Wetland ecological stability evaluation system and method

Abstract

The application provides a wetland ecological stability evaluation system and method, which first acquires ecological monitoring data of a target wetland in an ecological health management cycle; determines a purification recession index of self-purification capacity of the target wetland changing with time according to purification evaluation characteristics, and judges whether the target wetland is in an ecological stress state in the ecological health management cycle; if the target wetland is in the ecological stress state, determines an ecological resilience index of the target wetland according to the purification evaluation characteristics and a vegetation succession state of the target wetland in an ecological change trend, if the target wetland is in a benign self-maintenance stage, determines a health carrying capacity of the target wetland through the ecological resilience index and water storage interference characteristics of the target wetland; evaluates ecological stability of the target wetland through the health carrying capacity, and outputs a pre-diagnosis result of the ecological stability of the target wetland. Based on the above scheme, the pre-diagnosis of the full-dimension evaluation of the ecological stability of the wetland can be realized, so that the accuracy of the evaluation of the ecological stability of the wetland is improved.

Description

Technical Field

[0001] This application relates to the field of ecological health assessment technology, and more specifically, to a wetland ecological stability assessment system and method. Background Technology

[0002] As wetland ecological protection evolves from passive restoration to proactive prevention, ecological health assessment has become the core support for maintaining the sustainability of wetland ecosystems. Current mainstream methods mostly rely on monitoring single ecological indicators or comparing static data, or only conduct assessments on local characteristics such as self-purification capacity and vegetation status. Although these methods can initially reflect the ecological status of wetlands, they ignore the synergistic effect between external hydrological and meteorological disturbances and internal ecological responses, lack the ability to track dynamic changes in the ecosystem, and have limitations such as one-sided assessment dimensions and delayed early warning when dealing with complex disturbance scenarios, making it difficult to accurately represent the true state of ecological stability.

[0003] Current wetland ecological stability assessments face significant bottlenecks in terms of accuracy. The root cause lies in the lack of a quantitative analysis process for ecological stress states, recovery potential, and carrying capacity. Specifically, existing schemes rely on single static ecological indicators for assessment, failing to dynamically quantify the decline trend of self-purification capacity over time. This results in a lack of precise quantitative basis for judging ecological stress states and a lag in assessment. Alternatively, while stress states may be identified, the healthy self-sustaining potential of wetlands is not assessed, ignoring the crucial impact of recovery capacity on stability. Furthermore, the lack of quantified healthy carrying capacity leads to a lack of dynamic threshold support for stability assessments, remaining at a superficial level and failing to accurately reflect the dynamic changes and true stability of wetland ecosystems. Therefore, how to achieve pre-diagnosis of wetland ecological stability across all dimensions, thereby improving the accuracy of wetland ecological stability assessments, has become a challenging issue for the industry. Summary of the Invention

[0004] This application provides a wetland ecological stability assessment system and method, which can realize the pre-diagnosis of wetland ecological stability assessment in all dimensions, thereby improving the accuracy of wetland ecological stability assessment.

[0005] Firstly, this application provides a method for assessing the ecological stability of wetlands, including: Obtain ecological monitoring data of the target wetland during the ecological health management cycle; Based on the ecological monitoring data, the purification evaluation characteristics corresponding to the self-purification capacity of the target wetland are identified, and the purification decline index of the self-purification capacity of the target wetland over time is determined according to the purification evaluation characteristics. Then, based on the purification decline index, it is determined whether the target wetland is in an ecological stress state during the ecological health management cycle. If the target wetland is under ecological stress, the ecological resilience index of the target wetland is determined based on the purification evaluation characteristics and the vegetation succession status of the target wetland in the ecological change trend. The ecological resilience index is used to determine whether the health status of the target wetland is in a benign self-sustaining stage. If it is in a benign self-sustaining stage, the health carrying capacity of the target wetland is determined based on the ecological resilience index and the water storage disturbance characteristics of the target wetland. The ecological stability of the target wetland is assessed using the health carrying capacity, and a preliminary diagnosis of the target wetland's ecology is output.

[0006] In some embodiments, identifying purification evaluation features corresponding to the self-purification capacity of a target wetland based on the ecological monitoring data specifically includes: The feature vectors of the ecological monitoring data are constructed and their purification similarity is calculated with the feature vectors in the historical wetland purification case library. Based on the purification similarity calculation results, determine the purification correlation parameters corresponding to the self-purification capacity of the target wetland; The purification evaluation features corresponding to the self-purification capacity of the target wetland are extracted from the purification correlation parameters.

[0007] In some embodiments, determining the purification decline index of the target wetland's self-purification capacity over time based on the purification evaluation characteristics specifically includes: Determine the purification deviation index between the purification evaluation characteristics and the historical baseline purification status; The purification contribution during the ecological health management cycle is determined based on the purification deviation index. The purification contribution is used to determine the purification decline index of the target wetland's self-purification capacity over time.

[0008] In some embodiments, determining whether a target wetland is under ecological stress during the ecological health management cycle based on the purification and decline index specifically includes: The corresponding health status assessment template is dynamically loaded based on the target wetland type and evaluation target. The purification decline index is input into the health status judgment template and matched and verified against the preset static health baseline. Once the matching verification is completed, the status determination rule is triggered, and an ecological health diagnosis report of the target wetland within the ecological health management cycle is generated based on the health status determination template.

[0009] In some embodiments, if the wetland is under ecological stress, the ecological resilience index of the target wetland is determined based on the purification evaluation characteristics and the vegetation succession status of the target wetland in the ecological change trend, specifically including: Determine the state attribute parameters of the vegetation succession state of the target wetland in the ecological change trend; The ecological change rate of the target wetland is determined based on the purification evaluation characteristics and the state attribute parameters. The ecological resilience index of the target wetland is determined by the ecological change rate.

[0010] In some embodiments, determining whether the health status of a target wetland is in a benign self-sustaining stage using the ecological resilience index specifically includes: Determine the initial membership degree of the ecological resilience index relative to the preset ecological health level; Based on the resilience dynamic rules defined in the ecological management knowledge base, a health assessment matrix is ​​constructed to describe the relationship between the current ecological resilience index value and the historical ecological resilience change trend. The conclusion of the judgment of a benign self-sustaining state is determined based on the health assessment matrix and the initial membership degree.

[0011] In some embodiments, assessing the ecological stability of the target wetland using the health carrying capacity and outputting a pre-diagnostic result for the target wetland ecology specifically includes: Acquire hydrological and meteorological disturbance factors and ecological response data for the target wetland; The ecological stability index is obtained by feature fusion of the health carrying capacity, the hydro-meteorological disturbance factors, and the ecological response data. The ecological stability index is matched with a preset stability level threshold, and a pre-diagnostic result including ecological stability level, ecological risk factors and ecological regulation suggestions is output.

[0012] Secondly, this application provides a wetland ecological stability assessment system, including an ecological stability assessment unit, wherein the ecological stability assessment unit includes: The acquisition module is used to acquire ecological monitoring data of the target wetland during the ecological health management cycle; The processing module is used to identify the purification evaluation characteristics corresponding to the self-purification capacity of the target wetland based on the ecological monitoring data, and to determine the purification decline index of the self-purification capacity of the target wetland over time based on the purification evaluation characteristics, and then to determine whether the target wetland is in an ecological stress state during the ecological health management cycle based on the purification decline index. The processing module is also used to determine the ecological resilience index of the target wetland based on the purification evaluation characteristics and the vegetation succession status of the target wetland in the ecological change trend if it is under ecological stress, and to determine whether the health status of the target wetland is in a benign self-sustaining stage through the ecological resilience index. If it is in a benign self-sustaining stage, the health carrying capacity of the target wetland is determined through the ecological resilience index and the water storage disturbance characteristics of the target wetland. The execution module is used to assess the ecological stability of the target wetland based on the health carrying capacity and output the pre-diagnosis results of the target wetland ecology.

[0013] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for calling and running the computer program from the memory, so that the computer device performs the above-described wetland ecological stability assessment method.

[0014] Fourthly, this application provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the aforementioned wetland ecological stability assessment method.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: This application provides a wetland ecological stability assessment system and method. First, ecological monitoring data of the target wetland during its ecological health management cycle is acquired. Then, based on purification evaluation characteristics, a purification decline index is determined to reflect the wetland's self-purification capacity over time, indicating whether the wetland is under ecological stress during the ecological health management cycle. If under ecological stress, the wetland's ecological resilience index is determined based on purification evaluation characteristics and vegetation succession status within the ecological change trend. If the wetland is in a benign self-sustaining stage, its health carrying capacity is determined through the ecological resilience index and water storage disturbance characteristics. The ecological stability of the target wetland is assessed using its health carrying capacity, and a preliminary diagnosis of the wetland's ecology is output. Based on this scheme, a comprehensive pre-diagnosis of wetland ecological stability can be achieved, thereby improving the accuracy of wetland ecological stability assessment.

[0016] Therefore, this application assesses the ecological stability of a target wetland through the health carrying capacity and outputs a pre-diagnosis result of the target wetland's ecology. First, by determining the purification decline index, a dynamic quantitative characterization of the target wetland's self-purification capacity over time can be obtained, thus overcoming the limitations of relying on a single static indicator for assessment and achieving accurate capture of the self-purification capacity decline trend and quantitative determination of ecological stress status. This purification decline index transforms discrete ecological monitoring data into continuous trend parameters, providing a quantitative basis for identifying ecological stress status and solving the technical defects of delayed stress judgment and ambiguous basis in traditional assessments. Simultaneously, this purification decline index constructs a quantitative correlation between self-purification capacity and ecological stress status, forming a connecting node for the entire assessment chain, enabling the calculation of health carrying capacity to have an accurate preliminary judgment basis. Then, by determining the health carrying capacity, a quantitative parameter of the target wetland's tolerance threshold combined with water storage disturbance characteristics under ecological stress can be obtained, thus opening up the link between ecological resilience indicators and ecological stability assessment, making up for the shortcoming of lacking a dynamic carrying capacity threshold for assessment. The carrying capacity analysis couples the inherent ecological restoration potential of wetlands with external water storage disturbance factors, enabling precise definition of the carrying capacity of wetland ecological health. This allows stability assessment to move beyond surface-level judgment to the analysis of internal carrying mechanisms. Furthermore, this carrying capacity provides a quantitative basis for ecological stability level classification, ensuring that the output pre-diagnosis results not only include stability levels but also identify risk factors and targeted control recommendations. This effectively enhances the practicality of the pre-diagnosis results and promotes the precise development of comprehensive wetland ecological stability assessment. In summary, based on the above scheme, a comprehensive pre-diagnosis of wetland ecological stability can be achieved, thereby improving the accuracy of wetland ecological stability assessment. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is an exemplary flowchart of a wetland ecological stability assessment method according to some embodiments of this application; Figure 2 This is a schematic diagram of the process for determining health carrying capacity according to some embodiments of this application; Figure 3 This is a schematic diagram of the structure of an ecological stability assessment unit according to some embodiments of this application; Figure 4This is a schematic diagram of the structure of a computer device for implementing a wetland ecological stability assessment method according to some embodiments of this application. Detailed Implementation

[0019] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] refer to Figure 1 The figure is an exemplary flowchart of a wetland ecological stability assessment method according to some embodiments of this application. The wetland ecological stability assessment method mainly includes the following steps: In step 101, ecological monitoring data of the target wetland during the ecological health management cycle is obtained.

[0021] It should be noted that in this application, the ecological health management cycle is a continuous fixed-duration period set for wetland ecological health assessment; ecological monitoring data refers to the set of parameters for various dimensions of the wetland ecosystem; in specific implementation, the geographical boundaries of the target wetland are clearly defined, covering its core functional area, buffer zone, and surrounding potential impact area. A corresponding ecological health management cycle is set based on the wetland type and natural ecological evolution patterns. Subsequently, automatic water quality and hydrological sensors are deployed at key monitoring points such as the wetland inlet, core purification area, edge transition zone, and outlet, continuously collecting dynamic indicators such as chemical oxygen demand, ammonia nitrogen content, water storage, and water level at a frequency of once per hour. Every quarter, soil and vegetation samples are manually collected from the entire area at fixed times and sent to a professional laboratory for testing of precise indicators such as organic matter content and pollutant absorption. At the same time, high-resolution satellite remote sensing images or drone aerial photography are acquired to extract macroscopic indicators such as vegetation coverage and wetland area changes. Finally, outliers caused by faults and errors are removed from all acquired data using the 3σ principle. The dimensions of different indicators are standardized by Z-score, and missing data are filled using Kriging interpolation. The final result is ecological monitoring data of the wetland during the ecological health management cycle. In other embodiments, other methods can also be used to obtain ecological monitoring data, which are not limited here.

[0022] In step 102, the purification evaluation characteristics corresponding to the self-purification capacity of the target wetland are identified based on the ecological monitoring data, and the purification decline index of the self-purification capacity of the target wetland over time is determined based on the purification evaluation characteristics. Then, the target wetland is judged to be in an ecological stress state during the ecological health management cycle based on the purification decline index.

[0023] In some embodiments, identifying the purification evaluation characteristics corresponding to the self-purification capacity of a target wetland based on the ecological monitoring data can be achieved through the following steps: The feature vectors of the ecological monitoring data are constructed and their purification similarity is calculated with the feature vectors in the historical wetland purification case library. Based on the purification similarity calculation results, determine the purification correlation parameters corresponding to the self-purification capacity of the target wetland; The purification evaluation features corresponding to the self-purification capacity of the target wetland are extracted from the purification correlation parameters.

[0024] It should be noted that, in this application, the feature vector of ecological monitoring data is a structured numerical array transformed from multi-dimensional indicators related to wetland self-purification capacity in the ecological monitoring data; the historical wetland purification case library refers to a standardized database storing ecological monitoring data, self-purification capacity parameters, and corresponding feature vectors of wetlands of different types and health states; purification similarity calculation is a parameter that quantifies the degree of fit between the feature vector of the target wetland and the feature vector of historical cases; the purification similarity calculation result is a numerical result characterizing the degree of fit between the feature vector constructed from the ecological monitoring data of the target wetland and the feature vector in the historical wetland purification case library; purification correlation parameters refer to a set of quantitative indicators related to the self-purification capacity of the target wetland; and purification evaluation features are indicators characterizing the self-purification capacity status of the target wetland.

[0025] In specific implementation, the first step is to construct the feature vector of the ecological monitoring data and calculate the purification similarity with the feature vector in the historical wetland purification case library. This can be achieved in the following way: From the ecological monitoring data of the target wetland, indicators directly related to self-purification capacity, such as chemical oxygen demand removal rate, ammonia nitrogen degradation rate, microbial activity, and vegetation pollutant absorption, are selected. The Z-score standardization method is used to process each indicator, eliminating dimensional differences, and then the indicators are arranged in a preset order to form the feature vector of the target wetland. The sample data in the historical wetland purification case library comes from publicly available literature, measured data from research institutions, and data filed with ecological protection departments. Before being included in the library, outlier removal and consistency verification are performed to ensure validity. Each wetland purification case has its feature vector constructed according to the same indicators and order. The cosine similarity algorithm is used to calculate the purification similarity. Specifically, the dot product of the target vector and the historical case vector is calculated and divided by the product of the magnitudes of the two vectors to obtain a similarity value between 0 and 1. Then, the purification correlation parameters corresponding to the self-purification capacity of the target wetland can be determined based on the purification similarity calculation results. This can be achieved by statistically analyzing 100 sets of wetland sample experimental data to determine the purification similarity threshold as 0.7, and then selecting all wetland purification cases with similarity values ​​greater than 0.7 from the historical case library (if the number of matching cases is less than 3, the purification similarity threshold is lowered to 0.6 to ensure sufficient sample size). From the selected wetland purification cases, corresponding self-purification related parameters such as chemical oxygen demand removal efficiency, microbial degradation activity, and vegetation absorption coefficient are extracted. The comprehensive value of each parameter is calculated using a weighted average method, with the weight being the purification similarity value of the corresponding case (the higher the similarity, the greater the weight). This yields the purification-related parameters corresponding to the self-purification capacity of the target wetland. Finally, the purification evaluation features corresponding to the self-purification capacity of the target wetland can be extracted from the purification-related parameters in the following way: Principal component analysis is performed on the purification-related parameters. First, the covariance matrix of the purification-related parameter matrix is ​​calculated. By solving the eigenvalues ​​and eigenvectors of the covariance matrix, principal components with eigenvalues ​​greater than 1 are selected. Then, the contribution rate of each principal component is calculated. The purification-related parameters corresponding to the principal components with a cumulative contribution rate of 80% or higher are the purification evaluation features corresponding to the self-purification capacity of the target wetland.

[0026] In some embodiments, determining the purification decline index of the target wetland's self-purification capacity over time based on the purification evaluation characteristics can be achieved through the following steps: Determine the purification deviation index between the purification evaluation characteristics and the historical baseline purification status; The purification contribution during the ecological health management cycle is determined based on the purification deviation index. The purification contribution is used to determine the purification decline index of the target wetland's self-purification capacity over time.

[0027] It should be noted that, in this application, the historical baseline purification status refers to the long-term monitoring and statistical average of purification evaluation characteristics when wetlands of the same type are in a healthy and stable state; the purification deviation index is a parameter that quantifies the degree of difference between the purification evaluation characteristics of the target wetland and the historical baseline purification status; the purification contribution is a weighted parameter that measures the degree of influence of a single purification evaluation characteristic on the overall self-purification capacity of the wetland; and the purification decline index is a dynamic decline trend parameter that characterizes the degree of decline in self-purification capacity over time.

[0028] In specific implementation, firstly, the purification deviation index between the purification evaluation characteristics and the historical baseline purification state can be determined as follows: the historical baseline purification state value is derived from long-term monitoring data of similar healthy wetlands over 3-5 years, and the mean value is determined after verifying that the data conforms to a normal distribution through the KS test. For each purification evaluation characteristic, the purification deviation index is calculated using the relative deviation method, with the formula: Purification deviation index = (historical baseline value - current monitoring value) / historical baseline value. A positive result indicates that the characteristic is declining, while a negative result indicates that it is better than the baseline. Purification evaluation characteristic data for each sub-period within the ecological health management cycle are collected, and the index is calculated for each period according to the formula, thus obtaining all purification deviation indices. Then, the purification contribution within the ecological health management cycle can be determined based on the purification deviation index, using the entropy weight method to calculate the initial weight of each purification evaluation characteristic based on the purification deviation index data for each sub-period within the ecological health management cycle. First, calculate the mean and standard deviation of each purification evaluation feature's deviation index to obtain the coefficient of variation for each feature. Then, calculate the proportion of each feature's deviation index in each sub-period to the total deviation index of that feature throughout the entire cycle. Calculate the entropy and difference coefficient of each feature based on this proportion; a larger difference coefficient indicates a higher weight. The normalized difference coefficient becomes the initial weight. Next, use the mean of each feature's deviation index as a correction factor, substituting it into the formula: Purification Contribution = Initial Weight × Mean of Deviation Index / Sum of Mean Deviation Indexes, to complete the weight correction. Finally, the purification decline index, which measures the self-purification capacity of the target wetland over time, can be determined using the purification contribution. This can be achieved by using a weighted summation method, with the formula: Purification Decline Index = Σ(Purification Contribution × Corresponding Time Period Purification Deviation Index). During calculation, ensure that the contribution of each purification evaluation feature corresponds one-to-one with the deviation index of the corresponding time period. Divide the ecological health management cycle into multiple consecutive sub-periods, calculate the index value for each period, and construct a time series curve of the index. The index ranges from 0 to 1; the closer the value is to 1, the more severe the decline in self-purification capacity.

[0029] In some embodiments, determining whether a target wetland is under ecological stress during the ecological health management cycle based on the purification and decline index can be achieved through the following steps: The corresponding health status assessment template is dynamically loaded based on the target wetland type and evaluation target. The purification decline index is input into the health status judgment template and matched and verified against the preset static health baseline. Once the matching verification is completed, the status determination rule is triggered, and an ecological health diagnosis report of the target wetland within the ecological health management cycle is generated based on the health status determination template.

[0030] It should be noted that, in this application, the target wetland type refers to the wetland category classified according to the hydrological characteristics, vegetation community structure, and landform of the target wetland; the evaluation target refers to the ecological health assessment direction set for the target wetland; the health status judgment template refers to a standardized judgment document that includes a static health baseline and status judgment rules for different types of wetlands; the static health baseline refers to the statistical threshold of the purification decline index when the corresponding type of wetland is in a stable state in the health status judgment template; the status judgment rules refer to the logical conditions preset in the health status judgment template; and the ecological health diagnosis report refers to a standardized document that presents the ecological health status of the target wetland and the judgment basis.

[0031] In specific implementation, firstly, dynamically loading the corresponding health status assessment template according to the target wetland type and evaluation target can be achieved in the following way: Construct a health status assessment template library covering mainstream wetland types such as marshes, lakes, and rivers. The template data source is 3-5 years of long-term monitoring data of the same type of wetland registered with the ecological protection department. After KS normality test and outlier removal, the health status assessment templates are completed. The health status assessment templates are stored according to wetland type and evaluation target, with classification dimensions including short-term trend monitoring and long-term health assessment. During loading, the target wetland type and evaluation target keywords are input, and the health status assessment template library is searched through a fuzzy matching algorithm. When the matching degree reaches 90% or more, the corresponding health status assessment template is automatically loaded. Then, the purification and decline index is input into the health status assessment template, and the matching verification with the preset static health baseline can be achieved in the following way: The static health baseline is the statistical value of the purification and decline index of the same type of healthy wetland in the corresponding template. The calculation method is to select 100 sets of healthy wetland sample data and take the upper limit of the 95% confidence interval of its purification and decline index. During the matching verification, the purification decline index of the target wetland is input into the loaded health status assessment template. The deviation rate between the purification decline index and the static health baseline is calculated using the formula: Deviation rate = (Purification decline index - Static health baseline) / Static health baseline. Simultaneously, it is verified whether the value of the purification decline index is within the preset valid data range of the health status assessment template. Finally, after the matching verification is completed, the status assessment rules are triggered. Based on the health status assessment template, an ecological health diagnosis report for the target wetland within the ecological health management cycle can be generated in the following manner: the status assessment rules are preset logical conditions within the health status assessment template. Specifically, a deviation rate greater than 0 indicates an ecological stress state, a deviation rate equal to 0 indicates a stable state, and a deviation rate less than 0 indicates an excellent state. After the matching verification is completed, the status assessment rules are automatically triggered, extracting data such as the purification decline index value, deviation rate, and static health baseline. The assessment conclusions are then integrated according to a preset format to obtain the ecological health diagnosis report.

[0032] In step 103, if the target wetland is under ecological stress, the ecological resilience index of the target wetland is determined based on the purification evaluation characteristics and the vegetation succession status of the target wetland in the ecological change trend. The ecological resilience index is used to determine whether the health status of the target wetland is in a benign self-sustaining stage. If it is in a benign self-sustaining stage, the health carrying capacity of the target wetland is determined based on the ecological resilience index and the water storage disturbance characteristics of the target wetland.

[0033] In some embodiments, if the wetland is under ecological stress, the ecological resilience index of the target wetland can be determined based on the purification evaluation characteristics and the vegetation succession status of the target wetland in the ecological change trend by the following steps: Determine the state attribute parameters of the vegetation succession state of the target wetland in the ecological change trend; The ecological change rate of the target wetland is determined based on the purification evaluation characteristics and the state attribute parameters. The ecological resilience index of the target wetland is determined by the ecological change rate.

[0034] It should be noted that, in this application, ecological change trend refers to the direction and development trend of the target wetland ecology over time in terms of ecological elements such as vegetation community structure and self-purification function; vegetation succession state refers to the evolution of the wetland vegetation community over time in terms of species composition and structural complexity; state attribute parameters are a set of parameters that quantify the characteristics of the target wetland vegetation succession state; ecological change rate is a quantitative parameter that characterizes how fast the wetland ecological status changes over time; and ecological resilience index is a parameter that characterizes the wetland's ability to maintain structural stability and self-recover under ecological stress.

[0035] In specific implementation, firstly, determining the state attribute parameters of the target wetland's vegetation succession state within the ecological change trend can be achieved in the following way: Vegetation succession data is obtained through quarterly field surveys and annual remote sensing image analysis. Field surveys record species diversity, the proportion of dominant species, and vegetation cover, while remote sensing images extract spatial distribution changes in vegetation types. Succession direction, species richness change rate, and cover growth rate are selected as core indicators. The succession direction is assigned corresponding values ​​for positive, stable, and negative directions, respectively. The latter two indicators are calculated using (final value - initial value) / monitoring duration. The succession direction, species richness change rate, and cover growth rate are standardized using Z-scores to form the state attribute parameters of the vegetation succession state. Then, determining the ecological change rate of the target wetland based on the purification evaluation characteristics and the state attribute parameters can be achieved in the following way: Pollutant removal rate change rate and microbial activity change rate from the purification evaluation characteristics are selected as core factors, which, together with the three indicators in the state attribute parameters, form the input variables. The weights of each input variable are calculated using the entropy weight method. The information entropy and difference coefficient of each variable are calculated; the larger the difference coefficient, the higher the weight. Consistency checks ensure reasonable weight allocation. The standardized values ​​of each variable are multiplied by their corresponding weights and summed to obtain the comprehensive value for each sub-period. A linear fitting method is used to perform time series analysis on the comprehensive value. The slope of the fitted line represents the ecological change rate; a negative slope indicates ecological degradation, and a larger absolute value indicates faster degradation. Finally, the ecological resilience index of the target wetland is determined using the ecological change rate. This can be achieved by collecting data on the ecological change rate and actual recovery capacity of similar wetlands under ecological stress, establishing a benchmark database, and verifying the normal distribution of the data using the KS test to determine the conversion model between the ecological change rate and the ecological resilience index. The ecological resilience index is calculated using the exponential conversion formula: Ecological Resilience Index = e^(-k × |Ecological Change Rate|), where k is the conversion coefficient. This coefficient is calibrated based on 100 sets of healthy wetland data in the benchmark database, ensuring the index value ranges from 0 to 1; a larger value indicates stronger resilience.

[0036] It should be noted that in this application, if the area is not under ecological stress, the static health baseline in the health status assessment template is used as a reference. Combining the purification decline index with core indicators such as water quality compliance rate and vegetation coverage stability rate from ecological monitoring data, the entropy weight method is used to calculate the weights of each indicator and perform a weighted summation to classify the area into two health levels: "excellent" and "stable." A purification decline index below 80% of the static health baseline is classified as excellent, while an index between 80% and 100% of the static health baseline is classified as stable. Subsequently, differentiated dynamic monitoring frequencies are set for different health levels. Excellent-level wetlands are monitored at half the frequency of the ecological health management cycle, while stable-level wetlands maintain their original monitoring frequency. Simultaneously, time-series trend fitting is performed on the purification decline index and core indicators for each monitoring session. If two consecutive monitoring data show a negative trend towards the static health baseline, the monitoring frequency is automatically increased. Finally, based on the health level and indicator change trends, routine ecological maintenance recommendations are generated, including maintaining existing hydrological conditions and controlling surrounding disturbance sources, forming a complete closed loop for wetland health management.

[0037] In some embodiments, determining whether the target wetland is in a benign self-sustaining stage based on the ecological resilience index can be achieved through the following steps: Determine the initial membership degree of the ecological resilience index relative to the preset ecological health level; Based on the resilience dynamic rules defined in the ecological management knowledge base, a health assessment matrix is ​​constructed to describe the relationship between the current ecological resilience index value and the historical ecological resilience change trend. The conclusion of the judgment of a benign self-sustaining state is determined based on the health assessment matrix and the initial membership degree.

[0038] It should be noted that, in this application, the ecological health level refers to a standardized health status hierarchy for wetlands under ecological stress, based on ecological resilience index thresholds; the initial membership degree is a parameter quantifying the degree to which the current ecological resilience index belongs to the preset ecological health level; the ecological management knowledge base refers to a standardized database storing the ecological resilience change patterns and health status transformation conditions of wetlands of the same type; the resilience dynamic rule is a logical criterion describing the correlation between the historical change trend of the ecological resilience index and the health status; the current ecological resilience index value refers to the specific quantitative value of the ecological resilience of the target wetland calculated based on the rate of ecological change within the current ecological health management sub-period; the historical ecological resilience change trend refers to the ecological resilience index values ​​of the target wetland within multiple consecutive sub-periods within the ecological health management cycle; the health assessment matrix refers to a two-dimensional judgment carrier constructed with the current ecological resilience index range as rows and the historical change trend as columns; benign self-sustainability refers to the state in which a wetland, under ecological stress, can gradually recover to a healthy state without human intervention by relying on its own ecological regulation capacity.

[0039] In specific implementation, firstly, determining the initial membership degree of the ecological resilience index relative to the preset ecological health level can be achieved in the following way: Based on 3-5 years of ecological resilience monitoring data of similar wetlands, after KS normality test, three ecological health levels are divided: benign self-sustaining, awaiting recovery, and requiring artificial intervention. The threshold range of the resilience index corresponding to each level is then determined. Using a trapezoidal membership function, the membership degree value of each level is set to a range of 0-1. The ecological resilience index value of the current target wetland is substituted into the function to calculate the initial membership degree value of the index belonging to the three health levels respectively. Then, based on the resilience dynamic rules defined in the ecological management knowledge base, constructing a health assessment matrix to describe the relationship between the current ecological resilience index value and the historical ecological resilience change trend can be achieved in the following way: The data source of the ecological management knowledge base is the full-cycle data of ecological resilience changes of similar wetlands registered by the ecological protection department. From this data, resilience dynamic rules are extracted, including the health status logic corresponding to the three historical change trends of ecological resilience index: rising, stable, and falling. A 3×3 health assessment matrix is ​​constructed, using the current ecological resilience index level range as the row dimension and historical change trends as the column dimension. Each matrix cell corresponds to a preset health status assessment result. Finally, the judgment conclusion of a benign self-sustaining state is determined based on the health assessment matrix and the initial membership degrees. This can be achieved as follows: the initial membership degree values ​​are sorted, and the ecological health level with the highest membership degree is selected as the preliminary judgment result. The historical change trends of the target wetland's ecological resilience index are extracted, and the corresponding rows and columns are located in the health assessment matrix to obtain the assessment results in the health assessment matrix cells. A weighted fusion algorithm is used, setting the weight of the initial membership degree value to 0.6 and the weight of the matrix assessment result to 0.4. After weighted calculation, the final judgment conclusion is output.

[0040] In some embodiments, if the wetland is in a benign self-sustaining phase, the healthy carrying capacity of the target wetland is determined by the ecological resilience index and the water storage disturbance characteristics of the target wetland, with reference to... Figure 2 The diagram is a flowchart illustrating the determination of health carrying capacity in some embodiments of this application. In this embodiment, the determination of health carrying capacity can be achieved through the following steps: In step 1031, the ecological resilience index is converted into a health carrying capacity level, and the maximum disturbance tolerance threshold corresponding to each health carrying capacity level is determined. In step 1032, the disturbance sensitivity vector is extracted from the water storage disturbance characteristics of the target wetland; In step 1033, the health carrying capacity evaluation value under the current state is determined based on the health carrying capacity level and the interference sensitivity vector; In step 1034, it is determined whether the health carrying capacity evaluation value is better than the maximum disturbance tolerance threshold, and the health carrying capacity of the target wetland after the determination is passed is output.

[0041] It should be noted that in this application, the health carrying capacity level is a standardized classification that characterizes the level of wetland's ability to withstand disturbance; the maximum disturbance tolerance threshold refers to the upper limit of water storage disturbance that the wetland can withstand for each health carrying capacity level; the disturbance sensitivity vector refers to a structured numerical array that quantifies the sensitivity of each disturbance factor; the health carrying capacity evaluation value is a quantitative parameter that characterizes the actual carrying capacity of the wetland; and the health carrying capacity refers to the upper limit of external disturbance that the wetland can stably withstand.

[0042] In specific implementation, firstly, the ecological resilience index is converted into a health carrying capacity level, and the maximum disturbance tolerance threshold corresponding to each health carrying capacity level is determined. This can be achieved by collecting ecological resilience index and water storage disturbance tolerance data of similar wetlands for more than 5 years, and after KS normality test and outlier removal, dividing them into high, medium, and low health carrying capacity levels according to the range of resilience index values. The percentile method is used to determine the maximum disturbance tolerance threshold corresponding to each level, with the 90th percentile as the upper limit of the threshold to ensure that the threshold covers the tolerance level of the vast majority of healthy wetlands. Then, the disturbance sensitivity vector is extracted from the water storage disturbance characteristics of the target wetland. This can be achieved by selecting the water storage fluctuation amplitude, disturbance frequency, and number of extreme hydrological events as sensitivity factors. The data comes from the continuous monitoring records of hydrological sensors during the ecological health management cycle. The three factors were processed using the Z-score standardization method to eliminate dimensional differences. They were then arranged in a fixed order of "fluctuation amplitude – interference frequency – number of extreme events" to form a 3-dimensional interference sensitivity vector. Next, the health carrying capacity evaluation value under the current state was determined based on the health carrying capacity level and the interference sensitivity vector. This was achieved by calculating the weight of each factor in the interference sensitivity vector using the entropy weight method. The information entropy and difference coefficient of each factor were calculated; the larger the difference coefficient, the higher the weight. Consistency verification ensured that the weight allocation was reasonable. The standardized values ​​of each factor were multiplied by their corresponding weights and summed to obtain the comprehensive interference sensitivity value. Combining this with the correction coefficients for the health carrying capacity level, high, medium, and low levels are assigned correction coefficients of 1.0, 0.8, and 0.6 respectively. The final health carrying capacity evaluation value is calculated as: comprehensive disturbance sensitivity value × correction coefficient, with a value range of 0-1. Finally, it is determined whether the health carrying capacity evaluation value is better than the maximum disturbance tolerance threshold. The output of the target wetland's health carrying capacity after passing the determination can be achieved by comparing the health carrying capacity evaluation value with the maximum disturbance tolerance threshold corresponding to the health carrying capacity level. If the evaluation value is less than the threshold, it is determined to be "better than". After passing the determination, the disturbance tolerance parameters corresponding to the health carrying capacity level are extracted. Combined with the actual ecological characteristics of the target wetland, the specific health carrying capacity is output. This health carrying capacity includes the upper limit of pollutant load, the tolerance range of water storage fluctuations, and the number of extreme hydrological events tolerated.

[0043] It should be noted that in this application, if the wetland is not in a benign self-sustaining stage, it indicates that the wetland's own recovery capacity is insufficient to cope with the current ecological stress, and an artificial intervention management process needs to be initiated. First, based on the values ​​of ecological resilience indicators, the degree of reversal in vegetation succession, and the decline of purification evaluation characteristics, the entropy weight method is used to calculate the weight of each indicator and perform a weighted summation to classify three intervention levels: mild, moderate, and severe. Second, targeted intervention plans are formulated for different levels. Mild intervention focuses on regulating the magnitude of water storage disturbance and reducing the input of surrounding pollutants; moderate intervention combines vegetation replanting and hydrological rhythm restoration; and severe intervention implements wetland ecological reconstruction projects. Finally, the ecological resilience indicators and purification evaluation characteristics are tracked and monitored weekly, and the intervention effect is analyzed through time series fitting. If the indicators do not show an improvement trend for two consecutive months, the intervention plan is adjusted until the wetland ecological resilience indicators rise above the threshold of the benign self-sustaining stage, forming a closed-loop management system for artificial intervention, which will not be elaborated here.

[0044] In step 104, the ecological stability of the target wetland is assessed by the health carrying capacity, and a pre-diagnosis result of the target wetland ecology is output.

[0045] It should be noted that, in this application, hydrometeorological disturbance factors are a set of quantitative parameters characterizing the disturbance caused by external hydrometeorological conditions to the target wetland; ecological response data refers to monitoring data on changes in the structure and function of the target wetland ecosystem under hydrometeorological disturbances; ecological stability indicators refer to parameters that quantify the wetland ecosystem's ability to resist disturbance and maintain stability; stability level thresholds refer to quantitative references for determining the stability level of wetlands; ecological stability level is a state hierarchy characterizing the degree of wetland ecosystem's resistance to disturbance and maintenance of structural and functional stability; ecological risk factors refer to disturbance factors that lead to a decline in wetland ecological stability indicators; and ecological regulation recommendations refer to targeted management measures provided for wetland ecological protection and restoration.

[0046] In some embodiments, assessing the ecological stability of a target wetland based on its health carrying capacity and outputting a preliminary diagnosis of the target wetland's ecology can be achieved through the following steps: Acquire hydrological and meteorological disturbance factors and ecological response data for the target wetland; The ecological stability index is obtained by feature fusion of the health carrying capacity, the hydro-meteorological disturbance factors, and the ecological response data. The ecological stability index is matched with a preset stability level threshold, and a pre-diagnostic result including ecological stability level, ecological risk factors and ecological regulation suggestions is output.

[0047] In specific implementation, firstly, the hydrological and meteorological disturbance factors and ecological response data of the target wetland can be obtained in the following way: Hydrological and meteorological disturbance factors include indicators such as rainfall, temperature variation, wind speed, and frequency of extreme precipitation, with data sourced from continuous monitoring records of meteorological stations surrounding the target wetland and data filed with the ecological protection department; ecological response data includes indicators such as vegetation cover change rate, water quality compliance rate, and microbial activity change value, obtained through real-time sensor monitoring, quarterly field surveys, and remote sensing image inversion. Outliers are removed from both types of data using the 3σ principle, and the data is standardized using Z-score to form a standardized dataset, thus obtaining the hydrological and meteorological disturbance factors and ecological response data of the target wetland. Then, the ecological stability index is obtained by feature fusion of the health carrying capacity, the hydrological and meteorological disturbance factors, and the ecological response data, which can be achieved in the following way: a weighted fusion algorithm is used to perform feature fusion, first standardizing all three types of data—health carrying capacity, hydrological and meteorological disturbance factors, and ecological response data—to the 0-1 range. The weights of each indicator are determined using the entropy weighting method: The information entropy of each indicator in each data category is calculated, and the difference coefficient is obtained based on the information entropy. The larger the difference coefficient, the higher the weight. After normalization, the final weight is obtained. The standardized data of each category are multiplied by their corresponding weights, and the sum is obtained to obtain the ecological stability index. The index value ranges from 0 to 1, with larger values ​​indicating stronger stability. Finally, the ecological stability index is matched with a preset stability level threshold, and a pre-diagnostic result containing the ecological stability level, ecological risk factors, and ecological regulation suggestions is output. This can be achieved as follows: The stability level threshold is determined based on ecological stability index data from 100 groups of similar wetlands. After passing the KS normal distribution test, the percentile method is used to divide the data into high, medium, and low levels, clarifying the threshold range for each level. The calculated ecological stability index is matched with the threshold to determine the stability level. Core ecological risk factors are identified by combining the intensity ranking of hydrological and meteorological disturbance factors and anomalies in the ecological response data. For different levels and risk factors, regulation suggestions including disturbance control and ecological restoration are generated, forming a structured pre-diagnostic report.

[0048] Furthermore, in another aspect of this application, in some embodiments, this application provides a wetland ecological stability assessment system, which includes an ecological stability assessment unit, with reference to... Figure 3 The figure is a schematic diagram of the structure of an ecological stability assessment unit according to some embodiments of this application. The ecological stability assessment unit includes: an acquisition module 201, a processing module 202, and an execution module 203, which are described below: The acquisition module 201 in this application is mainly used to acquire ecological monitoring data of the target wetland during the ecological health management cycle; The processing module 202, in this application, is used to identify the purification evaluation characteristics corresponding to the self-purification capacity of the target wetland based on the ecological monitoring data, and to determine the purification decline index of the self-purification capacity of the target wetland over time based on the purification evaluation characteristics, and then to determine whether the target wetland is in an ecological stress state during the ecological health management cycle based on the purification decline index. It should be noted that the processing module 202 is also used to determine the ecological resilience index of the target wetland based on the purification evaluation characteristics and the vegetation succession status of the target wetland in the ecological change trend if it is under ecological stress, and to determine whether the health status of the target wetland is in a benign self-sustaining stage through the ecological resilience index. If it is in a benign self-sustaining stage, the health carrying capacity of the target wetland is determined through the ecological resilience index and the water storage disturbance characteristics of the target wetland. The execution module 203 in this application mainly assesses the ecological stability of the target wetland through the health carrying capacity and outputs the pre-diagnosis results of the target wetland ecology.

[0049] The foregoing has detailed examples of the wetland ecological stability assessment system and method provided in the embodiments of this application. It is understood that the corresponding apparatus, in order to achieve the above functions, includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the examples described in the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0050] In some embodiments, this application also provides a computer device, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for calling and running the computer program from the memory, so that the computer device performs the wetland ecological stability assessment method described above.

[0051] In some embodiments, reference Figure 4 The dashed lines in the figure indicate that the unit or module is optional. This figure is a schematic diagram of the structure of a computer device for implementing a wetland ecological stability assessment method according to an embodiment of this application. The wetland ecological stability assessment method described in the above embodiments can be achieved through… Figure 4The computer device shown is used to implement this, and the computer device includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device may be a terminal device, a server or a chip.

[0052] Processor 301 can be a general-purpose processor or a special-purpose processor. For example, processor 301 can be a central processing unit (CPU), which can be used to control computer devices, execute software programs, and process data from software programs. The computer device may also include a communication unit 305 for inputting (receiving) and outputting (transmitting) signals.

[0053] For example, the computer device may be a chip, and the communication unit 305 may be the input and / or output circuit of the chip, or the communication unit 305 may be the communication interface of the chip, which may be a component of a terminal device, network device or other device.

[0054] For example, the computer device may be a terminal device or a server, and the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.

[0055] The computer device may include one or more memories 302 storing a program 304. The program 304 can be executed by a processor 301 to generate instructions 303, causing the processor 301 to execute the method described in the above method embodiments according to the instructions 303. Optionally, the memory 302 may also store data (such as a target audit model). Optionally, the processor 301 may also read data stored in the memory 302, which may be stored at the same storage address as the program 304, or it may be stored at a different storage address than the program 304.

[0056] The processor 301 and memory 302 can be configured separately or integrated together, for example, integrated on the system on chip (SOC) of the terminal device.

[0057] It should be understood that each step of the above method embodiment can be completed by hardware logic circuits or software instructions in the processor 301. The processor 301 can be a CPU, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, such as discrete gate, transistor logic devices, or discrete hardware components.

[0058] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0059] For example, in some embodiments, this application also provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the above-described wetland ecological stability assessment method.

[0060] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0061] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for assessing ecological stability of a wetland, characterized by, Includes the following steps: Obtain ecological monitoring data of the target wetland during the ecological health management cycle; Based on the ecological monitoring data, the purification evaluation characteristics corresponding to the self-purification capacity of the target wetland are identified, and the purification decline index of the self-purification capacity of the target wetland over time is determined according to the purification evaluation characteristics. Then, based on the purification decline index, it is determined whether the target wetland is in an ecological stress state during the ecological health management cycle. If the target wetland is under ecological stress, the ecological resilience index of the target wetland is determined based on the purification evaluation characteristics and the vegetation succession status of the target wetland in the ecological change trend. The ecological resilience index is used to determine whether the health status of the target wetland is in a benign self-sustaining stage. If it is in a benign self-sustaining stage, the health carrying capacity of the target wetland is determined based on the ecological resilience index and the water storage disturbance characteristics of the target wetland. The ecological stability of the target wetland is assessed using the health carrying capacity, and a preliminary diagnosis of the target wetland's ecology is output.

2. The method as described in claim 1, characterized in that, The purification evaluation characteristics corresponding to the self-purification capacity of the target wetland identified based on the aforementioned ecological monitoring data specifically include: The feature vectors of the ecological monitoring data are constructed and their purification similarity is calculated with the feature vectors in the historical wetland purification case library. Based on the purification similarity calculation results, determine the purification correlation parameters corresponding to the self-purification capacity of the target wetland; The purification evaluation features corresponding to the self-purification capacity of the target wetland are extracted from the purification correlation parameters.

3. The method as described in claim 1, characterized in that, The purification decline index, which determines the self-purification capacity of the target wetland over time based on the aforementioned purification evaluation characteristics, specifically includes: Determine the purification deviation index between the purification evaluation characteristics and the historical baseline purification status; The purification contribution during the ecological health management cycle is determined based on the purification deviation index. The purification contribution is used to determine the purification decline index of the target wetland's self-purification capacity over time.

4. The method as described in claim 1, characterized in that, Determining whether a target wetland is under ecological stress during the ecological health management cycle based on the purification and decline index specifically includes: The corresponding health status assessment template is dynamically loaded based on the target wetland type and evaluation target. The purification decline index is input into the health status judgment template and matched and verified against the preset static health baseline. Once the matching verification is completed, the status determination rule is triggered, and an ecological health diagnosis report of the target wetland within the ecological health management cycle is generated based on the health status determination template.

5. The method as described in claim 1, characterized in that, If the wetland is under ecological stress, the ecological resilience index of the target wetland is determined based on the purification evaluation characteristics and the vegetation succession status of the target wetland in the ecological change trend. Specifically, this includes: Determine the state attribute parameters of the vegetation succession state of the target wetland in the ecological change trend; The ecological change rate of the target wetland is determined based on the purification evaluation characteristics and the state attribute parameters. The ecological resilience index of the target wetland is determined by the ecological change rate.

6. The method as described in claim 1, characterized in that, Determining whether the target wetland is in a healthy, self-sustaining phase using the aforementioned ecological resilience indicators specifically includes: Determine the initial membership degree of the ecological resilience index relative to the preset ecological health level; Based on the resilience dynamic rules defined in the ecological management knowledge base, a health assessment matrix is ​​constructed to describe the relationship between the current ecological resilience index value and the historical ecological resilience change trend. The conclusion of the judgment of a benign self-sustaining state is determined based on the health assessment matrix and the initial membership degree.

7. The method as described in claim 1, characterized in that, The assessment of the ecological stability of the target wetland based on the aforementioned health carrying capacity, and the output of the preliminary diagnostic results of the target wetland ecology, specifically include: Acquire hydrological and meteorological disturbance factors and ecological response data for the target wetland; The ecological stability index is obtained by feature fusion of the health carrying capacity, the hydro-meteorological disturbance factors, and the ecological response data. The ecological stability index is matched with a preset stability level threshold, and a pre-diagnostic result including ecological stability level, ecological risk factors and ecological regulation suggestions is output.

8. A wetland ecological stability assessment system, comprising an ecological stability assessment unit, characterized in that, The ecological stability assessment unit includes: The acquisition module is used to acquire ecological monitoring data of the target wetland during the ecological health management cycle; The processing module is used to identify the purification evaluation characteristics corresponding to the self-purification capacity of the target wetland based on the ecological monitoring data, and to determine the purification decline index of the self-purification capacity of the target wetland over time based on the purification evaluation characteristics, and then to determine whether the target wetland is in an ecological stress state during the ecological health management cycle based on the purification decline index. The processing module is also used to determine the ecological resilience index of the target wetland based on the purification evaluation characteristics and the vegetation succession status of the target wetland in the ecological change trend if it is under ecological stress, and to determine whether the health status of the target wetland is in a benign self-sustaining stage through the ecological resilience index. If it is in a benign self-sustaining stage, the health carrying capacity of the target wetland is determined through the ecological resilience index and the water storage disturbance characteristics of the target wetland. The execution module is used to assess the ecological stability of the target wetland based on the health carrying capacity and output the pre-diagnosis results of the target wetland ecology.

9. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to call and run the computer programs from the memory, so that the computer device performs the wetland ecological stability assessment method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions or code that, when executed on a computer, cause the computer to implement the wetland ecological stability assessment method as described in any one of claims 1 to 7.

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