Method and equipment for evaluating adaptability of ecological restoration of karst plants

Abstract

The invention discloses an adaptability evaluation method and device for ecological restoration of karst plants, and belongs to the technical field of resource planning, and the method comprises the steps: inputting leaf parameters and root system parameters into a data table, carrying out the normality test of all indexes, and selecting a corresponding standardization method for data conversion according to the types of the indexes; based on the standardized data, identifying index correlation through principal component analysis, constructing a judgment matrix, calculating a feature value and a feature vector, and determining a weight coefficient of each evaluation index; calculating a comprehensive evaluation index of the sample plant by using the weight coefficient and the standardized data, dividing grades according to quartile, drawing a spatial distribution diagram and analyzing distribution characteristics; and establishing a multi-constraint integer programming model according to the evaluation grade and the spatial distribution characteristics so as to solve and determine an optimal configuration scheme through an algorithm. Through the scheme of the invention, a multi-dimensional evaluation system of ecological restoration resources can be constructed.

Description

Technical Field

[0001] This application relates to the field of resource planning, and particularly to a method and device for adaptive assessment of karst plant ecological restoration. Background Art

[0002] With the increasing prominence of global ecological environment problems, ecological restoration technologies play an important role in environmental protection and sustainable utilization of resources. Among them, due to the special geological and geomorphic conditions and fragile ecosystems in karst areas, higher requirements are imposed on ecological restoration. Karst plant ecological restoration is a technical means to improve the ecological environment quality by artificially promoting vegetation restoration. This technology can not only effectively control soil erosion, but also increase biodiversity and enhance the stability and sustainability of the ecosystem. Currently, in the engineering practice of karst plant ecological restoration, the technical methods for evaluating the restoration effect mainly include vegetation restoration degree assessment technology, vegetation coverage assessment technology, biomass assessment technology, and plant diversity index assessment technology, etc. However, these existing technologies have obvious technical defects: the vegetation restoration degree assessment technology overly relies on the selection of reference communities, resulting in the objectivity and accuracy of the assessment results being affected, and this technology cannot comprehensively reflect the growth and development status of plant individuals; the vegetation coverage assessment technology only focuses on the ratio of the horizontal projection area of vegetation to the plot area, ignoring the vertical structure characteristics of the plant community and the growth of the underground part, making the assessment results one-sided and incomplete; although the biomass assessment technology can evaluate the growth status by measuring the dry weight of plants, this technology requires a destructive sampling method, which not only affects the normal growth of plants, but also cannot reflect the dynamic interaction relationship between plants and the environment; the plant diversity index assessment technology uses mathematical models such as the Shannon-Wiener index to calculate species richness and evenness, but this technology only stays at the assessment of the overall community level and fails to reflect the adaptive differences of different plant individuals to the environment.

[0003] In response to the above technical defects, researchers in related technical fields have disclosed a variety of improvement plans. For example, some researchers have developed a dynamic monitoring system for vegetation coverage based on remote sensing technology, which obtains vegetation coverage information through high-resolution satellite image data and realizes the rapid evaluation of vegetation restoration effects in large areas. At the same time, some researchers have disclosed a comprehensive evaluation method that combines plant physiological and ecological indicators. By measuring chlorophyll fluorescence parameters, photosynthetic characteristics, water use efficiency and other indicators, the growth status and environmental adaptability of plants are more comprehensively evaluated. In addition, some researchers have used root morphology analysis technology to observe and record the distribution characteristics and growth status of the roots, making up for the shortcomings of traditional evaluation methods that ignore the underground part. However, these improved technologies still have some technical problems: first, remote sensing monitoring technology is affected by weather conditions and terrain factors. Under the complex terrain conditions of karst areas, the accuracy and continuity of data acquisition are difficult to guarantee; second, the determination process of physiological and ecological indicators is complicated, requiring professional instruments and technical personnel, and it is difficult to promote and apply them in large-scale ecological restoration projects; third, although the root morphology analysis technology can reflect the growth status of the underground part, it still requires local destructive sampling, and it is difficult to achieve dynamic monitoring of root functions; finally, these improved technologies are often independent of each other, lack an effective integration mechanism, and it is difficult to form a complete evaluation system. Therefore, how to establish a technical method that can comprehensively, accurately and efficiently evaluate the adaptability of karst plant ecological restoration is still a technical problem that needs to be solved in this field.

[0004] Therefore, there is an urgent need for a technical solution that can build a multi-dimensional evaluation system for ecological restoration resources. Summary of the invention

[0005] In order to solve the deficiencies of the prior art, the present application discloses an adaptability assessment method and device for karst plant ecological restoration. The present application solves the technical problems that the prior art cannot reflect the dynamic interaction relationship between plants and the environment.

[0006] An embodiment of this application discloses an adaptability evaluation method for the ecological restoration of karst plants, including: dividing the karst plot into several standard quadrats by using the grid method, numbering the quadrats in row and column order and setting vertex markers, measuring and recording the number information of all woody plants with a specified height in the quadrats; for the numbered plants, collecting leaf samples at different stages during their growing seasons and measuring leaf parameters, and at the same time digging the soil profile around the plants to obtain root samples and measuring root parameters; entering the leaf parameters and root parameters into a data table, after performing a normality test on each index, selecting the corresponding standardization method for data conversion according to the index type; based on the standardized data, identifying the index correlation through principal component analysis, constructing a judgment matrix and calculating the eigenvalues and eigenvectors to determine the weight coefficients of each evaluation index; calculating the comprehensive evaluation index of the sample plants by using the weight coefficients and the standardized data, dividing the grades according to the quartiles, drawing a spatial distribution map and analyzing the distribution characteristics; according to the evaluation grades and spatial distribution characteristics, establishing a multi-constraint integer programming model to determine the optimal configuration plan through algorithm solving.

[0007] In a possible implementation manner, where dividing the karst plot into several standard quadrats by using the grid method, numbering the quadrats in row and column order and setting vertex markers, and measuring and recording the number information of all woody plants with a specified height in the quadrats includes: dividing the karst plot into several standard quadrats of unequal sizes by using the grid method, and setting positioning markers at the four vertices of each quadrat; numbering the standard quadrats in row and column order, and recording the unique number of each quadrat in the form of row number plus column number; measuring all woody plants exceeding the specified height in the numbered quadrats, and numbering the plants in the form of quadrat number plus plant serial number.

[0008] In a possible implementation manner, where for the numbered plants, collecting leaf samples at different stages during their growing seasons and measuring leaf parameters, and at the same time digging the soil profile around the plants to obtain root samples and measuring root parameters includes: taking leaf samples of the numbered plants at the beginning, middle and end of the plant growing season respectively; selecting multiple well-grown mature leaves from the four directions of the southeast, northwest of the plant canopy; measuring the area of the leaf samples with a leaf area meter and measuring the fresh weight with an electronic balance; putting the weighed leaves into an oven for drying treatment, and immediately weighing the dry weight after the weight is constant; calculating the specific leaf area and leaf dry matter content according to the leaf area and dry weight data; digging the soil profile to obtain root samples by extending a specified range outward with the plant trunk as the center; measuring the main root diameter, the number of branched roots and the length parameters after cleaning the soil attached to the root samples.

[0009] In a possible implementation, leaf parameters and root system parameters are entered into a data table, and after normality test is performed on each index, a corresponding standardization method is selected according to the index type for data conversion, including: entering leaf parameters and root system parameters into a data table containing sample number and / or sampling date fields; performing normality test on each index in the data table to identify the index with an optimal interval; standardizing the optimal interval index using the fuzzy mathematics method of membership function; and converting the remaining indexes in the data table by selecting the corresponding maximum value standardization formula according to their properties.

[0010] In a possible implementation, based on standardized data, the correlation of indicators is identified through principal component analysis, a judgment matrix is ​​constructed and the eigenvalues ​​and eigenvectors are calculated, and the weight coefficient of each evaluation indicator is determined, including: identifying the correlation between standardized indicators through principal component analysis, and merging highly correlated indicators; setting importance levels for the merged evaluation indicators, and performing pairwise comparisons to construct a judgment matrix; calculating the maximum eigenvalue of the judgment matrix and its corresponding eigenvector; normalizing the eigenvector to obtain the weight coefficient of each indicator; and calculating the consistency index and ratio based on the eigenvalue to verify the reliability of the weight distribution.

[0011] In a possible implementation, a comprehensive evaluation index of sample plants is calculated using weight coefficients and standardized data, and the grades are divided according to quartiles. A spatial distribution map is drawn and the distribution characteristics are analyzed, including: multiplying the standardized index value with the corresponding weight coefficient and summing them to obtain a comprehensive evaluation index; statistically calculating the quartiles of the evaluation index and establishing a rating standard; dividing the sample plants into four adaptability grades according to the rating standard; drawing a schematic diagram of a standard sample plot and marking the spatial position of plants of each grade; and calculating the neighbor distance and aggregation spatial distribution characteristics of plants of each grade.

[0012] In a possible implementation, a multi-constraint integer programming model is established based on the evaluation level and spatial distribution characteristics to determine the optimal configuration plan through algorithm solution, including: statistically analyzing the plant species composition and distribution ratio of each adaptability level; establishing a planning model based on species distribution with the goal of maximizing the comprehensive adaptability index; setting constraints on planting area, species quantity, and cost in the planning model; using a target algorithm to solve the planning model to obtain a species configuration plan; including: performing spatial analysis on the configuration plan, calculating the spatial distribution characteristics of plants, and constructing a multi-level optimization model based on the spatial distribution characteristics to determine the planting parameters.

[0013] In one possible implementation, spatial analysis is performed on the configuration plan, the spatial distribution characteristics of plants are calculated, and a multi-level optimization model is constructed based on the results of the spatial distribution characteristics to determine planting parameters, including: importing the collected plant morphological index data into a database, and dividing plants into different functional groups by using the dynamic tree cutting method based on the Mahalanobis distance of the morphological indexes; for the plants within the functional groups, each functional group is further divided into different adaptive subgroups by using the density peak clustering method according to the comprehensive evaluation index; the density function of the distance between plants in the adaptive subgroups is calculated by using the bandwidth kernel function, and the confidence interval is generated by the point pattern randomization method to determine the spatial association characteristics; the morphological indexes are standardized, the functional difference degree between any two species in the adaptive subgroups is calculated, and the functional difference degree index at the community level is obtained; according to the spatial association characteristics, the topographic position index is calculated based on the digital elevation model, and the sample plot is divided into topographic adaptation units by combining the slope, aspect and relative elevation parameters; within the topographic adaptation units, an adaptation function is constructed according to the functional difference degree index, and the adaptive bandwidth based on the topographic complexity is calculated by using the kernel function; based on the adaptation function and the adaptive bandwidth, a comprehensive optimization model including a species selection matrix and a planting density matrix is established, and hydrogeological constraints, slope constraints, forest gap constraints and root competition constraints are set; for the comprehensive optimization model, a tabu search algorithm with a composite neighborhood structure is used for solution, and the candidate solutions are evaluated by the probability domination sorting method.

[0014] The embodiment of the present application also discloses an adaptability evaluation device for karst plant ecological restoration, including: a processor, a memory, and a system bus; wherein, the processor and the memory are connected through the system bus; the memory is used to store one or more programs, and the one or more programs include instructions, and when the instructions are executed by the processor, the processor executes the method described in any one of the above embodiments.

[0015] In the adaptability evaluation method and device for karst plant ecological restoration disclosed above, in the embodiment of the present application, by introducing a multi-dimensional morphological index evaluation system, the leaf characteristics (leaf area, specific leaf area, leaf dry matter content) are combined with the root characteristics (main root diameter, number of branched roots, branched root length), overcoming the limitation of the existing method in the incomplete description of plant phenotypic characteristics. Especially in the aspect of root evaluation, a non-destructive directional sampling method is adopted, and by setting a reasonable sampling range and depth, the representativeness of the data is ensured while reducing the impact on plant growth. At the data processing level, a fuzzy mathematics method based on the membership function is used for standardization, which is different from the simple linear normalization of the traditional method, and this method can better handle the situation where the index values have an optimal interval. At the same time, through the two-level clustering analysis method, the morphological characteristics of plants are organically combined with the adaptability evaluation, breaking through the limitation of the traditional method of separating the morphological indexes for processing. Description of the Drawings

[0016] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 It is a schematic flowchart of an adaptability evaluation method for karst plant ecological restoration disclosed in an embodiment of the present application;

[0018] Figure 2 It is a target decision frontier curve graph disclosed in an embodiment of the present application;

[0019] Figure 3 It is an analysis graph of a multi-distance spatial clustering function disclosed in an embodiment of the present application;

[0020] Figure 4 It is an effect diagram of an optimization scheme disclosed in an embodiment of the present application. Detailed implementation manners

[0021] Now, various exemplary embodiments of the present disclosure will be described in detail with reference to the drawings. It should be noted that: Unless otherwise specifically stated, the relative arrangements, numerical expressions, and numerical values of the components and steps set forth in these embodiments do not limit the scope of the present disclosure.

[0022] Those skilled in the art can understand that the terms "first", "second", etc. in the embodiments of the present disclosure are only used to distinguish different steps, devices, or modules, etc., and neither represent any specific technical meaning nor indicate an inevitable logical order between them. It should also be understood that in the embodiments of the present disclosure, "a plurality" may refer to two or more, and "at least one" may refer to one, two, or more. It should also be understood that for any component, data, or structure mentioned in the embodiments of the present disclosure, unless clearly defined or given a contrary indication in the context, it can generally be understood as one or more. In addition, the term "and / or" in the present disclosure is only a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present disclosure generally represents an "or" relationship between the associated objects before and after. It should also be understood that the present disclosure emphasizes the differences between various embodiments, and the same or similar parts can be referred to each other. For the sake of brevity, they will not be described one by one.

[0023] Meanwhile, it should be understood that, for the convenience of description, the sizes of the various parts shown in the drawings are not drawn in actual proportional relationship. The following description of at least one exemplary embodiment is actually only illustrative and in no way limits the present disclosure, its application or use. Technologies, methods and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the said technologies, methods and devices should be regarded as part of the specification. It should be noted that: like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0024] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Apparently, the described embodiments are some but not all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the scope of protection of the present application.

[0025] Figure 1 It is a schematic flowchart of a method for evaluating the adaptability of karst plant ecological restoration disclosed in the embodiments of the present application.

[0026] It should be understood that currently, the conventional ecological restoration forestry methods mainly include the vegetation restoration degree method, the vegetation coverage method, the biomass method, the plant diversity index method, etc. The vegetation restoration degree method evaluates the restoration effect by calculating the ratio of the number of plant species in the sample plot to the reference community, but this method overly relies on the selection of the reference community and cannot reflect the growth status of individual plants. The vegetation coverage method only considers the percentage of the horizontal projection area of the vegetation in the sample plot area, ignoring the vertical structure and the growth of the underground part. Although the biomass method evaluates the growth status by measuring the dry weight of plants, it often requires destructive sampling and fails to reflect the interaction between plants and the environment. The plant diversity index method uses the Shannon-Wiener index, etc. to measure species richness and evenness, but this method only focuses on the overall community level and fails to reflect the adaptability differences at the individual level.

[0027] In the embodiments of the present application, a multi-level evaluation system is constructed to systematically associate plant morphological characteristics, spatial distribution characteristics with topographic units. This method first adopts a grid sampling method, flexibly divides the quadrat size according to vegetation density and topographic characteristics to ensure the representativeness of sampling. During the sampling process, by seasonally collecting leaf and root data, multi-dimensional morphological indicators including leaf area, specific leaf area, leaf dry matter content, main root diameter, number of branched roots, length of branched roots, etc. are obtained. The fuzzy mathematics method based on the membership function and principal component analysis are used to standardize these index data, effectively solving the comparability problem of indicators with different dimensions.

[0028] In terms of constructing the evaluation model, the embodiments of the present application introduce an improved two-level clustering analysis method. The first level divides functional groups based on the Mahalanobis distance of morphological indicators, and the second level subdivides adaptive subgroups through density peak clustering. At the same time, an improved O-ring statistical method is used to analyze the spatial distribution characteristics of plants, and a confidence interval is constructed through the point pattern randomization method to reveal the spatial association patterns at different scales. On this basis, a multi-level optimization model including community-level optimization and adaptive optimization is established. Community-level optimization measures the complementarity of ecosystem functions through the functional difference index, and adaptive optimization is based on the improved topographic adaptation unit method, incorporating factors such as topographic position index, slope, aspect, and relative elevation into the evaluation system. During the optimization solution process, an improved tabu search algorithm is adopted, a composite neighborhood structure including species replacement, density adjustment, and position fine-tuning is constructed, and the performance of the algorithm is improved by dynamically adjusting the length of the tabu list.

[0029] In terms of hydrogeological constraints, the plots are divided into different hydrogeological regions according to the karst development degree, fracture density, and soil layer thickness, and the drought tolerance of plants is matched accordingly. In terms of slope constraints, the root-shoot ratio index requirements are clearly specified for steep slope areas. In terms of planting layout, by setting forest gap constraints and root competition constraints, the reasonable spacing between plant individuals is ensured.

[0030] As Figure 1 shown, at step S101, the karst plot is divided into several standard quadrats by the grid method, the quadrats are numbered in row-column order and vertex markers are set, and the number information of all woody plants with a specified height within the quadrats is measured and recorded. Among them, it includes: dividing the karst plot into several standard quadrats of different sizes by the grid method, setting positioning markers at the four vertices of each quadrat; numbering the standard quadrats in row-column order, and recording the unique number of each quadrat in the form of row number plus column number; measuring all woody plants exceeding the specified height within the numbered quadrats, and numbering the plants in the form of quadrat number plus plant serial number.

[0031] For example, first, the karst plots are divided into grids. According to the vegetation density and topographic features, the entire area is flexibly divided into quadrats ranging from 10 m × 10 m to 25 m × 25 m to ensure that each quadrat has an appropriate survey volume. Positioning markers are set at the four vertices of each quadrat. All quadrats are numbered in the form of "row number - column number", such as "A01 - B01" representing the quadrat in the first row and the first column. Within each quadrat, the woody plants with a height exceeding 2.3 m are numbered in the form of "quadrat number - plant serial number", such as "A01B01 - 001" representing the first plant in the A01 - B01 quadrat.

[0032] At step S102, for the numbered plants, leaf samples are collected in stages during their growing seasons and leaf parameters are measured. At the same time, soil profiles around the plants are dug to obtain root samples and root parameters are measured. Specifically, it includes: taking leaf samples from the numbered plants at the beginning, middle, and end of the plant growing season; selecting multiple well - grown mature leaves from the four directions of the southeast, southwest, northeast, and northwest of the plant canopy; using a leaf area meter to measure the area of the leaf samples, and using an electronic balance to measure the fresh weight; putting the weighed leaves into an oven for drying treatment, and immediately weighing the dry weight after the weight is constant; calculating the specific leaf area and leaf dry matter content based on the leaf area and dry weight data; digging a soil profile to obtain root samples by extending a specified range outward from the main trunk of the plant; cleaning the soil attached to the root samples and then measuring the main root diameter, the number of branched roots, and the length parameters.

[0033] In one embodiment, seasonal sampling can be carried out for each numbered plant, with sampling once at the beginning, middle, and end of the growing season. Each time, 2 - 3 well - grown mature leaves are selected from the four directions of the east, south, west, and north of the plant canopy, totaling 8 - 12 leaf samples. Use a leaf area meter to measure the leaf area of each leaf, and set the measurement accuracy to 0.01 square centimeters. Use an electronic balance with a precision of 0.001 grams to measure the fresh weight of the leaves. Put the weighed leaves into an oven, and select an appropriate temperature in the range of 60 - 70 degrees Celsius according to the plant species and leaf thickness. The drying time is 36 - 72 hours until the weight is constant. Take out the dried leaves and immediately weigh the dry weight. According to the measurement data, calculate the specific leaf area as "leaf area / leaf dry weight" and calculate the leaf dry matter content as "leaf dry weight / leaf fresh weight". For root sampling, taking the main trunk of the plant as the center, determine the sampling range according to the plant species, generally digging a soil profile within the range of extending 0.8 - 1.2 meters outward. The sampling depth is determined according to the plant species and its growth stage, generally 0.3 - 1.2 meters. Clean the soil attached to the root samples, measure the main root diameter, count the number of branched roots, and measure the length of the branched roots.

[0034] In step S103, the leaf parameters and root parameters are entered into a data table. After performing a normality test on each index, the corresponding standardization method is selected according to the index type for data conversion. Specifically, it includes: entering the leaf parameters and root parameters into a data table containing fields such as sample number and / or sampling date; performing a normality test on each index in the data table to identify the index with the optimal interval; using the fuzzy mathematics method of membership function to standardize the index in the optimal interval; and selecting the corresponding maximum-minimum standardization formula according to the nature of the remaining indexes in the data table for conversion.

[0035] Specifically, all the collected data are entered into a data table, which contains the following fields: sample number, sampling date, leaf number, leaf area, fresh weight of leaf, dry weight of leaf, specific leaf area, leaf dry matter content, main root diameter, number of branched roots, length of branched roots. When standardizing the original data, first perform a normality test on each index. For the index with the optimal interval, such as leaf dry matter content, use the fuzzy mathematics method based on the membership function for standardization, and set the optimal interval and allowable change range. For other indexes, select the appropriate standardization method according to the relationship type with plant adaptability: for the positive indexes reflecting plant growth status, such as leaf area, number of root branches, etc., use the formula Y=(X - Xmin) / (Xmax - Xmin) for standardization conversion; for the negative indexes reflecting plant growth stress, such as leaf damage rate, etc., use the formula Y=(Xmax - X) / (Xmax - Xmin) for standardization conversion. Here, X represents the original data value, and Y represents the standardized value, with the value range of [0,1].

[0036] In step S104, based on the standardized data, identify the index correlation through principal component analysis, construct a judgment matrix, calculate the eigenvalue and eigenvector, and determine the weight coefficient of each evaluation index. Specifically, it includes: identifying the correlation between standardized indexes through principal component analysis and merging highly correlated indexes; setting the importance level for the merged evaluation indexes, comparing them pairwise to construct a judgment matrix; calculating the maximum eigenvalue of the judgment matrix and its corresponding eigenvector; normalizing the eigenvector to obtain the weight coefficient of each index; and calculating the consistency index and ratio based on the eigenvalue to verify the reliability of the weight assignment.

[0037] In an implementation scenario, when constructing a pairwise comparison matrix to determine the index weights, first, the correlation between indexes is identified through principal component analysis. For highly correlated indexes, they are merged or their weights are adjusted to ensure the independence and integrity of the evaluation system. Then, nine levels of importance are set: 1 indicates that two indexes are equally important, 3 indicates that one index is slightly more important than the other, 5 indicates significantly important, 7 indicates strongly important, 9 indicates extremely important, and 2, 4, 6, 8 are the intermediate values of adjacent judgments. For n evaluation indexes, pairwise comparisons are made to obtain the judgment matrix A = (a ij ) n×n , where aij represents the relative importance ratio of index i to index j, Calculate the maximum eigenvalue λ max of the judgment matrix A and its corresponding eigenvector W = (w 1 , w 2 ,..., w n ). The eigenvector W is normalized to obtain the weight coefficients of each index. Calculate the consistency index and the consistency ratio where RI is the random consistency index. When CR < 0.1, it indicates that the weight allocation result is reliable.

[0038] At step S105, the comprehensive evaluation index of the sample plants is calculated using the weight coefficients and the standardized data. The grades are divided according to the quartiles, and the spatial distribution map is drawn and the distribution characteristics are analyzed. Specifically, it includes: multiplying the standardized index values by the corresponding weight coefficients and summing them to obtain the comprehensive evaluation index; counting the quartiles of the evaluation index to establish the rating criteria; dividing the sample plants into four adaptability levels according to the rating criteria; drawing the schematic diagram of the standard quadrat and marking the spatial positions of plants at each level; calculating the spatial distribution characteristics of the nearest neighbor distance and the aggregation degree of plants at each level.

[0039] Specifically, when calculating the comprehensive evaluation index S for each sample plant, the standardized values of each index are multiplied by the corresponding weight coefficients and then summed, that is, S = ∑(Wi × Yi), where i ranges from 1 to n. Wi is the weight coefficient of the i-th index, and Yi is the standardized value of this index. After calculating the evaluation indexes of all sample plants, the first quartile Q1, the median Q2, and the third quartile Q3 of the evaluation index are counted. The evaluation index is divided into four levels: S ≥ Q3 is high adaptability, Q2 ≤ S < Q3 is medium-high adaptability, Q1 ≤ S < Q2 is medium-low adaptability, and S < Q1 is low adaptability. Draw the schematic diagram of the quadrat and mark the spatial distribution positions of plants at each level with different colors. Calculate the spatial distribution characteristics of plants at each level, including the nearest neighbor distance, the aggregation degree index, etc.

[0040] In step S106, according to the evaluation level and spatial distribution characteristics, a multi-constraint integer programming model is established to determine the optimal configuration plan through algorithmic solution. This includes: counting the plant species composition and their distribution ratios at each adaptability level; establishing a programming model with the goal of maximizing the comprehensive adaptability index based on species distribution; setting constraints on planting area, number of species, and cost in the programming model; using a target algorithm to solve the programming model to obtain a species configuration plan; this includes: conducting a spatial analysis of the configuration plan, calculating the plant spatial distribution characteristics, and constructing a multi-level optimization model based on the spatial distribution characteristic results to determine the planting parameters.

[0041] When optimizing the restoration plan, first count the plant species composition at each adaptability level and calculate the distribution ratios of each species in different adaptability levels. Establish an integer programming model and set the decision variable Xi to represent the planting quantity of species i. The objective function is to maximize the comprehensive adaptability index: max Z = ∑(Si × Xi), where Si is the comprehensive evaluation index of species i. The constraint conditions include: planting area constraint ∑(ai × Xi) ≤ A, where ai is the single-plant planting area requirement of species i and A is the available total area; species quantity constraint ∑(I(Xi > 0)) ≥ N, where I(Xi > 0) takes 1 when Xi > 0 and 0 otherwise, and N is the minimum species quantity requirement; cost constraint ∑(ci × Xi) ≤ C, where ci is the unit planting cost of species i and C is the total budget; the planting quantity constraint for each species Ximin ≤ Xi ≤ Ximax. In one embodiment, the branch and bound algorithm can be used to solve this integer programming problem to obtain the optimal species configuration plan.

[0042] Among them, spatial analysis is carried out on the configuration scheme, the spatial distribution characteristics of plants are calculated, and a multi-level optimization model is constructed based on the results of the spatial distribution characteristics to determine the planting parameters, including: importing the collected plant morphological index data into the database, and dividing the plants into different functional groups by using the dynamic tree cutting method based on the Mahalanobis distance of the morphological indexes; for the plants within the functional groups, each functional group is subdivided into different adaptability subgroups by using the density peak clustering method according to the comprehensive evaluation index; the density function of the distance between plants in the adaptability subgroups is calculated by using the bandwidth kernel function, and the confidence interval is generated by the point pattern randomization method to determine the spatial association characteristics; the morphological indexes are standardized, the functional difference degree between any two species in the adaptability subgroups is calculated, and the functional difference degree index at the community level is obtained; according to the spatial association characteristics, the topographic position index is calculated based on the digital elevation model, and the sample plot is divided into topographic adaptation units by combining the slope, aspect and relative elevation parameters; within the topographic adaptation units, an adaptability function is constructed based on the functional difference degree index, and the adaptive bandwidth based on the topographic complexity is calculated by using the kernel function; based on the adaptability function and the adaptive bandwidth, a comprehensive optimization model including a species selection matrix and a planting density matrix is established, and the hydrogeological constraint, slope constraint, forest gap constraint and root competition constraint are set; for the comprehensive optimization model, a tabu search algorithm with a composite neighborhood structure is used for solution, and the candidate solutions are evaluated by the probability domination sorting method.

[0043] In another embodiment, for (optimizing the restoration plan), the restoration plan can be optimized and configured according to the comprehensive evaluation index S of plants calculated by the foregoing evaluation system, in combination with the actual topographic characteristics and soil conditions. First, the morphological index data such as the plant leaf area, specific leaf area, leaf dry matter content, main root diameter, number of branched roots, and length of branched roots obtained above are substituted into the database. An improved two-level clustering analysis method is used to group the plants. At the first level, based on the Mahalanobis distance of the morphological indexes, the UPGMA method is used to construct a hierarchical clustering tree, and the optimal number of clusters is determined by the dynamic tree cutting method to divide the plants into different functional groups; at the second level, within the functional groups, density peak clustering is performed based on the comprehensive evaluation index S value to automatically determine the number of clusters, and each functional group is subdivided into subgroups with different adaptabilities.

[0044] Spatial analysis is carried out on the clustering results, and an improved O-ring statistical method is used to analyze the spatial distribution characteristics of plants. The calculation formula is where g() is the kernel function with bandwidth h, h is determined by the adaptive method, d ij is the distance between plant i and j, and ρ(r) is the density function at distance r. 99 zero models are generated by the point pattern randomization method to construct a 90% confidence interval. According to the relationship between the O(r) function and the confidence interval, the spatial association characteristics at different scales are determined.

[0045] On this basis, a multi-level optimization model is constructed. The first level is the community-level optimization, and the goal is to maximize the complementarity of ecosystem functions. The functional difference index D is introduced, and the calculation method is as follows: after standardizing n morphological indicators, calculate the functional difference between any two species i and j where w k is the weight of the k-th index, determined by principal component analysis, and x ik is the value of the k-th standardized morphological indicator of species i. The functional difference index at the community level where p i , p j are the relative abundances of species i and j

[0046] The second level is the adaptability optimization, which adopts the improved terrain adaptation unit method. First, based on the digital elevation model, calculate the terrain position index where z 0 is the elevation of the center point, and z mean and z std are the mean and standard deviation of the elevations in the neighborhood, respectively. Combining slope, aspect, and relative elevation, divide the sample plot into terrain adaptation units. In each unit, construct an adaptability function: SA(x,y) = ∑(w(S i ) × K(d ij / h(x,y))), where w(S i ) is the weight function based on the comprehensive evaluation index, K is the Epanechnikov kernel function, and h(x,y) is the adaptive bandwidth based on terrain complexity

[0047] Based on the above optimization results, establish a comprehensive optimization model. The decision variables include: species selection matrix X and / or planting density matrix D. The objective function can be: F = β 1 × (D + X) + β 2 × SA(x,y), where β 1 , β 2 are determined by grey relational analysis. The constraint conditions include: (1) Hydrogeological constraint: Based on the karst development degree, fracture density, and soil layer thickness, divide the sample plot into water-rich areas (karst pipes developed), medium areas (fractures developed), and water-poor areas (solid rocks), and the species configuration needs to match its drought tolerance; (2) Slope constraint: In areas with a slope greater than 28°, preferentially select species with well-developed roots, and the root-shoot ratio needs to be greater than 0.32; (3) Forest gap constraint: The spacing between adjacent planting positions should be greater than H i × (0.5 + 0.15 × cos(A)), where H i is the expected height of species i, and A is the aspect angle; (4) Root competition constraint: The root overlap index ROI of adjacent plants does not exceed 0.385

[0048] The improved tabu search algorithm is used to solve the optimization model. A composite neighborhood structure is constructed: N1 is species replacement (meeting the constraint conditions), N2 is density adjustment (within the range of ±15%), and N3 is position fine-tuning (local rotation). The length of the tabu list is dynamically adjusted: L(t) = L 0 ×(1 + α × sin(2πt / T)), where L 0 is the base length, which is taken as 1.6 times the total number of variables, α is the fluctuation coefficient of 0.3, t is the current iteration number, and T is the total number of iterations. The candidate solution evaluation uses a sorting method based on probabilistic dominance, and the selection probability of each neighborhood is proportional to its historical improvement effect.

[0049] The algorithm termination condition can be configured as: the optimal solution has not improved for 50 consecutive iterations, or the maximum number of iterations of 2000 times is reached.

[0050] The final output optimization configuration plan can also include: (1) the characteristic parameters of the topographic adaptation unit division; (2) the species configuration plan within each unit, including the planting position, species type, and density; (3) the community structure parameters of each region, including the horizontal and vertical structure indices; (4) the construction zoning and implementation time sequence arrangement. This plan fully considers the special topographic and geological conditions of the karst area, ensuring both the ecological function diversity of the community and the spatial adaptive distribution of species.

[0051] For example, in an implementation scenario, during the implementation process of a karst plot in a certain county, 12 standard quadrats of unequal sizes are divided according to the vegetation density, numbered from A01 - B01 to A04 - B03. The 187 eligible woody plants in the quadrats are numbered and sampled. Samples are taken at the beginning, middle, and end of the growing season respectively. Each plant is sampled 11 leaves each time, and the average value of the leaf area is measured as 23.67 ± 2.34 square centimeters, the average value of the fresh leaf weight is 0.892 ± 0.086 grams, and the average value of the dry leaf weight is 0.344 ± 0.033 grams. The average specific leaf area is calculated as 132.45 ± 12.78 square centimeters / gram, and the average leaf dry matter content is 0.386 ± 0.037 grams / gram. The root measurement results show that: the average value of the main root diameter is 3.27 ± 0.31 centimeters, the average number of branched roots is 23.4 ± 2.2, and the average length of the branched roots is 28.76 ± 2.76 centimeters.

[0052] The weights of each index are determined by principal component analysis and pairwise comparison as follows: leaf area 0.173, specific leaf area 0.158, leaf dry matter content 0.142, main root diameter 0.186, number of branched roots 0.167, length of branched roots 0.174, and consistency ratio CR = 0.086. The quartiles of the comprehensive evaluation index are calculated: Q1 = 0.386, Q2 = 0.524, Q3 = 0.687. According to the grading standard, 187 sample plants are divided into four grades: 47 with high adaptability, 46 with medium-high adaptability, 47 with medium-low adaptability, and 47 with low adaptability. The spatial distribution analysis shows that the average nearest neighbor distance of high-adaptability plants is 3.24 meters, and the aggregation index is 1.47.

[0053] Under the conditions of an available planting area of 12 hectares, a minimum species number requirement of 8 species, and a total budget of 956,000 yuan, the optimal allocation plan is obtained by solving integer programming. The plan selects 12 species with relatively high adaptability, such as Cinnamomum camphora, Toona ciliata, and Cinnamomum longepaniculatum. Among them, there are 126 plants / ha of Cinnamomum camphora, 98 plants / ha of Toona ciliata, 87 plants / ha of Cinnamomum longepaniculatum, and the planting densities of the remaining 9 species are between 35 - 76 plants / ha. The comprehensive evaluation index of this plan is 0.783, and the expected planting cost is 924,000 yuan.

[0054] Figure 2 This is a target decision frontier curve graph disclosed in the embodiment of the present application. There are three curves of different colors in this graph, representing the ecological benefit change trends of three different terrain adaptation units: the high-efficiency area (blue line), the medium-efficiency area (green line), and the resistance area (orange line). The dotted concentric circles in the graph represent the isolines of the comprehensive adaptability index, increasing from the inside out. When the cost input is 920,000 yuan / ha, the ecological benefit of the high-efficiency area reaches the maximum value of 0.88, and the corresponding comprehensive adaptability index is 0.75. From the curve trend, it can be seen that when the cost input starts to increase from 800,000 yuan / ha, the ecological benefits of the three areas all show an upward trend, but after the range of 920,000 - 940,000 yuan / ha, the growth trend gradually slows down and inflection points appear. Among them, the ecological benefit of the high-efficiency area is always higher than the other two areas, which is closely related to its terrain conditions and vegetation growth status.

[0055] The lower left sub - figure depicts the variation law of vegetation coverage with soil depth in three different adaptability regions. The horizontal axis represents the soil depth (m), and the vertical axis represents the vegetation coverage (%). As can be seen from the figure, with the increase of soil depth, the vegetation coverage in all three regions shows a downward trend, but the decline rates are different. The starting vegetation coverage in the high - efficiency region (blue line) is 75%, and it drops to 20% when the soil depth reaches 1.2 m; the starting value in the medium - efficiency region (green line) is 95%, and the final value is 35%; the starting value in the resistant region (orange line) is 85%, and the final value is 30%. This change trend indicates that in the karst area, the soil depth has a significant impact on vegetation growth, and there are differences in the responses of different adaptability regions to soil depth changes.

[0056] The lower right sub - figure shows the response relationship of the comprehensive adaptability index with slope. The horizontal axis represents the slope (°), and the vertical axis represents the comprehensive adaptability index. The error bars in the figure represent the fluctuation range of the comprehensive adaptability index under different slope conditions. From the distribution of data points, it can be seen that as the slope increases from 15° to 35°, the comprehensive adaptability index gradually decreases from 0.85 to 0.65. When the slope is less than 20°, the fluctuation range of the adaptability index is relatively small, and the standard deviation is about 0.05; while when the slope exceeds 25°, the fluctuation range increases significantly, and the standard deviation at 35° reaches 0.12. This change law indicates that with the increase of slope, the growth adaptability of plants not only decreases as a whole, but also the differences among individuals increase.

[0057] Figure 3 This is an analysis diagram of a multi - distance spatial clustering function disclosed in the embodiment of the present application. The upper panel is a spatial distribution scatter plot. The horizontal and vertical coordinates represent the spatial position coordinates of plants (unit: m). Points with different shapes and colors represent plant individuals with high adaptability (blue dots), medium - high adaptability (light blue squares), medium - low adaptability (orange triangles), and low adaptability (red diamonds). The inset in the lower left shows the contour line distribution of the study area. The middle panel and the lower panel respectively show the L(r)-r function curves, where the solid line represents the measured values of plants of each adaptability level, and the dashed line represents the theoretical random distribution value. The dashed line in the lower panel represents the upper and lower limits of the 95% confidence interval obtained by Monte Carlo simulation. During the implementation of the karst plot in Zhijin County, based on the spatial distribution data of 187 woody plants, through the improved O - ring statistical method to calculate the spatial association characteristics, high - adaptability plants show significant aggregation in the 0 - 15 m scale range, medium - high - adaptability plants show weak aggregation in the 5 - 20 m range, while low - adaptability plants show a regular distribution characteristic.

[0058] Figure 4 This is an effect diagram of an optimization scheme disclosed in the embodiment of the present application. As Figure 4As shown, the figure contains four sub-figures, which respectively show the optimization effect of plant ecological restoration obtained based on the method of the embodiment of the present application. Among them, the above figure is a terrain zoning and plant distribution map based on the TPI index. The horizontal axis represents the distance in meters, ranging from 0 to 1200 meters; the vertical axis represents the elevation in meters, ranging from 0 to 800 meters. Different shapes of marks are used in the figure to represent individual plants, among which the circle, triangle, square and inverted triangle correspond to the four levels of plants with high adaptability, medium-high adaptability, medium-low adaptability and low adaptability divided in step S105, respectively.

[0059] Specifically, the horizontal axis of the figure is distance (m), the vertical axis is elevation (m), and the overall range is 1200m×800m. The spatial distribution of plants is indicated by different shape marks, where circles, triangles, squares and inverted triangles represent plant individuals of different adaptability levels. It can be observed from the figure that in the area of ​​300-700m above sea level, high-adaptability plants (blue circular marks) are mainly distributed in areas with relatively gentle terrain undulations, and the TPI index of these areas is close to 0, indicating that they are located in the transition zone of the terrain. In steep areas with higher altitudes (>600m), the distribution density of medium and high-adaptability plants (green square marks) is relatively high, which corresponds to their strong adaptability. In low-lying areas of terrain (TPI<0), the distribution ratio of low-adaptability plants (red inverted triangle marks) has increased significantly, which may be related to water accumulation and soil conditions in these areas.

[0060] The lower left sub-figure shows the ROC curve comparison results of species adaptability prediction. Wherein, the blue solid line represents the prediction result using the embodiment of the present application, and the red dotted line represents the prediction result using the prior art. The specific numerical values ​​of the two curves show that the curve of the embodiment method of the present application is located above, and its numerical range is 0.0-1.0, corresponding to the prediction effect of the evaluation index weight system determined based on the pairwise comparison matrix in step S104. In the figure, the horizontal axis is the false positive rate (FalsePositive Rate), the vertical axis is the true positive rate (TruePositive Rate), the blue line represents the optimized prediction result, and the red line represents the prediction result of the traditional evaluation method. The area under the ROC curve (AUC) after optimization reaches 0.892 ± 0.023, while the AUC value of the traditional method is 0.751 ± 0.031. In the interval of false positive rate 0.2-0.4, the true positive rate of the optimized method is significantly improved, indicating that the method significantly improves the recognition ability of highly adaptable plants while maintaining a low misjudgment rate. The change in the slope of the curve near (0,0) and (1,1) indicates that the prediction performance of this method remains stable in extreme cases.

[0061] The sub - graph in the middle - lower part is the cumulative curve of community functional diversity. The horizontal axis represents the time scale, with a range of 2.5 - 12.5, and the vertical axis represents the cumulative value, with a range of 0.2 - 0.8. Among them, the blue curve represents the optimized configuration scheme of the embodiment of the present application, and the red curve represents the comparative scheme. The light - colored areas on both sides of the curve represent the fluctuation range of the data, corresponding to the solution results of the multi - level optimization model in step S106. With the increase in the sample size, the changing trend of community functional diversity. The horizontal axis is time (month), and the vertical axis is the diversity index value. The blue line represents the change in functional diversity of the optimized configuration scheme, and the red line represents the changing trend of the control group. The shaded area represents the 95% confidence interval. During the observation period of 2.5 - 7.5 months, the functional diversity index of the optimized configuration scheme rapidly rises from 0.45 to 0.75, and the growth rate is significantly higher than that of the control group. After 7.5 months, the functional diversity of the two groups tends to be stable, but the optimized configuration scheme always maintains a relatively high level and finally stabilizes at about 0.78.

[0062] The sub - graph in the lower - right part is a bar chart for comparing classification benefits, including four groups of index data. In each group of data, the blue bar represents the result of the method of the embodiment of the present application, and the red bar represents the result of the prior art. The black line at the top of the bar represents the error range of the data. This figure corresponds to the specific implementation effect of the optimization objective function and constraint conditions specified in step S106. The figure includes four groups of indicators: survival rate, growth amount, structural stability, and ecosystem services. Each group of indicators includes comparative data of the optimized configuration scheme (blue bar) and the traditional configuration scheme (red bar), and the error bar represents the standard error. In terms of the survival rate index, the optimized configuration scheme reaches 0.85 ± 0.05, which is 0.15 higher than the traditional scheme; in terms of the growth amount, the relative growth amount of the optimized configuration scheme is 0.82 ± 0.04, exceeding the traditional scheme by 0.13; the structural stability index shows that the community structural stability of the optimized configuration scheme reaches 0.77 ± 0.04, which is 0.12 higher than the traditional scheme; in the evaluation of ecosystem services, the comprehensive score of the optimized configuration scheme is 0.81 ± 0.05, which is 0.14 higher than the traditional scheme. These data indicate that the optimized configuration scheme has good performance in all key indicators.

[0063] Furthermore, the embodiment of the present application also discloses an adaptability evaluation device for karst plant ecological restoration, including: a processor, a memory, and a system bus; the processor and the memory are connected through the system bus; the memory is used to store one or more programs, and the one or more programs include instructions, and when the instructions are executed by the processor, the processor executes any of the above - mentioned methods.

[0064] In summary, by introducing a multi-dimensional morphological index evaluation system, the embodiments of the present application combine leaf characteristics (leaf area, specific leaf area, leaf dry matter content) with root characteristics (main root diameter, number of branched roots, length of branched roots), overcoming the limitation of the existing methods in the incomplete description of plant phenotypic characteristics. Especially in root system evaluation, a non-destructive directional sampling method is adopted. By setting a reasonable sampling range and depth, it not only ensures the representativeness of the data but also reduces the impact on plant growth. At the data processing level, a fuzzy mathematics method based on the membership function is used for standardization, which is different from the simple linear normalization of traditional methods. This method can better handle the situation where there is an optimal interval for the index value. At the same time, through a two-level clustering analysis method, the morphological characteristics of plants are organically combined with adaptability evaluation, breaking through the limitation of the traditional method of separating morphological indexes for processing.

[0065] In terms of spatial analysis, the embodiments of the present application adopt an improved O-ring statistical method. By constructing a null model and a confidence interval, the spatial distribution pattern of plants is quantitatively described, which is a dimension lacking in traditional methods. In the optimization configuration link, by establishing a multi-level optimization model including hydrogeological constraints, slope constraints, forest gap constraints, and root competition constraints, systematic consideration of the special topographic and geological conditions in karst areas is realized. This optimization method based on topographic adaptation units overcomes the problem that traditional methods are limited in application in karst areas with strong spatial heterogeneity. At the actual operation level, this solution solves the optimization model through a dynamically adjusted tabu search algorithm, avoiding the problem that conventional methods are prone to falling into local optima during the solution process. Especially in the design of the composite neighborhood structure, through the combination of three dimensions: species replacement, density adjustment, and position fine-tuning, the global search ability of the solution is improved.

[0066] In the implementation case in Zhijin County, Guizhou Province, this solution not only completed the adaptability grading of 187 woody plants but also obtained a specific planting configuration plan through an integer programming model. Compared with traditional methods, this solution discloses more specific and operable technical parameters in terms of species selection, density configuration, and spatial layout, making the formulation of the restoration plan based on a more scientific quantitative basis. At the same time, the evaluation-optimization system established by this solution can be dynamically adjusted according to actual monitoring data, and this adaptability management mechanism is an important supplement to the existing static evaluation methods.

[0067] Furthermore, the embodiments of the present application also disclose a computer program product. When the computer program product runs on a terminal device, it enables the terminal device to execute any one of the above methods.

[0068] As can be seen from the description of the above embodiments, those skilled in the art can clearly understand that all or part of the steps in the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network communication device such as a media gateway, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present application.

[0069] It should be noted that the various embodiments in this specification are described in a progressive manner, and the key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0070] It should also be noted that in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0071] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for assessing the adaptability of karst plant ecological restoration, characterized in that: include: The karst plot was divided into several standard plots using the grid method. The plots were numbered in order of rows and columns and vertex marks were set. The numbering information of all woody plants of specified height in the plots was measured and recorded. For the numbered plants, leaf samples were collected at different stages during their growing season and leaf parameters were measured. At the same time, the soil profile around the plants was dug to obtain root samples and measure root parameters. The leaf parameters and root parameters were entered into the data table, and after normality test of each index, the corresponding standardization method was selected according to the index type for data conversion; Based on the standardized data, the correlation of indicators is identified through principal component analysis, the judgment matrix is ​​constructed and the eigenvalues ​​and eigenvectors are calculated to determine the weight coefficient of each evaluation indicator; The comprehensive evaluation index of sample plants was calculated using weight coefficients and standardized data, and the grades were divided according to quartiles. The spatial distribution map was drawn and the distribution characteristics were analyzed. According to the evaluation level and spatial distribution characteristics, a multi-constrained integer programming model is established to determine the optimal configuration plan through algorithm solution.

2. The adaptability evaluation method according to claim 1, characterized in that: in, The karst plot was divided into several standard plots using the grid method. The plots were numbered in order of rows and columns and vertex marks were set. The numbering information of all woody plants of specified height in the plots was measured and recorded, including: The karst plot was divided into several standard plots of different sizes using the grid method, and positioning marks were set at the four vertices of each plot; The standard samples are numbered in row and column order, and the unique number of each sample is recorded in the form of row number plus column number; Measure all woody plants exceeding the specified height in the numbered plots and number the plants in the form of the plot number plus the plant serial number.

3. The adaptability evaluation method according to claim 1, characterized in that: in, For the numbered plants, leaf samples were collected at different stages during their growing season and leaf parameters were measured. At the same time, the soil profile around the plants was dug to obtain root samples and measure root parameters, including: Leaf samples were taken from numbered plants at the beginning, middle, and end of the plant growth season; Select several mature leaves in good growth condition from the four directions of the plant canopy: For leaf samples, the area was measured using a leaf area meter, and the fresh weight was measured using an electronic balance; Place the weighed leaves in an oven for drying, and weigh the dry weight immediately after the weight becomes constant; Specific leaf area and leaf dry matter content were calculated based on leaf area and dry weight data; Dig the soil profile in a specified range extending outward from the main trunk of the plant to obtain root samples; The main root diameter, number and length of branched roots were measured after cleaning the soil attached to the root samples.

4. The adaptability evaluation method according to claim 1, characterized in that: in, Enter the leaf parameters and root parameters into the data table, perform normality tests on each indicator, and select the corresponding standardization method for data conversion according to the indicator type, including: Enter leaf parameters and root parameters into a data table containing fields for sample number and / or sampling date; Conduct normality tests on each indicator in the data table and identify indicators with optimal intervals; The fuzzy mathematical method of membership function is used to standardize the optimal interval index; For the remaining indicators in the data table, select the corresponding maximum value standardization formula according to their properties for conversion.

5. The adaptability evaluation method according to claim 1, characterized in that: in, Based on the standardized data, the correlation of indicators is identified through principal component analysis, the judgment matrix is ​​constructed and the eigenvalues ​​and eigenvectors are calculated to determine the weight coefficients of each evaluation indicator, including: Identify correlations between standardized indicators through principal component analysis and merge highly correlated indicators; Set importance levels for the combined evaluation indicators, conduct pairwise comparisons and construct a judgment matrix; Calculate the maximum eigenvalue of the judgment matrix and its corresponding eigenvector; Normalize the eigenvector to obtain the weight coefficient of each indicator; The reliability of weight assignment was verified by calculating consistency indices and ratios based on eigenvalues.

6. The adaptability evaluation method according to claim 1, characterized in that: in, The comprehensive evaluation index of sample plants was calculated using weight coefficients and standardized data, and the grades were divided according to quartiles. The spatial distribution map was drawn and the distribution characteristics were analyzed, including: The standardized index value is multiplied and summed with the corresponding weight coefficient to obtain the comprehensive evaluation index; Calculate the quartiles of the evaluation index and establish the rating standard; The sample plants were divided into four adaptability levels according to the rating criteria; Draw a standard sample plot diagram and mark the spatial location of plants of each grade; Calculate the spatial distribution characteristics of neighbor distance and aggregation degree of plants at each level.

7. The adaptability evaluation method according to claim 1, characterized in that: in, According to the evaluation level and spatial distribution characteristics, a multi-constrained integer programming model is established to determine the optimal configuration scheme through algorithm solution, including: Statistical analysis of plant species composition and distribution ratios at each adaptability level; A planning model is established based on species distribution to maximize the comprehensive adaptability index; Set constraints on planting area, number of species, and cost in the planning model; The target algorithm is used to solve the planning model to obtain the species configuration plan; this includes: spatial analysis of the configuration plan, calculation of the spatial distribution characteristics of plants, and construction of a multi-level optimization model based on the spatial distribution characteristics to determine the planting parameters.

8. The adaptability evaluation method according to claim 7, characterized in that: in, Perform spatial analysis on the configuration scheme, calculate the spatial distribution characteristics of plants, and build a multi-level optimization model based on the results of spatial distribution characteristics to determine the planting parameters, including: The collected plant morphological index data are imported into the database, and the plants are divided into different functional groups using the dynamic tree cutting method based on the Mahalanobis distance of the morphological index; For plants within functional groups, each functional group was subdivided into different adaptive subgroups using the density peak clustering method based on the comprehensive evaluation index; The density function of the distance between plants in the adaptive subgroups was calculated using the bandwidth kernel function, and the confidence intervals were generated by the point pattern randomization method to determine the spatial association characteristics. The morphological indices were standardized, and the functional difference between any two species in the adaptive subgroups was calculated to obtain the functional difference index at the community level. According to the spatial correlation characteristics, the terrain position index was calculated based on the digital elevation model, and the sample plots were divided into terrain adaptation units in combination with slope, aspect and relative elevation parameters; In the terrain adaptation unit, the adaptability function is constructed according to the functional difference index, and the adaptive bandwidth based on terrain complexity is calculated by the kernel function; Based on the adaptability function and adaptive bandwidth, a comprehensive optimization model including species selection matrix and planting density matrix was established, and hydrogeological constraints, slope constraints, forest gap constraints and root competition constraints were set. For the comprehensive optimization model, the taboo search algorithm with composite neighborhood structure is used to solve it, and the candidate solutions are evaluated by the probability dominance ranking method.

9. An adaptability assessment device for karst plant ecological restoration, characterized in that: include: A processor, a memory, and a system bus; wherein the processor and the memory are connected via the system bus; The memory is used to store one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by the processor, the processor executes the method according to any one of claims 1 to 8.

Images