A method and system for analyzing basin drought-blue water scarcity coupling

Abstract

This application discloses a method and system for coupled analysis of drought and blue water scarcity in watersheds. The method includes: constructing a watershed grid-scale basic database; identifying drought in each grid cell in the watershed grid-scale basic database to obtain drought identification results; calculating the blue water scarcity index and multi-year average blue water scarcity index of each grid cell in the watershed grid-scale basic database; establishing a drought-blue water scarcity coupled response index system based on the drought identification results and blue water scarcity index; and outputting the spatiotemporal response results of blue water scarcity under the influence of drought events and the identification results of high-risk grid cells based on the drought-blue water scarcity coupled response index system and multi-year average blue water scarcity index. This application, based on a unified spatial unit, conducts a synergistic analysis of drought processes and blue water scarcity levels, which can directly reveal the specific impact process and degree of drought events on blue water scarcity levels, and can be widely applied in the field of watershed water resource management technology.

Description

Technical Field

[0001] This application relates to the field of watershed water resources management technology, and in particular to a watershed drought-blue water scarcity coupling analysis method and system. Background Technology

[0002] In current watershed management practices, the assessment of drought and water scarcity issues often relies on different operational modules, such as meteorological monitoring, hydrological statistics, and water use management. While these pieces of information can reflect abnormal precipitation, changes in water inflow, and water usage, in practical engineering applications, there is often a lack of a unified analytical method that directly correlates "drought events" with "changes in blue water scarcity." As a result, managers may know that precipitation is below average for a certain period and that water shortages exist in certain areas, but they struggle to quickly and quantitatively determine the extent to which these water shortages are caused by drought, and the extent to which drought amplifies the pressure on regional blue water resources. For engineering operations that require annual water resource allocation, drought assessment, water supply security assurance, and the identification of key risk areas, this information fragmentation directly impacts analytical efficiency and the effectiveness of decision-making.

[0003] Existing methods separate drought identification from blue water scarcity assessment and lack a unified coupled analysis framework. Although they can reflect the regional drought situation or water resource tension, they are difficult to further quantitatively reveal the magnitude, direction and spatial differentiation characteristics of changes in blue water scarcity after a drought event. In particular, it is difficult to identify which areas within the watershed are more likely to experience a significant increase in the contradiction between blue water supply and demand under the influence of drought. Summary of the Invention

[0004] The main objective of this application is to propose a watershed drought-blue water scarcity coupling analysis method and system that enables drought process identification, blue water scarcity calculation, and correlation analysis between the two.

[0005] To achieve the above objectives, one aspect of this application proposes a watershed drought-blue water scarcity coupling analysis method, comprising the following steps: Obtain basic grid-scale data for the target watershed and construct a basic grid-scale database for the watershed. Drought identification is performed on each grid cell in the watershed grid-scale basic database to obtain drought identification results; Calculate the blue water scarcity index and the multi-year average blue water scarcity index for each grid cell in the watershed grid-scale basic database; Based on the drought identification results and the blue water scarcity index, a drought-blue water scarcity coupled response index system is established. Based on the drought-blue water scarcity coupled response index system and the multi-year average blue water scarcity index, the spatiotemporal response results of blue water scarcity under the influence of drought events and the identification results of high-risk grid cells are output.

[0006] In some embodiments, obtaining grid-scale basic data of the target watershed and constructing a watershed grid-scale basic database specifically includes: Obtain the basic data at the grid scale; Based on the target watershed boundary, the basic data of the grid scale is spatially clipped and the time scale is unified to obtain grid cell data; Data cleaning is performed on the grid cell data to obtain the watershed grid-scale basic database containing precipitation, grid number, runoff, latitude and longitude, water consumption, and ecological water retention. The grid-scale basic data includes human water use data, precipitation data, ecological water demand-related data, runoff data, grid latitude and longitude information, and watershed boundaries.

[0007] In some embodiments, the step of performing drought identification on each grid cell in the watershed grid-scale basic database to obtain drought identification results specifically includes: Calculate the cumulative precipitation sequence of each grid cell over the time scale; The cumulative precipitation sequence was fitted using a Gamma distribution to obtain the probability density function; The cumulative probability distribution function is calculated based on the probability density function. The corrected cumulative probability is calculated based on the cumulative probability distribution function and the probability of zero precipitation. The modified cumulative probability is converted into a standard normal distribution variable to obtain the standardized precipitation index; Based on the standardized precipitation index, the drought identification result corresponding to each grid cell is extracted.

[0008] In some embodiments, the drought identification results include drought occurrence frequency, drought duration, and drought intensity. Extracting the drought identification results corresponding to each grid cell based on the standardized precipitation index specifically includes: The drought status of each grid cell is determined based on the standardized precipitation index. Based on the drought status, the number of time steps in which drought occurs in each of the grid cells during the study period is determined, and then the drought occurrence frequency is calculated based on the number of time steps and the total number of time steps. Set a drought state threshold, calculate the length of time during which the standardized precipitation index of each grid cell is continuously less than or equal to the drought state threshold, and obtain the drought duration; The drought intensity is obtained by summing the absolute values ​​of the standardized precipitation index over the duration of the drought.

[0009] In some embodiments, calculating the blue water scarcity index and the multi-year average blue water scarcity index of each grid cell in the watershed grid-scale basic database specifically includes: Based on the blue water resource supply and ecological water retention, the available blue water resource volume of each grid unit is calculated. If the amount of available blue water resources is less than zero, the amount of available blue water resources is set to zero, or the grid cell corresponding to the amount of available blue water resources is marked as an extremely water-scarce cell. If the available blue water resources are greater than zero, the blue water scarcity index of each grid cell is calculated based on human blue water consumption and the available blue water resources. The blue water scarcity index is averaged over time during the study period to obtain the multi-year average blue water scarcity index for each grid cell.

[0010] In some embodiments, the drought identification results include drought occurrence frequency and drought intensity. Based on the drought identification results and the blue water scarcity index, a drought-blue water scarcity coupled response index system is established, specifically including: Based on the drought identification results, each time step within the study period is divided into drought period and non-drought period; Based on the blue water scarcity index, calculate the first average blue water scarcity index of each grid cell during the drought period; Based on the blue water scarcity index, calculate the second average blue water scarcity index of each grid cell during the non-drought period; Based on the first average blue water scarcity index and the second average blue water scarcity index, calculate the absolute increment of the blue water scarcity response and the relative increase of the blue water scarcity response; The drought-blue water scarcity coupling strength is obtained by multiplying the drought intensity by the absolute increment of the blue water scarcity response, or by weighted summing the drought intensity, the drought occurrence frequency, and the relative increase of the blue water scarcity response.

[0011] In some embodiments, the spatiotemporal response results include a drought event time series map, a blue water scarcity difference map, a drought occurrence frequency spatial distribution map, a blue water scarcity classification map, and a coupled response intensity zoning map. The step of outputting the spatiotemporal response results of blue water scarcity under the influence of drought events and the high-risk grid cell identification results based on the drought-blue water scarcity coupled response index system and the multi-year average blue water scarcity index specifically includes: Based on the drought identification results, the drought event time series diagram is obtained; Based on the first average blue water scarcity index and the second average blue water scarcity index, a blue water scarcity difference map between the drought period and the non-drought period is obtained. Spatial mapping is performed on the drought occurrence frequency, the multi-year average blue water scarcity index, the absolute increment of the blue water scarcity response, the relative increase of the blue water scarcity response, and the drought-blue water scarcity coupling strength of all grid cells to obtain the spatial distribution map of the drought occurrence frequency, the blue water scarcity classification map, and the coupling response strength partition map of the target watershed. High-risk grid cells are determined based on the drought-blue water scarcity coupling strength, and the identification results of the high-risk grid cells are obtained.

[0012] To achieve the above objectives, another aspect of this application proposes a watershed drought-blue water scarcity coupling analysis system, comprising: The database construction module is used to obtain basic grid-scale data of the target watershed and construct a basic grid-scale database for the watershed. The drought identification module is used to identify drought in each grid cell of the watershed grid-scale basic database and obtain drought identification results. The blue water scarcity calculation module is used to calculate the blue water scarcity index and the multi-year average blue water scarcity index of each grid cell in the watershed grid-scale basic database. The drought-blue water scarcity coupling analysis module is used to establish a drought-blue water scarcity coupling response index system based on the drought identification results and the blue water scarcity index. The spatial heterogeneity identification and risk assessment module is used to output the spatiotemporal response results of blue water scarcity under the influence of drought events and the identification results of high-risk grid cells, based on the drought-blue water scarcity coupled response index system and the multi-year average blue water scarcity index.

[0013] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0014] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0015] The embodiments of this application include at least the following beneficial effects: The watershed drought-blue water scarcity coupling analysis method and system of this application first acquires grid-scale basic data of the target watershed and constructs a watershed grid-scale basic database; then, drought identification is performed on each grid unit in the watershed grid-scale basic database to obtain drought identification results; then, the blue water scarcity index and multi-year average blue water scarcity index of each grid unit in the watershed grid-scale basic database are calculated; furthermore, based on the drought identification results and blue water scarcity index, a drought-blue water scarcity coupling response index system is established; finally, based on the drought-blue water scarcity coupling response index system and multi-year average blue water scarcity index, the spatiotemporal response results of blue water scarcity under the influence of drought events and the identification results of high-risk grid units are output. This application, based on a unified spatial unit, performs a collaborative analysis of drought processes and blue water scarcity levels, quantitatively identifies the magnitude of changes and spatial differentiation characteristics of blue water resource pressure under drought conditions, and outputs analysis results that can directly serve engineering scheduling and management decisions. It can directly reveal the specific impact process and degree of drought events on blue water scarcity levels, thereby improving the accuracy, efficiency, and practicality of watershed water resource risk identification. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the embodiments of this application are described below. It should be understood that the drawings described below are only for the purpose of clearly illustrating some embodiments of the technical solutions in this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the steps of a watershed drought-blue water scarcity coupling analysis method provided in one embodiment of this application; Figure 2 This is a flowchart illustrating a watershed drought-blue water scarcity coupling analysis method provided in one embodiment of this application. Figure 3 This is a schematic diagram of the structure of a watershed drought-blue water scarcity coupling analysis system provided in one embodiment of this application; Figure 4 This is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0020] In engineering practice of watershed water resources management, drought processes often do not simply manifest as reduced precipitation, but rather evolve into a series of chain reactions, including decreased water inflow, reduced available water supply, intensified water intake and consumption conflicts, and increased water supply risks in localized areas. Particularly in watersheds that serve multiple functions, including urban and rural water supply, agricultural irrigation, industrial production, and ecological water replenishment, prolonged periods of low rainfall or intermittent droughts can easily lead to practical problems such as insufficient river flow, difficulties in water resource allocation, and increased pressure on water supply security in some areas. For management departments, simply knowing that a drought has occurred at a certain time is usually insufficient to support dispatching decisions. More importantly, it is crucial to further clarify: which areas experience a faster decline in usable blue water resources after a drought, which areas experience a more significant increase in water pressure, and which areas are most likely to face actual water shortage risks.

[0021] In engineering management, blue water resources are the resource type most directly related to water supply security and allocation. Whether it's surface water diversion, canal supply, reservoir regulation, or regional production and domestic water intake, all are closely related to the availability of blue water resources. Because different regions within a watershed have significant differences in inflow conditions, water use intensity, ecological constraints, and development and utilization methods, the impact of the same drought event on different regions is usually not consistent. Some regions, although experiencing less rainfall, will not experience significant water shortages in the short term due to relatively good basic water inflow conditions or relatively low water demand; other regions may be under high-intensity development and utilization, and once drought occurs, the balance between blue water supply and demand will rapidly deteriorate, leading to engineering problems such as irrigation restrictions, localized water shortages, and even difficulties in ensuring ecological water needs. Therefore, in practical applications, management departments pay more attention to the changing process of blue water scarcity under drought conditions, rather than just the meteorological drought assessment results.

[0022] In current watershed management practices, the assessment of drought and water scarcity issues often relies on different operational modules, such as meteorological monitoring, hydrological statistics, and water use management. While these pieces of information can reflect abnormal precipitation, changes in water inflow, and water usage, in practical engineering applications, there is often a lack of a unified analytical method that directly correlates "drought events" with "changes in blue water scarcity." As a result, managers may know that precipitation is below average for a certain period and that water shortages exist in certain areas, but they struggle to quickly and quantitatively determine the extent to which these water shortages are caused by drought, and the extent to which drought amplifies the pressure on regional blue water resources. For engineering operations that require annual water resource allocation, drought assessment, water supply security assurance, and the identification of key risk areas, this information fragmentation directly impacts analytical efficiency and the effectiveness of decision-making.

[0023] On the other hand, the actual management objects at the watershed scale are not a homogeneous whole, but rather consist of numerous spatial units with significantly different natural and water use conditions. Precipitation conditions, runoff formation capacity, ecological conservation requirements, agricultural irrigation needs, and urban water intake intensity can all vary significantly across different regions. While relying on overall watershed averages or administrative region statistics as the primary basis for judgment facilitates a macroscopic understanding of overall trends, it often fails to identify sudden increases in blue water scarcity in localized areas under drought conditions, easily overlooking areas truly requiring priority intervention and control. Especially against the backdrop of increasing demands for refined watershed management and regional regulation, engineering practice urgently needs an analytical method based on higher spatial resolution to accurately identify and differentiate the changes in blue water scarcity in different regions within the watershed under drought influence.

[0024] Furthermore, under the current trend of digital, refined, and visualized water resource management, engineering applications not only require accurate analysis results but also standardized, repeatable, and easily programmable analysis processes. These processes should be able to generate maps, zoning results, and risk identification outcomes to support the construction of operational platforms, daily monitoring and assessment, and management consultations. In other words, engineering practice requires not just a single drought assessment result or a single blue water scarcity result, but a comprehensive set of technical methods that seamlessly integrates basic data processing, drought identification, blue water scarcity calculation, response analysis, and risk zone identification to meet the practical operational needs of watershed water resource allocation, water supply security assessment, drought risk early warning, and key area management.

[0025] Existing watershed water resources management schemes have the following drawbacks: (1) Most of them are focused on drought identification or blue water scarcity assessment. The two are separate in terms of indicator system, data organization method and analysis process. There is a lack of technical path to unify and analyze the drought process and changes in blue water scarcity. Therefore, it is difficult to directly reveal the specific impact process and degree of drought events on the degree of blue water scarcity.

[0026] (2) Although some analyses are conducted using regional or grid-scale data, most still focus on the overall average results or the distribution results of a single indicator. They lack a detailed identification of the key process of “the magnitude of change in blue water scarcity under drought”, making it difficult to effectively depict the spatial heterogeneity of different regions within the basin under drought conditions and easily masking local high-risk areas.

[0027] (3) It usually focuses on describing the resource shortage under a certain period or multi-year average conditions. It lacks a unified and standardized quantitative method for the dynamic changes of blue water scarcity under drought disturbance. In particular, it is difficult to accurately answer questions such as whether drought leads to a significant increase in blue water scarcity, the extent of the increase, and whether the response in different regions is consistent, thus limiting the in-depth application of the results.

[0028] (4) The overall process integration is not high. It often requires multiple independent steps such as data processing, drought identification, water resource assessment and difference analysis to be completed separately. There is a lack of unified technical chain and standardized implementation method, which is not conducive to the further formation of reusable analysis tools or software systems and is difficult to meet the actual needs of refined management and engineering application of watershed water resources.

[0029] In view of this, this application proposes a watershed drought-blue water scarcity coupled analysis method. First, it acquires grid-scale basic data of the target watershed to construct a watershed grid-scale basic database. Next, it identifies drought in each grid cell in the watershed grid-scale basic database, obtaining drought identification results. Then, it calculates the blue water scarcity index and the multi-year average blue water scarcity index for each grid cell in the watershed grid-scale basic database. Furthermore, based on the drought identification results and the blue water scarcity index, it establishes a drought-blue water scarcity coupled response index system. Finally, based on the drought-blue water scarcity coupled response index system and the multi-year average blue water scarcity index, it outputs the spatiotemporal response results of blue water scarcity under the influence of drought events and the identification results of high-risk grid cells. This application, based on a unified spatial unit, performs a collaborative analysis of drought processes and blue water scarcity levels, quantitatively identifying the magnitude and spatial differentiation characteristics of blue water resource pressure under drought conditions, and outputting analytical results that can directly serve engineering scheduling and management decisions. It can directly reveal the specific impact process and degree of drought events on blue water scarcity, thereby improving the accuracy, efficiency, and practicality of watershed water resource risk identification.

[0030] Reference Figure 1 , Figure 1This is a flowchart illustrating the steps of a watershed drought-blue water scarcity coupling analysis method provided in one embodiment of this application. This application proposes a watershed drought-blue water scarcity coupling analysis method, which may include, but is not limited to, the following steps S101 to S105: Step S101: Obtain basic grid-scale data of the target watershed and construct a basic grid-scale database for the watershed; Step S102: Perform drought identification on each grid cell in the watershed grid-scale basic database to obtain drought identification results; Step S103: Calculate the blue water scarcity index and the multi-year average blue water scarcity index for each grid cell in the watershed grid-scale basic database; Step S104: Based on the drought identification results and the blue water scarcity index, establish a drought-blue water scarcity coupled response index system; Step S105: Based on the drought-blue water scarcity coupled response index system and the multi-year average blue water scarcity index, output the spatiotemporal response results of blue water scarcity under the influence of drought events and the identification results of high-risk grid cells.

[0031] Specifically, the watershed drought-blue water scarcity coupling analysis method proposed in this application is applicable to any watershed region with gridded precipitation, runoff, and human water use data. Its flowchart is shown below. Figure 2 As shown, this method uses grid cells as the basic analysis object. Under a unified spatial and temporal scale, it couples drought event identification, blue water resource availability calculation, blue water scarcity assessment, and blue water scarcity response analysis under drought conditions to achieve a refined quantitative characterization of water resource pressure changes in different regions within the watershed. Compared to traditional analysis methods based on administrative regions or overall watershed averages, this embodiment can identify the differentiated changes in blue water scarcity in different grid cells within the watershed under drought influence, revealing the amplifying effect of drought events on the watershed's water resource supply and demand imbalance. Steps S101 to S105 are sequentially linked to form a complete watershed drought-blue water scarcity coupled analysis process. This process is characterized by strong operability, high repeatability, and low dependence on complex numerical models.

[0032] It should be noted that the embodiments of this application integrate drought identification, blue water scarcity assessment, and blue water scarcity response analysis under drought conditions into the same technical process, achieving collaborative analysis under a unified spatial unit, unified time scale, and unified data structure. This framework solves the problem of separation between drought analysis and water resource pressure assessment in traditional methods, enabling a direct correspondence between drought processes and changes in blue water scarcity, thereby laying the foundation for subsequent response quantification and spatial identification.

[0033] As an optional implementation, step S101 can be further divided into the following steps S1011 to S1013: Step S1011: Obtain basic grid-scale data; Step S1012: Spatial clipping and temporal scale unification of the grid-scale basic data based on the target watershed boundary to obtain grid cell data; Step S1013: Clean the grid cell data to obtain a watershed grid-scale basic database containing precipitation, grid number, runoff, latitude and longitude, water consumption and ecological water retention. The grid-scale basic data includes human water use data, precipitation data, ecological water demand-related data, runoff data, grid latitude and longitude information, and watershed boundaries.

[0034] In some optional embodiments, this application first acquires grid-scale basic data of the target watershed and establishes a unified analysis database. This grid-scale basic data includes at least grid-scale precipitation data, runoff data, human water use data, ecological water demand-related data, and watershed boundary and grid latitude and longitude information. Next, the original grid-scale basic data is preprocessed. First, spatial clipping is performed based on the target watershed boundary. Then, data from different variables are unified to the same time scale, preferably a monthly scale. Afterward, quality checks are performed on the data of each grid unit, removing, interpolating, or marking missing, outlier, and invalid values. This results in a standardized database containing grid numbers, latitude and longitude, precipitation, runoff, water use, ecological water retention, and subsequent derived indicators—the watershed grid-scale basic database.

[0035] It should be noted that the embodiments of this application perform unified preprocessing on multi-source heterogeneous data such as precipitation, runoff, human water consumption, ecological water retention, and watershed boundaries. This includes spatial pruning, temporal scale unification, missing measurement value processing, outlier identification, and structured database construction. Through this step, data from different sources, with different attributes, and at different resolutions can be uniformly converted into standardized grid data that can be directly used in coupled analysis, improving the consistency, comparability, and repeatability of subsequent calculation results.

[0036] As an optional implementation, step S102 can be further divided into the following steps S1021 to S1026: Step S1021: Calculate the cumulative precipitation sequence of each grid cell over the time scale; Step S1022: Fit the cumulative precipitation sequence using the Gamma distribution to obtain the probability density function; Step S1023: Calculate the cumulative probability distribution function based on the probability density function; Step S1024: Calculate the corrected cumulative probability based on the cumulative probability distribution function and the probability of zero precipitation; Step S1025: Convert the corrected cumulative probability into a standard normal distribution variable to obtain the standardized precipitation index; Step S1026: Extract the drought identification results corresponding to each grid unit based on the standardized precipitation index.

[0037] Specifically, after completing the construction of the basic database, this embodiment of the application performs drought identification on each grid cell. In this embodiment, a standardized precipitation index is used. SPI As a drought identification indicator. For any grid cell i In time scale k The cumulative precipitation sequence is first calculated using the following formula for the grid over a continuous period of time. k Cumulative precipitation within one month : ; in, For grid cells i At any moment t Rainfall, For the corresponding k The cumulative precipitation over the month was then fitted to the cumulative precipitation series. Gamma Distribution, its probability density function It can be represented as: ; in, α For shape parameters, β For scale parameters, for Gamma The function. Then, based on the probability density function... The obtained cumulative probability distribution function Calculate the non-transcendental probability for a given precipitation amount, and consider the zero precipitation probability. The corrected cumulative probability is obtained. : ; Then adjust the cumulative probability Converting to a standard normal distribution variable yields the standardized precipitation index. : ; in, It is the inverse function of the standard normal distribution.

[0038] As an optional implementation, the drought identification results include drought occurrence frequency, drought duration, and drought intensity. Step S1026 can be further divided into the following steps S10261 to S10264: Step S10261: Determine the drought status of each grid unit based on the standardized precipitation index; Step S10262: Determine the number of time steps in which drought occurs in each grid cell during the study period based on the drought status, and then calculate the drought occurrence frequency based on the number of time steps and the total number of time steps; Step S10263: Set the drought state threshold, calculate the length of time that the standardized precipitation index of each grid cell is continuously less than or equal to the drought state threshold, and obtain the drought duration. Step S10264: Sum the absolute values ​​of the standardized precipitation index over the duration of the drought to obtain the drought intensity.

[0039] Specifically, according to the standardized precipitation index SPI Determine the drought status. For example, when... This indicates a dry condition. It can be determined to be a mild drought at this time. It can be determined to be a moderate drought at this time. The drought situation can be classified as severe or extreme drought. This is determined by calculating the standardized precipitation index for all grid cells at each time step. SPI This allows for the extraction of drought occurrence frequency, duration, and intensity for each grid cell. The drought occurrence frequency is... It can be represented as: ; in, For grid cells i The number of time steps during which drought occurred within the study period. N This represents the total number of time steps. The duration of a drought can be represented as a continuous set of conditions. The length of time, of which The preset drought state threshold; drought intensity This can be expressed as the duration of the period. SPI Cumulative sum of absolute values: ; in, For grid cells i No. j The intensity of the drought event, and These represent the start and end times of the drought event, respectively.

[0040] As an optional implementation, step S103 can be further divided into the following steps S1031 to S1034: Step S1031: Calculate the amount of usable blue water resources in each grid unit based on the blue water resource supply and ecological water retention. Step S1032: If the amount of usable blue water resources is less than zero, set the amount of usable blue water resources to zero, or mark the grid cell corresponding to the amount of usable blue water resources as an extremely water-scarce cell. Step S1033: If the amount of usable blue water resources is greater than zero, calculate the blue water scarcity index of each grid cell based on human blue water consumption and the amount of usable blue water resources. Specifically, after drought identification, this embodiment further constructs a blue water scarcity index to characterize the degree of supply and demand imbalance of blue water resources in each grid cell during the target period. For any grid cell... i and time t First, calculate the amount of usable blue water resources. In some alternative embodiments, the amount of blue water resources can be obtained by subtracting the ecological water demand from the natural runoff, i.e.: ; in, For grid cells i At any moment t The supply of blue water resources can be determined by selecting the runoff flow rate; This refers to the ecological water demand or ecological water retention at the corresponding time. If , then let Alternatively, the grid could be directly marked as an extremely water-scarce unit to avoid meaningless negative available water volumes. Subsequently, human blue water consumption... With available blue water resources The ratio is defined as the blue water scarcity index. : ; in, ε This is a very small positive number, used to avoid numerical overflow when the denominator is zero. If numerical smoothing is not required, it can also be used... Directly The value is assigned to the preset upper limit.

[0041] It should be noted that the embodiments of this application, in assessing blue water scarcity, not only consider the supply of blue water resources formed by natural runoff, but also use ecologically reserved water volume as a constraint on the amount of usable blue water resources, and combine it with human consumption of blue water to calculate the supply and demand relationship. This technical processing enables the obtained blue water scarcity results to more realistically reflect the water resource tension of the basin under actual engineering conditions, rather than merely remaining at the level of analysis on natural water volume changes.

[0042] Step S1034: Average the blue water scarcity index over the study period to obtain the multi-year average blue water scarcity index for each grid cell.

[0043] Furthermore, the scarcity of blue water during the study period can be averaged over time to obtain the multi-year average blue water scarcity index for each grid cell. : ; in, T This represents the total number of time periods in the study. For ease of tiered evaluation, it can be based on... or The size of the blue water is used to classify the scarcity level into levels such as no scarcity, slight scarcity, moderate scarcity, severe scarcity, and extreme scarcity.

[0044] As an optional implementation, the drought identification results include drought occurrence frequency and drought intensity. Step S104 can be further divided into the following steps S1041 to S1045: Step S1041: Based on the drought identification results, divide each time step within the study period into drought period and non-drought period; Step S1042: Calculate the first average blue water scarcity index of each grid cell during the drought period based on the blue water scarcity index. Step S1043: Calculate the second average blue water scarcity index for each grid cell during the non-drought period based on the blue water scarcity index. Step S1044: Calculate the absolute increment of blue water scarcity response and the relative increase of blue water scarcity response based on the first average blue water scarcity index and the second average blue water scarcity index. Step S1045: Calculate the product of drought intensity and absolute increment of blue water scarcity response to obtain drought-blue water scarcity coupling strength, or, perform a weighted summation of drought intensity, drought occurrence frequency and relative increase of blue water scarcity response to obtain drought-blue water scarcity coupling strength.

[0045] Specifically, after obtaining drought identification results and the blue water scarcity index, this application establishes a drought-blue water scarcity coupled response index system to quantify the impact of drought events on the degree of blue water scarcity. First, based on the aforementioned drought identification results, each time step within the study period is divided into drought and non-drought periods, or further divided into multiple levels such as mild drought, moderate drought, severe drought, and extreme drought. For any grid cell... i Calculate the average blue water scarcity index during dry and non-dry periods respectively: ; ; in, This represents the first average blue water scarcity index during a drought period. This represents the second average blue water scarcity index during non-drought periods. D Represents the set of drought periods. NThis represents the set during the non-drought period. and These represent the number of time steps within the corresponding set. Then, the absolute increment of the blue water scarce response is constructed. : ; And the relative increase in response to the scarcity of blue water : ; in, Used to characterize the absolute degree to which drought intensifies the scarcity of blue water. Used to characterize the relative amplification effect. If or This indicates that the scarcity of blue water is exacerbated in this grid cell under arid conditions; the larger the value, the stronger the coupling response.

[0046] To more comprehensively characterize the coupling relationship between drought and blue water scarcity, embodiments of this application can further construct a coupling strength index. For any grid cell i, the drought-blue water scarcity coupling strength can be defined based on the product of drought intensity and the blue water scarcity response increment or a normalized weighted sum. ,For example: ; or: ; in, and These represent the normalized drought intensity, absolute increase in blue water scarcity, drought frequency, and relative increase in blue water scarcity, respectively. Let be the weight coefficient, and satisfy... .

[0047] It should be noted that, in this embodiment of the application, by dividing the drought period into drought and non-drought periods, or periods of different drought levels, the absolute increase, relative increase, and coupling response intensity of blue water scarcity are further calculated, thereby quantitatively characterizing the amplification effect of drought events on blue water scarcity. This step enables this embodiment of the application not only to determine whether drought or scarcity exists, but also to directly reflect the degree and magnitude of the interaction between the two.

[0048] As an optional implementation, the spatiotemporal response results include a drought event time series map, a blue water scarcity difference map, a drought occurrence frequency spatial distribution map, a blue water scarcity classification map, and a coupled response intensity partitioning map. Step S105 can be further divided into the following steps S1051 to S1054: Step S1051: Based on the drought identification results, obtain the drought event time series diagram; Step S1052: Based on the first average blue water scarcity index and the second average blue water scarcity index, obtain the blue water scarcity difference map between the drought period and the non-drought period. Step S1053: Spatial mapping is performed on the drought occurrence frequency, multi-year average blue water scarcity index, absolute increment of blue water scarcity response, relative increase of blue water scarcity response, and drought-blue water scarcity coupling strength of all grid cells to obtain the spatial distribution map of drought occurrence frequency, blue water scarcity classification map, and coupling response strength partition map of the target watershed. Step S1054: Determine high-risk grid cells based on the drought-blue water scarcity coupling strength to obtain the high-risk grid cell identification results.

[0049] Specifically, by calculating the drought-blue water scarcity coupling strength It can identify high-risk grid cells that simultaneously have high drought exposure, strong drought intensity, and large blue water scarcity response, thereby enabling the identification of drought-blue water scarcity coupled hotspots.

[0050] Furthermore, embodiments of this application can perform spatial heterogeneity analysis on the coupling response results. Specifically, this involves performing spatial heterogeneity analysis on all grid cells. and Spatial mapping was performed to generate drought frequency distribution maps, blue water scarcity classification maps, and coupled response intensity zoning maps. Simultaneously, regional statistics were conducted on each indicator, calculating the area proportion, mean, median, and quantile characteristics of high-value areas to identify response differences among different regions within the watershed under drought conditions. If further analysis of the statistical relationship between drought and blue water scarcity is needed, the correlation coefficient between the two can be calculated, such as the Pearson correlation coefficient. ; in, This is used to measure the linear correlation between drought level and blue water scarcity in a given grid cell. Because SPI The smaller the value, the drier the product; therefore, it is generally expected to be... SPI and BWS The correlation is negative; the stronger the correlation, the more sensitive blue water scarcity is to drought disturbances.

[0051] After completing the above calculations, the output includes a drought event time series map, a drought frequency spatial distribution map, a blue water scarcity classification map, a blue water scarcity difference map between drought and non-drought periods, a coupled response intensity zoning map, and high-risk grid cell identification results. These can be further integrated into a drought-blue water scarcity grid analysis and visualization tool to achieve... SPI It includes functions such as calculation, blue water scarcity index calculation, comparative analysis of drought and non-drought periods, and result visualization.

[0052] It should be noted that this embodiment uses grid cells as a basis to perform spatial overlay analysis on drought frequency, blue water scarcity, and response intensity, identifying key areas within the watershed where the supply and demand imbalance of blue water is significantly exacerbated by drought. This technical feature effectively avoids the problem of overall average analysis masking local high-risk areas, making it more suitable for serving engineering needs such as zonal regulation, key area screening, and refined management.

[0053] The above describes the watershed drought-blue water scarcity coupling analysis method according to embodiments of this application. It can be recognized that embodiments of this application have the following advantages: First, this method organically integrates drought process identification, blue water scarcity assessment, and blue water scarcity response analysis under drought conditions into a single technical framework. It is not a simple patchwork of existing drought analysis and water resource assessment methods, but rather establishes a coupled analysis method oriented towards the watershed grid scale. This method can reveal the degree of impact and spatial differentiation characteristics of drought on blue water scarcity under a unified spatial unit, unified time scale, and unified data system, overcoming the limitation of existing technologies where drought identification and blue water scarcity assessment are independent.

[0054] Second, it can not only identify whether a region is experiencing drought, but also further quantitatively analyze whether the scarcity of blue water intensifies after a drought occurs, the extent of the intensification, and which areas are most significantly affected, thus revealing the formation process of watershed water resource risks more realistically. This technical solution can provide more direct and effective technical support for watershed water resource security assessment, water supply risk identification, drought-vulnerable area determination, and differentiated management, and has high theoretical and engineering application value.

[0055] Third, the data sources used are clearly defined, the calculation process is clear, and the indicator system is mature. Analysis can be directly conducted using existing gridded precipitation, runoff, human water use, and ecological water retention data, without relying on complex and difficult-to-implement specialized models or high-cost equipment, thus possessing strong operability and feasibility. After being implemented programmatically, it can quickly output drought identification results, blue water scarcity distribution results, and coupled response zoning results, effectively improving the efficiency of watershed management departments in conducting drought impact assessments and water resource risk assessments.

[0056] Fourth, a complete technical chain has been formed, from basic data preprocessing, drought event identification, and construction of the blue water scarcity index, to the analysis of differences between drought and non-drought periods, response intensity calculation, and high-risk area identification. Compared with the problems of scattered and poorly connected analysis links in existing technologies, this application has stronger process uniformity, analytical continuity, and result completeness. It can be used for single assessments or further expanded into a continuous monitoring and dynamic analysis system.

[0057] Fifth, by using grid cells as the basic analysis object, this technique can identify the spatial heterogeneity of blue water scarcity changes in different regions within the watershed under drought conditions, avoiding the problem of traditional overall average analysis masking local high-risk areas. This technical feature is more suitable for serving engineering needs such as regional regulation, key area screening, and refined management, and can provide more targeted basis for watershed water resource allocation and risk prevention and control measures.

[0058] VI. The process is highly standardized, the input data is clearly defined, and the output results are intuitive. It is easy to further develop into analysis software, visualization platforms, or business application tools. It is convenient to promote and apply in scenarios such as watershed water resources management, drought early warning, water supply security assessment, and regional risk investigation. It has good application prospects and promotion value.

[0059] Reference Figure 3 This application also provides a watershed drought-blue water scarcity coupling analysis system, including: The database construction module is used to obtain basic grid-scale data of the target watershed and construct a basic grid-scale database for the watershed. The drought identification module is used to identify drought in each grid cell of the watershed grid-scale basic database and obtain drought identification results; The Blue Water Scarcity Calculation Module is used to calculate the blue water scarcity index and the multi-year average blue water scarcity index of each grid cell in the watershed grid-scale basic database. The drought-blue water scarcity coupling analysis module is used to establish a drought-blue water scarcity coupling response index system based on drought identification results and blue water scarcity index. The spatial heterogeneity identification and risk assessment module is used to output the spatiotemporal response results of blue water scarcity under the influence of drought events and the identification results of high-risk grid cells, based on the drought-blue water scarcity coupled response index system and the multi-year average blue water scarcity index.

[0060] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0061] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0062] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0063] Please see Figure 4 , Figure 4 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 1002 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1002 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001 using the methods described in the embodiments of this application. Input / output interface 1003 is used to implement information input and output; The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004); The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0064] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0065] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0066] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0067] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0068] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0069] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0070] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0071] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0072] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0073] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0074] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0075] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0076] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0077] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0078] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0079] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A coupled analysis method for watershed drought and blue water scarcity, characterized in that, Includes the following steps: Obtain basic grid-scale data for the target watershed and construct a basic grid-scale database for the watershed. Drought identification is performed on each grid cell in the watershed grid-scale basic database to obtain drought identification results; Calculate the blue water scarcity index and the multi-year average blue water scarcity index for each grid cell in the watershed grid-scale basic database; Based on the drought identification results and the blue water scarcity index, a drought-blue water scarcity coupled response index system is established. Based on the drought-blue water scarcity coupled response index system and the multi-year average blue water scarcity index, the spatiotemporal response results of blue water scarcity under the influence of drought events and the identification results of high-risk grid cells are output.

2. The method according to claim 1, characterized in that, The acquisition of grid-scale basic data for the target watershed and the construction of a watershed grid-scale basic database specifically include: Obtain the basic data at the grid scale; Based on the target watershed boundary, the basic data of the grid scale is spatially clipped and the time scale is unified to obtain grid cell data; Data cleaning is performed on the grid cell data to obtain the watershed grid-scale basic database containing precipitation, grid number, runoff, latitude and longitude, water consumption, and ecological water retention. The grid-scale basic data includes human water use data, precipitation data, ecological water demand-related data, runoff data, grid latitude and longitude information, and watershed boundaries.

3. The method according to claim 1, characterized in that, The process of identifying drought in each grid cell of the watershed grid-scale basic database to obtain drought identification results specifically includes: Calculate the cumulative precipitation sequence of each grid cell over the time scale; The cumulative precipitation sequence was fitted using a Gamma distribution to obtain the probability density function; The cumulative probability distribution function is calculated based on the probability density function. The corrected cumulative probability is calculated based on the cumulative probability distribution function and the probability of zero precipitation. The modified cumulative probability is converted into a standard normal distribution variable to obtain the standardized precipitation index; Based on the standardized precipitation index, the drought identification result corresponding to each grid cell is extracted.

4. The method according to claim 3, characterized in that, The drought identification results include drought occurrence frequency, drought duration, and drought intensity. The extraction of the drought identification results corresponding to each grid cell based on the standardized precipitation index specifically includes: The drought status of each grid cell is determined based on the standardized precipitation index. Based on the drought status, the number of time steps in which drought occurs in each of the grid cells during the study period is determined, and then the drought occurrence frequency is calculated based on the number of time steps and the total number of time steps. Set a drought state threshold, calculate the length of time during which the standardized precipitation index of each grid cell is continuously less than or equal to the drought state threshold, and obtain the drought duration; The drought intensity is obtained by summing the absolute values ​​of the standardized precipitation index over the duration of the drought.

5. The method according to claim 1, characterized in that, The calculation of the blue water scarcity index and the multi-year average blue water scarcity index for each grid cell in the watershed grid-scale basic database specifically includes: Based on the blue water resource supply and ecological water retention, the available blue water resource volume of each grid unit is calculated. If the amount of available blue water resources is less than zero, the amount of available blue water resources is set to zero, or the grid cell corresponding to the amount of available blue water resources is marked as an extremely water-scarce cell. If the available blue water resources are greater than zero, the blue water scarcity index of each grid cell is calculated based on human blue water consumption and the available blue water resources. The blue water scarcity index is averaged over time during the study period to obtain the multi-year average blue water scarcity index for each grid cell.

6. The method according to claim 1, characterized in that, The drought identification results include drought occurrence frequency and drought intensity. Based on the drought identification results and the blue water scarcity index, a drought-blue water scarcity coupled response index system is established, specifically including: Based on the drought identification results, each time step within the study period is divided into drought period and non-drought period; Based on the blue water scarcity index, calculate the first average blue water scarcity index of each grid cell during the drought period; Based on the blue water scarcity index, calculate the second average blue water scarcity index of each grid cell during the non-drought period; Based on the first average blue water scarcity index and the second average blue water scarcity index, calculate the absolute increment of the blue water scarcity response and the relative increase of the blue water scarcity response; The drought-blue water scarcity coupling strength is obtained by multiplying the drought intensity by the absolute increment of the blue water scarcity response, or by weighted summing the drought intensity, the drought occurrence frequency, and the relative increase of the blue water scarcity response.

7. The method according to claim 6, characterized in that, The spatiotemporal response results include a drought event time series map, a blue water scarcity difference map, a drought occurrence frequency spatial distribution map, a blue water scarcity classification map, and a coupled response intensity zoning map. Based on the drought-blue water scarcity coupled response index system and the multi-year average blue water scarcity index, the spatiotemporal response results of blue water scarcity under the influence of drought events and the identification results of high-risk grid cells are output, specifically including: Based on the drought identification results, the drought event time series diagram is obtained; Based on the first average blue water scarcity index and the second average blue water scarcity index, a blue water scarcity difference map between the drought period and the non-drought period is obtained. Spatial mapping is performed on the drought occurrence frequency, the multi-year average blue water scarcity index, the absolute increment of the blue water scarcity response, the relative increase of the blue water scarcity response, and the drought-blue water scarcity coupling strength of all grid cells to obtain the spatial distribution map of the drought occurrence frequency, the blue water scarcity classification map, and the coupling response strength partition map of the target watershed. High-risk grid cells are determined based on the drought-blue water scarcity coupling strength, and the identification results of the high-risk grid cells are obtained.

8. A coupled analysis system for watershed drought and blue water scarcity, characterized in that, include: The database construction module is used to obtain basic grid-scale data of the target watershed and construct a basic grid-scale database for the watershed. The drought identification module is used to identify drought in each grid cell of the watershed grid-scale basic database and obtain drought identification results. The blue water scarcity calculation module is used to calculate the blue water scarcity index and the multi-year average blue water scarcity index of each grid cell in the watershed grid-scale basic database. The drought-blue water scarcity coupling analysis module is used to establish a drought-blue water scarcity coupling response index system based on the drought identification results and the blue water scarcity index. The spatial heterogeneity identification and risk assessment module is used to output the spatiotemporal response results of blue water scarcity under the influence of drought events and the identification results of high-risk grid cells, based on the drought-blue water scarcity coupled response index system and the multi-year average blue water scarcity index.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method of any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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