Methods and devices for monitoring soil salinity in facilities

Abstract

This invention provides a method and device for monitoring soil salinity in facilities, belonging to the field of soil salinity monitoring technology. This invention pre-defines a well-defined salinity decoupling function, mechanistically separating the combined effects of temperature, soil moisture content, and clay content on pore water conductivity to obtain standardized pore water conductivity, which is the essential physical quantity reflecting salinity levels. This solves the problems of ambiguous physical meaning of pore water conductivity values, environmental noise drowning out the true signal, and leading to misjudgments of salinity. This invention utilizes selected stable window data, with the optimization objective of minimizing the variance of the calculated pore water conductivity sequence. It employs a nonlinear least squares method to iteratively update the parameters in the salinity decoupling function online, significantly improving the model's local applicability and accuracy at that point. This solves the problems of inaccurate cross-point measurements and long-term performance degradation caused by unchanging model parameters.

Description

Technical Field

[0001] This invention relates to the field of soil salinity monitoring technology, specifically to a method and apparatus for monitoring soil salinity in facilities. Background Technology

[0002] With global population growth, arable land scarcity, and the escalating challenges of climate change, facility agriculture, represented by greenhouses, polytunnels, and plant factories, has become a key pathway to ensure food security and improve agricultural production efficiency and quality. Unlike traditional open-field agriculture, facility agriculture achieves precise control of growth factors such as light, temperature, water, fertilizer, and air through artificial environmental control, thereby enabling off-season, high-yield, and high-quality crop production. In this highly controllable system, the stability of the physicochemical properties of the soil (or soilless cultivation substrate) is crucial, as it serves as the medium in direct contact with crop roots.

[0003] In existing technologies, soil salinity changes are typically monitored by direct measurement using in-situ soil conductivity sensors or by decoupling methods based on fixed models. However, most existing commercial sensors only provide raw readings of pore water conductivity or use fixed, universal correction coefficients, which cannot dynamically and accurately decouple soil characteristics (texture) from real-time environmental conditions (temperature, water content) at specific monitoring points, resulting in large salinity estimation errors and low reliability.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method and apparatus for monitoring soil salinity in facilities, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The specific steps for monitoring soil salinity in facilities include:

[0008] Step 1: Collect facility soil data sequence within the test area. Facility soil data consists of pore water conductivity data, soil temperature data, soil moisture content data, and soil clay mass percentage data. Based on the facility soil salinity monitoring mechanism, a salinity decoupling function is pre-determined to describe the relationship between pore water conductivity and soil temperature, soil moisture content, soil clay mass percentage, and standard pore water conductivity.

[0009] Step 2: Divide the facility soil data sequence into several environmental windows of equal length, calculate the fluctuation value of soil temperature and the fluctuation value of soil moisture in each environmental window, and determine whether the facility soil environment in each environmental window is in a stable state based on the comparison result of the fluctuation value and the preset threshold.

[0010] Step 3: Select an environmental window in a stable state as the calibration window, stitch together the facility soil data within the calibration window to obtain a reference facility soil data sequence, and parameterize the reference facility soil data sequence based on the salt decomposition coupling function to obtain a standard pore water conductivity sequence. With the minimum fluctuation of the standard pore water conductivity sequence as the optimization objective, the parameters in the salt decomposition coupling function are updated online using the least squares method to obtain the optimized salt decomposition coupling function.

[0011] Step 4: Based on the optimized salt decoupling function, perform salt decoupling calculations on the subsequently collected facility soil monitoring data to obtain the pure pore water conductivity after removing the influence of temperature, water content, and clay content. Based on the pure pore water conductivity, according to the pre-established correspondence between conductivity and salt concentration, convert the pure pore water conductivity into the salt concentration of the facility soil to realize the monitoring of facility soil salinity.

[0012] Furthermore, the specific steps for obtaining the facility soil data sequence are as follows:

[0013] Set a uniform sampling time interval, and collect facility soil data for each data point at each sampling time;

[0014] The facility soil data collected in chronological order are arranged sequentially to form the original facility soil data sequence;

[0015] The original facility soil data sequence was preprocessed to obtain the facility soil data sequence.

[0016] Furthermore, the specific steps for determining the salt decomposition coupling function used to describe the relationship between pore water conductivity and soil temperature, soil moisture content, soil clay mass percentage, and standard pore water conductivity are as follows:

[0017] Set a temperature correction coefficient, determine the soil temperature difference by the difference between the soil temperature data and the standard temperature reference value, multiply the soil temperature difference by the temperature correction coefficient to obtain the temperature correction value, and add the temperature correction value to 1 to obtain the temperature correction function value.

[0018] Set the volumetric water content coefficient and common index parameter, calculate the soil water content value by raising the common index parameter to a power, and multiply the soil water content value by the volumetric water content coefficient to obtain the soil water content correction function value.

[0019] Set the clay content coefficient and the clay correction value, calculate the product of the clay content coefficient and the percentage of soil clay mass to obtain the clay content value, and add the clay content value and the clay correction value to obtain the clay correction function value;

[0020] The water content temperature value is obtained by multiplying the temperature correction function value, the soil moisture correction function value, and the clay correction function value. The standard pore water conductivity is obtained by comparing the pore water conductivity with the water content temperature value.

[0021] Furthermore, the specific steps for dividing the environment window are as follows:

[0022] Set the window length and sliding step size, slide the window on the facility soil data sequence with the sliding step size, place the first window in the initial position, and move the window forward according to the preset sliding step size until the window covers the end of the time series, thereby generating a series of time-continuous windows.

[0023] And for each generated window, perform the following operations:

[0024] Calculate the start and end times for each window;

[0025] After extracting data from the current window, continue sliding the window until the new start time plus the window length exceeds the end of the available data sequence.

[0026] Furthermore, the logic for determining whether an environment is stable is as follows:

[0027] Set temperature fluctuation thresholds and moisture content fluctuation thresholds;

[0028] Obtain the maximum and minimum temperature values ​​within each environmental window, and calculate the temperature difference value by measuring the difference between the maximum and minimum temperature values.

[0029] Obtain the maximum and minimum water content within each environmental window, calculate the difference between the maximum and minimum water content, and obtain the water content difference value.

[0030] When the temperature difference is less than the temperature fluctuation threshold and the moisture content difference is less than the moisture content fluctuation threshold, the environmental window is considered stable; otherwise, the environmental window is considered unstable.

[0031] Furthermore, the specific steps for constructing the loss function based on facility soil data are as follows:

[0032] Define the parameter vector to be optimized, where the optimized parameter vector consists of temperature correction coefficient, volumetric water content coefficient, clay content coefficient, clay correction value, and common index parameter, and set initial values ​​for them. Based on the initial values, calculate the model prediction output value for each group of samples.

[0033] The difference between the model's predicted output value and the mean predicted value for each group of samples is calculated to obtain the prediction residual. The square of the prediction residual is calculated to obtain the squared deviation from the mean. The sum of the squared deviations from the mean for all samples is calculated to obtain the total deviation from the mean. The total deviation from the mean is calculated and compared with the total deviation from the mean for all samples to obtain the loss function.

[0034] Furthermore, the parameters in the salt decoupling function are updated online using the least squares method to obtain the optimized salt decoupling function. The specific steps are as follows:

[0035] Based on the prediction residuals of each set of samples, a residual vector is constructed. A Jacobi matrix is ​​constructed with the residual vector as the independent variable. The product of the Jacobi matrix and the transpose of the Jacobi matrix is ​​calculated to obtain the normal matrix. The product of the inverse of the normal matrix, the Jacobi matrix, and the residual vector is calculated to obtain the first product. The sign of the first product is removed to obtain the parameter update increment.

[0036] Set convergence conditions. When the conditions are met, the iteration stops and the optimized parameter estimates are obtained.

[0037] Furthermore, the specific steps for converting the electrical conductivity of pure pore water into the salt concentration of the facility soil are as follows:

[0038] Set a proportionality coefficient and a conversion value, multiply the proportionality coefficient by the conductivity of pure pore water to obtain a second product, and add the second product to the conversion value to obtain the salt concentration of the facility soil.

[0039] The present invention also provides a facility soil salinity monitoring device, which is used to perform the above-described facility soil salinity monitoring method, comprising:

[0040] The function determination module collects the facility soil data sequence within the test area. The facility soil data consists of pore water conductivity data, soil temperature data, soil moisture content data, and soil clay mass percentage data. Based on the facility soil salinity monitoring mechanism, a salinity decoupling function is pre-determined to describe the relationship between pore water conductivity and soil temperature, soil moisture content, soil clay mass percentage, and standard pore water conductivity.

[0041] The state determination module divides the facility soil data sequence into several environmental windows of equal length, calculates the fluctuation values ​​of soil temperature and soil moisture content in each environmental window, and determines whether the facility soil environment in each environmental window is in a stable state based on the comparison results of the fluctuation values ​​with preset thresholds.

[0042] The function optimization module selects a stable environment window as the calibration window, stitches together the facility soil data within the calibration window to obtain a reference facility soil data sequence, and performs parameterization processing on the reference facility soil data sequence based on the salt decoupling function to obtain a standard pore water conductivity sequence. Taking the minimum fluctuation of the standard pore water conductivity sequence as the optimization objective, the parameters in the salt decoupling function are updated online using the least squares method to obtain the optimized salt decoupling function.

[0043] The salinity monitoring module, based on the optimized salinity decoupling function, performs salinity decoupling calculations on the subsequently collected facility soil monitoring data to obtain the pure pore water conductivity after removing the influence of temperature, water content, and clay content. Based on the pure pore water conductivity, according to the pre-established correspondence between conductivity and salinity concentration, the pure pore water conductivity is converted into the salinity concentration of the facility soil, thereby realizing the monitoring of facility soil salinity.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] This invention pre-defines a well-defined salt decoupling function to mechanistically separate the combined effects of temperature, soil moisture content, and clay content on pore water conductivity. The standardized pore water conductivity calculated by this function is directly related only to the salt ion concentration in the soil solution and is the essential physical quantity reflecting the salinity level. This solves the problems of ambiguous physical meaning of pore water conductivity values, environmental noise drowning out the true signal, and misjudgment of salinity. This invention divides the continuous monitoring data stream into environmental windows of equal length and automatically identifies relatively stable time periods based on preset temperature and moisture content fluctuation thresholds, ensuring the reliability of the optimized data foundation. This invention utilizes the selected stable window data, with the optimization objective of minimizing the variance of the calculated standard pore water conductivity sequence, and uses the nonlinear least squares method to iteratively update the parameters in the salt decoupling function online. This significantly improves the local applicability and accuracy of the model at that point and solves the problems of inaccuracy across points and long-term monitoring performance degradation caused by unchanging model parameters. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0047] Figure 2 The graph shows the fitted curves of pore water conductivity versus pore water conductivity under standard conditions.

[0048] Figure 3 The fitted curve is shown between the common index parameters and the variance.

[0049] Figure 4 This is a schematic diagram of the overall device of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0051] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0052] Example:

[0053] Please see Figures 1-3 The present invention provides a technical solution:

[0054] The specific steps for monitoring soil salinity in facilities include:

[0055] Step 1: Collect facility soil data sequences within the test area. Facility soil data consists of pore water conductivity data, soil temperature data, soil moisture content data, and soil clay mass percentage data. Based on the facility soil salinity monitoring mechanism, a salt decomposition coupling function is pre-determined to describe the relationship between pore water conductivity and soil temperature, soil moisture content, soil clay mass percentage, and standard pore water conductivity.

[0056] In this embodiment, the specific steps for obtaining the facility soil data sequence are as follows:

[0057] By setting a uniform sampling time interval and collecting facility soil data for each data point at each sampling time, then for the th The soil data for each sampling time point are represented as follows:

[0058]

[0059] In the formula, Indicates the first A vector of facility soil data at each sampling time; Indicates the first The electrical conductivity of pore water measured at the next sampling time; Indicates the first Soil temperature measured at the time of the next sampling; Indicates the first Soil moisture content measured at the time of the next sampling; Indicates the first Percentage of soil clay mass measured at the time of sampling;

[0060] Arranging the facility soil data collected in chronological order to form the original facility soil data sequence, the mathematical expression for the original facility soil data sequence is as follows:

[0061]

[0062] In the formula, This represents the original soil data sequence for the facility;

[0063] The original facility soil data sequence was preprocessed to obtain the facility soil data sequence.

[0064] In this embodiment, the specific steps for preprocessing the original facility soil data sequence are as follows:

[0065] Based on the physical reasonable range of soil parameters, the effective threshold range of each data component is set. Each data point in the original facility soil data sequence is traversed. If any data component exceeds its corresponding effective threshold range, the data point is marked as an outlier and all marked outliers are removed from the sequence.

[0066] For consecutive missing data segments caused by temporary sensor malfunctions or the removal of outliers, the following strategy is used for interpolation: For isolated or consecutive missing points with a number less than a preset threshold, linear interpolation is used for filling, i.e., for the first... For each missing point, interpolation is performed using the nearest valid data points before and after it. The mathematical expression for interpolation of a single point or short-term missing data is:

[0067]

[0068] In the formula, Indicates the number that needs to be estimated. The facility soil data vector at each sampling time, where Indicates the index number of the sampling moment where data is currently being processed but is missing; Indicates the first The most recent valid data point before the missing time, where This indicates the sampling time index number corresponding to the previous valid data point; Indicates the first The most recent valid data point after a missing time point, where This indicates the sampling time index number corresponding to the next valid data point;

[0069] For data segments with more than a preset threshold of consecutive missing points, they are considered invalid data segments and no imputation is performed. When dividing the environment window in the future, it is ensured that the window does not span multiple valid data segments.

[0070] To suppress the interference of high-frequency random noise on stability assessment and parameter optimization, smoothing filtering is applied to the time-series data. For any data point in the sequence, the smoothed value is... For its front and back Points (total) The arithmetic mean of the data points is given, and the mathematical expression for calculating the facility soil data vector after smoothing filtering is:

[0071]

[0072] In the formula, Indicates the first The facility soil data vector after smoothing and filtering at each sampling time, where boundary points are processed by mirroring or truncation; Indicates half the width of the window; Indicates the summation index variable;

[0073] The data processed through the above steps are rearranged in their original chronological order to form the final, quality-controlled sequence of facility soil data:

[0074]

[0075] In the formula, Indicates the first The preprocessed facility soil data vector at each valid sampling time. This represents the total number of valid data points after preprocessing.

[0076] In this embodiment, the specific steps for determining the salt decoupling function used to describe the relationship between pore water conductivity and soil temperature, soil moisture content, soil clay mass percentage, and standard pore water conductivity are as follows:

[0077] A temperature correction coefficient is set, and the soil temperature difference is determined by the difference between the soil temperature data and the standard temperature reference value. The soil temperature difference is multiplied by the temperature correction coefficient to obtain the temperature correction value. The temperature correction value is then added to 1 to obtain the temperature correction function value. The mathematical expression for calculating the temperature correction function value is as follows:

[0078]

[0079] In the formula, This represents the temperature correction function value, used to eliminate the effect of temperature on conductivity; Indicates the measured temperature; Indicates the temperature correction factor;

[0080] By setting the volumetric moisture content coefficient and common index parameter, the soil moisture content is obtained by raising the common index parameter to the power of the common index parameter. The soil moisture content value is then multiplied by the volumetric moisture content coefficient to obtain the soil moisture correction function value. The mathematical expression for calculating the soil moisture correction function value is as follows:

[0081]

[0082] In the formula, This represents the soil moisture correction function value; Indicates soil moisture content; Indicates the volumetric water content coefficient; Indicates the common index parameter;

[0083] By setting a clay content coefficient and a clay correction value, the clay content value is obtained by multiplying the clay content coefficient by the percentage of soil clay mass. The clay content value is then added to the clay correction value to obtain the clay correction function value. The mathematical expression for calculating the clay correction function value is as follows:

[0084]

[0085] In the formula, This represents the value of the clay particle correction function; Indicates the clay particle correction value; Indicates the clay content coefficient; This indicates the percentage of soil clay particles by mass.

[0086] Multiplying the temperature correction function value, soil moisture correction function value, and clay correction function value yields the water content temperature value. Comparing the pore water conductivity with the water content temperature value gives the pore water conductivity under standard conditions. The mathematical expression for calculating the pore water conductivity under standard conditions is:

[0087]

[0088] In the formula, This represents the electrical conductivity of pore water under standard conditions. This indicates the electrical conductivity of pore water.

[0089] In this embodiment, 50 sets of standard pore water conductivity data were obtained. The volumetric water content coefficient, clay content coefficient, clay correction value, common index parameter, and temperature correction coefficient were all fixed constants. The temperature correction coefficient was fixed at 0.018, the volumetric water content coefficient at 1.25, the clay content coefficient at 0.025, the clay correction value at 0.32, and the common index parameter at 0.65. Some of the data are shown in Table 1.

[0090] Table 1: Standard Pore Water Conductivity Data

[0091]

[0092] according to Figure 2 As shown in Table 1, both the conductivity of pore water and the conductivity of standard pore water increase strictly and monotonically with the sample number, without fluctuation or decline, and continuously cover a wide range from low to high. The conductivity of pore water is 0.326→7.114, and the conductivity of standard pore water is 0.185→2.649. The trends of the two are highly consistent and show an approximately linear growth relationship.

[0093] Step 2: Divide the facility soil data sequence into several environmental windows of equal length, calculate the fluctuation value of soil temperature and the fluctuation value of soil moisture in each environmental window, and determine whether the facility soil environment in each environmental window is in a stable state based on the comparison result of the fluctuation value and the preset threshold.

[0094] In this embodiment, the specific steps for dividing the environment window are as follows:

[0095] Set the window length and sliding step size, slide the window on the facility soil data sequence with the sliding step size, place the first window in the initial position, and move the window forward according to the preset sliding step size until the window covers the end of the time series, thereby generating a series of time-continuous windows.

[0096] And for each generated window, perform the following operations:

[0097] Calculate the start and end times for each window;

[0098] After extracting data from the current window, continue sliding the window until the new start time plus the window length exceeds the end of the available data sequence.

[0099] In this embodiment, the logic for determining whether the environment is stable is as follows:

[0100] Set temperature fluctuation thresholds and moisture content fluctuation thresholds;

[0101] Obtain the maximum and minimum temperature values ​​within each environmental window, and calculate the temperature difference value by measuring the difference between the maximum and minimum temperature values.

[0102] Obtain the maximum and minimum water content within each environmental window, calculate the difference between the maximum and minimum water content, and obtain the water content difference value.

[0103] When the temperature difference is less than the temperature fluctuation threshold and the moisture content difference is less than the moisture content fluctuation threshold, the environmental window is considered stable; otherwise, the environmental window is considered unstable.

[0104] Step 3: Select an environmental window in a stable state as the calibration window, stitch together the facility soil data within the calibration window to obtain a reference facility soil data sequence, and parameterize the reference facility soil data sequence based on the salt decomposition coupling function to obtain a standard pore water conductivity sequence. With the minimum fluctuation of the standard pore water conductivity sequence as the optimization objective, the parameters in the salt decomposition coupling function are updated online using the least squares method to obtain the optimized salt decomposition coupling function.

[0105] In this embodiment, the environmental window in a stable state is selected as the calibration window, and the facility soil data within the calibration window is stitched together to obtain the reference facility soil data sequence. The specific steps are as follows:

[0106] Iterate through all the partitioned environment windows and evaluate each window according to the stability judgment logic. Extract the indexes or data of all environment windows that are determined to be stable to form a set of stable windows;

[0107] Each stable environment window in the stable window set is directly defined as a calibration window. For each calibration window, the complete facility soil data subsequence contained therein is extracted.

[0108] The data contained in all calibration windows are sequentially pieced together according to the order of each window in the original time series to form a single, continuous reference facility soil data sequence.

[0109] In this embodiment, the actual soil salinity remains basically unchanged during a period of environmental stability. Therefore, the actual pore water conductivity should be approximately constant. Based on this understanding, by minimizing the sample variance of the standard pore water conductivity calculated by the model at n consecutive time points, the structural compensation factor parameters can be optimized in reverse to make the model output more stable and thus more accurately reflect the actual soil salinity state.

[0110] In this embodiment, the specific steps for constructing the loss function based on facility soil data are as follows:

[0111] Define the parameter vector to be optimized, where the optimized parameter vector consists of temperature correction coefficient, volumetric water content coefficient, clay content coefficient, clay correction value, and common index parameter, and set initial values ​​for them. Based on the initial values, calculate the model prediction output value for each group of samples.

[0112] The prediction residual is obtained by calculating the difference between the model's predicted output value and the mean predicted value for each sample group. The square of the prediction residual is then calculated to obtain the squared deviation from the mean. The sum of the squared deviations from the mean for all samples is then calculated to obtain the total deviation from the mean. The total deviation from the mean is then compared with the sum of the squared deviations from the mean for all samples to obtain the loss function. The mathematical expression for the loss function is as follows:

[0113]

[0114] That is, the objective function is:

[0115]

[0116] in,

[0117]

[0118] In the formula, The variance of the standard pore water conductivity sequence is used to measure... The degree of fluctuation in the value; This indicates the total number of soil data samples included in the calibration window; Indicates the sample index; Indicates all The arithmetic mean of the predicted values.

[0119] In this embodiment, 50 sets of common index parameters and standard pore water conductivity sequence variance data were obtained, and some of the data are shown in Table 2:

[0120] Table 2: Variance data of common index parameters and standard pore water conductivity series

[0121]

[0122] According to Table 2 and Figure 3It can be seen that the variance exhibits a strictly monotonically decreasing trend from index 1 to 50, without any rebound or fluctuation, continuously converging from the initial value of 15.732 to the final value of 0.412. This is the most significant and stable change characteristic in this parameter optimization process. The variance as a whole shows a non-linear decreasing convergence, with a faster rate of decrease in the early stage and a gradual slowdown in the later stage, exhibiting a typical iterative optimization convergence law: the total decrease in the first 0 iterations reached 9.175, with an average decrease of about 1.019 per step; the total decrease in the last 10 iterations was only 0.751, with an average decrease of about 0.083 per step, and the convergence rate decreased significantly. In contrast, the temperature correction coefficient, volumetric water content coefficient, clay content coefficient, clay correction value, and common index parameter all fluctuated slightly within a small range, without a significant monotonically increasing or decreasing trend. The magnitude of the numerical changes was much smaller than that of the variance, and the overall trend was towards stable oscillation.

[0123] In this embodiment, the specific steps for updating the parameters in the salt decoupling function online using the least squares method to obtain the optimized salt decoupling function are as follows:

[0124] Based on the prediction residuals of each set of samples, a residual vector is constructed. A Jacobian matrix is ​​then constructed using the residual vector as the independent variable. The product of the Jacobian matrix and its transpose is calculated to obtain the normal matrix. The product of the inverse of the normal matrix, the Jacobian matrix, and the residual vector is calculated to obtain the first product. The sign of the first product is removed to obtain the parameter update increment. The mathematical expression for calculating the parameter update increment is then:

[0125]

[0126] In the formula, This represents the increment of the parameter vector, and the parameter vector itself represents the increment of the parameter vector. The update step size, of which This indicates that the salt decomposition coupling function contains parameters to be determined, including the temperature correction coefficient. Volumetric water content coefficient Public index parameters Clay content coefficient Clay particle correction value The goal of optimization is to find a set of parameters. This allows for the calculation of the standard pore water conductivity sequence from the soil data sequence of the reference facility. The fluctuation is minimal; Represents the residual vector; Represents the residual vector Regarding parameters Jacobian matrix;

[0127] The mathematical expression for constructing the Jacobian matrix is:

[0128]

[0129] The mathematical expression for constructing the residual vector is:

[0130]

[0131] The parameter estimate for the next generation is determined by summing the parameter update increment with the parameter estimate of each generation. The mathematical expression for calculating the parameter estimate after the (k+1)th iteration is:

[0132]

[0133] In the formula, This represents the estimated value of the parameter in the k-th iteration; This represents the estimated value of the parameters after the (k+1)th iteration;

[0134] Set convergence conditions. When the conditions are met, the iteration stops and the optimized parameter estimates are obtained.

[0135] Step 4: Based on the optimized salt decoupling function, perform salt decoupling calculations on the subsequently collected facility soil monitoring data to obtain the pure pore water conductivity after removing the influence of temperature, water content, and clay content. Based on the pure pore water conductivity, according to the pre-established correspondence between conductivity and salt concentration, convert the pure pore water conductivity into the salt concentration of the facility soil to realize the monitoring of facility soil salinity.

[0136] In this embodiment, the specific steps for converting the electrical conductivity of pure pore water into the salt concentration of the facility soil are as follows:

[0137] By setting a proportionality coefficient and a conversion value, multiplying the proportionality coefficient by the conductivity of pure pore water to obtain a second product, and adding the second product to the conversion value, the salt concentration of the facility soil is obtained. Therefore, the mathematical expression for calculating the salt concentration of the facility soil is:

[0138]

[0139] In the formula, Indicates the salt concentration of the soil in the facility; Indicates the conversion of numerical values; Indicates the proportionality coefficient; This represents the electrical conductivity of pure pore water.

[0140] Please see Figure 4 The present invention also provides a facility soil salinity monitoring device, which is used to perform the above-described facility soil salinity monitoring method, including:

[0141] The function determination module collects the facility soil data sequence within the test area. The facility soil data consists of pore water conductivity data, soil temperature data, soil moisture content data, and soil clay mass percentage data. Based on the facility soil salinity monitoring mechanism, a salinity decoupling function is pre-determined to describe the relationship between pore water conductivity and soil temperature, soil moisture content, soil clay mass percentage, and standard pore water conductivity.

[0142] The state determination module divides the facility soil data sequence into several environmental windows of equal length, calculates the fluctuation values ​​of soil temperature and soil moisture content in each environmental window, and determines whether the facility soil environment in each environmental window is in a stable state based on the comparison results of the fluctuation values ​​with preset thresholds.

[0143] The function optimization module selects a stable environment window as the calibration window, stitches together the facility soil data within the calibration window to obtain a reference facility soil data sequence, and performs parameterization processing on the reference facility soil data sequence based on the salt decoupling function to obtain a standard pore water conductivity sequence. Taking the minimum fluctuation of the standard pore water conductivity sequence as the optimization objective, the parameters in the salt decoupling function are updated online using the least squares method to obtain the optimized salt decoupling function.

[0144] The salinity monitoring module, based on the optimized salinity decoupling function, performs salinity decoupling calculations on the subsequently collected facility soil monitoring data to obtain the pure pore water conductivity after removing the influence of temperature, water content, and clay content. Based on the pure pore water conductivity, according to the pre-established correspondence between conductivity and salinity concentration, the pure pore water conductivity is converted into the salinity concentration of the facility soil, thereby realizing the monitoring of facility soil salinity.

[0145] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0146] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0147] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0148] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for monitoring soil salinity in facilities, characterized in that, The specific steps include: Step 1: Collect facility soil data sequence within the test area. Facility soil data consists of pore water conductivity data, soil temperature data, soil moisture content data, and soil clay mass percentage data. Based on the facility soil salinity monitoring mechanism, a salinity decoupling function is pre-determined to describe the relationship between pore water conductivity and soil temperature, soil moisture content, soil clay mass percentage, and standard pore water conductivity. Step 2: Divide the facility soil data sequence into several environmental windows of equal length, calculate the fluctuation value of soil temperature and the fluctuation value of soil moisture in each environmental window, and determine whether the facility soil environment in each environmental window is in a stable state based on the comparison result of the fluctuation value and the preset threshold. Step 3: Select an environmental window in a stable state as the calibration window, stitch together the facility soil data within the calibration window to obtain a reference facility soil data sequence, and parameterize the reference facility soil data sequence based on the salt decomposition coupling function to obtain a standard pore water conductivity sequence. With the minimum fluctuation of the standard pore water conductivity sequence as the optimization objective, the parameters in the salt decomposition coupling function are updated online using the least squares method to obtain the optimized salt decomposition coupling function. Step 4: Based on the optimized salt decoupling function, perform salt decoupling calculations on the subsequently collected facility soil monitoring data to obtain the pure pore water conductivity after removing the influence of temperature, water content, and clay content. Based on the pure pore water conductivity, according to the pre-established correspondence between conductivity and salt concentration, convert the pure pore water conductivity into the salt concentration of the facility soil to realize the monitoring of facility soil salinity.

2. The method for monitoring soil salinity in facilities according to claim 1, characterized in that: The specific steps for obtaining facility soil data sequences are as follows: Set a uniform sampling time interval, and collect facility soil data for each data point at each sampling time; The facility soil data collected in chronological order are arranged sequentially to form the original facility soil data sequence; The original facility soil data sequence was preprocessed to obtain the facility soil data sequence.

3. The method for monitoring soil salinity in facilities according to claim 1, characterized in that: The specific steps for determining the salt decomposition coupling function used to describe the relationship between pore water conductivity and soil temperature, soil moisture content, soil clay mass percentage, and standard pore water conductivity are as follows: Set a temperature correction coefficient, determine the soil temperature difference by the difference between the soil temperature data and the standard temperature reference value, multiply the soil temperature difference by the temperature correction coefficient to obtain the temperature correction value, and add the temperature correction value to 1 to obtain the temperature correction function value. Set the volumetric water content coefficient and common index parameter, calculate the soil water content value by raising the common index parameter to a power, and multiply the soil water content value by the volumetric water content coefficient to obtain the soil water content correction function value. Set the clay content coefficient and the clay correction value, calculate the product of the clay content coefficient and the percentage of soil clay mass to obtain the clay content value, and add the clay content value and the clay correction value to obtain the clay correction function value; The water content temperature value is obtained by multiplying the temperature correction function value, the soil moisture correction function value, and the clay correction function value. The pore water conductivity under standard conditions is obtained by comparing the pore water conductivity with the water content temperature value.

4. The method for monitoring soil salinity in facilities according to claim 1, characterized in that: The specific steps for dividing the environment window are as follows: Set the window length and sliding step size, slide the window on the facility soil data sequence with the sliding step size, place the first window in the initial position, and move the window forward according to the preset sliding step size until the window covers the end of the time series, thereby generating a series of time-continuous windows. And for each generated window, perform the following operations: Calculate the start and end times for each window; After extracting data from the current window, continue sliding the window until the new start time plus the window length exceeds the end of the available data sequence.

5. The method for monitoring soil salinity in facilities according to claim 1, characterized in that: The logic for determining whether an environment is stable is as follows: Set temperature fluctuation thresholds and moisture content fluctuation thresholds; Obtain the maximum and minimum temperature values ​​within each environmental window, and calculate the temperature difference value by measuring the difference between the maximum and minimum temperature values. Obtain the maximum and minimum water content within each environmental window, calculate the difference between the maximum and minimum water content, and obtain the water content difference value. When the temperature difference is less than the temperature fluctuation threshold and the moisture content difference is less than the moisture content fluctuation threshold, the environmental window is considered stable; otherwise, the environmental window is considered unstable.

6. The method for monitoring soil salinity in facilities according to claim 1, characterized in that: The specific steps for constructing a loss function based on facility soil data are as follows: Define the parameter vector to be optimized, where the optimized parameter vector consists of temperature correction coefficient, volumetric water content coefficient, clay content coefficient, clay correction value, and common index parameter, and set initial values ​​for them. Based on the initial values, calculate the model prediction output value for each group of samples. The difference between the model's predicted output value and the mean predicted value for each group of samples is calculated to obtain the prediction residual. The square of the prediction residual is calculated to obtain the squared deviation from the mean. The sum of the squared deviations from the mean for all samples is calculated to obtain the total deviation from the mean. The total deviation from the mean is calculated and compared with the total deviation from the mean for all samples to obtain the loss function.

7. The method for monitoring soil salinity in facilities according to claim 1, characterized in that: The specific steps for updating the parameters of the salt decoupling function online using the least squares method to obtain the optimized salt decoupling function are as follows: Based on the prediction residuals of each set of samples, a residual vector is constructed. A Jacobi matrix is ​​constructed with the residual vector as the independent variable. The product of the Jacobi matrix and the transpose of the Jacobi matrix is ​​calculated to obtain the normal matrix. The product of the inverse of the normal matrix, the Jacobi matrix, and the residual vector is calculated to obtain the first product. The sign of the first product is removed to obtain the parameter update increment. The next generation of parameters is estimated based on the sum of the parameter update increment and the parameter estimates of each generation. Set convergence conditions. When the conditions are met, the iteration stops and the optimized parameter estimates are obtained.

8. The method for monitoring soil salinity in facilities according to claim 1, characterized in that: The specific steps for converting the electrical conductivity of pure pore water into the salt concentration of the facility soil are as follows: Set a proportionality coefficient and a conversion value, multiply the proportionality coefficient by the conductivity of pure pore water to obtain a second product, and add the second product to the conversion value to obtain the salt concentration of the facility soil.

9. A soil salinity monitoring device for facilities, characterized in that: The facility soil salinity monitoring device is used to implement the facility soil salinity monitoring method according to any one of claims 1-8, including: The function determination module collects the facility soil data sequence within the test area. The facility soil data consists of pore water conductivity data, soil temperature data, soil moisture content data, and soil clay mass percentage data. Based on the facility soil salinity monitoring mechanism, a salinity decoupling function is pre-determined to describe the relationship between pore water conductivity and soil temperature, soil moisture content, soil clay mass percentage, and standard pore water conductivity. The state determination module divides the facility soil data sequence into several environmental windows of equal length, calculates the fluctuation values ​​of soil temperature and soil moisture content in each environmental window, and determines whether the facility soil environment in each environmental window is in a stable state based on the comparison results of the fluctuation values ​​with preset thresholds. The function optimization module selects a stable environment window as the calibration window, stitches together the facility soil data within the calibration window to obtain a reference facility soil data sequence, and performs parameterization processing on the reference facility soil data sequence based on the salt decoupling function to obtain a standard pore water conductivity sequence. Taking the minimum fluctuation of the standard pore water conductivity sequence as the optimization objective, the parameters in the salt decoupling function are updated online using the least squares method to obtain the optimized salt decoupling function. The salinity monitoring module, based on the optimized salinity decoupling function, performs salinity decoupling calculations on the subsequently collected facility soil monitoring data to obtain the pure pore water conductivity after removing the influence of temperature, water content, and clay content. Based on the pure pore water conductivity, according to the pre-established correspondence between conductivity and salinity concentration, the pure pore water conductivity is converted into the salinity concentration of the facility soil, thereby realizing the monitoring of facility soil salinity.

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