Water system robustness optimization method based on multi-view factorial analysis

Abstract

The invention belongs to the technical field of water resource management, and particularly relates to a water system robustness optimization method based on multi-view factorial analysis, which comprises the following steps: establishing a regional input-output model, and quantifying water consumption implied in a product; departments playing a key role in a water system are comprehensively identified based on an intermediate product view angle, a final product view angle and a transmission center view angle; and taking the system robustness as a response variable for measuring sustainable development of the water system, taking the identified key department as a main factor, and performing multi-strategy scene simulation on the water system by utilizing a factorial analysis method to determine a strategy suitable for the key department. The whole life cycle of the water resource used in the production process is covered, the identified key departments are more comprehensive, and the key departments hidden in the supply chain are not easy to ignore; and the strategy suitable for each key department can be determined to promote sustainable utilization of water, so that the decision is more reasonable.

Description

Technical Field

[0001] The present invention belongs to the technical field of water resource management, and in particular relates to a water system robustness optimization method based on multi-perspective factor analysis. Background Art

[0002] Water resources are an indispensable input for socio-economic development, but they are facing sustainability challenges brought about by climate change. The massive consumption of fuels and the resulting continuous emission of greenhouse gases have profoundly changed the global hydrological system. In social production activities, water resources are traded along multifaceted supply chains and indirectly exacerbate environmental pressures through supply chains. Therefore, it is crucial to track the flow of water in products and services embedded in various sectors. For decision makers, it is often necessary to formulate strategies for key sectors to alleviate water resource pressure. These key sectors are usually sectors that consume a lot of water resources in the production process, or sectors that serve as major transmission centers and play a driving role in the entire supply chain. Identify the links between key sectors and improve their water resource utilization efficiency. In addition, different key sectors may be suitable for different strategic perspectives, such as production-based strategies or consumption-based strategies. Therefore, how to comprehensively identify the key sectors of water consumption and what strategies (production-based strategies or consumption-based strategies) to formulate for key sectors to promote the sustainability of water resources is an important issue.

[0003] In the process of determining the key water consumption sectors, existing technologies often only use a single perspective to determine them, such as identifying the sectors that directly consume a large amount of water resources as key sectors based on the production perspective, or determining the key final demand sectors that drive a large amount of water consumption through the supply chain based on the consumption perspective. This has led to the neglect of some sectors that play a key driving role, a pulling role, or a transmission center hidden in the supply chain. Traditional research mostly focuses on a specific strategy in a certain region, looking for the optimal water resource management decision for the sector under this specific strategy, but ignores the determination of suitable strategies for these sectors, that is, what strategies these key sectors should choose to be more conducive to the sustainability of water resources.

[0004] Therefore, a water system robustness optimization method based on multi-perspective factorial analysis is urgently needed to provide decision support for integrated water resources management. It can comprehensively identify key water consumption sectors from multiple perspectives and explore the individual and interactive effects of multiple factors on the sustainability of the water system from multiple strategic perspectives, so as to determine suitable strategies for key sectors and achieve water system robustness optimization based on multi-perspective factorial analysis. Summary of the invention

[0005] The purpose of the present invention is to provide a water system robustness optimization method based on multi-perspective factorial analysis, which is characterized by comprising the following steps:

[0006] Step A: Establish a regional input-output model, introduce the direct water consumption of each department, and quantify the water consumption implicit in the product;

[0007] Step B: Based on the water consumption implicit in the product, a multi-perspective input-output model is constructed to comprehensively identify key departments; the multi-perspective input-output model includes: a model based on the perspective of intermediate products, a model based on the perspective of final products, and a model based on the perspective of a transmission center;

[0008] Step C: taking system robustness as a response variable to measure the sustainable development of the water system, the key sectors identified in step B are taken as the main factors, and a multi-strategy scenario simulation of the water system is performed using a factorial analysis method to determine the strategies suitable for the key sectors, thereby optimizing the robustness of the water system.

[0009] The step A of establishing a regional input-output model includes:

[0010] Step A1: Construct a basic water balance model for the department;

[0011] Step A2, calculating the implicit water intensity;

[0012] Step A3: Quantify the water consumption implicit in the intermediate products and the water consumption implicit in the final products.

[0013] The basic water balance model for the construction sector includes:

[0014]

[0015] Where W 1×n is the direct water consumption matrix of each department; n is the number of departments in the region; the subscript 1×n represents the size of the matrix; Ψ 1×n is the implicit water intensity coefficient matrix, Ψ 1×n The element ψ i represents the implicit water consumption per unit product or service of sector i; Z n×n is the intermediate consumption coefficient matrix; is the diagonal matrix of total department output.

[0016] The calculation of implied water intensity includes:

[0017] Ψ 1×n =K 1×n (I n×n -A n×n ) -1

[0018] In the formula, the water resources input coefficient matrix The amount of water resources required by each sector to produce a unit of total output; I n×n is the unit matrix; An×n is the direct consumption coefficient matrix, where element a ij Represents the quantity of product i consumed to produce each unit of product j.

[0019] The water consumption implicit in the intermediate product is:

[0020]

[0021] The water consumption implicit in the final product is:

[0022]

[0023] In the formula, is the water consumption matrix implicit in the intermediate products of each sector; is the water consumption matrix implicit in the final product; F n×1 Final demand matrix for each department; is the diagonal matrix of implicit water intensity.

[0024] The model based on the perspective of intermediate products is:

[0025]

[0026] Where, IBP 1×n is the water input matrix implicit in the intermediate product; OBP n×1 is the water output matrix implicit in the intermediate products; IBP 1×n Elements of IBP i Represents the water input implicit in the intermediate products of various departments consumed in the process of producing final products by department i. The larger the value, the greater the role of the department in driving resource consumption in various sectors of the national economy. n×1 Elements of OBP i It represents the water output implicit in the intermediate products supplied to all other departments during the production of final products by department i. The larger the value, the greater the driving effect of the department on the production of other departments. i represents a column vector with all elements equal to 1; i′ is the transpose of i.

[0027] The final product perspective model is:

[0028]

[0029] In the formula, represents the domestic final demand matrix, represents the export matrix, Represents the import matrix; PBP n×1 is the water consumption matrix calculated based on the production end, CBP n×1 It is the water consumption matrix calculated based on the consumption end.

[0030] The transmission center perspective model is:

[0031] Assume that in a supply chain containing department i, department i has L1 upstream departments and L2 downstream departments, then the intermediate degree b of department i in the supply chain is i for:

[0032]

[0033] Where K is the water resources input coefficient matrix; is the L1 power of the direct consumption coefficient matrix; J i is an n×n matrix with all elements being 0 except the (i,i)th element being 1; is the direct consumption coefficient matrix L2; F is the domestic final demand matrix.

[0034] The multi-strategy scenario simulation of the water system using the factorial analysis method includes:

[0035] Step C1: Determine factors and factor levels under a multi-strategy scenario; the multi-strategy scenario includes: a production-based strategy and a consumption-based strategy;

[0036] Step C2: Determine the response variable and take the system robustness as the response variable of the water system;

[0037] Step C3: Conduct a factorial experiment to analyze the independent and interactive effects of key sectors on the sustainability of energy and water systems under multiple strategy scenarios, and determine which strategy is suitable for each key sector based on the results of the factorial analysis.

[0038] The factors and factor levels determined in the multi-strategy scenario include:

[0039] First, the factors of the two strategy scenarios are determined; the factors of the two strategy scenarios include: the direct consumption of water resources by each key sector as a factor based on the production strategy scenario and the final demand for each key sector as a factor based on the consumption strategy scenario;

[0040] Secondly, set high and low levels for the factors of the two strategy scenarios; the high level is 10% higher than the original factor threshold; the low level is 10% lower than the original factor threshold;

[0041] Finally, calculate the changes in other quantities brought about by the factor changes in the two strategy scenarios, including:

[0042] Simulation based on production strategy perspective:

[0043]

[0044] Simulation based on consumption strategy perspective:

[0045]

[0046] In the formula, the superscripts P and C represent the changes after applying the production-based strategy scenario and the consumption-based strategy, respectively; α i and β i They represent the factor levels of the key sector i based on the production strategy and consumption strategy scenarios respectively; Water consumption after applying the production-based strategy for sector i, w i is the water consumption of department i, is the implied water intensity matrix after applying the production-based strategy, is the diagonal matrix of total output, is the transpose of the intermediate input matrix, is the water consumption matrix after applying the production-based strategy, f i C The final demand after applying the consumption-based strategy for sector i, f i is the final demand of sector i, is the total output matrix after applying the consumption-based strategy, I is the unit matrix, A is the intermediate consumption coefficient matrix, For the final demand matrix after applying the consumption-based strategy, Water consumption after applying the consumption-based strategy for sector i, is the total output of sector i after applying the production-based strategy, x i is the total output of sector i, is the implied water intensity matrix after applying the consumption-based strategy, is the diagonal matrix of total output after applying the consumption-based strategy, is the water consumption matrix after applying the consumption-based strategy.

[0047] The system robustness as the response variable of the water system includes:

[0048]

[0049] In the formula, SR is the system robustness, As is the system efficiency, Ca is the system capacity, R is the system redundancy, and TST is p Represents the total system throughput, that is, the sum of the input and output flows of all departments, p ij For the matrix The elements represent the water resources flowing from sector i to sector j, T i represents the total water outflow of sector i, that is, T j represents all water resource flows into sector j, i.e. n is the number of departments in the study area.

[0050] The beneficial effects of the present invention are:

[0051] By applying a water system robustness optimization method based on multi-perspective factorial analysis disclosed in the present invention, the multi-perspective input-output analysis is combined with the multi-strategy scenario simulation based on factorial analysis, which can achieve:

[0052] (1) Quantify the amount of water embodied in products or services in the supply chain;

[0053] (2) Comprehensively identify the key sectors in the water system;

[0054] (3) Determine appropriate strategies for different key sectors, thereby providing decision support for the integrated management of water resources.

[0055] Compared with the integrated water resources management method in the prior art, the present invention has the following advantages:

[0056] First, in terms of identifying key sectors, we use three perspectives: intermediate products, final products, and transmission centers. These three perspectives cover the entire life cycle of water resources used in the production process. The key sectors identified are more comprehensive and are less likely to overlook some key sectors hidden in the supply chain.

[0057] Secondly, when formulating strategies for key departments, the present invention can determine which strategy is suitable for each key department to promote the sustainable use of water, make decisions more rational, and achieve robustness optimization of the water system based on multi-perspective factor analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic flow chart of a method for optimizing robustness of a water system based on multi-perspective factorial analysis according to the present invention;

[0059] Figure 2 Schematic diagram of the system framework of a method for optimizing robustness of a water system based on multi-perspective factorial analysis in an embodiment of the present invention;

[0060] Figure 3 Schematic diagram of the process of determining key departments from multiple perspectives in an embodiment of the present invention;

[0061] Figure 4 Schematic diagram of the process of determining the strategy suitable for the key departments in the embodiment of the present invention, wherein (a) is a schematic diagram of the impact of the key departments on the system robustness from different strategy perspectives, and (b) is a schematic diagram of the comparison of the impact on the robustness of the water system under different strategy scenarios. DETAILED DESCRIPTION

[0062] The present invention provides a method for optimizing the robustness of a water system based on multi-perspective factorial analysis. The present invention is further described in detail below in conjunction with the accompanying drawings.

[0063] like Figure 1 The embodiment of the present invention shown discloses a method for optimizing robustness of a water system based on multi-perspective factorial analysis, comprising:

[0064] Step A: Establish a regional input-output model, introduce the direct water consumption of each department, and quantify the water consumption implicit in the product;

[0065] Step B: Based on the water consumption implicit in the product, a multi-perspective input-output model is constructed to comprehensively identify key departments; the multi-perspective input-output model includes: a model based on the perspective of intermediate products, a model based on the perspective of final products, and a model based on the perspective of a transmission center;

[0066] Step C: taking system robustness as a response variable to measure the sustainable development of the water system, the key sectors identified in step B are taken as the main factors, and a multi-strategy scenario simulation of the water system is performed using a factorial analysis method to determine the strategies suitable for the key sectors, thereby optimizing the robustness of the water system.

[0067] In this embodiment, a method for optimizing the robustness of a water system based on multi-perspective factorial analysis is disclosed. A regional input-output model is established, the direct water resource consumption of each department is introduced, and the water consumption implicit in the product is quantified; the departments that play a key role in the water system are comprehensively identified based on the perspective of intermediate products, the perspective of final products, and the perspective of transmission centers; the system robustness is used as a response variable to measure the sustainable development of the water system, the identified key departments are used as the main factors, and the factorial analysis method is used to simulate the multi-strategy scenario of the water system to determine the strategies suitable for the key departments. The problem of incomplete identification of key departments in the water system and uncertainty of strategies suitable for different key departments is solved, providing support for decision-making in formulating targeted strategies for specific industries.

[0068] In this embodiment, the system framework of the water system robustness optimization method based on multi-perspective factorial analysis is as follows: Figure 2 As shown in the figure, firstly, the water implicit in products and services was quantified according to the basic environmental extended input-output model, then the key sectors in the water system were comprehensively identified from three perspectives: intermediate products, final products and transmission centers, and finally the strategies suitable for the identified key sectors were determined using the factorial analysis method.

[0069] The specific implementation process of each step is described below.

[0070] The step A of establishing a regional input-output model includes:

[0071] Step A1: Construct a basic water balance model for the department;

[0072] Step A2, calculating the implicit water intensity;

[0073] Step A3: Quantify the water consumption implicit in the intermediate products and the water consumption implicit in the final products.

[0074] In step A1, a basic water balance model for the sector is constructed;

[0075] The basic water balance model for the construction sector includes:

[0076]

[0077] Where W 1×n is the direct water consumption matrix of each department; n is the number of departments in the region; the subscript 1×n represents the size of the matrix; Ψ 1×n is the implicit water intensity coefficient matrix, Ψ 1×n The element ψ i represents the implicit water consumption per unit product or service of sector i; Z n×n is the intermediate consumption coefficient matrix; is the diagonal matrix of total department output.

[0078] In step A2, the implicit water intensity is calculated;

[0079] The calculation of implied water intensity includes:

[0080] Ψ 1×n =K 1×n (I n×n -A n×n ) -1

[0081] In the formula, the water resources input coefficient matrix The amount of water resources required by each sector to produce a unit of total output; I n×n is the unit matrix; A n×n is the direct consumption coefficient matrix, where element a ij Represents the quantity of product i consumed to produce each unit of product j.

[0082] In step A3, quantify the water consumption embodied in the intermediate and final products;

[0083] The water consumption implicit in the intermediate product is:

[0084]

[0085] The water consumption implicit in the final product is:

[0086]

[0087] In the formula, is the water consumption matrix implicit in the intermediate products of each sector; is the water consumption matrix implicit in the final product; F n×1 Final demand matrix for each department; is the diagonal matrix of implicit water intensity.

[0088] In step B, based on the water consumption implicit in the products, a multi-perspective input-output model is constructed to comprehensively identify key sectors;

[0089] Step B1: Construct a model based on the perspective of intermediate products;

[0090] The model based on the perspective of intermediate products is:

[0091]

[0092] Where, IBP 1×n is the water input matrix implicit in the intermediate product; OBP n×1 is the water output matrix implicit in the intermediate products; IBP 1×n Elements of IBP i Represents the water input implicit in the intermediate products of various departments consumed in the process of producing final products by department i. The larger the value, the greater the role of the department in driving resource consumption in various sectors of the national economy. n×1 Elements of OBP i It represents the water output implicit in the intermediate products supplied to all other departments during the production of final products by department i. The larger the value, the greater the driving effect of the department on the production of other departments. i represents a column vector with all elements equal to 1; i′ is the transpose of i.

[0093] Step B2: Construct a model based on the final product perspective;

[0094] The final product perspective model is:

[0095]

[0096] In the formula, represents the domestic final demand matrix, represents the export matrix, Represents the import matrix; PBP n×1 is the water consumption matrix calculated based on the production end, CBP n×1 It is the water consumption matrix calculated based on the consumption end.

[0097] In this embodiment, represents the domestic final demand matrix (including household consumption, government consumption, and capital formation, etc.), represents the export matrix, Represents the import matrix. The final products produced by a region after a series of production activities are to meet the consumption and export of the region. n×1 The main purpose is to identify the sectors that consume a large amount of water resources in the process of meeting these final demands. This is calculated based on the production perspective; however, for those exports whose final destination is not in the region, but imported products from other regions are consumed in the region, from the consumption perspective, only the resources implicit in the final demand and imports within the region should be calculated.

[0098] Step B3: Build a model based on the final product perspective;

[0099] The transmission center perspective model is:

[0100] Assume that in a supply chain containing department i, department i has L1 upstream departments and L2 downstream departments, then the intermediate degree b of department i in the supply chain is i for:

[0101]

[0102] Where K is the water resources input coefficient matrix; is the L1 power of the direct consumption coefficient matrix; J i is an n×n matrix with all elements being 0 except the (i,i)th element being 1; is the direct consumption coefficient matrix L2; F is the domestic final demand matrix.

[0103] In this embodiment, according to the structural path analysis theory, the energy and water consumption of products in the production process is decomposed into an infinite number of paths:

[0104]

[0105] Among them, KA t F represents the amount of water resources consumed in a supply chain path containing t departments (i.e., the t-layer production layer). The water resources absorbed by the system are transmitted from the starting department to the end department of these supply chains, and it is necessary to determine the departments that play a key role in the transmission process. These departments that serve as transmission centers in the water system are determined using the concept of betweenness, that is, the amount of water resources consumed by all supply chain paths passing through the department. Assuming that in a supply chain containing department i, department i has L1 upstream departments and L2 downstream departments, the betweenness of department i in the supply chain can be calculated by the following formula:

[0106]

[0107] Among them, (KA L1) i Represents the matrix kA L1 The i-th element of );(A L2 F) i Represents the matrix A L2 The i-th element of f (i.e., ); Matrix J i is an n×n matrix with all elements 0 except the (i,i)th element being 1. By extending the above formula to all supply chains containing department i, the betweenness of department i can be calculated:

[0108]

[0109] Among them, b i is the betweenness of sector i. The higher the value, the more water resources the sector transmits.

[0110] Step C: taking system robustness as a response variable to measure the sustainable development of the water system, the key sectors identified in step B are taken as the main factors, and a multi-strategy scenario simulation of the water system is performed using a factorial analysis method to determine the strategies suitable for the key sectors, thereby optimizing the robustness of the water system.

[0111] The multi-strategy scenario simulation of the water system using the factorial analysis method includes:

[0112] Step C1: Determine factors and factor levels under a multi-strategy scenario; the multi-strategy scenario includes: a production-based strategy and a consumption-based strategy;

[0113] Step C2: Determine the response variable and take the system robustness as the response variable of the water system;

[0114] Step C3: Conduct a factorial experiment to analyze the independent and interactive effects of key sectors on the sustainability of energy and water systems under multiple strategy scenarios, and determine which strategy is suitable for each key sector based on the results of the factorial analysis.

[0115] Step C1: Determine factors and factor levels in a multi-strategy scenario

[0116] In this embodiment, two scenarios, production strategy and consumption strategy, are selected for simulation to determine the most suitable strategy for each key department. Specifically, the factors of the two strategy scenarios are first determined: the direct consumption of water resources by each key department is used as a factor based on the production strategy scenario, and the final demand for each key department is used as a factor based on the consumption strategy scenario; secondly, each factor is set at two levels, high and low (i.e., increase by 10% or decrease by 10%); finally, the changes in other quantities caused by the changes in factors under different scenarios are calculated:

[0117] The factors and factor levels determined in the multi-strategy scenario include:

[0118] First, the factors of the two strategy scenarios are determined; the factors of the two strategy scenarios include: the direct consumption of water resources by each key sector as a factor based on the production strategy scenario and the final demand for each key sector as a factor based on the consumption strategy scenario;

[0119] Secondly, set high and low levels for the factors of the two strategy scenarios; the high level is 10% higher than the original factor threshold; the low level is 10% lower than the original factor threshold;

[0120] Finally, calculate the changes in other quantities brought about by the factor changes in the two strategy scenarios, including:

[0121] Simulation based on production strategy perspective:

[0122]

[0123] Simulation based on consumption strategy perspective:

[0124]

[0125] In the formula, the superscripts P and C represent the changes after applying the production-based strategy scenario and the consumption-based strategy, respectively; α i and β i They represent the factor levels of the key sector i based on the production strategy and consumption strategy scenarios respectively; Water consumption after applying the production-based strategy for sector i, w i is the water consumption of department i, is the implied water intensity matrix after applying the production-based strategy, is the diagonal matrix of total output, is the transpose of the intermediate input matrix, is the water consumption matrix after applying the production-based strategy, f i C The final demand after applying the consumption-based strategy for sector i, f i is the final demand of sector i, is the total output matrix after applying the consumption-based strategy, I is the unit matrix, A is the intermediate consumption coefficient matrix, For the final demand matrix after applying the consumption-based strategy, Water consumption after applying the consumption-based strategy for sector i, is the total output of sector i after applying the production-based strategy, x i is the total output of sector i, is the implied water intensity matrix after applying the consumption-based strategy, is the diagonal matrix of total output after applying the consumption-based strategy, is the water consumption matrix after applying the consumption-based strategy.

[0126] Step C2: Determine the response variable and take the system robustness as the response variable of the water system;

[0127] In this example, system robustness combines efficiency and redundancy and has been recognized as an important indicator for quantifying sustainability. In this example, system robustness is used as a response variable of the water system to measure the main effects and interaction effects of multiple key factors on the sustainability of the water system:

[0128] The system robustness as the response variable of the water system includes:

[0129]

[0130] R=Ca-As

[0131]

[0132] In the formula, SR is the system robustness, As is the system efficiency, Ca is the system capacity, R is the system redundancy, and TST is p Represents the total system throughput, that is, the sum of the input and output flows of all departments, p ij For the matrix The elements represent the water resources flowing from sector i to sector j, T i represents the total water outflow of sector i, that is, T j represents all water resource flows into sector j, i.e. n is the number of departments in the study area.

[0133] Step C3: Conduct a factorial experiment to analyze the independent and interactive effects of key sectors on the sustainability of energy and water systems under multiple strategy scenarios, and determine which strategy is suitable for each key sector based on the results of the factorial analysis.

[0134] In this embodiment, under different strategy scenarios, the response value of each key department to the system robustness is compared, and each key department is more suitable to adopt a strategy corresponding to a high system robustness response value.

[0135] In this embodiment, the process of determining key departments from multiple perspectives is as follows: Figure 3As shown in the figure, the analysis results show that animal husbandry (BAR), mining (MIN) and heavy manufacturing (HMA) have high contributions in all three perspectives, and these two sectors play a key role in the water system. The service industry has a high share in the perspective based on intermediate products and the perspective based on the transmission center, but it is very low in the perspective based on the final product, which shows that this sector transmits a large amount of water in the supply chain and sector transactions. Improving the production efficiency of the service industry can reduce the water input of upstream sectors, thereby indirectly alleviating water resource pressure. Vegetables, fruits and nuts (VFN) is the sector with the highest water consumption based on the production method, and light manufacturing (LMA) is the sector with the highest water consumption based on the consumption method. These two sectors cannot be ignored because they are not key water transmission centers. Therefore, vegetables, fruits and nuts (VFN), animal husbandry (BAR), mining (MIN), light manufacturing (LMA) and heavy manufacturing (HMA) are identified as key sectors of the water system.

[0136] Figure 4 The impact of the identified key sectors on system robustness from different strategic perspectives, where (a) is a schematic diagram of the impact of key sectors on system robustness from different strategic perspectives, and (b) is a schematic diagram of the comparison of the impact of water system robustness under different strategic scenarios. Figure 4 As can be seen in (a), under the production-based strategy, as the water consumption of VFN, MIN and LMA increases, the response of SR shows an upward trend, while the response of other key sectors shows a downward trend as the water consumption increases. Under the consumption-based strategy, except for VFN, the SR of other key sectors shows an upward trend as the final demand decreases. Compared with the consumption-based strategy, VFN has a higher individual impact on SR under the production-based strategy, mainly because VFN not only consumes the most water in the production-based method accounting, but also plays a key driving role in the water system. Increasing the water consumption of VFN from the production side will have a supply-driven effect on the production of various sectors, which is conducive to improving the robustness of the water system. Therefore, VFN is more suitable for a production-based strategy. For the other key sectors of the water system, the individual impact on SR under the consumption-based strategy is greater than that under the production-based strategy, because these sectors are key water transmission centers or key water consumption centers. Reducing the final demand for these sectors from the demand side is conducive to the sustainability of the water system. Based on the above analysis, the optimal strategy scenario is that VFN adopts a production-based strategy, and the other key sectors of the water system adopt a consumption-based strategy. Figure 4 (b) shows that the robustness of the water system under the optimal strategy scenario (S0) is better than that under the scenarios of only adopting production-based strategies or consumption-based strategies, which also indirectly highlights the importance of adopting different strategies for different sectors.

[0137] This embodiment is only a preferred specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A water system robustness optimization method based on multi-perspective factorial analysis, characterized in that: The steps include: Step A: Establish a regional input-output model, introduce the direct water consumption of each department, and quantify the water consumption implicit in the product; Step B: Based on the water consumption implicit in the product, a multi-perspective input-output model is constructed to comprehensively identify key sectors; The multi-perspective input-output model includes: an intermediate product perspective model, a final product perspective model, and a transmission center perspective model; Step C: taking system robustness as a response variable to measure the sustainable development of the water system, the key sectors identified in step B are taken as the main factors, and a multi-strategy scenario simulation of the water system is performed using a factorial analysis method to determine the strategies suitable for the key sectors, thereby optimizing the robustness of the water system.

2. The water system robustness optimization method based on multi-perspective factorial analysis according to claim 1 is characterized in that: The step A of establishing a regional input-output model includes: Step A1: Construct a basic water balance model for the department; Step A2, calculating the implicit water intensity; Step A3: Quantify the water consumption implicit in the intermediate products and the water consumption implicit in the final products.

3. The water system robustness optimization method based on multi-perspective factorial analysis according to claim 2 is characterized in that: The basic water balance model for the construction sector includes: Where W 1×n is the direct water consumption matrix of each department; n is the number of departments in the region; the subscript 1×n represents the size of the matrix; Ψ 1×n is the implicit water intensity coefficient matrix, Ψ 1×n The element ψ i represents the implicit water consumption per unit product or service of sector i; Z n×n is the intermediate consumption coefficient matrix; is the diagonal matrix of total department output.

4. The method for optimizing the robustness of a water system based on multi-perspective factorial analysis according to claim 2 is characterized in that: The calculation of implied water intensity includes: P 1×n =K 1×n (I n×n -A n×n ) -1 In the formula, the water resources input coefficient matrix The amount of water resources required by each sector to produce a unit of total output; I n×n is the unit matrix; A n×n is the direct consumption coefficient matrix, where element a ij Represents the quantity of product i consumed to produce each unit of product j.

5. The method for optimizing robustness of a water system based on multi-perspective factorial analysis according to claim 2 is characterized in that: The water consumption implicit in the intermediate product is: The water consumption implicit in the final product is: In the formula, is the water consumption matrix implicit in the intermediate products of each sector; is the water consumption matrix implicit in the final product; F n×1 Final demand matrix for each department; is the diagonal matrix of implicit water intensity.

6. The method for optimizing robustness of a water system based on multi-perspective factorial analysis according to claim 1 is characterized in that: The model based on the perspective of intermediate products is: Where, IBP 1×n is the water input matrix implicit in the intermediate product; OBP n×1 is the water output matrix implicit in the intermediate products; IBP 1×n Elements of IBP i Represents the water input implicit in the intermediate products of various departments consumed in the process of producing final products by department i. The larger the value, the greater the role of the department in driving resource consumption in various sectors of the national economy. n×1 Elements of OBP i It represents the water output implicit in the intermediate products supplied to all other departments during the production of final products by department i. The larger the value, the greater the driving effect of the department on the production of other departments. i represents a column vector whose elements are all 1; i′ is the transpose of i.

7. The method for optimizing robustness of a water system based on multi-perspective factorial analysis according to claim 1 is characterized in that: The final product perspective model is: In the formula, represents the domestic final demand matrix, represents the export matrix, Represents the import matrix; PBP n×1 is the water consumption matrix calculated based on the production end, CBP n×1 It is the water consumption matrix calculated based on the consumption end.

8. The method for optimizing robustness of a water system based on multi-perspective factorial analysis according to claim 1 is characterized in that: The transmission center perspective model is: Assume that in a supply chain containing department i, department i has L1 upstream departments and L2 downstream departments, then the intermediate degree b of department i in the supply chain is i for: Where K is the water resources input coefficient matrix; is the L1 power of the direct consumption coefficient matrix; J i is an n×n matrix with all elements being 0 except the (i,i)th element being 1; is the direct consumption coefficient matrix L2; F is the domestic final demand matrix.

9. The method for optimizing the robustness of a water system based on multi-perspective factorial analysis according to claim 1 is characterized in that: The multi-strategy scenario simulation of the water system using the factorial analysis method includes: Step C1: Determine factors and factor levels under a multi-strategy scenario; the multi-strategy scenario includes: a production-based strategy and a consumption-based strategy; Step C2: Determine the response variable and take the system robustness as the response variable of the water system; Step C3: Conduct a factorial experiment to analyze the independent and interactive effects of key sectors on the sustainability of energy and water systems under multiple strategy scenarios, and determine which strategy is suitable for each key sector through the factorial analysis results.

10. The method for optimizing robustness of a water system based on multi-perspective factorial analysis according to claim 9 is characterized in that: The factors and factor levels determined in the multi-strategy scenario include: First, the factors of the two strategy scenarios are determined; the factors of the two strategy scenarios include: the direct consumption of water resources by each key sector as a factor based on the production strategy scenario and the final demand for each key sector as a factor based on the consumption strategy scenario; Secondly, set high and low levels for the factors of the two strategy scenarios; the high level is 10% higher than the original factor threshold; the low level is 10% lower than the original factor threshold; Finally, calculate the changes in other quantities brought about by the factor changes in the two strategy scenarios, including: Simulation based on production strategy perspective: Simulation based on consumption strategy perspective: In the formula, the superscripts P and C represent the changes after applying the production-based strategy scenario and the consumption-based strategy, respectively; α i and β i They represent the factor levels of the key sector i based on the production strategy and consumption strategy scenarios respectively; Water consumption after applying the production-based strategy for sector i, w i is the water consumption of department i, is the implied water intensity matrix after applying the production-based strategy, is the diagonal matrix of total output, is the transpose of the intermediate input matrix, is the water consumption matrix after applying the production-based strategy, f i C The final demand after applying the consumption-based strategy for sector i, f i is the final demand of sector i, is the total output matrix after applying the consumption-based strategy, I is the unit matrix, A is the intermediate consumption coefficient matrix, For the final demand matrix after applying the consumption-based strategy, Water consumption after applying the consumption-based strategy for sector i, is the total output of sector i after applying the production-based strategy, x i is the total output of sector i, is the implied water intensity matrix after applying the consumption-based strategy, is the diagonal matrix of total output after applying the consumption-based strategy, is the water consumption matrix after applying the consumption-based strategy; The system robustness as the response variable of the water system includes: R=Ca-As In the formula, SR is the system robustness, As is the system efficiency, Ca is the system capacity, R is the system redundancy, and TST is p Represents the total system throughput, that is, the sum of the input and output flows of all departments, p ij For the matrix The elements represent the water resources flowing from sector i to sector j, T i represents the total water outflow of sector i, that is, T j represents all water resource flows into sector j, i.e. n is the number of departments in the study area.

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