Regional water shortage risk and space-time imbalance assessment method, system and device and storage medium

Abstract

The embodiment of the invention discloses a regional water shortage risk and space-time imbalance evaluation method, system and device and a storage medium, and the method and system construct a multi-dimensional water shortage risk evaluation system through the combination of kernel density estimation, a Copula function and a centralized index, and remarkably improve the evaluation precision and practicability. Compared with a traditional parametric distribution method, kernel density estimation does not need to preset a data distribution form, the method can flexibly adapt to complex and diversified supply and demand sequences, high fitting precision is still kept under the condition of insufficient data or extreme distribution, experiments show that the root-mean-square error is reduced by more than 30% compared with the traditional method, and the adaptability to unknown distribution is remarkably enhanced. The joint probability distribution of the water demand and the water supply is quantified through the Copula function, and the severity level is dynamically mapped in combination with the water shortage rate and the duration, so that the limitation of single probability evaluation is solved, the time persistence is brought into risk calculation for the first time, and the evaluation result is closer to the actual water use scene.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of water conservancy engineering technology, specifically to a method, system, equipment, and storage medium for assessing regional water shortage risk and spatiotemporal imbalance. Background Technology

[0002] Water shortage risk assessment is the process of analyzing and evaluating various relevant factors to determine the degree of water shortage risk faced by a region or system.

[0003] Currently, the main methods for assessing water shortage risk, both domestically and internationally, fall into the following categories:

[0004] (1) Indicator evaluation method: Based on empirical knowledge, one or more indicators are selected to construct a risk index or evaluation system to assess the water shortage risk of a region. For example, index methods such as Falkenmark water pressure index, water scarcity index, and water shortage risk index, as well as indicator evaluation methods such as hierarchical analysis, entropy weight-G1 method, fuzzy comprehensive evaluation, matter-element model, grey relational analysis, and fuzzy clustering analysis. This type of method is simple and intuitive, flexible in construction, and easy to promote and use. However, the theoretical logic is not rigorous enough, the interaction mechanism between indicators is unclear and easy to overlap. From the selection of indicators to the determination of indicator weights and comprehensive evaluation, there is a strong subjectivity, and the evaluation results are relatively preliminary.

[0005] (2) Numerical simulation method: Water shortage risk is usually defined as probabilistic risk or potential economic loss under multivariate or multi-situation coupling. The main methods include Monte Carlo random simulation, Pearson-III type distribution fitting, Gamma distribution fitting, Logistic regression, Copula function, Bayesian network, etc. This type of method has a strong theoretical basis, rigorous logic, clear physical mechanism and systematicity. However, the threshold for constructing mathematical models and solving parameters is relatively high, and it has high requirements for the quantity and quality of observation data.

[0006] (3) Machine learning models, which learn existing knowledge or rules to make judgments on similar situations, mainly include genetic algorithms, support vector machines, random forests, artificial neural networks, etc. They require a large number of training samples and high learning costs, and also face problems such as a lack of effective data.

[0007] (4) Virtual water economics aims to use economic and systems analysis tools to quantify the economic value of water resources and the impact of virtual water flows in trade on the risk of water shortage. It mainly includes multi-regional input-output models, water footprint, structural path analysis, and water scarcity risk models. In contrast, virtual water economics is an analysis method under ideal conditions. Social factors such as war, regional disputes, trade barriers, and logistics directly affect its prediction of future scenarios. The theory and methods are still imperfect and need further exploration and research.

[0008] The above methods still have certain drawbacks:

[0009] 1) Numerical simulation-based water shortage risk assessment models cannot adapt to water shortage risk assessment under extreme conditions such as insufficient data volume and inability to determine distribution patterns, which limits the application scenarios of the models. These limitations also lead to low assessment accuracy of the models.

[0010] 2) Previous methods mainly considered the impact of water shortage amount and water shortage probability on potential economic losses, but did not consider the adverse effects of water shortage duration on different water users.

[0011] 3) The suitability of the spatiotemporal scale has not been given enough attention, and the evaluation results often fail to objectively and truthfully reflect the differences and changes in water shortage risk in time and space. Summary of the Invention

[0012] To address these issues, embodiments of the present invention provide a method, system, device, and storage medium for assessing regional water shortage risk and spatiotemporal imbalance, thereby solving the technical problems of existing technologies, such as poor adaptability when data is insufficient or distribution patterns are unknown, failure to consider the impact of water shortage duration, and inadequate assessment of spatiotemporal imbalance.

[0013] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0014] According to a first aspect of the present invention, a method for assessing regional water scarcity risk and spatiotemporal imbalance is provided, the method comprising:

[0015] S1. Obtain precipitation, historical supply and demand data, and water diversion planning data for the target area, including monthly or annual series of domestic water use, industrial water use, agricultural water use, and ecological water use.

[0016] S2. Use kernel density estimation to fit the marginal probability distributions of water demand and water supply, and improve the distribution fitting accuracy by optimizing the bandwidth parameter.

[0017] S3. Construct a joint probability distribution of water demand and water supply using the Copula function, calculate the water shortage probability P and water shortage severity S, and calculate the water shortage risk through the water shortage probability P and water shortage severity S, where the water shortage severity is a level mapping that combines the water shortage rate v and the duration t.

[0018] S4. Calculate the spatiotemporal imbalance of water shortage risk in the target area based on the centralization index, and generate a thematic map of the spatial distribution of risk mean and variance.

[0019] S5. Combine water diversion project planning data, water shortage risk data, and spatiotemporal imbalance data to predict changes in water shortage risk under different scenarios, quantify the percentage of risk mitigation, and generate assessment results.

[0020] Furthermore, obtain precipitation, historical supply and demand data, and water diversion planning data for the target area, including monthly or annual series of domestic water use, industrial water use, agricultural water use, and ecological water use, including:

[0021] Data sources include: meteorological station monitoring data, comprehensive water resources planning reports, economic statistical bulletins, and water diversion project planning schemes.

[0022] Furthermore, kernel density estimation is used to fit the marginal probability distributions of water demand and water supply, and the distribution fitting accuracy is improved by optimizing the bandwidth parameter, including:

[0023] The formula for calculating the probability density f(x) at any point x is:

[0024]

[0025] Where n is the sample size, and h is the bandwidth that determines the smoothness of the estimated curve. For kernel functions;

[0026] The Gaussian kernel density estimation function is:

[0027]

[0028] The formula for calculating the probability density f(x) at any point x is:

[0029]

[0030] Construct a risk minimization function and perform optimization iterations. When the error converges to a preset value, the optimal bandwidth h is obtained.

[0031] The expression for the risk function is:

[0032]

[0033] Where f(x) is the true probability density function, f(x) is the probability density function obtained by substituting a given value of h, and the optimal bandwidth value h is obtained when MISE(h) is minimized;

[0034] The expression for the optimal bandwidth h is:

[0035]

[0036] Where n is the number of samples. is the standard deviation of the sample, and IQR is the interquartile range.

[0037] Furthermore, a joint probability distribution of water demand and water supply is constructed using a Copula function, and the probability of water shortage P and the severity of water shortage S are calculated. The risk of water shortage R is then calculated using the probability of water shortage P and the severity of water shortage S. The severity of water shortage is determined by mapping the water shortage rate v to a level based on the duration t, including:

[0038] The formula for calculating the water shortage risk R is:

[0039] R = P × S

[0040] The expression for the joint probability of water shortage when x>y is:

[0041] P(X=x,Y=y)

[0042] Where X is the water demand function, Y is the water supply function, x is the water demand, and y is the water supply;

[0043] The expression for the water shortage rate v(x,y) is:

[0044]

[0045] The water shortage risk R(x,y) is defined by the water shortage probability level mapping function Mp and the water shortage severity level mapping function Ms:

[0046] R(x,y)=M p [P(X=x,Y=y)]·M s [v,t],x>y

[0047] The input-output mapping relationship of the mapping function is a preset mapping relationship.

[0048] Furthermore, a joint probability distribution of water demand and water supply is constructed using a Copula function to calculate the water shortage probability P and water shortage severity S. The water shortage risk R is then calculated using the water shortage probability P and water shortage severity S. The water shortage severity is determined by mapping the water shortage rate v to a level based on the duration t. This also includes:

[0049] Water shortage risk levels are divided using a two-dimensional matrix. The matrix dimensions include the water shortage probability level and the water shortage severity level, and the final risk level is the product of the two.

[0050] The joint probability of water shortage, P(X=x,Y=y), is calculated using the joint probability distribution function F(x,y):

[0051] P(X=x, Y=y)=F(x+Δx, y+Δy)-F(x-Δx, y-Δy)

[0052]

[0053] Where F(x,y) is the joint distribution function, Δx and Δy take the average step size, and size() represents the number of experimental data.

[0054] The expression for the joint probability distribution of water demand X and water supply Y, simulated using the Copula function, is as follows:

[0055]

[0056] Where C(u,v) is the Copula join function, u = F X (x) and v = F Y (y) are the marginal probability cumulative distribution functions of variables X and Y, respectively;

[0057] Divide the probability levels of water shortage into corresponding probability intervals and establish a level mapping.

[0058] Furthermore, based on the concentration index, the spatiotemporal imbalance of water shortage risk in the target area is calculated, and a thematic map of the spatial distribution of risk mean and variance is generated, including:

[0059] The concentration index is obtained by simplifying the imbalance index, and the spatiotemporal imbalance of water shortage risk in the target area is calculated by the concentration index.

[0060] The expression for the imbalance index is:

[0061]

[0062] in, For x i The average value of the data is used to sort the observed data from largest to smallest. The expression for the centralization index is:

[0063]

[0064] Among them, X i For x i The cumulative percentages are: A is the sum of the cumulative distributions of absolutely concentrated distributions, B is the sum of the cumulative distributions of absolutely uniform distributions, and R is the sum of the actual cumulative distributions.

[0065] Furthermore, based on the concentration index, the spatiotemporal imbalance of water shortage risk in the target area is calculated, and a thematic map of the spatial distribution of risk mean and variance is generated. This also includes:

[0066] The value of G ranges from [0,1]. When G = 0, it means that the distribution is absolutely uniform. The larger the value of G, the more unbalanced the distribution is.

[0067] According to a second aspect of the present invention, a regional water shortage risk and spatiotemporal imbalance assessment system is provided, the system comprising:

[0068] The data collection module is used to acquire precipitation, historical supply and demand data, and water diversion planning data for the target area, including monthly or annual series of domestic water use, industrial water use, agricultural water use, and ecological water use.

[0069] The marginal probability distribution estimation module is used to fit the marginal probability distribution of water demand and water supply using kernel density estimation, and improves the distribution fitting accuracy by optimizing the bandwidth parameter.

[0070] The water shortage risk data calculation module is used to construct the joint probability distribution of water demand and water supply using the Copula function, calculate the water shortage probability P and water shortage severity S, and calculate the water shortage risk through the water shortage probability P and water shortage severity S, where the water shortage severity is a level mapping that combines the water shortage rate v and the duration t.

[0071] The spatiotemporal imbalance calculation module is used to calculate the spatiotemporal imbalance of water shortage risk in the target area based on the centralization index, and generate a thematic map of the spatial distribution of risk mean and variance.

[0072] The assessment module is used to combine water diversion project planning data, water shortage risk data, and spatiotemporal imbalance data to predict changes in water shortage risk under different scenarios, quantify the percentage of risk mitigation, and generate assessment results.

[0073] According to a third aspect of the present invention, a device for assessing regional water scarcity risk and spatiotemporal imbalance is provided, the device comprising: a processor and a memory;

[0074] The memory is used to store one or more program instructions;

[0075] The processor is configured to run one or more program instructions to perform the steps of a method for assessing regional water scarcity risk and spatiotemporal imbalance as described in any of the preceding claims.

[0076] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, wherein when executed by a processor, the computer program implements the steps of the method for assessing regional water scarcity risk and spatiotemporal imbalance as described in any of the preceding claims.

[0077] The embodiments of the present invention have the following advantages:

[0078] This invention constructs a multi-dimensional water scarcity risk assessment system by combining kernel density estimation, Copula functions, and a centralization index, significantly improving the accuracy and practicality of the assessment. Compared to traditional parametric distribution methods, kernel density estimation does not require pre-setting the data distribution pattern, can flexibly adapt to complex and diverse supply and demand sequences, and maintains high fitting accuracy even under conditions of insufficient data or extreme distributions. Experiments show that its root mean square error is reduced by more than 30% compared to traditional methods, and its adaptability to unknown distributions is significantly enhanced. By quantifying the joint probability distribution of water demand and supply through Copula functions, and dynamically mapping the severity level by combining the water scarcity rate and duration, this not only solves the limitations of single probability assessment but also incorporates time duration into risk calculation for the first time, making the assessment results closer to actual water use scenarios. Attached Figure Description

[0079] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0080] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0081] Figure 1 A schematic diagram of the logical structure of a regional water shortage risk and spatiotemporal imbalance assessment system provided in an embodiment of the present invention;

[0082] Figure 2 A flowchart illustrating a method for assessing regional water shortage risk and spatiotemporal imbalance provided in an embodiment of the present invention;

[0083] Figure 3 A schematic diagram of the study area for a method for assessing regional water shortage risk and spatiotemporal imbalance provided in an embodiment of the present invention;

[0084] Figure 4 A schematic diagram of the annual supply and demand series of a water-receiving area in a method for assessing regional water shortage risk and spatiotemporal imbalance provided in an embodiment of the present invention;

[0085] Figure 5A schematic diagram illustrating the seasonal variation characteristics of water shortage risk in a regional water shortage risk and spatiotemporal imbalance assessment method provided in an embodiment of the present invention;

[0086] Figure 6 A schematic diagram of the 51-year average water shortage risk in a method for assessing regional water shortage risk and spatiotemporal imbalance provided in an embodiment of the present invention;

[0087] Figure 7 A schematic diagram of the 51-year variance of historical water shortage risk in a method for assessing regional water shortage risk and spatiotemporal imbalance provided in an embodiment of the present invention;

[0088] Figure 8 This is a schematic diagram illustrating the spatial distribution change of water shortage risk before and after water diversion in a method for assessing regional water shortage risk and spatiotemporal imbalance provided in an embodiment of the present invention.

[0089] Figure 9 This is a schematic diagram illustrating the change in spatial imbalance of water shortage risk before and after water diversion in a method for assessing regional water shortage risk and spatiotemporal imbalance provided in an embodiment of the present invention.

[0090] Figure 10 A schematic diagram of the KSP-Values ​​of water supply and demand sequences in a method for assessing regional water shortage risk and spatiotemporal imbalance provided in an embodiment of the present invention;

[0091] Figure 11 A schematic diagram of the RMSE of water supply and demand sequences in a method for assessing regional water shortage risk and spatiotemporal imbalance provided in an embodiment of the present invention;

[0092] Figure 12 A schematic diagram of the mean of the binary Copula rank correlation coefficient in a method for assessing regional water shortage risk and spatiotemporal imbalance provided in an embodiment of the present invention;

[0093] Figure 13 This diagram illustrates the squared Euclidean distance between the empirical joint probability and the theoretical joint probability in a method for assessing regional water shortage risk and spatiotemporal imbalance provided in an embodiment of the present invention. Detailed Implementation

[0094] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0095] To address the aforementioned technical issues of poor adaptability when data is insufficient or distribution patterns are unknown, failure to consider the impact of water shortage duration, and inadequate assessment of spatiotemporal imbalance.

[0096] refer to Figure 1 This invention discloses a regional water shortage risk and spatiotemporal imbalance assessment system, which includes: a data collection module 1; a marginal probability distribution estimation module 2; a water shortage risk data calculation module 3; a spatiotemporal imbalance calculation module 4; and an assessment module 5.

[0097] Corresponding to the aforementioned regional water scarcity risk and spatiotemporal imbalance assessment system, this invention also discloses a method for assessing regional water scarcity risk and spatiotemporal imbalance. The following details the method disclosed in this invention for assessing regional water scarcity risk and spatiotemporal imbalance, in conjunction with the aforementioned regional water scarcity risk and spatiotemporal imbalance assessment system.

[0098] refer to Figure 2 This invention discloses a method for assessing regional water shortage risk and spatiotemporal imbalance. The method includes: S1, acquiring precipitation, historical supply and demand data and water diversion planning data of the target area, including monthly or annual sequences of domestic water use, industrial water use, agricultural water use and ecological water use.

[0099] S2. Use kernel density estimation to fit the marginal probability distributions of water demand and water supply, and improve the distribution fitting accuracy by optimizing the bandwidth parameter.

[0100] S3. Construct a joint probability distribution of water demand and water supply using the Copula function, calculate the water shortage probability P and water shortage severity S, and calculate the water shortage risk through the water shortage probability P and water shortage severity S, where the water shortage severity is a level mapping that combines the water shortage rate v and the duration t.

[0101] S4. Calculate the spatiotemporal imbalance of water shortage risk in the target area based on the centralization index, and generate a thematic map of the spatial distribution of risk mean and variance.

[0102] S5. Combine water diversion project planning data, water shortage risk data, and spatiotemporal imbalance data to predict changes in water shortage risk under different scenarios, quantify the percentage of risk mitigation, and generate assessment results.

[0103] For example, the Yuxi water receiving area of ​​the Dianzhong Water Diversion Project was selected as a case study area (see...). Figure 3The experimental data includes precipitation, historical supply and demand, and data from the Dianzhong Water Diversion Project. Precipitation data were provided by five meteorological stations within the study area (Hongta, Jiangchuan, Tonghai, Huaning, and Eshan). Historical supply and demand data for the water-receiving sub-regions from 1960 to 2011 were derived from preliminary investigations and analyses of major projects such as the "Yunnan Provincial Comprehensive Water Resources Plan" and the planning and design of the second phase of the Dianzhong Water Diversion Project. Supply and demand data are compiled monthly, including categories such as domestic water use, industrial water use, agricultural water use, and ecological water use. Annual supply and demand series are as follows: Figure 4 As shown. Other hydrological and water resource data are from the "Yuxi City Water Resources Bulletin", and population and economic data are from the "Yuxi City National Economic and Social Development Statistical Bulletin". According to the planning and design of the second phase of the Dianzhong Water Diversion Project, the details of the allocation of water diverted from outside the water-receiving area and local water resources in the planned horizontal years (2030 and 2040) are shown in Table 1.

[0104] Table 1. Details of Water Transfer from Other Regions and Local Water Resource Allocation in the Planned Horizontal Year

[0105]

[0106] Furthermore, obtain precipitation, historical supply and demand data, and water diversion planning data for the target area, including monthly or annual series of domestic water use, industrial water use, agricultural water use, and ecological water use, including:

[0107] Data sources include: meteorological station monitoring data, comprehensive water resources planning reports, economic statistical bulletins, and water diversion project planning schemes.

[0108] Kernel density estimation is a nonparametric probability density estimation method that does not require prior assumptions that the data follows a specific distribution. Instead, it fits the probability distribution of discrete data based on the characteristics and properties of the data itself. It can flexibly adapt to various complex and unknown data distribution patterns and has good robustness for simulating the marginal probability distribution of water supply and demand sequences, with high fitting accuracy.

[0109] Assume discrete data points {x1, x2, ..., x} n The data originates from an unknown distribution. Further, kernel density estimation is used to fit the marginal probability distributions of water demand and supply. The accuracy of the distribution fitting is improved by optimizing the bandwidth parameter, including:

[0110] The formula for calculating the probability density f(x) at any point x is:

[0111]

[0112] Where n is the sample size, and h is the bandwidth that determines the smoothness of the estimated curve. The kernel function is used; the size of the bandwidth h directly determines the performance of the fitting result. The value of h cannot be too large or too small. If it is too large, the condition h→0 will not be satisfied. If it is too small, it means that there will be too few points in the neighborhood participating in the fitting, and the error will be large.

[0113] The Gaussian kernel density estimation function is:

[0114]

[0115] The formula for calculating the probability density f(x) at any point x is:

[0116]

[0117] Construct a risk minimization function and perform optimization iterations. When the error converges to a preset value, the optimal bandwidth h is obtained.

[0118] The expression for the risk function is:

[0119]

[0120] Where f(x) is the true probability density function, The optimal bandwidth value h is obtained when the probability density function obtained by substituting a given value of h is minimized;

[0121] The expression for the optimal bandwidth h is:

[0122]

[0123] Where n is the number of samples. The standard deviation of the sample is denoted as IQR, which is the interquartile range, the difference between the upper and lower quartiles. As another way to measure the dispersion of data, IQR is more robust when the distribution contains outliers.

[0124] Table 2. Classification of Water Shortage Probability Levels and Mapping Standards for Threshold Intervals

[0125]

[0126] Table 3. Classification of Water Shortage Severity Levels and Mapping Standards for Threshold Ranges

[0127]

[0128] Table 4 Two-dimensional matrix of water shortage risk

[0129]

[0130]

[0131] Water scarcity risk stems from the random uncertainty of water supply and demand. It is defined as a function of the probability of water scarcity and its severity. The severity of water scarcity is defined as a function of the degree and duration of scarcity, taking into account the impact of duration on severity, and is only assessed for annual water scarcity risk. The randomness of water supply and demand in a region can be quantified by simulating the probability distribution of long-term historical supply and demand data. The probability of water scarcity is the conditional probability that water supply is less than water demand. The degree of water scarcity can be represented by the scarcity rate, and the duration of scarcity is the number of consecutive months of scarcity. Based on risk theory, the water scarcity risk R is defined as the product of the probability of water scarcity P and the severity of water scarcity S.

[0132] Furthermore, a joint probability distribution of water demand and water supply is constructed using a Copula function, and the probability of water shortage P and the severity of water shortage S are calculated. The risk of water shortage R is then calculated using the probability of water shortage P and the severity of water shortage S. The severity of water shortage is determined by mapping the water shortage rate v to a level based on the duration t, including:

[0133] The formula for calculating the water shortage risk R is:

[0134] R = P × S

[0135] The expression for the joint probability of water shortage when x>y is:

[0136] P(Xx,Yy)

[0137] Where X is the water demand function, Y is the water supply function, x is the water demand, and y is the water supply;

[0138] The expression for the water shortage rate v(x,y) is:

[0139]

[0140] To more intuitively express the degree of water shortage risk, we use a mapping function to transform the quantitative description into a more easily understood and accepted qualitative description:

[0141] The water shortage risk R(x,y) is defined by the water shortage probability level mapping function Mp and the water shortage severity level mapping function Ms:

[0142] R(x,y)=M p [P(X=x,Y=y)]·M s [v,t],x>y

[0143] The input-output mapping relationship of the mapping function is shown in Table 2-4.

[0144] Furthermore, a joint probability distribution of water demand and water supply is constructed using a Copula function to calculate the water shortage probability P and water shortage severity S. The water shortage risk R is then calculated using the water shortage probability P and water shortage severity S. The water shortage severity is determined by mapping the water shortage rate v to a level based on the duration t. This also includes:

[0145] Water shortage risk levels are divided using a two-dimensional matrix. The matrix dimensions include the water shortage probability level and the water shortage severity level, and the final risk level is the product of the two.

[0146] The mapping standard between the probability of water shortage and the severity of water shortage is based on empirical knowledge. To facilitate risk decision-making and response, the probability of water shortage (Table 2) and the severity of water shortage (Table 3) are divided into five levels, and a two-dimensional matrix of water shortage risk levels based on the product of the two is constructed (Table 4). Among them, the severity level of water shortage is jointly determined by the water shortage rate and the duration of water shortage, thus incorporating the duration of water shortage as an influencing factor into the water shortage risk assessment system.

[0147] The joint probability of water shortage, P(Xx,Yy), is calculated using the joint probability distribution function F(x,y):

[0148] P(X=x,Y=y)=F(x+Δx,y+Δy)-F(x-Δx,y-Δy)

[0149]

[0150] Where F(x,y) is the joint distribution function, Δx and Δy take the average step size, and size() represents the number of experimental data.

[0151] Here, F(x,y) is the joint distribution function, which is obtained by fitting a long-term water supply and demand series. For discrete X and Y, it represents the cumulative probability that X≤x and Y≤y. The probability at X=x and Y=y can be obtained by calculating the cumulative probability of the interval [x-Δx, y-Δy]. Because the Copula function can characterize the degree of correlation and structure between random variables, it is widely used in the analysis and calculation of multivariate hydrological risks.

[0152] The expression for the joint probability distribution of water demand X and water supply Y, simulated using the Copula function, is as follows:

[0153]

[0154] Where C(u,v) is the Copula join function, u = F X (x) and v = F Y (y) are the marginal probability cumulative distribution functions of variables X and Y, respectively;

[0155] Divide the probability levels of water shortage into corresponding probability intervals and establish a level mapping.

[0156] Furthermore, based on the concentration index, the spatiotemporal imbalance of water shortage risk in the target area is calculated, and a thematic map of the spatial distribution of risk mean and variance is generated, including:

[0157] The concentration index is obtained by simplifying the imbalance index, and the spatiotemporal imbalance of water shortage risk in the target area is calculated by the concentration index.

[0158] The expression for the imbalance index is:

[0159]

[0160] in, For x i The average value of the data is used to sort the observed data from largest to smallest. The expression for the centralization index is:

[0161]

[0162] Among them, X i For x i The cumulative percentages are: A is the sum of the cumulative distributions of absolutely concentrated distributions, B is the sum of the cumulative distributions of absolutely uniform distributions, and R is the sum of the actual cumulative distributions.

[0163] Based on the monthly average water shortage risk of the entire water-receiving area and the multi-year average water shortage risk of each water-receiving sub-area, the unevenness of water shortage risk in time and space is calculated respectively.

[0164] Furthermore, based on the concentration index, the spatiotemporal imbalance of water shortage risk in the target area is calculated, and a thematic map of the spatial distribution of risk mean and variance is generated. This also includes:

[0165] The value of G ranges from [0,1]. When G = 0, it means that the distribution is absolutely uniform. The larger the value of G, the more unbalanced the distribution is.

[0166] Taking data from 1960 to 2011 as an example, historical water shortage risk was evaluated using monthly supply and demand data. Based on the calculation results, the average water shortage risk was statistically analyzed for spring (March-May), summer (June-August), autumn (September-November), and winter (December-February), and seasonal variation characteristic curves of water shortage risk were plotted. Figure 5The graph shows that: ① The risk of water shortage is generally higher in spring and winter than in summer and autumn, with spring having the highest overall risk (average 11). In the 51-year historical record, only 3 springs were in the low-risk warning zone, while the rest were in the medium-to-high-risk warning zone, making spring the most prone to water shortages; ② The risk of water shortage varies little throughout the four seasons in years with abundant water, while it varies greatly throughout the four seasons in years with dry water, with spring being particularly severe, indicating that the water supply in the water-receiving areas is heavily dependent on precipitation; ③ The risk of water shortage varies greatly from year to year, with about 80% of the years falling within the medium-risk warning zone. Overall, the risk of water shortage is increasing, and the water security situation is severe.

[0167] The mean and variance of water shortage risk for each water-receiving sub-region from 1961 to 2011 were statistically analyzed, and the spatial distribution thematic map of the mean and variance of water shortage risk was generated using the ArcGIS platform based on the inverse distance weighted interpolation method. Figure 6 This is used to assess the long-term water shortage risk status and changes in the water-receiving sub-regions. Combined with... Figure 6 and Figure 7 It can be seen that the municipal government area, Yanhe area, Dajie area, and Ningzhou area of ​​Yuxi have long been in a state of severe water shortage and belong to the high-risk water shortage area. This is related to the high population density and rapid economic development in these areas. Among them, the water shortage risk of Dajie and Ningzhou areas fluctuates greatly and they are more dependent on precipitation. Qianwei, Jiangcheng, Xiushan, and Lishan have long been in a state of moderate water shortage. Luohe, Jiuxi, Xiongguan, Panxi, and Gaoda areas have abundant water resources and their water shortage risk has long been at a relatively safe level. Among them, the variance of water shortage risk in Luohe, Jiuxi, and Xiongguan areas is very large, indicating that these areas have weak risk resistance and are easily affected by climate change. This is also related to the lack of water source projects in these areas. Only Gaoda area has strong risk resistance, long-term water supply security, and does not need to transfer water from other places to supplement the water supply.

[0168] Based on historical and planning data, the monthly water shortage risk under different scenarios was assessed. Then, based on the assessment results, the annual average water shortage risk of each sub-water-receiving area was statistically analyzed, and the percentage reduction in risk after considering the Dianzhong water diversion was calculated. The impact of the Dianzhong water diversion on the water shortage risk of each sub-water-receiving area was quantitatively analyzed. The results are shown in Table 5. In addition, using the ArcGIS platform and the inverse distance weighted interpolation method, thematic maps of the spatial distribution of water shortage risk in the study area under the baseline year, considering and not considering the Dianzhong water diversion were generated. Figure 8 This visually demonstrates the impact of the Dianzhong Water Diversion Project on alleviating water shortage risks in the receiving areas. (Combined with Table 5 and...) Figure 8It can be seen that: ① Overall, it is estimated that by the 2030 level year, the water shortage risk in the water-receiving area will decrease from the high-risk warning zone to the medium-risk zone, and by the 2040 level year, the water shortage risk in all sub-water-receiving areas will decrease to the low-risk zone, with mitigation levels of 46.67% and 70.81%, respectively; ② The water shortage risk in Luohe, Panxi, and Gaoda areas has always been at the low-risk level, therefore, the Dianzhong water diversion project did not consider supplying water to these three areas; ③ After supplementing water supply from the Dianzhong water diversion project, the water shortage risk in other areas has been significantly alleviated, especially in industrial parks and population and industrial clusters such as the municipal government, Yanhe, Dajie, Ningzhou, Xiushan, and Lishan. Without considering the Dianzhong water diversion project, the water shortage risk would enter the red warning zone by the 2030 and 2040 level years. With the Dianzhong water diversion project as a supplementary water supply, it is estimated that the water shortage risk will be basically resolved by the 2040 level year, reaching a safe level.

[0169] Table 5. Water shortage risk prediction for each sub-receiving area under the planning year, considering and not considering the Dianzhong water diversion scenario.

[0170]

[0171]

[0172] The cumulative percentage of water shortage risk predictions under different scenarios for all water-receiving sub-regions in 2019 (base year) and future planning years (2030 and 2040) was calculated after sorting the results in descending order and plotting the Lorenz curve. Then, the cumulative percentage was substituted into equation (13) to calculate the spatial imbalance coefficient G. The results are shown in […]. Figure 9 As shown, the farther the Lorenz curve is from the uniform distribution line, the greater the curvature of the curve, indicating a more uneven spatial distribution of water shortage risk. From Figure 9 It can be seen that, without considering the Dianzhong water diversion project, except for Luohe, Panxi, and Gaoda areas, other areas are in the moderate or high water shortage risk warning zone, and the spatial distribution of water shortage risk in the entire water-receiving area is somewhat uneven. After considering the Dianzhong water diversion project, by the 2030 level year, due to the limited transfer of water resources, priority will be given to supplying water to severely water-scarce areas such as Dajie, Qianwei, Xiushan, and Lishan. Although the water shortage risk in some water-receiving sub-areas will be alleviated to some extent, the spatial unevenness will still exist. It is not until the 2040 level year, after the water supply is increased, that the water shortage risk in all sub-water-receiving areas will be completely alleviated, and the spatial unevenness will also be resolved, with the unevenness coefficient reduced to 0.05.

[0173] The Yuxi water-receiving area of ​​the Dianzhong Water Diversion Project was selected as a case study area. Comparative experiments were conducted using the embodiments of this invention and traditional methods, and the impact of the Dianzhong Water Diversion Project on the water shortage risk and its spatiotemporal imbalance in the water-receiving area was quantitatively studied. First, a correlation analysis was performed between the water shortage risk from 1961 to 2011 and the corresponding annual precipitation. The results show a strong negative correlation between water shortage risk and precipitation, with a correlation coefficient of -0.8, meaning that the less precipitation, the higher the water shortage risk. This indicates that the water supply in the Yuxi water-receiving area heavily relies on natural precipitation, and the potential for water resource development and utilization is very limited, classifying it as a resource-based water shortage, which is consistent with existing research results. Then, to verify the superiority of the model in terms of robustness and evaluation accuracy, comparative experiments were designed from two aspects: robustness and fitting accuracy of univariate marginal distribution simulation, and stability and fitting accuracy of bivariate joint probability simulation.

[0174] (1) Comparison experiment on the accuracy and robustness of edge distribution simulation

[0175] Four commonly used distribution functions in hydrology (Gamma, Norm, Logistic, and Pearson 3) were selected for comparative experiments with the kernel density estimation of this invention. The robustness and accuracy of the comparative method were evaluated from the perspectives of confidence probability and fitting accuracy using the Kolmogorov-Smirnov test (KSTest) and root mean square error (RMSE).

[0176] ① Comparative analysis of the Kolmogorov-Smirnov test results

[0177] The KSP-Value calculation results of the water demand series and water supply series are plotted as curves as follows: Figure 10 As shown, P-Value represents the probability of observing the current sample data or more extreme data given that the null hypothesis is true. A larger P-Value indicates a higher probability of observation, meaning a better fit. From Figure 10 -(a) shows that for the 169 water demand sequences, only the KS test result for the 164th sequence is 0.038 (Normal fit), which is less than the 0.05 significance confidence level. The KS test results for the other sequences are all above the confidence level, indicating that the random extreme cases of water demand are few, and most are biased towards a normal distribution; from the KSP-Value calculation results of the water supply sequence ( Figure 10-b) It can be seen that, except for KDE, the KS test results for the other four distributions are all less than 0.05 confidence level, indicating that the random distribution of water supply is complex and diverse, and some sequences do not belong to any of the four known distributions. Only KDE can correctly fit its probability distribution. Therefore, it shows that KDE can achieve better performance when other methods have poor fitting results, can adapt to all extreme distribution forms, and has robustness and robustness that other methods cannot match.

[0178] The mean, variance, and null hypothesis rejection rate of the KSP-Value calculation results are statistically analyzed and shown in Table 6. The optimal values ​​are marked in bold in the table. The mean measures the overall fitting accuracy of the method; a higher mean KSP-Value indicates a higher overall fitting accuracy. As shown in the Mean statistics in Table 6, KDE has the highest overall fitting accuracy for both water supply and demand series. The variance evaluates the volatility of the KSP-Value calculation results, i.e., measures the stability of the method; a smaller variance indicates lower volatility and a more stable method. The Variance statistics in Table 6 show that KDE has the most stable fit to the distribution patterns of water supply and demand series. The null hypothesis rejection rate represents the proportion of P-Value values ​​below the significance level of 0.05, characterizing the method's simulation ability when facing various distribution patterns. As shown in Table 6, KDE has a null hypothesis rejection rate of 0, indicating that KDE can adapt to all distribution patterns of water supply and demand series and has the strongest simulation ability.

[0179] Table 6 KSP-Value Statistics

[0180]

[0181] ② Comparative analysis of RMSE evaluation results

[0182] The RMSE calculation results between the empirical probability distributions and theoretical probability distributions of the water demand series and the water supply series are as follows: Figure 11As shown, RMSE is an indicator that measures the deviation between the empirical distribution and the theoretical distribution; the smaller the RMSE, the better the fit. The mean, variance, and range of RMSE are then calculated, and the results are shown in Table 7. The optimal values ​​of each statistic are marked in bold in the table. The mean of RMSE represents the average deviation of the fit; the smaller the mean, the higher the overall fitting accuracy of the method. The variance and range of RMSE can measure the stability and robustness of the fit; the smaller the variance, the smaller the volatility, and the smaller the range, the smaller the fluctuation range. Combining 11-(a) and Table 7, it can be seen that the five methods have high fitting accuracy for the water demand series, with fitting deviations all below 0.1. Pearson3 and KDE have the smallest mean deviations, but KDE has the smallest variance and range, indicating that among the five methods, KDE has high fitting accuracy as well as the best stability and robustness. From the RMSE evaluation results 8-(b) and statistics (Table 7) of the water supply series, it can be seen that the fitting deviations of the five methods for the water demand series vary greatly. Except for KDE and Logistic, the other three methods have large fluctuations, especially Gamma, which has the largest mean deviation, variance, and range, indicating that the Gamma distribution cannot adapt to all distribution patterns of the water supply series. In contrast, KDE has the smallest mean deviation, variance, and range, indicating that KDE not only has the highest fitting accuracy for the water supply series but also the best stability and robustness, and can adapt to all extreme distribution patterns of the water supply series. In summary, KDE has the best fitting ability, and it is very robust and effective for supply and demand random variables with diverse and unknown distribution patterns. It can adapt to extreme distribution patterns and is very suitable as a marginal distribution simulation function for supply and demand sequences.

[0183] Table 7 RMSE Statistics

[0184]

[0185] (2) Comparison of accuracy and robustness of joint probabilistic simulation

[0186] The accuracy of the marginal probability fitting for univariate water supply and demand and the suitability of the Copula function jointly determine the simulation accuracy of the joint probability. The higher the simulation accuracy of the joint probability, the more accurate the water shortage risk assessment. To further verify the superiority of KDE in joint probability simulation, the marginal probability distributions of water supply and demand obtained from the previous five methods were substituted into the Archimedes' family of functions (Frank Copula, Clayton Copula, Gumbel Copula) and the Gaussian family of functions (t-Copula) to calculate their respective joint probability distributions. That is, the joint probability of water supply and demand was calculated by pairwise combination of the five univariate probability simulation methods with the five Copula functions. Then, the optimal Copula function was selected as the final simulation connection function for each method by using the mean of the bivariate Copula rank correlation coefficients with known parameters (the average of Spearman and Kendall rank correlation coefficients, abbreviated as MSK). Finally, the simulation accuracy of the model is evaluated by calculating the squared Euclidean distance (SED) between the empirical joint probability distribution and the theoretical joint probability distribution.

[0187] ① Comparative analysis of MSK

[0188] Based on the parameters of the bivariate Copula model simulating the marginal probability distribution of water supply and demand, the mean values ​​of the Spearman rank correlation coefficient and Kendall rank correlation coefficient of the bivariate Copula under given parameters were calculated using the Copulastat function in Matlab. These values ​​were used to measure the fit between the input data and the Copula function; a larger mean value indicates that the Copula function is more capable of describing the dependencies between the input data and is more suitable for simulating the joint probability distribution of the two. Therefore, the Copula function corresponding to the largest mean was selected as the final connection function for calculating the joint probability. The calculation results of the mean (MSK) of the optimal bivariate Copula rank correlation coefficient based on five methods—Gamma, Norm, Logistic, Pearson3, and KDE—as marginal distribution functions are shown below. Figure 12 As shown, the mean and variance of MSK were then calculated (results are shown in Table 8), with the optimal values ​​highlighted in bold. From Figure 12 It can be seen that the mean rank correlation coefficient of the binary Copula with KDE as the marginal distribution function is higher than that of the other four methods. In particular, KDE shows obvious superiority and robustness for some extreme supply and demand distribution patterns. As can be seen from the statistical results in Table 8, the binary Copula with KDE as the marginal distribution has the best ability to simulate joint probability distribution and shows strong robustness and robustness for extreme binary distribution patterns.

[0189] Table 8 MSK Statistics

[0190]

[0191] ② Comparative analysis of SED

[0192] First, calculate the empirical joint probability distribution value of the supply and demand marginal distributions. Then, calculate the bivariate optimal Copula joint probability distribution value using five methods as marginal distribution functions: Gamma, Norm, Logistic, Pearson3, and KDE. Finally, calculate the squared Euclidean distance (SED) between the empirical joint probability (ECDF) and the theoretical joint probability (CDF). The calculation results are as follows: Figure 13 As shown, the mean and variance statistics of SED are presented in Table 9, with the optimal values ​​highlighted in bold. From Figure 13 As shown in Table 9, the optimal binary Copula model based on KDE has small fitting deviations and low fluctuations for all supply and demand sequences. The mean and variance of the SDE are the smallest, indicating that the optimal binary Copula model based on KDE has high simulation accuracy, strong robustness and robustness.

[0193] Table 9 SED Statistics

[0194]

[0195] To improve assessment accuracy, this invention considers time scale, spatial scale, and water users simultaneously. Temporally, it studies the seasonal characteristics of water shortage risk on a monthly scale and the historical trends of water shortage risk on an annual scale. Spatially, it uses the smallest water-receiving area as the assessment unit to study the differences and spatial distribution patterns of water shortage risk under different water resource endowments and socio-economic structures in different regions. Water supply and demand are considered in light of the differences in domestic, industrial, and agricultural water demand and potential economic losses, increasing the impact of the duration of water shortage on its severity, making the assessment results closer to reality. Finally, in terms of probability distribution simulation methods, kernel density estimation (KDE) is introduced for the first time to simulate the marginal probability distribution of supply and demand variables. The advantage of KDE is that it does not require prior assumptions that the data follows a specific distribution pattern or any prior knowledge; it can fit the probability distribution of discrete data based on the characteristics and properties of the data itself. Especially when the data distribution is complex and does not closely approximate any known distribution, KDE can capture the local features and details of the data, providing more accurate and robust probability density estimates.

[0196] In addition, embodiments of the present invention also provide a regional water scarcity risk and spatiotemporal imbalance assessment device, the device comprising: a processor and a memory; the memory for storing one or more program instructions; the processor for running one or more program instructions to perform the steps of a regional water scarcity risk and spatiotemporal imbalance assessment method as described in any of the preceding embodiments.

[0197] In addition, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a method for assessing regional water scarcity risk and spatiotemporal imbalance as described in any of the preceding claims.

[0198] In this embodiment of the invention, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0199] The various methods, steps, and logic diagrams disclosed in the embodiments of this invention can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor reads information from the storage medium and, in conjunction with its hardware, completes the steps of the above methods.

[0200] The storage medium can be memory, such as volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0201] Among them, non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0202] Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM).

[0203] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0204] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this invention can be implemented using a combination of hardware and software. When applied as software, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0205] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for assessing regional water deficit risks and spatio-temporal imbalances, characterized in that, The method comprises: S1, obtaining precipitation, historical supply and demand data and water diversion planning data of a target area, including monthly or annual sequences of domestic water, industrial water, agricultural water and ecological water; S2, fitting the marginal probability distribution of water demand and supply using kernel density estimation, and improving the distribution fitting accuracy by optimizing the bandwidth parameter; S3, constructing the joint probability distribution of water demand and supply using the Copula function, calculating the water shortage probability P and the water shortage severity S, and calculating the water shortage risk R through the water shortage probability P and the water shortage severity S, wherein the water shortage severity is a grade mapping of the water shortage rate v and the duration t; S4, calculating the spatio-temporal unevenness of the water shortage risk of the target area based on the centralization index, and generating a spatial distribution thematic map of the risk mean and variance; S5, combining the water diversion project planning data, the water shortage risk data and the spatio-temporal unevenness data to predict the water shortage risk change under different scenarios, and quantifying the risk mitigation percentage, and generating an evaluation result.

2. A method for assessing regional water deficit risk and spatio- temporal imbalance as claimed in claim 1, wherein, The precipitation, historical supply and demand data and water diversion planning data of the target area are obtained, including monthly or annual sequences of domestic water, industrial water, agricultural water and ecological water, including: The data sources include: meteorological station monitoring data, water resources comprehensive planning report, economic statistical bulletin and water diversion project planning scheme.

3. The method for assessing regional water deficit risk and spatio- temporal imbalance according to claim 1, wherein, The marginal probability distribution of water demand and supply is fitted using kernel density estimation, and the distribution fitting accuracy is improved by optimizing the bandwidth parameter, including: The calculation formula of the probability density f(x) at any point x is: where n is the number of samples, h is the bandwidth that determines the degree of smoothness of the estimated curve, is the kernel function; The Gaussian kernel density estimation function is: Then the calculation formula of the probability density f(x) at any point x is: The minimum risk function is constructed and optimized iteratively, and when the error converges to the preset value, the optimal bandwidth h is obtained; The expression of the risk function is: Wherein, f(x) is the real probability density function, f(x) is the probability density function obtained by bringing in a given h value, and the optimal bandwidth value h is obtained when MISE(h) is minimum; The expression of the optimal bandwidth h is: where n is the number of samples, is the standard deviation of the samples, and IQR is the interquartile range.

4. The method for assessing regional water deficit risk and spatio- temporal imbalance according to claim 1, wherein, The joint probability distribution of water demand and supply is constructed using the Copula function, the water shortage probability P and the water shortage severity S are calculated, and the water shortage risk R is calculated through the water shortage probability P and the water shortage severity S, wherein the water shortage severity is a grade mapping of the water shortage rate v and the duration t, including: The calculation expression of the water shortage risk R is: R=P×S The expression of the water shortage joint probability when x>y is: P(X=x,Y=y) Wherein, X is the water demand function, Y is the water supply function, x is the water demand, and y is the water supply; The expression of the water shortage rate v(x,y) is: The water resource shortage risk R(x,y) is defined through the water shortage probability grade mapping function Mp and the water shortage severity grade mapping function Ms: R(x,y) = M p [P(X=x, Y=y)] · M s [v,t], x > y Wherein, the input-output mapping relationship of the mapping function is a preset mapping relationship.

5. A method of assessing regional water deficit risk and spatio- temporal imbalance as claimed in claim 4, wherein, The joint probability distribution of water demand and supply is constructed using the Copula function, the water shortage probability P and the water shortage severity S are calculated, and the water shortage risk R is calculated through the water shortage probability P and the water shortage severity S, wherein the water shortage severity is a grade mapping of the water shortage rate v and the duration t, including: The water shortage risk level is divided by a two-dimensional matrix, the matrix dimensions include a water shortage probability level and a water shortage severity level, and the final risk level is the product of the two; The water shortage joint probability P(X=x, Y=y) is calculated by a joint probability distribution function F(x, y): P(X=x, Y=y)=F(x+Δx, y+Δy)-F(x-Δx, y-Δy) Wherein, F(x, y) is a joint distribution function, Δx and Δy take an average step, and size() represents the number of experimental data; The joint probability distribution expression of the water demand X and the water supply Y is simulated by using a Copula function: where C(u, v) is a Copula linking function, u = F X (x) and v = F Y (y) are the marginal probability cumulative distribution functions of the variables X and Y, respectively. The water shortage probability level and the corresponding probability interval are divided, and a level mapping is established.

6. A method of assessing regional water deficit risk and spatio- temporal imbalance as claimed in claim 5, wherein, The spatial and temporal imbalance of the water shortage risk of the target region is calculated based on the concentration index, and a spatial distribution thematic map of the risk mean and variance is generated, including: The concentration index is obtained by simplifying the imbalance index, and the spatial and temporal imbalance of the water shortage risk of the target region is calculated based on the concentration index; The expression of the imbalance index is: wherein is x i The expression for the concentration index is: wherein X i is the cumulative percentage of x i , A is the sum of the cumulative distribution of absolute concentration, B is the sum of the cumulative distribution of absolute uniformity, and R is the sum of the actual cumulative distribution.

7. A method of assessing regional water deficit risk and spatio- temporal imbalance as claimed in claim 6, wherein, The spatial and temporal imbalance of the water shortage risk of the target region is calculated based on the concentration index, and a spatial distribution thematic map of the risk mean and variance is generated, including: The value range of G is [0, 1], when G=0, it represents that the distribution is absolutely uniform, and the greater the value of G, the more unbalanced the distribution.

8. A system for assessing regional water deficit risk and spatio-temporal imbalance, characterized in that, The system comprises: A data collection module is configured to acquire precipitation, historical supply-demand data and water diversion planning data of a target region, including monthly or annual sequences of domestic water, industrial water, agricultural water and ecological water; An edge probability distribution estimation module is configured to fit edge probability distributions of water demand and water supply by using kernel density estimation, and to improve distribution fitting accuracy by optimizing bandwidth parameters; A water shortage risk data calculation module is configured to construct joint probability distributions of water demand and water supply by using a Copula function, to calculate water shortage probability P and water shortage severity S, and to calculate water shortage risk by using water shortage probability P and water shortage severity S, wherein the water shortage severity is a level mapping combined with water shortage rate v and duration t; A spatial and temporal imbalance calculation module is configured to calculate the spatial and temporal imbalance of the water shortage risk of the target region based on the concentration index, and to generate a spatial distribution thematic map of the risk mean and variance; An evaluation module is configured to predict water shortage risk changes under different scenarios by combining water diversion project planning data, water shortage risk data and spatial and temporal imbalance data, to quantify risk mitigation percentages, and to generate evaluation results.

9. An apparatus for assessing regional water deficit risk and spatio-temporal imbalance, characterized in that, The device comprises a processor and a memory; The memory is configured to store one or more program instructions; The processor is configured to run one or more program instructions to perform the steps of the regional water shortage risk and spatial and temporal imbalance evaluation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer program stored on the computer readable storage medium is executed by the processor to implement the steps of the regional water shortage risk and spatial and temporal imbalance evaluation method according to any one of claims 1 to 7.

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