A method and device for detecting the boundary threshold of regional ecological carrying capacity

Abstract

The present invention discloses a method for detecting a regional ecological carrying capacity boundary threshold, including: constructing a regional ecological carrying capacity safety boundary framework; obtaining the seasonal time series and seasonal detrended time series of each ecological carrying capacity boundary control variable; respectively obtaining the single method thresholds of each ecological carrying capacity boundary control variable in different seasons; obtaining the multi-method integrated threshold detection results of the regional ecological carrying capacity boundary control variable in different seasons. The above technical solution can provide a basis for establishing a clear and objective regional ecological carrying capacity safety boundary framework, which is of great significance to ecological environment protection, resource development and utilization, social and economic development, etc.

Description

Technical Field

[0001] The present invention relates to the fields of earth science and ecological and environmental science and technology, and in particular to a method and device for detecting a boundary threshold value of regional ecological carrying capacity. Background Art

[0002] Ecological environment refers to the sum of human society and various natural factors around it. It is closely related to human life and production activities and is the basis for people's survival and development. Ecological carrying capacity is one of the important focuses of my country's ecological civilization construction. It is a reflection of the carrying capacity and pressure of the complex ecosystem of "natural resources-ecological environment-social economy" under the conditions of rational development and utilization of resources and a benign cycle of the environment. Only when the intensity of human activities in the region does not exceed the bearing and regulation capacity of the ecosystem can the regional social economy maintain long-term benign development. Therefore, timely understanding of the regional ecological carrying capacity is a prerequisite for understanding the current ecological security status and sustainable development capabilities of the region. It can not only provide a basis for solving regional ecological problems, but also help to form a virtuous cycle of social economy and ecological environment and ensure the sustainable development of the region.

[0003] The ecological carrying capacity boundary threshold is a series of disturbance thresholds of human activities on key ecological processes, which can be used to judge the early warning status of ecological carrying capacity and human activity intensity. However, in the current related work, the definition of various boundary thresholds is relatively vague, and there is a lack of a set of definite threshold setting methods. In addition, due to the certain spatial and temporal heterogeneity of boundary thresholds, the series of empirical thresholds defined in previous studies are difficult to be universally applied to different regions and time ranges. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a method and device for detecting the boundary threshold of regional ecological carrying capacity, so as to obtain the detection result of the boundary threshold of regional ecological carrying capacity.

[0005] The technical solution adopted by the present invention is:

[0006] A method for detecting a regional ecological carrying capacity boundary threshold, comprising:

[0007] According to regional characteristics and application needs, the regional ecological carrying capacity boundary control variables are screened out, and the regional ecological carrying capacity safety boundary framework is constructed based on the regional ecological carrying capacity boundary control variables;

[0008] Obtaining the historical time series of each ecological carrying capacity boundary control variable of the region, and obtaining the seasonal time series and seasonal detrended time series of each ecological carrying capacity boundary control variable based on the historical time series of each ecological carrying capacity boundary control variable;

[0009] According to the seasonal time series and seasonal detrended time series of the ecological carrying capacity boundary control variables, N critical point analysis methods are used to obtain the threshold values ​​of the ecological carrying capacity boundary control variables in different seasons, where N>1;

[0010] Based on respectively obtaining the threshold values ​​of the ecological carrying capacity boundary control variables in different seasons, a multi-method integrated threshold detection result of the regional ecological carrying capacity boundary control variables in different seasons is obtained.

[0011] Furthermore, the seasonal time series and seasonal detrended time series of each ecological carrying capacity boundary control variable are obtained based on the historical time series of each ecological carrying capacity boundary control variable, including:

[0012] According to the time corresponding to each element in the historical time series of the ecological carrying capacity boundary control variables, the historical time series of the ecological carrying capacity boundary control variables are divided into four seasonal time series to be processed: spring, summer, autumn and winter;

[0013] Deleting the missing values ​​in the four seasonal time series to be processed to obtain the seasonal time series of the ecological carrying capacity boundary control variables;

[0014] The seasonal time series of the ecological carrying capacity boundary control variables are detrended by the Nadaraya-Watson non-parametric method to obtain the seasonal detrended time series of the ecological carrying capacity boundary control variables.

[0015] Furthermore, the seasonal time series of the ecological carrying capacity boundary control variables are detrended by the Nadaraya-Watson nonparametric method to obtain the seasonal detrended time series of the ecological carrying capacity boundary control variables, including:

[0016] Using uniform kernel function and the bandwidth h is 0.05 of the length L of the seasonal time series of the boundary control variables of the ecological carrying capacity, and the approximate functional relationship between the estimated time T and the boundary control variable z The regression value of the control variable is obtained according to the approximate functional relationship, and the residual time series between the regression value and the true value is calculated to obtain the seasonal detrended time series of the control variables of each ecological carrying capacity boundary.

[0017] Furthermore, the threshold values ​​of the ecological carrying capacity boundary control variables in different seasons are obtained by using N critical point analysis methods, respectively, including:

[0018] According to the length L of the seasonal detrended time series of the boundary control variable, a time sliding window of size 0.5L is taken, and the lagged autocorrelation index of the sequence elements in the time sliding window is calculated along the seasonal detrended time series. The ecological carrying capacity boundary control variable value corresponding to the time step with the largest ρ value is selected as the seasonal threshold; where L is the length of the seasonal detrended time series of the boundary control variable, z t and z t+1 are the variable values ​​of the boundary control variables at the tth and t+1th time steps of the seasonal series, μ and σ respectively. z are the mean and variance of the boundary control variables within the time sliding window, respectively;

[0019] and / or,

[0020] According to the length L of the seasonal detrended time series of the boundary control variable, a time sliding window of size 0.5L is taken, and autoregressive fitting is performed on the sequence elements in the sliding window along the seasonal detrended time series to calculate the autoregressive coefficients of the sequence elements in the time sliding window. and the rate of return r = 1-α1, and the ecological carrying capacity boundary control variable value corresponding to the time step with the maximum α1 value and the minimum r value is selected as the seasonal threshold; where ε t is a Gaussian white noise process;

[0021] and / or,

[0022] According to the length L of the seasonal detrended time series of the boundary control variable, a time sliding window of size 0.5L is taken, and the standard deviation of the sequence elements in the time sliding window is calculated along the seasonal detrended time series. Further, the SD derivative SD′ is obtained, and the ecological carrying capacity boundary control variable value corresponding to the time step with the largest SD′ value is selected as the seasonal threshold;

[0023] and / or,

[0024] According to the length L of the seasonal detrended time series of the boundary control variable, a time sliding window of size 0.5L is taken, and the kurtosis coefficient of the sequence elements in the time sliding window is calculated along the seasonal detrended time series. Further, the derivative κ′ of κ is obtained, and the ecological carrying capacity boundary control variable value corresponding to the time step with the largest κ′ value is selected as the seasonal threshold;

[0025] and / or,

[0026] According to the length L of the seasonal detrended time series of the boundary control variable, a time sliding window of size 0.1L is taken, and the sequence elements in the time sliding window are autoregressed along the seasonal detrended time series. The square of the autoregressive fitting residual is further autoregressed with a lag of one time step. The obtained residual autoregression coefficient is used as the conditional heteroscedasticity index CH, and the mean of the ecological carrying capacity boundary control variable values ​​corresponding to the time steps with CH values ​​greater than 0 and significance p<0.05 is selected as the seasonal threshold;

[0027] and / or,

[0028] Using the DDJ model The seasonal time series of the ecological carrying capacity boundary control variable without detrending was fitted. According to the three statistics of conditional variance, total variance and jump intensity in the fitting model, the relationship between the statistic and the seasonal boundary control variable was plotted. The mean of the variable value corresponding to the time step with the largest statistic value was selected as the seasonal threshold of the boundary control variable obtained by the DDJ model method; where θ t are the parameters of the nonlinear function representing the deterministic and random parts of the original seasonal time series, dt is a small time increment, dW is white noise, μ(z t ,θ t ) is the drift function that measures the instantaneous deterministic change of the time series, σ D (z t ,θ t ) is the diffusion function that measures the standard deviation of small shocks occurring at each time step, is the jump function representing the intermittent large shock of the sequence, Z n ~N(0,σ Z 2 (θ t )), σ Z 2 (θ t ) is the average amplitude of the jump.

[0029] Further, based on the threshold values ​​of the ecological carrying capacity boundary control variables in different seasons respectively obtained, the multi-method integrated threshold detection results of the regional ecological carrying capacity boundary control variables in different seasons are obtained, including:

[0030] Based on the threshold values ​​of the ecological carrying capacity boundary control variables in different seasons, the highest and lowest values ​​are removed, and the average value of the remaining threshold values ​​is obtained to obtain the multi-method integrated threshold detection results of the regional ecological carrying capacity boundary control variables in different seasons.

[0031] Furthermore, the regional ecological carrying capacity safety boundary framework includes primary type, secondary type and ecological carrying capacity boundary control variables in sequence;

[0032] The primary type includes a plurality of secondary types, and the secondary type includes a plurality of ecological carrying capacity boundary control variables.

[0033] Furthermore, the first-level types include ecological resilience, ecological support and ecological pressure;

[0034] Furthermore, the secondary types of ecological resilience include: climate change, soil conditions, seawater environmental conditions, surface water environmental conditions, air quality, and land system conditions;

[0035] The secondary types of ecological support include: resource supply, environmental governance, social progress, and economic development;

[0036] The secondary types of ecological pressure include: resource consumption, environmental damage, population pressure, economic growth pressure, and natural disasters.

[0037] The ecological carrying capacity boundary control variables include: factors that may affect the regional ecological carrying capacity and reflect changes in the regional ecosystem carrying capacity and the pressure on the carrying objects.

[0038] According to the above method, the present invention also provides a device for detecting a threshold value of a regional ecological carrying capacity boundary, comprising:

[0039] A construction module is used to screen out regional ecological carrying capacity boundary control variables according to regional characteristics and application requirements, and to construct a regional ecological carrying capacity safety boundary framework based on the regional ecological carrying capacity boundary control variables;

[0040] A preprocessing module, used to obtain the historical time series of each ecological carrying capacity boundary control variable of the region, and obtain the seasonal time series and seasonal detrended time series of each ecological carrying capacity boundary control variable based on the historical time series of each ecological carrying capacity boundary control variable;

[0041] An analysis module, for obtaining threshold values ​​of each ecological carrying capacity boundary control variable in different seasons by using N critical point analysis methods according to the seasonal time series and seasonal detrended time series of each ecological carrying capacity boundary control variable, wherein N>1;

[0042] The output module is used to obtain the multi-method integrated threshold detection results of the regional ecological carrying capacity boundary control variables in different seasons based on the threshold values ​​of each ecological carrying capacity boundary control variable in different seasons.

[0043] The beneficial effects of the present invention are as follows: the method and device for detecting the boundary threshold value of regional ecological carrying capacity described in the present invention obtains the specific boundary threshold value of ecological carrying capacity in different seasons by analyzing the changes in the mathematical characteristics of the historical time series of regional boundary control variables when the ecosystem approaches the critical point. The process of obtaining the threshold value in this method is objective and clear, and the threshold value can be obtained in a specific area and time range, and it has strong versatility. It can provide a basis for establishing a clear and objective regional ecological carrying capacity safety boundary framework, which is of great significance to ecological environment protection, resource development and utilization, social and economic development, etc., and can be widely promoted and applied in industries such as environmental protection, agriculture, manufacturing, energy, and transportation. It has extremely high value in production, education, and research, and has good social and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 The present invention is a flow chart of a method for detecting a boundary threshold value of regional ecological carrying capacity.

[0046] Figure 2 It is a structural diagram of the regional ecological carrying capacity boundary threshold detection device of the present invention. DETAILED DESCRIPTION

[0047] The embodiment of the present application provides a method and device for detecting the boundary threshold value of regional ecological carrying capacity. By screening out the regional ecological carrying capacity boundary control variables according to regional characteristics and application requirements, a regional ecological carrying capacity safety boundary framework is constructed based on the regional ecological carrying capacity boundary control variables; the historical time series of each ecological carrying capacity boundary control variable in the region is obtained, and the seasonal time series and seasonal detrended time series of each ecological carrying capacity boundary control variable are obtained based on the historical time series of each ecological carrying capacity boundary control variable; based on the seasonal time series and seasonal detrended time series of each ecological carrying capacity boundary control variable, N critical point analysis methods are used to respectively obtain the threshold values ​​of each ecological carrying capacity boundary control variable in different seasons, where N>1; based on the threshold values ​​of each ecological carrying capacity boundary control variable in different seasons obtained respectively, a multi-method integrated threshold detection result of the regional ecological carrying capacity boundary control variable in different seasons is obtained.

[0048] The above technical solution obtains the specific ecological carrying capacity boundary thresholds of different regions in different seasons by analyzing the changes in the mathematical characteristics of the historical time series of regional boundary control variables when the ecosystem approaches the critical point. The process of obtaining the threshold in this method is objective and clear, and the threshold can be obtained in a specific region and time range, which has strong versatility. It can provide a basis for establishing a clear and objective regional ecological carrying capacity safety boundary framework, which is of great significance to ecological environment protection, resource development and utilization, social and economic development, etc., and can be widely promoted and applied in environmental protection, agriculture, manufacturing, energy, transportation and other industries. It has a very high value of production, education and research, and has good social and economic benefits.

[0049] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0050] First of all, the term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0051] The main implementation principles, specific implementation methods and corresponding beneficial effects of the technical solutions of the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0052] Embodiment 1

[0053] Please refer to the attached Figure 1 The present application embodiment provides a method for detecting a regional ecological carrying capacity boundary threshold, including:

[0054] S101: Screening out regional ecological carrying capacity boundary control variables according to regional characteristics and application requirements, and constructing a regional ecological carrying capacity safety boundary framework based on the regional ecological carrying capacity boundary control variables;

[0055] S102: Obtain the historical time series of each ecological carrying capacity boundary control variable of the region, and obtain the seasonal time series and seasonal detrended time series of each ecological carrying capacity boundary control variable based on the historical time series of each ecological carrying capacity boundary control variable;

[0056] S103: Based on the seasonal time series and seasonal detrended time series of the ecological carrying capacity boundary control variables, using N critical point analysis methods, respectively obtaining the threshold values ​​of the ecological carrying capacity boundary control variables in different seasons, wherein N>1;

[0057] S104: Based on the threshold values ​​of the ecological carrying capacity boundary control variables in different seasons respectively obtained, a multi-method integrated threshold detection result of the regional ecological carrying capacity boundary control variables in different seasons is obtained.

[0058] The regional ecological carrying capacity boundary threshold detection method adopted by the present invention can be applied to intelligent electronic devices, such as smart phones, which have data input and output parts and data processing capabilities. The above method can also be applied to servers and computer systems.

[0059] When the method is executed on an intelligent electronic device, server or computer system, S101 is executed to screen out regional ecological carrying capacity boundary control variables according to regional characteristics and application requirements, and to construct a regional ecological carrying capacity safety boundary framework based on the regional ecological carrying capacity boundary control variables.

[0060] Among them, the specific ecological carrying capacity boundary control variables include all factors that may affect the regional ecological carrying capacity, so as to reflect the changes in the carrying capacity of the regional ecosystem and the pressure of the carrying objects, and ensure that the regional ecological carrying capacity and ecological security can be effectively evaluated and detected. That is, factors that may affect the regional ecological carrying capacity and reflect the changes in the carrying capacity of the regional ecosystem and the pressure of the carrying objects, for example, the ecological carrying capacity boundary control variables of the present invention may include: temperature, precipitation, soil heavy metal content, seawater chemical oxygen demand, surface water dissolved oxygen content, air PM 2.5 concentration, biodiversity index, per capita cultivated land area, comprehensive utilization rate of industrial solid waste, per capita disposable income, per capita GDP, energy consumption per unit of GDP, intensity of pesticide use, population density, GDP growth rate, economic losses from natural disasters, etc.

[0061] In this field, the regional ecological carrying capacity safety boundary framework defines a series of disturbance thresholds of human activities on key ecological processes based on the intrinsic biophysical processes that regulate the stability of the ecosystem, and plans an ecological safety operating space for sustainable development for human production and life. In this framework, if the disturbance of the ecosystem by human society exceeds the safety boundary in the framework, it is very likely to cause the ecosystem to change from a safe state suitable for human survival to a high-risk state that is not conducive to the development of human society. Establishing a regional ecological carrying capacity safety boundary framework and adjusting and controlling the intensity of regional human activities according to the safety boundary will greatly reduce the risk of destroying the safety state of the ecosystem during economic and social development.

[0062] Specifically, the construction of the regional ecological carrying capacity safety boundary framework in the embodiment of the present invention includes primary types, secondary types and ecological carrying capacity boundary control variables in sequence.

[0063] The first-level type includes a plurality of second-level types, and the second-level type includes a plurality of ecological carrying capacity boundary control variables.

[0064] Preferably, three primary types are selected, namely ecological resilience (A1, including secondary types B1-B6), ecological support (A2, including secondary types B7-B10), and ecological pressure (A3, including secondary types B11-B15).

[0065] A total of 15 secondary types are selected, including climate change (B1), soil conditions (B2), seawater environmental conditions (B3), surface water environmental conditions (B4), air quality (B5), land system conditions (B6), resource supply (B7), environmental governance (B8), social progress (B9), economic development (B10), resource consumption (B11), environmental damage (B12), population pressure (B13), economic growth pressure (B14), and natural disasters (B15).

[0066] Specifically, the secondary types of ecological resilience include: climate change, soil conditions, seawater environmental conditions, surface water environmental conditions, air quality, land system conditions, etc.;

[0067] The secondary types of ecological support include: resource supply, environmental governance, social progress, economic development, etc.;

[0068] The secondary types of ecological pressure include: resource consumption, environmental damage, population pressure, economic growth pressure, and natural disasters.

[0069] More specifically, the ecological carrying capacity boundary control variables included in the climate change are: temperature, precipitation, relative humidity, atmospheric CO2 concentration, aerosol optical depth, stratospheric ozone concentration, energy balance at the top of the atmosphere, sea surface temperature, and sea level change;

[0070] The ecological carrying capacity boundary control variables included in the soil condition category are: soil temperature, soil moisture, soil pH, soil nitrogen content, soil phosphorus content, soil potassium content, soil cadmium content, soil lead content, soil zinc content, soil copper content, soil arsenic content, soil nickel content, and soil mercury content;

[0071] The ecological carrying capacity boundary control variables of seawater environmental conditions include: seawater pH, seawater dissolved oxygen concentration, chemical oxygen demand, five-day biochemical oxygen demand, seawater inorganic nitrogen concentration, seawater non-ionized nitrogen concentration, seawater active phosphate concentration, seawater hexavalent chromium concentration, seawater total chromium concentration, seawater copper concentration, seawater zinc concentration, seawater selenium concentration, seawater arsenic concentration, seawater lead concentration, seawater cadmium concentration, seawater nickel concentration, seawater mercury concentration, seawater sulfide concentration, seawater cyanide concentration, seawater volatile phenol concentration, seawater petroleum substance concentration, seawater radionuclide concentration 60Co, seawater radionuclide concentration 90 Sr, seawater radionuclide concentration 106 Rn, seawater radionuclide concentration 134 Cs, seawater radionuclide concentration 137 Cs;

[0072] The ecological carrying capacity boundary control variables included in the surface water environmental conditions category are: surface water pH, surface water dissolved oxygen concentration, surface water permanganate index, surface water chemical oxygen demand, surface water five-day biochemical oxygen demand, surface water total nitrogen concentration, surface water total phosphorus concentration, surface water copper concentration, surface water zinc concentration, surface water selenium concentration, surface water arsenic concentration, surface water lead concentration, surface water cadmium concentration, surface water total mercury concentration, surface water fluoride concentration, surface water cyanide concentration;

[0073] The ecological carrying capacity boundary control variables included in the air quality category are: air PM 2.5 Concentration, air PM 10 concentration, air O3 concentration, air NO2 concentration, air SO2 concentration, air CO concentration;

[0074] The ecological carrying capacity boundary control variables included in the terrestrial system condition category are: wetland protection rate, forest cover change rate, normalized difference vegetation index, gross primary productivity, biodiversity index, and species extinction rate;

[0075] The ecological carrying capacity boundary control variables included in the resource supply category are: per capita cultivated land area, per capita green area, per capita water resources, per capita grain output, per capita sea area, and per capita marine aquatic product output;

[0076] The ecological carrying capacity boundary control variables included in the environmental governance category are: industrial wastewater discharge compliance rate, industrial solid waste comprehensive utilization rate, industrial SO2 removal rate, urban domestic sewage treatment rate, domestic garbage harmless treatment rate, crop straw comprehensive utilization rate, ecological and environmental protection investment proportion of fiscal revenue, restored coastline length, and marine and fishery protection area proportion;

[0077] The ecological carrying capacity boundary control variables included in the social progress category are: annual per capita net income of farmers, annual per capita disposable income of urban residents, the proportion of primary and secondary school students receiving ecological civilization education, the rate of resource and environmental information disclosure, the proportion of public transportation in motorized travel, and the proportion of capital investment in urban municipal public facilities construction;

[0078] The ecological carrying capacity boundary control variables included in the economic development category are: gross national product (GDP), per capita GDP, and the proportion of the tertiary industry in GDP;

[0079] The ecological carrying capacity boundary control variables included in the resource consumption category are: energy consumption per unit of GDP, construction land area per 10,000 yuan of GDP, water consumption per 10,000 yuan of GDP, per capita domestic water consumption, the proportion of non-fossil energy in primary energy consumption, and seafood catch;

[0080] The ecological carrying capacity boundary control variables included in the environmental damage category are: industrial wastewater discharge per 10,000 yuan of GDP, industrial waste discharge per 10,000 yuan of GDP, industrial solid waste generation per 10,000 yuan of GDP, SO2 emission intensity, agricultural fertilizer application intensity, pesticide application intensity, regional environmental noise average, reclamation area, and natural coastline retention rate;

[0081] The ecological carrying capacity boundary control variables included in the population pressure category are: population density, natural population growth rate, urban registered unemployment rate, and urbanization level;

[0082] The ecological carrying capacity boundary control variables included in the economic growth pressure category are: GDP growth rate, primary industry growth rate, secondary industry growth rate, Engel coefficient, tourism industry income, marine development intensity, marine aquaculture area, cargo throughput, and passenger turnover;

[0083] The ecological carrying capacity boundary control variables included in the natural disaster category are: the number of casualties caused by natural disasters and the direct economic losses caused by natural disasters.

[0084] After constructing the regional ecological carrying capacity safety boundary framework according to regional characteristics and application requirements, execute step S102: obtain the historical time series of each ecological carrying capacity boundary control variable in the region, and obtain the seasonal time series and seasonal detrended time series of each ecological carrying capacity boundary control variable based on the historical time series of each ecological carrying capacity boundary control variable.

[0085] Specifically, the historical time series of the control variables of the regional ecological carrying capacity boundaries can be obtained through observations and statistical data over the years.

[0086] The seasonal time series and seasonal detrended time series of each ecological carrying capacity boundary control variable are obtained based on the historical time series of each ecological carrying capacity boundary control variable, including:

[0087] According to the time corresponding to each element in the historical time series of the ecological carrying capacity boundary control variables, the historical time series of the ecological carrying capacity boundary control variables are divided into four seasonal time series to be processed: spring, summer, autumn and winter;

[0088] The missing values ​​in the four seasonal time series to be processed are deleted to obtain the seasonal time series of the ecological carrying capacity boundary control variables.

[0089] The regression method can be used to complete the detrending task of the seasonal time series of the boundary control variable. The operation steps are to obtain the approximate functional relationship between time and the boundary control variable through regression, obtain the regression value of the control variable based on the approximate relationship, calculate the residual time series between the regression and the true value, and obtain the seasonal detrended time series of the boundary control variable.

[0090] The embodiment of the present invention performs detrending processing on the seasonal time series of the ecological carrying capacity boundary control variables by using the Nadaraya-Watson non-parametric method to obtain the seasonal detrended time series of the ecological carrying capacity boundary control variables.

[0091] Specifically, we can use the uniform kernel function and the bandwidth h is 0.05 of the length L of the seasonal time series of the boundary control variables of the ecological carrying capacity, and the approximate functional relationship between the estimated time T and the boundary control variable z The regression value of the control variable is obtained according to the approximate functional relationship, and the residual time series between the regression value and the true value is calculated to obtain the seasonal detrended time series of each ecological carrying capacity boundary control variable. The approximate functional relationship between time T and boundary control variable z is z=m(T)+ε i It is obtained using the Nadaraya-Watson nonparametric estimation method, where m(·) is the unknown regression function, ε i is a random error, is the Nadaraya-Watson estimate of m(T).

[0092] After obtaining the seasonal time series and seasonal detrended time series of the ecological carrying capacity boundary control variables, step S103 is performed to obtain the threshold values ​​of the ecological carrying capacity boundary control variables in different seasons by using N critical point analysis methods according to the seasonal time series and seasonal detrended time series of the ecological carrying capacity boundary control variables, where N>1. That is, the threshold values ​​of the regional ecological carrying capacity boundary control variables in different seasons are detected by combining multiple critical point analysis methods.

[0093] The above-mentioned multiple critical point analysis methods may specifically include the following:

[0094] The first one is threshold detection based on lagged autocorrelation. The detection method is: according to the length L of the seasonal detrended time series of the boundary control variable, a time sliding window of size 0.5L is taken, and the lagged autocorrelation index of the sequence elements in the time sliding window is calculated along the seasonal detrended time series. The ecological carrying capacity boundary control variable value corresponding to the time step with the largest ρ value is selected as the seasonal threshold; where L is the length of the seasonal detrended time series of the boundary control variable, z t and zt+1 are the variable values ​​of the boundary control variables at the tth and t+1th time steps of the seasonal series, μ and σ respectively. z are the mean and variance of the boundary control variables within the time sliding window, respectively.

[0095] The second and third methods are threshold detection based on autoregressive coefficients and return rates. The specific method is: according to the length L of the seasonal detrended time series of the boundary control variable, a time sliding window of size 0.5L is taken, and the sequence elements in the sliding window are autoregressively fitted along the seasonal detrended time series, and the autoregressive coefficients of the sequence elements in the time sliding window are calculated. and the rate of return r = 1-α1, and the ecological carrying capacity boundary control variable value corresponding to the time step with the maximum α1 value and the minimum r value is selected as the seasonal threshold; where ε t is a Gaussian white noise process.

[0096] The fourth method is the threshold detection method based on standard deviation. Specifically, according to the length L of the seasonal detrended time series of the boundary control variable, a time sliding window of size 0.5L is taken, and the standard deviation of the sequence elements in the time sliding window is calculated along the seasonal detrended time series. The SD derivative SD′ is further calculated, and the ecological carrying capacity boundary control variable value corresponding to the time step with the largest SD′ value is selected as the seasonal threshold.

[0097] The fifth method is based on the threshold detection of kurtosis coefficient. Specifically, according to the length L of the seasonal detrended time series of the boundary control variable, a time sliding window of size 0.5L is taken, and the kurtosis coefficient of the sequence elements in the time sliding window is calculated along the seasonal detrended time series. The derivative κ′ of κ is further obtained, and the ecological carrying capacity boundary control variable value corresponding to the time step with the largest κ′ value is selected as the seasonal threshold.

[0098] The sixth type is threshold detection based on conditional heteroskedasticity. Specifically, according to the length L of the seasonal detrended time series of the boundary control variable, a time sliding window of size 0.1L is taken, and the sequence elements in the time sliding window are autoregressed along the seasonal detrended time series. The square of the autoregressive fitting residual is further autoregressed with a lag of one time step. The residual autoregression coefficient obtained is used as the conditional heteroskedasticity indicator CH, and the mean of the ecological carrying capacity boundary control variable values ​​corresponding to the time steps with CH values ​​greater than 0 and significance p<0.05 is selected as the seasonal threshold.

[0099] The seventh type is threshold detection based on the drift-diffusion-jump model (DDJ), specifically: using the DDJ model The seasonal time series of the ecological carrying capacity boundary control variable without detrending was fitted. According to the three statistics of conditional variance, total variance and jump intensity in the fitting model, the relationship between the statistic and the seasonal boundary control variable was plotted. The mean of the variable value corresponding to the time step with the largest statistic value was selected as the seasonal threshold of the boundary control variable obtained by the DDJ model method; where θ t are the parameters of the nonlinear function representing the deterministic and random parts of the original seasonal time series, dt is a small time increment, dW is white noise, μ(z t ,θ t ) is the drift function that measures the instantaneous deterministic change of the time series, σ D (z t ,θ t ) is the diffusion function that measures the standard deviation of small shocks occurring at each time step, is the jump function representing the intermittent large shock of the sequence, Z n ~N(0,σ Z 2 (θ t )), σ Z 2 (θ t ) is the average amplitude of the jump.

[0100] Among them, the three statistics of conditional variance, total variance and jump strength in the fitting model are conditional variance Total variance and jump intensity and are the first-order conditional moment and the second-order conditional moment respectively;

[0101] After using the above-mentioned multiple critical point analysis methods to obtain the threshold values ​​of each ecological carrying capacity boundary control variable in different seasons, execute step S104: based on the threshold values ​​of each ecological carrying capacity boundary control variable in different seasons, obtain the multi-method integrated threshold detection results of the regional ecological carrying capacity boundary control variables in different seasons.

[0102] Specifically, based on the thresholds of the ecological carrying capacity boundary control variables in different seasons obtained above, the highest and lowest values ​​can be removed, and the average value of the remaining thresholds can be calculated to obtain the multi-method integrated threshold detection results of the regional ecological carrying capacity boundary control variables in different seasons.

[0103] Embodiment 2

[0104] Based on the method of the first embodiment, the present invention also provides a device for detecting a threshold value of a regional ecological carrying capacity boundary, including:

[0105] A construction module is used to screen out regional ecological carrying capacity boundary control variables according to regional characteristics and application requirements, and to construct a regional ecological carrying capacity safety boundary framework based on the regional ecological carrying capacity boundary control variables;

[0106] A preprocessing module, used to obtain the historical time series of each ecological carrying capacity boundary control variable of the region, and obtain the seasonal time series and seasonal detrended time series of each ecological carrying capacity boundary control variable based on the historical time series of each ecological carrying capacity boundary control variable;

[0107] An analysis module, for obtaining threshold values ​​of each ecological carrying capacity boundary control variable in different seasons by using N critical point analysis methods according to the seasonal time series and seasonal detrended time series of each ecological carrying capacity boundary control variable, wherein N>1;

[0108] The output module is used to obtain the multi-method integrated threshold detection results of the regional ecological carrying capacity boundary control variables in different seasons based on the threshold values ​​of each ecological carrying capacity boundary control variable in different seasons.

[0109] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0110] Embodiment 3

[0111] A city in Lingnan, my country (hereinafter referred to as City F) was selected as an example to test the boundary threshold of ecological carrying capacity. The test process is as follows:

[0112] Construct a regional ecological carrying capacity safety boundary framework.

[0113] According to the natural environment characteristics of City F, nine boundary control variables reflecting five indicator types, namely climate change (including M1–M2), energy cycle (including M3–M5), soil state (including M6–M7) and biological land system (including M8–M9), were selected to constitute the regional ecological carrying capacity safety boundary framework, including: air temperature (M1), precipitation (M2), net solar radiation (M3), sensible heat flux (M4), latent heat flux (M5), soil surface temperature (M6), soil surface moisture (M7), leaf area index of high-density vegetation (evergreen trees, deciduous trees and woodlands) (M8) and leaf area index of low-density vegetation (crops, grasslands and shrubs) (M9).

[0114] Obtain historical time series data of regional ecological carrying capacity boundary control variables.

[0115] ERA5 reanalysis data was selected as the data source, and the monthly time series data of the ecological carrying capacity boundary control variables M1-M9 in City F from 1950 to 2020 were obtained and sorted.

[0116] Perform preprocessing operations on the boundary control variable time series.

[0117] This includes seasonal division, missing value elimination and detrending of the time series data of the above-mentioned nine ecological carrying capacity boundary control variables.

[0118] Threshold detection based on lagged autocorrelation.

[0119] For each seasonal detrended time series of the nine ecological carrying capacity boundary control variables, a time window of size 0.5L is opened, and the lagged autocorrelation index of the sequence elements in the time sliding window is calculated along the sequence. Where L is the length of the seasonal detrended time series of the boundary control variable, z t and z t+1 are the variable values ​​of the boundary control variable at the tth and t+1th time steps of the seasonal detrended time series, μ and σ respectively. z are the mean and variance of the boundary control variables in the time sliding window, respectively. For each seasonal detrended time series corresponding to the lagged autocorrelation indicator sequence, the original variable value corresponding to the time step with the largest ρ value is selected as the seasonal threshold of the boundary control variable obtained by the lagged autocorrelation method. The calculation results are shown in Table 1.

[0120]

[0121] Table 1 Calculation results of ecological carrying capacity boundary threshold of F city based on lagged autocorrelation

[0122] Threshold detection based on autoregressive coefficients.

[0123] For each seasonal detrended time series of the nine ecological carrying capacity boundary control variables, a time window of size 0.5L is opened, and the autoregressive coefficients of the sequence elements in the time sliding window are calculated along the sequence. where ε t is a Gaussian white noise process. For each seasonal detrended time series corresponding to the autoregressive coefficient sequence, the original variable value corresponding to the time step with the largest α1 value is selected as the seasonal threshold of the boundary control variable obtained by the autoregressive coefficient method. The calculation results are shown in Table 2.

[0124]

[0125] Table 2 Calculation results of ecological carrying capacity boundary threshold of F city based on autoregression coefficient

[0126] Threshold detection based on return rate.

[0127] For each seasonal detrended time series of the nine ecological carrying capacity boundary control variables, a time window of size 0.5L is opened, and the rate of return index r = 1-α1 of the sequence elements in the time sliding window is calculated along the sequence. For the rate of return index sequence corresponding to each seasonal detrended time series, the original variable value corresponding to the time step with the smallest r value is selected as the seasonal threshold of the boundary control variable obtained by the rate of return method. The calculation results are shown in Table 3.

[0128]

[0129]

[0130] Table 3 Calculation results of ecological carrying capacity boundary threshold of City F based on rate of return

[0131] Threshold detection based on standard deviation.

[0132] For each seasonal detrended time series of the nine ecological carrying capacity boundary control variables, a time window of size 0.5L is opened, and the standard deviation index of the sequence elements in the time sliding window is calculated along the sequence. For the autoregressive coefficient sequence corresponding to each seasonal detrended time series, its derivative SD′ with respect to time is further calculated, and the original variable value corresponding to the time step with the largest SD′ value is selected as the seasonal threshold of the boundary control variable obtained by the standard deviation method. The calculation results are shown in Table 4.

[0133]

[0134] Table 4 Calculation results of ecological carrying capacity boundary threshold of F city based on standard deviation

[0135] Threshold detection based on kurtosis coefficient.

[0136] For each seasonal detrended time series of the nine ecological carrying capacity boundary control variables, a time window of size 0.5L is opened, and the kurtosis coefficient of the sequence elements in the time sliding window is calculated along the sequence. For each seasonal detrended time series corresponding to the kurtosis coefficient sequence, its derivative κ′ with respect to time is further calculated, and the original variable value corresponding to the time step with the largest κ′ value is selected as the seasonal threshold of the boundary control variable obtained by the kurtosis coefficient method. The calculation results are shown in Table 5.

[0137]

[0138] Table 5 Calculation results of ecological carrying capacity boundary threshold of F city based on kurtosis coefficient

[0139] Threshold detection based on conditional heteroskedasticity.

[0140] For each seasonal detrended time series of the nine ecological carrying capacity boundary control variables, a time window of size 0.1L was opened. After autoregression of the sequence elements in the time sliding window along the sequence, the square of the autoregression fitting residual was further autoregressed with a lag of one time step. The slope of the residual autoregression was used as the conditional heteroskedasticity indicator CH. The mean of the original variable values ​​corresponding to several time steps with CH values ​​greater than 0 and significance p<0.05 was selected as the seasonal threshold of the boundary control variable obtained by the conditional heteroskedasticity method. The calculation results are shown in Table 6.

[0141]

[0142]

[0143] Table 6 Calculation results of ecological carrying capacity boundary threshold of F city based on conditional heteroscedasticity

[0144] Threshold detection based on the drift-diffusion-jump model.

[0145] The DDJ model was used to fit each seasonal time series (without detrending) of the nine ecological carrying capacity boundary control variables. The fitting form is: where θ t are the parameters of the nonlinear function representing the deterministic and random parts of the seasonal time series, dt is a small time increment, dW is white noise, μ(z t ,θ t ) is the drift function that measures the instantaneous deterministic change of the time series, σ D (z t ,θ t ) is the diffusion function that measures the standard deviation of small shocks occurring at each time step, is the jump function representing the intermittent large shock of the sequence (where Z n ~N(0,σ Z 2 (θ t )), σ Z 2 (θ t ) is the average amplitude of the jump). According to each fitted DDJ model, the corresponding conditional variance is further calculated Total variance and jump intensity For the three statistics, by drawing the relationship diagram between the statistics and the seasonal boundary control variables, the mean of the variable value corresponding to the time step with the largest statistic value is selected as the seasonal threshold of the boundary control variable obtained by the DDJ model method. The calculation results are shown in Table 7.

[0146]

[0147] Table 7 Calculation results of ecological carrying capacity boundary threshold of City F based on drift-diffusion-jump model

[0148] Integration of multi-method analysis results:

[0149] According to the thresholds detected by critical point analysis methods such as lagged autocorrelation, autoregressive coefficient, rate of return, standard deviation, kurtosis coefficient, conditional heteroskedasticity, and DDJ model, the highest and lowest detection values ​​are removed, and the average of the remaining detection results is obtained to obtain the final detection results of the boundary threshold of the ecological carrying capacity of City F based on the multi-method integration as shown in Table 8.

[0150]

[0151] Table 8 Multi-method integration results of ecological carrying capacity boundary threshold in City F

[0152] The present invention designs a method for combining the historical time series of the boundary control variables of the regional ecological carrying capacity with the critical point analysis method to obtain the boundary threshold of the regional ecological carrying capacity. The operation is simple and fast, the required data is highly accessible, and it can be widely used in the ecological carrying capacity safety boundary framework of different regions established according to regional characteristics and application requirements. At the same time, it also allows each boundary control variable to be analyzed separately, and has stronger practicality and operability in the relevant policy formulation and regional ecological carrying capacity management and regulation applications. At the same time, the present invention relates to a variety of critical point analysis methods, which obtain the critical point before the system state conversion and the boundary control variable situation near it according to a series of critical early warning signals that are very easy to detect before the ecosystem mutates. The mechanism and calculation are simple, and the sampling frequency and time independence of the detected data are not high. It can be applied to the critical slowing mutation, flickering mutation and other different types of ecosystem mutation critical point detection, and has extremely strong sensitivity and practical value. In addition, the present invention integrates the detection results of a variety of critical point analysis methods, further ensuring the accuracy, stability and reliability of the final boundary threshold.

[0153] The difference between the present invention and the prior art lies in that: compared with the empirical threshold definition method, the present invention combines the historical time series data of the ecological carrying capacity boundary control variables and obtains the ecological carrying capacity boundary threshold through objective spatiotemporal statistics and data mining methods; compared with the system dynamics simulation model method, the method described in the present invention is simple to calculate and does not need to consider the various elements in the ecosystem and the complex relationships and ecological processes between them; compared with ordinary data regression and transition threshold detection methods, the present invention mainly detects the critical point before the system state changes rather than the direct state transition point, and the obtained ecological carrying capacity boundary threshold has a better warning effect and is more in line with the application requirements of the ecological carrying capacity safety boundary framework.

[0154] Compared with the prior art, the advantages of the present invention are:

[0155] First, through a variety of critical point analysis methods, the mathematical characteristics of the historical time series of regional ecological carrying capacity boundary control variables when the ecosystem approaches the critical point are analyzed, and the threshold of the regional ecological carrying capacity boundary control variable is obtained. The detection process is clear and objective, overcoming the defect of vague definition of empirical threshold.

[0156] Second, threshold detection is performed based on historical time series data of regional ecological carrying capacity boundary control variables, which can be applied to different time and space ranges and has strong versatility.

[0157] Third, the boundary threshold result obtained by detection is the variable value of the ecological carrying capacity boundary control variable near the critical point before the ecosystem state transition occurs, which has higher application value in regional ecological carrying capacity security monitoring and early warning.

[0158] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the following claims.

[0159] It should be understood that the present invention is not limited to the precise structure described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

[0160] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting a regional ecological carrying capacity boundary threshold, characterized in that: According to regional characteristics and application needs, the regional ecological carrying capacity boundary control variables are screened out, and the regional ecological carrying capacity safety boundary framework is constructed based on the regional ecological carrying capacity boundary control variables; Obtaining the historical time series of each ecological carrying capacity boundary control variable of the region, and obtaining the seasonal time series and seasonal detrended time series of each ecological carrying capacity boundary control variable based on the historical time series of each ecological carrying capacity boundary control variable; According to the seasonal time series and seasonal detrended time series of the ecological carrying capacity boundary control variables, N critical point analysis methods are used to obtain the threshold values ​​of the ecological carrying capacity boundary control variables in different seasons, where N>1; Based on the threshold values ​​of the ecological carrying capacity boundary control variables in different seasons respectively obtained, a multi-method integrated threshold detection result of the regional ecological carrying capacity boundary control variables in different seasons is obtained; The seasonal time series and seasonal detrended time series of each ecological carrying capacity boundary control variable are obtained based on the historical time series of each ecological carrying capacity boundary control variable, including: According to the time corresponding to each element in the historical time series of the ecological carrying capacity boundary control variables, the historical time series of the ecological carrying capacity boundary control variables are divided into four seasonal time series to be processed: spring, summer, autumn and winter; Deleting the missing values ​​in the four seasonal time series to be processed to obtain the seasonal time series of the ecological carrying capacity boundary control variables; The seasonal time series of the ecological carrying capacity boundary control variables are detrended by the Nadaraya-Watson nonparametric method to obtain the seasonal detrended time series of the ecological carrying capacity boundary control variables, including: Using uniform kernel function and the bandwidth h is 0.05 of the length L of the seasonal time series of the boundary control variables of the ecological carrying capacity, and the approximate functional relationship between the estimated time T and the boundary control variable z The regression value of the control variable is obtained according to the approximate functional relationship, and the residual time series between the regression value and the true value is calculated to obtain the seasonal detrended time series of the control variables of each ecological carrying capacity boundary.

2. The method for detecting a regional ecological carrying capacity boundary threshold according to claim 1, characterized in that: The N critical point analysis methods are used to obtain the threshold values ​​of the ecological carrying capacity boundary control variables in different seasons, including: According to the length L of the seasonal detrended time series of the boundary control variable, a time sliding window of size 0.5L is taken, and the lagged autocorrelation index of the sequence elements in the time sliding window is calculated along the seasonal detrended time series. The ecological carrying capacity boundary control variable value corresponding to the time step with the largest ρ value is selected as the seasonal threshold; where L is the length of the seasonal detrended time series of the boundary control variable, z t and z t+1 are the variable values ​​of the boundary control variables at the tth and t+1th time steps of the seasonal series, μ and σ respectively. z are the mean and variance of the boundary control variables within the time sliding window, respectively; and / or, According to the length L of the seasonal detrended time series of the boundary control variable, a time sliding window of size 0.5L is taken, and autoregressive fitting is performed on the sequence elements in the sliding window along the seasonal detrended time series to calculate the autoregressive coefficients of the sequence elements in the time sliding window. and the rate of return r = 1-α1, and the ecological carrying capacity boundary control variable value corresponding to the time step with the maximum α1 value and the minimum r value is selected as the seasonal threshold; where ε t is a Gaussian white noise process; and / or, According to the length L of the seasonal detrended time series of the boundary control variable, a time sliding window of size 0.5L is taken, and the standard deviation of the sequence elements in the time sliding window is calculated along the seasonal detrended time series. Further obtain the SD derivative SD ′ , and select SD ′ The ecological carrying capacity boundary control variable value corresponding to the time step with the largest value is taken as the seasonal threshold; and / or, According to the length L of the seasonal detrended time series of the boundary control variable, a time sliding window of size 0.5L is taken, and the kurtosis coefficient of the sequence elements in the time sliding window is calculated along the seasonal detrended time series. Further obtain the derivative κ of κ ′ , and choose κ ′ The ecological carrying capacity boundary control variable value corresponding to the time step with the largest value is taken as the seasonal threshold; and / or, According to the length L of the seasonal detrended time series of the boundary control variable, a time sliding window of size 0.1L is taken, and the sequence elements in the time sliding window are autoregressed along the seasonal detrended time series. The square of the autoregressive fitting residual is further autoregressed with a lag of one time step. The obtained residual autoregression coefficient is used as the conditional heteroscedasticity index CH, and the mean of the ecological carrying capacity boundary control variable values ​​corresponding to the time steps with CH values ​​greater than 0 and significance p<0.05 is selected as the seasonal threshold; and / or, Using the DDJ model The seasonal time series of the ecological carrying capacity boundary control variable without detrending was fitted. According to the three statistics of conditional variance, total variance and jump intensity in the fitting model, the relationship between the statistic and the seasonal boundary control variable was plotted. The mean of the variable value corresponding to the time step with the largest statistic value was selected as the seasonal threshold of the boundary control variable obtained by the DDJ model method; where θ t are the parameters of the nonlinear function representing the deterministic and random parts of the original seasonal time series, dt is a small time increment, dW is white noise, μ(z t ,θ t ) is the drift function that measures the instantaneous deterministic change of the time series, σ D (z t ,θ t ) is the diffusion function that measures the standard deviation of small shocks occurring at each time step, is the jump function representing the intermittent large shock of the sequence, Z n ~N(0,σ Z 2 (θ t )), σ Z 2 (θ t ) is the average amplitude of the jump.

3. The method for detecting the boundary threshold of regional ecological carrying capacity according to claim 1 is characterized by: The method of obtaining the threshold values ​​of the ecological carrying capacity boundary control variables in different seasons based on the respective acquisition of the threshold values ​​of the ecological carrying capacity boundary control variables in different seasons and obtaining the multi-method integrated threshold value detection results of the regional ecological carrying capacity boundary control variables in different seasons includes: Based on the threshold values ​​of the ecological carrying capacity boundary control variables in different seasons, the highest and lowest values ​​are removed, and the average value of the remaining threshold values ​​is obtained to obtain the multi-method integrated threshold detection results of the regional ecological carrying capacity boundary control variables in different seasons.

4. A method for detecting a regional ecological carrying capacity boundary threshold according to any one of claims 1 to 3, characterized in that: The regional ecological carrying capacity safety boundary framework includes primary type, secondary type and ecological carrying capacity boundary control variables in sequence; The primary type includes a plurality of secondary types, and the secondary type includes a plurality of ecological carrying capacity boundary control variables.

5. A method for detecting a regional ecological carrying capacity boundary threshold according to claim 4, characterized in that: The primary types include ecological resilience, ecological support and ecological pressure.

6. A method for detecting a regional ecological carrying capacity boundary threshold according to claim 5, characterized in that: The secondary types of ecological resilience include: climate change, soil conditions, seawater environmental conditions, surface water environmental conditions, air quality, and land system conditions; The secondary types of ecological support include: resource supply, environmental governance, social progress, and economic development; The secondary types of ecological pressure include: resource consumption, environmental damage, population pressure, economic growth pressure, and natural disasters.

7. The method for detecting the boundary threshold of regional ecological carrying capacity according to claim 4 is characterized by: The ecological carrying capacity boundary control variables include: factors that may affect the regional ecological carrying capacity and reflect changes in the regional ecosystem carrying capacity and the pressure on the carrying objects.

8. A device for detecting the boundary threshold of regional ecological carrying capacity, characterized in that: include: A construction module is used to screen out regional ecological carrying capacity boundary control variables according to regional characteristics and application requirements, and to construct a regional ecological carrying capacity safety boundary framework based on the regional ecological carrying capacity boundary control variables; A preprocessing module, used to obtain the historical time series of each ecological carrying capacity boundary control variable of the region, and obtain the seasonal time series and seasonal detrended time series of each ecological carrying capacity boundary control variable based on the historical time series of each ecological carrying capacity boundary control variable; An analysis module, for obtaining threshold values ​​of each ecological carrying capacity boundary control variable in different seasons by using N critical point analysis methods according to the seasonal time series and seasonal detrended time series of each ecological carrying capacity boundary control variable, wherein N>1; An output module, for obtaining the multi-method integrated threshold detection results of the regional ecological carrying capacity boundary control variables in different seasons based on the threshold values ​​of the ecological carrying capacity boundary control variables in different seasons respectively obtained; The seasonal time series and seasonal detrended time series of each ecological carrying capacity boundary control variable are obtained based on the historical time series of each ecological carrying capacity boundary control variable, including: According to the time corresponding to each element in the historical time series of the ecological carrying capacity boundary control variables, the historical time series of the ecological carrying capacity boundary control variables are divided into four seasonal time series to be processed: spring, summer, autumn and winter; Deleting the missing values ​​in the four seasonal time series to be processed to obtain the seasonal time series of the ecological carrying capacity boundary control variables; The seasonal time series of the ecological carrying capacity boundary control variables are detrended by the Nadaraya-Watson nonparametric method to obtain the seasonal detrended time series of the ecological carrying capacity boundary control variables, including: Using uniform kernel function and the bandwidth h is 0.05 of the length L of the seasonal time series of the boundary control variables of the ecological carrying capacity, and the approximate functional relationship between the estimated time T and the boundary control variable z The regression value of the control variable is obtained according to the approximate functional relationship, and the residual time series between the regression value and the true value is calculated to obtain the seasonal detrended time series of the control variables of each ecological carrying capacity boundary.

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