211 results about "Stratified sampling" patented technology

The invention discloses an offshore red tide multi-parameter real-time monitoring method and monitoring system based on a buoy array. The method comprises the following steps: S1, performing array deployment and topology configuration according to a sea area sensitivity degree, tide characteristics and a historical red tide trajectory; according to the invention, through risk differentiation buoy array deployment and redundancy topology, traditional isolated monitoring limitation is solved, and sea area full coverage and red tide diffusion tracking are realized; physical-chemical-biological multi-source sensing and in-situ detection are combined with data fusion, so that the problem of single parameter is solved, and the monitoring accuracy is improved; by means of liftable stratified sampling and in-situ algae species and toxicity analysis, the vertical monitoring blank is filled up, and the complete life cycle process of red tide from occurrence to regression is completely captured; through linkage of edge-cloud multi-model early warning and field equipment, a'monitoring-early warning-disposal 'closed loop is formed, and the red tide monitoring real-time performance and the emergency response capability are greatly improved.

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.

A crop genotype-to-phenotype general prediction method based on multi-task learning comprises the following steps: collecting genotype and phenotype data sets of five crops, performing quality control and coding on genotype data, and performing standardization processing on phenotype data; dividing the data set into a training set and a test set in proportion by adopting a stratified sampling method; by taking genotype data as input and phenotype data as output, a universal prediction model based on a multi-task learning framework is constructed, information sharing among different phenotype prediction tasks is realized, and potential relations between complex phenotypes and genotypes are accurately captured. According to the method, phenotypes can be accurately predicted according to genotypes in the early stage of breeding, so that materials with target characters are rapidly screened, the breeding period is effectively shortened, the breeding efficiency is remarkably improved, and an efficient and universal technical scheme is provided for crop breeding and related research.

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 relates to the technical field of data processing, and discloses an urban space evolution detection method and system based on machine learning. The method comprises the following steps: carrying out space-time stratified sampling on city multi-scale data to obtain block-level, district-level and city-level feature tensors; calculating a collaborative weight matrix through a multi-scale space attention mechanism; a collaborative feature vector is obtained through mapping of a collaborative order degree encoder; performing time sequence interpolation processing by using a space-time cooperation trajectory decoder to obtain a dynamic cooperation index; and performing multi-level fusion on the dynamic collaboration index to obtain a collaboration measurement result. The problem that urban spatial evolution detection is lack of multi-scale cooperative relationship quantitative analysis and dynamic evolution trajectory recognition capability is solved.

The application discloses a cell heterogeneity analysis method, device and computer readable storage medium for large-scale single cell sequencing data. The application combines kernel non-negative matrix factorization with deep neural network to construct a clustering method called KNMF-DNN. The method selects some representative subsets from the overall data, and uses the kernel non-negative matrix factorization method to cluster the subsets; then, the representative subsets are used as a training set, the remaining data is used as a test set, the labels obtained by clustering are used to classify the remaining data through the deep neural network, so that the clustering of the whole data is realized. In addition, the application also uses the idea of stratified sampling to select the most representative samples, and the best division of the clustering samples and the classification samples is determined by the KL divergence. Experimental results show that, compared with other latest methods, the KNMF-DNN has more excellent results on three real scRNA sequencing data sets.

The invention relates to the technical field of soil and underground water collaborative monitoring, and discloses a soil and underground water collaborative monitoring stratified sampling and field detection integrated system, which comprises a stratified sampling module, a field detection module, a collaborative data processing module and a central control module, the system is also integrated with an early warning feedback unit. Synergistic stratified sampling of soil and underground water is achieved through the stratified sampling module, samples at different depth layers are automatically obtained through a programmable depth control device and a multifunctional sampler, and meanwhile in-situ rapid detection is conducted by integrating a multi-parameter sensor and a spectral analysis unit through the field detection module. Key indexes are measured in real time, instant feedback and calibration of data are supported, and automation and data accuracy of the monitoring process are guaranteed; soil and underground water monitoring data are fused through the collaborative data processing module, and the risk early warning and decision support capability of the monitoring data is ensured.

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 present invention discloses a stratified water sampling device for water quality testing, which belongs to the field of environmental monitoring and includes a mother chamber and a sampling device. The mother chamber includes an installation chamber and a pressure chamber. The installation chamber is arranged on the top of the pressure chamber, and the top of the installation chamber is open. The sampling device is arranged in the installation chamber. The sampling device includes a collecting bottle and an opening and closing mechanism, and the opening and closing mechanism is slidably arranged in the collecting bottle. The present invention can more accurately perform fixed-depth stratified sampling of water bodies, and can cope with various water body conditions by adjusting the water pressure. According to needs, multiple devices of the present invention can be bundled and centrally released through external equipment. A single release can complete the sampling of multi-layer water bodies, which is more efficient. In addition, the split design of the collecting bottle makes it easier to take and transport after sampling is completed.

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 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 utility model discloses a depth-adjustable seawater quality sampling device which comprises a submersible, a remote control module and a positioning module are arranged in the submersible, the depth-adjustable seawater quality sampling device further comprises a sampling mechanism, and the sampling mechanism is arranged on the submersible; according to the depth-adjustable seawater quality sampling device, through the first remote ball valve and the second remote ball valve, on-off of water flow can be accurately controlled, the water flow can be conveniently controlled at different stages, then the internal pressure of the sampling shell is controlled, and through cooperation with the one-way valve, water flow backflow can be prevented, and it is guaranteed that seawater flows in the preset direction; according to the invention, mixing of water samples at different depths is avoided, the sampling accuracy is ensured, the underwater propeller rotates to generate water flow, and the first one-way drainage valve is matched, so that the previously stored relatively shallow sea water can be drained to the outside, stratified sampling of sea water at different depths is facilitated, and the sampling accuracy is further improved.

The utility model relates to the technical field of chemical engineering, in particular to a liquefied gas stratified sampling device which comprises a spherical tank, a closed sampling box, a tank inlet pipeline, a radar liquid level meter director pipe, a sampling pipeline outside the tank, a first branch line, a second branch line and a third branch line, the closed sampling box is arranged outside the spherical tank; the tank inlet pipeline is connected with an inlet of the spherical tank; the radar liquid level meter director tube is directly inserted into the spherical tank through a radar liquid level meter at the top of the spherical tank, and an upper liquid level sampling tube, a middle liquid level sampling tube and a lower liquid level sampling tube are sequentially arranged at the upper position, the middle position and the lower position of the radar liquid level meter director tube. An upper sampling pipeline, a middle sampling pipeline and a lower sampling pipeline are installed in the tank, an operator takes materials in the tank through a closed sampler at the upper point, the middle point and the lower point for real-time test and analysis, and the qualified product leaves a factory.

The invention discloses a variable grid group state monitoring method, system and device and a medium. The method comprises the steps that multi-dimensional climate data are collected, and joint feature representation is generated through association fusion; obtaining the influence degree of each climatic factor parameter and the operation parameter of the power distribution network through multi-dimensional stratified sampling; constructing a meteorological power grid coupling index matrix, and obtaining node vulnerability; constructing a variable network group vulnerability thermodynamic diagram; generating a variable grid group load curve by comparing the correction deviation; and generating a variable grid group state monitoring report. According to the method, multi-dimensional climate data is associated and fused to generate joint feature representation; obtaining the influence degree of each climatic factor parameter change on the operation parameters of the power distribution network through multi-dimensional stratified sampling, and constructing a meteorological power grid coupling index matrix to obtain node vulnerability; finally, a variable grid group state monitoring report is generated, the problems of fixed grid deviation and centralized processing lag are solved, the monitoring accuracy and timeliness are improved, and scientific decision support is provided for power grid operation and maintenance.

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 provides a water pollution detection device and a detection method for geological environment investigation, belongs to the field of water pollution detection, and aims to solve the problems of complicated operation and cross contamination of samples during underground water stratified sampling. Comprising N fixed sleeve units, a fixing mechanism, N sampling pipes, a switching mechanism, N filling mechanisms, N detection mechanisms, a suction pipe and a suction mechanism, and N is greater than 1; by arranging a plurality of fixed sleeve units and sampling pipes, and matching with a sealing piece and a switching mechanism, independent sampling at different depths can be realized, so that sample mixing among different water layers is effectively prevented, and the accuracy and representativeness of a detection result are ensured; through cooperation of the elastic piece and the fixing mechanism, the device can be tightly attached to the wall of a drill hole to adapt to different hole diameters and geological conditions, meanwhile, the suction mechanism can discharge liquid in the downward exploration process, and downward exploration resistance and disturbance are reduced.

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.