A quantitative evaluation method for ecological environment restoration

Abstract

The present application relates to the technical field of ecological environment restoration evaluation, in particular to a quantitative evaluation method of ecological environment restoration. It comprises constructing an ecological environment restoration quantitative evaluation model and performing targeted quantitative regional framing according to the ecological environment restoration quantitative evaluation model. The present application divides the restoration region into multiple unit detection regions, maps the corresponding carbon sink quantity values and influence vector values to the corresponding restoration region simulation diagram, generates the carbon sink quantity change trend evolution path and the influence vector change path, obtains the influence vector change path of the single influence vector value according to the influence vector mapping set, performs path interweaving marking in combination with the carbon sink quantity change trend evolution path, feeds back the representation state of the current unit detection region through the path interweaving type and quantity, calculates the weight score of each unit detection region through the ecological environment restoration quantitative evaluation model, and realizes targeted quantitative regional framing, thereby reducing the quantitative evaluation workload.

Description

Technical Field

[0001] This invention relates to the field of ecological environment restoration assessment technology, and more specifically, to a quantitative assessment method for ecological environment restoration. Background Technology

[0002] Ecological environment restoration refers to the process of assisting damaged ecosystems in restoring their structure, function, and ecosystem services through human intervention. With the increasing prominence of global climate change, the carbon sequestration and enhancement benefits of ecological restoration projects have received widespread attention. Accurately quantifying and assessing the carbon sink volume in ecological restoration areas is crucial for verifying restoration effectiveness, optimizing restoration plans, participating in carbon trading markets, and achieving dual-carbon goals.

[0003] Currently, the carbon storage change method is mainly used for quantitative assessment of ecological environment restoration. This method calculates carbon sequestration by comparing changes in the total carbon pool before and after restoration, and is widely recognized as a highly accurate assessment method. This method typically requires the establishment of fixed monitoring plots within the restoration area. By monitoring indicators such as vegetation biomass and soil organic carbon within these plots over a long period, the carbon storage changes in the entire restoration area can be estimated.

[0004] However, in current technical practices, in order to reduce monitoring costs and workload, random sampling or simple systematic sampling is commonly used to select monitoring plots. Specifically, technicians usually randomly set up a certain number of unit plots within the total area of ​​the remediation area according to a preset sampling ratio, and then calculate the average value of the measured data from these plots to estimate the total carbon content of the entire remediation area.

[0005] The aforementioned traditional sampling methods have significant technical limitations. First, the environmental factors influencing carbon sequestration exhibit high spatial heterogeneity. Studies have shown that various environmental factors, such as vegetation cover, soil texture, topography, hydrology, and human disturbance, all significantly impact carbon storage, and there are complex interactive reinforcing effects among these factors. Within the same remediation area, environmental conditions may vary considerably across different locations, leading to significant spatial differentiation in carbon sequestration potential, carbon sequestration rate, and carbon storage levels.

[0006] In existing technologies, the lack of systematic consideration of the spatial heterogeneity of environmental factors and the failure to adaptively select sample plots based on the changing characteristics of environmental conditions result in the selected unit areas failing to truly represent the carbon sequestration status under different environmental conditions within the remediation area. Specifically, on the one hand, random sampling may miss key areas with unique environmental conditions but significant carbon sequestration contributions; on the other hand, for areas with drastic changes in environmental conditions and significant differences in remediation effectiveness, fixed sampling methods cannot achieve targeted monitoring of areas with different types of changes.

[0007] The resulting technical consequences are: reduced targeting of sample plot testing, significant discrepancies between measured data and the actual state of carbon sequestration in the region; difficulty in accurately reflecting the actual remediation effects in each region based on the assessment results extrapolated from the sampling data; and inability to accurately identify regions with good and poor remediation effects and their causes, thus making it difficult to provide a reliable scientific basis for optimizing and adjusting subsequent remediation work.

[0008] To address the aforementioned issues, there is an urgent need for a quantitative assessment method for ecological environment restoration that can incorporate changes in environmental factors and enable adaptive selection of monitoring plots. Summary of the Invention

[0009] The purpose of this invention is to provide a quantitative assessment method for ecological environment restoration in order to solve the problems mentioned in the background art.

[0010] To achieve the above objectives, a quantitative assessment method for ecological environment restoration is provided, comprising the following steps: S1. Based on the geographical distribution of the remediation area, select its boundaries, obtain the carbon storage detection standard, divide the remediation area into multiple unit detection areas, mark them with serial numbers, and generate a simulation diagram of the remediation area. S2. Collect the influence vector values ​​of carbon sink in each unit detection area, use a clustering algorithm to cluster the influence vector values, divide the unit detection area into influence trends based on the clustering results, and establish an influence vector mapping set. S3. Calculate the carbon sink in each unit detection area and map the influence vector mapping set to the corresponding unit detection area; S4. Compare the carbon sink values ​​of adjacent unit detection areas and select a continuous and uninterrupted carbon sink change trend evolution path in the simulation diagram of the repair area. S5. Obtain the influence vector change path of the single influence vector value based on the influence vector mapping set, and mark the path interweaving with the carbon sink change trend evolution path to construct a quantitative restoration area dataset and mark the interweaving unit detection area at different locations.

[0011] S6. Construct a quantitative assessment model for ecological environment restoration, extract quantitative parameters of each intersecting unit detection area, select targeted quantitative areas based on the quantitative assessment model for ecological environment restoration, and generate a quantitative assessment path for ecological environment restoration.

[0012] As a further improvement to this technical solution, the method for generating a simulation diagram of the repair area in S1 includes the following steps: S1.1 Obtain the area of ​​the unit detection area according to the carbon storage detection standard, and divide the remediation area into multiple unit detection areas; S1.2 Construct a Cartesian coordinate system within the divided selection area. The horizontal coordinate of the selection area is marked as x, and the vertical coordinate is marked as y. The coordinate points represent the location distribution of each unit detection area.

[0013] As a further improvement to this technical solution, the influence vector in S2 includes soil moisture content, organic matter content, vegetation coverage, and altitude.

[0014] As a further improvement to this technical solution, the method for establishing the influence vector mapping set in S2 includes the following steps: S2.1. Use sampling to collect the actual values ​​of each influence vector in the detection area of ​​different units, obtain the range of change of each influence vector value in the currently selected area, and construct a set of ranges of change of individual influence vector values ​​in the area. S2.2. Cluster processing is performed based on the range of numerical changes of individual influence vectors in each region; S2.3 Select high, medium, and low values ​​for the range of change of each influence vector; S2.4. Using high, medium, and low values ​​as initial cluster centers, corresponding cluster sets are formed respectively; S2.5 Extract the influence vector values ​​of each unit's influence area in the selected area, calculate the difference between them and the corresponding initial cluster center, and assign the corresponding influence vector values ​​to the cluster set with the smallest difference to form an influence vector mapping set.

[0015] As a further improvement to this technical solution, the algorithm for calculating the carbon sink amount of each unit detection area in S3 is as follows: ; in, Indicates carbon sequestration. This represents the ending carbon storage of the current unit's monitored area. This represents the initial carbon storage of the current unit's monitored area. This represents the molecular weight coefficient for the conversion of carbon into carbon dioxide. Indicates the evaluation period.

[0016] As a further improvement to this technical solution, the method for selecting the continuous and uninterrupted trend evolution path of carbon sink change in S4 adopts the method of comparing adjacent regions, and the specific steps are as follows: S4.1 Select the center area of ​​the selected region as the selection starting point; S4.2. Compare the changes in carbon sink values ​​between the unit detection areas adjacent to the selected starting point in turn to obtain the corresponding carbon sink difference and the initial evolution trend. S4.3. Using the compared unit detection region as the starting point, repeat step S4.2 to obtain the evolution trend between it and the adjacent unit detection regions, and determine whether it is consistent with the initial evolution trend. If the alignment remains consistent, the detection regions of adjacent units in the current comparison are retained; If the evolution trends are inconsistent, the detection regions of adjacent units in the current comparison are removed; S4.4. Establish a threshold for the length of the change trend evolution path. When the path length formed by multiple adjacent unit detection areas with the same evolution trend exceeds the threshold for the length of the change trend evolution path, the path formed by multiple adjacent unit detection areas is marked as the carbon sink change trend evolution path, and the evolution path is connected by connecting lines. S4.5 Repeat steps S4.2-S4.4 until all unit detection areas have completed the comparison work.

[0017] As a further improvement to this technical solution, the method for constructing the quantized repair region dataset in S5 includes the following steps: S5.1. Obtain the path of change of the influence vector of a single influence vector value by comparing adjacent regions; S5.2 Obtain the path through the detection area of ​​each unit, including the evolution path of carbon sink change trend and the path of influence vector change; S5.3 Remove the unit detection areas that do not pass through the carbon sink change trend evolution path, and retain the unit detection areas that do pass through the carbon sink change trend evolution path. S5.4 Summarize the individual influence vector change paths for the retained unit detection areas and obtain the number of identical influence vector change paths traversed by each retained unit detection area; S5.5. The path of influence vector change that passes through the most is taken as the representation path of the current unit detection region, and the other paths of influence vector change that pass through the current unit detection region are marked as shallow paths. The representation paths of each unit detection region are obtained, and combined with their corresponding numbers, a quantitative repair region dataset is constructed.

[0018] As a further improvement to this technical solution, the quantification parameters of the interlaced unit detection area in S6 include the number of carbon sink change trend evolution paths, the number of characterization paths, and the number of shallow paths.

[0019] As a further improvement to this technical solution, the method for targeted quantitative region selection in S6 includes the following steps: S6.1. Divide the weights of each quantization parameter and calculate the weight score of each unit detection area; S6.2. Set weight score thresholds and compare the weight scores for each unit's detection area; Unit detection regions whose weight scores exceed the weight score threshold are retained and marked as targeted quantization regions; Remove the detection regions whose weight scores do not exceed the weight score threshold; S6.3 Extract the serial number of each targeted quantization region, and divide the detection order according to the corresponding weight score, and the weight score is positively correlated with the detection order; S6.4. Based on the detection order, connect the simulated schematic diagram of the restoration area to form a quantitative assessment path for ecological environment restoration.

[0020] As a further improvement to this technical solution, the algorithm for calculating the weight score of each unit detection region in S6.1 is as follows: ; in This indicates the number of paths through which the trend of carbon sink changes evolves. Indicates the number of characterization paths. This represents the weight score of the unit detection area. Indicates the number of shallow paths. The weights represent the path. This represents the weight of shallow paths.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows: In this quantitative assessment method for ecological environment restoration, the restoration area is divided into multiple unit monitoring areas. The corresponding carbon sink values ​​and influence vector values ​​are mapped to the corresponding simulation diagrams of the restoration area, generating the evolution path of carbon sink change trends and the change path of influence vectors. The influence vector change path of individual influence vector values ​​is obtained based on the influence vector mapping set. The path interweaving is marked in conjunction with the evolution path of carbon sink change trends. The characterization status of the current unit monitoring area is fed back through the path interweaving type and quantity. The weight score of each unit monitoring area is calculated through the ecological environment restoration quantitative assessment model, realizing targeted quantitative area selection and reducing the workload of quantitative assessment. Attached Figure Description

[0022] Figure 1 This is a diagram illustrating the overall method steps of the present invention; Figure 2 This is a schematic diagram simulating the repair area of ​​the present invention; Figure 3 This is a diagram illustrating the repair area route of the present invention; Figure 4 This is a schematic diagram of the method steps for generating a repair area simulation according to the present invention; Figure 5This is a diagram illustrating the steps of the method for establishing an influence vector mapping set according to the present invention; Figure 6 This is a flowchart illustrating the steps of the method for selecting the evolution path of carbon sink change trend in this invention. Figure 7 This is a diagram illustrating the steps of the method for constructing a quantitative repair region dataset according to the present invention. Figure 8 This is a step diagram illustrating the method for targeted quantitative region selection according to the present invention. Detailed Implementation

[0023] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Please see Figure 1 As shown, a quantitative assessment method for ecological environment restoration is provided, including the following steps: S1. Based on the geographical distribution of the remediation area, select its boundaries, obtain the carbon storage detection standard, divide the remediation area into multiple unit detection areas, mark them with serial numbers, and generate a simulation diagram of the remediation area. S2. Collect the influence vector values ​​of carbon sink in each unit detection area, use a clustering algorithm to cluster the influence vector values, divide the unit detection area into influence trends based on the clustering results, and establish an influence vector mapping set. S3. Calculate the carbon sink in each unit detection area and map the influence vector mapping set to the corresponding unit detection area; S4. Compare the carbon sink values ​​of adjacent unit detection areas and select a continuous and uninterrupted carbon sink change trend evolution path in the simulation diagram of the repair area. S5. Obtain the influence vector change path of the single influence vector value based on the influence vector mapping set, and mark the path interweaving with the carbon sink change trend evolution path to construct a quantitative restoration area dataset and mark the interweaving unit detection area at different locations. S6. Construct a quantitative assessment model for ecological environment restoration, extract quantitative parameters of each intersecting unit detection area, select targeted quantitative areas based on the quantitative assessment model for ecological environment restoration, and generate a quantitative assessment path for ecological environment restoration.

[0025] In practical application, during the quantitative assessment of ecological environment restoration, the uneven terrain distribution of the actual restoration area can affect subsequent location marking and carbon sequestration detection. Therefore, to ensure the orderly progress of the assessment, this scheme uses the geographical location of the restoration area to define its boundaries. Figure 2 As shown, the edges of the overall restoration area are uneven. After selection, the selected area (i.e., the square area in the figure) is chosen as the restoration area, and the corresponding edge areas are removed. Secondly, due to the large size of the entire restoration area, the workload of overall quantitative assessment is too large, and it is difficult to complete the testing in a short time. Once the assessment period is extended, environmental changes will affect the assessment results, leading to a decrease in the accuracy of the assessment. Therefore, in order to ensure the assessment effect, this scheme adopts the method of dividing into landmark areas. The assessment results of the landmark areas are fed back to the overall restoration area. That is, by obtaining the carbon storage detection standard, the restoration area is divided into multiple unit detection areas. The carbon storage detection standard here is related to the ecosystem type of the current restoration area. For example, when the current restoration area is a forest or arbor forest, the selected unit detection area needs to be more than 600㎡ (including at least 100 trees). When the restoration area is a wetland or herbaceous vegetation, the selected unit detection area needs to be more than 400㎡.

[0026] like Figure 4 As shown, after dividing the area of ​​each monitoring zone according to the ecosystem type of the restoration area, a Cartesian coordinate system is constructed within the divided area, as follows: Figure 2 As shown, the corresponding selected area is labeled with x on the horizontal axis and y on the vertical axis. The coordinates represent the location of each unit detection area. For example... Figure 2 The unit detection areas A, B, and C in the diagram are represented as A(4,3), B(6,4), and C(7,2). The position distribution of the current unit detection area in the selected area is fed back through the corresponding coordinate points, generating a simulation diagram of the repair area.

[0027] Furthermore, since the amount of carbon sink is affected by various environmental factors, the environmental factors corresponding to different unit detection areas in the remediation area are different. Therefore, it is necessary to extract the environmental factors for each unit detection area in advance, obtain their variation patterns, and compare the changes in environmental factors with the changes in carbon sink to obtain the influence relationship of each environmental factor on carbon sink. Based on this, this scheme collects the influence vector values ​​of carbon sink in each unit detection area. The influence vector represents the environmental factors, including soil moisture content, organic matter content, vegetation cover, and altitude. Different influence vectors will have different effects on carbon sink under different geographical environments. For example, soil moisture content has a non-linear effect on carbon sink. In arid areas, increased soil moisture significantly promotes carbon sink, meaning that the two are positively correlated under this environment. In humid areas, excessive soil moisture reduces carbon sink by promoting cloud formation and inhibiting photosynthetically active radiation.

[0028] To address the influence vector states under different geographical environments, this scheme employs a clustering algorithm to cluster the influence vector values. Based on the clustering results, the influence trend is divided for each unit detection area, and an influence vector mapping set is established, such as... Figure 5 As shown, the specific solution is as follows: First, actual values ​​of each influence vector in the detection area of ​​different units are collected using a sampling method. The range of change of each influence vector value in the currently selected area is obtained, and a set of ranges of change of individual influence vector values ​​in the region is constructed. ,in to These represent the influence vector values ​​for different unit detection areas, where n represents the total number of samples. to The values ​​gradually increase, based on the range of changes in the individual influence vector values ​​for each region. Clustering is performed by selecting high, medium, and low values ​​for each influence vector's range of values. The high value is the influence vector value with the highest value in the range of values, the medium value is the average value in the range of values, and the low value is the influence vector value with the lowest value in the range of values. The high, medium, and low values ​​are used as initial cluster centers to form corresponding cluster sets. The influence vector values ​​of each unit influence area in the selected area are extracted and the differences are calculated with the corresponding initial cluster centers. That is, the difference between the influence vector value and the high, medium, and low values ​​is calculated. The corresponding influence vector values ​​are assigned to the cluster set with the smallest difference, forming an influence vector mapping set. That is, a single influence vector is divided into three influence vector mapping sets: the high value influence vector mapping set, the medium value influence vector mapping set, and the low value influence vector mapping set.

[0029] Furthermore, after the remediation work is completed, in order to ensure the effectiveness of the remediation evaluation, it is necessary to perform an overall carbon sequestration calculation for each unit's monitoring area. This involves calculating the carbon sequestration of each unit's monitoring area, and the specific calculation algorithm is as follows:

[0030] ; in, Indicates carbon sequestration. This represents the ending carbon storage of the current unit's monitored area. This represents the initial carbon storage of the current unit's monitored area. This represents the molecular weight coefficient for the conversion of carbon into carbon dioxide. This indicates the evaluation period, which is generally 1-5 years.

[0031] After calculating the carbon sink volume for each unit detection area, the carbon sink volume values ​​of adjacent unit detection areas are compared. A continuous and uninterrupted carbon sink volume change trend evolution path is selected in the simulation diagram of the remediation area, using a method of comparing adjacent areas. Figure 6 As shown, the specific selection method is as follows: First, the central area of ​​the selected region is chosen as the starting point. That is, the unit detection area located at the center of the current selected region is used as the starting point for the evolution path selection. The changes in carbon sequestration values ​​between adjacent unit detection areas are compared sequentially to obtain the corresponding carbon sequestration difference and the initial evolution trend (increase or decrease). The above steps are repeated with the compared unit detection area as the starting point to obtain its evolution trend with adjacent unit detection areas. It is determined whether it is consistent with the initial evolution trend. If consistent, the currently compared adjacent unit detection areas are retained; otherwise, if inconsistent, they are discarded. A threshold for the length of the change trend evolution path is set, generally 4-6 unit detection area lengths. When the path length formed by multiple adjacent unit detection areas with consistent evolution trends exceeds the change trend evolution path length threshold, the path formed by these multiple adjacent unit detection areas is marked as a carbon sequestration change trend evolution path. The evolution paths are connected by connecting lines. The above steps are repeated until all unit detection areas have been compared. Figure 3 As shown in the figure, evolution paths ①, ②, ③ and ④ are the corresponding carbon sink change trend evolution paths, represented by dashed lines. The direction of the arrow indicates that the carbon sink value corresponding to the arrow direction gradually increases.

[0032] To obtain the influence trend of each influence vector on carbon sink, it is necessary to map the influence vector mapping set to the corresponding unit detection region. The influence vector change path of individual influence vector values ​​is then obtained through adjacent region comparison. Figure 3 As shown, one of the influence vector change paths includes I, II, and III, which are connected by solid lines. The arrows indicate that the influence vector values ​​gradually increase along the direction of the arrows. The other influence vector change path includes 1 and 2, which are connected by dotted lines. The arrows indicate that the influence vector values ​​gradually increase along the direction of the arrows.

[0033] Furthermore, since the paths of influence vector changes and the evolution paths of carbon sink change trends intersect within the selected area, the types and numbers of paths in the intersecting unit detection regions differ. The more types and numbers of intersecting paths in a unit detection region, the more comprehensive the assessment information that the current unit detection region can reflect. Based on this, this solution uses the carbon sink change trend evolution path to mark path intersectations, obtains the intersecting unit detection regions corresponding to each influence vector, and constructs a quantitative remediation region dataset, such as... Figure 7 As shown, the specific construction method is as follows: First, the paths traversed by each unit detection region are obtained (including the evolution path of carbon sink change trend and the path of influence vector change). Since the quantitative assessment is related to the carbon sink of the current region, during the screening of intersecting unit detection regions, unit detection regions that do not have a carbon sink change trend evolution path are removed, and unit detection regions that have a carbon sink change trend evolution path are retained. For the retained unit detection regions, the single influence vector change paths are summarized, and the number of identical influence vector change paths traversed by each retained unit detection region is obtained. The influence vector change path with the most traversals is taken as the characterization path of the current unit detection region, and the remaining influence vector change paths traversing the current unit detection region are marked as shallow paths. The characterization paths of each unit detection region are obtained, and combined with their corresponding indices, a quantitative repair region dataset is constructed.

[0034] Furthermore, even after filtering some unit detection areas through path representation, the number of remaining unit detection areas is still too large. Therefore, this solution constructs a quantitative assessment model for ecological environment restoration, extracts quantitative parameters for each intertwined unit detection area, and performs targeted quantitative area selection based on the ecological environment restoration quantitative assessment model. Specific quantitative parameters include the number of carbon sink change trend evolution paths. Number of representation paths and the number of shallow paths The weights of each quantization parameter are assigned, and the weight score of each unit detection region is calculated. The specific calculation method and formula are as follows: ; in This represents the weight score of the unit detection area. The weights represent the path. This represents the weight of shallow paths.

[0035] like Figure 8 As shown, after calculating the weight scores, a weight score threshold is set, and the weight scores for each unit detection area are compared. Unit detection regions whose weight scores exceed the weight score threshold are retained and marked as targeted quantization regions; Unit detection regions with weight scores not exceeding the weight score threshold will be removed.

[0036] Extract the serial number of each targeted quantitative region, and divide the detection order according to the corresponding weight score. The weight score is positively correlated with the detection order. That is, the higher the weight score of the targeted quantitative region, the higher the detection order after a restoration cycle. Connect the detection order in the simulation diagram of the restoration region to form the ecological environment restoration quantitative assessment path, which serves as the subsequent restoration assessment detection path. Detect the order according to the ecological environment restoration quantitative assessment path.

[0037] This invention divides the remediation area into multiple unit detection areas, generates a simulation diagram of the remediation area, maps the corresponding carbon sink value and influence vector value to the corresponding simulation diagram of the remediation area, generates the evolution path of carbon sink change trend and influence vector change path, obtains the influence vector change path of individual influence vector values ​​based on the influence vector mapping set, and marks the path interweaving with the carbon sink change trend evolution path. The path interweaving type and quantity provide feedback on the characterization status of the current unit detection area. Finally, the weight score of each unit detection area is calculated through the ecological environment restoration quantitative assessment model, and the unit area is screened and selected based on the weight score, realizing targeted quantitative area selection and reducing the workload of quantitative assessment.

[0038] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A quantitative assessment method for ecological environment restoration, characterized in that: Includes the following steps: S1. Based on the geographical distribution of the remediation area, select its boundaries, obtain the carbon storage detection standard, divide the remediation area into multiple unit detection areas, mark them with serial numbers, and generate a simulation diagram of the remediation area. S2. Collect the influence vector values ​​of carbon sink in each unit detection area, use a clustering algorithm to cluster the influence vector values, divide the unit detection area into influence trends based on the clustering results, and establish an influence vector mapping set. S3. Calculate the carbon sink in each unit detection area and map the influence vector mapping set to the corresponding unit detection area; S4. Compare the carbon sink values ​​of adjacent unit detection areas and select a continuous and uninterrupted carbon sink change trend evolution path in the simulation diagram of the repair area. S5. Obtain the influence vector change path of the single influence vector value based on the influence vector mapping set, and mark the path interweaving with the carbon sink change trend evolution path to construct a quantitative restoration area dataset and mark the interweaving unit detection area at different locations. S6. Construct a quantitative assessment model for ecological environment restoration, extract quantitative parameters of each intersecting unit detection area, select targeted quantitative areas based on the quantitative assessment model for ecological environment restoration, and generate a quantitative assessment path for ecological environment restoration.

2. The quantitative assessment method for ecological environment restoration according to claim 1, characterized in that: The method for generating a simulation diagram of the repair area in S1 includes the following steps: S1.1 Obtain the area of ​​the unit detection area according to the carbon storage detection standard, and divide the remediation area into multiple unit detection areas; S1.2 Construct a Cartesian coordinate system within the divided selection area. The horizontal coordinate of the selection area is marked as x, and the vertical coordinate is marked as y. The coordinate points represent the location distribution of each unit detection area.

3. The quantitative assessment method for ecological environment restoration according to claim 1, characterized in that: The influence vectors in S2 include soil moisture content, organic matter content, vegetation coverage, and altitude.

4. The quantitative assessment method for ecological environment restoration according to claim 1, characterized in that: The method for establishing the influence vector mapping set in S2 includes the following steps: S2.

1. Use sampling to collect the actual values ​​of each influence vector in the detection area of ​​different units, obtain the range of change of each influence vector value in the currently selected area, and construct a set of ranges of change of individual influence vector values ​​in the area. S2.

2. Cluster processing is performed based on the range of numerical changes of individual influence vectors in each region; S2.3 Select high, medium, and low values ​​for the range of change of each influence vector; S2.

4. Using high, medium, and low values ​​as initial cluster centers, corresponding cluster sets are formed respectively; S2.5 Extract the influence vector values ​​of each unit's influence area in the selected area, calculate the difference between them and the corresponding initial cluster center, and assign the corresponding influence vector values ​​to the cluster set with the smallest difference to form an influence vector mapping set.

5. The quantitative assessment method for ecological environment restoration according to claim 1, characterized in that: The algorithm for calculating the carbon sink of each unit detection area in S3 is as follows: ； in, Indicates carbon sequestration. This represents the ending carbon storage of the current unit's monitored area. This represents the initial carbon storage of the current unit's monitored area. This represents the molecular weight coefficient for the conversion of carbon into carbon dioxide. Indicates the evaluation period.

6. The quantitative assessment method for ecological environment restoration according to claim 1, characterized in that: The method for selecting the continuous and uninterrupted trend evolution path of carbon sink change in S4 adopts the method of comparing adjacent regions, and the specific steps are as follows: S4.1 Select the center area of ​​the selected region as the selection starting point; S4.

2. Compare the changes in carbon sink values ​​between the unit detection areas adjacent to the selected starting point in turn to obtain the corresponding carbon sink difference and the initial evolution trend. S4.

3. Using the compared unit detection region as the starting point, repeat step S4.2 to obtain the evolution trend between it and the adjacent unit detection regions, and determine whether it is consistent with the initial evolution trend. If the alignment remains consistent, the detection regions of adjacent units in the current comparison are retained; If the evolution trends are inconsistent, the detection regions of adjacent units in the current comparison are removed; S4.

4. Establish a threshold for the length of the change trend evolution path. When the path length formed by multiple adjacent unit detection areas with the same evolution trend exceeds the threshold for the length of the change trend evolution path, the path formed by multiple adjacent unit detection areas is marked as the carbon sink change trend evolution path, and the evolution path is connected by connecting lines. S4.5 Repeat steps S4.2-S4.4 until all unit detection areas have completed the comparison work.

7. The quantitative assessment method for ecological environment restoration according to claim 6, characterized in that: The method for constructing the quantized repair region dataset in S5 includes the following steps: S5.

1. Obtain the path of change of the influence vector of a single influence vector value by comparing adjacent regions; S5.2 Obtain the path through the detection area of ​​each unit, including the evolution path of carbon sink change trend and the path of influence vector change; S5.3 Remove the unit detection areas that do not pass through the carbon sink change trend evolution path, and retain the unit detection areas that do pass through the carbon sink change trend evolution path. S5.4 Summarize the individual influence vector change paths for the retained unit detection areas and obtain the number of identical influence vector change paths traversed by each retained unit detection area; S5.

5. The path of influence vector change that passes through the most is taken as the representation path of the current unit detection region, and the other paths of influence vector change that pass through the current unit detection region are marked as shallow paths. The representation paths of each unit detection region are obtained, and combined with their corresponding numbers, a quantitative repair region dataset is constructed.

8. The quantitative assessment method for ecological environment restoration according to claim 1, characterized in that: The quantification parameters of the interlaced unit detection region in S6 include the number of carbon sink change trend evolution paths, the number of characterization paths, and the number of shallow paths.

9. The quantitative assessment method for ecological environment restoration according to claim 8, characterized in that: The method for targeted quantitative region selection in S6 includes the following steps: S6.

1. Divide the weights of each quantization parameter and calculate the weight score of each unit detection area; S6.

2. Set weight score thresholds and compare the weight scores for each unit's detection area; Unit detection regions whose weight scores exceed the weight score threshold are retained and marked as targeted quantization regions; Remove the detection regions whose weight scores do not exceed the weight score threshold; S6.3 Extract the serial number of each targeted quantization region, and divide the detection order according to the corresponding weight score, and the weight score is positively correlated with the detection order; S6.

4. Based on the detection order, connect the simulated schematic diagram of the restoration area to form a quantitative assessment path for ecological environment restoration.

10. The quantitative assessment method for ecological environment restoration according to claim 9, characterized in that: The algorithm for calculating the weight score of each unit detection region in S6.1 is as follows: ； in This indicates the number of paths through which the trend of carbon sink changes evolves. Indicates the number of characterization paths. This represents the weight score of the unit detection area. Indicates the number of shallow paths. The weights represent the path. This represents the weight of shallow paths.

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