apparatus comprises a laser, a sample cell, a thermostatic bath, a photoelectric imaging device, a control unit and a PC, wherein the sample cell is light-transmissive at the front and rear, and one or both of walls of a light-transmissive face is / are of a right-trapezoidal wedge-shaped structure; the thermostatic bath can perform temperature control on a liquid sample; and laser light emitted by the laser penetrates the liquid sample in the sample cell, and finally forms an image on the photoelectric imaging device placed in a transmission direction. The method comprises: when laser light passes through a sample in a sample cell, the laser light being refracted at an interface between different media, such that the direction of an emergent optical path changes, wherein the variable of the change is related to the refractive index of the sample; the laser light forming an image on a photoelectric imaging device after penetrating the sample; and calculating the refractive index of the sample on the basis of the relationship between the position of an imaging point and the refractive index. The present invention achieves measurement of the refractive index of a liquid with an accuracy better than 0.05%.

The utility model discloses a modularized fluorescence analysis and detection device, which relates to the technical field of fluorescence analyzers and particularly comprises a first shell which is a control part, a display screen is arranged at the top of the first shell, a control mainboard is arranged in the first shell, and the modularized fluorescence analysis and detection device further comprises a second shell connected with the first shell; the second shell is a detection part, and a frame body for placing a sample cell is arranged in the second shell; a fluorescence receiving part is arranged at the top in the second shell, an irradiation part is arranged at the bottom in the second shell, the fluorescence receiving part is located above the sample cell, and the irradiation part is located below the sample cell. During use, the first electric actuator can be controlled to move by operating the keys, so that the positions of the fluorescence receiving part and the irradiation part are adjusted, and a proper distance is kept between the optical filter group at the bottom of the fluorescence receiving part and the sample cell; and the light beams emitted by the irradiation part can be driven by controlling the first electric actuator to be completely concentrated on the sample cell.

The invention discloses a time-varying reliability analysis method based on a double-layer BP neural network, and relates to the field of time-varying structure reliability analysis, and the method comprises the steps: generating a sample pool based on a joint probability density function which affects a random design variable of a to-be-analyzed time-varying structure; calculating weights of sample points, screening part of the sample points and establishing a first training data set; an inner-layer BP neural network model is constructed and trained, the minimum value of the time-varying performance function corresponding to each sample point in the first training data set about time is calculated, and a second training set is formed; an outer-layer BP neural network model is constructed, and a second training set is adopted for training; the reliability of a time-varying structure is calculated by adopting a trained outer-layer BP neural network model, the minimum value of a time-varying performance function about time is solved through an inner-layer model, and the outer-layer model is constructed based on parameters of sample points and the minimum value solved by the inner-layer model, is used for calculating the failure probability and has good applicability. The method can be used for time-varying reliability analysis of complex structures.

The invention relates to the technical field of fluorescence detection, in particular to a three-dimensional fluorescence detection correction method based on double light sources, and the correction method comprises the following steps: obtaining double light source optical parameters, sample cell geometric parameters and original fluorescence intensity data, the geometric parameters of the sample pool comprise the length, width, height, pool wall reflection coefficient and sample attenuation coefficient of the sample pool; and constructing a light intensity distribution model in a three-dimensional space of the sample cell based on the optical parameters of the double light sources and the geometric parameters of the sample cell. According to the invention, accurate characterization of the light intensity of any spatial point in the sample cell is realized, light intensity deviation caused by various factors is quantified from the source, personalized correction is carried out on each spatial point, the influence caused by optical path difference at different positions, light source fluctuation and element error is effectively offset, and the accuracy and accuracy of the detection result are improved. The consistency of detection values of standard substances with the same concentration in different areas of the sample pool is greatly improved, and the linearity of a standard curve is remarkably optimized.

The invention discloses a multi-dimensional spectrum detection device based on combination of a transmission surface and a scattering surface, and belongs to the technical field of spectrum detection.The device comprises a camera obscura, a light source part, a detection part and a sample pool, the detection part and the sample pool are arranged in the camera obscura, the light source part comprises a light source and an angle fixing base, and the light source is connected to the angle fixing base through an optical fiber; an achromatic collimator is arranged on the end surface of the angle fixing seat and is arranged below the sample pool; the detection part comprises an emergent light detector arranged right above the midpoint of the sample cell and a plurality of scattered light detectors arranged on the side surface of the sample cell, and the emergent light detector collects transmission light spots generated after the sample is irradiated and obtains transmission information; the scattered light detector collects scattered light spots generated after the sample is irradiated and obtains a scattered spectrum image. According to the invention, the limitation of a single detection mode is effectively reduced, the distribution characteristics of scattered light at different angles and background information of transmission light are utilized to form complementary calibration, and the spectral quantitative analysis precision of the substance component content is improved.

The invention provides an edge real-time video analysis-oriented resource efficient continuous learning method and system. The method comprises the steps of real-time video analysis and model retraining; an online model precision reduction predictor oriented to the RoI granularity is designed; continuously collecting the precision reduction degree of the lightweight model compared with the high-precision model; then designing a double-layer mixed sample pool, and based on the designed RoI granularity-oriented online model precision reduction predictor; randomly introducing a certain proportion of RoI historical samples to relieve the problem of disastrous forgetting; and finally, developing a deep reinforcement learning DRL-based retraining progress controller. Based on the evaluation result of each round, dynamically estimating a model convergence state to determine a retraining termination opportunity; by introducing a behavioral clone BC loss function, the controller converges the optimal strategy more quickly in an offline training stage. The technical scheme of the invention has an important positive significance for improving the SP income in the MEC environment.

The invention relates to the technical field of livestock measurement, in particular to a beef cattle body size intelligent measurement device and method, in the beef cattle body size intelligent measurement device and method, hoof joint point pixel coordinates are converted and angle differences are calculated in combination with a lexicographical sequence block reordering algorithm, identity binding codes are accurately generated, uniqueness and stability of beef cattle individual recognition are ensured, and the accuracy of beef cattle body size measurement is improved. The method effectively reduces the identification confusion risk when multiple beef cattle are measured at the same time, generates a simulation sample through local pixel block rendering and generative adversarial network, screens a sample according with a threshold according to authenticity scores, improves the sample richness, optimizes the coverage of a model training set, extracts body size fields and position coding values, and calculates correlation coefficients between fields. Low-correlation field pairs are removed, only high-correlation fields are reserved for cross combination, a modeling sample pool is constructed, the representativeness of samples and variable combination diversity are enhanced, and the fitting precision and popularization capability of a body size estimation model are improved.

The invention discloses a method for measuring the molar concentration of nano-particles. The method comprises the following steps: diluting a nano-particle suspension with a high-molecular polymer solution or dispersing nano-particle powder into the high-molecular polymer solution; transferring the suspension liquid to a spacing net on the side of a sample pool in an electrophoresis tank; nano-particles migrate from the sample pool to the buffer pool under the driving of the electric field and are uniformly distributed on the spacing net in a single-particle manner; performing single-particle imaging on the spacing net by using an optical microscope; and counting the number of the nanoparticles in the image so as to measure the molar concentration of the nanoparticles in the sample. The method can be used under the condition that the properties of the nanoparticles are unknown or no standard substance exists, the detection object has no additional condition, and the accuracy is high.

This application belongs to the field of aircraft structure simulation calculation, and specifically relates to a method for constructing a high-dimensional output aircraft structure global proxy model, including: step 1, according to the probability distribution f of the structural model input variable X X (x), extract N input samples {x (1) ,…,x (N)} T , build a sample pool for global modeling. Step 2: Select N0 input samples from the sample pool and substitute each input sample into the finite element model for calculation to obtain the corresponding output response sample. Step 3: Perform PCA dimensionality reduction decomposition on the output response sample and convert the output response sample into the mean-centered output principal component sample. Step 4: Construct the Kriging model g with the input sample output principal component sample. K (X); Step 5, use the variance learning function to learn the Kriging model g K (X) is updated to obtain the Kriging model g for calculation K (X); Step 6: Calculate the Kriging model g K (X), perform matrix reconstruction to obtain a high-dimensional output prediction model.

The invention discloses an AI-enabled real-time electrochemical detection system, which is composed of a real-time electrochemical detection module and an embedded AI data processing and analysis module, the real-time electrochemical detection module comprises an electrochemical electrode system, a sample pool and an electrochemical workstation, the surface of a working electrode is modified with a probe of a specific recognition target, and when the probe is combined with the target, the probe of the specific recognition target can be detected by the embedded AI data processing and analysis module. The working electrode interface charge transfer impedance changes in real time to generate a dynamic electric signal, and the electrochemical workstation provides electric input and collects the signal; the embedded AI data processing and analysis module is used for denoising the dynamic electric signals through an adaptive filtering algorithm based on wavelet transform, extracting time-frequency domain joint features through a 1D-CNN and LSTM mixed deep learning model, and establishing a nonlinear mapping relation between the signals and target concentration. Through AI enabling electrochemical detection, the defects of low time efficiency, large error and low information utilization rate of the traditional technology are comprehensively overcome, and an innovative solution is provided for high-precision, rapid and anti-interference target substance detection.

The invention relates to the technical field of coal-fired power plant desulfurization system control, and discloses a dynamic load response desulfurization slurry circulating pump intelligent optimization regulation and control method comprising the following steps: S1, data acquisition; s2, data preprocessing; s3, constructing a model and training the model; s4, iteratively optimizing the model; and S5, optimal regulation and control of the circulating pump. According to the intelligent optimization regulation and control method for the desulfurization slurry circulating pump based on dynamic load response, big data transmitted by operation of desulfurization system equipment is comprehensively collected, a data cleaning method is adopted, and noise and abnormal values in the data are removed, so that the accuracy of model construction and the reliability of model training and prediction are guaranteed, and the method is suitable for large-scale popularization and application. The method comprises the following steps of: establishing a model, performing reasonable circulation and test division on data, constructing a sample pool, thereby improving the generalization ability of the model, establishing a prediction model by adopting a support vector machine, enabling the model to have higher fitting precision, and enhancing the optimization operation mode of the slurry circulating pump to adapt to the complexity of boiler load and working condition change.

The invention discloses a variant reconstruction AUV shape optimization design method based on knowledge migration. The method comprises the steps that a historical database is constructed through historical design and optimization of a single aircraft; carrying out data selection so as to construct a target domain database in a target domain task of shape design optimization of the multi-body structured aircraft; generating a target domain sampling data set through sampling; establishing an intermediate domain task and a corresponding intermediate sample set for shape design optimization of the dual-body aircraft; constructing an agent model of the target domain task and the intermediate domain task; establishing a migration integration model based on a three-domain task; carrying out iterative optimization on sample points in the target domain sampling data set by utilizing the integrated migration model, and constructing a sample pool; and constructing a multi-body structured aircraft appearance hydrodynamic simulation calculation framework, performing hydrodynamic simulation based on the sample points in the sample pool, and taking the design parameters contained in the finally obtained sample point with the minimum sailing resistance as a multi-body structured aircraft appearance design scheme.

The embodiment of the invention relates to an annotation quality control method and device with active learning ability, and the method comprises the steps: constructing and training a difficulty evaluation model and a quality prediction model before executing a first document annotation task; in the labeling process of each piece, labeling personnel are dynamically distributed based on the evaluation result of the difficulty evaluation model and the personnel information base, the labeling quality is monitored in real time by using the quality prediction model in the labeling process, the labeling result is subjected to quality evaluation according to rules when labeling is finished, and the personnel information base and the labeling sample pool are updated; after the first document task is executed, whether active learning needs to be carried out or not is confirmed regularly according to the recent evaluation condition, if yes, typical records are selected from the labeled sample pool to update the verification data set, and the difficulty evaluation model and the quality prediction model are upgraded based on the updated verification data set. According to the invention, the efficiency can be improved, the cost can be reduced, real-time early warning and dynamic task allocation can be realized, and the quality control quality can be actively improved.

The invention discloses a security detection method and device for network transmission data, and belongs to the field of data security. The method comprises the following steps: performing security detection on network transmission data by using a security detection model and a clustering model; and outputting a detection result based on comparison of detection results of the security detection model and the clustering model. When the comparison result is inconsistent, calculating a comprehensive safety coefficient, and storing the network transmission data into an experience sample pool; and respectively carrying out model optimization on the security detection model and the clustering model by utilizing samples in the empirical sample pool. According to the scheme, the supervised security detection model and the unsupervised clustering model are used for parallel detection, and bimodal high-precision detection is generated; through cross-modal conjoint analysis of two modal models, doubtful network transmission data is stored in an experience sample pool, parallel optimization of a supervised security detection model and an unsupervised clustering model is realized by using a sample of the experience sample pool, online closed-loop hot update of the models is realized, dynamic perception of novel attacks is improved, and the misjudgment rate is reduced.

This invention discloses a refractive index detection sensing device, comprising a support platform. A sample cell is adjustablely mounted at the center of the top of the support platform, and the sample cell contains a 3C-SiC nanocore-shell structure and a background medium. An incident unit is adjustablely mounted on one side of the support platform relative to the sample cell, and a detection unit is adjustablely mounted on the other side of the support platform relative to the sample cell. The 3C-SiC nanocore-shell structure comprises a 3C-SiC nanosphere core and a shell medium enclosing the 3C-SiC nanosphere core. The radius of the 3C-SiC nanosphere core is a, and the radius of the shell medium is b. The 3C-SiC nanocore-shell structure is embedded in a background medium with a uniform relative permittivity. This invention exhibits a detection sensitivity under a nonlocal model that is more than ten orders of magnitude higher than that under a local model, and can accurately capture minute changes in refractive index on the order of 0.01.

The invention belongs to the field of radar automatic target recognition, and relates to an SAR target recognition method and device based on projection features and improved self-learning. The method comprises the following steps: respectively processing each sample parameter of a training sample by a classifier T respectively trained by each prototype set to obtain integrated projection features of the sample parameters; initializing an SVM classifier based on the labeled sample feature set; taking out part of samples from the unlabeled sample feature set, and constructing a temporary sample pool; classifying the samples in the temporary sample pool by using an SVM classifier; evaluating the classification confidence of the samples in the temporary sample pool by using the classification posterior probability; adding a plurality of samples with high confidence into the labeled sample feature set, and retraining the SVM classifier; repeating for multiple times until an optimal SVM classifier is obtained; and target identification is carried out based on the optimal SVM classifier. The target recognition performance under the condition of a small number of labeled samples and a large number of unlabeled samples is effectively improved.

The present invention discloses an active learning medical image segmentation method based on superpixels and diversity, comprising the following steps: splitting images in a medical image dataset into small samples of fixed size, constructing a labeled sample pool and an unlabeled sample pool; establishing a medical image segmentation model and a similarity assessment model, and setting the labeling budget required in the active learning strategy; constructing an active learning strategy based on information content and diversity, evaluating samples in the unlabeled pool, screening out the most valuable unlabeled samples, having them annotated by experts and adding them to the labeled sample pool, deducting the labeling budget, and iterating training; updating the medical image segmentation model parameters, iterating the active model until the labeling budget is zero; and segmenting samples that lack labeling. The present invention overcomes the drawback of traditional active learning query methods that only focus on the amount of sample information, adds a measure of the relationship between samples, and further improves the accuracy of medical image segmentation when using a small number of labeled samples.

The invention discloses a training method and device of a sensor equipment deployment model, a sensor equipment deployment method and device, equipment and a computer program product. The method comprises the steps of obtaining training sample data from a sample pool of the sensor equipment deployment model; according to the training sample data, training a sensor equipment deployment model by using a reinforcement learning algorithm to obtain an initial sensor equipment deployment scheme output by the model; optimizing the initial sensor equipment deployment scheme by using a genetic algorithm to obtain an optimized sensor equipment deployment scheme; and iteratively updating parameters of the model according to the initial sensor equipment deployment scheme. According to the method, the sensor equipment deployment model is trained in a mode of combining the reinforcement learning algorithm and the genetic algorithm, a closed loop of reinforcement learning local optimization-genetic algorithm global expansion-reinforcement learning re-optimization is formed, and the training effect of the sensor equipment deployment model and the sensing effect after sensor equipment deployment are improved; and the manual deployment cost is reduced.

The application discloses a kind of power material evaluation sample dynamic construction method and device, method includes: obtaining historical original evaluation file and carrying out format conversion, obtain structured initial evaluation sample pool;The entropy of each initial evaluation sample in initial evaluation sample pool is analyzed and is filtered, and evaluation sample set is obtained;Initial evaluation sample in evaluation sample set is standardly marked, and is combined with pre-constructed thought chain model to carry out thought chain analysis, and obtain standard evaluation triplets;According to preset evaluation reward model, standard evaluation triplets are evaluated and screened, and power material evaluation sample library is generated;In response to real-time original evaluation file, the update of power material evaluation sample library is completed, and the dynamic construction of power material evaluation sample library is completed.Based on the construction of power material evaluation sample library, it is updated to ensure the quality of evaluation sample in power material evaluation sample library, and provide good data basis for model training.

The invention discloses an electric power material bid evaluation sample dynamic construction method and device, and the method comprises the steps: obtaining a historical original bid evaluation file, and carrying out the format conversion, and obtaining a structured initial bid evaluation sample pool; the entropy value of each initial bid evaluation sample in the initial bid evaluation sample pool is analyzed and screened, and a bid evaluation sample set is obtained; performing standard labeling on initial bid evaluation samples in the bid evaluation sample set, and performing thinking chain analysis in combination with a pre-constructed thinking chain model to obtain a standard bid evaluation triple; according to a preset evaluation reward model, performing evaluation screening on the standard bid evaluation triad, and generating an electric power material bid evaluation sample library; and in response to the real-time original bid evaluation file, updating the electric power material bid evaluation sample library, and completing dynamic construction of the electric power material bid evaluation sample library. And updating is performed on the basis of constructing the electric power material bid evaluation sample library, so that the quality of bid evaluation samples in the electric power material bid evaluation sample library is ensured, and a good data basis is provided for model training.

The application discloses a system and method for detecting SF6 gas decomposition products based on a spectrum method, and the system comprises an SF6 gas and decomposition product collecting device, a gas sample pool, a high-purity argon gas bottle, a laser device, an argon gas collecting pool, a spectrum analysis and detection device and a gas recharging device; the SF6 gas and decomposition product collecting device, the high-purity argon gas bottle, the laser device, the argon gas collecting pool, the spectrum analysis and detection device and the gas recharging device are all connected with the gas sample pool; the gas sample pool is embedded with a one-side wideband total reflection mirror and a quartz window sheet, the wideband total reflection mirror is symmetrically provided with a first arc-shaped total reflection mirror and a second arc-shaped total reflection mirror which are perpendicular to the wideband total reflection mirror, and the quartz window sheet is opposite to the wideband total reflection mirror; and the spectrum analysis and detection device collects scattered light through the quartz window sheet. The application has high detection sensitivity, fast detection speed and can realize real-time online analysis and detection.

The application relates to a device and a method for detecting saturated vapor pressure of aerosol single droplets, and belongs to the technical field of saturated vapor pressure detection. The device solves the problem of large detection result error of the existing aerosol single droplet saturated vapor pressure measuring device and method. The single droplet optical tweezers suspension device of the device comprises a sample cell, the sample cell comprises a concentric outer ring wall and an inner ring wall, the outer ring wall is connected with one side of the inner ring wall, a single droplet inlet penetrating the outer ring wall and the inner ring wall is arranged at the connecting position; a plurality of gas inlets and gas outlets penetrating the ring wall and being spaced apart are arranged on the outer ring wall and the inner ring wall, the gas inlets and the gas outlets on the outer ring wall are staggered with the gas inlets and the gas outlets on the inner ring wall; the diameter of the gas inlets and the diameter of the gas outlets are both smaller than the diameter of the single droplet inlet. The device can reduce the error caused by the wall effect on the saturated vapor pressure.

The invention provides an audit code generation model training method and device and related equipment, the audit code generation model training method comprises the following steps: obtaining audit sample data in a sample pool, the audit sample data being used for representing data of an audit field; performing word segmentation processing on the audit sample data to obtain a plurality of word metadata; according to the multiple pieces of word metadata, an audit code initial generation model is trained, an audit code generation model is obtained, and the initial generation model is a preset code generation universal model. Therefore, secondary training is carried out on the code generation general model through the multiple lexical metadata, so that the understanding degree of the obtained audit code generation model for professional knowledge in the audit field can be improved, and the accuracy of codes generated by the audit code generation model is improved.

This application discloses a method, apparatus, and device for optimizing a large model targeting a specific vertical domain, belonging to the field of artificial intelligence. The method includes: acquiring a sample pool, which includes general domain data and vertical domain data; constructing current-round training data based on the sample pool, and training an initial large model based on the current-round training data to obtain a target large model; constructing test questions for the vertical domain data based on the technical standards of the target vertical domain; generating response results for the test questions using the target large model; determining domain knowledge blind spots from the vertical domain data based on the understanding of the technical standards based on the response results; updating the current-round training data based on the domain knowledge blind spots to obtain the next-round training data, and training the target large model again based on the updated next-round training data.

The invention provides a large model entity alignment prompting method and system. The method comprises the following steps of: obtaining semantic embedding representation by utilizing joint embedding and fine tuning of a knowledge graph and a text corpus; retrieving a text block set related to the semantics of the query entity in a text corpus through a k-nearest neighbor algorithm retrieval technology to obtain a retrieval result; searching k neighbor entity pairs of the query entity pair from an expert labeled sample pool based on the key information, and taking the k neighbor entity pairs as positive samples; searching k neighbor entity pairs of the query entity pair from a public data set based on key information as supplementary examples; constructing the positive samples and the supplementary samples into a demonstration sample set; converting the demonstration sample set into a natural language text to obtain a sample text representation; and inputting the retrieval result and the sample text representation into the large model as an entity alignment prompt. According to the large model entity alignment prompting method and system, the reasoning ability of the large model in the entity alignment task is remarkably improved.

This disclosure relates to the field of reliability technology, specifically to a method for global sensitivity analysis of a radome structure. The method includes: obtaining important sampled samples of the input variables of the radome structure and a sample pool of the distribution parameters of the input variables; obtaining a training set for a Kriging failure model of the radome structure based on the important sampled samples, and training the Kriging failure model using the training set; when it is determined that each sample in the sample pool meets preset conditions, substituting the samples of the distribution parameters into the Kriging failure model to obtain the global sensitivity analysis result of the radome structure. This disclosure can improve the performance and reliability of radome structures.