Ecological restoration management and control method and system for interplanting white poplar and jerusalem artichoke in windy and

Abstract

The invention discloses an ecological restoration management and control method and system for interplanting poplar and jerusalem artichoke in a windy and sandy area, and relates to the technical field of ecological engineering management and control, and the method comprises the steps: firstly collecting an environment parameter EP1 before planting, and calculating a wind erosion potential index W of each grid unit, so as to match differentiated planting planning parameters, and achieve the scientific layout according to local conditions; after planting, an environment parameter EP2 after planting is obtained, the daily compensation water amount ET is calculated, precise irrigation of the composite canopy is achieved, and the water resource utilization rate is increased. Meanwhile, a biological hazard index I is calculated, a patrol scheduling scheme APL is generated, and pest control is converted into active early warning from passive response. And finally, an irrigation scheduling scheme BPL is generated and sent to an automatic irrigation control terminal, a patrol scheduling scheme APL is sent to patrol personnel, key parameters are stored in a log data set HIS, a complete management and control process from data acquisition, scientific analysis to accurate execution is completed, and long-term health and stability of an ecological restoration project are ensured.

Description

Technical Field

[0001] This invention relates to the field of ecological engineering management technology, specifically to a method and system for ecological restoration and management based on intercropping poplar and Jerusalem artichoke in wind-blown sand areas. Background Technology

[0002] In the field of ecological restoration technology, vegetation restoration in ecologically fragile environments such as sandy areas is a key branch. Within this branch, constructing stable ecosystems using specific plant communities is the mainstream technical approach. Among these, intercropping with herbaceous or shrub species has attracted significant attention due to its superior sand-fixing and soil-improving effects. In practical applications, the drought-resistant and wind-fixing poplar, combined with the well-developed and adaptable Jerusalem artichoke, forms a promising poplar-Jerusalem artichoke intercropping ecological restoration system. However, simply planting plants is only the beginning of ecological restoration. How to manage this complex and dynamically changing biological system in a long-term, scientific manner to ensure its survival, development, and ultimate realization of its ecological functions is crucial to the success of the restoration project. Therefore, a management method and system specifically designed for poplar-Jerusalem artichoke intercropping ecological restoration in sandy areas has become a technology with a clear demand. This technology can be applied to the management of large areas of desertified land, the construction and maintenance of protective forests, vegetation restoration after mine reclamation, and soil and water conservation in large-scale engineering projects such as wind farms.

[0003] However, current practices of intercropping poplar and Jerusalem artichoke in ecological restoration generally suffer from several technical shortcomings in existing management methods. First, during the planning phase of restoration projects, a uniform or experience-based planting layout is typically adopted, lacking a refined and quantitative assessment of key environmental factors such as wind erosion intensity and micro-topography within the project area, resulting in a lack of targeted planting plans. Second, in the long-term maintenance phase, irrigation management is relatively extensive, relying heavily on fixed time periods or broad regional soil moisture conditions for decision-making, failing to accurately respond to the differentiated water needs of poplar and Jerusalem artichoke, two plants with different heights and root depths, within the same plot. Finally, monitoring and control of biological hazards are lagging, typically only responding passively after significant damage from borers such as longhorn beetles is observed, lacking early, proactive warning mechanisms based on pest growth rhythms and host spatial distribution.

[0004] These technological deficiencies directly led to a series of abnormal effects during the operation of ecological restoration projects. Blindly planning the layout resulted in low seedling survival rates in severely wind-eroded areas such as windward slopes, making it difficult to form effective protective barriers. Conversely, in low-lying areas with better environmental conditions, seedling resources were wasted, leading to low overall input-output efficiency. Extensive irrigation methods not only resulted in a huge waste of precious water resources in arid, wind-blown areas, but also failed to effectively relieve water stress in plants due to inaccurate water supply, weakening their growth and even increasing their risk of attracting borers. Passive disaster prevention had particularly severe consequences. Once pests reached a visible level, irreversible structural damage was often caused to the poplar forest, affecting the restoration cycle at best and leading to large-scale tree death at worst, wasting significant initial investments and significantly increasing subsequent replanting and maintenance costs. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for ecological restoration and management based on intercropping poplar and Jerusalem artichoke in windy and sandy areas, which solves the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method and system for ecological restoration and management of intercropping poplar and Jerusalem artichoke in wind-blown sand areas, comprising the following steps: S1. Before planting, environmental parameters are collected by using anemometers deployed on-site, drone surveys, and laboratory analysis. S2. Based on the environmental parameters before planting, the wind erosion potential index of each grid cell is calculated and the planting planning parameters are matched from the preset planting knowledge base. S3. After planting, the environmental parameters after planting are obtained by connecting to the weather station network and experimental analysis through meteorological sensors and API interfaces, and the daily water compensation required for each grid unit is calculated by combining the Penman-Montes formula. S4. Based on the environmental parameters after planting, calculate the biological hazard index of the target pests on poplar, and generate an inspection and dispatch plan based on the biological hazard index. S5. Based on the daily water compensation requirement of each grid unit, generate an irrigation scheduling plan and send it to the automated irrigation control terminal in the project area. Send the inspection scheduling plan to the inspection personnel and store the environmental parameters, daily water compensation requirement and biological hazard index collected after planting into the log dataset.

[0007] Preferably, S1 includes S11; S11. Before planting, environmental parameters EP1 were collected by using anemometers deployed on site, drone surveys, and laboratory analysis. These parameters included wind parameters MP collected by anemometers, topographic parameters TLP collected by drone surveys, and wind and sand impact parameters WSP obtained through laboratory analysis. The wind parameters MP include the average wind speed u and wind direction angle w1 within the observation period, collected using an anemometer. The topographic parameters TLP include the digital elevation model (DEM) generated from the collected 3D coordinate point cloud data CPD, and the slope θ and aspect α of N grid cells are calculated using a domain analysis algorithm on the DEM, where N represents the total number of grid cells. The wind and sand influence parameters WSP include the soil erodibility factor K obtained using the EPIC model formula, the reference critical wind speed u0 obtained using the wind tunnel experiment method, the wind speed influence index n obtained using the multi-gradient wind speed and sand transport measurement method, and the direction sensitivity coefficient c obtained using the variable angle base wind tunnel experiment method.

[0008] Preferably, S2 includes S21; S21. Based on the pre-planting environmental parameters EP1, and according to the regional division of the Digital Elevation Model (DEM), N grid cells are obtained. Combining the wind shear force model on the ground surface and the relationship between wind direction angle w1 and slope aspect α, the wind erosion potential index W of the i-th grid cell is calculated. i ; Among them, the wind erosion potential index W of the i-th grid cell i The calculation expression is as follows: ; In the formula, i represents the identifier in N grid cells, exp represents the natural exponential function, and Σ represents the traversal symbol.

[0009] Preferably, S2 includes S22; S22. Based on the wind erosion potential index W of the i-th grid cell i The planting planning parameters for the i-th grid cell are obtained by matching the planting knowledge base set up by relevant staff. These parameters include the number of Jerusalem artichoke rows L1 between the poplar rows, the planting spacing D1 between Jerusalem artichoke plants, the planting row spacing L2 between the poplar rows, and the planting spacing D2 between the poplar plants. A deep ditch is set up next to each row of poplars, and a shallow ditch is dug between every two rows of Jerusalem artichokes.

[0010] Preferably, S3 includes S31; S31. After planting, the environmental parameters EP2 after planting are obtained by connecting to the weather station network and conducting experimental analysis through meteorological sensors and API interfaces. The meteorological sensors include a net radiation meter, a soil heat flux plate and an anemometer. Post-planting environmental parameter EP2 is included in the i-th grid cell, and the net radiation R is collected by the net radiation table. i Soil heat flux G collected by soil heat flux plate i It connects to the weather station network via API to collect the predicted highest temperature T for the day. MAX Minimum temperature T MIN Average temperature T and atmospheric pressure PA i Based on the average air temperature Tjunction and standard thermodynamic formulas, the slope Δs of the saturated vapor pressure-temperature curve was calculated. i Based on the atmospheric pressure PA junction and the formula for calculating the hygrometer constant, the hygrometer constant γ is calculated. i According to the predicted highest temperature T MAX Minimum temperature T MIN Given the average temperature T, and combining the saturated vapor pressure calculation formula and the actual vapor pressure calculation formula, the saturated vapor pressure es is calculated respectively. i and actual water vapor pressure ea i According to the average temperature T i and atmospheric pressure PA i Using the ideal gas equation, the air density ρA is calculated. i ; Within a preset sampling period t, the leaf area index (LAI) of Jerusalem artichoke canopy was obtained using destructive sampling and a leaf area meter. （i1，t） The leaf area index (LAI) of poplar canopy was obtained using digital hemispherical photography. （i2，t） By using a stomatalometer to directly measure the stomatal resistance data Pm at the single-leaf level on the leaves of poplar and Jerusalem artichoke, we obtained data. （i，t） According to the Jerusalem artichoke canopy leaf area index (LAI) （i1，t） Poplar canopy leaf area index (LAI) （i2，t） and porosity data Pm at the single-leaf level （i，t） By combining the upscaling model, the canopy resistance r of Jerusalem artichoke was obtained. （i1c，t） and poplar canopy resistance r （i2c，t） ; Within a preset sampling period t, the average canopy height H of Jerusalem artichoke was obtained through manual sampling measurements. （i1，t） And the average canopy height H of poplar （i2，t） Multiple anemometers were adjusted to the average canopy height H of Jerusalem artichoke. （i1，t） And the average canopy height H of poplar （i2，t） Collect the wind speed u of the Jerusalem artichoke canopy at the corresponding height. i1 and the wind speed of the poplar canopy u i2 Based on the average canopy height H of Jerusalem artichoke （i1，t） Average canopy height of poplar H （i2，t）、 Jerusalem artichoke canopy wind speed u at corresponding height i1and the wind speed of the poplar canopy u i2 Combining the logarithmic wind profile theory in micrometeorology, the aerodynamic drag r of the average canopy of Jerusalem artichoke was calculated. （i1v，t） And the average aerodynamic drag of the poplar canopy r （i2v，t） .

[0011] Preferably, S3 includes S32; S32. Based on the obtained post-planting environmental parameter EP2, and using the Penman-Montes formula, calculate the water evaporated per second per unit area under the current environmental conditions. Multiply this result by the total time of day and the area of ​​the i-th grid cell to obtain the daily water compensation requirement ET for Jerusalem artichoke in the i-th grid cell. i1 The daily water compensation requirement for poplar trees (ET) i2 ; Among them, the daily water demand compensation ET in the i-th grid cell i The calculation expression is as follows: ; In the formula, j represents the identifiers for Jerusalem artichoke and poplar; j=1 represents Jerusalem artichoke, and j=2 represents poplar. i λ represents the area of ​​the i-th grid cell calculated during grid generation, cp represents the preset specific heat of air at constant pressure, specifically 1005 J / (kg·K), and λ represents the preset latent heat of vaporization of water, specifically 2.45 × 10⁻⁶. 6 J / kg.

[0012] Preferably, S4 includes S41; S41. Based on the post-planting environmental parameter EP2, calculate the cumulative effective accumulated temperature Da from the completion of planting to day d, and calculate the biological hazard index I of the target pest to poplar based on the cumulative distribution function in the Weibull distribution. The biological hazard index I of the target pest on poplar is calculated as follows: ; ; In the formula, y represents the total number of days traversal identifier, d represents the number of days elapsed from the completion of planting to the current day, and when the date of day d is the same as the date of the completion of planting, d is reset to 1 and the counting starts again, T ydenoted as the average temperature on day y, obtained from the post-planting environmental parameter EP2; T0 represents the developmental starting temperature of the target pest, obtained through hatching and development experiments on the target pest; η represents the scale parameter of the Weibull distribution, representing the characteristic total effective accumulated temperature required for the pest to complete one generation of development, obtained through hatching and development experiments on the target pest; β represents the shape parameter of the Weibull distribution, representing the asymmetry of the peak shape of the target pest population activity, obtained through data fitting of historical target pest activity data.

[0013] Preferably, S4 includes S42; S42. Compare the biohazard index I of the target pest on poplar with the preset biohazard index threshold I. th A comparison is made, and an inspection and dispatch plan APL is generated based on the comparison results, including the first-level biological hazard index threshold I1 and the second-level biological hazard index threshold I2. If the biological hazard index I of the target pest on poplar is less than the first-level biological hazard index threshold I1, then there is no need to generate the patrol scheduling plan APL. If the first-level biological hazard index threshold I1 ≤ the biological hazard index I of the target pest on poplar < the second-level biological hazard index threshold I2, then the patrol scheduling plan APL is generated. The current period is the appropriate time for the control of the target pest. Patrol personnel need to deploy tree traps and spray contact insecticides. If the biological hazard index I of the target pest on poplar trees is greater than or equal to the secondary biological hazard index threshold I2, then an inspection and dispatch plan APL will be generated. The current period is the peak period of the target pest. The inspection personnel need to strengthen the monitoring of healthy trees, clean up the damaged trees, and trap and kill the target pest.

[0014] Preferably, S5 includes S51; S51. Based on the daily water demand compensation ET of Jerusalem artichokes in the i-th grid. i1 The daily water compensation requirement for poplar trees (ET) i2 Generate an irrigation scheduling scheme BPL, whereby BPL is the daily water compensation ET for Jerusalem artichokes in the i-th grid. i1 As the input for shallow furrow irrigation, the daily water requirement compensation ET of the poplars in the i-th grid is used. i2 As the input for deep trench irrigation, the irrigation scheduling plan is sent to the automated irrigation control terminal in the project area, and the inspection scheduling plan (APL) is sent to the inspection personnel, who then execute the inspection scheduling plan (APL). The post-planting environmental parameters EP2 and the daily water requirement ET for Jerusalem artichokes in the i-th grid are calculated. i1 The daily water compensation requirement for poplar trees (ET) i2 The biohazard index I of the target pest on poplar is stored in the log dataset HIS.

[0015] An ecological restoration and management system for intercropping poplar and Jerusalem artichoke in windy and sandy areas includes a pre-planting data acquisition module, a planting planning module, a daily water demand compensation analysis module, a biological hazard analysis module, and a scheduling and execution module. The pre-planting data acquisition module includes a pre-planting data acquisition unit, which collects environmental parameters before planting by using anemometers deployed on-site, drone surveys, and laboratory analysis. The planting planning module includes a wind erosion potential analysis unit and a planting planning generation unit. The wind erosion potential analysis unit divides the wind erosion area to be planted into multiple grid units based on the environmental parameters before planting and calculates the wind erosion potential index of each grid unit. The planting planning generation unit matches planting planning parameters from a preset planting knowledge base based on the wind erosion potential index. The daily water demand compensation analysis module includes a post-planting data acquisition unit and a daily water demand compensation analysis unit. After planting, the post-planting data acquisition unit obtains post-planting environmental parameters by connecting to the meteorological station network and experimental analysis through meteorological sensors and API interfaces. The daily water demand compensation analysis unit calculates the daily water demand compensation for each grid unit based on the post-planting environmental parameters and the Penman-Montes formula. The meteorological sensors include a net radiation meter, a soil heat flux plate, and an anemometer. The biological hazard analysis module includes a biological hazard analysis unit and an inspection and dispatch plan generation unit. The biological hazard analysis unit calculates the biological hazard index of the target pests on poplar based on the environmental parameters after planting, and the inspection and dispatch plan generation unit generates an inspection and dispatch plan based on the biological hazard index. The scheduling execution module includes a scheduling execution unit. The scheduling execution unit generates an irrigation scheduling plan based on the daily water compensation requirement of each grid unit and sends it to the automated irrigation control terminal. It also sends the inspection scheduling plan to the inspection personnel and stores the environmental parameters, daily water compensation requirement, and biological hazard index collected after planting into the log dataset.

[0016] This invention provides a method and system for ecological restoration and management of intercropping poplar and Jerusalem artichoke in wind-blown sand areas, which has the following beneficial effects: (1) In response to the problems of extensive planning and layout, serious water waste, and delayed disaster response in existing ecological restoration projects, this solution introduces a quantitative environmental assessment model and a dynamic crop physiological model to transform the traditional, experience-based restoration model into a data-driven, predictable scientific management process. This solution involves detailed planning at the beginning of the project and precise maintenance during the operation period, which can not only significantly improve the survival rate and health of the restored vegetation, but also achieve water-saving utilization in wind-blown sand areas and proactively avoid the risk of biological disasters, thereby reducing the overall cost and failure risk of the entire ecological restoration project.

[0017] (2) In the initial planning stage of the restoration project, this invention abandons the uniform planting method. By collecting environmental parameters EP1 before planting, the project area is gridded, and the wind erosion potential index W of the i-th grid unit is quantitatively calculated. i The wind erosion potential index W based on the i-th grid cell. i From a pre-defined planting knowledge base, differentiated planting planning parameters are matched to the grid units of the wind erosion potential index W, including the number of Jerusalem artichoke rows (L1) between poplar rows, the planting spacing (D1) of Jerusalem artichokes, the row spacing (L2) of poplars, and the planting spacing (D2) of poplars. This site-specific layout ensures that plant resources are prioritized for deployment in the areas most in need of protection, solving the problems of uneven survival rates and inconsistent protective effects in traditional solutions, making the initially constructed ecological barrier more resilient and stable.

[0018] (3) After completing the scientific planting layout, this invention provides a precise and dynamic post-planting maintenance plan. By continuously monitoring the post-planting environmental parameter EP2 and combining it with the Penman-Montes formula, the daily water requirement ET for Jerusalem artichokes in the i-th grid unit can be calculated. i1 The daily water compensation requirement for poplar trees (ET) i2 This calculation method replaces the extensive, uniform irrigation model, accurately meeting the differentiated water needs of different plants in the double-canopy structure and significantly improving water resource utilization efficiency. Simultaneously, based on the post-planting environmental parameter EP2, the biohazard index I of target pests on poplar is calculated, transforming traditional passive control into proactive risk warning. Finally, precise irrigation scheduling plans (BPL) and inspection scheduling plans (APL) are automatically generated and dispatched for execution, ensuring continuous, real-time data-driven scientific management of the restored ecosystem. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the steps of an ecological restoration and management method for intercropping poplar and Jerusalem artichoke in windy and sandy areas according to the present invention. Figure 2 This is a schematic diagram of an ecological restoration and management system for intercropping poplar and Jerusalem artichoke in windy and sandy areas according to the present invention. Figure 3 A graph showing the daily water requirement and the average aerodynamic drag of the plant canopy. Figure 4 This is a graph showing the daily water requirement and the plant canopy resistance data. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0021] Example 1 This invention provides a method and system for ecological restoration and management based on intercropping poplar and Jerusalem artichoke in wind-blown sand areas. Please refer to [link / reference]. Figure 1 This includes the following steps: S1. Before planting, environmental parameters are collected by using anemometers deployed on-site, drone surveys, and laboratory analysis. S2. Based on the environmental parameters before planting, the wind erosion potential index of each grid cell is calculated and the planting planning parameters are matched from the preset planting knowledge base. S3. After planting, the environmental parameters after planting are obtained by connecting to the weather station network and experimental analysis through meteorological sensors and API interfaces, and the daily water compensation required for each grid unit is calculated by combining the Penman-Montes formula. S4. Based on the environmental parameters after planting, calculate the biological hazard index of the target pests on poplar, and generate an inspection and dispatch plan based on the biological hazard index. S5. Based on the daily water compensation requirement of each grid unit, generate an irrigation scheduling plan and send it to the automated irrigation control terminal in the project area. Send the inspection scheduling plan to the inspection personnel and store the environmental parameters, daily water compensation requirement and biological hazard index collected after planting into the log dataset.

[0022] In this embodiment, the method transforms the traditional, experience-based restoration model into a data-driven, predictable scientific management process. First, before planting, this method collects pre-planting environmental parameters EP1 through on-site anemometers, UAV surveys, and laboratory analysis. Based on this, the wind erosion potential index W of the area to be planted is gridded, and the wind erosion potential index W is quantitatively calculated for each grid unit. Differentiated planting planning parameters are then matched from a pre-set planting knowledge base based on the wind erosion potential index W. This process abandons the uniform, experience-based, extensive layout method of the prior art. Through site-specific scientific planning, it ensures that plant resources are rationally deployed in different wind erosion threat areas, solving the problems of low seedling survival rates and uneven protective effects caused by improper layout. After completing the scientifically laid-out planting work, this method enters the dynamic maintenance stage. By obtaining the post-planting environmental parameters EP2 and combining them with the Penman-Montes formula, the daily water compensation requirements ET1 and ET2 for Jerusalem artichoke and poplar can be calculated for each grid unit. This calculation method can accurately respond to the differentiated water requirements of the double-layered composite canopy, replacing the traditional extensive irrigation model. While conserving precious water resources in wind-blown sandy areas, it effectively avoids plant growth stress caused by water imbalance. Simultaneously, based on the post-planting environmental parameter EP2, this method calculates the biohazard index I of target pests on poplar trees within each grid cell using an established dynamic evaluation model. When the biohazard index I exceeds a preset biohazard index threshold I... th Upon successful implementation, an inspection and scheduling plan (APL) is generated. This transforms the traditional passive prevention approach, which responds only after pests occur, into a proactive early warning system based on scientific prediction, enabling the forward-looking avoidance of significant losses caused by delayed responses to biological disasters such as longhorn beetles. Finally, this method generates an irrigation scheduling plan (BPL) based on the calculated daily water compensation requirements ET1 for Jerusalem artichoke and ET2 for poplar, and distributes it to the automated irrigation control terminal. Simultaneously, the inspection and scheduling plan (APL) is sent to inspection personnel. All collected post-planting environmental parameters (EP2), calculated daily water compensation requirements ET1 and ET2 for Jerusalem artichoke and poplar, and the biohazard index (I) are stored in the log dataset (HIS), completing a full management and control process from data collection and scientific analysis to precise execution, ensuring the long-term health and stability of the ecological restoration project.

[0023] Example 2 This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: S1 includes S11; S11. Before planting, environmental parameters EP1 were collected by using anemometers deployed on site, drone surveys, and laboratory analysis. These parameters included wind parameters MP collected by anemometers, topographic parameters TLP collected by drone surveys, and wind and sand impact parameters WSP obtained through laboratory analysis. The wind parameters MP include the average wind speed u and wind direction angle w1 within the observation period, collected using an anemometer. The topographic parameters TLP include the digital elevation model (DEM) generated using the collected 3D coordinate point cloud data CPD. The slope θ and aspect α of N grid cells are calculated using the domain analysis algorithm on the DEM, where N represents the total number of grid cells. The wind and sand influence parameters WSP include the soil erodibility factor K obtained using the EPIC model formula, the reference critical wind speed u0 obtained using the wind tunnel experiment method, the wind speed influence index n obtained using the multi-gradient wind speed and sand transport measurement method, and the direction sensitivity coefficient c obtained using the variable angle base wind tunnel experiment method. The specific method for obtaining the soil erodibility factor K using the EPIC model formula is as follows: In the area to be planted, topsoil samples were collected using a soil drill and sent to the laboratory. Using equipment such as a laser particle size analyzer, the sand content Psa and silt content Psi of the soil samples were analyzed and obtained. The soil erodibility factor K was calculated using the EPIC model formula. The EPIC model formula is expressed as follows: ; The specific method for obtaining the reference critical wind speed u0 using wind tunnel experiments is as follows: Soil samples collected from the planting area are naturally air-dried and then evenly spread in a sample tray at the bottom of the wind tunnel. A high-precision laser particle sensor or high-speed camera is placed downstream of the sample tray to capture the initial movement of sand particles. The wind tunnel speed is increased slowly from 0 in increments of 0.1 m / s, and the laser particle sensor monitors the concentration of particulate matter in the air in real time. When the sensor reading first shows a sustained and significant jump, the wind speed at this time is recorded. This wind speed is the critical wind speed for sand lifting of the soil sample. This process is repeated for multiple samples from different plots, and the average value is taken as the reference critical wind speed u0. The specific method for obtaining the direction sensitivity coefficient c using the variable-angle base wind tunnel experiment is as follows: A wind tunnel with an electrically adjustable sample base simulating slope θ and aspect α was used. A sand collector was placed downstream of the sample pan. With a fixed wind speed and slope θ, multiple different wind direction and aspect angles |α-w1| were set by rotating the base, for example, in 15° increments, simulating multiple angles from 0° to 90°. At each angle, erosion was performed for a fixed time, and the mass of wind-collected sand was precisely weighed. The obtained angle-sand loss data was compared with the objective function. The optimal coefficient obtained by performing nonlinear regression fitting is the direction sensitivity coefficient c. S2 includes S21; S21. Based on the pre-planting environmental parameters EP1, and according to the regional division of the Digital Elevation Model (DEM), N grid cells are obtained. Combining the wind shear force model on the ground surface and the relationship between wind direction angle w1 and slope aspect α, the wind erosion potential index W of the i-th grid cell is calculated. i ; Among them, the wind erosion potential index W of the i-th grid cell i The calculation expression is as follows: ; In the formula, i represents the identifier in N grid cells, exp represents the natural exponential function, and Σ represents the traversal symbol; S2 includes S22; S22. Based on the wind erosion potential index W of the i-th grid cell i The planting planning parameters for the i-th grid cell are obtained by matching the planting knowledge base set up by relevant staff. These parameters include the number of Jerusalem artichoke rows L1 between the poplar rows, the planting spacing D1 between Jerusalem artichoke plants, the planting row spacing L2 between the poplar rows and the planting spacing D2 between the poplar plants. A deep ditch is set up next to each row of poplars and a shallow ditch is dug between every two rows of Jerusalem artichokes. The planting knowledge base was established through specialized field trials and analysis of historical project data. Before the project started, small-scale experimental plots were set up, and parallel experiments were conducted with different planting density gradients and configuration combinations. Observations were carried out for 1-2 years. Combined with the collection and analysis of historical data from past ecological restoration projects in the project site or under similar climate and soil conditions, the survival rate, growth status, and windbreak and sand-fixing effects of different planting configurations were summarized. Successful planting patterns and parameters were extracted, and the planting planning parameters most suitable for local conditions were obtained. The planting knowledge base uses the wind erosion potential index W. i The interval is used as the query index, and a set of planting planning parameters, including the number of Jerusalem artichoke rows L1, the planting spacing of Jerusalem artichokes D1, the planting row spacing of poplar L2, and the planting spacing of poplar D2, are used as the output values. The specific contents of the planting knowledge base are shown in Table 1: Table 1:

[0024] The specific content of the planting knowledge base in Table 1 above is only the planting planning parameters suitable for the planting area in this embodiment. If entering a new windy and sandy area with different climate or soil characteristics, the parameters in the planting knowledge base must be localized and verified and calibrated through special field trials and historical project data analysis.

[0025] In this embodiment, the pre-planting environmental parameters EP1 are first collected. This process includes: collecting wind parameters MP, including average wind speed u and wind direction angle w1, using an anemometer; collecting three-dimensional coordinate point cloud data CPD through UAV surveying and generating a digital elevation model (DEM), and then calculating topographic parameters TLP, such as slope θ and aspect α, for each grid cell; and obtaining wind and sand influence parameters WSP, including soil erodibility factor K, reference critical wind speed u0, wind speed influence index n, and direction sensitivity coefficient c, through laboratory analysis and wind tunnel experiments. The special advantage of this process is that it transforms the natural environment of a region into a set of precise digital inputs, so that the entire planning process no longer relies on subjective experience, but is based on transparent and reproducible data. Subsequently, based on the collected pre-planting environmental parameters EP1, this method calculates the wind erosion potential index W for the i-th grid cell out of N grid cells. i Next, the wind erosion potential index W of the i-th grid cell is... i By matching with a planting knowledge base, a set of differentiated planting planning parameters is generated for each individual grid cell, specifically including the number of Jerusalem artichoke rows L1, the planting spacing of Jerusalem artichokes D1, the planting row spacing of poplars L2, and the planting spacing of poplars D2. Another particular advantage of this embodiment is that it calculates the wind erosion potential index W of the i-th grid cell. i This core indicator establishes a quantitative and physically meaningful bridge between the current environmental conditions and engineering design. The final output, which includes differentiated layout schemes with specific planting parameters, is an inevitable result of this rigorous quantitative analysis. It ensures that every inch of land in the restoration project receives the optimal and most targeted design, thereby constructing an ecological barrier that is more resilient and stable than traditional solutions.

[0026] Example 3 This embodiment is an explanation based on Embodiment 2. Please refer to it. Figure 1 , Figure 3 and Figure 4 Specifically: S3 includes S31; S31. After planting, the environmental parameters EP2 after planting are obtained by connecting to the weather station network and conducting experimental analysis through meteorological sensors and API interfaces. The meteorological sensors include a net radiation meter, a soil heat flux plate and an anemometer. Post-planting environmental parameter EP2 is included in the i-th grid cell, and the net radiation R is collected by the net radiation table. i Soil heat flux G collected by soil heat flux plate i It connects to the weather station network via API to collect the predicted highest temperature T for the day. MAX Minimum temperature T MIN Average temperature T and atmospheric pressure PA i Based on the average air temperature Tjunction and standard thermodynamic formulas, the slope Δs of the saturated vapor pressure-temperature curve was calculated. i Based on the atmospheric pressure PA junction and the formula for calculating the hygrometer constant, the hygrometer constant γ is calculated. i According to the predicted highest temperature T MAX Minimum temperature T MIN Given the average temperature T, and combining the saturated vapor pressure calculation formula and the actual vapor pressure calculation formula, the saturated vapor pressure es is calculated respectively. i and actual water vapor pressure ea i According to the average temperature T i and atmospheric pressure PA i Using the ideal gas equation, the air density ρA is calculated. i ; Within a preset sampling period t, the leaf area index (LAI) of Jerusalem artichoke canopy was obtained using destructive sampling and a leaf area meter. （i1，t） The leaf area index (LAI) of poplar canopy was obtained using digital hemispherical photography. （i2，t） By using a stomatalometer to directly measure the stomatal resistance data Pm at the single-leaf level on the leaves of poplar and Jerusalem artichoke, we obtained data. （i，t） According to the Jerusalem artichoke canopy leaf area index (LAI) （i1，t） Poplar canopy leaf area index (LAI) （i2，t） and porosity data Pm at the single-leaf level （i，t） By combining the upscaling model, the canopy resistance r of Jerusalem artichoke was obtained. （i1c，t） and poplar canopy resistance r （i2c，t） ; Within a preset sampling period t, the average canopy height H of Jerusalem artichoke was obtained through manual sampling measurements. （i1，t） And the average canopy height H of poplar （i2，t） Multiple anemometers were adjusted to the average canopy height H of Jerusalem artichoke. （i1，t） And the average canopy height H of poplar （i2，t） Collect the wind speed u of the Jerusalem artichoke canopy at the corresponding height. i1 and the wind speed of the poplar canopy u i2 Based on the average canopy height H of Jerusalem artichoke （i1，t） Average canopy height of poplar H （i2，t）、 Jerusalem artichoke canopy wind speed u at corresponding height i1and the wind speed of the poplar canopy u i2 Combining the logarithmic wind profile theory in micrometeorology, the aerodynamic drag r of the average canopy of Jerusalem artichoke was calculated. （i1v，t） And the average aerodynamic drag of the poplar canopy r （i2v，t） ; S3 includes S32; S32. Based on the obtained post-planting environmental parameter EP2, and using the Penman-Montes formula, calculate the water evaporated per second per unit area under the current environmental conditions. Multiply this result by the total time of day and the area of ​​the i-th grid cell to obtain the daily water compensation requirement ET for Jerusalem artichoke in the i-th grid cell. i1 The daily water compensation requirement for poplar trees (ET) i2 ; Among them, the daily water demand compensation ET in the i-th grid cell i The calculation expression is as follows: ; In the formula, j represents the identifiers for Jerusalem artichoke and poplar; j=1 represents Jerusalem artichoke, and j=2 represents poplar. i λ represents the area of ​​the i-th grid cell calculated during grid generation, cp represents the preset specific heat of air at constant pressure, specifically 1005 J / (kg·K), and λ represents the preset latent heat of vaporization of water, specifically 2.45 × 10⁻⁶. 6 J / kg; Daily water demand compensation ET in the third grid cell i The calculation example is as follows: The area of ​​the third grid cell, S3, is 1000m². 2 ; Post-planting environmental parameters EP2: Net radiation R3: 175W / m 2 Soil heat flux G3: 18 W / m 2 Average temperature T: 25℃; Atmospheric pressure PA3: 101kPa; Slope of the saturated vapor pressure-temperature curve Δ3: 0.1886 kPa / ℃, saturated vapor pressure es3: 3.17 kPa, actual vapor pressure ea3: 1.97 kPa, hygrometer constant γ3: 0.067 kPa / ℃, air density ρA3: 1.17 kg / m³ 3 ; Jerusalem artichoke canopy resistance r （31c，t） 80s / m, average aerodynamic drag of Jerusalem artichoke canopy r （31v，t） 150s / m; Poplar canopy resistance r （32c，t） 65s / m, average aerodynamic drag of poplar canopy r （32v，t）45s / m; Daily water demand compensation ET in the i-th grid cell i The specific calculations are as follows: ; ; In this embodiment, the extensive and uniform irrigation model in the background technology is upgraded to a high-precision simulation based on the physiological and aerodynamic aspects of a double-canopy. To achieve precise irrigation of the composite canopy, this method first collects the post-planting environmental parameter EP2 after planting. This process includes: in the i-th grid cell, collecting net radiation R using meteorological sensors such as a net radiation meter and a soil heat flux plate. i and soil heat flux G i The highest temperature T is obtained by connecting to the weather station network via API. MAX Minimum temperature T MIN Meteorological data were collected and the slope Δ of the saturated water vapor pressure-temperature curve was calculated. i hygrometer constant γ i Derivative parameters, etc. A unique advantage of this embodiment is that it no longer treats the poplar-Helianthus alba complex community as a vague whole, but rather, through experimental analysis, precisely deconstructs this ecosystem into two independent physiological and aerodynamic layers within the model. Specifically, the method obtains the leaf area index (LAI) of the Helianthus alba canopy using destructive sampling and digital hemispherical photography, respectively. （i1，t） And the leaf area index (LAI) of poplar canopy （i2，t） And a porosimeter was used to measure the porosimetry data Pm at the single-leaf level. （i，t） Then, the canopy resistance r of Jerusalem artichoke was calculated using an upscaling model. （i1c，t） and poplar canopy resistance r （i2c，t） Furthermore, by measuring the average canopy height H of Jerusalem artichokes... （i1，t） And the average canopy height H of poplar （i2，t） And collect the Jerusalem artichoke canopy wind speed u at the corresponding height. i1 and the wind speed of the poplar canopy u i2 This method can also accurately calculate the average aerodynamic drag r of the Jerusalem artichoke canopy. （i1v，t） And the average aerodynamic drag of the poplar canopy r （i2v，t） This accurately characterizes the complex turbulent exchange properties in the two-layer structure. After obtaining the complete post-planting environmental parameters EP2, based on these highly accurate and measured parameters, and using the Penman-Montes formula, the daily water requirement ET for Jerusalem artichoke in the i-th grid cell was calculated. i1 The daily water compensation requirement for poplar trees (ET) i2Therefore, the two daily water compensation values ​​output are not simple estimates, but rather the result of in-depth simulation of the physiological activities and physical processes of the two plants under real-world conditions, achieving unprecedented irrigation precision for complex intercropping systems.

[0027] Example 4 This embodiment is an explanation based on Embodiment 3. Please refer to it. Figure 1 Specifically: S4 includes S41; S41. Based on the post-planting environmental parameter EP2, calculate the cumulative effective accumulated temperature Da from the completion of planting to day d, and calculate the biological hazard index I of the target pest to poplar based on the cumulative distribution function in the Weibull distribution. The biological hazard index I of the target pest on poplar is calculated as follows: ; ; In the formula, y represents the total number of days traversal identifier, d represents the number of days elapsed from the completion of planting to the current day, and when the date of day d is the same as the date of the completion of planting, d is reset to 1 and the counting starts again, T y denoted as the average temperature on day y, obtained from the post-planting environmental parameter EP2; T0 represents the developmental starting temperature of the target pest, obtained through hatching and development experiments on the target pest; η represents the scale parameter of the Weibull distribution, representing the total effective accumulated temperature required for the pest to complete one generation of development, obtained through hatching and development experiments on the target pest; β represents the shape parameter of the Weibull distribution, representing the asymmetry of the peak shape of the target pest population activity, obtained through data fitting of historical target pest activity data. The specific details of the hatching and development experiment on the target pest are as follows: Taking the main trunk-boring pests of poplar, such as the yellow-spotted longhorn beetle, collect a sufficient number of larvae or pupae of the same age and place them in at least 5 constant temperature incubators, covering the possible development temperature range of the pest, such as 15℃, 20℃, 25℃, 30℃ and 35℃. Randomly and equally place the samples into incubators at different temperatures and record their development progress every day until all of them emerge as adults. Calculate the average number of days required to complete one generation of development at each temperature. Plot the temperature as the X-axis and the development rate as the Y-axis and perform linear regression analysis. The intersection of the regression line and the X-axis is the development starting temperature T0. The reciprocal of the slope of the regression line is the scale parameter η of the Weibull distribution required to complete one generation of development. Historical data from insect monitoring stations in the project area over the past few years were collected, especially the weekly trapping records of adult yellow-spotted longhorn beetles. The trapping numbers of each year were converted into a distribution curve with the cumulative effective accumulated temperature Da as the time axis. Then, using the nonlinear fitting tool in the statistical software, the Levenberg-Marquardt algorithm was used to fit these historical data distribution curves to the probability density function of the Weibull distribution. The optimal parameter output by the fitting process is the shape parameter β. S4 includes S42; S42. Compare the biohazard index I of the target pest on poplar with the preset biohazard index threshold I. th A comparison is made, and an inspection and dispatch plan APL is generated based on the comparison results, including the first-level biological hazard index threshold I1 and the second-level biological hazard index threshold I2. If the biological hazard index I of the target pest on poplar is less than the first-level biological hazard index threshold I1, then there is no need to generate the patrol scheduling plan APL. If the first-level biological hazard index threshold I1 ≤ the biological hazard index I of the target pest on poplar < the second-level biological hazard index threshold I2, then the patrol scheduling plan APL is generated. The current period is the appropriate time for the control of the target pest. Patrol personnel need to deploy tree traps and spray contact insecticides. If the biological hazard index I of the target pest on poplar is greater than or equal to the secondary biological hazard index threshold I2, then the patrol dispatch plan APL is generated. The current period is the peak period of the target pest. Patrol personnel need to strengthen the monitoring of healthy trees, clean up the damaged trees, and trap and kill the target pest. S5 includes S51; S51. Based on the daily water demand compensation ET of Jerusalem artichokes in the i-th grid. i1 The daily water compensation requirement for poplar trees (ET) i2 Generate an irrigation scheduling scheme BPL, whereby BPL is the daily water compensation ET for Jerusalem artichokes in the i-th grid. i1 As the input for shallow furrow irrigation, the daily water requirement compensation ET of the poplars in the i-th grid is used. i2As the input for deep trench irrigation, the irrigation scheduling plan is sent to the automated irrigation control terminal in the project area, and the inspection scheduling plan (APL) is sent to the inspection personnel, who then execute the inspection scheduling plan (APL). The post-planting environmental parameters EP2 and the daily water requirement ET for Jerusalem artichokes in the i-th grid are calculated. i1 The daily water compensation requirement for poplar trees (ET) i2 The biohazard index I of the target pest on poplar is stored in the log dataset HIS.

[0028] In this embodiment, to achieve proactive early warning of biological disasters, the biological damage index I of the target pest to poplar is calculated based on the post-planting environmental parameter EP2. This calculation process first calculates the cumulative effective accumulated temperature Da from the completion of planting to the current day; this calculation only adds the average temperature T of day y. y The portion exceeding the developmental threshold temperature T0 of the target pest. Then, using the accumulated effective temperature Da as input, and combining it with the scale parameter η and shape parameter β of the Weibull distribution calibrated experimentally, the biohazard index I is finally calculated based on the cumulative distribution function in the Weibull distribution. A unique advantage of this embodiment is that it no longer passively waits for pests to occur, but rather quantifies the risk over time by calculating the biohazard index I, predicting the pest's active time window using the accumulated effective temperature Da. Subsequently, this method uses the calculated biohazard index I... i Compared with the preset biological hazard index threshold I th Real-time comparison is performed; once the preset biohazard index threshold I is exceeded... th The patrol scheduling plan (APL) is immediately generated, and differentiated prevention and control instructions are issued based on different risk conditions. This patrol scheduling plan (APL), based on quantitative comparison, replaces the traditional indiscriminate and inefficient patrol mode. Finally, this method calculates the daily water compensation ET required by Jerusalem artichokes in the i-th grid. i1 The daily water compensation requirement for poplar trees (ET) i2 The irrigation scheduling plan (BPL) is generated and sent to the automated irrigation control terminal in the project area. Simultaneously, the inspection scheduling plan (APL) is sent to inspection personnel, and all critical data is stored in the log dataset (HIS). This unifies the deployment of irrigation and disaster prevention, two previously separate management objectives, signifying the realization of a multi-objective, collaborative intelligent management and control system.

[0029] Example 5 A management and control system for intercropping poplar and Jerusalem artichoke in wind-blown sandy areas, please refer to Figure 2 Specifically, it includes a pre-planting data acquisition module, a planting planning module, a daily water demand compensation analysis module, a biological hazard analysis module, and a scheduling execution module; The pre-planting data acquisition module includes a pre-planting data acquisition unit, which collects environmental parameters before planting by using anemometers deployed on-site, drone surveys, and laboratory analysis. The planting planning module includes a wind erosion potential analysis unit and a planting planning generation unit. The wind erosion potential analysis unit divides the wind erosion area to be planted into multiple grid units based on the environmental parameters before planting and calculates the wind erosion potential index of each grid unit. The planting planning generation unit matches planting planning parameters from a preset planting knowledge base based on the wind erosion potential index. The daily water demand compensation analysis module includes a post-planting data acquisition unit and a daily water demand compensation analysis unit. After planting, the post-planting data acquisition unit obtains post-planting environmental parameters by connecting to the meteorological station network and experimental analysis through meteorological sensors and API interfaces. The daily water demand compensation analysis unit calculates the daily water demand compensation for each grid unit based on the post-planting environmental parameters and the Penman-Montes formula. The meteorological sensors include a net radiation meter, a soil heat flux plate, and an anemometer. The biological hazard analysis module includes a biological hazard analysis unit and an inspection and dispatch plan generation unit. The biological hazard analysis unit calculates the biological hazard index of the target pests on poplar based on the environmental parameters after planting, and the inspection and dispatch plan generation unit generates an inspection and dispatch plan based on the biological hazard index. The scheduling execution module includes a scheduling execution unit. The scheduling execution unit generates an irrigation scheduling plan based on the daily water compensation requirement of each grid unit and sends it to the automated irrigation control terminal. It also sends the inspection scheduling plan to the inspection personnel and stores the environmental parameters, daily water compensation requirement, and biological hazard index collected after planting into the log dataset.

[0030] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for ecological restoration and management of intercropping poplar and Jerusalem artichoke in wind-blown sand areas, characterized in that: Includes the following steps: S1. Before planting, environmental parameters are collected by using anemometers deployed on-site, drone surveys, and laboratory analysis. S2. Based on the environmental parameters before planting, the wind erosion potential index of each grid cell is calculated and the planting planning parameters are matched from the preset planting knowledge base. S3. After planting, the environmental parameters after planting are obtained by connecting to the weather station network and experimental analysis through meteorological sensors and API interfaces, and the daily water compensation required for each grid unit is calculated by combining the Penman-Montes formula. S4. Based on the environmental parameters after planting, calculate the biological hazard index of the target pests on poplar, and generate an inspection and dispatch plan based on the biological hazard index. S5. Based on the daily water compensation requirement of each grid unit, generate an irrigation scheduling plan and send it to the automated irrigation control terminal in the project area. Send the inspection scheduling plan to the inspection personnel and store the environmental parameters, daily water compensation requirement and biological hazard index collected after planting into the log dataset.

2. The method for ecological restoration and management of intercropping poplar and Jerusalem artichoke in wind-blown sand areas according to claim 1, characterized in that: S1 includes S11; S11. Before planting, environmental parameters EP1 were collected by using anemometers deployed on site, drone surveys, and laboratory analysis. These parameters included wind parameters MP collected by anemometers, topographic parameters TLP collected by drone surveys, and wind and sand impact parameters WSP obtained through laboratory analysis. The wind parameters MP include the average wind speed u and wind direction angle w1 within the observation period, collected using an anemometer. The topographic parameters TLP include the digital elevation model (DEM) generated from the collected 3D coordinate point cloud data CPD, and the slope θ and aspect α of N grid cells are calculated using a domain analysis algorithm on the DEM, where N represents the total number of grid cells. The wind and sand influence parameters WSP include the soil erodibility factor K obtained using the EPIC model formula, the reference critical wind speed u0 obtained using the wind tunnel experiment method, the wind speed influence index n obtained using the multi-gradient wind speed and sand transport measurement method, and the direction sensitivity coefficient c obtained using the variable angle base wind tunnel experiment method.

3. The method for ecological restoration and management of intercropping poplar and Jerusalem artichoke in wind-blown sand areas according to claim 2, characterized in that: S2 includes S21; S21. Based on the pre-planting environmental parameters EP1, and according to the regional division of the Digital Elevation Model (DEM), N grid cells are obtained. Combining the wind shear force model on the ground surface and the relationship between wind direction angle w1 and slope aspect α, the wind erosion potential index W of the i-th grid cell is calculated. i ; Among them, the wind erosion potential index W of the i-th grid cell i The calculation expression is as follows: ； In the formula, i represents the identifier in N grid cells, exp represents the natural exponential function, and Σ represents the traversal symbol.

4. The method for ecological restoration and management of intercropping poplar and Jerusalem artichoke in wind-blown sand areas according to claim 3, characterized in that: S2 includes S22; S22. Based on the wind erosion potential index W of the i-th grid cell i The planting planning parameters for the i-th grid cell are obtained by matching the planting knowledge base set up by relevant staff. These parameters include the number of Jerusalem artichoke rows L1 between the poplar rows, the planting spacing D1 between Jerusalem artichoke plants, the planting row spacing L2 between the poplar rows, and the planting spacing D2 between the poplar plants. A deep ditch is set up next to each row of poplars, and a shallow ditch is dug between every two rows of Jerusalem artichokes.

5. The method for ecological restoration and management of intercropping poplar and Jerusalem artichoke in wind-blown sandy areas according to claim 4, characterized in that: S3 includes S31; S31. After planting, the environmental parameters EP2 after planting are obtained by connecting to the weather station network and conducting experimental analysis through meteorological sensors and API interfaces. The meteorological sensors include a net radiation meter, a soil heat flux plate and an anemometer. Post-planting environmental parameter EP2 is included in the i-th grid cell, and the net radiation R is collected by the net radiation table. i Soil heat flux G collected by soil heat flux plate i It connects to the weather station network via API to collect the predicted highest temperature T for the day. MAX Minimum temperature T MIN Average temperature T and atmospheric pressure PA i Based on the average air temperature Tjunction and standard thermodynamic formulas, the slope Δs of the saturated vapor pressure-temperature curve was calculated. i Based on the atmospheric pressure PA junction and the formula for calculating the hygrometer constant, the hygrometer constant γ is calculated. i According to the predicted highest temperature T MAX Minimum temperature T MIN Given the average temperature T, and combining the saturated vapor pressure calculation formula and the actual vapor pressure calculation formula, the saturated vapor pressure es is calculated respectively. i and actual water vapor pressure ea i According to the average temperature T i and atmospheric pressure PA i Using the ideal gas equation, the air density ρA is calculated. i ; Within a preset sampling period t, the leaf area index (LAI) of Jerusalem artichoke canopy was obtained using destructive sampling and a leaf area meter. （i1，t） The leaf area index (LAI) of poplar canopy was obtained using digital hemispherical photography. （i2，t） By using a stomatalometer to directly measure the stomatal resistance data Pm at the single-leaf level on the leaves of poplar and Jerusalem artichoke, we obtained data. （i，t） According to the Jerusalem artichoke canopy leaf area index (LAI) （i1，t） Poplar canopy leaf area index (LAI) （i2，t） and porosity data Pm at the single-leaf level （i，t） By combining the upscaling model, the canopy resistance r of Jerusalem artichoke was obtained. （i1c，t） and poplar canopy resistance r （i2c，t） ; Within a preset sampling period t, the average canopy height H of Jerusalem artichoke was obtained through manual sampling measurements. （i1，t） And the average canopy height H of poplar （i2，t） Multiple anemometers were adjusted to the average canopy height H of Jerusalem artichoke. （i1，t） And the average canopy height H of poplar （i2，t） Collect the wind speed u of the Jerusalem artichoke canopy at the corresponding height. i1 and the wind speed of the poplar canopy u i2 Based on the average canopy height H of Jerusalem artichoke （i1，t） Average canopy height of poplar H （i2，t）、 Jerusalem artichoke canopy wind speed u at corresponding height i1 and the wind speed of the poplar canopy u i2 Combining the logarithmic wind profile theory in micrometeorology, the aerodynamic drag r of the average canopy of Jerusalem artichoke was calculated. （i1v，t） And the average aerodynamic drag of the poplar canopy r （i2v，t） .

6. The method for ecological restoration and management of intercropping poplar and Jerusalem artichoke in wind-blown sand areas according to claim 5, characterized in that: S3 includes S32; S32. Based on the obtained post-planting environmental parameter EP2, and using the Penman-Montes formula, calculate the water evaporated per second per unit area under the current environmental conditions. Multiply this result by the total time of day and the area of ​​the i-th grid cell to obtain the daily water compensation requirement ET for Jerusalem artichoke in the i-th grid cell. i1 The daily water compensation requirement for poplar trees (ET) i2 ; Among them, the daily water demand compensation ET in the i-th grid cell i The calculation expression is as follows: ； In the formula, j represents the identifiers for Jerusalem artichoke and poplar; j=1 represents Jerusalem artichoke, and j=2 represents poplar. i λ represents the area of ​​the i-th grid cell calculated during grid generation, cp represents the preset specific heat of air at constant pressure, specifically 1005 J / (kg·K), and λ represents the preset latent heat of vaporization of water, specifically 2.45 × 10⁻⁶. 6 J / kg.

7. A method for ecological restoration and management of intercropping poplar and Jerusalem artichoke in wind-blown sand areas according to claim 6, characterized in that: S4 includes S41; S41. Based on the post-planting environmental parameter EP2, calculate the cumulative effective accumulated temperature Da from the completion of planting to day d, and calculate the biological hazard index I of the target pest to poplar based on the cumulative distribution function in the Weibull distribution. The biological hazard index I of the target pest on poplar is calculated as follows: ； ； In the formula, y represents the total number of days traversal identifier, d represents the number of days elapsed from the completion of planting to the current day, and when the date of day d is the same as the date of the completion of planting, d is reset to 1 and the counting starts again, T y denoted as the average temperature on day y, obtained from the post-planting environmental parameter EP2; T0 represents the developmental starting temperature of the target pest, obtained through hatching and development experiments on the target pest; η represents the scale parameter of the Weibull distribution, representing the characteristic total effective accumulated temperature required for the pest to complete one generation of development, obtained through hatching and development experiments on the target pest; β represents the shape parameter of the Weibull distribution, representing the asymmetry of the peak shape of the target pest population activity, obtained through data fitting of historical target pest activity data.

8. The method for ecological restoration and management of intercropping poplar and Jerusalem artichoke in wind-blown sand areas according to claim 7, characterized in that: S4 includes S42; S42. Compare the biohazard index I of the target pest on poplar with the preset biohazard index threshold I. th A comparison is made, and an inspection and dispatch plan APL is generated based on the comparison results, including the first-level biological hazard index threshold I1 and the second-level biological hazard index threshold I2. If the biological hazard index I of the target pest on poplar is less than the first-level biological hazard index threshold I1, then there is no need to generate the patrol scheduling plan APL. If the first-level biological hazard index threshold I1 ≤ the biological hazard index I of the target pest on poplar < the second-level biological hazard index threshold I2, then the patrol scheduling plan APL is generated. The current period is the appropriate time for the control of the target pest. Patrol personnel need to deploy tree traps and spray contact insecticides. If the biological hazard index I of the target pest on poplar trees is greater than or equal to the secondary biological hazard index threshold I2, then an inspection and dispatch plan APL will be generated. The current period is the peak period of the target pest. The inspection personnel need to strengthen the monitoring of healthy trees, clean up the damaged trees, and trap and kill the target pest.

9. A method for ecological restoration and management of intercropping poplar and Jerusalem artichoke in wind-blown sand areas according to claim 8, characterized in that: S5 includes S51; S51. Based on the daily water demand compensation ET of Jerusalem artichokes in the i-th grid. i1 The daily water compensation requirement for poplar trees (ET) i2 Generate an irrigation scheduling scheme BPL, whereby BPL is the daily water compensation ET for Jerusalem artichokes in the i-th grid. i1 As the input for shallow furrow irrigation, the daily water requirement compensation ET of the poplars in the i-th grid is used. i2 As the input for deep trench irrigation, the irrigation scheduling plan is sent to the automated irrigation control terminal in the project area, and the inspection scheduling plan (APL) is sent to the inspection personnel, who then execute the inspection scheduling plan (APL). The post-planting environmental parameters EP2 and the daily water requirement ET for Jerusalem artichokes in the i-th grid are calculated. i1 The daily water compensation requirement for poplar trees (ET) i2 The biohazard index I of the target pest on poplar is stored in the log dataset HIS.

10. A system for ecological restoration and management of intercropping poplar and Jerusalem artichoke in wind-blown sandy areas, applied to the method for ecological restoration and management of intercropping poplar and Jerusalem artichoke in wind-blown sandy areas as described in any one of claims 1 to 9, characterized in that: It includes a pre-planting data acquisition module, a planting planning module, a daily water demand compensation analysis module, a biological hazard analysis module, and a scheduling execution module; The pre-planting data acquisition module includes a pre-planting data acquisition unit, which collects environmental parameters before planting by using anemometers deployed on-site, drone surveys, and laboratory analysis. The planting planning module includes a wind erosion potential analysis unit and a planting planning generation unit. The wind erosion potential analysis unit divides the wind erosion area to be planted into multiple grid units based on the environmental parameters before planting and calculates the wind erosion potential index of each grid unit. The planting planning generation unit matches planting planning parameters from a preset planting knowledge base based on the wind erosion potential index. The daily water demand compensation analysis module includes a post-planting data acquisition unit and a daily water demand compensation analysis unit. After planting, the post-planting data acquisition unit obtains post-planting environmental parameters by connecting to the meteorological station network and experimental analysis through meteorological sensors and API interfaces. The daily water demand compensation analysis unit calculates the daily water demand compensation for each grid unit based on the post-planting environmental parameters and the Penman-Montes formula. The meteorological sensors include a net radiation meter, a soil heat flux plate, and an anemometer. The biological hazard analysis module includes a biological hazard analysis unit and an inspection and dispatch plan generation unit. The biological hazard analysis unit calculates the biological hazard index of the target pests on poplar based on the environmental parameters after planting, and the inspection and dispatch plan generation unit generates an inspection and dispatch plan based on the biological hazard index. The scheduling execution module includes a scheduling execution unit. The scheduling execution unit generates an irrigation scheduling plan based on the daily water compensation requirement of each grid unit and sends it to the automated irrigation control terminal. It also sends the inspection scheduling plan to the inspection personnel and stores the environmental parameters, daily water compensation requirement, and biological hazard index collected after planting into the log dataset.

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