283 results about "Sample mass" patented technology

The invention relates to the technical field of deep learning and small sample learning, in particular to a small sample capacity training method based on deep learning, which comprises the steps of 1, cross-domain data adaptation and feature alignment, 2, meta-knowledge distillation and prototype enhancement, 3, attention-guided small sample fine adjustment, and 4, model uncertainty quantification and iterative optimization. According to the small sample capacity training method based on deep learning, through cross-domain feature alignment, meta-knowledge distillation, prototype enhancement and dynamic iterative optimization, the problems of model overfitting and weak generalization ability in a small sample scene are solved, high-precision model training when the sample size is less than or equal to 50 is realized, and the training efficiency is improved. The method is suitable for data scarce scenes such as medical images and minority language processing.

The invention discloses a material structured data-oriented generation and screening method and device. The method comprises the steps of S1, acquiring material structured data; s2, according to the material structured data, judging whether treatment is needed or not by evaluating the ratio of the characteristic quantity to the sample quantity and the insight of a data set, and if treatment is needed, performing the step S3; s3, effectively generating the structural data of the material by using the depth generative model oriented to the structural data; s4, the generated samples are evaluated through multiple dimensions and weighted integration is carried out; and S5, according to an evaluation result, performing multi-dimensional high-quality sample screening by fusing domain knowledge. By adopting the technical scheme of the invention, effective generation of high-quality material structured data is realized.

The invention relates to the technical field of material physical property testing, and discloses an injectable hydrogel wound dressing tensile strength testing method, which comprises: establishing a correlation model of the mass and the maximum fracture load of a standard sample at different temperatures, and the correlation model of the initial rigidity and the temperature; measuring the mass and the actual maximum fracture load of the to-be-measured sample, and determining the actual initial rigidity of the to-be-measured sample; according to the method, the actual initial stiffness is calculated, the equivalent temperature of the test is converted by utilizing the actual initial stiffness, the standard theoretical maximum fracture load is calculated in combination with the sample mass and the corresponding model, and the performance is judged by comparing the two, so that the measurement of the geometric dimension of an irregular sample is avoided by constructing the correlation between the mass and the mechanical performance; the quality control problem of an in-situ forming material is solved, and the reliability of a judgment result in an industrial environment is further ensured by utilizing mechanical response data of the same test through an integrated temperature self-adaptive calibration mechanism.

The invention provides a joint optimization method for a stacking sequence and a grabbing posture of a neutron measurement system, and relates to the field of data processing. According to the method, multi-source monitoring data of a neutron measurement system are collected, shadow voxels are constructed in combination with a three-dimensional space model, a neutron resonance shadow domain is formed through clustering superposition, and a dangerous concentration cloud cluster and a safe rarefied area are marked; mapping candidate stacking positions and postures according to sample mass distribution, geometric dimensions, centroid positions and irradiation sensitive characteristics, generating a stacking mode and determining a stacking sequence; performing path risk assessment on the grabbing postures, and selecting the grabbing postures of which the crossing risk meets a threshold value; superposing micro-random jitter on the path to suppress resonance phase locking; and abnormal events are monitored in real time during operation, and a risk domain is updated to execute hierarchical unloading control, so that the instantaneous impact and damage risk is reduced. According to the technical scheme, the probability of instantaneous impact risks caused by stacking overall micro-drift and grabbing track overlapping can be effectively reduced, and therefore the safety of the system is guaranteed.

The invention discloses an index transaction monitoring method and device, electronic equipment and a storage medium, and relates to the technical field of transaction monitoring. The threshold range is dynamically generated based on the historical business data to adapt to the change of data distribution, the adaptive statistical test algorithm is selected in combination with the data sample size and the distribution characteristics to improve the accuracy of anomaly judgment, and personalized early warning output is performed according to the region and the business type of the auditing point. According to the technical scheme, data dynamic change and business scene diversity can be flexibly coped with, so that the technical problem that the reliability of an early warning result is reduced in the face of missing data, abnormal values and complex seasonal fluctuation, and then the real-time control and decision-making efficiency of an operator on a business operation state is influenced can be solved; the technical effects of improving the accuracy and reliability of business development index abnormity identification, reducing the false report and missing report rate, and enhancing the real-time control capability and decision-making efficiency of the operator for the business operation state are achieved.

The invention discloses a large-scale customization quality prediction method based on transfer learning. The invention designs a large-scale customization quality prediction method for solving the problems that structures among variables are difficult to excavate and the quality of products with few samples is difficult to predict due to modular production, customized assembly and inconsistent number of different product samples in large-scale customization production. The method comprises the following steps: firstly, mining a causal relationship among source domain variables by utilizing a PC causal discovery method aiming at source domain data with a richer sample size, carrying out structure migration on a part of fixed structures from a source domain to a target domain by utilizing mechanism knowledge in order to obtain the causal relationship among target domain variables, and carrying out target domain few-sample amplification through a generative adversarial network; a causal relationship is complemented in a target domain after variable amplification through a PC causal discovery method, a target domain variable causal graph structure is obtained, then a heterogeneous causal graph attention network is utilized to carry out source domain and target domain model training, and target domain quality prediction is carried out through an MAML transfer learning method with MMD distance weighting. Aiming at the characteristics of large-scale customized production of customized products, the transfer learning-based quality prediction method suitable for large-scale customized production is formed.

The invention relates to a port crude oil unloading speed regulation and control prediction method based on physical constraints, and belongs to the technical field of crude oil storage and transportation automation control, and the method comprises the following steps: S1, constructing a coupling equation of pipeline outlet pressure from an oil transfer arm to a reservoir area and storage tank flow speed; s2, constructing a physical constraint neural network model; s3, constructing a loss function formula of the physical constraint neural network model based on the coupling equation; and S4, performing adaptive training on the physical constraint neural network model based on historical data, and updating network parameters of the model. According to the method, the physical law of pipeline fluid movement is fused into the loss function in the form of constraint conditions in the model training stage, the degree of dependence of the model on the pure data sample size is remarkably reduced, and accurate regulation and control of the oil discharge rate under the complex working condition are achieved.

The invention belongs to the technical field of uncertainty probability characterization analysis, and discloses a thermoplastic composite material leading edge structure design allowable value probability characterization method and system under a small sample condition, and the method comprises the steps: defining a plurality of candidate probability distribution models; fitting each model based on the original sample data and calculating an AIC value and a BIC value; a dynamic weight factor alpha is calculated according to the sample size n, and then a hybrid information criterion HIC value is calculated; generating a plurality of sample sets through Bootstrap self-service sampling, recalculating the HIC value on each sample set, and counting the selected optimal frequency of each model; and determining an optimal probability distribution model according to the frequency, wherein the optimal probability distribution model is used for representing a design allowable value. According to the method, the dynamic weight factor alpha is introduced, AIC and BIC criteria are effectively unified, optimal balance between prediction precision and model complexity is achieved under the condition of small samples, and engineering practicability and robustness are remarkably improved.

The invention discloses a sample size calculation method and system for quantitative consistency evaluation. The method comprises the following steps: firstly, determining MDL and a clinical consistency boundary value, then collecting pre-test data, and determining a required sample size through iterative calculation. Then, verification samples are extracted from the overall data for consistency analysis, and the robustness of a calculation result of the verification sample size is simulated through Bootstrap; according to the method, MDL and least square regression are fused for the first time, a sample size formula for quantitative consistency evaluation is constructed, the reliability and effectiveness of clinical test data are ensured by providing a new sample size calculation method, more accurate sample size estimation is provided for clinical research, and therefore the quality of clinical decisions is improved.

The invention discloses a rapid yield analysis method based on xgboost proxy model truncation sampling, and the method comprises the following steps: generating a first random sample point set, and obtaining a training set through simulation; constructing a proxy simulator, and training the proxy simulator by using the training set; generating a second random sample point set, and inputting the second random sample point set into the proxy simulator to obtain a pseudo data set; establishing a filter, determining two hyperspherical surfaces based on the pseudo data set, dividing a process parameter space, evaluating a safety coefficient and the probability that a sample point enters a real simulator, and simulating the sample point of the process parameter space according to the probability to obtain a real sample set; iteratively optimizing the hyper-spherical surface and the proxy simulator by using the real sample set until convergence to obtain a final safety coefficient; and generating a third random sample point set, carrying out truncation sampling and failure discrimination by combining a real simulator and an agent simulator, and carrying out statistics on the final yield. According to the method, the xgboost network is adopted as an agent model to reduce the simulation cost, the sample size is reduced through truncation sampling, and rapid and accurate prediction of the yield of the integrated circuit is realized.

The invention provides an oil-containing shale porosity evaluation method, and belongs to the technical field of shale oil-gas exploration and development, and the method comprises the steps: obtaining a plurality of samples of a target oil-containing shale; measuring the free hydrocarbon content of the first sample; measuring the total volume, the skeleton volume and the sample mass of the second sample; measuring the crude oil density of the region where the target oil shale sample is located; determining the porosity value of the target oil-containing shale when the target oil-containing shale does not contain oil according to the total volume and the skeleton volume of the second sample, and calculating the porosity value of the target oil-containing shale when the target oil-containing shale contains oil according to the crude oil density, the free hydrocarbon content of the first sample, the total volume of the second sample, the skeleton volume and the sample mass; and determining the porosity of the target oil-containing shale sample according to the porosity value when the oil is not contained and the porosity value when the oil is contained. According to the method provided by the invention, the accuracy of porosity evaluation is improved, and meanwhile, as the test equipment is easily carried to the well site, the requirement of instantly testing the porosity in the well site is met.

The invention belongs to the technical field of medical data processing, and discloses a preoperative multi-complication risk prediction method and system based on structured clinical data, and the method comprises the steps: inputting the causal association between risk factors and complication nodes into the edge of a knowledge graph, calculating the statistical correlation between all complications, and supplementing the statistical correlation into the knowledge graph, and performing network embedding training on the knowledge graph to form a first-stage model, performing preliminary risk assessment on complications, modeling the knowledge graph in a graph neural network mode, and performing joint training with the first-stage model to form a second-stage model to output a final complication probability. According to the method, the interpretability and cross-domain consistency of the model can be improved through deep fusion of the medical knowledge graph and the multi-relational graph convolutional network, stability and calibration performance are still kept in a specialist with scarce sample size, and the problem that a traditional black box model cannot be interpreted is avoided; and the practical application value can be evaluated conveniently.

The invention discloses a CAD (Computer Aided Design) model gap defect detection method and device based on deep learning, which are mainly applied to the field of computer aided design and engineering. The method comprises the following six core steps: firstly, constructing a data set covering various typical gap defects, and expanding the sample size through a data amplification technology; then coding processing is carried out on geometric features of the model, and a deep convolutional neural network is built on the basis; and finally completing model training and gap defect detection. A corresponding device is composed of a data acquisition module, an amplification module, a geometric feature coding module, a detection analysis module and the like, and a matched computer readable storage medium stores program codes for realizing the method. According to the method, the gap defect detection precision is remarkably improved, the adaptability to the complex geometrical morphology is enhanced, and the model generalization ability is effectively improved. Popularization and application of the method can promote intelligent upgrading of a detection process, reduce dependence on experience of engineers, improve detection efficiency and provide technical support for automatic geometric cleaning.

The invention discloses a cognitive function screening system and method based on an MMSE (Minimum Mean Square Error) prediction model, and relates to the technical field of data analysis, the method comprises the following steps: collecting physical examination index data, removing missing records, adopting a multiple interpolation method for interpolation, and obtaining multiple sets of complete data sets; a continuous prediction model is constructed, MMSE continuous prediction values are obtained, and a sensitivity analysis report is generated; constructing a first-stage classification model, adaptively dividing an optimal threshold combination, and dividing a sample into a high-confidence region, a to-be-discriminated region and a low-confidence region; if the sample size of the to-be-discriminated region is higher than a preset training threshold value, constructing an enhanced feature set, and constructing a second-stage classification model; when the prediction probability reaches the optimal re-discrimination threshold value, the classification result in the first stage is corrected, and otherwise, the classification result is maintained; if not, maintaining the classification result; and integrating the classification results to obtain a final classification result of all the samples.

The invention discloses a method for judging and optimizing a raw material ratio in microwave synergistic pyrolysis, and relates to the technical field of solid waste recycling and biomass energy utilization. In order to overcome the defect that in-depth research and engineering application of a microwave pyrolysis process are restricted in the prior art, the technical scheme provided by the invention comprises the following steps: preparing samples from oily sludge and palm kernel shells according to a preset mass ratio, and mixing to obtain a mixed raw material; establishing an inert atmosphere and forming a controlled reaction system; applying microwave heating and collecting a sample mass change curve and temperature change data; comparing the mass change curve with a theoretical curve formed by weighted stacking of a single raw material pyrolysis curve to obtain a difference result; judging the synergistic effect strength among the raw materials according to the difference result, and outputting a synergistic effect judgment result; and determining an optimal ratio based on a synergistic effect judgment result. The method is suitable for raw material ratio judgment and optimization work of solid waste and biomass under the microwave synergistic pyrolysis condition.

The invention belongs to the technical field of biology, and discloses a cattle SNP chip and application thereof, the detection object of the liquid phase chip comprises 7,829 SNP sites located on a common cattle reference genome AR-UCD 1.2, and the position information of the SNP sites is shown in the specification table 3. The liquid chip provided by the invention highlights the specificity of the local cattle variety in the Tibet autonomous region, and by analyzing the local cattle population structure, sites capable of effectively distinguishing the population are screened, so that the problems of strong universality and poor specificity of a commercial chip are solved. The liquid-phase chip can also realize flexible selection and upgrading of sites, and breaks through the limitation that a solid-phase chip cannot flexibly supplement or update the sites. The whole method can be stably operated on the premise of limited sample size and complex population structure, and is suitable for genetic resource protection and application scenarios of rare varieties. The method can also be expanded into a standardized typing tool, and is suitable for various applications such as local cattle germplasm registration, variety identification, region identification and the like.

The invention relates to a light high-entropy alloy yield strength machine learning prediction method, which comprises the following steps: S1, obtaining an original data set of a light high-entropy alloy sample, the original data set comprising the yield strength of the light high-entropy alloy sample and corresponding experimental process conditions; s2, extracting feature parameters for describing samples based on the original data set; s3, dividing the original data set into a training set and a test set; s4, screening an optimal feature subset from the feature parameters, and calculating a secondary feature set based on the optimal feature subset; and S5, taking the yield strength of the light high-entropy alloy sample as a target variable, taking the secondary feature set as an independent variable, and adopting XGBoost trained by the original data set to construct a quantitative prediction model of the yield strength of the light high-entropy alloy. Compared with the prior art, the method has the advantages that the physical interpretability is enhanced, the generalization ability is improved, the dependence on the sample size is reduced, and the like.

The invention relates to the technical field of clinical test statistical design and analysis optimization, and discloses a clinical test statistical design optimization method borrowing external data, and the method comprises the steps: collecting target test data and associated external data, and dynamically evaluating the consistency of the external data and a current test, constructing a multi-model library comprising a frequency theory method, a Bayesian hierarchical model, MAP prior and EMAP prior, selecting an optimal statistical design algorithm, and dynamically borrowing external data; therefore, on the premise of controlling the class-I error rate and maximizing the test efficiency, effective utilization of external data is realized, the current test sample size is reduced, the test efficiency is improved, and the research robustness is improved. The system comprises a data acquisition and preprocessing module, a joint modeling module, an algorithm matching engine and a dynamic optimization execution module, and supports a multi-source data interface, conflict data cleaning and an abnormal result feedback mechanism.

The invention provides a laying hen feed raw material sample screening method and system based on multi-source variability, and relates to the technical field of data processing analysis, and the method comprises the steps: obtaining multi-source attribute data and conventional component content data of raw materials, mapping the attribute data into tensor modal dimensions through multi-source variability tensor construction processing, and obtaining a multi-source variation tensor model; the component data is used as a characteristic component, and a multi-source variability tensor is obtained through decoupling and compression. Variability spectrum decomposition processing is carried out, local rank spectrum decomposition is carried out along producing areas, time and component dimensions, and a variability spectrum vector set is obtained; and identifying a candidate modeling sample set through multi-scale extremum and sparsity screening. And evaluating the contribution degree and sensitivity of the sample to a standard ileum amino acid digestibility prediction equation through a leave-one-out method and sensitivity analysis, and screening out an optimal modeling sample. According to the method, the multi-source variation information of the raw materials can be integrated, the variation spectrum is comprehensively covered with the minimum sample size, and the precision and generalization ability of the prediction model are remarkably improved.

This invention relates to the field of nuclear safety equipment testing technology. It provides a method for optimizing parameters in accelerated reliability testing of nuclear safety-grade valves. The method involves the following steps: establishing a correlation model between accelerated stress and life parameters; calculating the acceleration factor and formulating an initial test plan; deriving the theoretical sample size based on the success-failure test method combined with confidence requirements; constructing an extended coefficient table using the range-extended test method to optimize test parameters; generating multiple accelerated test plans through chi-square distribution verification; eliminating plans that do not meet the life characteristic threshold; determining the optimal sample size, number of failures, and test cycle through comprehensive test cost and risk analysis; and finally outputting a complete test plan including the action interval time. This method solves the problems of limited test samples, insufficient failure data, low confidence levels, and difficulty in adapting accelerated models in existing high-reliability valve technologies.

The invention relates to the technical field of silk identification, in particular to a method for identifying common mulberry leaves and industrial feed silkworm rearing raw silk, which comprises the following steps: S1, cutting a sample to below 5mm; s2, performing thermal decomposition on the sheared sample to obtain the proportion of residual substances, and testing the sample by using a thermogravimetric analyzer under different atmosphere conditions and heating rate conditions; s3, drawing a curve graph, and obtaining a thermogravimetric curve of the sample by taking the thermal decomposition temperature as an abscissa and the mass residue percentage of the sample as an ordinate. According to the method, the essential difference of the common mulberry leaves and the silk for breeding silkworms with industrial feed in mineral substance residue is utilized, the mineral substance content of the raw silk produced by the silkworms fed with the feed is relatively high, and the content of the mineral substances is relatively high; the content of mineral substances in the raw silk produced by silkworms eating mulberry leaves is low, the mineral substances in the silk are derived from food, the addition amount of the mineral substances in feed is high, and the purpose of identifying the attributes of the raw silk is achieved by burning off organic matters in the raw silk and evaluating the proportion of inorganic ash residues.

The application is a kind of double robust causal dose-response curve estimation method based on high-dimensional independent variables, comprising the following steps: 1) constructing a new target function based on the modified adaptive LASSO method to realize dimension reduction; 2) constructing a double weighted distance correlation coefficient DWDC to select the optimal lambda n ; 3) using DR estimator to estimate the dose-response curve. The method mainly aims at the characteristics of the potential confounding variable set contained in the health medical big data, such as high dimension, nonlinear relationship between health outcome and (or) exposure factor, etc. In the framework of GOAL method, a double robust estimation method of causal dose-response curve is provided, which is called GOALDeR method. A large number of statistical simulations show that the estimation accuracy and precision of GOALDeR method are better than those of existing methods, and the method has double robustness and is less affected by the correlation structure between covariates and the n / p (sample size / covariate dimension) ratio.

The technical scheme of the invention discloses a method for judging weights of associated elements of process parameter data and quality data. The invention provides a method for analyzing relevance between process parameter data and quality data and endowing the process parameter data with weights. Aiming at the current situations that the industrial quality data acquisition sample size is small and part of values are easily identified as abnormal values when a traditional method similar to standard deviation is used, the method adopts a grouping comparison strategy when abnormal data are removed in data preprocessing, and effectively retains part of quality characteristics with large deviation. And a small amount of data still having dispute after deletion and selection is subjected to artificial expert review, so that the quality characteristics are further reserved, and the dependence on artificial deletion and selection is reduced. According to the method disclosed by the invention, in order to solve the problems that a model generated by machine learning is relatively poor in interpretability and relatively few in verification, an SHAP algorithm is adopted to carry out association construction on the model, process parameter weights are output, meanwhile, an output result is verified, and the correctness of a weight conclusion is ensured.

This application relates to the field of equipment performance testing and evaluation technology, and provides a method and system for equivalent reduction of equipment test sample size. By constructing a data fusion evaluation model based on actual and numerical test data, the fidelity of actual test response estimation under small sample conditions is improved. An equivalent measure is designed and calculated by fusing the mean square error of the evaluation model from a single data source with a preset test confidence level, and a box plot of the equivalent measure from a single data source is obtained. By connecting the lower quartile and upper quartile of each obtained box plot, equivalent composite confidence intervals for actual and numerical simulation tests are plotted. Based on these equivalent composite confidence intervals, the theoretically required actual test sample size is equivalently reduced to the numerical simulation test sample size, achieving an equivalent reduction of the actual test sample size by the numerical simulation test, thus solving the problem of equipment performance evaluation under insufficient actual test samples.

The utility model belongs to the technical field of urine sampling, in particular to a urine sample sampling device for animal husbandry and veterinary, which comprises an assembly plate, vertical rods are fixedly mounted on two sides of the top of the assembly plate, tension springs are movably mounted on the surfaces of the vertical rods, positioning plates are fixedly mounted on two sides of the assembly plate, and the positioning plates are movably mounted on the surfaces of the vertical rods. A clamping groove is formed in one side of the top of the front face of the positioning plate, a positioning rod is fixedly installed in the clamping groove, an abutting plate is movably installed on the surface of the positioning rod, a liquid inlet hopper conical face clamping base is fixedly installed on the top of the front face of the abutting plate, and a sampling measuring cup arc-shaped clamping plate is fixedly installed at the bottom of the front face of the abutting plate. Through the structural design of the assembling plate, the vertical rod, the tension spring, the positioning plate, the positioning rod, the abutting plate, the liquid inlet hopper conical surface clamping seat, the sampling measuring cup arc-shaped clamping plate, the connecting plate and the hanging rod, the function of rapidly disassembling and assembling the liquid collecting hopper and the sample measuring cup is achieved, so that the disassembling and assembling efficiency of the liquid collecting hopper and the sample measuring cup can be improved, and then the pollution risk between urine samples is reduced.

The invention relates to a data processing technology, and discloses a collaborative constraint-based industrial time series data enhancement method, which comprises the following steps of: acquiring industrial time series data, and executing data preprocessing operation on the industrial time series data by utilizing a data preprocessing module to obtain standard industrial time series data; inputting standard industrial time series data into the improved variational auto-encoder model to generate a data initial sample; inputting the initial data sample into a conditional generative adversarial network for data optimization operation to obtain an optimized data sample; performing industrial time sequence correlation constraint on the optimized data sample by using a time sequence feature constraint model to obtain an industrial constraint data sample; and performing sample evaluation and sample screening on the industrial constraint data sample to obtain a target generation data sample. The invention further provides an industrial time series data enhancement device and equipment based on the cooperative constraint and a medium. The data quality of the generated data can be ensured, and the sample size of the generated data can be improved.

The utility model discloses an integrated urine specimen collector, including the outer layer urine storage collection pipe, the inside of outer layer urine storage collection pipe is provided with the inner layer urine collector, the opening position of outer layer urine storage collection pipe is connected with the screw cap, is used for carrying out effective sealing to outer layer urine storage collection pipe, still include: the left side fixed connection of inner layer urine collector inside has fixed cylinder. This integrated urine specimen collector adopts two-piece design, and the inner layer pullable urine collection core has self -adaptation caliber function, and the outer layer is equipped with screw cap, and the inner layer is equipped with stretchable folding handle, and the bottom 8ml storage pipe one side has three urine leakage holes, and the other side is no hole, can promote collection efficiency, ensure that the specimen is safe, simplify the operation and accurate control sample amount, and when need to inspect urine, only need to unscrew the upper layer's inspection cover, and the probe is inserted from the hollow place of screw cap and can carry out the inspection, thereby make the inspection operation more simple, accurate.

The method of detecting SNP combinations in GWAS data based on the crowned porcupine algorithm of the genetic frequency control pool belongs to the field of intersection of computer data processing and genetics. Four different individual generation strategies are designed, namely the global guidance strategy, the collaborative perturbation strategy, the local exploration strategy and the convergence acceleration strategy; a gene sampling module with a frequency suppression mechanism, namely the genetic frequency control pool, is introduced; a method of automatically inferring the maximum detectable interaction order based on the sample size is introduced; a significance detection mechanism combining the K2 score and the G test is adopted; the method of the present invention significantly improves the computational efficiency and detection accuracy, and effectively identifies high-order SNP combinations related to complex diseases.

The invention discloses a statistical data acquisition method and device, electronic equipment and a storage medium, and relates to the technical field of statistical data acquisition. The method comprises the steps of determining a first influence weight of a related hierarchical variable on a target variable by calculating an information gain of the related hierarchical variable of a target region on the target variable, outputting a second influence weight of the related hierarchical variable on the target variable by utilizing a trained LightGBM neural network, performing linear weighting processing on the first influence weight and the second influence weight, and obtaining a second influence weight of the related hierarchical variable on the target variable. Obtaining weighted influence weights of the related hierarchical variables on the target variables, taking the related hierarchical variable corresponding to the maximum value of the weighted influence weights as a sampling hierarchical variable, applying the determined sampling hierarchical variable to stratified sampling of the target region, and dividing the target region into a plurality of levels according to the sampling hierarchical variable, and respectively calculating sample size calculation results of each hierarchy under proportional distribution and optimal distribution, and determining the sample size of stratified sampling of each hierarchy according to the calculation results.

The invention belongs to the technical field of foam performance testing, and relates to a comprehensive measurement device for foam characteristics in a thermal radiation environment, comprising: a container for holding a sample, the container being made of a transparent material; the image observation module is arranged on one side of the container, and the shooting end of the image observation module faces the container; the heating module is used for heating the sample in the container, and the heating module is used for acquiring heating data; the foam weighing module is used for weighing the mass of the foam sample in the container and acquiring mass change data of the foam sample; and the foam precipitation liquid in the container flows into the precipitation liquid measuring module through the guide pipe, and the precipitation liquid measuring module is used for acquiring the mass of the precipitation liquid in real time. The device can simulate the flame radiation environment, comprehensively measure and analyze the foam form change, the drainage behavior (liquid content and liquid evaporation capacity), the heat insulation property, the heat stability and other properties of foam, and provide powerful support for research and development and performance evaluation of a fire extinguishing agent.