Vegetation recovery system based on moss

Abstract

The invention discloses a moss-based vegetation restoration system, and relates to the technical field of moss ecological restoration, the system realizes active capture, collection and directional drainage of trace water resources in the environment by introducing condensation potential analysis, flow guide membrane layout optimization and water guide adjustment, and the utilization efficiency of condensate water is significantly improved. Compared with a traditional mode which only depends on natural rainfall or passively covers a thin film, the arrangement path of the flow guide film can be scientifically planned according to environment monitoring data and topographic features, efficient convergence of condensate water is guaranteed, and the flow guide film can be effectively collected through real-time iterative adjustment of flow guide film microstructure parameters. Water supply requirements under different seasons, day and night and climate changes are dynamically met, and therefore the problem of water shortage in the early moss planting stage is effectively solved. The flow guide membrane surface micro-slope structure parameters are subjected to adjustable design, iterative adjustment is performed according to the real-time condensate water forming amount, relatively stable water supply is maintained, and condensate water loss is reduced.

Description

Technical Field

[0001] This invention relates to the field of moss ecological restoration technology, specifically to a moss-based vegetation restoration system. Background Technology

[0002] In extreme environments such as high altitudes and arid regions, traditional vegetation restoration faces numerous challenges, including water scarcity, soil dryness, and harsh environments, resulting in low plant survival rates and unsatisfactory ecological restoration effects. Particularly in arid and cold mountainous areas, where rainfall is scarce and uneven, insufficient soil moisture supply has become a key bottleneck restricting vegetation restoration and ecological reconstruction. How to effectively capture and utilize trace water resources in the environment to achieve sustainable water supply and promote plant growth has become an urgent problem to be solved in the field of ecological restoration.

[0003] Mosses, as primitive and highly adaptable non-vascular plants, possess unique structural characteristics and physiological mechanisms, enabling them to efficiently absorb and utilize condensation or dew from the air. The porous structure of moss cell walls and surfaces allows them to absorb trace amounts of moisture from the environment. Even under conditions of minimal rainfall, they can meet their basic water needs through a micro-water film formed by condensation, thus maintaining their life activities and slow growth. Moss-based vegetation restoration technologies, utilizing their natural ability to absorb condensation, can effectively improve water use efficiency, enhance microecological conditions in arid environments, and promote soil water cycling and biodiversity restoration.

[0004] However, traditional methods for restoring moss vegetation largely rely on natural precipitation or surface runoff, or sometimes use mulch films to promote the collection and storage of condensate. These methods lack active capture and effective guidance of condensate, leading to underutilization of condensate resources. The condensate supply is unstable, resulting in long moss growth cycles and low survival rates. Furthermore, traditional methods struggle to achieve efficient collection and directional drainage of condensate, causing it to run off on the soil surface, wasting valuable micro-water resources, limiting vegetation growth and ecological restoration, and failing to meet the dynamic water needs of vegetation. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a moss-based vegetation restoration system to solve the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a moss-based vegetation restoration system, comprising:

[0007] The condensation potential analysis module is used to divide the area to be restored into multiple environmental monitoring sub-areas, collect environmental data, including diurnal temperature range, air humidity, surface wind speed and soil moisture content, analyze whether the sub-areas have the potential to form condensate, and output a first control signal or a second control signal to start the deployment of the condensate diversion membrane.

[0008] After receiving the first control signal, the sub-region suitability index SI of each sub-region is constructed and evaluated to obtain the suitability level classification results and generate a suitability level map, and the first control signal is updated.

[0009] The condensate diversion membrane deployment module is used to collect the digital elevation model (DEM) of each sub-region to construct the sub-region grid and the weighted deployment probability map (WBP) after receiving the deployment instruction of the first control signal.

[0010] Construct the aspect suitability factor for the j-th cell in each sub-region and slope suitability factor The terrain adaptation coefficient of the j-th pixel is calculated using a weighted summation method. Then, the system automatically plans the condensate diversion membrane deployment path to obtain the optimal path sequence for each sub-region.

[0011] The moisture guidance module is used to configure the initialization parameters of the micro-slope structure on the surface of the condensate guide film based on the optimal path sequence and the first and second suitability levels of the weighted distribution probability map (WBP) for each sub-region. These parameters include the initial guide slope. Initial flow texture depth Initial flow texture width Initial guide flow limiting angle And the arrangement of the trenches; and based on the weighted layout probability map WBP, after setting the planting points for bryophytes, the bryophytes were evenly sown.

[0012] The amount of condensate formed in the sub-region, Q, is collected and recorded in real time and compared with the preset minimum water volume for moss growth, Qmin. If it is insufficient, a water shortage warning signal is output, and the initial parameters are iteratively adjusted until the condensate supply Q reaches the minimum water volume threshold for moss growth, Qmin.

[0013] Preferably, the condensation potential analysis module includes an environmental data acquisition unit, a condensation potential calculation unit, and an instruction output unit;

[0014] The environmental data acquisition unit is used to divide the area to be restored into multiple environmental monitoring sub-zones. Each sub-zone is equipped with an environmental sensor array to acquire environmental data of the area to be restored in real time or at regular intervals. The environmental data includes:

[0015] The diurnal temperature difference ΔT, in °C, is calculated by continuously collecting the difference between the highest and lowest temperatures over 24 hours.

[0016] Air humidity (RH), measured by a humidity sensor, is expressed as % (%).

[0017] Surface wind speed Vg, measured by a low-power anemometer, is in m / s.

[0018] Soil moisture content S, measured by a soil moisture sensor, is expressed as volumetric moisture content (%).

[0019] The condensation potential calculation unit is used to calculate the potential for condensation formation in a sub-region based on the collected environmental data. When all of the following conditions are met, the sub-region is considered to have condensation potential and a first control signal is output:

[0020] ;

[0021] in, The preset minimum condensation temperature difference threshold is set to 6℃;

[0022] The preset minimum air humidity threshold is set to 60%.

[0023] The maximum wind speed limit is set at 2.5 m / s;

[0024] The maximum soil moisture content is set at 20%.

[0025] The instruction output unit is used to output the condensate diversion membrane deployment instruction based on the condensation potential result;

[0026] When all environmental conditions meet the judgment criteria, the first control signal is output. The first control signal includes: a deployment instruction, the location and range of the sub-area;

[0027] If any environmental parameter fails to meet the requirements, a second control signal is output to prevent the condensate drainage membrane from being installed until the environment improves or is reassessed.

[0028] Preferably, the condensation potential analysis module further includes a first marking unit, which is used to receive a first control signal, record current environmental data, construct and evaluate the sub-region suitability index SI, obtain suitability level classification results and generate a suitability level map. The suitability level classification results include a first suitability level, a second suitability level and a third unsuitability level.

[0029] The sub-region suitability index (SI) is obtained as follows:

[0030]

[0031] in, This indicates the reference value for the maximum diurnal temperature range. Indicates the corresponding humidity factor; This represents the maximum wind speed reference value. It represents the maximum saturated water content of the local soil, and the maximum value of water filling the pores per unit volume of soil.

[0032] Among them, the corresponding humidity factor Obtained through the piecewise normalization formula:

[0033] ;

[0034] In the formula, , , and Indicates weight, ;

[0035] When the sub-region's suitability index SI > 0.7, there is sufficient condensate, resulting in the first suitability level.

[0036] When the sub-region's suitability index is 0.4 ≤ SI ≤ 0.7, a second suitability level is generated;

[0037] When the subregion's suitability index SI < 0.4, a third level of unsuitability is generated.

[0038] Preferably, the condensate diversion membrane deployment module includes a sub-area deployment scheduling unit and a deployment path planning unit;

[0039] The sub-area deployment scheduling unit is used to include the first and second suitability levels into the next round of deployment queue, update the first control signal, and include the third unsuitability level into the second control signal, without deploying it.

[0040] The sub-region deployment scheduling unit includes a slope map and aspect map extraction sub-unit, a grid division unit, and a pixel adaptive analysis unit:

[0041] Slope and aspect maps are extracted into sub-units. These are used to acquire the Digital Elevation Model (DEM) for each sub-region, extract slope and aspect maps, and import land cover layers obtained through remote sensing interpretation or UAV aerial surveys. After identifying rocky outcrops, gravel deposits, vegetation cover, or dried-up gullies, the DEM is divided into grid cells. These cells are then used for spatial resampling and interpolation of the DEM, forming sub-regional GeoTIFF grids as the input base map for subsequent path planning. The pre-divided suitability level map is imported, and the weighting function is set as follows:

[0042] The first suitability level has a weight of 1.0, the second suitability level has a weight of 0.6, and the third unsuitability level has a weight of 0.2. The suitability level map is converted into a deployment priority layer, that is, the deployment attraction weight of each cell, and a weighted deployment probability map WBP is generated.

[0043] The pixel adaptive analysis unit is used to identify and obtain the aspect angle of the j-th pixel in each sub-region. Obtain the original cosine value of the j-th pixel. :

[0044]

[0045] in, Let the slope angle of the j-th pixel be... This indicates the optimal slope angle, such as 180° for a south-facing slope;

[0046] And the slope suitability factor of the j-th pixel The cosine value is truncated to a non-negative form to obtain the slope suitability factor for the j-th pixel. :

[0047]

[0048] pass Truncation indicates that if ≥0, will be retained; if If <0, replace it with 0;

[0049] Identify and obtain the slope of the j-th pixel in each sub-region. The slope suitability factor for the j-th pixel is obtained by using segmented conditions. :

[0050] ;

[0051] Combining the slope suitability factor of the j-th pixel and slope suitability factor Satisfying weight Calculate the terrain adaptation coefficient for the j-th pixel. :

[0052] .

[0053] Preferably, path planning units are deployed to adjust the terrain adaptation coefficient of the j-th pixel. Converted into cost value The conversion method is as follows:

[0054] ;

[0055] in, It is a small constant to prevent division by zero;

[0056] The value of the j-th pixel Substitute the values ​​into the sub-region grid to obtain the cost grid map;

[0057] After constructing a cost raster map containing the cost value of each cell, the connectivity between nodes is established according to the preset adjacency rules, including four-adjacency or eight-adjacency, and the movement cost between nodes is calculated based on the cost value and the geometric distance between cells.

[0058] Add the starting node to the open list and initialize its path cost to zero;

[0059] In each iteration, the node with the minimum total cost is selected from the open list, moved to the closed list, and its adjacent nodes are processed in turn:

[0060] If the adjacent node is not in the closed list, update the path cost of the adjacent node according to the sum of the current node's path cost and the movement cost, and record the path association relationship;

[0061] If the update results in the path cost of adjacent nodes being lower than that of existing records, then the original record is replaced.

[0062] When the endpoint node is added to the closed list, the optimal path sequence from the starting point to the endpoint is obtained by backtracking along the path association relationship;

[0063] Using the above methods, the optimal path sequence based on the cost grid is obtained for each sub-region.

[0064] In this method, if a heuristic estimate based on the node position and the endpoint position is added to the total cost calculation, then A* search is implemented; if no estimate is added, then Dijkstra search is implemented.

[0065] Preferably, the heuristic estimate is a prediction of the path cost between the node location and the target endpoint location, used to guide the path search algorithm to prioritize exploring paths to the target, specifically including:

[0066] Obtain the coordinate information of the current node and the target node in the cost grid, including their horizontal and vertical positions;

[0067] Calculate the distance difference between the two nodes in the horizontal and vertical directions;

[0068] Based on the adjacency rules, the optimal distance calculation method is selected, including:

[0069] When dealing with four adjacent nodes, if only vertical and horizontal movement is allowed, the sum of the absolute values ​​of the differences between the horizontal and vertical distances is calculated, which is the Manhattan distance.

[0070] For eight-adjacency nodes, if diagonal movement is allowed, the straight-line distance between the two nodes is calculated, i.e., the Euclidean distance.

[0071] The distance value obtained is the heuristic estimate, which is used as the estimated cost from the current node to the target node in path search.

[0072] Preferably, the moisture guiding module includes a micro-slope structure design unit on the surface of the condensate guiding membrane;

[0073] The condensate drainage membrane surface microslope structure design unit is used to configure the initialization parameters of the condensate drainage membrane surface microslope structure according to the optimal path sequence based on the cost grid and the first and second adaptability levels in the weighted deployment probability map (WBP) for each sub-region, including:

[0074] For the sub-region of the first adaptability level, the micro-slope structure on the surface of the condensate drainage membrane is set as follows:

[0075] Initial guide slope The range is 4° to 6°;

[0076] Initial flow texture depth The range is from 1.4 mm to 1.6 mm;

[0077] Initial flow texture width The range is from 2.8 mm to 3.2 mm;

[0078] Initial guide flow limiting angle The range is 25° to 35°;

[0079] The trenches are arranged in a regular V-shape to ensure efficient and concentrated flow of condensate to the target area under the membrane.

[0080] For sub-regions with secondary biocompatibility levels, the micro-slope structure on the surface of the condensate drainage membrane is set as follows:

[0081] Initial guide slope The range is 2° to 3°;

[0082] Initial flow texture depth The range is from 1.0 mm to 1.3 mm;

[0083] Initial flow texture width The range is 2.0 mm to 2.5 mm;

[0084] Initial guide flow limiting angle The range is 30° to 40°;

[0085] The trenches are arranged in an irregular rectangular or wavy pattern to adapt to the complex terrain of areas with low biodiversity and improve the efficiency of condensate dispersion and collection.

[0086] Preferably, the moisture guidance module also includes a moss planting unit and a moisture supply monitoring unit:

[0087] A moss planting unit is used to identify a set of sub-region pixels of the first suitability level and the second suitability level extracted from a weighted layout probability map (WBP) based on the WBP, form a preliminary candidate set of areas suitable for colonization, and calculate the spatial intersection of the preliminary candidate set of areas suitable for colonization and the area where the micro-slope structure of the condensate water diversion membrane is laid, that is:

[0088] The priority colonization area = the candidate area of the suitability level ∩ the area where the condensate water diversion membrane is laid, so as to obtain the colonization sub-region;

[0089] Equidistant moss plant colonization points are preset in the colonization sub-region, including:

[0090] For the sub-region of the first suitability level, the spacing between the colonization points is set to 0.3 m to 0.5 m;

[0091] For the sub-region of the second suitability level, the spacing between the colonization points is set to 0.6 m to 0.8 m;

[0092] Then the moss is evenly sown or implanted into the colonization sub-region under the condensate water diversion membrane by mechanical or manual means.

[0093] Preferably, a water supply monitoring unit is used to estimate the daily average supply volume of the condensate water under the condensate water diversion membrane in real time or at regular intervals, including:

[0094] The condensate water formation volume Q of the sub-region is collected and recorded in real time, with the unit of ml / h; a preset minimum starting growth water volume threshold Qmin for moss is set, and the condensate water formation volume Q of the sub-region is compared and analyzed with Qmin; when Q ≥ Qmin, it is determined that the current water supply meets the growth requirements of the moss, and a normal water supply state signal is output; when Q < Qmin, it is determined that the current water supply is insufficient, and a water shortage warning signal is output.

[0095] Preferably, the water diversion module further includes a correction unit. When receiving the water shortage warning signal, the correction unit obtains the initialization parameters of the current micro-slope structure of the condensate water diversion membrane, including the initial diversion slope the initial diversion texture depth the initial diversion texture width and the initial diversion current-limiting angle , and performs a decreasing adjustment according to the preset adjustment step and parameter range, and the condensate water supply volume Q is recollected through the water supply monitoring unit; it is judged whether Q after adjustment reaches the minimum starting growth water volume threshold Qmin of the moss. If it reaches, the iteration is stopped and the current parameters are maintained; if it does not reach and the parameters can still be adjusted, the iteration adjustment continues until the condensate water supply volume Q reaches the minimum starting growth water volume threshold Qmin of the moss.

[0096] The present invention provides a vegetation restoration system based on moss. It has the following beneficial effects:

[0097] This invention introduces modules such as condensation potential analysis, flow-guiding membrane layout optimization, and moisture guidance regulation, realizing the active capture, collection, and directional diversion of trace water resources in the environment, significantly improving the utilization efficiency of condensate. Compared with traditional methods that rely solely on natural precipitation or passive film covering, this invention not only scientifically plans the layout path of the flow-guiding membrane based on environmental monitoring data and topographic features to ensure efficient condensate collection, but also dynamically adapts to water supply demands under different seasons, diurnal cycles, and climate changes through real-time iterative adjustments to the microstructural parameters of the flow-guiding membrane, thereby effectively alleviating the water shortage problem in the early stages of moss colonization. Based on environmental monitoring data analysis of condensation potential and combined with topographic features to optimize the layout location and path of the condensate flow-guiding membrane, the collection efficiency of condensate is improved. By adjusting the micro-slope structural parameters of the flow-guiding membrane surface and iteratively adjusting them according to the real-time condensate formation, the system can maintain a relatively stable water supply under different environmental conditions, reducing condensate loss. In the early stages of moss colonization, it can improve water use efficiency and soil surface moisture, thereby improving the survival rate and growth stability of moss to a certain extent. Attached Figure Description

[0098] Figure 1 This is a schematic diagram of the system flow of the present invention. Detailed Implementation

[0099] 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, and 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.

[0100] Example 1

[0101] Please see Figure 1 This invention provides a moss-based vegetation restoration system, comprising:

[0102] The condensation potential analysis module is used to divide the area to be restored into multiple environmental monitoring sub-areas, collect environmental data, including diurnal temperature range, air humidity, surface wind speed and soil moisture content, analyze whether the sub-areas have the potential to form condensate, and output a first control signal or a second control signal to start the deployment of the condensate diversion membrane.

[0103] After receiving the first control signal, the sub-region suitability index SI of each sub-region is constructed and evaluated to obtain the suitability level classification results and generate a suitability level map, and the first control signal is updated.

[0104] The condensate diversion membrane deployment module is used to collect the digital elevation model (DEM) of each sub-region to construct the sub-region grid and the weighted deployment probability map (WBP) after receiving the deployment instruction of the first control signal.

[0105] Construct the aspect suitability factor for the j-th cell in each sub-region and slope suitability factor The terrain adaptation coefficient of the j-th pixel is calculated using a weighted summation method. Then, the system automatically plans the condensate diversion membrane deployment path to obtain the optimal path sequence for each sub-region.

[0106] The moisture guidance module is used to configure the initialization parameters of the micro-slope structure on the surface of the condensate guide film based on the optimal path sequence and the first and second suitability levels of the weighted distribution probability map (WBP) for each sub-region. These parameters include the initial guide slope. Initial flow texture depth Initial flow texture width Initial guide flow limiting angle And the arrangement of the trenches; and based on the weighted layout probability map WBP, after setting the planting points for bryophytes, the bryophytes were evenly sown.

[0107] The amount of condensate formed in the sub-region, Q, is collected and recorded in real time and compared with the preset minimum water volume for moss growth, Qmin. If it is insufficient, a water shortage warning signal is output, and the initial parameters are iteratively adjusted until the condensate supply Q reaches the minimum water volume threshold for moss growth, Qmin.

[0108] In this embodiment, the present invention, by introducing modules such as condensation potential analysis, flow-guiding membrane layout optimization, and moisture guidance regulation, achieves active capture, collection, and directional diversion of trace water resources in the environment, significantly improving the utilization efficiency of condensate. Compared with traditional methods that rely solely on natural precipitation or passive film covering, the present invention can not only scientifically plan the layout path of the flow-guiding membrane based on environmental monitoring data and topographic features to ensure efficient condensate collection, but also dynamically adapt to the water supply needs under different seasons, diurnal cycles, and climate changes through real-time iterative adjustment of the microstructure parameters of the flow-guiding membrane, thereby effectively alleviating the water shortage problem in the early stage of moss colonization. Based on environmental monitoring data analysis of condensation potential and combined with topographic features to optimize the layout location and path of the condensate flow-guiding membrane, the collection efficiency of condensate is improved. By adjusting the micro-slope structure parameters of the flow-guiding membrane surface in an adjustable manner and iteratively adjusting them according to the real-time condensate formation, the system can maintain a relatively stable water supply under different environmental conditions, reducing condensate loss. In the early stage of moss colonization, it can improve water utilization and soil surface moisture, thereby improving the survival rate and growth stability of moss to a certain extent.

[0109] Example 2

[0110] This embodiment is an explanation of Embodiment 1. For details, please refer to... Figure 1 The condensation potential analysis module includes an environmental data acquisition unit, a condensation potential calculation unit, and an instruction output unit;

[0111] The environmental data acquisition unit is used to divide the area to be restored into multiple environmental monitoring sub-zones. Each sub-zone is equipped with an environmental sensor array to acquire environmental data of the area to be restored in real time or at regular intervals. The environmental data includes:

[0112] The diurnal temperature difference ΔT, in °C, is calculated by continuously collecting the difference between the highest and lowest temperatures over 24 hours.

[0113] Air humidity (RH), measured by a humidity sensor, is expressed as % (%).

[0114] Surface wind speed Vg, measured by a low-power anemometer, is in m / s.

[0115] Soil moisture content S, measured by a soil moisture sensor, is expressed as volumetric moisture content (%).

[0116] The condensation potential calculation unit is used to calculate the potential for condensation formation in a sub-region based on the collected environmental data. When all of the following conditions are met, the sub-region is considered to have condensation potential and a first control signal is output:

[0117] ;

[0118] in, The preset minimum condensation temperature difference threshold is set to 6℃;

[0119] The preset minimum air humidity threshold is set to 60%.

[0120] The maximum wind speed limit is set at 2.5 m / s;

[0121] The maximum soil moisture content is set at 20%.

[0122] The instruction output unit is used to output the condensate diversion membrane deployment instruction based on the condensation potential result;

[0123] When all environmental conditions meet the judgment criteria, the first control signal is output. The first control signal includes: a deployment instruction, the location and range of the sub-area;

[0124] If any environmental parameter fails to meet the requirements, a second control signal is output to prevent the condensate drainage membrane from being installed until the environment improves or is reassessed.

[0125] In most arid / semi-arid scenarios, when the diurnal temperature range is less than about 5°C, the nighttime temperature often fails to drop to the dew point (especially under moderate RH conditions). When ΔT ≥ 6°C, surface quenching is more pronounced, and the frequency and total amount of condensation increase significantly. Therefore, setting 6°C as the minimum condensation temperature difference threshold is engineering feasible. The amount of condensable water vapor in the air increases linearly with increasing RH. If the RH is too low, even with a large temperature difference, insufficient water vapor in the air makes it difficult to form collectable condensate. When the RH is below 50%, the difference between the dew point and the ambient temperature is too large, resulting in a low probability of condensation. When the RH ≥ 60%, observable condensation is more likely to occur under a certain temperature difference, so 60% is set as an empirical lower limit. Wind speed affects boundary layer thickness and heat and mass transfer. Higher wind speeds carry away the humid air generated by cooling near the membrane surface and increase evaporation, thus inhibiting condensation accumulation. Conversely, lower wind speeds cause localized humidity retention, which is conducive to condensation. In field deployments and small-scale wind tunnel / in-situ observations, condensation decreases significantly when the wind speed exceeds 2–3 m / s. A maximum wind speed limit of 2.5 m / s is chosen as the engineering-standard limit, balancing operability and safety margin. Moist soils have higher heat capacity and evaporation potential; high soil moisture content at night compensates for nighttime cooling through evaporation and convection, reducing the surface cooling rate and consequently decreasing condensation efficiency. Conversely, dry soils cool more quickly at night, promoting condensation. A volumetric moisture content below 20% is generally considered dry (depending on soil type), facilitating rapid surface heat release and generating a larger surface temperature difference at night; therefore, 20% is a generally applicable upper limit.

[0126] In this embodiment, by dividing the area to be restored into multiple environmental monitoring sub-regions and deploying an environmental sensor array, key environmental parameters, including diurnal temperature range, air humidity, surface wind speed, and soil moisture content, can be acquired in real time or periodically. Based on these parameters, condensation potential can be calculated, enabling quantitative judgment of deployment conditions. Using preset multi-parameter threshold conditions, condensate drainage membrane deployment instructions can be output promptly when the environment is suitable, avoiding blind construction when conditions are not met and reducing resource waste. Simultaneously, this module can provide accurate environmental baseline data for subsequent water guidance and moss planting, improving condensate utilization efficiency and the targeted and adaptable nature of vegetation restoration.

[0127] Example 3

[0128] This embodiment is an explanation of Embodiment 1. For details, please refer to... Figure 1The condensation potential analysis module also includes a first marking unit, which is used to receive a first control signal, record current environmental data, construct and evaluate the sub-region suitability index SI, obtain suitability level classification results and generate a suitability level map. The suitability level classification results include a first suitability level, a second suitability level and a third unsuitability level.

[0129] The sub-region suitability index (SI) is obtained as follows:

[0130]

[0131] in, This indicates the reference value for the maximum diurnal temperature range. Indicates the corresponding humidity factor; This represents the maximum wind speed reference value. This indicates the maximum saturated water content of the local soil. , , and Indicates weight, and , , , ; The weights were determined by multiple regression analysis of multiple sets of field monitoring data and actual condensate measurements. The analysis results showed that the contribution rates of temperature difference and humidity were both higher than 30%, while the contribution rates of wind speed and soil moisture content were both around 15%. Therefore, the weights were set as follows: temperature difference 0.35, humidity 0.35, wind speed 0.15, and soil moisture content 0.15, to take into account both generation potential and ecological needs.

[0132] Among them, the corresponding humidity factor Obtained through the piecewise normalization formula:

[0133] ;

[0134] The greater the temperature difference between day and night, especially when the temperature drops sharply in the morning and evening, the easier it is for water vapor in the air to cool and condense into dew. A large temperature difference between day and night provides the temperature driving force for the formation of condensate, which is the key to the "condensation water supply" mechanism of condensate. A suitable temperature difference range can promote effective dew condensation. If the temperature difference is too small, the condensation power will be insufficient, and if the temperature difference is too large, it may lead to rapid evaporation of water vapor.

[0135] This indicates that the higher the relative humidity, the richer the water vapor content in the air, and the greater the potential for condensation formation. An air humidity (RH) exceeding 60% is conducive to the formation of dew or condensation. The corresponding humidity factor... The higher the temperature, the better it helps maintain the surface water film and increases the water absorption capacity of plants such as moss;

[0136] This represents the normalized surface wind speed factor. Higher wind speeds increase water evaporation and reduce condensation accumulation efficiency. Therefore, the formula uses... This indicates the negative correlation with wind speed;

[0137] Indicates the degree of soil dryness; when the soil moisture content is high, →1,1− →0 indicates low soil dryness and low demand for condensate; when soil moisture content is low... →0,1− →1 indicates high soil dryness and high dependence on condensation; → indicates dry soil, where condensation is more important and adaptability is enhanced by condensation.

[0138] When the sub-area suitability index SI > 0.7, it indicates that the current environmental monitoring sub-area has very suitable environmental conditions, sufficient condensate, and generates the first suitability level.

[0139] When the sub-area suitability index is 0.4≤SI≤0.7, it indicates that the environment of the environmental monitoring sub-area is suitable but slightly weak, with sufficient condensate, thus generating a second suitability level.

[0140] When the sub-region's suitability index SI < 0.4, it indicates that the environmental conditions of the environmental monitoring sub-region are poor, with insufficient condensate, resulting in a third level of unsuitability.

[0141] This invention achieves quantitative assessment and grading of the environmental suitability of areas to be restored by setting a first marker unit in the condensation potential analysis module and constructing a sub-region suitability index (SI). Based on the normalization and weighted combination of multiple environmental factors such as diurnal temperature range, relative humidity, surface wind speed, and soil moisture content, the SI condenses complex environmental information into comparable numerical indicators, providing clear and repeatable criteria for deciding whether each sub-region is suitable for the deployment of condensate drainage membranes and moss colonization. This mechanism offers the following technical benefits: First, it reduces blind or inefficient deployment by prioritizing deployment when SI ≥ 0.7 (first suitability), thus minimizing resource waste during construction in unfavorable environments. Second, it supports a zoned differentiation strategy, prioritizing limited resources for sub-zones with relatively sufficient condensation potential and water support, thereby improving overall project efficiency. Third, it is linked to real-time monitoring and subsequent water supply feedback, enabling dynamic updates to suitability classifications and achieving closed-loop optimization of deployment and planting. Fourth, it provides quantitative input for subsequent path planning, micro-slope structure design, and moss planting, facilitating controllability and effectiveness evaluation during implementation. In summary, this marker unit and SI index system help improve the targeting and reliability of condensate utilization, thereby enhancing water security and vegetation restoration success rates for moss planting to some extent.

[0142] Example 4

[0143] This embodiment is an explanation of Embodiment 1. For details, please refer to... Figure 1 The condensate diversion membrane deployment module includes a sub-area deployment scheduling unit and a deployment path planning unit;

[0144] The sub-area deployment scheduling unit is used to include the first and second suitability levels into the next round of deployment queue, update the first control signal, and include the third unsuitability level into the second control signal, without deploying it.

[0145] The sub-region deployment scheduling unit includes a slope map and aspect map extraction sub-unit, a grid division unit, and a pixel adaptive analysis unit:

[0146] Slope and aspect maps are extracted into sub-units. These are used to acquire the Digital Elevation Model (DEM) for each sub-region, extract slope and aspect maps, and import land cover layers obtained through remote sensing interpretation or UAV aerial surveys. After identifying rocky outcrops, gravel deposits, vegetation cover, or dried-up gullies, the DEM is divided into grid cells. These cells are then used for spatial resampling and interpolation of the DEM, forming sub-regional GeoTIFF grids as the input base map for subsequent path planning. The pre-divided suitability level map is imported, and the weighting function is set as follows:

[0147] The first suitability level has a weight of 1.0, the second suitability level has a weight of 0.6, and the third unsuitability level has a weight of 0.2. The suitability level map is converted into a deployment priority layer, that is, the deployment attraction weight of each cell, and a weighted deployment probability map WBP is generated.

[0148] Slope map (unit: °, range: 0–90°)

[0149] Slope aspect diagram (unit: °, 0–360°, 0 represents due north, 90 represents due east);

[0150] GeoTIFF sub-grids are composed of regularly arranged two-dimensional grids, and each grid unit is called a "cell".

[0151] The pixel adaptive analysis unit is used to identify and obtain the aspect angle of the j-th pixel in each sub-region. Obtain the original cosine value of the j-th pixel. :

[0152]

[0153] in, Let the slope angle of the j-th pixel be... This indicates the optimal slope angle, such as 180° for a south-facing slope;

[0154] If the slope is directly aligned with the optimal direction → angle difference = 0° → cos(0°) = 1;

[0155] If the slope direction is exactly opposite (difference of 180°) → cos(180°) = -1;

[0156] When the deviation is between 90° and 180°, the cos(value) will be negative (representing a reverse direction).

[0157] When the deviation is less than 90°, cos(value) is positive (indicating that the orientation is close).

[0158] And the slope suitability factor of the j-th pixel The cosine value is truncated to a non-negative form to obtain the slope suitability factor for the j-th pixel. Turning one's back is considered uncomfortable.

[0159]

[0160] pass Truncation indicates that if ≥0, will be retained; if If <0, replace it with 0;

[0161] Identify and obtain the slope of the j-th pixel in each sub-region. The slope suitability factor for the j-th pixel is obtained by using segmented conditions. :

[0162] ;

[0163] in, It is a gentle slope, very easy to deploy → Rating: 1.0 (out of 10)

[0164] It is a moderate slope, which can be deployed but is somewhat difficult → reduce to 0.7;

[0165] It is a steep slope, making deployment difficult → reduced to 0.3;

[0166] Very steep → Unsuitable for deployment, directly 0.0;

[0167] Combining the slope suitability factor of the j-th pixel and slope suitability factor Satisfying weight Calculate the terrain adaptation coefficient for the j-th pixel. :

[0168] .

[0169] Slope suitability factor weight =0.6;

[0170] Slope suitability factor weight =0.4;

[0171] In this embodiment, the working principle of the condensate drainage membrane deployment module is based on the obtained suitability level map, and introduces terrain adaptability analysis of slope and aspect factors to achieve quantitative optimization of the condensate membrane deployment area. First, based on the environmental monitoring and condensation potential analysis results, the system divides the sub-regions into a first suitability level, a second suitability level, and a third unsuitability level, and assigns weights of 1.0, 0.6, and 0.2 respectively, forming a preliminary distribution of deployment attractiveness. Subsequently, the module uses a digital elevation model (DEM) to extract the slope and aspect information of the sub-regions. The aspect suitability analysis is based on the cosine similarity principle, comparing the aspect of each pixel in the sub-region with the preset optimal orientation (e.g., 180° south slope). If the angle difference is less than 90°, the orientation is considered favorable, and the aspect suitability factor is obtained by normalizing the cosine value; when the angle difference exceeds 90°, the cosine value is negative, considered unfavorable, and truncated to 0 to reflect its decreased condensation potential. The slope suitability factor is assigned using a segmented scoring method: gentle slopes (≤10°) are easy to deploy and receive a score of 1.0; moderate slopes (10°–20°) receive a score of 0.7; steep slopes (20°–35°) receive a score of 0.3; slopes greater than 35° are considered infeasible and receive a score of 0. Finally, the module weights the suitability level, slope suitability factor, and aspect suitability factor to obtain the terrain adaptability coefficient for each pixel, generating a deployment priority map. The advantage of this method is that it combines environmental suitability with construction feasibility, ensuring that the deployment location has a high potential for condensate formation while avoiding wasting resources on steep, unfavorable, or adverse terrain. This significantly improves membrane material utilization efficiency and condensate production per unit area, while reducing the difficulty of manual deployment and maintenance costs.

[0172] Example 5

[0173] This embodiment is an explanation of Embodiment 1. For details, please refer to... Figure 1 Deploy path planning units to adjust the terrain adaptation coefficient of the j-th pixel. Converted into cost value The conversion method is as follows:

[0174] ;

[0175] in, It is a small constant to prevent division by zero;

[0176] The value of the j-th pixel Substitute the values ​​into the sub-region grid to obtain the cost grid map;

[0177] After constructing a cost raster map containing the cost value of each cell, the connectivity between nodes is established according to the preset adjacency rules, including four-adjacency or eight-adjacency, and the movement cost between nodes is calculated based on the cost value and the geometric distance between cells.

[0178] Add the starting node to the open list and initialize its path cost to zero;

[0179] In each iteration, the node with the minimum total cost is selected from the open list, moved to the closed list, and its adjacent nodes are processed in turn:

[0180] If the adjacent node is not in the closed list, update the path cost of the adjacent node according to the sum of the current node's path cost and the movement cost, and record the path association relationship;

[0181] If the update results in the path cost of adjacent nodes being lower than that of existing records, then the original record is replaced.

[0182] When the endpoint node is added to the closed list, the optimal path sequence from the starting point to the endpoint is obtained by backtracking along the path association relationship;

[0183] Using the above methods, the optimal path sequence based on the cost raster is obtained for each sub-region. The optimal path is a continuous path composed of multiple pixels (grid cells).

[0184] Path sequence = a list of all the cells visited along this path, arranged in the order of access;

[0185] In this method, if a heuristic estimate based on the node position and the endpoint position is added to the total cost calculation, then A* search is implemented; if no estimate is added, then Dijkstra search is implemented.

[0186] Heuristic estimates are predictions of the path cost between a node's location and the target endpoint. These estimates guide the pathfinding algorithm to prioritize exploring paths to the target. Specifically, they include:

[0187] Obtain the coordinate information of the current node and the target node in the cost grid, including their horizontal and vertical positions;

[0188] Calculate the distance difference between the two nodes in the horizontal and vertical directions;

[0189] Based on the adjacency rules, the optimal distance calculation method is selected, including:

[0190] When dealing with four adjacent nodes, if only vertical and horizontal movement is allowed, the sum of the absolute values ​​of the differences between the horizontal and vertical distances is calculated, which is the Manhattan distance.

[0191] For eight-adjacency nodes, if diagonal movement is allowed, the straight-line distance between the two nodes is calculated, i.e., the Euclidean distance.

[0192] The distance value obtained is the heuristic estimate, which is used as the estimated cost from the current node to the target node in path search.

[0193] The deployment of path planning units converts the terrain adaptability coefficient of each pixel into a cost raster map, providing basic data for path search. The cost value design ensures that areas with high suitability correspond to lower costs, and areas with low suitability correspond to higher costs, thereby guiding path planning to prioritize suitable deployment areas and improving the rationality and practicality of the light-concentrating membrane deployment. Node connectivity is established using four-adjacency or eight-adjacency rules, and the movement cost between nodes is calculated by combining geometric distance and cost, enabling path search to consider both spatial distance and environmental suitability.

[0194] By employing an iterative update mechanism of open and closed lists, the path with the minimum total cost is gradually sought, and path relationships are recorded to ensure that the found path is not only the shortest but also has optimal environmental conditions, thereby improving the efficiency and effectiveness of condensate drainage membrane deployment. Compared to the traditional Dijkstra algorithm, the A* algorithm, which introduces heuristic estimates, effectively reduces the search range, accelerates path planning, and guarantees path optimality.

[0195] In this embodiment, path planning fully considers terrain adaptability, avoiding deployment in unsuitable areas and reducing construction difficulty and cost. By rationally planning the path layout, the condensate drainage membrane covers the most suitable location, maximizing the collection and guidance effect of condensate. The path planning algorithm ensures the continuity and optimality of the planned path, reducing the risk of path breakage and condensate loss. Heuristic estimation is used to guide the search, reducing unnecessary node expansion, improving the algorithm's real-time performance, and making it suitable for dynamic environment monitoring and rapid response.

[0196] Example 6

[0197] This embodiment is an explanation of Embodiment 1. For details, please refer to... Figure 1 The moisture guiding module includes a micro-slope structure design unit on the surface of the condensate guiding membrane;

[0198] The condensate drainage membrane surface microslope structure design unit is used to configure the initialization parameters of the condensate drainage membrane surface microslope structure according to the optimal path sequence based on the cost grid and the first and second adaptability levels in the weighted deployment probability map (WBP) for each sub-region, including:

[0199] For the sub-region of the first adaptability level, the micro-slope structure on the surface of the condensate drainage membrane is set as follows:

[0200] Initial guide slope The range is 4° to 6°;

[0201] Initial flow texture depth The range is from 1.4 mm to 1.6 mm;

[0202] Initial flow texture width The range is from 2.8 mm to 3.2 mm;

[0203] Initial guide flow limiting angle The range is 25° to 35°;

[0204] The trenches are arranged in a regular V-shape to ensure efficient and concentrated flow of condensate to the target area under the membrane.

[0205] For sub-regions with secondary biocompatibility levels, the micro-slope structure on the surface of the condensate drainage membrane is set as follows:

[0206] Initial guide slope The range is 2° to 3°;

[0207] Initial flow texture depth The range is from 1.0 mm to 1.3 mm;

[0208] Initial flow texture width The range is 2.0 mm to 2.5 mm;

[0209] Initial guide flow limiting angle The range is 30° to 40°;

[0210] The trenches are arranged in an irregular rectangular or wavy pattern to adapt to the complex terrain of areas with low biodiversity and improve the efficiency of condensate dispersion and collection.

[0211] In this embodiment, by meticulously designing the micro-slope structure on the surface of the condensate drainage membrane, differentiated structural parameters are configured for sub-zones with different suitability levels, achieving efficient guidance and rational distribution of condensate. For the first suitability level sub-zone, a larger drainage slope and texture size, along with a regular V-shaped groove arrangement, helps to quickly concentrate condensate, ensuring sufficient water supply and promoting dense moss growth and improved ecological restoration. For the second suitability level sub-zone, a smaller drainage slope and texture size, combined with an irregular rectangular or wavy groove arrangement, adapts to complex terrain, promotes the dispersion and collection of condensate, ensures balanced water distribution under weaker environmental conditions, and improves moss survival rate. This structural design optimizes the condensate flow path, reduces water evaporation and loss, and enhances the system's water use efficiency and stability.

[0212] Example 7

[0213] This embodiment is an explanation of Embodiment 1. For details, please refer to... Figure 1 The moisture guidance module also includes a moss planting unit and a moisture supply monitoring unit:

[0214] The moss planting unit is used to identify sub-region pixel sets of the first and second suitability levels extracted from the weighted layout probability map (WBP), forming a preliminary candidate set of habitable areas. The spatial intersection of this preliminary candidate set of habitable areas and the condensate drainage membrane micro-slope structure layout area is then calculated.

[0215] Priority planting area = candidate area of ​​suitable habitat level ∩ area where condensate diversion membrane is laid, to obtain planting sub-regions;

[0216] Pre-determine equidistant planting points for bryophytes within the planting sub-region, including:

[0217] For sub-areas with the first suitability level, the spacing between planting points is set to 0.3 to 0.5 meters to ensure high moss coverage density and strong water absorption capacity, which is conducive to the formation of a continuous vegetation layer and improves the ecological restoration effect.

[0218] For sub-regions with the second suitability level, the spacing between planting points is set to 0.6 to 0.8 meters. The plant density is appropriately reduced to adapt to the weaker environmental conditions, ensuring that the moss has enough room to grow and reducing resource competition, while maintaining a certain coverage rate.

[0219] Then, the moss is evenly sown or implanted into the planting sub-area under the condensate drainage membrane by mechanical or manual means.

[0220] In this embodiment, sub-regions suitable for moss growth are accurately identified based on the Weighted Layout Probability Map (WBP). Combined with the spatial intersection of the micro-slope structure layout areas using the condensate drainage membrane, priority planting areas are determined to ensure that the moss planting locations meet both ecological suitability requirements and receive sufficient condensate supply. By setting reasonable planting point spacing within sub-regions of different suitability levels, the first suitability level sub-region uses a denser spacing of 0.3 to 0.5 meters to ensure moss coverage density and water absorption capacity, promoting the formation of a continuous vegetation layer and ecosystem stability. The second suitability level sub-region uses a spacing of 0.6 to 0.8 meters to reduce competitive pressure, adapt to weaker environmental conditions, and ensure healthy moss growth. The uniform sowing or implantation of moss, whether mechanical or manual, effectively achieves uniform water utilization and optimized plant cover, contributing to improved vegetation survival rate and ecological restoration effects.

[0221] Example 8

[0222] This embodiment is an explanation of Embodiment 1. For details, please refer to... Figure 1 A moisture supply monitoring unit is used to estimate the average daily supply of condensate under the condensate diversion membrane in real time or at regular intervals, including:

[0223] Collect and record the condensate formation amount Q of the sub-region in real time, with the unit of milliliters per hour; preset the minimum starting growth water volume threshold Qmin for moss, and compare and analyze the condensate formation amount Q of the sub-region with Qmin; when Q≥Qmin, it is determined that the current water supply meets the moss growth requirements, and a normal water supply status signal is output; when Q<Qmin, it is determined that the current water supply is insufficient, and a water shortage warning signal is output.

[0224] The water diversion module further includes a correction unit. When receiving the water shortage warning signal, the correction unit obtains the initialization parameters of the current condensate diversion film micro-slope structure, including the initial diversion slope , the initial diversion texture depth , the initial diversion texture width and the initial diversion current-limiting angle , and performs a decreasing adjustment according to the preset adjustment step and parameter range, and re-collects the condensate supply amount Q through the water supply monitoring unit; determines whether Q after adjustment reaches the minimum starting growth water volume threshold Qmin of moss. If it reaches, the iteration is stopped and the current parameters are maintained; if it does not reach and the parameters can still be adjusted, continue the iterative adjustment until the condensate supply amount Q reaches the minimum starting growth water volume threshold Qmin of moss.

[0225] And perform the following iterative adjustment based on this parameter set: First, determine whether each parameter is within the allowable adjustment range. If there is a parameter that does not reach the maximum or minimum limit, adjust each parameter in turn according to the preset step (set the diversion slope to increase by 0.2°, the diversion texture depth to increase by 0.05 mm, the diversion texture width to increase by 0.05 mm, the diversion current-limiting angle to decrease by 1°), and apply the adjusted parameters to the design of the condensate diversion film micro-slope structure again after adjustment.

[0226] It should be noted that: the dynamic adjustment of the diversion slope (set: increase by 0.2°), the adjustment of the diversion slope is achieved by arraying and integrating piezoelectric ceramic micro-displacers or thermal-driven micro-actuators under the base of the flexible diversion film. The piezoelectric ceramic micro-displacer will produce precise and predictable deformation (elongation or shortening) when a voltage is applied. A plurality of independent piezoelectric micro-columns or micro-beam arrays are used as the support structure of the diversion film. The control system applies different voltages to the piezoelectric elements at different positions in the array to make them generate differential vertical displacements. Set, to increase the slope Simply increase the voltage supporting the actuator at the "uphill" end of the flow guide membrane, causing its elongation to exceed that of the actuator at the "downhill" end. The piezoelectric effect is characterized by fast response speed (microseconds) and high displacement resolution (current technology reaches the nanometer level). By using a high-precision digital-to-analog converter (DAC) to output control voltage, angle change control with a precision of 0.2° or even higher can be achieved. (Flow guide texture depth) With width The dynamic adjustment (setting: increments of 0.05 mm) of the flow-guiding texture, acting as a microscopic water-guiding channel, can be achieved by employing microstructures made of electroactive polymers or shape memory alloys. The flow-guiding texture itself or its sidewalls are constructed from electroactive polymer materials. These materials undergo significant changes in volume or shape (contraction or expansion) under the influence of an electric field. Adjusting the depth... The bottom of the grooves in the texture can be a thin EAP film. By changing the voltage applied to it, its thickness can be altered, thus changing the effective depth of the grooves. Adjusting the width ( The groove sidewalls of the texture are made of EAP (Electric Aperture Plate), which expands or contracts laterally by applying voltage, thus precisely adjusting the groove width. The deformation of the EAP is directly related to the electric field strength, and micron-level (0.001 mm) deformation control is achieved through closed-loop control. (Current limiting angle) The dynamically adjustable (set: decreasing by 1°) flow-limiting angle is essentially the angle of a miniature baffle or gate located at the edge of the flow-guiding region. Its adjustment is achieved through a MEMS micro-hinge structure. At a specific location on the flow-guiding membrane, a mechanical structure consisting of miniature links and hinges is fabricated using semiconductor processing techniques. The driving force for this structure can originate from an electrostatic comb actuator, a piezoelectric thin-film actuator, or a miniature shape memory alloy wire. When the control system issues an adjustment command, it applies an electrical signal to the MEMS actuator. The electrostatic actuator generates electrostatic force, driving the miniature baffle to rotate around the micro-hinge; the piezoelectric thin film then bends, pushing the baffle to change its angle.

[0227] In this embodiment, by monitoring the amount of condensate formation in real time and combining it with a dynamic parameter adjustment mechanism, the system can automatically optimize the micro-slope structure design of the condensate diversion membrane for different environmental conditions, effectively improving the collection efficiency and diversion effect of condensate, ensuring that the moss vegetation obtains a stable and sufficient water supply, and significantly improving the growth rate and survival rate of moss; at the same time, this iterative adjustment process can adapt to environmental changes, realize the rational use of water resources, reduce water waste, and improve the ecological restoration effect in extreme drought and high-altitude cold environments.

[0228] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0229] The above formulas are all derived from software simulation using a large amount of data and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A moss-based revegetation system, characterized in that, The method comprises the following steps: a condensation potential analysis module is used to divide the area to be restored into a plurality of environmental monitoring sub-areas, collect environmental data including daily and night temperature difference, air humidity, ground surface wind speed and soil water content, analyze whether the sub-area has the potential to form condensate water, and output a first control signal or a second control signal for starting condensate water diversion film arrangement; after receiving the first control signal, a sub-area fitness index SI of each sub-area is constructed and evaluated to obtain a fitness level division result and generate a fitness level map, and the first control signal is updated; a condensate water diversion film arrangement module is used to collect a digital elevation model DEM of each sub-area to construct a sub-area grid and a weighted arrangement probability map WBP after receiving the arrangement instruction of the first control signal; Constructing the aspect suitability factor of the jth pixel of each subzone and the slope suitability factor , and obtaining the terrain adaptation coefficient of the jth pixel by a weighted summation method After that, the layout path of the condensate diversion film is automatically planned to obtain the optimal path sequence of each subzone; The moisture guiding module is used for configuring initialization parameters of the surface micro-slope structure of the condensate water guiding film according to the optimal path sequence of each sub-area and the first fitness level and the second fitness level of the weighted layout probability graph WBP, including: initial guiding slope , initial guiding texture depth , initial guiding texture width , initial guiding flow limiting angle , and groove arrangement mode; and based on the weighted layout probability graph WBP, moss planting points are set and then moss is uniformly sown. the amount of condensate water formed in the sub-area Q is collected and recorded in real time, and compared with a preset minimum moss growth water amount Qmin; if the amount is insufficient, a water shortage warning signal is output, and the initial parameters are iteratively adjusted until the condensate water supply amount Q reaches the minimum moss growth water amount threshold Qmin.

2. The moss-based revegetation system of claim 1, wherein, The condensation potential analysis module comprises an environmental data collection unit, a condensation potential calculation unit and an instruction output unit. The environmental data collection unit is used to divide the area to be restored into a plurality of environmental monitoring sub-areas, deploy an environmental sensor array in each sub-area, and collect environmental data of the area to be restored in real time or at a fixed time, wherein the environmental data comprises: a daily and night temperature difference ΔT, which is calculated by continuously collecting the highest and lowest air temperature within 24 hours, and the unit is ℃; air humidity RH, which is measured by a humidity sensor, and the unit is %; ground surface wind speed Vg, which is measured by a low-power anemometer, and the unit is m / s; soil water content S, which is measured by a soil moisture sensor, and the unit is volume water content %; The condensation potential calculation unit is used to calculate the potential capacity of the sub-area to form condensate water based on the collected environmental data. When all the following conditions are met, it is considered that the sub-area has condensation potential, and the first control signal is output: ， ， ， ； wherein, is a preset minimum condensing temperature difference threshold, which is set to 6°C; The preset minimum air humidity threshold is set as 60%. For maximum wind speed limit, set to 2.5 m / s; Upper limit of soil moisture, set to 20%; The instruction output unit is used to output a condensate water diversion film arrangement instruction according to the condensation potential result. When all environmental conditions meet the judgment conditions, the first control signal is output, which includes arrangement instruction, position and range of the sub-area. When any environmental parameter does not meet the conditions, the second control signal is output, and the condensate water diversion film is not arranged, waiting for environmental improvement or re-evaluation.

3. The moss-based revegetation system of claim 2, wherein, The condensation potential analysis module further comprises a first marking unit, which is used to receive the first control signal, record the current environmental data, construct and evaluate the sub-area fitness index SI to obtain a fitness level division result and generate a fitness level map, and the fitness level division result comprises a first fitness level, a second fitness level and a third unfitness level. The sub-area fitness index SI is obtained in the following way: wherein, represents a maximum daily temperature difference reference value, represents a corresponding humidity factor; represents a maximum wind speed reference value, represents a maximum saturation water content of the local soil; wherein the relative humidity factor is obtained by the piecewise normalization formula: ； wherein , , and represent weights, and ; when the sub-area fitness index SI is greater than 0.7, the first fitness level is generated; when the sub-area fitness index is 0.4≤SI≤0.7, the second fitness level is generated; when the sub-area fitness index SI is less than 0.4, the third unfitness level is generated.

4. The moss-based revegetation system of claim 1, wherein, The condensate water flow guide film arrangement module comprises a sub-area arrangement scheduling unit and an arrangement path planning unit; The sub-area arrangement scheduling unit is used for arranging the first and second adaptability levels into a secondary arrangement queue, updating the first control signal, and arranging the third unadaptability level into the second control signal and not arranging; The sub-area arrangement scheduling unit comprises a slope map and slope direction map extraction sub-unit, a grid division unit and a pixel adaptability analysis unit; The slope map and slope direction map extraction sub-unit is used for collecting a digital elevation model (DEM) of each sub-area, extracting a slope map and a slope direction map, and importing a land cover layer obtained through remote sensing interpretation or unmanned aerial survey, identifying a rock exposed area, a sand and gravel accumulation area, a plant covered area or a dry gully type, and then through the grid division unit, performing spatial resampling and interpolation processing on the digital elevation model (DEM) to form a sub-area grid GeoTIFF as an input base map for subsequent path planning, importing the divided adaptability level map, and setting a weight function as follows: The weight value of the first adaptability level is 1.0, the weight value of the second adaptability level is 0.6, and the weight value of the third unadaptability level is 0.2; the adaptability level map is converted into an arrangement priority layer, i.e. the arrangement attraction weight of each pixel, and a weighted arrangement probability map (WBP) is generated; The pixel adaptive analysis unit is configured to identify a slope direction angle of the jth pixel in each sub-region , and obtain a raw cosine value of the jth pixel : wherein, is the aspect angle of the jth pixel, denotes the most suitable aspect angle; The cosine value of the slope aspect suitability factor of the jth pixel is truncated to a non-negative form to obtain the slope aspect suitability factor of the jth pixel The cosine value of the slope aspect suitability factor of the jth pixel is truncated to a non-negative form to obtain the slope aspect suitability factor of the jth pixel : By truncation means that if ≥ 0, it is preserved, if < 0, it is replaced by 0; identifying the slope of the jth pixel of each subzone using a piecewise condition to assign a jth pixel slope suitability factor : ； a slope aspect suitability factor of the jth pixel and a slope suitability factor , satisfying the weight , a terrain adaptation coefficient of the jth pixel is calculated : 。 5. The moss-based revegetation system of claim 4, wherein, The laying path planning unit is configured to convert the terrain adaptation coefficient of the jth image element into a value of a currency , and the conversion mode is: ​ ； wherein is a small constant, preventing division by zero; the cost value of the jth pixel is substituted into the sub-region grid to obtain a cost grid map; After a cost grid map containing the generation value of each pixel is constructed, a connected relationship between nodes is established according to a preset adjacency rule, including four-adjacency or eight-adjacency, and a movement cost between nodes is calculated based on the generation value and the geometric distance between pixels; The starting node is added to an open list and its path cost is initialized to zero; In each iteration, the node with the minimum total cost is selected from the open list, moved to the closed list, and its adjacent nodes are processed in turn: If the adjacent node is not in the closed list, the path cost of the adjacent node is updated according to the sum of the path cost of the current node and the movement cost, and the path association relationship is recorded; If the update results in a path cost of the adjacent node lower than the existing record, the original record is replaced; When the end node is added to the closed list, the optimal path sequence from the starting node to the end node is obtained by backtracking along the path association relationship; Through the above method, the optimal path sequence of each sub-area based on the cost grid is obtained; In the method, if a heuristic estimation value based on the position of the node and the position of the end node is added in the total cost calculation, A* search is realized; if the estimation value is not added, Dijkstra search is realized.

6. The moss-based revegetation system of claim 5, wherein, The heuristic estimation value is a prediction of the path cost between the position of the node and the position of the target end node, which is used to guide the path search algorithm to preferentially explore the path leading to the target, and specifically comprises: Obtaining coordinate information of the current node and the target node in the cost grid map, including horizontal and vertical positions; Calculating the distance difference between the two nodes in the horizontal and vertical directions; According to the adjacency rule, the optimal distance calculation method is selected, including: For four-adjacency, if only up, down, left and right movements are allowed, the sum of the absolute values of the horizontal and vertical distance differences, i.e. the Manhattan distance, is calculated; For eight-adjacency, if diagonal movement is allowed, the straight-line distance between the two nodes, i.e. the Euclidean distance, is calculated; The distance value obtained is a heuristic estimation value used as an estimated cost of the current node to the target node in path searching.

7. The moss-based revegetation system of claim 6, wherein, The water guiding module comprises a condensate water guiding film surface micro-slope structure design unit; The condensate water guiding film surface micro-slope structure design unit is configured to configure initialization parameters of the condensate water guiding film surface micro-slope structure for each sub-region based on the optimal path sequence of the cost grid and the first fitness level and the second fitness level in the weighted layout probability map WBP, including: For the sub-region of the first fitness level, the condensate water guiding film surface micro-slope structure is set as: Initial slope of the flow guide ranging from 4° to 6°; Initial flow directing texture depth ranging from 1.4 mm to 1.6 mm; Initial flow directing texture width ranging from 2.8 mm to 3.2 mm; initial flow guiding and flow restricting angle in the range of 25° to 35°; The groove arrangement mode is regular V-shaped arrangement, which ensures efficient and concentrated condensate water guiding to the target area under the film. For the sub-region of the second fitness level, the condensate water guiding film surface micro-slope structure is set as: Initial slope of the flow guide in the range of 2° to 3°; Initial flow directing texture depth ranging from 1.0 mm to 1.3 mm; Initial flow directing texture width ranging from 2.0 mm to 2.5 mm; initial flow guiding and flow restricting angle in the range of 30° to 40°; The groove arrangement mode is irregular rectangular or wavy arrangement, which is suitable for complex terrain in the lower fitness area and improves the dispersion and collection efficiency of condensate water.

8. The moss-based revegetation system of claim 1, wherein, The water guiding module further comprises a moss planting unit and a water supply monitoring unit: The moss planting unit is configured to identify a sub-region pixel set of the first fitness level and the second fitness level in the weighted layout probability map WBP based on the weighted layout probability map WBP, form a preliminary plantable area candidate set, and calculate the spatial intersection of the preliminary plantable area candidate set and the condensate water guiding film micro-slope structure layout area, i.e. The priority planting area = the fitness level candidate area intersection the condensate water guiding film layout area, to obtain the planting sub-region; Equal-distance moss planting points are preset in the planting sub-region, including: For the sub-region of the first fitness level, the planting point spacing is set to 0.3-0.5 m; For the sub-region of the second fitness level, the planting point spacing is set to 0.6-0.8 m; Then the moss is uniformly scattered or planted under the condensate water guiding film by mechanical or manual means.

9. The moss-based revegetation system of claim 8, wherein, The water supply monitoring unit is configured to estimate the daily average supply of condensate water under the condensate water guiding film in real time or at regular intervals, including: The condensate water formation amount Q of the sub-region is collected and recorded in real time, with the unit being milliliter / hour; a preset minimum starting growth water amount threshold Qmin is compared with the condensate water formation amount Q of the sub-region; when Q≥Qmin, it is determined that the current water supply meets the growth demand of the moss, and a normal water supply state signal is output; when Q<Qmin, it is determined that the current water supply is insufficient, and a water shortage warning signal is output.

10. The moss-based revegetation system of claim 9, wherein, The moisture guiding module further comprises a correction unit, which, when receiving the moisture deficiency warning signal, acquires initialization parameters of the current condensate water guiding film micro-slope structure, including initial guiding slope , initial guiding texture depth , initial guiding texture width , and initial guiding flow limiting angle , performs stepwise decrement adjustment according to a preset adjustment step and parameter range, and reacquires the condensate water supply amount Q through the moisture supply monitoring unit; it is judged whether the adjusted Q reaches the moss minimum starting growth water threshold Qmin, if yes, the iteration is stopped and the current parameters are maintained; if not and the parameters can still be adjusted, the iteration adjustment is continued until the condensate water supply amount Q reaches the moss minimum starting growth water threshold Qmin.

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