Environmental bearing capacity analysis and early warning method and system

Abstract

The invention provides an environment bearing capacity analysis and early warning method and system, and is applied to the technical field of data processing. According to the method, data forward processing and data standardization processing are carried out on environment bearing capacity information after data cleaning, and target index features are generated; processing the target index features to generate cloud model parameter information; performing weight distribution processing on the cloud model parameter information to generate weight information corresponding to each index; processing the target index features based on the weight information corresponding to each index, and generating an average membership degree of the environmental bearing capacity, a fluctuation range value of the membership degree and a noise level value of the membership degree; and processing the average membership degree of the environmental bearing capacity, the fluctuation range value of the membership degree and the noise level value of the membership degree based on the target environmental bearing capacity early warning model, and generating environmental bearing capacity early warning information of the to-be-evaluated region.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an environmental carrying capacity analysis and early warning method and system. Background Art

[0002] Traditional environmental carrying capacity analysis and early warning methods have been widely used in practice, providing some support for regional development planning. However, environmental systems are complex, and data are subject to uncertainty, imprecision, and incompleteness. Traditional methods have limitations when processing these data, making it difficult to effectively quantify the impact of data uncertainty on assessment results. For example, in water quality monitoring, data is subject to errors due to the precision of monitoring equipment and spatial and temporal limitations. However, traditional indicator system methods directly use raw data for calculations, resulting in assessment results that may deviate from reality.

[0003] Environmental factors change continuously over time. Traditional assessment methods, which mostly focus on static evaluations, are unable to reflect the dynamic evolution of these factors in real time. When assessing the carrying capacity of a region's atmospheric environment, it is difficult to reflect the dynamic impact of factors such as industrial development and energy structure adjustments on atmospheric pollutant emissions and environmental quality, making it impossible to provide timely and accurate dynamic information for environmental management.

[0004] Furthermore, environmental carrying capacity involves multiple interconnected systems, and traditional approaches often analyze each system in isolation, ignoring the complex interactions between them. Traditional methods for assessing the environmental carrying capacity of tourist attractions assess the ecological environment and tourism reception capacity separately, but fail to fully consider the impact of tourism activities on the ecological environment and the feedback of ecological changes on sustainable tourism development. As a result, the assessment results fail to fully and accurately reflect the true environmental carrying capacity of the attraction.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore includes information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0006] The present application aims to provide an environmental carrying capacity analysis and early warning method and system that, at least to a certain extent, overcomes the challenges of existing technologies. The system cleans environmental carrying capacity data, classifying it into normally and non-normally distributed data. These data are then processed to filter out outliers and interpolate missing values, resulting in cleaned data. The cleaned data is then normalized and normalized, generating different types of indicators based on their properties and converting them into a unified, comparable form. This results in target indicator characteristics, laying the foundation for subsequent analysis. Cloud model parameter information is calculated based on the target indicator characteristics. Feature values ​​are filtered to obtain desired features. Entropy features are then calculated based on the target indicator characteristics, and the three are combined to obtain super-entropy features. Simultaneously, the cloud model parameter information is processed to generate data standard deviations and correlation coefficient matrices. This information is used to determine the comprehensive weight information for each indicator and normalizes this information to obtain objective weight information. Furthermore, a cloud evaluation scale and comparison matrix are constructed to calculate relative weights. Finally, the weights of each indicator are determined by integrating subjective and objective weights. The target indicator characteristics are processed using the weights of each indicator to obtain the average membership, fluctuation range, and noise level of the environmental carrying capacity. Inputting these values ​​into the target environmental carrying capacity early warning model and comparing them with the preset thresholds can determine the early warning level of the environmental carrying capacity of the area to be assessed and generate corresponding early warning information. This will help improve the scientific nature of environmental carrying capacity assessments and provide a more reliable basis for environmental planning, management and decision-making, thereby promoting regional sustainable development.

[0007] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0008] According to one aspect of the present application, a method for analyzing and warning of environmental carrying capacity is provided, including: obtaining environmental carrying capacity information of an area to be assessed and a target environmental carrying capacity warning model; performing data cleaning processing on the environmental carrying capacity information of the area to be assessed to generate environmental carrying capacity information after data cleaning; performing data positive transformation and data standardization processing on the environmental carrying capacity information after data cleaning to generate target indicator characteristics; processing the target indicator characteristics to generate cloud model parameter information; performing weight distribution processing on the cloud model parameter information to generate weight information corresponding to each indicator; processing the target indicator characteristics based on the weight information corresponding to each indicator to generate an average membership of the environmental carrying capacity, a fluctuation range value of the membership, and a noise level value of the membership; processing the average membership of the environmental carrying capacity, the fluctuation range value of the membership, and the noise level value of the membership based on the target environmental carrying capacity warning model to generate environmental carrying capacity warning information for the area to be assessed.

[0009] Another aspect of the present application is an environmental carrying capacity analysis and early warning device, characterized in that it includes: an acquisition module for acquiring environmental carrying capacity information of the area to be assessed and a target environmental carrying capacity early warning model; a processing module for performing data cleaning processing on the environmental carrying capacity information of the area to be assessed to generate environmental carrying capacity information after data cleaning; performing data positive and data standardization processing on the environmental carrying capacity information after data cleaning to generate target indicator characteristics; processing the target indicator characteristics to generate cloud model parameter information; performing weight distribution processing on the cloud model parameter information to generate weight information corresponding to each indicator; processing the target indicator characteristics based on the weight information corresponding to each indicator to generate an average membership of the environmental carrying capacity, a fluctuation range value of the membership, and a noise level value of the membership; processing the average membership of the environmental carrying capacity, the fluctuation range value of the membership, and the noise level value of the membership based on the target environmental carrying capacity early warning model to generate environmental carrying capacity early warning information of the area to be assessed.

[0010] According to another aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a second processor, the above-mentioned environmental carrying capacity analysis and early warning method is implemented.

[0011] This application provides a method and system for analyzing and early warning environmental carrying capacity. The server first obtains environmental carrying capacity information and a target early warning model for the area to be assessed. The server then cleans the environmental carrying capacity information, classifying it into normally and non-normally distributed data. These data are then processed separately to filter outliers and interpolate missing values, resulting in cleaned data. The cleaned data is then normalized and normalized, generating different types of indicators based on their properties and converting them into a unified, comparable form. This results in the target indicator characteristics, laying the foundation for subsequent analysis.

[0012] Cloud model parameter information is calculated based on the target indicator characteristics. Feature values ​​are filtered to obtain the desired characteristics. Entropy characteristics are then calculated based on the target indicator characteristics, and the three are then combined to obtain the super-entropy characteristics. Simultaneously, the cloud model parameter information is processed to generate data standard deviations and correlation coefficient matrices. This information is used to determine the comprehensive weight information for each indicator and normalize it to obtain objective weight information. Relative weight values ​​are calculated by constructing a cloud evaluation scale and comparison matrix. Finally, the weights of each indicator are determined by integrating subjective and objective weights. The target indicator characteristics are processed using the weights of each indicator to obtain the average membership, fluctuation range, and noise level of the environmental carrying capacity. These values ​​are input into the target environmental carrying capacity early warning model and compared with preset thresholds to determine the early warning level of the environmental carrying capacity of the assessed area. Corresponding early warning information is generated to assist in relevant decision-making, ensuring accurate understanding of the environmental carrying capacity situation and appropriate response.

[0013] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flow chart showing an environmental carrying capacity analysis and early warning method provided by an embodiment of the present application;

[0015] Figure 2 A schematic structural diagram of an environmental carrying capacity analysis and early warning device provided in one embodiment of the present application is shown. DETAILED DESCRIPTION

[0016] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0017] The following combination Figure 1 The following describes the analysis and early warning method of environmental carrying capacity according to an exemplary embodiment of the present application. Figure 1 As shown, the method is applied to the server and includes:

[0018] S101, obtaining environmental carrying capacity information of the area to be assessed and a target environmental carrying capacity early warning model.

[0019] In one embodiment, for a coastal city, environmental carrying capacity information is available from a wide range of sources. Regarding natural resources, water resource data, such as annual available freshwater, can be obtained from local water resources departments. Data from the past five years shows that annual available freshwater fluctuates within a [specific range], influenced by precipitation and water demand. Land resource data, including cultivated land area and construction land area, can be obtained through land use surveys conducted by the land department. In recent years, cultivated land area has decreased slightly due to urbanization. Mineral resource data covers reserves and extraction volumes, and local mineral resource management departments maintain detailed records. Regarding environmental quality, air pollution indicators, such as the annual average PM2.5 concentration, are provided by real-time monitoring stations and have fluctuated within a [specific numerical range] over the past few years. Water quality indicators include pollutant levels in rivers and oceans. Monitoring data from environmental protection departments indicates that water quality in some sea areas is affected by land-based pollution. Soil pollution indicators are obtained through soil sampling and analysis, with some areas experiencing certain levels of heavy metal exceeding standards. Regarding ecological resilience, biodiversity data can be obtained through biodiversity surveys, vegetation cover is determined through a combination of remote sensing and field measurements, and ecological resilience is assessed based on monitoring data from relevant ecological restoration projects. Regarding socioeconomics, population density data, available from statistical authorities, shows a year-on-year upward trend. Industrial output data comes from the local statistical bureau, and environmental protection investment data can be obtained from government fiscal expenditures and corporate environmental investment statistics. These data reflect the multifaceted state of this coastal city's environmental system and provide rich information for environmental carrying capacity analysis.

[0020] The target environmental carrying capacity early warning model integrates the Analytic Hierarchy Process (AHP), cloud model theory, and the CRITIC expert fusion weighting method. The AHP is used to construct a multi-level indicator system for systematic decomposition and analysis of environmental carrying capacity; the cloud model theory is used to address uncertainty and ambiguity in data; and the CRITIC expert fusion weighting method combines objective data statistics with expert subjective knowledge to determine indicator weights, making the model results more scientific and reliable. The model decomposes environmental carrying capacity into a target layer, a criterion layer, and an indicator layer. The target layer, representing "regional environmental carrying capacity," is the core objective of the entire assessment. The criterion layer, based on the complexity of the environmental system, is divided into key subsystems such as natural resources, environmental quality, ecological resilience, and socioeconomics. The indicator layer comprises specific quantifiable indicators within each criterion layer, such as annual available water volume and cultivated land area under the natural resources criterion layer; and annual average PM2.5 concentration and COD emissions under the environmental quality criterion layer.

[0021] The target environmental carrying capacity early warning model first obtains the environmental carrying capacity information of the area to be assessed and performs data cleaning, including outlier processing and missing value filling. The data is then forward-oriented and standardized, converting different types of indicators into a unified and comparable form. The cloud model parameters are then calculated to determine the weight of each indicator. Based on these parameters and weights, hierarchical aggregation calculations are performed from the indicator layer to the criterion layer and then to the target layer to obtain the average membership of the environmental carrying capacity, the fluctuation range of the membership, and the noise level of the membership. According to the preset thresholds corresponding to different levels of carrying capacity, combined with the target environmental carrying capacity early warning model, the warning level of the environmental carrying capacity of the area to be assessed is determined, and the corresponding warning information is generated.

[0022] S102: Perform data cleaning on the environmental carrying capacity information of the area to be assessed to generate cleaned environmental carrying capacity information.

[0023] In one embodiment, the environmental carrying capacity information of the area to be assessed is classified and processed to generate normally distributed data and non-normally distributed data. The collected environmental carrying capacity information contains many indicator data, such as annual available water volume, annual average concentration of PM2.5, population density, etc. The distribution type of these data is judged, and it is found that the annual available water volume data approximately obeys the normal distribution, while the population density data is affected by various factors such as urban development planning and policies, and presents a non-normal distribution. This step is the basis for the subsequent use of different processing methods for different distribution data, because the data characteristics of normal distribution and non-normal distribution are different, and different methods are required to deal with outliers.

[0024] Normally distributed data is processed to generate the mean and standard deviation for each indicator. The first outlier is then generated based on the mean and standard deviation of each indicator. For data on annual available water, which approximates a normal distribution, the mean and standard deviation are calculated. Assume that statistical calculations show that the city's annual available water volume over the past five years has a mean of μ = 5 billion cubic meters and a standard deviation of σ = 500 million cubic meters. According to the 3σ principle, the outlier determination condition is |x - μ| > 3σ. Therefore, the first outlier range is defined as data points less than 50 - 3 × 5 = 3.5 billion cubic meters or greater than 50 + 3 × 5 = 6.5 billion cubic meters. This is based on the characteristics of a normal distribution, identifying outliers within a certain probability range and excluding data that deviates from the normal range to prevent misleading subsequent analysis.

[0025] The box plot method is used to process non-normally distributed data and generate a second outlier. For non-normally distributed data such as population density, the box plot method is used. The quartiles are calculated. Assuming Q1 = 500 people / km² and Q3 = 800 people / km², the IQR = Q3-Q1 = 300 people / km². The second outlier range is defined as data points with a value less than Q1-1.5IQR = 800-1.5×300 = 50 people / km² or greater than Q1+1.5IQR = 800+1.5×300 = 1250 people / km². The box plot method is suitable for non-normally distributed data and can effectively identify outliers in the data. Based on the first and second outliers, the environmental carrying capacity information of the assessed area is filtered for outliers, generating noise-filtered environmental carrying capacity information. Data points with annual water availability less than 3.5 billion cubic meters and greater than 6.5 billion cubic meters, as well as population densities less than 50 people per square kilometer and greater than 1,250 people per square kilometer, were considered outliers. After screening, the noise-filtered environmental carrying capacity information was obtained, eliminating the interference of these outliers.

[0026] Missing values ​​are interpolated in the noise-filtered environmental carrying capacity information to generate cleaned environmental carrying capacity information. If missing data exist in the noise-filtered environmental carrying capacity information, interpolation is required. For example, if industrial output data for some years contains missing values, the missing rate is less than 20%, and the correlation between variables is high, the KNN method is used for interpolation. Assume that after calculating the Euclidean distance between the sample containing the missing value and the other samples, the five nearest neighboring samples are selected. If the average industrial output value of these five neighboring samples is 50 billion yuan, then the missing value is filled with 50 billion yuan. If data cannot be interpolated using KNN, such as missing data for certain soil pollution indicators and low correlation between variables, the MICE algorithm is used for multiple interpolation. Missing values ​​are predicted using an iterative regression model to generate multiple complete data sets. The results are then aggregated to fill the missing values, thus obtaining cleaned environmental carrying capacity information and laying the foundation for subsequent accurate environmental carrying capacity analysis.

[0027] S103, performing data positive and data standardization processing on the environmental carrying capacity information after data cleaning to generate target indicator characteristics.

[0028] In one implementation, the environmental carrying capacity information of the area to be assessed is processed to generate target layer features, criterion layer features, and indicator layer features. The target layer feature is "regional environmental carrying capacity", which is the core of the comprehensive assessment. The criterion layer features are determined based on the complexity of the environmental system. The natural resource criterion layer (B1) covers water resources, land resources, and mineral resources related features; the environmental quality criterion layer (B2) includes air, water quality, and soil pollution indicator features; the ecological resilience criterion layer (B3) has biodiversity, vegetation coverage, and ecological resilience features; the socio-economic criterion layer (B4) involves population density, industrial output value, and environmental protection investment features. The indicator layer features are a refinement of the criterion layer. For example, under the natural resource criterion layer (B1), the annual available water volume (C1, unit: 100 million m 3 ) and cultivated land area (C2, unit: km 2 ) is a specific indicator; Under the environmental quality standard layer (B2), the annual average concentration of PM2.5 (C3, μg / m 3 ) and COD emissions (C4, tons / year) are used as specific indicators. These characteristics constitute the preliminary classification and quantification of environmental carrying capacity information, laying the foundation for subsequent analysis.

[0029] The characteristics of the target layer, criterion layer, and indicator layer are processed to generate large-scale indicators, small-scale indicators, intermediate-scale indicators, and interval-scale indicators. In this coastal city's environmental carrying capacity analysis, indicators are categorized based on their nature and impact on environmental carrying capacity. Indicators such as annual available water volume and vegetation coverage, where larger values ​​contribute to improved environmental carrying capacity, are classified as large-scale indicators. Indicators such as annual average PM2.5 concentration and COD emissions, where smaller values ​​are preferred, are classified as small-scale indicators. Indicators with optimal values, such as soil nutrient content, are classified as intermediate-scale indicators, where optimal soil fertility is achieved within a certain range. For example, if the dissolved oxygen content in a particular sea area is specified to be optimal for marine ecosystems within a specific range of [5-7] mg / L, this is an interval-scale indicator. This categorization facilitates targeted processing of indicators and makes different types of indicators comparable.

[0030] Based on the first calculation formula, the small index is positively processed to generate a positive large index. The first calculation formula is: i,new =max{x1,x2,…,x n}-x i ; where x i,new is a large indicator after positive transformation, x iis the original small index value, max{x1,x2,…,x n} is the maximum value of all original small indicators. Taking the annual average concentration of PM2.5 as an example, assuming that the annual average concentration of PM2.5 in the coastal city and its surrounding areas in the past five years is x1 = 35μg / m 3 , x2=40μg / m 3 , x3=30μg / m 3 , x4=45μg / m 3 , x5=38μg / m 3 , where the maximum value max{x1,x2,x3,x4,x5}=45μg / m 3 For x i =40 μg / m 3 This data, according to the first calculation formula x i,new =max{x1,x2,…,x n}-x i , the value after positive transformation is 4540=5. Through positive transformation, small indicators are transformed into a form where the larger the better, which is convenient for subsequent unified calculation and comparison.

[0031] Based on the second calculation formula, the intermediate indicators are positively processed to generate positive large indicators. The second calculation formula is: Among them, x' i,new is a large index after positive transformation, M is the maximum absolute deviation, x' i is the original intermediate index value, x' best is the target value of the intermediate indicator. Assume that the optimal value x' of a nutrient content in the urban soil best =50mg / kg, the nutrient content of the soil in a certain area is x' i =45mg / kg. First calculate M=max{|x' i -x' best |}, find the maximum absolute value of the difference from the optimal value among multiple sample data. Assume that the maximum absolute value of the difference between other sample data and 50mg / kg is 10mg / kg, that is, M=10, according to the second calculation formula The forward value is This makes the intermediate indicators conform to the calculation logic of the larger the better after being positively converted.

[0032] Based on the third calculation formula, the interval-type indicator is positively processed to generate a positive large-scale indicator. The third calculation formula is: M=max{a-minx' i ' ,max{x' i '}-b}; where x'i, ‘ new is a large-scale indicator after positive conversion, M is the maximum absolute deviation, a and b are the lower and upper limits of the interval indicator, respectively, x' i ' is the original interval index value. For the dissolved oxygen content (interval index) in a certain sea area of ​​the coastal city, the optimal interval is [5, 7] mg / L. Assume that a certain monitoring value is x' i ' =4mg / L, first calculate M=max{5-minx' i ' ,max{x' i '}-7}, if the minimum value of the dissolved oxygen content in the historical monitoring data of the sea area is 3mg / L and the maximum value is 8mg / L, then M=max{5-3,8-7}=2. According to the third calculation formula, Through this formula, the interval-type indicator is converted into a unified positive indicator.

[0033] Based on the fourth calculation formula, several large indicators are standardized to generate target indicator characteristics. The fourth calculation formula is: Among them, X norm After positive processing, a series of large indicators are obtained. For example, the annual available water volume and the average annual concentration of PM2.5 after positive processing are used as examples. Assuming that the original data of annual available water volume is x 11 =4 billion cubic meters, x 12 =5 billion cubic meters, x 13 =3.5 billion cubic meters, its minimum value min{x 11 ,x 12 ,x 13}=3.5 billion cubic meters, the maximum value max{x 11 ,x 12 ,x 13}=5 billion cubic meters. According to the fourth calculation formula, for x 11 =4 billion cubic meters, the standardized value is All large-scale indicators after forward transformation are calculated, and indicators of different dimensions and magnitudes are unified into the interval [0, 1] to eliminate dimensional differences and generate target indicator characteristics, providing a standardized data basis for subsequent cloud model parameter calculation and environmental carrying capacity evaluation.

[0034] S104: Process the target indicator characteristics to generate cloud model parameter information.

[0035] In one embodiment, the target indicator characteristics are subjected to feature screening processing to generate expected features, wherein the expected features are used to characterize the characteristic values ​​in the target indicator characteristics that are greater than a preset threshold. After data standardization and positive processing, the target indicator characteristic data related to the environmental carrying capacity of the coastal city are obtained, including multiple indicator data such as annual available water volume and positively calculated annual average concentration of PM2.5. A preset threshold is set to 0.6 (this threshold can be determined based on actual research needs and data characteristics) to screen the target indicator characteristics. Taking the annual available water volume indicator as an example, its standardized data are [0.4, 0.7, 0.5, 0.8, 0.65], of which the characteristic values ​​greater than 0.6 are 0.7, 0.8, and 0.65. These values ​​constitute the expected features of the annual available water volume indicator. The expected features reflect the relatively better part of the indicator in the data distribution. In the environmental carrying capacity assessment, they represent data features that have a positive contribution to the environmental carrying capacity and perform relatively well.

[0036] The target indicator characteristics and the expected characteristics are processed to generate entropy characteristics, where the entropy characteristics are used to characterize the degree of dispersion of the target indicator characteristics around the expected characteristics. The entropy characteristics are used to characterize the degree of dispersion of the target indicator characteristics around the expected characteristics. Taking the annual available water quantity indicator as an example, the mean value Ex of the expected characteristic (assuming it is calculated to be 0.72) is calculated according to the entropy characteristic calculation formula Among them, En is the entropy feature, which is used to express the uncertainty of the target indicator feature; avg(X'-Ex) is the average deviation between the target indicator feature X' and the expected feature Ex. For the data point 0.4, (X'-Ex) = (0.4-0.72) = -0.32. Similar calculations are performed on all data points and the average deviation avg(X'-Ex) is taken. Assuming that avg(X'-Ex) = 0.15 after calculation, the entropy feature The larger the entropy value, the greater the degree of dispersion of the target indicator characteristics around the expected characteristics, that is, the higher the uncertainty of the data; in this example, the entropy value of 0.13 for the annual available water indicator reflects the dispersion of its data around the expected characteristics. If the entropy value is small, it means that the indicator data is relatively concentrated near the expected characteristics.

[0037] The target indicator features, expected features and entropy features are processed to generate super entropy features. The calculation formula for super entropy features is: Among them, He is the super entropy feature, which is used to express the uncertainty of the entropy feature; N is the number of samples, X' iis the characteristic value of the i-th sample. Taking the annual available water indicator as an example, with a sample size of N = 5 (i.e., the five data points [0.4, 0.7, 0.5, 0.8, 0.65] mentioned above), according to the super entropy characteristic calculation formula, for data point 0.4, |0.40.72|0.13 = 0.19. For each data point, the square is calculated and the sum is taken, assuming the result is 0.05. A larger super entropy value indicates greater uncertainty in the entropy characteristic itself and a higher level of data noise. In this scheme, the super entropy value of 0.11 for the annual available water indicator represents its entropy fluctuations. A larger super entropy value indicates poor data stability and the presence of numerous interfering factors that may affect the assessment of its uncertainty.

[0038] Cloud model parameter information is generated based on the expected, entropy, and super-entropy characteristics. In the cloud model, the expected characteristic represents the typical value or center point of a qualitative concept in the quantitative domain. Mathematically, it is the mean of the cloud droplet distribution and corresponds to the x-value (ideal center) corresponding to a membership degree μ(x) = 1. For the annual available water indicator for this coastal city, the calculated expected characteristic mean Ex = 0.72. This value indicates that, based on the data characteristics, 0.72 can be used as a representative "center" value for the city's annual available water in the environmental carrying capacity assessment. If the ideal state of environmental carrying capacity-related indicators is considered a standard, 0.72 represents a reference value for annual available water that is relatively close to the ideal state in the current dataset. For example, in a comprehensive assessment of the city's environmental carrying capacity, if 1 represents the most ideal annual available water state (indicating full satisfaction of all urban development needs and no water resource pressure) and 0 represents the worst state, then 0.72 means that the annual available water state is at a relatively good level, but there is room for improvement.

[0039] The entropy characteristic has a dual function: qualitatively, it reflects the ambiguity of the concept, or its coverage; quantitatively, it characterizes the standard deviation of a random distribution and determines the dispersion of cloud droplet distribution, or the fluctuation range of membership. For the annual available water indicator, the entropy characteristic En = 0.13. From the perspective of coverage, it reflects the ambiguity of the concept of annual available water. A larger value indicates a wider coverage, meaning that the indicator varies across different scenarios. Regarding dispersion, an entropy value of 0.13 indicates that the annual available water data has a moderate degree of dispersion around the expected characteristic of 0.72. A smaller entropy value, such as close to 0, indicates that the data is relatively concentrated around the expected characteristic, and the fluctuation of annual available water is relatively stable. A larger entropy value indicates a large degree of data dispersion, with significant differences in annual available water across different samples. In actual environmental carrying capacity assessments, this reflects the fluctuation of annual available water due to various factors. An entropy value of 0.13 indicates that while annual available water fluctuates in a city, the fluctuation is within a certain acceptable range.

[0040] The excess entropy characteristic is used to characterize the uncertainty of the entropy characteristic itself and reflects the degree of cloud droplet condensation. Intuitively, the greater the excess entropy, the thicker the cloud layer and the blurrier the edges; when He = 0, the cloud degenerates into a precise Gaussian distribution. The excess entropy characteristic He of this city's annual available water indicator is 0.11, indicating that the uncertainty of the entropy characteristic is within a certain level. A higher excess entropy value, such as greater than 0.2, indicates large fluctuations in the entropy value and high data noise levels. This suggests that there may be numerous uncertainties affecting the assessment of annual available water. For example, measurement errors and climate anomalies in specific years can significantly interfere with the annual available water data. The current excess entropy value of 0.11 indicates that while there are certain interfering factors when assessing the uncertainty of annual available water, the overall entropy estimate is relatively reliable, and the model is reasonably stable.

[0041] The above-mentioned expected characteristic mean Ex = 0.72, entropy characteristic En = 0.13, and excess entropy characteristic He = 0.11 together constitute the cloud model parameter information for the annual available water quantity indicator of this coastal city. These parameters are interrelated and can more comprehensively reflect the characteristics of annual available water quantity in environmental carrying capacity assessment, providing a scientific basis for subsequent analysis and decision-making. For example, when formulating urban water resources planning, these parameters can be used to understand the stability, range of variation, and degree of uncertainty of annual available water quantity, thereby rationally planning water resource development, utilization, and protection strategies.

[0042] S105: Perform weight distribution processing on the cloud model parameter information to generate weight information corresponding to each indicator.

[0043] In one embodiment, cloud model parameter information is processed to generate a data standard deviation for each indicator. The data standard deviation is used to characterize the score variance between indicators and reflects the degree of dispersion between indicators. Assume that we have an environmental carrying capacity evaluation indicator system for coastal cities, which includes the following indicators: annual available water volume (C1), cultivated land area (C2), annual average PM2.5 concentration (C3), and COD emissions (C4) (this is just a simple example). The data standard deviation of each indicator needs to be calculated to characterize the degree of dispersion between indicators. Assume that after data cleaning and standardization, the values ​​of each indicator are: annual available water volume (C1): [0.4, 0.6, 0.5, 0.7, 0.8]; cultivated land area (C2): [0.3, 0.5, 0.4, 0.6, 0.7]; annual average PM2.5 concentration (C3): [0.8, 0.7, 0.6, 0.5, 0.4]; COD emissions (C4): [0.2, 0.3, 0.4, 0.5, 0.6].

[0044] The formula for calculating standard deviation is: in, is the mean of the jth index, σ jis the standard deviation of the jth indicator. The formula for the standard deviation is

[0045] The cloud model parameter information is processed to generate the linear correlation coefficients between the indicators. Based on the linear correlation coefficients between the indicators, the correlation coefficient matrix and indicator conflict information are generated. The correlation coefficient matrix is ​​used to characterize the correlation between the indicators. The linear correlation coefficients between these indicators in the cloud model parameter information are calculated to characterize the correlation between them. The linear correlation coefficient calculation formula is: Thus, the correlation coefficient matrix is ​​obtained Specifically, r12 = 0.8, indicating a high positive correlation between annual water availability and cultivated land area, indicating similar trends in these two indicators. r13 = -0.6, indicating a negative correlation between annual water availability and annual average PM2.5 concentration, indicating opposite trends. r14 = 0.2, indicating a low positive correlation between annual water availability and COD emissions, indicating a moderate but not strong correlation between the two indicators. The same applies to other parameters.

[0046] The correlation coefficient matrix is ​​used to characterize the correlation between indicators. For further analysis, we can characterize the conflict between indicators by taking the opposite number of the correlation coefficient. The greater the conflict, the higher the redundancy between indicators and the lower their importance. Calculate the index conflict information, where δ j It reflects the conflict of the jth indicator relative to other indicators. The larger the value, the more intense the conflict. m represents the total number of indicator data. Suppose we calculate the following conflict information: r1: 0.2 (low conflict), r2: 0.2 (low conflict), r3: 0.4 (medium conflict), r4: 0.3 (medium conflict). The data standard deviation and indicator conflict information of each indicator are processed to generate the comprehensive weight information of each indicator. Comprehensive weight calculation By combining the data standard deviation and conflict information of the indicator, we can generate the comprehensive weight information of each indicator. The comprehensive weight information is calculated by combining the data standard deviation and conflict information. The specific formula is as follows: C j =σ j δ j , where C j is the comprehensive weight information of the jth indicator, σ j is the standard deviation of the indicator, δ j It is the conflict score of the indicator. The greater the comprehensive weight information, the higher the importance of the indicator in the evaluation.

[0047] Normalize the comprehensive weight information of each indicator to generate the objective weight information of each indicator. In order to ensure that the sum of the weights of each indicator is 1, it is necessary to normalize these comprehensive weight information to obtain the objective weight information of each indicator. The specific formula is as follows: Among them, ω j is the objective weight of the jth indicator, and m is the total number of indicators.

[0048] The cloud model parameter information is processed to generate a cloud evaluation scale. Specifically, the calculation formula of the cloud evaluation scale is as follows:

[0049]

[0050] The cloud model parameter information includes the following four indicators: annual available water volume (C1), cultivated land area (C2), annual average PM2.5 concentration (C3), and COD emissions (C4). The cloud model uses expectation (Ex), entropy (En), and excess entropy (He) to reflect the importance of the indicators. Assuming that the cloud model parameters for each indicator have been calculated, a cloud evaluation scale is generated as follows:

[0051] index Ex En He C1 0.65 0.1 0.05 C2 0.7 0.08 0.03 C3 0.3 0.2 0.1 C4 0.5 0.15 0.08

[0052] A comparison matrix of evaluation indicators of cloud models and cloud evaluation scales was constructed. The golden section method was used to allocate the parameters of the cloud model, and a cloud parameter table with a 9-level cloud importance scale was generated, as follows:

[0053]

[0054]

[0055] After the importance scale cloud parameters are given, the evaluation index comparison matrix of the cloud model and the cloud evaluation scale is constructed:

[0056]

[0057] Where n is the number of indicators to be evaluated, the expected value of the diagonal elements Ex=1, and En and He are both 0. The two indicators are compared pairwise and the formula is used Obtain the specific values ​​of each indicator and generate an evaluation indicator comparison matrix.

[0058] The example is as follows, the evaluation index comparison matrix is:

[0059]

[0060] Process each indicator in the evaluation indicator comparison matrix of the cloud model and cloud evaluation scale to generate the relative weight value of each indicator. Use the square root method to calculate the relative weight value of each indicator, as follows: The relative weight value W' of indicator i i (Ex' i ,En' i ,He' i ), the relative weight value of indicator j

[0061] Among them, λ max is the maximum eigenvalue of the matrix, which is used to calculate the relative weight of the index, Ex i ' is the expected value of the normalized index i, H e ' is the entropy of the normalized index i, En i ' is the normalized super entropy of index i, Ex i is the expected value of index i, En i is the entropy of index i, which is used to characterize the uncertainty of the index, H e is the super entropy of index i, which is used to express the uncertainty of entropy, Ex i,j is the entropy of indicator i in the evaluation scale, which indicates the uncertainty or fuzziness of indicator i. The larger the entropy, the higher the uncertainty of the indicator. i,j It is the expected value of indicator i in the evaluation scale, indicating the typical value or central value of indicator i, and reflecting the main characteristics of the indicator.

[0062] The calculations yielded the following relative weights: annual available water (C1): 0.2, cultivated land (C2): 0.35, annual average PM2.5 concentration (C3): 0.20, and COD emissions (C4): 0.25. These relative weights reflect the importance of each indicator in the environmental carrying capacity assessment.

[0063] The objective weight information of each indicator and the relative weight value of each indicator are normalized to generate the weight information corresponding to each indicator. The objective weight of each indicator is calculated using the CRITIC method, and combined with the relative weight of the expert scores, the subjective and objective preference coefficient α is used for fusion.

[0064] Assume that the objective weight calculated by the CRITIC method is:

[0065] index Objective weight C1 0.25 C2 0.30 C3 0.15 C4 0.30

[0066] Assume that the relative weights of expert ratings are:

[0067] index Relative weight C1 0.20 C2 0.35 C3 0.20 C4 0.25

[0068] The subjective and objective preference coefficient α = 0.5 is used for fusion, and w = αw s +(1-α)w o Calculate the comprehensive weight, where w is the comprehensive weight of the indicator; w s Refers to the weight calculated by the cloud model expert scoring method, that is, the relative weight; w o Refers to the weight calculated by the THECRITIC method, that is, the objective weight; α is the subjective and objective preference coefficient, which is taken as 0.5 in the present invention.

[0069] ω C 1=0.5×0.25+0.5×0.20=0.225;ω C 2 = 0.5 × 0.30 + 0.5 × 0.35 = 0.325;

[0070] ω C 3=0.5×0.15+0.5×0.20=0.175;ω C 4=0.5×0.30+0.5×0.25=0.275.

[0071] index Comprehensive weight C1 0.23 C2 0.31 C3 0.17 C4 0.28

[0072] S106 , processing the target indicator characteristics based on the weight information corresponding to each indicator to generate an average membership degree of the environmental carrying capacity, a fluctuation range value of the membership degree, and a noise level value of the membership degree.

[0073] In one embodiment, the criterion layer indicators in the target indicator characteristics are processed based on the weight information corresponding to the criterion layer indicators to generate the membership information of the criterion layer. Assume that we have an environmental carrying capacity evaluation index system for coastal cities, including four criterion layers: natural resources (B1), environmental quality (B2), ecological resilience (B3) and social economy (B4). Each criterion layer has multiple specific indicators, for example: natural resources (B1): annual available water (C1), cultivated land area (C2)

[0074] Environmental quality (B2): Annual average concentration of PM2.5 (C3), COD emissions (C4)

[0075] Ecological resilience (B3): biodiversity (C5), vegetation coverage (C6)

[0076] Social Economy (B4): Population Density (C7), Industrial Output (C8)

[0077] Assume that we have calculated the weights for each indicator using the cloud model and the CRITIC-Expert Fusion Weighting Method. For example, the weight of annual available water (C1) is 0.2, the weight of cultivated land (C2) is 0.1, the weight of annual average PM2.5 concentration (C3) is 0.15, the weight of COD emissions (C4) is 0.1, the weight of biodiversity (C5) is 0.1, the weight of vegetation cover (C6) is 0.1, the weight of population density (C7) is 0.15, and the weight of industrial output value (C8) is 0.2.

[0078] For each criterion layer, we calculate the membership information of the criterion layer based on the weight information of its subordinate indicators. For example, for natural resources (B1):

[0079] Based on the calculation formula of the criterion layer membership value, we can know that Criteria layer B k The membership degree, w j C is the indicator layer j The weight, μ Cj C is the indicator layer j The membership degree is n, n is the number of indicator layers, and m is the number of criterion layers. in, and are the membership values ​​of annual available water volume and cultivated land area, respectively.

[0080] Based on the weight information corresponding to the target layer indicators, the membership information of the criterion layer is processed to generate the membership information of the target layer. Assume that we have assigned weights to each criterion layer, for example:

[0081] Natural resources (B1): 0.2; Environmental quality (B2): 0.2;

[0082] Ecological resilience (B3): 0.3; Socio-economic (B4): 0.3;

[0083] The calculation formula of the target layer membership value is: Among them, μ A is the membership degree of the target layer, is the weight of the criterion layer.

[0084] Based on these weights, we can calculate the membership information of the target layer (regional environmental carrying capacity):

[0085]

[0086] The membership information of the target layer is processed to generate the average membership of the environmental carrying capacity. Assume that we obtain multiple membership values ​​of the target layer through Monte Carlo simulation. Where N = 1000. We calculate the average membership: Among them, Ex is the average membership of environmental carrying capacity, N is the number of samples of membership information of the target layer, is the target layer membership of the i-th sample. Assume that the average value of the simulation results is 0.65.

[0087] The membership information of the target layer and the average membership of the environmental carrying capacity are processed to generate the fluctuation range value of the membership. First, the mean of the absolute deviation between the membership and the average membership is calculated. in, is the average absolute deviation of the membership from the mean, and Ex is the average membership of the environmental carrying capacity. After calculation, the mean deviation is 0.12. At this time, the fluctuation range of the membership is calculated. Among them, En is the fluctuation range of membership.

[0088] The fluctuation range of the membership is processed to generate the noise level value of the membership. First, the variance of the membership deviation from the mean is calculated. Assuming the calculated variance is 0.03, at this time, calculate the noise level value of the membership degree,

[0089] S107 , processing the average membership degree, the fluctuation range value of the membership degree, and the noise level value of the membership degree based on the target environmental carrying capacity early warning model to generate environmental carrying capacity early warning information for the area to be assessed.

[0090] In one implementation, average membership thresholds, membership fluctuation range thresholds, and membership noise level thresholds corresponding to different levels of carrying capacity are constructed. In environmental carrying capacity assessment, carrying capacity is divided into several levels, such as "low," "medium," "high," and "very high." Each level corresponds to a different average membership, membership fluctuation range, and noise level threshold. Suppose we determine the following thresholds based on historical data and expert opinions:

[0091] Low carrying capacity: average membership threshold: ≤0.4; fluctuation range threshold: ≥0.2; noise level threshold: ≥0.1.

[0092] Medium bearing capacity: average membership threshold: 0.4<≤0.6; fluctuation range threshold: 0.1<≤0.2; noise level threshold: 0.05<≤0.1.

[0093] High carrying capacity: average membership threshold: 0.6<≤0.8; fluctuation range threshold: <0.1; noise level threshold: <0.05.

[0094] Extremely high carrying capacity: average membership threshold: >0.8; fluctuation range threshold: <0.05; noise level threshold: <0.02.

[0095] Based on the target environmental carrying capacity warning model, the average membership threshold corresponding to different levels of carrying capacity, the membership fluctuation range threshold, and the membership noise level threshold, the average membership, membership fluctuation range, and membership noise level values ​​of the environmental carrying capacity are processed to generate the warning level of the environmental carrying capacity of the area to be assessed. Assume that we have environmental carrying capacity data for the area to be assessed, and after calculation, we obtain the following values:

[0096] Average membership: 0.65; fluctuation range: 0.15; noise level: 0.087.

[0097] Based on the above thresholds, we can determine the carrying capacity level of the area:

[0098] The average membership degree of 0.65 falls within the “high carrying capacity” range.

[0099] The fluctuation range of 0.15 exceeds the fluctuation range threshold of "high carrying capacity".

[0100] The noise level value of 0.087 exceeds the noise level value threshold of "very high load capacity".

[0101] Based on the above information, we can conclude that the carrying capacity of this area is at the "high carrying capacity" level, but with large fluctuations and high noise levels.

[0102] The warning level of the environmental carrying capacity of the area to be assessed is processed to generate environmental carrying capacity warning information for the area to be assessed. Based on the calculation results and threshold judgment, we can generate the following warning information: Carrying capacity level: High carrying capacity. Warning information: Although the current carrying capacity is at a high level, the fluctuation range is large (0.12) and the noise level is also high (0.08). It is recommended to strengthen monitoring and data analysis to ensure the stability and reliability of the data. At the same time, due to the large fluctuations, it is recommended to exercise caution in planning and decision-making. In this way, we can combine the average membership, fluctuation range, and noise level values ​​to generate detailed environmental carrying capacity warning information to help decision makers better understand and respond to changes in environmental carrying capacity.

[0103] The server first obtains the environmental carrying capacity information and target early warning model for the area to be assessed. It then cleans the environmental carrying capacity information, classifying it into normally and non-normally distributed data. These data are then processed to filter out outliers and interpolate missing values, resulting in cleaned data. The cleaned data is then normalized and standardized, generating different types of indicators based on their properties and converting them into a unified and comparable form. This results in the target indicator characteristics, laying the foundation for subsequent analysis.

[0104] Cloud model parameter information is calculated based on the target indicator characteristics. Feature values ​​are filtered to obtain the desired characteristics. Entropy characteristics are then calculated based on the target indicator characteristics, and the three are then combined to obtain the super-entropy characteristics. Simultaneously, the cloud model parameter information is processed to generate data standard deviations and correlation coefficient matrices. This information is used to determine the comprehensive weight information for each indicator and normalize it to obtain objective weight information. Relative weight values ​​are calculated by constructing a cloud evaluation scale and comparison matrix. Finally, the weights of each indicator are determined by integrating subjective and objective weights. The target indicator characteristics are processed using the weights of each indicator to obtain the average membership, fluctuation range, and noise level of the environmental carrying capacity. These values ​​are input into the target environmental carrying capacity early warning model and compared with preset thresholds to determine the early warning level of the environmental carrying capacity of the assessed area. Corresponding early warning information is generated to assist in relevant decision-making, ensuring accurate understanding of the environmental carrying capacity situation and appropriate response.

[0105] In one embodiment, Figure 2 As shown, the present application also provides an environmental carrying capacity analysis and early warning device, comprising:

[0106] An acquisition module 201 is used to obtain environmental carrying capacity information of the area to be assessed and a target environmental carrying capacity early warning model;

[0107] The processing module 202 is used to perform data cleaning on the environmental carrying capacity information of the area to be assessed to generate the environmental carrying capacity information after data cleaning; perform data positive and data standardization on the environmental carrying capacity information after data cleaning to generate target indicator characteristics; process the target indicator characteristics to generate cloud model parameter information; perform weight distribution processing on the cloud model parameter information to generate weight information corresponding to each indicator; process the target indicator characteristics based on the weight information corresponding to each indicator to generate the average membership of the environmental carrying capacity, the fluctuation range value of the membership, and the noise level value of the membership; process the average membership of the environmental carrying capacity, the fluctuation range value of the membership, and the noise level value of the membership based on the target environmental carrying capacity early warning model to generate the environmental carrying capacity early warning information of the area to be assessed.

[0108] Each embodiment in this application is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the analysis and early warning method for evaluating environmental carrying capacity, electronic device, electronic device, and readable storage medium embodiments, since they are basically similar to the above-mentioned analysis and early warning method embodiments for environmental carrying capacity, the description is relatively simple, and the relevant parts can be referred to the partial description of the above-mentioned analysis and early warning method embodiments for environmental carrying capacity.

Claims

1. A method for analyzing and warning environmental carrying capacity, characterized in that: include: Obtain environmental carrying capacity information of the area to be assessed and the target environmental carrying capacity early warning model; Perform data cleaning on the environmental carrying capacity information of the area to be assessed to generate cleaned environmental carrying capacity information; Perform data positive and data standardization on the environmental carrying capacity information after data cleaning to generate target indicator characteristics; Process the target indicator characteristics to generate cloud model parameter information; Perform weight distribution processing on cloud model parameter information to generate weight information corresponding to each indicator; Based on the weight information corresponding to each indicator, the target indicator characteristics are processed to generate the average membership of environmental carrying capacity, the fluctuation range value of the membership, and the noise level value of the membership; Based on the target environmental carrying capacity early warning model, the average membership of the environmental carrying capacity, the fluctuation range value of the membership and the noise level value of the membership are processed to generate the environmental carrying capacity early warning information of the area to be assessed.

2. The method according to claim 1, wherein Perform data cleaning on the environmental carrying capacity information of the area to be assessed to generate cleaned environmental carrying capacity information, including: Classify the environmental carrying capacity information of the area to be assessed and generate normal distribution data and non-normal distribution data; Processing the normally distributed data to generate the mean and standard deviation of each indicator, and generating the first outlier based on the mean and standard deviation of each indicator; Processing non-normal distribution data based on the box plot method to generate a second outlier; performing outlier screening on the environmental carrying capacity information of the area to be assessed based on the first outlier and the second outlier to generate noise-filtered environmental carrying capacity information; The environmental carrying capacity information after noise filtering is interpolated for missing values ​​to generate environmental carrying capacity information after data cleaning.

3. The method according to claim 1, wherein The environmental carrying capacity information after data cleaning is processed for data positiveness and data standardization to generate target indicator characteristics, including: Process the environmental carrying capacity information of the area to be assessed to generate target layer features, criterion layer features, and indicator layer features; Process the target layer features, criterion layer features and indicator layer features to generate large indicators, small indicators, intermediate indicators and interval indicators; Perform positive processing on the small indicator based on the first calculation formula to generate a positive large indicator; Perform positive processing on the intermediate indicators based on the second calculation formula to generate positive large indicators; Based on the third calculation formula, the interval-type indicator is positively processed to generate a positive large-scale indicator; Based on the fourth calculation formula, several large indicators are standardized to generate target indicator characteristics; The first calculation formula is: i,new =max{x1,x2,…,x n }-x i ; where x i,new is a large indicator after positive transformation, x i is the original small index value, max{x1,x2,…,x n } is the maximum value of all original small indicators; The second calculation formula is: M=max{|x' i -x' best |}; where x' i,new is a large index after positive transformation, M is the maximum absolute deviation, x' i is the original intermediate index value, x' best is the target value of the intermediate indicator; The third calculation formula is: M=max{a-min x″ i ,max{x″ i }-b}; Where x″ i,new is a large-scale indicator after positive transformation, M is the maximum absolute deviation, a and b are the lower and upper limits of the interval indicator, respectively, and x″ i is the original interval indicator value; The fourth calculation formula is: Among them, X norm Target indicator characteristics.

4. The method according to claim 3, wherein Process the target indicator characteristics to generate cloud model parameter information, including: Performing feature screening on the target indicator features to generate expected features, wherein the expected features are used to represent feature values ​​in the target indicator features that are greater than a preset threshold; The target indicator features and the expected features are processed to generate entropy features, where the entropy features are used to characterize the degree of dispersion of the target indicator features around the expected features; The target indicator feature, expected feature and entropy feature are processed to generate a super entropy feature, wherein the super entropy feature is used to characterize the uncertainty of the entropy feature; Generate cloud model parameter information based on expected features, entropy features and super entropy features; The calculation formula for entropy characteristics is: Among them, En is the entropy feature, which is used to express the uncertainty of the target indicator feature; avg(X'-Ex) is the average deviation between the target indicator feature X' and the expected feature Ex; The calculation formula for calculating the super entropy characteristic is: Among them, He is the super entropy feature, which is used to express the uncertainty of the entropy feature; N is the number of samples, X' i is the eigenvalue of the i-th sample.

5. The method according to claim 4, wherein Perform weight distribution processing on the cloud model parameter information to generate weight information corresponding to each indicator, including: The cloud model parameter information is processed to generate the data standard deviation of each indicator. The data standard deviation is used to characterize the score variance between indicators and reflects the degree of dispersion between indicators. Processing the cloud model parameter information to generate linear correlation coefficients between indicators, and generating a correlation coefficient matrix and indicator conflict information based on the linear correlation coefficients between indicators. The correlation coefficient matrix is ​​used to characterize the correlation between indicators. Process the data standard deviation and indicator conflict information of each indicator to generate the comprehensive weight information of each indicator; Normalize the comprehensive weight information of each indicator to generate objective weight information of each indicator; Processing cloud model parameter information to generate a cloud evaluation scale; Construct a comparison matrix of evaluation indicators of cloud models and cloud evaluation scales; Process each indicator in the evaluation indicator comparison matrix of the cloud model and the cloud evaluation scale to generate the relative weight value of each indicator; The objective weight information of each indicator and the relative weight value of each indicator are normalized to generate the weight information corresponding to each indicator.

6. The method according to claim 1, wherein Based on the weight information corresponding to each indicator, the target indicator characteristics are processed to generate the average membership of the environmental carrying capacity, the fluctuation range of the membership, and the noise level of the membership, including: Processing the criterion layer indicators in the target indicator characteristics based on the weight information corresponding to the criterion layer indicators to generate the criterion layer membership information; The membership information of the criterion layer is processed based on the weight information corresponding to the target layer index to generate the membership information of the target layer; Process the membership information of the target layer to generate the average membership of the environmental carrying capacity; The membership information of the target layer and the average membership of the environmental carrying capacity are processed to generate the fluctuation range value of the membership; The fluctuation range value of the membership degree is processed to generate the noise level value of the membership degree.

7. The method according to claim 1, wherein Based on the target environmental carrying capacity early warning model, the average membership degree, the fluctuation range value of the membership degree, and the noise level value of the membership degree are processed to generate environmental carrying capacity early warning information for the area to be assessed, including: Construct the average membership threshold, membership fluctuation range threshold and membership noise level threshold corresponding to different levels of carrying capacity; Based on the target environmental carrying capacity early warning model, the average membership threshold corresponding to different levels of carrying capacity, the membership fluctuation range threshold, and the membership noise level threshold, the average membership, membership fluctuation range, and membership noise level values ​​of the environmental carrying capacity are processed to generate the early warning level of the environmental carrying capacity of the area to be assessed; The warning level of the environmental carrying capacity of the area to be assessed is processed to generate environmental carrying capacity warning information for the area to be assessed.

8. An environmental carrying capacity analysis and early warning device, characterized in that: The device comprises: An acquisition module is used to obtain the environmental carrying capacity information of the area to be assessed and the target environmental carrying capacity early warning model; The processing module is used to perform data cleaning on the environmental carrying capacity information of the area to be assessed to generate the environmental carrying capacity information after data cleaning; perform data positive and data standardization on the environmental carrying capacity information after data cleaning to generate target indicator characteristics; process the target indicator characteristics to generate cloud model parameter information; perform weight distribution processing on the cloud model parameter information to generate weight information corresponding to each indicator; process the target indicator characteristics based on the weight information corresponding to each indicator to generate the average membership of the environmental carrying capacity, the fluctuation range value of the membership and the noise level value of the membership; process the average membership of the environmental carrying capacity, the fluctuation range value of the membership and the noise level value of the membership based on the target environmental carrying capacity early warning model to generate the environmental carrying capacity early warning information of the area to be assessed.

9. An electronic device, characterized in that: include: a first processor; and a memory for storing executable instructions of the first processor; The first processor is configured to execute the environmental carrying capacity analysis and early warning method according to any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the second processor, the environmental carrying capacity analysis and early warning method according to any one of claims 1 to 7 is implemented.

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