211 results about "Soil wetness" patented technology

The invention, which relates to the technical field of landslide monitoring, discloses an optical fiber sensing landslide dynamic response monitoring system comprising an event monitoring module, a sampling optimization module, a landslide risk analysis module and an early warning level adjustment module. By comparing the offset of adjacent data points in real time, tiny displacement abnormity can be quickly identified, instant marks are provided for early geological activities, and the sampling frequency is automatically adjusted according to real-time monitoring data, so that data acquisition is finer, meanwhile, storage and processing of irrelevant data are reduced, the operation efficiency of the system is effectively improved, and the working efficiency of the system is improved. Real-time data and a historical landslide mode are finely compared, comprehensive analysis is carried out in combination with key indexes such as earth surface displacement and soil humidity, the system can accurately calculate the probability of occurrence of the landslide, the accuracy and timeliness of early warning are greatly improved, and casualties and property losses caused by landslide disasters are effectively reduced.

The invention relates to the technical field of water and soil conservation monitoring, and discloses a remote sensing image-based water and soil conservation monitoring method, which comprises the following steps of: 1, integrating optical, infrared and microwave remote sensing data, and constructing a soil information atlas; step 2, capturing soil humidity change in each season by using long-time-sequence data according to the soil information, and determining the influence of rainfall, snow melting and evaporation on the reflectivity; step 3, training data of each wave band through a deep neural network according to the influence information of the reflectivity; through an adaptive learning algorithm and a model parameter regular updating mechanism, the problem that in the prior art, characteristic spectrums of wet soil, vegetation and bare soil are overlapped due to seasonal changes is effectively solved, after the seasonal modulation factors are introduced, the system can accurately capture influences of natural factors such as rainfall, snow melting and evaporation on the soil reflectivity, and the soil reflectivity is improved. And model parameters are adaptively adjusted, so that the interference of reflectivity fluctuation on a monitoring result in wet and drought states is eliminated, and the stability of long-term monitoring is ensured.

The invention discloses a power distribution network three-core cable fault distance measurement analysis method and system, and relates to the technical field of fault distance measurement, and the key points of the technical scheme are that the method comprises the steps: carrying out the empirical mode decomposition of three-phase voltage and current signals; generating a dynamic correction wave velocity based on the cable type, the environment temperature and the soil humidity parameter; determining the first arrival time of the wave head of the initial traveling wave according to the instantaneous frequency abrupt change point of the alpha-mode component, screening the second arrival time of the first reflected wave by using the amplitude gradient of the zero-mode component, and calculating the arrival time difference; and calculating an initial fault distance, and calling the impedance matrix to iteratively correct the initial fault distance. According to the method, the dynamic wave velocity is coupled with multiple factors, the limitation of single parameter correction is broken through, and the problem of transient drift of the wave velocity is solved; and the STFT high-frequency resolution and the wavelet low-frequency stability are fused, and the ridge continuity is optimized in combination with dynamic programming, so that the traveling wave head detection precision can be improved.

The invention relates to the technical field of agricultural planting, and discloses a crop planting suggestion generation method adapting to different climate conditions. Real-time climate data are collected through a multi-source climate sensor, climate dynamic feature data are generated through a spatial-temporal feature fusion algorithm, and planting strategy parameters are obtained by inputting the climate dynamic feature data into a pre-trained mixed decision model. And constructing a multi-objective optimization model taking the crop adaptability matching degree and the resource utilization efficiency as objectives, and globally optimizing a planting scheme by adopting a hierarchical dynamic programming algorithm integrating fuzzy logic constraint and an adaptive decomposition strategy. A multi-level regulation and control framework comprising a decision-making layer, an adaptation layer and an execution layer is constructed, the decision-making layer generates a global planting sequence, the adaptation layer uses a sliding window optimization algorithm to adjust local parameters, the execution layer regulates and controls soil humidity and illumination intensity based on a robust feedback control algorithm, and finally a planting control instruction is output. And accurate and efficient climate adaptability planting decision is realized.

The invention relates to the technical field of intelligent gardens and precise irrigation, and particularly discloses an intelligent irrigation monitoring method and system. According to the method, a multi-source sensor array is deployed, Kalman filtering is adopted to fuse environmental data, and a three-dimensional state vector input reinforcement learning model is constructed to generate an irrigation decision; predicting a vegetation water demand by combining a gradient lifting decision tree, forming a graded irrigation strategy and converting the graded irrigation strategy into a water pump control instruction; soil humidity feedback data are collected in real time, decision model parameters and filtering rules are dynamically adjusted, and closed-loop optimization is achieved. Through multi-source data fusion and a self-adaptive decision-making mechanism, the irrigation precision and the water resource utilization rate are remarkably improved, meanwhile, the response capacity of the system to the vegetation growth dynamic state and the environment change is enhanced, and the beneficial effects of optimization process closed loop, decision dynamic adaptation and controllable resource consumption are achieved.

The invention relates to the technical field of geological disaster detection, in particular to a geological disaster hidden danger point detection method and system. Comprising the following steps: S1, synchronously acquiring vegetation canopy penetration data, multispectral soil humidity spatial distribution data and lithology distribution data by adopting an unmanned aerial vehicle carried image data acquisition device and a multi-band laser radar, and generating an orthoimage and a three-dimensional point cloud data set; s2, fusing the orthoimage and the three-dimensional point cloud data through an aerial triangulation encryption algorithm to establish a three-dimensional model, loading the multispectral soil humidity spatial distribution data into the three-dimensional model to generate a dynamic digital elevation model, and generating a contour line topological structure through a TIN triangulation network algorithm; s3, extracting a slope factor, topographic relief, vegetation coverage, fracture distance and a lithologic factor based on the three-dimensional model to construct a geological disaster hidden danger system; according to the system, the geological disaster occurrence time can be estimated, and the rescue and protection treatment period can be effectively planned based on the geological disaster occurrence time.

The invention discloses a Chinese herbal medicine intelligent drip irrigation regulation and control system based on end-cloud cooperation, and relates to the technical field of agricultural intelligent irrigation, and the system comprises an edge equipment end which is deployed in a Chinese herbal medicine planting area, comprises a multi-mode sensor module, a communication module and an irrigation execution mechanism, and is used for collecting soil humidity, illumination intensity and meteorological data in real time, and sending the collected data to a cloud server; and the drip irrigation valve is controlled through the irrigation executing mechanism. According to the Chinese herbal medicine intelligent drip irrigation regulation and control system based on end-cloud collaboration, through an end-cloud collaboration architecture and a hierarchical decision-making mechanism, the drip irrigation regulation and control accuracy and real-time performance in a Chinese herbal medicine planting environment are improved. A local AI inference engine of the edge equipment end is combined with a dynamic learning module, an optimization instruction can be automatically generated during network fluctuation, dependence on real-time communication of the cloud end is reduced, and continuity and reliability of irrigation operation in complex terrains are ensured.

The invention discloses an agricultural meteorological disaster monitoring and early warning method and system based on a remote sensing technology, and the method comprises the steps: generating a multi-dimensional data set through obtaining and processing multi-source remote sensing data, and generating a comprehensive data set through weighted average fusion. Then, extracting land surface temperature data, comparing the land surface temperature data with a historical value, calculating a temperature deviation value, and generating temperature anomaly distribution data; and calculating a disaster intensity index by combining the soil humidity and vegetation index data change trend, and generating disaster intensity distribution data. The data meteorology is imported into a driving simulation system, disaster evolution is simulated, and the disaster influence range and duration are predicted. And if the prediction data exceeds an early warning threshold, generating early warning data including disaster categories, influence areas and prediction time, and generating disaster risk distribution data by using a spatial interpolation method for dynamic monitoring. According to the invention, the accuracy and timeliness of disaster monitoring and early warning are improved.

The invention discloses a karst development area geological disaster identification method and system based on artificial intelligence, relates to the field of geological disaster early warning and risk assessment, and solves the problems that it is difficult to integrate geology and related data, it is difficult to construct a model to predict disaster categories based on historical disaster cases and real-time monitoring data, and the risk assessment efficiency is improved. The technical problem of lack of association of a prediction result with a geographic space to generate a risk distribution map and dynamically update a high-risk area is solved; comprising the following steps: introducing multi-source heterogeneous data, and combining historical geological disaster cases; preprocessing the data; extracting underground water level change rate, soil humidity and vibration frequency characteristics; combining a plurality of features into a feature matrix, and projecting the feature matrix to a principal component space; after training the model by using a random forest algorithm, optimizing the constructed model by combining cross validation with a Bayesian algorithm; the model prediction result is converted into a risk level value through a mapping rule; and generating a risk level distribution map based on the GIS data.

The invention provides an emergency communication method and system based on a multi-mode early warning broadcast terminal. Wherein the early warning function of the multi-modal terminal is activated and a dynamic feature library is constructed by defining disaster triggering conditions of combination of soil humidity and rainfall intensity; periodically scanning surface micro-deformation by using a radar array, calculating a mountain displacement rate in combination with Doppler frequency shift measurement, and generating a terrain risk thermodynamic diagram; a low-power-consumption long-distance wireless netlike self-organizing communication link is constructed, multi-hop redundancy transmission is started when a public network is interrupted, and an early warning data packet is segmented and forwarded; according to the early warning level cooperatively judged by the dynamic feature library and the radar array, the forwarding priority of the data packet is dynamically allocated, synchronous matching of an acousto-optic early warning signal and a disaster risk gradient is realized, and a hierarchical response link for linkage of terrain deformation monitoring and emergency communication is formed. According to the technical scheme provided by the invention, the emergency response efficiency and the coverage range are improved.

The invention relates to the technical field of forestry intelligent management and control, and discloses a forestry optimization management and control method and system based on artificial intelligence. The method comprises the following steps: collecting multi-source environment monitoring data such as soil humidity, illumination intensity and vegetation growth indexes in a forestry area, aligning a time sequence through a dynamic time warping algorithm, and separating steady-state and transient components; detecting anomalies through an isolated forest algorithm and generating a thermodynamic diagram, and obtaining a potential disease and pest outbreak area through causal reasoning in combination with historical records; constructing a dynamic growth reference curve, and identifying a disturbance sensitive area by using a generative adversarial network and a convolutional neural network; and integrating and generating a management and control priority partition map, optimizing resource allocation through deep reinforcement learning, outputting a task sequence, and driving equipment to execute irrigation, fertilization or pest control operation. According to the method, multiple artificial intelligence algorithms are fused, accurate control over the whole forestry process is achieved, and the method adapts to the dynamically-changing forestry environment.

The invention discloses a soil drought risk early warning system and early warning method based on machine learning, and belongs to the field of soil drought early warning, and the system comprises a data collection module, a data restoration and fusion module, a space-time drought coupling prediction model construction module, and a real-time early warning and model updating module. Wherein the data acquisition module is used for acquiring wind cloud meteorological data and MODIS optical image data; the data restoration and fusion module is used for generating an all-weather soil humidity chart; the space-time drought coupling prediction model construction module is used for constructing a space-time drought coupling prediction model; and the real-time early warning and model updating module is used for obtaining the dynamic drought index and dynamically updating the dynamic drought index. By adopting the soil drought risk early warning system and early warning method based on machine learning, high-precision drought risk prediction and decision support are realized through multi-source data fusion, a space-time coupling prediction model and a dynamic grading early warning mechanism.

The invention discloses a UWB navigation system and method based on visual feature fusion, the system comprises a UWB positioning module, a visual perception module, a data fusion processing module and a navigation control module, the UWB positioning module adaptively generates UWB anchor point layout, calculates UWB coordinates in real time, and then obtains UWB positioning data according to fruit tree coverage rate and soil humidity calibration; the visual perception module collects orchard images to recognize fruit trees and obstacles, obtains visual coordinates and corrects the visual coordinates to obtain visual positioning data; the data fusion processing module calculates weights of the UWB positioning data and the visual positioning data, fuses the UWB positioning data and the visual positioning data according to the weights to obtain fusion coordinates, and performs error correction to obtain optimal coordinates; and the navigation control module calculates a target path point according to the optimal coordinate, the obstacle recognition result and the weeding operation path, and controls the weeding robot to run to the target path point. According to the invention, high-precision navigation of the weeding robot in a complex orchard environment is realized.

The invention relates to the technical field of water conservancy flood prevention monitoring, and discloses a water conservancy flood prevention monitoring and early warning method and system. The method comprises the following steps: acquiring multi-source hydrological monitoring data including real-time rainfall, riverway water level, soil humidity and weather forecast data, and constructing a comprehensive hydrological information basis; flood risk analysis is carried out based on the data, a flood risk prediction signal and a flood situation evolution signal are synchronously generated, and the flood occurrence probability and the development trend are clarified. And combining the flood risk prediction signal with the multi-source data to determine flood prevention regulation and control parameters and generate an optimized flood prevention signal. Flood prevention facility operation is adjusted according to the optimized flood prevention signal and the flood situation evolution signal, an early warning control signal is output, the two signals are processed at the same time to generate a comprehensive early warning index, and the severity of the flood situation is visually reflected. According to the method, hydrological data multi-dimensional fusion is realized, flood prevention monitoring and early warning are promoted to change from single data dependence to multi-source collaboration, and the flood situation response efficiency is improved.

The invention belongs to the technical field of ecological information, and particularly relates to a Qinghai-Tibet Plateau land-gas coupling strength prediction method and system based on a CMIP6 model and emergence constraints. The method comprises the following steps: collecting monthly total precipitation and soil humidity data of a historical period and a future period in nine earth system models in a CMIP6 model, and constructing a data set; performing data preprocessing on the data set through spatial resolution unification and multi-source data fusion; constructing an emergence constraint model based on the preprocessed data; and calculating a land-gas coupling strength index according to a coupling measurement method based on the emergence constraint model. According to the method, by means of emerging constraint, the uncertainty of the model is reduced, the standard deviation of the land-gas coupling strength after constraint is reduced by 65.12%, the prediction precision of the space-time trend is improved, and the problems that a CMIP6 model is underestimated to the northeast and southwest of the Qinghai-Tibet Plateau and is overestimated to the west and northwest of the Qinghai-Tibet Plateau are solved.

The invention discloses a crop irrigation optimization method and device based on digital twinning and three-dimensional reconstruction. The method comprises the following steps: collecting crop growth data and video data of environmental parameters in real time through a multi-mode sensing network; performing frame extraction on the video data to obtain a multi-view image sequence, and generating a crop three-dimensional point cloud model based on a multi-angle reconstruction algorithm; semantic segmentation is carried out on the three-dimensional point cloud of the crop, phenotypic features of the crop are extracted, and environmental data are fused to construct a crop growth digital twinborn body; establishing a soil humidity prediction model, inputting time sequence environment data and crop phenotypic characteristic data, and outputting a soil humidity prediction value in future time; finally, irrigation strategy simulation is executed in the digital twin platform, the optimal irrigation amount is determined through a minimization cost function, an optimal irrigation instruction is issued to a physical irrigation actuator, and closed-loop control is achieved. Through three innovations of three-dimensional phenotype extraction, environment coupling modeling and dynamic strategy simulation, the problems of response lag and extensive decision of a traditional method are solved.

The invention discloses a debris flow intelligent monitoring and early warning system. The system comprises a sensing layer which is responsible for deploying a multi-parameter sensor matrix, constructing a space-air-ground integrated monitoring system, collecting debris flow formation environment key parameters in real time, performing preliminary data preprocessing, generating an encrypted compressed data packet and sending the encrypted compressed data packet to a transmission layer; the transmission layer is used for receiving the encrypted and compressed data packet through a 5G and Beidou short message dual-channel redundancy transmission technology and forwarding the encrypted and compressed data packet to the analysis layer; and the analysis layer is responsible for constructing a hybrid early warning model driven by a physical mechanism and data, fusing decrypted and decompressed data, dynamically capturing a coupling relationship among rainfall, soil humidity and terrain gradient parameters, learning a multi-factor evolution mode of a historical debris flow event, generating a dynamically adjusted critical threshold curved surface, and performing multi-parameter joint early warning. The early warning information is output to the application layer; and the application layer is responsible for triggering graded early warning and driving multiple departments to cooperatively respond based on the early warning information.

The invention discloses a rainfall type landslide early warning method based on multi-source environment threshold and nonlinear fusion. The method comprises the following steps: constructing a landslide disaster-inducing factor set; introducing soil humidity, vegetation indexes and evapotranspiration, and calculating a threshold parameter corresponding to the rainfall type landslide event to obtain an environment threshold point set; according to the landslide disaster-inducing factor set, constructing and training a landslide susceptibility model based on progressive learning; adopting a double-layer nonlinear fusion model based on multi-source data fusion and quality perception gating, and combining a landslide susceptibility model to obtain comprehensive risk probability distribution; and performing graded early warning based on the comprehensive risk probability distribution to complete rainfall type landslide early warning. According to the method, on the basis of traditional rainfall parameters, early-stage disaster-pregnant environment factors are introduced, and a multi-source environment threshold value is formed; fuzzy logic reasoning is adopted to realize nonlinear fusion of the threshold information and the landslide susceptibility base map; and dynamically reflecting a regional environmental condition evolution process in combination with an annual iterative updated susceptibility layer, and realizing finer and more adaptive space grading early warning.

The invention discloses a cotton drought identification management system based on image identification, and relates to the technical field of cotton drought management systems. The system comprises an image acquisition module, a data transmission module and an image recognition and processing module, an improved convolutional neural network model is adopted to process and analyze acquired images, then a trained convolutional neural network model is utilized to perform feature extraction and classification on the preprocessed images, and whether cotton is in a drought state or not is judged; the drought evaluation and decision-making module is used for establishing a drought evaluation model by adopting a fuzzy comprehensive evaluation method according to the result of the image recognition and processing module in combination with the growth stage of the cotton and the soil humidity information and evaluating the drought degree of the cotton, and the user interaction module is used for providing a visual operation interface for a user. The system can efficiently and accurately identify and manage the drought of the cotton.

This invention provides an agricultural drought prediction method and system integrating spatiotemporal feature learning, comprising: data collection and preprocessing; constructing a drought prediction model, including a spatial feature extraction module, a spatiotemporal feature learning module, and a sequence prediction module; constructing a drought event identification and prediction model; the drought event identification and prediction model analyzes the standardized soil moisture index (SMI) sequence for the next M consecutive days predicted by the sequence prediction module, extracts drought events, analyzes the spatial centroid movement characteristics of severe drought events, and predicts the future spatiotemporal changes of drought. This invention comprehensively considers the temporal and spatial attributes of drought, utilizes high-precision raster meteorological data, and inputs multiple hydrometeorological elements into a pre-trained convolutional neural network coupled with a deep learning model to predict future drought indicators. While maintaining model complexity within an appropriate range, it efficiently provides early warning of drought, offering technical guidance for drought prevention and water resource management.

The invention discloses a green ecological energy-saving irrigation roof system and a using method thereof, and relates to the technical field of building roofs. A green ecological energy-saving irrigation roof system comprises a root blocking waterproof layer, a blind ditch structure layer, a non-woven fabric filter layer, a planting layer and a vegetation layer which are arranged on a roof from bottom to top, a rainwater collecting device is arranged in the blind ditch structure layer, an irrigation device is arranged on the planting layer, the rainwater collecting device is communicated with a water storage tank, and the water storage tank is communicated with the irrigation device through a water suction pump; a soil humidity sensor is arranged in the planting layer, the soil humidity sensor and the water suction pump are both connected with the controller, and the solar power supply device is used for supplying power to the water suction pump, the soil humidity sensor and the controller. The solar power supply device is arranged on the roof and used for supplying power to electric equipment in the whole roof system, power supply depending on mains supply is avoided, near-zero-energy-consumption operation is achieved, the building capacity and energy consumption self-balance requirements are met, and the purposes of environment friendliness and energy conservation are achieved.

According to the technical scheme, the water-saving irrigation device for agricultural technology popularization is characterized by comprising a water storage tank, a water supply hole is formed in one side of the water storage tank, a water supply pipe is connected to the interior of the water supply hole, a flow dividing box is connected to one side of the water supply pipe, a water pump is connected to the interior of the water storage tank, and one end of the water pump is connected with the water supply pipe; the plurality of mounting holes are formed in one side of the shunting box; the irrigation assembly is arranged on one side of the flow dividing box and used for irrigating farmland, when irrigation water is conveyed to the water seepage pipe through the water supply pipe, micropores are formed in the outer wall of a pipeline of the water seepage pipe, stable water irrigation can be achieved, and pressure control can be conducted on the conveyed irrigation water through a pressure adjusting valve; waste caused by too fast water flow or uneven irrigation caused by insufficient water flow is avoided, soil humidity is monitored in real time through a soil humidity sensor, the irrigation amount of the water seepage pipes at different positions is controlled in cooperation with a first electromagnetic valve, utilization of irrigation water is optimized, the water saving effect is improved, and waste of water resources is reduced.

The invention discloses a method for interplanting amorphophallus bulbifer through photovoltaic shading. The method comprises the steps that interplanting areas are established between photovoltaic array lines; the interplanting area is divided into a fixed shading area and a variable light-transmitting area according to the projection range of the photovoltaic panels, the fixed shading area is located in the area under the photovoltaic panels, and the variable light-transmitting area is located in a light-transmitting zone between the adjacent photovoltaic panels; planting furrows are formed in the fixed shading area according to the line spacing of 40-50 cm; selecting amorphophallus bulbifer tubers, and performing field planting on the planting bed; a sunshade net sliding rail system driven by a stepping motor is arranged in the variable light-transmitting area, an illumination intensity sensor is installed on a sliding rail, the sensor is connected with a controller, and the controller automatically adjusts the horizontal displacement of the sunshade net every day according to a solar altitude calculation model and real-time illumination data; and establishing an intelligent drip irrigation system. According to the method, a stable shading environment and appropriate soil humidity can be provided for the amorphophallus bulbifer, manual operation is reduced, the automation level of the interplanting process is improved, and the specific requirements of crops for the growth environment are met.

The invention discloses a dynamic drought transmission threshold identification method and system, and relates to the technical field of drought transmission dynamic monitoring. Time sequence basic data is acquired, the time sequence basic data is segmented through a sliding window with a preset window length, and segmentation data of each window is acquired; calculating each meteorological drought index in a preset continuous time scale and a soil humidity index in a month scale for the segmented data; calculating a correlation coefficient between each meteorological drought index in a preset continuous time scale and a soil humidity index in a month scale based on a Pearson's correlation coefficient, and obtaining a preset time scale corresponding to the maximum correlation coefficient as drought transmission time; and calculating a drought transmission threshold value based on the drought transmission time, the soil humidity index of one month scale, combined with the joint probability distribution and the first-order Bayesian network. According to the invention, dynamic identification of different levels of drought transmission thresholds is realized.

The invention provides an agricultural pest correlation analysis and diagnosis method and system based on a knowledge graph, and the method comprises the steps: firstly collecting a multi-source agricultural monitoring data set, including crop growth stage parameters, meteorological fluctuation parameters and the like, of a target region, and then processing meteorological and soil humidity parameters through an environment feature extraction model to generate a multi-dimensional environment feature set; processing disease and pest symptom image data by using a symptom feature extraction model to generate a symptom feature set, calculating dynamic correlation degree parameters of the two based on a knowledge graph topological structure, constructing a disease and pest cause probability distribution diagram, and then performing joint reasoning on crop growth stage parameters and the cause probability distribution diagram by using a pre-trained disease and pest diagnosis model to obtain a disease and pest disease diagnosis result. And finally, matching an optimal control strategy set according to the multi-level disease and insect pest diagnosis results, and inputting the optimal control strategy set into an agricultural decision-making system to trigger control equipment regulation and control operation, thereby realizing scientific and effective disease and insect pest diagnosis and control.

The invention discloses a mining area watering cart driving control method based on animal avoidance real-time re-planning, and the method achieves the intelligent obstacle avoidance through multi-modal sensor fusion detection, deep learning driven target recognition, three-dimensional dynamic path planning, a multistage acousto-optic early warning system and adaptive speed adjustment. And a radar, a camera, a bionic compound eye vision module and the like are adopted, so that the detection response time is shortened, and the false alarm rate is reduced. Constraint conditions such as terrain gradient and soil humidity are integrated through an RRT algorithm, continuous processing of path curvature is achieved, the watering cart can complete path reconstruction without complete stop in an animal penetration scene, a graded early warning mechanism triggers three-level acousto-optic warning according to the animal approaching degree, and a dynamic speed adjusting model is matched to adjust the speed of the watering cart. And the spraying operation continuity is ensured while the safety allowance is ensured. According to the invention, through the environment interference compensation, the geological disaster early warning interface and the driver behavior simulation unit, the operation safety and the operation efficiency in the complex environment of the mining area are obviously improved.

The invention discloses a high-precision slope monitoring device based on a Beidou system and a detection method. The method comprises the steps that at least three Beidou positioning base stations and at least three Beidou positioning monitoring stations located at different preset positions of a target slope are arranged; the inclination angle, the soil humidity and the rainfall of the target slope are collected through an inclination angle sensor, a soil humidity sensor and a rain gauge; taking the slope displacement of the target slope as a state variable, and establishing a slope deformation nonlinear state equation according to the inclination angle, the soil humidity and the rainfall; establishing an observation equation between the Beidou positioning data and the slope displacement; establishing a slope stability evaluation model according to the slope deformation nonlinear state equation and the observation equation; and outputting the stability grade and risk early warning information of the target slope according to the slope stability evaluation model. According to the invention, the complex slope stability analysis process is automated through the slope stability evaluation model, the slope stability level and risk early warning information are output, and the evaluation efficiency and precision are improved.

The invention relates to the technical field of flood control and disaster reduction, in particular to an intelligent flood control adaptive regulation and control system based on the Internet of Things and artificial intelligence. Comprising a multi-source sensing network module, a flood prediction module, a self-adaptive regulation and control module and an edge calculation module, wherein the multi-source sensing network module deploys a high-precision sensor network at a key position of a whole watershed and is used for collecting water level, flow velocity, rainfall and soil humidity data in real time, and the multi-source sensing network module adopts a dynamic self-adaptive sampling strategy; and the multi-source sensing network module transmits data to the cloud distributed database through a 5G network and performs verification by fusing satellite remote sensing data and a GIS spatial topological relation. The IoT and AI algorithms are deeply integrated and applied to the flood control system, intelligent linkage and self-adaptive regulation and control of the facilities in the whole watershed can be achieved, the flood prediction precision is improved, the automatic balance and dynamic cooperation capability of the facilities is finally enhanced, and disaster losses caused by response lag are effectively reduced.

The invention discloses a landslide risk monitoring method and system based on multi-source data, and relates to the field of geological monitoring early warning, and the method comprises the steps: obtaining the environment monitoring data of a to-be-monitored slope area, and constructing a multi-dimensional feature matrix according to the environment monitoring data; performing risk prediction classification based on the multi-dimensional feature matrix according to a risk classifier to obtain a landslide risk level, and performing prediction based on the multi-dimensional feature matrix according to a neural network model to obtain a landslide displacement prediction quantity; calculating a landslide stability coefficient according to the environmental monitoring data based on a limit equilibrium method, calculating a rainfall induction threshold by combining the rainfall data and the soil humidity data, and obtaining a landslide comprehensive risk coefficient by integrating the landslide stability coefficient and the rainfall induction threshold; and determining a landslide risk monitoring result of the to-be-monitored slope area in combination with the landslide risk grade, the landslide displacement prediction amount and the landslide comprehensive risk coefficient. According to the application, the technical problem that the existing landslide risk monitoring accuracy is poor can be solved.

The invention is applicable to the technical field related to seedling raising, and provides a ligustrum gloridum high-stem grafting seedling raising device which comprises a base and a cultivation pot arranged on the base. The precise irrigation mechanism comprises a lifting ring, a supporting ring, a supporting frame and an irrigation assembly, an electric telescopic rod used for controlling the working state of the irrigation assembly is further arranged between the supporting ring and the lifting ring, and a control assembly is further arranged on the supporting ring; the humidity sensor is used for detecting soil humidity; through cooperative arrangement of a precise irrigation mechanism and a humidity sensor, the problems that according to an existing seedling raising mode, simple water spraying operation can only be conducted by experience, environment parameters cannot be precisely regulated and controlled, environment fluctuation is likely to be too large, grafted seedlings suffer from too high humidity or too drought, and diseases are caused are solved; the success rate of seedling raising and the quality of nursery stocks are greatly influenced.