A water system robustness optimization method based on multi-view factor analysis

Abstract

The application discloses to the technical field of water resource management, and particularly relates to a water system robustness optimization method based on multi-view factor analysis, which comprises the following steps: establishing a regional input-output model to quantify water consumption hidden in products; comprehensively identifying departments playing a key role in the water system from the perspectives of intermediate products, final products and transmission centers; taking the system robustness as a response variable for measuring the sustainable development of the water system, taking the identified key departments as main factors, and using the factor analysis method to perform multi-strategy scenario simulation on the water system to determine the suitable strategy for the key departments. The entire life cycle of water resources 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 easily ignored; and the suitable strategy for each key department can be determined to promote the sustainable use of water, so that the decision is more rational.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of water resources management, and particularly relates to a water system robustness optimization method based on multi-perspective factor analysis. BACKGROUND

[0002] Water resources are indispensable inputs for social and economic development, but water resources are facing sustainability challenges brought by climate change. The massive consumption of fuel and the consequent continuous emission of greenhouse gases have profoundly changed the global hydrological system. In social production activities, water resources are traded along multiple supply chains and indirectly exacerbate environmental pressure through supply chains. Therefore, it is crucial to track the flow of water in products and services in various departments. For decision makers, it is often necessary to develop strategies for key departments to alleviate water resource pressure, and these key departments are usually departments that consume a large amount of water resources in the production process, or departments that act as main transmission centers and play a driving role in the entire supply chain. It is necessary to determine the connection between key departments and improve the water resource utilization efficiency of key departments. In addition, different key departments may be suitable for different strategy perspectives, such as production-based strategy or consumption-based strategy. Therefore, how to comprehensively identify key water-consuming departments and what strategy (production-based strategy or consumption-based strategy) should be developed for key departments to promote water resource sustainability is an important problem.

[0003] In the prior art, in the process of determining key water-consuming departments, only a single perspective is often used to determine, such as determining departments that directly consume a large amount of water resources as key departments based on a production perspective, or determining key final demand departments that drive a large amount of water resource consumption through a consumption-based perspective. This leads to the fact that some departments that play a key driving role, pulling role or transmission center role hidden in the supply chain are ignored. Traditional researches mostly focus on a specific strategy in a certain region and find the optimal water resource management decision for the departments under this specific strategy, but ignore the determination of the strategy suitable for these departments, that is, which strategy should be selected by these key departments to be more conducive to the sustainability of water resources.

[0004] Therefore, there is an urgent need for a water system robustness optimization method based on multi-perspective factor analysis to provide decision support for comprehensive water resource management, which can comprehensively identify key water-consuming departments from multiple perspectives and explore the individual and interactive effects of various factors on the sustainability of the water system from multiple strategy perspectives to determine the strategy suitable for key departments, so as to realize the water system robustness optimization based on multi-perspective factor analysis. SUMMARY

[0005] The application aims to provide a water system robustness optimization method based on multi-perspective factor analysis, which is characterized by comprising the following steps:

[0006] Step A, establishing a regional input-output model, introducing direct water consumption of each department, and quantifying water consumption hidden in products;

[0007] Step B, based on water consumption hidden in products, constructing a multi-perspective input-output model to comprehensively identify key departments; the multi-perspective input-output model includes: a model based on intermediate products, a model based on final products, and a model based on transmission centers;

[0008] Step C, taking system robustness as a response variable for measuring sustainable development of the water system, taking the key departments identified in step B as main factors, and using factorial analysis method to simulate multiple strategies of the water system to determine the strategy suitable for the key departments, so as to realize optimization of the robustness of the water system.

[0009] The establishment of the regional input-output model in step A includes:

[0010] Step A1, constructing a basic water balance model of the department;

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

[0012] Step A3, quantifying water consumption hidden in intermediate products and water consumption hidden in final products.

[0013] The construction of the basic water balance model of the department includes:

[0014]

[0015] In the formula, W 1×n is a direct water consumption matrix of each department; n is the number of departments in the region; the subscript 1x n represents the size of the matrix; Ψ 1×n is a hidden water intensity coefficient matrix, Ψ 1×n The element ψ i in the middle represents the water consumption hidden in a unit product or service of department i; Z n×n is an intermediate input matrix; is a diagonal matrix of total output of the department.

[0016] The calculation of the hidden water intensity includes:

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

[0018] In the formula, the water resource input coefficient matrix is the amount of water resources required for each department to produce a unit of total output; I n×n is a unit matrix; An×n is the direct consumption coefficient matrix, where element a ij represents the amount of product j consumed per unit of product i produced.

[0019] The water consumption embodied in intermediate products is:

[0020]

[0021] The water consumption embodied in final products is:

[0022]

[0023] where, is the water consumption matrix embodied in intermediate products of each sector; is the water consumption matrix embodied in final products; F n×1 is the final demand matrix of each sector; is the diagonal matrix of water intensity.

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

[0025]

[0026] where, IBP 1×n is the water input matrix embodied in intermediate products; OBP n×1 is the water output matrix embodied in intermediate products; IBP 1×n element IBP i represents the water input embodied in intermediate products of all sectors consumed in the process of producing final products by sector i; OBP n×1 element OBP i represents the water output embodied in intermediate products supplied to all other sectors in the process of producing final products by sector i; i represents a column vector with all elements being 1; i' is the transpose of i.

[0027] The model based on the perspective of final products is:

[0028]

[0029] where, 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 side, CBP n×1 is the water consumption matrix calculated based on the consumption side.

[0030] The model based on the perspective of transmission center is:

[0031] Assuming 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] In the formula, K is the water resource input coefficient matrix; is the L1 power of the intermediate consumption coefficient matrix; J i is a matrix whose (i, i) element is 1 and other elements are 0; n×n ; is the L2 power of the intermediate consumption coefficient matrix; F is the domestic final demand matrix.

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

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

[0036] Step C2: determining a response variable, taking the system robustness as the response variable of the water system;

[0037] Step C3: performing a factorial experiment, analyzing the independent and interactive effects of the key departments on the energy and water system sustainability under the multi-strategy scenario, and determining the strategy suitable for each key department through the factorial analysis result.

[0038] The determination of the factors and factor levels under the multi-strategy scenario comprises:

[0039] Firstly, determining the factors of the two strategy scenarios; the factors of the two strategy scenarios comprise the direct consumption amount of water resources of each key department as the factor of the production strategy-based scenario and the final demand amount of each key department as the factor of the consumption strategy-based scenario;

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

[0041] Finally, calculating the changes of other quantities caused by the changes of the factors under the two strategy scenarios, comprising:

[0042] Production strategy-based perspective simulation:

[0043]

[0044] Consumption strategy-based perspective simulation:

[0045]

[0046] where superscripts P and C represent the change in the application of the production-based policy scenario and the consumption-based policy, respectively; a i and β i represent the factor level of key sector i based on the production-based policy and the consumption-based policy scenario, respectively; is the water use of sector i after the application of the production-based policy, w i is the water use of sector i, is the matrix of water intensity embodied after the application of the production-based policy, is the diagonal matrix of total output of sectors, is the transpose of the intermediate input matrix, is the matrix of water use after the application of the consumption-based policy, f i C is the final demand of sector i after the application of the consumption-based policy, f i is the final demand of sector i, is the matrix of total output after the application of the consumption-based policy, I is the identity matrix, A is the matrix of intermediate consumption coefficients, is the matrix of final demand after the application of the consumption-based policy, is the water use of sector i after the application of the consumption-based policy, is the total output of sector i after the application of the production-based policy, x i is the total output of sector i, is the matrix of water intensity embodied after the application of the consumption-based policy, is the diagonal matrix of total output after the application of the consumption-based policy, is the matrix of water use after the application of the consumption-based policy.

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

[0048]

[0049] R = Ca - As

[0050]

[0051] where SR is the system robustness, As is the system efficiency, Ca is the system capacity, R is the system redundancy, TST p represents the total system throughput, i.e., the sum of all sector inflows and outflows, p ij is the element of matrix representing the water resources flowing from sector i to sector j, T i represents the total water outflow from sector i, i.e., T jrepresents all water flows into sector j, i.e. n is the number of sectors in the study region.

[0052] The present application has the beneficial effects of:

[0053] The water system robustness optimization method based on multi-perspective factor analysis disclosed in the present application combines multi-perspective input-output analysis with multi-strategy scenario simulation based on factor analysis, and can achieve:

[0054] (1) quantifying the amount of water implied in the products or services of the supply chain;

[0055] (2) comprehensively identifying sectors that play a key role in the water system;

[0056] (3) determining the strategies suitable for different key sectors, thereby providing decision support for the comprehensive management of water resources.

[0057] Compared with the prior art water resource comprehensive management method, the present application has the following advantages:

[0058] Firstly, in terms of determining key sectors, three perspectives based on intermediate products, final products and transmission centers are determined, which cover the entire life cycle of water resources used in the production process, and the identified key sectors are more comprehensive and less likely to overlook some key sectors implied in the supply chain.

[0059] Secondly, in terms of developing strategies for key sectors, the present application can determine the strategy suitable for each key sector to promote sustainable use of water, making the decision more rational, and achieving water system robustness optimization based on multi-perspective factor analysis. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 The figure is a flowchart of the water system robustness optimization method based on multi-perspective factor analysis of the present application;

[0061] Figure 2 The figure is a system framework diagram of the water system robustness optimization method based on multi-perspective factor analysis in the embodiment of the present application;

[0062] Figure 3 The figure is a schematic diagram of the determination process of key sectors under multi-perspective in the embodiment of the present application;

[0063] Figure 4 The figure is a schematic diagram of the determination process of the strategy suitable for the key sector in the embodiment of the present application, wherein (a) is a schematic diagram of the influence of the key sector on the system robustness under different strategy perspectives, and (b) is a schematic diagram of the comparison of the influence of the water system robustness under different strategy scenarios. DETAILED DESCRIPTION

[0064] The present invention provides a water system robustness optimization method based on multi-perspective factorial analysis, which is further described in detail below with reference to the accompanying drawings.

[0065] like Figure 1 The embodiment of the present invention shown discloses a water system robustness optimization method based on multi-perspective factor analysis, including:

[0066] Step A: Establish a regional input-output model, introduce the direct water consumption of each department, and quantify the water consumption implicit in the products;

[0067] Step B: Based on the water consumption implicit in the products, 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 transmission centers;

[0068] Step C: Using system robustness as a response variable to measure the sustainable development of the water system, the key sectors identified in step B are used as the main factors, and a factorial analysis method is used to conduct a multi-strategy scenario simulation of the water system to determine the strategies suitable for the key sectors, thereby optimizing the robustness of the water system.

[0069] This embodiment discloses a method for optimizing the robustness of a water system based on multi-perspective factorial analysis. This method establishes a regional input-output model, introduces the direct water resource consumption of each sector, and quantifies the water consumption implicit in products. Key sectors within the water system are comprehensively identified from the perspectives of intermediate products, final products, and transmission centers. Using system robustness as a response variable for measuring the sustainable development of the water system and the identified key sectors as primary factors, factorial analysis is used to conduct multi-strategy scenario simulations of the water system to determine appropriate strategies for key sectors. This method addresses issues such as incomplete identification of key sectors within the water system and uncertainty about appropriate strategies for different key sectors, providing support for decision-makers in developing targeted strategies for specific industries.

[0070] 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, the water implicit in products and services was first quantified based on 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. Finally, the factor analysis method was used to determine the strategies suitable for the identified key sectors.

[0071] The specific implementation process of each step is described below.

[0072] The establishment of the regional input-output model in step A includes:

[0073] Step A1, constructing a basic water balance model of the department;

[0074] Step A2, calculating the implicit water intensity;

[0075] Step A3, quantifying the water consumption amount of the intermediate product and the water consumption amount of the final product.

[0076] In Step A1, a basic water balance model of the department is constructed;

[0077] The basic water balance model of the department comprises:

[0078]

[0079] In the formula, W 1×n is a direct water consumption matrix of each department; n is the number of departments of the region; the subscript 1x n represents the size of the matrix; Ψ 1×n is an implicit water intensity coefficient matrix, Ψ 1×n The element ψ i in the middle represents the water consumption amount implied by a unit product or service of department i; Z n×n is an intermediate input matrix; is a diagonal matrix of total output of the department.

[0080] In Step A2, the implicit water intensity is calculated;

[0081] The calculation of the implicit water intensity comprises:

[0082] Ψ 1×n = K 1×n (I n×n -A n×n ) -1

[0083] In the formula, the water resource input coefficient matrix is the water resource amount required by each department for producing a unit total output; I n×n is a unit matrix; A n×n is a direct consumption coefficient matrix, wherein the element a ij represents the quantity of product i consumed for producing a unit product j.

[0084] In Step A3, the water consumption amount of the intermediate product and the final product is quantified;

[0085] The water consumption amount of the intermediate product is:

[0086]

[0087] The water consumption amount of the final product is:

[0088]

[0089] wherein, is the water consumption matrix of the intermediate products among departments; is the water consumption matrix of the final products; n×1 is the final demand matrix of the departments; is the diagonal matrix of the water intensity.

[0090] In step B, based on the water consumption amount implied in the products, a multi-perspective input-output model is constructed to comprehensively identify key departments;

[0091] Step B1: constructing a model based on the perspective of intermediate products;

[0092] The model based on the perspective of intermediate products is:

[0093]

[0094] wherein, IBP 1×n is the water input matrix of the intermediate products; OBP n×1 is the water output matrix of the intermediate products; IBP 1×n The element IBP i of OBP n×1 represents the water consumption of the intermediate products of all departments in the process of producing final products by department i, and the greater the value, the greater the resource consumption pulling effect of the department on the national economy; OBP i The element OBP i of OBP n×1 represents the water output of the intermediate products supplied to all other departments in the process of producing final products by department i, and the greater the value, the greater the driving effect of the department on the production of each department; i represents a column vector with all elements being 1; i' is the transpose of i.

[0095] Step B2: constructing a model based on the perspective of final products;

[0096] The model based on the perspective of final products is:

[0097]

[0098] wherein, 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 is the water consumption matrix calculated based on the consumption end.

[0099] In this embodiment, representing the final demand matrix (including household consumption, government consumption, and capital formation, etc.), representing the export matrix, representing the import matrix. A region produces final products through a series of production activities to meet the final demand within the region and export, PBP n×1 The main purpose is to identify the sectors that consume large amounts of water resources in the process of meeting these final demands, which is calculated from the production perspective; however, for those final destinations of export products, the imported products from other regions are consumed within the region, so from the consumption perspective, only the resources implied in the final demand and imports within the region should be calculated.

[0100] Step B3: Constructing the transmission center perspective model;

[0101] The transmission center perspective model is:

[0102] Assuming that in a supply chain containing sector i, sector i has L1 upstream sectors and L2 downstream sectors, then the intermediate degree b i of sector i in the supply chain is:

[0103]

[0104] where K is the water resource input coefficient matrix; is the L1 power of the intermediate consumption coefficient matrix; J i is a matrix whose (i, i) element is 1 and other elements are 0; n×n is the L2 power of the intermediate consumption coefficient matrix; F is the domestic final demand matrix.

[0105] In this embodiment, according to the structural path analysis theory, the energy and water resource consumption of products in the production process is decomposed into infinite paths:

[0106]

[0107] where KA t F represents the amount of water resources consumed in a supply chain path containing t sectors (i.e. t layers of production). The water resources absorbed by the system are transmitted from the starting sector to the ending sector of these supply chains, and it is necessary to determine the sectors that play a key role in the transmission process. These sectors that use the concept of intermediate degree in the water system as transmission centers are determined, i.e. the amount of water resources consumed by all supply chain paths passing through the sector. Assuming that in a supply chain containing sector i, sector i has L1 upstream sectors and L2 downstream sectors, then the intermediate degree b

[0108] ​

[0109] Among them, (KA L1 ) i Represents the matrix kA L1 The i-th element of );(A L2 F) i Represents matrix A L2 The i-th element of f (i.e., ); Matrix J i It is a matrix where all elements except the (i,i)th element are 1 and all other elements are 0. n×n Matrix. Extending the above formula to all supply chains containing department i, the betweenness of department i can be calculated:

[0110]

[0111] Among them, b i is the betweenness of sector i. The higher the value, the more water resources the sector transmits.

[0112] Step C: Using system robustness as a response variable to measure the sustainable development of the water system, the key sectors identified in step B are used as the main factors, and a factorial analysis method is used to conduct a multi-strategy scenario simulation of the water system to determine the strategies suitable for the key sectors, thereby optimizing the robustness of the water system.

[0113] The multi-strategy scenario simulation of the water system using the factorial analysis method includes:

[0114] 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;

[0115] Step C2: Determine the response variable and take the system robustness as the response variable of the water system;

[0116] Step C3: Conduct a factorial experiment to analyze the independent and interactive impacts 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.

[0117] Step C1: Determine factors and factor levels in a multi-strategy scenario

[0118] In the present embodiment, two scenarios, a production-based strategy and a consumption-based strategy, are selected to simulate in order to determine the most suitable strategy for each key sector. Specifically, first, the factors for the two strategy scenarios are determined: the direct consumption of water resources by each key sector as the factor for the production-based strategy scenario, and the final demand for each key sector as the factor for the consumption-based strategy scenario; second, each factor is set to have a high and a low level (i.e. an increase or decrease of 10%); and finally, the changes in other quantities brought about by the changes in the factors in the different scenarios are calculated:

[0119] The determination of the factors and the factor levels in the multi-strategy scenarios comprises:

[0120] First, the factors for the two strategy scenarios are determined; the factors for the two strategy scenarios include: the direct consumption of water resources by each key sector as the factor for the production-based strategy scenario and the final demand for each key sector as the factor for the consumption-based strategy scenario;

[0121] Second, the high level and the low level are set for the factors for the two strategy scenarios; the high level is an increase of 10% based on the original factor threshold value; and the low level is a decrease of 10% based on the original factor threshold value;

[0122] Finally, the changes in other quantities brought about by the changes in the factors in the two strategy scenarios are calculated, including:

[0123] Simulation from the perspective of the production-based strategy:

[0124]

[0125] Simulation from the perspective of the consumption-based strategy:

[0126]

[0127] wherein the superscripts P and C represent the changes after the application of the production-based strategy scenario and the consumption-based strategy, respectively; a i and β i represent the factor levels for the key sector i based on the production-based strategy scenario and the consumption-based strategy scenario, respectively; is the water consumption of the sector i after the application of the production-based strategy, w i is the water consumption of the sector i, is the implicit water intensity matrix after the application of the production-based strategy, is the diagonal matrix of the total output of the sector, is the transpose of the intermediate input matrix, is the water consumption matrix after the application of the production-based strategy, f i C is the final demand of the sector i after the application of the consumption-based strategy, f i is the final demand of the sector i, is the total output matrix after applying the consumption-based strategy, I is the identity matrix, and A is the intermediate consumption coefficient matrix, is the final demand matrix after applying the consumption-based strategy, is the water use of sector i after applying the consumption-based strategy, 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 total output diagonal matrix after applying the consumption-based strategy, is the water use matrix after applying the consumption-based strategy.

[0128] Step C2: Determine the response variable, take the system robustness as the response variable of the water system;

[0129] In this embodiment, the system robustness, which combines efficiency and redundancy, has been recognized as an important indicator for quantifying sustainability. In this embodiment, the system robustness is taken as the response variable of the water system to measure the main effect and interaction effect of multiple key factors on the sustainability of the water system:

[0130] Said taking the system robustness as the response variable of the water system comprises:

[0131]

[0132] R = Ca- As

[0133]

[0134] In the formula, SR is the system robustness, As is the system efficiency, Ca is the system capacity, R is the system redundancy, TST p represents the total throughput of the system, i.e., the sum of the input and output flow of all sectors, p ij is the element of the matrix , which represents the water resources flowing into sector j from sector i, T i represents all water outflows of sector i, i.e. T j represents all water resources flowing into sector j, i.e. n is the number of sectors in the study area.

[0135] Step C3: Perform a factorial experiment to analyze the independent and interactive effects of key sectors on the sustainability of the energy and water system under multiple strategy scenarios, and determine which strategy is suitable for each key sector through the results of the factorial analysis.

[0136] In this embodiment, the response value of each key department to the system robustness is compared under different strategy scenarios, and each key department is more suitable to adopt the strategy corresponding to the high system robustness response value.

[0137] In this embodiment, the determination process of the key department under multi-perspective is as shown in Figure 3 The analysis result shows that the livestock industry (BAR), the mining industry (MIN) and the heavy manufacturing industry (HMA) have high contribution under the three perspectives, and these two departments play a key role in the water system. The service industry has a high proportion in the intermediate product-based perspective and the transmission center-based perspective, but it is low in the final product-based perspective, which shows that the department transmits a large amount of water in the supply chain and department transaction process. Improving the production efficiency of the service industry can reduce the water input of the upstream department, thereby indirectly relieving the water resource pressure. The vegetable, fruit and nut industry (VFN) is the department with the largest water consumption in the production-based method accounting, and the light manufacturing industry (LMA) is the department with the largest water consumption in the consumption-based method accounting, so these two departments cannot be ignored because they are not key water transmission centers. Therefore, the vegetable, fruit and nut industry (VFN), the livestock industry (BAR), the mining industry (MIN), the light manufacturing industry (LMA) and the heavy manufacturing industry (HMA) are determined as the key departments of the water system.

[0138] Figure 4 To identify the impact of the identified key departments on the system robustness under different strategy perspectives, (a) is a schematic diagram of the impact of the key departments on the system robustness under different strategy perspectives, and (b) is a schematic diagram of the comparison of the water system robustness impact under different strategy scenarios. From Figure 4 As can be seen from (a), under the production-based strategy, the response of SR shows an upward trend with the increase of the water consumption of VFN, MIN and LMA, while the response of the remaining key departments shows a downward trend with the increase of the water consumption. Under the consumption-based strategy, the SR of the remaining key departments shows an upward trend with the decrease of the final demand, except for VFN. Compared with the consumption-based strategy, the individual impact of VFN on SR is higher under the production-based strategy, which is 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 can promote the production of each department, thereby helping to improve the robustness of the water system. Therefore, VFN is more suitable to adopt the production-based strategy. The individual impact of the remaining key departments of the water system on SR under the consumption-based strategy is greater than that under the production-based strategy, because these departments are key water transmission centers or key water consumption centers, and reducing the final demand for these departments from the demand side is conducive to the sustainability of the water system. According to the above analysis, the optimal strategy scenario is that VFN adopts the production-based strategy, and the remaining key departments of the water system adopt the consumption-based strategy. Figure 4(b) The water system robustness under the optimal policy scenario (S0) is better than that under the production-based policy or consumption-based policy scenario, which also highlights the importance of different policies for different sectors.

[0139] This embodiment is only a preferred embodiment of the present application, but the protection scope of the present application is not limited to this, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to 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 products; Step B: Based on the water consumption implicit in the products, 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: Using system robustness as a response variable to measure the sustainable development of the water system, and the key sectors identified in Step B as the main factors, a multi-strategy scenario simulation of the water system is conducted using a factorial analysis method to determine the strategies that are suitable for the key sectors, thereby optimizing the robustness of the water system; The establishment of the regional input-output model in step A includes: Step A1: Construct the basic water balance model of 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; The basic water balance model of 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 input matrix; is the diagonal matrix of the total output of the sector; 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 identity matrix; A n×n is the direct consumption coefficient matrix, where element a ij represents the quantity of product i consumed per unit of product j produced; The water consumption implicit in the intermediate product is: The water consumption implicit in the final product is: Where, 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; The intermediate product perspective model 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 OBP represents the water input implicit in the intermediate products of various departments consumed in the production of final products by department i; n×1 Elements of OBP i represents the water output implicit in the intermediate products supplied to all other sectors during the production of final products by sector i; i represents a column vector whose elements are all 1; i′ is the transpose of i; The final product perspective model is: Where, 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 is the water consumption matrix calculated based on the consumer end; 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 betweenness of department i in the supply chain is b i for: Where K is the water resources input coefficient matrix; is the L1 power of the intermediate consumption coefficient matrix; J i is an n×n matrix with all elements 0 except the (i,i)th element being 1; is the L2 power of the intermediate consumption coefficient matrix; F is the domestic final demand matrix; 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 impacts of key sectors on the sustainability of energy and water systems under multiple strategy scenarios, and determine which strategy is appropriate for each key sector based on the factorial analysis results; The factors and factor levels determined in a multi-strategy scenario include: First, factors for two strategic scenarios are determined; the factors for the two strategic 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, we calculate the changes in other quantities brought about by the factor changes under the two strategy scenarios, including: Simulation based on production strategy perspective: Simulation based on consumption strategy perspective: Where, 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 Represent the factor levels of key sector i based on 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 the total output of the sector, 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 identity matrix, A is the intermediate consumption coefficient matrix, is 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 is a matrix The elements represent the water resources flowing from sector i to sector j, T i represents the total water outflow of sector i, i.e. 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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