285 results about "Stratified sampling" patented technology

The invention relates to the technical field of carbon sequestration capacity monitoring, in particular to a carbon sequestration capacity monitoring and evaluating method for an island ecosystem, which comprises the following steps: S1, multi-source data fusion and habitat partitioning: dividing an island habitat type partitioning map; s2, stratified sampling and carbon reserve actual measurement: measuring the carbon content, and constructing a carbon reserve actual measurement database; s3, constructing a dynamic carbon sink model: carrying out a whole growth cycle environment simulation experiment on sea grass and algae in different habitats, and establishing the dynamic carbon sink model taking habitat types as classification units; s4, carbon sequestration capability quantification and bottleneck diagnosis: calculating carbon sequestration contribution values of various habitat types, identifying low-efficiency carbon sequestration areas through contribution value comparison, and analyzing environmental limitation factors of the low-efficiency carbon sequestration areas; and S5, integrating a sink increasing technology and generating a path: designing a habitat specific sink increasing scheme for the identified low-efficiency carbon sink area. According to the invention, high-precision monitoring, intelligent evaluation and system optimization of the island ecosystem carbon sequestration capability are realized.

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.

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 provides a network intrusion detection method and system, a terminal and a storage medium, and the method comprises the steps: determining a target sampling strategy according to the sample number of preprocessed data, and carrying out the sampling of the preprocessed data according to the target sampling strategy, and obtaining a sampling sample; inputting the sampled sample into a network intrusion detection model for feature extraction, deep information extraction and multi-head attention mechanism calculation to obtain a global feature vector; performing data type prediction according to the global feature vector, and calculating model loss based on a weight balance loss function dynamically adjusted based on the category number; training the network intrusion detection model according to the model loss; and inputting to-be-detected network data into the converged network intrusion detection model for intrusion detection to obtain a network intrusion detection result. According to the embodiment of the invention, the pre-processed data is sampled in a layered sampling mode, so that the phenomenon of unbalanced sample data category is effectively prevented.

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 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 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 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.

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 discloses a soil solidification effect detection and analysis system and method, and relates to the field of soil solidification effect detection.The soil solidification effect detection and analysis method comprises the steps that based on the sedimentation characteristics of a solidification material, soil is divided into a bottom sedimentation sudden change zone, a middle sedimentation gradual change zone and a top sedimentation stable zone in the depth direction, and the minimum operation thickness of a sampling tool serves as the reference layer thickness; dividing non-equidistant sampling layers according to different multiples in each settlement zone to intercept columnar soil samples; performing an initial dynamic load test on the original soil sample, then performing circulating saturated water drying treatment on the same soil sample, and applying the completely same dynamic load mode again; a water circulation effect in a natural environment is simulated by performing vacuum water saturation and constant-temperature drying circulation treatment on a soil sample for multiple times and combining a dynamic load test after water damage. Through non-equal-interval stratified sampling and multi-round dry-wet cycle testing, stratified dynamic and high-precision quantitative evaluation of the water damage resistance of soil solidification is achieved.

The invention provides a method and system for automatically classifying merchants, and the method comprises the steps: obtaining multi-source data, and generating a structured merchant data set; performing feature engineering on the merchant data set, constructing a merchant multi-dimensional feature vector, and performing multi-dimensional marking on the multi-dimensional feature vector to obtain marked merchant feature data; performing data set division and preprocessing on the commercial tenant feature data by using random sampling, stratified sampling and cross validation technologies to obtain a model training data set, training a commercial tenant classification model, and generating a trained commercial tenant classification model; classifying the commercial tenants by using the trained commercial tenant classification model, and calculating scores of the commercial tenants based on a set grade division standard and a scoring rule system to obtain a commercial tenant grade scoring result; and comparing the commercial tenant grade scoring result with a preset risk threshold value, and generating a commercial tenant classification and risk assessment result. According to the invention, the accuracy of commercial tenant classification can be effectively improved.

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 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 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 relates to the technical field of underground water collection, and discloses an anti-pollution underground water stratified sampling device which comprises a sampling pump and a stratified pipe, the stratified pipe comprises a first pipe end on the head portion, a second pipe end on the tail portion and a plurality of pipe sections in the middle, and sealing covers are fixed to the first pipe end, the second pipe end and the end portions of the pipe sections. A connecting part is mounted between every two adjacent sealing covers; a segmentation opening is formed in the middle of the pipe section, the interior of the pipe section is divided into an upper cavity and a lower cavity through the segmentation opening, pressure collecting parts driven to move under air pressure are installed in the upper cavity and the lower cavity, and the pressure collecting parts move towards the interior of the lower cavity to enable water to enter the lower cavity through negative pressure. When water samples are collected, each layer of water sample is physically isolated by the independent cavity structure, the water samples with relative depths are extracted more accurately, and various water samples with different depths are collected at one time under the action of the air pressure of the sampling pump, so that excessive disturbance to the water body is avoided.

The invention relates to the technical field of environment monitoring, in particular to an environment monitoring instrument capable of regularly sampling and detecting, which is characterized in that a protection box is arranged on the upper side of a concentric-square-shaped floating plate through a concentric-square-shaped frame, a conveying mechanism is arranged in the protection box, a sampling mechanism is arranged on the protection box and the conveying mechanism together, and an extraction mechanism is arranged on the sampling mechanism and the concentric-square-shaped frame together; through cooperation of the conveying mechanism and the sampling mechanism, timing sampling can be carried out on the specific depth of a monitored water area in a continuous time period, and synchronous stratified sampling can be carried out on different water layers in the same area in a short time period; meanwhile, the sampling device can avoid cross mixing of samples at all depths due to factors such as water disturbance or equipment movement through physical isolation; through cooperation of the conveying mechanism, the sampling mechanism and the extraction mechanism, residual substances in a sampling container can be automatically cleaned before sampling each time, and meanwhile, samples in the container can be automatically extracted and conveyed.

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 belongs to the technical field of ore processing, and discloses mine ore sampling equipment which comprises a moving seat and universal wheels fixedly arranged at four corners of the bottom of the moving seat, and the moving seat is U-shaped; the crushing mechanism is located at the top of the moving seat, and the crushing mechanism is used for crushing ore to be sampled; the partition sampling mechanism is arranged on the periphery of the crushing mechanism, and through cooperation of a sampling barrel, a sealing sleeve, a driving assembly, an adjusting assembly and other structures, partition sampling of ore is conveniently achieved; the problems that in the sampling process of existing ore sampling equipment, single sample storage limitation exists, continuous stratified sampling cannot be achieved, when samples at different places or depths need to be sampled, a sample storage container needs to be manually and frequently replaced, the operation efficiency is low, and the sampling time is short are solved. And sample confusion or pollution may be caused by misoperation.

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.