1597 results about "Soil humidity" patented technology

Disclosed is an intelligent monitoring method for soil moisture status based on remote sensing technology, including the following steps: dividing a monitoring region into a plurality of sub-regions by a processing end, acquiring image data of various sub-regions on the basis of remote sensing satellites, acquiring multi-source data related to soil moisture status of the monitoring region on the basis of sensor devices, and pre-processing the image data and the multi-source data; comprehensively analyzing the image data and the multi-source data, and assessing soil moisture status of the various sub-regions; and comprehensively analyzing overall soil moisture status of the monitoring region according to the soil moisture status of the various sub-regions. The soil moisture status of the monitoring region is assessed after the multi-source data is comprehensively analyzed by the monitoring method, bringing about a more comprehensive and accurate analysis.

The invention relates to the technical field of intelligent agriculture and ecological internet of things, in particular to an ecological irrigation decision dynamic optimization method and related equipment. Comprising the following steps: acquiring multi-source environment data, acquiring a soil profile humidity gradient in real time through a soil humidity sensor array, acquiring future rainfall probability distribution, a temperature change rate and a wind speed predicted value in combination with a weather forecast interface, and synchronously accessing a geographic information system to acquire terrain elevation and crop distribution data. The three technical bottlenecks of model dimension collapse, parameter estimation instability and optimization response lag in a traditional irrigation decision-making system are systematically solved by constructing a space-time coupling analysis framework and a closed-loop optimization mechanism of multi-source heterogeneous data.

The invention discloses a precise irrigation decision analysis method and system based on crop water demand, and particularly relates to the technical field of crop irrigation. A monitoring area is divided, soil humidity sensors with different depths are installed, moisture state data of a root zone are monitored in real time, moisture gradient change characteristics and moisture retention time deviation characteristics are extracted and analyzed, a moisture fluctuation abnormal index is calculated, the moisture fluctuation abnormal index is compared with a threshold value, and the moisture fluctuation abnormal index is calculated. The method comprises the following steps: dividing underground water fluctuation into high, middle and low underground water fluctuation abnormal levels, and taking corresponding irrigation adjustment measures according to different levels of fluctuation: taking a drainage measure to reduce excessive soil moisture for high-level abnormal fluctuation; crop water demand model parameters are dynamically adjusted through medium-grade fluctuation; a normal irrigation strategy is maintained by low-grade fluctuation, so that negative effects of underground water fluctuation and excessive irrigation on crop root zones and soil structures can be effectively avoided, soil permeability and water storage capacity are maintained, long-term productivity of farmland is guaranteed, and irrigation efficiency and water resource utilization rate are improved.

The invention relates to the technical field of slope stability data identification, and particularly provides a multi-sensor fusion slope stability intelligent analysis system and an edge calculation method.The system comprises an environment sensing subsystem, a data processing subsystem and an edge calculation subsystem, the environment sensing subsystem monitors the change of the soil moisture content through a soil humidity sensor, and micro movement data of a slope are obtained through a displacement sensor; using a pressure sensor to sense the change of the ground pressure; meanwhile, pre-screening out abnormal data to form a fused data set; the data analysis subsystem is used for converting abnormal data of the fusion data set into a quantitative index of slope stability, and deducing a potential sliding risk of the slope by analyzing the relevance of the soil water content, the tiny movement data or the ground pressure; and the decision support subsystem provides specific reinforcement countermeasures according to the analysis result of the potential sliding risk. According to the method, the potential sliding risk of the side slope is comprehensively deduced by analyzing the relevance of the soil water content, the tiny movement data and the ground pressure.

The invention relates to the technical field of agricultural irrigation management, in particular to a method and system for estimating the agricultural irrigation water consumption of an irrigation area. The method comprises the following steps: collecting soil, weather and crop data through a sensor and a detector, processing the collected data through an edge computing node, and then selecting an optimal transmission path to transmit the data through an Internet of Things gateway based on an https protocol; an irrigation area agricultural irrigation water consumption prediction model is constructed based on an intelligent algorithm of deep learning, the model stores, preprocesses and analyzes received data, then modeling and prediction are carried out on soil humidity, meteorological conditions and crop growth conditions, and finally an irrigation water consumption estimation result is obtained; and updating the irrigation area agricultural irrigation water consumption prediction model to ensure the accuracy and timeliness of model prediction. According to the method, through an advanced sensor technology, the Internet of Things, big data analysis and a machine learning algorithm, accurate estimation and optimal management of the agricultural irrigation water consumption of the irrigation area can be realized.

The invention relates to the field of intelligent irrigation, in particular to an intelligent irrigation method and device based on the Internet of Things, and the method comprises the steps: constructing a soil standard model, obtaining soil data in real time, inputting the soil data into the soil standard model, and outputting a region division result through the soil standard model; based on a regional division result, judging whether the soil data meets an irrigation condition or not; when the soil data meets the irrigation condition, acquiring meteorological data, water pressure data and historical irrigation data; according to the meteorological data, the water pressure data and the historical irrigation data, irrigation parameters needed by the corresponding area of the soil are predicted; and determining corresponding irrigation behaviors in combination with the soil data and the irrigation parameters of the corresponding areas. Soil data is monitored in real time, so that the system can dynamically adjust an irrigation plan, invalid irrigation after rainfall is avoided, a targeted irrigation strategy is made according to the soil humidity level and topographic features, slope water and soil loss is prevented, and it is ensured that the flat ground is prevented from being too wet to cause root system oxygen deficit.

The invention provides a surface soil humidity multi-source data fusion and downscaling method and system. The surface soil humidity multi-source data fusion and downscaling method comprises the following steps: performing quality evaluation and standardization processing on multi-source soil humidity data; carrying out anomaly detection and restoration by adopting a hierarchical spatial-temporal feature extractor; carrying out regional partitioning based on an adaptive boundary generation algorithm and a soft boundary transition model; data fusion and error control are realized through a regional cascade fusion network and an error chain tracking algorithm; and carrying out downscaling processing by utilizing a terrain adaptive downscaling framework and a multi-scale residual learning model to obtain a soil humidity data product with the resolution of 1km. The soil humidity data of complex terrains and high vegetation coverage areas can be effectively processed, the data fusion precision and downscaling effect are remarkably improved, and the technical problems of a traditional method in the aspects of data quality evaluation, anomaly detection, partition boundary processing, error control and the like are solved.

The invention relates to the technical field of environment regulation, in particular to a corn cultivation environment intelligent regulation method based on multi-sensor control, which comprises the following steps: S1, generating a soil moisture three-dimensional distribution diagram in real time through humidity sensor arrays arranged at different depths of a corn root zone; s2, combining infrared temperature data of the canopy and the three-dimensional distribution diagram of the soil moisture, and calculating a moisture motion mismatch degree through transpiration-permeation conduction collaborative analysis; s3, generating a user-defined irrigation mode based on the water movement mismatch degree, and controlling the irrigation duration, the stop interval and the water flow rate. According to the method, coupling modeling is carried out on the plant physiological water demand behavior and the soil moisture supply characteristic, the limitation that a traditional irrigation system only depends on a soil humidity threshold value or meteorological index judgment is broken through, and the biological adaptability and the aging responsiveness of field moisture regulation and control are improved.

The invention relates to the technical field of seepage monitoring, and discloses a seepage monitoring system based on a hydraulic engineering project. The system collects water level, flow velocity, seepage flow, soil humidity, soil temperature and soil pressure data through a data collection module, a data analysis module calculates standard soil humidity, standard water flow velocity, standard soil pressure, standard soil temperature and standard seepage flow according to the data, establishes a mathematical model, calculates seepage abnormal indexes, and calculates the seepage abnormal indexes according to the standard soil humidity, the standard water flow velocity, the standard soil pressure, the standard soil temperature and the standard seepage flow. The risk assessment and early warning module performs threshold judgment on the seepage abnormal indexes, sends out an overrun signal when an overrun condition occurs, predicts a future seepage trend by using a moving average method at the moment, generates a future seepage trend broken line graph and a seepage risk assessment report, and uniformly transmits the future seepage trend broken line graph and the seepage risk assessment report to the emergency response module; and when the emergency response module receives the overrun signal, an emergency plan is started, and related managers are informed of the emergency plan, the future seepage trend broken line graph and the seepage risk assessment report in a short message mode.

The invention provides an intelligent agricultural environment monitoring system based on the Internet of Things, and relates to the technical field of data processing. The feature extraction and analysis module is used for performing segmented statistical analysis on the soil humidity original data according to the soil humidity original data in combination with historical soil humidity data, historical environment temperature data and preset near-period soil humidity feedback data, and automatically extracting a humidity change rate and an abnormal trend index data set under multiple time windows; the self-learning judgment module is used for dynamically adjusting the current judgment threshold value based on a self-learning algorithm, judging the soil humidity original data in combination with the humidity change rate and the abnormal trend index, and generating a judgment signal for judging whether local irrigation response is triggered or not; the response control module is used for automatically generating an irrigation equipment starting instruction; the irrigation execution module is used for executing irrigation operation according to the irrigation equipment starting instruction; according to the invention, the accuracy of agricultural environment monitoring is improved.

The invention provides a farmland root zone soil humidity prediction method, system and device and a storage medium, and belongs to the technical field of soil humidity prediction, and the method comprises the steps: obtaining multi-source remote sensing data of a farmland; downscaling the spatial resolution of the multi-source remote sensing data to obtain a kernel normalized difference vegetation index kNDVI of a first spatial resolution, a surface soil humidity SSM of a second spatial resolution and meteorological data ERA5 of a third spatial resolution; a root zone soil humidity RZSMt at the t moment is given, the kNDVI, the SSM and the ERA5 at the same moment and the SSM at the t + 1 moment are jointly input into a soil humidity prediction model, and the root zone soil humidity RZSMt + 1 at the t + 1 moment is simulated; and taking the current model output as the model input of the next step, and repeating the step to complete the prediction of the soil humidity of the root zone every day. According to the soil humidity prediction model provided by the invention, the problems of sensitivity and complexity in remote sensing inversion can be effectively solved, and relatively high-precision irrigation area scale root zone soil humidity prediction is realized.

The invention relates to the field of intelligent agriculture, and discloses an intelligent drip irrigation control method, system and device and a storage medium, and the method comprises the following steps: data collection: obtaining soil humidity data, environmental meteorological data and crop growth state data of a target area through a plurality of sensors, and transmitting the data to a data processing module; the system comprises a data acquisition module used for acquiring soil humidity data, environmental meteorological data and crop growth state data and transmitting the acquired data to a data processing and storage module; the device comprises a machine case shell, and an intelligent control chip, a wireless communication module, an electromagnetic valve and a water flow meter are installed in the machine case shell. By integrating a sensor network, intelligent analysis and a real-time feedback mechanism, the water demand of crops is accurately calculated, irrigation is automatically adjusted, and water resource utilization is optimized; a personalized irrigation plan is made by combining a deep neural network and time sequence analysis, and the water requirements of crops in different growth stages are ensured.

The invention relates to the technical field of irrigation, and discloses an intelligent energy-saving irrigation management system and method based on environmental monitoring, and the method comprises the steps: obtaining the daily water demand of crops in a farmland through a crop growth monitoring module, setting the water flow of irrigation according to the daily water demand, then dividing the farmland into a plurality of regions, then environment parameters of soil humidity, temperature and illumination intensity of each area are monitored in real time through an environment monitoring module, and then the environment parameters are processed and analyzed through a data processing module to obtain current irrigation indexes of each area; whether the set irrigation water flow meets the irrigation requirements of all the areas or not is judged based on the current irrigation indexes of all the areas, if the areas do not meet the irrigation requirements, water supplementing is conducted on the areas in a sprinkling irrigation mode, then the water supplementing water flow is calculated based on the environment parameters, and it is guaranteed that the water content of all the areas of the farmland is consistent; therefore, the growth vigor of crops in each area is consistent.

The invention discloses a slope stability evaluation and landslide early warning system and method based on strain monitoring, and relates to the technical field of geotechnical engineering safety monitoring, in the slope stability evaluation system, strain monitoring optical cables are arranged in a plurality of drill holes formed in a slope in a penetrating mode and are fully coupled with slope soil; laser is emitted to the strain monitoring optical cable through a Brillouin optical time domain reflectometer, and a Brillouin scattering light signal is received from the strain monitoring optical cable; brillouin frequency shift is determined according to the Brillouin scattered light signals, then transverse shear force is determined according to the Brillouin frequency shift, the slope stability coefficient of the slope is calculated according to the change of the transverse shear force, and whether landslide is likely to happen or not is further judged in combination with soil humidity measured by the humidity monitoring optical cable, and early warning is conducted in time. Compared with an existing slope monitoring technology, the slope monitoring system has the advantages of being high in precision, wide in monitoring range, resistant to electromagnetic interference, resistant to corrosion and the like, and real-time effective monitoring of different deformation stages of a slope can be achieved.

The invention relates to the technical field of irrigation control, in particular to a water and fertilizer integrated irrigation control method based on a wheat growth cycle, which comprises the following steps: collecting soil humidity and wheat growth state data, and screening key soil and biological parameters influencing wheat growth by applying a random forest algorithm; and predicting the requirements of the wheat on water and nutrition in the differential growth stage according to the key parameters. According to the method, the water and nutrition requirements of wheat in different growth stages are accurately predicted by adopting a random forest algorithm, resource allocation is directly carried out according to the actual needs of plants, the problems of resource waste and insufficient supply of a traditional irrigation system are effectively avoided, and a dynamic adjustment strategy is based on current soil and plant states and also based on a dynamic adjustment strategy. And a water and fertilizer supply plan is optimized by using historical and predicted data, the capability of responding to environmental changes is enhanced, irrigation and fertilization parameters are finely adjusted by combining real-time feedback circulation with a PID controller, and the accuracy and effectiveness of each time of irrigation and fertilization are further ensured.

The invention discloses an agricultural irrigation area water resource intelligent management system and method, and relates to the field of intelligent management, and the method comprises the steps: building a correlation model of a crop growth stage and a basic irrigation threshold value through real-time collection of original weather station data, future 24-hour weather forecast, crop types, planting dates and other multi-dimensional data; and in combination with soil humidity sensor data and a channel pump station state, the basic irrigation starting threshold is dynamically adjusted by using an FAO formula or a deep learning algorithm to form a dynamic irrigation decision threshold. And then multi-source data is input into an irrigation decision module, an irrigation starting and stopping instruction is generated by judging the relation between the irrigation state and the soil humidity threshold value, and closed-loop management is achieved. Thus, the limitation of traditional irrigation static threshold management is broken through, and through meteorological data dynamic adaptation and multi-source data fusion decision making, on the premise of guaranteeing crop growth water demand, the water resource allocation efficiency is optimized, and the intelligent management level of an agricultural irrigation area is improved.

The invention discloses a real-time monitoring and early warning method for the leakage state of a long-line type public facility, and relates to the technical field of monitoring and early warning of the leakage state of the long-line type public facility, and the method specifically comprises the following steps: analyzing environment dynamic response information stored in a central monitoring system; respectively generating a soil humidity change coefficient, an environment pressure response index and a temperature influence compensation coefficient; constructing a leakage risk assessment model for the generated soil humidity change coefficient, the environment pressure response index and the temperature influence compensation coefficient, and generating a leakage risk coefficient; and determining a preset leakage risk coefficient threshold value, comparing the determined leakage risk coefficient threshold value with the generated leakage risk coefficient, evaluating whether a leakage risk exists in the pipe gallery area close to the earth surface, and respectively generating a normal signal and an abnormal signal according to an evaluation result. According to the invention, the problem of underground pipe gallery leakage monitoring is solved, abnormal signals are accurately distinguished, and the monitoring accuracy and safety are improved.

The invention relates to the technical field of self-adaptive control, in particular to a tobacco field variable fertilization control system and method, and the system comprises a sensing data collection module which collects soil humidity data through a soil humidity sensor, monitors the growth data of crops at the same time, and carries out the synthesis to obtain real-time soil and growth data; and performing time sequence analysis on the real-time soil and growth data, identifying a data trend and an abnormal point, and generating a trend analysis result. According to the method, the soil humidity and the crop growth data are collected, and the time sequence analysis is combined, so that the trend and the abnormality are quickly identified, and the prediction accuracy is improved. The convolutional neural network is used for carrying out recognition and probability evaluation on diseases and insect pests, so that disease and insect pest management is more accurate, and response is quicker. Through the technologies, the system can dynamically adjust irrigation parameters based on real-time data, optimize water distribution, and ensure that an irrigation strategy is perfectly matched with crop requirements and potential risks.

The invention discloses a hilly slope irrigation control system based on artificial intelligence, and the system comprises a wind speed and wind direction detection module which is used for obtaining wind speed and wind direction data; the sprinkler head adjusting module is used for adjusting the angle and the spraying distance of a sprinkler head based on the wind speed and wind direction data; the soil humidity detection module is used for being connected with a soil humidity sensor and determining irrigation requirements according to data of the soil humidity sensor; the artificial intelligence control module is used for optimizing the irrigation time and the irrigation amount through an artificial intelligence algorithm; the sprinkling irrigation system control module is used for controlling the sprinkling irrigation system to irrigate according to the optimization result; the hilly slope irrigation control system based on artificial intelligence reduces the phenomenon of non-uniform growth of crops, facilitates healthy and uniform growth of the crops, and ensures that the crops can obtain a proper amount of water supply, so that normal growth and development of the crops are promoted, the yield and quality of the crops are improved, and the agricultural production benefits are further improved.

The invention relates to the technical field of farmland fertilization management, and discloses an intelligent fertilization management method and system based on machine learning. The method comprises the steps that a soil parameter set and an environment parameter set of a target farmland are collected, soil parameters comprise soil humidity, nitrogen phosphorus and potassium content and pH value, and environment parameters comprise illumination intensity, temperature and rainfall; constructing a soil nutrient dynamic change model according to historical data, and predicting a soil nutrient consumption trend in a future preset period; generating an initial fertilization scheme based on the nutrient consumption trend and the crop growth stage characteristics; monitoring the growth state of crops in real time by using a multi-mode sensor, and obtaining a leaf surface color index, a stem height and a fruit development progress to form a growth state data set; and inputting the growth state data set and the initial fertilization scheme into a fertilization decision model, and comparing growth state deviation to adjust the nutrient distribution ratio to generate an optimized fertilization scheme.

The invention belongs to the field of irrigation systems, and provides an intelligent water-saving irrigation system based on the Internet of Things, which comprises a control terminal, and the control terminal comprises a control module, a communication module, a data analysis module and a power supply module; the data acquisition module comprises a meteorological sensor, a soil humidity sensor and a plant growth state monitor which are electrically connected with the communication module; the irrigation execution module comprises an adjustable electric valve and a variable frequency water pump which are controlled in a partition mode, and irrigation operation is conducted according to the instruction sent by the control module; by arranging the control terminal, the data acquisition module and the irrigation execution module, the soil humidity and the plant water demand condition can be accurately sensed, the system can avoid unnecessary irrigation, water resource waste is effectively reduced, full automation and intelligentization of the irrigation process are achieved, manual intervention is reduced, and the working efficiency is improved.

The invention provides a landslide disaster recognition method and system based on multi-source data fusion, and relates to the technical field of landslide disaster recognized.The landslide disaster recognition method comprises the steps that firstly, sensors are evenly arranged in a landslide area to obtain soil humidity, underground water level, horizontal displacement and vertical displacement, and meteorological data at the same moment are recorded; thirdly, preprocessing the collected data, extracting a feature matrix by applying principal component analysis, then constructing a landslide risk model based on a deep learning model, and performing training by taking a landslide risk index as a label; and on the basis, analyzing a landslide risk index and meteorological data to obtain a landslide risk probability. And finally, monitoring slope body data in real time, preprocessing the slope body data, inputting the preprocessed slope body data into the trained model, obtaining a current landslide risk index, calculating a risk probability, and comparing the risk probability with a preset threshold value to judge a landslide safety state, thereby providing a scientific basis for landslide disaster early warning.

The invention is suitable for the field of spatio-temporal data prediction, and provides a CNS model soil humidity prediction method fusing dynamic spatio-temporal pruning and a WOA algorithm, and the method comprises the steps: building an initial CNS model based on a multiple convolution module, a BiLSTM module and an attention mechanism module; taking the soil time sequence data as the input of an initial CNS model, and performing preliminary training on the initial CNS model; performing dynamic pruning on each module of the preliminarily trained model by adopting a dynamic space-time pruning strategy; model parameters are updated through a back propagation algorithm, and the CNS model used for soil humidity prediction is obtained. The CNS model integrates a convolutional neural network CNN, a space-time diagram convolutional network STGCN, a bidirectional long short-term memory network BiLSTM and an attention mechanism layer Attention, multi-modal data from different data sources can be comprehensively processed and analyzed, and the CNS model shows higher precision and robustness in a soil humidity prediction task; through combination of dynamic pruning and WOA algorithm optimization, the model can maintain stable prediction performance.

The invention relates to an irrigation amount control method and system for prune cultivation. The method comprises the steps of performing timestamp calibration and dynamic weight distribution processing according to soil data, meteorological data and transpiration data to obtain a multi-modal data set; based on the multi-modal data set, microclimate abnormal event detection and soil humidity correction processing are carried out, and anti-interference soil water content data are obtained; according to the anti-interference soil moisture content data and preset prune growth stage parameters, carrying out root system layered water absorption dynamic modeling treatment to obtain a deep root system moisture demand prediction value; according to the deep root system moisture demand prediction value and the transpiration data, irrigation amount dynamic planning processing is carried out, and the target irrigation amount is obtained. According to the method, through multi-source data fusion, root system water absorption modeling processing and dynamic irrigation planning, the target irrigation amount of prune cultivation can be accurately determined, the accuracy and scientificity of irrigation management are remarkably improved, and powerful guarantee is provided for high-quality growth of prunes and efficient utilization of water resources.

The invention relates to the technical field of sowing control, in particular to a precise control system and method for cooperative fertilization and sowing, and the method comprises the following steps: monitoring soil humidity distribution data of a target farmland by using a soil humidity sensor, and calculating a soil permeability coefficient based on the soil humidity distribution data in combination with a three-dimensional terrain model and a soil permeability coefficient; constructing a soil humidity gradient model by adopting a finite element analysis method; based on the soil humidity gradient model and in combination with target fertilizer characteristics, establishing a fertilizer diffusion model, simulating the diffusion range and concentration distribution of the fertilizer under the soil humidity gradient condition, and outputting the fertilizer diffusion radius and the concentration gradient distribution of the fertilizer in soil through multivariate regression analysis; and based on the fertilizer diffusion model, calculating the fertilization amount and the fertilization depth in combination with the crop demand parameters. According to the method, the soil humidity gradient model is established, and fertilizer waste and environmental pollution caused by non-uniform fertilization or excessive fertilization amount are avoided.

The invention relates to the technical field of internet-of-things control, in particular to a wheat-corn annual water nitrogen content adjusting system, which comprises a data sensing module for acquiring soil humidity and nitrogen content data in a wheat-corn growth cycle, filtering the soil humidity and nitrogen content data and generating processed soil data; based on the processed soil data, a set standard threshold value is compared, and a soil state result is obtained. According to the method, the adjustment strategy is dynamically generated by further combining water and nitrogen demand standards of different growth stages, instant weather forecast data is synchronously analyzed when the strategy is generated, multi-dimensional environmental factors such as precipitation, wind speed, temperature and air humidity are considered, and an optimization adjustment scheme which better conforms to real-time demands of crops and environmental changes is generated. In the execution process, an instruction received by the irrigation and fertilization machinery is not a static single execution instruction any more, but is iteratively executed based on real-time feedback and environmental dynamics.

The invention relates to the technical field of agriculture, and discloses an intelligent fertilization technical measure and method for improving the fertilizer utilization rate, and the intelligent fertilization technical measure comprises a soil sensor module which is used for monitoring various parameters of soil in real time, including but not limited to soil nutrient content, soil humidity, soil acidity and alkalinity, soil temperature and soil texture; the soil sensor module can collect monitored parameter data with high precision and high frequency, and transmit the data to the central control unit through the wireless communication module; the fertilizer storage and conveying module is internally provided with a plurality of independent fertilizer storage bins, and each storage bin can store different types of fertilizers. According to the present invention, the type, the use amount and the application time of the fertilizer can be precisely controlled according to the growth requirements of different crops and the soil state through the precise fertilization mode so as to ensure the efficient absorption of the fertilizer by the crops, such that the fertilizer utilization rate is significantly improved, and compared with the traditional fertilization mode, the fertilizer waste can be reduced, and the agricultural production cost can be reduced.

A post-construction settlement prediction and risk assessment system for a soft soil foundation comprises a data acquisition module which is arranged at a key position of the soft soil foundation and comprises a soil humidity sensor, a pressure sensor and a displacement sensor; the data analysis module is used for preprocessing the data acquired by the data acquisition module, extracting key features from the preprocessed original data and taking the key features as model input; the settlement prediction module comprises a finite element model and a prediction model, inputs the model into the finite element model, outputs settlement results under different load conditions, and outputs corresponding settlement curves and prediction trends in combination with the prediction model; and the risk assessment module is used for outputting a corresponding risk level according to a set threshold value based on the prediction trend of the settlement prediction module. An efficient technical means is provided for monitoring and predicting the settlement risk, a scientific basis is provided for decision making in the construction process, and the safety and economical efficiency of engineering are improved.

The invention provides a multi-parameter GNSS-R soil humidity inversion method based on a random forest model. The multi-parameter GNSS-R soil humidity inversion method comprises the following steps: satellite-borne GNSS-R data quality control; satellite-borne GNSS-R data observation quantity extraction and surface reflectance calculation are carried out; eliminating satellite-borne GNSS-R observation data influenced by the open water area; carrying out surface reflectance gridding; preprocessing the SMAP data; constructing an inversion model; and carrying out spaceborne GNSS-R soil humidity inversion and precision evaluation. The method has the beneficial effects that by utilizing the performance advantages of processing a non-linear relationship, good generalization performance, strong robustness and the like of the random forest, after observation data influenced by a water body is accurately eliminated, an inversion model is constructed by combining multi-parameter auxiliary data of a soil humidity active and passive remote sensing satellite; the space-time coverage, the inversion precision and the reliability of soil humidity data are remarkably improved, and powerful data support is provided for the fields of climate forecast, agricultural production, hydrological modeling and the like.

The invention discloses an SAR (Synthetic Aperture Radar) soil surface water content inversion method and device based on deep learning, and the method comprises the steps: constructing a deep learning regression model for predicting the volume content of soil water through combining SAR data, auxiliary data and in-situ sensor data; a ConvNeXt model is adopted to construct a parallel convolution encoder to solve multi-source data with different input resolutions, and back scattering coefficients, spectral information, digital elevation model high-resolution data and soil sand content, sludge content, clay content and stacking density low-resolution data are fused into soil moisture estimation. And the inference capability of unknown data is improved. The model obtained by training can realize large-range and high-resolution automatic estimation of the water content of the soil surface layer. According to the invention, the influence of radiation transmission noise is avoided, the inversion precision of a vegetation coverage area is improved, and large-range high-resolution automatic processing of soil humidity products is realized.