Bridge scour warning method, device, equipment and storage medium

Abstract

The present invention discloses a bridge scour warning method, device, equipment, and storage medium. The method includes the following steps: obtaining average water velocity and water depth data measured around a bridge pier; calculating corresponding scour depth data based on the average water velocity and water depth data; and comparing the scour depth data with warning depth data to determine whether a bridge warning should be triggered. This application can accurately monitor bridge scour depth data, thereby improving the reliability of warnings and effectively preventing losses caused by bridge scour disasters.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge monitoring, and in particular to a bridge scour early warning method, device, equipment and storage medium. Background Art

[0002] The interference of bridge piers with water flow causes localized scour of the riverbed around the piers. This scour not only endangers the safety of upstream and downstream buildings and nearby embankments, but can also lead to water damage to the piers themselves. Currently, the most common scour monitoring method relies on manual monitoring using ship-borne echo sounder and photoelectric detection equipment. This method is suitable for areas with slow currents, good underwater environments, and the absence of large obstacles. Its advantages are low cost and wide coverage. However, its disadvantages are that scour disasters often occur during flood season, when water flows are fast and debris is abundant, making scour monitoring difficult. Therefore, early warning systems based on scour monitoring struggle to obtain accurate scour monitoring data when scour disasters are occurring, thus compromising their reliability.

[0003] Therefore, how to obtain accurate scour detection data and thus improve the reliability of early warning is a technical problem that needs to be solved urgently. Summary of the Invention

[0004] The main purpose of the present invention is to provide a bridge scour early warning method, device, equipment and storage medium, which can accurately monitor the scour depth data of the bridge, thereby improving the reliability of the early warning and effectively preventing the losses caused by bridge scour disasters.

[0005] In a first aspect, the present application provides a bridge scour early warning method, wherein the method comprises the steps of:

[0006] Obtain average water velocity and water depth data measured around the bridge piers;

[0007] Calculating corresponding scour depth data based on the average water flow velocity data and water depth data;

[0008] The scour depth data is compared with the warning depth data to determine whether a bridge warning is triggered.

[0009] In conjunction with the first aspect above, as an optional implementation method, the early warning platform is used to collect hydrological and meteorological early warning information in the area where the bridge is located, wherein the hydrological early warning information includes: flood level and flow information, and the meteorological early warning information includes: rainstorm rainfall, typhoon level and tidal wave information;

[0010] The hydrological warning information and meteorological warning information are input into a pre-trained first deep learning model to obtain average water flow velocity data and water depth data measured around the bridge pier.

[0011] In conjunction with the first aspect above, as an optional implementation, scour depth data measured around the bridge piers is collected according to a preset first preset rule, wherein the first preset rule is to test the riverbed elevation once before the bridge piers are scoured and once after the bridge piers are scoured;

[0012] collecting average water velocity and water depth data measured around the bridge pier according to a preset second preset rule, wherein the second preset rule is the average flow velocity and water depth during the bridge pier scouring period;

[0013] Establishing a scour data set using the scour depth data and the average flow velocity data;

[0014] The scour data set is input into a pre-trained second deep learning model to obtain corresponding scour depth data.

[0015] In combination with the first aspect above, as an optional implementation method, the first deep learning model is iteratively updated using the real-time collected measured scour data of bridge piers to optimize the first deep learning model.

[0016] In combination with the first aspect above, the second deep learning model is iteratively updated using the real-time collected measured scour data of bridge piers to optimize the second deep learning model.

[0017] In combination with the first aspect above, as an optional implementation method, the warning depth value can be customized according to needs;

[0018] When the scour depth data is greater than or equal to the warning depth value, determining to trigger a bridge warning;

[0019] When the scour depth data is less than the warning depth value, the bridge warning is not triggered.

[0020] In combination with the first aspect above, as an optional implementation method, the warning level is determined according to the scouring depth;

[0021] When the scouring depth reaches a first threshold, it is judged as a blue warning;

[0022] When the scouring depth reaches a second threshold, it is determined to be a yellow warning;

[0023] When the scouring depth reaches a third threshold, it is determined to be an orange warning;

[0024] When the scouring depth reaches a fourth threshold, it is determined to be a red warning.

[0025] In a second aspect, the present application provides a bridge scour warning device, which includes:

[0026] An acquisition module, which is used to obtain average water flow velocity data and water depth data measured around the bridge pier;

[0027] a calculation module, configured to calculate corresponding scour depth data based on the average water flow velocity data and the water depth data;

[0028] The processing module is used to compare the scour depth data with the warning depth data to determine whether to trigger a bridge warning.

[0029] In a third aspect, the present application further provides an electronic device comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method described in any one of the first aspects is implemented.

[0030] In a fourth aspect, the present application further provides a computer-readable storage medium storing computer program instructions, which, when executed by a computer, enables the computer to execute any one of the methods described in the first aspect.

[0031] This application provides a bridge scour early warning method, device, equipment, and storage medium. The method includes the following steps: obtaining average water velocity and water depth data measured around a bridge pier; calculating corresponding scour depth data based on the average water velocity and water depth data; and comparing the scour depth data with early warning depth data to determine whether a bridge early warning should be triggered. This application accurately monitors bridge scour depth data, thereby improving the reliability of early warnings and effectively preventing losses caused by bridge scour disasters.

[0032] It should be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0034] Figure 1 This is a flow chart of a bridge scour early warning method provided in an embodiment of the present application;

[0035] Figure 2 This is a schematic diagram of a bridge scour warning device provided in an embodiment of the present application;

[0036] Figure 3 A schematic diagram of an electronic device provided in an embodiment of the present application;

[0037] Figure 4 A schematic diagram of a computer-readable program medium provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0039] Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Some of the blocks shown in the drawings are functional entities that do not necessarily correspond to physically or logically separate entities.

[0040] The embodiments of the present application are further described in detail below with reference to the accompanying drawings.

[0041] Reference Figure 1 , Figure 1 The figure shows a flow chart of a bridge scour warning method provided by the present invention. Figure 1 As shown, the method includes the steps of:

[0042] Step S101: Obtain average water flow velocity data and water depth data measured around the bridge pier.

[0043] Specifically, the early warning platform collects hydrological and meteorological warning information in the area where the bridge is located, wherein the hydrological warning information includes: flood level and flow information, and the meteorological warning information includes: rainstorm rainfall, typhoon level and tidal wave information;

[0044] The hydrological warning information and meteorological warning information are input into a pre-trained first deep learning model to obtain average water flow velocity data and water depth data measured around the bridge pier.

[0045] For ease of understanding, an example is given. According to preset rules (average water flow velocity, water depth and scour depth over a certain period of time), the average water flow velocity, water depth and scour depth data during the measurement period around the bridge piers are collected when a measured flood occurs to form a (scour) data set, and sent to the pre-trained first deep learning model; the pre-trained first deep learning model, through data preprocessing and model training, uses the water flow velocity and water depth data around the bridge piers as the input layer and the scour depth as the output layer, so that for every input of a flow velocity and a water depth data, a corresponding scour depth data can be obtained.

[0046] It can be understood that the average water flow velocity, water depth and scour depth data of the preset time are collected to establish a data set, and the data set is input into the pre-trained model. Through data preprocessing and model training, the water flow velocity and water depth data around the pier are used as the input layer, and the scour depth is used as the output layer, so that for every input of a flow velocity and a water depth data, a corresponding scour depth data can be obtained.

[0047] Step S102: Calculate corresponding scour depth data based on the average water flow velocity data and water depth data.

[0048] Specifically, scour depth data measured around the bridge piers are collected according to a preset first preset rule, wherein the first preset rule is to test the riverbed elevation once before the bridge piers are scoured and once after the bridge piers are scoured;

[0049] collecting average water velocity and water depth data measured around the bridge pier according to a preset second preset rule, wherein the second preset rule is the average flow velocity and water depth during the bridge pier scouring period;

[0050] Establishing a scour data set using the scour depth data and the average flow velocity data;

[0051] The scour data set is input into a pre-trained second deep learning model to obtain corresponding scour depth data.

[0052] For easier understanding, let's take an example: The riverbed elevation is measured once before and once after a pier scour. This means scour depth data is collected around the piers. The average flow velocity and water depth during the pier scour period are then collected. This scour depth data and average flow velocity data are used to create a scour dataset. This dataset is then input into a pre-trained second deep learning model to obtain the corresponding scour depth data. In other words, scour depth data can be obtained by inputting water depth and flow data into the second deep learning model.

[0053] In one embodiment, the first deep learning model is iteratively updated using the real-time measured scour data of the bridge piers to optimize the first deep learning model.

[0054] The second deep learning model is iteratively updated using the real-time measured scour data of the bridge piers to optimize the second deep learning model.

[0055] It is understandable that by collecting the above-mentioned measured data, iteratively upgrading deep learning model 1 (first deep learning model) and deep learning model 2 (second deep learning model), the accuracy of the early warning system can be continuously improved.

[0056] Step S103: Compare the scour depth data with the warning depth data to determine whether a bridge warning is triggered.

[0057] Specifically, the warning depth value can be customized according to needs;

[0058] When the scour depth data is greater than or equal to the warning depth value, determining to trigger a bridge warning;

[0059] When the scour depth data is less than the warning depth value, the bridge warning is not triggered.

[0060] In one embodiment, the warning level is determined based on the scour depth;

[0061] When the scouring depth reaches a first threshold, it is judged as a blue warning;

[0062] When the scouring depth reaches a second threshold, it is determined to be a yellow warning;

[0063] When the scouring depth reaches a third threshold, it is determined to be an orange warning;

[0064] When the scouring depth reaches a fourth threshold, it is determined to be a red warning.

[0065] For ease of understanding, an example is given. The rules for selecting hydrological warning information or meteorological warning parameters are as follows: for large rivers in the runoff basin, the flood warning level is selected; for small rivers in mountainous areas in the runoff basin, the rainstorm warning level is selected; for sea areas, the typhoon warning level is selected.

[0066] Common hydrological warning levels are as follows: (1) Blue warning: water level (flow) approaches the warning water level (flow); (2) Yellow warning: water level (flow) reaches or exceeds the warning water level (flow); (3) Orange warning: water level (flow) recurrence period reaches or exceeds 10 to 20 years; (4) Red warning: water level (flow) reaches or exceeds the flood control design water level (flow).

[0067] Common weather warning levels are as follows:

[0068] Heavy rain warning: (1) Blue warning: Rainfall will reach 50 mm or more within 12 hours, or has reached 50 mm or more and is likely to continue. (2) Yellow warning: Rainfall will reach 50 mm or more within 6 hours, or has reached 50 mm or more and is likely to continue. (3) Orange warning: Rainfall will reach 50 mm or more within 3 hours, or has reached 50 mm or more and is likely to continue. (4) Red warning: Rainfall will reach 100 mm or more within 3 hours, or has reached 100 mm or more and is likely to continue.

[0069] Typhoon Warning: (1) Blue Warning: The area may or has been affected by a tropical cyclone within 24 hours, with the average wind speed on the coast or land reaching level 6 or above, or gusts reaching level 8 or above and may continue. (2) Yellow Warning: The area may or has been affected by a tropical cyclone within 24 hours, with the average wind speed on the coast or land reaching level 8 or above, or gusts reaching level 10 or above and may continue. (3) Orange Warning: The area may or has been affected by a tropical cyclone within 12 hours, with the average wind speed on the coast or land reaching level 10 or above, or gusts reaching level 12 or above and may continue. (4) Red Warning: The area may or has been affected by a tropical cyclone within 6 hours, with the average wind speed on the coast or land reaching level 12 or above, or gusts reaching level 14 or above and may continue.

[0070] Taking a Yangtze River Bridge as an example, it is located in the Hankou section of the middle reaches of the Yangtze River in the runoff basin. Therefore, its hydrological warning or meteorological warning parameter is selected as the flood warning level of the Hankou section of the middle reaches of the Yangtze River.

[0071] Training phase: (1) When the flow rate at the Yangtze River Hankou station is greater than 30,000 m3 / s, the monitoring device is activated to obtain the average flow velocity, average water depth, and scour depth data during the period, and the data are input into training module 1 to obtain deep learning model 1. (2) After the information collection module collects the flood warning level, the monitoring device is activated to obtain the average flow velocity and average water depth during the flood period, and the data are input into training module 2 to obtain deep learning model 2.

[0072] Early warning stage: (1) The information collection module collects the flood warning level, inputs it into the deep learning model 2, and obtains the predicted average flow velocity and average water depth. The obtained average flow velocity and average water depth are then input into the deep learning model 1, and the scour depth is output. When the preset scour depth is reached, an early warning is issued.

[0073] When the flow rate at the Yangtze River Hankou Station is greater than 30,000 m3 / s, the monitoring device is activated to obtain the average flow velocity and average water depth during the period, input the deep learning model 1, output the scour depth, and issue an early warning when the preset scour depth is reached.

[0074] Preset scour depth: (1) Blue warning: The preset scour depth is 60% of the design scour depth. (2) Yellow warning: The preset scour depth is 80% of the design scour depth. (3) Orange warning: The preset scour depth is 90% of the design scour depth. (4) Red warning: The preset scour depth is 100% of the design scour depth.

[0075] It should be noted that corresponding protection strategies should be formulated according to different warning levels.

[0076] It is understandable that local scour of bridges is mainly related to the bridge structure, water flow conditions and riverbed characteristics. For a selected bridge, its structural type and riverbed characteristics are fixed, so its scour depth mainly varies with the water flow conditions (water flow velocity, water depth). Based on this, a large amount of measured data can be used to combine the scour depth information that is difficult to monitor with the water flow velocity and water depth that are easy to measure to establish a deep learning model, including: a scour depth monitoring device, which obtains a scour depth measurement value according to preset rules (testing the riverbed elevation once before the flood and once after the flood); a water flow monitoring device, which obtains a water flow velocity and a water depth according to preset rules (the average flow velocity and water depth before and between the flood); establishing a data set, and obtaining multiple matching scour depths, water flow velocities, and water depths based on the uninterrupted monitoring of the above-mentioned monitoring device; and establishing a deep learning model of scour depth, water flow velocity, and water depth through feature training of a deep learning algorithm based on the above-mentioned data set.

[0077] There is a significant correlation between the water flow velocity and water depth at a specific measuring point and the hydrological warning and meteorological warning information. Therefore, after receiving the hydrological warning information or meteorological warning information, one water flow velocity and one water depth value are obtained according to the preset rules within the warning period. After repeating the above process multiple times, the relevant data are established into a data set. Through deep learning algorithm feature matching, a deep learning model2 of water flow velocity, water depth and hydrological warning and meteorological warning is established.

[0078] Through the collected hydrological warning information or meteorological warning information, the trained deep learning model 1 and deep learning model 2 are used to output the scour depth during the warning period, and by comparing it with the preset scour warning depth, the bridge scour warning information is issued.

[0079] Reference Figure 2 , Figure 2 FIG. 1 is a schematic diagram of a bridge scour warning device provided by the present invention, as shown in FIG. Figure 2 As shown, the device includes:

[0080] Acquisition module 201: It is used to acquire average water flow velocity data and water depth data measured around the bridge pier.

[0081] Calculation module 202: used to calculate corresponding scour depth data based on the average water flow velocity data and water depth data.

[0082] The processing module 203 is used to compare the scour depth data with the warning depth data to determine whether to trigger a bridge warning.

[0083] Furthermore, in one possible implementation, an acquisition module is configured to utilize the early warning platform to collect hydrological and meteorological early warning information of the area where the bridge is located, wherein the hydrological early warning information includes flood level and flow information, and the meteorological early warning information includes rainstorm amount, typhoon level, and tidal wave information;

[0084] The hydrological warning information and meteorological warning information are input into a pre-trained first deep learning model to obtain average water flow velocity data and water depth data measured around the bridge pier.

[0085] Furthermore, in a possible implementation, the calculation module is further configured to collect scour depth data measured around the bridge pier according to a first preset rule, wherein the first preset rule is to test the riverbed elevation once before the bridge pier is scoured and once after the bridge pier is scoured;

[0086] collecting average water velocity and water depth data measured around the bridge pier according to a preset second preset rule, wherein the second preset rule is the average flow velocity and water depth during the bridge pier scouring period;

[0087] Establishing a scour data set using the scour depth data and the average flow velocity data;

[0088] The scour data set is input into a pre-trained second deep learning model to obtain corresponding scour depth data.

[0089] Furthermore, in a possible implementation, the processing module is also used to iteratively update the first deep learning model using the real-time collected measured scour data of the bridge piers to optimize the first deep learning model.

[0090] Furthermore, in a possible implementation manner, the processing module is also used to iteratively update the second deep learning model using the real-time collected measured scour data of the bridge piers to optimize the second deep learning model.

[0091] Furthermore, in a possible implementation, the processing module is further configured to customize the warning depth value according to needs;

[0092] When the scour depth data is greater than or equal to the warning depth value, determining to trigger a bridge warning;

[0093] When the scour depth data is less than the warning depth value, the bridge warning is not triggered.

[0094] Furthermore, in a possible implementation manner, the processing module is further configured to determine a warning level according to the scouring depth;

[0095] When the scouring depth reaches a first threshold, it is judged as a blue warning;

[0096] When the scouring depth reaches a second threshold, it is determined to be a yellow warning;

[0097] When the scouring depth reaches a third threshold, it is determined to be an orange warning;

[0098] When the scouring depth reaches a fourth threshold, it is determined to be a red warning.

[0099] As can be understood, this application solves the difficulty of monitoring scour depth during floods by converting difficult-to-monitor scour depth data into easily measurable water velocity and depth data. This application also converts meteorological and hydrological warning information into bridge scour warning information, providing time for bridge scour prevention work and effectively preventing losses caused by bridge scour disasters.

[0100] Refer to the following Figure 3 The electronic device 300 according to this embodiment of the present invention will be described. Figure 3 The electronic device 300 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0101] like Figure 3 As shown, electronic device 300 is implemented as a general-purpose computing device. Components of electronic device 300 may include, but are not limited to, the aforementioned at least one processing unit 310, the aforementioned at least one storage unit 320, and a bus 330 connecting various system components (including storage unit 320 and processing unit 310).

[0102] The storage unit stores program codes, which can be executed by the processing unit 310, so that the processing unit 310 performs the steps according to various exemplary embodiments of the present invention described in the above “Example Method” section of this specification.

[0103] The storage unit 320 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 321 and / or a cache memory unit 322 , and may further include a read-only memory unit (ROM) 323 .

[0104] The storage unit 320 may also include a program / utility 324 having a set (at least one) of program modules 325, such program modules 325 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0105] Bus 330 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0106] The electronic device 300 can also communicate with one or more external devices (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 300, and / or any device that enables the electronic device 300 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication can occur via an input / output (I / O) interface 350. Furthermore, the electronic device 300 can also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 360. As shown, the network adapter 360 communicates with other modules of the electronic device 300 via a bus 330. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 300, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0107] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0108] According to the solution of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above-mentioned method of this specification is stored. In some possible implementations, various aspects of the present invention may also be implemented in the form of a program product, which includes program code. When the program product is executed on a terminal device, the program code is used to cause the terminal device to perform the steps according to various exemplary embodiments of the present invention described in the "Exemplary Methods" section of this specification.

[0109] refer to Figure 4As shown, a program product 400 for implementing the above method according to an embodiment of the present invention is described. The program product 400 may be a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0110] The program product may be implemented in any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0111] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0112] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0113] The program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0114] Furthermore, the above-described figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention and are not intended to be limiting. It is readily understood that the processes illustrated in the above-described figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0115] The foregoing is merely a list of specific embodiments of the present application, intended to enable those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the broadest scope consistent with the principles and novel features of the present application.

[0116] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

Claims

1. A bridge scour early warning method, characterized in that: include: Obtain average water velocity and water depth data measured around the bridge piers; Calculating corresponding scour depth data based on the average water flow velocity data and water depth data; Comparing the scour depth data with the warning depth data to determine whether a bridge warning is triggered; The early warning platform is used to collect hydrological and meteorological warning information in the area where the bridge is located, wherein the hydrological warning information includes: flood level and flow information, and the meteorological warning information includes: rainstorm rainfall, typhoon level and tidal wave information; Inputting the hydrological warning information and meteorological warning information into a pre-trained first deep learning model to obtain average water flow velocity data and water depth data measured around the bridge pier; collecting scour depth data measured around the bridge piers according to a first preset rule, wherein the first preset rule is to test the riverbed elevation once before the bridge piers are scoured and once after the bridge piers are scoured; collecting average water velocity and water depth data measured around the bridge pier according to a preset second preset rule, wherein the second preset rule is the average flow velocity and water depth during the bridge pier scouring period; Establishing a scour data set using the scour depth data and the average flow velocity data; The scour data set is input into a pre-trained second deep learning model to obtain corresponding scour depth data.

2. The method according to claim 1, characterized in that Also includes: The first deep learning model is iteratively updated using the real-time measured scour data of the bridge piers to optimize the first deep learning model.

3. The method according to claim 1, characterized in that Also includes: The second deep learning model is iteratively updated using the real-time measured scour data of the bridge piers to optimize the second deep learning model.

4. The method according to claim 1, wherein The comparing the scour depth data with the warning depth data to determine whether to trigger a bridge warning includes: Customize the warning depth value according to your needs; When the scour depth data is greater than or equal to the warning depth value, determining to trigger a bridge warning; When the scour depth data is less than the warning depth value, the bridge warning is not triggered.

5. The method according to claim 4, characterized in that Also includes: Determine the warning level according to the scour depth; When the scouring depth reaches a first threshold, it is judged as a blue warning; When the scouring depth reaches a second threshold, it is determined to be a yellow warning; When the scouring depth reaches a third threshold, it is determined to be an orange warning; When the scouring depth reaches a fourth threshold, it is determined to be a red warning.

6. A bridge scour warning device, characterized in that: include: An acquisition module, which is used to obtain average water flow velocity data and water depth data measured around the bridge pier; a calculation module, configured to calculate corresponding scour depth data based on the average water flow velocity data and the water depth data; a processing module, configured to compare the scour depth data with the warning depth data to determine whether to trigger a bridge warning; The acquisition module is further used to use the early warning platform to collect hydrological warning information and meteorological warning information in the area where the bridge is located, wherein the hydrological warning information includes: flood level and flow information, and the meteorological warning information includes: rainstorm rainfall, typhoon level and tidal wave information; Inputting the hydrological warning information and meteorological warning information into a pre-trained first deep learning model to obtain average water flow velocity data and water depth data measured around the bridge pier; The calculation module is further configured to collect scour depth data measured around the bridge pier according to a preset first preset rule, wherein the first preset rule is to test the riverbed elevation once before the bridge pier is scoured and once after the bridge pier is scoured; collecting average water velocity and water depth data measured around the bridge pier according to a preset second preset rule, wherein the second preset rule is the average flow velocity and water depth during the bridge pier scouring period; Establishing a scour data set using the scour depth data and the average flow velocity data; The scour data set is input into a pre-trained second deep learning model to obtain corresponding scour depth data.

7. An electronic device, characterized in that: The electronic device comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that The computer program instructions are stored therein, and when the computer program instructions are executed by a computer, the computer is caused to execute the method according to any one of claims 1 to 5.

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