251 results about "Soil characteristics" patented technology

The invention discloses a drip irrigation parameter dynamic regulation and control method combined with multi-point sensor monitoring, and relates to the technical field of irrigation regulation and control, the method comprises the following steps: monitoring a multi-point sensor arranged in a target area to obtain target monitoring data; performing actual demand analysis on the target crop to obtain actual demand data; analyzing the target soil permeability of the target area to obtain a target irrigation period; and performing irrigation processing according to the actual demand data and the target irrigation period to obtain irrigation data. The technical problems that in the prior art, real-time requirements of crops cannot be accurately obtained, irrigation is difficult to implement according to soil characteristics, and the agricultural production efficiency and sustainability are limited are solved. The effects that multiple types of monitoring data are obtained by arranging the multi-point sensors in the target area, precise irrigation is achieved, the crop growth environment is optimized, good physicochemical properties of soil are maintained, and the technical effects of high efficiency and sustainability of agricultural production are improved are achieved.

The invention discloses a microbiological culture scheme customization recommendation method and system based on soil data analysis, and relates to the technical field of microbiological culture plants.The method comprises the steps that soil samples at different positions and depths are collected, soil characteristic data are analyzed, and data processing is carried out; microbial community information is obtained through soil DNA extraction, PCR amplification and high-throughput sequencing, and distribution and functions of microorganisms are evaluated in combination with data preprocessing and diversity analysis; selecting proper microbial strains according to soil characteristics and microbial community analysis results; a microbial culture scheme is combined with agricultural management measures; the conditions of soil and microorganisms are monitored in real time, and a microorganism culture scheme is dynamically adjusted based on a feedback mechanism; through field trials and advanced performance evaluation indexes, the effect of the microbial culture scheme is verified for a long time. The method has high adaptability and sustainability, and customized microorganism management schemes can be provided in different agricultural environments.

The invention provides a regulation factor threshold effect evaluation method and device for vegetation response sudden drought characteristics, equipment and a storage medium. Relates to the technical field of drought risk prediction and influence evaluation. The method comprises the following steps: acquiring hydro meteorological data, vegetation productivity data, topographic feature data, soil characteristic data and biological variable data of a target area; constructing a drought suddenness index, extracting a drought suddenness event according to the drought suddenness index, and determining a drought suddenness feature of the drought suddenness event; extracting vegetation productivity abnormal data before and after the sudden drought event, and determining the response characteristic difference of the vegetation to sudden drought; the method comprises the following steps: determining influence factors of vegetation on sudden drought response characteristics, fusing XGBoost, a random forest regression model and an SHAP interpretability analysis method, analyzing regulatory factors of the vegetation response sudden drought characteristics, and determining a threshold effect of the regulatory factors. According to the method, the nonlinear cooperative regulation effect of multiple factors on vegetation response sudden drought characteristics can be disclosed more comprehensively.

The invention relates to the field of civil engineering, and discloses a foundation pit finite element model parameterization rapid modeling method which comprises the following steps: constructing a finite element model through foundation pit geometric information and soil characteristics, simulating soil response by adopting a nonlinear constitutive relation and a random process, and realizing foundation pit stability optimization and dynamic adjustment; the invention further provides a foundation pit finite element model parameterization rapid modeling system which comprises an input module, a modeling module, a constitutive model module, a random process module, a simulation module, an optimization module, a mesh refining module and a real-time monitoring module. According to the method, the foundation pit finite element model is automatically generated, the Kachanov-Delace nonlinear soil constitutive model is introduced, the soil elastic modulus random field is generated based on the Gaussian process, and the model parameters are dynamically adjusted in real time, so that the modeling process is simplified, the calculation precision is improved, the nonlinear behavior and dynamic response of the soil are accurately simulated, and the method is suitable for large-scale popularization and application. And the stability and the safety in the foundation pit design and construction process are obviously improved.

The invention relates to the technical field of agricultural information, discloses a corn precise topdressing prescription map generation method based on unmanned aerial vehicle multispectral remote sensing, and effectively solves the key problems of insufficient monitoring precision, poor environmental adaptability, low decision intelligent level and the like in the traditional fertilization technology. Through the unmanned aerial vehicle multispectral imaging technology, efficient and accurate monitoring of the corn nitrogen nutrition condition is achieved; in combination with a machine learning algorithm, an intelligent fertilization decision model adapted to different environmental conditions is established, and the fertilization precision in a complex farmland scene is remarkably improved; and a multi-source data fusion technology is adopted, and crop growth, soil characteristics and meteorological factors are comprehensively considered, so that the fertilization scheme is more scientific and reasonable. Compared with a traditional method, the method has the advantages that accurate variable fertilization can be realized according to actual needs of crops, the utilization rate of the fertilizer is increased, environmental pollution is reduced, and reliable technical support is provided for sustainable development of modern agriculture.

The invention discloses an intelligent fertilizer distribution control system based on Beidou positioning, and relates to the technical field of agricultural automation, and the system comprises the steps: building a fertilization simulation model, and obtaining the relation data of the disc opening size and the fertilization amount of a fertilizer distributor through an experiment; soil analysis data and remote sensing image data are collected and preprocessed; generating a fertilization prescription map by using a multi-objective optimization model based on the preprocessed soil analysis data and remote sensing image data according to the soil characteristics and crop requirements of the field; high-precision positioning data are obtained through a Beidou positioning system, and the fertilization prescription map and the high-precision positioning data are fused to complete field space calibration; based on the field space calibration result and the high-precision positioning data, adopting a Dijkstra algorithm to generate an optimal fertilizer spreading path; in combination with the real-time speed of the tractor and the fertilization simulation model, the disc opening size of the fertilizer spreader is dynamically regulated and controlled, and the fertilization amount is accurately controlled by using a control algorithm; the tractor corner and the field boundary are monitored in real time, and the fertilizer distributor is automatically closed.

The invention relates to the technical field of intelligent agriculture, and particularly discloses an orchard intelligent water and fertilizer integration-based edge end fruit tree fertilizer demand prediction method, which comprises the following steps of: constructing each fruit tree as a graph node, and fusing canopy spatial characteristics extracted by unmanned aerial vehicle multispectral remote sensing and nutrient time sequence characteristics monitored by a soil sensor; space influence weights among nodes are calculated according to the terrain, the soil texture and the space distance, and a fruit tree individual relation graph is established; then, through a space-time diagram convolutional network, aggregating space neighborhood features and capturing time dynamic changes, and outputting target state features subjected to multi-scale enhancement; when the canopy conflicts with the soil characteristic indication, combined diagnosis is carried out based on a neighborhood consistency index and an adjacent node state, systematic nutrient stress and local non-nutrient factors are distinguished, and final fertilizer demand prediction is generated after the conflict is eliminated; a digital fertilization prescription map is generated through spatial interpolation, soil characteristics and slope correction, and the water and fertilizer integrated equipment is driven to execute precise operation.

The invention relates to the technical field of agriculture, in particular to a precise fertilization variable control system and method applied to a spiral fertilizer applicator, and the method comprises five steps of data acquisition, data processing, variable control, fertilization optimization and execution control. Field environment data are collected in real time through various sensors, a field environment information graph is generated by adopting a multi-modal data fusion technology, and based on the information, the fertilization amount, the fertilization angle and the fertilization time are adjusted in real time by adopting a self-adaptive algorithm; further combining crop growth requirements and soil characteristics, optimizing a fertilization scheme through a genetic algorithm, and finally, accurately adjusting the fertilization amount and the fertilization area by a fertilizer applicator according to the optimized scheme, so as to ensure the precision and efficiency of the fertilization operation. According to the invention, through precise fertilization control, the efficient utilization of resources is realized, meanwhile, the negative influence of the fertilizer on the environment is reduced, and a sustainable fertilization solution is provided for agricultural production.

A method is provided for determining soil properties for an area of land, from soil spectrum data. In an embodiment, the method includes receiving soil spectrum data records from hyperspectral sensors that represent a mean soil spectrum of a specific geo-location of the area of land and removing interference signals from the spectrum data records to create soil spectral bands. The method also includes predicting a plurality of soil property datasets based on a partial least-square regression and the soil spectral bands and selecting specific soil property datasets from the plurality of soil property datasets to represent soil properties of the specific geo-location, wherein the specific soil property datasets include property data and spectral band data for spectral bands used to determine the property data. The specific soil property datasets may then be used to generate a crop prescription of recommended hybrid seeds or population densities for the specific geo-location.

The disclosure relates generally to methods and systems for ground water prediction using integration of satellite and auxiliary observations. Conventional techniques in the art for the ground water level prediction are not accurate and efficient for predicting the ground water level. The present disclosure combines time-series archived remote sensing data with a wide array of past auxiliary datasets specific to a given region for precise prediction of future ground water condition, to predict the ground water situation i.e. rising or declining for a given region. These encompass diverse information such as weather conditions, soil properties, agricultural data, population statistics, water resources information, industrial details, drought records, seismic data, and surface deformation patterns. The present disclosure also considers different future scenarios which can be built on future cropping patterns, forecasted weather conditions, use of irrigation system in future, and future population growth of the given region.

The invention relates to the technical field of agricultural precise management, and discloses an unmanned aerial vehicle precise variable rate fertilization method, which comprises the following steps: collecting vegetation indexes and color feature data of crops in a target plot through an unmanned aerial vehicle carrying a multispectral sensor and an RGB camera; acquiring soil characteristic data of the target land parcel, wherein the soil characteristic data comprises nitrogen, phosphorus and potassium nutrient content, humidity and pH value in soil; the invention further provides an unmanned aerial vehicle precise variable rate fertilization system which comprises a data acquisition module, and the data acquisition module is configured to acquire vegetation indexes, color characteristics, soil characteristics and meteorological data of the target land parcel through a multispectral sensor, an RGB camera, a soil sensor and a meteorological sensor carried by the unmanned aerial vehicle. Through multi-source data fusion, dynamic fertilization adjustment and path optimization, precise variable fertilization is realized, the fertilizer utilization efficiency is improved, the scientificity and efficiency of fertilization operation are improved, and meanwhile, the environmental influence is reduced.

The present invention provides an emergency supervision method for a smart city lifeline project and an Internet of Things large model system, which relate to the field of emergency supervision technology. The Internet of Things large model system includes an emergency supervision user platform, an emergency supervision service platform, an emergency supervision management platform, an emergency supervision sensor network platform, and an emergency supervision object platform. The method is executed by the emergency supervision management platform, and includes: in response to the gas environment characteristics corresponding to the target pipeline meeting the early warning conditions, every preset period: based on the spatial connectivity information corresponding to the target pipeline, determining the target sensor and obtaining the corresponding air flow data; based on the gas transportation data, spatial connectivity information, air flow data and regional soil characteristics of the target pipeline, determining the estimated diffusion amplitude of the target gas; based on the estimated diffusion amplitude, determining the inspection frequency and sampling frequency to obtain soil samples during the inspection; and receiving leakage warnings. The present invention can achieve accurate identification of leakage risks and timely warnings.

The invention relates to the technical field of artificial intelligence, and provides a soil texture recognition model construction method, a soil texture recognition model construction device, a computer readable storage medium and electronic equipment, and the soil texture recognition model construction method comprises the steps: screening key soil texture features from a plurality of ocean soil texture features; the feature importance score of the key soil feature is higher than a preset score threshold; collecting training data associated with the key soil characteristics, and training a plurality of initial machine learning models by using the training data; and according to a preset performance evaluation index, screening out a final target machine learning model for ocean soil texture identification from the plurality of initial machine learning models. According to the invention, through an automatic modeling mechanism combining feature optimization and multi-model performance screening, the classification precision and engineering applicability of the ocean soil texture identification model are improved.

The invention discloses a slope stability analysis method and system based on data analysis, belongs to the technical field of slope safety analysis, and aims to solve the problems of dependence on hypothesis simplified model and neglect of real-time dynamic change in the prior art. S2, dynamic environment factor analysis; s3, slope stability analysis; s4, establishing an intelligent decision-making and early warning system; s5, system integration and user interaction; s6, performing system optimization and continuous learning; by comprehensively considering complex soil body characteristics such as heterogeneity and anisotropy of the slope and dynamic changes of environmental factors such as external rainfall, meteorology and earthquakes, the system can provide a more real and accurate slope stability analysis result, an intelligent algorithm and a data analysis technology are adopted, and the stability of the slope is improved. The dependence on a simplified physical model and a static hypothesis is reduced, so that the scientificity and the practicability of an analysis result are improved.

The invention provides a slope displacement monitoring method and system for a high and steep slope in a loess area, and the method comprises the steps: carrying out the preprocessing of data through a point cloud denoising algorithm according to an initial point cloud data set, filtering noise points and irrelevant reflection points caused by a complex terrain, and generating an optimized refined point cloud data set; aiming at the refined point cloud data set, constructing a digital surface model of the side slope by utilizing a three-dimensional reconstruction algorithm, carrying out local detail correction on the model in combination with soil loosening characteristics of a loess area, and determining a preliminary morphological structure of the side slope; performing time sequence data acquisition on the preliminary morphological structure to obtain slope three-dimensional model data of multiple time periods, and analyzing the dynamic evolution trend of morphological change to obtain slope deformation feature distribution; according to the slope deformation characteristic distribution, a finite element analysis method is adopted to conduct simulation calculation on stress distribution of the high and steep slope, the stability risk of a deformation area is judged, and potential landslide risk points are determined.

A system and method for characterizing matter, for example, soil organic content is disclosed. A radiation and electric field sensor measure sample properties before, during and after irradiation. Calibrations are developed relating those measurements to useful properties of matter, for example, soil density and organic content. As an example of an embodiment of the disclosed invention an instrument attachment for portable X-ray fluorescence instrumentation was prototyped enabling concurrent volumetric soil organic matter quantification. This primary prototype outperformed more expensive emerging visible-near infrared multivariate instrumentation using parsimonious soil specific simple linear regression (R2 ranged 0.85-0.97) enabling rapid, parallel, nondestructive, cost-effective acquisition of soil elemental concentrations together with organic content data.

The invention discloses a lolium perenne seedling returning nitrogen reduction amount regulation and control method based on soil multifunctional evaluation, and relates to the technical field of agricultural planting.According to the method, a closed-loop dynamic regulation and control system is constructed, aiming at the soil characteristic difference after lolium perenne seedlings are returned to the field, precise and personalized management of the application amount of nitrogen fertilizer is achieved, and the application amount of nitrogen fertilizer is improved. According to the method, the nitrogen fertilizer compensation amount is dynamically adjusted according to the soil multifunctionality real-time evaluation result of the key growth stage of crops, and the yield fluctuation caused by blind reduction of application is effectively avoided, so that the stability and improvement of the crop yield are ensured while the chemical nitrogen fertilizer input is remarkably reduced, and the economic benefit is increased. The technical scheme that physical, chemical and biological indexes of soil are comprehensively evaluated in the key growth stage of crops, and the nitrogen fertilizer correction amount is dynamically calculated based on the deviation between the evaluation result and the target function range is adopted, so that the accurate regulation and control of the nitrogen fertilizer application amount can be realized; therefore, the stable yield of crops is guaranteed and the soil health level is continuously improved while the application of chemical nitrogen fertilizer is reduced.

The invention relates to the technical field of mixed fertilizer manufacturing, in particular to a biological soil conditioner and a preparation method thereof.The biological soil conditioner is prepared from organic materials, microbial agents, inorganic fertilizer and other additives, according to a formula, the soil biological improver is prepared from preparation raw materials of the soil biological improver, and the formula comprises the following components in parts by mass: M1, M1 is a first preset mass part, and M1 is set to be 57 parts; the mass part of the microbial agent is M2, M2 is a second preset mass part, and M2 is set to be 8 parts; the mass part of the inorganic fertilizer is M3, M3 is a third preset mass part, and M3 is set to be 15 parts; the mass part of other additives is M4, M4 is a fourth preset mass part, and M4 is set to be 20 parts. Local materials can be used according to soil characteristics of the high-altitude area, the soil structure of the high-altitude area is improved, fertility is improved, plant growth is promoted, and soil ecological balance is maintained.

The invention provides a grounding resistance online monitoring method and system, a storage medium and electronic equipment, and relates to the technical field of grounding resistance monitoring. According to the technical scheme provided by the invention, the geological type of the transformer substation and the reference resistance threshold curve corresponding to the geological type are acquired, the second grounding resistance value is calculated by combining the first grounding resistance value of the branch node in the grounding network, and the comprehensive resistance correction amount is determined based on the soil characteristic parameters; personalized adjustment of the reference resistance threshold curve is realized, and a target resistance threshold curve suitable for a specific transformer substation is obtained. The actually measured third grounding resistance value is compared with the corresponding threshold value of the final resistance threshold value curve, the abnormal state of the grounding network can be accurately identified, the problems of false alarm and missing alarm caused by a fixed threshold value are avoided, and the accuracy of online monitoring of the grounding resistance is improved.

The invention discloses an urban water collection capability evaluation system and method based on ecological data analysis, and relates to the technical field of urban water collection. Comprising the following steps: a soil information acquisition module divides a to-be-evaluated region into sub-regions, acquires soil feature information and calculates a soil water holding capacity index; and the water holding capacity analysis module is combined with the average vegetation coverage density and the soil permeability to correct the water holding capacity index of the soil. The evaporation potential analysis module is used for calculating the water evaporation capacity and generating an evaporation potential index based on the maximum bearing volume of the water body and the meteorological parameters. And the terrain factor analysis module corrects the evaluation coefficient and considers the ground average slope angle. And the catchment capability evaluation module dynamically adjusts a catchment capability evaluation threshold according to the average rainfall and the soil humidity, and finally generates an urban catchment capability evaluation result. The system improves the adaptability and effectiveness of urban catchment capacity evaluation.

Various embodiments for an autonomous forest fire detection, alerting and mitigation device includes a subterranean data gatherer having a plurality of soil temperature and soil moisture sensors installed at a plurality of different depths in a soil. A data assimilator that has a configured threshold change depending on the plurality of different depths in the soil of the plurality of soil temperature and soil moisture sensors, the soil properties, the duration of the fire, and a time delay duration. The data assimilator receives a data transmission packet from the subterranean data gatherer and detects a fire and generates a fire alert when the plurality of soil temperature and soil moisture sensors all breach the configured threshold change. A smart water pump is also started to slow the fire or completely stop it. The subterranean data gatherer is installed at the plurality of different depths in the soil.

The invention discloses a method for promoting microbial balance and improving the ecological function of soil through straw returning, and relates to the technical field of agricultural soil improvement. In a raw material pretreatment link, microbial agents A and B are added and fixed in a biochar pore structure, and a good foundation is laid for subsequent field returning through operations of pyrolysis carbonization, biological enzyme addition, foliage spraying of a microbial activator and the like. During field returning operation, measures such as specific deep ploughing depth, straw covering thickness and soil improvement agent adding are adopted according to different soil characteristics. In the fertigation process, the relative water content of soil is accurately controlled, fertilization is conducted according to the specific nitrogen-phosphorus-potassium proportion, and drip irrigation and spray irrigation modes are combined. The change of soil microbial communities is monitored by a high-throughput sequencing technology every 15 days, and the soil oxidation-reduction potential is measured by an oxidation-reduction potential instrument and is regulated and controlled in time. The problem of traditional straw returning is effectively solved, and the soil ecological function and the crop yield are remarkably improved.

The invention discloses an AIoT water and fertilizer integrated regulation and control method and system based on a crop growth dynamic model, and relates to the technical field of water and fertilizer regulation and control, and the method comprises the steps: firstly obtaining multi-dimensional environment parameters and soil characteristics through an AIoT sensing network, and deeply analyzing a restriction mechanism of soil moisture on nutrient effectiveness based on the crop growth dynamic model; and quantifying the nutrient effectiveness coefficient. And then, inputting the coefficient into a water-fertilizer coupling prediction model, and dynamically deducing the actual nutrient absorption amount and the moisture evapotranspiration amount of the crop in a specific water-fertilizer interaction environment, so as to realize digital representation of the physiological demand. And further, in combination with future meteorological information and cost constraints, multi-target collaborative optimization is carried out by using a rolling time domain optimization algorithm, and an optimal regulation and control strategy considering growth requirements and economic benefits is generated and driven to be executed. In this way, quantitative and accurate regulation and control of the water and fertilizer synergistic effect can be achieved, and therefore the resource utilization efficiency and agricultural production economic benefits in the whole growth cycle of crops are greatly improved.

The application provides an in-situ passivation remediation method for heavy metals in farmland soil, and belongs to the field of heavy metal passivation in soil. The in-situ passivation remediation method for heavy metals in farmland soil is composed of the following steps: premixing, primary fermentation and secondary fermentation. The premixing is to uniformly mix pig manure, chicken manure, a composite passivation agent, a microbial inoculum and farmland soil, to adjust the C / N ratio and the water content of the mixture, to obtain a premix, and then to perform primary fermentation and secondary fermentation in an aerobic environment. The in-situ passivation remediation method for heavy metals in farmland soil can effectively combine the passivation agent with the compost fermentation, avoid the adverse effects of the fermentation components on the passivation agent during the composting process, improve the adaptability to different soil characteristics, realize stable and efficient treatment of different characteristic soils, further improve the in-situ passivation remediation effect on heavy metals in farmland soil, and effectively avoid the situation that the heavy metals in the soil are released again after the soil heavy metals are passivated.

The invention discloses a soil remediation technology assessment method and system, and the method comprises the steps: extracting key parameters of target soil, obtaining a series of remediation schemes described in a parameterization manner from a pre-established soil remediation scheme database, and processing the data through employing an embedded coding technology and a vectorization coding technology based on deep learning, and mining semantic information of the parameterized description of the target soil and the parameterized description of the soil remediation scheme. And further, semantic query is performed on the target soil and each remediation scheme, so that deep matching between the target soil characteristics and the remediation schemes is realized, and the remediation scheme of the target soil is intelligently recommended based on a matching result. Thus, the soil and the remediation scheme can be accurately matched, the screening accuracy of the soil remediation scheme is improved, the recommendation result is made to better meet the actual soil requirement, then a direct and scientific action guide is provided for the soil remediation project, and efficient implementation of remediation work is guaranteed.

The invention relates to the technical field of soil measurement, in particular to a soil organic carbon density and reserve estimation method which comprises the following steps: S1, collecting basic information of multi-region and multi-type soil samples; s2, constructing a basic nonlinear regression model according to the basic information; s3, introducing a multi-source correction factor based on the basic nonlinear regression model; s4, dynamically adjusting the weight of the multi-source correction factor according to the basic information through an adaptive algorithm to form a transferable model; and S5, by utilizing a model transfer learning method, when the method is used, correction factors can be adaptively adjusted according to different geographical and soil characteristics through a dynamic weight mechanism, so that the accuracy of the transferable model in remote prediction can be improved, the dynamic adjustment of the weight and sensitivity coefficient does not depend on manual setting, and the accuracy of the transferable model in remote prediction can be improved. And the transferable model is automatically adjusted according to a statistical rule, so that subjective intervention is reduced, and the generalization ability is further improved.

The invention provides a variable-rate fertilization control system and method, and relates to the technical field of variable-rate fertilization control. A soil water quantity diffusion model is established through a water quantity diffusion model module according to soil characteristics of a region to be fertilized, a drip irrigation fertilization range is determined through a drip irrigation range calculation module, and the volume of the drip irrigation fertilization range is calculated; the fertilization amount calculation module is used for performing real-time concentration detection on the types of fertilizer elements in a drip irrigation fertilization range in which the fertilizer element detection plants are located, and judging the demand quantity and the lowest absorption content of the plants on each type of fertilizer elements in a fertilization period; the fertilization safety module calculates the maximum fertilization amount according to the soil inorganic salt content and the volume of the drip irrigation fertilization range, formulates a fertilization strategy through the concentration matching module, calculates the fertilization drip irrigation speed and the fertilization drip irrigation water amount according to the water amount diffusion model and the drip irrigation fertilization range, and calculates the fertilizer preparation concentration of each fertilizer element type according to the fertilization drip irrigation water amount.

The invention discloses a slope stability prediction method in heavy rainfall weather, and relates to the technical field of road slopes. The method comprises the following steps: collecting soil characteristic data before and after rainfall in a road slope sub-region, and fitting a water-soil characteristic function and a saturated permeation function through a least square method; the method comprises the following steps: establishing a road slope three-dimensional model by using a laser radar, obtaining soil parameters of each point of the road slope three-dimensional model based on an inverse distance square weighting method, and obtaining simulated initial soil matrix suction and simulated initial unsaturated permeability coefficients; simulating moisture movement through an unsaturated seepage control equation to obtain simulated soil moisture saturation and simulated pore water pressure of each point, screening risk points through a preset threshold value, and generating risk clusters through density clustering; and searching a potential sliding surface in the risk cluster by adopting a particle swarm optimization algorithm, calculating the safety coefficient of the sliding surface in combination with a soil strip method, and quantifying the area of the sliding surface. And fusing the safety coefficient and the area proportion to calculate a slope risk index and divide stability grades.

The invention relates to the technical field of geological disaster monitoring, in particular to a geological disaster real-time monitoring platform and method based on intelligent AI. The method comprises the following steps: acquiring multi-source monitoring data; analyzing the multi-source monitoring data, and determining the stability of the soil body; determining soil body characteristic parameters based on soil body stability; obtaining environmental hydrological data, and determining a geologic structure dynamic response mode according to the environmental hydrological data; and determining a geological disaster risk level according to the soil body characteristic parameters and the geological structure dynamic response mode. Soil body characteristic parameters are determined, quantitative indexes of building specificity are provided, and customized risk assessment is supported. The method comprises the following steps: acquiring environmental hydrological data, determining a dynamic response mode of a geologic structure according to the environmental hydrological data, capturing real-time behavior characteristics of the geologic structure under environmental change, and identifying a weak area of the geologic structure. And according to the soil body characteristic parameters and the geological structure dynamic response mode, determining the geological disaster risk level, realizing building level early warning, and quantifying the risk evolution rate.

The invention discloses soil vertical batch sampling equipment and method for environmental geological survey. The equipment comprises a main body supporting mechanism and a distributed sampling mechanism arranged on the main body supporting mechanism, the main body supporting mechanism comprises a main body supporting plate which is horizontally arranged; the distributed sampling mechanism comprises a sampling mechanism supporting shell arranged at the sampling lifting through groove, a sampling outer drill rod extending vertically is fixed to the lower end of the sampling mechanism supporting shell, and a sampling inner drill rod is rotationally connected into the sampling outer drill rod; the side wall of the sampling outer drill rod is provided with a plurality of outer drill rod side surface sampling holes which are communicated inside and outside, and the side wall of the sampling inner drill rod is provided with a plurality of inner drill rod side surface sampling holes which are communicated inside and outside; the depth of the sampling outer drill rod and the sampling inner drill rod inserted into the soil can be accurately controlled by utilizing the sampling static pressure driving mechanism, which is crucial for researching the characteristic distribution of the soil at different depths, and the distribution condition of the soil components in the vertical direction can be determined by accurately controlling the sampling depth.