Small watershed geological disaster meteorological risk early warning method and device based on massive regular grids

Abstract

The invention provides a small watershed geological disaster meteorological risk early warning method and device based on massive regular grids, and the method comprises the steps: carrying out the data cell numbering of a small watershed and a rainfall grid, constructing a first B-Tree index according to the cell number of the small watershed, and constructing a second B-Tree index according to the cell number of the rainfall grid; the area ratio of each rainfall grid to the small watershed to which the rainfall grid belongs is calculated, and an index relation table of the small watershed and the rainfall grids is established; querying an index relation table of the small watershed and the rainfall grids according to the first B-Tree index, querying rainfall data of the rainfall grids in the index relation table in a preset time period by using the second B-Tree index according to the index relation table of the small watershed and the rainfall grids, and further counting the average rainfall of the small watershed in the preset time period; and calculating the risk early warning level of the small watershed according to the constructed small watershed average rainfall threshold level judgment table and the risk early warning level judgment matrix. According to the invention, space operation time can be reduced, and early warning efficiency can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of meteorological early warning, and in particular relates to a method and device for early warning of meteorological risks of geological disasters in a small watershed based on massive regular grids. Background Art

[0002] Geological disasters are one of the important factors that threaten the safety of human life and property and the ecological environment. Small watersheds have become high-incidence areas of geological disasters due to their complex topography and changing geological conditions, as well as the impact of human activities. Timely and accurate meteorological risk warnings for geological disasters in small watersheds are of vital importance to protecting people's lives and property, rationally planning land use, and effectively carrying out disaster prevention and mitigation work.

[0003] However, the existing early warning methods for geological disaster meteorological risks in small watersheds have many shortcomings. On the one hand, the traditional methods have low data management and retrieval efficiency when processing massive data. The amount of information such as spatial vector data and rainfall grid data in small watersheds is huge, and the existing data storage and indexing methods are difficult to quickly and accurately obtain the required information, resulting in a long early warning process and unable to meet real-time requirements.

[0004] On the other hand, existing methods often lack scientific and effective data fusion and analysis methods when considering multiple influencing factors for risk warning. For example, when combining rainfall data with the level of geological disaster susceptibility, the actual relationship between different rainfall grids and small watersheds is not fully considered, resulting in inaccurate calculation of the average rainfall in small watersheds, which in turn affects the accuracy of risk warning. Moreover, when judging the risk warning level, there is a lack of a systematic judgment matrix based on actual data, and more reliance is placed on empirical judgment, which makes the reliability and stability of the warning results poor.

[0005] In addition, traditional methods are not adaptable enough to deal with the complex and changeable geographical environment of small watersheds and the diverse types of geological disasters. The geological conditions, topography and meteorological characteristics of different small watersheds vary greatly. The existing general early warning methods are difficult to meet the personalized needs of each small watershed, resulting in poor early warning effects. Summary of the invention

[0006] In order to solve the problems existing in the background technology, one aspect of the present invention provides a small watershed geological disaster meteorological risk early warning method based on massive regular grids, comprising:

[0007] S1: Obtain the spatial vector data of the small watershed, the rainfall grid data covering the small watershed, and the geological disaster susceptibility level of the small watershed;

[0008] S2: Number the data cells of the small watershed and the rainfall grid respectively, and construct the first B-Tree index according to the cell number of the small watershed, and construct the second B-Tree index according to the cell number of the rainfall grid;

[0009] S3: superimpose the spatial vector data of the small watershed and the rainfall grid data, calculate the area ratio of each rainfall grid to the small watershed to which it belongs, and establish an index relationship table between the small watershed and the rainfall grid;

[0010] S4: querying the index relationship table of the small watershed and the rainfall grid according to the first B-Tree index, querying the rainfall data of the rainfall grid in the index relationship table in the preset time period using the second B-Tree index according to the index relationship table of the small watershed and the rainfall grid, and then calculating the average rainfall of the small watershed in the preset time period;

[0011] S5: Obtaining the rainfall level of the small watershed in the preset time period according to the constructed small watershed average rainfall threshold level judgment table and the average rainfall of the small watershed in the preset time period;

[0012] S6: Obtain the risk warning level of the small watershed according to the constructed risk warning level judgment matrix based on the rainfall level and geological disaster susceptibility level of the small watershed, the rainfall level of the small watershed in a preset time period and the geological disaster susceptibility level of the small watershed.

[0013] Another aspect of the present invention provides a small watershed geological disaster meteorological risk warning device based on massive regular grids, including a processor and a memory; the memory is used to store computer programs; the processor is connected to the memory, and is used to execute the computer programs stored in the memory, so that the small watershed geological disaster meteorological risk warning device based on massive regular grids executes the small watershed geological disaster meteorological risk warning method based on massive regular grids.

[0014] Another aspect of the present invention provides a computer-readable storage medium storing a program, which, when executed by a processor, implements the method for early warning of meteorological risks of geological disasters in small watersheds based on massive regular grids.

[0015] The present invention has at least the following beneficial effects

[0016] The present invention numbers the data cells of the small watershed and the rainfall grid respectively, and constructs a first B-Tree index according to the cell number of the small watershed, and constructs a second B-Tree index according to the cell number of the rainfall grid, which greatly improves the storage and retrieval efficiency of data. The B-Tree index has good balance and efficient search performance, can quickly locate the required data, reduce data processing time, meet the real-time requirements of geological disaster meteorological risk warning, enable early warning information to be transmitted in time, and gain more time for disaster prevention and mitigation. The present invention superimposes the spatial vector data of the small watershed and the rainfall grid data, calculates the area ratio of each rainfall grid to the small watershed to which it belongs, and establishes an index relationship table of the small watershed and the rainfall grid. This method can fully consider the actual impact of different rainfall grids on the small watershed, so as to more accurately count the average rainfall of the small watershed in the preset time period. Accurate rainfall data is an important basis for risk warning, improves the accuracy of rainfall calculation, and also improves the accuracy of the entire risk warning. The present invention constructs a small watershed average rainfall threshold level judgment table and a risk warning level judgment matrix based on the rainfall level of the small watershed and the geological disaster susceptibility level, and organically combines the rainfall level of the small watershed with the geological disaster susceptibility level through a scientific matrix judgment method. Compared with the traditional empirical judgment, this judgment method based on actual data and scientific models greatly improves the reliability and stability of the risk warning level judgment, makes the warning results more accurate and reliable, and provides a more valuable reference for decision makers. The method of the present invention is based on massive regular grid data and can better adapt to the complex and changeable geographical environment and diversified geological disaster types of different small watersheds. By conducting targeted processing and analysis on the spatial vector data, rainfall grid data, etc. of a specific small watershed, it can meet the personalized warning needs of each small watershed, improve the versatility and applicability of the warning method, and can play a good role in the meteorological risk warning of geological disasters in small watersheds in different regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0018] Figure 2 This is a schematic diagram of small watershed numbering in an embodiment of the present invention;

[0019] Figure 3 A schematic diagram of the numbering of rainfall forecast grids in an embodiment of the present invention;

[0020] Figure 4 Schematic diagram of actual rainfall grid numbering in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0022] See also Figure 1 One aspect of the present invention provides a small watershed geological disaster meteorological risk early warning method based on massive regular grids, comprising:

[0023] S1: Obtain the spatial vector data of the small watershed, the rainfall grid data covering the small watershed, and the geological disaster susceptibility level of the small watershed;

[0024] Preferably, the rainfall grid data covering the small watershed includes: forecast rainfall grid data and actual rainfall grid data.

[0025] S2: Number the data cells of the small watershed and the rainfall grid respectively, and construct the first B-Tree index according to the cell number of the small watershed, and construct the second B-Tree index according to the cell number of the rainfall grid;

[0026] Preferably, step S2 comprises:

[0027] S21: Independent cell numbering is performed for each small watershed; independent cell numbering is performed for the forecast rainfall grid and the actual rainfall grid respectively;

[0028] S22: constructing a first B-Tree index according to the cell numbers of the small watershed, and constructing a second B-Tree index according to the cell numbers of the forecast rainfall grid and the actual rainfall grid.

[0029] See also Figure 2 , Figure 3 and Figure 4In this embodiment, a small watershed unit is prepared for the small watershed unit, and a small watershed number (LY_4172) is established; rainfall grid data covering the small watershed unit is prepared. There are two types of rainfall grids, one is forecast rainfall and the other is actual rainfall. Due to the different grid sizes, they need to be numbered separately. Among them, there are 75 forecast rainfall grids, numbered YB_15916, YB_15917, ..., YB_17902; there are 371 actual rainfall grids, numbered (SK_100090, SK_100091, ..., SK_112255). The first B-Tree index is established with the cell code of the small watershed, and the second B-Tree index is established with the numbers of the forecast rainfall grid and the actual rainfall grid to accelerate the reading of rainfall grid rainfall values. Prepare the data on the susceptibility level of geological disasters in small watersheds, and the susceptibility level is expressed by Y i It is represented by, i is the number of the small watershed. i The value range is high (Y i =3), medium prone (Y i =2), low incidence (Y i =1) and non-prone (Y i =0) four levels. B-Tree is a self-balancing multi-way search tree. We assume that a B-Tree with a degree of 3 is constructed (in actual applications, the degree will be adjusted according to the amount of data and performance requirements).

[0030] The present invention greatly improves the storage and retrieval efficiency of data by encoding cells of small watershed and rainfall grid data and constructing B-Tree index. B-Tree index has good balance and efficient search performance, can quickly locate required data, reduce data processing time, meet the real-time requirements of geological disaster meteorological risk warning, enable warning information to be transmitted in time, and buy more time for disaster prevention and mitigation.

[0031] S3: superimpose the spatial vector data of the small watershed and the rainfall grid data, calculate the area ratio of each rainfall grid to the small watershed to which it belongs, and establish an index relationship table between the small watershed and the rainfall grid;

[0032] Preferably, the calculation of the area ratio of each rainfall grid to the small watershed to which it belongs includes: superimposing the spatial vector data and rainfall grid data of the small watershed to find the forecast rainfall grid and the actual rainfall grid overlapping with the small watershed; calculating the area ratio of each overlapping forecast rainfall grid to the small watershed, and establishing an index relationship table between the small watershed and the forecast rainfall grid according to the cell number of the small watershed and the cell number of the forecast rainfall grid; calculating the area ratio of each overlapping actual rainfall grid to the small watershed, and establishing an index relationship table between the small watershed and the actual rainfall grid according to the cell number of the small watershed and the cell number of the actual rainfall grid.

[0033] In this embodiment, the rainfall grid data is superimposed on the small watershed spatial vector data, and a small watershed unit and rainfall grid coding index relationship table is established to calculate the rainfall grid area ratio associated with a single small watershed, and the area ratio is retained to 8 decimal places, as shown in Table 1 and Table 2:

[0034] Table 1 Index relationship table of small watersheds and forecast rainfall grids

[0035]

[0036] Table 2 Index relationship table of small watershed and actual rainfall grid

[0037]

[0038] S4: querying the index relationship table of the small watershed and the rainfall grid according to the first B-Tree index, querying the rainfall data of the rainfall grid in the index relationship table in the preset time period using the second B-Tree index according to the index relationship table of the small watershed and the rainfall grid, and then calculating the average rainfall of the small watershed in the preset time period;

[0039] Preferably, the statistical calculation of the average rainfall in the small watershed in the preset time period includes: obtaining the rainfall of each forecast rainfall grid and the actual rainfall grid in the preset time period according to the rainfall data of the rainfall grid data in the preset time period, traversing and calculating the forecast average rainfall and the actual average rainfall of each small watershed in the preset time period, and summing the forecast average rainfall and the actual average rainfall of each small watershed in the preset time period to obtain the average rainfall of each small watershed in the preset time period.

[0040] Preferably, the calculation of the forecast average rainfall and the actual average rainfall of each small watershed in a preset time period includes:

[0041]

[0042] V t =V r +V f

[0043] Among them, V t It represents the average rainfall of each small watershed in the preset time period, V r It represents the average rainfall forecast for a small watershed in a preset time period, V f Indicates the actual average rainfall in the small watershed during the preset time period; V i Indicates the rainfall forecast for the rainfall grid i in the preset time period; △S i V represents the area ratio of the forecast rainfall grid i to the small watershed; j Indicates the rainfall of the real-time rainfall grid j in the preset time period; △S jIt represents the area ratio of the actual rainfall grid j to the small watershed; n represents the number of forecast rainfall grids in the index relationship table between the small watershed and the rainfall grid; m represents the number of actual rainfall grids in the index relationship table between the small watershed and the rainfall grid.

[0044] In this embodiment, the forecast rainfall data is used as a trigger condition to automatically obtain the rainfall grid forecast rainfall and actual rainfall data.

[0045] Taking XXXX-XX-XX:XX-XXXX-XX-XX:XX as the storage label (such as 2024-09-3007:00-2024-09-3009:00), the forecast rainfall data for different times and different future forecast time periods such as 2 hours, 6 hours, 12 hours, and 24 hours are pushed at one time, and the total forecast rainfall in each grid in this time period is obtained, as shown in Table 3.

[0046] Table 3 Precipitation grid rainfall value table within a certain forecast period

[0047]

[0048] The real-time rainfall is pushed data hourly. The time and corresponding time period in the forecast rainfall storage tag are used as index conditions to search for the real-time rainfall in the corresponding time period. XXXX-XX-XX:XX-XXXX-XX-XX:XX is used as the storage tag to store the hourly real-time rainfall data. The real-time rainfall in the time period corresponding to the forecast rainfall storage tag is accumulated to obtain the total real-time rainfall in the corresponding time period of each real-time rainfall grid. The calculation formula is:

[0049]

[0050] Among them, V ri is the rainfall in hour i that matches the forecast rainfall period; V r(t) Each live rainfall grid corresponds to the total live rainfall in the time period.

[0051] Table 4 Hourly rainfall values ​​in a certain forecast period of the actual rainfall grid

[0052]

[0053] The area-weighted method is used to calculate the average rainfall forecast within the small watershed unit. The calculation formula is:

[0054]

[0055] Among them, V r Indicates the predicted average rainfall in the small watershed during the preset time period; △S i V represents the area ratio of the forecast rainfall grid i to the small watershed;i It represents the rainfall of forecast rainfall grid i; n represents the number of forecast rainfall grids in the index relationship table between small watersheds and forecast rainfall grids.

[0056] The area-weighted method is used to calculate the actual average rainfall within the small watershed unit. The calculation formula is:

[0057]

[0058] Among them, V f Indicates the actual average rainfall in the small watershed during the preset time period; △S j V represents the area ratio of the actual rainfall grid j to the small watershed; j Represents the rainfall amount of the actual rainfall grid j.

[0059] The average rainfall of the small watershed unit forecast and the actual rainfall is added together to obtain the average rainfall of the small watershed in the preset time period. The calculation formula is:

[0060] V t =V r +V f

[0061] Among them, V t It is the average rainfall in the small watershed during the preset time period.

[0062] The present invention superimposes the spatial vector data of the small watershed and the rainfall grid data, calculates the area ratio of each rainfall grid to the small watershed, and establishes an index relationship table. This method can fully consider the actual impact of different rainfall grids on the small watershed, so as to more accurately count the average rainfall of the small watershed in the preset time period. Accurate rainfall data is an important basis for risk warning, which improves the accuracy of rainfall calculation and the accuracy of the entire risk warning.

[0063] S5: Obtaining the rainfall level of the small watershed in the preset time period according to the constructed small watershed average rainfall threshold level judgment table and the average rainfall of the small watershed in the preset time period;

[0064] Preferably, the constructed small watershed average rainfall threshold level judgment table includes: dividing the rainfall threshold level into five levels: no risk, low risk, medium risk, high risk, and extremely high risk; if the average rainfall V of the small watershed in the preset time period is t ≤x 1 , then the rainfall level of the small watershed in the preset time period is classified as risk-free; if the average rainfall of the small watershed in the preset time period is x 1 <V t ≤x 2 , then the rainfall level of the small watershed in the preset time period is classified as low risk; if the average rainfall of the small watershed in the preset time period is x2 <V t ≤x 3 , then the rainfall level of the small watershed in the preset time period is classified as medium risk; if the average rainfall of the small watershed in the preset time period is x 3 <V t ≤x 4 , then the rainfall level of the small watershed in the preset time period is classified as high risk; if the average rainfall of the small watershed in the preset time period is x 4 <V t , the rainfall level of the small watershed in the preset time period is classified as extremely high risk, and a judgment table of the average rainfall threshold level of the small watershed is constructed.

[0065] According to the "one basin, one threshold" plan, a rainfall threshold level judgment table is established for each small basin. The rainfall threshold levels are divided into five levels: no risk, low risk, medium risk, high risk, and extremely high risk.

[0066] Table 5: Judgment table of rainfall threshold level for each small watershed

[0067]

[0068] The average rainfall V in the small watershed during the preset time period t Compare with the small watershed average rainfall threshold level judgment table to obtain the rainfall level P of the small watershed i , the judgment formula is as follows:

[0069] iV t ≤x 1 , P t = "No risk",

[0070] else ifV t ≤x 2 , P t = "Low risk"

[0071] else ifV t ≤x 3 , P t = “Medium risk”

[0072] else ifV t ≤x 4 , P t = “High risk”

[0073] elseP t = “Extremely high risk”

[0074] S5: Construct a risk warning level judgment matrix based on the rainfall level and geological disaster susceptibility level of the small watershed, and obtain the risk warning level of the small watershed according to the rainfall level of the small watershed in the preset time period and the geological disaster susceptibility level of the small watershed.

[0075] Preferably, the risk warning level judgment matrix includes:

[0076] The risk warning level is divided into five categories: no warning, blue warning, yellow warning, orange warning and red warning; the geological disaster susceptibility level of small watershed is divided into four categories: non-prone, low-prone, medium-prone and high-prone; the geological disaster susceptibility level of small watershed is defined as Y t , the rainfall level of the small watershed is defined as P t ; The risk warning level of the small watershed is defined as W t , then the risk warning level judgment matrix is ​​defined as:

[0077]

[0078] Identify the risk warning level of the small watershed based on the defined risk warning level judgment matrix:

[0079] W t = warningMatrix[Y t ][P t ]

[0080] Among them, warningMatrix represents the risk warning level judgment matrix; warningMatrix[Y t ][P t ] indicates the Pth t Row Y t Column value; P t =1 means the rainfall level in the small watershed is risk-free; P t =2 means the rainfall level in the small watershed is low risk; P t =3 means the rainfall level in the small watershed is medium risk; P t =4 means the rainfall level in the small watershed is high risk; P t =5 means that the rainfall level in the small watershed is extremely high risk; Y t =1 means that the geological disaster susceptibility level of the small watershed is not prone to occur; Y t =2 means that the geological disaster susceptibility level of the small watershed is low; Y t =3 means that the geological disaster susceptibility level of the small watershed is medium; Y t =4 means that the geological disaster susceptibility level of the small watershed is high; W t =0 means that the risk warning level of the small watershed is none; W t=1 means the risk warning level of the small watershed is blue; W t =2 means the risk warning level of the small watershed is yellow; t =3 means the risk warning level of the small watershed is orange; W t =4 means the risk warning level of the small watershed is red.

[0081] In this embodiment, a risk warning level judgment table based on the rainfall level in the small watershed and the geological disaster susceptibility level is established, and the warning levels are divided into five categories: no warning, blue warning, yellow warning, orange warning and red warning.

[0082] Table 6 Risk warning level judgment table based on rainfall level and geological disaster susceptibility level in small watersheds;

[0083]

[0084] Using small watershed units as indexes, matrix judgment is performed to divide the risk warning level into five categories: no warning, blue warning, yellow warning, orange warning, and red warning. The geological disaster susceptibility level of small watersheds is divided into four categories: non-prone, low-prone, medium-prone, and high-prone. The geological disaster susceptibility level of small watersheds is defined as Y t , the rainfall level of the small watershed is defined as P t ; The risk warning level of the small watershed is defined as W t , then the risk warning level judgment matrix is ​​defined as:

[0085]

[0086] Identify the risk warning level of the small watershed based on the defined risk warning level judgment matrix:

[0087] W t = warningMatrix[Y t ][P t ]

[0088] Among them, the geological disaster susceptibility level of small watersheds is evaluated through the relevant "Geological Hazard Hazard Assessment Specification" (DZ / T0286-2015).

[0089] The present invention constructs a small watershed average rainfall threshold level judgment table and a risk warning level judgment matrix based on rainfall level and geological disaster susceptibility level, and organically combines the rainfall level and geological disaster susceptibility level of the small watershed through a scientific matrix judgment method. Compared with traditional empirical judgment, this judgment method based on actual data and scientific models greatly improves the reliability and stability of risk warning level judgment, making the warning results more accurate and reliable, and providing more valuable reference for decision makers.

[0090] Another aspect of the present invention provides a small watershed geological disaster meteorological risk warning device based on massive regular grids, including a processor and a memory; the memory is used to store computer programs; the processor is connected to the memory, and is used to execute the computer programs stored in the memory, so that the small watershed geological disaster meteorological risk warning device based on massive regular grids executes the small watershed geological disaster meteorological risk warning method based on massive regular grids.

[0091] Another aspect of the present invention provides a computer-readable storage medium storing a program, which, when executed by a processor, implements the method for early warning of meteorological risks of geological disasters in small watersheds based on massive regular grids.

[0092] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0093] In summary, the present invention numbers the data cells of the small watershed and the rainfall grid respectively, and constructs the first B-Tree index according to the cell number of the small watershed, and constructs the second B-Tree index according to the cell number of the rainfall grid, which greatly improves the storage and retrieval efficiency of the data. The B-Tree index has good balance and efficient search performance, can quickly locate the required data, reduce data processing time, meet the real-time requirements of geological disaster meteorological risk warning, so that the warning information can be conveyed in time, and more time is gained for disaster prevention and mitigation. The present invention superimposes the spatial vector data of the small watershed and the rainfall grid data, calculates the area ratio of each rainfall grid to the small watershed to which it belongs, and establishes an index relationship table between the small watershed and the rainfall grid. This method can fully consider the actual impact of different rainfall grids on the small watershed, so as to more accurately count the average rainfall in the small watershed in the preset time period. Accurate rainfall data is an important basis for risk warning, which improves the accuracy of rainfall calculation and also improves the accuracy of the entire risk warning. The present invention constructs a small watershed average rainfall threshold level judgment table and a risk warning level judgment matrix based on the rainfall level of the small watershed and the geological disaster susceptibility level, and organically combines the rainfall level of the small watershed with the geological disaster susceptibility level through a scientific matrix judgment method. Compared with the traditional empirical judgment, this judgment method based on actual data and scientific models greatly improves the reliability and stability of the risk warning level judgment, makes the warning results more accurate and reliable, and provides a more valuable reference for decision makers. The method of the present invention is based on massive regular grid data and can better adapt to the complex and changeable geographical environment and diversified geological disaster types of different small watersheds. By conducting targeted processing and analysis on the spatial vector data, rainfall grid data, etc. of a specific small watershed, it can meet the personalized warning needs of each small watershed, improve the versatility and applicability of the warning method, and can play a good role in the meteorological risk warning of geological disasters in small watersheds in different regions.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A method for early warning of meteorological risk of geological disasters in small watersheds based on massive regular grids, characterized in that: include: S1: Obtain the spatial vector data of the small watershed, the rainfall grid data covering the small watershed, and the geological disaster susceptibility level of the small watershed; S2: Number the data cells of the small watershed and the rainfall grid respectively, and construct the first B-Tree index according to the cell number of the small watershed, and construct the second B-Tree index according to the cell number of the rainfall grid; S3: superimpose the spatial vector data of the small watershed and the rainfall grid data, calculate the area ratio of each rainfall grid to the small watershed to which it belongs, and establish an index relationship table between the small watershed and the rainfall grid; S4: querying the index relationship table of the small watershed and the rainfall grid according to the first B-Tree index, querying the rainfall data of the rainfall grid in the index relationship table in the preset time period using the second B-Tree index according to the index relationship table of the small watershed and the rainfall grid, and then calculating the average rainfall of the small watershed in the preset time period; S5: Obtaining the rainfall level of the small watershed in the preset time period according to the constructed small watershed average rainfall threshold level judgment table and the average rainfall of the small watershed in the preset time period; S6: Obtain the risk warning level of the small watershed according to the constructed risk warning level judgment matrix based on the rainfall level and geological disaster susceptibility level of the small watershed, the rainfall level of the small watershed in a preset time period and the geological disaster susceptibility level of the small watershed.

2. According to claim 1, a method for early warning of meteorological risks of geological disasters in small watersheds based on massive regular grids is characterized in that: The rainfall grid data covering the small watershed includes: forecast rainfall grid data and actual rainfall grid data.

3. The method for early warning of geological disaster meteorological risks in small watersheds based on massive regular grids according to claim 2 is characterized in that: The step S2 comprises: S21: Independent cell numbering is performed for each small watershed; independent cell numbering is performed for the forecast rainfall grid and the actual rainfall grid respectively; S22: constructing a first B-Tree index according to the cell numbers of the small watershed, and constructing a second B-Tree index according to the cell numbers of the forecast rainfall grid and the actual rainfall grid.

4. The method for early warning of geological disaster meteorological risks in small watersheds based on massive regular grids according to claim 3 is characterized in that: The calculation of the area ratio of each rainfall grid to the small watershed to which it belongs includes: superimposing the spatial vector data and rainfall grid data of the small watershed to find the forecast rainfall grid and the actual rainfall grid overlapping with the small watershed; calculating the area ratio of each overlapping forecast rainfall grid to the small watershed, and establishing an index relationship table between the small watershed and the forecast rainfall grid according to the cell number of the small watershed and the cell number of the forecast rainfall grid; calculating the area ratio of each overlapping actual rainfall grid to the small watershed, and establishing an index relationship table between the small watershed and the actual rainfall grid according to the cell number of the small watershed and the cell number of the actual rainfall grid.

5. The method for early warning of geological disaster meteorological risks in small watersheds based on massive regular grids according to claim 4 is characterized in that: The statistical calculation of the average rainfall in the small watershed in the preset time period includes: obtaining the rainfall of each forecast rainfall grid and the actual rainfall grid in the preset time period according to the rainfall data in the preset time period, traversing and calculating the forecast average rainfall and the actual average rainfall of each small watershed in the preset time period, and summing the forecast average rainfall and the actual average rainfall of each small watershed in the preset time period to obtain the average rainfall of each small watershed in the preset time period.

6. The method for early warning of geological disaster meteorological risks in small watersheds based on massive regular grids according to claim 5 is characterized in that: The average rainfall of each small watershed in the preset time period includes: V t =V r +V f Among them, V t It represents the average rainfall of each small watershed in the preset time period, V r It represents the average rainfall forecast for a small watershed in a preset time period, V f Indicates the actual average rainfall in the small watershed during the preset time period; V i Indicates the rainfall forecast for the rainfall grid i in the preset time period; △S i V represents the area ratio of the forecast rainfall grid i to the small watershed; j Indicates the rainfall of the real-time rainfall grid j in the preset time period; △S j It represents the area ratio of the actual rainfall grid j to the small watershed; n represents the number of forecast rainfall grids in the index relationship table between the small watershed and the rainfall grid; m represents the number of actual rainfall grids in the index relationship table between the small watershed and the rainfall grid.

7. The method for early warning of meteorological risk of geological disasters in small watersheds based on massive regular grids according to claim 2 is characterized in that: The constructed small watershed average rainfall threshold level judgment table includes: dividing the rainfall threshold level into five levels: no risk, low risk, medium risk, high risk, and extremely high risk; if the average rainfall V of the small watershed in the preset time period is t ≤x1, the rainfall level of the small watershed in the preset time period is classified as risk-free; if the average rainfall of the small watershed in the preset time period x1<V t ≤x2, the rainfall level of the small watershed in the preset time period is classified as low risk; if the average rainfall of the small watershed in the preset time period x2<V t ≤x3, the rainfall level of the small watershed in the preset time period is classified as medium risk; if the average rainfall of the small watershed in the preset time period x3<V t ≤x4, the rainfall level of the small watershed in the preset time period is classified as high risk; if the average rainfall of the small watershed in the preset time period is x4 <V t , the rainfall level of the small watershed in the preset time period is classified as extremely high risk, and a judgment table of the average rainfall threshold level of the small watershed is constructed.

8. The method for early warning of geological disaster meteorological risks in small watersheds based on massive regular grids according to claim 2 is characterized in that: Preferably, the risk warning level judgment matrix includes: The risk warning level is divided into five categories: no warning, blue warning, yellow warning, orange warning and red warning; the geological disaster susceptibility level of small watershed is divided into four categories: non-prone, low-prone, medium-prone and high-prone; the geological disaster susceptibility level of small watershed is defined as Y t , the rainfall level of the small watershed is defined as Pt; the risk warning level of the small watershed is defined as W t , then the risk warning level judgment matrix is ​​defined as: Identify the risk warning level of the small watershed based on the defined risk warning level judgment matrix: W t =warningMatrix[Y t ][P t ] Among them, warningMatrix represents the risk warning level judgment matrix; warningMatrix[Y t ][P t ] indicates the Pth t Row Y t Column value; P t =1 means the rainfall level in the small watershed is risk-free; P t =2 means the rainfall level in the small watershed is low risk; P t =3 means the rainfall level in the small watershed is medium risk; P t =4 means the rainfall level in the small watershed is high risk; P t =5 means that the rainfall level in the small watershed is extremely high risk; Y t =1 means that the geological disaster susceptibility level of the small watershed is not prone to occur; Y t =2 means that the geological disaster susceptibility level of the small watershed is low; Y t =3 means that the geological disaster susceptibility level of the small watershed is medium; Y t =4 means that the geological disaster susceptibility level of the small watershed is high; W t =0 means that the risk warning level of the small watershed is none; W t =1 means the risk warning level of the small watershed is blue; W t =2 means the risk warning level of the small watershed is yellow; t =3 means the risk warning level of the small watershed is orange; W t =4 means the risk warning level of the small watershed is red.

9. A small watershed geological disaster meteorological risk early warning device based on massive regular grids, characterized in that: It includes a processor and a memory; the memory is used to store computer programs; the processor is connected to the memory and is used to execute the computer programs stored in the memory, so that the small watershed geological disaster meteorological risk warning device based on massive regular grids can execute the small watershed geological disaster meteorological risk warning method based on massive regular grids as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a program, characterized in that: When the program is executed by the processor, the method for early warning of meteorological risks of geological disasters in small watersheds based on massive regular grids as described in any one of claims 1 to 8 is implemented.

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