Bridge water damage prediction method, device, equipment and medium

Abstract

The invention provides a bridge water damage prediction method and device, equipment and a medium. The method comprises the steps of obtaining position information of a target bridge; based on the position information of the target bridge, determining grid meteorological data of at least one key meteorological grid covered by an upstream basin of the target bridge within the prediction time period; the precipitation amount in the meteorological data of each grid is extracted; and obtaining flood damage risk prediction information of the target bridge based on the precipitation by using the bridge flood damage prediction model. In this way, accurate prediction of bridge water damage is achieved.

Description

Technical Field

[0001] This application relates to the technical field of road traffic disaster prediction, and particularly to a method, device, equipment and medium for predicting bridge water damage. Background Art

[0002] Bridge water damage refers to the phenomenon that a bridge is damaged due to the action of water. There are many limitations in traditional countermeasures for bridge water damage. For example, using sensors such as video, fiber optic sensors, and radar to monitor bridge water damage events can detect problems, but can only provide remedies after the event and cannot intervene in advance; at the same time, although the monitoring points are set on the bridge, the water damage problems often have ectopicity, that is, the problems appear on the bridge, but the root causes are located in the upstream rivers, resulting in difficulties in tracing the source. Summary of the Invention

[0003] In view of this, this application provides a method, device, equipment and medium for predicting bridge water damage, which can predict bridge water damage through the precipitation in the upstream basin, so as to achieve accurate prediction of bridge water damage.

[0004] In a first aspect, a method for predicting bridge water damage is provided, including: obtaining the location information of a target bridge; based on the location information of the target bridge, determining the grid meteorological data of at least one key meteorological grid covered by the upstream basin of the target bridge during a prediction time period; extracting the precipitation in each grid meteorological data; and using a bridge water damage prediction model to obtain water damage risk prediction information of the target bridge based on the precipitation.

[0005] In a second aspect, a device for predicting bridge water damage includes: a location information obtaining module for obtaining the location information of a target bridge; a grid data determining module for determining the grid meteorological data of at least one key meteorological grid covered by the upstream basin of the target bridge during a prediction time period based on the location information of the target bridge; a precipitation extraction module for extracting the precipitation in each grid meteorological data; and a prediction information obtaining module for using a bridge water damage prediction model to obtain water damage risk prediction information of the target bridge based on the precipitation.

[0006] In a third aspect, an electronic device is provided, including a processor, a memory, and a program stored on the memory and capable of running on the processor. When the program is executed by the processor, the steps of any one of the methods for predicting bridge water damage provided in the embodiments of this application are implemented.

[0007] In a fourth aspect, a computer-readable storage medium is provided, on which instructions are stored. When the instructions are executed by a processor, the steps of any one of the methods for predicting bridge water damage provided in the embodiments of this application are implemented.

[0008] In summary, the bridge water damage method, device, equipment, and medium provided by this application have the following beneficial effects: By the geographical location of the target bridge, the key meteorological grids covered by the boundary area of its upstream basin are dynamically matched, and by obtaining the grid meteorological data of the key meteorological grids in the predicted time period, the meteorological data of each area in the upstream basin can be captured more accurately. Extract the precipitation in each grid meteorological data and use the bridge water damage prediction model based on the precipitation to obtain the water damage risk prediction information of the target bridge, thereby integrating the precipitation data of multiple key meteorological grids in the upstream basin, avoiding the limitations of single-site data, and using the integrated precipitation data through the bridge water damage prediction model to output the bridge water damage risk prediction information, realizing the accurate and early prediction of bridge water damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0010] Figure 1 The flowchart showing a bridge water damage prediction method provided by an embodiment of the present application;

[0011] Figure 2 The schematic diagram showing the key meteorological grids of the target bridge provided by an embodiment of the present application;

[0012] Figure 3 The schematic diagram showing the structure of a bridge water damage prediction device provided by an embodiment of the present application;

[0013] Figure 4 The schematic diagram showing the structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] In order to make the above and other features and advantages of the present application clearer, the present application will be further described below with reference to the drawings. It should be understood that the specific embodiments given herein are for the purpose of explaining to those skilled in the art and are merely exemplary, not restrictive.

[0015] In the following description, many specific details are set forth to provide a thorough understanding of the present application. However, it is obvious to those skilled in the art that the present application does not need to be practiced with specific details. In other cases, well-known steps or operations are not described in detail to avoid obscuring the present application.

[0016] The inventor of the present invention found that the water damage of bridges generally results from excessive precipitation in the upstream watershed of the bridges. Therefore, the technical solution of the present application is proposed. By integrating the precipitation amounts in multiple regions of the upstream watershed and using the bridge water damage prediction model, accurate prediction of bridge water damage can be achieved.

[0017] On the one hand, an embodiment of the present application provides a bridge water damage prediction method, which is applied to a bridge water damage prediction device. Figure 1 The flowchart of a bridge water damage prediction method provided by an embodiment of the present application is shown, as Figure 1 shown. The bridge water damage prediction method may include the following steps.

[0018] S11, obtain the location information of the target bridge.

[0019] The target bridge involved in an embodiment of the present application can be any bridge. The location information of the target bridge may include the longitude and latitude of the target bridge.

[0020] In an embodiment of the present application, the location information of the target bridge can be obtained in various ways, such as from the electronic file of the bridge, the geographic information system or the electronic map.

[0021] In addition, after obtaining the location information of the target bridge, the accuracy of the location information of the target bridge is verified.

[0022] S12, based on the location information of the target bridge, determine the grid meteorological data of at least one key meteorological grid covered by the upstream watershed of the target bridge within the prediction time period.

[0023] The prediction time period involved in an embodiment of the present application can be a time period starting from a certain future moment. Optionally, the duration of the prediction time period can be 72 hours.

[0024] The upstream watershed of the target bridge involved in an embodiment of the present application refers to all the catchment areas located upstream in the water flow direction of the location of the target bridge. The upstream watershed of the target bridge can be analyzed in combination with the hydrological information of the river where the target bridge is located.

[0025] The key meteorological grid involved in an embodiment of the present application can be a meteorological grid that can affect the occurrence of bridge water damage and is a meteorological grid covered by the upstream watershed of the target bridge. In addition, there is no limit to the spatial resolution of the key meteorological grid. The spatial resolution of the key meteorological grid can be 1 km * 1 km, 2 km * 2 km, 5 km * 5 km, 10 km * 10 km, etc.

[0026] The grid meteorological data involved in an embodiment of the present application may include various meteorological data, such as temperature, precipitation, humidity, wind direction, wind speed, and weather phenomena, etc. The grid meteorological data belongs to meteorological prediction data and can be obtained through the application programming interface of the meteorological platform.

[0027] In an embodiment of the present application, the bridge water damage prediction device may determine at least one key meteorological grid covered by the upstream basin of the target bridge according to the location information of the target bridge, and obtain the grid meteorological data of the key meteorological grid within the prediction time period from the meteorological platform.

[0028] It should be noted that the spatial resolution of each key meteorological grid covered by the upstream basin is the same. However, the spatial resolutions of the key meteorological grids covered by the upstream basins of different bridges may be different.

[0029] S13, Extract the precipitation in each grid meteorological data.

[0030] The precipitation involved in an embodiment of the present application refers to the precipitation at each time step within the prediction time period. Optionally, the time step may be 1 hour.

[0031] S14, Use the bridge water damage prediction model to obtain the water damage risk prediction information of the target bridge based on the precipitation.

[0032] The bridge water damage prediction model involved in an embodiment of the present application may be constructed based on a machine learning model, such as constructed using a deep learning model. The bridge water damage prediction model is trained based on the historical water damage information of the bridge and the historical precipitation. Among them, the historical water damage information includes the key meteorological grid information of the bridge and the water damage level.

[0033] In an embodiment of the present application, the precipitation of each key meteorological grid at each time step within the prediction time period is used as the input of the bridge water damage prediction model. The input data of the bridge water damage prediction model includes the key meteorological grid and the corresponding precipitation at each step.

[0034] The water damage risk prediction information involved in an embodiment of the present application at least includes the water damage occurrence probability and the water damage level.

[0035] In another embodiment of the present application, in order to more comprehensively predict the bridge water damage information, the water damage risk prediction information may further include the disaster-causing grid and the disaster-causing time period. Among them, the disaster-causing grid and the disaster-causing time period refer to the key meteorological grid that causes the bridge water damage and the precipitation time period that causes the bridge water damage.

[0036] In this way, by outputting the disaster-causing grid and the disaster-causing time period, the reason for the bridge water damage can be traced from the source, so as to solve the problem of bridge water damage from the source.

[0037] It should be noted that the output of the bridge water damage prediction model can be multi-task output, such as outputting classification tasks (i.e., predicting the probability of water damage), regression tasks (i.e., predicting the level of water damage), and interpretive tasks (i.e., predicting the disaster-causing grids and disaster-causing time periods).

[0038] In some of the above embodiments, by the geographical location of the target bridge, the key meteorological grids covered by the boundary area of its upstream basin are dynamically matched, and by obtaining the grid meteorological data of the key meteorological grids in the prediction time period, the meteorological data of each area in the upstream basin can be captured more accurately. The precipitation in each grid meteorological data is extracted, and based on the precipitation, the bridge water damage prediction model is used to obtain the water damage risk prediction information of the target bridge, so as to integrate the precipitation data of multiple key meteorological grids in the basin, avoid the limitations of single-site data, and use the integrated precipitation data to output the water damage risk prediction information of the bridge through the bridge water damage prediction model, realizing the early and accurate prediction of bridge water damage.

[0039] Since when the area of the boundary area of the upstream basin is large, the number of meteorological grids covered by the boundary area of the upstream basin is large, which makes the bridge water damage prediction model need to process a large amount of data and has a large calculation amount, which is not conducive to the prediction of bridge water damage. Therefore, a different selection strategy is proposed to select meteorological grids according to the size of the area of the boundary area of the upstream basin, so as to reduce the data that the model needs to process and reduce the model calculation amount when the area of the boundary area is large, which is conducive to the prediction of bridge water damage.

[0040] In some embodiments, S12, based on the position information of the target bridge, determining the grid meteorological prediction data of at least one key meteorological grid covered by the upstream basin of the target bridge in the prediction time period, includes: determining the boundary area of the upstream basin of the target bridge based on the position information of the target bridge and the river hydrological information; determining the selection strategy of the key meteorological grid according to the comparison result between the area of the boundary area of the upstream basin and a preset threshold; obtaining at least one key meteorological grid covered by the upstream basin according to the selection strategy; and obtaining the grid meteorological prediction data of at least one key meteorological grid in the prediction time period.

[0041] In an embodiment of the present application, the target river passing through the target bridge can be determined according to the position information of the target bridge, the hydrological information of the target river can be matched from the river hydrological information, and the boundary area of the upstream basin of the target bridge can be obtained by watershed extraction according to the hydrological information of the target river through a hydrological analysis tool.

[0042] In another embodiment of the present application, when the location information of the target bridge matches the location information of the bridges in the historical record, the boundary area range of the upstream basin in the historical record can be directly used as the boundary area range of the upstream basin of the target bridge for this prediction.

[0043] The selection strategy involved in an embodiment of the present application is the strategy of selecting key meteorological grids. The selection strategy may include a basic strategy and an optimization strategy. Among them, the basic strategy is the grid selection strategy when the area of the boundary area range of the upstream basin is small (i.e., not greater than the preset threshold), that is, each meteorological grid covered by the boundary area range of the upstream basin is used as the key meteorological grid covered by the upstream basin.

[0044] The optimization strategy is the grid selection strategy when the area of the boundary area range of the upstream basin is large (i.e., greater than the preset threshold). Compared with the basic strategy, the optimization strategy deletes some unimportant meteorological grids from the meteorological grids covered by the boundary area range of the upstream basin.

[0045] The preset threshold involved in an embodiment of the present application can be set according to user requirements. For example, the preset threshold can be 10 square kilometers.

[0046] In an embodiment of the present application, the area of the boundary area range of the upstream basin can be compared with the preset threshold to obtain the comparison result of the area of the boundary area range of the upstream basin and the preset threshold. And according to the comparison result, a suitable selection strategy is determined, and the determined selection strategy is executed to obtain all the key meteorological grids covered by the upstream basin.

[0047] Figure 2 The schematic diagram showing the key meteorological grids of the target bridge provided by an embodiment of the present application is as Figure 2 shown, and some of the key meteorological grids (i.e., Grid 1 - Grid 6) covered by the upstream basin of the target bridge. It should be noted that Figure 2 only the positions of the key meteorological grids relative to the bridge are shown.

[0048] In some embodiments, according to the comparison result of the area of the boundary area range of the upstream basin and the preset threshold, the selection strategy of the key meteorological grids is determined, including: when the area of the boundary area range of the upstream basin is not greater than the preset threshold, determining the selection strategy of the key meteorological grids as the basic strategy; when the area of the boundary area range of the upstream basin is greater than the preset threshold, determining the selection strategy of the key meteorological grids as the optimization strategy.

[0049] In an embodiment of the present application, the optimization strategy includes screening out multiple key meteorological grids from all the meteorological grids covered by the boundary area range according to the hydrological characteristics of the upstream basin.

[0050] The hydrological characteristics of the upstream basin involved in an embodiment of the present application may include precipitation characteristics, topographic characteristics, and runoff characteristics. Among them, the topographic characteristics include, but are not limited to, the slope difference and elevation difference of the basin. The precipitation characteristics may include the monthly average precipitation, etc. The runoff characteristics may include the monthly runoff, water level, and water velocity.

[0051] In an embodiment of the present application, the hydrological characteristics of the upstream basin can be obtained from the meteorological data provided by the meteorological platform, the runoff monitoring data provided by the hydrological station, and the electronic map.

[0052] In an embodiment of the present application, multiple key meteorological grids can be screened from all meteorological grids covered by the boundary area range by using screening conditions. Among them, the screening conditions are set according to the hydrological characteristics. For example, the screening conditions may include taking the meteorological grids corresponding to the areas with more precipitation within the boundary area range as key meteorological grids, taking the meteorological grids corresponding to the areas with larger runoff within the boundary area range as key meteorological grids, and taking the meteorological grids corresponding to the high-altitude areas within the boundary area range as key meteorological grids, etc.

[0053] That is to say, according to the hydrological characteristics of the upstream basin, determine the areas within the boundary area range that meet the screening conditions, and determine all meteorological grids covered by the boundary area range, and screen out the meteorological grids corresponding to the areas that meet the screening conditions (i.e., key meteorological grids) from them.

[0054] In the above embodiment, screening key meteorological grids through the hydrological characteristics of the upstream basin can efficiently screen out key meteorological grids that are strongly related to the basin hydrological characteristics, providing a reliable basis for subsequent water damage prediction.

[0055] Since the terrain complexity affects the spatial resolution of meteorological grids, for example, complex terrain requires high spatial resolution, and flat terrain requires low resolution. Therefore, in some embodiments, before determining the selection strategy of key meteorological grids according to the comparison result between the area of the boundary area range of the upstream basin and the preset threshold, the bridge water damage prediction method further includes: selecting the spatial resolution of key meteorological grids that matches the terrain complexity according to the terrain complexity of the upstream basin.

[0056] The terrain complexity involved in an embodiment of the present application represents the proportion of complex terrain. In an embodiment of the present application, calculate the terrain complexity of the upstream basin. When the terrain complexity is less than the preset complexity and the proportion of complex terrain in the upstream basin is relatively small, a low spatial resolution can be selected as the spatial resolution of the key meteorological grid; when the terrain complexity is not less than the preset complexity and the proportion of complex terrain in the upstream basin is relatively large, a high spatial resolution can be selected as the spatial resolution of the key meteorological grid. Among them, the low spatial resolution is a spatial resolution not greater than 3km×3km. The high spatial resolution is a spatial resolution greater than 3km×3km.

[0057] In some of the above embodiments, according to the terrain complexity of the upper stream basin, the spatial resolution of the key meteorological grids can be flexibly selected, so as to achieve dynamic switching of the resolution, and reduce the problem that the prediction of bridge water damage is not accurate enough due to the use of a spatial resolution that does not match the complex terrain.

[0058] In some embodiments, after extracting the precipitation in each of the grid meteorological data in S13, the bridge water damage prediction further includes: determining the rendering color of each key meteorological grid according to the correspondence between the precipitation and the rendering color and the precipitation of each key meteorological grid.

[0059] The rendering color involved in an embodiment of the present application refers to the color used to render the key meteorological grids in the visualization interface. The correspondence between the precipitation and the rendering color refers to the mapping rule between the precipitation and the rendering color.

[0060] In an embodiment of the present application, different precipitation amounts can be divided into different precipitation intensities according to the meteorological precipitation intensity classification, and different precipitation intensities can correspond to different rendering colors. That is, the precipitation amounts of each key meteorological grid at each time step within the prediction time period are obtained, and the key meteorological grids are rendered according to the rendering colors corresponding to the precipitation amounts.

[0061] For example, when the precipitation of a certain key meteorological grid exceeds 60 mm, it indicates heavy precipitation. The rendering color of the key meteorological grid is determined to be red, and the key meteorological grid is rendered as red on the visualization interface.

[0062] In some of the above embodiments, determining the rendering color of each key meteorological grid according to the precipitation amount can render each key meteorological grid on the visualization interface in different colors, directly presenting the precipitation distribution of the basin, and facilitating users to quickly discover dangerous precipitation areas.

[0063] In some embodiments, after obtaining the water damage risk prediction information of the target bridge based on the precipitation amount by using the bridge water damage prediction model in S14, the bridge water damage early warning further includes: generating and pushing an early warning message when the water damage risk prediction information meets the early warning conditions.

[0064] The early warning conditions involved in the embodiments of the present application can be to issue an early warning when the probability of water damage exceeds a preset probability value. The early warning message at least includes the bridge name, bridge location, water damage level, and the time of water damage occurrence.

[0065] In one embodiment of the present application, when the warning condition is met, the push mechanism is immediately activated, and according to the preferences and actual situation of the target audience, a suitable push channel is selected to push the warning information. For example, for drivers, channels such as in-vehicle navigation systems and mobile phone apps can be selected for pushing; for the public, channels such as social media, radio, and television can be selected for pushing; for traffic management departments, it is pushed to the management platform through the network.

[0066] In the above embodiment, when the weather prediction information meets the preset alarm condition, warning information can be generated and pushed in a timely and accurate manner, providing effective warnings for road managers, drivers, and the public.

[0067] In some embodiments, in S14, after obtaining the water damage risk prediction information of the target bridge based on the precipitation using the bridge water damage prediction model, the bridge water damage warning further includes: determining the corresponding processing decision according to the water damage risk prediction information.

[0068] In one embodiment of the present application, a processing decision library can be established in advance, and for different water damage probabilities and levels, the corresponding processing decision is determined from the processing decision library.

[0069] The relationship between the water damage probability and level and the processing decision can be shown in Table 1.

[0070] Table 1

[0071]

[0072] Another aspect of the embodiments of the present application provides a bridge water damage prediction device. Figure 3 The structure diagram of a bridge water damage prediction device provided by an embodiment of the present application is shown, as Figure 3 shown, the bridge water damage prediction device 30 may include the following several modules.

[0073] A location information acquisition module 31, configured to acquire the location information of the target bridge.

[0074] A grid data determination module 32, configured to determine the grid meteorological data of at least one key meteorological grid covered by the upstream basin of the target bridge during the prediction time period based on the location information of the target bridge.

[0075] A precipitation extraction module 33, configured to extract the precipitation in each of the grid meteorological data.

[0076] A prediction information obtaining module 34, configured to obtain the water damage risk prediction information of the target bridge based on the precipitation using the bridge water damage prediction model.

[0077] In the above embodiments, through the geographical location of the target bridge, the key meteorological grids covered by the boundary area range of its upstream basin are dynamically matched, and by obtaining the grid meteorological data of the key meteorological grids in the prediction time period, the meteorological data of each area in the upstream basin can be captured more accurately. The precipitation in each grid meteorological data is extracted, and the water damage risk prediction information of the target bridge is obtained by using the bridge water damage prediction model based on the precipitation, so as to integrate the precipitation data of multiple key meteorological grids in the basin, avoid the limitations of single-site data, and the water damage risk prediction information of the bridge can be output by using the integrated precipitation data through the bridge water damage prediction model, realizing the accurate prediction of bridge water damage in advance.

[0078] In some embodiments, the grid data determination module 32 is specifically configured to determine the boundary area range of the upstream basin of the target bridge based on the position information of the target bridge and the river hydrological information; determine the selection strategy of the key meteorological grids according to the comparison result between the area of the boundary area range of the upstream basin and a preset threshold; obtain at least one key meteorological grid covered by the upstream basin according to the selection strategy; and obtain the grid meteorological prediction data of at least one key meteorological grid in the prediction time period.

[0079] In some embodiments, the selection strategy includes a basic selection strategy and an optimization selection strategy. The grid data determination module 32 is further configured to determine that the selection strategy of the key meteorological grids is the basic strategy when the area of the boundary area range of the upstream basin is not greater than the preset threshold; and determine that the selection strategy of the key meteorological grids is the optimization strategy when the area of the boundary area range of the upstream basin is greater than the preset threshold.

[0080] In some embodiments, the bridge water damage prediction device 30 may further include a resolution selection module, configured to select the spatial resolution of the key meteorological grids matching the terrain complexity according to the terrain complexity of the upstream basin before determining the selection strategy of the key meteorological grids according to the comparison result between the area of the boundary area range of the upstream basin and a preset threshold.

[0081] In some embodiments, the bridge water damage prediction device 30 may further include a color determination module, configured to determine the rendering color of each key meteorological grid according to the correspondence between the precipitation and the rendering color and the precipitation of each key meteorological grid after extracting the precipitation in each grid meteorological data.

[0082] In some embodiments, the bridge water damage prediction device 30 may further include a warning module, configured to generate and push a warning message when the water damage risk prediction information meets the warning conditions.

[0083] In some embodiments, the bridge flood damage prediction device 30 may further include a decision-making determination module, configured to determine a corresponding processing decision according to the flood damage risk prediction information.

[0084] It should be understood that the specific features, operations, and details described above regarding the method of the present application can be similarly applied to the devices and systems of the present application, or vice versa. Additionally, each step of the method of the present application described above can be executed by the corresponding components or units of the device or system of the present application.

[0085] It should be understood that each module / unit of the device of the present application can be implemented in whole or in part by software, hardware, firmware, or a combination thereof. Each module / unit can be embedded in the processor of the electronic device in the form of hardware or firmware or independent of the processor, or stored in the memory of the electronic device in the form of software for the processor to call to execute the operations of each module / unit. Each module / unit can be implemented as an independent component or module, or two or more modules / units can be implemented as a single component or module.

[0086] In another aspect of the present application, an electronic device is provided. Figure 4 The structural schematic diagram of an electronic device provided according to an embodiment of the present application is shown, as Figure 4 shown, the electronic device 40 includes a processor 41, a memory 42, and a program stored on the memory and capable of running on the processor. When the program is executed by the processor, it implements the steps of the bridge flood damage prediction method provided in any of the above embodiments.

[0087] In one embodiment, the electronic device 40 may include a processor, a memory, a network interface, a communication interface, etc. connected through a system bus. The processor of the electronic device 40 can be used to provide necessary computing, processing, and / or control capabilities. The memory of the electronic device 40 may include a non-volatile storage medium and an internal memory. The non-volatile storage medium may store an operating system, a computer program, etc. The internal memory can provide an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface and communication interface of the electronic device 40 can be used to connect and communicate with external devices through a network.

[0088] In another aspect of the present application, a computer-readable storage medium is provided. Instructions are stored on the computer-readable storage medium. When the instructions are executed by the processor, they implement the steps of the bridge flood damage prediction method provided in any of the above embodiments.

[0089] Those skilled in the art can understand that the method steps of this application can be completed by a computer program instructing relevant hardware such as electronic devices or processors. The computer program can be stored in a non-transitory computer-readable storage medium, and when the computer program is executed, the steps of this application are caused to be executed. Depending on the situation, any reference to a memory, storage, or other medium in this article may include non-volatile or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tapes, floppy disks, magneto-optical data storage devices, optical data storage devices, hard disks, solid state disks, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0090] The technical features described above can be combined arbitrarily. Although all possible combinations of these technical features are not described, any combination of these technical features should be considered to be covered by this specification as long as such a combination does not exist in contradiction.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them; although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for predicting bridge water damage, characterized in that, Including: Obtain the location information of the target bridge; Based on the location information of the target bridge, determine the grid meteorological data of at least one key meteorological grid covered by the upstream basin of the target bridge within the prediction time period; Extract the precipitation in each of the grid meteorological data; Utilize the bridge water damage prediction model to obtain the water damage risk prediction information of the target bridge based on the precipitation.

2. The method according to claim 1, characterized in that, The determining the grid meteorological prediction data of at least one key meteorological grid covered by the upstream basin of the target bridge within the prediction time period based on the location information of the target bridge includes: Based on the location information of the target bridge and the river hydrological information, determine the boundary area range of the upstream basin of the target bridge; According to the comparison result between the area of the boundary area range of the upstream basin and a preset threshold, determine the selection strategy for the key meteorological grid; According to the selection strategy, obtain at least one key meteorological grid covered by the upstream basin; Obtain the grid meteorological prediction data of the at least one key meteorological grid within the prediction time period.

3. The method according to claim 2, characterized in that, The selection strategy includes a basic selection strategy and an optimized selection strategy. According to the comparison result between the area of the boundary area range of the upstream basin and a preset threshold, determining the selection strategy for the key meteorological grid includes: When the area of the boundary area range of the upstream basin is not greater than the preset threshold, determine that the selection strategy for the key meteorological grid is the basic strategy; When the area of the boundary area range of the upstream basin is greater than the preset threshold, determine that the selection strategy for the key meteorological grid is the optimized strategy.

4. The method according to claim 3, characterized in that, The optimized strategy includes screening out multiple key meteorological grids from all meteorological grids covered by the boundary area range according to the hydrological characteristics of the upstream basin.

5. The method according to any one of claims 2 to 4, characterized in that Before determining the selection strategy for the key meteorological grid according to the comparison result between the area of the boundary area range of the upstream basin and a preset threshold, it further includes: selecting the spatial resolution of the key meteorological grid that matches the terrain complexity according to the terrain complexity of the upstream basin.

6. The method according to any one of claims 1-4, characterized in that, After extracting the precipitation in each of the grid meteorological data, it further includes: Determine the rendering color of each key meteorological grid according to the correspondence between the precipitation and the rendering color and the precipitation of each key meteorological grid.

7. The method according to claim 1, characterized in that The water damage risk prediction information includes the probability of water damage occurrence and the water damage level.

8. A bridge water damage prediction device, characterized in that, Including: A location information acquisition module for obtaining the location information of the target bridge; A grid data determination module for determining the grid meteorological data of at least one key meteorological grid covered by the upstream basin of the target bridge within the prediction time period based on the location information of the target bridge; A precipitation extraction module for extracting the precipitation in each of the grid meteorological data; A prediction information obtaining module for obtaining the water damage risk prediction information of the target bridge based on the precipitation by using the bridge water damage prediction model.

9. An electronic device, characterized in that, Including a processor, a memory, and a program stored on the memory and executable on the processor. When the program is executed by the processor, it implements the steps of the bridge water damage prediction method as described in any one of claims 1 - 7.

10. A computer-readable storage medium, characterized in that, Instructions are stored on the computer-readable storage medium, and when the instructions are executed by a processor, the steps of the bridge washout prediction method according to any one of claims 1-7 are implemented.

Images