404 results about "Soil humidity" patented technology

The invention discloses a flood disaster risk intelligent early warning method fusing multi-source information, and relates to the technical field of flood disaster prediction.The method comprises the steps that multi-source data of meteorology, hydrology, terrain, remote sensing, ground monitoring terminals and the like are obtained, and a unified data set is constructed through time synchronization, space resampling, abnormal value elimination and missing data filling; constructing multi-dimensional spatio-temporal features based on factors such as rainfall process, topographic features, surface coverage, drainage capacity, river network structure and soil humidity; inputting the spatial-temporal characteristics into a multi-source information fusion model, and predicting a flood occurrence probability in a future time period; flood risk indexes are generated according to the prediction probability, real-time monitoring and historical samples, and regional risks are divided into different grades; early warning information is automatically generated and issued according to the risk level; and the early warning result is fed back and corrected through field monitoring and patrol data, so that self-adaptive updating of early warning parameters and a model structure is realized.

The invention discloses a Xinanjiang model correction method based on CLDAS soil humidity assimilation. Soil moisture constants such as field capacity FC and wilting point WP are calculated; corresponding and mapping of a CLDAS observation layer and a model three-layer soil structure (WU, WL and WD) are realized through layered interpolation. For the problem of missing measurement of the CLDAS, a reconstruction model based on space-time Transform is introduced for filling, pixel-level uncertainty is output, and an EnKF observation covariance matrix is constructed based on the pixel-level uncertainty. And then dynamically updating the three-layer soil water content state of the model by using ensemble Kalman filtering, thereby reducing the influence of soil humidity initial estimation and simulation deviation on flood simulation. The flood simulation and real-time forecasting precision can be effectively improved, the method has the advantages of being stable in data source, clear in physical significance and high in applicability, and a new technical approach is provided for state initialization and digital hydrology research of a distributed hydrological model.

The invention relates to the technical field of rotary cultivators, in particular to a rotary cultivator with a residual film recycling function. Comprising a front wheel; first rotating shafts are fixedly connected to one sides of the two front wheels, second rotating shafts are fixedly connected to one sides of the two rear wheels, a recycling machine shell is jointly and rotationally connected between the two first rotating shafts and the two second rotating shafts, and a tillage machine is arranged on one side of the recycling machine shell. Through cooperation of a water pump, a second threaded rod, a spraying bin and a spray head, broken films on a screen mesh plate can be dissolved, soil blocks can be decomposed, the problem that a large amount of time is spent on sorting residual films and straw by workers in the follow-up process is solved, a first motor, an air pump, a second motor and the water pump are controlled through a controller, and the working efficiency is improved. Complex residual film recovery and soil treatment processes are integrated into a highly automatic and intelligent operation system, the soil humidity, the residual film density and the impurity content are monitored in real time through a sensor, the suction force of an air pump and the water pressure of a water pump are intelligently adjusted, and soil moisture loss and energy consumption are reduced to the maximum extent while it is ensured that residual films are efficiently recovered.

The invention relates to the technical field of intelligent irrigation, in particular to an intelligent watering control method and system for agricultural production, and the method comprises the steps: data collection, global data reconstruction, soil moisture content prediction, watering strategy optimization, sensor layout optimization, control execution and model updating. In the prior art, a pure mathematical method is generally adopted to calculate global humidity from sparse point data, and multi-dimensional environment information cannot be effectively fused, so that the reconstruction precision is limited and the physical credibility is insufficient; a physical information enhanced confrontation generation network is adopted to carry out global perception, sparse measurement values, geographic coordinates and real-time environment data are fused into conditional tensors to be input into a generator, and training is guided by forcing sensor data consistency in a loss function and introducing moisture diffusion physical constraints; the method can generate a high-resolution and physically reasonable global soil humidity distribution diagram, and significantly improves the reconstruction precision and reliability from sparse points to planar information in a complex farmland environment.

The invention relates to the technical field of water and soil loss dynamic monitoring and early warning methods in water conservancy projects, and discloses a water and soil loss dynamic monitoring and early warning method, which comprises the following steps: synchronously acquiring multi-dimensional data of terrain, rainfall, soil humidity and vegetation coverage through unmanned aerial vehicle remote sensing and a ground sensor network; registering and correcting the multi-source heterogeneous data based on a space-time fusion algorithm; the improved SWAT model is combined with a machine learning algorithm to dynamically simulate a water and soil loss process; and triggering graded early warning according to a preset risk threshold and generating prevention and control suggestions. The system comprises a data acquisition module, a data fusion processing module, a water and soil loss simulation prediction module and an early warning response module. According to the scheme, the real-time performance, the spatial resolution and the early warning accuracy of water and soil loss monitoring are remarkably improved, and efficient decision support is provided for ecological protection of water conservancy projects.

The invention belongs to the cross technical field of hydrology and meteorology, data analysis and machine learning, and particularly discloses a soil humidity space-time multi-step prediction model construction method and a storage medium, and the method comprises the following steps: constructing a data set; a construction graph convolution module is used for constructing a connection matrix based on time sequence correlation of soil humidity data in a training data set, performing k-hop expansion and normalization processing on the connection matrix to obtain a k-hop normalized connection matrix, and extracting wide-area spatial features and remote heterogeneous dependence of soil humidity based on the construction graph convolution module; and a prediction model is constructed, the prediction model comprises GConvLSTM units and ConvLSTM units, the GConvLSTM units and the ConvLSTM units are crossed or stacked in sequence, a layer memory flow architecture is formed, and an initial KGCCLSTM prediction model is obtained. According to the invention, high-precision multi-step prediction of the soil humidity in a complex scene can be realized.

The invention relates to the technical field of intelligent agricultural intelligent irrigation control, and discloses an intelligent irrigation analysis control system based on a crop growth period. The system comprises a crop image sequence, soil humidity profile data and a regional meteorological sequence. And the data analysis module is used for performing multi-stage growth state interpretation on the image sequence, generating a crop structure state profile in each growth period, performing vertical moisture exchange identification on the soil humidity data, and generating a root zone moisture dynamic map. And the habitat mapping and stress evaluation module synchronously executes habitat adaptability mapping and moisture stress risk mapping. And the irrigation decision module corrects the habitat mapping result according to the stress evaluation result and generates an irrigation decision profile. And the control instruction generation module generates a partition valve control instruction in combination with the decision profile and the alternate irrigation rule. According to the invention, deep fusion analysis of the crop growth state, the root zone moisture and the meteorological environment is realized, and the irrigation decision accuracy and the water resource utilization efficiency are improved.

The invention discloses an Internet of Things big data agricultural informatization monitoring and management system and an implementation method thereof, through monitoring, analyzing and researching environmental factors such as illumination, soil humidity and temperature in related environments of crops, and in cooperation with related peripheral electronic elements, an implementation system with operability is formed; therefore, the plant growth environment is regulated and controlled, and finally the purpose of regulating and controlling the plant growth environment is achieved. Meanwhile, people can regulate and control the temperature, the illumination intensity and the soil humidity in the plant growth environment in real time through the network. The monitoring system comprises greenhouse field data acquisition, an environment sensor can acquire parameters such as temperature, humidity, illumination intensity, soil moisture and the like inside and outside the greenhouse in real time, the parameters are presented to a management user in a visual mode, and various sound-light alarm information is provided according to requirements of planted crops. The real-time monitoring effect is achieved in the field of agricultural ecological environment monitoring, real-time monitoring of the agricultural ecological environment is achieved, and high efficiency and high quality of the farm production process are guaranteed.

The invention discloses a knowledge graph construction method for alternative planting of poppy, which comprises the following steps: acquiring soil humidity, illumination intensity, air temperature and rainfall data from Internet of Things sensing nodes deployed in an alternative planting area, analyzing a crop growth state in combination with a remote sensing image, and generating a structured environment observation tuple; entity recognition and relation extraction are carried out on policy and regulation texts, agricultural technology manual and historical planting records, and an initial triple set is constructed; inputting the structured environment observation tuple and the initial triple set into a graph neural network encoder to generate a node embedding vector; on the basis of the node embedding vectors, calculating semantic association weights among entities by adopting a graph attention mechanism, and constructing a dynamic adjacency matrix; the objective of the invention is to solve the problems of lack of semantic association, fragmentation of knowledge expression, weak reasoning ability and insufficient cross-domain collaboration of multi-source heterogeneous data in existing alternative planting management.

The invention provides an automatic irrigation device and method based on a soil humidity sensor, and relates to the technical field of water-saving sprinkling irrigation. The automatic irrigation device comprises a distributed soil humidity sensor network buried in soil of an area to be irrigated, a central controller connected with the distributed soil humidity sensor network and an irrigation execution unit electrically connected with the central controller. By arranging a distributed soil humidity sensor network and constructing a three-dimensional distribution model of soil moisture, the inherent defect that traditional single-point measurement is over-summarized is thoroughly overcome; by means of the automatic irrigation device, the real moisture conditions of each subarea and different depths in the irrigation area can be accurately mastered, so that independent and on-demand irrigation of each subarea is achieved, the high uniformity of the soil humidity of the whole field is effectively guaranteed, the optimal moisture environment is provided for crop growth, and the crop yield and quality are directly improved.

The invention relates to the technical field of pipeline safety monitoring, in particular to a long-distance pipeline risk event distinguishing monitoring system and method based on artificial intelligence. The artificial intelligence-based long-distance pipeline risk event distinguishing monitoring method comprises the following steps of S10, acquiring humidity time sequence data and anode output current time sequence data of soil in an area where a target pipe section is located, and respectively drawing a humidity time curve and a current time curve; acquiring a vibration signal acquired by the optical fiber sensing network; and S20, periodically fluctuating according to the current-time curve. According to the method, the relation between the regional self-adaptive humidity saturation interval and the corrosion rate is established, the huge cost for customizing a vibration analysis model for each region is avoided, the event recognition accuracy is improved under high-risk scenes such as coating damage by utilizing deep coupling of the corrosion state and vibration, and the event recognition efficiency is improved. And passive alarm is converted into active and accurate early warning based on humidity prediction.

The application relates to the field of garden greening management automation, and discloses a garden greening intelligent irrigation method, system, controller and spray head, the method comprising: collecting environmental information; constructing a soil moisture dynamic equation and a plant growth model according to plant information and environmental information; calculating a spray head opening time based on the dynamic equation and the growth model; controlling the spray head to open and close according to the opening time; and dynamically adjusting the spray head opening time based on the environmental information after the spray head is opened. The application constructs a soil moisture dynamic equation based on soil humidity and weather changes, and calculates a spray head opening time based on the dynamic equation and the growth model, so that efficient and intelligent irrigation control is realized. Compared with an existing timing automatic irrigation system, the application effectively avoids water resource waste caused by fixed cycle watering, while ensuring the accuracy and timeliness of water supply, meets the needs of modern water saving and accurate maintenance, and has remarkable environmental and economic benefits.

The invention relates to the technical field of soil humidity prediction, in particular to a high-precision soil humidity extraction method capable of supporting cross-regional migration in a mountain land surface environment, which comprises the following steps: training a 3D-CNN-LSTM model in a source domain by using multi-source environmental factor data, constructing a basic prediction framework, predicting frame by frame through a target domain sliding window mechanism, and obtaining a prediction result. Acquiring a soil humidity prediction map with the resolution ratio of 30 meters, and evaluating the performance of the model by adopting MSE, MAE and R2 indexes; through monthly time sequence images and spatial statistical analysis, annual scale and monthly scale space-time distribution laws are extracted; performing population initialization, crossover variation and selection on the candidate feature set through a genetic algorithm; and constructing a 3D-CNN-LSTM fusion framework, extracting local spatial-temporal characteristics through a three-dimensional convolution kernel, capturing time dependence in combination with LSTM, and outputting a predicted value to obtain a cross-regional soil humidity prediction model. Existing research mostly focuses on regional scale or short-term dynamic, and time-space change characteristics are difficult to consider under a complex terrain background.

A system of integrated water and fertilizer planting and industrial collaboration for a terraced field in an ecologically fragile area includes a hardware part, a software part, a water balance and osmosis model, a water and fertilizer optimization and adjustment model, and an industry collaboration model. The hardware part includes a terraced field intelligent irrigation equipment, soil moisture sensors, a meteorological station, a fertilizer application device, an underground water storage system, and a solar power supply. The system introduces the water balance and osmosis model to effectively manage water resources in terraced field. By integrating data from the soil sensors and meteorological station, it automatically optimizes the methods of irrigation and fertilization, preventing water waste and soil erosion. The water and fertilizer optimization and adjustment model can dynamically adjust the application of nitrogen, potassium, and phosphorus fertilizers based on real-time environmental data and crop requirements.

The invention discloses an ecological environmental pollution risk assessment method and system based on big data, and relates to the technical field of environmental pollution assessment, and the method comprises the steps: taking a rainstorm event as a trigger, constructing an event alignment data set, fusing rainfall radar, remote sensing covering, soil humidity, topographic convergence, farming activity agency, and river and lake water quality and flow related data, and obtaining a risk assessment result; inverting a sub-basin nutritive salt potential releasable amount index and generating an event propagation weight, combining with an event-driven coupling evaluation model constructed by historical events, outputting a transient river-entering load and a composite risk index, and further giving a risk contribution sequence and a minimum intervention set; and integrated early warning and decision support of source control before the rainstorm, interception in the rainstorm and restoration after the rainstorm is realized.

This invention discloses a method and system for deep fusion detection of multimodal soil nutrients, belonging to the field of data processing. The method includes the following steps: real-time reception of multimodal data from a sensor suite deployed in the field. The received multimodal data includes soil reflectance spectral data collected by a near-infrared spectral sensor. Compared with the prior art, the advantages of this invention are: real-time reception of spectral, electrochemical, and image data, extraction of deep features after preprocessing and alignment, and introduction of a condition-aware network. The weights of each modality are dynamically adjusted based on auxiliary information such as soil moisture and organic matter content, and finally, a weighted fusion is performed to output the predicted nutrient value. This fully utilizes the complementarity of multimodal information, overcoming the environmental sensitivity and instability of single technologies while retaining their respective advantages, achieving rapid, accurate, and robust soil nutrient detection in the field.

The utility model discloses a codonopsis pilosula seedling raising bed with irrigation equipment, which comprises a seedling raising box main body, a seedling raising bed laying groove arranged in the seedling raising box main body, a water collecting groove arranged at the bottom in the seedling raising box main body, a pebble soil support frame structure arranged between the seedling raising bed laying groove and the water collecting groove, and a water collecting tank arranged at the bottom of the outer side of the seedling raising box main body, a water outlet communicated with the water collecting tank and the water collecting tank is formed in one side of the bottom of the seedling raising box body, a water suction pump is arranged on the outer side of the water collecting tank and provided with a circulating water pumping pipe communicated with the water collecting tank, and the water suction pump is provided with a water outlet pipe extending to the position above the seedling raising box body. A plurality of sprayers are arranged at the bottom of the distributive pipe. Compared with the prior art, the device has the advantages that the device is supported and separated through the internal planting layer, continuous spraying can be guaranteed, soil humidity can be guaranteed, the overall seedling raising quality and survival rate can be guaranteed, meanwhile, water recycling can be guaranteed, and water resources are saved.

The utility model provides an Acanthopanax sessiliflorus planting soil humidity detection device, and relates to the technical field of Acanthopanax sessiliflorus planting soil humidity detection devices, the Acanthopanax sessiliflorus planting soil humidity detection device comprises a humidity detector, one side surface of the humidity detector is provided with a protection mechanism, the protection mechanism comprises a fixed pipe, the inner part of the fixed pipe is sleeved with an extrusion ring, and the extrusion ring is provided with a clamping groove; the extrusion ring is slidably connected with the fixing pipe, a guide block is connected into the fixing pipe in a sleeved mode, the guide block is slidably connected with the fixing pipe, and the guide block is fixedly connected with the extrusion ring. According to the humidity detector, the detection rod of the humidity detector is protected by arranging the protection mechanism, meanwhile, soil humidity detection of the detection rod is not affected, the fixed pipe is arranged and covers the detection rod, and then the fixed pipe can enter the soil along with the detection rod, so that the purpose of protecting the detection rod is achieved, and the detection rod is prevented from being damaged. Meanwhile, soil can enter the fixing pipe.

The invention belongs to the technical field of intelligent perception and agricultural informatization, and particularly relates to a non-contact soil humidity detection system and method based on polarization imaging. The problems that an existing traditional soil humidity detection method is poor in real-time performance and strong in dependence on illumination, and a contact type detection system and method cannot represent a real physical state in a characterization mode and cannot conduct mobile measurement are solved. The system specifically comprises an illumination control unit, a polarization imaging unit connected with the illumination control unit through a data bus, an attitude measurement unit connected with the polarization imaging unit through a data bus, and a data processing and analysis unit connected with the polarization imaging unit and the attitude measurement unit through data buses. And the display and output module is connected with the data processing and analyzing unit through a data bus.

The utility model relates to the technical field of garden management, in particular to a garden soil humidity monitoring device which comprises a base plate, a through groove is formed in the middle of the top face of the base plate, a fixing plate is installed above the base plate, and a lifting plate capable of reciprocating up and down is installed above the fixing plate. And a display screen is rotationally mounted on the top surface of the lifting plate. The utility model has the advantages that the base plate is used as a device main body, the fixed plate is arranged above the base plate, the lifting plate capable of reciprocating up and down is arranged above the fixed plate, the display screen is rotationally arranged above the fixed plate, the mounting seat is arranged below the fixed plate, the screw rod is arranged on the mounting seat, and the screw rod is in threaded fit with the fixed plate. The humidity probe is mounted on the mounting seat, when the humidity sensor is used, the humidity probe can be conveniently inserted into soil by screwing the screw rod, and meanwhile, monitoring data can be transmitted to the display screen through the cable, so that a worker can conveniently check related parameters at different positions and at different heights.

This application relates to the field of smart agriculture technology, specifically to a method and apparatus for assessing the drought resistance capacity of intercropping of rice and beans. The method includes: acquiring daily soil moisture variation data and environmental parameter variation data of the cultivated land to be assessed; decoupling the soil moisture variation data based on the environmental parameter variation data to obtain humidity variation parameters, including transpiration-humidity variation parameters and temperature-humidity variation parameters; inputting the humidity variation parameters, environmental parameter variation data, and daily soil moisture variation data as samples into a preset soil assessment model to update the preset soil drought resistance assessment model; and inputting temperature change prediction data for the time to be predicted and the time to be predicted into the updated preset soil drought resistance assessment model to obtain the assessment result. This method allows for a more accurate assessment of soil drought resistance by inputting future temperature changes into the corrected prediction model.

The invention provides an intelligent agricultural production control method and system based on artificial intelligence, and relates to the technical field of intelligent agriculture, and the method comprises the steps that S1, an unmanned plane carries out the real-time monitoring of a farmland every 10 minutes, and collects environment data, and the environment data comprises soil humidity, temperature, illumination intensity and air humidity; s2, analyzing the environmental data based on a deep learning algorithm and generating an irrigation control strategy; s3, automatically adjusting the operation parameters of the unmanned aerial vehicle according to the irrigation control strategy, wherein the operation parameters comprise irrigation time, irrigation water amount and irrigation area. According to the intelligent agricultural production control method and system based on artificial intelligence, real-time monitoring and data acquisition are performed on a farmland through the unmanned aerial vehicle, environmental data are analyzed by using a deep learning algorithm, and an irrigation control strategy is generated, so that accurate water resource management is realized, and the irrigation time, the irrigation water amount and the irrigation area are automatically adjusted; therefore, efficient use of water resources is ensured.

The utility model relates to the technical field of irrigators, in particular to a Bluetooth irrigator with hierarchical networking, which comprises a first Bluetooth module, a second Bluetooth module, a power supply module, an MCU, a key display module, a power supply dynamic control module, a valve driving module and a valve group, the MCU is a microcontroller core logic processing unit of the irrigator and is connected with the first Bluetooth module and the second Bluetooth module, and the valve group is connected with the key display module and the power supply dynamic control module. The MCU is connected with the Bluetooth module I and the Bluetooth module II and is used for communicating with the Bluetooth module I and the Bluetooth module II, the MCU is also connected with the power supply module and is used for providing a driving power supply for the MCU, and the MCU is also connected with the power supply dynamic control module and is used for controlling the power supply of the Bluetooth module II. According to the utility model, the data of the soil humidity sensors are acquired to carry out irrigation logic control, hierarchical networking is carried out on the soil humidity sensors, and the Bluetooth irrigator plays a role similar to a relay and forwards the data to the gateway, so that the networking distance of the soil humidity sensors is expanded, and the reliability of the system is also improved.

The utility model belongs to the technical field of farmland irrigation, and particularly relates to an intelligent farmland irrigation spray head capable of automatically adjusting water quantity, which comprises a base, a connecting pipe is mounted at the top of the base, a spray head is connected to the top of the connecting pipe, a controller is mounted at the top of the base, and a soil humidity sensor is arranged on the outer wall of the connecting pipe. The wiring end of the soil humidity sensor is connected with a detection probe, and the outer wall of the connecting pipe is connected with a water inlet pipe. According to the utility model, the detection probe is inserted into soil, then the soil humidity sensor is used for collecting soil humidity information, and then the controller is used for controlling the electric telescopic rod to drive the adjusting cap to ascend and descend according to the soil humidity, so that when the first water outlet hole and the nozzle hole are overlapped, sprayed water can be sprayed out according to the caliber of the first water outlet hole; and when the second water outlet hole is overlapped with the nozzle hole, the sprayed water is sprayed out according to the caliber of the second water outlet hole, so that the automatic adjustment of the water quantity is realized.

The application relates to the irrigation technical field and discloses a smart energy-saving irrigation management system and method based on environment monitoring, which acquires the daily water requirement of crops in farmland through a crop growth monitoring module, sets the water flow of irrigation according to the daily water requirement, divides the farmland into several regions, then monitors the soil humidity, temperature and light intensity environmental parameters of each region in real time through an environment monitoring module, then processes and analyzes the environmental parameters through a data processing module to obtain the current irrigation indexes of each region, judges whether the set water flow of irrigation meets the irrigation requirements of each region based on the current irrigation indexes of each region, if some region does not meet the requirements, the region is watered through a sprinkling irrigation mode, then the water flow of the water supplement is calculated based on the environmental parameters, the water content of each region of the farmland is ensured to be consistent, and therefore the growth of crops in each region is ensured to be consistent.

The application provides a soil erosion risk early warning method and device based on dust data, which comprises the following steps: performing grid processing and screening on a region to be analyzed to obtain an effective grid region; obtaining NDVI data and soil moisture data of the effective grid region; determining a slope length factor and a slope factor of each effective grid region; determining net dust data in the region to be analyzed by eliminating the influence of wind direction; determining a dust emission value of each effective grid region based on the NDVI data, the soil moisture data and the net dust data; calculating the NDVI data, the soil moisture data, the slope length factor, the slope factor and the dust emission value by using a neural network algorithm to determine a soil erosion intensity prediction value; combining the soil erosion intensity prediction value to determine a soil erosion value of each effective grid region; determining a soil erosion risk grid based on the current soil erosion value and historical soil erosion data, and performing risk early warning on the soil erosion risk grid.

1. Name of the product in this design: Screen and button of plant sensor (SENSO). 2. Purpose of this design: The overall product is a plant sensor used to monitor data such as soil moisture for plant growth; the protected parts are the screen and buttons of the plant sensor, which are used as the screen and buttons of the plant sensor. 3. The key design features of this product are the shape of the part to be protected, especially the combination and arrangement of the screen opening and multiple buttons. 4. The image or photograph that best illustrates the design's key points: 3D view 1. 5. Other situations requiring explanation: In the view, the dashed lines represent areas that do not require protection, while the solid lines represent areas that require protection.

The utility model discloses a flower seedling cultivation soil humidity monitoring device which comprises a shell, a storage groove is formed in the upper end of the shell, supporting blocks are symmetrically installed on the inner wall of the storage groove, a circuit board is arranged at the bottom in the shell, a monitoring rod is installed at the lower end of the shell and electrically connected with the circuit board, and a monitoring displayer is arranged in the storage groove. A connecting line is installed at the lower end of the monitoring displayer and electrically connected with the circuit board, a clamping mechanism is arranged at the left end of the shell, and an adjusting mechanism is arranged at the bottom of the shell. The height of the monitoring display can be adjusted and fixed according to use requirements, the monitoring rod is inserted into the land for soil humidity monitoring, a worker can check the soil humidity through the raised monitoring display only by standing and does not need to stoop to check the monitoring display, and the device is convenient, fast, time-saving and labor-saving.

The invention is suitable for the technical field of soil humidity remote sensing inversion, and provides a soil humidity remote sensing inversion method and system based on surface water balance constraint. The humidity detection data of a plurality of preset sampling points are obtained, remote sensing monitoring is carried out, and remote sensing image data are obtained; constructing a humidity correlation function; calculating a first soil humidity at the remote sensing inversion position based on the humidity detection data; performing thermal inertia analysis on the remote sensing image data, and calculating second soil humidity at a remote sensing inversion position by combining a humidity correlation function; on the basis of surface water balance constraints, influence weight data are determined, and the target soil humidity of the remote sensing inversion position is calculated. According to the method, the first soil humidity can be calculated based on the humidity detection data, the second soil humidity can be calculated based on the remote sensing image data, the influence weight data can be determined, the target soil humidity of the remote sensing inversion position can be calculated, a multi-parameter and multi-model complex calculation processing process is not needed, and the relatively accurate target soil humidity can be obtained.