A method, system, storage medium and program product for automatically detecting soil moisture

Abstract

A method, system, storage medium and program product for automatic soil moisture detection, which relates to the field of testing or analyzing materials by measuring the chemical or physical properties of materials. In the method, soil conductivity signals at the same depth are constructed as a closed-loop data sequence, and gradient values ​​are calculated; local abnormal areas are identified according to the gradient value distribution characteristics in the annular array at the same depth; soil moisture penetration paths are established based on the spatial distribution of local abnormal areas; time domain reflection sensors corresponding to the same soil moisture penetration paths are divided into the same group; corrected soil conductivity signals are determined; the moisture penetration rate of each group of time domain reflection sensors is calculated; the corrected soil conductivity signals are subjected to spatiotemporal weighted correction according to the moisture penetration rate to obtain target soil conductivity signals; the target soil conductivity signals are converted into soil moisture content to generate comprehensive soil moisture data. The application improves the accuracy of detecting soil moisture.

Description

Technical Field

[0001] The present application belongs to the field of testing or analyzing materials by measuring the chemical or physical properties of the materials, and in particular, to a method, system, storage medium and program product for automatically detecting soil moisture. Background Art

[0002] In agricultural irrigation management and water resource scheduling, soil moisture monitoring is a key task, which directly affects crop growth and water resource utilization efficiency. Traditional soil moisture monitoring mainly relies on manual sampling and laboratory analysis. This method not only has a long monitoring cycle and consumes a lot of human resources, but also has difficulty in achieving continuous monitoring and reflecting the dynamic changes of soil moisture in a timely manner.

[0003] In related technologies, a series of conductivity sensors can be deployed in farmland to measure soil conductivity to indirectly infer soil moisture content, and the data can be transmitted to a central control system for analysis, thereby realizing automated monitoring of soil moisture conditions.

[0004] However, due to the interference of external environmental factors and the limitations of the sensor's own performance, the monitoring data obtained often show instability. Unstable monitoring data reflects inaccurate soil moisture conditions, which in turn reduces the accuracy of subsequent resource scheduling. Summary of the invention

[0005] The present application provides a soil moisture automatic detection method, system, storage medium and program product for improving the accuracy of soil moisture detection and thus improving the accuracy of resource scheduling.

[0006] In a first aspect, the present application provides a method for automatically detecting soil moisture conditions, which obtains soil conductivity signals detected by time domain reflectometry sensors in a preset farmland area, wherein the time domain reflectometry sensors are arranged in a circular array in soil layers of different depths, wherein the circular array at each depth is composed of a plurality of time domain reflectometry sensors, and the circular arrays at adjacent depths are arranged in an alternating manner in the horizontal direction;

[0007] The soil conductivity signals of the annular array at the same depth are constructed into a closed-loop data sequence, and the gradient values ​​of the soil conductivity signals at adjacent depths in the closed-loop data sequence are calculated;

[0008] Identify local abnormal areas based on the gradient value distribution characteristics in the annular array at the same depth;

[0009] Establish soil moisture infiltration paths based on the spatial distribution of local anomaly areas;

[0010] The time domain reflectometry sensors corresponding to the same soil moisture penetration path are divided into the same group;

[0011] Using the soil conductivity signal corresponding to a depth less than a preset depth to verify the rationality of the soil conductivity signal corresponding to a depth not less than the preset depth, and using the soil conductivity signal corresponding to a depth not less than the preset depth to reversely correct the soil conductivity signal corresponding to a depth less than the preset depth, to obtain a corrected soil conductivity signal;

[0012] Based on the soil moisture infiltration path and the corrected soil conductivity signal, the moisture infiltration rate of each group of time domain reflectometry sensors is calculated;

[0013] Performing spatiotemporal weighted correction on the corrected soil conductivity signal according to the water infiltration rate to obtain the target soil conductivity signal;

[0014] Convert the target soil conductivity signal into soil moisture content to generate comprehensive soil moisture data.

[0015] By adopting the above technical solution, time domain reflection sensors are arranged in circular arrays at different depths of soil layers, and the circular arrays at adjacent depths are arranged in an interlaced manner in the horizontal direction, thereby increasing the spatial coverage of the sensors. The soil conductivity signals of the circular arrays at the same depth are constructed as a closed-loop data sequence and the gradient values ​​are calculated, which can effectively identify local abnormal areas. The soil moisture infiltration path established in combination with the spatial distribution can accurately reflect the characteristics of moisture migration. By grouping sensors with the same infiltration path and performing signal verification and correction, the impact of single sensor failure or abnormality is reduced. The moisture infiltration rate calculated based on the infiltration path and the correction signal provides a dynamic reference basis for the spatiotemporal weighted correction, so that the corrected soil conductivity signal is more consistent with the actual moisture migration law, improves the accuracy and reliability of soil moisture measurement, and takes into account the dynamic characteristics of soil moisture migration, so that the monitoring results can better reflect the actual soil moisture conditions of farmland.

[0016] In combination with some embodiments of the first aspect, in some embodiments, identifying a local abnormal area according to a gradient value distribution feature in an annular array at the same depth specifically includes:

[0017] Calculate the mean and standard deviation of the gradient values ​​in the annular array;

[0018] The annular array is divided into several sectors, and the deviation of the gradient value from the mean value in each sector is calculated;

[0019] When the gradient value deviation in any sector exceeds a preset multiple of the standard deviation, the sector is marked as an abnormal sector;

[0020] The angle difference between adjacent abnormal sectors is calculated, and when the angle difference is less than a preset angle threshold, the adjacent abnormal sectors are merged into a local abnormal area.

[0021] By adopting the above technical solution, a statistical benchmark for data anomaly judgment is established by calculating the mean and standard deviation of the gradient values ​​in the annular array. The annular array is divided into several sectors and the deviation is calculated to make the identification of abnormal areas more directional and accurate. The preset multiples of the standard deviation are used as the judgment threshold, which not only ensures the sensitivity of abnormal identification, but also reduces the misjudgment caused by oversensitivity. By calculating the angle difference between adjacent abnormal sectors and merging them according to the preset angle threshold, spatially continuous abnormal areas can be effectively clustered, reducing excessive fragmentation of abnormal areas. This abnormal area identification method based on statistical characteristics and spatial correlation can accurately capture the unevenness of soil moisture distribution, improve the accuracy of moisture infiltration path identification, and make subsequent sensor grouping and signal correction more targeted.

[0022] In combination with some embodiments of the first aspect, in some embodiments, based on the soil moisture penetration path and the corrected soil conductivity signal, calculating the moisture penetration rate of each group of time domain reflectometry sensors specifically includes:

[0023] establishing a sequence of time windows, each time window containing a preset number of corrected soil conductivity signals;

[0024] Calculate the rate of change of the corrected soil conductivity signal between annular arrays at adjacent depths on the same soil moisture infiltration path;

[0025] The instantaneous penetration rate in each time window was calculated based on the rate of change and the distance between adjacent depths;

[0026] The instantaneous penetration rate is subjected to exponential smoothing to obtain the water penetration rate of each group of time domain reflectometry sensors.

[0027] By adopting the above technical solution, the corrected soil conductivity signal is processed by time window sequence, which improves the time continuity and stability of infiltration rate calculation. By calculating the signal change rate between adjacent depth ring arrays on the same infiltration path, the water migration characteristics in the vertical direction are directly reflected. Exponential smoothing of the instantaneous infiltration rate reduces the influence of random fluctuations. The obtained water infiltration rate has good time continuity and representativeness, which improves the timeliness and accuracy of soil moisture monitoring.

[0028] In combination with some embodiments of the first aspect, in some embodiments, before obtaining the soil conductivity signal detected by the time domain reflectometry sensor in the preset farmland area, the method further includes:

[0029] Obtain terrain data and soil texture data of the preset farmland area;

[0030] Determine the water flow direction based on terrain data, and divide the preset farmland area into several sub-areas based on the water flow direction;

[0031] Determine the layout depth and radius of the circular array in each sub-area according to soil texture data;

[0032] Determine the installation location of the time domain reflectometer based on the deployment depth and radius.

[0033] By adopting the above technical solution, the topographic data and soil texture data of the preset farmland area are used to plan the sensor layout, making the spatial layout of the monitoring network more scientific. The water flow direction determined by the topographic data can reflect the overall trend of regional water migration, and the sub-areas divided based on this have similar hydrological characteristics. In the sub-area, the layout depth and radius of the circular array are determined according to the soil texture data, so that the sensor layout position can fully consider the spatial differences in soil characteristics. This sensor layout method based on topography and soil characteristics makes the spatial structure of the monitoring network adapt to the hydrogeological characteristics of the farmland, improves the representativeness and reliability of the monitoring data, and enables the collected soil conductivity signal to better reflect the regional moisture characteristics.

[0034] In conjunction with some embodiments of the first aspect, in some embodiments, determining the water flow direction according to the terrain data specifically includes:

[0035] Extract slope and aspect information from terrain data and construct a water flow accumulation network based on the slope and aspect information;

[0036] Calculate the confluence of the water flow accumulation network, and compare the confluence with the preset threshold to obtain the key confluence point;

[0037] The direction of water flow is determined based on the water connectivity between key confluence points.

[0038] By adopting the above technical solution, the natural flow path of water in the farmland area can be accurately simulated by extracting the slope and aspect information from the terrain data and constructing a water flow accumulation network. By calculating the flow rate based on the water flow accumulation network and comparing it with the preset threshold, the key confluence points that have an important impact on water movement can be identified. The water flow direction is determined based on the water flow connectivity between the key confluence points, making the analysis of the water movement law in the farmland area more in line with the actual situation and improving the scientificity and accuracy of the sub-area division. A quantitative relationship between terrain characteristics and water movement is established, making the system layout more adapted to the actual terrain conditions of the farmland.

[0039] In combination with some embodiments of the first aspect, in some embodiments, after determining the installation position of the time domain reflectometer according to the deployment depth and radius, the method further includes:

[0040] Collecting the initial soil conductivity signal of each time domain reflectometry sensor;

[0041] Calculate the spatial autocorrelation of the initial soil conductivity signal;

[0042] When the spatial autocorrelation is lower than a preset autocorrelation threshold, the corresponding installation position is adjusted.

[0043] By adopting the above technical solution, the rationality of the sensor layout position can be evaluated by collecting the initial soil conductivity signal of each time domain reflectometry sensor and calculating the spatial autocorrelation. When the spatial autocorrelation is detected to be lower than the preset autocorrelation threshold, the sensor installation position is adjusted in time, which improves the data quality of the sensor network and reduces the limitation that the traditional fixed layout scheme is difficult to adapt to the complex soil environment. Through the spatial correlation analysis of the initial data, the system can identify unreasonable sensor layout positions and make targeted adjustments, which improves the data representativeness of the sensor network and the monitoring system's ability to respond to changes in the soil environment, so that the collected data more accurately reflects the actual situation of soil moisture. The dynamic optimization of the sensor layout position is achieved, and the spatial continuity and reliability of the monitoring data are improved.

[0044] In combination with some embodiments of the first aspect, in some embodiments, when the spatial autocorrelation is lower than a preset autocorrelation threshold, adjusting the corresponding installation position specifically includes:

[0045] Calculate the influence radius of the area where the spatial autocorrelation is lower than the preset autocorrelation threshold;

[0046] Construct a sensor spatial coverage network based on influence radius and identify overlapping areas and blind spots in the spatial coverage network;

[0047] Calculate the optimal adjustment direction and optimal distance according to the coverage of the overlapping area;

[0048] The installation position is adjusted step by step in the optimal adjustment direction according to the optimal distance.

[0049] By adopting the above technical solution, by calculating the influence radius of the area corresponding to the spatial autocorrelation lower than the preset autocorrelation threshold, the sensor spatial coverage network is constructed, and the overlapping areas and blind areas are systematically identified, the spatial coverage effect of the sensor deployment can be comprehensively evaluated. The optimal adjustment direction and optimal distance are calculated based on the coverage of the overlapping area, making the sensor position adjustment more accurate and efficient. By gradually adjusting the installation position in the optimal adjustment direction according to the optimal distance, the spatial coverage uniformity of the sensor network is improved, the spatial resolution of the sensor network is improved, and the utilization efficiency of monitoring resources is optimized.

[0050] In the second aspect, an embodiment of the present application provides an automatic soil moisture detection system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0051] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a system, causes the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0052] In a fourth aspect, an embodiment of the present application provides a computer program product, characterized in that when the computer program product runs on a system, the system executes the method described in any possible implementation manner in the first aspect.

[0053] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0054] 1. The present application provides a method for automatically detecting soil moisture. By arranging time domain reflection sensors in circular arrays at different depths of soil layers, and staggering the circular arrays at adjacent depths in the horizontal direction, the spatial coverage of the sensor is increased. The soil conductivity signals of the circular arrays at the same depth are constructed as a closed-loop data sequence and the gradient values ​​are calculated, which can effectively identify local abnormal areas. The soil moisture infiltration path established in combination with the spatial distribution can accurately reflect the characteristics of moisture migration. By grouping sensors with the same infiltration path and performing signal verification and correction, the impact of single sensor failure or abnormality is reduced. The moisture infiltration rate calculated based on the infiltration path and the correction signal provides a dynamic reference basis for spatiotemporal weighted correction, so that the corrected soil conductivity signal is more in line with the actual law of moisture migration, improves the accuracy and reliability of soil moisture measurement, and takes into account the dynamic characteristics of soil moisture migration, so that the monitoring results can better reflect the actual moisture conditions of farmland.

[0055] 2. The present application provides a method for automatic detection of soil moisture conditions, which uses the terrain data and soil texture data of preset farmland areas to plan sensor layout, making the spatial layout of the monitoring network more scientific. The water flow direction determined according to the terrain data can reflect the overall trend of regional water migration, and the sub-areas divided based on this have similar hydrological characteristics. The layout depth and radius of the circular array are determined in the sub-area based on the soil texture data, so that the sensor layout position can fully consider the spatial differences in soil characteristics. This sensor layout method based on terrain and soil characteristics makes the spatial structure of the monitoring network adapt to the hydrogeological characteristics of the farmland, improves the representativeness and reliability of the monitoring data, and enables the collected soil conductivity signal to better reflect the regional moisture characteristics.

[0056] 3. The present application provides a method for automatic detection of soil moisture. By collecting the initial soil conductivity signal of each time domain reflectance sensor and calculating the spatial autocorrelation, the rationality of the sensor layout position can be evaluated. When it is detected that the spatial autocorrelation is lower than the preset autocorrelation threshold, the sensor installation position is adjusted in time, which improves the data quality of the sensor network and reduces the limitations of traditional fixed layout solutions that are difficult to adapt to complex soil environments. Through the spatial correlation analysis of the initial data, the system can identify unreasonable sensor layout positions and make targeted adjustments, which improves the data representativeness of the sensor network and enhances the monitoring system's ability to respond to changes in the soil environment, so that the collected data more accurately reflects the actual situation of soil moisture. Dynamic optimization of sensor layout positions is achieved, and the spatial continuity and reliability of monitoring data are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flow chart of a method for automatically detecting soil moisture in an embodiment of the present application.

[0058] Figure 2 It is another flow chart of a method for automatically detecting soil moisture in an embodiment of the present application.

[0059] Figure 3 It is a schematic diagram of the physical device structure of a soil moisture automatic detection system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0061] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0062] The following uses an embodiment and combines Figure 1 , a method for automatically detecting soil moisture in an embodiment of the present application is described:

[0063] See also Figure 1 , which is a flow chart of a method for automatically detecting soil moisture conditions in an embodiment of the present application.

[0064] S101, obtaining a soil conductivity signal detected by a time domain reflectometry sensor in a preset farmland area;

[0065] The system obtains soil conductivity signals detected by time domain reflectometry sensors in a preset farmland area. The time domain reflectometry sensors are arranged in circular arrays at different soil depths. The circular array at each depth is composed of several time domain reflectometry sensors, and the circular arrays at adjacent depths are staggered in the horizontal direction.

[0066] In this step, the system first needs to obtain the soil conductivity signal detected by the time domain reflectometry sensors deployed in the preset farmland area. The time domain reflectometry sensors are deployed in the soil layers of different depths in the form of a circular array. Each depth of the circular array is composed of a number of time domain reflectometry sensors, and the circular arrays of adjacent depths are staggered in the horizontal direction. This layout can achieve all-round and three-dimensional detection of soil layers of different depths, and obtain more comprehensive and accurate soil conductivity signal data.

[0067] In order to obtain the soil conductivity signal in the preset farmland area, the system can be carried out in the following specific ways: first, time domain reflection sensors are arranged in the preset farmland area, and they are arranged in the form of circular arrays in soil layers of different depths, and the circular arrays at adjacent depths are staggered in the horizontal direction; secondly, the soil conductivity signals detected by each time domain reflection sensor are collected by the data acquisition unit, and the collected data are transmitted to the data processing unit for subsequent analysis and processing.

[0068] S102, constructing soil conductivity signals of the annular array at the same depth into a closed-loop data sequence, and calculating the gradient values ​​of soil conductivity signals at adjacent depths in the closed-loop data sequence;

[0069] In this step, the system needs to construct the soil conductivity signals of the circular array at the same depth into a closed-loop data sequence, and then calculate the gradient values ​​of adjacent soil conductivity signals in the closed-loop data sequence. The construction of the closed-loop data sequence can realize the data correlation analysis between each time domain reflectometry sensor in the circular array at the same depth, and the calculation of the gradient values ​​of adjacent soil conductivity signals can reflect the spatial variation trend and distribution characteristics of soil conductivity.

[0070] Specifically, the system can realize the construction of closed-loop data sequence and the calculation of gradient value in the following ways: first, the time domain reflectometers in the same depth annular array are numbered in clockwise or counterclockwise order according to their spatial positions to form a closed data acquisition sequence; then, the soil conductivity signal values ​​collected by each sensor are read in sequence according to the numbering order to construct a closed-loop data sequence; finally, each pair of adjacent soil conductivity signal values ​​in the closed-loop data sequence is differentially calculated to obtain the gradient values ​​of adjacent soil conductivity signals.

[0071] S103, identifying a local abnormal area according to the gradient value distribution characteristics in the annular array at the same depth;

[0072] The system identifies local abnormal areas based on the gradient value distribution characteristics in the annular array at the same depth, including:

[0073] Calculate the mean and standard deviation of the gradient values ​​in the annular array;

[0074] The annular array is divided into several sectors, and the deviation of the gradient value from the mean value in each sector is calculated;

[0075] When the gradient value deviation in any sector exceeds a preset multiple of the standard deviation, the sector is marked as an abnormal sector;

[0076] The angle difference between adjacent abnormal sectors is calculated, and when the angle difference is less than a preset angle threshold, the adjacent abnormal sectors are merged into a local abnormal area.

[0077] In this step, the system needs to identify local abnormal areas based on the gradient value distribution characteristics in the same depth annular array. The gradient value distribution characteristics reflect the spatial variation trend and distribution law of soil conductivity. By analyzing the distribution characteristics of the gradient values, abnormal areas of soil conductivity changes can be found, and then the abnormal situation of soil moisture can be judged.

[0078] In order to identify local abnormal areas, the system can adopt the following specific methods: first, calculate the mean and standard deviation of all gradient values ​​in the circular array as basic indicators to measure the distribution characteristics of gradient values; then, divide the circular array into several sectors according to preset angle intervals, and calculate the deviation between the gradient value and the mean in each sector. When the gradient value deviation in any sector exceeds the preset multiple of the standard deviation, the sector can be marked as an abnormal sector; finally, by calculating the angle difference between adjacent abnormal sectors, when the angle difference is less than the preset angle threshold, the adjacent abnormal sectors can be merged into a local abnormal area.

[0079] In the process of identifying local abnormal areas, there may be problems such as inaccurate or missed abnormal area identification due to unreasonable abnormal sector division or improper setting of abnormal area merging conditions. To solve this problem, the system can introduce an adaptive sector division algorithm to dynamically adjust the angle interval and number of sectors so that it can better adapt to different soil conductivity distribution conditions and improve the recognition accuracy of abnormal sectors.

[0080] S104, establishing a soil moisture infiltration path based on the spatial distribution of the local abnormal area;

[0081] In this step, the system needs to establish the soil moisture infiltration path based on the spatial distribution of the local abnormal area identified in step S103. The spatial distribution of the local abnormal area reflects the area where the soil conductivity changes abnormally, and these abnormal changes are often closely related to the infiltration and migration of soil moisture. By analyzing the spatial distribution law of the local abnormal area, the vertical and horizontal infiltration paths of soil moisture can be inferred, providing an important reference for the subsequent soil moisture analysis.

[0082] In order to establish the soil moisture infiltration path, the system can adopt the following specific technical solutions: first, the local abnormal areas identified in step S103 are numbered and marked according to their spatial positions to form an abnormal area distribution map; then, the distribution sequence of the abnormal areas in the vertical direction is analyzed, the connectivity relationship between the abnormal areas in the soil layers at different depths is identified, and the vertical infiltration path between the abnormal areas is established; finally, the distribution characteristics of the abnormal areas in the horizontal direction are analyzed, the connectivity relationship between the abnormal areas in the soil layer at the same depth is identified, and the horizontal infiltration path between the abnormal areas is established. By integrating the infiltration path information in both the vertical and horizontal directions, a complete soil moisture infiltration path network can be constructed.

[0083] S105, dividing the time domain reflectometry sensors corresponding to the same soil moisture penetration path into the same group;

[0084] In this step, the system needs to group the time domain reflectometry sensors on the same infiltration path into the same group according to the soil moisture infiltration path established in step S104. Since soil moisture often moves along a specific path during infiltration and migration, the soil conductivity signals monitored by the sensors on the same infiltration path have strong correlation and consistency. By grouping these sensors into the same group, the overall analysis and description of the soil moisture migration process on the same infiltration path can be achieved, thereby improving the pertinence and effectiveness of soil moisture monitoring.

[0085] In order to realize the grouping of time domain reflectometry sensors, the system can adopt the following specific methods: first, traverse the soil moisture infiltration path established in step S104, and identify the time domain reflectometry sensors covered on each infiltration path; then, according to the correspondence between the sensor and the infiltration path, the sensors covering the same infiltration path are grouped together to form several sensor groups; finally, each sensor group is numbered and marked, and a mapping relationship between the grouped sensors and the infiltration path is established. In this way, the effective grouping of time domain reflectometry sensors can be realized, providing a basis for subsequent data analysis and processing.

[0086] In the process of sensor grouping, there may be problems such as inaccurate infiltration path establishment or sensor position offset, which may lead to sensor grouping errors or omissions. In order to solve this problem, the system can introduce a sensor grouping optimization algorithm to automatically adjust and optimize sensor grouping by comprehensively analyzing factors such as the spatial position relationship between sensors, the correlation of soil conductivity signals, and the topological structure of the infiltration path, thereby improving the accuracy and completeness of the grouping.

[0087] S106, using the soil conductivity signal corresponding to the depth less than the preset depth to verify the rationality of the soil conductivity signal corresponding to the depth not less than the preset depth, and using the soil conductivity signal corresponding to the depth not less than the preset depth to reversely correct the soil conductivity signal corresponding to the depth less than the preset depth, to obtain a corrected soil conductivity signal;

[0088] In this step, the system needs to use the shallow soil conductivity signal to verify the rationality of the deep soil conductivity signal, and use the deep soil conductivity signal to reversely correct the shallow soil conductivity signal, so as to obtain the corrected soil conductivity signal. Since soil moisture and salt have the characteristics of downward penetration and accumulation in the vertical direction, the conductivity signal of the shallow soil is often affected by the water and salt conditions of the deep soil. By cross-validating and correcting the shallow and deep conductivity signals, the reliability and accuracy of the soil conductivity signal can be effectively improved, providing more accurate data support for subsequent soil moisture analysis.

[0089] In order to realize the verification and correction of soil conductivity signals, the system can adopt the following specific technical solutions: first, the time domain reflectometry sensors are divided into two categories: shallow sensors and deep sensors according to their burial depths, wherein the depth corresponding to the shallow sensors is less than the preset depth, and the depth corresponding to the deep sensors is not less than the preset depth; then, the soil conductivity signals of the shallow sensors are used to analyze their changing trends and distribution characteristics, and based on this, it is judged whether the soil conductivity signals of the deep sensors are reasonable, and abnormal or unreasonable deep signals are marked; finally, the soil conductivity signals of the deep sensors are used to reversely correct the conductivity signals of the shallow sensors, and by establishing a mathematical mapping relationship between the shallow and deep conductivity signals, the shallow signals are corrected and optimized to obtain corrected soil conductivity signals.

[0090] S107, calculating the water penetration rate of each group of time domain reflectometry sensors based on the soil water penetration path and the corrected soil conductivity signal;

[0091] The system calculates the water penetration rate of each group of time domain reflectometry sensors based on the soil water penetration path and the corrected soil conductivity signal, including:

[0092] establishing a sequence of time windows, each time window containing a preset number of corrected soil conductivity signals;

[0093] Calculate the rate of change of the corrected soil conductivity signal between annular arrays at adjacent depths on the same soil moisture infiltration path;

[0094] The instantaneous penetration rate in each time window was calculated based on the rate of change and the distance between adjacent depths;

[0095] The instantaneous penetration rate is subjected to exponential smoothing to obtain the water penetration rate of each group of time domain reflectometry sensors.

[0096] In this step, the system uses the established soil moisture infiltration path and the corrected soil conductivity signal to calculate the moisture infiltration rate of each group of time domain reflectometry sensors. The moisture infiltration rate represents the vertical infiltration distance or flux of soil moisture per unit time, and is an important indicator for measuring the soil moisture migration capacity and efficiency. By calculating the infiltration rate in different areas, the spatiotemporal distribution characteristics and change laws of farmland soil moisture can be understood, providing a decision-making basis for agricultural irrigation and water resources management.

[0097] Specifically, the system first establishes a series of time windows, each of which contains a certain number of corrected soil conductivity signals. Then, the system calculates the conductivity signal change rate between sensor groups at adjacent depths on the same soil moisture infiltration path, that is, the magnitude of conductivity change per unit time. Based on the conductivity change rate and the distance between adjacent depths, the system can estimate the instantaneous infiltration rate of water in each time window. Finally, the system performs exponential smoothing on the instantaneous infiltration rate to obtain the average moisture infiltration rate of each group of sensors during the entire monitoring process.

[0098] When calculating the water infiltration rate, the system can consider a variety of influencing factors, such as soil texture, bulk density, initial water content, etc., select appropriate soil moisture characteristic curve models, such as Van Genuchten model, Brooks-Corey model, etc., convert the conductivity signal into soil water potential or volumetric water content, and improve the accuracy of infiltration rate estimation. At the same time, the system can also apply digital signal processing techniques, such as filtering, denoising, trend analysis, etc., to pre-process the original conductivity signal and extract effective change information.

[0099] In practical applications, there may be problems such as drastic changes in conductivity signals and unstable estimated instantaneous infiltration rates in certain time windows, which will affect the overall evaluation of the infiltration rate. To this end, the system can introduce an adaptive time window mechanism to dynamically adjust the window size and step size according to the changing characteristics of the conductivity signal, so as to capture the key moments of the water migration process and filter out the interference of random noise. In addition, the system can also calibrate and attribute the water infiltration rate in combination with external input information such as precipitation and irrigation, revealing the response law of soil water migration to environmental factors.

[0100] S108, performing spatiotemporal weighted correction on the corrected soil conductivity signal according to the water infiltration rate to obtain a target soil conductivity signal;

[0101] In this step, the system uses the calculated water infiltration rate to perform further spatiotemporal weighted correction on the corrected soil conductivity signal to obtain the final target soil conductivity signal. The water infiltration rate reflects the ability of soil moisture to migrate in time and space, and the infiltration rate in different regions and at different times may vary significantly. Introducing the infiltration rate into the correction process of the conductivity signal can make the correction result more accurately reflect the actual distribution state of soil moisture and reduce signal distortion or lag caused by water migration.

[0102] In the specific implementation, the system can design a spatiotemporal weighted correction model to comprehensively consider the time series characteristics and spatial correlation of the conductivity signal. In the time dimension, the system can assign different weights to the conductivity signals at different times according to the size of the water infiltration rate. The greater the infiltration rate, the higher the weight, and vice versa. This can highlight the conductivity signal during the active period of water migration and weaken the signal during the relatively stable period. In the spatial dimension, the system can use spatial interpolation methods such as Kriging interpolation and inverse distance weighting to perform spatial correction of the conductivity signal of the gridded farmland area according to the spatial distribution characteristics of the infiltration rate, so that it conforms to the changing trend of the infiltration rate in the local area.

[0103] S109: Convert the target soil conductivity signal into soil moisture content to generate comprehensive soil moisture data.

[0104] In this step, the system converts the target soil conductivity signal after spatiotemporal weighted correction into soil moisture data, generating comprehensive soil moisture information reflecting the soil moisture status of farmland. Soil moisture content is a direct indicator for evaluating soil moisture, indicating the water content per unit mass or volume of soil. By establishing a quantitative relationship between soil conductivity and moisture content, the conversion of conductivity signal to moisture content can be achieved, and layered, partitioned, continuous and dynamic soil moisture data can be obtained, providing intuitive and interpretable information support for agricultural production and management.

[0105] In the specific implementation, the system can select an appropriate soil moisture characteristic curve model, such as the Van Genuchten model, Brooks-Corey model, etc., to establish a functional relationship between conductivity and water content. The parameters of the moisture characteristic curve may vary for different types of soil, and the parameters need to be determined and calibrated based on soil texture, bulk density and other properties. In addition, since soil conductivity is also affected by factors such as temperature and salinity, the system can introduce a multivariate correction model to comprehensively consider the impact of various environmental factors on the conductivity-water content conversion and improve the accuracy of water content estimation.

[0106] In the above embodiment, by arranging time domain reflection sensors in circular arrays at different depths of soil layers, and staggering the circular arrays at adjacent depths in the horizontal direction, the spatial coverage of the sensor is increased. The soil conductivity signal of the circular array at the same depth is constructed as a closed-loop data sequence and the gradient value is calculated, so that the local abnormal area can be effectively identified, and the soil moisture infiltration path established in combination with the spatial distribution can accurately reflect the characteristics of moisture migration. By grouping sensors with the same infiltration path and performing signal verification and correction, the impact of single sensor failure or abnormality is reduced. The moisture infiltration rate calculated based on the infiltration path and the correction signal provides a dynamic reference basis for the spatiotemporal weighted correction, so that the corrected soil conductivity signal is more in line with the actual moisture migration law, improves the accuracy and reliability of soil moisture measurement, and takes into account the dynamic characteristics of soil moisture migration, so that the monitoring results can better reflect the actual soil moisture conditions of the farmland.

[0107] The above embodiment describes in detail the specific implementation process of automatic soil moisture detection. However, in practical applications, in order to make the layout of the monitoring system more reasonable and improve the accuracy of monitoring data, it is necessary to scientifically plan and optimize the layout of sensors before detection. Figure 2 , another soil moisture automatic detection method in the embodiment of the present application is described:

[0108] See also Figure 2 , is another flow chart of a method for automatically detecting soil moisture conditions in an embodiment of the present application.

[0109] S201, obtaining terrain data and soil texture data of a preset farmland area;

[0110] The system first needs to obtain the terrain data and soil texture data of the preset farmland area. The terrain data can include information such as surface elevation, slope, and slope direction. This information can be obtained through drone aerial surveys, satellite remote sensing, etc., or by using existing DEM (digital elevation model) data. Soil texture data mainly includes the mechanical composition of the soil (i.e. the content of soil particles of different particle sizes), which can be obtained through field sampling and analysis, or by using existing data such as soil texture maps.

[0111] In addition to topography and soil texture, the system can also obtain other data related to soil moisture, such as meteorological data (rainfall, evapotranspiration, etc.), crop type and growth status, etc. These data can help determine the temporal and spatial distribution of soil moisture. In addition, if some soil moisture monitoring equipment has been deployed in the farmland area, the system can also obtain the historical monitoring data of these equipment to guide new sensor deployment plans.

[0112] S202, determining the water flow direction according to the terrain data, and dividing the preset farmland area into a plurality of sub-areas according to the water flow direction;

[0113] The system determines the direction of water flow based on terrain data. Specifically, it extracts slope and aspect information from terrain data and builds a water flow accumulation network based on the slope and aspect information.

[0114] Calculate the confluence of the water flow accumulation network, and compare the confluence with the preset threshold to obtain the key confluence point;

[0115] The water flow direction is determined according to the water connectivity between key confluence points, and the preset farmland area is divided into several sub-areas according to the water flow direction.

[0116] This step uses terrain data to analyze the law of water migration in the farmland area and provide a basis for regional division for sensor deployment. The system first determines the direction of water flow based on the terrain data. Since water flows from high to low under the action of gravity, the direction of water flow is closely related to the slope and aspect of the terrain. Based on DEM data, the system can use flow direction algorithms (such as D8, Dinf, etc.) to calculate the water flow direction of each grid to form a water flow direction map. Then, according to the law of water accumulation, the area of ​​the catchment area upstream of each grid is calculated to form a catchment flow map. The catchment flow map can reflect the spatial distribution of surface runoff. Areas with large catchment flow usually have higher soil moisture.

[0117] According to the flow map, the system can divide the farmland into several sub-areas. One way to divide the farmland is to use the ridge line (i.e., the place where the flow changes suddenly) as the boundary, and divide the farmland into multiple watershed areas. The water movement within each watershed area is relatively independent, which is suitable for the deployment of a group of sensors. Another way to divide the farmland is to use the equal flow line as the boundary, and divide the farmland into several flow level areas. The water conditions in different levels of areas vary greatly, and the density of sensor deployment can be different.

[0118] When dividing the sub-areas, the system also needs to consider the shape and size of the sub-areas. Sub-areas that are too narrow or fragmented are not conducive to sensor networking, and sub-areas that are too large may ignore the differences in moisture in the area. Therefore, the system can take into account the geometric characteristics of the sub-areas on the basis of satisfying the hydrological logic, and perform appropriate merging or segmentation to make them more regular in shape and moderate in area.

[0119] S203, determining the layout depth and radius of the annular array in each sub-area according to the soil texture data;

[0120] Soil texture has a significant impact on soil water retention and water conductivity. The speed and range of water migration in soils of different textures are different, and accordingly, the layout of sensors should also be different. Generally speaking, the coarser the texture (such as sandy soil), the better the permeability, the faster the water infiltrates, and the sensors need to be laid deeper; the finer the texture (such as clay), the worse the permeability, the water mainly migrates in the surface soil, and the sensors can be laid shallower. At the same time, the coarser the texture, the larger the range of water migration, and the sensor's monitoring radius can be set larger; conversely, the finer the texture, the limited range of water migration, and the sensor's monitoring radius needs to be set smaller.

[0121] The system can establish a corresponding relationship model between texture parameters and sensor layout parameters based on soil texture data. One modeling method is based on a mechanism model, such as using physical models such as the Richards equation to describe soil moisture movement, to estimate the moisture penetration depth and diffusion range under different texture conditions, and then determine the sensor layout depth and spacing. Another modeling method is based on an empirical model, such as monitoring a large number of points on soils of different textures, and using regression analysis and other methods to fit the empirical relationship between texture parameters and optimal layout parameters.

[0122] S204, determining the installation position of the time domain reflectometry sensor according to the deployment depth and radius;

[0123] This step further determines the specific installation location of the sensor based on the layout parameters determined in the previous step. The time domain reflectometry sensor is a sensor that measures soil moisture content using the principle of electromagnetic wave reflection. Its installation location will affect the representativeness and accuracy of the measurement. The sensor needs to be installed in a position that can represent the average moisture content of the entire monitoring depth range, and a certain distance needs to be maintained between different sensors to avoid signal interference.

[0124] The system can be laid out in a circular array, with multiple sensors laid out along the isobaths in each sub-area. Isobaths refer to a series of lines parallel to the surface and of equal depth. It can be roughly assumed that the soil moisture conditions on the isobaths are similar. The sensors are evenly laid out along the isobaths, which can better reflect the average soil moisture conditions at that depth. The radius of the circular array is the spacing between the isobaths, which is determined in the previous step. The larger the radius, the better the spatial representativeness of the measurement, but the higher the number of sensors and the higher the cost.

[0125] On the basis of the ring array, the system can also set up several radial measurement lines in each sub-area, and deploy some sensors on the measurement lines as supplementary monitoring points to improve the reliability of monitoring. These sensors can be deployed in areas with large terrain fluctuations and large soil texture variations to capture local differences in soil moisture. At the same time, the distance between the sensor and the farmland boundary, irrigation facilities, etc. also needs to be controlled within a certain range to reduce interference from external factors.

[0126] S205, collecting the initial soil conductivity signal of each time domain reflectometry sensor;

[0127] This step is to collect the initial reading of the sensor after the sensor is installed, which will serve as the baseline value for subsequent monitoring. Soil conductivity is an important indicator for evaluating soil moisture content. The time domain reflectometry sensor estimates the conductivity by measuring the soil dielectric constant, and then infers the moisture content. The initial conductivity signal reflects the soil moisture condition when the sensor is installed, and is the starting point for subsequent monitoring and analysis.

[0128] When collecting the initial signal, the system needs to control the time and frequency of collection. Generally, it should be carried out during a period when the soil moisture conditions are relatively stable, such as a period of time after no interference such as rainfall or irrigation. The collection frequency should be high enough to obtain stable and reliable data. At the same time, the system also needs to record the environmental conditions during the collection, such as temperature and humidity, so that necessary corrections can be made during subsequent data analysis.

[0129] In addition to collecting the initial signal, the system can also use this step to calibrate and verify the sensor. By synchronously measuring the soil moisture content with other methods (such as drying weighing method), a calibration relationship between the sensor reading and the actual moisture content is established, and regular verification and updates are performed. This can improve the measurement accuracy and stability of the sensor. In addition, the system can also collect the initial signal, detect the working status of the sensor, and promptly discover and eliminate faults to ensure the continuity and reliability of the monitoring data.

[0130] S206, calculating the spatial autocorrelation of the initial soil conductivity signal;

[0131] This step uses spatial statistics to analyze the spatial correlation of the initial sensor readings and evaluate the rationality of the sensor layout plan. Spatial autocorrelation refers to the similarity of attribute values ​​in adjacent areas in space, which reflects the continuity and heterogeneity of attributes such as soil conductivity in space. Calculating spatial autocorrelation can reveal the degree of matching between the sensor layout density and the scale of soil conductivity variation, providing a basis for optimizing the layout plan.

[0132] Commonly used spatial autocorrelation indices include Moran's I, Geary's C, etc., which are all calculated based on attribute values ​​and spatial weight matrices. The spatial weight matrix reflects the adjacency or distance relationship between spatial objects. The system can calculate the spatial weight matrix based on the sensor coordinates, and then calculate the spatial autocorrelation index based on the attribute value (i.e., the initial conductivity signal) and the weight matrix. The value of the index is generally between -1 and 1. A value greater than 0 indicates a positive correlation (i.e., adjacent sensor readings are similar), a value less than 0 indicates a negative correlation, and a value equal to 0 indicates no correlation. The larger the absolute value of the index, the stronger the spatial autocorrelation.

[0133] Ideally, the soil conductivity monitored by the sensor should have strong spatial autocorrelation, that is, the readings of adjacent sensors should be relatively close. This means that the density of sensor deployment matches the spatial variation scale of soil conductivity and can better reflect the spatial distribution of soil moisture. If the spatial autocorrelation is very weak, it means that the readings of adjacent sensors are very different, and the spatial variation of soil moisture may occur on a relatively small scale, and the deployment density needs to be further increased; conversely, if the spatial autocorrelation is very strong, it means that the sensors are too densely deployed, and the readings of adjacent sensors are highly similar. The deployment density can be appropriately reduced to save costs.

[0134] S207: When the spatial autocorrelation is lower than a preset autocorrelation threshold, adjust the corresponding installation position.

[0135] When the spatial autocorrelation is lower than the preset autocorrelation threshold, the system adjusts the corresponding installation position, including:

[0136] Calculate the influence radius of the area where the spatial autocorrelation is lower than the preset autocorrelation threshold;

[0137] Construct a sensor spatial coverage network based on influence radius and identify overlapping areas and blind spots in the spatial coverage network;

[0138] Calculate the optimal adjustment direction and optimal distance according to the coverage of the overlapping area;

[0139] The installation position is adjusted step by step in the optimal adjustment direction according to the optimal distance.

[0140] This step dynamically adjusts the installation position of the sensor based on the results of the spatial autocorrelation analysis to better adapt it to the spatial variation characteristics of soil conductivity. The preset autocorrelation threshold reflects the requirement of sensor deployment for soil moisture monitoring accuracy. When the actual spatial autocorrelation is lower than the threshold, it means that the current deployment scheme is difficult to meet the accuracy requirements and needs to be adjusted and optimized. The purpose of the adjustment is to match the sensor deployment with the variation scale of soil conductivity, so as not to miss important variation information or be too dense and waste resources.

[0141] When adjusting the installation position, the system first needs to evaluate the problems with the current layout plan. By analyzing the local indicators of spatial autocorrelation, the system can find areas with particularly low spatial autocorrelation, which are often the focus of adjustment. Furthermore, the system needs to estimate the spatial scale of soil conductivity variation. Tools such as semivariograms can be used to analyze the change of spatial autocorrelation with distance and determine the dominant scale of variation. Based on the above analysis, the system can adjust the position and spacing of sensors in a targeted manner.

[0142] One adjustment strategy is to increase the density of sensors in areas with low spatial autocorrelation to capture soil moisture variation information in the area. The additional sensors can be deployed around the original sensors with a spacing smaller than the original sensors to form a local high-density monitoring network. Another strategy is to rearrange the spacing of all sensors according to the dominant variation scale to adapt it to the variation scale. This may require a large-scale rearrangement of the original sensors. The two strategies can be used in combination to meet monitoring needs while taking into account the economy of deployment.

[0143] In the above embodiment, the topographic data and soil texture data of the preset farmland area are used to plan the sensor layout, making the spatial layout of the monitoring network more scientific. The water flow direction determined according to the topographic data can reflect the overall trend of regional water migration, and the sub-areas divided based on this have similar hydrological characteristics. The layout depth and radius of the circular array are determined according to the soil texture data in the sub-area, so that the sensor layout position can fully consider the spatial differences in soil characteristics. This sensor layout method based on topography and soil characteristics makes the spatial structure of the monitoring network adapt to the hydrogeological characteristics of the farmland, improves the representativeness and reliability of the monitoring data, and enables the collected soil conductivity signal to better reflect the regional moisture characteristics.

[0144] The following describes the system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , is a schematic diagram of the physical device structure of a soil moisture automatic detection system provided in an embodiment of the present application.

[0145] It should be noted that Figure 3 The structure of the system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0146] like Figure 3 As shown, the system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through a bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0147] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a liquid crystal display (LCD) and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0148] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0149] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.

[0150] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0151] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiment; or may exist independently without being assembled into the system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiment.

[0152] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0153] As used in the above embodiments, the term "when..." may be interpreted as "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted as "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0154] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0155] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A method for automatically detecting soil moisture, characterized in that: include: Acquire soil conductivity signals detected by time domain reflectometry sensors in a preset farmland area, wherein the time domain reflectometry sensors are arranged in a circular array in soil layers at different depths, wherein the circular array at each depth is composed of a plurality of the time domain reflectometry sensors, and the circular arrays at adjacent depths are arranged alternately in a horizontal direction; The soil conductivity signals of the annular array at the same depth are constructed into a closed-loop data sequence, and the gradient values ​​of the soil conductivity signals at adjacent depths in the closed-loop data sequence are calculated; Identify local abnormal areas according to the gradient value distribution characteristics in the annular array at the same depth; Establishing a soil moisture infiltration path based on the spatial distribution of the local abnormal area; The time domain reflectometry sensors corresponding to the same soil moisture penetration path are divided into the same group; Using the soil conductivity signal corresponding to a depth less than a preset depth to verify the rationality of the soil conductivity signal corresponding to a depth not less than the preset depth, and using the soil conductivity signal corresponding to a depth not less than the preset depth to reversely correct the soil conductivity signal corresponding to a depth less than the preset depth, to obtain a corrected soil conductivity signal; Calculating the moisture penetration rate of each group of the time domain reflectometry sensors based on the soil moisture penetration path and the corrected soil conductivity signal; Performing spatiotemporal weighted correction on the corrected soil conductivity signal according to the water infiltration rate to obtain a target soil conductivity signal; The target soil conductivity signal is converted into soil moisture content to generate comprehensive soil moisture data.

2. The method according to claim 1, characterized in that The identifying of the local abnormal area according to the gradient value distribution characteristics in the annular array at the same depth specifically includes: Calculating the mean and standard deviation of the gradient values ​​in the annular array; Dividing the annular array into a plurality of sectors, and calculating the deviation of the gradient value in each sector from the mean value; When the gradient value deviation in any sector exceeds a preset multiple of the standard deviation, marking the sector as an abnormal sector; The angle difference between adjacent abnormal sectors is calculated, and when the angle difference is less than a preset angle threshold, the adjacent abnormal sectors are merged into a local abnormal area.

3. The method according to claim 1, characterized in that The calculating the moisture penetration rate of each group of the time domain reflectometry sensors based on the soil moisture penetration path and the corrected soil conductivity signal specifically includes: Establishing a time window sequence, each of the time windows containing a preset number of the calibrated soil conductivity signals; calculating the rate of change of the corrected soil conductivity signal between the annular arrays at adjacent depths on the same soil moisture infiltration path; Calculating the instantaneous penetration rate in each of the time windows according to the change rate and the distance between the adjacent depths; The instantaneous penetration rate is subjected to exponential smoothing processing to obtain the water penetration rate of each group of the time domain reflectometry sensors.

4. The method according to claim 1, characterized in that Before acquiring the soil conductivity signal detected by the time domain reflectometry sensor in the preset farmland area, the method further includes: Obtain terrain data and soil texture data of the preset farmland area; Determine the water flow direction according to the terrain data, and divide the preset farmland area into a plurality of sub-areas according to the water flow direction; Determine the layout depth and radius of the annular array in each of the sub-areas according to the soil texture data; The installation position of the time domain reflectometry sensor is determined according to the deployment depth and radius.

5. The method according to claim 4, characterized in that Determining the water flow direction according to the terrain data specifically includes: Extracting slope and aspect information from the terrain data, and constructing a water flow accumulation network based on the slope and aspect information; Calculating the confluence of the water flow accumulation network, and comparing the confluence with a preset threshold to obtain a key confluence point; The water flow direction is determined according to the water flow connectivity between the key confluence points.

6. The method according to claim 4, characterized in that After determining the installation position of the time domain reflectometry sensor according to the deployment depth and radius, the method further includes: Collecting an initial soil conductivity signal from each of the time domain reflectometry sensors; Calculating the spatial autocorrelation of the initial soil conductivity signal; When the spatial autocorrelation is lower than a preset autocorrelation threshold, the corresponding installation position is adjusted.

7. The method according to claim 6, characterized in that When the spatial autocorrelation is lower than a preset autocorrelation threshold, adjusting the corresponding installation position specifically includes: Calculate the influence radius of the area where the spatial autocorrelation is lower than a preset autocorrelation threshold; Constructing a sensor spatial coverage network based on the influence radius, and identifying overlapping areas and blind areas in the spatial coverage network; Calculate the optimal adjustment direction and the optimal distance according to the coverage of the overlapping area; The installation position is gradually adjusted in the optimal adjustment direction according to the optimal distance.

8. A soil moisture automatic detection system, characterized in that: The system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to execute the method according to any one of claims 1 to 7.

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