A method for evaluating sand-fixing effect based on microbial inoculants

Abstract

The present application provides a kind of sand-fixing effect evaluation method based on microbial inoculant, it is related to desertification control technical field, the present application carries out multidimensional early evaluation to target sand-fixing area and candidate management unit, including sand accumulation characteristics, microtopography and microclimate environmental factor collection.Candidate microorganism sand-fixing scheme set is established, environmental adaptation degree, management synergy degree and sand-fixing strategy optimization index are calculated, and screening and sequencing are carried out according to access threshold value. The scheme set is finely screened using the evaluation model, which comprehensively considers biological activity attenuation entropy, sand-fixing layer micro-cementation, wind erosion resistance ability deviation and long-term stability gradient to determine the hierarchical implementation area and differentiated sand-fixing strategy. The wind erosion resistance ability deviation and biological activity attenuation entropy of the implementation area are continuously monitored, and the sand-fixing layer strength and stability are determined according to the preset convergence condition. If the effect does not converge as expected, hierarchical strategy adjustment or manual intervention is started.

Description

Technical Field

[0001] This invention relates to the field of desertification control technology, specifically to a method for evaluating the sand-fixing effect based on microbial agents. Background Technology

[0002] Located in an arid desert region, the grotto area experiences strong winds and abundant sand. Fine sand particles accumulate and rub against each other under the influence of the wind, threatening not only the stability of the grottoes themselves but also causing irreversible physical wear and burial of the precious murals and painted sculptures. Traditional sand-fixing techniques, such as setting up straw checkerboard sand barriers, nylon mesh, or planting psammophytes, while effective in general sand control, show increasingly limited limitations in special settings like grottoes where landscape harmony, material compatibility, and long-term maintenance costs are paramount. These traditional methods often focus primarily on windbreaks, reducing wind and sand erosion through physical barriers, but fail to consider the impact of particle size distribution, pore structure, and microenvironment of different sand layers (such as loose surface sand versus dense deep sand) on sand-fixing effectiveness. Traditional assessment methods are particularly inadequate for biological sand-fixing technologies like microbially-induced calcium carbonate (MICP) crystallization.

[0003] Microbial sand fixation technology enhances the wind erosion resistance of sand layers by inducing the formation of calcium carbonate cement between sand grains using microbial metabolites. However, this technology faces challenges in practical application, particularly in precise evaluation and strategy optimization. Existing microbial sand fixation schemes generally lack systematic consideration of key technical indicators during their formulation and implementation. For example, they fail to adequately assess the impact of different sand media on microbial activity and proliferation rates, leading to low efficiency in agent application. Furthermore, they lack effective real-time monitoring and quantitative evaluation of the cement formation rate, distribution uniformity, and microstructural stability during microbial-induced calcium carbonate crystallization. Especially under complex micro-topography and variable microclimate conditions, the uniformity of calcium carbonate crystallization directly affects the overall strength and durability of the sand-fixing layer, and existing technologies often struggle to accurately grasp the dynamic changes and spatial distribution of this micro-cementation. In addition, traditional sand fixation strategies lack specificity, failing to perform graded optimization based on regional wind erosion risk levels, specific sand layer characteristics, and microbial activity potential. This results in traditional experience-based schemes being unable to adapt to complex and variable sandy environments, and the sand fixation effect failing to meet expectations. More importantly, during the formation and stabilization of the sand-fixing layer, there is a lack of effective early warning and feedback adjustment mechanisms for the dynamic changes in the decline of microbial activity, the deviation of the sand-fixing layer's wind erosion resistance, and the long-term stability gradient. Once the sand-fixing effect deviates from expectations, it is often impossible to initiate strategy upgrades or artificial intervention in a timely and scientific manner, thus delaying the best time for governance. Summary of the Invention

[0004] The purpose of this invention is to provide a method for evaluating the sand fixation effect based on microbial agents, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for evaluating the sand-fixing effect based on microbial inoculants, comprising the following steps: A multi-dimensional preliminary assessment was conducted on the target sand-fixing area and candidate treatment units. The preliminary assessment included collecting sand accumulation characteristic parameters, micro-topographic features, and microclimate environmental factors. A candidate microbial sand fixation scheme set is established. The candidate microbial sand fixation scheme set is obtained by comprehensively calculating the environmental adaptability coefficient, the governance synergy index and the sand fixation strategy optimization index, and the candidate governance units are screened and sorted in descending order according to the preset admission threshold. In the multi-objective decision optimization framework, an evaluation model is used to refine the selection of candidate microbial sand fixation schemes. The evaluation model is based on a comprehensive consideration of biological activity decay entropy, micro-cementation degree of sand fixation layer, wind erosion resistance deviation and long-term stability gradient to determine the graded implementation areas and their corresponding differentiated sand fixation strategies. Continuously monitor the deviation of wind erosion resistance and the entropy of biological activity decay in the implementation area, and determine whether the sand fixation layer has reached the preset strength and stability requirements based on the preset convergence conditions. Based on the judgment results, if the sand fixation effect fails to converge as expected, a graded strategy adjustment or artificial intervention early warning mechanism will be activated; if the sand fixation layer reaches the preset strength and stability requirements, a sand fixation effect confirmation marker will be triggered and subsequent monitoring and maintenance cycles will be planned.

[0006] Compared with the prior art, the beneficial effects of the present invention are: This invention improves the targeting and scientific rigor of microbial sand-fixing strategies by constructing a multi-dimensional evaluation model that includes bioactivity decay entropy, microscopic cementation of the sand-fixing layer, wind erosion resistance deviation, and long-term stability gradient, and by classifying treatment units in conjunction with wind erosion risk coefficients. This overcomes the limitations of existing "one-size-fits-all" solutions that cannot adapt to complex sandy environments, ensuring that measures are tailored to local conditions and implemented precisely in the grotto area, thereby enhancing the effectiveness of sand-fixing schemes and resource utilization efficiency.

[0007] This invention improves the responsiveness and success rate of microbial sand-fixing engineering by establishing a continuous monitoring and dynamic judgment mechanism based on wind erosion resistance deviation and biological activity decay entropy, and by initiating a graded adaptive strategy adjustment when the sand-fixing effect does not meet expectations. This solves the problem of the lack of an effective early warning and feedback adjustment mechanism in existing technologies, avoids the passive situation of not being able to intervene in time when the sand-fixing effect deviates from expectations, ensures that the sand-fixing project in the grotto area can be optimized in a timely manner according to the actual progress, and improves the reliability of the treatment.

[0008] This invention improves the adaptability and long-term stability of microbial sand fixation technology in grotto areas by integrating wind erosion risk assessment, multi-index quantitative evaluation, hierarchical treatment strategies, and a full-process management framework of dynamic monitoring and adaptive adjustment. This overcomes the limitations of traditional sand fixation methods in terms of landscape harmony, material compatibility, and maintenance costs, and compensates for the shortcomings of existing microbial sand fixation technologies in precise assessment and optimization strategies, providing a more sustainable and scientific solution for wind and sand protection of precious cultural relics. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the overall process of the sand fixation effect evaluation method based on microbial agents of the present invention.

[0010] Figure 2 This is a schematic diagram illustrating the overall method flow of the present invention. Detailed Implementation

[0011] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0012] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0013] Example 1: Please see Figures 1 to 2 The present invention provides a technical solution: A method for evaluating the sand-fixing effect based on microbial inoculants, comprising the following steps: S1: Conduct a multi-dimensional preliminary assessment of the target sand-fixation area and candidate treatment units. The preliminary assessment includes collecting sand accumulation characteristic parameters, micro-topographic features, and microclimate environmental factors. A candidate microbial sand fixation scheme set was established. The candidate microbial sand fixation scheme set was selected and sorted in descending order based on the comprehensive calculation of environmental adaptability coefficient, governance synergy index and sand fixation strategy optimization index, and the candidate governance units were screened and sorted in descending order according to the preset admission threshold. The steps in S1 for establishing a candidate microbial sand fixation scheme set and scheme screening specifically include: obtaining the environmental compatibility coefficient between the treatment unit u and the target sand fixation area through a multi-scale environmental sensing network and calculating the treatment synergy index; the environmental compatibility coefficient is obtained by real-time collection of sand layer porosity, initial water content and microclimate fluctuation range in the treatment unit u area, and obtaining it through weighted logic calculation; the treatment synergy index is obtained by comparing the sand particle size distribution, topographic slope characteristics and wind erosion potential level of the treatment unit u area, and outputting a value characterizing the difficulty of sand fixation layer construction; the sand fixation strategy optimization index is obtained by calculating the product of the environmental compatibility coefficient and the treatment synergy index.

[0014] Units in the candidate treatment unit set that are below the preset admission threshold are removed, and the remaining units are arranged in descending order of the sand fixation strategy selection index and added to the candidate microbial sand fixation scheme set.

[0015] This embodiment focuses on a typical sand-affected area on the roof of a grotto. This area has complex environmental characteristics, including gently sloping sandy land and dune slopes with gradients of 15-30 degrees. The sand particle size distribution varies from fine sand (<0.25mm) to coarse sand (>0.5mm), and it faces the challenge of a large diurnal temperature range (up to 20℃) and drastic wind speed variations. Traditional sand-fixing methods are ill-suited to this diverse terrain and environment, and their assessments lack refined guidance. This invention aims to optimize the formulation and implementation of microbial sand-fixing schemes by constructing a refined three-dimensional field model of sand-fixing potential and introducing an intelligent assessment mechanism.

[0016] The steps for establishing a candidate set of microbial sand fixation schemes and screening schemes in S1 specifically include: S11. Obtain the environmental adaptability coefficient of the treatment unit; The environmental adaptability coefficient is obtained by real-time collection of sand layer porosity, initial moisture content, and microclimate fluctuation range in the treatment unit u region through a multi-scale environmental sensing network, and calculated based on a preset weighted logic. The multi-scale environmental sensing network includes: an UAV-borne hyperspectral / multispectral sensor for macroscopic topography and vegetation cover analysis; a ground-based wireless sensor network for real-time monitoring of microclimate parameters such as sand layer temperature, humidity, and wind speed; and a portable soil analyzer for rapid on-site detection of sand layer particle size distribution, porosity, and initial moisture content. This network integrates the above-mentioned multi-source heterogeneous data to construct a comprehensive dynamic perception capability for the sand-fixing area.

[0017] Environmental adaptability coefficient, denoted as C adapt (u) is used to characterize the adaptability of the candidate treatment unit u to the environmental conditions of the microbial agent. Its calculation formula is:

[0018] in, uThe porosity of the sand layer in candidate treatment unit u is represented, and its value ranges from [0,1]. max This represents the theoretical maximum porosity of the region. M u表示 The average sand layer moisture content of candidate treatment unit u (e.g., an average value obtained in real time through ground-based wireless sensor networks at a depth of 0–5 cm, expressed as a percentage), M opt M represents the optimal moisture content for microbial activity (expressed as a percentage). max,dev This indicates the maximum permissible moisture content deviation (expressed as a percentage). This parameter primarily considers the overall moist environment required for microbial survival and metabolism. ΔT u ΔT represents the daily fluctuation range (in degrees Celsius) of the surface temperature of the microclimate sand layer in candidate governance unit u. max This represents the maximum temperature fluctuation range (in degrees Celsius) that the microorganisms can tolerate. The max(0,…) function ensures that when the temperature fluctuation exceeds the maximum tolerance range, the contribution of this term is not negative, but drops to zero, indicating that the environment is no longer suitable. q1, q2, and q3 are the normalized weights of each dimension, and their sum is 1. In this embodiment, q1=0.4 (porosity is given priority, as it directly affects the bacterial solution wetting effect), q2=0.4 (moisture content is given priority, as it directly affects microbial activity), and q3=0.2 (considering the impact of temperature fluctuations on microbial stability).

[0019] S12. Construct a governance synergy index for each candidate governance unit; obtain the governance synergy index by comparing the sediment particle size distribution and topographic slope characteristics of candidate governance unit u region, and based on a preset compatibility evaluation function. The governance synergy index is denoted as I. synergy (u) is used to characterize the degree of matching between the physical characteristics of the candidate treatment unit u and the microbial sand fixation method. Its calculation formula is as follows:

[0020] Among them: G u C represents the particle size distribution characteristics of the sediment in candidate treatment unit u (including fine sand, medium sand, and coarse sand). size (G u The value represents the compatibility score for microbial cementation efficiency calculated based on this particle size distribution, and its range is [0,1]. u C represents the topographic slope characteristics (including gentle, mild, and steep slopes) of candidate governance unit u. slope (S uThe score is calculated based on the terrain slope to determine the compatibility between construction difficulty and sand fixation layer stability. Its value range is [0,1] (e.g., a higher score corresponds to a gentler slope, and a lower score corresponds to a steeper slope). WERI(u) represents the wind erosion risk coefficient of candidate treatment unit u. 0 represents the lowest wind erosion risk, and 1 represents the highest wind erosion risk. It is included in the calculation in the form of (1-WERI(u)) to reflect the inverse relationship that a lower wind erosion risk corresponds to a higher degree of treatment synergy. w1, w2, and w3 are the contribution weights of each feature, and their sum is 1. In this embodiment, w1=0.4 (particle size distribution directly affects the cementation effect), w2=0.3 (slope affects construction efficiency and the physical stability of the sand fixation layer), and w3=0.3 (wind erosion potential affects the long-term effect of the sand fixation layer).

[0021] It should be noted that the particle size distribution compatibility score C size (G u The matching logic for ) is as follows: The efficiency of microbial sand fixation (MICP) is closely related to the specific surface area, pore structure, and permeability of sand particles to microbial inoculum. Fine sand has a larger specific surface area, providing more sites for microbial attachment and calcium carbonate crystal growth; at the same time, the pores formed by fine sand are generally smaller, which is conducive to the uniform distribution of the inoculum and the effective deposition of calcium carbonate, thus forming a denser cemented structure. However, excessively fine particles may lead to poor permeability, affecting the deep penetration of the inoculum. Coarse sand has a small specific surface area, large pores, and the inoculum is easily lost, making cementation difficult and resulting in a sand-fixing layer with lower strength. u The particle size distribution of sand samples from candidate treatment unit u is determined by standard sieve analysis (e.g., according to ASTM-D422 standard), specifically, the particle size distribution compatibility score Csize(G). u The effective particle size D is determined based on the measured particle size distribution of candidate treatment unit u. After performing a sieving experiment on the sand sample from candidate treatment unit u, the effective particle size D is obtained. 10 Median particle size D 50 and limiting particle size D 60 And calculate the non-uniformity coefficient C. u :

[0022] In the formula, D 10 D represents the particle size corresponding to a cumulative throughput of 10%. 50 D represents the median particle size corresponding to a cumulative pass rate of 50%. 60 This indicates the particle size corresponding to a cumulative pass rate of 60%, both in mm; C u This represents the coefficient of non-uniformity.

[0023] When C uIf the particle size distribution score is greater than 10, or if the sieving curve shows obvious missing particle sizes, it is judged as extremely non-uniform sand, and the particle size distribution compatibility score is calculated according to the following formula:

[0024] When C u When the value is not greater than 10 and the sieving curve is continuous, compare D50 with the preset particle size boundary value and calculate according to the following formula:

[0025] The principle of the formula is that D 50 The smaller the sand grain, the larger its specific surface area, the more microbial attachment sites and calcium carbonate crystal nucleation sites there are, resulting in a higher score; D 50 The larger the pores, the easier it is for the bacterial solution to escape, and the lower the score; C u If the particle size is too large or the sieving curve shows missing particle sizes, it indicates that the sand sample has extremely uneven gradation and poor cementation continuity; therefore, a lower score is used. Example scores are shown in Table 1.

[0026] Table 1: Particle size distribution compatibility score C size (G u Example table

[0027] Terrain slope compatibility score C slope (S u The matching logic is that topographic slope directly affects the infiltration, retention, and uniform distribution of microbial inoculants in the sand layer. On steep slopes, the inoculants are easily lost rapidly under gravity, resulting in insufficient sand infiltration, limited microbial activity, and uneven calcium carbonate precipitation. Furthermore, after the sand-fixing layer is formed, it experiences greater gravitational shear stress on steep slopes, posing a challenge to its long-term stability. Topographic slope compatibility score C slope (S u The slope α is determined based on the average slope of the candidate treatment unit u, where α is in degrees. α is compared with the preset slope boundary value and calculated using the following formula:

[0028] In the formula, α represents the average slope of the candidate treatment unit u. The smaller the slope, the easier it is for the bacterial solution to permeate and remain evenly in the sand layer, and the less gravitational shear force the sand-fixing layer experiences, thus resulting in a higher score. Conversely, the larger the slope, the more significant the loss of the bacterial solution along the slope, and the worse the stability of the sand-fixing layer, thus resulting in a lower score. Therefore, those skilled in the art can determine the C based on the measured average slope. slope (S u Example scores are shown in Table 2.

[0029] Table 2: Terrain Slope Compatibility Score C slope(S u Example table

[0030] The principle behind the calculation of the wind erosion risk coefficient WERI(u) is that wind erosion potential refers to the inherent risk of wind erosion of the sand layer in a candidate treatment unit u before treatment. Areas with high wind erosion potential mean frequent sand grain movement, which continuously interferes with the attachment and growth of microorganisms and the formation of early calcium carbonate crystals, thus prolonging the sand fixation cycle and increasing the difficulty and risk of initial sand fixation failure. Even if a sand fixation layer is initially formed, a high wind erosion environment poses a greater threat to its long-term maintenance. Therefore, the lower the wind erosion potential, the better the initial synergy of sand fixation layer construction. The wind erosion risk coefficient (WERI) is used to quantify wind erosion potential, and its calculation includes the following parameters: Average wind speed, denoted as V u Long-term average wind speed data (in m / s) for candidate treatment unit u areas are collected through ground-based wireless sensor networks or small weather stations. Higher average wind speeds directly correspond to higher wind erosion risks.

[0031] The surface water content of the sand layer is denoted as M. surf,u The moisture content of the uppermost layer of sand (e.g., 0-1 cm depth) in candidate treatment unit u area is monitored in real time using a ground-based wireless sensor network. Lower surface moisture content reduces the adhesion between sand grains, thus increasing the risk of wind erosion. This parameter is related to the average sand moisture content M in S11. u The difference lies in the surface condition of the sand layer that is directly affected by wind.

[0032] Surface roughness index, denoted as R u By using a UAV-borne hyperspectral / multispectral sensor combined with image processing algorithms, the distribution of non-sand-based obstacles such as surface vegetation cover, rocks, or surface crusts in candidate governance units u is analyzed to calculate the surface roughness index (e.g., dimensionless value [0,1]). Higher surface roughness can effectively reduce near-surface wind speed, thereby reducing the risk of wind erosion. The calculation formula is as follows: R u =k1×VC u +k2×OD u +k3×CC u Wherein, VCu represents the vegetation cover of candidate governance unit u; the pixel ratio of vegetation cover is obtained by calculating the normalized differential vegetation index (NDVI) and thresholding the hyperspectral / multispectral images. Regions with NDVI values ​​greater than 0.2 are identified as vegetation, and their area proportion is calculated. Higher VC... u The value indicates a stronger ability to reduce wind speed and fix sand particles; OD uThis represents the density of non-sandy obstacles in candidate governance unit u. Rocks, gravel, or other non-sandy fixed objects are identified using land cover classification and target recognition algorithms on hyperspectral / multispectral images, and their area percentage within the unit region is calculated. For example, rocks are distinguished by setting spectral reflectance thresholds (e.g., red band reflectance greater than 0.3, near-infrared band reflectance less than 0.4), and their area percentage is calculated. Higher OD... u The value indicates that there are more structures on the Earth's surface that can generate turbulence and dissipate wind energy. CC u The surface crust coverage of candidate governance unit u is represented, with a value range of [0,1]. The area ratio of surface crust is calculated by performing spectral feature analysis and classification on hyperspectral / multispectral images (e.g., distinguishing between biological crust, physical crust, and bare sand). k1, k2, and k3 are the normalized weights of each component, and their sum is 1. In this embodiment, k1 is set to 0.4 (vegetation has the most direct effect on reducing wind speed, for example, by increasing aerodynamic roughness to reduce friction speed), k2 to 0.3 (obstacles provide physical resistance and turbulence effects), and k3 to 0.3 (surface crust provides surface stability and improves shear strength).

[0033] Combined with average wind speed V u , Sand surface moisture content M surf,u and surface roughness index R u The wind erosion risk coefficient WERI(u) is calculated using the following formula:

[0034] Among them, M surf,ref To reference the maximum surface moisture content, for example, the saturated moisture content, it is set to 20%. R ref To reference the maximum roughness index, the roughness of completely vegetated or complex terrain is set to 1.0. a1, a2, and a3 are the contribution weights of each feature, and their sum is 1. In this embodiment, a1 is set to 0.4 because wind speed is the main driving force, a2 to 0.3 because surface humidity affects sand particle adhesion, and a3 to 0.3, with roughness providing protection. S13, Based on the environmental adaptability coefficient C of S11 and S12 adapt (u) and governance synergy index I synergy The sand fixation strategy optimization index SSPI(u) is obtained by multiplying (u) by itself. Its calculation formula is: SSPI(u) = C adapt (u)×I synergy (u); The sand fixation strategy optimization index is used to comprehensively evaluate the overall suitability and potential effects of implementing microbial sand fixation schemes in candidate treatment units u.

[0035] S14. Screen and sort candidate treatment units; remove units in the candidate treatment unit set that are below the preset admission threshold τ, and sort the remaining candidate treatment units in descending order according to the sand fixation strategy optimization index SSPI(u), thereby establishing the final candidate microbial sand fixation scheme set.

[0036] The logic and source of the admission threshold τ are explained. This threshold is based on the minimum effect requirements and resource input benefit assessment of microbial sand fixation engineering. Its reasonable value range is (0, 0.5). In this embodiment, the minimum acceptable porosity is set to 60% of the theoretical maximum porosity (i.e., u / max ≥0.6), the initial moisture content deviates from the optimum value by no more than 30% (i.e., |Mu-M) opt | / M max,dev ≤0.3), the daily fluctuation range of the microclimate does not exceed 70% of the maximum value (i.e., ΔT). u / ΔT max (≤0.7). Simultaneously, the particle size distribution compatibility score must be no less than 0.6, the terrain slope compatibility score no less than 0.5, and the wind erosion potential compatibility score no less than 0.7. Based on historical engineering data and expert experience, the optimal entry threshold τ is set at 0.30. This means that only above this stringent physical baseline can candidate treatment units possess the minimum feasibility for implementing microbial sand fixation, thereby avoiding ineffective resource input and inefficient treatment at the source.

[0037] Specific examples: This embodiment focuses on a sand-affected area of ​​approximately 1000 square meters on the roof of a grotto, dividing it into 7 candidate treatment units (U-01 to U-07). Parameter settings: max =0.55 (maximum porosity), M opt =0.15 (optimal moisture content), M max,dev =0.10 (maximum moisture content deviation), ΔT max =25℃ (maximum temperature fluctuation). Weights q1=0.4, q2=0.4, q3=0.2; w1=0.4, w2=0.3, w3=0.3. Admission threshold τ=0.30.

[0038] The calculation process and results are shown in Tables 3 and 4.

[0039] Table 3: Detailed Parameter Calculation Table for Candidate Governance Units

[0040] Table 4: Calculation Table for Environmental Adaptability and Governance Synergy Assessment of Candidate Governance Units

[0041] Based on the calculation results, the SSPI values ​​of treatment units U-01, U-02, U-05, and U-07 exceeded the admission threshold τ=0.30. The system sorted these four units in descending order of SSPI value (0.641>0.469>0.382>0.350), thus establishing the final set of candidate microbial sand fixation schemes. For example, although treatment unit U-04 had a relatively good environmental adaptability (C... adapt (u)=0.730), but because the accumulated sand is coarse sand and the terrain slope is steep, the degree of synergy in governance is low (I synergy (u)=0.333), and the final SSPI value was 0.243, which was lower than the admission threshold, so it was judged as a "high-difficulty unit" and excluded. Although the treatment unit U-03 had good particle size distribution and slope compatibility, its initial moisture content was much higher than the optimal value, resulting in poor environmental adaptability (C). adapt (u)=0.318), and the final SSPI value is 0.253, so it was also excluded. Although the microclimate of governance unit U-07 fluctuates significantly, when 30℃>ΔT max =25℃, resulting in C adapt While the temperature term in (u) is 0, its high porosity, moderate initial moisture content, and fine particle size distribution indicate a relatively high overall environmental suitability. However, its extremely high wind erosion risk (WERI(u) = 0.924) severely impacts the synergy of its treatment efforts. Despite this, due to its high environmental suitability, its final SSPI value still exceeds the admission threshold, leading to its inclusion in the candidate microbial sand fixation scheme set. However, its low ranking indicates the need for special consideration regarding its high wind erosion risk. This screening mechanism effectively avoids investing resources in areas that are difficult to manage or have a low return on investment, ensuring the efficiency and effectiveness of subsequent sand fixation work.

[0042] The method provided in this embodiment, especially in stage S1, introduces a systematic, data-driven evaluation framework aimed at addressing the problems of incomplete evaluation and imprecise strategies faced by traditional sand fixation technologies and existing microbial sand fixation applications in complex and sensitive scenarios such as grottoes. Its core technical principle lies in conducting a multi-dimensional preliminary evaluation to quantitatively analyze the environmental adaptability and governance synergy of each candidate treatment unit in the target sand fixation area, thereby establishing a set of screened and ranked microbial sand fixation schemes. Specifically, this method uses a multi-scale environmental sensing network to collect environmental parameters such as sand layer porosity, initial moisture content, and microclimate fluctuation range in real time, and calculates the environmental adaptability coefficient based on a preset weighted logic. The environmental adaptability coefficient objectively reflects the degree to which the physical environment of the treatment unit adapts to the activity and survival of microbial agents, ensuring that microorganisms can function in a relatively favorable environment. This method calculates the governance synergy index by comparing physical characteristics such as sand particle size distribution, topographic slope characteristics, and wind erosion risk coefficient. Particle size distribution directly affects the efficiency of microbial cementation and the uniform distribution of calcium carbonate crystals, while topographic slope affects the penetration and retention of bacterial solutions and the physical stability of the sand-fixing layer. The wind erosion risk coefficient quantifies the degree of wind interference with microbial attachment and early cementation processes. The comprehensive consideration of these indicators ensures that the selected area not only has an environment suitable for microorganisms, but its sand layer and topography also have a good match with microbial sand-fixing technology. By multiplying the environmental suitability coefficient by the governance synergy index, a sand-fixing strategy optimization index is obtained. This index comprehensively evaluates the overall suitability and potential effects of microbial sand-fixing schemes in a specific governance unit. Based on this, an entry threshold is set, and units with optimization indices below this threshold are eliminated. The remaining units are then arranged in descending order of the index, forming the final set of candidate microbial sand-fixing schemes, laying the foundation for subsequent refined decision-making.

[0043] The beneficial effects of this embodiment are that it overcomes the limitations of traditional sand fixation methods in certain grottoes due to a lack of consideration for key factors such as the characteristics of different sand layers, microenvironment, and the uniformity of calcium carbonate crystallization. By accurately calculating the environmental adaptability coefficient and the governance synergy index, this method can identify the most suitable areas for microbial sand fixation, avoiding blind investment in unsuitable areas, thereby improving the success rate and resource utilization efficiency of sand fixation projects. Secondly, by introducing a wind erosion risk coefficient and incorporating it into the governance synergy index during the early assessment stage, this method can identify and avoid high-risk wind erosion areas early on, reducing the possibility of damage to the sand-fixing layer in its early stages, thus enhancing the reliability of the sand fixation effect. This data-driven quantitative evaluation method makes the formulation of sand fixation schemes more objective and scientific, replacing the previous strategy of relying on experience-based judgment. By screening and ranking candidate governance units, a more precise governance path can be provided for areas like grottoes with complex micro-topography and variable microclimates, ensuring that sand fixation measures can better adapt to regional differences. For example, the example uses the SSPI value to remove units such as U-04, U-03, and U-06, which helps to avoid investing resources in these high-difficulty or low-potential areas, thereby optimizing the overall sand fixation strategy and improving the efficiency and effectiveness of subsequent sand fixation work.

[0044] Example 2: Please see Figures 1 to 2 S2: In the multi-objective decision optimization framework, the evaluation model is used to screen the three-dimensional field model of sand fixation potential. The evaluation model is based on a comprehensive consideration of the bioactivity decay entropy, the micro-cementation degree of the sand fixation layer, the wind erosion resistance deviation and the long-term stability gradient. The optimal treatment unit u is determined from the candidate microbial sand fixation scheme set as the initial implementation area, and a candidate treatment strategy containing the microbial agent ratio and construction parameter combination is generated. The bioactivity decay entropy, sand-fixing layer microcementation degree, wind erosion resistance deviation, and long-term stability gradient of each treatment unit are constructed to generate a fused feature vector. The bioactivity decay entropy is obtained by acquiring the microbial agent activity index and decay rate of treatment unit u, calculating its information entropy over time, and characterizing the dynamic uncertainty of the biomineralization process. The sand-fixing layer microcementation degree is obtained by monitoring the uniformity of calcium carbonate crystallization caused by environmental disturbance in the early stage of sand fixation in treatment unit u, characterizing the effectiveness of biomineralization on sand grain cementation. The wind erosion resistance deviation is obtained by simultaneously acquiring the expected wind erosion resistance and the actual wind tunnel test results of treatment unit u during the sand-fixing layer formation stage, and calculating the performance deviation between the two. The long-term stability gradient is obtained by calculating the rate of change of the mechanical strength and microstructure parameters of the sand-fixing layer over time.

[0045] This step aims to construct an evaluation model based on the candidate microbial sand-fixing scheme set established in S1, conducting in-depth analysis and screening to determine the most suitable areas for initial treatment and formulate specific construction strategies. This evaluation model comprehensively considers multiple key indicators, including biological dynamics during the microbial sand-fixing process, physical cementation effect, resistance to external environmental disturbances, and long-term stability. The evaluation model receives the candidate microbial sand-fixing scheme set output by S1, calculates various evaluation indicators for each candidate treatment unit, and finally outputs the corresponding treatment unit and its corresponding treatment strategy.

[0046] The logic for determining the initial implementation area and governance area in S2 specifically includes: S21: Identify priority treatment units with high wind erosion risk; The evaluation model utilizes the wind erosion risk coefficient WERI(u) of the candidate treatment units u obtained in S12. All treatment units with WERI(u) higher than the preset wind erosion risk threshold are marked as a set of high-risk wind erosion units. In the evaluation model, treatment units with a wind erosion risk coefficient WERI(u) higher than the preset wind erosion risk threshold (e.g., 0.60) are marked as high-risk wind erosion units and classified as first-level implementation areas. The logic for this classification range is based on the sensitivity of microbial sand fixation technology to environmental disturbance, the protection needs of a certain grotto as a cultural heritage site, and the optimization considerations of resource allocation. WERI(u), as a normalized index, ranges from 0 to 1. 0.60 is considered the preferred value for classification, indicating that the wind erosion risk in this area has reached a moderately high level, significantly interfering with the attachment and growth of microbial agents and the early formation of calcium carbonate cement, potentially affecting the stability and uniformity of sand fixation effects. Therefore, the threshold is preferably set within the range of 0.50 to 0.75, aiming to balance the enthusiasm for early intervention with the rationality of resource investment: a threshold below this range may lead to an overreaction to risks, classifying a large number of controllable areas as high-risk and wasting resources; while a threshold above this range may lead to a lag in risk identification, causing reinforcement measures to be initiated only when wind erosion has already caused significant impact, increasing the difficulty of treatment and the risk of failure. The threshold within this preferred range can ensure that the system can promptly identify areas that truly require reinforcement strategies (such as the first sand fixation strategy), thereby effectively reducing the risk of initial sand fixation failure and ensuring the long-term effectiveness of the wind and sand protection project for the grotto and the safety of cultural relics.

[0047] S22: Determine the implementation area for the tiered system and generate candidate governance strategies; The assessment model first determines whether the set of high-wind-erosion-risk units is non-empty. If the set of high-wind-erosion-risk units is non-empty, the assessment model designates the treatment unit u of the set of high-wind-erosion-risk units as the first-level implementation area and generates the first sand-fixing strategy, including: preparing microbial agents at a concentration 20-30% higher than the conventional application concentration and applying them 2-3 times per week; combining them with biopolymers, such as xanthan gum or sodium alginate, for reinforcement; deploying physical sand-fixing barriers with a spacing of 5-10 meters between each barrier, including biodegradable bio-covering nets, high-density grass checkerboard sand barriers, or low windbreak nets; and configuring an intelligent irrigation system, configured to replenish water 3-5 times per day for 2-4 weeks. For the remaining treatment units in the candidate microbial sand-fixing scheme set of S1 that were not identified as having a high risk of wind erosion, the evaluation model comprehensively evaluates them based on the sand-fixing strategy optimization index SSPI(u) and the evaluation indicators constructed in stage S2. Specifically, it constructs the entropy of biological activity decay, the micro-cementation degree of the sand-fixing layer, the deviation of wind erosion resistance, and the long-term stability gradient, and combines them with the sand-fixing strategy optimization index SSPI(u) to comprehensively calculate the comprehensive evaluation score S. core (u); The bioactivity decay entropy, sand-fixing layer micro-cementation degree, wind erosion resistance deviation, and long-term stability gradient of each governance unit are constructed to generate a fused feature vector. The fused feature vector refers to all relevant quantitative indicators used in the evaluation model, including C calculated in stage S1. adapt (u), I synergy SSPI(u), WERI(u), and the following four newly constructed metrics in this S2 phase: The bioactivity decay entropy is obtained by acquiring the activity indicators (e.g., ATP content measured by ATP bioluminescence or respiratory activity measured by CO2 release) and decay rate of microbial agents in candidate treatment unit u. Microbial activity is continuously monitored during the initial stage of sand fixation (the first 30 days), and its activity level is discretized into several states. The probability distribution of these activity states at different time points is calculated, and the information entropy of their time distribution is calculated using the Shannon entropy formula, characterizing the dynamic uncertainty of the biomineralization process. Bioactivity decay entropy H decay The formula for calculating (u) is:

[0048] Where, p i(t) represents the probability that microbial activity is observed to be at the i-th discrete activity level at time t, where N is the total number of discrete activity levels. Specifically, discrete activity level refers to the finite state obtained by dividing the measured microbial activity values ​​according to the relative activity ratio. At time t, m(t) microbial activity samples are collected from candidate treatment unit u. The measured activity value of sample j is denoted as Aj(t), the initial activity value is denoted as Aj(0), and the relative activity rj(t) is calculated according to the following formula:

[0049] In this embodiment, relative activity is divided into four discrete activity levels: 0 ≤ rj(t) < 0.25 is a low activity level, 0.25 ≤ rj(t) < 0.50 is a slightly low activity level, 0.50 ≤ rj(t) < 0.75 is a medium activity level, and 0.75 ≤ rj(t) ≤ 1.00 is a high activity level. The number of samples falling into the i-th discrete activity level, ni(t), is counted, and the probability pi(t) is calculated according to the following formula:

[0050] A lower entropy value indicates a more stable and predictable decline in microbial activity, which is beneficial for the expected sand-fixing effect. The entropy of biological activity decline is used to quantify the dynamic uncertainty or stability of microbial inoculant activity changes during sand-fixing. A lower entropy value indicates a more regular and predictable decline in microbial activity, thus implying a more stable biomineralization reaction and a more reliable expectation of sand-fixing effect. Conversely, a higher entropy value indicates greater fluctuations in microbial activity and more uncertainty in the sand-fixing process.

[0051] The micro-cementation degree of the sand-fixing layer was obtained by performing microscopic analysis on sand samples from the initial stage of sand fixation (7 days after sand fixation was set) of the candidate treatment unit u, monitoring the uniformity of calcium carbonate crystallization caused by environmental disturbances (e.g., slight wind disturbance, temperature fluctuations). Specifically, the method included: accurately identifying and extracting the 3D spatial distribution of calcium carbonate cement through image processing and segmentation algorithms; dividing the sand samples into several equal-volume micro-sub-regions (e.g., cubic voxels or representative unit volumes with side lengths equal to several sand grain diameters); calculating the volume or mass percentage of calcium carbonate cement in each micro-sub-region as the local cementation content; and collecting the local cementation content values ​​of all micro-sub-regions to form a micro-dataset; and calculating the ratio of the mean (Mean) to the standard deviation (StdDev) of this micro-dataset to characterize the effectiveness of biomineralization in sand grain cementation. The micro-cementation degree D of the sand-fixing layer was then calculated. cement The formula for calculating (u) is:

[0052] in, The function ensures that the micro-cementation degree is at least 0. When D cement When (u) approaches 1, it indicates that the calcium carbonate cement is extremely uniformly distributed throughout the sand-fixing layer, and the cementing effect is ideal. When D cement When (u) approaches 0, it indicates that the distribution of calcium carbonate cement is highly uneven, and there may be a large number of unbonded areas or weak cemented links, resulting in poor cementation effect.

[0053] The deviation in wind erosion resistance was obtained by simultaneously acquiring the expected wind erosion resistance and the actual wind tunnel test results for the candidate treatment unit u region during the sand-fixing layer formation stage (set to 14 days after sand fixation), and calculating the performance deviation between the two. Based on the wind erosion risk coefficient WERI(u) calculated in S12, a reverse extrapolation was performed to predict the sand loss at a specific wind speed. Standard sand samples were collected from the candidate treatment unit u region, and the actual wind speed conditions were simulated in a controlled wind tunnel experiment to measure the sand loss per unit time and per unit area. The wind erosion resistance deviation ΔE was then calculated. wind The formula for calculating (u) is:

[0054] Wherein, PsL represents the mass of sand grains eroded or transported by wind from a unit area of ​​the earth's surface per unit time, predicted in wind tunnel testing; AsL represents the actual mass of sand grains eroded or transported by wind from a unit area of ​​the earth's surface per unit time; and the lower ΔE... wind The (u) value indicates that the actual effect is closer to the expectation, suggesting that the sand fixation scheme is highly predictable.

[0055] The long-term stability gradient is obtained by periodically sampling and analyzing the sand-fixing layer of the candidate treatment unit u, calculating the mechanical strength of the sand-fixing layer, specifically the unconfined compressive strength (UCS) and the rate of change of microstructural parameters (e.g., porosity, calcium carbonate content) over time. The specific method involves collecting sand samples at different time points after sand fixation (e.g., 30 days, 90 days, 180 days) and performing UCS tests, while simultaneously conducting Micro-CT analysis to obtain microstructural parameters. The slope of the UCS value or key microstructural parameters over time, i.e., the long-term stability gradient G, is calculated using methods such as linear regression or curve fitting. stability (u), the calculation formula is:

[0056] Among them, UCS t1 and UCS t2 These represent the unconfined compressive strength (dimensions: pressure, e.g., kPa) measured at time points t1 and t2, respectively. A gradient close to zero or a positive value indicates stable or slightly increased strength of the sand-fixing layer, while a large negative value indicates rapid strength decay. The long-term stability gradient G is also considered.stability (u) Normalization yields the long-term stability score S. stability (u):S stability (u)=max(0,1+G stability (u) / Max DegradationRate ), where Max DegradationRa t is the acceptable maximum intensity attenuation rate, with dimensions similar to G. stability (u) is the same. .

[0057] The evaluation model calculates the comprehensive evaluation score S for each candidate governance unit u using the following formula. core (u): S core (u)=wSSPI×SSPI(u)+wH×(1 H decay (u) / H max )+wD×D cement (u)+wE×(1 ΔE wind (u))+wS×Ss tability (u); where; Wherein, SSPI(u) is the preferred index for sand fixation strategy. (1) H decay (u) / H max ) represents the normalized entropy of biological activity decay, where H max Set to 2 bits. D cement (u) represents the micro-cementation degree of the sand-fixing layer. (1) ΔE wind (u) represents the normalized deviation of wind erosion resistance. Sstability(u) is the long-term stability score. wSSPI, wH, wD, wE, and wS are the weights of each index. In this embodiment, the preferred values ​​are set as wSSPI=0.20; wH=0.15; wD=0.25; wE=0.20; wS=0.20; A preset classification threshold is set, with a preferred value range of 0.70 to 0.75, and second-level implementation areas and third-level implementation areas are obtained by classification. In this embodiment, the preferred value of the classification threshold is set to 0.70. The comprehensive evaluation score S core(u) Areas with a classification threshold greater than or equal to the threshold are marked as Level 2 implementation areas. Level 2 implementation areas correspond to Level 2 sand fixation strategies, including: preparing microbial agents at standard concentrations and applying them once a week, with the agent formulation adjusted according to the regional microenvironment; applying optimized nutrient solutions, with the carbon source, nitrogen source, and trace elements in the nutrient solution in the correct ratio, and applying the nutrient solution using drip irrigation or micro-sprinkler irrigation technology; prioritizing the use of biodegradable mulch, such as bio-fiber blankets or straw mulch; configuring intelligent irrigation systems, with the system configured to provide precise water replenishment 1-2 times every 1-2 days for 4-8 weeks. The comprehensive evaluation score S core (u) Areas with concentrations less than the classification threshold are marked as Level 3 implementation areas. Level 3 implementation areas correspond to Level 3 sand fixation strategies, including: preparing microbial agents at concentrations 10-20% lower than the standard concentration, or delaying the application time; prioritizing small-scale pilot applications of microbial sand fixation schemes, with the pilot area covering 5-10% of the total area; establishing long-term environmental monitoring points with a monitoring frequency of once a month; and configuring intelligent irrigation systems, with the system configured to provide precise water replenishment 1-2 times per week for 8-12 weeks. Specific example data: This embodiment continues the screening results of S1 and evaluates the selected governance units (U-01, U-02, U-05, U-07) in the S2 stage. The final selected units in S1 and their SSPI values ​​and WERI(u) values ​​are shown in Table 5 below.

[0058] Table 5: Example of S1 filtering results

[0059] Based on the preset wind erosion risk threshold of S21, identify priority treatment units with high wind erosion risk; For U-01 (WERI(u)=0.501): below the wind erosion risk threshold of 0.60.

[0060] For U-02 (WERI(u)=0.835): It is above the wind erosion risk threshold of 0.60 and is marked as a high wind erosion risk unit.

[0061] For U-05 (WERI(u)=0.663): It is above the wind erosion risk threshold of 0.60 and is marked as a high wind erosion risk unit.

[0062] For U-07 (WERI(u)=0.924): It is above the wind erosion risk threshold of 0.60 and is marked as a high wind erosion risk unit.

[0063] Therefore, the set of high wind erosion risk units is {U-02, U-05, U-07}.

[0064] Determining the Level Implementation Area and Generating Candidate Governance Strategies (S22): Since the set of high wind erosion risk units is not empty, according to the logic of S22, the governance units of the high wind erosion risk unit set are designated as the Level 1 implementation area. Level 1 implementation area: {U-02, U-05, U-07}; For units with low wind erosion risk (i.e., U-01), the evaluation model comprehensively evaluates the sand fixation strategy optimization index SSPI(u) and the evaluation indicators constructed in stage S2. The final corresponding strategies are shown in Table 6.

[0065] Table 6: Example of S2 evaluation for candidate governance units:

[0066] In this embodiment, the core technical principle of stage S2 lies in constructing a multi-objective decision optimization framework. By introducing a series of refined evaluation indicators, the candidate microbial sand-fixing schemes selected in stage S1 are analyzed and graded in depth. This solves the problems of existing microbial sand-fixing technologies lacking specificity in strategy formulation and lacking quantitative evaluation of the dynamics of the sand-fixing process. Specifically, this method first identifies high-risk areas based on wind erosion risk coefficients, classifies them as first-level implementation areas, and configures them with the most enhanced and comprehensive first-level sand-fixing strategy. This approach of prioritizing high-risk areas is based on the urgency of wind and sand erosion in sensitive cultural heritage sites such as grottoes. Through multiple means such as high-concentration bacterial agents, biopolymer enhancement, physical barrier assistance, and high-frequency intelligent irrigation, it aims to quickly stabilize the sand layer and reduce the risk of initial damage.

[0067] For the remaining treatment units that are not at high wind erosion risk, this embodiment introduces four key evaluation indicators: bioactivity decay entropy, sand-fixing layer micro-cementation degree, wind erosion resistance deviation, and long-term stability gradient. These are combined with the sand-fixing strategy optimization index from stage S1 to calculate a comprehensive evaluation score. Bioactivity decay entropy assesses the stability of the biomineralization process by quantifying the dynamic uncertainty of microbial activity changes; sand-fixing layer micro-cementation degree assesses the effectiveness and durability of the cementation effect by monitoring the uniformity of calcium carbonate crystallization; wind erosion resistance deviation assesses the sand-fixing layer's resistance to external environmental disturbances by comparing expected and actual wind tunnel test results; and long-term stability gradient assesses the long-term durability of the sand-fixing layer by analyzing the rate of change of its mechanical strength and microstructural parameters over time. These indicators comprehensively and quantitatively characterize the intrinsic mechanism and external manifestations of microbial sand fixation from multiple dimensions, including biological, physical, environmental response, and long-term performance. By weighting and summing these indicators, a comprehensive evaluation score is obtained. Based on preset classification thresholds, the treatment units are divided into Level 2 implementation areas (corresponding to the second sand fixation strategy) and Level 3 implementation areas (corresponding to the third sand fixation strategy). The second sand fixation strategy emphasizes standard concentrations of microbial agents, optimized nutrient solutions, and biodegradable cover materials, focusing on refined management and continuous stability. The third sand fixation strategy adopts more cautious pilot applications and lower concentrations of microbial agents, aiming at exploratory treatment and long-term monitoring.

[0068] The beneficial effects of this embodiment lie in constructing a more adaptive and responsive sand fixation strategy decision-making system. By prioritizing the identification and intensified management of high-risk wind erosion areas, the most pressing wind and sand erosion threats in areas such as a certain grotto can be effectively addressed, avoiding the problem of traditional uniform solutions being ineffective in high-risk areas. The introduction of indicators such as bioactivity decay entropy and microscopic cementation degree allows the assessment of microbial sand fixation processes to move from macroscopic phenomena to microscopic mechanisms, improving the scientific rigor and accuracy of the assessment. For areas with complex conditions or limited potential, more cautious or intensified measures are adopted to avoid ineffective investment. This tiered implementation strategy improves the adaptability and success rate of microbial sand fixation technology in complex and variable environments.

[0069] Example 3: Please see Figures 1 to 2 S3. Continuously monitor and determine that the sand-fixing layer has reached the preset strength; the system continuously monitors the deviation of the wind erosion resistance of the initial implementation area. When the deviation value is lower than the preset convergence threshold (e.g., less than 5%) for G consecutive cycles (e.g., G = 3 consecutive cycles, each cycle 7 days) and the biological activity decay entropy tends to be stable (e.g., its change rate is less than 0.01 bits / day), it is determined that the sand-fixing layer of the treatment unit u area has reached the preset strength and stability requirements.

[0070] In this embodiment, the treatment unit U-01 is taken as an example. It has been designated as a second-level implementation area and the second sand fixation strategy has been implemented. S31. The system will deploy a series of sensors and a periodic sampling mechanism to continuously acquire the deviation of the wind erosion resistance capability (ΔE) in this area. wind (U-01) and bioactivity decay entropy (H decay (U-01) Data. Multiple distributed field wind erosion sensors were installed in the U-01 area. These sensors can estimate the loss trend of surface sand particles in real time using miniature differential pressure gauges or optical particle counters. Meanwhile, to ensure data accuracy, the system randomly collects 5-10 standard fixed sand samples from the U-01 area every 7 days (i.e., one cycle) and sends them to a portable wind tunnel laboratory for precise ΔE measurements. wind (u) Tests. These test results are compared with the expected sand loss calculated in S12 based on WERI(u) backpropagation to calculate the current ΔE. wind (U-01). The preset convergence threshold is less than 5%. The system will continuously track the value of ΔEwind(U-01).

[0071] Data example: Week 1: The sand-fixing layer is initially formed, with ΔEwind(U-01) at 12%.

[0072] Week 2: ΔE wind (U-01) decreased to 8%.

[0073] Week 3: ΔE wind (U-01) decreased to 6%.

[0074] Week 4: ΔE wind (U-01) decreased to 4.8% (first time below 5%). Week 5: ΔE wind (U-01) decreased to 4.2%. (Below 5% for the second consecutive week) Week 6: ΔE wind (U-01) decreased to 3.9%. (Below 5% for the third consecutive week) S32. Soil microbial samples were collected weekly (synchronized with wind erosion capacity deviation monitoring) at multiple fixed monitoring points in the U-01 area. Microbial activity indicators were obtained using a rapid on-site ATP bioluminescence analyzer or a portable CO2 emission measurement device. These activity indicators were discretized into several states, and their probability distributions at different time points were calculated, thereby calculating H. decay (U-01). Simultaneously, the system will calculate H. decay The weekly rate of change of (U-01).

[0075] The preset stability threshold is that its rate of change is less than 0.01 bits / day.

[0076] Data Example: Week 1: H decay (U-01) is 0.95 bits, with a weekly change rate of -0.03 bits / day.

[0077] Week 2: H decay (U-01) is 0.88 bits, with a weekly change rate of -0.01 bits / day.

[0078] Week 3: H decay (U-01) is 0.85 bits, with a weekly change rate of -0.005 bits / day. (First time below 0.01 bits / day) Week 4: H decay (U-01) is 0.84 bits, with a weekly change rate of -0.002 bits / day. (Below 0.01 bits / day for the second consecutive week) Week 5: H decay (U-01) is 0.838 bits, with a weekly change rate of -0.0003 bits / day. (Below 0.01 bits / day for the third consecutive week) Based on comprehensive assessment, ΔE is determined at the end of the 6th week. wind (U-01) has been below the 5% convergence threshold for three consecutive weeks (i.e., G=3 cycles). And H decay The rate of change of (U-01) has been below the stability threshold of 0.01 bits / day for three consecutive weeks. Based on the satisfaction of the above two conditions, the system determines that the sand-fixing layer in the treatment unit U-01 area has reached the preset strength and stability requirements and marks it as qualified. After identifying the first qualified mark, the system triggers the first sand-fixing effect confirmation mark, officially establishing the treatment unit U area as an effective sand-fixing area and triggering the first maintenance instruction, including: conducting a manual on-site inspection once a quarter, mainly checking the macroscopic integrity of the sand-fixing layer, vegetation growth, and whether there are new signs of wind erosion; and automatically triggering precise micro-sprinkler irrigation or drip irrigation 1-2 times a month when there is no precipitation for more than 15 consecutive days or the soil moisture is lower than the minimum moisture threshold, in order to maintain microbial activity and sand-fixing layer moisture.

[0079] If within the preset maximum monitoring period (e.g., 12 weeks), ΔE wind (U-01) failed to stay below the preset convergence threshold for G consecutive periods, or H decayIf the rate of change of (U-01) fails to stabilize for G consecutive cycles, the system will determine that the sand fixation effect has failed to converge as expected. G represents the number of consecutive periods meeting the standard, used to determine whether the sand fixation effect has remained stable over multiple consecutive monitoring periods. One monitoring period corresponds to one monitoring of wind erosion resistance deviation and one monitoring of biological activity attenuation entropy; in this embodiment, each 7 days is considered a monitoring period, and G is 3, indicating that the sand fixation effect is considered to have reached a stable convergence state only after the judgment conditions are met for 3 consecutive monitoring periods. Specifically, for wind erosion resistance deviation, if ΔEwind(U-01) is lower than the preset convergence threshold for G consecutive periods, it indicates that the wind erosion resistance of the sand fixation layer has continuously met the standard; for biological activity attenuation entropy, if H decay If the rate of change of (U-01) is below the preset stability threshold for G consecutive cycles, it indicates that the microbial activity decay process has stabilized. When both of the above conditions are met, the sand-fixing layer in the treatment unit U-01 region is determined to have reached the preset strength and stability requirements. In this embodiment, G = 3 cycles. Corresponding to the subsequent data example, ΔE wind (U-01) fell below the 5% convergence threshold for three consecutive weeks (weeks 4, 5, and 6), thus meeting the condition for continuous compliance with wind erosion resistance deviation; H decay The rate of change of (U-01) was below the stationary threshold of 0.01 bits / day for three consecutive weeks (weeks 3, 4, and 5), thus meeting the condition of continuous stationary bioactivity decay entropy. Since both monitoring results met the stability requirement for three consecutive weeks, the system determined that the sand-fixing layer in the U-01 area of ​​the treatment unit had met the preset strength and stability requirements. If the sand-fixing effect fails to converge as expected, the system will activate an adaptive adjustment mechanism, specifically including: If the current treatment unit is a Level 3 implementation area: the system will trigger a strategy upgrade command, upgrading the sand fixation strategy of the area to the Level 2 sand fixation strategy. Specific adjustments include: adjusting the concentration of microbial agents from 10-20% below the standard concentration to the standard concentration; adjusting the application frequency from delayed application or small-scale pilot application to once a week; and simultaneously, optimizing the carbon source, nitrogen source, and trace element ratio of the nutrient solution based on real-time monitoring data, and adjusting the intelligent irrigation system to provide precise water replenishment 1-2 times every 1-2 days.

[0080] If the current treatment unit is a Level 2 implementation area (such as U-01 in this embodiment): the system will trigger a strategy upgrade command to upgrade the sand fixation strategy of the area to the Level 1 sand fixation strategy. Specific adjustments include: adjusting the concentration of microbial agents from the standard concentration to 20-30% higher than the conventional application concentration; adjusting the application frequency from once a week to 2-3 times a week; simultaneously, combining biopolymers for reinforcement, deploying physical sand fixation barriers (e.g., a biodegradable biological cover net spaced 5-10 meters apart), and configuring an intelligent irrigation system to replenish water 3-5 times a day.

[0081] If the current remediation unit is a Level 1 implementation area: the system will trigger a manual intervention warning. Since the area has failed to converge despite implementing the highest level of enhanced sand fixation strategies, it indicates the possible existence of deeper environmental or biological obstacles. This warning will be sent to the project leader and technical experts, suggesting the need for on-site investigation and in-depth analysis of the area. This may involve more complex soil sample analysis, microbial community structure analysis, geological exploration, or detailed testing of environmental factors (such as extreme pH values ​​and heavy metal pollution) to diagnose the root cause and develop customized solutions, or even consider other remediation technologies besides non-microbial sand fixation.

[0082] The method provided in this embodiment, in stage S3, is based on a core technical principle of constructing a dynamic monitoring and adaptive adjustment mechanism based on dual key indicators (wind erosion resistance deviation and bioactivity decay entropy). This solves the problems of existing microbial sand fixation technologies lacking real-time, objective effect assessment and untimely strategy feedback during implementation. Specifically, this method quantifies the deviation between the actual performance and expectations of the sand fixation layer's wind erosion resistance by continuously deploying on-site wind erosion sensors and conducting regular wind tunnel tests, ensuring an objective assessment of the physical stability of the sand fixation effect. Simultaneously, through regular microbial sample collection and activity analysis, the bioactivity decay entropy and its rate of change are calculated, thereby dynamically tracking the biological activity state of the microbial agent in the sand fixation layer, which is the biological basis for successful microbial sand fixation. Only when these two key indicators meet the preset convergence conditions for multiple consecutive cycles does the system finally determine that the sand fixation layer has met the strength and stability requirements, triggering a sand fixation effect confirmation marker and subsequent monitoring and maintenance instructions.

[0083] The beneficial effects of this embodiment are that it provides a scientific and real-time basis for judging sand fixation effectiveness, avoiding the limitations of subjective judgment or single-indicator evaluation. Through the synergistic judgment of dual indicators, the physical strength and biological activity of the sand-fixing layer can be more comprehensively reflected, improving the accuracy and reliability of effectiveness judgment. More importantly, this embodiment establishes a hierarchical and adaptive strategy adjustment mechanism. When the sand fixation effect fails to converge as expected, the system will automatically initiate a strategy upgrade based on the current treatment unit's level. For example, upgrading a level 3 area to a level 2 strategy, or a level 2 area to a level 1 strategy, to increase treatment intensity and intervention measures. For areas where the highest level strategy has been implemented but still ineffective, a manual intervention warning is triggered, guiding experts to conduct in-depth diagnosis. This dynamic feedback and hierarchical adjustment mechanism ensures timely and scientific intervention when the sand fixation effect deviates from expectations, avoiding delays in the optimal treatment time, thereby improving the success rate and resource utilization efficiency of microbial sand fixation engineering, and providing practical support for long-term wind and sand control in special scenarios such as a grotto.

[0084] It should be noted that the evaluation model of this invention aims to achieve intelligent recommendation of risk level classification and sand fixation strategies for governance units during its training and output process. During the training phase of the evaluation model, the system collects a large amount of historical data, including but not limited to the wind erosion risk coefficient WERI(u) of different governance units, local climate conditions (such as average wind speed, rainfall, temperature, and humidity), soil physicochemical properties (such as sand particle size distribution and organic matter content), and actual effect data (such as the time required for the sand fixation layer to reach the preset strength, ΔE) of different sand fixation strategies implemented under similar conditions in the past under similar conditions. wind and H decay (The convergence status). After preprocessing, these multivariate data are input as features into a pre-defined machine learning algorithm model, such as a classification model based on decision trees, support vector machines, or neural networks. By analyzing this historical data, the model learns and constructs a mapping relationship between the input features and the optimal sand fixation strategy level, or learns to predict the success probability of adopting a certain strategy under specific conditions, thereby optimizing its internal parameters so that it can accurately identify the potential risks of different governance units and recommend corresponding governance measures.

[0085] During the model output phase, when a new governance unit needs to undergo sand fixation assessment, the system inputs the unit's current WERI(u) value and real-time monitored environmental parameters. The trained assessment model then rapidly performs calculations and inferences based on this input data, generating clear output results. These output results primarily include: first, a risk level determination for the governance unit, such as labeling it as a high-wind-erosion-risk unit; and second, a recommendation of the most suitable initial sand fixation strategy level based on the risk level and environmental conditions, such as directly suggesting that the unit adopt the first sand fixation strategy. Furthermore, the model can provide auxiliary decision-making information, such as predicting the expected time for the sand-fixing layer to reach the preset strength and stability under the current recommended strategy, or predicting ΔE. wind and H decay The convergence trend provides forward-looking guidance for continuous monitoring and adaptive adjustment in the S3 stage, ensuring the accuracy of resource allocation and the reliability of sand fixation effect.

[0086] Figure 1The illustration depicts a real-world scenario of wind and sand erosion at a grotto. One selected treatment unit is magnified, conceptually revealing the cemented structure formed between sand grains under the action of microbial agents—the foundation of this invention's biological sand-fixing technology. The technical roadmap derived from this scenario elaborates on each step. "Preliminary multi-dimensional assessment" corresponds to the step of "conducting a multi-dimensional preliminary assessment of the target sand-fixing area and candidate treatment units, including collecting parameters of sand accumulation characteristics, micro-topographic features, and microclimate environmental factors," indicating comprehensive environmental data collection before sand-fixing implementation. "Optimization of sand-fixing scheme set" corresponds to the step of "establishing a candidate microbial sand-fixing scheme set, which comprehensively calculates environmental adaptability coefficients, treatment synergy indicators, and sand-fixing strategy optimization indices, and filters and ranks candidate treatment units in descending order according to preset admission thresholds," indicating the selection of the most suitable sand-fixing scheme based on preliminary assessment data. "Refined Assessment and Strategy Formulation" corresponds to the steps of "using an assessment model to refine the selection of candidate microbial sand-fixing schemes within a multi-objective decision optimization framework. The assessment model is based on a comprehensive consideration of bioactivity decay entropy, microscopic cementation of the sand-fixing layer, wind erosion resistance deviation, and long-term stability gradient to determine the graded implementation areas and their corresponding differentiated sand-fixing strategies." This indicates that a multi-dimensional model is used for in-depth analysis to determine specific implementation strategies. "Dynamic Monitoring and Strategy Adjustment" corresponds to the steps of "continuously and dynamically monitoring the wind erosion resistance deviation and bioactivity decay entropy of the implementation area, and determining whether the sand-fixing layer has reached the preset strength and stability requirements based on preset convergence conditions; if the sand-fixing effect fails to converge as expected, a graded strategy adjustment or artificial intervention early warning mechanism is initiated; if the sand-fixing layer reaches the preset strength and stability requirements, a sand-fixing effect confirmation marker is triggered, and subsequent monitoring and maintenance cycles are planned." This indicates a closed-loop management process of continuous monitoring after sand-fixing implementation, effect judgment, and adaptive adjustment based on feedback. The attached diagram visually illustrates the entire chain of practical applications of this method, from macro-environmental assessment to micro-mechanism analysis, and finally to systematic decision-making and dynamic optimization. It should be noted that all calculation formulas in this application employ regression analysis, including but not limited to machine learning algorithms, to deeply analyze the collected parameters and identify their natural trends and interrelationships. Specialized software, such as Python's Scikit-learn library or the R language, is used to automatically generate mathematical models that match the data. Then, cross-validation and other methods are used to objectively evaluate the model performance, and continuous feedback and optimization are combined to ensure that the created formulas truly reflect the inherent laws of the data, thereby guaranteeing their effectiveness and accuracy. In all calculation formulas in this application, the parameters in each formula undergo dimensionless processing within a consistent range to ensure that different physical quantities are compared on the same scale; dimensionless processing techniques include, but are not limited to, min-max-normalization and Z-score standardization. The algorithm of this invention is implemented as a Python script. Before executing the core logic, the program first executes a data loading module (e.g., using the widely used pandas library in Python) configured to read the aforementioned spreadsheet file and load its contents into the program's working memory (e.g., a DataFrame data structure). Subsequent algorithm steps will directly query and retrieve the required configuration parameters from this in-memory data structure.

[0087] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for evaluating the sand-fixing effect based on microbial inoculants, characterized in that, The specific steps include: A multi-dimensional preliminary assessment was conducted on the target sand-fixing area and candidate treatment units. The preliminary assessment included collecting sand accumulation characteristic parameters, micro-topographic features, and microclimate environmental factors. The sand accumulation characteristic parameters included the median particle size, effective particle size, limiting particle size, sand layer porosity, and initial water content of the sand sample. The micro-topographic features included the average slope, slope aspect, and surface elevation difference. The microclimate environmental factors included the average wind speed, the daily fluctuation range of sand layer surface temperature, and the surface water content of the sand layer. A candidate microbial sand fixation scheme set is established. The candidate microbial sand fixation scheme set is obtained by comprehensively calculating the environmental adaptability coefficient, the governance synergy index and the sand fixation strategy optimization index, and the candidate governance units are screened and sorted in descending order according to the preset admission threshold. In the multi-objective decision optimization framework, an evaluation model is used to refine the selection of candidate microbial sand fixation schemes. The evaluation model is based on a comprehensive consideration of bioactivity decay entropy, sand layer micro-cementation degree, wind erosion resistance deviation, and long-term stability gradient to determine the graded implementation areas and their corresponding differentiated sand fixation strategies. Bioactivity decay entropy is equal to the sum of the products of the probability of negative microbial activity at different discrete activity levels and its base-2 logarithm. The formula for calculating sand layer micro-cementation degree is: sand layer micro-cementation degree is equal to the maximum value of the ratio of zero and one minus the standard deviation and mean of the local cementation content dataset. The formula for calculating wind erosion resistance deviation is: wind erosion resistance deviation is equal to the sand loss predicted by wind tunnel test minus the actual sand loss, divided by the sand loss predicted by wind tunnel test. The method for obtaining the micro-cementation degree of the sand-fixing layer is as follows: micro-analysis is performed on sand samples in the initial stage of sand fixation of candidate treatment units to monitor the uniformity of calcium carbonate crystallization caused by environmental disturbance. The degree is obtained by calculating the ratio of the average value to the standard deviation of the local cementation content dataset of calcium carbonate cement in several equal-volume micro-sub-regions. The method for obtaining the deviation of wind erosion resistance is as follows: during the sand fixation layer formation stage, the expected wind erosion resistance of the candidate treatment unit area and the actual wind tunnel test results are obtained simultaneously, and the performance deviation between the two is calculated. The long-term stability gradient is obtained by periodically sampling and analyzing the sand-fixing layer of the candidate treatment unit, calculating the rate of change of the unconfined compressive strength, porosity and calcium carbonate content of the sand-fixing layer over time, and normalizing them to obtain the long-term stability score. The deviation of wind erosion resistance and the entropy of biological activity decay in the implementation area are continuously and dynamically monitored, and the sand fixation layer is judged to meet the preset strength and stability requirements based on the preset convergence conditions. Based on the judgment results, if the sand fixation effect fails to converge as expected, a graded strategy adjustment or artificial intervention early warning mechanism will be activated; if the sand fixation layer reaches the preset strength and stability requirements, a sand fixation effect confirmation marker will be triggered and subsequent monitoring and maintenance cycles will be planned.

2. The method for evaluating the sand-fixing effect based on microbial inoculants according to claim 1, characterized in that, In the preliminary assessment, the collection of sedimentation characteristic parameters, micro-topographic features, and microclimate environmental factors aims to comprehensively obtain physical environmental data of the target sand-fixing area. The establishment of a candidate microbial sand-fixing scheme set includes: obtaining the environmental adaptability coefficient of candidate treatment units to characterize their adaptability to environmental conditions and the effects of microbial agents; constructing a treatment synergy index for candidate treatment units to characterize the degree of matching between their physical characteristics and microbial sand-fixing methods; calculating the sand-fixing strategy optimization index based on the product of the environmental adaptability coefficient and the treatment synergy index to comprehensively evaluate the overall suitability and potential effects of implementing microbial sand-fixing schemes in candidate treatment units; removing units below a preset entry threshold from the candidate treatment unit set, and arranging the remaining units in descending order of the sand-fixing strategy optimization index to enter the candidate microbial sand-fixing scheme set.

3. The method for evaluating the sand-fixing effect based on microbial inoculants according to claim 1, characterized in that, The environmental adaptability coefficient of the candidate treatment unit is obtained by: collecting the sand layer porosity, initial water content and microclimate fluctuation range of the treatment unit area in real time through a multi-scale environmental sensing network, and calculating the environmental adaptability coefficient based on a preset weighted logic. The multi-scale environmental sensing network includes UAV-borne hyperspectral and multispectral sensors, ground wireless sensor network and portable soil analyzer. The environmental adaptability coefficient is obtained by weighted summation of the normalized values ​​of the ratio of sand layer porosity to theoretical maximum porosity, the deviation between average sand layer moisture content and optimal moisture content for microbial activity, and the ratio of the daily fluctuation range of microclimate sand layer surface temperature to the maximum temperature fluctuation range that microorganisms can tolerate. The sum of the normalized weights of each dimension is one.

4. The method for evaluating the sand-fixing effect based on microbial inoculants according to claim 1, characterized in that, Construct governance synergy indicators for candidate governance units, including: By comparing the sediment particle size distribution and topographic slope characteristics of candidate governance unit areas, and calculating the governance synergy index based on a preset compatibility evaluation function; The governance synergy index is obtained by weighted summation of the compatibility scores of microbial cementation efficiency calculated based on particle size distribution, the compatibility scores of construction difficulty and sand fixation layer stability calculated based on topographic slope, and the normalized value of wind erosion risk coefficient, where the sum of the contribution weights of each feature is one.

5. The method for evaluating the sand-fixing effect based on microbial inoculants according to claim 4, characterized in that, The calculation of the wind erosion risk factor includes: The average wind speed, surface moisture content of sand, and surface roughness index of the candidate treatment unit area were obtained. The surface roughness index is obtained by weighted summation of surface vegetation cover, non-sandy obstacle density and surface crust cover of candidate treatment units, where the sum of the normalized weights of each component is one. The wind erosion risk coefficient is obtained by weighted summation of the normalized values ​​of the ratio of average wind speed to reference wind speed, the ratio of surface moisture content of sand layer to reference maximum surface moisture content, and the ratio of surface roughness index to reference maximum roughness index, where the sum of the contribution weights of each feature is one.

6. The method for evaluating the sand-fixing effect based on microbial inoculants according to claim 1, characterized in that, Treatment units with wind erosion risk exceeding a preset wind erosion risk threshold are identified as high-wind-erosion-risk units. If the set of high wind erosion risk units is not empty, then the treatment units in the set of high wind erosion risk units are determined as the first-level implementation areas, and the first sand fixation strategy is generated. For the remaining treatment units in the candidate microbial sand fixation schemes that were not identified as having a high risk of wind erosion, a comprehensive evaluation was conducted based on the sand fixation strategy optimization index and the evaluation indicators constructed by the evaluation model. The evaluation indicators included the entropy of biological activity decay, the micro-cementation degree of the sand fixation layer, the deviation of wind erosion resistance, and the long-term stability gradient. The comprehensive evaluation score was calculated by combining the sand fixation strategy optimization index. Based on the relationship between the comprehensive evaluation score and the preset classification threshold, the second-level and third-level implementation areas are determined, and the corresponding second or third sand fixation strategies are generated.

7. The method for evaluating the sand-fixing effect based on microbial inoculants according to claim 6, characterized in that, The method for obtaining the entropy of bioactivity decay is as follows: The activity index and decay rate of microbial agents in candidate treatment unit areas are obtained. By discretizing the microbial activity level into several states, the probability distribution of these activity states at different time points is calculated, and the bioactivity decay entropy over time is calculated using the Shannon entropy formula.

8. The method for evaluating the sand-fixing effect based on microbial inoculants according to claim 1, characterized in that, The sand fixation strategy adjustment includes: if the deviation of wind erosion resistance fails to fall below the preset convergence threshold within the preset maximum monitoring period, or the rate of change of biological activity decay entropy fails to stabilize, it is determined that the sand fixation effect has failed to converge as expected, and a graded strategy adjustment is initiated according to the current level of the governance unit: if the current governance unit is a level 3 implementation area, the sand fixation strategy for that area is upgraded to the level 2 sand fixation strategy; if the current governance unit is a level 2 implementation area, the sand fixation strategy for that area is upgraded to the level 1 sand fixation strategy; if the current governance unit is a level 1 implementation area, a manual intervention warning is triggered.

9. The method for evaluating the sand-fixing effect based on microbial inoculants according to claim 8, characterized in that, The instructions for triggering confirmation of sand fixation effect include: if the deviation of wind erosion resistance is lower than the preset convergence threshold within the preset maximum monitoring period, and the entropy of biological activity decay tends to be stable, the sand fixation layer in the treatment unit u area is determined to meet the preset strength and stability requirements, and the first qualified mark is made. After identifying the first qualified marker, the first sand fixation effect confirmation marker is triggered, officially establishing the treatment unit area as an effective sand fixation zone; the first maintenance command is triggered, and the subsequent monitoring and maintenance cycle is planned based on the long-term stability gradient, micro-cementation degree of the sand fixation layer, biological activity attenuation entropy and wind erosion resistance deviation of the area.

Images