A method and related device for identifying the spatiotemporal pattern evolution characteristics of watershed water resources

Abstract

This invention discloses a method and related apparatus for identifying the spatiotemporal evolution characteristics of watershed water resources. The method includes: acquiring an overview of watershed zoning and water resource quantity information; using a point conversion tool to convert the surface features of the zoning into point features at their geometric centers; using a spatiotemporal cube construction tool to create a spatiotemporal cube of water resources quantity in the watershed; using the Mann-Kendall statistical method to determine the trend of water resource quantity change in each zoning; using Sen's Slope Estimator to quantify the rate of change of water resources quantity in each zoning; and finally, visualizing the evolution characteristics. This invention, by constructing a spatiotemporal cube and embedding the time dimension into a spatial analysis framework, achieves joint analysis and dynamic presentation of the spatiotemporal evolution of water resources. It can quickly and accurately identify the evolution trend of watershed water resource patterns and has advantages such as comprehensive spatiotemporal analysis, high visualization, and dynamic expression.

Description

Technical Field

[0001] This invention relates to the fields of hydrology and water resources, specifically to a method and related apparatus for identifying the spatiotemporal pattern evolution characteristics of watershed water resources. Background Technology

[0002] The evolution of watershed water resource patterns encompasses both temporal and spatial evolution. Identifying the characteristics of this evolution is crucial for improving water resource utilization efficiency, optimizing water resource allocation, and promoting sustainable human water resource development.

[0003] Hagerstrand first proposed the spatiotemporal cube model, which was used to describe human migration. The model comprises three dimensions: two spatial dimensions and one temporal dimension. The temporal dimension is represented as the Z-axis, either continuous or discrete, while the spatial dimensions are represented as the X and Y axes. Through human understanding of the semantic geometry of time, this model intuitively describes the historical evolution of spatial entities. The development of the spatiotemporal cube model has had a significant impact on the research of temporal geographic information systems.

[0004] Water resources are closely related to human life and production, and scholars from various countries are committed to identifying the patterns and characteristics of watershed water resource evolution through various methods. Currently, commonly used methods for analyzing watershed water resource evolution trends include: statistical methods, such as the MK trend test and Sen's slope estimation for analyzing water resource time series trends; hydrological modeling; change point monitoring; spatial analysis; and machine learning. These methods have the following drawbacks: they typically treat time series and spatial pattern evolution separately, making it difficult to conduct joint spatiotemporal analysis; human-computer interaction is weak, requiring manual parameter setting; visualization is low, and dynamic expression is difficult.

[0005] Currently, the application of spatiotemporal cube technology is mainly in the fields of natural element evolution, land and resources planning data analysis, identification of speeding incident black spots on roads, and identification of high-frequency fire zones. It has not yet been applied to the identification of water resource evolution patterns. Summary of the Invention

[0006] The purpose of this invention is to provide a method and related apparatus for identifying the spatiotemporal pattern evolution characteristics of watershed water resources, which can more efficiently identify the evolution characteristics of watershed water resources patterns and improve the visualization of these characteristics.

[0007] To achieve the above objectives, this invention provides a method for identifying the spatiotemporal pattern evolution characteristics of watershed water resources, comprising the following steps:

[0008] S1. Obtain the zoning overview of the study watershed and the water resource quantity information of each zone;

[0009] S2. Using the point conversion tool, the zoning surface elements in the water resources information of the zoning are converted into point elements with their geometric centers to obtain the geometric center point elements of the water resources zoning of the study basin.

[0010] S3. Using the spatiotemporal cube construction tool, based on the geometric center point elements obtained in step S2 and the water resource quantity information of the partition obtained in step S1, create a spatiotemporal cube and construct the watershed water resource quantity spatiotemporal cube.

[0011] S4. Based on the spatiotemporal cube of water resources in the basin constructed in step S3, the Mann-Kendall statistical method is used to determine the trend of water resource changes in each water resource zone or grid cell, and the trend judgment result is obtained.

[0012] S5. Based on the spatiotemporal cube of water resources in the basin constructed in step S3, the rate of change of water resources in each water resource zone is quantified using the Sen's Slope Estimator statistical method to obtain the rate of change results.

[0013] S6. Based on the trend judgment results obtained in step S4 and the change rate results obtained in step S5, the evolution characteristics of the watershed water resources pattern are visualized and presented intuitively in the form of a spatiotemporal cube.

[0014] Furthermore, step S1 includes:

[0015] S11. Investigate and study the water resource zoning of the basin. Depending on the research purpose or whether the basin is a closed basin, choose to study surface water resources and groundwater resources separately or in a unified manner.

[0016] S12. Organize the water resources volume of each watershed water resource zone according to the time series, and organize it into the database according to the principle that the water resources volume of each zone corresponds to the time.

[0017] Furthermore, step S3 includes:

[0018] S31. Open the Create Spacetime Cube by Aggregation Point tool in ArcMap;

[0019] S32. Input element selection step S2 extracts the geometric center point of the water resources zoning of the research basin, and the time step is consistent with the statistical period of water resources data.

[0020] S33. Fill in the time field, determine the fields to be summarized and the aggregation shape, and create a time-space cube.

[0021] Furthermore, step S4 includes:

[0022] S41. Calculate the MK test statistic using the following formula. :

[0023] ;

[0024] ;

[0025] In the formula: and It is a time series and The data values ​​in the middle, Data points Quantity, As a reference point, compared with the other data points Compare;

[0026] S42. Calculate the statistic using the following formula. variance :

[0027] ;

[0028] In the formula, It is the number of data points. This refers to the number of bound groups. A bound group is a set of sample data values ​​that have the same attribute. Indicates the first The number of data points in the group bound to the group;

[0029] S43, Use and Calculate the standard normality test statistic. :

[0030] ;

[0031] The sign and magnitude of the Z-value indicate whether the trend of water resource change is upward or downward and its significance.

[0032] Furthermore, the determination of the upward or downward trend and its significance based on the sign and magnitude of the Z value specifically includes: if A positive value indicates an upward trend, and vice versa; if the goal is to test the trend of increase or decrease at a certain significance level α, then the absolute value of Z should be less than... When, accept the null hypothesis. .

[0033] A device for identifying the spatiotemporal pattern evolution characteristics of watershed water resources includes:

[0034] The data acquisition module is used to acquire the zoning overview of the study watershed and the water resource quantity information of each zoning.

[0035] The point conversion module is used to convert the water resource quantity information of the partition into point elements with their geometric centers using a point conversion tool, so as to obtain the geometric center point elements of the water resource partition of the study basin.

[0036] The spatiotemporal cube construction module is used to create spatiotemporal cubes based on the obtained geometric center point features and water resource quantity information of the partition using the spatiotemporal cube construction tool, and to construct the spatiotemporal cube of watershed water resource quantity.

[0037] The trend judgment module is used to judge the trend of water resource changes in each water resource zone or grid cell based on the constructed watershed water resource spatiotemporal cube and the Mann-Kendall statistical method, and obtain the trend judgment result.

[0038] The rate of change quantification module is used to quantify the rate of change of water resources in each water resource zone based on the constructed spatiotemporal cube of water resources in the basin, using the Sen's SlopeEstimator statistical method, and obtain the rate of change results.

[0039] The visualization module is used to receive the trend judgment results and the change rate results, and to visualize the evolution characteristics of the watershed water resources pattern, presenting them intuitively in the form of a spatiotemporal cube.

[0040] Furthermore, the data acquisition module is specifically used for:

[0041] S11. Investigate and study the water resource zoning of the basin. Depending on the research purpose or whether the basin is a closed basin, choose to study surface water resources and groundwater resources separately or in a unified manner.

[0042] S12. Organize the water resources volume of each watershed water resource zone according to the time series, and organize it into the database according to the principle that the water resources volume of each zone corresponds to the time.

[0043] Furthermore, the spacetime cube construction module is specifically used for:

[0044] S31. Open the Create Spacetime Cube by Aggregation Point tool in ArcMap;

[0045] S32. Input element selection step S2 extracts the geometric center point of the water resources zoning of the research basin, and the time step is consistent with the statistical period of water resources data.

[0046] S33. Fill in the time field, determine the fields to be summarized and the aggregation shape, and create a time-space cube.

[0047] Furthermore, the trend judgment module is specifically used for:

[0048] S41. Calculate the MK test statistic using the following formula. :

[0049] ;

[0050] ;

[0051] In the formula: and It is a time series and The data values ​​in the middle, Data points Quantity, As a reference point, compared with the other data points Compare;

[0052] S42. Calculate the statistic using the following formula. variance:

[0053] ;

[0054] In the formula, It is the number of data points. This refers to the number of bound groups. A bound group is a set of sample data values ​​that have the same attribute. Indicates the first The number of data points in the group bound to the group;

[0055] S43, Use and Calculate the standard normality test statistic. :

[0056] ;

[0057] The sign and magnitude of the Z-value indicate whether the trend of water resource change is upward or downward and its significance.

[0058] A system for identifying the spatiotemporal pattern evolution characteristics of watershed water resources includes: a computer-readable storage medium and a processor;

[0059] The computer-readable storage medium is used to store executable instructions;

[0060] The processor is used to read executable instructions stored in the computer-readable storage medium and execute the method for identifying the spatiotemporal pattern evolution characteristics of watershed water resources.

[0061] A non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for identifying the spatiotemporal pattern evolution characteristics of watershed water resources.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] Achieving integrated spatiotemporal modeling: In response to the shortcomings of traditional methods that separate the time series of water resources from the evolution of spatial patterns, this invention converts the area features into point features in step S2, and constructs a spatiotemporal cube of watershed water resources containing spatial and temporal dimensions in step S3. The temporal dimension is directly embedded into the spatial analysis framework, laying a data foundation for spatiotemporal joint analysis.

[0064] Achieving dynamic presentation of the evolution process: This invention is the first to systematically apply spatiotemporal cube technology to the field of water resource pattern evolution identification. Through step S6, the trend judgment and change rate results are visualized in three dimensions, and a time slider is added to dynamically display the changes in water resource quantity, which greatly improves the interpretability of the analysis results and the decision support capability. Attached Figure Description

[0065] Figure 1 This is a flowchart illustrating a method for identifying the spatiotemporal pattern evolution characteristics of watershed water resources according to the present invention.

[0066] Figure 2 This is a map showing the percentage of water resources in each region based on the multi-year average.

[0067] Figure 3 The graph shows the MK trend test results for water resources in each zone.

[0068] Figure 4 The results of Sen's slope analysis for each secondary water resource zone are shown. Detailed Implementation

[0069] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0070] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0071] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention, and no reference numerals in the claims should be construed as limiting the scope of the claims.

[0072] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. These other embodiments are also covered within the scope of protection of this invention.

[0073] It should also be understood that the specific embodiments described above are only used to explain the present invention, and the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

[0074] Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0075] All prior art documents cited in this specification are incorporated herein by reference in their entirety and are therefore part of the disclosure of this invention.

[0076] Example

[0077] like Figure 1 As shown, the first aspect of this invention provides a method for identifying the spatiotemporal pattern evolution characteristics of watershed water resources based on spatiotemporal cube technology. The method includes the following steps:

[0078] Step S1: Obtain the zoning overview of the study watershed and the water resource quantity information of each zoning.

[0079] Taking a certain river basin as an example, it has 12 secondary water resource zones. The data used is the total water resource volume of each secondary water resource zone of the river basin from 2006 to 2023, a total of 17 years. The unit of water resource volume is 100 million cubic meters. The multi-year average water resource volume of each zone accounts for a certain percentage. Figure 2 As shown in Table 1, the data processing example uses the time column, which is uniformly set to January 1st of each year for easy alignment of time steps; the value field represents the water resource volume of the partition; the region field is the alias for the secondary water resource partition; and the X and Y fields represent the coordinates of the center of each secondary region, used to create a spatiotemporal cube in the form of aggregation points.

[0080] Table 1. Example of data organization format.

[0081]

[0082] Step S2: Using the point conversion tool, the zoning surface features in the water resource quantity information of the zoning are converted into point features with their geometric centers to obtain the geometric center point features of the water resource zoning of the study basin.

[0083] Step S3: Using the Space-Time Pattern Mining toolbox in ArcMap, based on the geometric center point features obtained in Step S2 and the water resource quantity information of the partitions obtained in Step S1, create a spatiotemporal cube to construct the regional water resource quantity spatiotemporal cube. Step S3 includes the following steps:

[0084] Step S31: Open the Create Spacetime Cube by Aggregation Point tool in ArcMap.

[0085] Step S32: Select the geometric center point of a secondary water resources zone in a watershed as the input element. Enter the name and save location of the output spatiotemporal cube. The time field is "time", the time step is 1 year, the time step alignment is set to the start time, and the reference time is set to January 1, 2026, including only the date. Since the distance span is large and the spatial coordinates of each aggregation point are available, no further requirements are specified.

[0086] Step S33: Set the summary field to "rate", select "MEAN" for the statistical method, choose "ZEROS" for "Fill Empty bins with", and set the aggregation shape to "hexagon" or "fishing net". Click "OK" to create a spatiotemporal cube. Save the file as a NetCDF file, which contains watershed and water resource information for each time point.

[0087] Step S4: Based on the spatiotemporal cube of water resources in the basin constructed in Step S3, the Mann-Kendall statistical method is used to determine the trend of water resource changes in each water resource zone or grid cell, and the trend determination result is obtained. Step S4 includes the following steps:

[0088] Step S41: Calculate the MK test statistic using the following formula. :

[0089]

[0090]

[0091] In the formula: and It is a time series and The data values ​​in the middle, Data points Quantity, As a reference point, compared with the other data points Compare;

[0092] Step S42: Calculate the statistic using the following formula. variance:

[0093]

[0094] In the formula, It is the number of data points. It is the number of data points in the bound group (a bound group is a set of sample data values ​​with the same attribute) and express The number of values ​​in the group column;

[0095] Step S43, Use and Calculate the standard normality test statistic. :

[0096]

[0097] When Z is positive, it indicates that the water resources in the region have been increasing over the years; when Z is negative, it indicates that the water resources in the region have been decreasing over the years. The absolute value of Z indicates the significance of the upward or downward trend; the larger the absolute value, the higher the significance of the trend, as shown in Table 2.

[0098] Table 2 MK Test Table for Water Resources Change Trends

[0099]

[0100] In this embodiment, the trend of water resource changes and its significance are as follows: Figure 3 As shown.

[0101] Step S5: Based on the spatiotemporal cube of water resources in the basin constructed in Step S3, the rate of change of water resources in each water resource zone is quantified using the Sen's SlopeEstimator statistical method.

[0102] The calculation results of the rate of change are as follows Figure 4 As shown.

[0103] Step S6: Based on the trend judgment results obtained in Step S4 and the change rate results obtained in Step S5, the evolution characteristics of the watershed water resources pattern are visualized and presented intuitively in the form of a spatiotemporal cube. Step S6 includes the following steps:

[0104] Step S61: Open the created watershed spatiotemporal cube file in ArcScene and use visualization tools to perform 3D and 2D visualization.

[0105] Step S62: Add a time slider to the 3D spatiotemporal cube visualization to dynamically display the changes in water resources in the research watershed partitions.

[0106] Compared with the prior art, the present invention has the following beneficial effects:

[0107] 1. Breaking through the limitations of traditional spatiotemporal separation analysis, achieving spatiotemporal integrated modeling of water resource evolution characteristics.

[0108] To address the shortcomings of traditional methods that separate the analysis of water resource time series trends from the analysis of spatial patterns, making joint analysis difficult, this invention converts the area elements into point elements in step S2, and then constructs a watershed water resource spatiotemporal cube containing two spatial dimensions and one time dimension in step S3. By directly embedding the time dimension into the spatial analysis framework, it achieves a unified expression and storage of water resource quantity in the spatiotemporal dimensions, laying a data foundation for subsequent spatiotemporal joint analysis.

[0109] 2. Applying spatiotemporal cube technology to the field of water resources to achieve dynamic presentation of the evolution process.

[0110] This invention is the first to systematically apply spatiotemporal cube technology to the field of watershed water resource spatiotemporal pattern evolution feature identification, providing a complete methodology and system architecture. It offers a novel technical approach for water resource spatiotemporal analysis, overcoming the shortcomings of traditional methods such as low visualization and difficulty in dynamic expression. In step S6, the trend judgment results obtained in step S4 and the change rate results obtained in step S5 are visualized in three dimensions in ArcScene, and a time slider is added to the spatiotemporal cube to dynamically display the water resource quantity changes in the studied watershed partitions. This allows the spatiotemporal evolution process of water resource patterns to be presented in an intuitive and dynamic way, greatly improving the interpretability and decision support capabilities of the analysis results.

[0111] A second aspect of the present invention provides a device for identifying the spatiotemporal pattern evolution characteristics of watershed water resources, comprising:

[0112] The data acquisition module is used to acquire the zoning overview of the study watershed and the water resource quantity information of each zoning.

[0113] The point conversion module is used to convert the water resource quantity information of the partition into point elements with their geometric centers using a point conversion tool, so as to obtain the geometric center point elements of the water resource partition of the study basin.

[0114] The spatiotemporal cube construction module is used to create spatiotemporal cubes based on the obtained geometric center point features and water resource quantity information of the partition using the spatiotemporal cube construction tool, and to construct the spatiotemporal cube of watershed water resource quantity.

[0115] The trend judgment module is used to judge the trend of water resource changes in each water resource zone or grid cell based on the constructed watershed water resource spatiotemporal cube and the Mann-Kendall statistical method, and obtain the trend judgment result.

[0116] The rate of change quantification module is used to quantify the rate of change of water resources in each water resource zone based on the constructed spatiotemporal cube of water resources in the basin, using the Sen's SlopeEstimator statistical method, and obtain the rate of change results.

[0117] The visualization module is used to receive the trend judgment results and the change rate results, and to visualize the evolution characteristics of the watershed water resources pattern, presenting them intuitively in the form of a spatiotemporal cube.

[0118] Another aspect of the present invention provides a system for identifying the spatiotemporal pattern evolution characteristics of watershed water resources, comprising: a computer-readable storage medium and a processor;

[0119] The computer-readable storage medium is used to store executable instructions;

[0120] The processor is used to read executable instructions stored in the computer-readable storage medium and execute the watershed water resources spatiotemporal pattern evolution feature identification method described in the first aspect.

[0121] In another aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for identifying the spatiotemporal pattern evolution characteristics of watershed water resources as described in the first aspect.

[0122] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0124] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0125] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for identifying the spatiotemporal pattern evolution characteristics of watershed water resources, characterized in that... Includes the following steps: S1. Obtain the zoning overview of the study watershed and the water resource quantity information of each zone; S2. Using the point conversion tool, the zoning surface elements in the water resources information of the zoning are converted into point elements with their geometric centers to obtain the geometric center point elements of the water resources zoning of the study basin. S3. Using the spatiotemporal cube construction tool, based on the geometric center point elements obtained in step S2 and the water resource quantity information of the partition obtained in step S1, create a spatiotemporal cube and construct the watershed water resource quantity spatiotemporal cube. S4. Based on the spatiotemporal cube of water resources in the basin constructed in step S3, the Mann-Kendall statistical method is used to determine the trend of water resource changes in each water resource zone or grid cell, and the trend judgment result is obtained. S5. Based on the spatiotemporal cube of water resources in the basin constructed in step S3, the rate of change of water resources in each water resource zone is quantified using the Sen's Slope Estimator statistical method to obtain the rate of change results. S6. Based on the trend judgment results obtained in step S4 and the change rate results obtained in step S5, the evolution characteristics of the watershed water resources pattern are visualized and presented intuitively in the form of a spatiotemporal cube.

2. The method for identifying the spatiotemporal pattern evolution characteristics of watershed water resources according to claim 1, characterized in that, Step S1 includes: S11. Investigate and study the water resource zoning of the basin. Depending on the research purpose or whether the basin is a closed basin, choose to study surface water resources and groundwater resources separately or in a unified manner. S12. Organize the water resources volume of each watershed water resource zone according to the time series, and organize it into the database according to the principle that the water resources volume of each zone corresponds to the time.

3. The method for identifying the spatiotemporal pattern evolution characteristics of watershed water resources according to claim 1, characterized in that, Step S3 includes: S31. Open the Create Spacetime Cube by Aggregation Point tool in ArcMap; S32. Input element selection step S2 extracts the geometric center point of the water resources zoning of the research basin, and the time step is consistent with the statistical period of water resources data. S33. Fill in the time field, determine the fields to be summarized and the aggregation shape, and create a time-space cube.

4. The method for identifying the spatiotemporal pattern evolution characteristics of watershed water resources according to claim 1, characterized in that, Step S4 includes: S41. Calculate the MK test statistic using the following formula. : ； ； In the formula: and It is a time series and The data values ​​in the middle, Data points Quantity, As a reference point, compared with the other data points Compare; S42. Calculate the statistic using the following formula. variance : ； In the formula, It is the number of data points. This refers to the number of bound groups. A bound group is a set of sample data values ​​that have the same attribute. Indicates the first The number of data points in the group bound to the group; S43, Use and Calculate the standard normality test statistic. : ； The sign and magnitude of the Z-value indicate whether the trend of water resource change is upward or downward and its significance.

5. A device for identifying the spatiotemporal pattern evolution characteristics of watershed water resources, characterized in that, include: The data acquisition module is used to acquire the zoning overview of the study watershed and the water resource quantity information of each zoning. The point conversion module is used to convert the water resource quantity information of the partition into point elements with their geometric centers using a point conversion tool, so as to obtain the geometric center point elements of the water resource partition of the study basin. The spatiotemporal cube construction module is used to create spatiotemporal cubes based on the obtained geometric center point features and water resource quantity information of the partition using the spatiotemporal cube construction tool, and to construct the spatiotemporal cube of watershed water resource quantity. The trend judgment module is used to judge the trend of water resource changes in each water resource zone or grid cell based on the constructed watershed water resource spatiotemporal cube and the Mann-Kendall statistical method, and obtain the trend judgment result. The rate of change quantification module is used to quantify the rate of change of water resources in each water resource zone based on the constructed spatiotemporal cube of water resources in the basin, using the Sen's SlopeEstimator statistical method, and obtain the rate of change results. The visualization module is used to receive the trend judgment results and the change rate results, and to visualize the evolution characteristics of the watershed water resources pattern, presenting them intuitively in the form of a spatiotemporal cube.

6. The watershed water resources spatiotemporal pattern evolution characteristic identification device according to claim 5, characterized in that, The data acquisition module is specifically used for: S11. Investigate and study the water resource zoning of the basin. Depending on the research purpose or whether the basin is a closed basin, choose to study surface water resources and groundwater resources separately or in a unified manner. S12. Organize the water resources volume of each watershed water resource zone according to the time series, and organize it into the database according to the principle that the water resources volume of each zone corresponds to the time.

7. The watershed water resources spatiotemporal pattern evolution characteristic identification device according to claim 6, characterized in that, The spacetime cube construction module is specifically used for: S31. Open the Create Spacetime Cube by Aggregation Point tool in ArcMap; S32. Input element selection step S2 extracts the geometric center point of the water resources zoning of the research basin, and the time step is consistent with the statistical period of water resources data. S33. Fill in the time field, determine the fields to be summarized and the aggregation shape, and create a time-space cube.

8. The watershed water resources spatiotemporal pattern evolution characteristic identification device according to claim 6, characterized in that, The trend judgment module is specifically used for: S41. Calculate the MK test statistic using the following formula. : ； ； In the formula: and It is a time series and The data values ​​in the middle, Data points Quantity, As a reference point, and with the other data points Compare; S42. Calculate the statistic using the following formula. variance: ； In the formula, It is the number of data points. This refers to the number of bound groups. A bound group is a set of sample data values ​​that have the same attribute. Indicates the first The number of data points in the group bound to the group; S43, Use and Calculate the standard normality test statistic. : ； The sign and magnitude of the Z-value indicate whether the trend of water resource change is upward or downward and its significance.

9. A system for identifying the spatiotemporal pattern evolution characteristics of watershed water resources, comprising: Computer-readable storage media and processors; The computer-readable storage medium is used to store executable instructions; The processor is used to read executable instructions stored in the computer-readable storage medium and execute the method for identifying the spatiotemporal pattern evolution characteristics of watershed water resources as described in any one of claims 1-4.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for identifying the spatiotemporal pattern evolution characteristics of watershed water resources as described in any one of claims 1-4.

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