A bridge collapse scene intelligent monitoring system

Abstract

The application discloses a kind of bridge collapse scene intelligent monitoring system, belong to bridge safety monitoring technical field, comprising: first data acquisition module, first data processing module, analog bridge construction module, second data acquisition module, second data processing module, third data acquisition module, third data processing module, fourth data acquisition module, fourth data processing module, first data storage module, fifth data acquisition module, fifth data processing module and early warning module;The application can realize the monitoring of whole time period, improve the monitoring comprehensiveness and monitoring safety effect of system to bridge.

Description

Technical Field

[0001] This invention belongs to the field of bridge safety monitoring technology, specifically relating to an intelligent monitoring system for bridge collapse scenarios. Background Technology

[0002] Bridges are structures used to cross rivers, valleys, or other transportation lines, such as railways, roads, canals, and pipelines. During their use, they are subjected to repeated loads and erosion from the natural environment, which can lead to material fatigue damage. This can gradually reduce the safety performance of bridges and, in severe cases, even cause them to collapse. In order to understand the usage status of bridges in a timely manner and take appropriate measures, it is necessary to monitor the usage status of bridges in real time.

[0003] Chinese Patent No. 202210563906.9 discloses a bridge monitoring system based on multi-source data fusion, which includes the following modules:

[0004] The data acquisition module acquires the commonly inspected images corresponding to the commonly inspected coordinates based on the image acquisition device, and acquires the load data of the target bridge based on the deep inspection signal test. The load data includes static load data and dynamic load data.

[0005] The data analysis module determines the frequently detected locations based on the bridge model, converts these locations into frequently detected coordinates, analyzes the frequently detected images, and determines whether to generate a deep detection signal based on the analysis results. It also analyzes static load data using a static load standard curve to obtain static load analysis labels, analyzes dynamic load data using an intelligent evaluation model to obtain dynamic load analysis labels, and determines the state of the target bridge based on the dynamic load analysis labels and / or static load analysis labels. The intelligent evaluation model is obtained by training an artificial intelligence model using standard training data. The data analysis module analyzes the frequently detected images and determines whether to generate a deep detection signal based on the analysis results. This includes: identifying anomalies in the frequently detected locations in the images using image recognition algorithms, analyzing the number and distribution of abnormal frequently detected locations, and determining whether to generate a deep detection signal based on the analysis results.

[0006] The inventors of this application have discovered that the existing bridge monitoring system based on multi-source data fusion can only determine whether a deeper inspection is needed for the routine inspection locations of the bridge and monitor the bridge status during the deeper inspection, but cannot monitor the bridge status at all times. In some cases, the collapse of the bridge may occur during the interval between two inspections. Therefore, the effectiveness of bridge safety monitoring still needs to be improved. Summary of the Invention

[0007] To address the problems mentioned in the background section, this invention provides an intelligent monitoring system for bridge collapse scenarios. This system enables continuous monitoring of bridges throughout the entire time period, improving the comprehensiveness and safety effectiveness of bridge monitoring.

[0008] To achieve the above objectives, the present invention provides the following technical solution: an intelligent monitoring system for bridge collapse scenarios, comprising: a first data acquisition module, a first data processing module, a simulated bridge construction module, a second data acquisition module, a second data processing module, a third data acquisition module, a third data processing module, a fourth data acquisition module, a fourth data processing module, a first data storage module, a fifth data acquisition module, a fifth data processing module, and an early warning module, wherein:

[0009] The first data acquisition module collects as-built data, inspection and evaluation data, and surrounding environmental information of the bridge to be monitored.

[0010] The first data processing module identifies the type of reinforced concrete used in the construction of the bridge under monitoring through the as-built data, identifies the load-bearing capacity specified in the construction of the bridge under monitoring through the test and evaluation data, and identifies the environmental information around the bridge under monitoring, including temperature and humidity, through the surrounding environmental information data.

[0011] The simulated bridge construction module can construct multiple simulated bridges with the same type of reinforced concrete, load-bearing capacity, and environmental information as the bridge to be monitored.

[0012] The second data acquisition module collects load and reinforced concrete fatigue strength data of multiple simulated bridges under test without environmental information influence.

[0013] The second data processing module calculates the correlation between the fatigue strength of each load at different stages of the bridge by using load and reinforced concrete fatigue strength data from multiple simulated bridges.

[0014] The third data acquisition module collects fatigue strength data of reinforced concrete for multiple simulated bridges under no-load influence experiments.

[0015] The third data processing module calculates the correlation between the environmental information and the fatigue strength of the bridge at different stages using environmental information and reinforced concrete fatigue strength data from multiple simulated bridges.

[0016] The fourth data acquisition module collects fatigue strength data of reinforced concrete from multiple simulated bridges under natural environmental tests, including load and environmental information.

[0017] The fourth data processing module establishes a fitting curve of collapse risk level - reinforced concrete fatigue strength as a comparison template, and divides the fitting curve into safe interval, warning interval and dangerous interval respectively;

[0018] The first data storage module stores the aforementioned comparison template;

[0019] The fifth data acquisition module collects real-time load data and environmental information data of the bridge;

[0020] The fifth data processing module clarifies the time period for collecting bridge load data and environmental information data. By calculating the fatigue strength associated with the load in each period and the fatigue strength associated with the environmental information in each period, it calculates the influence of the load on the bridge fatigue strength. The collected bridge fatigue strength data are connected into a curve, and this curve is compared with the collapse risk degree-reinforced concrete fatigue strength fitting curve of the above comparison template. If the highest point of the curve is lower than the highest point of the safe zone, no warning is issued. If it is higher than the highest point of the safe zone but lower than the highest point of the warning zone, a first-level warning is issued. If it is higher than the highest point of the warning zone but lower than the highest point of the danger zone, a second-level warning is issued.

[0021] The early warning module issues corresponding warnings based on the data processing structure of the fifth data processing module.

[0022] Preferably, the early warning module includes a pop-up warning on the monitoring site's large screen and an audio warning via a mobile early warning app for monitoring personnel.

[0023] Preferably, it also includes a 3D modeling module and a 3D model display module, wherein:

[0024] The 3D modeling module receives the as-built data, inspection and evaluation data and surrounding environmental information of the bridge to be monitored collected by the first data acquisition module, clarifies the environmental information of the bridge and the surrounding buildings, and performs 3D modeling of the bridge and surrounding buildings. At the same time, it receives the bridge and surrounding building information collected in real time by the fifth data acquisition module and performs 3D modeling of the bridge's real-time status and surrounding buildings.

[0025] The 3D model display module displays the 3D model of the bridge and surrounding buildings constructed by the 3D modeling module in real time, so as to understand the condition of the bridge in a timely manner and take timely action.

[0026] Preferably, it also includes a maintenance data database, in which:

[0027] A maintenance data database stores maintenance measures for bridges at various fatigue strengths.

[0028] The fourth data processing module also includes associating the maintenance measures in the maintenance data database with the corresponding nodes of the warning interval and the danger interval of the collapse risk degree-reinforced concrete fatigue strength fitting curve.

[0029] The fifth data acquisition module extracts the maintenance measures for the corresponding points of the curve endpoint and the comparison template to the three-dimensional model display module;

[0030] The 3D model display module shows the maintenance measures for the corresponding point of the curve endpoint and the comparison template when the bridge fatigue strength enters the warning period.

[0031] Preferably, it also includes a sixth data acquisition module, wherein:

[0032] The sixth data acquisition module studies the collapse of each node of the bridge by applying different loads to the bridge construction module, and collects the failure path and failure range data of each node. The three-dimensional modeling module receives the collected failure range data of each node, models the failure range data of each node of the bridge, and displays it on the three-dimensional model display module with a red warning zone.

[0033] The 3D model display module is accessible via a shared login password, allowing it to be viewed simultaneously by various municipal engineering departments. This enables subsequent construction to avoid the area affected by bridge collapse.

[0034] Preferably, it also includes a seventh data acquisition module, wherein:

[0035] The seventh data acquisition module collects the location of people using existing buildings within the area affected by the bridge collapse;

[0036] The early warning module also includes a voice warning for the mobile phone early warning APP of the existing building users. Within a preset time period before the bridge collapse, the early warning APP of the users located within the bridge collapse damage range is sent according to the location of the users collected by the seventh data acquisition module, so as to help the users evacuate quickly and avoid injury.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] 1. This invention is based on constructing multiple simulated bridges with the same reinforced concrete type, load, and environmental information as the bridge to be monitored. It calculates the fatigue strength correlation of each load and environmental information at different stages of the bridge. Then, it calculates the real-time fatigue strength by comparing the real-time monitored load data with the calculated fatigue strength correlation of each load and environmental information at each stage. Finally, it compares the fatigue strength curve with the generated comparison template to determine the degree of bridge collapse risk and provides an appropriate level of early warning. This achieves intelligent monitoring of common bridge collapse issues, enabling monitoring at all times and improving the system's comprehensiveness and safety monitoring effect.

[0039] 2. This invention performs three-dimensional modeling based on the collected information, and updates the data in real time based on the collected load and fatigue data, so that monitoring personnel can understand the bridge's usage status and take timely action.

[0040] 3. The present invention has specific maintenance measures associated with each fatigue strength node. When the curve endpoint is compared with the matching point on the comparison template, the maintenance measures associated with the matching point are displayed so that maintenance can be carried out on the bridge.

[0041] 4. This invention provides three-dimensional modeling of buildings surrounding bridges, and the three-dimensional model is visualized and shared, so that municipal engineering departments can avoid dangerous areas of bridges during construction.

[0042] 5. This invention collects information on existing building users within the bridge's collapse zone and provides early warnings when the bridge is at risk of collapse, helping people in dangerous areas to evacuate in time. Attached Figure Description

[0043] Figure 1 This is a framework diagram of the present invention;

[0044] In the diagram: 1. First data acquisition module; 2. First data processing module; 3. Simulated bridge construction module; 4. Second data acquisition module; 5. Second data processing module; 6. Third data acquisition module; 7. Third data processing module; 8. Fourth data acquisition module; 9. Fourth data processing module; 10. First data storage module; 11. Fifth data acquisition module; 12. Fifth data processing module; 13. Early warning module; 14. 3D modeling module; 15. 3D model display module; 16. Maintenance data database; 17. Sixth data acquisition module; 18. Seventh data acquisition module. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] Example 1

[0047] Please see Figure 1 The present invention provides the following technical solution: an intelligent monitoring system for bridge collapse scenarios, comprising: a first data acquisition module 1, a first data processing module 2, a simulated bridge construction module 3, a second data acquisition module 4, a second data processing module 5, a third data acquisition module 6, a third data processing module 7, a fourth data acquisition module 8, a fourth data processing module 9, a first data storage module 10, a fifth data acquisition module 11, a fifth data processing module 12, and an early warning module 13, wherein:

[0048] The first data acquisition module 1 collects the as-built data, inspection and evaluation data and surrounding environmental information of the bridge to be monitored.

[0049] The first data processing module 2 determines the type of reinforced concrete used in the construction of the bridge under monitoring through the as-built data of the bridge under monitoring, determines the load-bearing capacity specified in the construction of the bridge under monitoring through the test and evaluation data, and determines the environmental information around the bridge under monitoring, including temperature and humidity, through the surrounding environmental information data.

[0050] Simulated bridge construction module 3 constructs multiple simulated bridges with the same type of reinforced concrete, load-bearing capacity, and environmental information as the bridge to be monitored.

[0051] The second data acquisition module 4 collects load and reinforced concrete fatigue strength data of multiple simulated bridges under test without environmental information influence.

[0052] The second data processing module 5 calculates the correlation degree of fatigue strength of each load at different stages of the bridge by using multiple simulated bridge load and reinforced concrete fatigue strength data.

[0053] The third data acquisition module 6 collects fatigue strength data of reinforced concrete for multiple simulated bridges under no-load influence experiments.

[0054] The third data processing module 7 calculates the correlation degree of each environmental information with the fatigue strength of the bridge at different stages by using environmental information and reinforced concrete fatigue strength data of multiple simulated bridges.

[0055] The fourth data acquisition module 8 collects fatigue strength data of reinforced concrete for multiple simulated bridges under natural environmental tests, including load and environmental information.

[0056] The fourth data processing module 9 establishes a collapse risk level-reinforced concrete fatigue strength fitting curve as a comparison template, and divides the fitting curve into safe intervals, warning intervals and dangerous intervals respectively;

[0057] The first data storage module 10 stores the above comparison template;

[0058] The fifth data acquisition module 11 collects the bridge's load data and environmental information data in real time;

[0059] The fifth data processing module 12 clarifies the period of the collected bridge load data and environmental information data. It calculates the influence of the load on the bridge fatigue strength by calculating the fatigue strength associated with the load and the fatigue strength associated with the environmental information in each period. It connects the collected bridge fatigue strength data into a curve and compares the curve with the collapse risk degree-reinforced concrete fatigue strength fitting curve of the above comparison template. If the highest point of the curve is lower than the highest point of the safe zone, no warning is issued. If it is higher than the highest point of the safe zone but lower than the highest point of the warning zone, a first-level warning is issued. If it is higher than the highest point of the warning zone but lower than the highest point of the danger zone, a second-level warning is issued.

[0060] The early warning module 13 issues corresponding early warnings based on the data processing structure of the fifth data processing module 12.

[0061] Specifically, the early warning module 13 includes pop-up warnings on the monitoring site's large screen and sound warnings via the monitoring personnel's mobile phone early warning APP.

[0062] The working principle of this embodiment is as follows: The first data acquisition module 1 collects the as-built data, inspection and evaluation data, and surrounding environmental information of the bridge to be monitored. The first data processing module 2 receives the data information collected by the first data acquisition module 1, and determines the type of reinforced concrete, the specified load-bearing capacity, and the surrounding environmental information, including temperature and humidity, of the bridge to be monitored based on the as-built data, inspection and evaluation data, and surrounding environmental information of the bridge to be monitored. The simulated bridge construction module 3 constructs multiple simulated bridges with the same type of reinforced concrete, load-bearing capacity, and environmental information as the bridge to be monitored based on the information determined by the first data processing module 2. The first simulation... During the first simulation, only the load was varied, without environmental information. The second data acquisition module 4 collected load and reinforced concrete fatigue strength data for multiple simulated bridges. The second data processing module 5 received the data from the second data acquisition module 4 and calculated the correlation between the fatigue strength of each load and the bridge at different stages. In the second simulation, only the environmental information was varied, without load. The third data acquisition module 6 collected environmental information and reinforced concrete fatigue strength data for multiple simulated bridges. The third data processing module 7 received the data from the third data acquisition module 6 and calculated the correlation between the environmental information and the fatigue strength of the bridge at different stages. The third... The experiment involves varying the load and environmental information. The fourth data acquisition module 8 collects fatigue strength data of the reinforced concrete of multiple simulated bridges. The fourth data processing module 9 receives the data from the fourth data acquisition module 8 and establishes a collapse risk level-reinforced concrete fatigue strength fitting curve as a comparison template. The fitting curve is divided into safe, warning, and dangerous zones, and the data is saved by the first data storage module 10. The fifth data acquisition module 11 collects the bridge's load data and environmental information data in real time. The fifth data processing module 12 receives the data from the fifth data acquisition module 11 and clarifies the collected bridge load data and environmental information data. During each period, the influence of the load on the fatigue strength of the bridge is calculated by the fatigue strength associated with the load and the fatigue strength associated with the environmental information of each period. The collected bridge fatigue strengths are connected into a curve, and the curve is compared with the collapse risk degree-reinforced concrete fatigue strength fitting curve of the above comparison template. If the highest point of the curve is lower than the highest point of the safe zone, no warning is given. If it is higher than the highest point of the safe zone but lower than the highest point of the warning zone, a first-level warning is given. If it is higher than the highest point of the warning zone but lower than the highest point of the danger zone, a second-level warning is given. The warning module 13 gives the corresponding warning according to the data processing structure of the fifth data processing module 12.

[0063] Example 2

[0064] The difference between this embodiment and Embodiment 1 is that:

[0065] Specifically, it also includes a 3D modeling module 14 and a 3D model display module 15, wherein:

[0066] The 3D modeling module 14 receives the as-built data, inspection and evaluation data and surrounding environmental information of the bridge to be monitored collected by the first data acquisition module 1, clarifies the environmental information of the bridge and the surrounding buildings, and performs 3D modeling of the bridge and surrounding buildings. At the same time, it receives the bridge and surrounding building information collected in real time by the fifth data acquisition module 11 and performs 3D modeling of the bridge's real-time status and surrounding buildings.

[0067] The 3D model display module 15 displays the 3D model of the bridge and surrounding buildings constructed by the 3D modeling module 14 in real time, so as to understand the condition of the bridge in a timely manner and take timely action.

[0068] The working principle of this embodiment is as follows: The 3D modeling module 14 receives the as-built data, inspection and evaluation data and surrounding environmental information of the bridge to be monitored collected by the first data acquisition module 1, clarifies the environmental information of the bridge and the surrounding buildings, and performs 3D modeling of the bridge and surrounding buildings. At the same time, it receives the bridge and surrounding building information collected in real time by the fifth data acquisition module 11, performs real-time updated 3D modeling of the bridge's real-time status and the real-time status of the surrounding buildings, and displays it through the 3D model display module 15 so as to understand the bridge's condition in a timely manner and take timely action.

[0069] Example 3

[0070] The difference between this embodiment and embodiment two is that:

[0071] Specifically, it also includes a maintenance data database 16, among which:

[0072] Maintenance data database 16 stores maintenance measures for bridges under various fatigue strengths;

[0073] The fourth data processing module 9 also includes data processing that associates the maintenance measures in the maintenance data database 16 with the corresponding nodes of the warning interval and the danger interval of the collapse risk degree-reinforced concrete fatigue strength fitting curve.

[0074] The fifth data acquisition module 11 extracts the maintenance measures of the corresponding points of the curve endpoint and the comparison template to the three-dimensional model display module 15;

[0075] The 3D model display module 15 displays the maintenance measures for the corresponding point of the curve endpoint and the comparison template when the bridge fatigue strength enters the early warning period.

[0076] The working principle of this embodiment is as follows: When the fifth data processing module 12 compares the curve with the collapse risk degree-reinforced concrete fatigue strength fitting curve of the above comparison template, it simultaneously extracts the maintenance measures in the maintenance data database 16 associated with the matching point of the curve endpoint and the collapse risk degree-reinforced concrete fatigue strength fitting curve and displays them in the three-dimensional model display module 15 so that monitoring personnel can maintain the bridge in a timely manner.

[0077] Example 4

[0078] The difference between this embodiment and Embodiment 3 is that:

[0079] Specifically, it also includes the sixth data acquisition module 17, in which:

[0080] The sixth data acquisition module 17 studies the collapse of each node of the bridge by applying different loads to the bridge construction module 3, and collects the failure path and failure range data of each node of the bridge. The three-dimensional modeling module 14 receives the collected failure range data of each node, models the failure range data of each node of the bridge, and displays it on the three-dimensional model display module 15 with a red warning zone.

[0081] The viewing of the 3D model display module 15 is based on a shared login password, which can be viewed simultaneously by various departments of the municipal engineering project, so as to avoid the damage range of the bridge collapse during subsequent construction.

[0082] The working principle of this embodiment is as follows: During the fourth test of the simulated bridge construction module 3, different loads are applied to study the collapse of each node of the bridge. The sixth data acquisition module 17 collects the damage path and damage range data of each node of the bridge. The three-dimensional modeling module 14 receives the damage range data of each node collected by the sixth data acquisition module 17, models the damage range data of each node of the bridge, and displays it on the three-dimensional model display module 15 with a red warning zone. Various departments of municipal engineering can log in to the system through a shared login password to view the three-dimensional model on the three-dimensional model display module 15 so as to avoid the bridge collapse damage range in the later construction.

[0083] Example 5

[0084] The difference between this embodiment and embodiment four is that:

[0085] Specifically, it also includes a seventh data acquisition module 18, in which:

[0086] The seventh data acquisition module 18 collects the location of people using existing buildings within the area affected by the bridge collapse;

[0087] The early warning module 13 also includes a voice warning for the mobile phone early warning APP of the existing building users. Within a preset time period before the bridge collapse, the early warning module 13 sends a warning command to the mobile phone early warning APP of the users who are located within the bridge collapse damage range based on the location of the users collected by the seventh data acquisition module 18, so as to help the users evacuate quickly and avoid injury.

[0088] The working principle of this embodiment is as follows: When the early warning module 13 issues a hazard warning, the seventh data acquisition module 18 collects the location of existing building users within the bridge collapse and damage range, and sends a warning command to the mobile early warning APP of the users who are located within the bridge collapse and damage range within a preset time period before the bridge collapse, so as to help the users evacuate quickly and avoid injury.

[0089] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart monitoring system for bridge collapse scenarios, characterized in that, include: The system comprises a first data acquisition module (1), a first data processing module (2), a simulated bridge construction module (3), a second data acquisition module (4), a second data processing module (5), a third data acquisition module (6), a third data processing module (7), a fourth data acquisition module (8), a fourth data processing module (9), a first data storage module (10), a fifth data acquisition module (11), a fifth data processing module (12), and an early warning module (13), wherein: The first data acquisition module (1) collects the as-built data, inspection and evaluation data and surrounding environmental information of the bridge to be monitored; The first data processing module (2) determines the type of reinforced concrete used in the construction of the bridge under monitoring through the as-built data of the bridge under monitoring, determines the load-bearing capacity specified in the construction of the bridge under monitoring through the test and evaluation data, and determines the environmental information around the bridge under monitoring, including temperature and humidity, through the surrounding environmental information data. The simulated bridge construction module (3) constructs multiple simulated bridges with the same type of reinforced concrete, load-bearing capacity and environmental information as the bridge to be monitored; The second data acquisition module (4) acquires load and reinforced concrete fatigue strength data of multiple simulated bridges under test without environmental information influence; The second data processing module (5) calculates the correlation degree of fatigue strength of each load at each stage of the bridge by using multiple simulated bridge loads and reinforced concrete fatigue strength data. The third data acquisition module (6) acquires multiple simulated bridge fatigue strength data of reinforced concrete under no-load influence experiment; The third data processing module (7) calculates the correlation degree of each environmental information with the fatigue strength of the bridge at each stage by using environmental information and reinforced concrete fatigue strength data of multiple simulated bridges. The fourth data acquisition module (8) acquires fatigue strength data of reinforced concrete for multiple simulated bridges under natural environmental tests including load and environmental information; The fourth data processing module (9) establishes a collapse risk degree-reinforced concrete fatigue strength fitting curve as a comparison template, and divides the fitting curve into safe interval, warning interval and dangerous interval respectively; The first data storage module (10) stores the above comparison template; The fifth data acquisition module (11) collects the load data and environmental information data of the bridge in real time; The fifth data processing module (12) clarifies the period of the collected bridge load data and environmental information data. It calculates the influence of the load on the bridge fatigue strength by calculating the fatigue strength associated with the load and the fatigue strength associated with the environmental information in each period. It connects the collected bridge fatigue strength into a curve and compares the curve with the collapse risk degree-reinforced concrete fatigue strength fitting curve of the above comparison template. If the highest point of the curve is lower than the highest point of the safe zone, no warning is given. If it is higher than the highest point of the safe zone but lower than the highest point of the warning zone, a first-level warning is given. If it is higher than the highest point of the warning zone but lower than the highest point of the danger zone, a second-level warning is given. The early warning module (13) performs corresponding early warnings based on the data processing structure of the fifth data processing module (12).

2. The intelligent monitoring system for bridge collapse scenarios according to claim 1, characterized in that: The early warning module (13) includes a pop-up warning on the large screen of the monitoring site and a sound warning on the mobile phone APP of the monitoring personnel.

3. The intelligent monitoring system for bridge collapse scenarios according to claim 1, characterized in that: It also includes a 3D modeling module (14) and a 3D model display module (15), wherein: The three-dimensional modeling module (14) receives the as-built data, inspection and evaluation data and surrounding environmental information of the bridge to be monitored collected by the first data acquisition module (1), clarifies the environmental information of the bridge and the surrounding buildings, performs three-dimensional modeling of the bridge and surrounding buildings, and simultaneously receives the bridge and surrounding building information collected in real time by the fifth data acquisition module (11), performs three-dimensional modeling of the bridge's real-time status and surrounding buildings. The 3D model display module (15) displays the 3D model of the bridge and surrounding buildings constructed by the 3D modeling module (14) in real time, so as to understand the situation of the bridge in a timely manner and take timely action.

4. The intelligent monitoring system for bridge collapse scenarios according to claim 3, characterized in that: It also includes a maintenance data database (16), in which: Maintenance data database (16) to store maintenance measures for bridges under various fatigue strengths; The fourth data processing module (9) further includes associating the maintenance measures in the maintenance data database (16) with the corresponding nodes of the warning interval and the danger interval of the collapse risk degree-reinforced concrete fatigue strength fitting curve; The fifth data acquisition module (11) extracts the maintenance measures of the curve endpoint and the corresponding point of the comparison template to the three-dimensional model display module (15); The three-dimensional model display module (15) displays the maintenance measures of the corresponding point of the curve endpoint and the comparison template when the bridge fatigue strength enters the early warning period.

5. The intelligent monitoring system for bridge collapse scenarios according to claim 3, characterized in that: It also includes a sixth data acquisition module (17), in which: The sixth data acquisition module (17) studies the collapse of each node of the bridge by applying different loads through the simulated bridge construction module (3), and collects the damage path and damage range data of each node of the bridge. The three-dimensional modeling module (14) receives the collected damage range data of each node, models the damage range data of each node of the bridge, and displays it on the three-dimensional model display module (15) with a red warning zone. The viewing of the 3D model display module (15) is based on the login password sharing and can be viewed by various departments of municipal engineering at the same time, so as to avoid the scope of bridge collapse and damage during construction.

6. The intelligent monitoring system for bridge collapse scenarios according to claim 1, characterized in that: It also includes a seventh data acquisition module (18), in which: The seventh data acquisition module (18) collects the location of existing building users within the area affected by the bridge collapse; The early warning module (13) also includes a sound warning from the mobile phone early warning APP of the existing building users. Within a preset time period before the bridge collapse, the early warning command is sent to the mobile phone early warning APP of the users who are located within the bridge collapse damage range based on the location of the users collected by the seventh data acquisition module (18) to help the users evacuate quickly and avoid injury.

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