Association analysis method and device based on carbon footprint data and species inhabitation data

Abstract

The invention relates to the technical field of ecological environment monitoring, in particular to a correlation analysis method and device based on carbon footprint data and species inhabitation data. The method comprises the following steps: acquiring species inhabitation data and carbon footprint data for a target area; utilizing the species inhabitation data and the carbon footprint data to construct a collaborative feature matrix; based on the collaborative feature matrix, calculating a habitat quality evaluation index for representing the comprehensive state of the habitat of the species and a net carbon footprint index for representing the net carbon exchange capacity of the target area; and through the habitat quality evaluation index and the net carbon footprint index, establishing a quantitative relationship between the species habitat data and the carbon footprint data in the ecological environment of the target area. Compared with the prior art, the internal relation between the data is mined through correlation analysis of the two kinds of data, and the quantitative relation between habitat evaluation and the carbon footprint is established. The problem of data splitting in traditional monitoring is solved, multi-dimensional and deeper comprehensive evaluation of the ecological environment is realized, and the comprehensiveness and accuracy of environment evaluation are improved.

Description

Technical Field

[0001] This application relates to the field of ecological environment monitoring technology, and in particular to a method and apparatus for correlation analysis based on carbon footprint data and species habitat data. Background Technology

[0002] In the field of ecological and environmental monitoring, species habitat monitoring refers to monitoring data (such as population size, population density, and activity areas) of a specific species (e.g., frogs, birds) within a certain area. Species habitat monitoring and carbon footprint monitoring are usually carried out as two independent operations. The former relies heavily on manual surveys and sampling, as well as fixed monitoring stations, which suffers from low sampling efficiency and incomplete spatial coverage. The latter mainly relies on satellite remote sensing and ground flux towers, which have limitations such as low spatial resolution and poor timeliness.

[0003] In recent years, when assessing the ecological environment of a certain region, the approach has often been single-task oriented, such as evaluating the ecological environment of a certain region solely based on species habitat data. This approach fails to reflect the specific changes in the ecological environment, leading to the omission of key ecological information and poor overall assessment accuracy. Summary of the Invention

[0004] In view of the above-mentioned shortcomings and deficiencies of the prior art, this application provides a method and apparatus for correlation analysis based on carbon footprint data and species habitat data. The main purpose is to solve the problem that the existing ecological environment assessment methods are unable to reflect the specific changes in the ecological environment, resulting in the omission of key ecological information and poor overall evaluation accuracy.

[0005] To achieve the above objectives, the main technical solutions adopted in this application include:

[0006] In a first aspect, embodiments of this application provide a correlation analysis method based on carbon footprint data and species habitat data, including:

[0007] Acquire species habitat data and carbon footprint data for the target area;

[0008] Using the species habitat data and the carbon footprint data, a synergistic feature matrix is ​​constructed; the synergistic feature matrix has parameter correlation relationships used in the quantitative assessment of the ecological environment, and the parameter correlation relationships are obtained by associating the correlated parameters in the species habitat data and the carbon footprint data;

[0009] Based on the aforementioned collaborative feature matrix, the habitat quality assessment index, which characterizes the overall status of species habitats, and the net carbon footprint index, which characterizes the net carbon exchange capacity of the target area, are calculated respectively.

[0010] The quantitative relationship between species habitat data and carbon footprint data in the target area's ecological environment is established using the habitat quality assessment index and the net carbon footprint index.

[0011] Optionally, the habitat quality assessment index includes a living environment index and a population health index;

[0012] Based on the collaborative feature matrix, a habitat quality assessment index for characterizing the overall state of a species' habitat is calculated, including: determining basic parameters in the collaborative feature matrix for calculating the living environment index and the population health index; and calculating the habitat quality assessment index based on the basic parameters and the preset weights corresponding to the basic parameters.

[0013] Optionally, after establishing a quantitative relationship between frog habitat data and carbon footprint data in the target area's ecological environment, basic parameters for calculating the survival environment index and population health index are determined in the collaborative feature matrix, including: determining a first basic parameter for calculating the survival environment index; the first basic parameter includes the comprehensive water quality, vegetation coverage, and water area ratio of the target area; and determining a second basic parameter for calculating the population health index based on the parameter correlation, the second basic parameter including frog population density, the ratio of juvenile to adult frogs, and species richness.

[0014] Optionally, calculating the habitat quality assessment index based on the basic parameters and their corresponding preset weights includes: calculating a standardized score for each basic parameter, and calculating the habitat quality assessment index based on the standardized score; the habitat quality assessment index is calculated using the following formula:

[0015] Habitat quality assessment index = (survival environment index + population health index) / 2;

[0016] The living environment index is calculated as follows: α1 × standardized score of comprehensive water quality + β1 × standardized score of vegetation coverage + θ1 × standardized score of water area proportion; where α1 is the preset weight corresponding to comprehensive water quality, β1 is the preset weight corresponding to vegetation coverage, and θ1 is the preset weight corresponding to water area proportion.

[0017] Population health index = α2 × population density standardized score + β2 × juvenile-to-adult ratio standardized score + θ2 × species richness standardized score; where α2 is the preset weight corresponding to population density, β2 is the preset weight corresponding to juvenile-to-adult ratio, and θ2 is the preset weight corresponding to species richness.

[0018] Optionally, based on the collaborative feature matrix, a net carbon footprint index is calculated to characterize the net carbon exchange capacity of the target region, including: calculating the carbon absorption and carbon emissions of the target region based on the collaborative feature matrix; and calculating the net carbon footprint index of the target region based on the carbon absorption and carbon emissions of the target region.

[0019] Optionally, establishing a quantitative relationship between species habitat data and carbon footprint data in the target area's ecological environment using the habitat quality assessment index and the net carbon footprint index includes: calculating the external disturbance index of the target area; using the net carbon footprint index as the dependent variable and the habitat quality assessment index and the external disturbance index as independent variables, and establishing a quantitative relationship between species habitat data and carbon footprint data in the target area's ecological environment based on a multiple regression analysis strategy.

[0020] Optionally, after establishing a quantitative relationship between the species habitat data and the carbon footprint data in the target area's ecological environment, the method further includes: dynamically assessing the ecological status of the target area based on the quantitative relationship and real-time monitoring data; and generating and executing a dynamic monitoring plan for the abnormal area when the assessment results meet preset abnormal conditions.

[0021] Optionally, the species habitat data and carbon footprint data of the target area are obtained through a combination of unmanned aerial vehicles (UAVs) and ground stations.

[0022] The acquisition of species habitat data and carbon footprint data for a target area includes: acquiring topographic data of the target area; dividing the target area based on the topographic data, at least into a first area and a second area, with the first area and the second area corresponding to different preset collection routes; controlling a drone to collect data from the first area and the second area along the preset collection routes within a target time period, thereby obtaining species habitat data and carbon footprint data collected by the drone; and acquiring species habitat data and carbon footprint data of the target area within the target time period uploaded by ground stations.

[0023] Optionally, after acquiring species habitat data and carbon footprint data for the target area, the method further includes: preprocessing the species habitat data and carbon footprint data; and aligning the species habitat data and carbon footprint data collected by the UAV with the species habitat data and carbon footprint data uploaded by ground stations in terms of time and space.

[0024] Secondly, embodiments of this application provide a correlation analysis device based on carbon footprint data and species habitat data, including:

[0025] The acquisition unit is configured to acquire species habitat data and carbon footprint data for a target area;

[0026] The processing unit is configured to construct a collaborative feature matrix using the species habitat data and the carbon footprint data; the collaborative feature matrix has parameter correlations used in the quantitative assessment of the ecological environment, and the parameter correlations are obtained by associating the correlated parameters in the species habitat data and the carbon footprint data;

[0027] The computing unit is configured to calculate, based on the collaborative feature matrix, a habitat quality assessment index that characterizes the overall state of a species' habitat and a net carbon footprint index that characterizes the net carbon exchange capacity of the target area.

[0028] The establishment unit is configured to establish a quantitative relationship for quantitatively assessing the ecological environment of the target area using the habitat quality assessment index and the net carbon footprint index.

[0029] By employing the above technical solution, this application provides a method and apparatus for correlation analysis based on carbon footprint data and species habitat data. First, species habitat data and carbon footprint data for a target area are acquired. Then, a co-feature matrix is ​​constructed using the species habitat data and carbon footprint data. This co-feature matrix contains parameter correlations used in the quantitative assessment of the ecological environment, obtained by associating correlated parameters in the species habitat data and carbon footprint data. Based on the co-feature matrix, a habitat quality assessment index characterizing the overall state of species habitats and a net carbon footprint index characterizing the net carbon exchange capacity of the target area are calculated. Through the habitat quality assessment index and the net carbon footprint index, a quantitative relationship between species habitat data and carbon footprint data in the target area's ecological environment is established. Compared with related technologies, this method utilizes species habitat data (such as population size and density) and carbon footprint data (such as net carbon exchange capacity) to mine the intrinsic connections between the data through fusion and correlation analysis, ultimately establishing a quantitative relationship between habitat assessment and carbon footprint. This solves the data fragmentation problem caused by the independence of the two data sets in traditional monitoring, achieving a multi-dimensional and deeper comprehensive assessment of the ecological environment, thereby improving the comprehensiveness and accuracy of environmental assessment. Attached Figure Description

[0030] Figure 1 A flowchart illustrating a correlation analysis method based on carbon footprint data and species habitat data provided in this application embodiment;

[0031] Figure 2 This is a schematic diagram of a correlation analysis device based on carbon footprint data and species habitat data provided in an embodiment of this application. Detailed Implementation

[0032] To better understand the above technical solutions, exemplary embodiments of this application will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application can be understood more clearly and thoroughly, and that the scope of this application can be fully conveyed to those skilled in the art.

[0033] To address the shortcomings of existing ecological environment assessment methods, which often fail to reflect specific changes in the ecological environment, leading to omissions of key ecological information and poor overall assessment accuracy, this application proposes a correlation analysis method based on carbon footprint data and species habitat data. This method can be applied to a correlation analysis system based on carbon footprint data and species habitat data, or to an ecological environment monitoring and analysis system. During operation, it can execute any of the correlation analysis methods based on carbon footprint data and species habitat data mentioned below. Figure 1 As shown, the method includes:

[0034] S101, acquire species habitat data and carbon footprint data for the target area.

[0035] In S101, the target area refers to the area where ecological environment monitoring is required. Species habitat data refers to habitat-related data collected for a specific species, such as the population size, population density, active area, and ratio of juveniles to adults in that habitat. Carbon footprint data includes basic data related to carbon footprint, such as CO2 concentration, methane emissions, temperature, and humidity. In this embodiment, it may specifically include data related to carbon absorption and carbon emissions, such as the vegetation type, vegetation cover, and atmospheric carbon dioxide concentration in the area.

[0036] S102 uses species habitat data and carbon footprint data to construct a collaborative feature matrix.

[0037] The co-feature matrix contains parameter correlations used in the quantitative assessment of the ecological environment. These correlations are obtained by linking correlated parameters in species habitat data and carbon footprint data. Specifically, the parameter correlations refer to the correlation between species, environment, and carbon footprint. A simple example is given below, as detailed in Table 1, to facilitate understanding of the co-feature matrix.

[0038] Table 1

[0039]

[0040] In this embodiment, frogs are used as an example for monitoring. For instance, a user sets up an analysis task to analyze the correlation between frog habitat data and carbon footprint data in a target area. This involves correlating frog population density with concurrent vegetation carbon storage and soil organic matter content, and correlating water quality parameters with parameters related to the frogs' living environment. The carbon footprint value corresponding to each set of frog population data can be clearly defined, as shown in Table 1. For example, if the frog density in target area A13 is 15 individuals / 100m², the corresponding net carbon footprint and environmental parameters such as vegetation cover of 80% and water pH of 7.1 can be correlated. This provides data support for establishing subsequent quantitative relationships.

[0041] It should be noted that the parameter correlations are not explicitly shown in Table 1. These correlations are derived by linking relevant parameters in species habitat data and carbon footprint data. This does not simply mean summarizing the two types of data in a table or database; rather, it establishes a link between parameters in a species' habitat data that are related to the carbon footprint data. For example, water quality parameters (expressed as pH) affect vegetation growth, which in turn affects vegetation carbon sequestration capacity, indirectly influencing carbon footprint data. This also determines the frog's habitat (such as population size and density). Vegetation cover is a key parameter for "vegetation carbon sequestration" in the carbon footprint and also provides hiding places for frogs. Water areas are the core breeding grounds for frogs, and the carbon sequestration capacity around water areas is significantly higher than in other areas. The proportion of water area is closely related to frog population activity. Different species require different selections of relevant parameters, which can be set based on the species' habits and historical experience. The purpose of establishing the collaborative feature matrix, in addition to summarizing the data, is mainly to correlate these related parameters, so as to provide a reliable data foundation for subsequent analysis of the deep relationship between the habitat quality assessment index and the net carbon footprint index.

[0042] S103, based on the collaborative feature matrix, calculates the habitat quality assessment index, which characterizes the overall status of species habitats, and the net carbon footprint index, which characterizes the net carbon exchange capacity of the target area.

[0043] Habitat quality assessment indices characterize the overall status of species habitats, while net carbon footprint indices characterize the net carbon exchange capacity of target areas. As mentioned above, there is a close correlation between some habitat data and carbon footprint data. However, analyzing the relationship between carbon footprints based solely on one type of habitat data is both cumbersome and prone to errors. Therefore, habitat quality assessment indices characterizing the overall status of species habitats and net carbon footprint indices characterizing the net carbon exchange capacity of target areas were constructed to replace the arbitrary nature of related technologies that rely on only one type of data for evaluation, thereby improving the accuracy and stability of ecosystem assessment.

[0044] S104 establishes a quantitative relationship between species habitat data and carbon footprint data in the target area's ecological environment through habitat quality assessment index and net carbon footprint index.

[0045] In this embodiment, firstly, species habitat data and carbon footprint data for the target area are acquired. A collaborative feature matrix is ​​constructed using these two datasets. This matrix contains parameter correlations used for quantitative assessment of the ecological environment, obtained by associating correlated parameters from the species habitat data and carbon footprint data. Based on the collaborative feature matrix, a habitat quality assessment index characterizing the overall state of species habitats and a net carbon footprint index characterizing the net carbon exchange capacity of the target area are calculated. Through these two indices, a quantitative relationship is established between species habitat data and carbon footprint data in the target area's ecological environment. Compared to related technologies, this method utilizes species habitat data and carbon footprint data, employing fusion and correlation analysis to uncover the intrinsic connections between the data, ultimately establishing a quantitative relationship between habitat assessment and carbon footprint. This solves the data fragmentation problem caused by the independence of the two datasets in traditional monitoring, enabling a multi-dimensional and deeper comprehensive assessment of the ecological environment, thereby improving the comprehensiveness and accuracy of environmental assessment.

[0046] Optionally, in S103, the habitat quality assessment index includes a survival environment index and a population health index. Specifically, based on the co-feature matrix, the habitat quality assessment index used to characterize the overall state of a species' habitat is calculated, including: determining the basic parameters used to calculate the survival environment index and the population health index in the co-feature matrix; and calculating the habitat quality assessment index based on the basic parameters and their corresponding preset weights.

[0047] In this embodiment, the habitat quality assessment index integrates the living environment index and the population health index. The living environment index reflects the physicochemical conditions of the habitat, while the population health index reflects the survival status of the species, avoiding the one-sidedness of evaluating solely based on the number of a single species. The net carbon footprint index integrates the region's carbon absorption and carbon emissions, reflecting the region's net contribution to the carbon cycle. By using these two integrated indices for correlation analysis, rather than directly using massive amounts of raw parameters, dimensionality reduction and noise interference can be effectively achieved, revealing a more profound intrinsic link between the ecosystem's carbon cycle and species habitat, thus making the assessment results more stable and accurate.

[0048] Furthermore, the analysis of the quantitative relationship between frog habitat data and carbon footprint data in the target area's ecological environment will be used as a feasible implementation method to illustrate the above steps. After establishing the quantitative relationship between frog habitat data and carbon footprint data in the target area's ecological environment, the basic parameters used to calculate the survival environment index and population health index are determined in the collaborative feature matrix. This includes: determining the first basic parameter for calculating the survival environment index; the first basic parameter includes the comprehensive water quality, vegetation coverage, and water area ratio of the target area; and determining the second basic parameter for calculating the population health index based on the parameter correlation, the second basic parameter includes frog population density, the ratio of juvenile to adult frogs, and species richness.

[0049] In this embodiment, when frogs are used as the specific monitoring species, the selection of basic parameters fully considers their living habits. The first basic parameter (used to calculate the survival environment index), including comprehensive water quality, vegetation coverage, and water area proportion, are key environmental factors determining frog survival and reproduction. The second basic parameter (used to calculate the population health index), including population density, the ratio of juvenile to adult frogs, and species richness, directly reflects the size, structure, and stability of the frog population. To reflect correlation, the purpose of constructing a collaborative feature matrix in S102 is to establish parameter correlations between these correlated data. For example, collecting pH and dissolved oxygen data shows that water quality affects vegetation growth, and vegetation carbon sequestration is a core component of the carbon footprint, directly determining the frog's living environment. Vegetation coverage is inferred from the Normalized Difference Vegetation Index (NDVI), a key parameter in the carbon footprint for "vegetation carbon sequestration," while also providing hiding places for frogs. In the water area proportion parameter, water areas are the core breeding area for frogs, and the carbon sequestration capacity around wetland water areas is significantly higher than in other areas. Population density is a direct reflection of frog survival status, and its changes are coupled with carbon footprint fluctuations. The ratio of juvenile to adult frogs reflects the population's reproductive capacity; in healthy populations, juveniles typically account for 30%-40%, corresponding to a stable carbon sink environment. Higher species richness indicates a more stable ecosystem and a more balanced carbon cycle (such as organic matter decomposition). Furthermore, both the first and second fundamental parameters can be obtained directly or indirectly from the synergistic feature matrix constructed in step S102, ensuring the data source for the evaluation model.

[0050] Furthermore, based on the basic parameters and the preset weights corresponding to the basic parameters, the habitat quality assessment index is calculated, including: calculating the standardized score corresponding to each basic parameter, and calculating the habitat quality assessment index based on the standardized score;

[0051] The habitat quality assessment index is calculated using the following formula:

[0052] Habitat quality assessment index = (survival environment index + population health index) / 2;

[0053] The living environment index is calculated as follows: α1 × standardized score of comprehensive water quality + β1 × standardized score of vegetation coverage + θ1 × standardized score of water area proportion; where α1 is the preset weight corresponding to comprehensive water quality, β1 is the preset weight corresponding to vegetation coverage, and θ1 is the preset weight corresponding to water area proportion.

[0054] Population health index = α2 × population density standardized score + β2 × juvenile-to-adult ratio standardized score + θ2 × species richness standardized score; where α2 is the preset weight corresponding to population density, β2 is the preset weight corresponding to juvenile-to-adult ratio, and θ2 is the preset weight corresponding to species richness.

[0055] In this embodiment, the standardized score is calculated to eliminate the differences in the dimensions and numerical ranges of different parameters, making them comparable.

[0056] Specifically, the standardization process converts the measured values ​​of each basic parameter into a standardized score of 0-100, eliminating dimensional differences (such as the different units of water quality pH value and population density). The standardization formula is divided into two categories based on the indicator attributes: "positive indicators" (the higher the value, the better) and "interval indicators" (the highest score is achieved within a suitable interval).

[0057] Positive indicators (such as vegetation cover, population density): Standardized score = (measured value - minimum value) / (maximum value - minimum value) × 100

[0058] For example: If the measured vegetation coverage of a certain area is 85%, and the minimum vegetation coverage of this wetland is 30% and the maximum is 90%, then the standardized score = (85-30) / (90-30)×100≈91.7 points.

[0059] For range indicators (such as water pH value, suitable range 6.5-7.5): if the measured value is within the range, the score = 100; if the measured value is <6.5 or >7.5, the score = 100 - |measured value - midpoint of the range| × 20 (for every deviation of 0.1, the score decreases by 2 points).

[0060] For example: If the pH value of the water in a certain area is 6.3 (0.2 below the lower limit of the suitable range), then the standardized score = 100 - 0.2 × 20 = 96 points.

[0061] Furthermore, the weighted summation of the indices is performed by weighting and summing the "living environment index" and the "population health index" separately, as shown in the following formula:

[0062] The living environment index is calculated as follows: (Standardized score of water pH value × 30%) + (Standardized score of vegetation coverage × 30%) + (Standardized score of water area percentage × 20%).

[0063] For example, if a region scores 96 for water quality, 91.7 for vegetation, and 85 for water area, then the living environment index = 96 × 0.3 + 91.7 × 0.3 + 85 × 0.2 ≈ 28.8 + 27.5 + 17 = 73.3 points.

[0064] Population health index = (Standardized score of population density × 50%) + (Standardized score of juvenile / adult ratio × 30%) + (Standardized score of species richness × 20%)

[0065] For example, if a region scores 88 points for density, 92 points for proportion, and 80 points for abundance, then the population health index = 88 × 0.5 + 92 × 0.3 + 80 × 0.2 = 44 + 27.6 + 16 = 87.6 points.

[0066] The final habitat quality assessment index combines the scores of the two dimensions mentioned above, and the average value is taken as the final assessment result.

[0067] It should be noted that the preset weights for different basic parameters are determined based on two factors: "the intensity of the impact of basic parameters on habitat quality" and "the closeness of their correlation with carbon footprint." These weights were derived through literature review and calibration with historical experimental data (correlation analysis of 10 wetland samples in the initial stage). The method of setting these weights is not limited and can be flexibly set according to the actual analysis task or historical experimental data. In this embodiment, the specific weight allocation is as follows:

[0068] The specific indicators and their weights in the assessment are based on the following weighting: Water quality parameter (pH value) 30% Water quality is a critical factor for frog survival (pH value 6.5-7.5 is the suitable range) and directly affects vegetation carbon sequestration efficiency, with the highest correlation. Vegetation coverage 30% Vegetation is a core contributor to the carbon footprint (accounting for more than 70% of carbon absorption) and also provides frogs with foraging / concealment places, with a significant dual impact. Water area ratio 20% Water is a necessary condition for frog reproduction, but its direct impact on the carbon footprint is lower than that of water quality and vegetation, with the second highest weight. Population health index Frog population density 50% Population density is a direct feedback indicator of habitat quality, and its coupling coefficient with the carbon footprint (r=0.76) is higher than other population indicators. Population health index Juvenile to adult frog ratio 30% Ratio reflects population sustainability and is positively correlated with carbon sink stability (r=0.68), with the second highest weight. Population health index Species richness 20% The impact of richness on habitat quality lags behind density and ratio, and its correlation with the carbon footprint is relatively low (r=0.52).

[0069] Optionally, based on the co-feature matrix, a net carbon footprint index is calculated to characterize the net carbon exchange capacity of the target region, including: calculating the carbon absorption and carbon emissions of the target region based on the co-feature matrix; and calculating the net carbon footprint index of the target region based on the carbon absorption and carbon emissions of the target region.

[0070] In this embodiment, the calculation of the net carbon footprint index also relies on data provided by the synergistic feature matrix. The net carbon footprint index is the difference between carbon absorption and carbon emissions. A value greater than 0 indicates that the region is a carbon sink, while a value less than 0 indicates a carbon source. This index quantifies the net role of a region in the carbon cycle.

[0071] Optionally, a quantitative relationship between species habitat data and carbon footprint data in the target area's ecological environment can be established using the habitat quality assessment index and net carbon footprint index. This includes: calculating the external disturbance index of the target area; using the net carbon footprint index as the dependent variable and the habitat quality assessment index and external disturbance index as independent variables, and establishing a quantitative relationship between species habitat data and carbon footprint data in the target area's ecological environment based on a multiple regression analysis strategy.

[0072] In this embodiment, an external disturbance index is introduced to characterize factors such as the intensity of human activities and the degree of invasive alien species as independent variables. This eliminates the interference of non-ecological factors on the quantitative relationship, allowing the model to more accurately reflect the intrinsic link between habitat quality and carbon footprint. In a feasible implementation, the quantitative relationship between species habitat data and carbon footprint data in the target area's ecological environment can be expressed as: Net Carbon Footprint = 0.6 × Habitat Quality Index + 0.2 × Vegetation Coverage - 0.1 × Human Disturbance Index. Analysis shows that for every 10% improvement in habitat quality, net carbon sink capacity increases by 6%-8%, thus reflecting the mutual influence between the two and significantly improving accuracy compared to related technologies.

[0073] Optionally, after establishing a quantitative relationship between species habitat data and carbon footprint data in the target area's ecological environment, the method further includes: dynamically assessing the ecological status of the target area based on the quantitative relationship and real-time monitoring data; and generating and executing a dynamic monitoring plan for the abnormal area when the assessment results meet preset abnormal conditions.

[0074] Specifically, the preset abnormal conditions include at least one of the following: the real-time monitored frog population density decreases by more than a first threshold compared to historical data or data from the surrounding area; the real-time monitored carbon dioxide concentration increases by more than a second threshold compared to historical baseline values ​​or concentrations in the surrounding area; and the habitat quality assessment index calculated based on real-time data is lower than a preset warning value.

[0075] Generate and execute a dynamic monitoring plan for the abnormal area, including: automatically generating a supplementary monitoring route for the UAV, with the core objective of flying from the current position of the UAV to the abnormal area and planning the shortest path; controlling the UAV to fly along the supplementary monitoring route and performing encrypted sampling in the abnormal area, wherein the spacing between sampling points in the abnormal area is smaller than the spacing between sampling points in the initial monitoring plan.

[0076] The generation and execution of a dynamic monitoring scheme for anomaly areas also includes: after flying to the anomaly area, adjusting the operating parameters of the sensors carried by the UAV, including at least one of the following: extending the recording time of the acoustic sensor in the anomaly area; increasing the sampling frequency of the multispectral sensor in the anomaly area.

[0077] It also includes emergency response steps: when a serious ecological anomaly is detected, an early warning message is automatically pushed to the management terminal; serious ecological anomalies include large-scale frog deaths or a sudden and significant increase in the intensity of regional carbon sources; the early warning message includes recommendations for key environmental factors to be verified based on quantitative relationship analysis.

[0078] In this embodiment, the established quantitative relationship can be used for dynamic assessment and early warning of actual monitoring. After accessing real-time or near-real-time monitoring data, the current habitat quality assessment index and net carbon footprint index can be quickly calculated, and the established quantitative relationship can be used for status assessment and rationality verification. When the assessment results are abnormal, such as the deviation between the actual measured carbon footprint data and the value predicted based on habitat quality exceeding a preset threshold, or a certain index itself deteriorating severely, it is determined that the abnormal conditions are met. The system will then automatically generate a dynamic monitoring plan to achieve rapid and accurate review of the abnormal area.

[0079] Optionally, species habitat data and carbon footprint data for the target area are collected collaboratively via drones and ground stations. Acquiring species habitat data and carbon footprint data for the target area includes: acquiring topographic data of the target area; dividing the target area based on the topographic data, at least into a first region and a second region, with different preset collection routes for each region; controlling drones to collect data from the first and second regions along preset collection routes within a target time period, obtaining the species habitat data and carbon footprint data collected by the drones; and acquiring species habitat data and carbon footprint data for the target area uploaded by ground stations within the target time period.

[0080] As a feasible implementation method, the system can first acquire high-precision terrain data, dividing the target area into a core area (a densely populated frog area or high carbon sink area), a buffer zone (a 500-meter radius around the core area), and an outer area. The sampling point spacing is 10 meters in the core area, 30 meters in the buffer zone, and 50 meters in the outer area. Furthermore, the core area and buffer zone can be used as the first region, and the outer area as the second region, or vice versa. The UAV uses a spiral flight path in the core area to ensure multi-angle observation, while a grid flight path is used in the buffer zone and outer area, reducing the total flight time by 30% compared to a single-mission flight path.

[0081] Optionally, after acquiring species habitat data and carbon footprint data for the target area, the method further includes: preprocessing the species habitat data and carbon footprint data; and aligning the species habitat data and carbon footprint data collected by the UAV with the species habitat data and carbon footprint data uploaded by ground stations in terms of time and space.

[0082] In this embodiment, data preprocessing includes data cleaning, format standardization, and imputation of missing values. Since the species habitat data and carbon footprint data collected by UAVs are continuous, while the species habitat data and carbon footprint data for the target area within the target time period uploaded by ground stations are discrete (e.g., a station corresponds to only some collection points within the area, and collection is also performed at intervals), spatiotemporal alignment is a prerequisite for data fusion. Temporal alignment uses high-precision timestamps as a benchmark for data matching; spatial alignment maps all data uniformly to a standard geographic grid (e.g., 10m × 10m), forming a structured dataset of "grid ID - timestamp - various parameters," laying the data foundation for constructing a collaborative feature matrix and accurate analysis.

[0083] Taking frog species as an example, the following describes an execution system for implementing the technical solutions proposed in any of the above embodiments, to facilitate the explanation of the specific execution process. The execution system includes:

[0084] Task Collaborative Planning Module:

[0085] This module generates a drone-based collaborative monitoring plan based on the ecological characteristics of the monitoring area, such as wetland type and vegetation coverage, and the monitoring targets, including frog population density and carbon storage changes. The specific process is as follows:

[0086] The target area is divided into layers: The system imports high-precision terrain data (1-meter resolution) and divides the monitoring area into a core area (a known frog-dense area or high carbon sink area), a buffer zone (a 500-meter radius around the core area), and an outer area, with different monitoring accuracies set for each (10-meter spacing between sampling points in the core area, 30-meter spacing in the buffer zone, and 50-meter spacing in the outer area).

[0087] Multi-sensor collaborative configuration: Equip the drone with a multi-task sensor combination: Optical camera and infrared thermal imager: used to identify individual frogs (infrared can distinguish the temperature difference between frogs and the environment) and vegetation type; Multispectral sensor: collects vegetation NDVI and chlorophyll content (for carbon storage estimation); Gas sensor: measures CO2 concentration in the air in real time (reflecting carbon emission intensity); Acoustic sensor: records frog calls (for population estimation).

[0088] Initial route generation: The software generates a "spiral + grid" composite route based on the area division and sensor parameters: the core area adopts a spiral route (to ensure multi-angle observation), while the buffer zone and the outer area adopt a grid route, which shortens the total flight time by 30% compared to a single mission route.

[0089] Multi-source data acquisition module:

[0090] This module enables collaborative data acquisition between drones and ground equipment, specifically in the following way:

[0091] Dynamic data collection by drones: The drones fly along the planned route and collect multi-source data simultaneously: optical / infrared images are captured every 10 seconds (5 seconds / time in the core area); multispectral data is sampled every 20 meters (10 meters / time in the core area); CO2 concentration is recorded every second, and GPS location is marked simultaneously; acoustic sensors record throughout the process, and directional recording is activated in key areas (such as around water bodies).

[0092] Ground-based data linkage: Ground-based sensor nodes (1 per 1 km²) synchronously collect: water quality parameters: pH value, dissolved oxygen, ammonia nitrogen content (affecting frog survival); soil parameters: temperature, humidity, organic matter content (affecting carbon decomposition); meteorological data: wind speed, temperature, precipitation (affecting carbon exchange and frog activity).

[0093] Real-time data transmission: The drone transmits data back to the system in real time via the 5G network, with ground data uploaded every 5 minutes to ensure data timeliness.

[0094] Data fusion processing module:

[0095] This module preprocesses and fuses multi-source data, extracting key parameters. Specifically:

[0096] Data preprocessing: Image data: Remove fog noise, stitch together panoramic images, identify individual frogs using a deep learning model (YOLOv8) (92% accuracy), and calculate population density; Multispectral data: Calculate NDVI and vegetation cover, and estimate vegetation carbon storage using a biomass model; Gas data: Correct for the effect of wind speed on CO2 concentration, and generate a spatial distribution map of carbon emission intensity; Acoustic data: Distinguish frog species using voiceprint recognition (88% accuracy), and estimate population size based on call frequency.

[0097] Multi-source data fusion: Spatiotemporal alignment: Aligning UAV data with ground data in terms of time (accurate to the second) and space (GPS coordinates);

[0098] Feature fusion: Construct a synergistic feature matrix of "frog-environment-carbon", such as correlating frog population density with contemporaneous vegetation carbon storage and soil organic matter content. Clearly defining the carbon footprint and environmental parameters corresponding to each set of frog population data provides underlying data support for subsequent quantitative calculations, avoiding errors in subsequent model validation due to data misalignment or missing data.

[0099] Habitat-carbon footprint correlation analysis module:

[0100] This module establishes a quantitative relationship between frog habitat quality and carbon footprint, specifically in the following way:

[0101] Habitat quality assessment: constructed from three dimensions

[0102] Living environment index: comprehensive water quality (weight 30%), vegetation coverage (30%), water area (20%), temperature (20%);

[0103] Population health index: based on population density, age structure (ratio of juvenile to adult frogs), and species richness;

[0104] Disturbance index: intensity of human activities (such as distance from roads) and degree of invasion by alien species.

[0105] Carbon footprint quantification: Calculating regional carbon sink / source intensity. Net carbon footprint = carbon uptake - carbon emissions, where carbon uptake is the annual carbon sequestration by vegetation (based on NDVI and biomass models); carbon emissions are CO2 released by soil respiration (estimated based on soil temperature and humidity models).

[0106] Furthermore, the habitat quality assessment uses "carbon footprint-related data + frog population data" as dual core inputs, quantifies habitat suitability through multi-dimensional indicators, and provides a unified assessment standard for subsequent "carbon footprint-habitat" verification. The specific construction logic, indicator sources, weight settings, and calculation methods are as follows:

[0107] The construction logic and data sources of the evaluation indicators

[0108] The evaluation index system is divided into two main dimensions: the habitat environment index (reflecting basic habitat conditions) and the population health index (reflecting the survival status of frogs). All index data come from previous multi-source collection and fusion results, i.e., the collaborative feature matrix, to ensure data authenticity and correlation. The specific correspondence is as follows:

[0109] The specific indicator data for the assessment dimensions are derived from the correlation logic between carbon footprint and population.

[0110] The water quality parameters in the survival environment index are collected by ground sensors. Water quality affects vegetation growth and directly determines the survival environment index of frogs.

[0111] Vegetation cover was calculated by back-calculating NDVI from UAV multispectral data. Vegetation cover is a key parameter in the carbon footprint for "vegetation carbon sequestration" and also provides hiding places for frogs.

[0112] The water area percentage in the habitat index was obtained by extracting water areas through image segmentation from UAV optical imagery. Water areas are the core breeding grounds for frogs, and the carbon sequestration capacity around wetland water areas is significantly higher than that of other areas.

[0113] Frog population density in the population health index can be obtained by counting through the fusion of infrared and optical images. Population density is a direct reflection of the survival status of frogs, and its changes are coupled with carbon footprint fluctuations.

[0114] The population health index, specifically the ratio of juvenile to adult frogs, is identified through infrared image features. It can distinguish differences in juvenile frog size and body temperature. The ratio of juvenile to adult frogs reflects the population's reproductive capacity. A healthy population has juvenile frogs accounting for 30%-40%, corresponding to a stable carbon sink environment.

[0115] Species richness in the population health index can be confirmed through acoustic sensor voiceprint recognition combined with image-assisted verification. Higher richness indicates a more stable ecosystem and a more balanced carbon cycle.

[0116] The preset weights of the aforementioned basic parameters are determined based on two factors: "the intensity of the indicator's impact on habitat quality" and "the closeness of its correlation with carbon footprint," and are derived through literature review and calibration with historical experimental data. Then, the standardized scores of each basic index are calculated, resulting in the habitat quality assessment index and the net carbon footprint index. A quantitative relationship between the two is established, for example, net carbon footprint = 0.6 × habitat quality index + 0.2 × vegetation cover - 0.1 × human disturbance index. Specific procedures can be found in the examples described in the above embodiments, and will not be repeated here.

[0117] Dynamic monitoring and adjustment module:

[0118] This module dynamically optimizes the monitoring strategy based on real-time analysis results, specifically including:

[0119] Anomaly identification: The system monitors data anomalies in real time, such as: a sudden drop in frog population density of more than 30%; CO2 concentration exceeding the threshold (e.g., 20% higher than the historical average); habitat quality index below the warning value, etc.

[0120] Dynamic route adjustment: After identifying anomalies, the UAV software automatically generates supplementary routes: prioritizes flying to the anomaly area, densifies sampling points (5-meter spacing); adjusts sensor parameters (such as extending acoustic recording time and increasing multispectral sampling frequency); calculates the shortest path to ensure arrival within 10 minutes (saving 40% of time compared to replanning the route).

[0121] Emergency response trigger: When a serious anomaly is detected (such as a large-scale death of frogs or a sudden increase in carbon sources), the system will automatically push an alert to the management personnel, along with an emergency monitoring plan (such as key sampling areas and environmental factors that need to be verified).

[0122] The correlation analysis method based on carbon footprint data and species habitat data provided by any of the above embodiments has at least the following technical effects:

[0123] (1) Multi-task collaborative monitoring mechanism: Breaking through the limitations of a single task, by integrating multiple sensors of UAVs and optimizing flight routes, the synchronous monitoring of frog habitats and carbon footprints can be achieved, improving data collection efficiency by 50% and solving the problem of low monitoring efficiency.

[0124] (2) Multi-source data fusion framework: Construct a multi-dimensional data fusion model of "UAV-Ground" to achieve spatiotemporal alignment of frog parameters, carbon parameters and environmental parameters, improve the analysis accuracy by 40% (frog population estimation error is reduced to 18%, carbon storage error is reduced to 15%), and solve the problem of low analysis accuracy.

[0125] (3) Quantitative analysis of the association between habitat and carbon footprint: For the first time, the coupling relationship between "habitat quality and carbon sequestration capacity" was quantified, revealing the mechanism by which frog activities affect the carbon cycle. For example, frog disturbance promotes the decomposition of soil organic matter, and an appropriate population density can increase carbon sequestration capacity by 12%; or, in a certain wetland, the population density of golden-striped frogs is significantly positively correlated with net carbon sequestration capacity (r=0.76). When the density of golden-striped frogs is 15-20 individuals / 100m², the carbon sequestration capacity reaches its peak. This finding provides a quantitative basis for protecting frogs and promoting carbon sequestration, while traditional methods cannot establish such a correlation. This solves the problem of missing data correlation.

[0126] (4) Dynamic adaptive monitoring strategy: Based on real-time data, abnormal areas are automatically identified, and the flight path and sampling frequency of UAVs are dynamically adjusted. The blind spot rate of key area monitoring is reduced from 20%-30% to below 5%, solving the problems of insufficient dynamic adaptability and incomplete spatial coverage.

[0127] Furthermore, as Figure 1 The specific implementation of the method shown in this embodiment provides a correlation analysis device based on carbon footprint data and species habitat data, such as... Figure 2 As shown, the device includes: an acquisition unit 201, a processing unit 202, a calculation unit 203, and a creation unit 204.

[0128] Acquisition unit 201 is configured to acquire species habitat data and carbon footprint data for a target area;

[0129] Processing unit 202 is configured to construct a collaborative feature matrix using the species habitat data and the carbon footprint data; the collaborative feature matrix has parameter correlation relationships used in the quantitative assessment of the ecological environment, and the parameter correlation relationships are obtained by associating the correlated parameters in the species habitat data and the carbon footprint data;

[0130] The calculation unit 203 is configured to calculate, based on the collaborative feature matrix, a habitat quality assessment index that characterizes the overall status of a species' habitat and a net carbon footprint index that characterizes the net carbon exchange capacity of the target area.

[0131] Establishment unit 204 is configured to establish a quantitative relationship for quantitatively assessing the ecological environment of the target area using the habitat quality assessment index and the net carbon footprint index.

[0132] In specific application scenarios, the calculation unit 203 is further configured to determine the basic parameters used to calculate the living environment index and the population health index in the collaborative feature matrix; and to calculate the habitat quality assessment index based on the basic parameters and the preset weights corresponding to the basic parameters.

[0133] In specific application scenarios, the computing unit 203 is further configured to determine the first basic parameters for calculating the living environment index; the first basic parameters include the comprehensive water quality, vegetation coverage and water area ratio of the target area; and based on the parameter correlation, determine the second basic parameters for calculating the population health index, the second basic parameters include frog population density, the ratio of juvenile to adult frogs and species richness.

[0134] In specific application scenarios, the calculation unit 203 is further configured to calculate the standardized score corresponding to each basic parameter, and calculate the habitat quality assessment index based on the standardized score.

[0135] In specific application scenarios, the computing unit 203 is further configured to calculate the carbon absorption and carbon emissions of the target region based on the collaborative feature matrix; and to calculate the net carbon footprint index of the target region based on the carbon absorption and carbon emissions of the target region.

[0136] In a specific application scenario, unit 204 is further configured to calculate the external disturbance index of the target area; using the net carbon footprint index as the dependent variable, and the habitat quality assessment index and the external disturbance index as independent variables, a quantitative relationship between the species habitat data and the carbon footprint data in the ecological environment of the target area is established based on a multiple regression analysis strategy.

[0137] In specific application scenarios, the establishment unit 204 is further configured to dynamically assess the ecological status of the target area based on the quantified relationship and real-time monitoring data; when the assessment result meets the preset abnormal conditions, a dynamic monitoring scheme for the abnormal area is generated and executed.

[0138] In a specific application scenario, the acquisition unit 201 is further configured to acquire terrain data of the target area; divide the target area based on the terrain data into at least a first area and a second area, with the first area and the second area corresponding to different preset collection routes; control the drone to collect data from the first area and the second area along the preset collection routes within a target time period, thereby obtaining species habitat data and carbon footprint data collected by the drone; and acquire the species habitat data and carbon footprint data of the target area within the target time period uploaded by the ground station.

[0139] In specific application scenarios, the acquisition unit 201 is further configured to preprocess the species habitat data and carbon footprint data; and to align the species habitat data and carbon footprint data collected by the UAV with the species habitat data and carbon footprint data uploaded by the ground station in terms of time and space dimensions.

[0140] It should be noted that other corresponding descriptions of the functional units involved in the correlation analysis device based on carbon footprint data and species habitat data provided in this embodiment can be found in [reference needed]. Figure 1 The corresponding descriptions in [the document] will not be repeated here.

[0141] Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause a computer device (such as personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of this application.

[0142] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented using software plus necessary general-purpose hardware platforms, or it can be implemented through hardware. By applying the solution of this embodiment, compared with related technologies, the intrinsic relationship between data is mined using two types of data correlation analysis to establish a quantitative relationship between habitat assessment and carbon footprint. This solves the problem of data fragmentation in traditional monitoring, enabling a multi-dimensional and deeper comprehensive assessment of the ecological environment, and improving the comprehensiveness and accuracy of environmental assessment.

[0143] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0144] In the description of this specification, the terms "one embodiment," "some embodiments," "embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0145] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make modifications, alterations, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A correlation analysis method based on carbon footprint data and species habitat data, characterized in that, include: Acquire species habitat data and carbon footprint data for the target area; A collaborative feature matrix is ​​constructed using the species habitat data and the carbon footprint data; The collaborative feature matrix has parameter correlations used in the quantitative assessment of the ecological environment. These parameter correlations are obtained by associating the species habitat data with the parameters that are correlated in the carbon footprint data. Based on the synergistic feature matrix, the habitat quality assessment index, which characterizes the overall status of species habitats, and the net carbon footprint index, which characterizes the net carbon exchange capacity of the target area, are calculated respectively. The quantitative relationship between species habitat data and carbon footprint data in the target area's ecological environment is established using the habitat quality assessment index and the net carbon footprint index.

2. The method according to claim 1, characterized in that, The habitat quality assessment index includes the living environment index and the population health index; Based on the aforementioned collaborative feature matrix, a habitat quality assessment index is calculated to characterize the overall state of a species' habitat, including: The basic parameters used to calculate the living environment index and the population health index are determined in the cooperative feature matrix; The habitat quality assessment index is calculated based on the basic parameters and their corresponding preset weights.

3. The method according to claim 2, characterized in that, Having established a quantitative relationship between frog habitat data and carbon footprint data in the target area's ecological environment, the basic parameters used to calculate the habitat index and population health index are determined in the collaborative feature matrix, including: Determine the first basic parameters for calculating the living environment index; the first basic parameters include the overall water quality, vegetation coverage, and water area ratio of the target area. Based on the parameter correlation, a second basic parameter for calculating the population health index is determined. The second basic parameter includes frog population density, the ratio of juvenile to adult frogs, and species richness.

4. The method according to claim 3, characterized in that, The calculation of the habitat quality assessment index based on the basic parameters and their corresponding preset weights includes: Calculate the standardized score corresponding to each basic parameter, and calculate the habitat quality assessment index based on the standardized score; The habitat quality assessment index is calculated using the following formula: Habitat quality assessment index = (survival environment index + population health index) / 2; The living environment index is calculated as follows: α1 × standardized score of comprehensive water quality + β1 × standardized score of vegetation coverage + θ1 × standardized score of water area proportion; where α1 is the preset weight corresponding to comprehensive water quality, β1 is the preset weight corresponding to vegetation coverage, and θ1 is the preset weight corresponding to water area proportion. Population health index = α2 × population density standardized score + β2 × juvenile-to-adult ratio standardized score + θ2 × species richness standardized score; where α2 is the preset weight corresponding to population density, β2 is the preset weight corresponding to juvenile-to-adult ratio, and θ2 is the preset weight corresponding to species richness.

5. The method according to claim 1, characterized in that, Based on the aforementioned collaborative feature matrix, a net carbon footprint index is calculated to characterize the net carbon exchange capacity of the target region, including: Based on the aforementioned collaborative feature matrix, the carbon absorption and carbon emissions of the target region are calculated. The net carbon footprint index of the target area is calculated based on the carbon absorption and carbon emissions of the target area.

6. The method according to claim 1, characterized in that, The process of establishing a quantitative relationship between species habitat data and carbon footprint data in the target area's ecological environment through the habitat quality assessment index and the net carbon footprint index includes: Calculate the external interference index of the target area; Using the net carbon footprint index as the dependent variable and the habitat quality assessment index and the external disturbance index as independent variables, a quantitative relationship between the species habitat data and the carbon footprint data in the target area's ecological environment is established based on a multiple regression analysis strategy.

7. The method according to claim 1, characterized in that, After establishing the quantitative relationship between the species habitat data and the carbon footprint data in the target area's ecological environment, the method further includes: Based on the quantitative relationship and real-time monitoring data, the ecological status of the target area is dynamically assessed. When the evaluation results meet the preset abnormal conditions, a dynamic monitoring plan for the abnormal area is generated and executed.

8. The method according to claim 1, characterized in that, The species habitat data and carbon footprint data of the target area were collected collaboratively using drones and ground stations. The acquisition of species habitat data and carbon footprint data for the target area includes: Acquire terrain data for the target area; The target area is divided based on the terrain data, into at least a first area and a second area, with different preset data collection routes corresponding to the first area and the second area. Within the target time period, the drone is controlled to collect data from the first and second areas along a preset collection route, thereby obtaining species habitat data and carbon footprint data collected by the drone. Acquire species habitat data and carbon footprint data for the target area during the target time period, uploaded by ground stations.

9. The method according to claim 8, characterized in that, After acquiring species habitat data and carbon footprint data for the target area, the method further includes: The species habitat data and carbon footprint data were preprocessed; Align the species habitat and carbon footprint data collected by drones with the species habitat and carbon footprint data uploaded by ground stations in terms of time and space.

10. A correlation analysis device based on carbon footprint data and species habitat data, characterized in that, include: The acquisition unit is configured to acquire species habitat data and carbon footprint data for a target area; The processing unit is configured to construct a collaborative feature matrix using the species habitat data and the carbon footprint data; The collaborative feature matrix has parameter correlations used in the quantitative assessment of the ecological environment. These parameter correlations are obtained by associating the species habitat data with the parameters that are correlated in the carbon footprint data. The computing unit is configured to calculate, based on the collaborative feature matrix, a habitat quality assessment index that characterizes the overall state of a species' habitat and a net carbon footprint index that characterizes the net carbon exchange capacity of the target area. The establishment unit is configured to establish a quantitative relationship for quantitatively assessing the ecological environment of the target area using the habitat quality assessment index and the net carbon footprint index.

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