465 results about "Sample area" patented technology

The invention provides a preparation method of a transmission electron microscope ultrathin sample with a cross section of an electron beam sensitive material, which comprises the following steps: selecting a specific region as a to-be-extracted sample region for the electron beam sensitive material by adopting an electron beam imaging technology; depositing a protective layer on the surface of the to-be-extracted sample area by adopting an electron beam and / or an ion beam; bombarding a to-be-extracted sample area by utilizing a high-energy ion beam, extracting an electron beam sensitive material cross section sample from the to-be-extracted sample area, and fixing the extracted sample to a metal bracket; performing pre-thinning treatment on the extracted sample by using a focused ion beam to obtain a first thinned sample of which the thickness is reduced to be less than a first thickness; and transferring the first thinned sample into a micro-beam fixed-point ion thinning system, and carrying out multi-time stepped low-energy ion beam fine thinning treatment on the first thinned sample by using inert gas ion micro-beams to obtain a second thinned sample which is clean in surface and free of a surface ion damage layer and has the thickness reduced to be less than a second thickness, and taking the second thinned sample as a transmission electron microscope ultrathin sample.

The invention provides a rice growth model construction method and system based on salt stress, and relates to the technical field of growth model.The rice growth model construction method comprises the steps that firstly, monitoring points are arranged in a planting sample area, and soil conductivity data of different depths are obtained through a multi-depth soil conductivity sensor and a Kriging interpolation method; collecting root system density, a multispectral remote sensing image and a leaf area index, and calculating a canopy salt stress index; measuring the concentration of related ions to obtain a sodium adsorption ratio, and establishing a salt stress coefficient by combining the soil conductivity and the irrigation volume; fusing canopy and soil stress indexes to generate a stress comprehensive index, and introducing a salinity feedback item to represent the crop compensation capability; and finally, constructing a hybrid machine learning model by adopting a convolutional neural network and a long-short-term memory network to realize rice growth dynamic prediction. According to the method, multi-dimensional salinity stress quantification of a soil-crop system is realized, and decision support is provided for salinization treatment in precision agriculture.

The invention relates to the technical field of forest image processing, in particular to a forest information extraction method based on a remote sensing image. The method comprises the following steps: obtaining remote sensing image data of a forest area and a GPS positioning sample place area, and carrying out sample area remote sensing image processing and vegetation index analysis to obtain vegetation index data corresponding to each forest type in the sample area; performing end member component reflectivity analysis on the remote sensing image data of the sample area to obtain spectral reflectivity values corresponding to all end member components of the forest in the sample area; performing mixed pixel decomposition on a corresponding sample area remote sensing image in the sample area remote sensing image data in combination with a linear mixed pixel decomposition model so as to generate an abundance map of each ground feature type of the sample area; and carrying out pixel forest information classification extraction on each ground feature type abundance map of the sample region to obtain each forest type distribution map of the sample region. According to the invention, different forest information in the remote sensing image can be obtained by integrating the surface reflectance and the vegetation index.

The invention relates to the technical field of microscopic biological observation, in particular to a microscopic biological observation method based on a digital microscope. Firstly, original point cloud of a sample area on an objective table is obtained through structured light scanning, noise points are removed, point cloud density is optimized by adjusting scanning intensity for multiple times, high-precision and low-noise preprocessed point cloud is generated, then a reference plane is fitted in combination with the structured light point cloud, and obstacle and non-obstacle features are extracted. Detecting a second center coordinate of the bounding box by using a YOLO model, obtaining a third center coordinate through Hough circle detection, generating a standard coordinate by fusing multi-source coordinates, generating a global path based on a grid map and an RRT algorithm, predicting a track in real time, calculating a minimum Euclidean distance with an obstacle, dynamically inserting a B spline transit point to avoid a dangerous area, and ensuring a safe distance. Finally, collecting sample images in real time along the path and extracting microorganism characteristics; the technical problem that the efficiency is low when a traditional microscope is used for acquiring a microorganism image in a sample is solved.

The invention relates to the technical field of performance detection, in particular to a heat insulation performance detection process for an energy-saving and environment-friendly curtain wall material. Comprising the following steps: S1, cutting a sample; step S2, performing pretreatment; s3, performance testing is carried out; s4, calculating the actual thermal conductivity of the to-be-detected sample according to the actual temperature difference and the heat source condition. According to the invention, the sample area is divided into two types and temperature distribution analysis is carried out to effectively judge the accuracy of the thermal imaging image, and corresponding regulation and control are carried out according to the analysis result, so that the reliability and accuracy of the test are improved, the sample is ensured to be uniformly heated in the test process, local overheating or supercooling is avoided, and the test efficiency is improved. By monitoring a real-time displacement field and environmental parameters, whether jitter or heat flow disturbance exists in the test process can be identified, and the reliability of test data is further ensured; and comparing the calculated actual heat conductivity with a related standard to judge whether the heat insulation performance of the sample meets the requirement or not.

The invention relates to the technical field of data analysis, and further relates to a forest pest and disease identification method based on data analysis, and the method comprises the steps: 1, obtaining an aviation orthoimage of a target forest region and a trapping point count in a set time interval, and associating the trapping count to a sample region unit according to the recent affiliation; 2, a composite simplex complex composed of a plaque layer, a sample area layer and a trapping layer is constructed on a forest map grid, counter examples are screened out through a two-stage verifier, and then suspected pest and disease damage triggering units are output; step 3, establishing a disease and insect pest atlas database, and describing typical modes by disease type topological fingerprints; and carrying out similarity judgment on the topological evidence packet and the disease type topological fingerprint to generate a disease type, a range and a credibility level, and carrying out layering labeling on a forest map grid. According to the method, the fine-grained structure change of diseases and insect pests can be captured in an early stage, and the recognition accuracy and interpretability are improved through multi-source evidence synthesis.

Disclosed are systems, devices and methods for quantifying unique features of an object such as an artistic work to identify and authenticate the object and specific characteristics thereof using multi-spectral diagnostic characterization techniques and analytical algorithms. In some aspects, a method for creating an identification for an object includes acquiring image data of an object in two or more electromagnetic spectrums along a coordinated array of sample regions of the object; analyzing the acquired image data to produce a quantitative data set including specific characteristics of the object associated with the two or more electromagnetic spectrums for each sample region; generating a digital identification associated with a unique data fingerprint, based on the specific characteristics, in which the digital identification solely corresponds to the object; and storing the generated digital identification.

The invention discloses a hyperspectrum-based wheat variety identification method. The method comprises the following steps: establishing a hyperspectrum identification model set of a target variety; checking and screening to obtain a target variety hyperspectral recognition model; and detecting the purity of the to-be-detected wheat variety in the wheat blind sample by using the variety identification hyperspectral model. And constructing a hyperspectral recognition model set of the target variety, wherein the modeling process comprises the following steps: confirming a wheat grain sample area in a spectrum acquisition process, extracting an average spectrum of a wheat grain modeling sample and constructing a recognition model. Screening and optimization of a target variety hyperspectral recognition model: selecting a to-be-detected sample with known target variety purity; determining a wheat grain sample area during sample spectrum collection; identifying a target variety in the to-be-detected wheat grain sample, calculating the purity of the target variety, and comparing the purity of the target variety with a true purity value of the target variety to obtain a hyperspectral identification model of the wheat target variety for identifying the target variety.

The invention relates to a soil microorganism recovery index processing method and system and electronic equipment. The method comprises the following steps: collecting a soil sample in a gradient sample area, and measuring soil microbiological indexes and soil attributes; taking the soil microorganism indexes as a mother sequence, taking the soil attributes as subsequences, calculating correlation coefficients and correlation degrees between the subsequences and the mother sequence, and determining key soil attributes influencing soil microorganism recovery; determining a weight value of the key soil attribute; based on the numerical values of the key soil attributes in the original ecological reference area and the damaged unrepaired area, combining the weight values of the key soil attributes, and adopting an improved soil restoration index model to calculate and obtain a soil microorganism restoration index; the technical problem that in existing mining area ecological restoration evaluation, an original ecological system is not used as a benchmark, and the soil microorganism restoration degree cannot be quantitatively represented is solved, and the technical effect of achieving objective, comparable and quantifiable accurate evaluation of the soil ecological function restoration degree is achieved.

The invention relates to a method for predicting landslide susceptibility by using a machine learning model and SBAS-InSAR deformation, and the method comprises the steps: obtaining an environment static factor and landslide catalog in a sample region, obtaining a dynamic factor based on an SBAS-InSAR technology, building a landslide prediction model based on a random forest algorithm, and carrying out the prediction of the landslide susceptibility. Inputting the static factor, the dynamic factor and the landslide record into a landslide prediction model for training; and inputting the environmental static factor and the dynamic factor of the to-be-detected area into the trained landslide prediction model to obtain a landslide susceptibility map. According to the landslide susceptibility mapping method, the earth surface deformation rate serves as a dynamic factor to be fused into landslide susceptibility evaluation, a scientific basis is provided for landslide prevention and disaster reduction, the earth surface rail rising deformation rate and the earth surface rail falling deformation rate are obtained, the earth surface deformation rate is integrated into a static factor data set, and the landslide susceptibility mapping accuracy is improved. After uniform data preprocessing is completed, static factor and dynamic factor data are input into the landslide prediction model for joint training and evaluation, and the method has the advantage of being high in precision.

The invention discloses a method and system for dynamically monitoring soft sandstone slope erosion, and relates to the technical field of geological disaster monitoring, and the method comprises the steps: building a monitoring sample area on a soft sandstone slope, obtaining initial terrain point cloud data, generating a reference digital elevation model, and recognizing a vegetation space pattern; collecting rainfall data in real time, and triggering an erosion monitoring program when rainfall parameters meet a preset triggering condition; acquiring real-time terrain point cloud data, generating a current digital elevation model and acquiring a slope fracture feature set; based on the elevation variation of the current digital elevation model and the reference digital elevation model, carrying out erosion intensity partitioning; determining an erosion regulation effect coefficient of each vegetation coverage subarea by combining vegetation spatial pattern data; and establishing an erosion risk assessment model, and outputting erosion sensitivity grading of the monitoring sample area and a key protection area identification result. According to the invention, automatic and high-precision dynamic monitoring of soft sandstone slope erosion is realized, and a scientific basis is provided for water and soil conservation and geological disaster prevention and control.

An intra prediction method includes: performing filtering processing on template samples corresponding to a target image block to obtain filtered template samples, where the template samples include at least part of reconstructed samples in a neighboring sample region of the target image block; obtaining, based on the filtered template samples, at least one intra prediction mode and an amplitude of the at least one intra prediction mode; determining, based on the amplitude of the at least one intra prediction mode, a target intra prediction mode from the at least one intra prediction mode; and determining, based on the target intra prediction mode, a target prediction value corresponding to the target image block.

A line-scan confocal scanning light-field microscopic imaging apparatus (10) and method. Said apparatus comprises: a microscope (100), a target biological sample being placed on a stage of the microscope (100); an excitation optical path (200), which is used for outputting a line-shaped light source for exciting fluorescence of the target biological sample; a line-scan hardware optical path (300), which is used for performing multi-dimensional scanning of the line-shaped light source to obtain a scanned light-field image; a microlens array (400), which is used for light-field modulation of the line-shaped light source to obtain a line-shaped light field; a camera (500), which is used for performing three-dimensional imaging on the basis of the line-shaped light field to obtain a three-dimensional image; and a control system (600), which is used for controlling the line-scan hardware optical path (300) and the camera (500) to execute a line-scan confocal action, so that the number of pixel rows exposed simultaneously at a same moment is consistent with a sample region illuminated by the line-shaped light source, so as to separate a sample fluorescence signal and a background fluorescence signal of the target biological sample, and, on the basis of the scanned light-field image and the three-dimensional image, generate a three-dimensional microscopic image with background fluorescence removed for the target biological sample. Therefore, the problem of background fluorescence in a scanning light-field microscopy system is solved.

The invention provides a cold and hot dual-purpose transmission electron microscope in-situ sample rod. The cold and hot dual-purpose transmission electron microscope in-situ sample rod comprises a handle, a rod body and a rod head, the handle part is provided with an external power supply interface and a cold source conduit interface, and can be externally connected with a heating power supply, a cold source and the like, so that current, a cooling medium and the like flow into the rod head part of the sample rod to realize a heating or refrigerating function. The rod head part is provided with an objective table, a cooling medium guide pipe, a sample loading device, a heat sensitive element and an electrode assembly. The sample fixing device is used for fixing a sample; the heat-sensitive chip is used for measuring the temperature of the sample area; the heating electrode part is used for heating the sample part after being connected with a power supply so as to realize transmission electron microscope observation of the sample in a high-temperature environment. By using the in-situ sample rod disclosed by the invention, direct heating and refrigeration can be realized to create high-temperature and low-temperature detection environments, and the experimental operation process of in-situ testing is simplified.

The invention relates to a consumable device for sweat-based monitoring, the consumable device comprising a proximal part configured to contact with a user, and further comprising: a) a sweat induction means on the proximal part, comprising: i. a sweat promoter configured to contact the skin of a user, and ii. a microfluidic structure; and b) a sweat interrogation means, located distally to the sweat induction means and comprising: i. a sample area; and ii. a passive fluid pump configured to induce the flow of the liquid through the sample area. The microfluidic structure is configured to convey fluid from the sweat promoter to the sample area. The invention also relates to a sweat-monitoring system comprising: a consumable device as above and a sweat-based monitoring device, the sweat-based monitoring device comprising sensing means and processing means.

The invention discloses a sample sending process monitoring system fusing image recognition and geographical location information, comprising a multi-source information acquisition module for acquiring image information around a sample and geographical location information of the sample in a sample sending process; the sample sending abnormal behavior recognition module based on image recognition is used for detecting pedestrian abnormal behaviors, camera abnormal shielding and camera angle abnormal adjustment in a sample area according to the image information; the sample sending abnormal behavior recognition module based on the geographic position information is used for extracting space-time trajectory characteristics of the sample according to the geographic position information of the sample and detecting a time abnormal trajectory, a space abnormal trajectory and a space-time abnormal trajectory according to the space-time trajectory characteristics; and the early warning module is used for generating an abnormal behavior signal or a non-abnormal signal according to the detection result of the sample sending abnormal behavior and the abnormal track behavior, and sending early warning information according to the abnormal behavior signal. The method has the advantages of small algorithm size, easy migration, low cost and the like, and can improve the user experience.

The invention relates to the technical field of detection sample sealing, in particular to an instant-drying type slide sample sealing protection device which comprises a slide tray provided with a slide placing groove used for placing a slide; the linear driving module is connected with the slide tray and is used for driving the slide tray to linearly act; the glue adding module comprises a glue dripping head located above the moving path of the slide tray and is used for adding glue to the sample area of the slide; the glue scraping module comprises a scraping blade, and the scraping blade is located above the moving path of the slide tray and used for scraping glue on the slide; and the glue drying module is positioned on one side of the glue scraping module and is used for drying glue on the slide. According to the method, a brand new process is adopted, cover glass does not need to be placed on the slide, the problem of cover glass fragmentation and the problem of mounting medium pasting are avoided, the problem of mutual intersection of smear lamination storage is solved, the storage efficiency of a smear cabinet is improved, and rapid smear sealing can be achieved.

The invention discloses a methane column concentration inversion method, device and equipment, a medium and a program product, relates to the technical field of remote sensing data processing, and is used for accurately determining the methane column concentration and avoiding the problem of low accuracy of a calculation result caused by neglecting actual working condition factors. The method comprises the following steps: acquiring meteorological parameters, methane distribution fitting information, radiance image data observed by a satellite and sample working conditions of a sample area in a natural gas emptying process; processing the radiance image data based on the initial inversion model to obtain an initial inversion result; the initial inversion result comprises the column concentration of methane in a plurality of resolution grids in the space above the sample area; determining correction parameters based on the meteorological parameters, the methane distribution fitting information and the initial inversion result; and correcting the initial inversion model based on the correction parameter to obtain a target inversion model corresponding to the sample working condition.

The invention relates to a soil freezing process and hyperspectral imaging real-time synchronous testing device, and belongs to the technical field of frozen soil physical testing and hyperspectral imaging. The device comprises a freezing bin, a sample box, a hyperspectral imaging system and a light source system. The freezing bin is of a hollow structure with a groove, an observation window is formed in the top of the freezing bin, and temperature control modules are arranged at the two ends respectively. The sample box consists of a bottom groove plate and a top plate, and a sample area for filling a soil sample is formed between the bottom groove plate and the top plate; a plurality of thermocouple sensors are integrated in the sample box, one end of each thermocouple sensor is closed, and the other end of each thermocouple sensor is open; the hyperspectral imaging system comprises a hyperspectral camera and a data processing module; the light sources are symmetrically distributed on the two sides of the hyperspectral camera. Through structure integration and function coupling, high fusion of hyperspectral image acquisition and temperature synchronous monitoring in the soil freezing process is achieved, and the device has the advantages of being high in test precision, good in imaging quality, convenient to operate, low in cost and the like.

The invention relates to the technical field of numerical simulation, in particular to a tunnel construction whole process numerical simulation method based on non-contact measurement, which comprises the following steps: S1, acquiring a tunnel structure corresponding to the shape and size of a tunnel, a tunnel construction method and construction conditions corresponding to construction steps, dividing the tunnel into a plurality of sample areas according to a tunnel structure and construction conditions; s2, the settlement amount of a measuring point in each sample area is obtained, the center of the tunnel is set as a starting point, and a settlement distribution diagram is obtained according to the distance between the measuring point and the center of the tunnel; s3, according to the settlement distribution diagram, vertical displacement of the bottom of the tunnel, normal displacement of the two sides of the tunnel and the settlement area generated during tunnel construction under the corresponding construction steps are obtained, and the maximum settlement factor and the correction factor are obtained through calculation; s4, identifying a maximum settlement point and a maximum change point in the tunnel, and using the maximum change point as a basis to perform simulation by using the finite element model; and the construction efficiency and quality are improved.

The invention provides a blood cell detection method and system based on a microscopic multispectral image, and relates to the field of blood cell detection.The blood cell detection method based on the microscopic multispectral image comprises the following steps that S01, pre-scanning is carried out, performing low-resolution pre-scanning on a sample area of the input cell smear, and screening a plurality of candidate view fields based on a preset view field selection algorithm; s02, performing multi-direction scanning according to the spatial distribution of the candidate visual fields. Through the cooperation of the above structures, the system has the following beneficial effects: 1, multiple sampling can be carried out, the optimal view can be selected, and the recognition efficiency can be improved; secondly, a plurality of single-spectrum band images can be synthesized into a full-light image, and compared with a traditional single-wavelength image, more cell characteristics and details can be provided; and thirdly, the focal length can be dynamically adjusted, and accurate focusing on a plurality of specific layers is realized.

The invention discloses an intelligent avalanche susceptibility assessment method fused with a deep residual network, and relates to the technical field of geological disaster risk assessment. According to the method, the spatial precision is high, the 12.5 m high-resolution DEM and 10 multi-source evaluation factors are adopted, the influence of the microtopography on the stability of accumulated snow can be accurately recognized, and the avalanche point hit rate reaches 82.5% and is far higher than that of a traditional method (generally lt: 70%); the model is excellent in performance, the interpretability of a shallow model is reserved through a two-stage framework of an integrated learning primary model and a residual network, nonlinear interaction among factors is captured through a deep network, and AUC of ResMLP is equal to 0.8496 and is improved by more than or equal to 4% compared with the primary model; the method is high in generalization stability, the prediction variance is reduced by about 12% on the premise that the training cost is not increased by introducing a TTA mechanism with Gaussian noise added, the method is particularly suitable for a high-altitude small sample area, and the overfitting problem is effectively relieved.

Disclosed are systems, devices and methods for quantifying unique features of an object such as an artistic work to identify and authenticate the object and specific characteristics thereof using multi-spectral diagnostic characterization techniques and analytical algorithms. In some aspects, a method for creating an identification for an object includes acquiring image data of an object in two or more electromagnetic spectrums along a coordinated array of sample regions of the object; analyzing the acquired image data to produce a quantitative data set including specific characteristics of the object associated with the two or more electromagnetic spectrums for each sample region; generating a digital identification associated with a unique data fingerprint, based on the specific characteristics, in which the digital identification solely corresponds to the object; and storing the generated digital identification.

The invention discloses a pepper remote sensing yield estimation system and method under a complex terrain. The method comprises the following steps: obtaining an unmanned aerial vehicle image of a sample area; carrying out classic vegetation index screening to form an optimal feature subset, and extracting the total area of a pepper plant canopy and the area of a single pepper plant canopy; obtaining a yield prediction value of the single pepper plant according to the canopy area of the single pepper plant; extracting a satellite image covering the target area and performing registration; marking the registered satellite image; screening out a series of key feature data sets from the optimal feature subset; and constructing a series of satellite image yield inversion models based on a mechanical learning algorithm, and screening out an optimal satellite image yield inversion model to predict the pepper yield of the target area. By utilizing the satellite remote sensing technology to estimate the yield of the Chinese prickly ash, the accuracy of yield prediction can be improved, the automation and intelligence level in precise estimation of the per unit yield of the Chinese prickly ash can be improved, large-area monitoring is realized, and the monitoring efficiency is improved.

The invention discloses an attribute feature driven scene-to-image cross-domain mapping sample set construction method, and belongs to the technical field of depth model compression, and the method comprises the steps: carrying out the augmentation of a basic sample set through employing an image feature transformation method, and obtaining a material library used for sample synthesis; the basic sample set comprises a simulation target material and a real shooting environment material; performing spatial position registration on an actual target and an actual environment in an actual application scene to obtain position information of the actual target in an environment coordinate system; selecting a corresponding simulation target material and a real shooting environment material from a material library based on an actual application scene; based on the position information of an actual target in an environment coordinate system, performing threshold segmentation on a selected simulation target material and fusing the selected simulation target material with a real shot environment material to obtain a high-confidence synthetic sample, so as to solve the problems of obvious segmentation of pixel blocks in a synthetic sample region, sample distortion and high reliability of the existing sample synthesis method. And therefore, a high-confidence sample set required by intelligent algorithm training cannot be effectively synthesized.

The invention discloses a water quality concentration acquisition and real-time detection system and method, and relates to the field of environmental monitoring, the system comprises a sampling scheduling module, an analysis detection module, a trigger judgment module, an interpolation modeling module and a linkage control module. According to the method, the historical concentration data and the environmental factors are modeled by introducing the machine learning algorithm, the migration trend of the pollutants in the water body can be predicted, the acquisition nodes are scheduled in advance to sample a high-concentration area possibly occurring, missing detection and delayed response are effectively avoided, and the early warning capability of pollution emergencies is improved. By fusing remote sensing images, ground sampling data and hydrodynamic information and utilizing an improved geographically weighted regression model and a graph neural network algorithm, concentration interpolation prediction is carried out on a non-point distribution area, and the problem that traditional point location type monitoring space coverage is insufficient is solved.

The invention provides an image enhancement method and device, a storage medium and electronic equipment, and the method is applied to the technical field of computers, and comprises the steps: obtaining a first sample data set which comprises a first sample image, a first sample text and a sample region coordinate, performing model training on the initial region selection large model based on the first sample data set to obtain a region selection large model, and obtaining a second sample data set which comprises a second sample image, a second sample text and a sample clear image, and performing model training on the initial image enhancement large model based on the second sample data set to obtain an image enhancement large model, and performing image enhancement processing based on the region selection large model and the image enhancement large model. According to the method, the local image can be determined through the area selection large model, the image enhancement large model is adopted to perform enhancement processing on the local image, the pertinence of image enhancement is improved, and the operation amount of image enhancement is also reduced.

The present disclosure provides a machine vision-based automatic focusing and automatic centering method and system. An object stage is controlled to move in an imaging distance range of an electron microscope, and images scanned by the electron microscope when the object stage is at different imaging distances are acquired. The image definition of an object stage image is calculated according to a gray-scale value of each pixel in the object stage image, the imaging position when an image definition value is the maximum is determined, and the object stage is controlled to move to the position to realize machine vision-based accurate focusing. After the accurate focusing is completed, the images that can clearly reflect the arrangement relationship between an indenter and a sample on the object stage are acquired by the electron microscope, and a midline of an indenter area and a midline of a sample area are aligned.

The invention relates to a method and a system for identifying a slow-dip-angle structural plane of a pumped storage upper reservoir, and the method comprises the following steps: selecting a plurality of surveyed pumped storage power station upper reservoir regions as sample regions, and obtaining the exploration sample data of the sample regions; gridding the sample area into a three-dimensional voxel grid, preprocessing exploration sample data, extracting exploration features of each voxel in the three-dimensional voxel grid, adding a sample label indicating whether the voxel belongs to a low-dip-angle structural plane, and putting the sample label into a training sample set; constructing a recognition model based on a machine learning model, and performing iterative training on the recognition model by using the training sample set to obtain a voxel low-dip-angle structural plane probability recognition model; and acquiring exploration data of a reservoir region on the pumped storage power station to be identified, acquiring a prediction probability of whether each voxel in the region belongs to a low-dip-angle structural plane or not by using the voxel low-dip-angle structural plane probability identification model, and further identifying the low-dip-angle structural plane in a target region.

The invention relates to the technical field of oil and gas geochemical experiments, in particular to a shale hot-pressing hydrocarbon generation and expulsion simulation system and method based on multi-type containers. The system comprises a hydrocarbon generation autoclave unit, a tube furnace heating unit, a hydraulic sealing unit, a liftable integrated platform unit, a pressure monitoring and product collecting and metering unit and a computer control unit, hydrocarbon generation and expulsion simulation of shale samples is completed through mutual cooperation of all the units, and uniform and accurate control over the temperature of a sample area is achieved. Automatically sealing and disassembling the hydrocarbon-generating high-pressure kettle, and rapidly cooling and cleaning the reaction kettle body after the experiment is completed; the temperature of the upper tube furnace and the temperature of the lower tube furnace are controlled by calculating the combined stable weight, the temperature difference consistency coefficient, the temperature control effectiveness and the target regulation and control temperature, samples of different types and specifications can be simulated by selecting different reaction containers, the sample chamber in the autoclave body can be uniformly heated in the heating process, and the heating efficiency of the autoclave body is improved. And the accuracy and reliability of the experiment can be improved.