197 results about "Proportional Stratified Sampling" patented technology

The invention discloses an intelligent recommendation method for optimizing advertisement keyword combination through cross validation, and relates to the technical field of advertisement technology and search engine marketing, which comprises the following steps: constructing a heterogeneous data set through multi-modal data fusion, and layering according to data sparseness: training a Transform-XL time sequence model by adopting time cross validation of a dynamic K value in a high resource layer; a graph neural network association graph is introduced into a low resource layer, semantic expression of a long tail word is enhanced, a stratified sampling-transfer learning two-channel mechanism is designed, and the generalization ability is improved in combination with exposure frequency weighting and a parameter freezing strategy; developing a Bayesian fusion engine, and dynamically weighting a high / low resource layer prediction result by using an improved Materon kernel function Gaussian process; and generating a confidence interval based on neural quantile regression, and outputting an optimal keyword combination sequence under ROI-risk-diversity constraint in combination with multi-target Pareto optimization. According to the method, the cold start efficiency and the long-tail resource utilization rate are improved, and high-robustness decision support is provided for advertisement putting.

The invention provides an artificial intelligence auxiliary antenna design method based on a Gaussian regression process, and the method comprises the steps: employing a script interface to interact with a simulation platform, automatically completing the parameterized modeling process of an antenna model, and setting an excitation condition and a radiation boundary; a multi-dimensional space stratified sampling method is adopted to generate a uniformly distributed sample point set in the multi-dimensional parameter space, and electromagnetic simulation is carried out; preprocessing the data output by simulation to form a data structure suitable for machine learning model processing; integrating the trained MLNN model into a grey wolf optimization algorithm, and optimizing antenna design parameters; and performing electromagnetic simulation on the optimal parameter obtained by optimization, comparing a prediction result with actual simulation data, and verifying the performance of the optimization engine in the target frequency band. According to the method, the problems of insufficient sample generation efficiency, insufficient parameter sensitivity analysis, limited model generalization ability, low convergence speed of an optimization algorithm and the like in the prior art can be solved.

The invention relates to the field of water resource quality detection, in particular to a sampling device for water resource quality detection. The invention provides a water resource quality detection sampling device which comprises a sampling barrel, a barrel cover is arranged at an opening of the sampling barrel, clamping seats are symmetrically distributed on one side, close to the barrel cover, of the sampling barrel, clamping pins are arranged in the clamping seats, the barrel cover is locked through cooperation of the clamping pins and the clamping seats, and the sampling barrel is connected with the clamping seats. A plurality of sampling box bodies for containing water samples are vertically arranged in the sampling barrel, a water pump is arranged in the sampling barrel, and a water inlet pipe of the water pump extends to the outer side of the sampling barrel. The stratified sampling box body and the independent flow guide assembly are combined with the servo motor to drive the staggered box cover, so that accurate stratified collection of water samples at different depths is realized, and mixed pollution is avoided; a water flow switching assembly is additionally arranged, a pipeline is automatically flushed after sampling, a residual water sample is discharged into a liquid storage box, cross contamination is reduced, independence and representativeness of the water sample are guaranteed, and detection accuracy is improved.

The invention belongs to the technical field of geological exploration, particularly relates to a high-adaptability multi-type geological exploration sampling device and an accurate sampling method.The high-adaptability multi-type geological exploration sampling device and the accurate sampling method aim to solve the problems that a drill bit is stuck and stratified sampling is difficult, and the high-adaptability multi-type geological exploration sampling device comprises a base, a vertical plate, a lifting plate and a driving cylinder with the drill bit; a first annular groove is formed in the outer wall of the driving cylinder and matched with the first circular ring, downward-pressing drilling and upward-lifting resetting of the drill bit are achieved, a second annular groove is formed in the drill bit, and the flow direction of hydraulic oil is controlled through a second circular ring connected with a fixing rod; during lifting, hydraulic oil is injected into the second hydraulic channel, the trapezoidal collecting box extends out to scrape hole wall samples, the electromagnet and the iron block are matched to adjust the position of the second circular ring, the working states of the teeth and the collecting box are switched, drill bit jamming is reduced, hole wall sampling at any depth is supported, and exploration efficiency is improved.

The invention provides a UWB-NLOS identification and distance measurement correction method based on a DEA-PSO hybrid optimization Iight GBM algorithm, and the method comprises the following steps: 1, building multi-dimensional features after data stratified sampling, obtaining an HCNLOS data set through two times of label screening, and generating initial positioning; step 2, adopting a DEA-PSO hybrid optimization algorithm to realize LightGBM hyper-parameter automatic adjustment and optimization, and improving parameter adjustment efficiency through dynamic fitness evaluation and collaborative search; 3, constructing a classification model and a regression model in parallel; and 4, fusing the corrected NLOS data and the original LOS / HCNLOS data, and outputting a high-precision coordinate through a positioning algorithm, thereby effectively improving the positioning robustness in a complex environment.

The invention discloses an expert matching method based on structured data, and the method comprises the following steps: firstly, calculating the objective weight and subjective weight of each result index through information and a Delphi method based on the structured data in a database, and generating an expert comprehensive academic score; then, keyword information input by a front end is obtained, the matching degree of keywords and expert fields is calculated by adopting a word frequency-inverse document frequency algorithm, and experts are grouped according to the matching degree; and finally, according to the scale of the expert database and the number of persons to be extracted, adaptively selecting a random extraction or hierarchical distribution extraction mode to determine a target expert. According to the method, through a dynamic weight calculation model and an intelligent stratified sampling mechanism, the accuracy and flexibility of expert matching are realized, the score is continuously optimized in combination with the historical participation performance data of the experts, and the scientificity and practicability of expert extraction are improved.

The invention discloses a grain depot intelligent sampling quality inspection system and method, and relates to the technical field of intelligent grain depot detection, and the method comprises the steps: collecting environment data, three-dimensional point cloud data and surface acoustic wave group velocity data, and carrying out the inversion calculation through a Love wave equation, and obtaining a three-dimensional density distribution diagram; calculating the density gradient in the vertical direction according to the three-dimensional density distribution diagram to obtain a stratified sampling parameter table; according to the stratified sampling parameter table, stratified puncturing is conducted on the grain pile, actual sampling depth data are generated, and a three-way sample valve is used for conducting sample distribution detection on the actual sampling depth data. Through layered detection of the three-way sample valve and near-infrared X-ray combined analysis, accurate detection of moisture, protein and heavy metal content is realized, and a detection result is matched with a density distribution diagram through a constrained Kriging interpolation method to generate a quality safety index.

The utility model relates to the field of sewage sampling, and discloses a sewage detection stratified sampling device, which comprises three sampling buckets, a support column and a sliding rod, the sampling buckets are internally provided with mounting cavities, the middle part of the support column is provided with a sliding chute, and the sliding rod slides in the sliding chute. The lower ends of the supporting columns penetrate through the sampling barrel and extend out of the sampling barrel, a plurality of sliding cavities are formed in the two sides, close to the sampling barrel, of the middle of the outer wall of a supporting column rod body, and connecting rods are fixedly connected to the two sides, close to the sampling barrel, of the middle of the outer wall of a sliding rod body. According to the stratified sampling device, when the stratified sampling device is used for sampling sewage, the stratified sampling device is in threaded connection with the filter screen on the outer wall of the threaded pipe, water plants, floating objects or other garbage in a sampling water area can be effectively prevented from blocking the water taking hole, the sampling process is ensured to be smoothly carried out, and in addition, the threaded connection mode is convenient to mount and dismount and simple and convenient to operate. And when the filter screen needs to be replaced or equipment needs to be maintained, a worker can quickly finish operation, so that the maintenance cost is reduced.

This invention provides an in-situ stratified rapid sampling device and method for river and lake sediment pollution, relating to the field of river and lake sediment pollution monitoring technology. The device includes: a main control system, a main sampling system, a mounting frame, a depth sounding module, a working vessel hull, and a crane. The depth sounding module is installed below the stern of the working vessel hull. The main control system and the crane are both mounted on the working vessel hull. The main sampling system is connected to the crane and fixed within the mounting frame. The depth sounding module is used to detect and determine water depth and the thickness of floating mud, flowing mud, and silt. The main control system is used to control the sampling process of the main sampling system and to read the water depth, sediment thickness, and determine whether the main sampling system has completely inserted into the sediment in real time. The main sampling system is used to insert into the sediment to perform stratified sampling of the sediment pore solution. This invention solves the problems of low sampling rate, inability to perform stratified sampling, deviation between stratified sediment samples and actual conditions, low efficiency, and large errors in detection results in existing technologies.

The invention relates to the technical field of biological sampling netting gears. A stratified sampling net comprises a trawl net frame and a netting assembly, the netting assembly comprises sub-netting bodies which are sequentially arranged from top to bottom, and the lower line of the upper sub-netting body in every two adjacent sub-netting bodies is fixedly connected with the upper line of the upper sub-netting body; the trawl frame comprises a main body frame, the main body frame comprises lateral slide rails, the lateral slide rails comprise a front side slide rail and a rear side slide rail which are arranged front and back, and the rear side slide rail is longitudinally and slidably connected with the sub-netting side ropes; the netting opening and closing control mechanism comprises a sliding transverse rod, an elastic telescopic assembly and an unlocking and releasing assembly, wherein the sliding transverse rod is longitudinally connected with the front side sliding rail in a sliding manner; the upper line of the uppermost sub-netting is connected with a sliding cross rod; the lower line of the sub-netting located at the lowermost part is connected with the other sliding cross rod; and the joints of the upper lines and the lower lines of the adjacent sub-nettings are connected with the same sliding cross rod. According to the invention, the requirement of stratified sampling is met.

The invention relates to the technical field of water quality sampling, and particularly discloses layered water quality sampling and classified storage equipment which comprises a sampling tank, the sampling tank comprises an upper shell and a lower shell, a self-locking telescopic rod is mounted at the top of the upper shell, a control switch is arranged at the tail end of the self-locking telescopic rod, an opening and closing assembly is arranged on the upper shell, and a water inlet is formed in the lower shell; a sampling assembly and a driving assembly are arranged on the inner side of the lower shell; the depth of the sampling tank in a water body is accurately adjusted through the self-locking telescopic rod, and meanwhile, ordered and accurate layered sampling of water samples of different water layers is realized by combining the linkage design of the driving assembly, the opening and closing assembly and the sampling assembly; compared with traditional sampling equipment, the sampling equipment has the advantages that the problem of disordered water sample collection is avoided, and a plurality of water layer samples can be obtained through sequential alternation of the multiple sampling barrels without adjusting the position of the equipment for multiple times, so that the sampling efficiency is greatly improved, and the collected water samples can truly reflect the water quality condition of each water layer.

The invention provides a method for analyzing and predicting the water stability of an asphalt mixture, which comprises the following steps of: acquiring multi-dimensional service environment, material composition and standard water stability test data, and carrying out standardization and stratified sampling division on the multi-dimensional service environment, the material composition and the standard water stability test data; a context sensing multi-layer perceptron network supporting feature parameter sharing and gradient accumulation is constructed, and the importance weight of each dimension of features under different data distributions is automatically evaluated and normalized. An attention mechanism and a graph convolutional network are introduced, association between features is quantified, and a key high-order interaction relationship is deduced; by means of K-fold cross validation and meta-learning optimization, the generalization ability of the model for new distribution data is improved, prediction accuracy and feature interpretation are improved, and intelligent analysis and proportion design of the water stability of the asphalt mixture are promoted.

The invention relates to an eDNA method for identifying bumblebee honeycombs in permafrost regions of Qinghai-Tibet Plateau. The eDNA method comprises the following steps: S1, positioning observation sample belts and arranging sampling points on the basis of multi-source data in combination with a Bayesian hierarchical model; s2, performing deep freezing and complete excavation on matrixes around and inside all the sampling points; s3, stratified sampling and anti-degradation treatment are carried out, and a spatial attribute database is established; s4, extracting and purifying eDNA (enhanced deoxyribonucleic acid); s5, determining a nest host based on PCR amplification, high-throughput sequencing and sequence comparative analysis; and S6, constructing a Bayesian hierarchical model in combination with biological information, and judging honeycomb characteristics. The method is simple, rapid and low in cost, not only is suitable for Qinghai-Tibet Plateau permafrost regions, has the core advantages of non-destructiveness and high sensitivity, but also is also suitable for underground insect nest identification research in other environment sensitive regions or regions difficult to physically explore in principle, and has wide application prospects. The method is especially suitable for monitoring rare, secret or difficult-to-approach organisms.

The invention provides a sediment stratified sampling device and a control method. The sediment stratified sampling device comprises a shell, a motor, a drill bit and a sample sampling pipe, the motor is arranged on the shell, and an output shaft of the motor penetrates out of the shell in the axial direction of the shell and is connected with the drill bit. The sample sampling tube at least comprises a first sampling tube and a second sampling tube, the first sampling tube and the second sampling tube extend out of the shell for sampling or retract into the shell after sampling is completed, and the end parts of the first sampling tube and the second sampling tube are respectively provided with a first sealing structure; and second sealing structures are arranged on the inner walls of the first sampling tube and the second sampling tube. The self-adaptive adjustment of the rotating speed of the motor and the double sealing of the sampling pipe generate a synergistic effect, so that the sampling efficiency is improved, the sampling quality is ensured, and the self-adaptive adjustment of the rotating speed of the motor can automatically adjust the sampling intensity and the sampling rate according to the difference of sediments.

The invention discloses a time sequence coal reservoir permeability prediction method based on improved deep learning, and belongs to the technical field of coal bed gas exploration and development and deep learning. According to the method, standardized preprocessing is performed on logging data, a time sequence sample is constructed by using a sliding window, stratified sampling is introduced to optimize K-fold cross validation, and an AdamW optimizer is adopted to train a double-layer LSTM network, so that the problems of low precision and poor generalization ability of a traditional prediction method are effectively solved. Experimental results show that the method can accurately capture the time sequence characteristics of the logging data, data balance is ensured through stratified sampling, overfitting is inhibited in combination with weight attenuation, and the prediction result has high stability and reliability.

The invention relates to the technical field of deep learning, in particular to an intelligent prospecting model construction method based on a knowledge graph and data deep learning, which comprises the following steps: collecting multi-source geological data to perform time decomposition and spatial stratified sampling to extract features, calculating an attachment weight through semantic coding to construct a geological knowledge structure, and constructing a geological prospecting model; the method comprises the steps of extracting spatial-temporal features in a convolution mode, combining with semantic deviation degree weighted fusion to generate a hierarchical feature coupling result, carrying out aggregation analysis on spatial-temporal correlation to extract consistent components, judging a metallogenic response relation through conditional probability reasoning, fitting a model, calculating deviation, adjusting weights, analyzing semantic consistency, and carrying out classification and aggregation to generate an intelligent prospecting optimization result. The feature precision is improved through time decomposition and spatial stratified sampling of multi-source geological data, data association is enhanced through semantic coding, time-space consistency is enhanced through convolution extraction and semantic fusion, multi-scale features are balanced through hierarchical coupling, the ore-forming relation judgment accuracy is improved through probabilistic reasoning, and the result precision and stability are remarkably improved.

The invention relates to the technical field of tunnel rockburst prediction, in particular to a rockburst prediction method based on a machine learning model, and the method comprises the following steps: S1, collecting rockburst case data, and constructing a database; s2, box diagram analysis is carried out on each type of data, abnormal values in the data are removed, and original rockburst case data are obtained; s3, a Borderline-SMOTE algorithm is adopted to carry out dynamic screening and synthesis on a minority class of samples in the original rockburst case data, and a more balanced data set is generated; s4, performing standardization processing on the rockburst data set, and dividing the rockburst data set into a training set and a test set through stratified sampling; s5, training a plurality of machine learning models by using the divided training set to obtain an optimal training model, and performing parameter tuning by using the divided test set to obtain an optimal hyper-parameter; and S6, based on the optimal hyper-parameter, establishing a rockburst grade prediction model, selecting an optimal model result, determining a rockburst grade, integrating prediction results of a plurality of rockburst grade prediction models, and outputting the rockburst grade.

The utility model discloses a cutting ring for measuring soil bulk density by stratified sampling. The cutting ring comprises a cutting ring main body, the outer side of the supporting mechanism is of a rectangular structure, and a cutting ring body capable of moving up and down is arranged in the middle of the supporting mechanism; the system further comprises; the soil scraping mechanism comprises a pedal, a moving mechanism and a scraper main body; the pedal is arranged on the outer side of the supporting mechanism, the inner side of the pedal is connected with the scraper body through the moving mechanism, and the scraper body stretches out and is stored under the action of the moving mechanism and is used for cutting the bottom of the scraper body to scrape redundant soil. According to the cutting ring for measuring the soil bulk density through stratified sampling, scales are arranged outside the connecting rod, when the whole cutting ring body is taken out, the cutting ring body can be taken out along with rising of the connecting rod, outside soil does not need to be dug, and therefore stratified sampling work can be conducted at the same soil taking position.

The utility model discloses a water quality stratified sampling device which comprises a plurality of outer sleeves in threaded connection, a plurality of sampling barrels which are rotationally arranged in the outer sleeves and are in one-to-one correspondence with the outer sleeves, a rotating handle for driving the sampling barrels to rotate, and a lower-layer sealing cover arranged at the bottom of the outer sleeve at the lowest end, the upper end and the lower end of the outer sleeve are open, a water inlet is formed in the side wall of the outer sleeve, a sampling opening corresponding to the water inlet is formed in the side wall of each sampling barrel, the outer wall of each sampling barrel is tightly connected with the inner wall of the outer sleeve, a cross-shaped inserting plate is arranged at the upper end of each sampling barrel, a cross-shaped inserting groove matched with the cross-shaped inserting plate is formed in the lower end of each sampling barrel, and the sampling barrels are connected end to end. The stratified sampling device has the beneficial effects that the device is simple in structure and relatively convenient to assemble and disassemble, and the stratified sampling effect can be realized through a plurality of flexibly assembled outer sleeves and sampling barrels according to the sampling requirements of water quality at different depth levels.

The present invention proposes a method for evaluating the link quality of an unmanned and manned collaborative network based on random forest, belonging to the fields of wireless communication networks and unmanned and manned collaborative ad-hoc networks. First, the present invention constructs an index system for link quality evaluation; then constructs a random forest model and balances the sample data by the stratified sampling method; next, uses the sparrow search algorithm to optimize the hyperparameters of the random forest model, improves the position update method of the discoverer in the sparrow search algorithm by introducing the butterfly optimization algorithm, and adopts an adaptive shrinking search space mechanism to limit the search space of the population in each iteration; finally, realizes the evaluation of the network link quality based on the random forest classification model optimized by the improved sparrow search algorithm. The present invention improves the accuracy and convergence speed of link quality evaluation, and enhances the stability and reliability of the interconnection and interoperability of the unmanned and manned collaborative network.

The invention relates to a melamine identification method of 1D-VDCNN-BiLSTM fused with attention characteristics, and belongs to the field of identification of melamine in food. The method comprises the following four stages: 1) sample data division and preprocessing: completing preprocessing of sampling data: preprocessing a sample set by adopting a KS method; (2) constructing a one-dimensional ultra-deep convolutional neural network (1D-VD-CNN): improving a two-dimensional CNN model into the 1D-CNN for melamine one-dimensional data, and then adding an ultra-deep VD structure to construct the one-dimensional ultra-deep convolutional neural network (1D-VD-CNN); 3) structural design of a bidirectional long short-term memory network (BiLSTM); and 4) constructing a melamine identification model fused with the attention mechanism, and training, verifying and testing the model. Wherein the data classification is changed into layered sampling from traditional pure random sampling. By establishing a novel melamine identification method with high precision and strong interpretability, intelligent technical support is provided for food safety risk prevention and control.

This invention provides a method for high-speed train bearing fault diagnosis and selection based on multi-model comprehensive evaluation. The method first obtains bearing fault feature vectors, then divides the training and test sets using 8:2 stratified sampling, mitigating sample imbalance bias through a sample weighting strategy. Next, it constructs a diagnostic system comprising five models, including random forest and support vector machine, and employs optimal hyperparameters for parallel training. Subsequently, a multi-dimensional evaluation system for accuracy and efficiency is established, and model reliability is verified using a confusion matrix. Finally, model selection is tailored to specific scenarios: multilayer perceptrons are used for real-time monitoring, gradient boosting trees for high-precision scenarios, and random forests or K-nearest neighbors for lightweight deployment. This achieves scientific multi-model selection, balancing diagnostic accuracy, real-time performance, and stability, adapting to the maintenance needs of high-speed trains, and demonstrating strong engineering practicality.

The invention discloses an arid region underground water detection stratified sampling device, and belongs to the technical field of water quality detection, the arid region underground water detection stratified sampling device comprises a sampling barrel, at least two sealing assemblies are arranged in an inner cavity of the sampling barrel, a plurality of stratified sampling cavities are formed through the sealing assemblies, sample separation barrels are arranged in the stratified sampling cavities, and a plurality of sample separation boxes are arranged in the sample separation barrels in a surrounding manner; a trigger assembly is arranged on the inner side of the sample separation barrel corresponding to the sample separation box. According to the invention, through the designed expansion liquid bag, a medium can be fed into the expansion liquid bag through external pumping equipment, the expansion liquid bag can expand after liquid injection, the expansion liquid bag is unfolded to seal the opposite spaces on the two sides of the sampling barrel, a multi-partition layered sampling structure can be formed, and the sampling efficiency is improved by sealing different partitions. Compared with a traditional sampling mechanism with a single sampling cavity and valve switching, mixing of water at different layers can be avoided, and the sampling data precision is improved.

The application provides a soil monitoring sampling method, system, device and medium, the region to be monitored is divided into grid by spatial position and historical soil information, forming a candidate sampling unit with spatial correlation, in each candidate sampling unit, the physical parameters representing the state of the soil surface and shallow layer are collected, and a high-dimensional feature vector is generated; based on all high-dimensional feature vectors, a dynamic sampling priority scoring model reflecting the degree of spatial difference of soil is constructed, and a target sampling unit is determined; a depth stratified sampling strategy is performed on the target sampling unit, the sampling depth interval is adaptively determined, and then a representative soil sample is obtained; the soil sample is associated with the corresponding spatial position, sampling depth and soil state information, and structured soil sampling data is generated. The scheme of the application can realize the spatial-depth joint optimization of soil sampling, and accurately capture the real state and spatial variation law of the soil environment.

The invention discloses a crop disease severity dynamic estimation method and system based on multi-scale fusion, provides a spatial stratified sampling strategy, realizes refined partitioning based on growth potential difference and disease risk clustering, ensures that sampling points cover field key areas, and meanwhile, improves the accuracy of crop disease severity estimation. The dynamic time compensation sampling strategy is combined with the field disease development situation and environmental factor self-adaptive adjustment sampling to provide high-quality and dynamic data support for estimation; multi-source features such as scab quantity and edge roughness are introduced into the leaf scale to describe scab morphology and development difference in a fine granularity manner, and the plant scale is fused with weights such as leaf space position to highlight contribution of functional leaves and key leaves to the health of the whole plant; the spatial distribution pattern of the field diseases is effectively reflected by combining the field scale with the growth potential partition weight, the risk partition weight, the health degree and the like of the plants, and finally, the constructed dynamic time integral model is fused with the propagation rate and the fertility sensitivity to obtain a dynamic comprehensive estimation result of the accumulated influence of the diseases in the monitoring period.

The invention discloses a multi-scale time sequence prediction system and method for adaptive hierarchical frequency, which realizes standardized processes of data preprocessing, dynamic hierarchical sampling, frequency attention modeling, cross-scale attention fusion and prediction output through modular architecture design, is clear in interface between modules, is easy to deploy and expand, and is high in practicability. The problem of non-uniform system architecture in the prior art is solved; in the dynamic stratified sampling step, through region division and dynamic sampling rate distribution, adaptive matching of a down-sampling rate and a local frequency characteristic of a non-stationary sequence is realized; in the frequency attention modeling step, through frequency domain conversion and attention weight distribution, the sensitivity of the system to periodicity and frequency characteristics is enhanced, and key frequency components can be focused according to prediction task requirements; according to the cross-scale attention fusion step, through feature dimension unification and cross-scale correlation calculation, the dependency among the scale features is effectively modeled, redundant information is filtered, and the fusion efficiency and the prediction precision are improved.

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 utility model discloses a stratified sampling device which comprises a sampling outer pipe structure, a sampling inner pipe structure is movably inserted into the upper end opening of the sampling outer pipe structure, a torsion spring is connected between the sampling outer pipe structure and the sampling inner pipe structure, and the upper end opening of the sampling inner pipe structure is fixedly connected with a collecting structure in a spiral mode. The sampling outer tube structure comprises an outer sampling tube, a plurality of outer sample inlets and a lower handle, the outer sample inlets are formed in the surface of the outer sampling tube, and the outer sample inlets are uniformly distributed in the surface of the outer sampling tube. According to the stratified sampling device disclosed by the utility model, during sampling, the stratified sampling device only needs to be automatically loosened after being screwed by 90 degrees, then automatically returns under the elastic force of the torsional spring, and accurately seals the end opening of the outer sample inlet, so that the operation is simpler, the sampling is faster, and in addition, through a collecting structure, the sampling efficiency is improved. Sampled samples can be rapidly collected, and multi-point rapid sampling can be achieved.

The utility model belongs to the technical field of oil sampling, and discloses a sampler for offshore oil exploitation, which comprises a sampling mechanism, a sampling mechanism and a control mechanism, the stratified sampling assembly comprises a sampling barrel, three layers of sample cavities are formed in the sampling barrel, three sets of electric valves communicated with the three layers of sample cavities are installed on the outer side of the sampling barrel, and three sets of pumping pipes used for pumping petroleum samples in the sample cavities are arranged on the outer side of the sampling barrel; compared with the prior art, the stratified sampling device has the following beneficial effects that the stratified sampling assembly is designed and is connected with the hanging ring through the mooring rope, so that the sampler can be lowered into a vertical sampling pipeline, when the sampler reaches a first sampling layer, a group of electric valves are opened, and under the action of pressure difference and gravity, a petroleum sample of the layer flows into a corresponding sample cavity to complete collection; the first sampling layer continues to be lowered after collection, and the other two sets of electric valves are opened when the first sampling layer reaches other preset sampling layers, so that stratified sampling of petroleum in different depth layers is achieved.

The utility model relates to the technical field of geological sample sampling devices, and discloses a geological stratified sampling device for geological mining, which comprises a movable base provided with a central hole, and a limiting ring rotatably connected in the central hole; the sampling barrel selectively penetrates through the central hole; the telescopic sleeves are sequentially connected in a sliding mode from the outer side to the inner side in the vertical direction, the telescopic sleeve on the outermost side is connected with the inner side wall of the limiting ring in a sliding mode, the inner side wall of the telescopic sleeve on the innermost side is connected with the sampling barrel in a sliding mode, and the sampling barrel can drive the telescopic sleeve on the innermost side to synchronously move downwards. And then the multiple sections of telescopic sleeves from the inner side to the outer side are driven to be sequentially embedded in the drill hole. The sampling barrel can drive the telescopic sleeve on the innermost side to synchronously move downwards, then the multiple sections of telescopic sleeves are driven to be sequentially embedded in a drill hole, collapse of the drill hole is avoided, and layered multi-time sampling of stratums with different depths is achieved.