Ecological risk dynamic simulation evaluation and prediction early warning method based on substance metabolism process

Abstract

The invention relates to an ecological risk dynamic simulation evaluation and prediction early warning method based on a material metabolism process, and the method comprises the following steps: 1, ecological risk impact recognition: precisely analyzing the urban metabolism process through dynamic material flow analysis, and constructing an urban metabolism network model; step 2, ecological risk dynamic simulation evaluation: the risk impact of each metabolic subject is incorporated into an input-output model, and an urban ecological risk diffusion model is constructed; step 3, ecological risk prediction and early warning: constructing a prediction model of ecological risk impact indexes and influence factors based on an expandable random environmental influence evaluation model; the method has the advantages that key metabolic risk points are mined through risk impact recognition and quantification of all metabolic subjects, influence linkage conduction process simulation, risk index calculation and metabolic risk prediction and early warning, and stable operation of an urban agglomeration system is ensured; starting from the whole metabolic process of urban source supply and the like, the mechanism of metabolic risk is systematically explored.

Description

Technical Field

[0001] The present invention belongs to the technical field of ecological environment, and specifically relates to an ecological risk dynamic simulation assessment and prediction and early warning method based on material metabolic process. Background Art

[0002] In an era of rapid urbanization and industrialization, the use of urban materials and energy has increased significantly, causing the use of some resources to exceed the city's carrying capacity, making it possible for urban systems to induce metabolic risks, and urban sustainable development goals face severe challenges. If urban ecological risks can be dynamically simulated and predicted and warned in a timely and accurate manner, it will be possible to effectively prevent the spread of risks and take targeted preventive measures, which is of great significance.

[0003] At the same time, the urban organizational form strengthens the connection between urban departments and enhances the interdependence between subjects, which further triggers the transmission of metabolic risks in the urban metabolic network system. The cascade transmission effect of risks in the network is even greater than the impact of the shock itself. Understanding the risk shocks faced by the system and their linkage transmission process is the basis for a comprehensive understanding of the risks of urban (the cities in this article also include urban agglomerations) ecosystems.

[0004] The invention patent with the publication number of Chinese invention patent application CN116070906A discloses a risk identification and assessment method based on a complex product supply chain. The invention identifies and calculates the initial risk of each supplier in a complex product from the supplier's perspective, and then further considers the risk transmission and calculates the risk of the complex product supplier supply chain.

[0005] However, the existing technologies, including the above-mentioned invention patent applications, still have the following problems: First, the identification of risk shocks only stays on external events, without considering the improper operating mechanism within the system, and the risk shocks of urban metabolic networks have not yet been paid attention to; second, most of the existing technologies focus on a single type of risk shock, and there are relatively few technologies that systematically explore metabolic risks from the perspective of the entire metabolic process, including source supply, intermediate transformation and terminal demand; third, the existing technologies fail to disassemble the risk transmission chain and transmission path, making the multi-level transmission chain and indirect risk points of risk shocks blurred; finally, the existing urban ecological risk prediction methods often fail to consider the complexity and interdependence of urban ecosystem components, ignore the diffusion effect of risks, and cannot comprehensively and accurately quantify and predict urban ecological risks. There are also few methods that can provide graded warnings for ecological risks from a departmental scale. Summary of the invention

[0006] In view of the problems existing in the prior art, the present invention proposes a model framework for dynamic simulation assessment and prediction and early warning of urban ecological risks. First, the metabolic process of the city is analyzed through dynamic material flow analysis, a metabolic network model is constructed, and the metabolic risk shock is identified from multiple perspectives throughout the process, and the risk shock is quantified in combination with its corresponding risk threshold; secondly, the input-output model is used to simulate the transmission process of risk shock in the metabolic network to clarify the degree and scope of risk transmission; finally, the classic risk analysis is used to evaluate and predict the metabolic risk index, identify key risk points, and provide a scientific basis for ecological risk classification and early warning. The present invention aims to provide a feasible solution for evaluating urban metabolic risks and provide a reference for promoting urban resilience and sustainable development.

[0007] The method for dynamic simulation assessment and prediction and early warning of ecological risks based on the material metabolism process of the present invention comprises the following steps:

[0008] Step 1: Identification of ecological risk shocks: Accurately analyze the metabolic process of the city through dynamic material flow analysis, build an urban metabolic network model, identify metabolic risk shocks from multiple perspectives throughout the entire process, and quantify ecological risk shocks based on the risk thresholds corresponding to risk shock indicators;

[0009] Step 2: Dynamic simulation and assessment of ecological risks: Incorporate the risk impact of each metabolic entity into the input-output model, build an urban ecological risk diffusion model, simulate the transmission process of different types of risk impacts based on the metabolic network, quantify the risk diffusion amount and risk diffusion path based on the amplification effect of the linkage, determine the probability and amount of risk impacts to each department in the city, and evaluate the ecological risk index based on classical risk theory;

[0010] Step 3. Ecological risk prediction and early warning: Based on the scalable stochastic environmental impact assessment model, a prediction model for ecological risk impact indicators and influencing factors is constructed. Further establish a variety of future development scenarios and determine the future factor values. Calculate the future changes in metabolic process risk impact indicators to achieve the prediction of future ecological risk impacts and ecological risk indexes. At the same time, refer to the classic risk matrix and construct a rectangular coordinate system with the horizontal axis as the impact possibility and the vertical axis as the sum of diffusion risks. According to the risk critical line, the matrix is ​​divided into different levels to achieve ecological risk classification and early warning.

[0011] Furthermore, the step 1 is specifically as follows:

[0012] Identify the metabolic risk shocks of cities, including the construction of urban metabolic network model and the identification of metabolic risk shocks:

[0013] Construct an urban metabolic network model based on the complex material supply and demand relationship between various social and economic sectors in the city, with the social and economic sectors of the city as nodes, the material flow relationship between sectors as paths, and the corresponding relationship between materials and sectors to map and generate an urban metabolic network model;

[0014] Aiming at the three important links of source resource exploitation and consumption, intermediate metabolic network circulation characteristics, and terminal stock products providing services and pollutant emissions, metabolic risk shocks are divided into four types: imbalance in key resource supply and demand, incomplete material circulation, ecological environmental damage, and unfair distribution of derivative products and services. The metabolic risk shocks faced by cities are identified from the whole metabolic process.

[0015] Quantify the metabolic risk impact of cities, specifically:

[0016] Based on the basic development needs of human beings, a literature survey was conducted to determine the risk thresholds corresponding to the above-mentioned different risk impact indicators. The metabolic risk impact faced by the city was quantified by combining the difference between the risk impact and the risk threshold, as shown in the following formula (1):

[0017] in:

[0018]

[0019] In formula (1), UA_RI is the metabolic risk shock of the urban agglomeration, RI ij is the initial risk of the j-th risk category in the i-th city, RV is the risk shock of different risk indicators, Threhold j is the risk threshold corresponding to different risk indicators, SI is population or GDP, and the calculation process uses population or GDP to determine the risk indicator unit.

[0020] Furthermore, the step 2 specifically includes the following steps:

[0021] Step 2.1 Dynamic simulation calculation of risk shocks: construct a differential transmission model based on the input-output model to simulate the transmission process of different types of risk shocks and clarify the scale and path of risk transmission:

[0022] The Ghosh model is used to construct a key resource supply and demand imbalance shock transmission model, and the diffusion matrix ΔCR is calculated based on the Ghosh inverse matrix and the supply shock matrix Δz. * , which is used to characterize the impact of a node supply shock on other nodes in the metabolic process, as shown in the following formula (2):

[0023] ΔCR * =Δz(IA * ) -1 ......(2),

[0024] In formula (2), ΔCR * is the diffusion matrix, which represents the amount of indirect risk transmission borne by the receptor node after the cascade diffusion of the supply-driven risk shock, and Δz is the supply shock matrix;

[0025] The transmission model of the impact of ecological environmental damage and unfair distribution of products and services is constructed using the Leontief model. The induction matrix ΔCR is calculated based on the Leontief inverse matrix and the demand shock matrix Δy to characterize the linkage effect of a demand shock at a certain node on the remaining nodes in the metabolic process, as shown in the following formula (3):

[0026] ΔCR=(IA) -1 Δy......(3),

[0027] In formula (3), ΔCR is the induction matrix, which represents the risk transmission amount borne by each receptor node after the initial risk cascade diffusion driven by demand, Δy-demand shock matrix;

[0028] The Ghosh model and Leontief model are used for the impact transmission of incomplete material flow according to the impact direction of upstream and downstream. The Ghosh model is used for the impact of a certain node on the downstream node. The supply matrix and the distribution coefficient impact matrix ΔA are used. * (corresponding to the row change of the node) calculate the diffusion matrix ΔCR * The linkage effect of the upstream node on a node is calculated using the Leontief model. The induction matrix ΔCR is calculated based on the demand matrix and the consumption coefficient impact matrix ΔA (corresponding to the column change of the node). The specific calculation is as follows (4)-(5):

[0029] ΔCR=(I-ΔA) -1 y......(4),

[0030] ΔCR * =z(I-ΔA * ) -1 ......(5),

[0031] In the above formulas (4)-(5), ΔA-consumption coefficient impact matrix represents the linkage effect of the upstream node on a certain node, ΔA * -Distribution coefficient impact matrix, which represents the impact of a node on downstream nodes;

[0032] Dismantle indirect risks and identify risk transmission paths: Use structural path analysis to dismantle indirect risk transmission and track the transmission chain and indirect risk points of risk shocks, as shown in the following formulas (6)-(7):

[0033]

[0034]

[0035] In formulas (6)-(7), (A) t Δy,Δz(A) t- the amount of cascade conduction occurring at the tth layer; i, j, k, l-represent different department nodes in the city, s-the total number of department nodes in the city; a ij It indicates how much of the unit output of department i is allocated to department j, reflecting the direct promotion effect; It indicates how much product i is consumed by the unit production of department j, reflecting the direct pull effect;

[0036] Step 2.2 Ecological risk assessment: Referring to the classical risk calculation method, i.e., risk = probability * consequence, the possibility of coupled shocks, supply and demand risk impact, pollution risk impact, and product and service risk impact are four dimensions to develop an ecological risk index and systematically assess the ecological risks of each metabolic entity, as shown in the following formula (8):

[0037]

[0038] In formula (8), the possibility of a node facing a shock is represented by the ratio of the frequency of the total transmission path passing through the node to the total frequency of passing through all nodes, and the impact of the shock is the sum of supply and demand, pollution, and product and service risks. The former and the latter are represented by the transmission amount of the shock.

[0039] Furthermore, the step 3 is specifically as follows:

[0040] Step 3.1 Determine the possible influencing factors of ecological risk impact indicators, including urban socio-economic factors, technical factors and policy factors, and construct a prediction model of ecological risk impact indicators and influencing factors, as shown in the following formula (9);

[0041] lnI=lna+blnP+cLNA+dlnT+lne,

[0042] In the above formula (9), I is the ecological risk impact index, P, A and T are the influencing factors, a is the constant term, b, c and d are the elasticity of the influencing factors on the urban risk impact index, that is, the degree of influence of the influencing factors, and e is the error term;

[0043] Step 3.2 Determine the lower limit of the ecological risk impact indicator threshold based on the survey, and determine the upper limit of the risk impact indicator threshold using species sensitivity analysis;

[0044] Step 3.3 Determine the changes in future influencing factors based on literature research, government bulletins or the average growth rate in the past ten years, and combine different scenarios for each factor;

[0045] Step 3.4 Input the corresponding future factor values ​​under each combination scenario into the prediction model, and quantify the size of future risk impact based on the relationship between the model output results and the threshold. On this basis, the future ecological risk index is predicted. Referring to the classic risk matrix, a rectangular coordinate system is constructed with the horizontal axis as the impact possibility and the vertical axis as the total diffusion risk. The horizontal and vertical data are scaled to between 0 and 10 to construct a risk grading matrix of 10×10. The elements in the risk grading matrix are ecological risk indexes. According to the risk critical line, that is, possibility × total diffusion risk = 10, 20, 30…90, the matrix is ​​divided into 10 parts, representing different levels of risk as shown below:

[0046]

[0047] The superior technical effects of the present invention over the prior art in this technical field are:

[0048] 1. The ecological risk dynamic simulation assessment and prediction and early warning method based on the material metabolic process described in the present invention can identify and quantify the risk impact of each metabolic entity, simulate the linkage transmission process, calculate the risk index, and predict and warn of metabolic risks, so as to explore key metabolic risk points and ensure the stable operation of the urban agglomeration system.

[0049] 2 The ecological risk dynamic simulation assessment and prediction and early warning method based on the material metabolism process described in the present invention starts from the entire metabolic process of urban source supply, intermediate transformation and terminal demand, systematically explores the mechanism of metabolic risk, comprehensively and accurately quantifies and predicts urban ecological risks, and has broad prospects for promotion and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The figure is a flow chart of the method for dynamic simulation assessment and prediction and early warning of ecological risks based on the material metabolism process of the present invention.

[0051] Figure 2 Concept diagram for urban metabolic risk shock identification and risk assessment.

[0052] Figure 3 Schematic diagram of the dynamic simulation of metabolic risk shocks in the Fujian Triangle urban agglomeration.

[0053] Figure 4 This is the metabolic risk impact result diagram of the Fujian Triangle Urban Agglomeration.

[0054] Figure 5 This is a diagram of the scale structure of metabolic risk transmission in the Fujian Triangle Urban Agglomeration.

[0055] Figure 6 This is a diagram showing the transmission path of risk impacts in the Fujian Triangle urban agglomeration.

[0056] Figure 7This is the metabolic risk prediction result of the Fujian Triangle Urban Agglomeration.

[0057] Figure 8 This is the metabolic risk classification warning result map of the Fujian Triangle Urban Agglomeration. DETAILED DESCRIPTION

[0058] The following is a further detailed description of the ecological risk dynamic simulation assessment and prediction and early warning method based on the material metabolism process of the present invention in combination with the accompanying drawings and specific implementation methods.

[0059] The ecological risk dynamic simulation assessment and prediction and early warning method based on the material metabolism process is as follows: Figure 1 As shown, the specific implementation process is as follows:

[0060] Step 1: Identification of ecological risk impacts

[0061] S1.1 Construction of urban metabolic network model: Based on the ew-MFA accounting criteria, dynamic material flow model and existing urban metabolic accounting framework, the metabolic network model of urban agglomeration is constructed with social and economic sectors as nodes and material transfer between sectors as paths. The boundary of the metabolic system of urban agglomeration is consistent with the geographical boundary of urban agglomeration. The social and economic sectors of each city are divided into 11 sectors: agriculture, forestry, animal husbandry and fishery (AG), mining (MIN), processing and manufacturing (MFG), recycling and processing (RE), construction (CONST), transportation (TRANS), energy processing and conversion (EPCI), electricity, heat, gas, water production and supply (EHGW), other service industries (OS), residents’ consumption (RES), and waste treatment (WM). In addition, there are two main bodies outside the system: natural environment and other economies. Material resources enter the social and economic system of urban agglomeration through local environmental exploitation and import or transfer from other economies, and generate material flow, transformation and exchange between multiple sectors within the system, and finally discharge pollutants or waste into the environment, while exporting or transferring material resources to other economies. In addition, a part of them forms stock products to provide services for humans.

[0062] S1.2 Identification of urban metabolic risk shocks: Figure 2 As shown, based on the simulation of the metabolic process of urban agglomerations, the present invention mainly focuses on three types of metabolic risk shocks: imbalance in supply and demand of key resources, damage to the ecological environment, and unfair distribution of derivative products and services. Supply and demand shocks are characterized by external supply rate, energy consumption, and non-energy consumption; pollution shocks are characterized by carbon dioxide emissions, unrecycled stock, and industrial solid waste emissions; and product and service supply shortage shocks are characterized by building environment stock and durable goods stock.

[0063] S1.3 Quantification of metabolic risk shocks faced by urban agglomerations: Based on the basic development needs of human beings, a literature survey is conducted to determine the risk threshold corresponding to the risk shock, and the metabolic risk shocks faced by urban agglomerations are quantified by combining the risk shock and the risk threshold, such as Figure 4 As shown, the specific calculation is as follows:

[0064]

[0065]

[0066] In the above formula, UA_R is the metabolic risk shock of the urban agglomeration, RI ij is the risk shock of the j-th risk category in the i-th city, RV j is the historical actual data of different risk indicators, Threhold j is the threshold value corresponding to different risk indicators, SI is population or GDP, and its calculation process uses population or GDP to determine the risk indicator unit.

[0067] Table 1 shows the risk shock thresholds for the implementation cases

[0068]

[0069] In Table 1: 1 is the intensity index per unit GDP, the unit is kg / yuan; 2 is the per capita index, the unit is t / cap; a is the lower limit of the threshold, b is the upper limit of the threshold;

[0070] Step 2: Dynamic simulation and assessment of ecological risks

[0071] S2.1 Dynamic simulation calculation of risk shocks: Based on the input-output model, a differential transmission model is constructed to simulate the transmission process of different types of risk shocks and clarify the scale and path of risk transmission, such as Figure 3 and Figure 5 As shown;

[0072] The Ghosh model is used to construct the transmission model of the imbalance between supply and demand of key resources. The diffusion matrix ΔCR is calculated based on the Ghosh inverse matrix and the supply shock matrix Δz. * , which is used to characterize the impact of a node supply shock on other nodes in the metabolic process, as follows:

[0073] ΔCR * =Δz(IA * ) -1 ,

[0074] In the above formula, ΔCR * is the diffusion matrix, which represents the amount of risk transmission borne by each receptor node after the cascade diffusion of the supply-driven risk shock, and Δz is the supply shock matrix;

[0075] The transmission model of ecological environmental damage and unfair distribution of products and services is constructed using the Leontief model. The induction matrix ΔCR is calculated based on the Leontief inverse matrix and the demand shock matrix Δy to characterize the linkage effect of a demand shock at a certain node on the remaining nodes in the metabolic process, as shown in the following formula:

[0076] ΔCR=(IA) -1 Δy,

[0077] In the above formula, ΔCR is the induction matrix, which represents the risk transmission amount borne by each receptor node after the initial risk cascade diffusion driven by demand, Δy-demand shock matrix;

[0078] The Ghosh model and Leontief model are used for the impact transmission of incomplete material flow according to the impact direction of upstream and downstream. The Ghosh model is used for the impact of a certain node on the downstream node. The supply matrix and the distribution coefficient impact matrix ΔA are used. * (corresponding to the row change of the node) calculate the diffusion matrix ΔCR * The linkage effect of the upstream node on a certain node adopts the Leontief model, and the induction matrix ΔCR is calculated according to the demand matrix and the consumption coefficient impact matrix ΔA (corresponding to the column change of the node). The specific calculation is as follows:

[0079] ΔCR=(I-ΔA) -1 y,

[0080] ΔCR * =z(I-ΔA * ) -1 ,

[0081] In the above formula, ΔA-consumption coefficient impact matrix, represents the linkage effect of the upstream node on a certain node, ΔA * -Distribution coefficient impact matrix, which represents the impact of a node on downstream nodes;

[0082] Dismantle indirect risks and identify risk transmission paths: Use structural path analysis to dismantle indirect risk transmission, such as Figure 6 As shown in the figure, the transmission chain and indirect risk points of risk shocks are tracked as follows:

[0083]

[0084]

[0085]

[0086] In the above formula, (A) t Δy,Δz(A) t- the amount of risk cascade transmission occurring at the tth layer; i, j, k, l-represent different department nodes in the city, s-the total number of department nodes in the city; a ij It indicates how much of the unit output of department i is allocated to department j, reflecting the direct promotion effect; It indicates how much product i is consumed by the unit production of department j, reflecting the direct pull effect;

[0087] S2.2 Ecological risk assessment: Figure 2 As shown, referring to the classic risk calculation method (risk = probability * consequence), the four dimensions of coupling shock possibility, supply and demand risk impact, pollution risk impact, and product and service risk impact are developed to systematically evaluate the ecological risks of each metabolic entity, as shown in the following formula:

[0088]

[0089] In the above formula, the possibility of a node facing a shock is represented by the ratio of the frequency of the total transmission path passing through the node to the total frequency of passing through all nodes. The impact of the shock is the sum of supply and demand, pollution, and product and service risks, where the former and the latter are represented by the transmission amount of the shock;

[0090] Step 3: Ecological risk prediction and early warning

[0091] S3.1 Determine the risk impact indicators, namely the building environment stock, durable goods stock, carbon dioxide emissions, unrecycled solid waste, industrial solid waste, external supply rate, energy demand and non-energy demand influencing factors, including population P, regional GDP B, urbanization rate C, industrial structure D, energy structure F, stock life structure G, production department recovery rate H, consumption department recovery rate I, urban mine ratio J, material intensity K, energy consumption intensity L, and construct the prediction model of risk impact indicators and influencing factors as follows:

[0092] lnR=lna+plnP+bLNB+clnC+dlnD+flnF+glnG+hlnH+ilnI+jlnJ+klnK+llnL++lne,

[0093] In the above formula, R is the risk shock index, a is the constant term, b, c and d are the elasticity of the influencing factors on urban metabolism, that is, the degree of influence of the influencing factors, and e is the error term;

[0094] S3.2 Determine the lower limit of the metabolic index threshold based on the survey, and determine the upper limit of the risk impact index threshold using species sensitivity analysis;

[0095] S3.3 Determine the changes in future influencing factors based on the average growth rate in the past ten years or five years, taking the population P and the past ten years as an example, that is, The future forecasts of regional GDP B, urbanization rate C, industrial structure D, energy structure F, stock life structure G, production sector recovery rate H, consumption sector recovery rate I, urban-mine ratio J, material intensity K, and energy intensity L are the same as above;

[0096] S3.4 Input the future factor values ​​into the prediction model, and quantify the risk impact and the total ecological risk impact of each sector based on the relationship between the model output results and the threshold value. Figure 7 ;

[0097] Referring to the classic risk matrix, a rectangular coordinate system is constructed with the horizontal axis representing the impact possibility and the vertical axis representing the total diffusion risk. The horizontal and vertical data are scaled to between 0 and 10 to construct a risk grading matrix of 10×10. The elements in the risk grading matrix are ecological risk indexes. Based on the risk critical line, i.e., the total possibility × diffusion risk = 10, 20, 30…90, the matrix is ​​divided into 10 parts. See Figure 8 , the risk levels of different levels are expressed as follows:

[0098]

[0099] Table 2 shows the ecological risk index and graded warning results of the embodiment

[0100]

[0101]

[0102] The above description is only a preferred embodiment of the present invention and does not limit the present invention. For those skilled in the art, the present invention may have various changes and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for dynamic simulation assessment and prediction of ecological risks based on material metabolism process, comprising the following steps: Step 1: Identification of ecological risk shocks: Accurately analyze the metabolic process of the city through dynamic material flow analysis, build an urban metabolic network model, identify metabolic risk shocks from multiple perspectives throughout the entire process, and quantify ecological risk shocks based on the risk thresholds corresponding to risk shock indicators; Step 2: Dynamic simulation and assessment of ecological risks: Incorporate the risk impact of each metabolic entity into the input-output model, build an urban ecological risk diffusion model, simulate the transmission process of different types of risk impacts based on the metabolic network, quantify the risk diffusion amount and risk diffusion path based on the amplification effect of the linkage, determine the probability and amount of risk impacts to each department in the city, and evaluate the ecological risk index based on classical risk theory; Step 3. Ecological risk prediction and early warning: Based on the scalable stochastic environmental impact assessment model, a prediction model for ecological risk impact indicators and influencing factors is constructed. Further establish a variety of future development scenarios and determine the future factor values. Calculate the future changes in metabolic process risk impact indicators to achieve the prediction of future ecological risk impacts and ecological risk indexes. At the same time, refer to the classic risk matrix and construct a rectangular coordinate system with the horizontal axis as the impact possibility and the vertical axis as the sum of diffusion risks. According to the risk critical line, the matrix is ​​divided into different levels to achieve ecological risk classification and early warning.

2. According to the method for dynamic simulation assessment and prediction and early warning of ecological risks based on material metabolism process according to claim 1, the step 1 is specifically: Identify the metabolic risk shocks of cities, including the construction of urban metabolic network model and the identification of metabolic risk shocks: Construct an urban metabolic network model based on the complex material supply and demand relationship between various social and economic sectors in the city, with the social and economic sectors of the city as nodes, the material flow relationship between sectors as paths, and the corresponding relationship between materials and sectors to map and generate an urban metabolic network model; Aiming at the three important links of source resource exploitation and consumption, intermediate metabolic network circulation characteristics, and terminal stock products providing services and pollutant emissions, metabolic risk shocks are divided into four types: imbalance in key resource supply and demand, incomplete material circulation, ecological environmental damage, and unfair distribution of derivative products and services. The metabolic risk shocks faced by cities are identified from the whole metabolic process. Quantify the metabolic risk impact of cities, specifically: Based on the basic development needs of human beings, a literature survey was conducted to determine the risk thresholds corresponding to the above-mentioned different risk impact indicators. The metabolic risk impact faced by the city was quantified by combining the difference between the risk impact and the risk threshold, as shown in the following formula (1): in: In formula (1), UA_RI is the metabolic risk shock of the urban agglomeration, RI ij is the initial risk of the j-th risk category in the i-th city, RV is the risk shock of different risk indicators, Threhold j is the risk threshold corresponding to different risk indicators, SI is population or GDP, and the calculation process uses population or GDP to determine the risk indicator unit.

3. According to the method for dynamic simulation assessment and prediction of ecological risks based on material metabolism process of claim 1, the step 2 specifically comprises the following steps: Step 2.1 Dynamic simulation calculation of risk shocks: construct a differential transmission model based on the input-output model to simulate the transmission process of different types of risk shocks and clarify the scale and path of risk transmission: The Ghosh model is used to construct a key resource supply and demand imbalance shock transmission model, and the diffusion matrix ΔCR is calculated based on the Ghosh inverse matrix and the supply shock matrix Δz. * , which is used to characterize the impact of a node supply shock on other nodes in the metabolic process, as shown in the following formula (2): ΔCR * =Δz(I-A * ) -1 ......(2), In formula (2), ΔCR * is the diffusion matrix, which represents the amount of indirect risk transmission borne by the receptor node after the cascade diffusion of the supply-driven risk shock, and Δz is the supply shock matrix; The transmission model of the impact of ecological environmental damage and unfair distribution of products and services is constructed using the Leontief model. The induction matrix ΔCR is calculated based on the Leontief inverse matrix and the demand shock matrix Δy to characterize the linkage effect of a demand shock at a certain node on the remaining nodes in the metabolic process, as shown in the following formula (3): ΔCR=(I-A) -1 Δy......(3), In formula (3), ΔCR is the induction matrix, which represents the risk transmission amount borne by each receptor node after the initial risk cascade diffusion driven by demand, Δy-demand shock matrix; The Ghosh model and Leontief model are used for the impact transmission of incomplete material flow according to the impact direction of upstream and downstream. The Ghosh model is used for the impact of a certain node on the downstream node. The supply matrix and the distribution coefficient impact matrix ΔA are used to * (corresponding to the row change of the node) calculate the diffusion matrix ΔCR * The linkage effect of the upstream node on a node is calculated using the Leontief model. The induction matrix ΔCR is calculated based on the demand matrix and the consumption coefficient impact matrix ΔA (corresponding to the column change of the node). The specific calculation is as follows (4)-(5): ΔCR=(I-ΔA) -1 y......(4), ΔCR * =z(I-ΔA * ) -1 ......(5), In the above formulas (4)-(5), ΔA-consumption coefficient impact matrix represents the linkage effect of the upstream node on a certain node, ΔA * -Distribution coefficient impact matrix, which represents the impact of a node on downstream nodes; Dismantle indirect risks and identify risk transmission paths: Use structural path analysis to dismantle indirect risk transmission and track the transmission chain and indirect risk points of risk shocks, as shown in the following formulas (6)-(7): In the above formulas (6)-(7), (A) t Δy,Δz(A) t - the amount of cascade conduction occurring at the tth layer; i, j, k, l-represent different department nodes in the city, s-the total number of department nodes in the city; a ij It indicates how much of the unit output of department i is allocated to department j, reflecting the direct promotion effect; It indicates how much product i is consumed by unit production of department j, reflecting the direct pull effect; Step 2.2 Ecological risk assessment: Referring to the classical risk calculation method, i.e., risk = probability * consequence, the possibility of coupled shocks, supply and demand risk impact, pollution risk impact, and product and service risk impact are four dimensions to develop an ecological risk index and systematically assess the ecological risks of each metabolic entity, as shown in the following formula (8): In formula (8), the possibility of a node facing a shock is represented by the ratio of the frequency of the total transmission path passing through the node to the total frequency of passing through all nodes, and the impact of the shock is the sum of supply and demand, pollution, and product and service risks. The former and the latter are represented by the transmission amount of the shock.

4. The ecological risk dynamic simulation assessment and prediction and early warning method based on the material metabolism process of claim 1, wherein step 3 is specifically: Step 3.1 Determine the possible influencing factors of ecological risk impact indicators, including urban socio-economic factors, technical factors and policy factors, and construct a prediction model of ecological risk impact indicators and influencing factors, as shown in the following formula: lnI=lna+blnP+cLNA+dlnT+lne......(8), In the above formula (8), I is the ecological risk impact index, P, A and T are the influencing factors, a is the constant term, b, c and d are the elasticity of the influencing factors on the urban risk impact index, that is, the degree of influence of the influencing factors, and e is the error term; Step 3.2 Determine the lower limit of the ecological risk impact indicator threshold based on the survey, and determine the upper limit of the risk impact indicator threshold using species sensitivity analysis; Step 3.3 Determine the changes in future influencing factors based on literature research, government bulletins or the average growth rate in the past ten years, and combine different scenarios for each factor; Step 3.4 Input the corresponding future factor values ​​under each combination scenario into the prediction model, and quantify the size of future risk impact based on the relationship between the model output results and the threshold. On this basis, the future ecological risk index is predicted. Referring to the classic risk matrix, a rectangular coordinate system is constructed with the horizontal axis as the impact possibility and the vertical axis as the total diffusion risk. The horizontal and vertical data are scaled to between 0 and 10 to construct a risk grading matrix of 10×10. The elements in the risk grading matrix are ecological risk indexes. According to the risk critical line, that is, possibility × total diffusion risk = 10, 20, 30…90, the matrix is ​​divided into 10 parts, representing different levels of risk as shown below:

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