Bridge safety early warning method and device based on bridge and tunnel structure data sharing and storage medium

Abstract

The application provides a bridge safety early warning method and device based on bridge tunnel structure data sharing and a storage medium. The early warning method comprises the following steps: acquiring meteorological data sets at each sampling point based on meteorological sensors of multiple sampling points; acquiring meteorological sharing indexes; acquiring structure data at each collection point based on structure monitoring sensors of multiple collection points; acquiring structure sharing indexes; obtaining bridge body structure health indexes; obtaining bridge structure evaluation indexes based on the structure sharing indexes and the bridge body structure health indexes; acquiring weather forecast information at the bridge; and obtaining bridge safety evaluation indexes s based on the bridge structure evaluation indexes, the meteorological sharing indexes and dynamically adjusted weights. * The obtained bridge safety evaluation indexes s * are compared with a safety threshold, and early warning is triggered when the bridge safety evaluation indexes s * are greater than the safety threshold. The early warning method provided by the application can realize bridge safety early warning while saving the deployment of bridge body meteorological sensors.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge monitoring, and in particular to a bridge safety early warning method, device and storage medium based on bridge and tunnel structure data sharing. Background Art

[0002] In the construction of roads, railways and other transportation, many bridges and tunnels need to be built. In order to cross special terrains such as deep valleys and mountains, bridges across deep valleys and mountains are often built, as well as tunnels connected to bridges in the mountains on both sides. This article calls them bridge-tunnel structures. Figure 1 .

[0003] To ensure safety, bridges and tunnels are typically inspected using sensors and related testing equipment during operation. These tests typically include testing the integrity of bridge pile foundations, the quality of structural concrete, and the durability of structural concrete.

[0004] In the process of implementing the present invention, the inventors discovered that the prior art has at least the following problems:

[0005] Currently, monitoring and early warning systems for bridge and tunnel structures are primarily limited to monitoring the bridge and tunnel structures themselves. Research has yet to explore the use of sensor data from surrounding structures for collaborative analysis and risk assessment. Furthermore, environmental factors, in addition to the structural health of the bridge itself, generally influence bridge safety. Therefore, to collect meteorological and environmental data, bridges are typically equipped with multiple dedicated sensors, further increasing system power consumption and complexity. Independent monitoring of each structure also means data cannot be shared. Especially within the same geographic area, multiple sensors often duplicate the same environmental and meteorological data, leading to high system deployment and maintenance costs.

[0006] Therefore, a bridge safety early warning method, equipment and storage medium based on bridge and tunnel structure data sharing are needed. Summary of the Invention

[0007] In view of this, embodiments of the present invention provide a bridge safety early warning method, device, and storage medium based on bridge and tunnel structure data sharing to at least solve one of the problems in the prior art.

[0008] In a first aspect, an embodiment of the present invention provides a bridge safety early warning method based on bridge and tunnel structure data sharing, the early warning method comprising:

[0009] Based on meteorological sensors located at multiple sampling points at different locations within a set distance range corresponding to the type of target bridge classified by span size and configured in a tunnel and / or slope on at least one side of the target bridge, a meteorological dataset at each sampling point is obtained, including wind speed, rainfall, and temperature data;

[0010] Obtain meteorological sharing indicators based on the meteorological datasets of multiple sampling points, the distances between the sampling points and the target bridge, and the sensor health;

[0011] Acquiring structural data at each collection point based on structural monitoring sensors disposed at a tunnel entrance and exit and / or a slope on at least one side of a target bridge, wherein the structural data is one of stress, displacement, and vibration data;

[0012] Normalize the structural data obtained at all acquisition points and obtain a structural sharing index based on the normalized structural data, the distance between the acquisition point and the target bridge, and the sensor health;

[0013] Based on the structural data acquired by the target bridge's own structural monitoring sensors, including stress, displacement, and vibration data of multiple key parts, the bridge's structural health indicators are obtained;

[0014] Obtaining a bridge structure evaluation index based on the structural sharing index and the bridge body structural health index;

[0015] Obtain weather forecast information at the bridge, and obtain the bridge safety assessment index s based on the bridge structure assessment index, meteorological sharing index and the weight dynamically adjusted according to the weather forecast information at the bridge. * , 0≤s * ≤1; wherein, the bridge safety assessment index s * The size of the bridge represents the different safety levels. * The larger it is, the more dangerous the bridge condition is;

[0016] The bridge safety evaluation index s obtained * Compared with the safety threshold that is dynamically adjusted according to the weather forecast information at the bridge, when the bridge safety assessment index s * If the value is greater than the safety threshold, an early warning is triggered.

[0017] In a second aspect, an embodiment of the present invention further provides a bridge safety early warning device based on bridge and tunnel structure data sharing, the early warning device comprising:

[0018] a memory for storing computer-executable instructions;

[0019] The processor is used to implement the early warning method of the above technical solution when executing the computer executable instructions stored in the memory.

[0020] In a third aspect, an embodiment of the present invention further provides a storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to execute the early warning method of the above technical solution.

[0021] Additional advantages, objects, and features of the present invention will be set forth in part in the following description and will become apparent to those skilled in the art upon examination of the following or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained by the structures particularly pointed out in the description and drawings.

[0022] Those skilled in the art will understand that the purposes and advantages that can be achieved by the present invention are not limited to the above specific descriptions, and the above and other purposes that can be achieved by the present invention will be more clearly understood based on the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are intended to provide a further understanding of the present invention, constitute a part of this application, and do not constitute a limitation of the present invention. The components in the drawings are not drawn to scale, but are merely for the purpose of illustrating the principles of the present invention. To facilitate the illustration and description of certain portions of the present invention, corresponding portions in the drawings may be exaggerated, that is, may be larger than other components in an exemplary device actually manufactured according to the present invention. In the drawings:

[0024] Figure 1 A schematic diagram of an existing bridge and tunnel structure;

[0025] Figure 2 is a flow chart of an early warning method according to an embodiment of the present invention;

[0026] Figure 3 is a schematic diagram of an early warning device according to an embodiment of the present invention;

[0027] Figure 4 FIG. 1 is a schematic diagram of an early warning system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0029] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, the accompanying drawings only show structures and / or processing steps closely related to the solutions according to the present invention, while other details that are not closely related to the present invention are omitted.

[0030] It should be emphasized that the term "include / comprises" when used herein refers to the existence of features, elements, steps or components, but does not exclude the existence or addition of one or more other features, elements, steps or components.

[0031] It should also be noted that, unless otherwise specified, the term "connection" herein may refer not only to a direct connection but also to an indirect connection involving an intermediate.

[0032] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the accompanying drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0033] First, refer to Figure 2 The bridge safety early warning method 100 based on bridge and tunnel structure data sharing according to an embodiment of the present application is described. Figure 2 As shown, the early warning method 100 may include steps S110 to S180, which are specifically as follows:

[0034] In step S110, based on meteorological sensors at multiple sampling points located at different positions within a set distance range corresponding to the type of the target bridge classified by span size and configured in a tunnel and / or slope on at least one side of the target bridge, a meteorological data set at each sampling point is obtained, including wind speed, rainfall and temperature data.

[0035] In step S120 , a meteorological sharing index is obtained based on the meteorological data sets of the plurality of sampling points, the distance between the sampling points and the target bridge, and the sensor health.

[0036] In step S130, based on structural monitoring sensors configured at multiple collection points of tunnel entrances and exits and / or slopes on at least one side of the target bridge, structural data at each collection point is obtained, where the structural data is one of stress, displacement and vibration data.

[0037] In step S140 , the structural data acquired at all acquisition points are normalized, and a structural sharing index is acquired based on the normalized structural data, the distance between the acquisition point and the target bridge, and the sensor health.

[0038] In step S150, based on the target bridge's own structural data acquired by its own structural monitoring sensors, including stress, displacement and vibration data of multiple key parts, the bridge's structural health index is obtained.

[0039] In step S160, a bridge structure evaluation index is obtained based on the structure sharing index and the bridge body structure health index.

[0040] In step S170, weather forecast information at the bridge is obtained, and the bridge safety assessment index s is obtained based on the bridge structure assessment index, the meteorological sharing index and the weight dynamically adjusted according to the weather forecast information at the bridge. * , 0≤s * ≤1; wherein, the bridge safety assessment index s * The size of the bridge represents the different safety levels. * The larger the value, the more dangerous the bridge condition.

[0041] In step S180, the bridge safety evaluation index s is obtained. * Compared with the safety threshold that is dynamically adjusted according to the weather forecast information at the bridge, when the bridge safety assessment index s * If the value is greater than the safety threshold, an early warning is triggered.

[0042] In an embodiment of the present application, first, multiple meteorological data sets of different locations of tunnels and / or slopes within a set distance range from the target bridge are obtained, and meteorological sharing indicators are obtained based on the multiple meteorological data sets; multiple structural data of tunnel entrances and / or slopes are obtained, and the structural sharing indicators are further obtained after the data are normalized; then the structural health index of the bridge body is obtained, and the bridge structure evaluation index is obtained based on the structural sharing index and the bridge body structural health index. Then, the dynamically adjusted meteorological sharing index weight and safety threshold are determined according to the weather forecast information at the bridge, and the bridge safety evaluation index s is obtained based on the bridge structure evaluation index, the meteorological sharing index and the dynamically adjusted weight. * , and finally the bridge safety evaluation index s * Compare the size with the safety threshold and determine whether to trigger an early warning based on the situation.

[0043] For example, collected meteorological datasets and structural data on slopes and tunnels can be uploaded to the cloud via 4G / 5G networks or the Internet of Things. This data is not only used for structural health analysis of slopes and tunnels, but can also be used by bridge structures. The bridge structure establishes a connection with the cloud server via a preset network protocol, regularly sending requests to obtain meteorological and structural shared indicators. After receiving the request, the cloud processes and returns the meteorological and structural shared indicators. Based on its own edge computing devices, the bridge structure integrates the received information with its own monitoring data. Before data integration, the edge computing devices can perform local preprocessing on the monitoring data to filter out noise, and then perform risk assessment and status judgment, reducing the configuration of its own meteorological sensors and achieving lightweight design.

[0044] From the description of the above process, it can be seen that according to the early warning method 100 of the embodiment of the present application, by sharing the data of the slope and tunnel sensors around the bridge (including meteorological and structural data information), it is avoided to repeatedly install meteorological sensors on the bridge body, thereby reducing system cost and power consumption. In addition, the relevant shared indicators of the tunnels and slopes around the bridge are integrated, and the weather information at the bridge is considered to obtain the bridge safety assessment index s. * The result is compared with the size of the safety threshold that is dynamically adjusted according to the weather forecast information at the bridge to achieve bridge safety early warning.

[0045] Among them, Figure 2 In the example, step S110, step S130, and step S150 are shown as being performed sequentially. This is merely an example. It is understood that the order of step S110, step S130, and step S150 is not limited. For example, the three steps can be performed simultaneously.

[0046] The contents of the above steps of the early warning method 100 according to the embodiment of the present application will be described in detail below.

[0047] For common bridges, sensors are typically deployed at key locations to monitor the safety of the bridge structure in real time. For example, structural monitoring sensors are installed at the pier foundation or main beam to monitor various structural parameters.

[0048] For example, displacement sensors are installed in bridge pier foundations to capture displacement data, monitor changes in water flow on the pier foundations, and assess foundation stability. Strain sensors and accelerometers are deployed in the main beam support area, mid-span, beam end nodes, and at the crossbeam-main beam connection to capture beam stress and vibration data for real-time monitoring of the beam's stress state and vibration response. These are just some examples; the deployment of sensors in other key locations is not detailed here.

[0049] Then, based on the stress, displacement and vibration data obtained by sensors at key parts of the bridge, all the acquired stress, displacement and vibration data are normalized. For all the stress data after normalization, the bridge body stress index S is obtained through weighted calculation according to the weights corresponding to the key parts. local Using the same method, the bridge body vibration index V local , Bridge body displacement index D local Finally, the bridge structure health index M is obtained by the following formula: local ,

[0050]

[0051] The above content is the specific content of step S150 of the embodiment of the present application. Therefore, step S150 will not be repeated in the following.

[0052] In an embodiment of the present application, in step S110, based on meteorological sensors at multiple sampling points located at different positions within a set distance range corresponding to the type of the target bridge classified by span size and configured in a tunnel and / or slope on at least one side of the target bridge, a meteorological data set at each sampling point is obtained, including wind speed, rainfall and temperature data.

[0053] It should be noted that for sensors utilizing structures surrounding bridges (slopes, tunnels), different data sources are used in different situations. Bridges can be categorized by span size into small, medium, large, and extra-large bridges. In this embodiment, when using meteorological sensors at sampling points around bridges to obtain meteorological data, specific and strict restrictions are placed on their locations.

[0054] Specifically, for small bridges (with a total span of multiple holes between 8 and 30 meters and a span of single holes between 5 and 20 meters), the meteorological monitoring data collected by meteorological sensors at sampling points of surrounding structures such as slopes and tunnels within 10 meters of the target bridge are used; for medium bridges (with a total span of multiple holes between 30 and 100 meters and a span of single holes between 20 and 40 meters), the meteorological monitoring data collected by meteorological sensors at sampling points of surrounding structures such as slopes and tunnels within 20 meters of the bridge are used; for large bridges (with a total span of multiple holes between 100 and 1000 meters and a span of single holes between 40 and 1500 meters) and extra-large bridges (with a total span of multiple holes greater than 1000 meters and a span of single holes greater than 150 meters), the meteorological monitoring data collected by meteorological sensors at sampling points of surrounding structures such as slopes and tunnels within 50 meters of the bridge are used.

[0055] Specifically, for ease of understanding and description, let's assume the target bridge is a multi-span bridge with a total span of 200 meters. Six and seven sampling points are placed on the two slopes of the target bridge, respectively. A total of 12 sampling points are set within a tunnel on one side of the target bridge, within 50 meters of the bridge. Meteorological sensors at each sampling point capture three meteorological data points: wind speed, rainfall, and temperature, forming a meteorological dataset.

[0056] For example, within a sampling period of 10 or 30 minutes, the meteorological sensor at each sampling point collects three meteorological data points: wind speed, rainfall, and temperature. This data set forms a meteorological dataset. Alternatively, the meteorological sensor at each sampling point acquires 10 values ​​of the corresponding meteorological data at a sampling interval Δt within a sampling period. The average of these 10 values ​​is used as the final value of the corresponding meteorological data for subsequent calculations.

[0057] In an embodiment of the present application, in step S120 , a meteorological sharing index is obtained based on the meteorological data sets of the multiple sampling points, the distance between the sampling points and the target bridge, and the sensor health.

[0058] Specifically, the following may be included:

[0059] Based on the distance between the sampling point and the target bridge, determine the first influencing factor ω of the meteorological dataset of the i-th sampling point i ,

[0060]

[0061] Among them, d i is the distance between each sampling point and the target bridge. i Sensor health, ranging from 0 to 1, indicates the health of the sensor. Higher health indicates higher values. Sensor health is a technical parameter currently available. For details, see "Sensor Health Evaluation Method Based on Fuzzy Set Theory," Cao Zhenghong and Shen Jihong, Journal of Electric Machines and Control, Vol. 14, No. 5, May 2010. This paper provides a quantitative comprehensive evaluation indicator for sensor health: sensor health, which is not described here.

[0062] Based on the determined first impact factor ω i The meteorological sharing index M is determined by using the meteorological data sets of multiple sampling points weather

[0063]

[0064] Where M is the number of sampling points, W i 、R i and T i are the wind speed, rainfall and temperature data in the meteorological dataset at the i-th sampling point.

[0065] In an embodiment of the present application, in step S130, structural data at each collection point is acquired based on structural monitoring sensors configured at multiple collection points at tunnel entrances and exits and / or slopes on at least one side of the target bridge. The structural data at each collection point is one of stress, displacement, and vibration data.

[0066] For tunnel structures, stress sensors can be installed at the arch crown and arch foot at the entrance and exit, displacement sensors can be installed at the midpoint and bottom of the sidewall, and vibration sensors can be installed at key locations of the tunnel lining and support structure. For slope structures, displacement sensors can be installed at the top and bottom of the slope, vibration sensors can be installed at different heights and locations on the slope, and stress sensors can be installed at potential sliding surfaces, crack areas, or geological structure changes on the slope.

[0067] For example, 12 data collection points were placed on the slope of one side of the target bridge, and 18 data collection points were placed at the tunnel entrances and exits on both sides of the target bridge. At each of these 30 data collection points, the structural monitoring sensor acquires one of the following types of data: stress, displacement, and vibration. For example, 10 stress data points, 10 displacement data points, and 10 vibration data points were acquired, respectively.

[0068] In an embodiment of the present application, in step S140, the structural data acquired at all acquisition points are normalized, and a structural sharing index is acquired based on the normalized structural data, the distance between the acquisition point and the target bridge, and the sensor health.

[0069] First, since the data units and dimensions of stress, vibration and displacement are different, these data must be standardized before fusion, such as normalization, to make them comparable.

[0070]

[0071] Among them, S j Indicates the jth stress data among all the stress data obtained, V k Indicates the kth vibration data obtained, D l Indicates the lth displacement data obtained, S j ', V k ', D l ' are the normalized data. max(S), max(V), and max(D) represent the historical maximum values ​​of the corresponding class data, and min(S), min(V), and min(D) represent the historical minimum values ​​of the corresponding class data.

[0072] Then, based on the distance between the acquisition point and the target bridge, the second influencing factor β of the structural data at different acquisition points is determined. s(v,d),j(k,l) ,

[0073]

[0074] Among them, d j(k,l) Indicates the distance between the acquisition point where the structural data of the corresponding sequence number is located and the target bridge. j(k,l) Indicates the health of the sensor that obtains the structural data of the corresponding sequence number.

[0075] Here β s(v,d),j(k,l) This is β in the following text s,j , β v,k , β d,l The total expression of .

[0076] Based on the determined second impact factor β s(v,d),j(k,l)The structure sharing index M is determined by the obtained normalized structure data struct ,

[0077]

[0078] Where N represents the number of stress data obtained, β s,j represents the second impact factor corresponding to the jth stress data obtained, P represents the number of vibration data obtained, β v,k represents the second impact factor corresponding to the kth vibration data obtained, Q represents the number of displacement data obtained, β d,l Indicates the second impact factor corresponding to the obtained lth displacement data.

[0079] Taking the example of obtaining 10 stress data, 10 displacement data and 10 vibration data from 30 acquisition points on the slopes on both sides of the target bridge and the tunnel entrance and exit in the previous article, N, P and Q are all set to 10.

[0080] In step S160, a bridge structure evaluation index is obtained based on the structure sharing index and the bridge body structure health index.

[0081] Specifically, based on the structural sharing index M struct and the bridge structure health index M local Obtain bridge structure evaluation index R struct

[0082] R struct =γ·M struct +δ·M local

[0083] Where γ and δ are the weights of the structural sharing index and the bridge structure health index, respectively. γ + δ = 1. For example, γ is 0.3 and δ is 0.7.

[0084] In step S170, weather forecast information at the bridge is obtained, and the bridge safety assessment index s is obtained based on the bridge structure assessment index, the meteorological sharing index and the weight dynamically adjusted according to the weather forecast information at the bridge. * , 0≤s * ≤1. Among them, the bridge safety assessment index s * The size of the bridge represents the different safety levels. * The larger the value, the more dangerous the bridge condition.

[0085] Specifically, the bridge safety assessment index s is obtained * ,

[0086] s * =λ·R struct +μ·Mweather

[0087] wherein, λ and μ are the weights of the bridge structure evaluation index and the weather sharing index respectively. λ + μ = 1.

[0088] It should be noted that λ and μ need to be adaptively dynamically adjusted according to the weather information at the bridge, rather than fixed. For example, the weight of the weather sharing index should be increased and the weight of the bridge structure evaluation index should be relatively reduced under extreme weather conditions.

[0089] The weight of the weather sharing index is dynamically adjusted according to the weather forecast information at the bridge,

[0090] μ' = μ'' x f

[0091] wherein, μ' is the weight of the weather sharing index under extreme weather, μ'' is the weight of the weather sharing index under non-extreme weather. Extreme weather refers to wind speed > 20 m / s or rainfall > 50 mm / h. f is an adjustment coefficient, f > 1. f is a certain value, for example, the value is 1.5.

[0092] For example, according to the weather forecast information at the bridge, it is determined that the weather information at the bridge is non-extreme weather, for example, at this time λ takes the value of 0.6 and μ takes the value of 0.4. When it is determined that the weather information at the bridge is extreme weather, it is changed to λ taking the value of 0.4 and μ taking the value of 0.6.

[0093] In step S180, the obtained bridge safety evaluation index s * is compared with the safety threshold dynamically adjusted according to the weather forecast information at the bridge. When the bridge safety evaluation index s * is greater than the safety threshold, the warning is triggered.

[0094] It should be noted that the safety threshold also needs to be adaptively dynamically adjusted according to the weather information at the bridge, rather than fixed. During extreme weather, the safety threshold for triggering the warning will be automatically reduced to cope with the sudden risk.

[0095] The safety threshold is dynamically adjusted according to the weather forecast information at the bridge, which specifically refers to:

[0096] t' = t'' - e

[0097] wherein, t' is the safety threshold under extreme weather, t'' is the safety threshold under non-extreme weather, 0.1 ≤ e ≤ 0.2, e is a certain value. For example, according to the weather forecast information at the bridge, it is determined that the weather information at the bridge is non-extreme weather, for example, at this time the safety threshold takes the value of 0.85. When it is determined that the weather information at the bridge is extreme weather, the safety threshold is reduced to take the value of 0.75.

[0098] When the bridge safety evaluation index s *When the level exceeds the safety threshold, an early warning is triggered. This typically indicates that the bridge's safety has reached an extremely high risk, with damage or failure imminent, and urgent measures must be taken immediately. Early warning information can be sent to relevant management departments and on-site monitoring personnel via various channels (such as text messages, emails, and mobile apps). Integrated GPS and navigation systems can also provide real-time voice or text warnings to drivers and pedestrians on the bridge, reminding them to avoid the bridge.

[0099] In addition, the bridge safety assessment index s can also be * Further set up multiple levels to deal with different risk levels and achieve graded processing. For example, taking the safety threshold of 0.85 as an example, when s * If the safety threshold exceeds 0.85, the system will immediately trigger a level 1 warning. This usually means that the structure has reached an extremely high risk and may be damaged or fail at any time. Emergency measures must be taken immediately to remind vehicles and people to avoid the bridge. * In the medium risk range (0.65≤s * <0.85), the system triggers a secondary warning and recommends that managers conduct further inspections of the structure and take appropriate preventive measures. * In the low risk range (such as 0.35≤s * <0.65), the system will issue a level 3 warning, prompting monitoring personnel to pay attention to possible structural abnormalities and continue to observe the changing trend. * In the risk-free range (0≤s * <0.35), indicating that the bridge is safe.

[0100] Based on the above description, the early warning method according to the embodiment of the present application avoids the repeated arrangement of meteorological sensors and the repeated collection of meteorological data by sharing data, thereby reducing costs and power consumption. In addition, the relevant shared indicators of tunnels and slopes around the bridge are integrated, and the weather information at the bridge is considered to obtain the bridge safety assessment index s. * By comparing the results with safety thresholds dynamically adjusted based on weather forecast information at the bridge site, it is possible to issue warnings based on different risk levels and provide bridge safety alerts. This method enhances bridge monitoring and early warning capabilities through multi-source data sharing and intelligent analysis.

[0101] refer to Figure 3The embodiment of the present application further provides an early warning device 200 for implementing the early warning method 100 according to the embodiment of the present application. The early warning device 200 includes a processor 210 and a memory 220. The early warning device 200 may include one or more processors 210 and one or more memories 220. The memory 220 stores an executable program executed by the processor 210. When the executable program is executed by the processor 210, the processor 210 executes the early warning method 100 according to the embodiment of the present application described above.

[0102] The processor 210 may be a central processing unit (CPU) or other processing units having data processing capabilities and / or instruction execution capabilities.

[0103] The memory 220 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 210 may run the program instructions to implement the client functions and / or other desired functions in the embodiments of the present application described herein (implemented by the processor). Various applications and various data may also be stored in the computer-readable storage medium, such as various data used and / or generated by the application.

[0104] The early warning device 200 may also include an input device and an output device, and these components are interconnected through a bus system and / or other forms of connection mechanisms. Figure 3 The components and structures of the early warning device 200 shown are merely exemplary and non-limiting. The early warning device 200 may also have other components and structures as needed.

[0105] The input device may be a device used by a user to input instructions, and may include one or more of a keyboard, a mouse, a microphone, a touch screen, etc. In addition, the input device may also be any interface for receiving information.

[0106] The output device may output various information (eg, images or sounds) to the outside (eg, a user), and may include one or more of a display, a speaker, etc. In addition, the output device may also be any other device with an output function.

[0107] Exemplarily, the example early warning device 200 for implementing the early warning method 100 according to the embodiment of the present application can be applied to terminal devices (such as mobile phones), tablet computers, laptops, ultra-mobile personal computers (UMPCs), handheld computers, netbooks, personal digital assistants (PDAs), wearable devices (such as smart watches, smart glasses or smart helmets, etc.), augmented reality (AR), virtual reality (VR) devices, smart home devices, car computers and other electronic devices. The embodiments of the present application do not impose any restrictions on this.

[0108] Those skilled in the art can understand the specific operations of the early warning device 200 for implementing the early warning method 100 according to the embodiment of the present application in combination with the contents described above. For the sake of brevity, the specific details are not repeated here, and only some main operations of the processor 210 are described.

[0109] In one embodiment of the present application, when the executable program is executed by the processor 210, the processor 210 performs the following steps:

[0110] Based on meteorological sensors at multiple sampling points located at different positions within a set distance range corresponding to the type of target bridge classified by span size and configured in tunnels and / or slopes on at least one side of the target bridge, a meteorological data set at each sampling point is obtained, including wind speed, rainfall, and temperature data; based on the meteorological data sets obtained at the multiple sampling points, the distance between the sampling points and the target bridge, and the sensor health, a meteorological sharing index is obtained; based on structural monitoring sensors at multiple collection points at tunnel entrances and / or slopes on at least one side of the target bridge, structural data at each collection point is obtained, wherein the structural data is one of stress, displacement, and vibration data. Normalize the structural data obtained at all acquisition points, and obtain a structural sharing index based on the normalized structural data, the distance between the acquisition point and the target bridge, and the sensor health; obtain the structural health index of the bridge body based on the structural data of the target bridge itself obtained by the structural monitoring sensor of the target bridge itself, including stress, displacement and vibration data of multiple key parts; obtain a bridge structure evaluation index based on the structural sharing index and the bridge body structural health index; obtain weather forecast information at the bridge, and obtain a bridge safety evaluation index s based on the bridge structure evaluation index, meteorological sharing index and the weight dynamically adjusted according to the weather forecast information at the bridge. * , 0≤s * ≤1; wherein, the bridge safety assessment index s *The size of the bridge represents the different safety levels. * The larger the value, the more dangerous the bridge condition. The bridge safety assessment index s * Compared with the safety threshold that is dynamically adjusted according to the weather forecast information at the bridge, when the bridge safety assessment index s * If the value is greater than the safety threshold, an early warning is triggered.

[0111] The above example shows the early warning method 100 according to the embodiment of the present application. Figure 4 The following describes an early warning system 300 provided in another aspect of an embodiment of the present application.

[0112] Reference Figure 4 The following describes an example early warning system 300 for implementing the early warning method of an embodiment of the present application. The early warning system 300 may include a meteorological data set acquisition module 310, a meteorological shared index acquisition module 320, a structural data acquisition module 330, a structural shared index acquisition module 340, a structural health index acquisition module 350, a structural assessment index acquisition module 360, a safety assessment index acquisition module 370, and an early warning module 380. Among them:

[0113] The meteorological data set acquisition module 310 is used to: obtain a meteorological data set at each sampling point, including wind speed, rainfall, and temperature data, based on meteorological sensors located at multiple sampling points at different locations within a set distance range corresponding to the type of the target bridge classified by span size from the target bridge, which are configured in the tunnel and / or slope on at least one side of the target bridge.

[0114] The meteorological sharing index acquisition module 320 is used to acquire a meteorological sharing index based on the acquired meteorological data sets of the multiple sampling points, the distance between the sampling points and the target bridge, and the sensor health.

[0115] The structural data acquisition module 330 is used to: obtain structural data at each collection point based on structural monitoring sensors configured at multiple collection points of tunnel entrances and exits and / or slopes on at least one side of the target bridge, wherein the structural data is one of stress, displacement and vibration data.

[0116] The structural sharing index acquisition module 340 is used to normalize the structural data acquired at all acquisition points and acquire the structural sharing index based on the normalized structural data, the distance between the acquisition point and the target bridge, and the sensor health.

[0117] The structural health index acquisition module 350 is used to obtain the structural health index of the target bridge based on the structural data of the target bridge itself acquired by the structural monitoring sensors of the target bridge, including stress, displacement and vibration data of multiple key parts.

[0118] The structural evaluation index acquisition module 360 ​​is used to obtain a bridge structure evaluation index based on the structural sharing index and the bridge body structural health index.

[0119] The safety assessment index acquisition module 370 is used to obtain weather forecast information at the bridge, and obtain the bridge safety assessment index s based on the bridge structure assessment index, meteorological sharing index and the weight dynamically adjusted according to the weather forecast information at the bridge. * , 0≤s * ≤1; wherein, the bridge safety assessment index s * The size of the bridge represents the different safety levels. * The larger the value, the more dangerous the bridge condition.

[0120] The early warning module 380 is used to: obtain the bridge safety assessment index s * Compared with the safety threshold that is dynamically adjusted according to the weather forecast information at the bridge, when the bridge safety assessment index s * If the value is greater than the safety threshold, an early warning is triggered.

[0121] The early warning system 300 proposed in this embodiment of the present invention reduces the deployment of sensors on the bridge itself by sharing meteorological and structural data about the target bridge's surrounding environment, achieving a lightweight design for the early warning system. The system utilizes sensor data from surrounding structures (slopes, tunnels), combined with real-time monitoring of the bridge itself, to conduct comprehensive risk assessments and early warnings. This innovative approach to data sharing effectively reduces the need for sensor installation on the bridge, reduces power consumption, and improves monitoring efficiency.

[0122] In addition, according to an embodiment of the present application, the present application further provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, it is used to execute the corresponding steps of the early warning method 100 of the embodiment of the present application. The storage medium may include, for example, a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0123] In addition, according to an embodiment of the present application, the present application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the monitoring method of the embodiment of the present application or the steps of the early warning method of the embodiment of the present application.

[0124] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely illustrative and are not intended to limit the scope of the present application. Various changes and modifications may be made therein by those skilled in the art without departing from the scope and spirit of the present application. All such changes and modifications are intended to be included within the scope of the present application as required by the appended claims.

[0125] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel 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.

[0126] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units described is merely a logical function division. In actual implementation, other division methods may be used, such as combining or integrating multiple units or components into another device, or ignoring or not performing some features.

[0127] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of this application and to form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

[0128] It should be noted that the above embodiments illustrate rather than limit the present application, and that a person skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbols placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present application may be implemented by means of hardware comprising several different elements and by means of appropriately programmed computers. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0129] The above description is merely a specific embodiment or illustration of a specific embodiment of the present application, and the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. The scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A bridge safety early warning method based on bridge and tunnel structure data sharing, characterized in that: The early warning method includes: Based on meteorological sensors located at multiple sampling points at different locations within a set distance range corresponding to the type of target bridge classified by span size and configured in a tunnel and / or slope on at least one side of the target bridge, a meteorological dataset at each sampling point is obtained, including wind speed, rainfall, and temperature data; Obtain meteorological sharing indicators based on the meteorological datasets of multiple sampling points, the distances between the sampling points and the target bridge, and the sensor health; Acquiring structural data at each collection point based on structural monitoring sensors disposed at a tunnel entrance and exit and / or a slope on at least one side of a target bridge, wherein the structural data is one of stress, displacement, and vibration data; Normalize the structural data obtained at all acquisition points and obtain a structural sharing index based on the normalized structural data, the distance between the acquisition point and the target bridge, and the sensor health; Based on the structural data acquired by the target bridge's own structural monitoring sensors, including stress, displacement, and vibration data of multiple key parts, the bridge's structural health indicators are obtained; Obtaining a bridge structure evaluation index based on the structural sharing index and the bridge body structural health index; Obtain weather forecast information at the bridge, and obtain the bridge safety assessment index s based on the bridge structure assessment index, meteorological sharing index and the weight dynamically adjusted according to the weather forecast information at the bridge. * , 0≤s * ≤1; wherein, the bridge safety assessment index s * The size of the bridge represents the different safety levels. * The larger it is, the more dangerous the bridge condition is; The bridge safety evaluation index s obtained * Compared with the safety threshold that is dynamically adjusted according to the weather forecast information at the bridge, when the bridge safety assessment index s * If the value is greater than the safety threshold, an early warning is triggered.

2. The early warning method according to claim 1, characterized in that: The acquisition of meteorological sharing indicators specifically includes: Based on the distance between the sampling point and the target bridge, determine the first influencing factor ω of the meteorological dataset of the i-th sampling point i , Among them, d i is the distance between the sampling point and the target bridge, h i is the sensor health; Based on the determined first impact factor ω i The meteorological sharing index M is determined by using the meteorological data sets of multiple sampling points weather Where M is the number of sampling points, W i 、R i and T i are the wind speed, rainfall and temperature data in the meteorological dataset at the i-th sampling point.

3. The early warning method according to claim 1, characterized in that: Normalize the structural data at all acquisition points, specifically: Among them, S j Indicates the jth stress data among all the stress data obtained, V k Indicates the kth vibration data obtained, D l represents the lth displacement data obtained, max(S), max(V), and max(D) represent the historical maximum values ​​of the corresponding class data.

4. The early warning method according to claim 3, characterized in that: The obtaining of the structural sharing indicator specifically includes: Determine the second influencing factor β of the structural data at different collection points based on the distance between the collection point and the target bridge s(v,d),j(k,l) , Among them, d j(k,l) Indicates the distance between the acquisition point where the structural data of the corresponding sequence number is located and the target bridge, h j(k,l) Indicates the health of the sensor that obtains the structural data of the corresponding sequence number; Based on the determined second impact factor β s(v,d),j(k,l) The structure sharing index M is determined by the obtained normalized structure data struct , Where N represents the number of stress data obtained, β s,j represents the second impact factor corresponding to the jth stress data obtained, P represents the number of vibration data obtained, β v,k represents the second impact factor corresponding to the kth vibration data obtained, Q represents the number of displacement data obtained, β d,l Indicates the second impact factor corresponding to the obtained lth displacement data.

5. The early warning method according to claim 1, characterized in that: The set distance corresponding to the type of target bridge classified by span size specifically refers to: When the target bridge is classified as a small bridge based on span size, the corresponding set distance is 10 meters; When the target bridge is classified as a medium bridge based on span size, the corresponding set distance is 20 meters; When the target bridge is classified as a large bridge or an extra-large bridge according to span size, the corresponding set distance is 50 meters.

6. The early warning method according to claim 4, characterized in that: The structure sharing index and the bridge body structure health index M local Obtain bridge structure evaluation index R struct , specifically: R struct =γ·M struct +δ·M local Among them, γ and δ are the weights of the structural sharing index and the bridge structure health index, respectively.

7. The early warning method according to claim 6, characterized in that: The bridge safety assessment index s is obtained * , specifically: s * =λ·R struct +μ·M weather Among them, λ and μ are the weights of bridge structure evaluation index and meteorological sharing index, respectively.

8. The early warning method according to claim 1, characterized in that: The weights dynamically adjusted according to the weather forecast information at the bridge specifically refer to: μ'=μ"×f Among them, μ' is the weight of the meteorological sharing index under extreme weather conditions, μ" is the weight of the meteorological sharing index under non-extreme weather conditions, f>1, and f is a certain value; The safety threshold dynamically adjusted according to the weather forecast information at the bridge specifically refers to: t'=t"-e Among them, t' is the safety threshold under extreme weather conditions, t" is the safety threshold under non-extreme weather conditions, 0.1≤e≤0.2, and e is a certain value.

9. A bridge safety early warning device based on bridge and tunnel structure data sharing, characterized in that: The early warning equipment includes: a memory for storing computer-executable instructions; The processor is configured to implement the early warning method according to any one of claims 1 to 8 when executing the computer executable instructions stored in the memory.

10. A storage medium storing computer instructions, wherein: The computer instructions are used to enable the computer to execute the early warning method according to any one of claims 1 to 8.

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