415 results about "Soil type" patented technology

The invention provides a green land carbon sink amount estimation method for ecological restoration planning, and the method comprises the steps: precisely collecting the vegetation coverage rate, species distribution, soil type and other data of a green land through integrating remote sensing images, unmanned aerial vehicle monitoring, a geographic information system and other technical means; the method comprises a vegetation carbon reserve model, a soil carbon reserve model and an environmental factor correction module, the vegetation carbon reserve is calculated by adopting an Allometric model, the soil carbon reserve is subjected to multi-layer estimation, and the carbon sink amount is corrected by utilizing meteorological conditions and topographic factors, so that the accuracy and applicability of estimation are improved, and the estimation accuracy is improved. According to the method, dynamic estimation of the carbon sink amount is realized through time sequence analysis and in combination with vegetation growth cycles and seasonal changes, and visual result display and decision support functions are provided. The method is widely applicable to various ecological environments such as urban green land, wet land and mountain land, and scientific basis is provided for ecological restoration planning and carbon sink monitoring.

The embodiment of the invention provides a soil nutrient spectrum detection and regulation method and system, and belongs to the technical field of soil nutrient spectrum detection and regulation. The method comprises the following steps: collecting soil multispectral original data and environmental data, generating a calibration matrix and a nonlinear correction parameter, executing optical reflectivity conversion, and synchronously fusing a time domain reflectometry dielectric constant and a spectral moisture index to obtain fused moisture content; selecting a nutrient diagnosis characteristic wave band pair to obtain a moisture inhibition type spectral index; and predicting the soil nutrient content by using a pre-trained transfer learning model to obtain a soil nutrient content prediction value, and performing fertilization regulation and control based on the fertilization decision risk coefficient. According to the invention, extreme soil moisture measurement deviation is solved through a soil type adaptive moisture fusion technology; through multi-source risk quantitative decision and PID self-adaptive regulation and control, the fertilization amount can be dynamically optimized, execution safety is guaranteed, and a detection-decision-execution precision agriculture closed loop is formed.

The invention relates to the technical field of wetland carbon evaluation, and discloses a wetland carbon flux evaluation and management system based on three-dimensional digital twinning. The system comprises a three-dimensional modeling module, a flux monitoring module, a model optimization module and a twin updating module. The three-dimensional modeling module integrates wetland geographic space coordinates, an ecological parameter set and elevation, vegetation coverage density and soil type layering information, constructs a three-dimensional terrain grid model, and generates a three-dimensional digital twinborn body with a geographic mark; the flux monitoring module is used for collecting data such as atmospheric temperature gradient based on real-time environmental parameters, extracting carbon flux change characteristics through time sequence analysis and outputting a dynamic distribution state value; the model optimization module uses an adaptive evolutionary algorithm to adjust the parameter matching degree, iteratively updates constraint conditions, and generates an optimization parameter set; and the twin updating module recognizes abnormal grid units according to the abnormal grid units, corrects attributes by combining measured data, calibrates a topological relation, and outputs an updated evaluation model.

The invention discloses an optimization method for spatial distribution of garden plants, which comprises the following steps: generating a high-precision 3D model of a garden terrain by adopting a plant spatial adaptive layout algorithm and utilizing a laser radar or unmanned aerial vehicle technology, generating a weight matrix according to soil types, illumination intensity and drainage characteristics, and applying a dynamic weight Voronoi algorithm in the terrain model to optimize the spatial distribution of the garden plants. The method comprises the following steps: generating a planting range of each plant, establishing a root distribution model by using an underground imaging technology, simulating distribution dynamic of moisture and nutrients among root systems in combination with underground hydrological data, collecting sensor data in real time by using a wireless network, designing a dynamic irrigation scheme, and optimizing the planting density and irrigation range of the plants. Data such as terrain and climate are integrated into a virtual garden, the virtual garden is ensured to be consistent with an actual state through interaction of a sensor and a digital twinborn model, plant growth, ecological benefit changes and disaster risks are predicted by using the digital model, and a user is allowed to modify and optimize the garden layout in real time through a visual interface.

The invention discloses a method for predicting the risk of a valley-type tailing pond being threatened by extra-small watershed flood, and the method comprises the steps: S1, obtaining the soil type, vegetation coverage and gradient of the extra-small watershed of the valley-type tailing pond, and the spatial distribution data of a valley, and building an underlying surface classification system; s2, non-linear key inducements in the rainfall-runoff process in the valley terrain extra-small watershed are extracted, dominant factors influencing runoff formation are recognized, and a non-linear confluence calculation numerical model is constructed; s3, constructing a distributed sensing monitoring network through a rainfall monitoring station and a runoff monitoring station; s4, constructing a layered three-dimensional hydrological hydrodynamic model of coupling rainfall runoff generation-slope confluence-shallow groundwater flow; and S5, establishing a risk assessment index system and a risk prediction model through a layered three-dimensional hydrological hydrodynamic model in combination with flood risk influence factors. According to the method, accurate prediction of the valley-type tailings pond under the flood threat in the extra-small watershed is realized, and the problem of poor predictability of the flood threat in the extra-small watershed is solved.

The invention relates to the technical field of safety evaluation, in particular to an ultra-deep soft soil foundation pit safety evaluation method and system, and the method comprises the following steps: obtaining the geological data of an ultra-deep soft soil foundation pit, recording the soil type, soil density, underground water level and temperature data, carrying out the data arrangement and formatting, and generating a geological data record sheet. According to the method, the detailed geological data of the ultra-deep soft soil foundation pit are acquired and arranged, the accuracy and the applicability of the basic data are enhanced, so that subsequent analysis is carried out according to clear actual conditions, and the timeliness and the accuracy of risk prediction are ensured by directly associating prediction of mechanical behaviors with risk assessment; the risk assessment model can be dynamically adjusted, the effectiveness and accuracy of early warning are improved, the risk dependency relationship model and the monitoring scheme are integrated, theoretical and actual operations are effectively linked, the systematicness and adaptability of construction safety management are effectively improved, and the accident prevention capability is remarkably improved.

The invention relates to the technical field of remote sensing monitoring equipment, and discloses tropical agricultural ecology remote sensing monitoring equipment which comprises an unmanned aerial vehicle platform, a remote sensing unmanned aerial vehicle is arranged on the unmanned aerial vehicle platform, and a monitoring sensor assembly is loaded on the remote sensing unmanned aerial vehicle. The monitoring sensor assembly comprises a high-resolution camera device, a hyperspectral sensor, a thermal infrared sensor and a laser radar, a data receiving module, a data processing module and a display module are arranged in the unmanned aerial vehicle platform, and the monitoring sensor assembly is mainly used for obtaining soil images and data information. The data processing module can judge soil ecological information according to images and data, and the monitoring sensor assembly and the data processing module are matched to obtain the erosion degree and fertility condition of soil in the planting area, the moisture content, the soil type, the moisture evaporation condition and the slope and direction information of the soil. The growth state of plants in a planting area and the ecological environment of soil are monitored, and producers are better assisted in formulating targeted agricultural management measures.

The invention discloses a construction method for governing and repairing desertification degraded grassland in an arid area, and relates to the technical field of sand governing. The method comprises the following steps: evaluating a native vegetation type, vegetation coverage, a soil degradation and desertification degree, a soil type and a water resource condition; performing classified measures according to natural conditions, and determining different types of dune (flowing, fixed or semi-fixed) treatment measures; construction preparation; grid sand barriers are laid on the windward sides of the moving dunes and the semi-fixed dunes, and sand stabilization operation is conducted in a mechanical and manual combined mode; digging pits to plant indigenous shrubs subjected to special treatment, and using rooting water retention bags to improve the survival rate; the granular organic fertilizer and the seeds are mixed and stirred according to a certain proportion and then sown into soil at a time, the operation frequency is reduced, and precise fertilization is achieved. Through effective sand prevention and control measures, the sand wind damage is reduced, the living environment of farmers and herdsmen is protected, and the living quality is improved.

The invention discloses a method for automatically keeping a chassis of mobile intelligent equipment horizontal on a slope, and relates to the technical field of mobile intelligent equipment.The method comprises the following specific steps that terrain detection is conducted, specifically, the current terrain condition is detected in real time through a sensor system installed on the mobile intelligent equipment, and the terrain condition is determined; comprising slope angles, soil types and rock distribution information; by integrating the sensor system and the central processing unit, real-time monitoring and intelligent analysis of slope topography and road surface conditions are achieved, mobile intelligent equipment can rapidly sense and adapt to various complex topographies including soft soil slopes, rugged rock slopes and the like, and by automatically adjusting the level of the chassis, the slope topography and the road surface conditions can be monitored and analyzed in real time. And the stability and the operation performance of the equipment on different terrains are ensured, so that the terrain adaptability of the equipment is remarkably improved, the mobile intelligent equipment can play a role in a wider environment, and the application range and the practicability of the equipment are improved.

The invention relates to the technical field of intelligent agriculture and precise irrigation, in particular to an intelligent control system for agricultural irrigation. Comprising a water-saving valve module, a sensing layer module, a control layer module, an execution layer module and a data and application layer module, and the water-saving valve module is used for achieving microsecond level flow regulation and control through piezoelectric ceramic driving and energy recovery and integrating a self-maintenance function. According to the invention, the LSTM network and the Duelling DQN algorithm are utilized to accurately discriminate the soil type and formulate an extreme weather coping scheme, dynamic optimization of an irrigation strategy is realized based on low-power-consumption edge calculation and an intelligent control algorithm, the adaptability and decision accuracy of the system are greatly improved, multiple advanced technologies are integrated, and the system is suitable for large-scale popularization and application. Comprising a magneto-rheological buffer, a TSN valve controller, an SMA emergency pressure release valve and a variable frequency water pump driver, pressure balance of a pipe network, high-precision synchronization of valves and safe treatment of sudden failures are ensured, and the stability and flexibility of an irrigation system are comprehensively improved.

The invention discloses a farmland soil health evaluation method and system, and relates to the field of soil health evaluation. The method comprises the following steps: acquiring remote sensing image data in a target area, decomposing color features of a remote sensing image by using an HIS model, acquiring a hue value, a saturation value and a brightness value of a unit grid, constructing a color feature vector set, clustering elements in the color feature vector set of the unit grid into K unit grid types, corresponding the unit grid types to a soil type, and constructing a soil model. Soil in a target area is divided into K categories, adjacent unit grid areas of the same type are combined into independent areas, the number of sampling points in any independent area is determined, soil sampling is conducted at the sampling points, the concentration value of all pollutants in soil samples of the independent areas is measured, and the distribution situation of all the pollutants in the target area is obtained. And calculating a single pollution index of the pollutant in the independent area according to the obtained concentration value of the dyed object, and dividing the pollution degree grade of the independent area.

The invention provides a farmland yield prediction method and system based on soil parameter inversion, and the method specifically comprises the steps: collecting the soil profile information and earth surface three-dimensional information of a target farmland through a ground penetrating radar and a laser radar, and carrying out the precise inversion of the water content and porosity of soil based on a water-porosity inversion model, therefore, a high-precision three-dimensional soil distribution model is constructed, crop growth monitoring data is combined, a farmland yield prediction model is established, the relationship among soil structure characteristics, crop growth indexes and meteorological factors is quantified, and high-precision farmland yield prediction is realized. According to the method, through accurate modeling of the double-pore structure and optimization of the multiple linear regression prediction model, yield prediction is more accurate and reliable, and the method is particularly suitable for farmland management under different soil types and meteorological conditions.

The invention belongs to the technical field of geotechnical engineering testing and site investigation, and provides a shear wave velocity estimation method based on a piezocone penetration test, which can accurately and conveniently estimate the shear wave velocity. The method specifically comprises the following steps of S1, carrying out a pore pressure static sounding test on a plurality of drill holes in a site, and obtaining pore pressure static sounding basic parameters and shear wave velocity of each drill hole tested in the depth direction, S2, interpreting derived parameters according to the pore pressure static sounding basic parameters, S3, carrying out soil layer division, soil type identification and soil body type judgment, and S4, carrying out soil layer classification, soil type identification and soil body type judgment. S4, selecting data tested by the same soil body type as an analysis object, performing fitting analysis on the scatter diagrams of the cone tip resistance and the shear wave velocity of the piezocone penetration test to obtain a fitting formula, and S5, obtaining normalized cone tip resistance and normalized shear wave velocity based on the cone tip resistance and the shear wave velocity, substituting the normalized cone tip resistance and the normalized shear wave velocity into the fitting formula in the step S4, and introducing a soil classification index to obtain a soil classification result. And obtaining a calculation expression of the optimal estimation shear wave.

The invention discloses an intelligent inrush current irrigation method based on meteorological data and Internet of Things monitoring. The method comprises the following steps: A1, weather data acquisition and judgment; a2, soil humidity monitoring and demand judgment; a3, calculating irrigation duration; a4, carrying out inrush irrigation period division and dynamic control; the total irrigation duration is decomposed into an inrush current period, and period parameters are dynamically optimized; a5, executing intelligent irrigation; internet weather data and soil humidity monitoring are integrated, intelligent irrigation decision making is achieved, and irrigation accuracy and timeliness are improved. By combining the soil texture, the meteorological data and the real-time soil humidity, the irrigation duration and the irrigation period number are dynamically calculated, the problems of parameter distortion and soil type neglecting in a traditional method are solved, the irrigation effect is ensured, and the irrigation uniformity is improved.

The invention discloses a drainage basin water quality pollution source flux identification method based on an SWAT model, computer equipment and a storage medium, and the method comprises the steps: obtaining topographic data, land utilization data, soil type data and meteorological data, and enabling the topographic data, the land utilization data, the soil type data and the meteorological data to form a drainage basin spatial database; based on the preprocessed digital elevation model, determining a water flow direction and setting a reasonable water collection area threshold value, and dividing sub-basins to construct a basin calculation unit network topology structure; obtaining pollution source data, and constructing an SWAT-CUP model according to the pollution source data; performing system calibration and verification on the watershed hydrological process by adopting an SUFI-2 algorithm in the SWAT-CUP model; carrying out system calibration and verification on the total nitrogen load of the drainage basin by adopting an SWAT-CUP model; and based on the calibrated SWAT hydrological model, the input condition of the pollution source is changed. The method can cover the whole watershed and quantify the contribution rate of different pollution sources; cost is high, operation is simple, and long-term change can be dynamically simulated.

The invention discloses a soil improvement and soil quality optimization comprehensive management method based on big data analysis, and the method comprises the following steps: S1, collecting and preprocessing multi-source soil data, and constructing a soil data input matrix; s2, initializing parameters of the fuzzy C-means clustering model, and constructing a model basic structure; s3, introducing a hosting crab optimization algorithm, and optimizing clustering model parameters; s4, executing clustering analysis, and generating a fuzzy soil type distribution map; s5, constructing an improved strategy scheme space, and fusing crop types, historical records and resource information; s6, making a multi-target scoring standard, and selecting an optimal improvement strategy; s7, deploying an optimal improvement strategy to a farmland operation system, and collecting soil feedback data in real time; and S8, updating model input and parameters based on feedback data, and re-executing optimization to realize a closed loop. According to the invention, big data and an intelligent optimization technology are fused, and accurate matching and efficient configuration of soil improvement strategies are realized.

The invention relates to the technical field of mechanical parameter adjustment, in particular to a method and system for adjusting operation parameters of agricultural machinery and a storage medium. The method comprises the following steps: acquiring multispectral image data of a farmland through a multispectral camera; performing image segmentation and feature extraction on the multispectral image data, and identifying different soil types, crop growth states and weed distribution to obtain farmland feature map data; performing tillage difficulty prediction of different areas on the farmland feature map data by using a deep learning algorithm to obtain tillage difficulty prediction data; acquiring part image data of the agricultural machinery; and carrying out edge detection and morphological analysis on the image data of the part, and identifying the wear state of the part so as to obtain wear state data. According to the method, deep learning is utilized to carry out tillage difficulty prediction and part wear dynamic modeling, so that the analysis capability of a complex farmland environment and a mechanical state is improved, and the prediction accuracy is enhanced.

The invention discloses a soil fertility intelligent assessment method and system based on image recognition, and relates to the technical field of data processing, and the method comprises the steps: obtaining land image information of a to-be-assessed region, the land image information comprising a multispectral image, a thermal imaging image and an RGB image of the to-be-assessed region; carrying out image preprocessing on the land image information, and carrying out vegetation coverage and health assessment according to the multispectral image: speculating soil characteristics according to the RGB image: speculating the humidity change of a soil surface layer according to the temperature distribution of a thermal imaging image, and identifying a drought region and a region with sufficient moisture; integrating the high-resolution texture features of the RGB image, the spectral information of the multispectral image and the speculation result of the temperature distribution of the thermal imaging image to generate a comprehensive analysis result; and classifying soil types and evaluating fertility grades according to a comprehensive analysis result. The problem that in the prior art, in intelligent soil fertility evaluation, workers need to carry out on-site sampling examination is solved, and the technical effect of improving the soil fertility evaluation efficiency is achieved.

The invention discloses a soil type prediction model training method, drawing method and system based on multi-modal feature fusion and spatial neighborhood constraint, and the training method comprises the steps: constructing a multi-source heterogeneous digital soil drawing data sample set which comprises a continuous environment variable, a discrete environment variable and a multi-source remote sensing image; extracting the characteristic quantity of the data sample set and carrying out multi-modal adaptive characteristic fusion, and adaptively determining a fusion weight by using a cross attention mechanism to obtain a joint characteristic quantity; constructing a composite loss function including a spatial neighborhood smooth regular term and classification cross entropy loss; and performing joint optimization training on a preset deep learning model according to the joint characteristic quantity and the composite loss function until a preset requirement is met, and determining a soil type prediction model. According to the embodiment of the invention, the accuracy and spatial continuity of the mapping result expression output by the model and the generalization performance of the model can be effectively improved, and the method can be widely applied to the technical field of digital soil mapping.

The invention provides an operation monitoring method for a steam pipe network. The operation monitoring method comprises the following steps: acquiring pipeline burial depth information, a leakage signal, temperature, pressure and soil thermal conductivity change information of an underground steam pipe network; constructing a pipe network space model, marking pipe fitting positions, obtaining soil types, and calculating attenuation coefficients of leakage signals in different soil media; simulating a leakage signal propagation path based on a pipe network space model, obtaining a complex area of a pipe network structure according to the pipe burial depth information, and if the leakage signal is superposed or interfered in the complex area, separating out an independent signal component to obtain an initial propagation direction of the leakage signal; according to the position of the leakage point, in combination with the pipeline burial depth and the soil type attribute, a regression analysis method is adopted to calculate the quantitative relation between the intensity of the leakage signal and the leakage degree, and an evaluation result of the leakage degree is obtained.

The invention relates to the technical field of ecological restoration scene modeling, in particular to an ecological restoration scene modeling method and system platform applied to territorial space planning, and the method comprises the following steps: based on a successful ecological restoration technical scheme case and a to-be-restored land parcel unit. According to the method, normalization processing is carried out on multi-dimensional attributes between successful ecological restoration technical scheme cases and to-be-restored land parcel units, a source-target land parcel feature fingerprint set is constructed, and specific attributes such as annual average temperature, precipitation, soil types, pH values, altitudes, native vegetation types and labor cost are combined; modeling is realized on data source selection granularity and expression dimension. Furthermore, an importance coefficient is set according to the influence degree of each dimension in a historical repair success case, feature difference weight calculation is guided, and a technical migration adaptation degree score is obtained by combining normalized difference calculation, so that a quantifiable source-place adaptation capability evaluation index is established, and the limitation that judgment depends on experience in the prior art is broken through.

The invention discloses a soil three-dimensional pore structure feature distribution rule comprehensive index construction method. Relates to the technical field of crossing of soil science and computer technology, in particular to the technical field of analyzing soil three-dimensional pore structure feature distribution rules by constructing comprehensive indexes. According to the method, the three-dimensional pore structure feature distribution rule index is constructed based on the three-dimensional Minkowski functional estimation value, and key parameters such as soil porosity, specific surface area and connectivity can be comprehensively and visually reflected. The method comprises the following steps: obtaining a binarized image and carrying out the preprocessing of the binarized image; cutting the pixel matrix data set to obtain a representative basic volume data set of the soil pore structure; the Minkowski functional estimation value in the three-dimensional space is calculated; analyzing Minkowski functional estimation values on the basis of skewness and kurtosis characteristics in statistics; and based on the pore structure distribution characteristic parameters of different soil types, constructing a three-dimensional pore structure characteristic distribution rule index.

The invention relates to the related technical field of climate monitoring, in particular to a freezing circle climate change monitoring method and system suitable for an Asian alpine region, and the method comprises the steps: configuring a monitoring network, receiving a frozen soil multi-source monitoring data set, constructing a frozen soil and climate coupling response model, embedding a region climate assessment engine, and generating a frozen soil degradation risk grade distribution map. And judging whether risk reminding is triggered or not according to the dynamic frozen soil degradation threshold value, and if the risk reminding is triggered, marking an abnormal region in the frozen soil degradation risk level distribution diagram. The technical problems that Asian alpine region freezing circle climate change monitoring abnormity reminding mostly depends on static data, the dynamic evolution process of frozen soil cannot be accurately captured, and the frozen soil degradation risk identification precision is limited are solved, a frozen soil and climate coupling response model constructed based on multi-source data is achieved, and the frozen soil degradation risk identification accuracy is improved in combination with a dynamic degradation threshold value set according to the frozen soil type. And risk reminding is triggered in time and an abnormal region is marked, so that the frozen soil degradation risk identification precision is improved.

The invention belongs to the technical field of pile foundation construction, and particularly relates to an inversion method of a pile foundation construction and geological exploration technology, which comprises the following steps of: 1, collecting geological exploration data; the method comprises the following steps: acquiring property data of an underground soil layer through drilling and in-situ testing operation, including the thickness, compactness and soil type of the soil layer; and calculating the numerical values of the thickness, the compactness and the soil type of the soil layer through a static sounding test method. According to the inversion method of the pile foundation construction and geological exploration technology, accurate geological exploration data can be provided, specific characteristics of an underground soil layer are calculated through the inversion method, and accurate parameters are provided for pile foundation design. Meanwhile, the interaction between the pile foundation and the soil layer is simulated through a finite element method, deformation and stress distribution in the construction process are predicted, and a scientific basis is provided for engineering design. In addition, settlement and pile sinking resistance of the pile foundation need to be monitored in real time, so that a construction scheme is adjusted in time, and construction safety and quality are ensured.

The invention belongs to the technical field of cross-country path planning, and provides a dynamic cross-country path planning method for land surface state simulation, which comprises the following steps: S1, acquiring a cross-country map, parameters, a starting point and an ending point of a target vehicle, current surface water content, future rainfall data, air temperature data and radiation data; s2, determining the future soil moisture content; s3, rasterizing the cross-country map, and identifying a landform type, a road surface type, a soil type and a gradient; s4, determining the size relationship between the unit grounding pressure and the future soil bearing capacity; s5, dividing a passable area R2 and an impassable area T2; s6-S9, determining air resistance, road surface resistance, horizontal subsidence resistance and slope resistance; s10, calculating the maximum driving speed of the vehicle in each grid; s11, generating a maximum driving speed matrix based on the maximum driving speed in the passable area R2 and the speed value in the impassable area T2; and S12, based on the maximum driving speed matrix, utilizing an improved A * algorithm to plan a cross-country path.

The invention specifically relates to a forest stock dynamic exploration system and method based on unmanned aerial vehicle multispectral fusion, and relates to the technical field of forest resource monitoring. A data preprocessing module; a feature extraction module; a model construction and estimation module; and a result output and visualization module. According to the method, a multi-dimensional data fusion and dynamic calibration mechanism changes the limitation of a traditional fixed soil regulation coefficient; when the soil regulation coefficient is obtained, four core factors of soil type, terrain illumination, soil humidity and texture partition distance are comprehensively considered; interference of different soil backgrounds, terrain illumination and humidity changes on vegetation index calculation is effectively eliminated, the enhanced vegetation index can reflect the coverage and health condition of forest vegetation more truly, a high-precision data basis is provided for subsequent forest stock estimation based on the vegetation index, and compared with a traditional method, the method has the advantage that the method is more accurate. And the data accuracy and reliability are improved.

The invention discloses a garden plant root rapid oxygen release device, and relates to the technical field of environmental protection, and the technical scheme is that the garden plant root rapid oxygen release device comprises a pipe body, one end of the pipe body is provided with a cap, the pipe body and the cap are fixedly connected through threads, the other end of the pipe body is provided with a drill bit, one side of the drill bit is fixedly provided with a pointed cone, and the bottom of the cap is fixedly provided with a telescopic rod; a spring is fixedly arranged outside the telescopic rod in a sleeving mode, one end of the telescopic rod is fixedly connected with a pressing plate, a plurality of water-soluble oxygen release particles are arranged in the pipe body, a partition plate is arranged between every two adjacent water-soluble oxygen release particles, and a diaphragm is fixedly arranged on one side of each partition plate. The method is simple in operation, provides instant oxygen supply for the root system, does not depend on specific soil types and climate conditions, can be used in various environments, overcomes the defect of environmental limitation, meanwhile, is not limited by the growth cycle and adaptability of plants, and can take effect quickly.

The invention relates to the technical field of soil moisture content monitoring, and discloses a method and a system for accurately monitoring soil moisture content through remote sensing inversion of an unmanned aerial vehicle. Acquiring original spectral data of different soil types and environmental conditions through a spectral sensor to obtain an initial multispectral image data set; dividing soil types by using a support vector machine classification method according to pixel reflection characteristics to form a soil type grouping set; calculating a multispectral band reflectivity variance for each group, and screening candidate bands to construct a set; processing the candidate wave band set through a random forest regression model, adjusting the weight in combination with an environment variable, and performing iterative screening to form an optimized wave band combination sequence; extracting spectral features of the optimized wave band, and carrying out weighted fusion on the soil variation region to output a processed image; and calculating the soil moisture content by using a linear inversion model based on the pixel reflectivity of the processed image, and outputting a distribution diagram when the deviation reaches the standard. The method is suitable for different soil and environments, the wave band is accurately selected, the precision is improved, and high-quality data is provided for agricultural and environmental governance.

The invention discloses an accounting method for carbon sequestration and emission reduction of soil. The accounting method comprises the following steps: S1, acquiring the average soil temperature # imgabs0 # average soil humidity # imgabs1 # average soil acidity and alkalinity # imgabs2 # average soil microbe number # imgabs3 # average soil microbe activity # imgabs4 # average environment temperature # imgabs5 # average environment humidity # imgabs6 # S2 of selected type soil from a moment t to a moment t + delta t; inputting the average soil temperature # imgabs7 #, the average soil humidity # imgabs8 #, the average soil acidity and alkalinity # imgabs9 #, the average soil microbe number # imgabs10 #, the average soil microbe activity # imgabs11 #, the average environment temperature # imgabs12 #, the average environment humidity # imgabs13 # and the soil type of the selected type of soil into a trained soil carbon sequestration emission reduction amount accounting model; and obtaining the carbon sequestration amount and greenhouse gas emission reduction amount of the selected type of soil. The method can quickly and accurately reflect the actual carbon sequestration and emission reduction conditions of the soil.

The invention provides a slope monitoring system based on a physical model, and relates to the technical field of slope physical test analysis, and the IPC classification number belongs to G01N. According to the slope monitoring system based on the physical model, the monitoring and evaluation capability of a risk area is remarkably improved by measuring soil types, geological material characteristics and plant root system state data included in ground materials; the system combines historical landslide fault data and risk area division, can dynamically generate a warning threshold value, and ensures real-time prediction of slope instability. In addition, aiming at comprehensive analysis of the plant root system state, the system can quantify the influence of the root system on the slope stability by generating a root system evaluation index; the threshold calibration unit provides a dynamic warning threshold adjustment strategy to ensure that the stable state of the slope is reflected in real time; meanwhile, the system can achieve more accurate risk assessment by generating an unstable landslide evaluation index, thereby improving the efficiency and reliability of slope monitoring.