A flood warning method and system for a river-crossing bridge, a medium and an electronic device

Abstract

This application provides a method, system, medium, and electronic device for flood prevention early warning of cross-river bridges. The method includes: collecting multi-source basic information about the cross-river bridge; collecting multi-dimensional real-time data on the upstream and downstream pier areas of the bridge; constructing a bridge-river channel flooding model based on the multi-source basic information; performing simulation analysis based on the bridge-river channel flooding model to generate an intelligent early warning mechanism; and making early warning judgments based on the multi-dimensional real-time data using the intelligent early warning mechanism, and outputting early warning response results. The solution provided in this application can improve the early warning capability for flood prevention of cross-river bridges.

Description

Technical Field

[0001] This application belongs to the field of flood prevention and early warning technology, specifically involving a flood prevention and early warning method, system, medium and electronic equipment for cross-river bridges based on multi-source data fusion and dynamic threshold calculation, which is particularly suitable for collaborative early warning of bridge structural safety and river flood discharge safety under extreme climate conditions. Background Technology

[0002] With the rapid development of my country's transportation infrastructure, the number of bridges spanning rivers has surged. While ensuring smooth traffic flow, these bridges also face increasingly severe flood control challenges. Existing monitoring methods are mostly focused on single water level or flow data, lacking the ability to proactively detect the key risk factor of "abnormal water obstruction." Although some bridges have deployed water level gauges, they generally suffer from data silos, statically set warning thresholds, and a lack of on-site verification mechanisms. When abnormal water obstruction occurs, the water level difference increases sharply, easily leading to abnormally high upstream water levels, triggering floods exceeding standard levels, and threatening the safety of upstream and downstream villages and infrastructure. For example, during a flood season in a certain year, a landslide caused by torrential rain upstream of a bridge spanning a river brought a large amount of trees and debris into the river, causing partial water obstruction at the bridge piers. Because traditional water level gauges only monitor water level changes and do not detect sudden changes in head difference, and are not linked to video monitoring, the system did not trigger an early warning. By the time the water level exceeded the warning line, it had already caused partial flooding of upstream villages. This incident exposed the serious deficiencies of single-sensor monitoring and static threshold settings. Therefore, improving the foresight, accuracy, and reliability of flood warnings for cross-river bridges has become a pressing technical challenge. Summary of the Invention

[0003] The purpose of this application is to provide a flood prevention early warning method, system, medium and electronic equipment for cross-river bridges to solve the problems of delayed early warning, high false alarm and missed alarm rates, and lack of field verification and closed-loop optimization capabilities in the prior art.

[0004] This application provides a flood prevention early warning method for cross-river bridges, including the following steps:

[0005] Collect multi-source basic information on bridges spanning rivers;

[0006] Real-time collection of multi-dimensional data on the upstream and downstream areas of the bridge and the pier area;

[0007] A bridge-river channel water stagnation model is constructed based on the aforementioned multi-source basic information;

[0008] Based on the bridge-river stagnation model, a simulation analysis is performed to generate an intelligent early warning mechanism.

[0009] Based on the aforementioned intelligent early warning mechanism, early warning judgments are made on the multi-dimensional real-time data, and early warning handling results are output.

[0010] Furthermore, the multi-source basic information includes: bridge structural parameters, river cross-section data, flood control standards, characteristic water levels, water conservancy project scheduling information, and historical flood event data.

[0011] Furthermore, the real-time acquisition includes:

[0012] Radar level gauges were simultaneously deployed upstream and downstream of the bridge to collect water level data and head difference data.

[0013] Water depth sensors and current meters were installed in key areas of the bridge piers to collect water depth and flow velocity data.

[0014] Deploy remote high-definition cameras upstream and downstream of the bridge to capture real-time images of the water surface;

[0015] Rain gauges and soil moisture sensors were installed upstream of the bridge to collect rainfall and soil moisture data.

[0016] Furthermore, the simulation analysis includes:

[0017] Using the bridge-river damming model, the damming height of bridges under different protection standard flood scenarios is simulated to obtain the normal damming height.

[0018] Based on historical flood event data, statistical analysis was conducted on the water level at the actual flood events to obtain historical water level data.

[0019] Furthermore, the dynamic early warning threshold is calculated using the following formula:

[0020] ΔH_threshold = μ + k × σ,

[0021] Where μ is the mean of the normal pleural height, σ is the standard deviation of the normal pleural height, and k is an adjustable coefficient with a value range of 1.5 to 3.0.

[0022] Furthermore, the intelligent early warning mechanism includes a three-level progressive early warning system:

[0023] The first-level risk warning is triggered when upstream rainfall exceeds a preset threshold and soil moisture reaches saturation.

[0024] The second-level anomaly warning is triggered when the head difference data exceeds the dynamic warning threshold three times in a row, or when the water depth change rate exceeds 0.1 m / min.

[0025] The third level of confirmation warning is triggered when the second level warning is activated. Real-time footage from the bridgehead is retrieved and image recognition algorithms confirm the presence of floating debris or abnormal water surface disturbances, thus upgrading the warning to a confirmation warning.

[0026] Furthermore, the method also includes:

[0027] Record the handling results and actual flood impact of each early warning event;

[0028] Based on the recorded data, the parameters of the bridge-river channel water retention model are dynamically calibrated using a supervised learning algorithm;

[0029] The dynamic warning threshold is updated based on the calibration results to achieve continuous optimization of model performance.

[0030] This application also provides a flood prevention early warning system for cross-river bridges, including a data information acquisition module, a model construction module, an analysis and early warning module, and an early warning and response module, with each module's function corresponding to the above-mentioned method steps.

[0031] This application also provides an electronic device and a computer-readable storage medium having a program storing the above-described method for executing the technical solution of this application.

[0032] Beneficial effects:

[0033] (1) By integrating multi-source information such as upstream rainfall, soil moisture, head difference, water depth change rate and water surface image, risk perception is significantly advanced, and the warning window can be advanced by more than 30 minutes, which wins valuable time for flood control preparation and early intervention. In extreme rainfall scenarios, the response time is shortened by about 60% compared with traditional methods;

[0034] (2) Through cross-validation of hydraulic indicators and visual evidence, the false alarm rate can be reduced to below 5%, and the false alarm rate can be controlled within 2%, significantly improving the reliability of early warning;

[0035] (3) Construct a three-level progressive early warning mechanism to realize a precise response chain from "risk prediction" to "anomaly identification" and then to "on-site confirmation" to improve the efficiency of emergency management;

[0036] (4) A closed-loop path of “early warning-response-feedback-optimization” has been constructed. The model parameters and dynamic thresholds are continuously calibrated through machine learning models. The system has long-term evolution capabilities and can cope with the uncertainties brought about by climate change and river channel changes. Attached Figure Description

[0037] Figure 1 : A schematic flowchart of the flood prevention and early warning method for cross-river bridges described in this application embodiment.

[0038] Figure 2 This is a schematic diagram illustrating the process of simulation analysis using a bridge-river channel water retention model as described in the embodiments of this application.

[0039] Figure 3 This is a schematic diagram illustrating the process of constructing an intelligent early warning mechanism as described in an embodiment of this application.

[0040] Figure 4 The main flowchart of the flood prevention and early warning method for cross-river bridges described in this application embodiment is shown below.

[0041] Figure 5 : A schematic diagram of the structure of the cross-river bridge flood warning system described in this application embodiment.

[0042] Figure 6 : A schematic diagram of the structure of the electronic device described in the embodiments of this application. Detailed Implementation

[0043] The preferred embodiments of this application will be described in detail below with reference to the accompanying drawings. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0044] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0045] With the rapid expansion and continuous densification of my country's transportation infrastructure network, the number of cross-river bridges, as key nodes, has increased exponentially. While maintaining the smooth flow of regional transportation arteries and supporting economic and social development, their own structure and impact on river flood discharge also constitute a weak link in the flood control system, facing increasingly severe and complex safety challenges. With the increasing frequency of extreme weather events and the increased intensity and uncertainty of rainstorms, river hydrological conditions are becoming more complex, challenging traditional flood control standards based on historical statistics. The compression effect of bridges and their piers on water flow naturally causes backwater, but when "abnormal water obstruction" occurs in the river channel due to the accumulation of trees, floating debris, or local erosion deformation, hydraulic conditions will deteriorate sharply. At this time, the water level difference will increase abnormally, leading to an abnormally high upstream water level. This "cumulative effect" can easily cause upstream shoreline overflow and dike failure even before the design flood standard is reached. Simultaneously, the downstream may also be impacted by sudden discharge, posing a dual threat to riverside villages, farmland, and infrastructure.

[0046] Current monitoring methods mostly focus on single water level or cross-sectional flow data, which is a "post-event" or "phenomenon-based" perception, lacking the ability to proactively and directly identify the key risk factor of "abnormal water blockage." Although some important bridges have deployed water level gauges, "data silos" are prevalent—data from each point is isolated and not integrated with multi-source information such as upstream rainfall, soil moisture, and video monitoring. More critically, early warning thresholds are mostly set based on static hydrological calculations, failing to fully consider the dynamic nonlinear characteristics of river evolution, engineering activities, and extreme weather, resulting in either delayed and ineffective early warnings or frequent false alarms. Furthermore, the lack of a verification process from data anomalies to on-site conditions leaves management decisions without intuitive basis and emergency response efficiency low.

[0047] Therefore, breaking through the limitations of traditional monitoring and constructing a pre-warning method for flood prevention of cross-river bridges has become a bottleneck problem in the current flood prevention and safety management of bridges.

[0048] At least in response to the above-mentioned problems, the following embodiments of this application provide a flood prevention early warning method, system, medium and electronic equipment for cross-river bridges.

[0049] The technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0050] Figure 1 This is a flowchart illustrating a flood warning method for cross-river bridges according to one embodiment of this application. Figure 1 As shown, the flood prevention early warning method for cross-river bridges includes the following steps S11 to S16.

[0051] Step S11: Collect multi-source basic information of the bridge across the river.

[0052] In one embodiment of this application, the multi-source basic information of the cross-river bridge includes: bridge structural parameters, river cross-section data, flood control standards, characteristic water levels, water conservancy project scheduling information, and historical flood event data.

[0053] For example, bridge structural parameters include span, pier and abutment arrangement and bridge deck bottom height; river cross-sectional data include roughness and water surface slope; flood control standards and characteristic water levels include river embankments, bridges and key upstream and downstream protection objects.

[0054] Step S12: Collect multi-dimensional real-time data of the upstream and downstream pier areas of the bridge.

[0055] In one embodiment of this application, the real-time acquisition includes:

[0056] a) Radar level gauges are simultaneously deployed upstream and downstream of the bridge to collect water level data and head difference data.

[0057] For example, radar level gauges are simultaneously deployed upstream and downstream of the bridge to collect real-time data on the water level and head difference of the river.

[0058] b. Deploy water depth sensors and flow velocity meters in key areas of the bridge piers to collect water depth and flow velocity data.

[0059] For example, water depth sensors and current meters are installed in key areas of the bridge piers. Water depth sensors are used to collect water depth data in real time, and current meters are used to monitor the water flow velocity data and water depth change rate in real time.

[0060] c. Deploy remote high-definition cameras upstream and downstream of the bridge to collect real-time images of the water surface.

[0061] For example, remote high-definition cameras are deployed at key locations upstream and downstream of the bridge to visually monitor floating objects and water surface conditions.

[0062] d. Install rain gauges and soil moisture sensors upstream of the bridge to collect rainfall and soil moisture data.

[0063] For example, rain gauges and soil moisture sensors are installed simultaneously upstream of the bridge. Rain gauges are used to measure rainfall, and soil moisture sensors are used to collect soil moisture data in real time. Combined with watershed water situation early warning and forecast information, the risk of upstream water inflow is assessed.

[0064] Step S13: Construct a bridge-river channel sludge model based on the multi-source basic information. For example, the bridge-river channel sludge model is constructed using HEC-RAC (Hydrologic Engineering Center's River Analysis System).

[0065] Step S14: Perform simulation analysis based on the bridge-river channel water stagnation model to generate an intelligent early warning mechanism.

[0066] Figure 2 This diagram illustrates the process of simulation analysis using a bridge-river channel waterlogging model in one embodiment of this application. Figure 2 As shown, the process of simulation analysis using the bridge-river channel water dam model includes the following steps S21 to S24.

[0067] Step S21: Using the bridge-river channel backwater model, simulate the backwater height for different flood protection standards to obtain the normal backwater height.

[0068] For example, the protection standards for different protected objects include 10-year return periods, 20-year return periods, 30-year return periods, 50-year return periods, 100-year return periods, 200-year return periods, and 300-year return periods. The normal backwater height is calculated using a bridge-channel backwater model under different protection standards for different protected objects, yielding the backwater height generated by the bridge structure under normal conditions.

[0069] Step S22: Based on historical flood event data, statistically analyze the water level rise during actual floods to obtain historical water level rises. The historical water level rise is the bridge water level rise obtained from historical data at the time of the actual flood.

[0070] Step S23: Output a dynamic early warning threshold based on the normal water level and the historical water level.

[0071] Specifically, the normal mean and normal standard deviation of the normal flood level are obtained based on the normal flood level. The normal mean represents the average normal flood level under different standard flood conditions, and the normal standard deviation represents the dispersion of the normal flood level caused by floods of different magnitudes; the larger the flood, the higher the flood level.

[0072] The dynamic warning threshold, i.e., the abnormal liquid level difference warning value, is calculated based on the normal mean and normal standard deviation of the normal plenum height. The dynamic warning threshold is expressed as follows:

[0073] The dynamic warning threshold (ΔH_threshold) = normal mean μ + k × normal standard deviation σ. Where μ is the mean of the normal dammed water height, σ is the standard deviation of the normal dammed water height, and k is an adjustable coefficient ranging from 1.5 to 3.0. The dynamic warning threshold is the water level value; when the dammed water height at the bridge reaches this level, it indicates that the river water level has significantly exceeded the normal fluctuation range, suggesting a possible anomaly.

[0074] Step S24: Construct the intelligent early warning mechanism based on the dynamic early warning threshold.

[0075] Step S15: Based on the intelligent early warning mechanism, perform early warning judgment on the multi-dimensional real-time data and output the early warning handling result. In one embodiment of this application, early warning judgment is performed according to the intelligent early warning mechanism to obtain early warning information. Early warning handling is then performed based on the early warning information to obtain the early warning handling result.

[0076] Figure 3 This is a schematic diagram illustrating the process of constructing an intelligent early warning mechanism in one embodiment of this application. For example... Figure 3As shown, the intelligent early warning mechanism includes a three-level progressive early warning system: the first-level risk warning is triggered when the upstream rainfall exceeds a preset threshold and the soil moisture reaches saturation; the second-level anomaly warning is triggered when the head difference data exceeds the dynamic early warning threshold three times consecutively, or the water depth change rate exceeds 0.1 m / min; and the third-level confirmation warning is triggered when, after the second-level warning is triggered, the real-time image of the bridgehead is retrieved and confirmed by the image recognition algorithm that there is floating debris accumulation or abnormal water surface disturbance, and then upgraded to a confirmation warning.

[0077] For example, when rainfall upstream of the bridge exceeds a threshold and soil moisture reaches saturation, a first-level risk warning is automatically triggered, and the operation platform is displayed with "Upward water inflow risk increased." If the real-time measured head difference data exceeds the dynamic warning threshold three times consecutively, or the water depth change rate exceeds a set value (e.g., 0.1 m / min), it is determined to be an "abnormal water obstruction risk," and a second-level abnormal warning is automatically initiated. When the second-level abnormal warning is triggered, real-time footage from a remote camera is automatically retrieved, and image recognition algorithms are used to analyze the real-time footage to obtain evidence of the river surface conditions, such as the presence of floating debris accumulation and abnormal water surface disturbances. If such evidence is found, the warning is upgraded to a third-level confirmation warning, and the emergency response process is initiated.

[0078] In one embodiment of this application, early warning information can be uploaded to a central early warning platform in real time via a wireless network (e.g., 4G / 5G / NB-IoT). The platform automatically pushes the information to relevant duty personnel, management personnel, flood control headquarters, and upstream and downstream joint defense units. The pushed content includes: early warning level, measured head difference, water depth change rate, on-site images, risk assessment report, and recommended response measures.

[0079] Step S16: Based on the early warning and handling results, perform closed-loop dynamic optimization of the bridge-river channel water stagnation model.

[0080] Furthermore, the method also includes: recording the handling results and actual flood impact of each early warning event; dynamically calibrating the parameters of the bridge-river channel waterlogging model using a supervised learning algorithm based on the recorded data; and updating the dynamic early warning threshold according to the calibration results to achieve continuous optimization of model performance. The system records the handling results and actual flood impact of each early warning event, and continuously optimizes the bridge-river channel waterlogging model and dynamic early warning threshold using machine learning algorithms, constructing a closed-loop iteration of "early warning—response—feedback—optimization" to improve the long-term early warning accuracy of the system.

[0081] The following section will provide a detailed description of the flood warning method for cross-river bridges provided in this application through a specific example. It should be noted that the content of this example is only used to explain and illustrate the flood warning method for cross-river bridges provided in this application, and is not intended to limit the scope of protection of this application in any way. In specific applications, corresponding steps can be added or deleted based on this example according to actual needs. Figure 4 This is a flowchart illustrating the main flow chart of the flood warning method for cross-river bridges in this example. Figure 4 As shown, the flood warning for cross-river bridges in this example includes the following steps.

[0082] Step S100 involves collecting multi-source basic information such as bridge structural parameters, river cross-section data, flood control standards and characteristic water levels, water conservancy project scheduling information and historical flood event data for cross-river bridges, and constructing a bridge-river channel water stagnation model based on the collected multi-source basic information.

[0083] Step S200: Based on the bridge-river backwater model, simulate the backwater height of the bridge under different protection standard flood scenarios and analyze historical data to obtain the normal backwater height and the actual historical backwater height.

[0084] Step S300: Based on the normal water level and the actual historical water level, set a dynamic early warning threshold according to the regional risk level.

[0085] Step S400: Collect liquid level data, water depth, water flow velocity and images in the upstream and downstream areas of the bridge and the pier area to obtain multi-dimensional real-time data of the cross-river bridge.

[0086] Step S500: Set up a three-level intelligent early warning mechanism based on multi-dimensional real-time data of the cross-river bridge and dynamic early warning thresholds.

[0087] Step S600: Based on the intelligent early warning mechanism, an early warning judgment is made to obtain early warning information, and based on the early warning information, early warning processing is performed to obtain the early warning handling result.

[0088] Step S700: Based on the early warning and response results and the actual impact of flooding, the bridge and river channel waterlogging model is dynamically and continuously optimized using machine learning algorithms.

[0089] In summary, this application achieves significantly earlier risk perception by integrating multi-source information such as upstream rainfall and soil moisture, thus greatly advancing the warning window and gaining valuable time for flood prevention preparation and early intervention, transforming passive response into proactive defense. Cross-validation and fusion analysis of key hydraulic indicators (such as head difference and its rate of change) with on-site video evidence from bridges crossing the river effectively overcomes the limitations of single sensors being susceptible to interference, thereby accurately identifying core risk factors such as "abnormal water blockage," minimizing false alarms and missed alarms, and ensuring the reliability and authority of the warning signals.

[0090] Simultaneously, a three-tiered progressive early warning mechanism is adopted, precisely matching risk signals with response actions. From the first-level risk warning based on meteorological and hydrological models, to the second-level anomaly warning based on real-time hydraulic anomalies, and then to the third-level confirmation warning based on visual verification, a clear hierarchical and well-defined action guidance chain is formed. This achieves precise connection and efficient transformation from risk perception to control actions, significantly improving the scientific nature of emergency management and the efficiency of resource allocation. Furthermore, a closed-loop optimization path of "early warning—response—feedback—optimization" is constructed, which can automatically correct model parameters and optimize dynamic early warning thresholds, enabling the system to have continuous evolution capabilities and ensuring that its long-term operational performance continuously improves with the accumulation of experience, effectively addressing the uncertainties brought about by climate change and river channel changes.

[0091] The scope of protection of the flood prevention and early warning method for cross-river bridges described in this application is not limited to the order of steps listed in this embodiment. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principles of this application is included within the scope of protection of this application.

[0092] This application also provides a cross-river bridge flood warning system, which can implement the cross-river bridge flood warning method described in this application. However, the implementation device of the cross-river bridge flood warning method described in this application includes, but is not limited to, the structure of the cross-river bridge flood warning system listed in this embodiment. All structural modifications and substitutions of the prior art made based on the principles of this application are included within the protection scope of this application.

[0093] Figure 5 The diagram shown is a structural schematic of a flood warning system for a cross-river bridge according to one embodiment of this application. Figure 5As shown, the cross-river bridge flood control early warning system 1 includes: a data information acquisition module 11, a model construction module 12, an analysis and early warning module 13, and an early warning response module 14. The data information acquisition module 11 is used to collect multi-source basic information about the cross-river bridge and to collect multi-dimensional real-time data. The model construction module 12 is used to construct a bridge-river channel flooding model based on the multi-source basic information. The analysis and early warning module 13 is used to perform simulation analysis based on the bridge-river channel flooding model and generate an intelligent early warning mechanism. The early warning response module 14 is used to make early warning judgments based on the multi-dimensional real-time data according to the intelligent early warning mechanism and output early warning response results.

[0094] It should be noted that, Figure 5 The modules in the cross-river bridge flood warning system 1 shown are... Figure 1 The steps in the flood prevention and early warning method for cross-river bridges correspond one by one, and will not be repeated here.

[0095] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0096] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of this application, depending on actual needs. For example, the functional modules / units in the various embodiments of this application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0097] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0098] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program implements the cross-river bridge flood warning method provided in this application. Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state hard disk, magnetic tape, floppy disk, optical disk, and any combination thereof. The above storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0099] This application embodiment may also provide an electronic device. Figure 6 The diagram shown is a structural schematic of an electronic device 200 according to an embodiment of this application. Figure 6 As shown, in this embodiment, the electronic device 200 includes a memory 201 and a processor 202.

[0100] The memory 201 is used to store computer programs. In some possible implementations, the memory 201 may include various media capable of storing program code, such as ROM, RAM, magnetic disk, USB flash drive, memory card, or optical disk.

[0101] In this embodiment, memory 201 may include a computer system readable medium in the form of volatile memory, such as RAM and / or cache memory. Electronic device 200 may further include other removable / non-removable, volatile / non-volatile computer system storage media. Memory 201 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0102] The processor 202 is connected to the memory 201 and is used to execute the computer program stored in the memory 201 so that the electronic device 200 can perform the flood prevention early warning method for the cross-river bridge.

[0103] For example, processor 202 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc. In other embodiments, processor 202 may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0104] In some implementations, the electronic device 200 provided in this application embodiment may further include a display 203. The display 203 is communicatively connected to the memory 201 and the processor 202, and is used to display a graphical user interface (GUI) related to the flood warning method for cross-river bridges.

[0105] In this embodiment, the display 203 may include a display screen (display panel). In some implementations, the display panel may be configured using a liquid crystal display (LCD), an organic light-emitting diode (OLED), or other similar forms. Furthermore, the display 203 may also be a touch panel (touchscreen, touch screen), which may include a display screen and a touch-sensitive surface. When the touch-sensitive surface detects a touch operation on or near it, it transmits the information to the processor 202 to determine the type of touch event. Subsequently, the processor 202 provides corresponding visual output on the display device based on the type of touch event.

[0106] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0107] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A flood prevention early warning method for cross-river bridges, characterized in that, Includes the following steps: Collect multi-source basic information on bridges spanning rivers; Real-time collection of multi-dimensional data from upstream and downstream pier areas of the bridge; A bridge-river channel water stagnation model is constructed based on the aforementioned multi-source basic information; Based on the bridge-river stagnation model, a simulation analysis is performed to generate an intelligent early warning mechanism. Based on the aforementioned intelligent early warning mechanism, early warning judgments are made on the multi-dimensional real-time data, and early warning handling results are output.

2. The flood prevention early warning method for cross-river bridges according to claim 1, characterized in that, The multi-source basic information includes: bridge structural parameters, river cross-section data, flood control standards, characteristic water levels, water conservancy project scheduling information, and historical flood event data.

3. The flood prevention early warning method for cross-river bridges according to claim 1, characterized in that, The real-time data acquisition includes: Radar level gauges were simultaneously deployed upstream and downstream of the bridge to collect water level data and head difference data. Water depth sensors and current meters were installed in key areas of the bridge piers to collect water depth and flow velocity data. Deploy remote high-definition cameras upstream and downstream of the bridge to capture real-time images of the water surface; Rain gauges and soil moisture sensors were installed upstream of the bridge to collect rainfall and soil moisture data.

4. The flood prevention early warning method for cross-river bridges according to claim 1, characterized in that, The simulation analysis includes: Using the bridge-channel flood model, the flood height was simulated under different flood protection standards to obtain the normal flood height. Based on historical flood event data, statistical analysis was conducted on the water level at the actual flood events to obtain historical water level data.

5. The flood prevention early warning method for cross-river bridges according to claim 4, characterized in that, The dynamic early warning threshold is calculated using the following formula: ΔH_threshold = μ + k × σ; Where μ is the mean of the normal pleural height, σ is the standard deviation of the normal pleural height, and k is an adjustable coefficient with a value range of 1.5 to 3.

0. The intelligent early warning mechanism is constructed based on the dynamic early warning threshold.

6. The flood prevention early warning method for cross-river bridges according to claim 1, characterized in that, The intelligent early warning mechanism includes a three-level progressive early warning system: The first-level risk warning is triggered when upstream rainfall exceeds a preset threshold and soil moisture reaches saturation. The second-level abnormal warning is triggered when the head difference data exceeds the dynamic warning threshold three times in a row, or when the water depth change rate exceeds 0.1m / min. The third level of confirmation warning is triggered when the second level warning is activated. Real-time footage from the bridgehead is retrieved and image recognition algorithms confirm the presence of floating debris or abnormal water surface disturbances, thus upgrading the warning to a confirmation warning.

7. The flood warning method for cross-river bridges according to any one of claims 1 to 6, characterized in that, Also includes: Record the handling results and actual flood impact of each early warning event; Based on the recorded data, the parameters of the bridge-river channel water retention model are dynamically calibrated using a supervised learning algorithm; The dynamic warning threshold is updated based on the calibration results to achieve continuous optimization of model performance.

8. A flood early warning system for cross-river bridges, characterized in that, include: The data acquisition module is used to collect multi-source basic information of cross-river bridges and collect multi-dimensional real-time data. The model building module is used to build a bridge and river channel water retention model based on the multi-source basic information; The analysis and early warning module is used to perform simulation analysis based on the bridge-river channel water stagnation model and generate an intelligent early warning mechanism. The early warning and handling module is used to make early warning judgments on the multi-dimensional real-time data based on the intelligent early warning mechanism and output the early warning and handling results.

9. An electronic device, characterized in that, include: A memory on which computer programs are stored; The processor, communicatively connected to the memory, is used to execute the computer program to implement the flood warning method for cross-river bridges as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by an electronic device, it implements the flood warning method for cross-river bridges as described in any one of claims 1 to 7.

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