Evaluation method of water-energy-grain stochastic system

Abstract

The invention relates to an evaluation method of a water-energy-grain stochastic system. The evaluation method of the water-energy-grain stochastic system comprises the following steps: constructing the water-energy-grain stochastic system under the risk influence, and constructing a corresponding stochastic differential equation for the water-energy-grain stochastic system; based on the stochastic differential equation, performing stochastic simulation on a plurality of state variables in the water-energy-grain stochastic system by using a stochastic simulation method to obtain an instantaneous probability and a steady-state probability; and deriving an elastic index according to the instantaneous probability and the steady-state probability, and evaluating the water-energy-grain stochastic system based on the elastic index. According to the embodiment of the invention, the evaluation analysis accuracy under the risk influence can be improved.

Description

Technical Field

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

[0002] The impact of low flow and high water temperature is increasingly closely embedded in the water-energy-food linkage, and system resilience assessment has become a new perspective for water resources management. The complexity of the water-energy-food system is reflected in two aspects: first, in the context of global warming, the occurrence of extreme weather and climate conditions is "random"; second, in the context of human activities, there is "correlation" between the water resources subsystem, energy subsystem, and food subsystem. The rare low flow-high water temperature event in the xx basin in the summer of 2022 brought unprecedented challenges to water resources supply, agricultural production, and energy security. Drought caused lakes to shrink and rivers to dry up, and the concurrent high temperature caused increased evaporation of vegetation and water bodies, resulting in a sharp decline in water resources; drought caused rivers to dry up and hydropower generation to be greatly reduced, while high temperature caused a surge in electricity demand, an imbalance in electricity supply and demand, and an energy crisis; water sources in irrigation areas could not be guaranteed during droughts, and high temperature heat damage significantly reduced crop yields, causing damage to agriculture. Water resource crises, energy crises, and food crises are closely related to climate change. Resilience reflects the ability of a system to resist risks and return to a stable state. Faced with severe threats from extreme climate events such as high temperatures and droughts, climate resilience has become a research focus. Assessing the resilience of the water-energy-food system can improve our understanding of the complex and interconnected water resource system, which is crucial to maintaining the normal state and stable operation of the system.

[0003] However, existing research mainly focuses on the ability of low-dimensional systems to cope with risks, while ignoring the transmission and transfer of risks within high-dimensional systems. Summary of the invention

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

[0005] In a first aspect, the present disclosure provides an evaluation method for a water-energy-food stochastic system, comprising:

[0006] Constructing a water-energy-food stochastic system under the influence of risk, and constructing a corresponding stochastic differential equation for the water-energy-food stochastic system;

[0007] Based on the stochastic differential equation, a stochastic simulation method is used to perform stochastic simulation on multiple state variables in the water-energy-food stochastic system to obtain instantaneous probability and steady-state probability;

[0008] A elasticity index is derived according to the instantaneous probability and the steady-state probability, and the water-energy-food stochastic system is evaluated based on the elasticity index.

[0009] In a second aspect, the present disclosure provides an evaluation device for a water-energy-food stochastic system, comprising:

[0010] An equation building module, used to build a water-energy-food stochastic system under the influence of risk, and to build a corresponding stochastic differential equation for the water-energy-food stochastic system;

[0011] A simulation processing module, used for performing random simulation on multiple state variables in the water-energy-food random system based on the stochastic differential equation using a random simulation method to obtain instantaneous probability and steady-state probability;

[0012] A system evaluation module is used to deduce a elasticity index according to the instantaneous probability and the steady-state probability, and to evaluate the water-energy-food stochastic system based on the elasticity index.

[0013] In a third aspect, the present disclosure provides an evaluation device for a water-energy-food random system, 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 evaluation method of the water-energy-food random system of the first aspect.

[0017] In a fourth aspect, the present disclosure provides a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to implement the evaluation method of the water-energy-food stochastic system of the first aspect.

[0018] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages:

[0019] The evaluation method of the water-energy-food random system of the embodiment of the present disclosure can construct a water-energy-food random system under the influence of risks, and construct a corresponding random differential equation for the water-energy-food random system. Then, based on the random differential equation, a random simulation method is used to randomly simulate multiple state variables in the water-energy-food random system to obtain instantaneous probability and steady-state probability. Then, according to the instantaneous probability and the steady-state probability, a elasticity index is deduced, and the water-energy-food random system is evaluated based on the elasticity index. Thus, a water-energy-food random system under the influence of risks and a corresponding random differential equation are constructed. By randomly simulating multiple state variables, the instantaneous probability and the steady-state probability are obtained, thereby deducing the elasticity index. The water-energy-food random system is evaluated in combination with the elasticity index, thereby improving the accuracy of evaluation and analysis under the influence of risks. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0021] Figure 1 A schematic diagram of a flow chart of an evaluation method for a water-energy-food stochastic system provided in an embodiment of the present disclosure;

[0022] Figure 2 A schematic diagram of the structure of a water-energy-food random system provided by an embodiment of the present disclosure;

[0023] Figure 3 A schematic diagram of a water-energy-food random system variable provided in an embodiment of the present disclosure;

[0024] Figure 4 A schematic flow chart of another method for evaluating a random water-energy-food system provided by an embodiment of the present disclosure;

[0025] Figure 5 A schematic diagram of the structure of an evaluation device for a water-energy-food random system provided by an embodiment of the present disclosure;

[0026] Figure 6 A schematic diagram of the structure of an evaluation device for a water-energy-food random system provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] Embodiments of the present disclosure will be described in more detail below 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 being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0028] It should be understood that the various steps described in the method implementation of the present disclosure can be performed in different orders and / or performed in parallel. In addition, the method implementation may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0029] The term "including" and its variations used herein are open inclusions, 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"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0030] It should be noted that the concepts such as "first" and "second" mentioned in the present 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.

[0031] It should be noted that the modifications of "one" and "plurality" 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, it should be understood as "one or more".

[0032] The names of the messages or information exchanged between the 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.

[0033] In order to solve the above problems, the present disclosure provides an evaluation method for a water-energy-food random system. Figure 1-Figure 4 The evaluation method of the water-energy-food stochastic system provided in the embodiment of the present disclosure is described in detail.

[0034] Figure 1 A flow chart of an evaluation method for a water-energy-food stochastic system provided by an embodiment of the present disclosure is shown.

[0035] In the disclosed embodiment, the evaluation method of the water-energy-food random system may be performed by an electronic device, wherein the electronic device may include but is not limited to devices such as computer devices, cloud servers or cloud server clusters.

[0036] like Figure 1 As shown, the evaluation method of the water-energy-food stochastic system may include the following steps.

[0037] S110. Construct a water-energy-food random system under the influence of risk, and construct a corresponding stochastic differential equation for the water-energy-food random system.

[0038] In the disclosed embodiment, the electronic device may construct a water-energy-food random system under the influence of risk, and construct a corresponding stochastic differential equation for the water-energy-food random system.

[0039] Optionally, the water-energy-food stochastic system can be used to characterize the complex water resource system in a certain region.

[0040] Optionally, the stochastic differential equation may be a differential equation containing random terms or random processes, and these random terms may include random parameters, random initial values ​​or random boundary values.

[0041] Specifically, the electronic device can construct a water-energy-food random system under the influence of risks, and construct a corresponding stochastic differential equation for the water-energy-food random system.

[0042] Figure 2 A schematic structural diagram of a water-energy-food random system provided by an embodiment of the present disclosure is shown.

[0043] like Figure 2As shown in the figure, electronic devices can construct a random water-energy-food system under the influence of risks. The water-energy-food system can be summarized into four main components: water supply system, energy system, food system and social system. The xx economic zone can be summarized as a virtual reservoir with water storage capacity, which can store water resources necessary for human production and life, and flow into agricultural irrigation and industrial power generation through the water supply system. The water supply system can provide water resources for the energy system and the food system, of which only a small part is non-consumable water, and most of the rest is consumable water. After use, it will carry pollutants and return to the ecological environment in the form of return water. The energy system mainly considers power plants, including hydropower plants and non-hydropower plants (such as thermal power plants, wind power plants and photovoltaic plants). The power generation of hydropower plants is greatly affected by flow, and the power generation of thermal power plants is greatly affected by water temperature. Hydropower plants can convert the potential energy of water into electrical energy, so they can be summarized as an aggregated power plant located in the middle of the xx economic zone. The water used by hydropower plants is non-consumable, and the upstream water can be reused by downstream water users. Part of the cooling water of thermal power plants is consumable. Common cooling methods include direct cooling, circulating cooling and air cooling. Among them, direct cooling is often used in areas with abundant water resources such as along the river. Its water intake and withdrawal process will interfere with the natural flow of water sources, emit thermal pollution, and affect aquatic populations. Due to the construction of reservoirs and the burning of fossil fuels, human activities related to power generation will have an impact on the environment. The food system mainly considers agricultural products and aquatic products. The output of agricultural products is greatly affected by flow, and the output of aquatic products is greatly affected by water temperature. Agricultural production requires irrigation water, and rivers, lakes and underground aquifers provide fresh water support. When the precipitation in the basin decreases and the drought continues to develop, the flow of the xx mainstream decreases, the water level in the two lake basins decreases, and the water storage in small reservoirs in some areas is seriously insufficient, which will cause a reduction in grain production. Fishery production cannot be separated from water. Water temperature is an important hydrological factor that affects the survival of fish. When the water temperature exceeds the critical temperature, it will cause fish metabolic disorders, and even cause death in severe cases. The social system is closely related to the water-energy-food system. Human policy adjustments such as the construction of cascade reservoirs, low-carbon transformation of thermal power, the xx fishing ban plan, and water diversion for irrigation have affected the water-energy-food nexus.

[0044] S120. Based on the stochastic differential equation, a random simulation method is used to perform random simulation on multiple state variables in the water-energy-food random system to obtain instantaneous probability and steady-state probability.

[0045] In the disclosed embodiment, the electronic device may perform random simulation on multiple state variables in the water-energy-food random system based on the random differential equation using a random simulation method to obtain instantaneous probability and steady-state probability.

[0046] Alternatively, the stochastic simulation method (Monte Carlo) may be a numerical simulation method that takes probabilistic phenomena as a research object.

[0047] Alternatively, the state variables can be used to describe the complex relationships within the water-energy-food system.

[0048] Optionally, the instantaneous probability may be a probability value used to characterize a probability density function at a specific point.

[0049] Alternatively, the steady-state probability can be the probability distribution of a random variable taking a specific value in a system over a long period of time.

[0050] Specifically, after constructing the stochastic differential equation, the electronic device can use a stochastic simulation method to perform random simulation on multiple state variables in the water-energy-food random system according to the stochastic differential equation to obtain instantaneous probability and steady-state probability.

[0051] S130. Derive a elasticity index according to the instantaneous probability and the steady-state probability, and evaluate the water-energy-food stochastic system based on the elasticity index.

[0052] In the disclosed embodiment, the electronic device may deduce a elasticity index according to the instantaneous probability and the steady-state probability, and evaluate the water-energy-food stochastic system based on the elasticity index.

[0053] Optionally, the elasticity index may be a pre-set index for evaluation.

[0054] Specifically, after obtaining the instantaneous probability and the steady-state probability, the electronic device can deduce a elasticity index according to the instantaneous probability and the steady-state probability, and evaluate the water-energy-food stochastic system based on the elasticity index.

[0055] Therefore, in the disclosed embodiment, a water-energy-food random system under the influence of risk can be constructed, and a corresponding stochastic differential equation can be constructed for the water-energy-food random system. Then, based on the stochastic differential equation, a random simulation method is used to randomly simulate multiple state variables in the water-energy-food random system to obtain instantaneous probability and steady-state probability. Then, elasticity indicators are deduced based on the instantaneous probability and the steady-state probability, and the water-energy-food random system is evaluated based on the elasticity indicator. Thus, a water-energy-food random system under the influence of risk and a corresponding stochastic differential equation are constructed. By randomly simulating multiple state variables, instantaneous probability and the steady-state probability are obtained, thereby deducing elasticity indicators. The water-energy-food random system is evaluated in combination with the elasticity indicator, thereby improving the accuracy of evaluation and analysis under the influence of risk.

[0056] Optionally, the multiple state variables introduced into the water-energy-food stochastic system to describe the complex relationships within the water-energy-food system may include water resources, per capita water consumption, per capita electricity generation, per capita food production, population and climate risks.

[0057] Optionally, multiple auxiliary variables introduced into the water-energy-food stochastic system to describe regional socio-economic conditions may include gross regional product, technological level, and total wastewater discharge.

[0058] Optionally, multiple intermediate variables introduced into the water-energy-food stochastic system to describe the connection between the water-energy-food system and socio-economic conditions may include flow, water temperature, inflow, outflow, rainfall, evapotranspiration, hydropower generation, thermal power generation, agricultural product output and aquatic product output.

[0059] Figure 3 A schematic diagram of the structure of a water-energy-food random system variable provided by an embodiment of the present disclosure is shown.

[0060] like Figure 3 As shown in Figure 2, in order to describe the complex relationship within the water-energy-food system in the study area, six state variables are introduced, such as Figure 3 The middle rectangle includes: water resources (S), which represents the amount of water resources stored in the xx economic zone; per capita water consumption (W), which represents the per capita water use intensity of the water supply system, separating the impact of total water use and population; per capita electricity generation (E), which represents the per capita power supply level of the energy system, separating the total power generation of hydropower plants and thermal power plants and the impact of population; per capita food (F), which represents the per capita food supply level of the food system, separating the total output of agricultural products and aquatic products and the impact of population; population (N), which represents the scale of human society in the region; climate risk (A), which represents the climate disasters that can be perceived by human society and may cause system collapse. The research focuses on the threats of low flow and high water temperature extreme events.

[0061] In order to describe the socio-economic conditions of the study area, three auxiliary variables are introduced, such as Figure 3 The circle in the figure includes: gross regional product (G), which represents the total benefits obtained from the energy and food systems and can be estimated by the market water price; technological level (K), which represents the ability of human beings to control and repair the natural environment, scientific and technological research and development, and technological innovation; total wastewater discharge (C), which represents pollutants discharged into the ecosystem, which will affect regional water quality and disrupt the survival of plants and animals.

[0062] In order to describe the connection between the water-energy-food system and socio-economic conditions in the study area, 10 intermediate variables were introduced, such as Figure 3The hexagons in the figure include: the flow (Q) and water temperature (T) of a station, as environmental variables, which can describe the hydrological conditions of the xx economic zone; the inflow (I), outflow (O), rainfall (P) and evapotranspiration (ET), which can describe the storage capacity of water resources in the xx economic zone; the hydropower generation (Eq), thermal power generation (Et), agricultural product output (Fq) and aquatic product output (Ft), which can describe the impact of environmental variables on the energy and food systems.

[0063] Figure 3 The feedback diagram of the water-energy-food system under the influence of risks is displayed, including positive feedback and negative feedback paths. The energy and food systems play similar roles: on the one hand, human society invests water resources in electricity production and food output, creating more resources to accumulate wealth and increasing regional GDP; the accumulation of wealth can prompt scientific and technological workers to invest more funds in education, research and other fields, thereby promoting the improvement of scientific and technological levels; the continuous improvement of technological levels, such as the increase in the proportion of wind and solar power generation and water-saving irrigation, will promote the energy and food systems to develop in a better direction, forming a positive feedback path. On the other hand, the accumulation of wealth can promote regional economic development and promote the continuous growth of population; the increase in population stimulates the level of local water resource exploitation, resulting in a lack of sufficient water resources in the energy and food systems, thereby inhibiting the development of the energy and food systems; the scale of low flow and high water temperature extreme events under the climate crisis continues to escalate, and climate risks pose a serious threat to the security of the energy and food systems, forming a negative feedback path. Similarly, it can be explained from the perspective of feedback paths. Figure 3 The changes of other variables in .

[0064] Optionally, the stochastic differential equations include a hydrological equation, an energy equation, a food equation, a water use equation, a population equation and a climate risk equation.

[0065] Optionally, S110 may specifically include: determining an ordinary differential equation of the water-energy-food stochastic system; introducing Gaussian white noise of risk impact into the ordinary differential equation to construct the stochastic differential equation.

[0066] In an embodiment of the present disclosure, the electronic device can determine the ordinary differential equation of the water-energy-food stochastic system.

[0067] Among them, the ordinary differential equation corresponding to the deterministic dynamics model can be:

[0068]

[0069] Where x is a multidimensional state variable that evolves over time t (e.g., per capita water supply, per capita electricity generation, and population). The parameter λ is a multidimensional vector that describes the deterministic changes in the system (e.g., population growth rate and technology wealth ratio are used as economic and social parameters, respectively). The function f(·) is a multidimensional matrix that describes the dynamic evolution law of the system. Here, x, λ, and f(·) are all multidimensional, that is, x = (x1, x2, …, x n ) T ∈R n ,λ∈R s and f:R n ×R s →R n , n is the number of variables, and s is the number of parameters.

[0070] Furthermore, the electronic device may introduce Gaussian white noise of risk influence into the ordinary differential equation to construct the stochastic differential equation.

[0071] Specifically, the above ordinary differential equation ignores the external interference that the system may suffer. This interference can be understood as randomness. The electronic device can introduce Gaussian white noise affected by the risk as randomness into the ordinary differential equation to construct the stochastic differential equation.

[0072] Among them, the Ito integral form of the stochastic differential equation is:

[0073] dX t =f(X t )dt+g(X t )·dW t ,X(0)=X0;0≤t≤T (2).

[0074] Where X is a multidimensional random variable that evolves over time t. Parameter r is a multidimensional vector that describes the random disturbance to the system (the impact of climate risk events). The larger r is, the greater the random disturbance is. t is Gaussian white noise, which can be understood as the formal derivative of the Wiener process.

[0075] Gaussian white noise can simply and effectively describe the random interference to the system. The function f(·) is a multidimensional matrix that describes the dynamic evolution law of the system. The function g(·) is a multidimensional matrix that describes the magnitude of the interference to the system. Similar to the deterministic dynamic equation, r, W and g(·) are all multidimensional, that is, r∈R 1 , W∈R n and g:R n ×R s →R n 1 is the number of parameters. The symbol “·” indicates that the equation is solved for X using the Ito integral form. t. X(0)=X0 is the initial condition, and [0,T] is the time interval.

[0076] It should be noted that when g(·) = c, that is, g(·) is a constant, the random term is simplified to additive noise; when g(X t ; r) = R·X t , that is, when g(·) is not a constant, the random term is represented by multiplicative noise. R is a diagonal matrix, and the elements on the diagonal satisfy the condition R ii =r i Both additive and multiplicative noise can be used to describe random processes. Generally speaking, multiplicative noise is more widely used in population dynamics, biological processes, and environmental systems, especially when the system is disturbed by the external environment. Therefore, this study selected multiplicative noise to explain sudden climate risk events.

[0077] It is worth noting that deterministic variables are generally represented by lowercase letters (e.g., x), while random variables are usually represented by uppercase letters (e.g., X). Deterministic variables are described by ordinary differential equations, usually in the form of derivatives, i.e. The random variable is described by a stochastic differential equation, which is usually presented in the form of a differential, i.e. dX t =….

[0078] The Euler-Maruyama algorithm (EM) is a numerical method for solving stochastic differential equations. It is a generalization of the Euler algorithm, which is only suitable for solving ordinary differential equations. In order to apply the EM algorithm to formula (2), we first discretize the interval Δt = T / L, where L is an integer. The recursive formula of the equation is as follows:

[0079]

[0080] Δt=Rδt (4).

[0081] Among them, the time step Δt is an integer multiple R of the step length δt of the Wiener process. i -W i-1 is the variation of the Wiener process, and the formula is as follows:

[0082]

[0083] Where, ΔW j Is of the form The study sets δt=2 -8 , R=4.

[0084] Optionally, when constructing a water-energy-food stochastic system under the influence of risks, it includes: describing the interrelationships, using stochastic differential equations to describe the uncertainties outside the system and the ties within the system; determining constitutive relationships, and estimating the socio-economic relationships, driving functions and basic parameters in the stochastic differential equations through actual data.

[0085] In some embodiments of the present disclosure, six stochastic differential equations are used to describe the interrelationships of the water-energy-food stochastic dynamic system, which respectively characterize the evolution of water resources (S), per capita electricity generation (E), per capita food (F), per capita water consumption (W), population (N) and climate risk (A).

[0086] The hydrological equation describes the dynamic balance of water resources (S) in the xx economic zone. Water resources are replenished by precipitation and inflow, most of which return to the atmosphere in the form of evapotranspiration, a small part is consumed by agriculture, industry and domestic water, and finally discharged to downstream areas and estuaries in the form of outbound flow. It can be specifically expressed as:

[0087] dS=[I+P-ET-O-WN·10 -4 ]·dt+r S S·dW s (6).

[0088] Among them, I is the inflow flow; P is precipitation; ET is soil evaporation and vegetation transpiration; O is the outflow water volume, which is linearly related to the inflow flow I according to the linear reservoir hypothesis, with a linear coefficient of 1.5; WN is the product of per capita water consumption (W) and population (N), which represents the total amount of consumable water used for residents' lives, industrial and agricultural production.

[0089] Per capita electricity generation and per capita food production are closely related to water resources. * It is a standardized value that stimulates humans to use water resources for production and life, which can be expressed as:

[0090]

[0091] Among them, S * The amount of water resources is lower than the critical value S crit The critical value of water resources is the maximum amount of water resources that stimulates humans to use water resources for production and life. When the amount of water resources is lower than the critical value, the amount of water resources is insufficient, and water use for irrigation and power generation is restricted. At this time, the increase in water resources will promote the increase in food production and power generation. When the amount of water resources is higher than the critical value, the amount of water resources is sufficient, and food production and total power generation will not be affected.

[0092] The energy equation describes the dynamic changes of per capita electricity generation (E). The increase in water resources and the improvement of scientific and technological levels have a positive effect on the growth of per capita electricity generation. The study considers hydropower generation and thermal power generation. Since hydropower generation is affected by flow and thermal power generation is affected by water temperature, per capita electricity generation will be affected by climate risks. The increase in climate risks has a negative inhibitory effect on the growth of per capita electricity generation. Among them, the energy equation can be specifically expressed as:

[0093] dE=[e S (S * )+e K (K)+e A (A * )]·dt+r e E·dW e (8).

[0094] Among them, S * is the standardized value below the critical value of water resources; K is the level of science and technology; A * is a standardized value above the climate risk threshold. When the water resources are below the threshold S crit When water resources are insufficient, water use for power generation is limited. The per capita power generation will increase with the increase of water resources. This kind of impact is represented by the driving function e S (S * ). The improvement of scientific and technological level, for example, increasing the proportion of wind and solar power generation, will promote the increase of per capita power generation. This kind of impact is represented by the driving function e K (K) indicates that with climate change, the extreme risk of low flow and high water temperature will increase. Drought will lead to reduced river flow, which is not conducive to hydropower production; high temperature will lead to higher river water temperature, reducing the power generation efficiency of thermal power plants. In general, only when the climate risk is higher than the critical value A crit The impact of climate on the energy system will only be apparent when the climate changes. This impact is driven by the driving function e A (A * )express.

[0095] The food equation describes the dynamic changes of per capita food (F). The increase in water resources and the improvement of scientific and technological levels have a positive effect on the growth of per capita food. The study considers agricultural product output and aquatic product output. Since agricultural product output is affected by flow and aquatic product output is affected by water temperature, per capita food will be affected by climate risks. The increase in climate risks has a negative inhibitory effect on the growth of per capita food. Among them, the food equation can be expressed as:

[0096] dF=[f S (S * )+f K (K)+f A (A *)]·dt+r f F·dW f (9).

[0097] Among them, S * is the standardized value below the critical value of water resources; K is the level of science and technology; A * is a standardized value above the climate risk threshold. When the water resources are below the threshold S crit When water resources are insufficient and irrigation water is restricted, the per capita grain output will increase with the increase of water resources. This kind of impact is represented by the driving function e S (S * ). The improvement of scientific and technological level, for example, increasing the proportion of water-saving irrigation, will promote the increase of per capita food production. This kind of impact is represented by the driving function e K (K) indicates that with climate change, the extreme risk of low flow and high water temperature will increase. Drought will lead to reduced river flow, which is not conducive to food production; high temperature will lead to higher river water temperature, affecting the growth and reproduction of aquatic organisms. In general, only when the climate risk is higher than the critical value A crit The impact of climate on the food system will only appear when the climate changes, and this impact is driven by the driving function e A (A * )express.

[0098] The water use equation describes the dynamic changes of per capita water consumption (W). The improvement of scientific and technological level has a negative inhibitory effect on the growth of per capita water consumption. The growth of per capita power generation and per capita grain production will increase the development and utilization of water resources, and have a positive promoting effect on the growth of per capita water consumption. Per capita water consumption can be specifically expressed as:

[0099] dW=[w K (K)+w E (E)+w F (F)]·dt+r w W·dW w (10).

[0100] Among them, K is the level of science and technology; E is the average power generation; F is the average grain production. The positive effects of average power generation and average grain production on average water consumption are respectively represented by the driving function w E (E) and w F (F) indicates that the improvement of scientific and technological level can improve the efficiency of human use of water resources and curb the growth of per capita water consumption. This kind of impact is represented by the driving function w K(K) indicates that technological progress has a wide range of impacts on water use. On the one hand, the improvement of water use efficiency and technical means such as sewage purification and treatment can reduce the consumption of water resources. On the other hand, mechanization will promote the development of industrial and agricultural activities, further increasing the consumption of water resources. In general, the increase in water use caused by the intensification of human activities is far less than the amount of water saved by sewage treatment and improved water use efficiency. Therefore, the study assumes that technological progress will significantly reduce per capita water use.

[0101] The population equation describes the dynamic changes of the population (N) in the xx economic zone. In the classic Logistic model, the total population eventually tends to approach its environmental carrying capacity. Population growth is not only determined by the natural growth rate, but also affected by economic and environmental factors. Integrating these two types of influences into the Logistic model can be specifically expressed as:

[0102]

[0103] Among them, G is the gross regional product; C is the total amount of wastewater discharged; N max is the environmental carrying capacity of the population in the xx economic zone. The regional GDP will stimulate population growth, which is determined by the driving function n G (G) represents; the total amount of wastewater discharge limits the growth of population, which is determined by the driving function n C (C) indicates.

[0104] The climate risk equation describes the dynamic changes of low flow-high water temperature risk (A), which can be expressed as:

[0105] dA=k a A·dt+r a A·dW a (7).

[0106] Among them, k a is the growth rate of climate risk. It can be calculated using formulas (8)-(12).

[0107]

[0108] Among them, Q obs and T obs are the multi-year average flow and multi-year average water temperature observed at a certain station from 20yy to 20yy; k q and k t Q is the flow change slope and water temperature change slope obtained by performing MK trend analysis on the flow and water temperature of a station under the RCP4.5 scenario; crit and T critThey are the thresholds calculated from the flow and water temperature of a certain station respectively. The threshold can be understood as the sum of the mean term and the trend term. The threshold will change over time. When the flow (or water temperature) has an upward (or downward) trend, the flow (or water temperature) threshold will also increase (or decrease) accordingly.

[0109]

[0110] Among them, A q is the risk of low flow, only when the flow Q is less than the threshold Q crit , it will be triggered only when , otherwise the risk of low flow is 0; similarly, A t is the risk of high water temperature. crit It will be triggered only when the water temperature reaches 0. q and r t are the noise intensity of flow and water temperature, respectively, which represent the uncertainty of flow and water temperature changes and can be obtained by calculating the ratio of the standard deviation and mean of flow and water temperature observed at a certain station from 20yy to 20yy.

[0111]

[0112] Among them, k a is the growth rate of climate risk; a is the climate attenuation factor. When a low flow-high water temperature extreme event occurs, the growth rate of climate risk increases with the low flow risk A q and high water temperature riskA t When there is no low flow-high water temperature extreme event, the growth rate of climate risk is a fixed negative value (i.e., ζ a ), indicating that climate risk is decreasing.

[0113] Among them, A * Is above the climate risk threshold A crit The standardized value of crit The impact of climate change on energy and food systems will only become apparent when the climate changes.

[0114] The constitutive relationship refers to the transformation relationship between the internal variables of the water-energy-food random dynamic system of the xx economic belt, which can reflect the long-term evolution characteristics of the system. The constitutive relationship determines the evolution direction of the water-energy-food random dynamic system and can be fitted and estimated through historical observation data. The study divides the constitutive relationship into two categories: (1) social-economic relationship, which describes the mutual transformation relationship between various economic and social elements in the xx economic belt; (2) driving function relationship, which characterizes the driving or inhibitory effect of economic and social elements or auxiliary variables on state variables. Finally, the basic parameters in the stochastic differential equation are estimated through actual data.

[0115] The socio-economic relationship is the transformation relationship between basic economic and social elements and state variables, or between economic and social elements, which can be reasonably estimated through measured data. The socio-economic relationship of the water-energy-food stochastic dynamic system is represented by three descriptive equations, corresponding to the dynamic changes of three auxiliary variables: regional gross domestic product (G), technological level (K), and total wastewater discharge (C).

[0116]

[0117] Among them, G * It is the standardized regional GDP; the regional GDP (G) can be expressed as a function of per capita water consumption (W) and population (N). An increase in any of these two variables will lead to an increase in the regional GDP.

[0118]

[0119] Among them, K * It is the standardized technological level; the technological level (K) is determined by the economic input invested in technological development. The larger the regional GDP (G), the higher the technological level.

[0120]

[0121] Among them, C * is the standardized total wastewater discharge; the total wastewater discharge (C) can be expressed as a function of per capita water consumption (W), population (N) and technological level (K). The total wastewater discharge is affected by two aspects: on the one hand, the food and energy systems produce pollutants, and the total wastewater discharge will increase with the increase of per capita water consumption and population size; on the other hand, the continuous improvement of scientific and technological level will significantly improve the pollutant treatment capacity and reduce the pollution level, and the total wastewater discharge will decrease with the increase of scientific and technological level.

[0122] The steps of deriving the driving function are summarized as follows: First, calculate the total growth rate TR (X i ); Secondly, according to the weight coefficient w i,j (d i,j 0 distributes the total growth rate to different driving factors d i,j The weight of the driving factor can be determined based on measured data and expert experience. i For example, if the total growth rate is TR(X i ), then the driving factor d i,j The state variable X caused by i The growth rate can be expressed as:

[0123]

[0124] Among them, Λ i,j (d i,j ) is the sign function, reflecting the driving factor d i,j For the state variable X i The promoting (or inhibiting) effect of i,j (d i,j ) is the weight function, reflecting the driving factor d i,j For the state variable X i The weight of the total growth rate. Note that the sum of the weights of all driving factors is 1; v i,j (d i,j ) is a scaling function that can convert the driving factor d i,j The range of variation is scaled to [0,1]; n is the number of driving factors. Usually, the total growth rate TR (X i ) will change over time, which invisibly increases the difficulty of modeling. Therefore, in order to simplify the model, the study uses the multi-year average of the growth rate to characterize the evolution trajectory of the water-energy-food stochastic dynamic system.

[0125] The study sets the observed data of each state variable and auxiliary variable in 20yy as the initial value of the stochastic differential equation. In order to explore the full state space of the system, the initial value of the system should be randomly distributed, so the initial value of the variable can be expressed as:

[0126] X0=X obs ·(1+r·ξ) (21).

[0127] Among them, X0 is a vector composed of the initial values ​​of each variable. obs is the actual observed value of each variable in 20yy; ξ is a randomly generated Gaussian white noise with a mean of 0 and a variance of 1; r is the scaling factor of the noise intensity.

[0128] Optionally, S130 may specifically include: calculating the Euclidean norm between the instantaneous probability and the steady-state probability; and when the Euclidean norm approaches zero, deriving the elasticity index through a steady-state probability density and a probability potential function.

[0129] In the embodiment of the present disclosure, the electronic device may calculate the Euclidean norm between the instantaneous probability and the steady-state probability.

[0130] Specifically, a discrete Wiener process is randomly generated to simulate the Gaussian white noise that affects the state variables; the EM algorithm is used to deduce the numerical solutions of the six stochastic differential equations. It should be noted that once the discrete Wiener process is given at the same time, a simulation of the system is achieved. In order to obtain reliable numerical results, this step is repeated many times, and the kernel density estimation method is used to simulate the instantaneous and steady-state probabilities; the Euclidean norm (L2 norm) between the instantaneous and steady-state probabilities is calculated. When the L2 norm tends to zero, the system is usually considered to have reached a stable state. The L2 norm of the random variable at each time cutoff is defined as follows:

[0131] ‖X‖2=(|X1| 2 +|X2| 2 +|X3| 2 +…+|X n | 2 ) 1 / 2 (28).

[0132] Where X represents a random variable, n represents the number of discrete simulated values ​​of the random variable, and ‖X‖2 represents the L2 norm of the random variable, which is obtained by taking the square root of the sum of the squares of the elements that make up the random variable.

[0133] X n =p s (x n )-p(x n ,t) (29).

[0134] Among them, X n Indicates p s (x n ) and p(x n ,t). n It should tend to zero as time goes by. s (x n ) represents the steady-state probability density, p(x n ,t) represents the instantaneous probability density.

[0135] Figure 4 A flow chart of another method for evaluating a water-energy-food random system provided by an embodiment of the present disclosure is shown.

[0136] like Figure 4As shown, the electronic device can construct a water-energy-food random system under the influence of risk, and construct a corresponding stochastic differential equation for the water-energy-food random system, that is, determine the ordinary differential equation of the water-energy-food random system; introduce the Gaussian white noise affected by the risk into the ordinary differential equation to construct the stochastic differential equation. Then, based on the stochastic differential equation, a random simulation method (Monte Carlo) is used to perform random simulation on multiple state variables in the water-energy-food random system to obtain instantaneous probability and steady-state probability. Finally, the elasticity index is derived according to the instantaneous probability and the steady-state probability, that is, the Euclidean norm (L2 norm) between the instantaneous probability and the steady-state probability is calculated; when the Euclidean norm tends to zero, the elasticity index is derived through the steady-state probability density and the probability potential function, and the water-energy-food random system is evaluated based on the elasticity index.

[0137] Figure 5 A schematic structural diagram of an evaluation device for a water-energy-food random system provided by an embodiment of the present disclosure is shown.

[0138] like Figure 5 As shown, the evaluation device 500 for the water-energy-food stochastic system may include an equation building module 510 , a simulation processing module 520 and a system evaluation module 530 .

[0139] The equation construction module 510 can be used to construct a water-energy-food stochastic system under the influence of risks, and to construct a corresponding stochastic differential equation for the water-energy-food stochastic system.

[0140] The simulation processing module 520 can be used to perform random simulation on multiple state variables in the water-energy-food random system based on the stochastic differential equation using a random simulation method to obtain instantaneous probability and steady-state probability.

[0141] The system evaluation module 530 can be used to derive a resilience index according to the instantaneous probability and the steady-state probability, and evaluate the water-energy-food stochastic system based on the resilience index.

[0142] Therefore, in the disclosed embodiment, a water-energy-food random system under the influence of risk can be constructed, and a corresponding stochastic differential equation can be constructed for the water-energy-food random system. Then, based on the stochastic differential equation, a random simulation method is used to randomly simulate multiple state variables in the water-energy-food random system to obtain instantaneous probability and steady-state probability. Then, elasticity indicators are deduced based on the instantaneous probability and the steady-state probability, and the water-energy-food random system is evaluated based on the elasticity indicator. Thus, a water-energy-food random system under the influence of risk and a corresponding stochastic differential equation are constructed. By randomly simulating multiple state variables, instantaneous probability and the steady-state probability are obtained, thereby deducing elasticity indicators. The water-energy-food random system is evaluated in combination with the elasticity indicator, thereby improving the accuracy of evaluation and analysis under the influence of risk.

[0143] In some embodiments of the present disclosure, the multiple state variables in the water-energy-food stochastic system include water resources, per capita water consumption, per capita electricity generation, per capita food production, population and climate risk.

[0144] In some embodiments of the present disclosure, the stochastic differential equations include hydrological equations, energy equations, food equations, water use equations, population equations and climate risk equations.

[0145] In some embodiments of the present disclosure, the equation construction module 510 may specifically include an equation determination unit and an equation construction unit.

[0146] The equation determination unit can be used to determine the ordinary differential equation of the water-energy-food stochastic system.

[0147] The equation construction unit can be used to introduce Gaussian white noise of risk influence into the ordinary differential equation to construct the stochastic differential equation.

[0148] In some embodiments of the present disclosure, the system evaluation module 530 may specifically include a data calculation unit and an indicator derivation unit.

[0149] The data calculation unit may be configured to calculate a Euclidean norm between the instantaneous probability and the steady-state probability.

[0150] The index derivation unit can be used to derive the elastic index through steady-state probability density and probability potential function when the Euclidean norm tends to zero.

[0151] It should be noted that Figure 5 The water-energy-food random system evaluation device 500 shown can perform Figure 1-Figure 4 The various steps in the method embodiment shown in the figure are implemented Figure 1-Figure 4The various processes and effects in the method embodiment shown are not described in detail here.

[0152] Figure 6 A schematic diagram of the structure of an evaluation device for a water-energy-food random system provided by an embodiment of the present disclosure is shown.

[0153] In some embodiments of the present disclosure, Figure 6 The evaluation device of the water-energy-food random system shown can be an electronic device. Specifically, the electronic device can include but is not limited to devices such as computer devices, cloud servers or cloud server clusters.

[0154] like Figure 6 As shown, the evaluation device of the water-energy-food random system may include a processor 601 and a memory 602 storing computer program instructions.

[0155] Specifically, the processor 601 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.

[0156] The memory 602 may include a large capacity memory for information or instructions. By way of example and not limitation, the memory 602 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive or a combination of two or more of these. Where appropriate, the memory 602 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 602 may be inside or outside the integrated gateway device. In a particular embodiment, the memory 602 is a non-volatile solid-state memory. In a particular embodiment, the memory 602 includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (Electrically Erasable Programmable ROM, EEPROM), an electrically rewritable ROM (EAROM) or a flash memory, or a combination of two or more of these.

[0157] The processor 601 reads and executes the computer program instructions stored in the memory 602 to perform the steps of the water-energy-food random system evaluation method provided in the embodiment of the present disclosure.

[0158] In one example, the evaluation device of the water-energy-food random system may further include a transceiver 603 and a bus 604. Figure 6 As shown, the processor 601, the memory 602 and the transceiver 603 are connected via a bus 604 and communicate with each other.

[0159] The bus 604 includes hardware, software, or both. For example, but 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 604 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

[0160] 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 evaluation method of the water-energy-food random system provided by the embodiments of the present disclosure.

[0161] The above-mentioned storage medium may, for example, include a memory 602 of computer program instructions, and the above-mentioned instructions may be executed by a processor 601 of the water-energy-food random system evaluation device to complete the water-energy-food random system evaluation method provided by the embodiment of the present disclosure. Optionally, the storage medium may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a ROM, a random access memory (Random Access Memory, RAM), a compact disc read-only memory (Compact Disc ROM, CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0162] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such 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 device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0163] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be 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 will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating a water-energy-food stochastic system, characterized in that: include: Constructing a water-energy-food stochastic system under the influence of risk, and constructing a corresponding stochastic differential equation for the water-energy-food stochastic system; Based on the stochastic differential equation, a stochastic simulation method is used to perform stochastic simulation on multiple state variables in the water-energy-food stochastic system to obtain instantaneous probability and steady-state probability; A elasticity index is derived according to the instantaneous probability and the steady-state probability, and the water-energy-food stochastic system is evaluated based on the elasticity index.

2. The method according to claim 1, characterized in that The multiple state variables in the water-energy-food stochastic system include water resources, per capita water consumption, per capita electricity generation, per capita food production, population and climate risk.

3. The method according to claim 1, characterized in that The stochastic differential equations include hydrological equations, energy equations, food equations, water use equations, population equations and climate risk equations.

4. The method according to claim 1, characterized in that: The corresponding stochastic differential equation is constructed for the water-energy-food stochastic system, including: determining ordinary differential equations for the water-energy-food stochastic system; The Gaussian white noise of risk influence is introduced into the ordinary differential equation to construct the stochastic differential equation.

5. The method according to claim 1, characterized in that The deducing a linear indicator according to the instantaneous probability and the steady-state probability includes: calculating the Euclidean norm between the instantaneous probability and the steady-state probability; When the Euclidean norm approaches zero, the elastic index is derived through steady-state probability density and probability potential function.

6. An evaluation device for a water-energy-food random system, characterized in that: include: An equation building module, used to build a water-energy-food stochastic system under the influence of risk, and to build a corresponding stochastic differential equation for the water-energy-food stochastic system; A simulation processing module, used for performing random simulation on multiple state variables in the water-energy-food random system based on the stochastic differential equation using a random simulation method to obtain instantaneous probability and steady-state probability; A system evaluation module is used to deduce a elasticity index according to the instantaneous probability and the steady-state probability, and to evaluate the water-energy-food stochastic system based on the elasticity index.

7. The device according to claim 6, characterized in that The equation building module includes: An equation determination unit, used for determining the ordinary differential equation of the water-energy-food stochastic system; The equation construction unit is used to introduce the Gaussian white noise of risk influence into the ordinary differential equation to construct the stochastic differential equation.

8. The device according to claim 6, characterized in that The system evaluation module includes: A data calculation unit, used for calculating the Euclidean norm between the instantaneous probability and the steady-state probability; The index derivation unit is used to derive the elastic index through the steady-state probability density and the probability potential function when the Euclidean norm tends to zero.

9. An evaluation device for a water-energy-food random system, characterized in that: include: processor; A memory for storing executable instructions; Wherein, the processor is used to read the executable instructions from the memory and execute the executable instructions to implement the evaluation method of the water-energy-food random system described in any one of claims 1-5 above.

10. 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 the processor, the processor implements the evaluation method of the water-energy-food random system described in any one of claims 1-5.

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