358 results about "Sample area" patented technology

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 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.

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 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.

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 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.

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 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 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.

A method, a device, a storage medium and an equipment for observing hydrogen reservoir components in soil are provided by the present disclosure, including: obtaining the hydrogen components of soil pore water, the hydrogen components of organic matter, and the hydrogen components of lattice water corresponding to each sample; and obtaining the total hydrogen reservoir components of the target sample plot based on the hydrogen components of soil pore water, the hydrogen components of organic matter, and the hydrogen components of lattice water corresponding to the samples. The target sample plot is provided with a preset number of sampling soil profile points, each of the sampling soil profile points corresponds to n samples, and the sampling depth interval between any two adjacent samples in the same sampling soil profile point is consistent, so that the hydrogen reservoir components in the target sample plot can be accurately obtained.

The present invention is entitled "System and method for full-scale sampling to perform material testing on a steam turbine rotor." The present invention provides a method for generating a material test sample for performing material testing on a conventional steam turbine rotor, the conventional steam turbine rotor having an inter-blade region rotor surface and an inlet region rotor surface adjacent to the inter-blade region rotor surface. The method includes forming an annular ring of rotor material in a sample area, and forming a material test sample from a portion of the annular ring. A conventional steam turbine rotor is also described, the conventional steam turbine rotor including an inter-blade region rotor surface and an inlet region rotor surface adjacent to the inter-blade region rotor surface. The steam turbine rotor has a groove formed therein, and wherein the groove is machined to be capable of removing material from the steam turbine rotor to form a sample, the sample being configured to be capable of performing material property testing and operating an improved steam turbine rotor with extended thermal stresses compared to a conventional steam turbine rotor.

This application relates to a method, apparatus, and equipment for soil erosion analysis. A pre-set model is trained by comprehensively considering soil erosion driving factor data, spatial structure characteristics, and soil erosion modulus data of the sample area. During training, the spatial autocorrelation of the prediction residuals of the soil erosion prediction model is ensured to meet a pre-set non-significant condition, thereby overcoming the problem of existing machine learning models neglecting spatial characteristics. In the prediction stage, the trained model is used in conjunction with the soil erosion driving factor data and spatial structure characteristics of the target prediction area to obtain accurate soil erosion modulus data, thus obtaining reliable soil erosion analysis information.

The invention relates to the technical field of wheat quality detection, and discloses a wheat quality rapid detection system based on a near infrared spectrum. The system comprises five modules: a spectral feature dynamic acquisition module, an environmental parameter coupling analysis module, a spectral precision evaluation module, a detection state control module and a spectral resource optimization module. The spectral characteristic dynamic acquisition module acquires near infrared spectral reflectivity data of a wheat sample, and divides sample areas with different detection attention degrees; the environment parameter coupling analysis module collects growth environment parameters, and integrates the growth environment parameters with the spectral data of the corresponding region to generate an environment coupling analysis data set; the spectral precision evaluation module calculates the real-time detection accuracy according to the difference between the historical reference of the sample area in the data set and the real-time detection quality index; the detection state control module switches the load state of the spectrum acquisition channel in combination with the accuracy and a preset threshold value; and the spectrum resource optimization module allocates resources according to the load state. The system improves detection pertinence, reliability and resource utilization rate, and meets rapid detection requirements.

This invention provides a portable colorimetric detection system for astaxanthin based on a smartphone, belonging to the field of colorimetric detection technology. Its features include: an optical detection platform for accommodating the Yeast lipolyticis fermentation broth sample and providing a standardized optical detection environment; a smartphone with its camera pointed at the sample area of ​​the optical detection platform for acquiring images of the fermentation broth sample; a color analysis module embedded in or connected to the smartphone for extracting color feature parameters from the acquired images; and a quantitative model module pre-stored with a linear regression model based on the color parameters of the fermentation broth and the astaxanthin concentration, the model being constructed based on the (R+G) / 2 parameter, used to calculate the astaxanthin concentration in the sample based on the color feature parameters. The advantages of this invention are: simplified detection steps, convenient and rapid sample detection, and real-time sample monitoring.

The invention discloses a coniferous forest individual tree crown accurate segmentation method based on semi-supervised learning, and belongs to the technical field of forestry resource intelligent monitoring and remote sensing image processing. The method comprises the following steps: carrying out RGB image and laser radar data acquisition and preprocessing on a target area, constructing a CHM and generating a single tree crown pseudo label set; a part of typical sample areas are selected from the DOM, and a small number of real tree crown labels with spatial representativeness are obtained. And constructing an instance segmentation deep neural network architecture Mask R-CNN, and training a single tree crown instance segmentation deep learning model by adopting a DOM and a pseudo tag. And adopting a step-by-step fine tuning strategy, and introducing a real crown label to perform fine tuning on the trained model. According to the method, the unmanned aerial vehicle remote sensing technology and a semi-supervised learning framework are fused, pseudo label generation and a progressive fine tuning strategy are combined, low-cost and high-precision single tree crown segmentation is realized, and the method has the remarkable characteristics of high labeling efficiency, high model adaptability and reliable monitoring result.

The utility model discloses a real-time remote monitoring device for soil crack development, which comprises a monitoring shed arranged around a sample area and being a closed space formed above the sample area; the viewfinder is arranged at the top of the monitoring shed and is used for framing and photographing the observation frame in the sample area; the plurality of lamps are arranged on the inner wall of the monitoring shed above the observation frame; the soil moisture monitoring instrument is used for monitoring the moisture content of soil around the sample area. According to the utility model, the whole soil crack development process can be remotely monitored, crack images can be captured in real time, and reliable basis is provided for background analysis of the relationship between the crack width, length, curvature and the like and the water content.

The invention discloses a film thickness measurement method based on a nanometer anti-oxidation film, and relates to the technical field of nanometer film thickness measurement, the method comprises the following steps: co-heating a sample containing the nanometer anti-oxidation film and a volatile chelating agent, so that a film layer is converted into a volatile complex through a coordination reaction; condensing and collecting a gaseous product, and filtering and enriching the gaseous product on a filter membrane; measuring the masses of the filter membrane before and after enrichment as b and a respectively, and calculating the mass of a membrane layer product according to a formula m = b-a; according to the known sample area S and the film layer material density rho, the film thickness is calculated according to the formula h = m / (rho * S). According to the method, quantitative separation of the film layer is achieved through chemical coordination-sublimation, measurement errors caused by rough substrates and ultrathin film layers in a traditional method are effectively overcome, and the method has the advantages of being high in accuracy and high in anti-interference performance.

The invention relates to the technical field of rheological testing and material characterization, and provides a local rheological testing system and device based on electromagnetic driving and visual displacement tracking, and the system comprises a magnetic field generation module which is used for generating a stable magnetic field for magnetizing a magnetic needle in a sample area and a periodic gradient magnetic field for driving the magnetic needle, and a driving module, the magnetic field generation module is used for providing corresponding steady-state and alternating current excitation for the magnetic field generation module, and the image acquisition module is used for generating a uniform linear magnetic field gradient in a central area by a gradient field Maxwell configuration coil pair and an external trigger synchronization mechanism through the arrangement of the structures such as the gradient field Maxwell configuration coil pair and the external trigger synchronization mechanism; the driving force acting on the magnetic needle is ensured to be stable and repeatable; an external trigger synchronization mechanism controls driving signals to be strictly synchronized with image acquisition, and magnetic needle displacement and phase difference are accurately extracted by combining image recognition and a sub-pixel positioning algorithm, so that magnetic field fluctuation and time sequence errors are effectively reduced, the rheological parameter inversion precision is improved, and the problems of large errors and high cost in the prior art are solved.

The invention discloses a model training method and device, a target detection method and device, electronic equipment and a storage medium. The training method comprises the following steps: inputting a sample image and sample point cloud data collected for a sample region into a teacher model for target detection to obtain first feature information and a first detection result; inputting the sample image into a student model for target detection to obtain second feature information and a second detection result; determining target loss according to at least two of the first distillation loss, the second distillation loss and the third distillation loss; and at least adjusting parameters of the student model according to the target loss until a training ending condition is met, and taking the student detection model meeting the training ending condition as a target detection model. According to the method provided by the invention, the accuracy of target detection is effectively improved.

The present application belongs to the field of ecology and environmental monitoring technology, and relates to a kind of detection and analysis method for decomposition rate of leguminous sand-fixing shrub litter in field.The present application screens sample area by system grid method, collects and lays out litter bag, synchronously determines initial and current nitrogen content of soil, calculates net increment of soil nitrogen, determines horizontal diffusion boundary of nitrogen, and then obtains soil absorbed nitrogen amount;obtains equivalent dry matter loss amount based on nitrogen balance calibration, and finally determines litter decomposition rate by exponential decay fitting.The method solves the decomposition rate estimation deviation problem caused by nitrogen migration and soil absorption in traditional detection, can more accurately reflect the actual decomposition process in field, improves the reliability of monitoring data, and provides reliable technical support for nutrient cycling research and ecological restoration effect evaluation of desert ecosystem.