153 results about "Soil wetness" patented technology

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 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 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 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 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 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.

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.

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 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 cultivation method comprises the steps that the macadamia nut forest with the tree age larger than or equal to 5 years is selected, PVC permeable pipes with water seepage holes are laid at the bottoms of ditches, and the ditches are covered with gravel layers; ridging is conducted along the dead furrows, and a lower layer of fallen leaf crushed matter mixed decomposed farmyard manure, a middle layer of acid red soil and an upper layer of special konjak matrix containing functional microorganisms are constructed on ridge bodies in a layered mode; performing cold shock treatment on the seed taros, soaking the seed taros in chitosan-nano-selenium composite liquid, and coating the seed taros with nut oleoresin; the height of the sunshade net is dynamically regulated and controlled through an intelligent system to maintain the shading rate to be 60%-70%, and automatic drip irrigation is carried out based on a soil humidity sensor; harvesting and retaining rhizomes after konjak seedlings fall, removing konjak plants before nut fruit drop, and collecting dropped fruits by utilizing dead furrows. According to the method, the problems of water and light accurate regulation and control, root competition and harvesting conflicts are solved, waste recycling is achieved, the soft rot rate is decreased, the fruit drop loss rate is greatly decreased, and the comprehensive output value is remarkably increased.

The invention discloses a mountain torrent disaster early warning method based on multi-source data fusion, and the method comprises the steps: 1, building a hydrological-side slope coupling dynamic model based on a TRIGRS landslide dynamic model, and enabling the hydrological-side slope coupling dynamic model to integrate dynamic calculation modules of soil pore water pressure, soil moisture content and slip shear strength; 2, Beidou-R soil humidity inversion data and three-dimensional deformation field monitoring data are accessed in real time, and a fusion data layer is generated; 3, space-time calculation is conducted on the fusion data layer through the hydrology-slope coupling dynamic model, a dynamic forecast value of the landslide safety coefficient Fs is output, Fs = sliding face resistance / sliding force, and when Fslt, Fs = sliding face resistance / sliding force; 1, triggering landslide early warning; according to the method, the fusion data layer is subjected to space-time calculation through the hydrology-slope coupling dynamic model, the dynamic forecast value of the landslide safety coefficient Fs is output, rainfall-induced geological disaster early warning and forecasting are achieved, and the accuracy of geological disaster area risk early warning is improved.

The invention provides a multi-scheme collaborative yield forecasting method based on data assimilation and model parameter optimization, and belongs to the technical field of agricultural information, and the method comprises the steps: obtaining historical and real-time data of a target region and a target crop growth season; constructing a plurality of combined simulation schemes of the WOFOST model; carrying out data assimilation on the leaf area index and the soil humidity by utilizing an ensemble Kalman filter (EnKF) method and combining a Gaussian disturbance strategy; performing sensitivity analysis and optimization on photosynthetic parameters of the WOFOST model, determining an optimal photosynthetic parameter combination and operating the model; improving a water stress function; constructing a rolling updating yield prediction framework, and dynamically optimizing a yield prediction result; and dynamically selecting an optimal simulation strategy to simulate and forecast the yield. According to the method, the yield simulation precision and forecasting stability of the crop model under different moisture years are remarkably improved, and a reference is provided for developing a new meteorological year adaptive dynamic simulation framework of the crop model.

The invention relates to the technical field of meteorology and hydrology, and discloses a quantitative rainfall estimation method and system based on multi-source data correction radar echoes, and the method comprises the steps: collecting the multi-source data, such as rainfall station actually measured rainfall data, elevation data, hydrometric station flow, radar reflectivity, soil humidity and environmental attributes, in a target region; a Z-R relation parameter of each radar sampling grid is dynamically calibrated by combining a collaborative Kriging interpolation and physical constraint hybrid neural network model, and a radar reflectivity correction parameter of each grid is calculated by using a difference between rainfall station observation data and radar data; in real-time application, based on the corrected radar reflectivity and the calibrated Z-R parameter, the rainfall intensity of each grid is inverted, and the precision of quantitative rainfall estimation is improved.

The invention discloses a regional extreme drought and flood event prediction system based on a climate model, and relates to the technical field of climate model prediction and disaster early warning, and the system comprises a data input module which is used for receiving regional multivariable prediction data processed by downscaling of the climate model, and the multivariable prediction data comprises rainfall, soil humidity and evapotranspiration. According to the regional extreme drought and flood event prediction system based on the climate model, equation strong constraint and variational optimization are carried out on key variables such as rainfall, soil humidity and evapotranspiration through the physical constraint dynamic coupler, the physical law of water circulation mass conservation and surface energy balance is forcibly met, the distortion phenomenon is eliminated, and the prediction accuracy is improved. According to the method, physical correction residual errors are converted into weight factors, Copula function parameters are dynamically adjusted, it is ensured that joint probability distribution of extreme drought and flood events strictly follows a physical mechanism, the path of misinformation physical impossible events is blocked from the source, and the problem of prediction distortion caused by multivariate physical inconsistency in downscaling output of a climate model is solved.

The invention discloses an agricultural crop growth parameter collecting, monitoring and tracing all-in-one machine which comprises an all-in-one machine body, and a multi-parameter sensor module, a data collector, a wireless communication module and a power management module are arranged outside the all-in-one machine body. The system comprises an advanced multi-parameter sensor module, a high-efficiency data acquisition and processing system, a reliable wireless communication module, a data processing platform, an intelligent decision support system and a block chain data tracing platform, and is characterized in that the advanced multi-parameter sensor module, the high-efficiency data acquisition and processing system, the reliable wireless communication module and the intelligent decision support system are integrated; key growth parameters such as soil humidity, illumination intensity, air temperature and humidity and carbon dioxide concentration can be monitored in real time, and accurate irrigation and fertilization suggestions can be provided through data analysis. Besides, the system adopts a data tracing platform constructed by a block chain technology, the authenticity and non-tampering property of all monitoring data are ensured, and the transparency and credibility of the whole agricultural production process are enhanced, so that the quality and yield of agricultural products are effectively improved.

The invention provides an intelligent watershed flood prediction method and system based on a multi-modal large model, and relates to the technical field of hydrology and water resources, and the method comprises the steps: S1, obtaining multi-modal original data from a meteorological radar, satellite remote sensing, a soil humidity sensor, hydrological station monitoring equipment and a terrain and land utilization database, and respectively performing signal denoising, spatial interpolation, dimension standardization and timestamp synchronization on the original data. According to the drainage basin flood intelligent prediction method and system based on the multi-modal large model, unified modeling of multi-modal information such as rainfall, runoff, soil water content and terrain is realized through deep fusion of multi-source heterogeneous data and large model structure design. The constructed time sequence processing subnet and the spatial feature extraction subnet are specially optimized for time sequence dependence and spatial topological relation respectively, the high dependence of a traditional physical model on data quality and boundary conditions is overcome, and the defects of a pure data driving model in the aspects of complex nonlinear relation and generalization ability are overcome.

The invention relates to the technical field of mountain torrent disaster prediction model training, and discloses a mountain torrent disaster prediction model training method based on a hybrid optimization neural network, and the method comprises the steps: collecting key data related to mountain torrent disasters in real time through a multi-source sensor network, such as rainfall intensity, water level height, soil humidity and water velocity; the method comprises the following steps: acquiring data, performing data labeling based on historical disaster events, performing normalization processing on the acquired data by adopting a sliding window and clustering analysis, and constructing a deep neural network architecture which comprises multi-scale feature extraction, a time sequence attention enhancement module and a hybrid optimization training strategy. According to the method, through innovative weight initialization and two-stage optimization training, the model prediction precision and real-time performance are effectively improved, through online incremental model updating, the model can be continuously optimized under changing environmental conditions, disaster early warning signals are generated in real time, and powerful support is provided for mountain torrent disaster prevention and control.

The invention discloses an embedded frequency conversion control method and system for motor control, and relates to the technical field of motor control, and the method comprises the steps: laying an embedded frequency conversion control network for a target irrigation area, and the embedded frequency conversion control network comprises a data sensing module, an embedded micro-control module and a power driving module; based on the data sensing module, real-time soil humidity, real-time environment data and meteorological data are obtained and transmitted to the micro-control module; and the micro-control module performs multi-stage frequency conversion control analysis, generates a dynamic motor control instruction, sends the dynamic motor control instruction to the power driving module, and executes self-adaptive frequency conversion control. The technical problems that an existing irrigation system is insufficient in motor control accuracy, high in energy consumption and capable of affecting the irrigation energy efficiency are solved, and the technical effects that through embedded frequency conversion control, the dynamic adjustment of the motor operation frequency is achieved, the motor control accuracy is improved, the energy consumption is reduced, and then the irrigation energy efficiency is optimized are achieved.

The invention discloses a land coverage classification method based on time series data, which comprises the following steps of: acquiring multispectral image data, cutting according to a geographic boundary of a research area, and extracting surface reflectance data of the research area through wave band superposition; by calculating a normalized difference vegetation index and a tasseled cap transformation humidity component, surface vegetation phenology and soil humidity are captured; an optimal time sequence feature set is screened based on time sequence difference evaluation indexes, and data redundancy is reduced; a land coverage classification result of the research area is generated through a support vector machine classifier in combination with the optimal time sequence feature set; and obtaining a verification sample in combination with the Google Earth high-resolution image, calculating overall classification accuracy (OA) and a Kappa coefficient according to the verification sample and a land cover classification result, and evaluating the reliability of the land cover classification result. Through combination of time sequence data and an intelligent classification strategy, the classification precision of urban complex ground features is significantly improved.

The application discloses a UWB navigation system and method based on visual feature fusion, and the system comprises four modules of UWB positioning, visual perception, data fusion processing and navigation control. The UWB positioning module adaptively generates a UWB anchor point layout, and after calculating UWB coordinates in real time, UWB positioning data are obtained by calibration according to the coverage rate of fruit trees and soil humidity. The visual perception module collects orchard images, identifies fruit trees and obstacles, obtains visual coordinates and corrects visual positioning data. The data fusion processing module calculates the weight of UWB positioning data and visual positioning data, fuses UWB positioning data and visual positioning data according to the weight to obtain fusion coordinates, and corrects errors to obtain optimal coordinates. The navigation control module calculates target path points according to the optimal coordinates, obstacle recognition results and weeding operation paths, and controls the weeding robot to travel to the target path points. The application realizes high-precision navigation of the weeding robot in the complex environment of the orchard.

The application relates to the technical field of precision agriculture monitoring, and discloses a precision agriculture monitoring system based on adaptive wireless communication. The system comprises a multi-source environment acquisition module, a communication protocol adaptation module and a crop state evaluation module. The multi-source environment acquisition module obtains soil humidity data, illumination intensity data and meteorological parameter data through wireless sensor nodes arranged in farmland areas, and synchronizes the timestamps of the data to ensure the consistency of the data in the time dimension. The communication protocol adaptation module dynamically selects a wireless transmission frequency band according to the synchronous data volume, allocates communication time slots based on data priority, and generates adaptive transmission channel parameters to cope with the communication requirements under the conditions of data volume fluctuation and complex environment. The crop state evaluation module receives the transmission channel parameters, analyzes real-time environment data streams, and divides the current crop growth state into a growth standard state, a growth lag state or a growth fluctuation state. The system provides support for fine management of agricultural production.

The application provides a wheat wide seedling strip seedbed preparation device and a monitoring system thereof, which comprises a single fixed seat, a front ditch opener fixed seat, a front ditch opener, a fertilizer spreading device, an anti-abrasion plate, a rotary tiller group, a side plate, a soil retaining plate, a rear ditch opener, a soil humidity sensor, a metal water pipe, a profiling mechanism, a spring damper, a transmission box and a motor. The single fixed seat is provided with a through hole, and the single body is fixed to a seeding machine frame through U-shaped bolts. The seeding machine can be installed with multiple single bodies. The spacing between the single bodies is adjusted to adapt to the wide seedling strip seeding operation of different row spacings. The soil disturbance and power consumption during operation are reduced through wide seedling strip rotary tillage. The monitoring system comprises a rotary tiller group power distribution monitoring system and a lower irrigation monitoring system. The rotary tiller group power is automatically adjusted with the change of the forward speed and the working resistance, so that the seedbed preparation operation effect is improved. Through lower irrigation of the seedling strip, the problem of uneven emergence caused by the inconsistent soil moisture content of different plots in the field is solved.

The utility model discloses an independently adjustable drip irrigation device for blueberry cultivation, which comprises a water pump, a water inlet pipe, a connecting pipe, a filter tank and a drip irrigation pipe, the water inlet pipe is communicated with the input end of the water pump, the other end of the water inlet pipe is communicated with an external water source, the connecting pipe is communicated with the output end of the water pump, and the filter tank is communicated with the drip irrigation pipe. The filter box is communicated with the other end of the connecting pipe, and the drip irrigation pipe is communicated with the right side of the filter box. By arranging the flow adjusting block and the fixing assembly, the opening and closing degree of the drip irrigation holes can be adjusted according to needs, so that accurate control over the drip irrigation amount is achieved, blueberries in different areas can obtain a proper amount of water according to specific needs through the independent adjusting function, waste of water resources is reduced, and cost is reduced. The problems that accurate adjustment cannot be conducted according to soil humidity and crop growth stages of different areas, the maintenance cost of a traditional drip irrigation system is high, and especially under the condition that the filtering effect is poor, a water dropper is prone to being blocked are solved.

A shaping roller of a ridger belongs to the technical field of agricultural machinery. The device comprises a gear box, transmission shafts, a mounting frame, chain wheel transmission mechanisms and a shaping roller, the gear box is fixedly mounted on the ridger, the input end of the gear box is connected with a driving shaft, the output ends of the two sides of the gear box are connected with the transmission shafts respectively, and the ends of the transmission shafts on the two sides are connected with the two ends of the shaping roller through the chain wheel transmission mechanisms respectively. The transmission mechanism and the shaping rotating roller are both installed on the installation frame. The utility model aims to solve the problems that a shaping mechanism of an existing ridger is easy to drag and pile, poor in ridge body compaction degree and poor in ridge shape when the amount of straw in soil is large or the soil humidity is large, the shaping rotating roller adopted by the ridger is not easy to drag and pile, and meanwhile, the shaping rotating roller rotates on the ridge at a high speed, so that the ridge shape flatness is obviously improved, and the ridging effect is improved. The device is suitable for different types of soil and terrains, has good adaptability and is suitable for planting of various crops.

This invention relates to the field of irrigation technology for afforestation in barren mountains, specifically a monitoring and irrigation device for afforestation in barren mountains. It includes an electric six-wheeled chassis and a side-spray water supply structure connected to the chassis. The side-spray water supply structure includes a water tank, with a telescopic water supply unit fixedly connected to the tank, and two symmetrically arranged side-spray units fixedly connected to the tank. A point-based detection irrigation structure is also connected to the chassis. This structure includes a mobile carrier unit connected to a drone, a lifting limit unit connected to the chassis, multiple independent detection units connected to the lifting limit unit, and an aerial sprinkler unit movably connected to the lifting limit unit. Through the combination of the side-spray water supply structure and the point-based detection irrigation structure, this invention is suitable for complex mountainous terrain, accurately monitoring soil moisture and performing irrigation operations, thus improving the applicability and accuracy of irrigation operations in mountainous areas.

This invention discloses a method for improving the accuracy of rice irrigation forecasts in irrigation districts based on digital twins, belonging to the field of agricultural water conservancy engineering technology. Based on digital twin technology, this invention divides the irrigation district into sub-regions using GIS and constructs a digital mapping model; it establishes a dynamic adaptive calculation system with multi-model selection, combining meteorological data completeness, rice growth period, and historical errors, and calculates the appropriate model using a dynamic weighted adaptation algorithm, obtaining the final value from the input data; it introduces soil moisture and field management correction coefficients to construct a dynamic calculation model; combined with the calculation of real-time water requirements for rice, it generates a refined irrigation scheduling plan for the sub-regions and achieves coordinated irrigation; and it corrects parameters using a gradient descent algorithm to ensure forecast accuracy. This method, through its dynamic adaptive calculation system, solves the accuracy problem in scenarios with missing data and has broad application value.