Multi-species collaborative desert vegetation reconstruction method and system

Abstract

The application discloses a kind of multi-species cooperation's desert vegetation reconstruction method and system, belong to ecological restoration field, based on temperature and humidity change, fine particle soil proportion and wind erosion response lagging behind identification potential wind erosion-thermal response stress coupling area;Microclimate data is collected to construct water vapor dispersion tensor matrix;According to tensor matrix configuration pioneer plant and symbiotic plant combination, and inoculate dormant state mycorrhizal spore, form basic repair unit;Humidity control induction layer is constructed in its periphery, and bacteria proliferation simulation is carried out;Secondary wind erosion blocking structure is constructed by introducing structure induction type plant;Further introduce associated behavior insect and induce plant and fungi-root mutual feeling adjustment device, construct ecological interaction system;Finally output self-maintenance ability analysis index, guide multi-unit parallel repair;The application realizes microclimate regulation, rhizosphere symbiosis, wind erosion protection and the cooperative reconstruction of ecological stability, is suitable for wind erosion strong, drought heavy, microhabitat broken extreme degradation area.

Description

Technical Field

[0001] This invention relates to the field of ecological restoration, specifically to a method and system for the synergistic reconstruction of desert vegetation by multiple species. Background Technology

[0002] In extremely arid and desertified areas, long-term wind erosion, water erosion, and human disturbance have led to the breakage of the root system of native vegetation, severe damage to the topsoil structure, near extinction of microbial communities, loose soil aggregates, low organic matter content, and evaporation rates far exceeding precipitation, resulting in extremely low vegetation survival rates. In specific areas, such as shrub-grass transition zones in arid regions or desert-saline-alkali transition zones, localized "microclimate islands" exist, characterized by large diurnal temperature variations, unstable wind speeds, and drastic fluctuations in topsoil temperature, making it difficult for even drought-resistant plants to survive.

[0003] Currently, most desert vegetation restoration methods rely on planting single pioneer plants in small patches, supplemented by irrigation or water-retaining agents. However, these methods fail to address issues such as the lack of microbial communities in the rhizosphere, severe water vapor competition among plants, and a monotonous community structure. Especially in "sterile-exposed-strong wind" areas (such as wind erosion areas, bare sand belts on slope tops, and degraded salt crust areas), a single plant species cannot form an effective protective barrier and may even further deteriorate the surface structure due to root disturbance. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for multi-species collaborative desert vegetation reconstruction to address the shortcomings of the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for multi-species collaborative desert vegetation reconstruction, comprising:

[0006] S100: Obtain hourly temperature and humidity variation curves, fine-grained soil ratio distribution and wind erosion intensity index of different surface soil layers in the target desert area. Based on the diurnal temperature reversal frequency and wind erosion response hysteresis, identify potential wind erosion-thermal stress coupling zones Ri with overlapping boundaries, where i is the number of potential wind erosion-thermal stress coupling zones.

[0007] S200, in each potential wind erosion-thermal stress coupling zone Ri, collect the microclimate fluctuation pattern of different points within the solar cycle, and construct the water vapor convergence-dispersion tensor matrix Wi of the Ri region;

[0008] S300, based on the water vapor convergence and divergence tensor matrix Wi, select pioneer plant P1 and form a root complementary structure with symbiotic plant P2. At the same time, inoculate dormant mycorrhizal spore group M1 that can be activated by the allelochemicals of P2 roots to form the basic repair unit Ui.

[0009] S400, a composite moisture-wicking layer is constructed around the basic repair unit Ui to form a moisture-control induction layer Fi;

[0010] Based on the diurnal humidity range distribution map formed by the coupling effect of the water vapor convergence-dispersion tensor matrix Wi and the humidity-controlled induction layer Fi, S500 microbial community proliferation simulation was conducted:

[0011] S600, introduces structure-inducing plant P3 into each basic repair unit Ui to form a secondary wind erosion blocking structure Ci;

[0012] S700, when the secondary wind erosion blocking structure Ci structure has been stable for a set time and the regional evaporation inhibition rate has increased by ≥35%, then the associated behavior insect-induced plant P4 and mycorrhizal mutual induction regulating device are introduced into the basic repair unit Ui to reconstruct the basic repair unit Ui.

[0013] S800 outputs the self-sustainability analytical index of the basic repair unit Ui after reconstruction, and performs multi-unit parallel repair according to the expansion requirements of the target desert area.

[0014] Preferably, S200 includes:

[0015] Within the potential wind erosion-thermal stress coupling zone Ri, several micro meteorological collection points are set up according to the boundary diffusion gradient. The hourly temperature, humidity and solar radiation values ​​of each point are recorded over a continuous time period to construct the corresponding time series dataset.

[0016] Fourier spectrum analysis was performed on the temperature and humidity changes within the solar cycle at each collection point to extract the main fluctuation frequency and phase shift characteristics.

[0017] The response attributes of all sampling points are correlated according to spatial coordinates, and the direction and intensity of water vapor diffusion between adjacent points are calculated to generate a three-dimensional water vapor flow vector field.

[0018] Based on the aggregation and divergence characteristics of each node in the three-dimensional water vapor flow vector field, a water vapor aggregation and divergence tensor matrix Wi is constructed.

[0019] Preferably, the formation of the basic repair unit Ui includes:

[0020] Based on the grid cells in the water vapor aggregation tensor matrix Wi where the water vapor aggregation degree is higher than the average value of the whole area, the preferred planting location of pioneer plant P1 is determined, and plants with leaf transpiration regulation ability and adapted to low water vapor environmental gradient are selected as pioneer plant P1.

[0021] Outside the centripetal extension range of the root system of the pioneer plant P1, the symbiotic plant P2 is arranged in the opposite direction of the water vapor dissipation direction in the water vapor aggregation tensor matrix Wi, so that the symbiotic plant P2 and the pioneer plant P1 form a root complementary structure with a root depth difference of more than 5 cm.

[0022] The dormant mycorrhizal spore group M1, which remains stable in an inactive state and can be activated by root allelochemicals secreted by the symbiotic plant P2, is uniformly placed into the overlapping area of ​​the root complementary structure, so that the mycorrhizal spore group M1 enters the induction response stage after implantation.

[0023] Once the dormant mycorrhizal spore group M1 shows initial signs of hyphal expansion within the root complementary structure, the structure jointly formed by the pioneer plant P1, the symbiotic plant P2, and the mycorrhizal spore group M1 is defined as the basic repair unit Ui.

[0024] Preferably, the step of simulating microbial community proliferation includes:

[0025] Based on the water vapor convergence intensity of each grid cell in the water vapor convergence tensor matrix Wi, spatial matching is performed with the actual humidity monitoring data below the humidity control induction layer Fi to generate a humidity range distribution map containing the maximum difference between day and night humidity. Grid cells with a humidity range greater than 1.3 times the average value of the whole area are identified as potential areas for bacterial proliferation.

[0026] Within the potential area for microbial community proliferation, a microbial community proliferation probability model was constructed with humidity range, temperature fluctuation amplitude, and humidity-controlled induction layer coverage thickness as input factors. The theoretical proliferation probability of mycorrhizal spore group M1 at different time periods was calculated using the polynomial regression method.

[0027] The theoretical proliferation probability is compared with the measured hyphal expansion rate in the basic repair unit Ui. If the difference between the two is less than 20%, the corresponding area is defined as the effective proliferation zone of the microbial community.

[0028] Within the effective proliferation zone of the microbial community, the acceleration of the change in mycelial density over time is used as the microbial community activation index. When the microbial community activation index exceeds a preset threshold, it is determined that the basic repair unit Ui has entered the stable microbial community proliferation stage.

[0029] Preferably, the basic repair unit Ui reconstruction includes:

[0030] After the secondary wind erosion blocking structure Ci has been formed for 30 days and the regional evaporation inhibition rate has reached or exceeded 35%, the symbiotic insect-induced plant P4 is selected.

[0031] Fungal-root mutual induction regulating devices were uniformly buried in the rhizosphere region of P4 plant induced by symbiotic insects.

[0032] The establishment of ecological interaction chains can be determined by regularly monitoring the visit frequency of induced insects and the trend of mycelial density changes in the area.

[0033] When the access frequency increases by more than 50% and the mycelial density maintains a stable growth trend for more than 7 days, the basic restoration unit Ui will be upgraded to a multi-species synergistic ecological restoration system that includes P1, P2, P3, P4 and mycorrhizal regulatory structures.

[0034] Preferably, the self-sustaining capability analytical index of the basic repair unit Ui after output reconstruction includes:

[0035] The net primary productivity of plant communities, the rhizosphere microbial community diversity index, and the water vapor recirculation intensity of each basic restoration unit Ui were measured, and three-dimensional time series data were generated respectively.

[0036] The three data points are indexed using a fuzzy comprehensive evaluation model to output the self-sustaining capability analytical index, denoted as Si. Si ≥ 0.75 indicates that the basic repair unit Ui has parallel migration repair capability after reconstruction.

[0037] Based on the extended boundary and water vapor migration trend map of the target desert area, the region with the smallest difference in humidity gradient from the boundary of the reconstructed basic repair unit Ui is selected as the candidate region for the new repair unit.

[0038] The plant combination structure and humidity-controlled induction layer parameters corresponding to the reconstructed basic repair unit Ui with high Si value are copied as a whole to the candidate area and laid out in an interleaved-nested manner to achieve multi-unit parallel vegetation reconstruction.

[0039] This invention also provides a multi-species collaborative desert vegetation reconstruction system, comprising:

[0040] The environmental factor identification module obtains the hourly temperature and humidity change curves, fine-grained soil ratio distribution and wind erosion intensity index of different surface soil layers in the target desert area. Based on the diurnal temperature reversal frequency and wind erosion response lag, it identifies potential wind erosion-thermal stress coupling zones Ri with overlapping boundaries, where i is the number of potential wind erosion-thermal stress coupling zones.

[0041] The microclimate modeling module collects the microclimate fluctuation patterns at different locations within the solar cycle in each potential wind erosion-thermal stress coupling zone Ri, and constructs the water vapor convergence-dispersion tensor matrix Wi for the Ri region.

[0042] The plant combination construction module selects pioneer plant P1 based on the water vapor convergence and divergence tensor matrix Wi, and forms a root complementary structure with symbiotic plant P2. At the same time, it inoculates dormant mycorrhizal spores M1 that can be activated by the allelochemicals of P2 roots to form the basic repair unit Ui.

[0043] The moisture-control induction structure construction module constructs a composite moisture-wicking layer around the basic repair unit Ui, forming the moisture-control induction layer Fi;

[0044] The microbial community response simulation module uses the diurnal humidity range distribution map formed by the coupling effect of the water vapor convergence-dispersion tensor matrix Wi and the humidity-controlled induction layer Fi as a benchmark to simulate microbial community proliferation:

[0045] The wind erosion inhibition structure construction module introduces structure-inducing plants P3 into each basic repair unit Ui to form a secondary wind erosion blocking structure Ci.

[0046] The ecological interaction extension module introduces the symbiotic behavior insect-induced plant P4 and mycorrhizal mutual induction regulating device into the basic repair unit Ui when the secondary wind erosion blocking structure Ci has been stably existing for a set time and the regional evaporation inhibition rate has increased by ≥35% to reconstruct the basic repair unit Ui.

[0047] The ecological migration and replication module outputs the self-sustainability analysis index of the basic repair unit Ui after reconstruction, and performs multi-unit parallel repair according to the expansion needs of the target desert area.

[0048] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0049] 1. This invention, by constructing a complete vegetation reconstruction process centered on environmental factor identification, water vapor dynamic modeling, multi-species root system complementarity, targeted microbial community activation, wind erosion suppression, ecological behavior regulation, and multi-unit parallel migration, proposes for the first time a technical path guided by water vapor convergence-dispersion tensor matrix and humidity range distribution to guide plant combination configuration and microbial community activation. Compared with existing methods that rely on single vegetation restoration or physical cover for water control, this invention not only achieves closed-loop regulation of the microclimate in the restoration area, but also significantly improves the survival rate of vegetation communities and soil stability by synergistically constructing a multi-scale wind erosion-resistant structure through a moisture-control induction layer and structure-inducing plants.

[0050] 2. This invention introduces a self-sustaining capacity analytical index as a quantitative basis for the migration and replication of restoration units, making the ecological restoration process evaluable, adaptable, and replicable. Through a fuzzy comprehensive evaluation model and a spatial gradient response mechanism, a highly adaptive multi-unit ecological restoration strategy is realized, applicable to extremely degraded areas with strong heterogeneity and significant combined effects of wind erosion and drought, providing a scalable and sustainable solution for the reconstruction of large-scale desert ecosystems. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0052] Figure 1 This is a flowchart of the method of the present invention.

[0053] Figure 2 This is a flowchart of the system modules of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Example 1, please refer to Figure 1 As shown in this embodiment, a multi-species collaborative desert vegetation reconstruction method includes:

[0056] S100: Obtain hourly temperature and humidity variation curves, fine-grained soil ratio distribution, and wind erosion intensity index of different surface soil layers in the target desert area. Based on the diurnal temperature reversal frequency and wind erosion response hysteresis, identify potential wind erosion-thermal stress coupling zones Ri with overlapping boundaries, where i is the number of potential wind erosion-thermal stress coupling zones.

[0057] Several representative points were selected in the target desert area and divided into three soil layers according to vertical depth: the first interval is 0 to 5 cm above the ground, which is the area directly affected by the microclimate; the second interval is 5 to 20 cm, which is the area of ​​active root system; and the third interval is 20 to 50 cm, which is the area for temperature and humidity buffering and mycelial penetration observation.

[0058] Soil temperature and humidity monitoring devices were deployed in each soil layer, recording data every 30 minutes for at least 15 days to cover diurnal cyclical changes. The collected data formed hourly temperature and humidity variation curves, denoted as T(z,t) and H(z,t), respectively, where z is the soil depth and t is the time point.

[0059] At each of the aforementioned locations, soil samples were taken from the three layers and particle size analysis was performed. A laser particle size analyzer was used for dry sieving to extract the mass percentage of particles smaller than 0.05 mm, denoted as F(z), representing the distribution of fine-grained soil at different depths. Interpolation analysis was then performed on the F(z) values ​​at each location to construct a three-dimensional distribution map of fine-grained soil at various depths throughout the target area, in order to analyze the spatial differences in surface wind erosion resistance.

[0060] Surface wind speed data acquisition devices were deployed in the target area to record wind speed changes at heights of 10 cm and 2 meters, denoted as V1(t) and V2(t), respectively. The sampling interval was 30 minutes, and the sampling period was no less than 15 days. Based on the American wind erosion model, the wind erosion intensity index E(t) was calculated, defined as follows: At each time point t, the wind erosion intensity index was calculated. , where α is an empirical constant ranging from 0.3 to 0.7, and F(0) represents the proportion of fine-grained soil in the top 0–5 cm soil layer. The higher E(t), the greater the risk of wind erosion at that time point.

[0061] Analyze the temperature change curve T(z,t) at each point to determine the number of reversals of the diurnal temperature difference within the daily cycle. Specifically, if the temperature change trend changes from warming to cooling or vice versa more than once within 24 hours, it is recorded as one temperature reversal.

[0062] The number of reversals, Nrev, is counted daily, and the average reversal frequency, frev, is calculated within the period as Nrev / Tdays, where Tdays is the number of sampling days.

[0063] E(t) and T(0,t) are time-series registered, and cross-correlation analysis is used to calculate the maximum cross-correlation time lag τ between the temperature change signal and the wind erosion intensity signal. If τ is greater than or equal to 2 hours, the location is considered to have significant thermal change-wind erosion response lag.

[0064] The cumulative wind erosion intensity (Esum), average temperature reversal frequency (frev), and maximum cross-correlation time lag (τ) at each location were spatially superimposed to construct a three-dimensional raster dataset. The DBSCAN algorithm, based on density clustering, was then used to cluster the raster data. The clustering criteria were: within the same cluster unit, Esum must be more than 20% higher than the regional average, frev must be greater than 1 time per day, and τ must be greater than 2 hours. Cluster units meeting these three conditions were identified as potential wind erosion-thermal stress coupling zones.

[0065] Each coupling region is labeled Ri, where i is a number, and Ri represents the i-th potential wind erosion-thermal stress coupling region. Within each Ri, it will be used for the construction of subsequent plant synergistic units.

[0066] S200, in each potential wind erosion-thermal stress coupling zone Ri, collect the microclimate fluctuation pattern of different points within the solar cycle, and construct the water vapor convergence-dispersion tensor matrix Wi of the Ri region;

[0067] Within each identified potential wind erosion-thermal stress coupling zone Ri, sampling points are deployed based on the boundary diffusion gradient principle. Specifically, with the center of the Ri region as the origin, one micro meteorological sampling point is deployed every 5 meters or less towards the edge, forming a grid array of no less than 9 sampling points, ensuring that the spacing between sampling points does not exceed 10% of the spatial scale of the local evaporation zone.

[0068] Each micro meteorological data collection point needs to be equipped with a highly sensitive temperature sensor, a relative humidity sensor, and a total solar radiation sensor. The data collection time should be no less than 72 consecutive hours, with a sampling interval of 1 hour. The obtained temperature, humidity, and solar radiation values ​​constitute three types of time series datasets, denoted as T(t), H(t), and R(t), respectively, where t represents a time point.

[0069] Fourier transforms were performed on the temperature sequence T(t) and humidity sequence H(t) collected at each sampling point to extract the main frequency components and phase features within a 24-hour period.

[0070] Let the dominant frequency of the temperature sequence at a certain sampling point be f1, the dominant phase be φ1, the dominant frequency of humidity be f2, and the dominant phase be φ2; then the microclimate response attribute of this point can be defined as a quadruple S=(f1,φ1,f2,φ2), which is used to represent the response intensity and relative time delay behavior of this point to the solar cycle.

[0071] The response attribute quadruple of each sampling point is bound to its spatial coordinates (x, y, z) in the Ri region to construct a spatial attribute dataset D={(xi,yi,zi,f1i,φ1i,f2i,φ2i)}.

[0072] Based on dataset D, Euclidean distance is determined for all point pairs (i.e., between adjacent sampling points). A set of point pairs P={(i,j)} with a distance of less than 10 meters is selected. The difference Δf2 of the humidity dominant frequency f2 and the dominant phase difference Δφ2 between each pair of points are calculated. Combined with the distance dij between the two points, the water vapor diffusion vector is constructed as follows:

[0073] For each pair of sampling points (i,j), the direction of water vapor diffusion is defined as from the side with smaller φ2 to the side with larger φ2, and the diffusion intensity is... , where β is an empirical adjustment coefficient, ranging from 0.5 to 1.5.

[0074] The diffusion vectors between all sampling points are spatially superimposed to generate a three-dimensional water vapor flow vector field within the Ri region, denoted as V(x,y,z), which is used to describe the net migration trend of water vapor in the microclimate.

[0075] Based on the three-dimensional water vapor flow vector field V(x,y,z), tensor analysis is used to calculate the divergence and curl of each grid cell:

[0076] For each location point M(x,y,z), extract the water vapor diffusion vectors in the six adjacent directions centered on it, and construct a third-order tensor W(x,y,z). The tensor elements are composed of the water vapor flow difference in each direction, which are used to describe the aggregation and dispersion intensity and directionality of the point.

[0077] By merging all tensors W on M(x,y,z), a complete water vapor accumulation-dispersion tensor matrix Wi can be constructed to characterize the spatial distribution pattern of water vapor accumulation and dispersion zones driven by microclimate within the Ri region. This tensor matrix Wi will serve as important input data for subsequent plant combination selection and mycorrhizal spore inoculation site layout. Its accumulation regions are used to prioritize the deployment of water vapor-sensitive plants, while its dispersion regions are used to guide the optimization of water vapor compensation pathways.

[0078] S300, based on the water vapor convergence-dispersion tensor matrix Wi, select pioneer plant P1 and form a root complementary structure with symbiotic plant P2. At the same time, inoculate dormant mycorrhizal spore group M1 that can be activated by the allelochemical substances of P2 roots to form the basic repair unit Ui.

[0079] First, the degree of water vapor accumulation in each grid cell of the water vapor accumulation-dispersion tensor matrix Wi is quantified. The net water vapor accumulation intensity is represented by the sum of the main diagonal values ​​of the tensor at each grid point, denoted as G(x,y), where x and y are the two-dimensional spatial coordinates of the grid point in the Ri region.

[0080] The average value G_avg of G(x,y) in Wi was calculated, and all grid cells with G(x,y) ≥ 1.2 × G_avg were selected as priority implantation areas with high water vapor accumulation. These locations were chosen as candidate implantation sites for the pioneer plant P1.

[0081] Among the candidate sites mentioned above, based on the fluctuation range of annual average evaporation and soil moisture content in the Ri region, plant species with controllable leaf transpiration rate and stomatal closure regulation mechanism were selected as pioneer plants P1, such as semi-shrubs or drought-resistant perennials with powdery mildew leaf reflective layer or leaf curling response structure.

[0082] After identifying the planting site of the pioneer plant P1, the centripetal expansion radius R1 of its root system is measured, which is usually in the range of 0.3 to 0.6 meters, depending on the variety of P1 and the soil conditions.

[0083] Outside the maximum horizontal expansion range of each P1 root system, 6 to 8 symbiotic plant P2 implantation points are arranged circumferentially at 15° intervals, and arranged in the opposite direction of water vapor dissipation in the water vapor convergence and divergence tensor matrix Wi to ensure that the water vapor return direction is conducive to the growth of symbiotic plant P2.

[0084] Select herbaceous or leguminous plants with deep taproot structures that can quickly root downwards under drought conditions and have nitrogen-fixing functions as symbiotic plants P2, and ensure that their root depth is at least 5 cm higher than that of P1 to form a "shallow-deep complementary" root structure.

[0085] Within the vertically overlapping area of ​​the complementary root systems of pioneer plant P1 and symbiotic plant P2, pre-selected dormant mycorrhizal spore groups M1 are shallowly embedded at a grid spacing of 20 cm × 20 cm.

[0086] The mycorrhizal spore group M1 consists of granulated microclusters with a membrane structure, containing signal cascade pathway proteins that can be activated by specific allelochemicals. These allelochemicals must be specific flavonoids secreted by the roots of the symbiotic plant P2, with an activation threshold concentration of 10 μmol / L or higher.

[0087] Upon contact with the aforementioned allelochemicals, mycorrhizal spore group M1 enters a responsive state, initiating extracellular esterase activity, promoting the release of extracellular polysaccharides, and subsequently initiating hyphal germination. If, within 72 hours of continuous observation, the initial hyphal extension length is observed to be greater than 3 mm in the overlapping area, the initial hyphal activation is considered successful.

[0088] When the mycorrhizal spore group M1 completes its initial hyphal expansion and the first hyphal attachment point appears in the root junction area between P1 and P2, the structural combination can be defined as the basic repair unit Ui.

[0089] The basic restoration unit Ui includes: at least one pioneer plant P1, at least one symbiotic plant P2, and mycorrhizal spores M1 in an activated state in the overlapping area of ​​their roots. The three form a stable plant-root-mycorrhizal triple mutual promotion structure for subsequent ecological restoration unit replication and expansion.

[0090] S400, a composite moisture-wicking layer is constructed around the basic repair unit Ui to form a moisture-control induction layer Fi.

[0091] Using the center point of the basic restoration unit Ui as the origin, and based on the maximum canopy radius R_c of the surrounding plant community, the annular layout range of the humidity-controlled induction layer Fi is extended outward by a distance of 1.5 times R_c. Specifically, the layout area is an annular zone with a radius of 1.5 × R_c centered on the center of Ui, with a bandwidth of not less than 30 cm.

[0092] The construction of the moisture-control induction layer Fi needs to cover the entire surface layer of the annular area to minimize the area of ​​exposed sand and soil, control the escape path of surface water vapor, and achieve the effect of inward moisture accumulation.

[0093] The moisture-control induction layer Fi is composed of two natural materials with complementary properties: one is a material of fallen leaves and branches F1, and the other is a biodegradable hydrophobic fiber membrane material F2.

[0094] The fallen leaves and branches material F1 is derived from native forest litter in the target area or similar climate zone. It needs to be air-dried, crushed to a length of 2 to 5 cm, and the moisture content controlled below 15%. It has high surface porosity and moderate adsorption capacity.

[0095] The biodegradable hydrophobic fiber membrane material F2 is a nonwoven membrane based on polycaprolactone and coconut fiber composite. The thickness is controlled between 1 mm and 2 mm. It has a hydrophobic surface microstructure and a contact angle greater than 105°, which allows water vapor to accumulate below but inhibits its diffusion to the outside.

[0096] Both materials must undergo physical sterilization treatment (such as hot steam treatment) before use to prevent contamination by exogenous bacteria, and be stacked in a weight ratio of 3:1 for later use.

[0097] In the area surrounding the basic repair unit Ui constructed in step S300 (i.e., within a radius of 1.5 × R_c centered on Ui), the surface is first lightly compacted to a depth of 2 cm to enhance surface adhesion stability. The laying operation is then performed in the following sequence:

[0098] First, lay down a layer of dead branches and fallen leaves material F1 with a thickness of 1 cm to 1.5 cm to form an adsorption layer with a microporous structure;

[0099] A biodegradable hydrophobic fiber membrane material F2 is superimposed on it to completely cover F1, forming a surface hydrophobic induction layer.

[0100] A vent is made every 50 centimeters, with a diameter not exceeding 3 centimeters, to fine-tune the water vapor pressure difference and maintain a slightly positive pressure state below the induction layer.

[0101] The total thickness of the composite layer after installation is controlled between 1.5 cm and 3 cm, and the installation width is not less than the width of the annular area from the outer edge of Ui to 1.5×R_c, to ensure continuous coverage of the airflow disturbance area.

[0102] The effectiveness of the moisture-inducing layer Fi is evaluated using the evaporation inhibition rate and the water vapor recirculation index: the evaporation inhibition rate is defined as the difference between the surface evaporation rate of the paved area and the unpaved area, divided by the evaporation rate of the unpaved area, with evaporation measured per unit time; the evaporation inhibition rate must be greater than 35%. The water vapor recirculation index is defined as the average water vapor flux inward from the boundary of the moisture-inducing layer divided by the average flux outward from the boundary; a value greater than 1.2 is considered to indicate effective recirculation function. If both the evaporation inhibition rate and the water vapor recirculation index meet the standards within 72 hours of continuous monitoring, the moisture-inducing layer Fi is deemed to have been successfully constructed and possesses moisture-inducing function.

[0103] S500 uses the diurnal humidity range distribution map formed by the coupling effect of water vapor convergence and dispersion tensor matrix Wi and humidity control induction layer Fi as a benchmark to simulate bacterial community proliferation.

[0104] First, the water vapor convergence intensity value of each grid cell in the water vapor convergence tensor matrix Wi is read. The water vapor convergence intensity is defined as the sum of the main diagonal elements of the grid tensor, and is used to represent the net convergence capacity of water vapor in a local region.

[0105] A humidity monitoring device was deployed at the corresponding location below the humidity-inducing layer Fi to collect hourly humidity data for 48 consecutive hours, constructing a humidity time series H(t). The difference between the maximum and minimum humidity values ​​during the day and night was extracted from this series and defined as the humidity range ΔH.

[0106] The water vapor concentration intensity in Wi is matched one-to-one with the measured humidity range below Fi according to spatial coordinates to generate a two-dimensional humidity range distribution map. The average humidity range value ΔH_avg of all grid cells in the entire distribution map is calculated.

[0107] Grid cells with a humidity range ΔH greater than 1.3 times ΔH_avg were designated as potential areas for mycorrhizal spore groups M1 to proliferate, indicating regions where the mycorrhizal spore group M1 might achieve a higher proliferation rate.

[0108] Within the potential zone for bacterial community proliferation, a bacterial community proliferation probability model was constructed using the humidity range ΔH, temperature fluctuation amplitude ΔT, and the coverage thickness L_f of the humidity-controlled induction layer Fi as input factors. The temperature fluctuation amplitude ΔT is the difference between the maximum and minimum values ​​of a continuous 48-hour temperature sequence.

[0109] A three-variable polynomial regression model was established: P = f(ΔH, ΔT, L_f), where P represents the theoretical proliferation probability of mycorrhizal spore group M1, and the function f is in second-order polynomial form, with coefficients obtained from previous experimental calibration. Each input factor in the model was normalized to prevent dimensional differences from affecting the regression results.

[0110] By inputting the values ​​of ΔH, ΔT, and L_f for each grid region, the theoretical proliferation probability P(t) for each region at different time periods is calculated. The higher the P(t), the more favorable the bacterial community proliferation conditions in that region.

[0111] Within the basic remediation unit Ui, the hyphal expansion rate of mycorrhizal spore group M1 was measured. The hyphal expansion rate was defined as the increment of hyphal length per unit time, and continuous changes were recorded over at least 24 hours using microscopic observation methods.

[0112] The measured mycelial expansion rate was converted into the spatial proliferation rate R_obs per unit time, and compared with the theoretical proliferation probability P(t) output by the community proliferation probability model. If the difference is less than 0.2, meaning the difference between the two is less than 20%, then the growth behavior of the microbial community in this area is considered to be highly consistent with the model prediction. Areas that meet the above conditions are designated as effective microbial proliferation zones, indicating that the mycorrhizal spore group M1 can continuously obtain suitable moisture and temperature-humidity environment support in this area.

[0113] Within the effective proliferation zone of the mycelium, the mycelial density M_d(t) was measured periodically. The mycelial density was defined as the dry weight of mycelium per unit volume, obtained by the drying method.

[0114] The first derivative of mycelial density over time is calculated and defined as the mycelial density growth rate; then the second derivative over time is calculated, i.e., the mycelial density growth acceleration, which is defined as the community activation index A_c.

[0115] Based on previous culture experiments, the threshold for the microbial community activation index was set at 0.05 g / cm³ / h². If A_c is higher than this threshold, it indicates that the microbial community has entered a rapid proliferation phase, with mycelium exhibiting an exponential growth trend in space. When the microbial community activation index exceeds the above threshold, it is determined that the basic repair unit Ui has entered a stable microbial community proliferation phase, providing a stable foundation for the subsequent formation of a synergistic structure between plant roots and mycelial networks.

[0116] S600 introduces structure-inducing plants P3 into each basic repair unit Ui to form a secondary wind erosion blocking structure Ci. The structure-inducing plants P3 are plant types with the following characteristics: the aboveground parts exhibit flexible swaying characteristics under wind speed, with a wind-induced deformation angle greater than 30 degrees; the near-ground layer (0 to 10 cm) has a dense stem cluster or stolon structure; the root system has strong lateral root expansion and particle binding ability, and can combine with sand grains to form solidified masses; the plant height does not exceed 60 cm at maturity to reduce its own wind resistance.

[0117] Based on the above functional requirements, in practical applications, priority should be given to selecting local dominant species such as Calligonum mongolicum and Stipa grandis, or hybrid creeping Kentucky bluegrass varieties obtained through screening and breeding.

[0118] Using the center of the basic restoration unit Ui as the origin of the coordinate system, an inner buffer zone is formed based on the maximum canopy width of the pioneer plant P1 and the symbiotic plant P2. The P3 layout zone is constructed by extending outward by no less than 0.5 meters. The layout area is a ring-shaped area R_out with a width of 0.5 to 1 meter.

[0119] Within the R_out area, planting points P3 are arranged in a hexagonal equilateral grid pattern, with the distance between adjacent plants controlled between 20 and 30 centimeters to ensure that the leaves of each plant can cross and shade each other during the maturity stage, forming a continuous low-level vegetation belt.

[0120] Before sowing or transplanting, lightly loosen the soil surface to a depth of no more than 5 cm to prevent damage to the original moisture-controlling induction layer Fi structure, and place P3 seedlings or seeds according to the plant hole location.

[0121] The secondary wind erosion blocking structure Ci is defined as: a complex of wind energy slowing and sand particle stabilization formed by the aboveground flexible structure of structure-inducing plant P3, underground sand-fixing root system and mycorrhizal network.

[0122] On the 30th day after P3 plants survived, a wind erosion disturbance simulation experiment was conducted, using an artificial wind source (wind speed of 8 meters per second) to erode the area for 10 minutes. The amount of surface sand lost per unit time before and after wind erosion was analyzed by laser particle size imaging. If ΔS was less than 40% of the amount lost in the unplanted area, it was considered that a preliminary wind erosion blocking effect had been formed.

[0123] Simultaneously, the wind speed disturbance amplitude ΔV at a height of 5 cm above the ground surface is monitored in the same area, defined as the difference between the maximum and minimum wind speed fluctuation values. If ΔV is less than 30% of the wind source speed, it indicates that the surface airflow has been effectively disturbed and dispersed by the P3 plant structure.

[0124] Once the above two indicators are met, the structure formed in the area can be identified as a secondary wind erosion blocking structure Ci, serving as an additional reinforcement unit for extending the protective function of the basic repair unit Ui.

[0125] Because the root system of the structure-inducing plant P3 can extend between the basic remediation unit Ui and its surrounding area, and combined with the mycelium produced by the mycorrhizal spore group M1 in Ui, a microstructured consolidation network of mycelium-root hair-sand grains can be formed in the rhizosphere. This consolidation network is characterized by increased sand grain aggregation and decreased gas content per unit volume, enhancing surface stability. Furthermore, through the dissipation effect of airflow disturbances, it spatially reduces the propagation of wind stress within Ui, forming a two-layered ecological structural barrier of "internal stability and external mitigation." Therefore, the formation of the secondary wind erosion blocking structure Ci not only has its own wind erosion inhibition effect but also significantly improves the structural stability and external stress response capability of the basic remediation unit Ui.

[0126] S700: When the secondary wind erosion blocking structure Ci has been stably present for a set time and the regional evaporation inhibition rate has increased by ≥35%, then the associated behavior insect-induced plant P4 and mycorrhizal mutual induction regulating device are introduced into the basic repair unit Ui to reconstruct the basic repair unit Ui.

[0127] Continuous monitoring of the surface cover stability and regional evaporation inhibition rate of the secondary wind erosion blocking structure Ci. The evaporation inhibition rate is defined as the reduction in the evaporation rate of the moisture-controlled induction layer Fi per unit time compared to the evaporation rate of the uncovered bare land, expressed by the formula: Evaporation inhibition rate = (Evaporation rate of bare land − Evaporation rate of Fi region) / Evaporation rate of bare land × 100%.

[0128] If the Ci structure does not exhibit degradation phenomena such as collapse or band breakage within 30 consecutive days, and the measured evaporation inhibition rate is consistently higher than 35%, then the conditions for the formation of an ecological external disturbance barrier are met.

[0129] Based on this, P4, an insect-induced plant with specific volatile signaling function, was selected. Its characteristics include: releasing monoterpenoids or sesquiterpenoids during flowering; attracting local pollinating insects (such as Hymenoptera and Coleoptera) or underground root-attracting beetles; and having roots with moderate to high activity, capable of continuous material exchange with soil microorganisms.

[0130] Plant P4 is arranged in strips around the basic restoration unit Ui, with a strip width of 1 meter and a distance of no less than 0.3 meters from the edge of Ui, to prevent the pioneer plant P1 and the structure-inducing plant P3 from being obscured.

[0131] In the rhizosphere zone of P4 plant induced by symbiotic insects, fungal-root mutual inductance regulating devices are evenly buried with a spacing of no less than 4 per square meter.

[0132] The mycorrhizal-root interaction regulating device is a slow-release vegetative capsule structure, composed of a biodegradable porous shell and a core microcapsule structure, internally encapsulating the following components:

[0133] Signal-level oligosaccharide molecules (such as β-1,3-glucan and its derivatives) are used to activate signal recognition pathways in mycelial networks in soil;

[0134] Plant-derived symbiotic regulatory peptides (such as NCR-like proteins) can promote the recognition and symbiotic response of roots to exogenous hyphae.

[0135] The regulating device has a microporous structure with a pore size of 10 to 50 micrometers. After implantation, it can swell with the plant root secretions and release effective ingredients, with a release period of more than 14 days.

[0136] After the P4 and mutual inductance regulating devices are deployed, ecological interaction monitoring will be conducted in the Ui area. Monitoring content includes:

[0137] Insect visit frequency: The number of target insects visiting the P4 inflorescence or rhizosphere within 24 hours was recorded using an infrared trigger camera or insect trap device, and the average daily visit frequency was calculated.

[0138] Hyphae density variation: Using the hyphal staining extraction method, samples were taken every 3 days in the rhizosphere of Ui, and the dry weight of hyphae per unit volume was measured to construct a continuous time density sequence.

[0139] When the access frequency increases by more than 50% compared to the baseline data before introduction, and the mycelial density shows a linear or exponential growth trend for 7 consecutive days (with a daily growth rate of no less than 5%), it is determined that a preliminary ecological interaction chain has been established in the area.

[0140] Based on the achievement of the above-mentioned ecological interaction chain, the pioneer plant P1, symbiotic plant P2, structure-inducing plant P3, symbiotic behavior insect-inducing plant P4, and the mycorrhizal-root mutual induction regulatory structure already introduced in the current basic restoration unit Ui will be reconstructed in the basic restoration unit Ui.

[0141] S800 outputs the self-sustainability analytical index of the basic repair unit Ui after reconstruction, and performs multi-unit parallel repair according to the expansion requirements of the target desert area.

[0142] For each reconstructed basic repair unit Ui, the following three key indicators of ecological stability were collected:

[0143] Net primary productivity of plant communities: defined as the net increase in photosynthetic carbon fixed by plants per unit area, measured by monthly field sampling and harvesting, in grams per square meter per day;

[0144] Rhizosphere microbial community diversity index: Illumina high-throughput sequencing was used to sequence and analyze the 16S rRNA and ITS regions of fungi and bacteria in the rhizosphere soil of Ui, and diversity was calculated based on the Shannon index or Chao1 index.

[0145] Water vapor recirculation intensity: Based on the constructed water vapor convergence-dispersion tensor matrix Wi, combined with real-time humidity monitoring data under the humidity control induction layer Fi, the net water vapor inflow per unit time is determined by the differential gradient direction, with the unit being grams per square meter per hour.

[0146] All three data points were recorded as a time series, collected once every 7 days, for a continuous period of more than 60 days. The data were uniformly modeled in three dimensions, that is, a time axis t was introduced into the xy spatial coordinates to form a three-dimensional ecological response curve.

[0147] A fuzzy comprehensive evaluation model with three input factors was constructed, denoted as A, B, and C, respectively, for net primary productivity of plant communities, rhizosphere microbial diversity index, and water vapor recirculation intensity. Based on previous experimental statistics, the weights of the three factors were set to 0.4, 0.4, and 0.2, respectively, representing the importance of community structure, underground interactions, and microclimate regulation.

[0148] The time series curves of each indicator are normalized to the interval between 0 and 1. The original data are then mapped to a standard scoring space using a fuzzy membership function (Gaussian type), outputting a fuzzy comprehensive evaluation index for each Ui, denoted as Si. The value of Si ranges from 0 to 1. A threshold of Si ≥ 0.75 is set as the judgment condition, indicating that the Ui unit has reached the replicable standard for migration and restoration in terms of ecological process sustainability, self-regulation capacity, and disturbance buffering mechanism.

[0149] Within the entire target desert region, humidity gradient curves at the boundaries of each region are identified by overlaying topographic and meteorological data onto a water vapor migration trend map (synthesized from multiple Wi data). The humidity gradient is defined as the change in humidity per unit distance, expressed as a percentage per meter.

[0150] The edge humidity gradient value G1 of each Ui unit with Si≥0.75 is extracted and compared with the edge gradient value G2 of the candidate region of the expansion area. Regions that satisfy |G1−G2|≤5% are selected as candidate regions for new remediation units to ensure continuous humidity conditions after migration, which is suitable for rapidly establishing microclimate and microbial community connections.

[0151] All plant configuration parameters (including the density and arrangement of P1, P2, P3, and P4) and the material type and laying thickness of the moisture-control induction layer Fi in the basic restoration unit Ui, which is determined to have high self-sustaining capacity, are replicated as a whole as a combined template.

[0152] Within the candidate area, a staggered-nested layout is adopted: that is, plant boundaries are staggered between adjacent Ui units, and overlapping areas share the humidity-controlled induction layer structure to enhance edge connectivity and water vapor synergy.

[0153] During continuous deployment, an ecological monitoring zone is set up for every 3 newly added Ui units to measure the Si value of the newly added units, ensuring that the overall reconstruction stability is within a controllable range.

[0154] Example 2, please refer to Figure 2 As shown in this embodiment, a multi-species collaborative desert vegetation reconstruction system includes:

[0155] The environmental factor identification module obtains the hourly temperature and humidity change curves, fine-grained soil ratio distribution and wind erosion intensity index of different surface soil layers in the target desert area. Based on the diurnal temperature reversal frequency and wind erosion response lag, it identifies potential wind erosion-thermal stress coupling zones Ri with overlapping boundaries, where i is the number of potential wind erosion-thermal stress coupling zones.

[0156] The microclimate modeling module collects the microclimate fluctuation patterns at different locations within the solar cycle in each potential wind erosion-thermal stress coupling zone Ri, and constructs the water vapor convergence-dispersion tensor matrix Wi for the Ri region.

[0157] The plant combination construction module selects pioneer plant P1 based on the water vapor convergence and divergence tensor matrix Wi, and forms a root complementary structure with symbiotic plant P2. At the same time, it inoculates dormant mycorrhizal spores M1 that can be activated by the allelochemicals of P2 roots to form the basic repair unit Ui.

[0158] The moisture-control induction structure construction module constructs a composite moisture-wicking layer around the basic repair unit Ui, forming the moisture-control induction layer Fi;

[0159] The microbial community response simulation module uses the diurnal humidity range distribution map formed by the coupling effect of the water vapor convergence-dispersion tensor matrix Wi and the humidity-controlled induction layer Fi as a benchmark to simulate microbial community proliferation:

[0160] The wind erosion inhibition structure construction module introduces structure-inducing plants P3 into each basic repair unit Ui to form a secondary wind erosion blocking structure Ci.

[0161] The ecological interaction extension module introduces the symbiotic behavior insect-induced plant P4 and mycorrhizal mutual induction regulating device into the basic repair unit Ui when the secondary wind erosion blocking structure Ci has been stably existing for a set time and the regional evaporation inhibition rate has increased by ≥35% to reconstruct the basic repair unit Ui.

[0162] The ecological migration and replication module outputs the self-sustainability analysis index of the basic repair unit Ui after reconstruction, and performs multi-unit parallel repair according to the expansion needs of the target desert area.

[0163] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for multi-species synergistic desert vegetation reconstruction, characterized in that: The method comprises the following steps: S100, acquiring the hourly temperature and humidity change curve, fine particle soil proportion distribution and wind erosion intensity index of different surface soil layers in the target desert area, identifying the potential wind erosion-thermal variation stress coupling area Ri with boundary overlap according to the diurnal temperature inversion frequency and wind erosion response lag; S200, collecting the microclimate fluctuation mode of different points in the sunshine period in each potential wind erosion-thermal variation stress coupling area Ri, and constructing the water vapor gathering and scattering tensor matrix Wi of the area Ri; S300, selecting the pioneer plant P1 according to the water vapor gathering and scattering tensor matrix Wi, and forming a root system complementary structure with the symbiotic plant P2, and inoculating the dormant state mycorrhizal spore group M1 which can be activated by the root system allelochemicals of P2 to form the basic repair unit Ui; S400, constructing a composite moisture guide layer outside the basic repair unit Ui to form a humidity control induction layer Fi; S500, taking the day and night humidity range distribution diagram formed under the coupling action of the water vapor gathering and scattering tensor matrix Wi and the humidity control induction layer Fi as the reference, and carrying out the simulation of the bacterial population proliferation; S600, introducing the structure-induced plant P3 into each basic repair unit Ui to form the secondary wind erosion blocking structure Ci; S700, when the secondary wind erosion blocking structure Ci is stable for more than a set time and the regional evaporation inhibition rate is increased by more than 35%, introducing the accompanying behavior insect-induced plant P4 and the bacteria-root mutual sensing regulator into the basic repair unit Ui to reconstruct the basic repair unit Ui; S800, outputting the self-sustaining capacity analysis index of the reconstructed basic repair unit Ui, and carrying out multi-unit parallel repair according to the expansion demand of the target desert area; wherein the method for obtaining the self-sustaining capacity analysis index is: measuring the plant community net primary productivity, root zone bacterial population diversity index and water vapor return intensity of each basic repair unit Ui, and generating three-dimensional time series data respectively; and carrying out index processing on the three data through a fuzzy comprehensive evaluation model to output the self-sustaining capacity analysis index.

2. The method for multi-species synergistic desert vegetation reconstruction according to claim 1, characterized in that: The S200 comprises: a plurality of micro-weather collection points are arranged in the potential wind erosion-thermal variation stress coupling area Ri according to the boundary diffusion gradient, the hourly temperature, humidity and solar radiation values of each point in a continuous time period are recorded, and a corresponding time series data set is constructed; Fourier spectrum analysis is performed on the temperature and humidity change in the sunshine period of each collection point, and the main fluctuation frequency and phase shift characteristics are extracted; the response properties of all sampling points are associated according to the spatial coordinates, the water vapor diffusion direction and intensity between adjacent points are calculated, and a three-dimensional water vapor flow vector field is generated; based on the gathering and scattering characteristics of each node of the three-dimensional water vapor flow vector field, the water vapor gathering and scattering tensor matrix Wi is constructed.

3. The method for multi-species synergistic desert vegetation reconstruction according to claim 1, characterized in that: The method for forming the basic repair unit Ui comprises: based on the grid elements with water vapor gathering degree higher than the average value of the whole area in the water vapor gathering and scattering tensor matrix Wi, the priority implantation position of the pioneer plant P1 is determined, and the plant with leaf transpiration regulation ability and suitable for low water vapor environment gradient is selected as the pioneer plant P1. Outside the centripetal expansion range of the root system of the pioneer plant P1, the symbiotic plant P2 is arranged according to the reverse direction of water vapor dispersion in the water vapor dispersion and convergence tensor matrix W1, so that the symbiotic plant P2 and the pioneer plant P1 form a root system complementary structure with a difference of more than 5 cm in root system depth; The dormant mycorrhizal spore group M1 which is stable in an inactive state and can be activated by the root system allelochemicals secreted by the symbiotic plant P2 is uniformly placed in the overlapping area of the root system complementary structure, so that the mycorrhizal spore group M1 enters an induced response stage after implantation; When the dormant mycorrhizal spore group M1 shows initial mycelium expansion in the root system complementary structure, the structure composed of the pioneer plant P1, the symbiotic plant P2 and the mycorrhizal spore group M1 is defined as a basic restoration unit Ui.

4. The method for multi-species synergistic desert vegetation reconstruction according to claim 1, characterized in that: The simulation of the proliferation of the microbial population comprises: According to the water vapor convergence intensity of each grid unit in the water vapor dispersion and convergence tensor matrix W1, the actual humidity monitoring data under the humidity control induction layer Fi is spatially matched to generate a humidity range distribution diagram containing the maximum difference in diurnal humidity, and the grid unit with a humidity range greater than 1.3 times the average value of the whole area is determined as a potential area for the proliferation of the microbial population; In the potential area for the proliferation of the microbial population, a microbial population proliferation probability model is constructed with the humidity range, temperature fluctuation amplitude and humidity control induction layer coverage thickness as input factors, and the theoretical proliferation probability of the mycorrhizal spore group M1 in different time periods is calculated by a polynomial regression method; The theoretical proliferation probability is compared with the measured mycelium expansion speed in the basic restoration unit Ui, and if the difference is less than 20%, the corresponding area is defined as an effective microbial population proliferation area; In the effective microbial population proliferation area, the acceleration of the mycelium density change with time is taken as the microbial population activation index, and when the microbial population activation index exceeds a preset threshold value, it is determined that the basic restoration unit Ui has entered a stable stage of microbial population proliferation.

5. The method for multi-species synergistic desert vegetation reconstruction according to claim 1, characterized in that: The reconstruction of the basic restoration unit Ui comprises: After the secondary wind erosion blocking structure Ci is formed for 30 days and the regional evaporation inhibition rate reaches or exceeds 35%, a companion behavior insect-induced plant P4 is selected; A fungus-root mutual sensing adjustment device is uniformly buried in the rhizosphere area of the companion behavior insect-induced plant P4; By regularly monitoring the access frequency of the induced insects and the change trend of the mycelium density in the area, the establishment of the ecological interaction chain is determined; When the access frequency is increased by more than 50% and the mycelium density maintains a stable growth trend for more than 7 days, the basic restoration unit Ui is upgraded to a multi-species collaborative ecological restoration system containing P1, P2, P3, P4 and a fungus-root adjustment structure.

6. The method for multi-species synergistic desert vegetation reconstruction according to claim 1, characterized in that: The output of the self-sustaining ability analysis index of the reconstructed basic restoration unit Ui comprises: The net primary productivity of the plant community, the root zone microbial population diversity index and the water vapor return intensity of each basic restoration unit Ui are measured to generate three-dimensional time series data respectively; The three data are processed by a fuzzy comprehensive evaluation model to output the self-sustaining ability analysis index, denoted as Si, wherein Si≥0.75 indicates that the reconstructed basic restoration unit Ui has parallel migration repair capability; Based on the target desert area expansion boundary and water vapor migration trend map, the area with the minimum humidity gradient difference between the reconstructed basic repair unit Ui boundary and the selected and reconstructed basic repair unit Ui boundary is selected as the new repair unit candidate area; The plant combination structure corresponding to the reconstructed basic repair unit Ui with high Si value and the humidity control induction layer parameters are copied to the candidate area as a whole, and the staggered-nested layout is adopted to realize multi-unit parallel vegetation reconstruction.

7. A multi-species synergistic desert vegetation reconstruction system for implementing a multi-species synergistic desert vegetation reconstruction method according to any one of claims 1-6, characterized in that: It includes: An environmental factor identification module obtains the hourly temperature and humidity change curve, fine particle soil proportion distribution, and wind erosion intensity index of different surface soil layers in the target desert area, and identifies the potential wind erosion-thermal variation stress coupling area Ri based on the diurnal temperature reversal frequency and wind erosion response lag, where i is the number of potential wind erosion-thermal variation stress coupling areas; A microclimate modeling module collects the microclimate fluctuation pattern of different points in the sun cycle in each potential wind erosion-thermal variation stress coupling area Ri, and constructs the water vapor gathering and scattering tensor matrix Wi of the Ri area; A plant combination construction module selects a pioneer plant P1 based on the water vapor gathering and scattering tensor matrix Wi, and forms a root system complementary structure with a symbiotic plant P2, and inoculates a dormant state mycorrhizal spore group M1 that can be activated by the root system allelochemicals of P2 to form a basic repair unit Ui; A humidity control induction structure construction module constructs a composite humidity guide layer around the basic repair unit Ui to form a humidity control induction layer Fi; A microbial population response simulation module simulates the proliferation of the microbial population based on the day-night humidity range distribution map formed under the coupling action of the water vapor gathering and scattering tensor matrix Wi and the humidity control induction layer Fi: An erosion inhibition structure construction module introduces a structure-induced plant P3 into each basic repair unit Ui to form a secondary wind erosion blocking structure Ci; An ecological interaction expansion module introduces a companion behavior insect-induced plant P4 and a fungus-root mutual sensing regulator into the basic repair unit Ui when the secondary wind erosion blocking structure Ci is stable for more than a set time and the regional evaporation inhibition rate increases by ≥35%, and reconstructs the basic repair unit Ui; An ecological migration replication module outputs the self-sustaining ability analysis index of the reconstructed basic repair unit Ui, and performs multi-unit parallel repair according to the expansion requirements of the target desert area.

Images