Water-Energy-Food System Nexus Analysis Method

Abstract

This disclosure relates to a method for analyzing the linkages of a water-energy-food system. The method includes: decoupling linkages based on causal relationships within the water-energy-food system to determine corresponding order parameters and eigenvectors; constructing a collaborative network between multiple order parameters and performing centrality analysis to determine the temporal trends of the water-energy-food system; and constructing a Bayesian network between multiple eigenvectors and performing sensitivity analysis to determine the spatial distribution pattern of the water-energy-food system. This embodiment of the disclosure improves the accuracy of linkage analysis of the water-energy-food system.

Description

Technical Field

[0001] The present disclosure relates to the technical field of water resource management, and in particular to a linkage analysis method for a water-energy-food system. Background Art

[0002] As human activities intensify, multiple resource systems (such as the water-energy-food system) are becoming increasingly interconnected, and these complex nexus relationships have become a new focus of interdisciplinary research. Water, energy, and food are closely intertwined, forming the foundation of human survival and strategic resources for the long-term stability of a nation. As three essential resources, water, energy, and food face increasing demand and limited supply. It is estimated that by 2030, global water, energy, and food consumption will increase by 40%, 50%, and 35%, respectively. Since the concept of water-energy-food was proposed at the Bonn Conference, governments and organizations around the world have invested heavily in major R&D projects focused on this nexus, known as the FEW nexus. Research on the water-energy-food nexus can help understand the dynamic interactions between these systems and provide decision-making insights and methodologies for regional sustainable development.

[0003] However, existing research primarily considers the construction of static ties, ignoring the impact of temporal and spatial variations on these ties. Economic development, resource scarcity, and climate change pose significant challenges to the water-energy-food system, necessitating integrated resource management to improve synergies among subsystems. However, previous research has insufficiently characterized the dynamic mechanisms and evolutionary characteristics of these ties, failing to reveal the conditional dependencies between subsystems. The synergistic patterns of the water-energy-food system across temporal and spatial scales remain unclear. Summary of the Invention

[0004] In order to solve the above technical problems, the present disclosure provides a linkage analysis method for the water-energy-food system.

[0005] In a first aspect, the present disclosure provides a method for analyzing the linkages of water, energy, and food systems, comprising:

[0006] Decouple the causal relationships in the water-energy-food system and determine the corresponding order parameters and eigenvectors;

[0007] Constructing a collaborative network among a plurality of the order parameters and performing centrality analysis to obtain a temporal trend of the water-energy-food system;

[0008] A Bayesian network is constructed between multiple eigenvectors and sensitivity analysis is performed to obtain the spatial distribution pattern of the water-energy-food system.

[0009] In a second aspect, the present disclosure provides a water-energy-food system linkage analysis device, comprising:

[0010] The relationship decoupling module is used to decouple the causal relationships in the water-energy-food system and determine the corresponding order parameters and eigenvectors;

[0011] The first construction module is used to construct a collaborative network between multiple order parameters and perform centrality analysis to obtain the temporal trend of the water-energy-food system;

[0012] The second construction module is used to construct a Bayesian network between multiple feature vectors and perform sensitivity analysis to obtain the spatial distribution pattern of the water-energy-food system.

[0013] In a third aspect, the present disclosure provides a water-energy-food system linkage analysis device, comprising:

[0014] processor;

[0015] a memory for storing executable instructions;

[0016] The processor is used to read executable instructions from the memory and execute the executable instructions to implement the water-energy-food system linkage analysis method of the first aspect.

[0017] In a fourth aspect, the present disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor implements the water-energy-food system linkage analysis method of the first aspect.

[0018] The technical solution provided by the embodiments of the present disclosure has the following advantages over the prior art:

[0019] The linkage analysis method of the water-energy-food system of the embodiment of the present disclosure can decouple the linkage relationship according to the causal relationship in the water-energy-food system, determine the corresponding order parameters and eigenvectors, then construct a collaborative network between multiple order parameters and perform centrality analysis to obtain the temporal trend of the water-energy-food system, and then construct a Bayesian network between multiple eigenvectors and perform sensitivity analysis to obtain the spatial distribution pattern of the water-energy-food system. Therefore, by decoupling the linkage relationship of the water-energy-food system, order parameters and eigenvectors are obtained, and centrality analysis and sensitivity analysis are performed respectively, the temporal trend of the water-energy-food system and the spatial distribution pattern are obtained, thereby improving the accuracy of the linkage analysis of the water-energy-food system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the originals and elements are not necessarily drawn to scale.

[0021] Figure 1 A flow chart of a water-energy-food system linkage analysis method provided in an embodiment of the present disclosure;

[0022] Figure 2 A schematic diagram of the relationship between characteristic vectors in a water-energy-food system provided by an embodiment of the present disclosure;

[0023] Figure 3 A flow chart of another method for analyzing the linkages of water-energy-food systems provided in an embodiment of the present disclosure;

[0024] Figure 4 A schematic structural diagram of a water-energy-food system linkage analysis device provided in an embodiment of the present disclosure;

[0025] Figure 5 A schematic structural diagram of a water-energy-food system linkage analysis device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0026] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0027] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0028] As used herein, the term "including" and its variations are open-ended, i.e., "including but not limited to." The term "based on" means "based, at least in part, on." The term "one embodiment" means "at least one embodiment," the term "another embodiment" means "at least one additional embodiment," and the term "some embodiments" means "at least some embodiments." Other terms are defined in the following description.

[0029] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0030] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0031] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0032] In order to solve the above problems, the present disclosure provides a method for analyzing the linkages of water, energy and food systems. Figure 1-Figure 3 The water-energy-food system linkage analysis method provided by the embodiment of the present disclosure is described in detail.

[0033] Figure 1 A flow chart of a water-energy-food system linkage analysis method provided by an embodiment of the present disclosure is shown.

[0034] In an embodiment of the present disclosure, the water-energy-food system linkage analysis method may be performed by an electronic device, which may include but is not limited to devices such as a computer device, a cloud server, or a cloud server cluster.

[0035] like Figure 1 As shown, the water-energy-food system linkage analysis method may include the following steps.

[0036] S110. Decouple the linkages based on the causal relationships in the water-energy-food system and determine the corresponding order parameters and eigenvectors.

[0037] In an embodiment of the present disclosure, the electronic device can decouple the linkage relationship based on the causal relationship in the water-energy-food system and determine the corresponding order parameters and eigenvectors.

[0038] Alternatively, the water-energy-food system can be used to characterize the complex water resource system of a region.

[0039] Alternatively, the causal relationship may be used to characterize the linkages in the water-energy-food system, wherein the causal relationship may include constituent causality, direct causality, and indirect causality.

[0040] Alternatively, the order parameter can be a description of the ordered structure of the system, which helps to understand the trade-offs and synergies at the nexus of water-energy-food systems.

[0041] Optionally, the eigenvectors may be vectors corresponding to different order parameters.

[0042] Specifically, in the water-energy-food system, electronic devices can decouple the water-energy-food nexus according to causal relationships, such as constitutive causality, direct causality, and indirect causality, thereby obtaining order parameters and eigenvectors.

[0043] S120: Construct a collaborative network among the plurality of order parameters and perform centrality analysis to obtain a temporal variation trend of the water-energy-food system.

[0044] In an embodiment of the present disclosure, the electronic device can construct a collaborative network between multiple order parameters and perform centrality analysis to obtain the temporal change trend of the water-energy-food system.

[0045] Optionally, the collaborative network may be a network that characterizes the collaborative relationship between multiple order parameters.

[0046] Specifically, after obtaining the order parameters, the electronic device can construct a collaborative network between multiple order parameters and perform centrality analysis to obtain the temporal change trend of the water-energy-food system.

[0047] S130. Construct a Bayesian network between the plurality of characteristic vectors and perform sensitivity analysis to obtain the spatial distribution pattern of the water-energy-food system.

[0048] In an embodiment of the present disclosure, the electronic device can construct a Bayesian network between multiple feature vectors and perform sensitivity analysis to obtain the spatial distribution pattern of the water-energy-food system.

[0049] Optionally, the Bayesian network may be a network that represents the relationship between multiple feature vectors.

[0050] Specifically, after obtaining the feature vector, the electronic device can construct a Bayesian network between multiple feature vectors and perform sensitivity analysis to obtain the spatial distribution pattern of the water-energy-food system.

[0051] Therefore, in the embodiment of the present disclosure, it is possible to decouple the linkage relationship according to the causal relationship in the water-energy-food system, determine the corresponding order parameters and characteristic vectors, then construct a collaborative network between multiple order parameters and perform centrality analysis to obtain the temporal trend of the water-energy-food system, and then construct a Bayesian network between multiple characteristic vectors and perform sensitivity analysis to obtain the spatial distribution pattern of the water-energy-food system. Therefore, by decoupling the linkage relationship of the water-energy-food system, order parameters and characteristic vectors are obtained, and centrality analysis and sensitivity analysis are performed respectively to obtain the temporal trend of the water-energy-food system and the spatial distribution pattern, thereby improving the accuracy of the linkage analysis of the water-energy-food system.

[0052] Optionally, S110 may specifically include: based on the causal relationship, decoupling the water-energy-food linkage in the water-energy-food system to obtain a corresponding subsystem; and determining the order parameter and the characteristic vector corresponding to the subsystem according to basic attributes.

[0053] In the embodiment of the present disclosure, the electronic device can decouple the water-energy-food relationship in the water-energy-food system based on the cause-effect relationship to obtain the corresponding subsystem.

[0054] Specifically, based on the causal relationship, the electronic device can decouple the water-energy-food relationship in the water-energy-food system to obtain corresponding subsystems, namely, a water subsystem, an energy subsystem and a food subsystem.

[0055] Furthermore, the electronic device may determine the order parameter and the eigenvector corresponding to the subsystem according to basic properties.

[0056] For example, based on the four basic attributes of water, energy, and food resources—supply, demand, efficiency, and pollution—12 order parameters are selected, each of which is decomposed into a set of eigenvectors. Multidimensional eigenvectors are then combined to form a vector space.

[0057] Figure 2 A schematic diagram of the relationship between characteristic vectors in a water-energy-food system provided by an embodiment of the present disclosure is shown.

[0058] like Figure 2As shown, based on the causal relationship, the electronic device decouples the water-energy-food linkage within the water-energy-food system, obtaining corresponding subsystems: the water subsystem, the energy subsystem, and the food subsystem. Based on the four basic properties of water, energy, and food resources (supply, demand, efficiency, and pollution), 12 order parameters are selected, such as F1, F2, F3, F4, E1, E2, E3, E4, W1, W2, W3, and W4. Each order parameter is then decomposed into a set of eigenvectors. The multidimensional eigenvectors are combined to form a vector space, such as f1, f2, f3, f4, f5, f6, f7, f8, f9, f10, e1, e2, e3, e4, e5, e6, e7, e8, e9, w1, w2, w3, w4, w5, w6, w7, w8, w9, w10, w11, w12, w13, and w14.

[0059] Optionally, S120 may specifically include: constructing a collaborative network between multiple order parameters and performing centrality analysis to identify the main controlling factors affecting the linkage relationship; analyzing the main controlling factors to obtain the temporal change trend of the water-energy-food system.

[0060] In the disclosed embodiment, the electronic device can construct a collaborative network between multiple order parameters and perform centrality analysis to identify the main controlling factors that influence the linkage relationship. The main controlling factors can also be analyzed to obtain the temporal trend of the water-energy-food system.

[0061] Specifically, the electronic device can construct a collaborative network between multiple order parameters and perform centrality analysis, so as to identify the main controlling factors affecting the link relationship, and further analyze the main controlling factors to obtain the temporal change trend of the water-energy-food system.

[0062] The steps for building a collaborative network are as follows:

[0063] (1) Representation of order parameters:

[0064] Each order parameter V at different times can be represented by a set of eigenvectors v. The i-th order parameter V i and the jth order parameter V j The values ​​in year t and year t+1 can be represented by the eigenvectors as follows:

[0065]

[0066] Where n is the i-th order parameter V i The eigenvector v i The number of m is the number of order parameters V j The eigenvector v j The number of .

[0067] (2) Rate of change of eigenvector:

[0068] The rate of change of the eigenvector can be expressed by the change in the eigenvectors of adjacent years as follows:

[0069]

[0070] Where, and Represents the first eigenvector v in year t and year t+1 respectively l and the kth eigenvector v k The rate of change.

[0071] (3) Influence of eigenvector:

[0072] The closer the rate of change of two eigenvectors is, the greater the influence of the eigenvectors is, and the closer it is to 1. In year t, any two eigenvectors v 1 and v k The influence between them can be expressed as follows:

[0073]

[0074] (4) Influence matrix of order parameters:

[0075] In the tth year, any two order parameters V i and V j The influence matrix between can be expressed as follows:

[0076]

[0077] In the formula, the influence matrix It is n×m-dimensional because the dimensions of the two order parameters are n and m respectively.

[0078] (5) Synergy of order parameters:

[0079] In the tth year, any two order parameters V i and V j The degree of coordination between them can be expressed as:

[0080]

[0081] In the formula, |·| represents the absolute value of the vector, and ‖·‖ represents the bi-norm of the vector or matrix.

[0082] (6) Threshold of synergy:

[0083] When building a network based on collaboration, a threshold r needs to be set. th , assuming there are N order parameters, we can calculate However, the connection between two order parameters can only be established when the synergy is greater than the threshold. If an order parameter has a strong synergistic correlation with other order parameters, it is considered to dominate the water-energy-food relationship and can easily affect or be affected by other order parameters. If the threshold of the synergy degree is too high, too many invalid connections will be discarded, and few connections will be retained, making it difficult to quantify the complete relationship. Therefore, according to the research, the average synergy degree over many years is selected as the threshold, that is, r th = 0.5. If the calculated value between two nodes is higher than the average value, it is considered a valid connection, otherwise it is considered an invalid connection. Therefore, the influence of the order parameter with low synergy on the system centrality can be excluded.

[0084] (7) Calculation of various centrality indices:

[0085] Degree centrality is the most intuitive and simplest metric for measuring nodes. Each order parameter represents a node. The degree centrality of a node can be interpreted as the number of connections it has with other nodes. Nodes with higher degrees are considered important nodes because they can spread their influence to more nodes. The absolute degree centrality of the i-th node is as follows:

[0086]

[0087] Where, ∑ j p ij is the number of connections a node has with other nodes in the network.

[0088] Betweenness centrality describes the "bridge"-like role of a node, reflecting the "transitional" connections between all possible node pairs in the network. A node's betweenness centrality can be interpreted as the number of times a node communicates with any node pair in the network. The higher the betweenness centrality of a node, the stronger its communication ability in the network. For the i-th node, absolute betweenness centrality represents the ratio of paths between all node pairs that pass through a given node, and is defined as follows:

[0089]

[0090] Where nodes j and k are any pair of nodes. jk represents the number of paths between nodes j and k, g jk (i) represents the number of paths between nodes j and k that pass through node i.

[0091] Closeness centrality describes the independence of a given node from other nodes in a network. The closeness centrality of a node can be interpreted as the sum of the shortest distances from the given node to any other node. The node with the highest closeness centrality is usually considered to be at the center of the network. The absolute closeness centrality of the i-th node is calculated as follows:

[0092]

[0093] Where N is the number of nodes (i.e., order parameters). max Indicates the maximum distance between two nodes in the network, d ij represents the shortest distance between nodes i and j.

[0094] Optionally, S130 may specifically include: constructing a Bayesian network between multiple characteristic vectors and performing sensitivity analysis to obtain the sensitivity of the main controlling factors in different spaces; analyzing the sensitivity to obtain the spatial distribution pattern of the water-energy-food system.

[0095] In an embodiment of the present disclosure, the electronic device can construct a Bayesian network between multiple feature vectors and perform sensitivity analysis to obtain the sensitivity of the main controlling factors in different spaces; analyze the sensitivity to obtain the spatial distribution pattern of the water-energy-food system.

[0096] Specifically, the electronic device can construct a Bayesian network between multiple feature vectors and perform sensitivity analysis to obtain the sensitivity of the main controlling factors in different spaces, and further analyze the sensitivity to obtain the spatial distribution pattern of the water-energy-food system.

[0097] Among them, the joint probability distribution of the Bayesian network is expressed as follows:

[0098]

[0099] In order to evaluate whether the output variable is sensitive to the changes of the input nodes in the Bayesian network, sensitivity analysis indicators are often used when evaluating the Bayesian network model. Mutual Information (MI) is calculated based on entropy reduction. The calculation formula is as follows:

[0100]

[0101] Where q represents the state of the output variable Q, and e represents the input variable E. H is the entropy. H(·|·) represents the conditional entropy. Finally, for ease of comparison, MI is rescaled to a relative value (between 0% and 100%).

[0102] Figure 3 A flow chart of another water-energy-food system linkage analysis method provided by an embodiment of the present disclosure is shown.

[0103] like Figure 3As shown, electronic devices can decouple the causal relationships within the water-energy-food system, such as constructing causal, direct, and indirect causal relationships, to obtain corresponding subsystems: the water subsystem, the energy subsystem, and the food subsystem. Next, based on the four basic properties of water, energy, and food resources—supply, demand, efficiency, and pollution—12 order parameters are selected, each decomposed into a set of eigenvectors. Multidimensional eigenvectors are combined to form a vector space. For example, based on the four basic properties of water, energy, and food resources—supply, demand, efficiency, and pollution—12 order parameters are selected, such as F1, F2, F3, F4, E1, E2, E3, E4, W1, W2, W3, and W4. Each order parameter is then decomposed into a set of eigenvectors. Multidimensional feature vectors are combined to form a vector space, such as f1, f2, f3, f4, f5, f6, f7, f8, f9, f10, e1, e2, e3, e4, e5, e6, e7, e8, e9, w1, w2, w3, w4, w5, w6, w7, w8, w9, w10, w11, w12, w13, w14.

[0104] Then, the electronic device can construct a collaborative network between multiple order parameters and perform centrality analysis to identify the main controlling factors affecting the link relationship, and further analyze the main controlling factors to obtain the temporal change trend of the water-energy-food system.

[0105] For example, the study explored temporal trends in the water-energy-food system. Water, energy, and food compete with each other, forming a constantly evolving dynamic network. Synergy network analysis can enhance understanding of the dynamic connections between related subsystems. The results show that the degree of synergy within the water-energy-food nexus peaked in 2007 and reached its lowest point in 2013, with the overall synergy within the nexus showing a fluctuating downward trend. This research suggests that improving resource efficiency can significantly promote synergy within the food and energy subsystems.

[0106] Finally, the electronic device can construct a Bayesian network between multiple feature vectors and perform sensitivity analysis to obtain the sensitivity of the main controlling factors in different spaces, and further analyze the sensitivity to obtain the spatial distribution pattern of the water-energy-food system.

[0107] For example, the study explored the spatial distribution of the water-energy-food nexus. Water use in the upstream of the XX region is sensitive to food-related variables, while water use in the downstream is sensitive to energy-related variables. Therefore, agricultural development in the upstream economic zone should focus on environmental protection, improving food structure, and increasing irrigation coefficients and yields; while industrial development in the downstream economic zone should focus on ecological restoration and promoting a shift in energy production methods. The research's significance lies in developing a network analysis framework to better understand the spatiotemporal variations in the water-energy-food nexus within the XX region.

[0108] Figure 4 A schematic structural diagram of a water-energy-food system linkage analysis device provided by an embodiment of the present disclosure is shown.

[0109] like Figure 4 As shown, the water-energy-food system linkage analysis device 400 may include a relationship decoupling module 410 , a first construction module 420 and a second construction module 430 .

[0110] The relationship decoupling module 410 can be used to decouple the linkage relationship according to the causal relationship in the water-energy-food system and determine the corresponding order parameters and eigenvectors.

[0111] The first construction module 420 can be used to construct a collaborative network among the plurality of order parameters and perform centrality analysis to obtain the temporal variation trend of the water-energy-food system.

[0112] The second construction module 430 can be used to construct a Bayesian network between the multiple feature vectors and perform sensitivity analysis to obtain the spatial distribution pattern of the water-energy-food system.

[0113] Therefore, in the embodiment of the present disclosure, it is possible to decouple the linkage relationship according to the causal relationship in the water-energy-food system, determine the corresponding order parameters and characteristic vectors, then construct a collaborative network between multiple order parameters and perform centrality analysis to obtain the temporal trend of the water-energy-food system, and then construct a Bayesian network between multiple characteristic vectors and perform sensitivity analysis to obtain the spatial distribution pattern of the water-energy-food system. Therefore, by decoupling the linkage relationship of the water-energy-food system, order parameters and characteristic vectors are obtained, and centrality analysis and sensitivity analysis are performed respectively to obtain the temporal trend of the water-energy-food system and the spatial distribution pattern, thereby improving the accuracy of the linkage analysis of the water-energy-food system.

[0114] In some embodiments of the present disclosure, the causal relationship includes constituent causation, direct causation, and indirect causation.

[0115] In some embodiments of the present disclosure, the relationship decoupling module 410 may specifically include a relationship decoupling unit and a data determination unit.

[0116] The relationship decoupling unit can be used to decouple the water-energy-food linkage relationship in the water-energy-food system based on the causal relationship to obtain the corresponding subsystem.

[0117] The data determination unit can be used to determine the order parameter and the eigenvector corresponding to the subsystem according to basic attributes.

[0118] In some embodiments of the present disclosure, the first construction module 420 may specifically include a first construction unit and a first analysis unit.

[0119] The first construction unit can be used to construct a collaborative network between multiple order parameters and perform centrality analysis to identify the main controlling factors that affect the tie relationship.

[0120] The first analysis unit can be used to analyze the main controlling factors to obtain the temporal variation trend of the water-energy-food system.

[0121] In some embodiments of the present disclosure, the second construction module 430 may specifically include a second construction unit and a second analysis unit.

[0122] The second construction unit can be used to construct a Bayesian network between the plurality of feature vectors and perform sensitivity analysis to obtain the sensitivity of the main controlling factors in different spaces.

[0123] The second analysis unit can be used to analyze the sensitivity and obtain the spatial distribution pattern of the water-energy-food system.

[0124] It should be noted that Figure 4 The water-energy-food system linkage analysis device 400 shown can perform Figure 1-Figure 3 The various steps in the method embodiment shown are implemented Figure 1-Figure 3 The various processes and effects in the illustrated method embodiment are not described in detail here.

[0125] Figure 5 A schematic structural diagram of a water-energy-food system linkage analysis device provided by an embodiment of the present disclosure is shown.

[0126] In some embodiments of the present disclosure, Figure 5 The water-energy-food system link analysis device shown may be an electronic device, which may include but is not limited to a computer device, a cloud server, or a cloud server cluster.

[0127] like Figure 5As shown, the water-energy-food system linkage analysis device may include a processor 501 and a memory 502 storing computer program instructions.

[0128] Specifically, the processor 501 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0129] Memory 502 may include a large-capacity memory for information or instructions. By way of example, and not limitation, memory 502 may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disk, a magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 502 may include removable or non-removable (or fixed) media. Where appropriate, memory 502 may be internal or external to the integrated gateway device. In certain embodiments, memory 502 is non-volatile solid-state memory. In certain embodiments, memory 502 includes read-only memory (ROM). Where appropriate, the ROM may be mask-programmed ROM, programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0130] The processor 501 reads and executes the computer program instructions stored in the memory 502 to perform the steps of the water-energy-food system linkage analysis method provided in the embodiment of the present disclosure.

[0131] In one example, the water-energy-food system link analysis device may further include a transceiver 503 and a bus 504. Figure 5 As shown, the processor 501 , the memory 502 and the transceiver 503 are connected via a bus 504 and communicate with each other.

[0132] The bus 504 includes hardware, software, or both. By way of example and not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, the bus 504 may include one or more buses. Although embodiments herein describe and illustrate a particular bus, this application contemplates any suitable bus or interconnect.

[0133] The embodiments of the present disclosure also provide a computer-readable storage medium, which may store a computer program. When the computer program is executed by a processor, the processor implements the water-energy-food system linkage analysis method provided by the embodiments of the present disclosure.

[0134] The aforementioned storage medium may, for example, include a memory 502 containing computer program instructions. These instructions may be executed by a processor 501 of the water-energy-food system linkage analysis device to implement the water-energy-food system linkage analysis method provided in the embodiments of the present disclosure. Alternatively, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, or optical data storage device.

[0135] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising" is intended to cover non-exclusive inclusion, so that a process, method, article or apparatus that includes a list of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article or apparatus.

[0136] The foregoing description is intended only to provide specific embodiments of the present disclosure, intended to enable those skilled in the art to understand and implement the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the embodiments described herein, but rather to be construed in the broadest manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for analyzing the linkages between water, energy and food systems, characterized by: include: Decouple the causal relationships in the water-energy-food system and determine the corresponding order parameters and eigenvectors; Constructing a collaborative network among a plurality of the order parameters and performing centrality analysis to obtain a temporal trend of the water-energy-food system; Constructing a Bayesian network between a plurality of the eigenvectors and performing sensitivity analysis to obtain the spatial distribution pattern of the water-energy-food system; The step of constructing a collaborative network among the plurality of order parameters and performing centrality analysis to obtain the temporal trend of the water-energy-food system includes: Constructing a collaborative network between multiple order parameters and performing centrality analysis to identify the main controlling factors that affect the tie relationship; The main controlling factors are analyzed to obtain the temporal variation trend of the water-energy-food system.

2. The method according to claim 1, characterized in that The causal relationship includes constituent causation, direct causation and indirect causation.

3. The method according to claim 1, characterized in that The decoupling of the causal relationship in the water-energy-food system and the determination of the corresponding order parameters and eigenvectors include: Based on the causal relationship, the water-energy-food relationship in the water-energy-food system is decoupled to obtain corresponding subsystems; The order parameter and the eigenvector corresponding to the subsystem are determined according to the attributes.

4. The method according to claim 1, wherein The step of constructing a Bayesian network between the plurality of eigenvectors and performing sensitivity analysis to obtain the spatial distribution pattern of the water-energy-food system includes: Constructing a Bayesian network between multiple feature vectors and performing sensitivity analysis to obtain the sensitivity of the main controlling factors in different spaces; The sensitivity is analyzed to obtain the spatial distribution pattern of the water-energy-food system.

5. A water-energy-food system linkage analysis device, characterized in that: include: The relationship decoupling module is used to decouple the causal relationships in the water-energy-food system and determine the corresponding order parameters and eigenvectors; The first construction module is used to construct a collaborative network between multiple order parameters and perform centrality analysis to obtain the temporal trend of the water-energy-food system; The second construction module is used to construct a Bayesian network between multiple feature vectors and perform sensitivity analysis to obtain the spatial distribution pattern of the water-energy-food system; The first building block includes: The first construction unit is used to construct a collaborative network between multiple order parameters and perform centrality analysis to identify the main controlling factors that affect the tie relationship; The first analysis unit is used to analyze the main controlling factors to obtain the temporal variation trend of the water-energy-food system.

6. The device according to claim 5, characterized in that The relationship decoupling module includes: a relationship decoupling unit, configured to decouple the water-energy-food relationship in the water-energy-food system based on the causal relationship to obtain corresponding subsystems; A data determination unit is used to determine the order parameter and the eigenvector corresponding to the subsystem according to the attributes.

7. A water-energy-food system linkage analysis device, characterized in that: include: processor; a memory for storing executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the water-energy-food system linkage analysis method according to any one of claims 1 to 4.

8. A non-volatile computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the water-energy-food system linkage analysis method according to any one of claims 1 to 4.

Images