A method for evaluating the effect of a soybean field plough layer structure conditioning technology in saline-alkali soil

Abstract

This invention relates to the field of agricultural data analysis, and more particularly to a method for evaluating the effect of topsoil structure conditioning technology in saline-alkali soybean fields. The method includes: collecting and preprocessing stratified soil physicochemical data within the conditioning assessment period to obtain basic stratified soil physicochemical data; obtaining a spatial obstruction penalty factor by coupling analysis of the pore height difference characteristics and soil electrical conductivity spatial distribution characteristics of the shallow and middle-lower layers; obtaining a habitat loss factor by jointly analyzing the aeration pore deviation characteristics and soil electrical conductivity deviation characteristics of the middle-lower layers; and obtaining a comprehensive evaluation index of the conditioning effect in saline-alkali land by jointly correcting the basic conditioning evaluation score using the spatial obstruction penalty factor and habitat loss factor. Based on this, the evaluation result of the topsoil structure conditioning technology in saline-alkali soybean fields is obtained, thereby reducing the masking effect of the overall topsoil mean evaluation on salt accumulation in the middle-lower layers and root zone habitat deterioration, and improving the accuracy of the conditioning effect evaluation.

Description

Technical Field

[0001] This invention relates to the field of agricultural data analysis technology, and in particular to a method for evaluating the effect of soil layer structure conditioning technology in soybean fields in saline-alkali land. Background Technology

[0002] In soybean cultivation on saline-alkali land, topsoil structure conditioning typically involves deep loosening, application of soil conditioners, and irrigation / drainage leaching to improve soil pore structure and salinity distribution. To evaluate the effectiveness of these conditioning measures, existing technologies usually involve field sampling to obtain physicochemical indicators such as topsoil bulk density, total porosity, aeration porosity, and soil electrical conductivity. A comprehensive evaluation model is then constructed using principal component analysis, indicator normalization, and linear weighting. The evaluation results, characterizing the effectiveness of topsoil structure conditioning, are obtained through comprehensive calculation of these soil physicochemical indicators.

[0003] However, in the actual conditioning process of saline-alkali land, the improvement of soil pore structure and salt migration with water may be asynchronous in the vertical space of the topsoil. When the pore structure of the shallow topsoil is improved while the middle and lower topsoil still maintain a high degree of compaction, the shallow salt carried downward by irrigation or precipitation is easily hindered by the structure of the middle and lower layers, causing salt to accumulate in the interface area between the shallow and middle / lower layers or in the middle / lower topsoil. Existing evaluation methods usually average the soil physicochemical data at different depths, so that the positive data generated by the increase in shallow porosity and decrease in electrical conductivity compensate for the abnormal state in the middle and lower layers, making it difficult to accurately reflect the vertical pore height difference and abnormal spatial distribution of salt.

[0004] Meanwhile, the main root and root nodule communities of soybean are primarily distributed in the middle and lower soil layers, and their growth and nitrogen fixation processes are highly sensitive to soil aeration and salinity. Existing linear weighted evaluation methods typically treat aeration porosity and soil electrical conductivity as independent evaluation indicators, lacking a mechanism for jointly characterizing the combined habitat deviation caused by insufficient aeration and excessive salinity in the middle and lower root zones. This easily leads to inconsistencies between the evaluation results of conditioning effects and the actual habitat conditions in the soybean root zone. Therefore, accurately characterizing the spatial misalignment of structure and salinity between different soil layers during the evaluation of the conditioning effects of soybean cultivation in saline-alkali land, and correcting the basic evaluation results by combining them with the actual habitat conditions in the middle and lower root zones, has become an urgent problem to be solved in the field of conditioning effect evaluation for soybean fields in saline-alkali land. Summary of the Invention

[0005] In view of this, the present invention aims to propose an evaluation method for the effect of soil layer structure conditioning technology in soybean fields in saline-alkali land, in order to solve the problem that existing evaluation methods are unable to identify vertical salinity misalignment and deterioration of the middle and lower root zone habitat, resulting in an overestimation of the conditioning effect.

[0006] To achieve the above objectives, the technical solution of the present invention is implemented as follows:

[0007] A method for evaluating the effectiveness of topsoil structure conditioning technology in soybean fields in saline-alkali land, the method comprising:

[0008] Step S1: Collect and preprocess the stratified soil physicochemical data during the conditioning assessment period to obtain the basic stratified soil physicochemical data;

[0009] Step S2: By coupling analysis of the pore height difference characteristics and soil electrical conductivity spatial distribution characteristics of the shallow and middle-lower tillage layers, the spatial hindrance penalty factor is obtained.

[0010] Step S3: Obtain habitat loss factors by jointly analyzing the deviation characteristics of aeration pores and soil electrical conductivity in the middle and lower tillage layers;

[0011] Step S4: The basic conditioning evaluation score is jointly corrected by the spatial obstruction penalty factor and the habitat loss factor to obtain the comprehensive evaluation index of the conditioning effect of saline-alkali land;

[0012] Step S5: By conducting time-series analysis on the comprehensive evaluation index of the conditioning effect of saline-alkali land, the evaluation results of the conditioning effect of the topsoil structure in soybean fields in saline-alkali land are obtained.

[0013] Furthermore, the process of collecting and preprocessing stratified soil physicochemical data during the conditioning and assessment period to obtain stratified soil physicochemical basic data includes:

[0014] Soil data collection points were set up in the target saline-alkali soybean field after the implementation of the topsoil structure conditioning technology, and the topsoil of the target saline-alkali soybean field was divided into shallow topsoil and middle and lower topsoil along the soil depth direction.

[0015] Set the data collection cycle within the conditioning and evaluation period. According to the preset data collection cycle, use in-situ field sensors or manual ring samplers to collect data synchronously and layer by layer from the shallow tillage layer and the middle and lower tillage layer. Obtain the original data of total porosity of shallow tillage layer, soil electrical conductivity of shallow tillage layer, total porosity of middle and lower tillage layer, aeration porosity of middle and lower tillage layer, and soil electrical conductivity of middle and lower tillage layer.

[0016] Outlier removal and time-series smoothing were performed on the raw data of total porosity, soil electrical conductivity, total porosity, aeration porosity, and soil electrical conductivity of the shallow, middle, and lower-level cultivated layers, respectively, to obtain the data of total porosity, soil electrical conductivity, aeration porosity, and soil electrical conductivity of the shallow, middle, and lower-level cultivated layers. These data were then used as the basic physicochemical data of the stratified soils.

[0017] Furthermore, the spatial hindrance penalty factor is obtained by coupling analysis of the pore height difference characteristics and soil electrical conductivity spatial distribution characteristics between the shallow and middle-lower tillage layers, including:

[0018] By performing longitudinal difference normalization on the total porosity data of the shallow and middle-lower layers of the topsoil, and extracting the spatial distribution characteristics of the soil electrical conductivity data of the shallow and middle-lower layers of the topsoil, longitudinal porosity drop characteristic data and salinity spatial distribution characteristic data were obtained.

[0019] By performing spatial hindrance coupling mapping on longitudinal pore drop characteristic data and salinity spatial distribution characteristic data, the spatial hindrance penalty factor is obtained.

[0020] Furthermore, the process involves performing vertical difference normalization on the total porosity data of the shallow and middle-lower layers of the cultivated layer, and extracting spatial distribution features from the soil electrical conductivity data of the shallow and middle-lower layers of the cultivated layer to obtain vertical porosity drop feature data and salinity spatial distribution feature data, including:

[0021] For any target data collection period within the conditioning assessment cycle, extract the total porosity data of the shallow topsoil, the total porosity data of the middle and lower topsoil, the soil electrical conductivity data of the shallow topsoil, and the soil electrical conductivity data of the middle and lower topsoil corresponding to the target data collection period from the stratified soil physicochemical basic data.

[0022] When the total porosity data of the shallow cultivated layer is greater than that of the middle and lower cultivated layers, the difference between the two is used as the forward porosity drop assessment for the target data acquisition period. When the total porosity data of the shallow cultivated layer is less than or equal to that of the middle and lower cultivated layers, the forward porosity drop assessment for the target data acquisition period is set to a constant of 0. The total porosity data of the shallow and middle and lower cultivated layers are squared respectively, and the two squared results are added together. The square root of the sum is used as the porosity joint modulus assessment for the target data acquisition period. The forward porosity drop assessment is used as the numerator, the porosity joint modulus assessment is used as the denominator, and the resulting fraction is used as the longitudinal porosity drop characteristic data for the target data acquisition period.

[0023] The total soil conductivity data for the target data collection period is calculated by adding the electrical conductivity data of the shallow topsoil layer and the middle and lower topsoil layer. The result is used as the total soil conductivity assessment for the target data collection period. The shallow topsoil conductivity data is used as the numerator, the total soil conductivity assessment is used as the denominator, and the resulting fraction is used as the distribution ratio of shallow topsoil conductivity for the target data collection period. Similarly, the middle and lower topsoil conductivity data is used as the numerator, the total soil conductivity assessment is used as the denominator, and the resulting fraction is used as the distribution ratio of middle and lower topsoil conductivity for the target data collection period.

[0024] Natural logarithmic mappings were performed on the proportions of shallow and mid-to-lower layer conductivity distributions, respectively. The result of multiplying the proportion of shallow conductivity distribution by the corresponding natural logarithmic mapping was used as the logarithmically weighted evaluation of the shallow layer distribution, and the result of multiplying the proportion of mid-to-lower layer conductivity distribution by the corresponding natural logarithmic mapping was used as the logarithmically weighted evaluation of the mid-to-lower layer distribution. The logarithmically weighted evaluations of the shallow and mid-to-lower layer distributions were then added together, and the negative of the sum was used as the concentration spatial distribution dispersion index corresponding to the target data acquisition period.

[0025] When the electrical conductivity data of the lower and middle cultivated soil layers is greater than that of the shallow cultivated soil layers, the difference between the electrical conductivity data of the lower and middle cultivated soil layers and the shallow cultivated soil layers is used as the electrical conductivity inversion difference assessment corresponding to the target data collection period. When the electrical conductivity data of the lower and middle cultivated soil layers is less than or equal to that of the shallow cultivated soil layers, the electrical conductivity inversion difference assessment corresponding to the target data collection period is set to a constant of 0. The concentration spatial distribution dispersion index is used as the numerator, the natural logarithm of constant 2 is used as the denominator, and the corresponding fraction is used as the concentration distribution normalization assessment. The electrical conductivity inversion difference assessment is used as the numerator, the total soil electrical conductivity assessment is used as the denominator, and the corresponding fraction is used as the electrical conductivity inversion ratio assessment. The result of multiplying the concentration distribution normalization assessment and the electrical conductivity inversion ratio assessment is used as the salinity spatial distribution characteristic data corresponding to the target data collection period.

[0026] Furthermore, the step of obtaining the spatial hindrance penalty factor by performing spatial hindrance coupling mapping processing on the longitudinal pore drop feature data and the salinity spatial distribution feature data includes:

[0027] For any target data acquisition period within the conditioning and evaluation cycle, obtain the longitudinal pore drop characteristic data and salinity spatial distribution characteristic data corresponding to the target data acquisition period;

[0028] The result of multiplying the longitudinal pore drop characteristic data and the salinity spatial distribution characteristic data corresponding to the target data acquisition cycle is used as the spatial hindrance coupling evaluation corresponding to the target data acquisition cycle.

[0029] The difference between constant 1 and the spatial hindrance coupling evaluation corresponding to the target data acquisition period is used as the spatial hindrance penalty factor corresponding to the target data acquisition period.

[0030] Furthermore, the habitat loss factor is obtained by jointly analyzing the aeration porosity deviation characteristics and soil electrical conductivity deviation characteristics of the middle and lower tillage layers, including:

[0031] By performing a one-way deviation mapping process on the aeration porosity data of the middle and lower tillage layers and the basic reference aeration porosity, aeration porosity deviation characteristic data is obtained.

[0032] By performing a one-way deviation mapping process on the soil electrical conductivity data of the middle and lower cultivated layers and the critical threshold of soil electrical conductivity, soil electrical conductivity deviation characteristic data is obtained.

[0033] By performing joint distance mapping and attenuation processing on the aeration pore deviation characteristic data and the soil electrical conductivity deviation characteristic data, the habitat loss factor was obtained.

[0034] Furthermore, the step of obtaining aeration porosity deviation characteristic data by performing unidirectional deviation mapping processing on the aeration porosity data of the middle and lower tillage layers and the basic reference aeration porosity includes:

[0035] The baseline reference aeration porosity is preset based on the root nodule respiration requirements of the target soybean variety; for any target data collection period within the conditioning assessment cycle, the aeration porosity data of the middle and lower tillage layers corresponding to the target data collection period is extracted from the stratified soil physicochemical baseline data.

[0036] When the aeration porosity data of the middle and lower tillage layers corresponding to the target data acquisition period is less than the basic reference aeration porosity, the difference between the basic reference aeration porosity and the aeration porosity data of the middle and lower tillage layers is used as the aeration porosity deficiency assessment for the target data acquisition period; when the aeration porosity data of the middle and lower tillage layers corresponding to the target data acquisition period is greater than or equal to the basic reference aeration porosity, the aeration porosity deficiency assessment for the target data acquisition period is set to a constant of 0.

[0037] The ventilation porosity deficiency assessment corresponding to the target data acquisition cycle is used as the numerator, the baseline reference ventilation porosity is used as the denominator, and the resulting fraction is used as the ventilation porosity deviation characteristic data corresponding to the target data acquisition cycle.

[0038] Furthermore, the step of obtaining soil conductivity deviation characteristic data by performing a one-way deviation mapping process on the soil electrical conductivity data of the middle and lower cultivated layers and the critical threshold of soil electrical conductivity includes:

[0039] Based on the root nodule salt tolerance characteristics of the target soybean variety, a critical threshold for soil electrical conductivity is preset; for any target data collection period within the conditioning assessment cycle, the soil electrical conductivity data of the middle and lower tillage layers corresponding to the target data collection period is extracted from the stratified soil physicochemical basic data;

[0040] When the soil electrical conductivity data of the middle and lower cultivated layers corresponding to the target data collection period is greater than the critical threshold of soil electrical conductivity, the difference between the soil electrical conductivity data of the middle and lower cultivated layers and the critical threshold of soil electrical conductivity is used as the soil electrical conductivity exceeding the limit assessment for the target data collection period; when the soil electrical conductivity data of the middle and lower cultivated layers corresponding to the target data collection period is less than or equal to the critical threshold of soil electrical conductivity, the soil electrical conductivity exceeding the limit assessment for the target data collection period is set to a constant of 0.

[0041] The soil conductivity exceedance assessment corresponding to the target data acquisition period is used as the numerator, the critical threshold of soil conductivity is used as the denominator, and the resulting fraction is used as the soil conductivity deviation characteristic data corresponding to the target data acquisition period.

[0042] Furthermore, the habitat loss factor is obtained by performing joint distance mapping and attenuation processing on the aeration pore deviation characteristic data and the soil electrical conductivity deviation characteristic data, including:

[0043] For any target data collection period within the conditioning assessment cycle, acquire the aeration porosity deviation characteristic data and soil electrical conductivity deviation characteristic data corresponding to the target data collection period;

[0044] The square of the deviation of the aeration pores from the characteristic data corresponding to the target data acquisition period is used as the assessment of the deviation intensity of the aeration pores corresponding to the target data acquisition period, and the square of the deviation of the soil electrical conductivity from the characteristic data corresponding to the target data acquisition period is used as the assessment of the deviation intensity of the soil electrical conductivity corresponding to the target data acquisition period.

[0045] The result of adding the aeration pore deviation intensity assessment and the soil electrical conductivity deviation intensity assessment is used as the joint habitat deviation intensity assessment corresponding to the target data collection period, and the square root of the joint habitat deviation intensity assessment is used as the joint habitat deviation distance corresponding to the target data collection period.

[0046] The negative of the joint habitat deviation distance corresponding to the target data collection period is subjected to an exponential mapping with the natural constant as the base, and the corresponding mapping result is used as the habitat loss factor corresponding to the target data collection period.

[0047] Furthermore, the comprehensive evaluation index of saline-alkali land conditioning effect is obtained by jointly correcting the basic conditioning evaluation score through spatial obstruction penalty factor and habitat loss factor, including:

[0048] For any target data collection period within the conditioning assessment cycle, extract the shallow topsoil total porosity data, shallow topsoil electrical conductivity data, middle and lower topsoil total porosity data, middle and lower topsoil aeration porosity data, and middle and lower topsoil electrical conductivity data corresponding to the target data collection period from the stratified soil physicochemical basic data.

[0049] The average values ​​of the physicochemical data of the same type of soil in the shallow and middle-lower layers were calculated, and the average values ​​of the physicochemical data of the whole soil in the topsoil were normalized and linearly weighted by preset weights. The calculation results were used as the basic conditioning evaluation score corresponding to the target data collection period.

[0050] Obtain the spatial obstruction penalty factor and habitat loss factor corresponding to the target data collection period, and use the calculation result of multiplying the spatial obstruction penalty factor and habitat loss factor as the joint correction assessment corresponding to the target data collection period;

[0051] The result of multiplying the basic conditioning evaluation score corresponding to the target data collection period with the joint correction evaluation is used as the comprehensive evaluation index of the conditioning effect of saline-alkali land corresponding to the target data collection period.

[0052] Compared with the prior art, the present invention has the following advantages:

[0053] This invention discloses a method for evaluating the effectiveness of topsoil structure conditioning technology in saline-alkali soybean fields. By jointly characterizing the porosity difference between the shallow and middle-lower layers and the spatial distribution characteristics of soil electrical conductivity, it can identify spatial misalignments where the shallow structure improves after conditioning, but infiltration obstruction and salt accumulation still exist in the middle and lower layers. This avoids the masking effect of improved shallow layer data on abnormal data in the middle and lower layers in traditional whole-layer mean evaluation. Therefore, it can more accurately reflect the vertical migration of water and salt and the degree of improvement in topsoil structure after conditioning in saline-alkali land, improving the ability to identify hidden problems such as localized salt accumulation and vertical porosity faults, and making the conditioning effect evaluation results more consistent with the actual field improvement status. Furthermore, this invention combines the deviation of the aeration porosity and soil electrical conductivity of the middle and lower layers from the soybean root nodule suitable habitat benchmark to jointly quantify the hypoxia and salt stress in the core root zone, and incorporates the spatial obstruction state and root zone habitat state into the dynamic correction process of the basic conditioning evaluation results. This avoids overestimating the effectiveness of soil conditioning based solely on improvements in overall soil physicochemical indicators, making the evaluation results more consistent with the actual growth environment of soybean taproots and root nodules, and providing a more reliable quantitative basis for the acceptance of saline-alkali land conditioning measures, secondary deep loosening, subsequent leaching, and recultivation management. Attached Figure Description

[0054] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0055] Figure 1 This is a flowchart illustrating the method for evaluating the effect of a soil layer structure conditioning technology in soybean fields in saline-alkali land, as described in an embodiment of the present invention. Detailed Implementation

[0056] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0057] See Figure 1 This is a flowchart of a method for evaluating the effect of a soil layer structure conditioning technology in soybean fields in saline-alkali land, as provided in Embodiment 1 of the present invention. Figure 1 As shown, a method for evaluating the effectiveness of a technology for conditioning the topsoil structure in soybean fields in saline-alkali land may include:

[0058] Step S1: Collect and preprocess the stratified soil physicochemical data during the conditioning and assessment period to obtain the basic stratified soil physicochemical data.

[0059] First, soil data collection locations are set up in the target saline-alkali soybean field after the implementation of the topsoil structure conditioning technology. The topsoil of the target saline-alkali soybean field is divided into shallow topsoil and middle and lower topsoil along the soil depth direction. It should be noted that in the embodiment of the present invention, 0 to 10 cm is set as shallow topsoil and 10 to 20 cm is set as middle and lower topsoil.

[0060] The data collection cycle within the conditioning and evaluation period is set. In this embodiment of the invention, the data collection cycle is set as follows: according to the preset data collection cycle, the shallow tillage layer and the middle and lower tillage layer are synchronously collected by field in-situ sensors or manual ring samplers. The original data of total porosity of shallow tillage layer, soil electrical conductivity of shallow tillage layer, total porosity of middle and lower tillage layer, aeration porosity of middle and lower tillage layer, and soil electrical conductivity of middle and lower tillage layer are obtained.

[0061] Outlier removal and time-series smoothing were performed on the raw data of total porosity, soil electrical conductivity, total porosity, aeration porosity, and soil electrical conductivity of the shallow, middle, and lower-level cultivated layers, respectively, to obtain the data of total porosity, soil electrical conductivity, aeration porosity, and soil electrical conductivity of the shallow, middle, and lower-level cultivated layers. These data were then used as the basic physicochemical data of the stratified soils.

[0062] Thus, the basic physicochemical data of the stratified soil were obtained by collecting and preprocessing the stratified soil physicochemical data during the conditioning and assessment period.

[0063] Step S2: By coupling analysis of the pore height difference characteristics and soil electrical conductivity spatial distribution characteristics of the shallow and middle-lower tillage layers, the spatial hindrance penalty factor is obtained.

[0064] After obtaining the physicochemical data of the layered soils after conditioning in saline-alkali land, under ideal leaching conditions, the salt in the entire topsoil should be uniformly diluted or infiltrated, resulting in a small difference in electrical conductivity between the shallow and middle-lower soil layers, or an overall low electrical conductivity. However, if the conditioning depth is limited, leading to vertical porosity faults within the topsoil, the salt carried downwards by the water flow will remain in the middle-lower layers, causing an inverted concentration accumulation of electrical conductivity between the shallow and middle-lower layers. Existing assessment algorithms use global mean calculations, mathematically smoothing out the spatial variation characteristics of this vertical physical fault and chemical enrichment, leading to biases in the assessment of local stress. To quantify this fault-induced inverted accumulation, this method extracts the scalar difference in measured porosity between the shallow and middle-lower layers, and combines it with the modulus projection of the porosity of the two layers to construct a structural fault dispersion index reflecting the degree of physical seepage blockage. Simultaneously, it calculates the proportion of electrical conductivity in the shallow and middle-lower layers within the overall observation domain, and uses logarithmic weighted mapping to construct a concentration spatial distribution dispersion index characterizing the degree of salt imbalance. By combining these two spatial dispersion indicators, the system accurately captures the surface-to-internal salinity inversion characteristic caused by water retention. This computational structure transforms abnormal spatial distribution differences into negative constraint weights on the base score, thereby adaptively reducing the skewed scores generated in the mean-based algorithm due to improved shallow data, and quantifying the impact of vertical barriers on the overall conditioning effect.

[0065] In summary, this invention first performs vertical difference normalization on the total porosity data of the shallow and lower-middle-level cultivated layers, and extracts spatial distribution characteristics from the soil electrical conductivity data of the shallow and lower-middle-level cultivated layers to obtain vertical porosity drop characteristic data and salinity spatial distribution characteristic data. Specifically, for any target data collection period within the conditioning assessment cycle, the total porosity data of the shallow cultivated layer, the total porosity data of the lower-middle-level cultivated layer, the soil electrical conductivity data of the shallow cultivated layer, and the soil electrical conductivity data of the lower-middle-level cultivated layer corresponding to the target data collection period are extracted from the stratified soil physicochemical basic data. When the total porosity data of the shallow cultivated layer is greater than that of the lower-middle-level cultivated layer, the difference between the total porosity data of the shallow cultivated layer and the lower-middle-level cultivated layer is used as the positive porosity drop assessment corresponding to the target data collection period. When the total porosity data of the shallow cultivated layer is less than or equal to that of the lower-middle-level cultivated layer, the positive porosity drop assessment corresponding to the target data collection period is set to a constant of 0. The total porosity data of the shallow and middle-lower layers were squared separately. The two squared results were then added together, and the square root of the sum was used as the porosity joint modulus assessment for the target data acquisition period. The forward porosity drop assessment was used as the numerator, and the porosity joint modulus assessment was used as the denominator. The resulting fraction was used as the longitudinal porosity drop characteristic data for the target data acquisition period. The soil electrical conductivity data of the shallow and middle-lower layers was added together to obtain the total soil electrical conductivity assessment for the target data acquisition period. The soil electrical conductivity data of the shallow layer was used as the numerator, and the total soil electrical conductivity assessment was used as the denominator. The resulting fraction was used as the shallow layer electrical conductivity distribution ratio for the target data acquisition period. The soil electrical conductivity data of the middle-lower layers was used as the numerator, and the total soil electrical conductivity assessment was used as the denominator. The resulting fraction was used as the middle-lower layer electrical conductivity distribution ratio for the target data acquisition period.

[0066] Natural logarithmic mappings were performed on the distribution proportions of shallow and mid-to-lower layers of electrical conductivity, respectively. The result of multiplying the shallow layer electrical conductivity distribution proportion by the corresponding natural logarithmic mapping result was used as the logarithm-weighted evaluation of the shallow layer distribution, and the result of multiplying the mid-to-lower layer electrical conductivity distribution proportion by the corresponding natural logarithmic mapping result was used as the logarithm-weighted evaluation of the mid-to-lower layer distribution. The logarithm-weighted evaluations of the shallow and mid-to-lower layers were added together, and the negative of the sum was used as the concentration spatial distribution dispersion index for the target data collection period. When the electrical conductivity data of the mid-to-lower layer topsoil was greater than that of the shallow topsoil, the difference between the mid-to-lower layer topsoil electrical conductivity data and the shallow topsoil electrical conductivity data was used as the conductivity inversion difference evaluation for the target data collection period. When the electrical conductivity data of the mid-to-lower layer topsoil was less than or equal to that of the shallow topsoil, the conductivity inversion difference evaluation for the target data collection period was set to a constant of 0. The concentration spatial distribution dispersion index is used as the numerator, and the natural logarithm of constant 2 is used as the denominator. The resulting fraction is used as the concentration distribution normalization assessment. The conductivity inversion difference assessment is used as the numerator, and the total soil conductivity assessment is used as the denominator. The resulting fraction is used as the conductivity inversion proportion assessment. The product of the concentration distribution normalization assessment and the conductivity inversion proportion assessment is used as the salinity spatial distribution characteristic data corresponding to the target data collection period.

[0067] After obtaining the salinity spatial distribution characteristic data corresponding to the target data acquisition period, the spatial hindrance coupling mapping process is further performed on the longitudinal porosity drop characteristic data and the salinity spatial distribution characteristic data to obtain the spatial hindrance penalty factor. Specifically, for any target data acquisition period within the conditioning assessment period, the longitudinal porosity drop characteristic data and the salinity spatial distribution characteristic data corresponding to the target data acquisition period are obtained. The result of multiplying the longitudinal porosity drop characteristic data and the salinity spatial distribution characteristic data corresponding to the target data acquisition period is used as the spatial hindrance coupling assessment corresponding to the target data acquisition period. The difference between the constant 1 and the spatial hindrance coupling assessment corresponding to the target data acquisition period is used as the spatial hindrance penalty factor corresponding to the target data acquisition period.

[0068] In one implementation, assume the first The total porosity data of the shallow tillage layer corresponding to each data collection cycle is as follows: ;No. The total porosity data of the middle and lower tillage layers corresponding to each data collection period are as follows: ;No. The shallow topsoil conductivity data corresponding to each data collection period are: ;No. The soil conductivity data for the middle and lower tillage layers corresponding to each data collection period are as follows: ;No. The distribution of shallow conductivity corresponding to each data acquisition cycle is as follows: ;No. The proportion of the middle and lower layer conductivity distribution corresponding to each data acquisition cycle is: Then the first The formula for calculating the spatial distribution dispersion index of concentration corresponding to each data acquisition cycle is:

[0069]

[0070] in, Indicates the first Concentration spatial distribution dispersion index corresponding to each data acquisition cycle; Indicates the first The percentage of shallow conductivity distribution corresponding to each data acquisition cycle; Indicates the first The percentage of conductivity distribution in the middle and lower layers corresponding to each data acquisition cycle; This represents the logarithmic function with the natural constant e as the base.

[0071] Furthermore, the first The formula for calculating the spatial obstruction penalty factor corresponding to each data acquisition cycle is:

[0072]

[0073] in, Indicates the first Spatial obstruction penalty factor corresponding to each data acquisition cycle; Indicates the first Total porosity data of shallow tillage layer corresponding to each data collection cycle; Indicates the first Total porosity data of the middle and lower tillage layers corresponding to each data collection cycle; Represents the maximum value function; Indicates the first Concentration spatial distribution dispersion index corresponding to each data acquisition cycle; Indicates the first The electrical conductivity data of shallow topsoil corresponding to each data collection cycle; Indicates the first The electrical conductivity data of the middle and lower tillage layers corresponding to each data collection period.

[0074] It should be noted that, to address the issue that traditional global mean algorithms mask the vertical hindrance and anomalous enrichment of salt, this design corrects this bias by utilizing correlation structure and distribution characteristics. The left bracket portion of the formula utilizes... Extract the positive porosity drop and divide it by the vector magnitude of the porosity of the two layers in two-dimensional space. This ratio term maps the difference in vertical structural gradient. When the shallow porosity is greater than that of the middle and lower layers, it indicates the existence of a physical infiltration barrier, and the ratio approaches 1. If the pores of the upper and lower layers are well connected, the maximum value function truncates it to 0, and the algorithm does not intervene in the conditioning evaluation under normal conditions. The right-hand side of the formula focuses on analyzing the salinity inversion characteristics. It utilizes the concentration spatial distribution dispersion index. Divided by the theoretical maximum distribution benchmark As a moderating factor, it influences the right side. The chemical concentration inversion ratio represents this. When salts precipitate and accumulate in the lower layers due to impeded osmosis, this condition is met. Greater than Under certain conditions, the concentration dispersion index amplifies this spatial distribution imbalance, causing the right-hand composite term to output positive proportional data. These two structural features are correlated through multiplication, ensuring that the subtraction term will only produce a positive value when both structural faulting and salt accumulation at the bottom layer occur simultaneously, resulting in a positive final output. If the factor is less than 1, the overly high score in the algorithm's basic evaluation model due to superior shallow data is automatically deducted. If the conditioning technology improves the structure of the entire tillage layer and the salt is fully leached, and none of the above abnormal conditions are met, the deduction item is reset to zero, the spatial obstruction penalty factor is equal to 1, and the model retains the normal conditioning effect score. This calculation mechanism corrects the evaluation error of single-layer mean calculation under spatial heterogeneity conditions.

[0075] Thus, the spatial obstruction penalty factor was obtained by coupling analysis of the pore height difference characteristics and soil electrical conductivity spatial distribution characteristics of the shallow and middle-lower tillage layers.

[0076] Step S3: By jointly analyzing the deviation characteristics of aeration pores and soil electrical conductivity in the middle and lower tillage layers, habitat loss factors are obtained.

[0077] Step S2's constructed spatial obstruction penalty factor effectively identified and reduced the artificially inflated scores caused by salt inversion accumulation due to faulting, resolving the relative misalignment problem masked by upper and lower layer data. However, in the actual growth environment of soybeans in saline-alkali soil, besides focusing on the relative obstruction of salt between upper and lower layers, the main root system and root nodule community of soybeans are concentrated in the middle and lower layers. The absolute physicochemical environmental conditions of the soil at this depth play a decisive role in crop survival. If only the relative height difference between upper and lower layers is relied upon for correction, the impact of the absolute quality deterioration of the middle and lower layer environment will be ignored. Soybean root nodules have objective physiological tolerance limits to soil oxygen supply (determined by aeration porosity) and salt concentration (determined by electrical conductivity). In particular, when insufficient soil aeration porosity leads to micro-hypoxia, the tolerance of root nodules to salt osmotic pressure will further decrease, forming an interaction between low oxygen and high salt that exacerbates stress. Existing independent weighted algorithms calculate porosity and salt separately, lacking characterization of the interactive effects of these environmental factors. Therefore, this step further focuses on the core root zone of soybean, extracting the measured aeration porosity and soil electrical conductivity of this layer. Combined with the physiological tolerance benchmark for soybean root nodule survival, an Euclidean distance reflecting the degree of deviation from multiple environmental factors is constructed in a two-dimensional coordinate system to calculate the habitat loss factor. This factor is used to further correct for the biological stress effects that conventional mean algorithms fail to reflect when the absolute value of the deep environment exceeds the crop's physiological tolerance limit.

[0078] In summary, this invention first obtains aeration porosity deviation characteristic data by performing a one-way deviation mapping process between the aeration porosity data of the middle and lower tillage layers and the baseline reference aeration porosity. Specifically, the baseline reference aeration porosity is preset according to the root nodule respiration requirements of the target soybean variety. For any target data acquisition period within the conditioning assessment cycle, the aeration porosity data of the middle and lower tillage layers corresponding to the target data acquisition period is extracted from the stratified soil physicochemical baseline data. When the aeration porosity data of the middle and lower tillage layers corresponding to the target data acquisition period is less than the baseline reference aeration porosity, the difference between the baseline reference aeration porosity and the aeration porosity data of the middle and lower tillage layers is used as the aeration porosity deficiency assessment for the target data acquisition period. When the aeration porosity data of the middle and lower tillage layers corresponding to the target data acquisition period is greater than or equal to the baseline reference aeration porosity, the aeration porosity deficiency assessment for the target data acquisition period is set to a constant of 0. The ventilation porosity deficiency assessment corresponding to the target data acquisition cycle is used as the numerator, the baseline reference ventilation porosity is used as the denominator, and the resulting fraction is used as the ventilation porosity deviation characteristic data corresponding to the target data acquisition cycle.

[0079] After obtaining the aeration pore deviation characteristic data, the soil conductivity deviation characteristic data is further obtained by performing a one-way deviation mapping process on the soil conductivity data of the middle and lower tillage layers and the critical threshold of soil conductivity. Specifically, the critical threshold of soil conductivity is preset based on the root nodule salt tolerance characteristics of the target soybean variety. For any target data collection period within the conditioning assessment cycle, the soil conductivity data of the middle and lower tillage layers corresponding to the target data collection period is extracted from the stratified soil physicochemical basic data. When the soil conductivity data of the middle and lower tillage layers corresponding to the target data collection period is greater than the critical threshold of soil conductivity, the difference between the soil conductivity data of the middle and lower tillage layers and the critical threshold of soil conductivity is used as the soil conductivity exceeding the limit assessment for the target data collection period. When the soil conductivity data of the middle and lower tillage layers corresponding to the target data collection period is less than or equal to the critical threshold of soil conductivity, the soil conductivity exceeding the limit assessment for the target data collection period is set to a constant of 0. The soil conductivity exceedance assessment corresponding to the target data acquisition period is used as the numerator, the critical threshold of soil conductivity is used as the denominator, and the resulting fraction is used as the soil conductivity deviation characteristic data corresponding to the target data acquisition period.

[0080] After obtaining the soil electrical conductivity deviation characteristic data, the habitat loss factor is obtained by performing joint distance mapping and attenuation processing on the aeration porosity deviation characteristic data and the soil electrical conductivity deviation characteristic data. Specifically, for any target data collection period within the conditioning assessment cycle, the aeration porosity deviation characteristic data and the soil electrical conductivity deviation characteristic data corresponding to the target data collection period are obtained. The square of the aeration porosity deviation characteristic data corresponding to the target data collection period is used as the aeration porosity deviation intensity assessment, and the square of the soil electrical conductivity deviation characteristic data corresponding to the target data collection period is used as the soil electrical conductivity deviation intensity assessment. The sum of the aeration porosity deviation intensity assessment and the soil electrical conductivity deviation intensity assessment is used as the joint habitat deviation intensity assessment corresponding to the target data collection period, and the square root of the joint habitat deviation intensity assessment is used as the joint habitat deviation distance corresponding to the target data collection period. The negative of the joint habitat deviation distance corresponding to the target data collection period is subjected to exponential mapping with the natural constant as the base, and the corresponding mapping result is used as the habitat loss factor corresponding to the target data collection period.

[0081] In one implementation, assume the first The aeration porosity data for the middle and lower tillage layers corresponding to each data collection period are as follows: The critical threshold for soil electrical conductivity is The basic reference ventilation porosity is Then the first The formula for calculating the habitat loss factor corresponding to each data collection cycle is:

[0082]

[0083] in, Indicates the first Habitat depletion factor corresponding to each data collection cycle; This represents an exponential function with the natural constant e as the base. Indicates the basic reference ventilation porosity; Indicates the first Data on the aeration porosity of the middle and lower tillage layers corresponding to each data collection cycle; Indicates the first The electrical conductivity data of the middle and lower tillage layer soil corresponding to each data collection cycle; This represents the critical threshold for soil electrical conductivity.

[0084] It should be noted that, to address the issues of existing algorithms neglecting the interactive effects of environmental factors through independent weighting and masking local absolute environmental degradation through mean calculation, this design introduces exponential decay based on two-dimensional Euclidean distance for supplementary correction. The first term inside the formula... This constitutes a unidirectional constraint ratio for ventilation porosity. A positive deviation value will only be output when the measured ventilation porosity in the middle and lower layers is lower than the healthy reference value, characterizing the degree of hypoxic stress; the second item... This constitutes a unidirectional constraint ratio for salinity concentration. A positive deviation value is only output when the conductivity of the lower layers exceeds the tolerance threshold, characterizing the degree of salinity stress. By summing the squares of the two deviation ratios and taking the square root, the formula objectively maps the geometric amplification effect of simultaneous hypoxia and high salinity using a two-dimensional Euclidean distance. When the deep environment is favorable (sufficient ventilation and salinity below the threshold), the Euclidean distance is 0, and the exponential function... An output value of 1 indicates no degradation in the algorithm score. However, when limited conditioning leads to simultaneous occurrences of compaction and hypoxia in the deep root zone, both deviations increase synchronously. The summation of squares mechanism significantly increases the overall Euclidean distance, and the decaying characteristics of the exponential function cause the habitat degradation factor to adaptively converge towards zero, outputting a decay coefficient significantly less than 1. This factor quantifies the stress degree of hypoxia and high salinity interaction on the deep root zone of soybeans. This ensures accurate constraints under the mean-based algorithm system, whether due to relative gradient misalignment caused by salinity blockage or absolute environmental deterioration exceeding physiological limits.

[0085] Thus, the habitat loss factor was obtained by jointly analyzing the deviation characteristics of aeration pores and soil electrical conductivity in the middle and lower tillage layers.

[0086] Step S4: The basic conditioning evaluation score is jointly corrected by the spatial obstruction penalty factor and the habitat loss factor to obtain the comprehensive evaluation index of the conditioning effect of saline-alkali land.

[0087] To quantify the spatial fault features and deep stress features extracted in the previous steps into the final evaluation results, this method combines the spatial obstruction penalty factor extracted in step S2 and the habitat loss factor extracted in step S3, using them in a weighted manner within the existing basic linear weighted evaluation model. The spatial obstruction penalty factor specifically reduces false high scores generated in mean calculation due to favorable shallow data, while the habitat loss factor adaptively truncates the evaluation score when the absolute value of the deep environment exceeds the crop's physiological limits. Through multiplicative fusion of these two factors, the original static mean-weighted model is reconstructed into a dynamic correction model with the ability to perceive longitudinal spatial heterogeneity and verify biological environmental limits.

[0088] Specifically, for any target data collection period within the conditioning assessment cycle, the following data are extracted from the stratified soil physicochemical data: total porosity of the shallow topsoil, soil electrical conductivity of the shallow topsoil, total porosity of the middle and lower topsoil, aeration porosity of the middle and lower topsoil, and soil electrical conductivity of the middle and lower topsoil corresponding to the target data collection period. The mean values ​​of the physicochemical data for the same soil type in the shallow and middle / lower topsoil are calculated, and the resulting mean values ​​of the physicochemical data for the entire topsoil layer are normalized and linearly weighted with preset weights. The calculated results are used as the basic conditioning evaluation score for the target data collection period. The spatial obstruction penalty factor and habitat loss factor for the target data collection period are obtained, and the product of these two factors is used as the joint correction assessment for the target data collection period. The product of the basic conditioning evaluation score and the joint correction assessment for the target data collection period is used as the comprehensive evaluation index of the saline-alkali land conditioning effect for the target data collection period.

[0089] It should be noted that, in this embodiment of the invention, based on the agronomic characteristics of soybean, the preset basic reference aeration porosity to ensure normal respiration of soybean root nodules is 15%; the preset critical threshold for soil electrical conductivity tolerability of soybean root nodules is... The two parameters mentioned above can be adjusted according to the actual scenario and are not required. These two parameters serve as a benchmark to ensure the objective accuracy of the model when calculating the deviation of the environment.

[0090] Thus, the comprehensive evaluation index of the conditioning effect of saline-alkali land was obtained by jointly correcting the basic conditioning evaluation score through spatial obstruction penalty factor and habitat loss factor.

[0091] Step S5: By conducting time-series analysis on the comprehensive evaluation index of the conditioning effect of saline-alkali land, the evaluation results of the conditioning effect of the soil structure in soybean fields in saline-alkali land are obtained.

[0092] The comprehensive evaluation index of saline-alkali land conditioning effect for each data collection period within the conditioning assessment cycle is obtained and arranged in chronological order of data collection to construct time-series data of the comprehensive evaluation index of saline-alkali land conditioning effect. Trend analysis is performed on the time-series data of the comprehensive evaluation index of saline-alkali land conditioning effect to obtain the changes in conditioning effect between each data collection period, and corresponding dynamic evaluation results of the conditioning effect of the topsoil structure are generated.

[0093] By correlating the time-series data of the comprehensive evaluation index of the conditioning effect of saline-alkali land, the changing trend of the conditioning effect, and the corresponding data collection cycle, an evaluation result of the conditioning effect of the soil layer structure of soybean fields in saline-alkali land is formed and output to the agricultural management decision-making terminal. This is used to characterize the dynamic improvement of the soil layer structure and root zone habitat after the implementation of conditioning measures, and to provide data basis for subsequent adjustment of conditioning measures and recultivation management.

[0094] Thus, the time-series analysis of the comprehensive evaluation index of the conditioning effect on saline-alkali land was completed, and the evaluation results of the conditioning technology effect on the topsoil structure of soybean fields in saline-alkali land were obtained.

[0095] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating the effectiveness of topsoil structure conditioning technology in soybean fields in saline-alkali land, characterized in that, The method includes: Step S1: Collect and preprocess the stratified soil physicochemical data during the conditioning assessment period to obtain the basic stratified soil physicochemical data; Step S2: By coupling analysis of the pore height difference characteristics and soil electrical conductivity spatial distribution characteristics of the shallow and middle-lower tillage layers, the spatial hindrance penalty factor is obtained. Step S3: Obtain habitat loss factors by jointly analyzing the deviation characteristics of aeration pores and soil electrical conductivity in the middle and lower tillage layers; Step S4: The basic conditioning evaluation score is jointly corrected by the spatial obstruction penalty factor and the habitat loss factor to obtain the comprehensive evaluation index of the conditioning effect of saline-alkali land; Step S5: By conducting time-series analysis on the comprehensive evaluation index of the conditioning effect of saline-alkali land, the evaluation results of the conditioning effect of the topsoil structure in soybean fields in saline-alkali land are obtained.

2. The method for evaluating the effect of topsoil structure conditioning technology in saline-alkali soybean fields according to claim 1, characterized in that, The process involves collecting and preprocessing stratified soil physicochemical data during the conditioning and assessment period to obtain basic stratified soil physicochemical data, including: Soil data collection points were set up in the target saline-alkali soybean field after the implementation of the topsoil structure conditioning technology, and the topsoil of the target saline-alkali soybean field was divided into shallow topsoil and middle and lower topsoil along the soil depth direction. Set the data collection cycle within the conditioning and evaluation period. According to the preset data collection cycle, use in-situ field sensors or manual ring samplers to collect data synchronously and layer by layer from the shallow tillage layer and the middle and lower tillage layer. Obtain the original data of total porosity of shallow tillage layer, soil electrical conductivity of shallow tillage layer, total porosity of middle and lower tillage layer, aeration porosity of middle and lower tillage layer, and soil electrical conductivity of middle and lower tillage layer. Outlier removal and time-series smoothing were performed on the raw data of total porosity, soil electrical conductivity, total porosity, aeration porosity, and soil electrical conductivity of the shallow, middle, and lower-level cultivated layers, respectively, to obtain the data of total porosity, soil electrical conductivity, aeration porosity, and soil electrical conductivity of the shallow, middle, and lower-level cultivated layers. These data were then used as the basic physicochemical data of the stratified soils.

3. The method for evaluating the effect of topsoil structure conditioning technology in saline-alkali soybean fields according to claim 1, characterized in that, The spatial hindrance penalty factor is obtained by coupling analysis of the pore height difference characteristics and soil electrical conductivity spatial distribution characteristics between the shallow and middle-lower layers of cultivated land, including: By performing longitudinal difference normalization on the total porosity data of the shallow and middle-lower layers of the topsoil, and extracting the spatial distribution characteristics of the soil electrical conductivity data of the shallow and middle-lower layers of the topsoil, longitudinal porosity drop characteristic data and salinity spatial distribution characteristic data were obtained. By performing spatial hindrance coupling mapping on longitudinal pore drop characteristic data and salinity spatial distribution characteristic data, the spatial hindrance penalty factor is obtained.

4. The method for evaluating the effect of topsoil structure conditioning technology in saline-alkali soybean fields according to claim 3, characterized in that, The process involves normalizing the longitudinal differences in total porosity data between the shallow and middle-lower layers of cultivated soil, and extracting spatial distribution characteristics from the soil electrical conductivity data of the shallow and middle-lower layers of cultivated soil to obtain longitudinal porosity drop characteristic data and salinity spatial distribution characteristic data, including: For any target data collection period within the conditioning assessment cycle, extract the total porosity data of the shallow topsoil, the total porosity data of the middle and lower topsoil, the soil electrical conductivity data of the shallow topsoil, and the soil electrical conductivity data of the middle and lower topsoil corresponding to the target data collection period from the stratified soil physicochemical basic data. When the total porosity data of the shallow cultivated layer is greater than that of the middle and lower cultivated layers, the difference between the two is used as the forward porosity drop assessment for the target data acquisition period. When the total porosity data of the shallow cultivated layer is less than or equal to that of the middle and lower cultivated layers, the forward porosity drop assessment for the target data acquisition period is set to a constant of 0. The total porosity data of the shallow and middle and lower cultivated layers are squared respectively, and the two squared results are added together. The square root of the sum is used as the porosity joint modulus assessment for the target data acquisition period. The forward porosity drop assessment is used as the numerator, the porosity joint modulus assessment is used as the denominator, and the resulting fraction is used as the longitudinal porosity drop characteristic data for the target data acquisition period. The total soil conductivity data for the target data collection period is calculated by adding the electrical conductivity data of the shallow topsoil layer and the middle and lower topsoil layer. The result is used as the total soil conductivity assessment for the target data collection period. The shallow topsoil conductivity data is used as the numerator, the total soil conductivity assessment is used as the denominator, and the resulting fraction is used as the distribution ratio of shallow topsoil conductivity for the target data collection period. Similarly, the middle and lower topsoil conductivity data is used as the numerator, the total soil conductivity assessment is used as the denominator, and the resulting fraction is used as the distribution ratio of middle and lower topsoil conductivity for the target data collection period. Natural logarithmic mappings were performed on the proportions of shallow and mid-to-lower layer conductivity distributions, respectively. The result of multiplying the proportion of shallow conductivity distribution by the corresponding natural logarithmic mapping was used as the logarithmically weighted evaluation of the shallow layer distribution, and the result of multiplying the proportion of mid-to-lower layer conductivity distribution by the corresponding natural logarithmic mapping was used as the logarithmically weighted evaluation of the mid-to-lower layer distribution. The logarithmically weighted evaluations of the shallow and mid-to-lower layer distributions were then added together, and the negative of the sum was used as the concentration spatial distribution dispersion index corresponding to the target data acquisition period. When the electrical conductivity data of the lower and middle cultivated soil layers is greater than that of the shallow cultivated soil layers, the difference between the electrical conductivity data of the lower and middle cultivated soil layers and the shallow cultivated soil layers is used as the electrical conductivity inversion difference assessment corresponding to the target data collection period. When the electrical conductivity data of the lower and middle cultivated soil layers is less than or equal to that of the shallow cultivated soil layers, the electrical conductivity inversion difference assessment corresponding to the target data collection period is set to a constant of 0. The concentration spatial distribution dispersion index is used as the numerator, the natural logarithm of constant 2 is used as the denominator, and the corresponding fraction is used as the concentration distribution normalization assessment. The electrical conductivity inversion difference assessment is used as the numerator, the total soil electrical conductivity assessment is used as the denominator, and the corresponding fraction is used as the electrical conductivity inversion ratio assessment. The result of multiplying the concentration distribution normalization assessment and the electrical conductivity inversion ratio assessment is used as the salinity spatial distribution characteristic data corresponding to the target data collection period.

5. The method for evaluating the effect of topsoil structure conditioning technology in saline-alkali soybean fields according to claim 3, characterized in that, The process of obtaining a spatial hindrance penalty factor by performing spatial hindrance coupling mapping on longitudinal pore drop characteristic data and salinity spatial distribution characteristic data includes: For any target data acquisition period within the conditioning and evaluation cycle, obtain the longitudinal pore drop characteristic data and salinity spatial distribution characteristic data corresponding to the target data acquisition period; The result of multiplying the longitudinal pore drop characteristic data and the salinity spatial distribution characteristic data corresponding to the target data acquisition cycle is used as the spatial hindrance coupling evaluation corresponding to the target data acquisition cycle. The difference between constant 1 and the spatial hindrance coupling evaluation corresponding to the target data acquisition period is used as the spatial hindrance penalty factor corresponding to the target data acquisition period.

6. The method for evaluating the effect of topsoil structure conditioning technology in saline-alkali soybean fields according to claim 1, characterized in that, The habitat loss factor is obtained by jointly analyzing the deviation characteristics of aeration pores and soil electrical conductivity in the middle and lower tillage layers, including: By performing a one-way deviation mapping process on the aeration porosity data of the middle and lower tillage layers and the basic reference aeration porosity, aeration porosity deviation characteristic data is obtained. By performing a one-way deviation mapping process on the soil electrical conductivity data of the middle and lower cultivated layers and the critical threshold of soil electrical conductivity, soil electrical conductivity deviation characteristic data is obtained. By performing joint distance mapping and attenuation processing on the aeration pore deviation characteristic data and the soil electrical conductivity deviation characteristic data, the habitat loss factor was obtained.

7. The method for evaluating the effect of topsoil structure conditioning technology in saline-alkali soybean fields according to claim 6, characterized in that, The process involves performing a one-way deviation mapping between the aeration porosity data of the middle and lower tillage layers and the baseline reference aeration porosity to obtain aeration porosity deviation characteristic data, including: The baseline reference aeration porosity is preset based on the root nodule respiration requirements of the target soybean variety; for any target data collection period within the conditioning assessment cycle, the aeration porosity data of the middle and lower tillage layers corresponding to the target data collection period is extracted from the stratified soil physicochemical baseline data. When the aeration porosity data of the middle and lower tillage layers corresponding to the target data acquisition period is less than the basic reference aeration porosity, the difference between the basic reference aeration porosity and the aeration porosity data of the middle and lower tillage layers is used as the aeration porosity deficiency assessment for the target data acquisition period; when the aeration porosity data of the middle and lower tillage layers corresponding to the target data acquisition period is greater than or equal to the basic reference aeration porosity, the aeration porosity deficiency assessment for the target data acquisition period is set to a constant of 0. The ventilation porosity deficiency assessment corresponding to the target data acquisition cycle is used as the numerator, the baseline reference ventilation porosity is used as the denominator, and the resulting fraction is used as the ventilation porosity deviation characteristic data corresponding to the target data acquisition cycle.

8. The method for evaluating the effect of topsoil structure conditioning technology in saline-alkali soybean fields according to claim 6, characterized in that, The process involves performing a one-way deviation mapping between the electrical conductivity data of the middle and lower cultivated layers and the critical threshold of soil electrical conductivity to obtain soil electrical conductivity deviation characteristic data, including: Based on the root nodule salt tolerance characteristics of the target soybean variety, a critical threshold for soil electrical conductivity is preset; for any target data collection period within the conditioning assessment cycle, the soil electrical conductivity data of the middle and lower tillage layers corresponding to the target data collection period is extracted from the stratified soil physicochemical basic data; When the soil electrical conductivity data of the middle and lower cultivated layers corresponding to the target data collection period is greater than the critical threshold of soil electrical conductivity, the difference between the soil electrical conductivity data of the middle and lower cultivated layers and the critical threshold of soil electrical conductivity is used as the soil electrical conductivity exceeding the limit assessment for the target data collection period; when the soil electrical conductivity data of the middle and lower cultivated layers corresponding to the target data collection period is less than or equal to the critical threshold of soil electrical conductivity, the soil electrical conductivity exceeding the limit assessment for the target data collection period is set to a constant of 0. The soil conductivity exceedance assessment corresponding to the target data acquisition period is used as the numerator, the critical threshold of soil conductivity is used as the denominator, and the resulting fraction is used as the soil conductivity deviation characteristic data corresponding to the target data acquisition period.

9. The method for evaluating the effect of a technology for conditioning the topsoil structure in soybean fields in saline-alkali land according to claim 6, characterized in that, The habitat loss factor is obtained by performing joint distance mapping and attenuation processing on the aeration pore deviation characteristic data and the soil electrical conductivity deviation characteristic data, including: For any target data collection period within the conditioning assessment cycle, acquire the aeration porosity deviation characteristic data and soil electrical conductivity deviation characteristic data corresponding to the target data collection period; The square of the deviation of the aeration pores from the characteristic data corresponding to the target data acquisition period is used as the assessment of the deviation intensity of the aeration pores corresponding to the target data acquisition period, and the square of the deviation of the soil electrical conductivity from the characteristic data corresponding to the target data acquisition period is used as the assessment of the deviation intensity of the soil electrical conductivity corresponding to the target data acquisition period. The result of adding the aeration pore deviation intensity assessment and the soil electrical conductivity deviation intensity assessment is used as the joint habitat deviation intensity assessment corresponding to the target data collection period, and the square root of the joint habitat deviation intensity assessment is used as the joint habitat deviation distance corresponding to the target data collection period. The negative of the joint habitat deviation distance corresponding to the target data collection period is subjected to an exponential mapping with the natural constant as the base, and the corresponding mapping result is used as the habitat loss factor corresponding to the target data collection period.

10. The method for evaluating the effect of topsoil structure conditioning technology in saline-alkali soybean fields according to claim 1, characterized in that, The method involves jointly correcting the basic conditioning evaluation score using spatial obstruction penalty factors and habitat loss factors to obtain a comprehensive evaluation index for the conditioning effect of saline-alkali land, including: For any target data collection period within the conditioning assessment cycle, extract the shallow topsoil total porosity data, shallow topsoil electrical conductivity data, middle and lower topsoil total porosity data, middle and lower topsoil aeration porosity data, and middle and lower topsoil electrical conductivity data corresponding to the target data collection period from the stratified soil physicochemical basic data. The average values ​​of the physicochemical data of the same type of soil in the shallow and middle-lower layers were calculated, and the average values ​​of the physicochemical data of the whole soil in the topsoil were normalized and linearly weighted by preset weights. The calculation results were used as the basic conditioning evaluation score corresponding to the target data collection period. Obtain the spatial obstruction penalty factor and habitat loss factor corresponding to the target data collection period, and use the calculation result of multiplying the spatial obstruction penalty factor and habitat loss factor as the joint correction assessment corresponding to the target data collection period; The result of multiplying the basic conditioning evaluation score corresponding to the target data collection period with the joint correction evaluation is used as the comprehensive evaluation index of the conditioning effect of saline-alkali land corresponding to the target data collection period.

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