Quantitative identification method of water resource bearing capacity driving factor influence and related device

Abstract

The invention provides a quantitative identification method for water resource bearing capacity driving factor influence and a related device, and relates to the field of hydrology and ecology. The electronic equipment divides the to-be-analyzed region into a plurality of sub-regions, takes each sub-region as a target sub-region, and determines a plurality of adjacent sub-regions from the plurality of sub-regions; dividing the target sub-region and the plurality of adjacent sub-regions into a plurality of region sets according to respective water resource bearing capacity indexes; and according to the overall discrete index of the plurality of adjacent sub-regions and the target sub-region for the water resource bearing capacity and the local discrete index of the to-be-analyzed factor in each region set, obtaining the correlation degree of the to-be-analyzed factor for the water resource bearing capacity. In this way, compared with the mode that the whole research area is regarded as a statistical whole to be analyzed, the problem that geographic local differences are ignored due to the fact that global stationarity hypothesis is relied on is solved.

Description

Technical Field

[0001] This application relates to the field of hydrology and ecology, and more specifically, to a method and related apparatus for quantitatively identifying the influence of driving factors on water resource carrying capacity. Background Technology

[0002] Factors influencing water resource carrying capacity refer to natural and anthropogenic factors that can significantly alter a region's water resource support capacity. Therefore, by studying these factors, we can identify which factors dominate water resource carrying capacity in terms of spatial location and intensity, thus providing a scientific basis for the optimal allocation of water resources.

[0003] However, existing technologies mostly rely on the assumption of global stationarity, ignoring the local variability of geographical processes, which makes the analysis of influencing factors unreasonable. Summary of the Invention

[0004] To overcome at least one deficiency in the prior art, this application provides a method and related apparatus for quantitatively identifying the influence of driving factors on water resource carrying capacity. By dividing multiple regional sets based on the water resource carrying capacity index, it can more accurately identify which factors play a dominant role in water resource carrying capacity at the local scale. Compared with analyzing the entire study area as a statistical whole, it effectively solves the problem of ignoring local geographical differences due to the reliance on the assumption of global stationarity.

[0005] Firstly, this application provides a method for quantitatively identifying the influence of driving factors on water resource carrying capacity, the method comprising: The region to be analyzed is divided into multiple sub-regions, and each sub-region is used as a target sub-region. Multiple adjacent sub-regions are determined from the multiple sub-regions. The target sub-region and the multiple adjacent sub-regions are divided into multiple region sets according to their respective water resource carrying capacity indices, wherein the multiple region sets correspond one-to-one with multiple water resource carrying capacity intervals; Based on the overall discrete index of water resource carrying capacity of the multiple adjacent sub-regions and the target sub-region, and the local discrete index of the factors to be analyzed in each of the regions, the correlation degree of the factors to be analyzed with water resource carrying capacity is obtained.

[0006] Secondly, this application provides a device for quantitatively identifying the influence of driving factors on water resource carrying capacity, the device comprising: The region segmentation module is used to divide the region to be analyzed into multiple sub-regions, and to use each sub-region as a target sub-region, and to determine multiple adjacent sub-regions from the multiple sub-regions; The regional classification module is used to divide the target sub-region and the multiple adjacent sub-regions into multiple regional sets according to their respective water resource carrying capacity indices, wherein the multiple regional sets correspond one-to-one with multiple water resource carrying capacity intervals; The factor evaluation module is used to obtain the correlation degree of the factor to be analyzed with respect to water resource carrying capacity based on the overall discrete index of the multiple adjacent sub-regions and the target sub-region with respect to water resource carrying capacity, and the local discrete index of the factor to be analyzed in each of the regions.

[0007] Thirdly, this application provides a storage medium storing a computer program that, when executed by a processor, implements the method for quantitatively identifying the influence of driving factors on water resource carrying capacity.

[0008] Fourthly, this application provides an electronic device, which includes a processor and a memory. The memory stores a computer program, which, when executed by the processor, implements the quantitative identification method for the influence of driving factors of water resource carrying capacity.

[0009] Compared with the prior art, this application has the following beneficial effects: In the quantitative identification method and related apparatus for the influence of water resource carrying capacity driving factors provided in this application, the electronic device divides the area to be analyzed into multiple sub-regions, and takes each sub-region as the target sub-region, and determines multiple adjacent sub-regions from the multiple sub-regions; the target sub-region and the multiple adjacent sub-regions are divided into multiple region sets according to their respective water resource carrying capacity indices; wherein, the multiple region sets correspond one-to-one with multiple water resource carrying capacity intervals; based on the overall dispersion index of water resource carrying capacity of multiple adjacent sub-regions and the target sub-region, and the local dispersion index of the factor to be analyzed in each region set, the correlation degree of the factor to be analyzed with water resource carrying capacity is obtained.

[0010] Thus, by dividing the region into multiple sets based on the water resource carrying capacity index, it is possible to more accurately identify which factors play a dominant role in water resource carrying capacity at the local scale. Compared with analyzing the entire study area as a statistical whole, this solves the problem of ignoring local geographical differences due to the reliance on the assumption of global stationarity. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 One of the flowcharts for a method to quantitatively identify the influence of driving factors on water resource carrying capacity provided in this application embodiment; Figure 2 This is a schematic diagram illustrating the principle of adjacent sub-region selection provided in an embodiment of this application; Figure 3 A second schematic flowchart illustrating the quantitative identification method for the influence of driving factors of water resource carrying capacity provided in this application embodiment; Figure 4 A schematic diagram of the structure of the quantitative identification device for the influence of water resource carrying capacity driving factors provided in the embodiments of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of this application (hereinafter referred to as "the embodiments") clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0014] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0015] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0016] In the description of this application, it should be noted that the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0017] Based on the above statement, as introduced in the background section, existing technologies mostly rely on the assumption of global stationarity and ignore the local variability of geographical processes, making the analysis of influencing factors unreasonable.

[0018] Specifically, traditional statistical methods, spatial econometric models, and most machine learning methods treat the entire study area as a whole, assuming that the intensity and mode of influence of the same driving factor on water resource carrying capacity remain consistent within the region. However, this assumption of global stationarity conflicts with real geographical processes. This is because water resource formation is controlled by large-scale climate background as well as influenced by local geographical and human factors.

[0019] It should be noted that the defects in the solutions in the prior art are the result of practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed by the embodiments of this application in the following text should be regarded as contributions to this application in the process of invention and creation, and should not be understood as technical content known to those skilled in the art.

[0020] Based on the discovery of the above-mentioned technical problems, this embodiment provides a method for quantitatively identifying the influence of driving factors on water resource carrying capacity. For example... Figure 1 As shown, the method includes: S1 divides the region to be analyzed into multiple sub-regions, and uses each sub-region as the target sub-region, and determines multiple adjacent sub-regions from the multiple sub-regions.

[0021] S2 divides the target sub-region and multiple adjacent sub-regions into multiple region sets according to their respective water resource carrying capacity indices.

[0022] Among them, multiple regional sets correspond one-to-one with multiple water resource carrying capacity intervals.

[0023] S3. Based on the overall discrete index of water resource carrying capacity of multiple adjacent sub-regions and the target sub-region, and the local discrete index of the factors to be analyzed in each region, the correlation degree of the factors to be analyzed with water resource carrying capacity is obtained.

[0024] This can be understood as follows: because the division of regional sets is based on a real water resource carrying capacity index, it can more sensitively reflect the actual differences in the impact of the factors under analysis in regions with different carrying capacity levels. Therefore, by dividing multiple regional sets based on the water resource carrying capacity index, it is possible to more accurately identify which factors play a dominant role in water resource carrying capacity at the local scale. Compared to analyzing the entire study area as a statistical whole, this solves the problem of ignoring local geographical differences due to reliance on the assumption of global stationarity.

[0025] This can be understood as follows: compared to analyzing the entire study area as a statistical whole, this embodiment divides the region set based on the actual water resource carrying capacity index, obtains the difference in the influence of the factor to be analyzed in different carrying capacity level regions, compares it with the discrete characteristics in the global scope, and thus judges the relationship between the factor and water resource carrying capacity, thereby improving the rationality of the analysis results.

[0026] It is worth noting that the electronic device used to implement the quantitative identification method for the influence of water resource carrying capacity driving factors can be, but is not limited to, mobile terminals, computers, and servers. The server can be a single server or a group of servers. The server group can be centralized or distributed (e.g., the servers can be a distributed system). In some embodiments, the server can be local or remote relative to the user terminal. In some embodiments, the server can be implemented on a cloud platform; by way of example only, the cloud platform can include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, inter-cloud, multi-cloud, etc., or any combination thereof. In some embodiments, the server can be implemented on an electronic device having one or more components.

[0027] Taking a computer as an example, this computer can be a desktop or laptop. The hardware environment for building this computer requires a processor of at least an Intel Core i7-10700K, at least 32GB of RAM, and at least 2TB of hard drive space to meet the needs of multi-source data storage. It also needs a dedicated graphics card, such as an NVIDIA GeForce RTX 3060 or higher, to support efficient rendering of remote sensing data. The software environment requires Windows 10 Professional (64-bit) operating system, ArcGIS 10.8 for spatial data processing and scale conversion, and Python 3.9 as the programming language, configured with libraries such as GDAL, Pandas, and NumPy for batch data processing. These hardware and software requirements together ensure the stable and efficient execution of the quantitative identification method for the driving factors of water resource carrying capacity.

[0028] To make the solution provided in this embodiment clearer, a computer is used as an example below, combined with... Figure 1 Each step of the method is described in detail. However, it should be understood that the operations in the flowchart may not be implemented in sequence, and steps without logical contextual relationships may be reversed in order or implemented simultaneously. Furthermore, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowchart, or remove one or more operations from the flowchart. Figure 1 As shown, the method includes: S1 divides the region to be analyzed into multiple sub-regions, and uses each sub-region as the target sub-region, and determines multiple adjacent sub-regions from the multiple sub-regions.

[0029] This embodiment can be understood as follows: by dividing the area to be analyzed into multiple sub-regions and taking each sub-region as a target sub-region, the adjacent sub-regions within a certain range around it are determined, thereby constructing a localized spatial analysis window to identify the driving factors of water resource carrying capacity in the target sub-region.

[0030] In practical applications, the entire area to be analyzed (e.g., the entire land area of ​​China) is uniformly processed into a grid composed of numerous square cells with sides of 1 kilometer. These sub-regions are arranged continuously in space, collectively covering the entire study area. When analyzing the impact mechanism of water resource carrying capacity at a certain location, the computer will sequentially analyze each sub-region as a target sub-region. For the currently selected target sub-region, the computer will identify multiple adjacent sub-regions distributed around the target sub-region from all sub-regions according to a preset sliding window size.

[0031] For example, such as Figure 2 As shown, when the sliding window size is set to 7, it means that the target sub-region is centered on the target sub-region and the distance is extended outward by 3 units to determine the adjacent sub-regions, forming a neighborhood region consisting of 49 sub-regions in a total of 7×7.

[0032] Based on the above example's explanation of the target sub-region and neighboring regions, please refer to [link / reference]. Figure 1 Next, we will continue with... Figure 1 Step S2 will be explained below: S2 divides the target sub-region and multiple adjacent sub-regions into multiple region sets according to their respective water resource carrying capacity indices.

[0033] In this embodiment, multiple regional sets correspond one-to-one with multiple water resource carrying capacity intervals. This can be understood as follows: this embodiment classifies the target sub-region and multiple adjacent sub-regions according to their respective water resource carrying capacity indices, forming multiple regional sets with clearly defined numerical ranges.

[0034] In practical applications, the water resources carrying capacity index can be calculated by constructing a multi-dimensional assessment system that includes the availability of water resources, water resources utilization efficiency, ecological environment water demand satisfaction, and socio-economic development level, and then using the entropy weight-TOPSIS comprehensive evaluation method. This index can reflect the supporting capacity of each sub-region for the water resources system.

[0035] The computer divides these water resource carrying capacity indices into several non-overlapping water resource carrying capacity intervals based on their actual distribution. Each water resource carrying capacity interval corresponds to a set of regions. In other words, all sub-regions falling within the same water resource carrying capacity interval are grouped into the same set of regions.

[0036] For example, different discretization methods can be used to define water resource carrying capacity intervals during the partitioning process. As an alternative, the quantile method can be used, which calculates the percentile of all water resource carrying capacity indices within the current sliding window. , , , , and Then, the entire numerical range is divided into five equal intervals, so that each region set contains approximately the same number of sub-region samples.

[0037] In another implementation, equal-interval discretization can be used. That is, first find the minimum and maximum values ​​of the water resource carrying capacity index within the current window, and then divide this total range evenly into five sub-intervals of equal length. Each sub-interval is a water resource carrying capacity interval, and a corresponding region set is generated.

[0038] In this way, by dividing the target sub-region and multiple adjacent sub-regions into multiple regional sets according to their respective water resource carrying capacity indices, and making these regional sets correspond one-to-one with the water resource carrying capacity intervals, sub-regions with similar water resource carrying capacity are located in the same regional set.

[0039] Based on the above embodiments illustrating multiple region sets, please refer to [link to previous document]. Figure 1 Next, we will continue with... Figure 1 Step S3 will be explained below: S3. Based on the overall discrete index of water resource carrying capacity of multiple adjacent sub-regions and the target sub-region, and the local discrete index of the factors to be analyzed in each region, the correlation degree of the factors to be analyzed with water resource carrying capacity is obtained.

[0040] It should be understood that in practical applications, water resource carrying capacity is affected by a variety of natural and human factors. Therefore, the factor to be analyzed is any one of the factors in a set, which includes total water resources, precipitation, evaporation, terrestrial water storage, groundwater, nighttime light, and soil moisture. This set of factors covers natural factors in the hydrological cycle, such as precipitation replenishment, water evaporation, and changes in groundwater storage, while also considering human factors reflecting the level of socio-economic development, specifically represented by nighttime light. This indicator can be obtained through satellite remote sensing data and indirectly reflects the level of economic activity in the region, and the intensity of economic activity also affects water resource consumption.

[0041] For the aforementioned multiple factors, this embodiment allows for the selection of any one factor from the factor set as the current factor to be analyzed independently in each iteration. For example, precipitation can be selected as the factor to be analyzed in one calculation, while nighttime light can be selected in another, and so on, completing the analysis of all seven factors one by one. Each factor to be analyzed is supported by data from several years, including not only spatial 1-kilometer resolution raster distribution information but also factor indicators from multiple years, thus integrating temporal and spatial variation characteristics and improving the reliability of the analysis results.

[0042] For example, taking precipitation as the factor to be analyzed, for each 1 km × 1 km sub-region, annual or monthly observations are provided for multiple consecutive years (e.g., 2000 to 2020). The value at each pixel location records the actual precipitation at the corresponding geographical location within a specific time period, forming a three-dimensional data structure with two spatial dimensions (longitude and latitude) and one temporal dimension (year). When analyzing a target sub-region of this area, the computer extracts 49 sub-regions within a 7 × 7 window centered on that location, as well as the annual precipitation data for each sub-region over these 21 years.

[0043] It should also be understood that the indicators of the aforementioned factors often correspond to multiple sources and may employ different geographic projection methods and spatial resolutions. For example, some data are based on latitude and longitude grids, while others use other coordinate systems, and the original pixel size may be 3 kilometers, 5 kilometers, or even present an irregular grid structure. Therefore, directly using these misaligned data for analysis will lead to inaccurate correspondence of values ​​at the same geographical location, resulting in misalignment or coverage bias.

[0044] Therefore, before step S3, the computer also acquires the original factor indices corresponding to the factors to be analyzed in the region to be analyzed; converts the original factor indices into Albers equal area projection, and uses interpolation to sample them to the resolution corresponding to each sub-region, thereby obtaining the factor indices for each sub-region.

[0045] This can be understood as follows: before formally analyzing each factor, this embodiment performs unified spatial benchmark processing and resolution alignment on the original factor indices of each factor to ensure that all factors to be analyzed are strictly aligned with the water resource carrying capacity index in terms of spatial location and scale.

[0046] In practical applications, the computer first acquires the original factor indices corresponding to the factors to be analyzed in the region to be analyzed, such as remote sensing inversion or ground observation data for precipitation, nighttime light, or soil moisture. These original factor indices often come from different institutions, different sensors, or different years, and their original spatial reference systems vary. Specifically, some use a latitude and longitude rectangular coordinate system, while others do not even define projection information. To eliminate these differences, the computer uniformly converts all original factor indices to an Albers equal-area projection.

[0047] After unifying the projection, the computer further uses bilinear interpolation to resample all the original factor indicators, adjusting their spatial resolution to a uniform 1 kilometer, perfectly consistent with the granularity of the sub-region division. This means that each sub-region corresponds to a clear and unique pixel, thus solving the problems of missing data and inconsistent resolution.

[0048] During this process, the computer can also save all processed data in the standard GeoTIFF format and organize it according to the naming rule of "index name_year_standardized.tif" for easy retrieval by time later. For example, "precipitation_2015_standardized.tif" represents the spatial distribution map of precipitation in 2015 after Albers projection transformation, 1-kilometer resampling, and standardization.

[0049] Thus, by acquiring the original factor indicators and converting them into Albers equal-area projections, and then sampling them to a resolution of 1 kilometer corresponding to each sub-region through interpolation, the problem of projection chaos and scale inconsistency in multi-source spatial data is solved.

[0050] Based on the description of the factors to be analyzed in the above embodiments, it is worth noting that traditional methods, when analyzing the driving factors of water resource carrying capacity, usually employ global statistical models or average calculations based on administrative regions, which are difficult to accurately reflect the subtle spatial differences between different geographical locations. Especially in areas with complex terrain and significant climate variations, the intensity of the same driving factor may vary drastically in different places. Therefore, without quantifying these local variations, it is impossible to truly reconstruct the actual impact of each factor on water resource carrying capacity. In view of this, this embodiment provides the following optional implementation methods for step S3: S3-1: Obtain multiple water resource carrying capacity indices of multiple adjacent sub-regions and the target sub-region, and use the variance among multiple water resource carrying capacity indices as the overall discrete index.

[0051] S3-2, obtain multiple factor indicators of the factors to be analyzed in each regional set, and use the variance between the multiple factor indicators as the local discrete index of each regional set.

[0052] This embodiment can be understood as follows: by calculating the overall difference between the water resource carrying capacity index of multiple adjacent sub-regions and the target sub-region, as well as the local difference of the factor to be analyzed in different regional sets, the influence of the factor on the spatial differentiation of water resource carrying capacity is quantified.

[0053] In practical applications, the computer extracts water resource carrying capacity index data for all 49 sub-regions across multiple years from a 7×7 sliding window centered on the target sub-region. This can be understood as water resource carrying capacity index values ​​from T years and 49 spatial locations, reflecting the overall distribution of water resource carrying capacity within that local area. Based on this, the computer calculates the deviation of these water resource carrying capacity indices from their average value, i.e., the total variance. The expression is:

[0054] In the formula, Indicates the first The water resource carrying capacity index value corresponding to each sub-region This represents the mean of the water resource carrying capacity index for all sub-regions within the sliding window. The total variance... It is used as an overall discrete index of water resource carrying capacity for multiple adjacent sub-regions and the target sub-region, reflecting the degree of dispersion of water resource carrying capacity in the local region corresponding to the entire sliding window.

[0055] Meanwhile, within the same sliding window, the computer processes each region set independently. Each region set is a collection of sub-regions with similar numerical levels, defined based on the water resource carrying capacity index. For each region set, the computer acquires multiple factor indicators of the factor to be analyzed within its corresponding range, such as remote sensing observations of precipitation or nighttime light intensity, and calculates the variance of these factor indicators relative to the mean of their respective region set, as a local discrete indicator. Assuming there are k region sets, the precipitation of the factor to be analyzed is summed to obtain the sum of the internal variances of all region sets. The expression is:

[0056] In the formula, Indicates the first The variance of a set of regions This represents the average value of the concentrated precipitation in the region. It is used as the sum of local discrete indices of multiple regional sets.

[0057] S3-3, based on the overall discrete index and the local discrete index of multiple regional sets, the correlation degree of the factors to be analyzed with water resource carrying capacity is obtained.

[0058] As an optional implementation, the computer obtains the ratio between the sum of local discrete indices of multiple region sets and the overall discrete indices; based on the ratio, the correlation degree is obtained, wherein the ratio and the correlation degree are inversely correlated. The relationship between the ratio and the correlation degree satisfies:

[0059] In the formula, Indicates the degree of relevance. Indicates the ratio, This represents the sum of local discrete indices for multiple region sets. This represents the overall dispersion index. The expression for correlation degree can be understood as follows: Reflecting the overall spatial differences in water resource carrying capacity within a local area, This reflects the differences that still exist in the factors under analysis within areas with similar water resource carrying capacity. If a factor can explain the spatial distribution of water resource carrying capacity, then the carrying capacity levels in similar areas should tend to be consistent. The smaller, at this time The larger the value; conversely The smaller the value.

[0060] Further research revealed that the intensity of the same factor's influence varies significantly across large regions and geographical locations. For example, precipitation may dominate carrying capacity in humid southern regions. It is noteworthy that without a clear visual representation of spatial variability, it is difficult to support refined water resource zoning management decisions. Therefore, if... Figure 3 As shown, the quantitative identification method for the influence of water resource carrying capacity driving factors provided in this embodiment further includes: S4. Generate a spatial distribution map of correlation degree based on the correlation degree corresponding to multiple sub-regions.

[0061] S5 identifies hot and cold areas from the spatial distribution of correlation.

[0062] This embodiment can be understood as presenting the correlation results calculated for each sub-region in the form of a map, and identifying concentrated areas with significant influence intensity from it, so that the spatial distribution pattern of water resource carrying capacity driving factors can be presented intuitively.

[0063] In practical applications, a spatial distribution map of correlation degrees is generated based on the correlation degrees corresponding to multiple sub-regions. Each sub-region in this spatial distribution map corresponds to a raster cell with a resolution of 1 km. The value represents the explanatory power of a certain factor under analysis on water resource carrying capacity at that location, with a uniform value range between 0 and 1. The closer the value is to 1, the stronger the influence of the factor in the local area. The data is then saved as a GeoTIFF format file. To save storage space, the LZW compression algorithm can also be applied during output to reduce file size without loss.

[0064] Therefore, for each driving factor, such as precipitation or nighttime light, a separate results file is generated, named "q_factor_name.tif". For example, "q_precipitation.tif" records the distribution of precipitation as a factor on water resource carrying capacity across the country. Based on this, the computer further visualizes the spatial distribution map of the correlation degree, using continuously gradient color bars to represent changes in the q-value, such as transitioning from blue (low value) to red (high value), allowing users to immediately see where this factor plays a dominant role.

[0065] During this process, the computer will automatically identify Areas with values ​​significantly higher than the surrounding areas are called hotspot areas. Areas with generally low values ​​are called cold spots. These areas typically reflect the stability of driving mechanisms under specific geographical conditions. For example, in arid inland river basins, groundwater may serve as the primary water source for a long time, forming stable hot spots; while in the rain-rich southern regions, the influence of the same factor is weaker, resulting in cold spots.

[0066] In this embodiment, to address the computational demands of the sea-level quantum region within the study area, a multi-process-based parallel computing framework is employed to improve overall computational efficiency. This framework treats each sub-region as a target sub-region and performs corresponding correlation calculation tasks for each target sub-region, achieving efficient processing of the sea-level quantum region. During actual operation, the number of working processes can be dynamically determined based on the number of processor cores, typically set to the total number of cores minus two, to reserve resources for other basic operations and ensure overall operational stability.

[0067] In practice, a process pool can be created using ProcessPoolExecutor to decompose all sub-regions to be analyzed into independent task units, with each task containing the coordinates of that sub-region. This includes related data references, such as a set of water resource carrying capacity indices and multiple factor indicator sets corresponding to each factor to be analyzed. This data is uniformly organized and managed in memory, allowing various parallel processes to access it as needed. During parallel scheduling, the computer automatically allocates currently idle computing tasks to available processes and tracks completed tasks in real time using a method called `as_completed()`, asynchronously obtaining their output results, thereby improving task throughput efficiency.

[0068] During this process, the computer is also programmed to output progress information every 100 sub-region correlation calculations completed, facilitating the monitoring of the entire analysis process. Simultaneously, to ensure the integrity and reliability of the calculation process, if a sub-region fails to complete a calculation due to data anomalies or reading problems, the anomaly will be recorded but the execution of other tasks will not be halted, ensuring that the generation of results for other sub-regions is not affected.

[0069] Based on the same inventive concept as the method for quantitatively identifying the influence of driving factors on water resource carrying capacity provided in this embodiment, this embodiment also provides a device for quantitatively identifying the influence of driving factors on water resource carrying capacity. This device includes at least one software functional module that can be stored in a memory or embedded in an electronic device. The processor in the electronic device executes the executable module stored in the memory. For example, the software functional modules and computer programs included in this device. Please refer to... Figure 4 Functionally, the device may include: The region segmentation module 11 is used to divide the region to be analyzed into multiple sub-regions, and to use each sub-region as a target sub-region, and to determine multiple adjacent sub-regions from the multiple sub-regions; The regional classification module 12 is used to divide the target sub-region and multiple adjacent sub-regions into multiple regional sets according to their respective water resource carrying capacity indices. Among them, the multiple regional sets correspond one-to-one with multiple water resource carrying capacity intervals. The factor assessment module 13 is used to obtain the correlation degree of the factors to be analyzed with respect to water resources carrying capacity based on the overall discrete index of water resources carrying capacity of multiple adjacent sub-regions and the target sub-region, as well as the local discrete index of the factors to be analyzed in each region.

[0070] In this embodiment, the region segmentation module 11 is used to implement Figure 1 In step S1, the region classification module 12 is used to implement... Figure 1 In step S2, the factor evaluation module 13 is used to implement... Figure 1 Step S3 in the above process. Therefore, for a detailed description of each of the above modules, please refer to the specific implementation method of the corresponding step.

[0071] Optionally, the factor evaluation module 13 is specifically used for: Multiple water resource carrying capacity indices of multiple adjacent sub-regions and the target sub-region are obtained, and the variance among multiple water resource carrying capacity indices is used as the overall discrete index. The factors to be analyzed are obtained in each regional set, and the variance among the factors is used as the local discrete index of each regional set. Based on the overall discrete index and the local discrete indexes of multiple regional sets, the correlation degree of the factors to be analyzed with water resource carrying capacity is obtained.

[0072] Optionally, the factor evaluation module 13 is specifically used for: Obtain the ratio between the sum of local discrete indices of multiple region sets and the overall discrete indices; The correlation degree is obtained from the ratio, where the ratio and the correlation degree are inversely correlated.

[0073] Optionally, before determining the overall discrete index of water resource carrying capacity for multiple adjacent sub-regions and the target sub-region, and the local discrete index of the factors to be analyzed in each region, the factor evaluation module 13 is further used for: Obtain the original factor indicators corresponding to the factors to be analyzed in the region to be analyzed; The original factor indices are converted into Albers equal-area projections and then sampled to the resolution corresponding to each sub-region using interpolation to obtain the factor indices for each sub-region.

[0074] Optionally, the factor evaluation module 13 is also used for: Generate a spatial distribution map of correlation degree based on the correlation degree corresponding to multiple sub-regions; Hotspot areas and coldspot areas are identified from the spatial distribution of correlation.

[0075] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0076] It should also be understood that if the above embodiments are implemented as software functional modules and sold or used as independent products, they 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 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 several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0077] Therefore, this embodiment also provides a storage medium, which is a computer-readable storage medium. This storage medium stores a computer program, which, when executed by a processor, implements the quantitative identification method for the influence of water resource carrying capacity driving factors provided in this embodiment. The storage medium can be any medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0078] Please refer to Figure 5 , Figure 5 This embodiment provides an electronic device for implementing a method for quantitatively identifying the influence of driving factors on water resource carrying capacity. The electronic device may include a processor 22 and a memory 21. The memory 21 stores a computer program, and the processor reads and executes the computer program in the memory 21 corresponding to the above-described embodiments to implement the method for quantitatively identifying the influence of driving factors on water resource carrying capacity provided in this embodiment.

[0079] See also Figure 5 The electronic device also includes a communication unit 23. The memory 21, processor 22 and communication unit 23 are electrically connected to each other directly or indirectly through system bus 24 to realize data transmission or interaction.

[0080] The memory 21 can be an information recording device based on any electronic, magnetic, optical, or other physical principles, used to record execution instructions, data, etc. In some embodiments, the memory 21 can be, but is not limited to, volatile memory, non-volatile memory, memory drive, etc.

[0081] In some embodiments, the volatile memory may be random access memory (RAM); in some embodiments, the non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, etc.; in some embodiments, the storage drive may be a disk drive, solid-state drive, any type of storage disk (such as optical disc, DVD, etc.), or similar storage media, or a combination thereof.

[0082] The communication unit 23 is used to send and receive data over a network. In some embodiments, the network may include a wired network, a wireless network, a fiber optic network, a telecommunications network, an intranet, the Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a public switched telephone network (PSTN), a Bluetooth network, a ZigBee network, or a near field communication (NFC) network, or any combination thereof. In some embodiments, the network may include one or more network access points. For example, the network may include wired or wireless network access points, such as base stations and / or network switching nodes, through which one or more components of the service request processing system can connect to the network to exchange data and / or information.

[0083] The processor 22 may be an integrated circuit chip with signal processing capabilities, and may include one or more processing cores (e.g., a single-core processor or a multi-core processor). By way of example only, the processor described above may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction-set Processor (ASIP), a Graphics Processing Unit (GPU), a Physics Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller unit, a Reduced Instruction Set Computing (RISC) computer, or a microprocessor, or any combination thereof.

[0084] Understandable. Figure 5 The structure shown is for illustrative purposes only. Electronic devices may also have more advanced features. Figure 5 Showing more or fewer components, or having with Figure 5 The different configurations shown. Figure 5 The components shown can be implemented using hardware, software, or a combination thereof.

[0085] It should be understood that the apparatus and methods disclosed in the above embodiments can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0086] The above descriptions are merely various embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for quantitatively identifying the influence of driving factors on water resource carrying capacity, characterized in that, The method includes: The region to be analyzed is divided into multiple sub-regions, and each sub-region is used as a target sub-region. Multiple adjacent sub-regions are determined from the multiple sub-regions. The target sub-region and the multiple adjacent sub-regions are divided into multiple region sets according to their respective water resource carrying capacity indices, wherein the multiple region sets correspond one-to-one with multiple water resource carrying capacity intervals; Based on the overall discrete index of water resource carrying capacity of the multiple adjacent sub-regions and the target sub-region, and the local discrete index of the factors to be analyzed in each of the regions, the correlation degree of the factors to be analyzed with water resource carrying capacity is obtained.

2. The method for quantitatively identifying the influence of driving factors on water resource carrying capacity according to claim 1, characterized in that, Based on the overall dispersion index of water resource carrying capacity of the multiple adjacent sub-regions and the target sub-region, and the local dispersion index of the factors to be analyzed in each of the region sets, the correlation degree of the factors to be analyzed with water resource carrying capacity is obtained, including: Obtain multiple water resource carrying capacity indices of the multiple adjacent sub-regions and the target sub-region, and use the variance among the multiple water resource carrying capacity indices as the overall discrete index; The factors to be analyzed are obtained in multiple factor indices for each of the regions, and the variance among the multiple factor indices is used as the local discrete index for each of the regions. Based on the overall discrete index and the local discrete index of the multiple regional sets, the correlation degree of the factor to be analyzed with water resource carrying capacity is obtained.

3. The method for quantitatively identifying the influence of driving factors on water resource carrying capacity according to claim 2, characterized in that, Based on the overall discrete index and the local discrete indices of the multiple regional sets, the correlation degree of the factors to be analyzed with water resource carrying capacity is obtained, including: Obtain the ratio between the sum of the local discrete indices of the multiple region sets and the overall discrete indices; The correlation degree is obtained based on the ratio, wherein the ratio is inversely correlated with the correlation degree.

4. The method for quantitatively identifying the influence of driving factors on water resource carrying capacity according to claim 3, characterized in that, The relationship between the ratio and the correlation degree satisfies: In the formula, Indicates the degree of correlation. This indicates the ratio. This represents the sum of local discrete indices for multiple region sets. This represents the overall discrete index.

5. The method for quantitatively identifying the influence of driving factors on water resource carrying capacity according to claim 1, characterized in that, Before basing the overall discrete index of water resource carrying capacity of the plurality of adjacent sub-regions and the target sub-region, and the local discrete index of the factors to be analyzed in each of the region sets, the method further includes: Obtain the original factor indicators corresponding to the factors to be analyzed in the region to be analyzed; The original factor indices are converted into Albers equal-area projections and sampled to the resolution corresponding to each sub-region using interpolation to obtain the factor indices for each sub-region.

6. The method for quantitatively identifying the influence of driving factors on water resource carrying capacity according to claim 1, characterized in that, The factor to be analyzed is any one of the factors in the factor set, which includes total water resources, precipitation, evaporation, terrestrial water storage, groundwater, nighttime light, and soil moisture.

7. The method for quantitatively identifying the influence of driving factors on water resource carrying capacity according to claim 1, characterized in that, The method further includes: Based on the correlation degree corresponding to the multiple sub-regions, a spatial distribution map of correlation degree is generated; Hotspot areas and coldspot areas are identified from the spatial distribution of the correlation degree.

8. A quantitative identification device for the influence of driving factors on water resource carrying capacity, characterized in that, The device includes: The region segmentation module is used to divide the region to be analyzed into multiple sub-regions, and to use each sub-region as a target sub-region, and to determine multiple adjacent sub-regions from the multiple sub-regions; The regional classification module is used to divide the target sub-region and the multiple adjacent sub-regions into multiple regional sets according to their respective water resource carrying capacity indices, wherein the multiple regional sets correspond one-to-one with multiple water resource carrying capacity intervals; The factor evaluation module is used to obtain the correlation degree of the factor to be analyzed with respect to water resource carrying capacity based on the overall discrete index of the multiple adjacent sub-regions and the target sub-region with respect to water resource carrying capacity, and the local discrete index of the factor to be analyzed in each of the regions.

9. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the quantitative identification method for the influence of water resource carrying capacity driving factors as described in any one of claims 1-7.

10. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing a computer program, which, when executed by the processor, implements the quantitative identification method for the influence of water resource carrying capacity driving factors as described in any one of claims 1-7.

Images