Facility soil salinity monitoring method and device

Abstract

The invention provides a facility soil salinity monitoring method and device, and relates to the technical field of soil salinity monitoring, a salt decomposition coupling function with a clear structure is preset, the composite influence of temperature, soil water content and clay content on the pore water conductivity is separated from the mechanism, and the standardized pore water conductivity is obtained; the method is an essential physical quantity reflecting the salinity level, and solves the problem that the physical significance of a pore water conductivity value is fuzzy, and environmental noise submerges a real signal, resulting in salt misjudgment. According to the method, the screened stable window data is utilized, the variance minimization of the calculated pore water conductivity sequence is taken as an optimization target, and a nonlinear least square method is adopted to iteratively update parameters in a salt decomposition decoupling function online, so that the local applicability and precision of the model at the point are remarkably improved; and the problems of inaccurate cross-point location and reduced long-term monitoring performance caused by unchanged model parameters are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of soil salinity monitoring, in particular to a facility soil salinity monitoring method and device. BACKGROUND

[0002] With the global population growth, farmland resource shortage and climate change challenges intensifying, facility agriculture represented by greenhouses, plant factories has become a key path to ensure food security and improve agricultural production efficiency and quality. Unlike traditional open-field agriculture, facility agriculture realizes precise regulation of light, temperature, water, fertilizer, air and other growth factors through artificial environmental control, thereby achieving off-season, high-yield and high-quality production of crops; in this highly controllable system, the stability of the physical and chemical properties of soil (or soilless culture medium) as the medium directly contacted by crop roots is crucial.

[0003] In the prior art, the soil salinity content change is usually monitored by direct measurement method of in-situ soil conductivity sensor or decoupling method based on fixed model, however, most of the existing commercial sensors only provide pore water conductivity raw readings, or use fixed and universal correction coefficients, which cannot dynamically and accurately decouple the soil properties (texture) of specific monitoring points and real-time changing environmental conditions (temperature, water content), resulting in large salinity estimation error and low reliability.

[0004] The above information disclosed in the background section is only used to enhance the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0005] The purpose of the present application is to provide a facility soil salinity monitoring method and device to solve the problems raised in the background.

[0006] To achieve the above purpose, the present application provides the following technical solutions: A facility soil salinity monitoring method, the specific steps comprising: Step 1: collecting facility soil data sequence in the area to be measured, the facility soil data consisting of pore water conductivity data, soil temperature data, soil water content data and soil clay mass percentage data, based on the facility soil salinity monitoring mechanism, a salinity decoupling function for describing the relationship between pore water conductivity and soil temperature, soil water content, soil clay mass percentage and standard pore water conductivity is determined in advance; Step 2: dividing the facility soil data sequence into several environmental windows of equal length, calculating the fluctuation value of soil temperature and the fluctuation value of soil water content in each environmental window, and determining whether the facility soil environment in each environmental window is in a stable state according to the comparison result of the fluctuation value and the preset threshold value; Step 3: screening out the stable state environment window as a calibration window, splicing the facility soil data in the calibration window to obtain a reference facility soil data sequence, parameterizing the reference facility soil data sequence based on the salt dissociation decoupling function, obtaining a standard pore water conductivity sequence, taking the minimum fluctuation of the standard pore water conductivity sequence as an optimization objective, and updating the parameters in the salt dissociation decoupling function online by using the least square method to obtain an optimized salt dissociation decoupling function; Step 4: based on the optimized salt dissociation decoupling function, the subsequent collected facility soil monitoring data is calculated to obtain the pure pore water conductivity after excluding the influence of temperature, water content and clay content, and based on the pure pore water conductivity, the pure pore water conductivity is converted into the salt concentration of the facility soil according to the pre-established corresponding relationship between the conductivity and the salt concentration, so as to realize the monitoring of the salt of the facility soil.

[0007] Further, the specific steps of obtaining the facility soil data sequence are: Setting a uniform sampling time interval, collecting facility soil data of each data point of the facility soil at each sampling time; Arranging the facility soil data collected in time sequence in turn to form an original facility soil data sequence; Pretreating the original facility soil data sequence to obtain a facility soil data sequence.

[0008] Further, the specific steps of determining the salt dissociation decoupling function for describing the relationship between the pore water conductivity and the soil temperature, the soil water content, the soil clay mass percentage and the standard pore water conductivity are: Setting a temperature correction coefficient, determining a soil temperature difference value through the difference between the soil temperature data and a standard temperature reference value, multiplying the soil temperature difference value and the temperature correction coefficient to obtain a temperature correction value, and adding the temperature correction value and 1 to obtain a temperature correction function value; Setting a volume water content coefficient and a common index parameter, calculating the common index parameter power of the soil water content to obtain a soil water value, multiplying the soil water value and the volume water content coefficient to obtain a soil water correction function value; Setting a clay content coefficient and a clay correction value, calculating the product of the clay content coefficient and the soil clay mass percentage to obtain a clay content value, and adding the clay content value and the clay correction value to obtain a clay correction function value; Multiplying the temperature correction function value, the soil water correction function value and the clay correction function value to obtain a water temperature value, and comparing the pore water conductivity with the water temperature value to obtain a standard pore water conductivity.

[0009] Further, the specific steps of dividing the environment window are: Set the window length and sliding step, slide the sliding step on the facility soil data sequence, place the first window at the initial position, move the window forward according to the preset sliding step, until the window covers the end of the time sequence, thereby generating a series of windows that are continuous in time; And for each generated window, the following operations are performed: Calculate the start time and end time of each window; After completing the data extraction of the current window, continue to slide the window until the new start time plus the window length exceeds the end of the available data sequence.

[0010] Further, the logic for determining whether the environment is stable is: Set the temperature fluctuation threshold and the water content fluctuation threshold; Obtain the maximum temperature value and the minimum temperature value in each environment window, and calculate the difference between the maximum temperature value and the minimum temperature value to obtain the temperature difference; Obtain the maximum water content and the minimum water content in each environment window, and calculate the difference between the maximum water content and the minimum water content to obtain the water content difference; When the temperature difference is less than the temperature fluctuation threshold and the water content difference is less than the water content fluctuation threshold, the environment window is determined to be stable, otherwise it is determined to be unstable.

[0011] Further, the specific steps for constructing the loss function based on the facility soil data are: Define the parameter vector to be optimized, wherein the optimized parameter vector is the temperature correction coefficient, the volume water content coefficient, the clay content coefficient, the clay correction value and the common exponential parameter, and set initial values for them, and calculate the model prediction output value of each sample based on the initial values; Calculate the difference between the model prediction output value of each sample and the mean of the prediction values to obtain the prediction residual, calculate the square of the prediction residual to obtain the deviation from the mean square, calculate the sum of all sample deviation from the mean square to obtain the total deviation from the mean, and compare the total deviation from the mean with all samples -1 to obtain the loss function.

[0012] Further, the specific steps for updating the parameters in the salt decomposition decoupling function using the least squares method are: Construct a residual vector based on the prediction residual of each sample, construct a Jacobian matrix with the residual vector as the independent variable, calculate the product of the Jacobian matrix and the transpose of the Jacobian matrix to obtain a normal matrix, calculate the inverse matrix of the normal matrix, the product of the Jacobian matrix and the residual vector to obtain the first product, and remove the symbol from the first product to obtain the parameter update increment; Set the convergence condition, when the condition is met, the iteration stops, and the optimized parameter estimate value is obtained.

[0013] Further, the specific steps of converting the pure pore water conductivity into the salt concentration of the facility soil are as follows: Setting a proportionality coefficient and a conversion value, multiplying the proportionality coefficient by the pure pore water conductivity to obtain a second product, and adding the second product to the conversion value to obtain the salt concentration of the facility soil.

[0014] The application further provides a facility soil salt concentration monitoring device for performing the facility soil salt concentration monitoring method described above, comprising: A function determination module acquires a facility soil data sequence in a to-be-measured region, the facility soil data being composed of pore water conductivity data, soil temperature data, soil water content data, and soil clay mass percentage data, and a salt decoupling function for describing the relationship between the pore water conductivity and the soil temperature, the soil water content, the soil clay mass percentage, and the standard pore water conductivity is determined in advance based on a facility soil salt concentration monitoring mechanism; A state determination module divides the facility soil data sequence into a plurality of environmental windows in equal length, calculates the fluctuation value of the soil temperature and the fluctuation value of the soil water content in each environmental window, and judges whether the facility soil environment in each environmental window is in a stable state according to the comparison result of the fluctuation values and a preset threshold value; A function optimization module selects the environmental windows in the stable state as calibration windows, splices the facility soil data in the calibration windows to obtain a reference facility soil data sequence, performs parameterization processing on the reference facility soil data sequence based on the salt decoupling function, obtains a standard pore water conductivity sequence, takes the minimum fluctuation of the standard pore water conductivity sequence as an optimization target, and updates the parameters in the salt decoupling function online by using the least square method to obtain an optimized salt decoupling function; A salt concentration monitoring module performs salt decoupling calculation on the subsequently acquired facility soil monitoring data based on the optimized salt decoupling function, obtains the pure pore water conductivity after eliminating the influence of the temperature, the water content, and the clay content, converts the pure pore water conductivity into the salt concentration of the facility soil according to the corresponding relationship between the conductivity and the salt concentration established in advance based on the pure pore water conductivity, and realizes the monitoring of the salt concentration of the facility soil.

[0015] Compared with the prior art, the application has the following beneficial effects: The application pre-sets a salt decomposition decoupling function with a clear structure, separates the combined effects of temperature, soil moisture content and clay content on the pore water conductivity from a mechanism, and the normalized pore water conductivity calculated through the function is directly related to the salt ion concentration in the soil solution and is an essential physical quantity reflecting the salt level, solving the problems of ambiguous physical meaning of the pore water conductivity value, environmental noise drowning the real signal and leading to salt misjudgment; the application divides the continuous monitoring data stream into equal length environmental windows, automatically identifies the time period with relatively stable environmental parameters based on the preset temperature and moisture content fluctuation threshold, and ensures the reliability of the optimized data basis; the application uses the screened stable window data, takes the minimization of the variance of the calculated standard pore water conductivity sequence as the optimization target, and uses the nonlinear least squares method to iteratively update the parameters in the salt decomposition decoupling function, significantly improves the local applicability and accuracy of the model at the point, and solves the problems of cross-point inaccuracy and long-term monitoring performance decline caused by constant model parameters. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 It is a whole method flowchart of the application; Figure 2 It is a fitting curve graph of the pore water conductivity and the pore water conductivity under the standard state; Figure 3 It is a fitting curve graph between the common exponential parameter and the variance; Figure 4 It is a whole device schematic diagram of the application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical scheme and advantages of the application clearer and more apparent, the application will be further described in detail below in combination with specific embodiments.

[0018] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the application should be understood as the usual meanings understood by those skilled in the art to which the application belongs. The "first", "second" and similar words used in the application do not represent any order, quantity or importance, but are only used to distinguish different components. "Include" or "contain" and similar words mean that the elements or objects before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connected" or "connected" and similar words are not limited to physical or mechanical connection, but can include electrical connection, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to represent the relative positional relationship, when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] EMBODIMENT: Please refer toFigures 1-3 The application provides a technical solution: The facility soil salt content monitoring method comprises the following specific steps: Step 1: Collecting facility soil data sequences in a to-be-measured area, the facility soil data comprising pore water conductivity data, soil temperature data, soil water content data and soil clay mass percentage data, and based on a facility soil salt content monitoring mechanism, a salt content decoupling function for describing the relationship between pore water conductivity and soil temperature, soil water content, soil clay mass percentage and standard pore water conductivity is determined in advance.

[0020] In this embodiment, the specific steps of obtaining the facility soil data sequence are as follows: A uniform sampling time interval is set, and facility soil data of each data point at each sampling time is collected, so that, for the i th sampling time, the facility soil data is represented as: In the formula, represents a facility soil data vector at the i th sampling time; represents the pore water conductivity measured at the i th sampling time; represents the soil temperature measured at the i th sampling time; represents the soil water content measured at the i th sampling time; represents the soil clay mass percentage measured at the i th sampling time; The facility soil data collected in time sequence is arranged in sequence to form an original facility soil data sequence, and the mathematical expression of the original facility soil data sequence is: In the formula, represents the original facility soil data sequence; The original facility soil data sequence is preprocessed to obtain a facility soil data sequence. In this embodiment, the specific steps of preprocessing the original facility soil data sequence are as follows: Based on the physical reasonable range of the 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 the corresponding effective threshold range, the data point is marked as an abnormal point, and all marked abnormal points are removed from the sequence.

[0021] ​​​​​​For the continuous missing data segment caused by temporary sensor failure or abnormal point rejection, the following strategy is adopted for interpolation: for isolated or continuous missing points less than the preset threshold, linear interpolation is used to fill in, that is, for the first missing point, the nearest valid data points before and after it are used for interpolation, and the mathematical expression for interpolating single point or short time missing is: For the data segment with more than the preset threshold of continuous missing points, it is considered as invalid data segment and no interpolation is performed, and when the environment window is divided subsequently, it is ensured that the window does not cross multiple valid data segments. ​​​​​​​​​​​​​​​​​​​​​​​​​

[0022] In the embodiment, the specific steps for determining the salt uncoupling function for describing the relationship between the pore water conductivity and the soil temperature, the soil moisture content, the soil clay mass percentage, and the standard pore water conductivity are as follows: A temperature correction coefficient is set, a soil temperature difference is determined by a difference between the soil temperature data and a standard temperature reference value, a temperature correction value is obtained by multiplying the soil temperature difference by the temperature correction coefficient, and a temperature correction function value is obtained by adding 1 to the temperature correction value, and a mathematical expression for calculating the temperature correction function value is: In the formula, The temperature correction function value is used to eliminate the influence of temperature on conductivity. The measured temperature is represented. The temperature correction coefficient is represented. A volume moisture content coefficient and a common index parameter are set, a soil moisture value is calculated by raising the common index parameter to the power of the soil moisture content, and a soil moisture correction function value is obtained by multiplying the soil moisture value by the volume moisture content coefficient, and a mathematical expression for calculating the soil moisture correction function value is: In the formula, The soil moisture correction function value is represented. The soil moisture content is represented. The volume moisture content coefficient is represented. The common index parameter is represented. A clay content coefficient and a clay correction value are set, a clay content value is calculated by multiplying the clay content coefficient by the soil clay mass percentage, and a clay correction function value is obtained by adding the clay correction value to the clay content value, and a mathematical expression for calculating the clay correction function value is: In the formula, The clay correction function value is represented. The clay correction value is represented. The clay content coefficient is represented. The soil clay mass percentage is represented. The temperature correction function value, the soil moisture correction function value, and the clay correction function value are multiplied to obtain a moisture temperature value, and the pore water conductivity is compared with the moisture temperature value to obtain the pore water conductivity under the standard state, and a mathematical expression for calculating the pore water conductivity under the standard state is: In the formula, The pore water conductivity under the standard state is represented. The pore water conductivity is represented.

[0023] In this embodiment, 50 sets of standard pore water conductivity data are obtained, wherein the volume water content coefficient, the clay content coefficient, the clay correction value, the common index parameter and the temperature correction coefficient are fixed constants, the temperature correction coefficient is fixed as 0.018, the volume water content coefficient is fixed as 1.25, the clay content coefficient is fixed as 0.025, the clay correction value is 0.32, and the common index parameter is fixed as 0.65, and part of the data is shown in Table 1: Table 1: Standard pore water conductivity data According to Figure 2 and Table 1, the pore water conductivity and the standard pore water conductivity both strictly and monotonically increase with the sample serial number, without fluctuation or falling back, and continuously cover a wide range from low to high, the pore water conductivity is 0.326→7.114, the standard pore water conductivity is 0.185→2.649, the change trends of the two are highly consistent, and the relationship is approximately linear.

[0024] Step 2: divide the facility soil data sequence into a plurality of environmental windows, calculate the fluctuation value of the soil temperature and the fluctuation value of the soil water content in each environmental window, and determine whether the facility soil environment in each environmental window is in a stable state according to the comparison result of the fluctuation value and the preset threshold.

[0025] In this embodiment, the specific steps of dividing the environmental window are as follows: Set the window length and the sliding step, slide the sliding step on the facility soil data sequence, place the first window at the initial position, move the window forward according to the preset sliding step, until the window covers the end of the time sequence, thereby generating a series of windows that are continuous in time; And for each generated window, the following operations are performed: Calculate the start time and the end time of each window; After completing the data extraction of the current window, continuously slide the window until the new start time plus the window length exceeds the end of the available data sequence.

[0026] In this embodiment, the logic for determining whether the environment is stable is as follows: Set the temperature fluctuation threshold and the water content fluctuation threshold; Obtain the maximum temperature value and the minimum temperature value in each environmental window, and calculate the difference between the maximum temperature value and the minimum temperature value to obtain the temperature difference; Obtain the maximum water content and the minimum water content in each environmental window, and calculate the difference between the maximum water content and the minimum water content to obtain the water content difference; When the temperature difference is less than the temperature fluctuation threshold and the water content difference is less than the water content fluctuation threshold, it is determined that the environmental window is stable, otherwise it is determined that the environmental window is unstable.

[0027] 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.

[0028] 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: 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; 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. 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.

[0029] 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.

[0030] In this embodiment, the specific steps for constructing the 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 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: That is, the objective function is: in, 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.

[0031] 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: Table 2: Variance data of common index parameters and standard pore water conductivity series According to Table 2 and Figure 3 It 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.

[0032] 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: 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: 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 the smallest; Represents the residual vector; Represents the residual vector Regarding parameters Jacobian matrix; The mathematical expression for constructing the Jacobian matrix is: The mathematical expression for constructing the residual vector is: 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: 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; Set convergence conditions. When the conditions are met, the iteration stops and the optimized parameter estimates are obtained.

[0033] 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.

[0034] 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: 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: 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.

[0035] 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: 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.

[0036] 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.

[0037] 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.

[0038] 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, depending on actual needs.

[0039] 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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