An intelligent optimization method for spatial layout of under-forest ginseng niche

Abstract

The present application relates to the technical field of smart forestry and under-forest planting planning, in particular to a kind of under-forest ginseng ecological niche space layout intelligent optimization method, comprising: benchmark construction and simulation steps: obtain forest land data to construct ideal habitat capacity model to generate potential energy grid chart, and carry out virtual planting simulation to generate simulated output data;Double-track residual generation step: generate real residual matrix in combination with historical data and potential energy chart, generate theoretical residual matrix in combination with simulation data and potential energy chart;Structure comparison and optimization step: calculate matrix structure similarity;Logic determination step: in response to similarity greater than threshold, determine as spatial layout factor and execute rearrangement optimization;Otherwise, it is determined as environmental factor and generates improvement prompt;The present application realizes the accurate decoupling between the environment background and the space layout of the low yield reason.

Description

Technical Field

[0001] This invention relates to the field of smart forestry and understory planting planning technology, specifically a method for intelligent optimization of the ecological niche spatial layout of ginseng under forest. Background Technology

[0002] In the process of planting and managing ginseng under forest cover, the forest microenvironment exhibits high spatial heterogeneity, and the plant growth process is constrained by both environmental background factors and intraspecific biological competition. Existing yield assessment and management schemes generally adopt extensive manual experience judgment or single-dimensional statistical analysis, lacking an ideal quantitative benchmark for yield based on the natural endowment of a specific plot, and are unable to accurately separate environmental background noise and plant spatial competition effects in complex understory habitats. Due to the lack of effective data support and counterfactual reasoning mechanisms, existing technologies struggle to quantitatively distinguish whether the limited productivity of target forest land is caused by insufficient environmental baseline factors such as soil fertility and light conditions, or by spatial configuration factors such as excessive planting density and uneven plant spacing. This lack of attribution logic leads to a lack of scientific basis for management decisions, which can easily result in blind policy implementation. For example, soil improvement may be incorrectly carried out in areas with low yields due to overcrowding, or planting density may be ineffectively adjusted in areas with poor environmental baseline factors, resulting in wasted operating costs and insignificant yield improvement.

[0003] Therefore, how to establish an intelligent optimization method that can accurately decouple the causes of production capacity constraints and provide targeted spatial rearrangement or environmental improvement strategies based on the diagnostic results has become an urgent technical problem to be solved. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides an intelligent optimization method for the ecological niche spatial layout of ginseng under forest cover. Specifically, the technical solution of this invention includes: Obtain 3D GIS data and historical management flow data of the target forest land; Based on the three-dimensional GIS data of forest land, an ideal habitat capacity model is constructed to generate a forest land ecological niche potential grid map; Based on preset competitive simulation parameters, a virtual planting layout simulation is performed on a forest ecological niche potential grid map to generate simulated prediction output data. Based on historical management flow data and forest ecological niche potential grid map, a real residual spatial distribution matrix is ​​generated; Based on the simulated predicted output data and the forest ecological niche potential grid map, a theoretical residual spatial distribution matrix is ​​generated; The actual residual space distribution matrix and the theoretical residual space distribution matrix are subjected to non-negative domain truncation mapping and gray-scale space normalization; the matrix structure similarity between the normalized actual residual space distribution matrix and the theoretical residual space distribution matrix is ​​calculated. If the matrix structure similarity is greater than the preset similarity threshold, the reason for the limited productivity of the target forest land is determined to be spatial layout factors, and spatial rearrangement optimization is performed. If the matrix structure similarity is less than or equal to the preset similarity threshold, the reason for the limited productivity of the target forest land is determined to be environmental background factors, and land improvement suggestions are generated.

[0005] Preferably, the three-dimensional GIS data of forest land includes slope data, aspect data, canopy closure data, and soil humus layer thickness grid data; Historical operational data includes survival rate data, biomass growth curve data, and time series data of disease occurrence records for historical planting batches.

[0006] Preferably, based on the three-dimensional GIS data of forest land, an ideal habitat capacity model is constructed to generate a forest land ecological niche potential grid map, including: based on the microenvironmental factors characterizing habitat suitability in the three-dimensional GIS data of forest land, a multidimensional regression analysis algorithm is used to establish the theoretical maximum biomass function of a single ginseng plant; Based on the theoretical maximum biomass function of a single ginseng plant, the theoretical maximum output value of each grid cell in the target forest land under the state of zero competition interference is calculated; Based on the theoretical maximum output value, a forest ecological niche potential energy grid map is generated.

[0007] Preferably, based on preset competition simulation parameters, a virtual planting layout simulation is performed on a forest ecological niche potential energy grid map to generate simulation prediction output data, including: obtaining preset ginseng allelopathy attenuation coefficient, root competition resistance coefficient and competition sensitivity coefficient; Virtual ginseng coordinate points are generated on the forest ecological niche potential grid map; for each virtual ginseng coordinate point, the distance data and tree age data of other plants in its neighborhood are calculated; Based on distance and tree age data, the cumulative competitive pressure value was obtained by weighting the ginseng allelopathy attenuation coefficient, competition sensitivity coefficient, and root competition resistance coefficient. Numerical attenuation processing is performed on the forest niche potential energy grid map based on the cumulative competitive pressure value to generate simulation prediction output data.

[0008] Preferably, based on historical management flow data and forest niche potential energy grid map, a real residual spatial distribution matrix is ​​generated, including: extracting actual output values ​​from historical management flow data; and calculating the first difference between the theoretical maximum output value and the actual output value at the corresponding coordinate point in the forest niche potential energy grid map. Based on the spatial distribution data of the first difference, construct the real residual spatial distribution matrix.

[0009] Preferably, based on the simulated predicted output data and the forest niche potential energy grid map, a theoretical residual spatial distribution matrix is ​​generated, including: extracting the simulated output values ​​from the simulated predicted output data; and calculating the second difference between the theoretical maximum output value and the simulated output value at the corresponding coordinate point in the forest niche potential energy grid map. Based on the spatial distribution data of the second difference, a theoretical residual spatial distribution matrix is ​​constructed.

[0010] Preferably, when the matrix structure similarity is greater than a preset similarity threshold, the reason for the limited productivity of the target forest land is determined to be spatial layout factors, and spatial rearrangement optimization is performed, including: adjusting the virtual coordinate position in the virtual planting layout simulation; Recalculate the adjusted theoretical residual space distribution matrix; When the sum of the absolute values ​​of all elements in the adjusted theoretical residual space distribution matrix is ​​minimized, the corresponding virtual coordinate positions are output as the optimal layout scheme.

[0011] Preferably, when the structural similarity is less than or equal to a preset similarity threshold, the reason for the limited productivity of the target forest land is determined to be environmental background factors, and a land improvement suggestion is generated, including: marking the target forest land as an abnormal land plot; Generate land improvement suggestions that include soil testing recommendations or disease identification instructions.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention introduces a counterfactual reasoning mechanism to construct an ideal vacuum state without competition as a benchmark, and accurately decouples the causes of low yield in reality from insufficient environmental background and improper spatial layout; it uses image processing technology to calculate the matrix structure similarity between the real residual spatial distribution matrix and the theoretical residual spatial distribution matrix, effectively solving the technical problem in traditional planting management that it is difficult to distinguish whether low yield is caused by spatial congestion or environmental limitations, thereby realizing a quantitative diagnosis of the causes of low yield. 2. This invention integrates multi-dimensional microenvironmental factors such as slope, canopy closure, and soil humus layer thickness from three-dimensional GIS data of forest land, and uses a multi-dimensional regression analysis algorithm to establish the theoretical maximum biomass function of a single ginseng plant. Based on this, an ideal habitat capacity model is constructed, which can accurately capture the heterogeneity differences of microhabitats within the forest land, establish a productivity ceiling with clear physical meaning, provide a high-precision quantitative benchmark for subsequent simulation and deduction, and ensure the objectivity of niche assessment. 3. This invention introduces non-negative domain truncation mapping and grayscale space standardization mechanism before similarity calculation to actively filter out noise interference from over-yielding areas, force the algorithm to focus on spatial structure comparison of under-yielding areas, and combine structural similarity index algorithm to comprehensively measure topological consistency from three dimensions: brightness, contrast and structure. This effectively avoids the risk of calculation results being distorted due to the large dynamic range of biomass values, and significantly improves the accuracy of capturing spatial distribution patterns in low-yielding areas. 4. This invention employs an intelligent hierarchical response strategy based on matrix structure similarity thresholds. When a problem is identified as a layout factor, it utilizes simulated annealing to perform spatial rearrangement optimization of virtual coordinates. When a problem is identified as an environmental factor, it generates targeted plot improvement suggestions, achieving a closed-loop process from problem diagnosis to solution. This hierarchical mechanism avoids ineffective computational consumption on non-layout problems, ensures the relevance and effectiveness of optimization solutions in complex agricultural scenarios, and achieves the optimal match between environmental carrying capacity and biological density. Attached Figure Description

[0013] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0015] Example 1: Please see Figure 1 A method for intelligent optimization of the ecological niche spatial layout of ginseng under forest, the method includes: acquiring three-dimensional GIS data of the target forest land and historical management flow data; Based on the 3D GIS data of forest land, an ideal habitat capacity model is constructed to generate a forest land ecological niche potential energy grid map; based on the preset competition simulation parameters, a virtual planting layout simulation is carried out on the forest land ecological niche potential energy grid map to generate simulated prediction output data. Based on historical management flow data and forest niche potential energy grid map, a realistic residual spatial distribution matrix is ​​generated; based on simulated predicted output data and forest niche potential energy grid map, a theoretical residual spatial distribution matrix is ​​generated; non-negative domain truncation mapping and grayscale standardization are performed on the realistic residual spatial distribution matrix and the theoretical residual spatial distribution matrix; the matrix structure similarity between the standardized realistic residual spatial distribution matrix and the theoretical residual spatial distribution matrix is ​​calculated. If the matrix structure similarity is greater than the preset similarity threshold, the reason for the limited productivity of the target forest land is determined to be spatial layout factors, and spatial rearrangement optimization is performed; if the matrix structure similarity is less than or equal to the preset similarity threshold, the reason for the limited productivity of the target forest land is determined to be environmental background factors, and land improvement suggestions are generated.

[0016] This embodiment provides an intelligent optimization method for the ecological niche spatial layout of ginseng under forest. The core logic of this method is to introduce a counterfactual deduction mechanism. By constructing an ideal vacuum state without competition as a benchmark, the underlying causes of low yield in reality are precisely decoupled from insufficient environmental background and improper spatial layout. The system performs data acquisition steps, obtaining 3D GIS data and historical management flow data of the target forest land through multi-source sensors. Simultaneously, the system acquires species types and distribution data of associated plants within the target forest land. Associated plants include at least one of the following: linden seedlings, hazel shrubs, Solomon's seal, Epimedium, or Adenophora stricta. Ecological niche optimization is achieved by constructing a three-layered composite community structure of larch, associated plants, and ginseng, thereby establishing a digital forest land twin base. The 3D GIS data is used to characterize immovable static environmental assets, while the historical management flow data characterizes the dynamic biological asset status. Based on the 3D GIS data, the system constructs an ideal habitat capacity model to generate a forest land ecological niche potential grid map. This model is built based on data-driven statistical principles, assuming that under standard planting density and standard self-thinning effect constraints, each pixel value in the output grid map represents the theoretical maximum biomass of that location under environmental factors limited only by light, soil, and water, without any biological competition, thus establishing the productivity ceiling of the plot. Based on preset competitive simulation parameters, a virtual planting layout simulation is carried out on the forest ecological niche potential energy grid map to generate simulated predicted output data. This is a positive stress test process. The system places virtual ginseng on the ideal potential energy map and forcibly activates the competitive parameters. The simulation data generated represents the theoretical output caused only by excessive planting density. Based on this, the system generates a real residual spatial distribution matrix according to historical management flow data and forest niche potential energy grid map. This matrix represents the total loss in the real world and records the total gap after all negative factors are superimposed. At the same time, based on the simulated predicted output data and forest niche potential energy grid map, a theoretical residual spatial distribution matrix is ​​generated. This matrix represents the theoretical loss caused purely by layout factors. The system calculates the matrix structure similarity between the actual residual spatial distribution matrix and the theoretical residual spatial distribution matrix, and uses image processing techniques to compare whether the spatial distribution patterns of the actual and theoretically deduced errors are consistent. Specifically, the system employs a structural similarity index. The algorithm performs calculations, and its core calculation is based on the following sliding window formula, where... Local window data representing the actual spatial distribution matrix of residuals. The local window data representing the theoretical residual spatial distribution matrix is ​​calculated using the following formula:

[0017] in, These are the local means of the actual residual matrix and the theoretical residual matrix within the sliding window, respectively. For local variance, For covariance, this formula comprehensively measures the topological similarity of two matrices from three dimensions: brightness, contrast, and structure. Compared to simple mean square error, It can more accurately detect whether the spatial distribution patterns of low-yield areas overlap; in this process, in order to solve the problem of directly using biomass values ​​with a high dynamic range, such as 0~5000g / m², it leads to... constant term in the formula Too large, for example hour Quantity reached To mask the fundamental flaw of subtle structural differences in the input matrix, the system introduces a grayscale space mapping mechanism before computation. Considering that the residual matrix may contain outliers due to local actual output exceeding theoretical values ​​(i.e., negative residuals), the system performs non-negative field truncation mapping processing, i.e., uniformly sets... The physical meaning of this processing strategy lies in actively filtering out noise from overproduction areas and forcing the algorithm to focus only on underproduction areas, i.e., comparing the spatial structure of positive residuals; the system extracts the global maximum value in the truncated real residual matrix and the theoretical residual matrix. Using the linear normalization formula:

[0018] Map all elements of the two matrices to the standard image grayscale range [0, 255]; based on this standardized data, the system will adjust the dynamic range constant in the formula. The value is reset to 255, and the constant term is calculated based on the general standard of structural similarity theory. The calculation formula is as follows:

[0019] in, To prevent the stability constant from having a denominator of zero, values ​​are taken according to industry standards. Substitute Calculated and This improvement ensures the constant term It will not dominate the numerator and denominator, thus making The indicator can effectively distinguish the structural differences of the residual matrix, avoiding the risk of failure where the calculation result always approaches 1; The value is calculated using a sliding window statistical method, and the final output is a scalar value in the range of [-1, 1]. In response to the matrix structure similarity being greater than the preset similarity threshold, for example, set to 0.6, the system determines that the low-yield area in reality and the simulated congested area highly overlap, determines that the reason for the limited production capacity is spatial layout factors, and performs spatial rearrangement optimization. In response to the matrix structure similarity being less than or equal to the preset similarity threshold, the system determines that the low-yield area in reality and the simulated congested area are inconsistent, indicating that there is environmental background noise that the model has not captured, determines that the reason for the limited production capacity is environmental background factors, and generates a plot improvement suggestion.

[0020] Example 2: The three-dimensional GIS data of forest land includes slope data, aspect data, canopy closure data, and soil humus layer thickness grid data; historical management flow data includes survival rate data of historical planting batches, biomass growth curve data, and time series data of disease occurrence records.

[0021] The system acquires slope and aspect data through airborne lidar or UAV oblique photography. These two data points are used to calculate soil erosion risk and sunshine duration. Given ginseng's biological characteristics of preferring diffused light and being susceptible to waterlogging, this determines the hydrothermal basis of its ideal habitat. The system also extracts canopy closure data, which indicates the degree to which the canopy blocks sunlight and directly determines the light transmittance under the forest canopy. This data is a key input for constructing a photosynthetic potential model. Simultaneously, the system acquires soil humus layer thickness grid data through ground-penetrating radar or interpolation algorithms, characterizing the spatial heterogeneity of soil fertility. Based on this, the system integrates historical operational data, the most crucial of which is the precise historical planting coordinates (x, y) of each ginseng plant, along with the actual yield data of a single plant at the harvest period corresponding to those coordinates, such as fresh weight or dry weight. This data comes from automatic weighing records or manual sampling yield measurement records at the time of harvest and forms the logical basis for subsequent digital twin mapping and point-to-raster conversion. Without this data, it is impossible to construct the actual residual matrix. In addition, the survival rate data of historical planting batches is used to correct for regional mortality risks, the biomass growth curve data provides a baseline rate of ginseng growth over time to train the growth function, and the time series data of disease occurrence records is used to help identify non-layout abnormal fluctuations in subsequent analysis. This embodiment ensures that the ideal habitat carrying capacity model can accurately capture the heterogeneity of the understory microenvironment by limiting specific multidimensional data inputs. In particular, the introduction of two key ecological factors, humus layer thickness and canopy closure, enables the model to distinguish between tiny habitat differences within the forest that are only a few meters apart, providing solid data support for high-precision gridded calculations and thus ensuring the physical realism of subsequent simulations.

[0022] Example 3: Based on forest land 3D GIS data, an ideal habitat carrying capacity model is constructed to generate a forest land ecological niche potential grid map. This includes: establishing a theoretical maximum biomass function for a single ginseng plant based on microenvironmental factors characterizing habitat suitability in the forest land 3D GIS data using a multidimensional regression analysis algorithm; calculating the theoretical maximum output value of each grid unit in the target forest land under a zero-competition interference state based on the theoretical maximum biomass function for a single ginseng plant; and generating a forest land ecological niche potential grid map based on the theoretical maximum output value.

[0023] This embodiment details the construction process of the ideal habitat carrying capacity model. Based on microenvironmental factors characterizing habitat suitability from forest 3D GIS data, the system utilizes a multidimensional regression analysis algorithm to establish the theoretical maximum biomass function for a single ginseng plant. This regression model is built by comprehensively considering habitat suitability indicators such as rainfall, temperature changes, and light transmittance. Prior to this, to eliminate spatial differences in multi-source heterogeneous data, the system performs a multi-layer raster alignment preprocessing step. This resamples slope data from lidar (e.g., 0.5-meter resolution), canopy closure data from remote sensing imagery (e.g., 1-meter resolution), and interpolated soil data to a standard resolution (e.g., 0.5m × 0.5m) using bilinear interpolation, and performs geographic coordinate registration to ensure the accuracy of subsequent input microenvironmental factor vectors. Strictly corresponding to the same physical location; To ensure that the model outputs the theoretical values ​​under non-competitive conditions, the system implements a sparse sample screening strategy during the data preprocessing stage, extracting only independent growth samples with fewer than 3 plants within a 0.5-meter radius of the historical database as the regression training set. It should be noted that although the screening radius is consistent with the neighborhood radius used in the subsequent calculation of competitive pressure, setting less than 3 plants as the screening criterion for no competition is based on the fact that prior statistical tests showed that at this low density, the inhibitory effect of competition among plants on biomass is negligible and masked by environmental noise. Therefore, it can be approximated as an ideal state of no competition, thus solving the compatibility problem between the training sample screening criterion and the subsequent competition simulation logic. The function model uses a second-order polynomial regression form to capture the interaction between environmental factors, as detailed below:

[0024] in, In this embodiment, the total number of input microenvironmental factors is [number]. ; The theoretical maximum biomass of a single ginseng plant under non-competitive conditions is expressed in g and is derived from regression analysis prediction. For the intercept term, , The regression coefficients were obtained by fitting the aforementioned independent growth sample set using the least squares method; furthermore, the regression coefficients... The training sample set needs to cover the microenvironmental characteristics under the cover of different companion plants to ensure that the theoretical maximum biomass function of a single ginseng plant can respond to the changes in the microenvironment regulated by the companion plants. This is the random error term, used to characterize the random disturbance of biomass by other unobserved variables besides the aforementioned microenvironmental factors. In this model, it is assumed that it follows a normal distribution with a mean of 0. ; For the input independent variables in the regression formula, explicitly referring to the preprocessed and encoded feature vector elements, the specific structure and definition are as follows: The slope data is normalized. Northbound index after linear mapping ; Eastward index after linear mapping ; The data represents the normalized canopy closure. This is the normalized soil humus layer thickness data; here, the system presets a slope threshold for determining terrain flatness, for example, set to 5 degrees; When substituting values ​​into the formula for calculation, the values ​​must be those that have undergone trigonometric function encoding and secondary mapping, such as... Instead of the original GIS angle values, this establishes a unique mapping relationship from the original data to the regression variables; The specific processing logic is as follows: Addressing the discontinuity between the 0° and 360° values ​​in the aspect data, the system performs trigonometric function encoding conversion, decomposing it into a northward index. and Eastward Index To ensure the weight balance of the input vector in the regression analysis, the system further linearly maps these two triangular components to the [0,1] interval, i.e. ; Here, the system presets a slope threshold for determining terrain flatness, for example, 5 degrees. When the grid slope is less than this threshold, i.e., flat, the system forces the original value to be reset. After the above linear mapping, the corresponding eigenvalue is 0.5, which is correctly represented in the regression model as a neutral effect without slope offset, avoiding the incorrect south / west orientation caused by directly substituting the zero value. The remaining variables are standardized using Min-Max to map the original GIS values ​​to the [0,1] interval; in this step, to strictly adhere to the data consistency principle of machine learning, the system explicitly limits the extreme value parameters used for standardization. It must originate from the global statistics of the historical training sample set containing at least 5 historical years and different habitat types when the regression function was established, and be solidified into the model metadata; When predicting new target forest land, if the feature value of the new plot exceeds the range of the historical extreme value, the system performs boundary truncation processing, that is, it takes 1 if it is greater than the maximum value and 0 if it is less than the minimum value. The system calls the fixed global extreme value to transform the new data instead of using the local extreme value of the current plot, thereby avoiding model prediction failure caused by data distribution drift. The regression coefficients are obtained by fitting the above independent growth sample set using the least squares method; This is the random error term, used to characterize the random disturbance of biomass by other unobserved variables besides the microenvironmental factors mentioned above. It is usually assumed that it follows a normal distribution with a mean of 0. Furthermore, considering that the second-order polynomial may experience overfitting at the edges of the feature space, leading to negative value overflow, the system adds a non-negativity constraint layer at the function output, i.e. This ensures the validity of the physical meaning of the output; simultaneously, this function The model is clearly anchored to the standard harvesting cycle, such as 5 years old. This means that the model predicts the theoretical maximum dry weight of ginseng when it grows to maturity under the microenvironmental conditions, in order to match the subsequent assessment needs of the final yield. The system divides the target forest into grid cells. For each grid cell, its corresponding microenvironmental factors are substituted into the above function to calculate the theoretical maximum output value of that grid cell under zero competition interference. The calculation formula is as follows:

[0025] in, coordinates The niche potential energy value at a location, in physical terms, represents the energy production limit at that location; To be based on the grid The theoretical maximum biomass of a single plant is calculated by substituting the microenvironmental factors at the location into the above regression function. This is the standard single-plant occupancy coefficient per unit area, which is determined based on the standard dense planting upper limit in the local ginseng planting technical specifications, for example, a value of 25 plants / square meter; In this embodiment, the self-thinning effect correction coefficient is set to a fixed empirical value of 0.65 to ensure a clear source of parameters. This value is not arbitrarily selected, but is based on the fitting calibration of the local ginseng population using Yoda's -3 / 2 self-thinning rule. The specific fitting process is as follows: The system retrieves data from the historical database of mature plots that are in a closed canopy state, i.e., where self-thinning has occurred, and extracts their average biomass per plant. With planting density logarithmic relation data A self-sparse line with a slope close to -1.5 was obtained by fitting the data using linear regression. Calculate the upper limit of standard dense planting The corresponding theoretical average weight per plant on this self-thinning line This is compared with the theoretical maximum biomass of a single plant under non-competitive conditions. To perform a comparison, that is This calculation process is based on statistical regression of locally measured data, rather than directly referencing literature values, thus eliminating model errors caused by regional differences. Its physical meaning represents the density achieved. At that time, the population biomass relative to the ideal maximum value of a single plant The total physical loss ratio; To further alleviate concerns about whether the competition effect is repeatedly modeled in the potential energy diagram and simulation, this embodiment clearly defines... Competing parameters with Example 4 Coupling mechanism: Defined as the baseline decay rate under standard competitive conditions, its value must satisfy... ,in Standard density The theoretical average competitive pressure is calculated as follows: A standard density is constructed... A virtual hexagonal densely planted lattice was used to calculate the cumulative pressure value of the central plant using the competitive pressure formula from Example 4. In this calculation, the age of the virtual plant was uniformly set to the standard harvesting cycle of the ginseng variety, for example, 5 years, to characterize the extreme competitive pressure at maturity. Thus ensuring It represents the expected production capacity under standard planting conditions, rather than the vacuum production capacity without competition; This means that subsequent residual calculations actually measure the degree of deviation of the current layout competitive pressure from the standard layout competitive pressure, thus assigning parameters. A clear reference correction significance, making it consistent with This forms a complementary rather than redundant physical relationship; if this coefficient is ignored, the calculation can be performed directly. This will cause the potential energy map to exhibit a physically unrealizable infinite background value, making the subsequent residual matrix... In Excessive weighting of items masks the structural differences between actual and simulated outputs, leading to... The indicator is not sensitive to layout factors; therefore, this coefficient is a necessary physical constraint to ensure the logical closure of the counterfactual deduction. Based on the theoretical maximum output value, the system maps the values ​​of all grid cells into grayscale images or matrices to generate a forest ecological niche potential grid map, which serves as the baseline map for subsequent simulations. This embodiment quantifies the inherent endowment of forest resources by establishing a theoretical maximum biomass function. The system can identify which areas are high potential energy zones and which are low potential energy zones. This provides an objective and physically clear baseline for subsequent assessment of whether the actual output meets the standards, enabling the niche assessment to shift from qualitative description to quantitative calculation.

[0026] Example 4: Based on preset competition simulation parameters, a virtual planting layout simulation is performed on a forest niche potential energy grid map to generate simulated prediction output data, including: obtaining preset ginseng allelopathy attenuation coefficient, root competition resistance coefficient, and competition sensitivity coefficient; generating virtual ginseng coordinate points on the forest niche potential energy grid map; for each virtual ginseng coordinate point, calculating the distance data and tree age data of other plants in its neighborhood; based on the distance data and tree age data, using the ginseng allelopathy attenuation coefficient, competition sensitivity coefficient, and root competition resistance coefficient for weighted calculation to obtain the cumulative competition pressure value; and performing numerical attenuation processing on the forest niche potential energy grid map based on the cumulative competition pressure value to generate simulated prediction output data.

[0027] This embodiment details the execution logic of virtual planting layout simulation, which is a process of mathematizing biological competition mechanisms. The system acquires preset competition simulation parameters. During this step, in addition to acquiring preset allelopathy attenuation coefficients, root competition resistance coefficients, and competition sensitivity coefficients, the competition sensitivity coefficient must also be acquired simultaneously. The subsequent formula for calculating the biomass per plant is:

[0028] If this coefficient is missing, the calculation will not be able to close the loop, therefore this coefficient is missing. It is an essential component of the preset competitive simulation parameters; These coefficients were determined through a pre-conducted controlled-variable pot experiment: two-plant groups with different distance gradients were set up, and biomass decline curves were fitted to determine the parameters; among them, the allelopathic attenuation coefficient was included. The value is typically between 1.8 and 2.5, representing the power-law decay of secretion concentration with distance; the competition sensitivity coefficient. For example, if the value is 0.2, it is determined through a standard density gradient pressure experiment, that is, planting plants of different densities under a uniform environment and fitting a biomass-pressure negative exponential response curve. This clarified the physical source of the parameter as the core parameter for numerical decay processing. In this step, to ensure the closed-loop consistency between the simulation logic and the aforementioned potential energy diagram construction logic, the system performs parameter co-verification: the parameters to be measured... Substitute the values ​​into the standard density model to calculate the expected attenuation ratio, and confirm that it matches the ratio set in Example 3. The values ​​match; if there is a significant deviation, such as the calculated values... If the relative error between the value and the preset value exceeds 5%, then adjustment will be prioritized. Value, i.e., update settings , to match The dynamic calibration mechanism, which represents the true competitive intensity, ensures that the virtual simulation is performed on a standard water level determined by the potential energy diagram, and solves the problem of ambiguity in the physical meaning of the model caused by the fragmentation of parameter definitions. Regarding the root system competing impedance coefficient To address the issue of unifying physical dimensions, the system defines it as a dimensionless scalar, and the calculation formula is as follows:

[0029] in, The measured root dry weight density is in g / cm³. It should be noted that, for the virtual simulation scenario in this embodiment, since the virtual plant does not have a physical entity for real-time measurement, the system forcibly sets this value. The value is the arithmetic mean of the root density of the same ginseng variety at maturity in historical operating data, such as the statistical mean. Thus Established as a fixed global parameter in the simulation operator, this ensures the solvability of the equations and the statistical significance of the simulation results; while As a standard reference density, this parameter is set as a fixed constant, for example, 0.8 g / cm³, which is determined according to the average dry density of the lignified root system of this ginseng variety at maturity. As a normalization benchmark, it does not change with individual plants, thereby ensuring the pure numerical characteristics of subsequent exponential calculations. A set of virtual ginseng coordinate points is generated on the forest ecological niche potential grid map. This step involves performing digital twin mapping, that is, directly reading the actual spatial coordinates (x, y) of each ginseng plant recorded in historical management data and mapping them one by one onto the virtual grid. This ensures that the spatial layout of the virtual simulation is completely consistent with the planting distribution in the real physical world. This is the basis for subsequent steps. The logical premise for structural similarity comparison; and for each target plant in the set, the cumulative competitive pressure value it experiences is calculated using the following formula:

[0030] in, The cumulative competitive stress value experienced by the target plant, in units of biological stress. With the target plant as the center and radius... The collection of neighboring plants within, among which The effective radius for root interaction is set based on the growth characteristics of ginseng roots, for example, a value of 0.5 meters; It is the attenuation coefficient of ginseng allelopathic effect; This is the root system competition impedance coefficient; The normalized tree age factor is dimensionless. When generating virtual ginseng coordinate points, the system introduces spatial pattern constraints of the associated plant layer, sets the ginseng planting hole to be located 1-2 meters to the side of the rows or clumps of associated plants, and performs coordinate verification to avoid the drip line of larch trunks; The formula for calculating the normalized tree age factor is:

[0031] in, The actual age of the neighboring plants. The standard mature harvesting age for this ginseng variety, for example, 5 years, is used to standardize the competitiveness of different growth stages; The normalized distance factor is dimensionless and is calculated using the following formula:

[0032] in, The Euclidean distance between the target plant and its neighboring plants is given. The recommended plant spacing in standard planting technical specifications, such as 0.2 meters, is used to quantify the degree of space overcrowding; The system performs a mapping calculation from discrete plants to a continuous grid based on cumulative competitive pressure values ​​to generate simulated predicted output data; specific steps include: single-plant biomass calculation: for each virtual ginseng coordinate point Retrieve the theoretical maximum biomass of a single plant at its corresponding coordinates. That is, extract coordinate points The microenvironmental factor values ​​were substituted into the regression function of Example 3 and combined with the calculated competitive pressure values. Calculate the simulated biomass of this plant under competitive conditions. The calculation formula is as follows:

[0033] in, Target plants Simulated biomass under competitive conditions, in grams; The theoretical maximum biomass per plant at this location is derived from Example 3; Competition sensitivity coefficient, unit: biological stress unit , characterizing the negative exponential response rate of biomass to competitive pressure; The cumulative competitive pressure value calculated above; rasterization aggregation and density normalization: initialize a zero matrix with the same resolution as the forest niche potential grid map. ; Traverse all virtual plants and determine their coordinates Determine its grid cell The simulated biomass of this plant The value is added to the value of that grid cell and divided by the physical area of ​​the grid cell. The calculation formula is as follows:

[0034] in, The actual physical area of ​​a single grid cell, for example ; Execute by The operation is crucial; it converts the accumulated absolute mass into biomass density, thus ensuring the accuracy of the simulated output data. Forest ecological niche potential grid map Strict consistency in physical dimensions avoids dimensional mismatch errors in subsequent residual calculations; the final matrix... This refers to the simulated and predicted output data, which physically represents the biomass density per unit area after considering spatial competition. This processing ensures strict alignment between the simulation data and the rasterized GIS data in terms of data structure and physical dimensions.

[0035] Example 5: Based on historical management flow data and forest niche potential energy grid map, a real residual spatial distribution matrix is ​​generated, including: extracting actual output values ​​from historical management flow data; calculating the first difference between the theoretical maximum output value and the actual output value at the corresponding coordinate point in the forest niche potential energy grid map; and constructing the real residual spatial distribution matrix based on the spatial distribution data of the first difference.

[0036] Based on the simulated predicted output data and the forest niche potential energy grid map, a theoretical residual spatial distribution matrix is ​​generated, including: extracting the simulated output values ​​from the simulated predicted output data; calculating the second difference between the theoretical maximum output value and the simulated output value at the corresponding coordinate point in the forest niche potential energy grid map; and constructing the theoretical residual spatial distribution matrix based on the spatial distribution data of the second difference. This embodiment describes the generation process of two key residual matrices, which are the core basis for diagnosis. Regarding the construction of the real-world residual spatial distribution matrix, the system extracts actual output values ​​from historical management flow data. Since historical flow data is typically stored as discrete plant coordinate points, while the potential energy map is continuous grid data, the system performs a point-to-grid mapping operation here: creating a zero matrix with the same resolution as the forest niche potential energy grid map; for each grid cell, retrieving all historical plant records falling within the spatial range of that cell, summing their corresponding actual biomass values, and dividing by the area of ​​the grid cell. This is converted to density values, thereby generating a continuous actual output grid. In other words, the actual output grid data defined in this embodiment is used as the subtrahend term to ensure that subsequent matrix subtraction operations are performed in terms of spatial dimension and physical dimensions. Strict alignment with the above; calculate the difference between it and the potential energy diagram, using the following formula:

[0037] in, These are the actual residual values, which physically represent the total capacity gap; it should be noted that the matrix generated here... It contains the original positive and negative floating-point values, where positive values ​​represent reduced production and negative values ​​represent overproduction, meaning the actual output exceeds the theoretical potential energy; Example 1's non-negative field truncation is performed when reading this matrix. The preprocessing steps during the calculation do not change the original matrix data structure generated in this step, so as to preserve the complete output difference information; This represents the theoretical maximum output value, derived from the potential energy diagram. The actual output value is derived from the aforementioned rasterized historical data. Based on the spatial distribution of this difference, the system constructs a real residual spatial distribution matrix. The high-value regions in this matrix represent areas where the actual output is far lower than the theoretical upper limit. Simultaneously, for the construction of the theoretical residual spatial distribution matrix, the system extracts the simulated output values ​​from the simulated predicted output data and calculates the difference between them and the potential energy map, as shown in the following formula:

[0038] in, This represents the theoretical residual value, with the physical meaning being the gap caused by layout factors; The numerical values ​​generated are from the simulation and originate from virtual simulation. Based on the spatial distribution of this difference, the system constructs a theoretical residual spatial distribution matrix. The high-value regions in this matrix represent the areas where production is reduced due to overcrowding in the theoretical simulation. This embodiment transforms the abstract production problem into a visualized image problem by constructing two sets of residual matrices: a real-world matrix and a theoretical matrix. The real-world matrix includes the combined effects of all unknown factors, while the theoretical matrix only includes the effects of layout factors. This differentiated matrix construction lays a solid data foundation for subsequent pattern matching using image similarity algorithms, achieving a visualized dimensionality reduction of the problem.

[0039] Example 6: When the matrix structure similarity is greater than the preset similarity threshold, the reason for the limited productivity of the target forest land is determined to be spatial layout factors, and spatial rearrangement optimization is performed, including: adjusting the virtual coordinate positions in the virtual planting layout simulation; recalculating the adjusted theoretical residual spatial distribution matrix; outputting the corresponding virtual coordinate positions as the optimal layout scheme when the sum of the absolute values ​​of all elements in the adjusted theoretical residual spatial distribution matrix is ​​minimized; the optimal layout scheme adopts a row or cluster configuration mode, and by adjusting the row spacing of the associated plants or the center distance of the clusters to 3-8 meters, the ginseng plants are placed in a microenvironment gradient with moderate light and humidity, so as to achieve synergistic improvement of ginseng quality and economic output of associated plants.

[0040] This embodiment details the spatial rearrangement optimization strategy executed after confirming that layout factors are causing low productivity; in response to the matrix structure similarity being greater than a preset threshold, indicating that the current low productivity is mainly caused by unreasonable spatial layout, the system immediately starts an iterative optimization process, which specifically uses the simulated annealing algorithm to avoid getting trapped in local optima; The system initializes the virtual coordinate positions in the virtual planting layout simulation and generates an initial layout scheme. The initial plan It directly inherits from the set of real coordinates generated based on historical data in Example 4, ensuring that the optimization path starts from the current actual problem state, rather than a random cold start; And set the initial temperature and cooling rate ; where the initial temperature The setting is based on the initial total residual value of the system. Multiples of, for example To ensure that the algorithm has enough energy to escape local optima in the early stages, and to reduce the cooling rate. This is set to an empirical constant between 0.95 and 0.99 to control the convergence rate; in each iteration, a random perturbation vector is applied to the coordinates in the set. Generate new solutions Disturbance Follows uniform distribution ,in This represents the maximum single movement step size, for example, 0.1 meters; In this process, boundary constraints and anti-overlap mechanisms are introduced. If the perturbed coordinates exceed the forest GIS boundary, boundary reflection processing is performed to restrict them to the effective area. If the distance between the new coordinates and the existing coordinates is less than the minimum survival threshold, such as 5cm, the perturbation is discarded and regenerated to ensure the physical feasibility of the layout. For the new layout scheme, the system re-executes the simulation steps and calculates the new theoretical residual spatial distribution matrix. At the same time, the optimization objective function is defined as the sum of the theoretical residuals across the entire field, as shown in the following formula:

[0041] in, The total theoretical production capacity loss is represented by the physical objective of optimization. Traverse the effective planting area The area Defined as a forest ecological niche potential grid map The set of all grid cells, excluding non-planted areas and invalid background areas; it should be noted that... and All are based on the same area density dimension, biomass / area. The difference between the two represents the yield loss per unit grid area. The summation over all grids is the total loss of the entire field, thus ensuring the dimensional consistency of the objective function calculation. To simulate output under the new layout scheme, the specific operation is based on the adjusted coordinate set. Recalculate the interplant distance and cumulative competitive pressure value The output distribution obtained after performing the gridded polymerization step of Example 4 again; Calculate the change in the objective function ;like If so, accept the new proposal directly; if Then, based on probability Accept the new plan; execute the cooling operation. The system searches for the set of coordinates that minimizes the objective function by repeating the above iterative process, and reaches a convergence condition, such as when the temperature is below a threshold. If the objective function value does not change significantly after N consecutive iterations, output the corresponding virtual coordinate position as the optimal layout scheme. It is worth noting that, due to the sum of the potential energy diagrams It is a constant determined by the environment, therefore minimizing the total theoretical residual is the key. Mathematically, this is equivalent to maximizing the total simulated output under the constraint of a constant number of plants. This optimization mechanism essentially drives virtual plants to migrate from low-potential or highly crowded areas to high-potential sparse areas, thereby achieving the optimal match between environmental carrying capacity and biological density.

[0042] Example 7: When the structural similarity is less than or equal to the preset similarity threshold, the reason for the limited productivity of the target forest land is determined to be environmental background factors, and land improvement prompts are generated, including: marking the target forest land as an abnormal land plot; generating land improvement prompts containing soil testing suggestions or disease investigation instructions; the integrated management instructions generated by the system for abnormal land plots also include: moderately thinning or pruning larch trees to adjust the top light, and shaping and pruning associated plants to maintain the ideal crown shape, thereby repairing the niche defect through biological intervention.

[0043] This embodiment illustrates the processing logic when the cause of low productivity is determined to be an environmental background factor; in response to the matrix structure similarity being less than or equal to a preset similarity threshold, it means that the actual low productivity distribution does not match the simulated crowded distribution, and the system determines that there is a hidden environmental constraint. The system marks areas with high values ​​in the actual residual matrix but low values ​​in the theoretical residual matrix as abnormal plots; the system calls the preset expert knowledge base to generate plot improvement suggestions; for example, in response to the area being located in a low-lying area, combined with GIS slope data, it generates instructions to check for root rot or water accumulation; in response to the area having no obvious topographic defects, it generates instructions to conduct soil heavy metal or trace element testing. This embodiment avoids forced optimization of the algorithm for non-layout problems; when the root cause of the problem is not in the layout, the system can intelligently stop the loss and no longer waste computing power to calculate coordinates. Instead, it directly guides human intervention to solve the underlying environmental problems. This hierarchical diagnosis mechanism reflects the system's deep understanding and adaptability to complex agricultural scenarios, ensuring the pertinence and effectiveness of the solution.

[0044] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for intelligent optimization of the ecological niche spatial layout of ginseng under forest cover, characterized in that, The method includes: acquiring three-dimensional GIS data of the target forest land and historical management flow data; Based on the aforementioned forest land 3D GIS data, an ideal habitat capacity model is constructed to generate a forest land ecological niche potential grid map; Based on preset competitive simulation parameters, a virtual planting layout simulation is performed on the forest ecological niche potential energy grid map to generate simulated predicted output data. Based on the historical operational flow data and the forest ecological niche potential energy grid map, a real residual spatial distribution matrix is ​​generated; Based on the simulated and predicted output data and the forest ecological niche potential energy grid map, a theoretical residual spatial distribution matrix is ​​generated; The actual residual space distribution matrix and the theoretical residual space distribution matrix are subjected to non-negative domain truncation mapping and gray-scale space normalization; the matrix structure similarity between the normalized actual residual space distribution matrix and the theoretical residual space distribution matrix is ​​calculated. If the similarity of the matrix structure is greater than a preset similarity threshold, the reason for the limited productivity of the target forest land is determined to be spatial layout factors, and spatial rearrangement optimization is performed. If the similarity of the matrix structure is less than or equal to the preset similarity threshold, the reason for the limited productivity of the target forest land is determined to be environmental background factors, and a land improvement suggestion is generated.

2. The intelligent optimization method for the ecological niche spatial layout of ginseng under forest cover according to claim 1, characterized in that, The forest land 3D GIS data includes slope data, aspect data, canopy closure data, and soil humus layer thickness grid data; The historical operational data includes survival rate data, biomass growth curve data, and time series data of disease occurrence records for historical planting batches.

3. The intelligent optimization method for the ecological niche spatial layout of ginseng under forest cover according to claim 1, characterized in that, The step of constructing an ideal habitat capacity model based on the forest land 3D GIS data to generate a forest land ecological niche potential grid map includes: establishing a theoretical maximum biomass function for a single ginseng plant based on the microenvironmental factors characterizing habitat suitability in the forest land 3D GIS data and using a multidimensional regression analysis algorithm. Based on the theoretical maximum biomass function of a single ginseng plant, the theoretical maximum output value of each grid cell in the target forest land under the state of zero competition interference is calculated; Based on the theoretical maximum output value, the forest ecological niche potential energy grid map is generated.

4. The intelligent optimization method for the ecological niche spatial layout of ginseng under forest cover according to claim 1, characterized in that, The virtual planting layout simulation is performed on the forest ecological niche potential energy grid map based on preset competition simulation parameters to generate simulation prediction output data, including: obtaining preset ginseng allelopathy attenuation coefficient, root system competition resistance coefficient and competition sensitivity coefficient; Virtual ginseng coordinate points are generated on the forest ecological niche potential grid map; for each virtual ginseng coordinate point, the distance data and tree age data of other plants in its neighborhood are calculated; Based on the distance data and the tree age data, the cumulative competitive pressure value is obtained by weighting the ginseng allelopathy attenuation coefficient, the competition sensitivity coefficient, and the root competition resistance coefficient. The forest niche potential energy grid map is numerically attenuated based on the accumulated competitive pressure value to generate the simulation prediction output data.

5. The intelligent optimization method for the ecological niche spatial layout of ginseng under forest cover according to claim 1, characterized in that, The step of generating a real residual spatial distribution matrix based on the historical management flow data and the forest niche potential energy grid map includes: extracting the actual output value from the historical management flow data; calculating the first difference between the theoretical maximum output value and the actual output value at the corresponding coordinate point in the forest niche potential energy grid map; and constructing the real residual spatial distribution matrix based on the spatial distribution data of the first difference.

6. The intelligent optimization method for the ecological niche spatial layout of ginseng under forest cover according to claim 1, characterized in that, The step of generating a theoretical residual spatial distribution matrix based on the simulated predicted output data and the forest niche potential energy grid map includes: extracting the simulated output values ​​from the simulated predicted output data; calculating the second difference between the theoretical maximum output value and the simulated output value at the corresponding coordinate point in the forest niche potential energy grid map; and constructing the theoretical residual spatial distribution matrix based on the spatial distribution data of the second difference.

7. The intelligent optimization method for the ecological niche spatial layout of ginseng under forest cover according to claim 1, characterized in that, When the similarity of the matrix structure is greater than a preset similarity threshold, the reason for the limited productivity of the target forest is determined to be spatial layout factors, and spatial rearrangement optimization is performed, including: adjusting the virtual coordinate positions in the virtual planting layout simulation; recalculating the adjusted theoretical residual spatial distribution matrix; and outputting the corresponding virtual coordinate positions as the optimal layout scheme when the sum of the absolute values ​​of all elements of the adjusted theoretical residual spatial distribution matrix is ​​minimized.

8. The intelligent optimization method for the ecological niche spatial layout of ginseng under forest cover according to claim 1, characterized in that, When the structural similarity is less than or equal to the preset similarity threshold, the reason for the limited productivity of the target forest land is determined to be environmental background factors, and a land improvement suggestion is generated, including: marking the target forest land as an abnormal land plot; generating the land improvement suggestion containing soil testing suggestions or disease investigation instructions.

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