Soil drainage capacity real-time monitoring method and system

Abstract

The invention belongs to the technical field of soil drainage capacity monitoring, particularly relates to a real-time monitoring method and system for soil drainage capacity, and aims to solve the problems that in the existing soil drainage capacity monitoring process, intelligent analysis on monitoring data is inconvenient, and monitoring equipment is inconvenient to protect and maintain, so that the monitoring evaluation effect is affected. According to the scheme, the method comprises the following steps that S1, a monitoring area is selected, and a plurality of monitoring points are arranged in the monitoring area; s2, selecting monitoring equipment, and installing the monitoring equipment to the monitoring points; s3, collecting soil moisture data of the monitoring points in real time through monitoring equipment; s4, the collected data are uploaded, and the data are arranged and processed; according to the invention, in the process of monitoring the drainage capacity of the soil, the monitoring data can be intelligently analyzed, and the monitoring equipment can be protected and maintained conveniently, so that the monitoring and evaluation effect can be ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of soil drainage capacity monitoring, and particularly relates to a method and system for real-time monitoring of soil drainage capacity. Background Art

[0002] Soil drainage capacity monitoring refers to the evaluation and measurement of the ability of soil to drain excess water. Through soil drainage capacity monitoring, agricultural workers can better understand the soil moisture management situation, so as to formulate more scientific irrigation and drainage plans, improve the yield and quality of crops, and at the same time save water resources and protect the ecological environment.

[0003] In the prior art, during the process of soil drainage capacity monitoring, it is not convenient to perform intelligent analysis on the monitoring data, and it is not convenient to protect and maintain the monitoring equipment, which in turn affects the monitoring and evaluation effect. Therefore, we propose a method and system for real-time monitoring of soil drainage capacity to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to solve the drawbacks in the prior art that during the process of soil drainage capacity monitoring, it is not convenient to perform intelligent analysis on the monitoring data, and it is not convenient to protect and maintain the monitoring equipment, which in turn affects the monitoring and evaluation effect, and to propose a method and system for real-time monitoring of soil drainage capacity.

[0005] A method and system for real-time monitoring of soil drainage capacity provided by the present application adopt the following technical solutions:

[0006] A method for real-time monitoring of soil drainage capacity includes the following steps:

[0007] S1: Select a monitoring area and set multiple monitoring points in the monitoring area;

[0008] S2: Select monitoring equipment and install the monitoring equipment at the monitoring points;

[0009] S3: Real-time collect soil moisture data of the monitoring points through the monitoring equipment;

[0010] S4: Upload the collected data and perform sorting and processing on the data;

[0011] S5: Compare the data of different monitoring points to judge the soil moisture distribution;

[0012] S6: Analyze the processed data to judge the soil drainage capacity.

[0013] The present invention also provides a real-time soil drainage capacity monitoring system, comprising: a sensor module, the sensor module is connected to a protection module and a control module, the control module is connected to a power supply module, a data acquisition module and a debugging module, the debugging module is used to ensure the correct installation and normal operation of each component, the debugging module is connected to a maintenance management module, the maintenance management module is used to regularly inspect and maintain the equipment, the data acquisition module is connected to a communication module, the communication module is connected to a server module, the server module is connected to an algorithm module, the algorithm module is connected to a generation module, the generation module is connected to a database module, a display module and a comparison module, the database module is connected to a data management module, the comparison module is connected to an intelligent analysis module, the display module is connected to an alarm module, and the alarm module is connected to a recommended optimization module.

[0014] Further, the sensor module includes a humidity sensor unit, a temperature sensor unit, a water level sensor unit and a rainfall sensor unit. The humidity sensor unit is connected to the temperature sensor unit, the temperature sensor unit is connected to the water level sensor unit, and the water level sensor unit and the rainfall sensor unit are connected. The humidity sensor unit is used to monitor the moisture content in the soil in real time, the temperature sensor unit is used to monitor the soil temperature, the water level sensor unit is used to monitor the groundwater level, and the rainfall sensor unit is used to monitor rainfall data.

[0015] Further, the data acquisition module includes a data collection unit, a data preprocessing unit and an upload unit. The data collection unit is connected to the data preprocessing unit, and the data preprocessing unit is connected to the upload unit.

[0016] Further, the data management module includes a query unit, an export unit and an analysis function unit. The query unit is connected to the export unit, and the export unit is connected to the analysis function unit.

[0017] Further, the communication module includes a wired communication unit and a wireless communication unit. The wired communication unit is connected to the wireless communication unit. The wireless communication unit uses a LoRa module, and the wired communication unit uses an RS485 communication method.

[0018] Further, the power supply module includes a battery power supply unit and a solar power supply unit. The battery power supply unit is connected to the solar power supply unit. The battery power supply unit and the solar power supply unit are used to provide continuous power supply for the sensor module and the control module.

[0019] Further, the server module includes a storage unit, an analysis unit and a processing unit. The storage unit is connected to the analysis unit, and the analysis unit is connected to the processing unit.

[0020] Further, the alarm module includes a threshold setting unit, a triggering unit, and a reminder unit. The threshold setting unit is connected to the triggering unit, and the triggering unit is connected to the reminder unit.

[0021] Further, the database module includes a historical storage unit, a recording unit, and a backup unit. The historical storage unit is connected to the recording unit, and the recording unit is connected to the backup unit. The database module is used for long-term storage of historical monitoring data for subsequent analysis and review.

[0022] In summary, the present application includes at least one of the following beneficial technical effects:

[0023] 1. In this solution, by installing the humidity sensor unit, temperature sensor unit, water level sensor unit, and rainfall sensor unit to each monitoring point respectively, multi-point monitoring can be performed. The sensor module can be protected and maintained through the protection module and the maintenance management module.

[0024] 2. In this solution, the control module controls the sensor module to work, monitors the soil temperature and humidity, underground water level, and rainfall. The data acquisition module collects the monitored data and uploads it to the server module through the communication module. The server module processes the collected data and transmits it to the algorithm module. The algorithm module uses a preset algorithm or model to analyze the soil humidity and temperature data and evaluate the soil drainage capacity.

[0025] 3. In this solution, the generation module generates an evaluation report and displays it through the display module. The comparison module can compare the data of different monitoring points to judge the soil moisture distribution. The intelligent analysis module can analyze the processed data to judge the soil drainage capacity. When the soil drainage capacity is lower than the set threshold, a notification and reminder are sent through the early warning module.

[0026] The present invention can facilitate the intelligent analysis of monitoring data during the monitoring process of soil drainage capacity, and facilitate the protection and maintenance of monitoring equipment, thereby ensuring the monitoring and evaluation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a flowchart of a method for real-time monitoring of soil drainage capacity proposed by the present invention;

[0028] Figure 2 is a structural block diagram of a system for real-time monitoring of soil drainage capacity proposed by the present invention;

[0029] Figure 3 is a structural block diagram of the sensor module of a system for real-time monitoring of soil drainage capacity proposed by the present invention;

[0030] Figure 4 Block diagram of the data acquisition module of a real-time soil drainage capacity monitoring system proposed by the present invention;

[0031] Figure 5 Block diagram of the data management module of a real-time soil drainage capacity monitoring system proposed by the present invention;

[0032] Figure 6 Block diagram of the communication module of a real-time soil drainage capacity monitoring system proposed by the present invention;

[0033] Figure 7 Block diagram of the power supply module of a real-time soil drainage capacity monitoring system proposed by the present invention;

[0034] Figure 8 Block diagram of the server module of a real-time soil drainage capacity monitoring system proposed by the present invention;

[0035] Figure 9 Block diagram of the alarm module of a real-time soil drainage capacity monitoring system proposed by the present invention;

[0036] Figure 10 Block diagram of the database module of a real-time soil drainage capacity monitoring system proposed by the present invention. Detailed implementation manners

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0038] Embodiment 1

[0039] Refer to Figure 1 , a real-time soil drainage capacity monitoring method, including the following steps:

[0040] S1: Select a monitoring area and set multiple monitoring points in the monitoring area;

[0041] S2: Select monitoring equipment and install the monitoring equipment at the monitoring points;

[0042] S3: Real-time collect soil moisture data of the monitoring points through the monitoring equipment;

[0043] S4: Upload the collected data and perform sorting and processing on the data;

[0044] S5: Compare the data of different monitoring points to judge the soil moisture distribution;

[0045] S6: Analyze the processed data to judge the soil drainage capacity.

[0046] Referring to Figure 2 , this embodiment also proposes a real-time soil drainage capacity monitoring system, including: a sensor module, the sensor module is connected to a protection module and a control module, the control module is connected to a power module, a data acquisition module and a debugging module, the debugging module is used to ensure that each component is correctly installed and works properly, the debugging module is connected to a maintenance management module, the maintenance management module is used to regularly check and maintain the equipment, the data acquisition module is connected to a communication module, the communication module is connected to a server module, the server module is connected to an algorithm module, and the algorithm module includes: soil moisture data analysis algorithm, soil temperature data analysis algorithm, groundwater level data analysis algorithm, rainfall data analysis algorithm and comprehensive analysis algorithm;

[0047] According to the actual monitoring scenario, determine the key factors affecting the soil drainage capacity as independent variables, such as soil moisture, temperature, groundwater level, rainfall, etc. For example, when monitoring the soil drainage capacity of farmland, soil moisture and rainfall may be the main independent variables, while the soil drainage capacity (such as drainage speed, drainage volume, etc.) is used as the dependent variable. According to different monitoring scenarios (such as farmland, forest, urban green space, etc.), adjust the selection and weight of variables. For example, in forest soil monitoring, vegetation coverage and soil organic matter content may have a greater impact on the drainage capacity and need to be included in the analysis;

[0048] Combine multiple methods such as regression analysis method, time series analysis method and machine learning algorithm to give full play to their respective advantages. For example, first use the regression analysis method to establish a preliminary relationship model between soil moisture and drainage capacity, then use the time series analysis method to capture the change trend of soil moisture over time, and finally optimize and predict the model through machine learning algorithms (such as neural networks). Before comprehensive analysis, preprocess the collected original data, including data cleaning (removing outliers, handling missing values, etc.), normalization or standardization, etc. At the same time, extract key features, such as time series features (such as mean, variance, trend, etc.) and spatial distribution features (such as humidity differences at different depths) of soil moisture, to improve the accuracy and efficiency of analysis. Verify the comprehensive analysis model through methods such as cross-validation and holdout validation, evaluate the accuracy and generalization ability of the model, and adjust and optimize the model according to the verification results, such as adjusting model parameters, adding or reducing variables, etc., to improve the prediction performance of the model;

[0049] The soil moisture data analysis algorithm includes: regression analysis method, time series analysis method and machine learning algorithm;

[0050] Regression analysis method

[0051] Simple linear regression: formula:

[0052] y = β0 + β1x + ∈

[0053] Among them, y is the dependent variable, x is the independent variable, β0 is the intercept, β1 is the slope, is the error term;

[0054] Goal: Find the best β0 and β1 to minimize the error between the predicted value and the actual value;

[0055] Multiple linear regression: Formula:

[0056] y = β0 + β1x1 + β2x2 + … + β n x n + ∈

[0057] Among them, y is the dependent variable, x1, x2, …, xn are the independent variables, β0 is the intercept, β1, β2, …, β n are the coefficients of their respective independent variables, is the error term;

[0058] Goal: Find the best β1, β2, …, β n to minimize the error between the predicted value and the actual value;

[0059] Logistic regression (for classification problems): Formula:

[0060]

[0061] Among them, P(y = 1|x) is the probability that the dependent variable y is 1 given the independent variable x, β0 is the intercept, β1, β2, …, β n are the coefficients of their respective independent variables;

[0062] Goal: Find the best β1, β2, …, β n to make the predicted probability closest to the actual classification;

[0063] Time series analysis method

[0064] Autoregressive moving average model (ARMA): Formula:

[0065]

[0066] Among them, X t is the value of the time series at time t, c is the constant term, is the autoregressive coefficient, θ j is the moving average coefficient, is the white noise sequence, p is the autoregressive order, q is the moving average order;

[0067] Goal: Capture the dynamic characteristics of the time series through autoregressive terms and moving average terms;

[0068] Autoregressive Integrated Moving Average Model (ARIMA): Formula:

[0069]

[0070] However, in the ARIMA model, the data needs to be differenced first to achieve a stationary state;

[0071] Differencing formula:

[0072] ΔX t = X t - X t-1

[0073] Objective: To make the non-stationary time series become a stationary series through differencing and then use the ARMA model for modeling;

[0074] Seasonal Autoregressive Integrated Moving Average Model (SARIMA): Formula:

[0075]

[0076] Among them, Φ s and Θ r are the seasonal autoregressive and seasonal moving average coefficients respectively, and S and R are the orders of seasonal autoregression and seasonal moving average respectively;

[0077] Objective: To capture the seasonal changes in the time series by introducing seasonal components;

[0078] Machine learning algorithms

[0079] Exponential smoothing method: Formula:

[0080] F t-1 = αX t + (1 - α)F t

[0081] Among them, F t+1 is the predicted value at the next moment, X t is the actual value at the current moment, F t is the predicted value at the current moment, and α is the smoothing coefficient, with a value range between 0 and 1;

[0082] Objective: To give higher weights to recent data through weighted averaging for predicting future values;

[0083] Holt-Winters exponential smoothing method: Formula:

[0084] F t+m = l t + mb t

[0085] l t = α(X t - s t-1 ) + (1 - α)(l t-1 + b t-1 )

[0086] b t = β(l t - l t-1 ) + (1 - β)b t-1

[0087] s t = γ(X t - l t ) + (1 - γ)s t-S

[0088] where F t+m is the predicted value at the m-th future moment, l t is the horizontal component, b t is the trend component, s t is the seasonal component, and α, β, γ are the smoothing coefficients for the horizontal, trend, and seasonal components respectively;

[0089] Objective: To predict future values by smoothing the horizontal, trend, and seasonal components respectively;

[0090] The soil temperature data analysis algorithm includes: one-dimensional heat conduction equation, two-dimensional heat conduction equation, and three-dimensional heat conduction equation;

[0091] One-dimensional heat conduction equation: The formula is:

[0092]

[0093] where u(x, t) represents the temperature at position x and time t, α is the thermal diffusivity, a physical quantity representing the heat conduction ability of the material, x is the position coordinate, and t is the time;

[0094] Two-dimensional heat conduction equation: The formula is:

[0095]

[0096] where u(x, y, t) represents the temperature at position (x, y) and time t, and the meanings of other symbols are the same as above;

[0097] Three-dimensional heat conduction equation: The formula is:

[0098]

[0099] where u(x, y, z, t) represents the temperature at position (x, y, z) and time t, and the meanings of other symbols are the same as above;

[0100] The groundwater level data analysis algorithm includes: a one-dimensional groundwater level dynamic model and a model considering the influence of rainfall and evaporation;

[0101] One-dimensional groundwater level dynamic model: Its basic form is:

[0102]

[0103] where h is the groundwater level height, t is the time, C is the Manning roughness coefficient, which reflects the influence of riverbed roughness on water flow, Q is the flow rate, x is the position coordinate along the river or channel, and q is the lateral inflow or seepage per unit length;

[0104] Model considering the influence of rainfall and evaporation: In practical applications, rainfall and evaporation are important factors affecting the groundwater level. These factors can be incorporated into the model to obtain an equation in the following form:

[0105]

[0106] where R is the rainfall intensity and E is the evaporation rate;

[0107] The rainfall data analysis algorithm includes: the cumulative distribution function method and the wavelet analysis method;

[0108] The cumulative distribution function (Cumulative Distribution Function, abbreviated as CDF) is an important concept in probability theory, which describes the probability that a random variable takes a value less than or equal to a specific value. The cumulative distribution function is usually denoted as F(x), and its definition is as follows:

[0109] F(x) = P(X ≤ x)

[0110] where F(x) is the cumulative distribution function of the random variable X, and P(X ≤ x) represents the probability that the random variable X takes a value less than or equal to x;

[0111] Wavelet analysis method: The formula is as follows:

[0112]

[0113] where f(t) is the signal to be analyzed; ψ(t) is the wavelet function; a is the scale factor, which controls the stretching of the wavelet function; b is the translation factor, which controls the translation of the wavelet function; W f (a, b) is the wavelet transform coefficient of the signal f(t) at scale a and position b;

[0114] Main steps of wavelet transform: 1. Select a wavelet function: Select an appropriate wavelet function according to the characteristics of the signal; 2. Calculate wavelet transform coefficients: Use the above formula to calculate the wavelet transform coefficients of the signal at different scales and positions; 3. Threshold processing: Perform threshold processing on the wavelet transform coefficients to remove noise; 4. Reconstruct the signal: Reconstruct the signal using the processed wavelet transform coefficients;

[0115] The comprehensive analysis algorithm includes: multiple regression analysis;

[0116] The basic form of the multiple linear regression model is:

[0117] y = β0 + β1x1 + β2x2 + … + β p x p + ∈

[0118] where y is the dependent variable (response variable); x1, x2, …, x p are independent variables (explanatory variables); β0 is the intercept term; β1, β2, …, β p are regression coefficients, indicating the degree of influence of each independent variable on the dependent variable; is the error term, usually assumed to follow a normal distribution with a mean of 0;

[0119] The algorithm module is connected to a generation module, the generation module is connected to a database module, a display module, and a comparison module, the database module is connected to a data management module, the comparison module is connected to an intelligent analysis module, the display module is connected to an alarm module, and the alarm module is connected to a recommended optimization module.

[0120] Refer to Figures 3 - 10, the sensor module includes a humidity sensor unit, a temperature sensor unit, a water level sensor unit, and a rainfall sensor unit. The humidity sensor unit is connected to the temperature sensor unit, the temperature sensor unit is connected to the water level sensor unit, and the water level sensor unit and the rainfall sensor unit are connected. The humidity sensor unit is used to monitor the moisture content in the soil in real time, the temperature sensor unit is used to monitor the soil temperature, the water level sensor unit is used to monitor the groundwater level, and the rainfall sensor unit is used to monitor rainfall data. The data acquisition module includes a data collection unit, a data preprocessing unit, and an upload unit. The data collection unit is connected to the data preprocessing unit, and the data preprocessing unit is connected to the upload unit. The data management module includes a query unit, an export unit, and an analysis function unit. The query unit is connected to the export unit, and the export unit is connected to the analysis function unit. The communication module includes a wired communication unit and a wireless communication unit. The wired communication unit is connected to the wireless communication unit. The wireless communication unit uses a LoRa module, and the wired communication unit uses an RS485 communication method. The power module includes a battery power supply unit and a solar power supply unit. The battery power supply unit is connected to the solar power supply unit. The battery power supply unit and the solar power supply unit are used to provide continuous power supply for the sensor module and the control module. The server module includes a storage unit, an analysis unit, and a processing unit. The storage unit is connected to the analysis unit, and the analysis unit is connected to the processing unit. The alarm module includes a threshold setting unit, a trigger unit, and a reminder unit. The threshold setting unit is connected to the trigger unit, and the trigger unit is connected to the reminder unit. The database module includes a historical storage unit, a recording unit, and a backup unit. The historical storage unit is connected to the recording unit, and the recording unit is connected to the backup unit. The database module is used to store historical monitoring data for a long time for subsequent analysis and review.

[0121] The implementation principle in this embodiment is as follows: When in use, select the monitoring area and set multiple monitoring points in the monitoring area. Install the humidity sensor unit, temperature sensor unit, water level sensor unit, and rainfall sensor unit at each monitoring point respectively. The sensor module can be protected by the protection module. The control module controls the sensor module to work, monitors the soil temperature and humidity, groundwater level, and rainfall. The data acquisition module collects the monitored data and uploads it to the server module through the communication module. The server module processes the collected data and transmits it to the algorithm module. The algorithm module uses a preset algorithm or model to analyze the soil humidity and temperature data, evaluate the soil drainage capacity, generate an evaluation report through the generation module, and display it through the display module. The comparison module can compare the data of different monitoring points to judge the soil moisture distribution. The intelligent analysis module can analyze the processed data to judge the soil drainage capacity. When the soil drainage capacity is lower than the set threshold, a notification and reminder are sent through the early warning module.

[0122] Embodiment Two

[0123] The difference between this embodiment and the first embodiment is as follows: The sensor module is connected to the protection module and the control module. The control module is connected to a power module, a data acquisition module, and a debugging module. The debugging module is used to ensure that each component is correctly installed and operates normally. The debugging module is connected to a maintenance management module, which is used to regularly inspect and maintain the equipment. The data acquisition module is connected to a communication module and a soil environment monitoring module. The communication module is connected to a server module, and the server module is connected to an algorithm module. The soil environment monitoring module is used to monitor soil texture, stone content, and soil layer data. By monitoring information such as soil texture, stone content, and soil layers, these factors will affect the porosity and permeability of the soil, and thus affect the drainage capacity. By comprehensively considering these factors, the error in soil volume water content can be reduced, the accuracy of the assessment of soil drainage capacity can be improved, more comprehensive soil characteristic data can be provided, the assessment of drainage capacity can be made more accurate, and it is helpful to formulate more reasonable soil management and improvement measures.

[0124] Embodiment Three

[0125] The difference between this embodiment and the first embodiment is as follows: The sensor module is connected to the protection module and the control module. The control module is connected to a power module, a data acquisition module, and a debugging module. The debugging module is used to ensure that each component is correctly installed and operates normally. The debugging module is connected to a maintenance management module, which is used to regularly inspect and maintain the equipment. The data acquisition module is connected to a communication module. The communication module is connected to a server module and a network security protection module. The server module is connected to an algorithm module. The network security protection module is used to strengthen the network security of remote monitoring and early warning, prevent data leakage and malicious attacks, and ensure the security of data transmission through technologies such as intrusion detection, firewalls, and data encryption, ensure the integrity and confidentiality of monitoring data, avoid data loss or tampering caused by network attacks, and improve the reliability of the system.

[0126] Embodiment Four

[0127] The difference between this embodiment and the first embodiment is as follows: The sensor module is connected to the protection module and the control module. The control module is connected to a power module, a data acquisition module, and a debugging module. The debugging module is used to ensure that each component is correctly installed and operates normally. The debugging module is connected to a maintenance management module, which is used to regularly inspect and maintain the equipment. The data acquisition module is connected to a communication module. The communication module is connected to a server module and an anti-interference module. The server module is connected to an algorithm module. The anti-interference module is used to perform anti-interference protection on the communication module, reduce the influence of electromagnetic interference, enhance the stability of data transmission, and improve the anti-interference ability of communication through technologies such as filtering, shielding, and signal enhancement, ensure that the monitoring data can be uploaded to the server in a timely and accurate manner, and avoid inaccurate analysis results caused by data loss or delay.

[0128] Example 5

[0129] The difference between this example and Example 1 is as follows: The sensor module is connected to the protection module and the control module. The control module is connected to a power module, a data acquisition module, and a debugging module. The debugging module is used to ensure that each component is correctly installed and operates normally. The debugging module is connected to a maintenance management module, which is used to regularly inspect and maintain the equipment. The data acquisition module is connected to a communication module and a preprocessing module. The communication module is connected to a server module, and the server module is connected to an algorithm module. The preprocessing module can adopt data processing algorithms such as median filtering method, arithmetic mean filtering method, and low-pass filtering method to preprocess the monitoring data, reduce random interference and errors, improve the quality and reliability of the data, provide a more accurate data basis for subsequent analysis, and improve the accuracy and reliability of the analysis results.

[0130] Experimental Example

[0131] Experimental Design

[0132] 1. Experimental Purpose: To verify the influence of different modules on the monitoring accuracy of soil drainage capacity;

[0133] 2. Experimental Materials:

[0134] Soil Samples: Soil samples were collected from the same area. One profile was taken every 20 cm, with a total of 5 layers, denoted as P1 - P5 in sequence;

[0135] 3. Experimental Setup: A variable - head water supply infiltration device was designed, with 2 rows of observation holes set up. One row was used to install soil tensiometers, and the other row was used to install soil sensors;

[0136] 3. Experimental Procedures:

[0137] Soil Sample Collection and Processing: The collected soil samples were transported to the laboratory to be air - dried, crushed after removing impurities, and set aside;

[0138] Soil Column Filling: The initial moisture content and wet density of the soil samples were measured, the dry density of each soil layer was calculated, and the soil samples were filled according to this dry bulk density;

[0139] Experimental Process Control: The initial water depth of surface water supply was set at 5 cm. Under the combined action of evaporation and infiltration, the water depth gradually decreased. When the water depth reached 0, the experiment was stopped. When closing the bottom water supply, the experimental indicators were measured at a frequency of 2 times a day, and the experiment lasted for 14 days;

[0140] Experimental Verification

[0141] Verification Method:

[0142] Data comparison: Compare the monitoring data of different embodiments with the conventional real-time monitoring scheme for soil drainage capacity to verify the influence of different modules on monitoring accuracy;

[0143] Statistical analysis: Use statistical methods to analyze the experimental data and evaluate the influence degree of different modules on monitoring accuracy;

[0144] Through the real-time monitoring scheme for soil drainage capacity proposed in Embodiment 1 to Embodiment 5, compare it with the conventional real-time monitoring scheme for soil drainage capacity. The experimental data is as follows:

[0145]

[0146]

[0147] The monitoring accuracy of Embodiment 1 to Embodiment 5 increases in turn, verifying that different modules have a significant promoting effect on monitoring accuracy. Especially for the preprocessing module added in Embodiment 5, through data filtering, random interference and errors are significantly reduced, thus improving the monitoring accuracy.

[0148] As mentioned above, it is only the preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, makes equivalent replacements or changes, and all should be covered by the protection scope of the present invention.

Claims

1. A real-time monitoring method for soil drainage capacity, characterized in that: It includes the following steps: S1: Select a monitoring area and set multiple monitoring points in the monitoring area; S2: Select monitoring equipment and install the monitoring equipment at the monitoring points; S3: Real-time collect soil moisture data of the monitoring points through the monitoring equipment; S4: Upload the collected data and sort out and process the data; S5: Compare the data of different monitoring points to judge the soil moisture distribution; S6: Analyze the processed data to judge the soil drainage capacity.

2. A real-time soil drainage capacity monitoring system, characterized in that: It includes: A sensor module, the sensor module is connected to a protection module and a control module, the control module is connected with a power module, a data acquisition module and a debugging module, the debugging module is used to ensure that each component is correctly installed and works properly, the debugging module is connected with a maintenance management module, the maintenance management module is used to regularly check and maintain the equipment, the data acquisition module is connected with a communication module, the communication module is connected with a server module, the server module is connected with an algorithm module, the algorithm module is connected with a generation module, the generation module is connected with a database module, a display module and a comparison module, the database module is connected with a data management module, the comparison module is connected with an intelligent analysis module, the display module is connected with an alarm module, and the alarm module is connected with a recommended optimization module.

3. The real-time soil drainage capacity monitoring system according to claim 2, characterized in that: The sensor module includes a humidity sensor unit, a temperature sensor unit, a water level sensor unit and a rain sensor unit. The humidity sensor unit is connected to the temperature sensor unit, the temperature sensor unit is connected to the water level sensor unit, and the water level sensor unit and the rain sensor unit are connected. The humidity sensor unit is used to monitor the moisture content in the soil in real time, the temperature sensor unit is used to monitor the soil temperature, the water level sensor unit is used to monitor the groundwater level, and the rain sensor unit is used to monitor rain data.

4. The real-time soil drainage capacity monitoring system according to claim 3, characterized in that: The data acquisition module includes a data collection unit, a data preprocessing unit and an upload unit. The data collection unit is connected to the data preprocessing unit, and the data preprocessing unit is connected to the upload unit.

5. The real-time soil drainage capacity monitoring system according to claim 4, characterized in that: The data management module includes a query unit, an export unit and an analysis function unit. The query unit is connected to the export unit, and the export unit is connected to the analysis function unit.

6. The real-time soil drainage capacity monitoring system according to claim 5, characterized in that: The communication module includes a wired communication unit and a wireless communication unit. The wired communication unit is connected to the wireless communication unit. The wireless communication unit uses a LoRa module, and the wired communication unit uses an RS485 communication method.

7. The real-time soil drainage capacity monitoring system according to claim 6, characterized in that: The power module includes a battery power supply unit and a solar power supply unit. The battery power supply unit is connected to the solar power supply unit. The battery power supply unit and the solar power supply unit are used to provide continuous power supply for the sensor module and the control module.

8. A real-time soil drainage capacity monitoring system according to claim 7, characterized in that: The server module includes a storage unit, an analysis unit and a processing unit. The storage unit is connected to the analysis unit, and the analysis unit is connected to the processing unit.

9. The real-time soil drainage capacity monitoring system according to claim 8, characterized in that: The alarm module includes a threshold setting unit, a trigger unit and a reminder unit. The threshold setting unit is connected to the trigger unit, and the trigger unit is connected to the reminder unit.

10. The real-time soil drainage capacity monitoring system according to claim 9, characterized in that: The database module includes a historical storage unit, a recording unit, and a backup unit. The historical storage unit is connected to the recording unit, and the recording unit is connected to the backup unit. The database module is used to store historical monitoring data for a long time, facilitating subsequent analysis and review.

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