Soil moisture content intelligent monitoring control method and system

Abstract

The invention relates to the technical field of soil physical property detection, in particular to a soil moisture content intelligent monitoring control method and system, and the method comprises the steps: multi-modal sensor data collection: deploying a multi-modal sensor array in a to-be-detected soil area; interference compensation and correction; self-adaptive calibration is carried out; water phase state distinguishing and soil moisture content index calculation; 4, soil moisture content evaluation: comprehensively evaluating the actual water content, the effective water content, the soil moisture content index and the water phase state information obtained in the step 4, and setting different grade threshold values to obtain a soil moisture content evaluation result; and intelligent control decision and execution: taking the soil moisture content evaluation result as input, and generating a corresponding control decision. The intelligent monitoring and controlling system for the soil moisture content can monitor the soil moisture condition in real time and can improve the precision and efficiency of agricultural irrigation through intelligent compensation, calibration and decision optimization, and therefore the purposes of water saving and efficient crop management are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of soil physical property detection, and in particular to a soil moisture intelligent monitoring and control method and system. Background Art

[0002] Soil moisture monitoring plays a vital role in agricultural production, water resources management, and ecological and environmental protection. However, there are still many problems in the existing soil moisture monitoring technology that need to be solved. On the one hand, when measuring soil moisture content, traditional capacitive sensors are easily interfered by environmental factors such as soil salinity and temperature, resulting in a significant decrease in measurement accuracy, which in turn affects the accurate assessment of the actual soil moisture content. On the other hand, most existing monitoring systems can only obtain the total moisture content of the soil, and it is difficult to effectively distinguish the different phases of water in the soil, such as liquid water, solid water, and gaseous water. This makes it difficult to analyze the dynamic changes of soil moisture and crop water requirements. Detailed information on the phase state of water is lacking, thus limiting the comprehensiveness and accuracy of soil moisture monitoring.

[0003] In addition, the calibration parameters of traditional soil moisture monitoring systems are usually fixed and cannot be automatically adjusted according to different soil types and environmental conditions, which leads to increased measurement errors in different regions and soil types, and serious restrictions on applicability and reliability. At the same time, existing soil moisture assessment methods mainly rely on a single moisture content indicator, lack a comprehensive assessment of soil moisture conditions, and are difficult to fully consider the actual state of soil moisture and crop water demand, which can easily lead to inaccurate irrigation decisions and affect crop growth and yield.

[0004] Finally, the intelligence level of existing monitoring systems is generally low. Most of them can only perform simple data collection and display, and lack intelligent data processing and decision support functions. When faced with complex soil environments and changeable meteorological conditions, they are unable to automatically perform data compensation, calibration and analysis, nor can they generate scientific irrigation decisions in real time based on soil moisture conditions, resulting in low irrigation efficiency and serious waste of water resources. Summary of the invention

[0005] Based on the above purpose, the present invention provides a soil moisture intelligent monitoring and control method, comprising the following steps:

[0006] S1: Multimodal sensor data acquisition. A multimodal sensor array is deployed in the soil area to be tested. The multimodal sensor array includes capacitive sensors, time domain reflectometry sensors, and thermal pulse sensors. The collected parameters include capacitance value C, dielectric constant , temperature T, thermal conductivity λ, soil salt content S;

[0007] S2: Interference compensation and correction, input the capacitance value C and dielectric constant collected by S1 And temperature T and soil salt content S, the compensation capacitance value C is obtained through the compensation formula corrected , Compensation for dielectric constant ;

[0008] S3: Adaptive calibration, the compensation capacitance value C corrected , Compensation for dielectric constant , temperature T and thermal conductivity λ are used as the input of the Bayesian optimization algorithm, and the Bayesian optimization algorithm is used to output the optimized calibration parameter P opt ;

[0009] S4: Water phase distinction and soil moisture index calculation, the compensation capacitance value C corrected , Compensation for dielectric constant , temperature T, optimized calibration parameter P opt As the input of the fuzzy logic algorithm, the water phase information in the soil is distinguished based on the fuzzy logic algorithm, and the actual water content θ and effective water content θ of the soil are calculated based on the distinction results and the optimized calibration parameters. eff and soil moisture index SI;

[0010] S5: Soil moisture assessment, the actual water content θ and effective water content θ obtained in S4 are eff And soil moisture index SI and water phase information, conduct comprehensive evaluation, set different level thresholds, and obtain soil moisture evaluation results;

[0011] S6: Intelligent control decision and execution, taking soil moisture assessment results as input to generate corresponding control decisions.

[0012] Preferably, in S2, the compensation formula is as follows: ;

[0013] Among them, C corrected is the compensation capacitance value;

[0014] C is the capacitance value obtained by initial measurement;

[0015] T is the soil temperature in degrees Celsius (℃);

[0016] S is the soil salt content;

[0017] a is the compensation coefficient of temperature interference on capacitance value, which means the amount of capacitance value that needs to be compensated for every unit change in temperature;

[0018] b is the compensation coefficient of soil salinity interference on capacitance value, which means the amount of capacitance value that needs to be compensated for every 1 unit change in soil salinity;

[0019] c is the comprehensive compensation coefficient, which is used to compensate for the interference of other unknown factors on capacitance measurement except temperature and salinity;

[0020] The dielectric constant compensation formula is: ;

[0021] in, To compensate for the dielectric constant; is the dielectric constant obtained by initial measurement;

[0022] T is the soil temperature in degrees Celsius (℃).

[0023] Preferably, in S3, the following steps are specifically included:

[0024] S3.1: Set the initial calibration parameter P0;

[0025] S3.2: Construct the likelihood function L(D|P), where D is the measured data, including capacitance C and dielectric constant , temperature T, thermal conductivity λ, soil salt content S, P represents the calibration parameter, and the likelihood function is the possibility of obtaining the measurement data D given the calibration parameter P. The likelihood function is as follows: ;

[0026] in, is the i-th set of measurement data, is a function of the calibration parameters P that maps the calibration parameters to the predicted values ​​of the measured data, is the noise variance of the i-th group of data, and n is the total amount of measured data;

[0027] S3.3: Determine the prior probability P(P), which is expressed as follows: ;

[0028] Where m is the number of calibration parameters;

[0029] j is the index parameter, is the calibration parameter of the jth group;

[0030] S3.4: Update the posterior probability P(P|D) through the Bayesian formula, the formula is as follows: ;

[0031] S3.5: Traverse the posterior probability distribution samples obtained by sampling, and find the corresponding distribution sample with the largest posterior probability, that is, the calibration parameter P with the largest posterior probability. opt In this process, S3.2-S3.5 are repeated continuously, and finally the optimized calibration parameters P are output.opt .

[0032] Preferably, in S4, the steps specifically include:

[0033] S4.1: Input variable is compensation capacitance value C corrected , Compensation for dielectric constant and temperature T, the output variable is the phase information of water in the soil, including free water, transition water and bound water;

[0034] S4.2: Define the compensation capacitor value C corrected Membership function and compensated dielectric constant The membership function of and the membership function of temperature T;

[0035] S4.3: Formulate fuzzy rules:

[0036] Rule 1: If the compensation capacitance value is high and the compensation dielectric constant is large and the temperature is high, the soil is mainly free water;

[0037] Rule 2: If the compensation capacitance value is low and the compensation dielectric constant is small and the temperature is low, the soil is mainly bound water;

[0038] Rule 3: If the compensation capacitance is medium, the compensation dielectric constant is medium, and the temperature is medium, then the soil is mainly composed of transition water;

[0039] S4.4: C corrected , and T are converted into fuzzy sets, the membership value of each input variable is calculated, and then the activation degree of each fuzzy rule is calculated, and finally the proportion of free water is obtained;

[0040] S4.5: Calculate the actual soil moisture content θ and effective soil moisture content θ eff and soil moisture index SI.

[0041] In S4.4, the free water fraction y is given by: ;

[0042] Among them, α1 is the activation degree of rule 1; α2 is the activation degree of rule 2, and α3 is the activation degree of rule 3;

[0043] The calculation formula of actual water content θ is as follows: ;

[0044] Among them, C corrected is the compensation capacitor value, is the compensation dielectric constant, T is the temperature, P optis the optimized calibration parameter, y is the proportion of free water;

[0045] Effective water contentθ eff The calculation formula is as follows: ;

[0046] Among them, θ res is the residual water content; the residual water content can be estimated by empirical methods (including model fitting methods), or obtained by natural air drying and mercury injection, or determined by the water content calculation formula established based on the interlayer hydration microstructure of montmorillonite (the water content when the first layer of montmorillonite is hydrated is the residual water content);

[0047] The calculation formula of soil moisture index SI is as follows: ;

[0048] Among them, θ fc is the field water capacity, θ wp is the wilting coefficient.

[0049] The method for determining field water holding capacity is the ring knife method: collect the original soil on the experimental plot, bring it back to the laboratory, make the soil sample saturated with water, place it on air-dried soil to drain the gravity water, and use the artificial drying method to determine the soil weight water content, which is the field water holding capacity. The method for determining the wilting coefficient is: plant the plant in a container, seal the soil surface with a mixture of paraffin and vaseline, and when the leaves of the plant wilt, place it in a place with suitable air humidity. At this time, the percentage of soil moisture to the weight of dried soil is the wilting coefficient.

[0050] Preferably, in S5, the assessment level of soil moisture condition includes the following:

[0051] Good: Soil moisture is considered good when the following conditions are met:

[0052] SI ≥ 0.8;

[0053] The actual water content θ is within the field water capacity θ fc Between 80% and 100%;

[0054] Effective water contentθ eff Sufficient to meet the needs of crop growth, that is, θ eff ≥θ min ,θ min It is the minimum effective water content determined according to the crop type;

[0055] Suitable: Soil moisture is considered suitable when the following conditions are met:

[0056] 0.5≤SI<0.8;

[0057] The actual water content θ is within the field water capacity θ fc between 50% and 80%;

[0058] Effective water contentθ eff Basically meet the crop growth, that is, θ min <θ eff ≤θ opt ,θ opt The optimum effective water content is determined according to the crop type;

[0059] Mild drought: When the following conditions are met, the soil moisture condition is judged as "mild drought":

[0060] 0.2≤SI<0.5;

[0061] The actual water content θ is within the field water capacity θ fc Between 20% and 50%;

[0062] Effective water contentθ eff Initially insufficient, i.e. θ eff <θ min；

[0063] Severe drought: When any of the following conditions are met, the soil moisture condition is judged as "severe drought":

[0064] SI < 0.2;

[0065] The actual water content θ is lower than the field capacity θ fc 20% of the total.

[0066] Preferably, in S6, the following strategies are specifically included:

[0067] When the soil moisture condition is "mild drought", if it is a field crop and there is sufficient water source, select drip irrigation or sprinkler irrigation, send an opening instruction to the irrigation equipment (such as the controller of the drip irrigation system, the water pump control unit of the sprinkler irrigation system), and set the irrigation time or irrigation amount;

[0068] When the soil moisture condition is "severe drought", regardless of the crop type, flood irrigation or a combination of rapid drip irrigation / sprinkler irrigation should be used to replenish soil moisture as quickly as possible, send an open command to the irrigation equipment, and set the irrigation duration or amount.

[0069] A soil moisture intelligent monitoring and control system includes a multi-modal data acquisition module, an interference compensation and correction module, an adaptive calibration module, a moisture phase distinction and index calculation module, a soil moisture evaluation module and an intelligent control decision and execution module. The modules are connected through a data bus and a control bus to realize data transmission and instruction execution.

[0070] The multimodal data acquisition module includes a sensor integration unit and a data preliminary perception unit; the interference compensation and correction module includes an interference analysis unit and a compensation correction unit; the adaptive calibration module includes an initial parameter setting unit and an optimization adjustment unit; the moisture phase distinction and index calculation module includes a moisture phase distinction unit and an index calculation unit; the soil moisture assessment module includes a threshold setting unit and a comprehensive assessment unit; the intelligent control decision and execution module includes a decision generation unit and an instruction execution unit.

[0071] Preferably, in the multimodal data acquisition module, the sensor integration unit integrates multiple types of sensors, including capacitive sensors, time domain reflectometry (TDR) sensors and thermal pulse sensors. These sensors are installed at different depths in the soil and can synchronously collect the capacitance value, dielectric constant, temperature, thermal conductivity and soil salt content S of the soil. The capacitive sensor is installed 10 cm below the soil surface to monitor the change of soil capacitance in real time; the time domain reflectometry sensor is installed 20 cm below the soil surface to measure the propagation characteristics of electromagnetic waves in the soil and obtain the soil dielectric constant; the thermal pulse sensor is installed 30 cm below the soil surface to monitor the change of soil thermal conductivity;

[0072] In the data preliminary perception unit, the capacitive sensor makes a preliminary judgment on the soil moisture content by sensing the changes in soil capacitance; the time domain reflectometer obtains the soil dielectric constant by measuring the propagation characteristics of electromagnetic waves in the soil, and then estimates the soil moisture content; the thermal pulse sensor assists in judging the moisture state of the soil by monitoring the changes in soil thermal conductivity, and distinguishes between liquid water and solid water components.

[0073] Preferably, it also includes a residual moisture content and field water holding capacity collection module, wherein the residual moisture content and field water holding capacity collection module includes a sample drying and weighing unit and a control and operation unit, and the control and operation unit controls the sample drying and weighing unit to dry the soil sample, and then weighs it to calculate the residual moisture content and field water holding capacity.

[0074] Beneficial effects of the present invention:

[0075] 1. By introducing multimodal sensors and intelligent data processing modules, the problem of "traditional capacitive sensors are easily disturbed by soil salinity and temperature, thus affecting the measurement accuracy" is overcome. By combining multiple sensors such as soil moisture, temperature, pH value, microbial activity, etc., the present invention can evaluate soil moisture conditions from multiple angles and comprehensively, avoiding the influence of external factors (such as salt and temperature) on a single sensor. The intelligent data processing module can also filter and correct the sensor data to further improve the measurement accuracy of soil moisture content.

[0076] 2. Existing systems often use a single measurement method and cannot effectively distinguish different phases of water in the soil (such as liquid water and solid water). The present invention uses multimodal measurement technology, combining multiple modules such as capacitive, moisture sensor, temperature sensor, and soil microbial activity sensor to achieve multi-dimensional monitoring of the moisture state in the soil. By comprehensively analyzing the moisture, temperature, and microbial activity of the soil, liquid water and solid water can be distinguished more accurately, thereby improving the comprehensiveness and accuracy of soil moisture monitoring.

[0077] 3. The fixed calibration parameters of the traditional soil moisture monitoring system have poor applicability in different regions and soil types, resulting in increased measurement errors. The present invention introduces an intelligent adaptive calibration mechanism to automatically adjust and optimize calibration parameters for different soil types and environmental conditions. This enables the system to perform personalized calibration according to the actual conditions of the soil, achieves efficient application across regions and soil types, and improves the accuracy and reliability of the system in different environments.

[0078] 4. Taking into account multi-dimensional factors such as moisture, temperature, and soil health, the system can provide refined management plans to help agricultural producers make better decisions on irrigation, fertilization, etc., improve the health of soil and crops, and thereby increase crop yield and quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0080] Figure 1 is a flow chart of the steps of the method of the present invention;

[0081] Figure 2 is a flow chart of the steps of method S3 of the present invention;

[0082] Figure 3 4 is a system block diagram of the system of the present invention. DETAILED DESCRIPTION

[0083] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0084] See also Figure 1-Figure 3The embodiment of the present invention provides a soil moisture intelligent monitoring and control method, which realizes accurate monitoring and intelligent control of soil moisture through the mutual coordination of various steps, and optimizes agricultural irrigation effects.

[0085] First, a multimodal sensor array is deployed in the soil area to be tested. This array includes capacitive sensors, time domain reflectometry sensors, and thermal pulse sensors. Each sensor is responsible for collecting different parameters, such as capacitance C, dielectric constant , temperature T, thermal conductivity λ, and soil salt content S. These sensors collect different types of soil data at the same time, providing multi-dimensional basic data for subsequent analysis. The diversity of data ensures a comprehensive understanding of soil moisture status.

[0086] Due to factors such as changes in the external environment and differences in soil properties, the sensor data may be subject to certain interference, affecting the measurement accuracy. Therefore, in step S2, the input raw data (capacitance value C, dielectric constant , temperature T and soil salt content S) are corrected by the preset compensation formula to obtain a more accurate capacitance value Ccorrected and dielectric constant , temperature T and soil salt content S. This step ensures the stability and reliability of the data and lays the foundation for subsequent calibration and analysis.

[0087] Next, the corrected data (Ccorrected, , T) and thermal conductivity λ are input into the adaptive calibration module. Using the Bayesian optimization algorithm, the system automatically adjusts the calibration parameters and outputs the optimized calibration parameters Popt. Bayesian optimization can automatically adjust according to the data samples, avoiding manual intervention, improving the accuracy and flexibility of calibration, and is especially suitable for diverse environments with different soil types and moisture states.

[0088] The corrected data and optimized calibration parameters will be input into the water phase distinction and soil moisture index calculation module. This module uses fuzzy logic algorithm to distinguish different water phases in the soil, and calculates the actual soil moisture content θ, effective moisture content θeff and soil moisture index SI in combination with the optimized calibration parameters. The fuzzy logic algorithm can handle complex soil moisture changes and improve the accuracy of soil moisture assessment.

[0089] Based on the soil moisture index obtained in step S4, the system comprehensively evaluates the actual moisture content θ, the effective moisture content θeff, the soil moisture index SI and the moisture phase information. By setting different thresholds, the evaluation results will generate different soil moisture levels. For example, if the actual moisture content is lower than the set threshold, it is judged as drought, otherwise it is suitable for irrigation. This evaluation result provides a basis for subsequent intelligent control decisions.

[0090] Finally, based on the soil moisture assessment results obtained in step S5, the system generates corresponding control decisions. According to the soil moisture level, the intelligent control system will start the automatic irrigation equipment for irrigation to ensure the water requirements of crops at different growth stages. Through this module, the system can efficiently and accurately control irrigation to avoid over-irrigation or water shortage.

[0091] Preferably, in S2, the compensation formula is as follows: ;

[0092] Among them, C corrected is the compensation capacitance value;

[0093] C is the capacitance value obtained by initial measurement;

[0094] T is the soil temperature in degrees Celsius (℃);

[0095] S is the soil salt content;

[0096] a is the compensation coefficient of temperature interference on capacitance value, which means the amount of capacitance value that needs to be compensated for every unit change in temperature;

[0097] b is the compensation coefficient of soil salinity interference on capacitance value, which means the amount of capacitance value that needs to be compensated for every 1 unit change in soil salinity;

[0098] c is the comprehensive compensation coefficient, which is used to compensate for the interference of other unknown factors on capacitance measurement except temperature and salinity;

[0099] The dielectric constant compensation formula is: ;

[0100] in, To compensate for the dielectric constant;

[0101] is the dielectric constant obtained by initial measurement;

[0102] T is the soil temperature in degrees Celsius (℃).

[0103] Preferably, in S4, the calculation formula of the actual water content θ is as follows: ;

[0104] Among them, C corrected is the compensation capacitor value, is the compensation dielectric constant, T is the temperature, P opt is the optimized calibration parameter;

[0105] Effective water contentθ eff The calculation formula is as follows: ;

[0106] Among them, θ res is the residual water content;

[0107] The calculation formula of soil moisture index SI is as follows: ;

[0108] Among them, θ fc is the field water capacity, θ wp is the wilting coefficient.

[0109] In a possible implementation, in step S3, a Bayesian optimization algorithm is used to perform adaptive calibration of soil moisture monitoring. Specifically, first, an initial calibration parameter set P0 is set, which is selected based on previous experience or experimental data in order to provide a starting point for the optimization process. The initial calibration parameters P0 may include relevant physical property parameters such as capacitance, dielectric constant, temperature, etc., which play a key role in actual soil measurement.

[0110] In S3.2, a likelihood function L(D|P) is constructed to measure the actual measured data D (including capacitance C, dielectric constant , temperature T, thermal conductivity λ, soil salt content S) will occur. Specifically, this function evaluates the quality of the calibration parameter P by comparing the difference between the model prediction value and the actual observation data. The noise variance σi in the formula represents the uncertainty of each set of measurement data, and n is the total amount of measurement data. The role of this step is to provide a correction direction for the parameters in a data-driven way. The likelihood function is as follows: ; is the i-th set of measurement data, is a function of the calibration parameters P that maps the calibration parameters to the predicted values ​​of the measured data, is the noise variance of the i-th group of data, and n is the total amount of measured data.

[0111] In step S3.3, based on the prior knowledge of the calibration parameters, the prior probability P(P) of the parameters is set. This probability reflects the probability distribution of the parameter P in the absence of any measurement data, and is usually determined based on experience or historical data. The prior distribution P(P) can take the form of uniform distribution, Gaussian distribution, etc., where m is the number of calibration parameters and j is the index parameter. Through this step, the initial probability of each calibration parameter can be reasonably estimated, providing a basis for subsequent Bayesian updates.

[0112] The prior probability P(P) is expressed as follows: ;

[0113] Where m is the number of calibration parameters;

[0114] j is the index parameter.

[0115] In step S3.4, the Bayesian formula is applied to update the posterior probability P(P|D), that is, to calculate the probability distribution of the calibration parameter P given the measurement data D. The Bayesian formula combines the prior probability P(P) with the likelihood function L(D|P) to obtain a new posterior probability distribution. The core of this process is to continuously revise the understanding of the calibration parameters based on the new measurement data so that the parameters more accurately reflect the actual soil moisture state. The formula is as follows: .

[0116] In S3.5, by traversing multiple candidate samples sampled from the posterior probability distribution, the distribution sample that maximizes the posterior probability is found, that is, the final optimized calibration parameter P is obtained. opt In order to ensure that the final calibration parameter P opt t is accurate and reliable. In this process, steps S3.2 to S3.4 need to be repeated continuously until the posterior probability is stable. Finally, the optimized calibration parameter P opt It will serve as the basis for further analysis and control of the system.

[0117] In a possible implementation manner, in S4, specifically including the following:

[0118] S4.1: Input variable is compensation capacitance value C corrected , Compensation for dielectric constant and temperature T, the output variable is the phase information of water in the soil, including free water, transition water and bound water;

[0119] S4.2: Define the compensation capacitor value C corrected Membership function and compensated dielectric constant The membership function of and the membership function of temperature T;

[0120] S4.3: Formulate fuzzy rules:

[0121] Rule 1: If the compensation capacitance value is high and the compensation dielectric constant is large and the temperature is high, the soil is mainly free water;

[0122] Rule 2: If the compensation capacitance value is low and the compensation dielectric constant is small and the temperature is low, the soil is mainly bound water;

[0123] Rule 3: If the compensation capacitance is medium, the compensation dielectric constant is medium, and the temperature is medium, then the soil is mainly composed of transition water;

[0124] S4.4: C corrected , and T are converted into fuzzy sets, the membership value of each input variable is calculated, and then the activation degree of each rule is calculated, and finally the proportion of free water is obtained;

[0125] S4.5: Calculate the actual soil moisture content θ and effective soil moisture content θ eff and soil moisture index SI.

[0126] In S4.4, the free water fraction y is given by: ;

[0127] Among them, α1 is the activation degree of rule 1; α2 is the activation degree of rule 2, and α3 is the activation degree of rule 3.

[0128] The calculation formula of actual water content θ is as follows: ;

[0129] Among them, C corrected is the compensation capacitor value, is the compensation dielectric constant, T is the temperature, P opt is the optimized calibration parameter, y is the proportion of free water;

[0130] Effective water contentθ eff The calculation formula is as follows: ;

[0131] Among them, θ res is the residual water content;

[0132] The calculation formula of soil moisture index SI is as follows: ;

[0133] Among them, θ fc is the field water capacity, θ wp is the wilting coefficient.

[0134] In one possible implementation, in step S5, the assessment level of soil moisture condition is based on multiple key parameters, such as soil moisture index (SI), actual moisture content (θ) and effective moisture content (θ eff ). These parameters reflect the moisture level of the soil and its suitability for crop growth, so it is necessary to comprehensively analyze these factors to determine the specific level of soil moisture conditions.

[0135] Specifically, the SI value, as the core indicator for measuring soil moisture status, determines the basic classification of soil moisture conditions. According to the SI value, soil moisture conditions can be divided into "good" (SI ≥ 0.8), "suitable" (0.5 ≤ SI < 0.8), "mild drought" (0.2 ≤ SI < 0.5), and "severe drought" (SI < 0.2). The SI value is derived from a combination of multiple factors, such as soil moisture, temperature, humidity, etc. A higher SI value indicates that the soil has sufficient water supply and is suitable for crop growth; a lower SI value means that the soil moisture is insufficient, which may affect crop growth.

[0136] θ (actual moisture content) refers to the existing moisture in the soil, while θ fc is the field water holding capacity, that is, the maximum amount of water that the soil can retain. When the actual water content θ is between 80% and 100% of the field water holding capacity, the soil moisture condition is judged to be "good"; when θ is between 50% and 80%, the soil moisture condition is "suitable"; if the actual water content θ is less than 20% of the field water holding capacity, it is judged to be "severe drought".

[0137] θ eff (Available moisture content) is the amount of water that is essential for crop growth, usually considering the portion of soil moisture that can be absorbed by plant roots.

[0138] When assessing soil moisture conditions, we need to focus on whether the available water content is sufficient to meet the growth needs of crops:

[0139] “Good” soil moisture requires effective water content θ eff ≥θ min (minimum effective moisture content), which can ensure the water needs of crops.

[0140] The “suitable” soil moisture condition requires the effective water content to be within θ min to θ opt Between opt It is the optimal effective moisture content for crops.

[0141] When the effective water content θ eff Below θ min When the drought reaches 90°C, the soil moisture condition is judged as “mild drought” or “severe drought”.

[0142] In one possible implementation, in step S6 of the soil moisture intelligent monitoring and control method, the focus is on adopting different irrigation strategies according to different soil moisture conditions (such as "mild drought" and "severe drought") to ensure that soil moisture is effectively replenished, thereby ensuring the healthy growth of crops.

[0143] Specifically, when the soil moisture is assessed as "mild drought", it is first judged based on the crop type and water source conditions. If the crop is a field crop and there is sufficient water, choose an appropriate irrigation method. In field crops, drip irrigation or sprinkler irrigation is considered to be a more efficient irrigation method. Drip irrigation can accurately supply water to the roots and reduce water waste; sprinkler irrigation can cover a larger area and is suitable for larger fields. Once the irrigation method is determined, the intelligent system sends a start command to the irrigation equipment, and sets the appropriate irrigation duration or amount. The set irrigation amount is adjusted according to soil moisture, crop needs and environmental conditions to avoid over-irrigation or insufficient water.

[0144] When soil moisture is assessed as "severe drought", rapid soil rehydration should be prioritized regardless of crop type. In this case, the choice of irrigation method is aimed at restoring soil moisture as quickly as possible. In severe drought, flood irrigation or a combination of rapid drip / sprinkler irrigation is more efficient. Flood irrigation can quickly cover a large area of ​​land and quickly increase soil moisture; while a combination of rapid drip or sprinkler irrigation can accurately replenish water in a shorter time to avoid excessive evaporation. The system automatically sets the corresponding irrigation duration or irrigation amount according to the irrigation method. Since the soil moisture content is extremely low in severe drought, a large amount of irrigation is required to ensure that soil moisture can be restored as soon as possible.

[0145] Flexible adjustment of irrigation methods and irrigation amounts according to changes in soil moisture. When soil moisture is sufficient but slightly lacking, precise, water-saving drip irrigation or sprinkler irrigation is used; in severe drought conditions, flood irrigation or a combination of irrigation methods that quickly replenishes water is the preferred choice. The intelligent monitoring system automatically selects irrigation plans based on real-time data from soil moisture assessment, thereby achieving intelligent control.

[0146] Correspondingly, the embodiment of the present invention also provides an intelligent monitoring and control system for soil moisture conditions, which realizes accurate monitoring and efficient control of soil moisture conditions through close cooperation between various modules.

[0147] The multimodal data acquisition module consists of a sensor integration unit and a data preliminary perception unit. The sensor integration unit is responsible for collecting moisture data at different levels in the soil (such as humidity, temperature, conductivity, etc.) and transmitting these raw data to other modules of the system through the data bus. The data preliminary perception unit performs a preliminary analysis of the collected raw data to determine the accuracy and reliability of the data. After data acquisition, the sensor module transmits the information to the interference compensation and correction module for subsequent processing.

[0148] The interference compensation and correction module consists of an interference analysis unit and a compensation correction unit. The interference analysis unit analyzes and evaluates the interference factors (such as environmental changes, equipment errors, etc.) in the raw data obtained from the acquisition module. The compensation correction unit compensates and corrects the data based on the results of the interference analysis to ensure that the data is accurate and reliable. The corrected data is then transmitted to the adaptive calibration module. The corrected data from the interference compensation and correction module will be sent to the adaptive calibration module for further calibration of the system.

[0149] The adaptive calibration module consists of an initial parameter setting unit and an optimization adjustment unit. The initial parameter setting unit sets preliminary soil parameters based on historical data or experience. The optimization adjustment unit adaptively optimizes the initial parameters based on real-time collected data to achieve accurate soil moisture assessment. The optimized and calibrated parameters are transmitted to the moisture phase differentiation and index calculation module. The optimized calibration parameters provide data support for subsequent soil assessment.

[0150] The moisture phase distinction and index calculation module includes a moisture phase distinction unit and an index calculation unit. The moisture phase distinction unit determines the phase of soil moisture (such as liquid, gas or solid) based on the optimized and calibrated data, providing a basis for subsequent moisture assessment. The index calculation unit calculates relevant soil moisture indicators such as soil moisture storage and available water based on the moisture phase data. The calculated moisture index and moisture phase data will be transmitted to the soil moisture assessment module.

[0151] The soil moisture assessment module consists of a threshold setting unit and a comprehensive assessment unit. The threshold setting unit sets different soil moisture assessment standards and determines various soil moisture conditions (such as wet, dry, etc.) according to the moisture index. The comprehensive assessment unit conducts a comprehensive assessment of the soil moisture according to the set threshold and various indicator data, and outputs the assessment results. The assessment results are transmitted to the intelligent control decision and execution module to make corresponding control decisions.

[0152] The intelligent control decision and execution module includes a decision generation unit and an instruction execution unit. The decision generation unit generates control instructions such as irrigation and fertilization based on the soil moisture assessment results, and makes decisions based on the priority set by the system. The instruction execution unit receives the decision instructions and performs corresponding operations (such as starting irrigation equipment, adjusting water flow, etc.). The instruction execution unit sends the instructions to the irrigation control equipment or other execution units through the control bus to realize the automatic operation of the system.

[0153] In one possible implementation, the multimodal data acquisition module can comprehensively and accurately collect data closely related to the soil moisture status by integrating capacitive sensors, time domain reflectometry sensors and thermal pulse sensors, providing basic data for intelligent monitoring and analysis of soil moisture conditions. These sensors are installed in the soil at different depths to form a three-dimensional monitoring network that can synchronously obtain multi-dimensional data of the soil.

[0154] Specifically, the capacitive sensor is installed 10 cm below the soil surface and is mainly used to monitor the change of soil capacitance in real time. The capacitance value of the soil is closely related to the moisture content. Therefore, the capacitive sensor can directly reflect the moisture content of the soil by sensing the change of soil capacitance. The capacitive sensor transmits the acquired capacitance data to the data preliminary perception unit, which performs preliminary processing and provides a data basis for subsequent moisture assessment.

[0155] The time domain reflectometry sensor is installed 20cm below the soil surface. It is mainly used to measure the propagation characteristics of electromagnetic waves in the soil, and then obtain the dielectric constant of the soil. The dielectric constant of the soil is closely related to the moisture content in the soil, especially the change of the dielectric constant can reflect the moisture state of the soil. The time domain reflectometry sensor measures the propagation speed of electromagnetic waves and the characteristics of the reflected waves, calculates the dielectric constant of the soil and provides it to the data preliminary perception unit for further moisture estimation.

[0156] The thermal pulse sensor is installed 30cm below the soil surface to monitor changes in soil thermal conductivity. The thermal conductivity of the soil is closely related to its moisture content and moisture state (liquid water and solid water). The thermal pulse sensor calculates the thermal conductivity of the soil and determines the moisture state of the soil by heating the pulse and measuring the temperature response change. The data is also transmitted to the data preliminary perception unit and combined with the data from other sensors to further improve the judgment of the soil moisture state.

[0157] In the data preliminary perception unit, the raw data collected by the capacitive sensor, time domain reflectometry sensor and thermal pulse sensor will be preliminarily processed. Through the data of the capacitive sensor, the system can preliminarily judge the moisture content of the soil. The data calculated by the time domain reflectometry sensor through the dielectric constant can further confirm the change in the moisture content of the soil. The thermal pulse sensor assists in judging the state of soil moisture and distinguishes between liquid water and solid water components. The function of this module is to integrate the data of each sensor to provide a preliminary assessment of the moisture state and changes of the soil.

[0158] In a possible embodiment, a residual moisture content and field water holding capacity collection module is also included, wherein the residual moisture content and field water holding capacity collection module includes a sample drying and weighing unit and a control and operation unit, and the control and operation unit controls the drying and weighing unit to dry the soil sample, and then weighs it to calculate the residual moisture content and field water holding capacity.

[0159] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0160] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A soil moisture intelligent monitoring and control method, characterized in that: The following steps are involved: S1: Multimodal sensor data acquisition. A multimodal sensor array is deployed in the soil area to be tested. The multimodal sensor array includes capacitive sensors, time domain reflectometry sensors, and thermal pulse sensors. The collected parameters include capacitance value C, dielectric constant , temperature T, thermal conductivity λ and soil salt content S; S2: Interference compensation and correction, input the capacitance value C and dielectric constant collected by S1 , temperature T and soil salt content S, the compensation capacitance value Ccorrected and the compensation dielectric constant are obtained through the compensation formula ; S3: Adaptive calibration, the compensation capacitance value Ccorrected and the compensation dielectric constant , temperature T and thermal conductivity λ are used as the input of the Bayesian optimization algorithm, and the Bayesian optimization algorithm is used to output the optimized calibration parameter P opt ; S4: Water phase distinction and soil moisture index calculation, compensation capacitance value Ccorrected, compensation dielectric constant , temperature T, optimized calibration parameter P opt As the input of the fuzzy logic algorithm, the fuzzy logic algorithm is used to distinguish the water phase information in the soil, and the actual soil water content θ and effective soil water content θ are calculated based on the distinction results, the residual water content and field water holding capacity obtained by the test, and the optimized calibration parameters. eff and soil moisture index SI; S5: Soil moisture assessment, the actual water content θ and effective water content θ obtained in S4 are eff And soil moisture index SI and water phase information, conduct comprehensive evaluation, set different level thresholds, and obtain soil moisture evaluation results; S6: Intelligent control decision and execution, taking soil moisture assessment results as input to generate corresponding control decisions.

2. The soil moisture intelligent monitoring and control method according to claim 1 is characterized in that: In S2, the capacitance compensation formula is as follows: ； Among them, C corrected is the compensation capacitance value; C is the capacitance value obtained by initial measurement; T is the soil temperature in degrees Celsius (℃); S is the soil salt content; a is the compensation coefficient of temperature interference on capacitance value, which means the amount of capacitance value that needs to be compensated for every unit change in temperature; b is the compensation coefficient of the interference of soil salt content on the capacitance value, which means the amount of capacitance value that needs to be compensated for every 1 unit change in soil salt content; c is the comprehensive compensation coefficient, which is used to compensate for the interference of other factors on capacitance measurement except temperature and salinity; The dielectric constant compensation formula is: ； in, To compensate for the dielectric constant; is the dielectric constant obtained by initial measurement; T is the soil temperature in degrees Celsius (℃).

3. The soil moisture intelligent monitoring and control method according to claim 2 is characterized in that: In S3, the following steps are specifically included: S3.1: Set the initial calibration parameter P0; S3.2: Construct the likelihood function L(D|P), where D is the measured data, including capacitance C and dielectric constant , temperature T, thermal conductivity λ, soil salt content S, P represents the calibration parameter, and the likelihood function is the possibility of obtaining the measurement data D given the calibration parameter P. The likelihood function is as follows: ； in, is the i-th set of measurement data, is a function of the calibration parameters P that maps the calibration parameters to the predicted values ​​of the measured data, is the noise variance of the i-th group of measurement data, and n is the total amount of measurement data; S3.3: Determine the prior probability P(P), which is expressed as follows: ； Where m is the number of calibration parameters; j is the index parameter, is the calibration parameter of the jth group; S3.4: Update the posterior probability P(P|D) using the Bayesian formula. The formula is as follows ； S3.5: Traverse the sampling to obtain the distribution samples corresponding to the posterior probability, and find the distribution sample with the largest posterior probability, that is, to obtain the calibration parameter P with the largest posterior probability. opt In this process, S3.2-S3.5 are repeated continuously, and finally the optimized calibration parameters P are output. opt .

4. The soil moisture intelligent monitoring and control method according to claim 3 is characterized in that: In S4, the specifics include: S4.1: Input variable is compensation capacitance value C corrected , Compensation for dielectric constant and temperature T, the output variable is the water phase information in the soil, including free water, transition water and bound water; S4.2: Define the compensation capacitor value C corrected Membership function and compensated dielectric constant The membership function of and the membership function of temperature T; S4.3: Formulate fuzzy rules: Rule 1: If the compensation capacitance value is high and the compensation dielectric constant is large and the temperature is high, the soil is mainly free water; Rule 2: If the compensation capacitance value is low and the compensation dielectric constant is small and the temperature is low, the soil is mainly bound water; Rule 3: If the compensation capacitance is medium, the compensation dielectric constant is medium, and the temperature is medium, then the soil is mainly composed of transition water; S4.4: C corrected , and T are converted into fuzzy sets, the membership value of each input variable is calculated, and then the activation degree of each fuzzy rule is calculated, and finally the proportion of free water is obtained; S4.5: Calculate the actual soil moisture content θ and effective soil moisture content θ eff and soil moisture index SI.

5. The soil moisture intelligent monitoring and control method according to claim 4 is characterized in that: In S4.4, the free water fraction y is given by: ； Among them, α1 is the activation degree of rule 1; α2 is the activation degree of rule 2, and α3 is the activation degree of rule 3; The calculation formula of actual water content θ is as follows: ； Among them, C corrected is the compensation capacitor value, is the compensation dielectric constant, T is the temperature, P opt is the optimized calibration parameter, y is the proportion of free water; Effective water contentθ eff The calculation formula is as follows: ； Among them, θ res is the residual water content; The calculation formula of soil moisture index SI is as follows: ； Among them, θ fc is the field water capacity, θ wp is the wilting coefficient.

6. The soil moisture intelligent monitoring and control method according to claim 5 is characterized in that: In S5, the assessment levels of soil moisture conditions include the following: Good: Soil moisture is considered good when the following conditions are met: SI ≥ 0.8; The actual water content θ is within the field water capacity θ fc Between 80% and 100%; Effective water contentθ eff Sufficient to meet the needs of crop growth, that is, θ eff ≥θ min ,θ min It is the minimum effective water content determined according to the crop type; Suitable: Soil moisture is considered suitable when the following conditions are met: 0.5≤SI<0.8; The actual water content θ is within the field water capacity θ fc between 50% and 80%; Effective water contentθ eff Basically meet the needs of crop growth, that is, θ min <θ eff ≤θ opt ,θ opt The optimum effective water content is determined according to the crop type; Mild drought: When the following conditions are met, the soil moisture condition is judged as "mild drought": 0.2≤SI<0.5; The actual water content θ is within the field water capacity θ fc Between 20% and 50%; Effective water contentθ eff Initially insufficient, i.e. θ eff <θ min； Severe drought: When any of the following conditions are met, the soil moisture condition is judged as "severe drought": SI < 0.2; The actual water content θ is lower than the field capacity θ fc 20% of the total.

7. The soil moisture intelligent monitoring and control method according to claim 6 is characterized in that: In S6, the following strategies are included: When the soil moisture condition is "mild drought", if it is a field crop and there is sufficient water, select drip irrigation or sprinkler irrigation, send an opening instruction to the irrigation equipment, and set the irrigation time or irrigation amount; When the soil moisture condition is "severe drought", regardless of the crop type, flood irrigation or a combination of fast drip irrigation / sprinkler irrigation is preferred to replenish soil moisture as quickly as possible, send an open command to the irrigation equipment, and set the irrigation duration or amount.

8. A soil moisture intelligent monitoring and control system, applied to a soil moisture intelligent monitoring and control method according to any one of claims 1 to 7, characterized in that: It includes multimodal data acquisition module, interference compensation and correction module, adaptive calibration module, water phase distinction and index calculation module, soil moisture assessment module and intelligent control decision and execution module. Each module is connected through a data bus and a control bus to realize data transmission and command execution. The multimodal data acquisition module includes a sensor integration unit and a data preliminary perception unit; The interference compensation and correction module includes an interference analysis unit and a compensation correction unit; The adaptive calibration module includes an initial parameter setting unit and an optimization adjustment unit; The water phase distinction and index calculation module includes a water phase distinction unit and an index calculation unit; The soil moisture assessment module includes a threshold setting unit and a comprehensive assessment unit; The intelligent control decision and execution module includes a decision generation unit and an instruction execution unit.

9. The soil moisture intelligent monitoring and control system according to claim 8, characterized in that: In the multimodal data acquisition module, the sensor integration unit integrates multiple types of sensors, including capacitive sensors, time domain reflectometry sensors and thermal pulse sensors. These sensors are installed at different depths in the soil and can synchronously collect the capacitance value, dielectric constant, temperature, thermal conductivity and soil salt content S of the soil. Among them, the capacitive sensor is installed 10 cm below the soil surface to monitor the change of soil capacitance in real time; the time domain reflectometry sensor is installed 20 cm below the soil surface to measure the propagation characteristics of electromagnetic waves in the soil and obtain the dielectric constant of the soil; the thermal pulse sensor is installed 30 cm below the soil surface to monitor the change of thermal conductivity of the soil; In the data preliminary perception unit, the capacitive sensor makes a preliminary judgment on the soil moisture content by sensing the changes in soil capacitance; the time domain reflectometer obtains the soil dielectric constant by measuring the propagation characteristics of electromagnetic waves in the soil, and then estimates the soil moisture content; the thermal pulse sensor assists in judging the moisture state of the soil by monitoring the changes in soil thermal conductivity, and distinguishes between liquid water and solid water components.

10. The soil moisture intelligent monitoring and control system according to claim 9, characterized in that: It also includes a residual moisture content and field water holding capacity collection module, wherein the residual moisture content and field water holding capacity collection module includes a sample drying and weighing unit and a control and operation unit. The control and operation unit controls the sample drying and weighing unit to dry the soil sample, and then weighs it to calculate the residual moisture content and field water holding capacity.

Images