Bridge state monitoring method, evaluation method and device based on cloud edge collaboration and multi-index fusion, and storage medium

Abstract

The application provides a bridge state monitoring method and evaluation method based on cloud edge cooperation and multi-index fusion, equipment and a storage medium, wherein the monitoring method comprises the following steps: acquiring a plurality of monitoring physical quantities corresponding to the position of each monitoring point of a target bridge in a monitoring period; performing Kalman filtering processing on a plurality of monitoring values of each monitoring physical quantity of each monitoring point; constructing an index matrix with the average value of all filtered monitoring values as an element; obtaining a health evaluation score of each monitoring point based on the index matrix and a weight matrix; obtaining a Sharpe value of each monitoring physical quantity of an abnormal monitoring point; obtaining the average change rate of the monitoring physical quantity corresponding to the maximum Sharpe value and the center distance between the abnormal monitoring point and the corresponding camera, and redetermining and adjusting the sampling frequency of the sensor of the abnormal monitoring point and the sampling angle of the camera. The monitoring method provided by the application can realize more efficient and more accurate monitoring of the target bridge.

Description

Technical Field

[0001] The present invention relates to the field of bridge monitoring technology, and in particular to a bridge status monitoring method, evaluation method, device and storage medium based on cloud-edge collaboration and multi-indicator fusion. Background Art

[0002] With the rapid development of modern transportation infrastructure, bridges, as key components of transportation networks, are crucial for ensuring smooth traffic flow. However, bridge structures are susceptible to long-term damage from the elements and traffic loads, making them particularly vulnerable to structural damage. This safety hazard is particularly acute in extreme weather conditions, such as strong winds, heavy rain, and earthquakes. Therefore, real-time monitoring of bridge status has become a routine task in bridge management and maintenance.

[0003] In the process of implementing the present invention, the inventors discovered that the prior art has at least the following problems:

[0004] Most existing bridge monitoring systems rely on a centralized architecture, where all data collected by monitoring devices must be transmitted over the network to a central server for processing and analysis. This model often faces challenges with large-scale, multi-source data, such as insufficient bandwidth, high data transmission latency, and excessive computational load, making real-time bridge monitoring difficult. Furthermore, if an anomaly occurs in a specific area of ​​the bridge, the existing monitoring devices, due to their fixed operation, may lack accurate detection accuracy, resulting in inadequate and delayed monitoring.

[0005] Therefore, a bridge condition monitoring method, evaluation method, equipment and storage medium based on cloud-edge collaboration and multi-indicator fusion are needed to at least partially solve the above technical problems. Summary of the Invention

[0006] In view of this, an embodiment of the present invention provides a bridge condition monitoring method, evaluation method, device and storage medium based on cloud-edge collaboration and multi-indicator fusion to solve at least one of the problems in the prior art.

[0007] In a first aspect, an embodiment of the present invention provides a bridge condition monitoring method based on cloud-edge collaboration and multi-indicator fusion, the monitoring method comprising:

[0008] Based on an array of monitoring devices installed at multiple monitoring points on a target bridge, multiple monitoring physical quantities corresponding to the location of each monitoring point on the target bridge during a monitoring period are obtained; wherein the array of monitoring devices at each monitoring point includes a camera that collects video information from the monitoring point, an edge computing terminal, and at least one sensor;

[0009] performing Kalman filtering on a plurality of monitoring values ​​corresponding to each monitored physical quantity acquired by the at least one sensor at different acquisition time sequences at each monitoring point within a monitoring period, through an edge computing terminal, to obtain a plurality of filtered monitoring values ​​corresponding to each monitored physical quantity, and further obtaining an average value of the filtered monitoring values ​​of each monitored physical quantity at each monitoring point within the monitoring period;

[0010] The same order is used to construct an indicator matrix with the average value of all filtered monitoring values ​​as its element;

[0011] Obtain the health assessment score of each monitoring point during the monitoring period based on the indicator matrix and the weight matrix composed of each monitored physical quantity of the target bridge, and compare the health assessment score of each monitoring point during the monitoring period with the upper and lower control thresholds determined based on the corresponding historical data;

[0012] If the health assessment score is within the upper and lower control thresholds, a first signal indicating that the bridge is normal is sent to the bridge control center via the edge computing terminal. If the health assessment score is not within the upper and lower control thresholds, the edge computing terminal compresses the filtered monitoring values ​​of multiple monitored physical quantities at the abnormal monitoring point and sends the compressed filtered monitoring values, monitoring point video information, and a second signal indicating that the bridge is abnormal to the bridge control center.

[0013] Obtain the Shapley value φ(σ) of each monitored physical quantity at the abnormal monitoring point, and determine the monitored physical quantity corresponding to the maximum Shapley value.

[0014]

[0015] Wherein, σ represents any one of the monitored physical quantities obtained at the abnormal monitoring point, N is the set of monitored values ​​of all monitoring points of the target bridge, S is any subset of all monitored values ​​of the abnormal monitoring point excluding the σ monitored physical quantities, S∪{σ} represents the set after adding any one of the σ monitored physical quantities to the subset S, |N| and |S| are the number of monitored values ​​in their respective sets, |N|! and |S|! represent the number of permutations, and v(S) represents the health assessment score obtained based on the subset indicator matrix formed by the average of the filtered monitored values ​​corresponding to the monitored values ​​of the subset S and the weight matrix;

[0016] The average rate of change of the monitored physical quantity corresponding to the maximum Shapley value during the monitoring period and the center distance between the abnormal monitoring point and the corresponding camera are obtained respectively, and the sampling frequency of the sensor for collecting the monitored physical quantity corresponding to the maximum Shapley value of the abnormal monitoring point and the sampling angle of the camera are re-determined and adjusted.

[0017] In a second aspect, an embodiment of the present invention provides a bridge condition assessment method based on cloud-edge collaboration and multi-index fusion, the assessment method comprising:

[0018] Based on an array of monitoring devices installed at multiple monitoring points on a target bridge, multiple monitoring physical quantities corresponding to the location of each monitoring point on the target bridge during a monitoring period are obtained; wherein the array of monitoring devices at each monitoring point includes a camera that collects video information from the monitoring point, an edge computing terminal, and at least one sensor;

[0019] The edge computing terminal performs Kalman filtering on multiple monitoring values ​​corresponding to each monitored physical quantity acquired by the at least one sensor at different acquisition time sequences within a monitoring period at each monitoring point to obtain multiple filtered monitoring values ​​corresponding to each monitored physical quantity, and further obtains an average value of the filtered monitoring values ​​of each monitored physical quantity at each monitoring point within the monitoring period;

[0020] The same order is used to establish an indicator matrix with the average value of the filtered monitoring values ​​as its element;

[0021] Based on the indicator matrix and the weight matrix composed of each monitored physical quantity of the target bridge, the health assessment score of the target bridge within the monitoring period is obtained;

[0022] Based on the deviation and change of the monitored physical quantities obtained at all monitoring points of the target bridge, the abnormality assessment score of the target bridge during the monitoring period is obtained;

[0023] A status evaluation value of the target bridge is obtained based on the health evaluation score and the abnormality evaluation score of the target bridge, wherein the size of the status evaluation value represents the health of the target bridge. The smaller the status evaluation value, the healthier the target bridge.

[0024] In a third aspect, an embodiment of the present invention further provides a monitoring device, the monitoring device comprising:

[0025] a memory for storing computer-executable instructions;

[0026] The processor is used to implement the monitoring method of the above technical solution when executing the computer executable instructions stored in the memory.

[0027] In a fourth aspect, an embodiment of the present invention further provides an evaluation device, the evaluation device comprising:

[0028] a memory for storing computer-executable instructions;

[0029] The processor is used to implement the evaluation method of the above technical solution when executing the computer executable instructions stored in the memory.

[0030] In a fifth 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 monitoring method or evaluation method of the above technical solution.

[0031] 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.

[0032] 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

[0033] 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:

[0034] Figure 1 Flowchart of a bridge condition monitoring method based on cloud-edge collaboration and multi-index fusion according to an embodiment of the present invention;

[0035] Figure 2 Flowchart of a bridge condition assessment method based on cloud-edge collaboration and multi-index fusion according to an embodiment of the present invention;

[0036] Figure 3 is a schematic diagram of a monitoring device according to an embodiment of the present invention;

[0037] Figure 4 FIG. 1 is a schematic diagram of a monitoring system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] First, refer to Figure 1 The bridge condition monitoring method 100 based on cloud-edge collaboration and multi-index fusion according to an embodiment of the present application is described. Figure 1 As shown, the monitoring method 100 may include steps S110 to S190, which are specifically as follows:

[0044] In step S110, based on an array of monitoring devices set at multiple monitoring points of the target bridge, multiple monitoring physical quantities corresponding to the positions of each monitoring point of the target bridge during the monitoring period are obtained; wherein the array of monitoring devices at each monitoring point includes a camera for collecting video information of the monitoring point, an edge computing terminal and at least one sensor.

[0045] In step S120, Kalman filtering is performed on multiple monitoring values ​​corresponding to each monitoring physical quantity obtained by the at least one sensor at different acquisition time sequences at each monitoring point through the edge computing terminal to obtain multiple filtered monitoring values ​​corresponding to each monitoring physical quantity, and further obtain the average value of the filtered monitoring values ​​of each monitoring physical quantity at each monitoring point in the monitoring period.

[0046] In step S130, an indicator matrix is ​​constructed in the same order with the average value of all filtered monitoring values ​​as an element.

[0047] In step S140 , a health assessment score of each monitoring point within the monitoring period is obtained based on the indicator matrix and a weight matrix composed of each monitored physical quantity of the target bridge.

[0048] In step S150, whether each monitoring point is abnormal is determined based on the upper and lower control thresholds determined according to the corresponding historical data based on the health assessment score of each monitoring point during the monitoring period.

[0049] In step S160, if the health assessment score is within the upper and lower control thresholds, a first signal indicating that the bridge is normal is sent to the bridge control center through the edge computing terminal.

[0050] In step S170, if the health assessment score is not within the upper and lower control thresholds, the filtered monitoring values ​​of multiple monitoring physical quantities of the abnormal monitoring point are compressed through the edge computing terminal, and the compressed filtered monitoring values, monitoring point video information and a second signal representing the bridge abnormality are sent to the bridge control center.

[0051] In step S180, the Shapley value φ(σ) of each monitored physical quantity at the abnormal monitoring point is obtained, and the monitored physical quantity corresponding to the maximum Shapley value is determined.

[0052]

[0053] Wherein, σ represents any one of the monitored physical quantities obtained at the abnormal monitoring point, N is the set of monitored values ​​of all monitoring points of the target bridge, S is any subset of all monitored values ​​of the abnormal monitoring point excluding the σ monitored physical quantities, S∪{σ} represents the set after adding any one of the σ monitored physical quantities to the subset S, |N| and |S| are the number of monitored values ​​in their respective sets, |N|! and |S|! represent the number of permutations, and v(S) represents the health assessment score obtained based on the subset indicator matrix formed by the average of the filtered monitored values ​​corresponding to the monitored values ​​of the subset S and the weight matrix.

[0054] In step S190, the average rate of change of the monitored physical quantity corresponding to the maximum Shapley value during the monitoring period and the center distance between the abnormal monitoring point and the corresponding camera are obtained respectively, and the sampling frequency of the sensor for collecting the monitored physical quantity corresponding to the maximum Shapley value of the abnormal monitoring point and the sampling viewing angle of the camera are determined and adjusted.

[0055] In an embodiment of the present application, first, a plurality of monitoring physical quantities corresponding to the positions of the monitoring points of each monitoring point of the target bridge are obtained based on the monitoring device array during the monitoring period, and a plurality of monitoring values ​​corresponding to each monitoring physical quantity obtained at each monitoring point are subjected to Kalman filtering through the edge computing terminal to obtain a plurality of corresponding filtered monitoring values, and an average value of the filtered monitoring values ​​is obtained, and then an indicator matrix with the average value of the filtered monitoring values ​​as an element is constructed, and a health assessment score of each monitoring point during the monitoring period is obtained based on the indicator matrix and the weight matrix, thereby judging whether each monitoring point is abnormal. If the monitoring point is normal, a signal indicating that the bridge is normal is sent to the bridge control center through the edge computing terminal. If there is an abnormality at a monitoring point, a compressed and filtered monitoring value, monitoring point video information, and a second signal representing the bridge abnormality are sent to the bridge control center. Then, the Shapley value of each monitored physical quantity at the abnormal monitoring point is obtained, and the monitored physical quantity corresponding to the maximum Shapley value is determined. The average rate of change of the monitored physical quantity corresponding to the maximum Shapley value within the monitoring period and the center distance between the abnormal monitoring point and the corresponding camera are obtained respectively. The sampling frequency of the sensor that collects the monitored physical quantity corresponding to the maximum Shapley value at the abnormal monitoring point and the sampling angle of view of the camera are re-determined. Finally, the corresponding sensor and camera are monitored and adjusted based on the re-determined sampling frequency of the sensor and sampling angle of view of the camera.

[0056] From the description of the above process, it can be seen that according to the monitoring method 100 of the embodiment of the present application, the edge computing technology based on the edge computing terminal is used to filter the collected monitoring values ​​at the monitoring point end and obtain the health assessment score of each monitoring point. When the monitoring point (bridge structure) is normal, it is only necessary to send a first signal representing the normal state of the bridge to the bridge control center. When an abnormality is found in the monitoring point, the compressed filtered monitoring values, the monitoring point video information and the second signal representing the abnormality of the bridge are transmitted for in-depth analysis by the bridge control center. Such a hierarchical transmission mechanism optimizes the data flow and saves bandwidth resources. At the same time, it ensures the timely transmission of key information in an emergency, reduces the transmission volume of irrelevant data, and thus improves the response speed and processing efficiency. At the same time, it integrates the dynamic adjustment of the camera angle and the adaptive control function of the sensor sampling frequency, accurately adjusts the shooting angle of the camera for the abnormal monitoring point, locks the abnormal area, and dynamically optimizes the sampling frequency of the sensor to improve the monitoring accuracy of the abnormal area and the timeliness and efficiency of data acquisition.

[0057] Among them, Figure 1 In the example, step S170 and step S180 are shown to be performed sequentially, which is only an example. It is understood that the order of step S170 and step S180 is not limited. For example, step S180 can be performed before step S170, or the two can be performed in parallel.

[0058] The contents of the above steps of the monitoring method 100 according to an embodiment of the present application will be described in detail below.

[0059] For common bridges, sensors are typically deployed at key locations to monitor their condition in real time. For example, high-precision sensors are installed at the pier foundations and main beams to monitor various structural parameters. Specifically, water flow pressure sensors and displacement sensors are installed at the pier foundations to capture impact force and displacement data. This allows for monitoring the impact force and displacement changes of the water flow on the pier foundations and assessing the foundation's stability and scour resistance. Strain sensors and accelerometers are deployed at the main beam supports, mid-span locations, beam end nodes, and crossbeam-main beam connections to capture beam stress and vibration data. This allows for real-time monitoring of the beam's stress state and vibration response, preventing structural damage caused by excessive loading or fatigue. For cable-stayed or suspension bridges, in addition to the aforementioned sensors, strain and displacement sensors can be deployed at the towers, anchorages, cable clamps, and at the cable-stay bridge-deck connection points to capture cable displacement and deformation data. This allows for precise monitoring of cable tension changes and displacement and deformation at connection points, ensuring the safety and stability of the cable system. The specific sensor installation plan will not be described here.

[0060] It should be noted that the sensor at each monitoring point in the embodiments of the present application can obtain monitoring values ​​of multiple different physical quantities at each monitoring point. For example, the sensor can obtain data on two, three, four, or other different physical quantities at each monitoring point. Therefore, the sensor at each monitoring point can be multiple sensors with a single monitoring function, or it can be an all-in-one sensor capable of measuring multiple physical quantities.

[0061] Furthermore, given the significant differences in structural stress and deformation characteristics between single-span and multi-span bridges, the sensor placement scheme differs. For single-span bridges, sensors are primarily located at supports and mid-span locations to monitor support reactions and mid-span deflections, ensuring overall structural stiffness and stability. For multi-span bridges, sensors are located at the mid-span, supports, piers, and joints between spans to ensure structural coordination and avoid stress concentration and structural damage caused by differential deformation between spans.

[0062] In an embodiment of the present application, step S110 obtains multiple monitored physical quantities corresponding to the location of each monitoring point on the target bridge during a monitoring period based on an array of monitoring devices installed at multiple monitoring points on the target bridge. The array of monitoring devices at each monitoring point includes a camera that collects video information from the monitoring point, an edge computing terminal, and at least one sensor.

[0063] Specifically, for ease of understanding and description, let's take a cable-stayed or suspension bridge as an example. Assume that 10 monitoring points are set up at the bridge's pier foundations and main beams, and 20 monitoring points are set up at the pylons, anchorages, cable clamps, and the connection points between the cable and the bridge deck. Water flow pressure sensors and displacement sensors are installed at each monitoring point on the bridge's pier foundations to capture pier impact force and pier displacement data. Strain sensors and accelerometers are installed at each monitoring point on the main beams to capture beam stress and vibration data. Strain sensors and displacement sensors are deployed at the pylons, anchorages, cable clamps, and the connection points between the cable and the bridge deck to capture cable displacement and deformation data. In other words, the target bridge has 30 monitoring points, capturing a total of six monitored physical quantities.

[0064] During a monitoring cycle, such as 10 minutes, 30 minutes, or one hour, a sensor at each monitoring point acquires a total of 30 monitoring data points for the corresponding monitored physical quantity at a sampling interval Δt. For example, the water flow pressure sensor at monitoring point 1 on the bridge pier foundation acquires 30 data points on the pier's impact force, and the displacement sensor acquires 30 data points on the pier's displacement. The water flow pressure sensor at monitoring point 4 on the bridge pier foundation acquires 30 data points on the pier's impact force, and the displacement sensor acquires 30 data points on the pier's displacement. The strain sensor at monitoring point 7 on the main beam acquires 30 data points on the beam's stress, and the acceleration sensor acquires 30 data points on the beam's vibration. The strain sensor at monitoring point 15 on the top of the cable tower acquires 30 data points on the cable's displacement, and the displacement sensor acquires 30 data points on the cable's deformation. The strain sensor at monitoring point 23 on the cable clamp acquires 30 data points on the cable's displacement, and the displacement sensor acquires 30 data points on the cable's deformation.

[0065] In an embodiment of the present application, in step S120, the edge computing terminal performs Kalman filtering on multiple monitoring values ​​corresponding to each monitoring physical quantity obtained by the at least one sensor at different acquisition time sequences within the monitoring period at each monitoring point, obtains multiple filtered monitoring values ​​corresponding to each monitoring physical quantity, and further obtains the average value of the filtered monitoring values ​​of each monitoring physical quantity at each monitoring point within the monitoring period.

[0066] To ensure real-time performance and response speed and ensure data accuracy, the edge computing terminal (existing technology equipment) at each monitoring point performs Kalman filtering on the multiple monitoring values ​​corresponding to the corresponding multiple monitoring physical quantities to obtain multiple filtered monitoring values ​​corresponding to each monitoring physical quantity.

[0067] Specifically,

[0068]

[0069] K k =P k|k-1 H T (HP k|k-1 H T +R) -1

[0070] in, It represents the monitoring value z of a monitoring physical quantity obtained by the sensor at the same sampling interval k times within the monitoring period. k The monitored value after filtering, K k is the Kalman gain, H is the state matrix, H T is the transposed matrix, R is the noise covariance matrix, P k|k-1 is the state estimation error covariance matrix at the acquisition moment.

[0071] Based on the multiple filtered monitoring values ​​corresponding to each monitored physical quantity, an average value of the filtered monitoring values ​​of each monitored physical quantity at each monitoring point within the monitoring period is further obtained. That is, all filtered monitoring values ​​are added together and then divided by k to obtain the average value of the filtered monitoring values ​​of each monitored physical quantity at each monitoring point within the monitoring period.

[0072] In the embodiment of the present application, in step S130, the same order is used to construct the indicator matrix X with the average value of all filtered monitoring values ​​as elements. Specifically,

[0073]

[0074] Where n is the total number of monitoring points, m is the number of types of monitored physical quantities obtained at all monitoring points of the target bridge, It is the average value of the filtered monitoring value of the jth monitoring physical quantity at the i-th monitoring point.

[0075] Still taking the cable-stayed bridge or suspension bridge as an example, the total number of monitoring points n is 30, and the number of types of monitored physical quantities m is 6. Each row of the indicator matrix X can be arranged in the order of pier impact force data, pier displacement data, beam stress data, beam vibration data, cable displacement data, and cable deformation data. For physical quantities that do not exist, zero is assigned. For example, for the monitoring point number 1 at the pier foundation of the bridge, that is, to Both are 0.

[0076] In the embodiment of the present application, in step S140, the health assessment score Y of each monitoring point in the monitoring period is obtained based on the indicator matrix and the weight matrix composed of each monitoring physical quantity of the target bridge. i .

[0077] Y points through health assessment of monitoring pointsi , can indicate the data health of the monitoring point in this monitoring cycle. Specifically,

[0078] Y i =X i W T

[0079]

[0080] W=[ω1,ω2,…,ω j ,…,ω m ]

[0081] Among them, ω j represents the weight of the jth monitored physical quantity, W T is the transposed matrix of W.

[0082] in,

[0083]

[0084] In an embodiment of the present application, in step S150, whether each monitoring point is abnormal is determined based on the upper and lower control thresholds determined according to the corresponding historical data based on the health assessment score of each monitoring point within the monitoring period.

[0085] Among them, the upper and lower control thresholds of each monitored physical quantity are determined based on the corresponding historical data and are respectively certain values.

[0086] In an embodiment of the present application, in step S160, if the health assessment score is within the upper and lower control thresholds, a first signal indicating that the bridge is normal is sent to the bridge control center through the edge computing terminal.

[0087] Specifically, if the health assessment score is within the upper and lower control thresholds, the bridge is considered normal. This allows the edge computing terminal to send only the initial signal indicating the bridge is healthy to the bridge control center, eliminating the need to send a large amount of real-time monitoring values ​​of various physical quantities and video information from monitoring points. This ensures efficient information transmission and minimizes the burden.

[0088] In an embodiment of the present application, if the health assessment score is not within the upper and lower control thresholds in step S170, the edge computing terminal compresses the filtered monitoring values ​​of the various monitored physical quantities at the abnormal monitoring point. The compressed, filtered monitoring values, the monitoring point video information, and a second signal indicating the bridge abnormality are then transmitted to the bridge control center.

[0089] If the health assessment score is not within the upper and lower control thresholds, it indicates an abnormal bridge condition. First, to significantly reduce network bandwidth usage, especially in situations with limited bandwidth or poor network conditions, and to ensure efficient data transmission, the edge computing terminal compresses the filtered monitoring values ​​of various monitored physical quantities at abnormal monitoring points to reduce data volume. The edge computing terminal then sends the compressed, filtered monitoring values, monitoring point video information, and a second signal representing the bridge anomaly to the bridge control center, providing bridge anomaly information and data for analysis.

[0090] In an embodiment of the present application, in step S180 , the Shapley value φ(σ) of each monitored physical quantity at the abnormal monitoring point is obtained, and the monitored physical quantity corresponding to the maximum Shapley value is determined.

[0091] When an abnormality occurs at a monitoring point, in order to improve the efficiency and accuracy of sensor monitoring without increasing the burden too much, a sensitivity analysis is performed on the monitoring physical quantity data obtained by different sensors at the abnormal monitoring point to evaluate the contribution of each monitoring physical quantity to the abnormality of the monitoring point, that is, to evaluate the influence of each monitoring physical quantity on the formation of the abnormality of the monitoring point.

[0092] Specifically,

[0093]

[0094] Wherein, σ represents any one of the monitored physical quantities obtained at the abnormal monitoring point, N is the set of monitored values ​​of all monitoring points of the target bridge, S is any subset of all monitored values ​​of the abnormal monitoring point excluding the σ monitored physical quantities, S∪{σ} represents the set after adding any one of the σ monitored physical quantities to the subset S, |N| and |S| are the number of monitored values ​​in their respective sets, |N|! and |S|! represent the number of permutations, and v(S) represents the health assessment score obtained based on the subset indicator matrix formed by the average of the filtered monitored values ​​corresponding to the monitored values ​​of the subset S and the weight matrix.

[0095] When the Shapley value of a certain monitored physical quantity at the abnormal monitoring point is determined to be the largest, it means that the data of the monitored physical quantity is most likely to be abnormal.

[0096] In an embodiment of the present application, in step S190, the average change rate of the monitored physical quantity corresponding to the maximum Shapley value during the monitoring period and the center distance between the abnormal monitoring point and the corresponding camera are respectively obtained, and the sampling frequency of the sensor for collecting the monitored physical quantity corresponding to the maximum Shapley value of the abnormal monitoring point and the sampling viewing angle of the camera are re-determined and adjusted.

[0097] Continuing from the above, after determining that the data of a certain monitored physical quantity is most likely to be abnormal, the average change rate of the monitored physical quantity corresponding to the maximum Shapley value during the monitoring period and the center distance between the abnormal monitoring point and the corresponding camera are obtained respectively. Among them, the average change rate of the monitored physical quantity corresponding to the maximum Shapley value during the monitoring period is

[0098]

[0099] Among them, z k It represents the monitoring value of the monitored physical quantity collected by the sensor for the kth time at the sampling interval Δt within the monitoring period.

[0100] Then, the sampling frequency f of the sensor for monitoring the physical quantity corresponding to the maximum Shapley value of the abnormal monitoring point is re-determined. new and the camera’s sampling angle θ new , specifically:

[0101] f new =f base ×(1+α×ΔP)

[0102]

[0103] Among them, f base The sampling frequency of the sensor that collects the maximum Shapley value corresponding to the monitoring physical quantity in this monitoring period, θ base is the sampling angle of the camera in this monitoring period, D detect is the center distance between the abnormal monitoring point and the corresponding camera, D max is the maximum distance covered by the camera, ΔP is the average rate of change of the monitored physical quantity corresponding to the maximum Shapley value during the monitoring period. α and β are the respective adjustment coefficients, which are fixed values.

[0104] With the re-determined sampling frequency f new and the camera's sampling angle θ new Monitor abnormal monitoring points to increase the monitoring frequency in abnormal situations, capture more detailed changes, and adjust the camera angle to focus the lens on the abnormal area to capture more relevant visual information.

[0105] Based on the above description, according to the monitoring method of the embodiment of the present application, data or signals can be transmitted hierarchically through edge computing technology based on edge computing terminals, thereby saving bandwidth resources, reducing the transmission volume of irrelevant data, and improving response speed and processing efficiency; at the same time, it integrates the dynamic adjustment of camera viewing angle and sensor sampling frequency control functions to improve the monitoring accuracy of abnormal areas and the timeliness and efficiency of data acquisition.

[0106] refer to Figure 3The embodiment of the present application further provides a monitoring device 300 for implementing the monitoring method 100 according to the embodiment of the present application. The monitoring device 300 includes a processor 310 and a memory 320. The monitoring device 300 may include one or more processors 310 and one or more memories 320. The memory 320 stores an executable program executed by the processor 310. When the executable program is executed by the processor 310, the processor 310 executes the monitoring method 100 according to the embodiment of the present application described above.

[0107] The processor 310 may be a central processing unit (CPU) or other processing units having data processing capabilities and / or instruction execution capabilities.

[0108] The memory 320 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 310 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.

[0109] The monitoring device 300 may also include input devices and output devices, and these components are interconnected via a bus system and / or other forms of connection mechanisms. Figure 3 The components and structure of the monitoring device 300 shown are merely exemplary and non-limiting. The monitoring device 300 may also have other components and structures as needed.

[0110] 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.

[0111] 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.

[0112] For example, the example monitoring device 300 for implementing the monitoring method 100 according to the embodiment of the present application can be applied to terminal devices (such as mobile phones), tablet computers, laptop computers, 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, and the embodiments of the present application do not impose any restrictions on this.

[0113] Those skilled in the art can understand the specific operations of the monitoring device 300 for implementing the monitoring 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 310 are described.

[0114] In one embodiment of the present application, when the executable program is executed by the processor 310, the processor 310 executes the following steps: based on the array of monitoring devices set at multiple monitoring points of the target bridge, obtain a plurality of monitoring physical quantities corresponding to the location of each monitoring point of the target bridge during the monitoring period according to the location of the monitoring point; perform Kalman filtering on the plurality of monitoring values ​​corresponding to each monitoring physical quantity obtained by the at least one sensor at different acquisition time sequences during the monitoring period at each monitoring point through the edge computing terminal to obtain a plurality of filtered monitoring values ​​corresponding to each monitoring physical quantity, and further obtain the average value of the filtered monitoring values ​​of each monitoring physical quantity at each monitoring point during the monitoring period; construct an indicator matrix with the average value of all filtered monitoring values ​​as an element in the same order; obtain the health assessment score of each monitoring point during the monitoring period based on the indicator matrix and the weight matrix composed of each monitoring physical quantity of the target bridge; The health assessment score of each monitoring point during the measurement period is used to determine whether each monitoring point is abnormal based on the upper and lower control thresholds determined by the corresponding historical data; if the health assessment score is within the upper and lower control thresholds, the edge computing terminal sends a first signal to the bridge control center indicating that the bridge is normal; if the health assessment score is not within the upper and lower control thresholds, the edge computing terminal compresses the filtered monitoring values ​​of multiple monitoring physical quantities of the abnormal monitoring point, and sends the compressed filtered monitoring values, monitoring point video information and a second signal indicating the abnormal bridge to the bridge control center; obtain the Shapley value of each monitoring physical quantity of the abnormal monitoring point, and determine the monitoring physical quantity corresponding to the maximum Shapley value; obtain the average change rate of the monitoring physical quantity corresponding to the maximum Shapley value during the monitoring period and the center distance between the abnormal monitoring point and the corresponding camera, determine and adjust the sampling frequency of the sensor for collecting the monitoring physical quantity corresponding to the maximum Shapley value of the abnormal monitoring point and the sampling angle of the camera.

[0115] The above exemplary shows the monitoring method 100 according to the embodiment of the present application. Figure 4 A monitoring system 400 provided in another aspect of an embodiment of the present application is described.

[0116] Reference Figure 4 The following describes an example monitoring system 400 for implementing the monitoring method of an embodiment of the present application. The monitoring system 400 may include an acquisition module 410, a filtering module 420, a matrix construction module 430, a health assessment score calculation module 440, a judgment and processing module 450, a Shapley value calculation module 460, and an adjustment module 470. Among them:

[0117] The acquisition module 410 is configured to acquire, based on an array of monitoring devices disposed at multiple monitoring points of the target bridge, a plurality of monitoring physical quantities corresponding to the positions of each monitoring point of the target bridge during a monitoring period.

[0118] The filtering module 420 is configured to perform Kalman filtering on the plurality of monitoring values corresponding to each monitoring physical quantity acquired by the at least one sensor at different acquisition time sequences in a monitoring period for each monitoring point based on the edge computing terminal, to obtain a plurality of filtered monitoring values corresponding to each monitoring physical quantity, and to further acquire an average value of the filtered monitoring values of each monitoring physical quantity of each monitoring point in the monitoring period.

[0119] The matrix construction module 430 is configured to construct an index matrix with the average values of all the filtered monitoring values as elements in the same order.

[0120] The health assessment score calculation module 440 is configured to acquire a health assessment score of each monitoring point in the monitoring period based on the index matrix and a weight matrix composed of each monitoring physical quantity of the target bridge.

[0121] The judgment and processing module 450 is configured to determine whether each monitoring point is abnormal based on the health assessment score of each monitoring point in the monitoring period and upper and lower control thresholds determined according to corresponding historical data, to send a first signal representing a normal bridge to the bridge control center if the health assessment score is within the upper and lower control threshold range, and to perform compression processing on the filtered monitoring values of the plurality of monitoring physical quantities of the abnormal monitoring point and send the compressed filtered monitoring values, the monitoring point video information and a second signal representing an abnormal bridge to the bridge control center if the health assessment score is not within the upper and lower control threshold range.

[0122] The Shapley value calculation module 460 is configured to acquire a Shapley value of each monitoring physical quantity of the abnormal monitoring point and determine the monitoring physical quantity corresponding to the maximum Shapley value.

[0123] The adjustment module 470 is configured to acquire an average change rate of the monitoring physical quantity corresponding to the maximum Shapley value in the monitoring period and a center distance between the abnormal monitoring point and the corresponding camera, respectively, and to determine and adjust a sampling frequency of the sensor and a sampling angle of the camera of the abnormal monitoring point for acquiring the monitoring physical quantity corresponding to the maximum Shapley value.

[0124] The monitoring system 400 provided by the embodiment of the application can transmit information for different conditions, save bandwidth resources, improve response speed and processing efficiency, and realize dynamic adjustment of the camera angle and adaptive regulation and control of the sensor sampling frequency, thereby improving the monitoring accuracy of the abnormal area and the timeliness and efficiency of data acquisition.

[0125] Reference Figure 2 The application also provides a bridge state evaluation method 200 based on cloud-edge collaboration and multi-index fusion. Figure 2As shown, the evaluation method 200 can include steps S210 to S260, as follows:

[0126] At step S210, based on the monitoring device array arranged at the plurality of monitoring points of the target bridge, the monitoring physical quantity corresponding to the position of each monitoring point of the target bridge is obtained in the monitoring period; wherein the monitoring device array of each monitoring point includes a camera for collecting video information of the monitoring point, an edge computing terminal and at least one sensor.

[0127] At step S220, the edge computing terminal performs Kalman filtering processing on the plurality of monitoring values corresponding to each monitoring physical quantity obtained by the at least one sensor at different collection time sequences in the monitoring period, to obtain a plurality of filtered monitoring values corresponding to each monitoring physical quantity, and further obtain the average value of the filtered monitoring values of each monitoring physical quantity of each monitoring point in the monitoring period.

[0128] At step S230, an index matrix X is established with the average values of the filtered monitoring values as elements in the same order.

[0129] At step S240, based on the index matrix and the weight matrix composed of each monitoring physical quantity of the target bridge, the health evaluation score of the target bridge in the monitoring period is obtained.

[0130] At step S250, based on the deviation and change of the monitoring physical quantity obtained by all the monitoring points of the target bridge, the abnormal evaluation score of the target bridge in the monitoring period is obtained.

[0131] At step S260, based on the health evaluation score and the abnormal evaluation score of the target bridge, the state evaluation value of the target bridge is obtained, wherein the size of the state evaluation value represents the health degree of the target bridge, and the smaller the state evaluation value, the healthier the target bridge.

[0132] According to the evaluation method 200 of the present application, steps S210 to S230 can refer to the related content of the monitoring method 100 of the present application, which is not repeated here.

[0133] The steps S240 to S260 are described as follows:

[0134] At step S240, based on the index matrix and the weight matrix composed of each monitoring physical quantity of the target bridge, the health evaluation score Y of the target bridge in the monitoring period is obtained, specifically:

[0135] Y=X·W T

[0136] Wherein, X is the index matrix, W Tis the transposed matrix of the weight matrix W composed of each monitored physical quantity of the target bridge.

[0137] In step S250, based on the deviation and change of the monitored physical quantities obtained at all monitoring points of the target bridge, an abnormality assessment score of the target bridge within the monitoring period is obtained.

[0138] Specifically, the abnormal assessment score of the target bridge during the monitoring period is A,

[0139] A = γ·deviation score + η·rate of change score

[0140] Among them, γ and η are their respective weights and are constant values.

[0141] Regarding the deviation score, the following formula is used to obtain it:

[0142] Deviation score = ∑(Pj*ω j )

[0143]

[0144] z=[z1,z2,…z k …,z p ]

[0145] The average deviation is the average of all deviations obtained. Pj represents the deviation score of the jth monitored physical quantity of the target bridge, z represents the sequence of monitored values ​​of the jth monitored physical quantity collected by the sensor at sampling interval Δt during the monitoring period, and Uj and Lj are the upper and lower control thresholds for the jth monitored physical quantity, respectively. The maximum allowable deviation is the maximum allowable deviation of different monitored physical quantities from the average deviation value to the boundary of the range [Lj, Uj] and is a fixed value.

[0146] Regarding the change rate score, the following formula is used to obtain it:

[0147] Change rate score = ∑(Vj*ω j )

[0148]

[0149] Where Vj represents the rate of change score of the jth monitored physical quantity of the target bridge. The average rate of change is the average of all the calculated rates of change. The maximum and minimum rates of change are the maximum and minimum values ​​of all the calculated rates of change.

[0150] In step S26, a condition assessment value F of the target bridge is obtained based on the health assessment score Y and the abnormality assessment score A of the target bridge according to their respective weights. The condition assessment value F represents the health of the target bridge. A smaller condition assessment value indicates a healthier target bridge.

[0151] For example, the weights of health assessment score Y and abnormality assessment score A are 0.6 and 0.4 respectively.

[0152] F=0.6·Y+0.4·A

[0153] Similarly, the present embodiment further provides an evaluation device (not shown) for implementing the evaluation method 200 according to the present embodiment. The evaluation device also includes a processor and a memory. The specific details are not described here, and reference can be made to the relevant content of the monitoring device 300 provided in the present embodiment for implementing the monitoring method 100 according to the present embodiment.

[0154] 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 perform the corresponding steps of the monitoring method 100 of the embodiment of the present application or the corresponding steps of the evaluation method 200 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.

[0155] 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 evaluation method of the embodiment of the present application.

[0156] 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.

[0157] 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.

[0158] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and the division of the units is merely a logical function division. In actual implementation, another division manner can be used, for example, a plurality of units or components can be combined or integrated into another device, or some features can be omitted or not executed.

[0159] In addition, those skilled in the art can understand that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments means to be within the scope of the present application and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0160] It should be noted that the above embodiments explain the present application but do not limit the present application, and those skilled in the art can design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs located between parentheses shall not constitute a limitation on 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 application can be implemented by means of both hardware and software, whether specifically mentioned or not. In a unit claim enumerating several means, the listed means can be embodied by one and the same item of hardware. The use of the words first, second and third, etc. does not imply any order. These words can be understood as names.

[0161] The above description is merely a specific implementation or explanation of the present application, and the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. The protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A bridge condition monitoring method based on cloud-edge collaboration and multi-index fusion, characterized in that: The monitoring method comprises: Based on an array of monitoring devices installed at multiple monitoring points on a target bridge, multiple monitoring physical quantities corresponding to the location of each monitoring point on the target bridge during a monitoring period are obtained; wherein the array of monitoring devices at each monitoring point includes a camera that collects video information from the monitoring point, an edge computing terminal, and at least one sensor; performing Kalman filtering on a plurality of monitoring values ​​corresponding to each monitored physical quantity acquired by the at least one sensor at different acquisition time sequences at each monitoring point within a monitoring period, through an edge computing terminal, to obtain a plurality of filtered monitoring values ​​corresponding to each monitored physical quantity, and further obtaining an average value of the filtered monitoring values ​​of each monitored physical quantity at each monitoring point within the monitoring period; The same order is used to construct an indicator matrix with the average value of all filtered monitoring values ​​as its element; Based on the indicator matrix and the weight matrix composed of each monitored physical quantity of the target bridge, the health assessment score of each monitoring point within the monitoring period is obtained; based on the health assessment score of each monitoring point within the monitoring period, the upper and lower control thresholds determined according to the corresponding historical data are used to determine whether each monitoring point is abnormal; If the health assessment score is within the upper and lower control thresholds, a first signal indicating that the bridge is normal is sent to the bridge control center via the edge computing terminal. If the health assessment score is not within the upper and lower control thresholds, the edge computing terminal compresses the filtered monitoring values ​​of multiple monitored physical quantities at the abnormal monitoring point and sends the compressed filtered monitoring values, monitoring point video information, and a second signal indicating that the bridge is abnormal to the bridge control center. Obtain the Shapley value of each monitored physical quantity at the abnormal monitoring point , determine the monitored physical quantity corresponding to the maximum Shapley value, in, Indicates any of the monitored physical quantities obtained at the abnormal monitoring point, is the set of monitoring values ​​of all monitoring points of the target bridge, Abnormal monitoring points do not include Any subset of all monitored values ​​other than the monitored physical quantity, Indicates adding The set of any monitored values ​​of a physical quantity, and is the number of monitored values ​​in the respective set, and Indicates the number of permutations, Represents a subset-based The average value of the filtered monitoring value corresponding to the monitoring value of is the health assessment score obtained by the subset indicator matrix formed by the elements and the weight matrix; The average rate of change of the monitored physical quantity corresponding to the maximum Shapley value during the monitoring period and the center distance between the abnormal monitoring point and the corresponding camera are obtained respectively, and the sampling frequency of the sensor for collecting the monitored physical quantity corresponding to the maximum Shapley value of the abnormal monitoring point and the sampling angle of the camera are re-determined and adjusted.

2. The monitoring method according to claim 1, characterized in that: The obtaining of a plurality of filtered monitoring values ​​corresponding to each monitored physical quantity specifically refers to: in, Indicates that the sensor is monitored with the same interval A monitoring value of a monitoring physical quantity obtained The monitored value after filtering, is the Kalman gain, is the state matrix, is the noise covariance matrix, is the state estimation error covariance matrix at the acquisition moment.

3. The monitoring method according to claim 1, characterized in that The same order is used to establish an indicator matrix with the average value of the filtered monitoring value as an element , specifically: in, is the total number of monitoring points, The number of types of monitored physical quantities obtained for all monitoring points of the target bridge, For the The monitoring point The average value of the filtered monitoring value of a monitored physical quantity.

4. The monitoring method according to claim 3, characterized in that: The health assessment score of each monitoring point in the monitoring period is obtained based on the indicator matrix and the weight matrix composed of each monitoring physical quantity of the target bridge. , specifically: in, Indicates the The weight of the monitored physical quantity, for The transposed matrix of .

5. The monitoring method according to claim 1, characterized in that: The sampling frequency of the sensor of the monitoring physical quantity corresponding to the maximum Shapley value collected at the abnormal monitoring point is re-determined and the camera's sampling angle of view , specifically: in, The sampling frequency of the sensor that collects the maximum Shapley value corresponding to the monitored physical quantity in this monitoring period, is the sampling angle of the camera in this monitoring period, is the center distance between the abnormal monitoring point and the corresponding camera, is the maximum distance covered by the camera, is the average rate of change of the monitored physical quantity corresponding to the maximum Shapley value during the monitoring period, and are their respective adjustment coefficients.

6. A bridge condition assessment method based on cloud-edge collaboration and multi-index fusion, characterized in that: The evaluation method includes: Based on an array of monitoring devices installed at multiple monitoring points on a target bridge, multiple monitoring physical quantities corresponding to the location of each monitoring point on the target bridge during a monitoring period are obtained; wherein the array of monitoring devices at each monitoring point includes a camera that collects video information from the monitoring point, an edge computing terminal, and at least one sensor; Performing Kalman filtering on a plurality of monitoring values ​​corresponding to each monitored physical quantity acquired by the at least one sensor at different acquisition time sequences within a monitoring period at each monitoring point through an edge computing terminal to obtain a plurality of filtered monitoring values ​​corresponding to each monitored physical quantity, and further obtaining an average value of the filtered monitoring values ​​of each monitored physical quantity at each monitoring point within the monitoring period; The same order is used to establish an indicator matrix with the average value of the filtered monitoring values ​​as its element; Based on the indicator matrix and the weight matrix composed of each monitored physical quantity of the target bridge, the health assessment score of the target bridge during the monitoring period is obtained, specifically: in, is the indicator matrix, It is the health assessment score, is the weight matrix composed of each monitoring physical quantity of the target bridge The transposed matrix of Based on the deviation and change of the monitored physical quantities obtained at all monitoring points of the target bridge, the abnormality assessment score of the target bridge during the monitoring period is obtained, specifically: in, is the abnormality assessment score, The target bridge Deviation score of the monitored physical quantity, The target bridge A score that monitors the rate of change of a physical quantity. Indicates that the sensor is sampling at intervals within the monitoring period. The collected A monitoring numerical sequence of a monitoring physical quantity, and They are The upper and lower control thresholds of the monitored physical quantity, and is the weight; A status evaluation value of the target bridge is obtained based on the health evaluation score and the abnormality evaluation score of the target bridge, wherein the size of the status evaluation value represents the health of the target bridge. The smaller the status evaluation value, the healthier the target bridge.

7. A monitoring device, characterized in that: The monitoring equipment includes: a memory for storing computer-executable instructions; The processor is configured to implement the monitoring method according to any one of claims 1 to 5 when executing the computer executable instructions stored in the memory.

8. An evaluation device, characterized in that The evaluation equipment includes: a memory for storing computer-executable instructions; The processor is configured to implement the evaluation method according to claim 6 when executing the computer executable instructions stored in the memory.

9. A storage medium storing computer instructions, wherein: The computer instructions are used to enable the computer to execute the monitoring method according to any one of claims 1 to 5, or the evaluation method according to claim 6.

Images