Producing area ecological safety and industrial development coupling coordination degree prediction method and application

Abstract

The invention discloses a production area ecological safety and industrial development coupling coordination degree prediction method. The method comprises the following steps: acquiring data of multiple groups of ecological safety and industrial development evaluation indexes and multiple groups of ecological safety and industrial development evaluation indexes in a historical time period; determining the coupling coordination degree of ecological safety and industrial development; calculating a production area ecological safety and industrial development coupling coordination degree in future to-be-predicted time; according to the method, the coupling coordination degree of ecological safety and industrial development is calculated by utilizing historical ecological safety and industrial development index data, the matching degree between the future ecological environment and industrial development can be estimated by utilizing the coupling coordination degree, and a theoretical basis and scientific guidance are provided for future development planning of a production area.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment research, and in particular to a method for predicting the coupling coordination degree between ecological security and industrial development in a production area and its application. Background Art

[0002] Economic and social development and rapid urbanization have brought more ecological and environmental problems. Exploring the coordination between economic development and the ecological environment, as well as the industrial resilience and suitability under the constraints of ecological security, has gradually become a hot topic in academic research. The resilience of industrial development is an important support and guarantee for the smooth operation of the social economy, while the ecological environment often constrains the resilience of industrial development. Only when the two are coupled and coordinated with each other can the overall optimization of regional development be achieved. As the main form of new urbanization, urban agglomerations are an important carrier for promoting cross-regional industrial cooperation and joint protection and governance of the ecological environment. How to always maintain the regional coordinated promotion of ecological environment and economic development is an urgent problem to be solved in the region.

[0003] In the existing technology, no relevant research has been conducted on the ecological security and industrial development of production areas, and the existing calculation methods and models in other fields are less applicable to the ecological security and industrial development of production areas, resulting in low prediction accuracy. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a method and application for predicting the coupling coordination degree between ecological security and industrial development in a production area.

[0005] A method for predicting the coupling coordination degree between ecological security and industrial development in a production area comprises the following steps:

[0006] Obtain data on multiple sets of ecological security and industrial development evaluation indicators and multiple sets of ecological security and industrial development evaluation indicators in historical time periods;

[0007] Determine the weight of each group of ecological security and industrial development evaluation indicator data;

[0008] Determine the coupling coordination degree between ecological security and industrial development through the ecological security and industrial development evaluation index data within the historical period and the weight of the ecological security and industrial development evaluation index data;

[0009] According to the coupling coordination degree between ecological security and industrial development in the historical time period, a prediction model is used to calculate the coupling coordination degree between ecological security and industrial development in the future predicted time period; wherein the prediction model adopts one of the grey prediction model, the vector autoregression model or the random forest model.

[0010] Note: Using the above method, we can use historical ecological security and industrial development indicator data to calculate the coupling coordination degree between the two. Then, using this coupling coordination degree, we can describe and estimate the degree of coordination between the future ecological environment and industrial development, and provide a theoretical basis and scientific guidance for the region's future development planning.

[0011] Furthermore, the ecological security and industrial development evaluation indicators are composed of an ecological security part and an industrial development part. The ecological security part includes ecological environmental pressure indicators, ecological environmental endowment indicators and ecological environmental protection indicators; the industrial development part includes economic development indicators, industrial structure indicators and growth quality indicators.

[0012] Note: The above indicator classification can more comprehensively describe the quantitative data included in ecological security and industrial development, and use these indicators to calculate and solve the coupling degree between the two, and then describe the connection and correlation between ecological security and industrial development. The above indicator classification can also change with time and changes in management objectives, so that the indicators can accurately represent ecological security and industrial development. For example, the ecological security indicators may be different depending on the region, and the regional industrial development will also change over time.

[0013] Furthermore, it also includes using a range standardization processing formula to perform range standardization processing on the data of the multiple groups of ecological security and industrial development evaluation indicators, and the range standardization processing formula is:

[0014]

[0015] Where, X ij is the original value of the jth indicator in the i-th year; max X ij is the maximum value and minX of the jth indicator in the i-th year ij is the minimum value of the jth indicator in the i-th year; x ij mX is the jth index value in the i-th year after the range standardization process; ij maxX ij or min X ij .

[0016] Note: Audit standardization according to the above formula can unify the scale of each indicator so that it can participate in the subsequent calculation process more accurately and simply. At the same time, it can highlight the differences between the data and facilitate the subsequent model calculation.

[0017] Furthermore, the determination of the weight of each group of ecological security and industrial development evaluation index data includes:

[0018] First, the indicator proportions are calculated through the data of multiple groups of ecological security and industrial development evaluation indicators; the calculation formula is:

[0019] Formula (2) is:

[0020]

[0021] Where P ij is the index proportion of the jth index in the i-th year, x ij is the jth index value in the i-th year after the range standardization process, and N is the year;

[0022] Then, the weight of the ecological security and industrial development evaluation index data is calculated by the index proportion; the calculation formula is:

[0023]

[0024] In the formula, w j is the weight of the jth indicator in the ecological security and industrial development evaluation index data, d j is the difference index of the jth indicator, where d j =1-e j , N is year.

[0025] Note: The above formula can be used to determine the weight of each indicator. The weight can be used to perform weighted processing according to the importance of each indicator to ensure the accuracy of the final result.

[0026] Furthermore, the calculation formula for determining the coupling coordination degree between ecological security and industrial development is:

[0027]

[0028] In the formula, D represents the degree of combined coordination between industrial development and ecological security when the composite benefit is maximized under given conditions. The smaller D is, the less coordinated it is; the larger D is, the more ideal the coordination state is; T is the comprehensive coordination index, and T = gF(z) + hF(y), g and h are weight parameters, F(z) is the ecological security index, and F(y) is the industrial development index; C is the coupling degree, and F(z) is the ecological security index, w j is the weight of the jth indicator in the ecological security and industrial development evaluation index data; z ij is the jth ecological security index value in the i-th year, F(y) is the industrial development index, y ij is the value of the j-th industry development index in the i-th year.

[0029] Furthermore, the F(z) is subjected to attenuation correction processing, and the attenuation correction formula is:

[0030]

[0031] In the formula, k i is the indicator decay function for the i-th year, n is the year, k i Satisfy k i =e -λt , t is the number of each indicator data per unit time, λ is the time decay rate, and e is the natural base.

[0032] Note: The above-mentioned attenuation correction can further correct the ecological security index and industrial development index to make them conform to the law of change of indicators over time, and then reflect the importance of data at different time points to future predictions, and improve the calculation results of coupling coordination.

[0033] Furthermore, the coupling coordination degree between ecological security and industrial development in the historical time period is calculated by using a prediction model to calculate the coupling coordination degree between ecological security and industrial development in the production area at a future time to be predicted, including:

[0034] The coupling coordination degree between ecological security and industrial development in multiple historical time periods is constructed into a time series;

[0035] Determine the trend, periodicity and key factors of the coupling coordination degree; then select a prediction model according to the time series and the trend, periodicity and key factors of the coupling coordination degree, and train the prediction model;

[0036] The prediction model is used to predict the coupling coordination degree prediction value. The input of the prediction model is the data of multiple groups of ecological security and industrial development evaluation indicators in the time series and historical time period; the output of the prediction model is the coupling coordination degree prediction value.

[0037] Note: The above method utilizes the construction of time series and the selection of prediction models to achieve the purpose of calculating the coupling coordination degree using the prediction model with high accuracy.

[0038] Further, the trend, periodicity and key factors of the coupling coordination degree are determined; then, according to the time series and the trend, periodicity and key factors of the coupling coordination degree, the prediction model is selected, including:

[0039] A period of historical time is selected to determine whether the coupling coordination degree of the period of historical time has an upward or downward trend and whether it has periodic changes; if at least one of the two exists, the prediction model adopts one of the gray prediction model and the vector autoregression model; if neither of the two has trend changes and periodic changes, the prediction model adopts the random forest model;

[0040] Determine the correlation between the coupling coordination degree of the historical time and the key factor; if the correlation is greater than or equal to 0.90, the prediction model adopts a grey prediction model; if the correlation is less than 0.90, the prediction model adopts a vector autoregression model.

[0041] Note: The above method can conveniently and quickly screen out the prediction model that is most suitable for the coupling coordination degree of ecological security and industrial development. The prediction model screened by this method has a high prediction accuracy.

[0042] The present invention also provides an application of a method for predicting the coupling coordination degree of ecological security and industrial development in a production area, which is applied to the process of regulating and managing ecological security and industrial development in a production area. The application method includes:

[0043] Classify the degree of coupling coordination between ecological security and industrial development in the production areas to be studied in the future, including low coordination level, medium coordination level and high coordination level;

[0044] When it is determined that the area to be studied is at a low coordination level, the key factors of the area to be studied are used to adjust the area to be studied until the area to be studied is at a medium coordination level or a high coordination level.

[0045] Note: The above application provides specific reference significance for the subsequent ecological environment and industrial development of a certain region.

[0046] The beneficial effects of the present invention are:

[0047] The method of the present invention can use historical ecological security and industrial development indicator data to calculate the coupling coordination degree between the two. The attenuation correction can be used to further correct the ecological security index and the industrial development index to make them conform to the law of indicator change over time, thereby reflecting the importance of data at different time points to future predictions, improving the calculation results of the coupling coordination degree, and then using the coupling coordination degree to describe and estimate the degree of coordination between the future ecological environment and industrial development, providing a theoretical basis and scientific guidance for the future development planning of the region. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is the ecological security index data chart for 2015-2022 of the embodiment of the present invention;

[0049] Figure 2 It is a data graph of the industrial development index from 2015 to 2022 of an embodiment of the present invention;

[0050] Figure 3 It is a data chart showing the change trend of the coordination degree C and the coupling coordination degree D from 2015 to 2022 of the embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to further illustrate the method adopted by the present invention and the effect achieved, the technical solution of the present invention will be clearly and completely described in combination with experiments below.

[0052] The method provided by the embodiment of the present invention relates to the technical field of ecological environment research, and in particular to a method and application for predicting the coupling coordination degree of ecological security and industrial development in a production area, which can be used to predict and calculate the coupling coordination degree of ecological security and industrial development in a production area. Specifically, the coupling coordination degree of the two is calculated by using the setting of ecological security and industrial development evaluation index data and their weights, and a prediction model is used to predict, so as to obtain the coupling coordination degree of the ecological environment and industrial development in a certain area at a certain time in the future, and the coupling coordination degree is used to provide a reference for regional planning.

[0053] In the existing technology, there are few related researches and calculations on these two. The existing calculation methods are more focused on the tourism, digital industry and other economic aspects. Meanwhile, the calculation and research methods in the existing technology are directly obtained by adopting the commonly used calculation models and methods without any adaptive improvement.

[0054] Embodiment 1: A method for predicting the coupling coordination degree between ecological security and industrial development in a production area, comprising the following steps:

[0055] S101. Obtain data on multiple sets of ecological security and industrial development evaluation indicators and multiple sets of ecological security and industrial development evaluation indicators in historical time periods;

[0056] The ecological security and industrial development evaluation indicators are composed of an ecological security part and an industrial development part. The ecological security part includes ecological environmental pressure indicators, ecological environmental endowment indicators and ecological environmental protection indicators; the industrial development part includes economic development indicators, industrial structure indicators and growth quality indicators.

[0057] For example, the ecological environment pressure indicators include the exhaust gas sulfur dioxide emission intensity sub-indicator, the unit GDP energy consumption sub-indicator, the unit GDP water consumption sub-indicator, the wastewater chemical oxygen demand emission intensity sub-indicator, and the fine particulate matter annual average concentration sub-indicator; the ecological environment endowment indicators include the urban air quality good days ratio sub-indicator, the surface water I ~ III ratio sub-indicator, the per capita park green space area sub-indicator, the forest coverage rate sub-indicator, and the ecological environment status index sub-indicator; the ecological environment protection indicators include the urban domestic waste harmless treatment rate sub-indicator, the urban sewage treatment rate sub-indicator, the general industrial solid waste comprehensive utilization rate sub-indicator, the hazardous waste The waste safe disposal rate sub-indicator; the economic development indicators include the per capita GDP sub-indicator, the per capita disposable income of urban residents sub-indicator, the per capita disposable income of farmers sub-indicator, and the general public budget revenue as a percentage of GDP sub-indicator; the industrial structure indicators include the primary industry output value as a percentage of GDP sub-indicator, the tertiary industry output value as a percentage of GDP sub-indicator, and the tertiary industry employment population proportion sub-indicator; the growth quality indicators include the GDP annual growth rate sub-indicator, the fixed asset investment as a percentage of GDP sub-indicator, the eco-tourism income as a percentage of the total output value of the service industry sub-indicator, the industrial added value growth rate sub-indicator above designated size and the tea output sub-indicator.

[0058] Specifically, the indicator data is obtained through existing literature and materials. For example, the research period is 2015-2022, and the total research period is 8 years. The data is obtained from the "Statistical Yearbook", "Statistical Communiqué of National Economic and Social Development", "China City Statistical Yearbook", "Eco-environmental Quality Bulletin" and other sources of a certain place.

[0059] The indicators include whole indicators and negative indicators, and specific examples are shown in the following table:

[0060] Table 1 Evaluation index system of coupling coordination between ecological security and industrial development

[0061]

[0062]

[0063] Note: “+” indicates a positive indicator, and “-” indicates a negative indicator.

[0064] The range standardization processing formula is used to perform range standardization processing on the data of the multiple groups of ecological security and industrial development evaluation indicators. The range standardization processing formula is:

[0065]

[0066] Where, X ij is the original value of the jth indicator in the i-th year; max X ij is the maximum value and minX of the jth indicator in the i-th yearij is the minimum value of the jth indicator in the i-th year; x ij is the jth index value in the ith year after the range standardization, between 0 and 1; mX ij is max X ij or min X ij .

[0067] Combining formula (1) and Table 1, the positive indicator uses the following formula (1-1):

[0068]

[0069] When the indicator is negative, the following formula (1-2) is used:

[0070]

[0071] S102, determining the weight of each group of ecological security and industrial development evaluation index data;

[0072] Determining the weight of each group of ecological security and industrial development evaluation index data includes:

[0073] First, the indicator proportions are calculated through the data of multiple groups of ecological security and industrial development evaluation indicators; the calculation formula is:

[0074]

[0075] Where P ij is the index proportion of the jth index in the i-th year, x ij is the jth index value in the i-th year after the range standardization process, and N is the year;

[0076] Then, the weight of the ecological security and industrial development evaluation index data is calculated by the index proportion; the calculation formula is:

[0077]

[0078] In the formula, w j is the weight of the jth indicator in the ecological security and industrial development evaluation index data, d j is the difference index of the jth indicator, where d j =1-e j , N is year.

[0079] S103, determining the coupling coordination degree between ecological security and industrial development through the ecological security and industrial development evaluation index data in the historical time period and the weight of the ecological security and industrial development evaluation index data;

[0080] The calculation formula for determining the coupling coordination degree between ecological security and industrial development is:

[0081]

[0082] In the formula, D represents the coupling coordination degree of industrial development and ecological security when the composite benefit is maximized under given conditions. The smaller D is, the less coordinated it is; the larger D is, the more ideal the coordination state is; T is the comprehensive coordination index, and T = gF(z) + hF(y), g and h are weight parameters, F(z) is the ecological security index, and F(y) is the industrial development index; C is the coupling degree, and F(z) is the ecological security index, w j is the weight of the jth indicator in the ecological security and industrial development evaluation index data; ij is the jth ecological security index value in the i-th year, F(y) is the industrial development index, y ij is the value of the jth industry development index in the i-th year;

[0083] The F(z) is subjected to attenuation correction processing, and the attenuation correction formula is:

[0084]

[0085] In the formula, k i is the indicator decay function for the i-th year, n is the year, k i Satisfy k i =e -λt , t is the number of each indicator data per unit time, λ is the time decay rate, and e is the natural base.

[0086] S104. Based on the coupling coordination degree between ecological security and industrial development in the historical time period, a prediction model is used to calculate the coupling coordination degree between ecological security and industrial development in the production area at the future predicted time; wherein the prediction model adopts one of a grey prediction model, a vector autoregression model or a random forest model.

[0087] The coupling coordination degree between ecological security and industrial development in the historical time period is calculated by using a prediction model to calculate the coupling coordination degree between ecological security and industrial development in the production area at a future time to be predicted, including:

[0088] ① Construct the coupling coordination degree between ecological security and industrial development in multiple historical time periods into a time series;

[0089] For example, the time series X = {x1, x2, ... x n}, there are n coupling coordination degrees arranged by year;

[0090] ② Determine the trend, periodicity and key factors of the coupling coordination degree; then select a prediction model according to the time series and the trend, periodicity and key factors of the coupling coordination degree, and train the prediction model;

[0091] Determine the trend, periodicity and key factors of the coupling coordination degree; then select the prediction model according to the time series and the trend, periodicity and key factors of the coupling coordination degree, including:

[0092] A period of historical time is selected to determine whether the coupling coordination degree of the period of historical time has an upward or downward trend and whether it has periodic changes; if at least one of the two exists, the prediction model adopts one of the gray prediction model and the vector autoregression model; if neither of the two has trend changes and periodic changes, the prediction model adopts the random forest model;

[0093] Determine the correlation between the coupling coordination degree of the historical time and the key factor; if the correlation is greater than or equal to 0.90, the prediction model adopts a grey prediction model; if the correlation is less than 0.90, the prediction model adopts a vector autoregression model.

[0094] Exemplarily, according to the above judgment, if there is an upward trend in the coupling coordination degree, and the correlation between the key factor and the coupling coordination degree is 0.95, the grey prediction model is adopted.

[0095] The method for determining the above-mentioned key factors is to use a correlation analysis method (such as Pearson correlation analysis method) to perform a correlation analysis on each sub-indicator and the coupling coordination degree to obtain a correlation. The key factors are sub-indicators with a correlation greater than 0.80.

[0096] Exemplarily, the method for training the prediction model is:

[0097] The training process is the process of continuously updating the initialized predictions using the training set. Specifically, it is the process of continuously updating the parameters of the prediction model. The above indicator data is divided into a training set, a validation set, and a test set, and the model training is started at the same time. The model training and evaluation process includes:

[0098] The first step is model training.

[0099] The training samples in the training set are input into the prediction model, the predicted value is calculated, and the loss function value between the predicted value and the true value is calculated, and the gradient backpropagation is performed, and the parameters of the model are updated according to the set optimization algorithm.

[0100] It should be understood that model training is a process of multiple rounds of iterations. Each round of iteration traverses the training set once, and each time a small batch of samples is obtained from it and sent to the model to perform forward calculations to obtain predicted values. The effect of model training is judged by observing the decreasing trend of the loss value in each round of iteration. For example, when the decreasing trend of the loss value remains unchanged, it means that the model tends to converge.

[0101] The second step is model evaluation.

[0102] The test set is input into the trained model for evaluation to obtain the predicted value, calculate the loss function value between the predicted value and the true value, and calculate the evaluation index value to facilitate the evaluation of the model effect.

[0103] The third step is model reasoning.

[0104] Input the coupling coordination data to be verified into the trained model to perform reasoning, and observe and verify whether the reasoning results meet expectations.

[0105] ③ The prediction model is used to predict the coupling coordination degree prediction value, the input of the prediction model is the data of multiple groups of ecological security and industrial development evaluation indicators in the time series and historical time period; the output of the prediction model is the coupling coordination degree prediction value.

[0106] For example, input: monthly data of indicators of ecological environment and industrial development in the past year; output: predicted value of coupling coordination for each month in the next year, and 95% confidence interval of the predicted value.

[0107] The grey prediction method is used to predict the coupling coordination degree between ecological security and industrial development. The specific steps are as follows:

[0108] First, let the time series X (0) ={x (0 )1,x (0) 2,...x (0) n There are n coupling coordination degrees, which are accumulated to generate a new sequence

[0109] The grey prediction model is:

[0110]

[0111] is the development coefficient, is the grey action, which is solved by the least square method;

[0112] According to the above prediction model, prediction can be made. Substitute the t value (i.e., the future prediction time) into the prediction model to obtain the cumulative prediction value of the coupling coordination degree: Then, the predicted value of coupling coordination degree at time t is obtained by cumulative reduction.

[0113] Finally, the grey prediction formula is tested for accuracy and the posterior difference ratio is calculated. In the formula, is the mean value of coupling coordination, and the probability of small error is calculated Among them, S1 is the mean square error of the coupling coordination degree; the results are shown in Table 2. If P and C are within the allowable range, the predicted value of this indicator can be calculated. Otherwise, it is necessary to re-correct the formula of the grey prediction model by analyzing the residual sequence, that is, repeat the iterative calculation. and

[0114] Table 2 Grey prediction accuracy test level standard

[0115] Accuracy level P C Accuracy level P C good >0.95 <0.35 reluctantly >0.70 <0.65 qualified >0.80 <0.50 Failure ≤0.70 ≥0.65

[0116] The embodiment of the present invention also provides an application of a method for predicting the coupling coordination degree of ecological security and industrial development in a production area, which is applied to the process of regulating and managing ecological security and industrial development in a production area. The application method includes:

[0117] Classify the degree of coupling coordination between ecological security and industrial development in the production areas to be studied in the future, including low coordination level, medium coordination level and high coordination level;

[0118] When it is determined that the area to be studied is at a low coordination level, the key factors of the area to be studied are used to adjust the area to be studied until the area to be studied is at a medium coordination level or a high coordination level:

[0119] In another implementation of the embodiment of the present invention, the coupling coordination degree is classified using the classification method shown in Table 3 below:

[0120] Table 3 Classification and criteria for coordinated development of ecological security and industrial development

[0121] Coupling Coupling level Coupling coordination Coupling coordination level [0～0.5) Low level coupling [0～0.5) Low level coordination [0.5～0.8) Lower level coupling [0.5～0.6) Lower level coordination [0.8～0.9) Moderate coupling [0.6～0.7) Moderate coordination [0.9～0.95) Higher level coupling [0.7～0.8) Higher level of coordination [0.95～1] High level coupling [0.8～1] High level of coordination

[0122] In the above Table 3, values ​​above 0.95 indicate high coupling.

[0123] Experimental example:

[0124] According to the above-mentioned embodiment method and examples, the present invention uses 6 cities / counties to perform actual model calculations to achieve the purpose of prediction. The following are the data results of six cities / counties (J1, J2, Z1, Z2, A1, A2) in a certain area: 1. The results of the ecological security-industrial development coupling coordination degree in 2022 are shown in Table 4:

[0125] Table 4 Ecological security-industrial development coupling coordination degree in 2022

[0126] City and County Coupling degree C Coupling type Coupling coordination degree D Coordination Type J1 0.952 High level coupling 0.721 Higher level of coordination J2 0.994 High level coupling 0.751 Higher level of coordination Z1 0.988 High level coupling 0.757 Higher level of coordination Z2 0.994 High level coupling 0.760 Higher level of coordination A1 0.993 High level coupling 0.684 Moderate coordination A2 0.981 High level coupling 0.661 Moderate coordination

[0127] It can be seen from Table 4 that the coupling degree in the region is high in 2022, and there is no low-level coordination. 2. The following table shows the results of the correlation analysis, which is used to determine the key factors;

[0128] Table 6 Correlation results between ecological security and industrial development coupling coordination

[0129]

[0130]

[0131] As shown in Table 5, economic development indicators such as GDP per capita (B11) and per capita disposable income of farmers (B13) have a significant positive correlation with the coupling coordination degree of the six counties (cities), among which GDP per capita (B11) has the largest positive impact coefficient on Z1 County, and per capita disposable income of farmers (B13) has the largest positive impact coefficient on Z2 County. The level of economic development directly affects the green transformation of the industry and strengthens the relationship between ecological security maintenance and industrial resilience development. For this reason, we should steadily promote high-quality economic development. For J1 County, the proportion of the primary industry in GDP (B21) has the greatest negative impact on the coupling coordination degree, with a correlation coefficient of -0.995. For J2 County, the annual average concentration of fine particulate matter (PM2.5) (A15) has the greatest negative impact on the coupling coordination degree, with a correlation coefficient of -0.920. For Z1 County, the intensity of exhaust sulfur dioxide emissions (A11) has the greatest negative impact on the coupling coordination degree, with a correlation coefficient of -0.981. For Z2 County, the intensity of exhaust sulfur dioxide emissions (A11) has the greatest negative impact, with a correlation coefficient of -0.984. For A1, the annual average concentration of fine particulate matter (PM2.5) (A15) has the greatest negative impact on the coupling coordination degree, with a correlation coefficient of -0.925; for A1 County, the proportion of ecotourism income in the total output value of the service industry (B33) has the greatest negative impact on the coupling coordination degree, with a correlation coefficient of -0.902.

[0132] III. Changes in the Ecological Security and Industrial Development Index of the Six Counties (Cities) from 2015 to 2022

[0133] Depend on Figure 1It can be seen that the overall development level of the ecological security system is between 0.1 and 0.7. The development level of the ecological security system in each county (city) shows a significant growth trend. The largest increase is in Z2, followed by J1. The ecological security index F(z) in 2022 increased by 6.13 times and 5.51 times compared with 2015, respectively. The ecological environment protection system index of Z2 since 2016 is significantly higher than that of other counties (cities), which is the main reason for the highest ecological security index among the six counties (cities), while Z1 is mainly due to the ecological environment endowment system index leading other counties (cities); A1 has the smallest ecological security index F(x), and the ecological security index F(x) in 2022 increased by 1.02 times compared with 2015. In 2022, the ecological security index F(x) of the six counties (cities) was lower than that in 2021. The highest F(x) was 0.618 in J1, and the lowest was 0.490 in A2. Among the subsystems of the ecological security system, the ecological environmental pressure index had the highest weight, which was 0.47 in 2022. The ecological environmental pressure index had the most obvious impact on the change of the ecological security index. Factors such as the emission of wastewater, waste gas, smoke, and industrial energy consumption and water consumption played a vital role in improving the ecological environment quality in the "one place and six counties" area.

[0134] Depend on Figure 2 It can be seen that the overall development level of the industrial development system is between 0.3 and 0.7. The development level of the industrial system in each county shows a steady growth trend. The city and county with the largest increase is Z1, and the industrial development index F(y) in 2022 increased by 86.98% compared with 2015; the smallest is A2, and A2, the industrial development index F(y) in 2022 increased by only 8.57% compared with 2015. This is closely related to the economic development level and resource endowment of different regions. Z1 and J1 are in areas with highly concentrated economic resources, which is conducive to the city's gathering of high-quality talents and accelerating the construction of new infrastructure, promoting industrial structure adjustment and improving the quality of economic growth, thereby improving the industrial development index, which is particularly evident in Z1. In 2022, the industrial development index F(y) of the six counties was lower than that in 2021. The highest F(y) was 0.604 in Z1, and the lowest was 0.468 in A1. This shows that the regional industrial development level has grown steadily in recent years and has a certain correlation with the quality of the ecological environment. Economic development supports and promotes ecological security and stability, and the improvement of ecological environment quality in turn feeds back to industrial development.

[0135] 4. Changes in coupling coordination degree from 2015 to 2022

[0136] like Figure 3 As shown in the figure, the coupling degree (C) between ecological security and industrial development is calculated through the coupling coordination model. Figure 3 , a.) and coupling coordination (D)( Figure 3, b.). The study found that the average values ​​of coupling and coupling coordination of the six counties (cities) were both fluctuating upward, with the average value of coupling between 0.55 and 1, an increase of 79.71%; while the average value of coupling coordination was between 0.40 and 0.73, an increase of 79.62%. From 2015 to 2022, the coupling and coupling coordination of the six counties (cities) were generally improved, the differences between regions were constantly narrowing, and ecological security and industrial development were more coordinated. Among them, the cities and counties with the largest increase were Z2, which were 1.49 times and 1.32 times of 2015, respectively. The coupling degree rose from the last place in 2015 to the first place in 2022, and the coupling coordination degree rose from the last place in 2015 to the first place in 2022; the smallest increase was A1, with an increase of 0.17 and 0.14, respectively, which were 71.22% and 45.57% of 2015. It can be seen that the coupling degree and coupling coordination degree of the "one place and six counties" region are correlated.

[0137] 5. Coupling coordination prediction results

[0138] The coupling coordination degree of the two systems in the six regions from 2015 to 2022 was taken as the analysis data, and the prediction time length was 8, and the coupling coordination degree (D) prediction from 2023 to 2030 was carried out. Table 6 shows that the development trend of the coupling coordination degree of ecological security and industrial development in these regions in the next few years will continue the change characteristics from 2015 to 2022, showing a slight upward trend, and will rise to a high level of coordination by 2030. The coordinated development of ecological security and industrial development in these regions will generally improve in the next few years. In 2026, Z2 will take the lead in reaching a fully coordinated level, but the coordinated development level of A1 will still rise slowly, and it is still necessary to break through its own dominant restrictive factors in future development to achieve coordinated and sustainable economic structure adjustment and ecological environmental protection.

[0139] Table 6 Prediction of the coordinated development of ecological security and industrial development

[0140] Forecast Year J1 J2 Z1 Z2 A1 A2 2023 0.833 0.811 0.827 0.873 0.769 0.738 2024 0.881 0.853 0.871 0.928 0.815 0.769 2025 0.932 0.898 0.917 0.986 0.864 0.801 2026 0.985 0.945 0.966 1.000 0.916 0835 2027 1.000 0.995 1.000 1.000 0.971 0.870 2028 1.000 1.000 1.000 1.000 1.000 0.906 2029 1.000 1.000 1.000 1.000 1.000 0.944 2030 1.000 1.000 1.000 1.000 1.000 0.983 Coordination level High level High level High level High level High level High level

[0141] The above research shows that the coupling degree (C) and coupling coordination degree (D) of ecological security and industrial development have shown a stable growth trend. According to the forecast, by 2030, the coupling coordination degree (D) of A1 County will not reach a fully coordinated development level. It is necessary to balance the relationship between development and protection through reasonable planning of industrial development and industrial layout in the process of regional integration, and continue to strengthen the modern industrial system. Affected by the combined influence of human activities such as fossil fuel combustion and VOCs emissions, as well as natural conditions such as regional topography, surface cover, and climate, the coupling coordination degree (D) of J2 and A1 regions is negatively correlated with the annual average concentration of fine particulate matter (PM2.5). The coupling coordination degree (D) of Z2 and Z1 counties is negatively correlated with the intensity of exhaust sulfur dioxide emissions. It is necessary to focus on PM2.5 control and pollutant reduction with industrial parks to improve the quality of the atmospheric environment.

[0142] The main influencing factor of the coupling coordination degree between J1 and A1 is the industrial structure; the industrial structure of A1 and A2 lags behind other regions to a certain extent, so they should develop characteristic industries based on their own ecological advantages. They can enhance the ecological resilience of the city through optimization of ecological land, urban renewal actions, and construction of ecological corridors, expand the channels for transformation of the value of natural ecological resources, and create an ecological cultural tourism and all-round tourism model.

[0143] In summary, in this experimental case, the ecological security index F(x) and industrial development index F(y) of the "one place and six counties" area from 2015 to 2022 are both in a continuous growth trend. The largest increase in the ecological security index F(x) is in Z2, and the smallest is in A1. The highest industrial development index F(y) is in Z1, which is 0.767, and the lowest is in A1, which is 0.468. From the perspective of time evolution, the average values ​​of the coupling degree (C) and coupling coordination degree (D) of ecological security and industrial development in the six counties (cities) are all in a fluctuating upward trend. From 2015 to 2022, the largest increase in the coupling degree (C) and coupling coordination degree (D) is in Z2, with increases of 0.61 and 0.48 respectively, and the smallest increase is in A1, with increases of 0.17 and 0.14 respectively. The annual average concentration of fine particulate matter (PM2.5) has a negative impact on J2 and A1, and the intensity of exhaust sulfur dioxide emissions has a negative impact on Z2 and Z1. The main influencing factor of the coupling coordination between J1 and A2 is the industrial structure. The industrial structural adjustment should be accelerated based on the regional integration advantages of the Yangtze River Delta. The main factors of the coupling coordination of each city and their influencing direction and degree of influence have obvious spatial heterogeneity. The development of the coupling coordination between regional ecological security and industrial development in the next few years will roughly continue the change characteristics from 2015 to 2022, showing a slight upward trend. By 2030, the coupling coordination of the six counties (cities) will reach a high level of coordination.

Claims

1. A method for predicting the coupling coordination degree between ecological security and industrial development in a production area, characterized in that: The following steps are involved: Obtain data on multiple sets of ecological security and industrial development evaluation indicators and multiple sets of ecological security and industrial development evaluation indicators in historical time periods; Determine the weight of each group of ecological security and industrial development evaluation indicator data; Determine the coupling coordination degree between ecological security and industrial development through the ecological security and industrial development evaluation index data within the historical period and the weight of the ecological security and industrial development evaluation index data; According to the coupling coordination degree between ecological security and industrial development in the historical time period, a prediction model is used to calculate the coupling coordination degree between ecological security and industrial development in the future predicted time period; wherein the prediction model adopts one of the grey prediction model, the vector autoregression model or the random forest model.

2. A method for predicting the coupling coordination degree of ecological security and industrial development in a production area as claimed in claim 1, characterized in that: The ecological security and industrial development evaluation indicators are composed of an ecological security part and an industrial development part. The ecological security part includes ecological environmental pressure indicators, ecological environmental endowment indicators and ecological environmental protection indicators; the industrial development part includes economic development indicators, industrial structure indicators and growth quality indicators.

3. The method for predicting the coupling coordination degree of ecological security and industrial development in a production area according to claim 1, characterized in that: It also includes performing range standardization processing on the data of the multiple groups of ecological security and industrial development evaluation indicators using a range standardization processing formula, and the range standardization processing formula is: Where, X ij is the original value of the jth indicator in the i-th year; maxX ij is the maximum value and minX of the jth indicator in the i-th year ij is the minimum value of the jth indicator in the i-th year; x ij mX is the jth index value in the i-th year after the range standardization process; ij maxX ij or minX ij .

4. A method for predicting the coupling coordination degree of ecological security and industrial development in a production area as claimed in claim 3, characterized in that: Determining the weight of each group of ecological security and industrial development evaluation index data includes: First, the indicator proportions are calculated through the data of multiple groups of ecological security and industrial development evaluation indicators; the calculation formula is: In formula (2), P ij is the index proportion of the jth index in the i-th year, x ij is the jth index value in the i-th year after the range standardization process, and N is the year; Then, the weight of the ecological security and industrial development evaluation index data is calculated by the index proportion; the calculation formula is: In formula (3), w j is the weight of the jth indicator in the ecological security and industrial development evaluation index data, d j is the difference index of the jth indicator, where d j =1-e j , N is year.

5. The method for predicting the coupling coordination degree of ecological security and industrial development in a production area according to claim 1, characterized in that: The calculation formula for determining the coupling coordination degree between ecological security and industrial development is: In the formula, D represents the coupling coordination degree of industrial development and ecological security when the compound benefit is maximized under given conditions. The smaller D is, the less coordinated it is; the larger D is, the more coordinated it is. T is the comprehensive coordination index, and T = gF(z) + hF(y), g and h are weight parameters, C is the coupling degree, and F(z) is the ecological security index, w j is the weight of the jth indicator in the ecological security and industrial development evaluation index data; ij is the jth ecological security index value in the i-th year, F(y) is the industrial development index, y ij is the value of the j-th industry development index in the i-th year.

6. A method for predicting the coupling coordination degree of ecological security and industrial development in a production area as claimed in claim 5, characterized in that: The F(z) is subjected to attenuation correction processing, and the attenuation correction formula is: In the formula, k i is the indicator decay function for the i-th year, n is the year, k i Satisfy k i =e -λt , t is the number of each indicator data per unit time, λ is the time decay rate, and e is the natural base.

7. The method for predicting the coupling coordination degree between ecological security and industrial development in a production area according to claim 1, characterized in that: The coupling coordination degree between ecological security and industrial development in the historical time period is calculated by using a prediction model to calculate the coupling coordination degree between ecological security and industrial development in the production area at a future time to be predicted, including: The coupling coordination degree between ecological security and industrial development in multiple historical time periods is constructed into a time series; Determine the trend, periodicity and key factors of the coupling coordination degree; then select a prediction model according to the time series and the trend, periodicity and key factors of the coupling coordination degree, and train the prediction model; The prediction model is used to predict the coupling coordination degree prediction value. The input of the prediction model is the data of multiple groups of ecological security and industrial development evaluation indicators in the time series and historical time period; the output of the prediction model is the coupling coordination degree prediction value.

8. The method for predicting the coupling coordination degree between ecological security and industrial development in seed production areas as claimed in claim 7, characterized in that: Determine the trend, periodicity and key factors of coupling coordination; Then, according to the trend, periodicity and key factors of the time series and coupling coordination, the prediction models are selected including: A period of historical time is selected to determine whether the coupling coordination degree of the period of historical time has an upward or downward trend and whether it has periodic changes; if at least one of the two exists, the prediction model adopts one of the gray prediction model and the vector autoregression model; if neither of the two has trend changes and periodic changes, the prediction model adopts the random forest model; Determine the correlation between the coupling coordination degree of the historical time and the key factor; if the correlation is greater than or equal to 0.90, the prediction model adopts a grey prediction model; if the correlation is less than 0.90, the prediction model adopts a vector autoregression model.

9. The application of the method for predicting the coupling coordination degree of ecological security and industrial development in a production area as claimed in claim 1, characterized in that: Apply it to the regulation and management of ecological security and industrial development in production areas.

10. The application of the method for predicting the coupling coordination degree of ecological security and industrial development in a production area as claimed in claim 9, characterized in that: Application methods include: Classify the degree of coupling coordination between ecological security and industrial development in the production areas to be studied in the future, including low coordination level, medium coordination level and high coordination level; When it is determined that the area to be studied is at a low coordination level, the key factors of the area to be studied are used to adjust the area to be studied until the area to be studied is at a medium coordination level or a high coordination level.

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