Hydrological correlation value checking method and system based on data driving

Abstract

The invention provides a hydrological correlation value checking method and system based on data driving. The method comprises the steps that to-be-queried water level data of a drainage basin, all water level observation data of the drainage basin within a preset duration and flow observation data corresponding to all the water level observation data are obtained; constructing each array of the drainage basin according to each water level observation data in the preset duration and the flow observation data corresponding to each water level observation data; determining a subscript value corresponding to the to-be-queried water level data; and searching flow data corresponding to the to-be-queried water level data in each array of the drainage basin based on the subscript value corresponding to the to-be-queried water level data. According to the technical scheme, the flow can be efficiently and accurately inquired in the large-scale water level data, meanwhile, the value inquiry data are stored through the array, the inquiry process is simplified, and the inquiry speed is remarkably increased.

Description

Technical Field

[0001] The present application relates to the field of hydrological relationship query, and in particular to a data-driven hydrological correlation value query method and system. Background Art

[0002] Hydrological relationship curve value query is an indispensable means in hydrological analysis. It is used to describe the relationship between water level and flow and is widely used in water resources management, flood forecasting and related fields. Existing value query methods include: fitting formula value query and proportional linear value query. The fitting formula value query method converts the data curve into a mathematical formula (such as power exponent, polynomial, etc.) by fitting the existing data. This method is fast and simple when querying, but it has high requirements on the linearity of the data. It may be difficult to fit a suitable formula for nonlinear data; proportional linear value query is a calculation method that infers the value of unknown points through the linear relationship between known data points, but the accuracy of proportional calculation is limited by the spacing between data points and the degree of data sparsity. In the case of uneven data distribution or large intervals, the calculation results may have large errors, affecting the accuracy of the analysis results. At the same time, with the rapid increase in the amount of hydrological data, due to frequent database access and heavy computing burden, the system response speed is slow, which is difficult to meet the needs of real-time data analysis. Therefore, it is urgent to propose a value query scheme with high query efficiency, high accuracy and reduced system load. Summary of the invention

[0003] The present application provides a data-driven hydrological correlation value query method and system to at least solve the technical problems of low query efficiency, low accuracy and high system load.

[0004] The first embodiment of the present application proposes a data-driven hydrological correlation value query method, the method comprising:

[0005] Obtain the water level data to be queried of the river basin, the water level observation data of the river basin within a preset time period, and the flow observation data corresponding to each water level observation data;

[0006] Constructing each array of the watershed according to each water level observation data within the preset time period and the flow observation data corresponding to each water level observation data;

[0007] Determine the subscript value corresponding to the water level data to be queried;

[0008] Based on the subscript value corresponding to the water level data to be queried, the flow data corresponding to the water level data to be queried is searched in each array of the river basin.

[0009] Preferably, before constructing each array of the watershed according to each water level observation data within the preset time period and the flow observation data corresponding to each water level observation data, the method further includes:

[0010] Determine the number of arrays of the watershed;

[0011] Determine whether the number of water level observation data within the preset time length is equal to the number of arrays of the watershed; if not, determine each missing water level observation data and its corresponding flow observation data by using equal-proportional linear interpolation method;

[0012] Based on the missing water level observation data and their corresponding flow observation data, the water level observation data within the preset time period and the flow observation data corresponding to each water level observation data are completed to obtain the completed water level observation data within the preset time period and the flow observation data corresponding to each water level observation data.

[0013] Further, the determining the number of arrays of the watershed includes:

[0014] Obtain the maximum and minimum values ​​of the water level observation data within the preset time period;

[0015] Determine the number of arrays of the watershed according to the maximum value and the minimum value;

[0016] The calculation formula for the number of arrays of the watershed is as follows:

[0017] N = (max-min) × 100

[0018] Where N is the number of arrays, max is the maximum value of the water level observation data, and min is the minimum value of the water level observation data.

[0019] Furthermore, the method of using the proportional linear interpolation method to determine each missing water level observation data and its corresponding flow observation data includes:

[0020] Find the first adjacent point and the second adjacent point of each missing data in each water level observation data within the preset time period and the flow observation data corresponding to each water level observation data;

[0021] Based on the first and second neighboring points of each missing data

[0022] From the first adjacent point of the missing data to the second adjacent point, the missing data is filled in according to the minimum unit of water level observation data, 0.01m.

[0023] Furthermore, constructing each array of the watershed according to each water level observation data within the preset time period and the flow observation data corresponding to each water level observation data includes:

[0024] Determine the subscript value of each water level observation data after completion within the preset time length;

[0025] Generate each array of the watershed based on the subscript value of each water level observation data and the flow observation data corresponding to each water level observation data;

[0026] Wherein, the array includes: the sub-value of the water level observation data and the flow observation data corresponding to the water level observation data.

[0027] Furthermore, the step of determining the subscript value of each water level observation data after completion within the preset time period includes:

[0028] Obtaining the minimum value of each water level observation data after completion within the preset time length;

[0029] Respectively determine the difference between each water level observation data completed within the preset time period and the minimum value;

[0030] Each of the differences is used as a subscript value of the water level observation data.

[0031] Furthermore, the method further comprises:

[0032] The determined missing water level observation data and their corresponding flow observation numbers are stored in a Redis database or a file-based local cache mechanism, and cache management and automatic update are performed on the data in the Redis database or the file-based local cache mechanism.

[0033] The second embodiment of the present application proposes a data-driven hydrological correlation value query system, including:

[0034] An acquisition module, used to acquire the water level data to be queried of the basin, the water level observation data of the basin within a preset time period, and the flow observation data corresponding to each water level observation data;

[0035] A construction module, used to construct each array of the watershed according to each water level observation data within the preset time period and the flow observation data corresponding to each water level observation data;

[0036] A determination module, used to determine the subscript value corresponding to the water level data to be queried;

[0037] A search module is used to search for flow data corresponding to the water level data to be queried in each array of the watershed based on the subscript value corresponding to the water level data to be queried.

[0038] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in the first aspect is implemented.

[0039] A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect of the present application.

[0040] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0041] The present application proposes a method and system for querying hydrological correlation values ​​based on data-driven, the method comprising: obtaining the water level data to be queried of the basin and the water level observation data of the basin within a preset time period and the flow observation data corresponding to each water level observation data; constructing each array of the basin according to the water level observation data within the preset time period and the flow observation data corresponding to each water level observation data; determining the subscript value corresponding to the water level data to be queried; searching for the flow data corresponding to the water level data to be queried in each array of the basin based on the subscript value corresponding to the water level data to be queried. The technical solution proposed in the present application can efficiently and accurately query the flow in large-scale water level data, and at the same time, by storing the query data in an array, the query process is simplified and the query speed is significantly improved.

[0042] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0044] Figure 1 A flow chart of a data-driven hydrological correlation value query method provided according to an embodiment of the present application;

[0045] Figure 2 A flood discharge curve diagram of flood discharge flow corresponding to each water level at each sluice opening provided according to an embodiment of the present application;

[0046] Figure 3 A schematic diagram of flood discharge flow data corresponding to each water level at each sluice gate opening provided according to an embodiment of the present application;

[0047] Figure 4 A first structural diagram of a data-driven hydrological correlation value query system provided according to an embodiment of the present application;

[0048] Figure 5 This is a second structural diagram of a data-driven hydrological correlation query system provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0049] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0050] The present application proposes a method and system for querying hydrological correlation values ​​based on data-driven, the method comprising: obtaining the water level data to be queried of the basin and the water level observation data of the basin within a preset time period and the flow observation data corresponding to each water level observation data; constructing each array of the basin according to the water level observation data within the preset time period and the flow observation data corresponding to each water level observation data; determining the subscript value corresponding to the water level data to be queried; searching for the flow data corresponding to the water level data to be queried in each array of the basin based on the subscript value corresponding to the water level data to be queried. The technical solution proposed in the present application can efficiently and accurately query the flow in large-scale water level data, and at the same time, by storing the query data in an array, the query process is simplified and the query speed is significantly improved.

[0051] The following describes a data-driven hydrological correlation value query method and system according to an embodiment of the present application with reference to the accompanying drawings.

[0052] Embodiment 1

[0053] Figure 1 A flow chart of a data-driven hydrological correlation value query method provided according to an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0054] Step 1: Obtain the water level data to be queried of the river basin, the water level observation data of the river basin within a preset time period, and the flow observation data corresponding to each water level observation data.

[0055] Step 2: construct each array of the watershed according to each water level observation data within the preset time period and the flow observation data corresponding to each water level observation data.

[0056] In the embodiment of the present disclosure, before step 2, the method further includes:

[0057] 1. Determine the number of arrays of the watershed;

[0058] The determining the number of arrays of the watershed comprises:

[0059] Obtain the maximum and minimum values ​​of the water level observation data within the preset time period;

[0060] Determine the number of arrays of the watershed according to the maximum value and the minimum value;

[0061] The calculation formula for the number of arrays of the watershed is as follows:

[0062] N = (max-min) × 100

[0063] Where N is the number of arrays, max is the maximum value of the water level observation data, and min is the minimum value of the water level observation data.

[0064] For example, read the original data, find the highest water level and the lowest water level, and determine the minimum and maximum water levels from the original data to prepare for subsequent array storage.

[0065] Sample data: Group 1, water level (m) 123.45, flow (m 3 / s) 1300; the second group, water level (m) 123.49, flow (m 3 / s) 1400; the third group, water level (m) 154.33, flow (m 3 / s)6000;

[0066] The lowest water level min: 123.45m, the highest water level max: 154.33m;

[0067] Array N = (max-min)×100 = (154.33-123.45)×100 = 3088.

[0068] 2. Determine whether the number of water level observation data within the preset time length is equal to the number of arrays of the watershed; if not, determine each missing water level observation data and its corresponding flow observation data by using the proportional linear interpolation method;

[0069] The method of using the proportional linear interpolation method to determine each missing water level observation data and its corresponding flow observation data includes:

[0070] Find the first adjacent point and the second adjacent point of each missing data in each water level observation data within the preset time period and the flow observation data corresponding to each water level observation data;

[0071] Based on the first and second neighboring points of each missing data

[0072] From the first adjacent point of the missing data to the second adjacent point, the missing data is filled in according to the minimum unit of water level observation data, 0.01m.

[0073] 3. Based on the missing water level observation data and their corresponding flow observation data, the water level observation data within the preset time period and the flow observation data corresponding to each water level observation data are completed to obtain the completed water level observation data within the preset time period and the flow observation data corresponding to each water level observation data.

[0074] For example, based on the existing data points, the flow corresponding to each water level with centimeter accuracy is calculated using the proportional linear interpolation method, such as the water level observation data and the corresponding flow observation data after supplementing the missing points as shown in Table 1;

[0075] Table 1

[0076] Water level(m) <![CDATA[Flow rate (m 3 / s)]]> 123.45 1300 123.46 1310 123.47 1330 123.48 1360 123.49 1400 … … 154.32 5800 154.33 6000

[0077] In the embodiment of the present disclosure, step 2 specifically includes:

[0078] 2-1. Determine the subscript value of each water level observation data after completion within the preset time length;

[0079] The step of determining the subscript value of each water level observation data after completion within the preset time period includes:

[0080] Obtaining the minimum value of each water level observation data after completion within the preset time length;

[0081] Respectively determine the difference between each water level observation data completed within the preset time period and the minimum value;

[0082] Each of the differences is used as a subscript value of the water level observation data.

[0083] 2-2. Generate each array of the basin based on the subscript value of each water level observation data and the flow observation data corresponding to each water level observation data;

[0084] Wherein, the array includes: the sub-value of the water level observation data and the flow observation data corresponding to the water level observation data.

[0085] For example, use the water level value divided by centimeters minus the minimum value as the array subscript, and store the corresponding flow value in the array, as follows:

[0086] Subscript = (water level - min) × 100. When the water level is 123.48, subscript = (123.48-123.45) × 100 = 3, and we can get a[3] = flow rate. 123.48 =1360 array.

[0087] Step 3: Determine the subscript value corresponding to the water level data to be queried.

[0088] For example, when querying the flow rate corresponding to the water level data of 123.48m, calculate the subscript value: subscript = (water level - min) × 100 = (123.48-123.45) × 100 = 3.

[0089] Step 4: Searching for flow data corresponding to the water level data to be queried in each array of the river basin based on the subscript value corresponding to the water level data to be queried.

[0090] For example, find the flow rate a[3] = 1360m 3 / s.

[0091] In an embodiment of the present disclosure, the method further includes:

[0092] The determined missing water level observation data and their corresponding flow observation numbers are stored in a Redis database or a file-based local cache mechanism, and cache management and automatic update are performed on the data in the Redis database or the file-based local cache mechanism.

[0093] It should be noted that, in order to further improve system performance, the query method provided in this embodiment introduces a calculation result cache mechanism. The calculated results are stored in the cache, so that subsequent identical or similar queries can be directly obtained from the cache without recalculation, which not only reduces the amount of calculation of the system, but also greatly improves the query response speed.

[0094] Among them, (1) result cache storage: after completing the proportional calculation, the calculation results are stored in the cache, using an in-memory database (such as Redis) or a file-based local cache mechanism to ensure that the data can be quickly accessed.

[0095] (2) Cache management: Regularly check and manage cache content, including eliminating expired data and updating cache policies to prevent cache data from becoming outdated or occupying too much space.

[0096] (3) Automatic update mechanism: When the data (such as water level flow meter) changes, the system can automatically detect these changes and trigger recalculation to update the cache content. This ensures the timeliness of the data and avoids errors caused by outdated data.

[0097] A data-driven hydrological correlation query method proposed in the above scheme is introduced in detail. Figure 2 The following is a flood discharge curve diagram of the flood discharge corresponding to each water level under each sluice gate opening:

[0098] 1. Based on Figure 2 Read the original data and get Figure 3 The data shown, find the highest water level and the lowest water level, determine the minimum and maximum water level from the original data, so as to prepare for the subsequent array storage;

[0099] It can be obtained that the lowest water level min: 57m, the highest water level max: 78m, array size = max-min = 78-57 = 21 elements.

[0100] 2. Proportional linear interpolation is used to find out the flow rates of all corresponding gate opening water levels;

[0101] According to the existing data points, the flow corresponding to each water level with centimeter accuracy is calculated using the proportional linear interpolation method, and the interpolation is performed under the same gate opening. For example, as shown in Table 2;

[0102]

[0103] 3. Build an array (or other data structure) based on the minimum and maximum values;

[0104] Store all proportional interpolation data into an array:

[0105] Use the water level value divided by centimeters minus the minimum value as the subscript of the array, and store the discharge value corresponding to the gate opening into the array.

[0106] 4. Lookup algorithm

[0107] Through the constructed array, the subscript is calculated according to the water level, and the flood discharge volume corresponding to the gate opening is directly found.

[0108] For example, query the corresponding discharge flow when the water level is 59m and the gate opening is 2:

[0109] Calculate the subscript: subscript = water level - min = 59-57 = 2;

[0110] Find the corresponding discharge flow when the gate opening is 2: a[2]['gate opening 2'] = 99m 3 / s;

[0111] Find the corresponding discharge flow when the gate opening is 4: a[2]['gate opening 4'] = 153m 3 / s.

[0112] In summary, the data-driven hydrological correlation query method proposed in this embodiment can efficiently and accurately query flow in large-scale water level data. At the same time, by storing the query data in an array, the query process is simplified and the query speed is significantly improved.

[0113] Embodiment 2

[0114] Figure 4 A structural diagram of a data-driven hydrological correlation value query system provided according to an embodiment of the present application is shown in FIG. Figure 4 As shown, the system comprises:

[0115] The acquisition module 100 is used to acquire the water level data to be queried of the basin, the water level observation data of the basin within a preset time period, and the flow observation data corresponding to each water level observation data;

[0116] A construction module 200, for constructing each array of the watershed according to each water level observation data within the preset time period and the flow observation data corresponding to each water level observation data;

[0117] A determination module 300 is used to determine the subscript value corresponding to the water level data to be queried;

[0118] The search module 400 is used to search for flow data corresponding to the water level data to be queried in each array of the watershed based on the subscript value corresponding to the water level data to be queried.

[0119] In the embodiment of the present disclosure, the acquisition module 100 is further used for:

[0120] Determine the number of arrays of the watershed;

[0121] Wherein, the determining the number of arrays of the watershed includes:

[0122] Obtain the maximum and minimum values ​​of the water level observation data within the preset time period;

[0123] Determine the number of arrays of the watershed according to the maximum value and the minimum value;

[0124] The calculation formula for the number of arrays of the watershed is as follows:

[0125] N = (max-min) × 100

[0126] Where N is the number of arrays, max is the maximum value of the water level observation data, and min is the minimum value of the water level observation data.

[0127] Determine whether the number of water level observation data within the preset time length is equal to the number of arrays of the watershed; if not, determine each missing water level observation data and its corresponding flow observation data by using equal-proportional linear interpolation method;

[0128] The method of using the proportional linear interpolation method to determine each missing water level observation data and its corresponding flow observation data includes:

[0129] Find the first adjacent point and the second adjacent point of each missing data in each water level observation data within the preset time period and the flow observation data corresponding to each water level observation data;

[0130] Based on the first and second neighboring points of each missing data

[0131] From the first adjacent point of the missing data to the second adjacent point, the missing data is filled in according to the minimum unit of water level observation data, 0.01m.

[0132] Based on the missing water level observation data and their corresponding flow observation data, the water level observation data within the preset time period and the flow observation data corresponding to each water level observation data are completed to obtain the completed water level observation data within the preset time period and the flow observation data corresponding to each water level observation data.

[0133] In the embodiment of the present disclosure, the building module 200 is also used for:

[0134] Determine the subscript value of each water level observation data after completion within the preset time length;

[0135] The step of determining the subscript value of each completed water level observation data within the preset time period includes:

[0136] Obtaining the minimum value of each water level observation data after completion within the preset time length;

[0137] Respectively determine the difference between each water level observation data completed within the preset time period and the minimum value;

[0138] Each of the differences is used as a subscript value of the water level observation data.

[0139] Generate each array of the watershed based on the subscript value of each water level observation data and the flow observation data corresponding to each water level observation data;

[0140] Wherein, the array includes: the sub-value of the water level observation data and the flow observation data corresponding to the water level observation data.

[0141] In the embodiments of the present disclosure, Figure 5 As shown, the system further includes: a cache module 500;

[0142] The cache module 500 is used to store the determined missing water level observation data and their corresponding flow observation numbers in a Redis database or a file-based local cache mechanism, and to cache and automatically update the data in the Redis database or the file-based local cache mechanism.

[0143] In summary, the data-driven hydrological correlation query system proposed in this embodiment can efficiently and accurately query flow in large-scale water level data, and at the same time, store the query data by array, which simplifies the query process and significantly improves the query speed.

[0144] Embodiment 3

[0145] In order to implement the above embodiments, the present disclosure further proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in Embodiment 1 is implemented.

[0146] Embodiment 4

[0147] In order to implement the above embodiments, the present disclosure further proposes a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in the first embodiment is implemented.

[0148] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0149] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0150] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A data-driven hydrological correlation value query method, characterized in that: The method comprises: Obtain the water level data to be queried of the river basin, the water level observation data of the river basin within a preset time period, and the flow observation data corresponding to each water level observation data; Constructing each array of the watershed according to each water level observation data within the preset time period and the flow observation data corresponding to each water level observation data; Determine the subscript value corresponding to the water level data to be queried; Based on the subscript value corresponding to the water level data to be queried, the flow data corresponding to the water level data to be queried is searched in each array of the river basin.

2. The method according to claim 1, characterized in that Before constructing each array of the watershed according to each water level observation data within the preset time period and the flow observation data corresponding to each water level observation data, the method further includes: Determine the number of arrays of the watershed; Determine whether the number of water level observation data within the preset time length is equal to the number of arrays of the watershed; if not, determine each missing water level observation data and its corresponding flow observation data by using the proportional linear interpolation method; Based on the missing water level observation data and their corresponding flow observation data, the water level observation data within the preset time period and the flow observation data corresponding to each water level observation data are completed to obtain the completed water level observation data within the preset time period and the flow observation data corresponding to each water level observation data.

3. The method according to claim 2, characterized in that The determining the number of arrays of the watershed comprises: Obtain the maximum and minimum values ​​of the water level observation data within the preset time period; Determine the number of arrays of the watershed according to the maximum value and the minimum value; The calculation formula for the number of arrays of the watershed is as follows: N = (max-min) × 100 Where N is the number of arrays, max is the maximum value of the water level observation data, and min is the minimum value of the water level observation data.

4. The method according to claim 3, characterized in that The method of using the proportional linear interpolation method to determine each missing water level observation data and its corresponding flow observation data includes: Find the first adjacent point and the second adjacent point of each missing data in each water level observation data within the preset time period and the flow observation data corresponding to each water level observation data; Based on the first and second neighboring points of each missing data From the first adjacent point of the missing data to the second adjacent point, the missing data is filled in according to the minimum unit of water level observation data, 0.01m.

5. The method according to claim 4, characterized in that The constructing of each array of the watershed according to each water level observation data within the preset time period and the flow observation data corresponding to each water level observation data comprises: Determine the subscript value of each water level observation data after completion within the preset time length; Generate each array of the watershed based on the subscript value of each water level observation data and the flow observation data corresponding to each water level observation data; Wherein, the array includes: the sub-value of the water level observation data and the flow observation data corresponding to the water level observation data.

6. The method according to claim 5, characterized in that The step of determining the subscript value of each water level observation data after completion within the preset time period includes: Obtaining the minimum value of each water level observation data after completion within the preset time length; Respectively determine the difference between each water level observation data completed within the preset time period and the minimum value; Each of the differences is used as a subscript value of the water level observation data.

7. The method according to claim 6, characterized in that The method further comprises: The determined missing water level observation data and their corresponding flow observation numbers are stored in a Redis database or a file-based local cache mechanism, and cache management and automatic update are performed on the data in the Redis database or the file-based local cache mechanism.

8. A data-driven hydrological correlation query system, characterized in that: The system comprises: An acquisition module, used to acquire the water level data to be queried of the basin, the water level observation data of the basin within a preset time period, and the flow observation data corresponding to each water level observation data; A construction module, used to construct each array of the watershed according to each water level observation data within the preset time period and the flow observation data corresponding to each water level observation data; A determination module, used to determine the subscript value corresponding to the water level data to be queried; A search module is used to search for flow data corresponding to the water level data to be queried in each array of the watershed based on the subscript value corresponding to the water level data to be queried.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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