A target tracking method and system for a bridge monitoring system

Abstract

The present invention provides a target tracking method and system for a bridge monitoring system, which relates to the technical field of data processing. The method includes: interactively obtaining the basic design data of the bridge to be monitored to generate an observation focus distribution result, distributing monitoring attention based on the monitoring accuracy of the bridge to be monitored and the observation focus distribution result to generate preset monitoring points, configuring a reference point group based on regional data and the preset monitoring points, and before monitoring the bridge to be monitored, performing reference point verification on the reference point group to generate a monitoring data set based on the reference point group. It configures tracking targets through a static deformation set and a dynamic deformation set, redistributes the monitoring attention of the tracking targets, and generates a monitoring warning message according to the tracking monitoring results. The present invention solves the technical problem in the prior art that the lack of control over bridge defects leads to low bridge safety, and realizes the precise positioning, tracking and control of bridge defects, improving the low bridge safety.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a target tracking method and system for a bridge monitoring system. Background Art

[0002] As an integral part of the transportation system, bridges have played an important role in the development and evolution of human civilization. With the development of modern technology and the continuous growth of transportation demand, large bridges (such as cross-sea bridges, long-span bridges, etc.) are increasingly appearing in people's vision. These bridges often cost hundreds of millions or even billions of yuan, and have important strategic significance in aspects such as transportation, military, and social life.

[0003] However, during the construction and use of bridges, due to the erosion of the environment and harmful substances, the effects of vehicles, wind, earthquakes, fatigue, human factors, etc., as well as the continuous degradation of the material's own properties, different degrees of damage and deterioration occur in various parts of the structure long before reaching the design life. If these damages cannot be detected and repaired in time, it will affect driving safety and shorten the service life of the bridge at best, and may even lead to the sudden destruction and collapse of the bridge at worst. Currently, in the existing technology, there is a lack of monitoring, tracking, and control of bridge defects, resulting in the technical problem of low bridge safety. Summary of the Invention

[0004] The present application provides a target tracking method and system for a bridge monitoring system, which is used to solve the technical problem of low bridge safety caused by the lack of control of bridge defects in the existing technology.

[0005] In view of the above problems, the present application provides a target tracking method and system for a bridge monitoring system.

[0006] In a first aspect, the present application provides a target tracking method for a bridge monitoring system, the method comprising: interactively obtaining the basic design data of the bridge to be monitored, and performing observation focus analysis based on the basic design data to generate an observation focus distribution result; configuring the monitoring accuracy of the bridge to be monitored, and distributing monitoring attention according to the monitoring accuracy and the observation focus distribution result to generate preset monitoring points; collecting the regional data within a predetermined area of the bridge to be monitored, and configuring a reference point group based on the regional data and the preset monitoring points, wherein the reference point group has a mapping relationship with the preset monitoring points, and each reference point group includes at least three reference points; before monitoring the bridge to be monitored, performing reference point verification on the reference point group, and when the verification passes, generating a monitoring data set based on the reference point group, wherein the monitoring data set includes a static deformation set and a dynamic deformation set; configuring a tracking target through the static deformation set and the dynamic deformation set; and redistributing the monitoring attention of the tracking target, and generating a monitoring warning information according to the tracking monitoring result.

[0007] In a second aspect, the present application provides a target tracking system for a bridge monitoring system. The system includes: an observation focus analysis module, which is used to interactively obtain the basic design data of the bridge to be monitored, and perform observation focus analysis based on the basic design data to generate an observation focus distribution result; a monitoring module, which is used to configure the monitoring accuracy of the bridge to be monitored, distribute monitoring attention according to the monitoring accuracy and the observation focus distribution result, and generate preset monitoring points; a data acquisition module, which is used to collect regional data within a predetermined area of the bridge to be monitored, and configure a reference point group based on the regional data and the preset monitoring points, wherein the reference point group has a mapping relationship with the preset monitoring points, and each reference point group includes at least three reference points; a reference point verification module, which is used to perform reference point verification on the reference point group before monitoring the bridge to be monitored, and generate a monitoring data set based on the reference point group after the verification passes, wherein the monitoring data set includes a static deformation set and a dynamic deformation set; a target configuration module, which is used to configure a tracking target through the static deformation set and the dynamic deformation set; a monitoring attention module, which is used to redistribute the monitoring attention of the tracking target and generate a monitoring warning message according to the tracking monitoring result.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0009] A target tracking method and system for a bridge monitoring system provided by the present application relate to the technical field of data processing, solve the technical problem in the prior art that the lack of control over bridge defects leads to low bridge safety, and realize the precise positioning tracking control of bridge defects, improving the low bridge safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a schematic flowchart of a target tracking method for a bridge monitoring system provided by the present application;

[0011] Figure 2 It is a schematic flowchart of obtaining an associated deviation result in a target tracking method for a bridge monitoring system provided by the present application;

[0012] Figure 3 It is a schematic flowchart of early warning management of a bridge to be monitored in a target tracking method for a bridge monitoring system provided by the present application;

[0013] Figure 4 It is a schematic flowchart of monitoring management of a bridge to be monitored in a target tracking method for a bridge monitoring system provided by the present application;

[0014] Figure 5 This application provides a schematic structural diagram of a target tracking system for a bridge monitoring system.

[0015] Explanation of reference numerals: Observation focus analysis module 1, monitoring module 2, data acquisition module 3, reference point verification module 4, target configuration module 5, monitoring attention module 6. Detailed implementation manners

[0016] This application provides a target tracking method and system for a bridge monitoring system to solve the technical problem in the prior art that the lack of control over bridge defects leads to low bridge safety.

[0017] Embodiment 1

[0018] As Figure 1 shown, the embodiment of this application provides a target tracking method for a bridge monitoring system, and the method includes:

[0019] Step S100: Interactively obtain the basic design data of the bridge to be monitored, and perform observation focus analysis based on the basic design data to generate an observation focus distribution result;

[0020] Specifically, a target tracking method for a bridge monitoring system provided by the embodiment of this application is applied to a target tracking system for a bridge monitoring system. To ensure accurate tracking of the target defects of the bridge in the later stage, it is necessary to first perform data interaction with the basic design data of the target bridge to be monitored through the system. The basic design data of the target bridge to be monitored may include pier height data, pier bottom longitudinal width data, transverse bridge data, span data, etc. By transmitting the basic design data of the target bridge to be monitored into the system, and at the same time performing 3D simulation analysis of the bridge to be monitored based on the basic design data, the analysis result is used as the bridge observation template diagram. During the process of observing the bridge observation template diagram, traverse the pixel blocks of the bridge observation, and use the pixel blocks with a clarity greater than 80% as the observation focus distribution points. Finally, output the observation focus distribution points as the observation focus distribution result of the bridge to be monitored, which serves as an important reference basis for realizing target tracking based on the bridge monitoring system in the later stage.

[0021] Step S200: Configure the monitoring accuracy of the bridge to be monitored, and distribute monitoring attention according to the monitoring accuracy and the observation focus distribution result to generate preset monitoring points;

[0022] Specifically, to improve the monitoring accuracy of the bridge monitoring system, it is necessary to configure the monitoring accuracy for the target bridge to be monitored, which means determining the monitoring accuracy value according to the basic design data of the target bridge to be monitored. The monitoring data can include single-point positioning accuracy, RTK positioning accuracy, etc. The single-point positioning accuracy can be set to less than 1.5 m, and for the RTK positioning accuracy, the horizontal accuracy can be set to plus or minus 2.5 mm, and the initial value accuracy can be set to plus or minus 2.5 mm. Further, based on the set monitoring accuracy and the above-obtained observation focus distribution results, distributed monitoring attention is carried out on the bridge to be monitored. The distributed monitoring attention mechanism refers to concentrating visual attention on different regions of the image. This attention mechanism is a special structure embedded in the machine learning model, used to automatically learn and calculate the contribution of the monitoring accuracy and the observation focus distribution results to the monitoring point data. The monitoring points with large data contributions are recorded as preset monitoring points, thus laying a foundation for realizing target tracking based on the bridge monitoring system.

[0023] Step S300: Collect regional data within a predetermined area of the bridge to be monitored, and configure a reference point group based on the regional data and the preset monitoring points. Among them, the reference point group has a mapping relationship with the preset monitoring points, and each reference point group includes at least three reference points;

[0024] Specifically, in order to divide the threshold monitoring points into groups in different regions of the bridge to be monitored, it is first necessary to divide the bridge to be monitored according to the basic design data to obtain a predetermined area, and extract the regional data of the bridge to be monitored included in this area. The regional data extracted in different predetermined areas all include the regional characteristics of the current area. Further, the reference point group of the bridge to be monitored is configured with the collected regional data and the preset monitoring points. The reference point group has a mapping relationship with the preset monitoring points, which means that when taking a value in the preset monitoring points, there is exactly one corresponding value in the reference point group, while when taking a value in the reference point group, there can be multiple corresponding values in the preset monitoring points, and each reference point group includes at least three reference points, that is, the preset monitoring points, laying a solid foundation for subsequent realizing target tracking based on the bridge monitoring system.

[0025] Step S400: Before monitoring the bridge to be monitored, perform a reference point verification on the reference point group. After the verification passes, generate a monitoring data set based on the reference point group, where the monitoring data set includes a static deformation set and a dynamic deformation set;

[0026] Furthermore, as Figure 2 shown, step S400 of this application further includes:

[0027] Step S410: Use any one of the reference points in the reference point group as the calibration reference point to perform deviation authentication on the other reference points in the current reference point group;

[0028] Step S420: Traverse all the reference points in the reference point group as standard reference points, and integrate to obtain a deviation authentication set;

[0029] Step S430: Locate the associated reference point group of the reference point group, and perform associated authentication of the reference point group through the associated reference point group to obtain an associated deviation result;

[0030] Step S440: Perform reference point verification through the deviation authentication set and the associated deviation result.

[0031] Furthermore, step S400 of the present application further includes:

[0032] Step S450: Interact the bridge passing data of the bridge to be monitored, and synchronously determine the weather environment data;

[0033] Step S460: Set a monitoring data interaction window, and within the monitoring data interaction window, perform monitoring sensor data interaction based on the bridge passing data and the weather environment data to obtain a data interaction result;

[0034] Step S470: Perform settlement and deformation evaluation of the bridge to be monitored through the data interaction result;

[0035] Step S480: Generate the static deformation set based on the settlement and deformation evaluation results.

[0036] Furthermore, step S400 of the present application further includes:

[0037] Step S490: Locate dynamic vehicles according to the bridge passing data;

[0038] Step S4100: Measure the speed of the dynamic vehicle through a speed measuring device and record the speed data;

[0039] Step S4110: Perform weight measurement of the dynamic vehicle based on a weighing sensor, and perform weight linear fitting according to the weight measurement result and the speed data to generate weight data;

[0040] Step S4120: Perform dynamic deformation monitoring during the period when the dynamic vehicle passes through the bridge to be monitored to generate a dynamic deformation monitoring result;

[0041] Step S4130: Map and identify the speed data, the weight data, and the dynamic deformation data, and generate a dynamic deformation set according to the mapping identification result.

[0042] Specifically, before monitoring a bridge to be monitored, calibrating the reference point group means selecting an arbitrarily chosen reference point within the reference point group as the calibration reference point, and then performing deviation authentication on the other reference points in the reference point group to which the calibration reference point belongs. That is, the reference points contained in the current reference point group except the calibration reference point are compared with the calibration reference point in turn. The reference points with a comparison error greater than 30% with the calibration reference point are authenticated as deviated reference points. Further, all the reference points in the reference point group are traversed as standard reference points, and the reference points with deviation authentication are integrated and recorded as the deviation authentication set. At the same time, the associated reference points in the reference point group are located. The associated reference points refer to that at least one or more reference points in the reference point group correspond to a reference point, that is, they change with the change of the reference point. All the located associated reference points in the reference point group are summarized and recorded as the associated reference point group, and the correlation degree between the reference points in the reference point group is correlated and authenticated through the associated reference point group. Taking the associated reference point group as the standard data, the correlation degree between the reference points contained in the reference point group is compared with the correlation degree between the associated reference points contained in the associated reference point group. When the correlation degree comparison error between the reference points in the reference point group is greater than 20%, it is extracted and recorded as the associated deviation result. Finally, the reference points in the reference point group are calibrated with the deviation authentication set and the associated deviation result. When the average error rate of the deviation authentication set and the associated deviation result is less than 10%, it is considered that the calibration is passed. After the reference point calibration is passed, the finally passed reference points are used as the monitoring data set of the reference point group. Among them, the monitoring data set includes a static deformation set and a dynamic deformation set

[0043] The static deformation set in the monitoring data set is first obtained through data interaction with the bridge traffic data of the bridge to be monitored. When interacting with the bridge traffic data, not only dynamic monitoring is carried out on the bridge location and inside the bridge, but also data within a fixed range around the bridge to be monitored needs to be pre-monitored, and the weather environment data around the bridge to be monitored is synchronously determined. To ensure the real-time nature of the monitoring data interaction for the bridge to be monitored, it is necessary to set a monitoring data interaction window. This monitoring data interaction window is a window used to collect and interact with the traffic data and weather environment data of the bridge to be monitored. At the same time, sensor data interaction is carried out based on the bridge traffic data and weather environment data to obtain a data interaction result. To minimize the external influence on the monitoring of the bridge to be monitored, data monitoring can be carried out during a period when the number of vehicles passing on the bridge to be monitored is small and the weather environment is excellent during the process of sensor data interaction. Further, the settlement and deformation of the bridge to be monitored are evaluated based on the data interaction result. This means evaluating the downward amplitude of the solid deep foundation of the bridge to be monitored and the shape change of the bridge structure in the data interaction result. The downward amplitude of the solid deep foundation of the monitored bridge and the shape change of the bridge structure have an inverse relationship with the evaluation result. If the downward amplitude of the solid deep foundation of the monitored bridge is large and the shape change of the bridge structure is large, the settlement and deformation evaluation will be low. Finally, the static deformation set of the bridge to be monitored is generated from the settlement and deformation evaluation result.

[0044] The dynamic deformation set in the monitoring data set is to perform real-time positioning on the dynamic vehicles existing on the bridge to be monitored according to the bridge traffic data. Further, speed measurement is performed on the dynamic vehicles inside the bridge through the speed measurement devices arranged on the bridge to be monitored, which means obtaining the speed of the dynamic vehicles based on the real-time positioning of the dynamic vehicles within a unit time and recording the speed data of each dynamic vehicle. Further, weight measurement is performed on the dynamic vehicles through the weight measurement sensors arranged on the bridge to be monitored, and at the same time, weight linear fitting is performed according to the weight measurement results and speed data, which means assuming that both the weight measurement results and speed data are observed quantities, and y is a function of the weight measurement results: y = f(x; b), where x is the weight measurement result, y is the speed data, and curve fitting is to seek the best estimated value of the parameter b through the observed values of the weight measurement results and speed data, and to seek the best theoretical curve y = f(x; b). When the function y = f(x; b) is a linear function of b, weight linear fitting is completed, thereby generating weight data. Further, dynamic deformation monitoring of the bridge to be monitored is performed during the period when the dynamic vehicle passes through the bridge to be monitored, which means monitoring the changes in the shape or volume of the bridge to be monitored caused by the external forces exerted by the dynamic vehicles in the traffic data, integrating the change data and recording it as the dynamic deformation monitoring result. Finally, the speed data, weight data, and dynamic deformation data are mapped and identified. One value is selected from the speed data and weight data included in the weight linear fitting data, and there is exactly one corresponding value for the dynamic deformation data, while for one value of the dynamic deformation data, there can be multiple corresponding values in the speed data and weight data included in the weight linear fitting data. On this basis, the mapping identification between the speed data, weight data, and dynamic deformation data is obtained, and the dynamic deformation set of the bridge to be monitored is generated according to the mapping identification result, realizing the function of target tracking based on the bridge monitoring system.

[0045] Step S500: Configure a tracking target through the static deformation set and the dynamic deformation set;

[0046] Specifically, using the static deformation set and dynamic deformation set of the target bridge to be monitored obtained above as standard data, extract the settlement data and deformation data generated by the target bridge to be monitored in the static environment included in the static deformation set, and use the extracted settlement data and deformation data as the first tracking target for the deformation of the target bridge to be monitored. Extract the speed data, weight data, and dynamic deformation data generated by the target bridge to be monitored in the dynamic environment included in the dynamic deformation set, and use the extracted speed data, weight data, and dynamic deformation data as the second tracking target for the deformation of the target bridge to be monitored. Finally, configure the tracking target for the target bridge to be monitored according to the first tracking target and the second tracking target, so as to be used as reference data for subsequent target tracking based on the bridge monitoring system.

[0047] Step S600: Redistribute the monitoring attention of the tracking target, and generate a monitoring warning message according to the tracking and monitoring results.

[0048] Furthermore, as Figure 3 shown, step S600 of this application further includes:

[0049] Step S610: Perform a location correlation analysis on the newly added tracking target and the tracking target to determine the correlation coefficient;

[0050] Step S620: Evaluate the correlation warning of the newly added tracking target and the tracking target based on the correlation coefficient;

[0051] Step S630: Perform warning management on the bridge to be monitored based on the correlation warning evaluation result.

[0052] Specifically, to ensure the accuracy of the tracking target of the bridge to be monitored, it is necessary to redistribute the monitoring attention of the tracking target. First, perform a location correlation analysis on the newly added tracking target and the tracking target, which refers to the frequency of describing the laws and patterns of the simultaneous appearance of the newly added tracking target and the tracking target, thereby determining the correlation coefficient. Further, evaluating the correlation warning of the newly added tracking target and the tracking target based on the correlation coefficient means that the higher the correlation coefficient between the newly added tracking target and the tracking target, the lower the safety of the bridge to be monitored. Further, perform warning management on the bridge to be monitored based on the correlation warning evaluation result. The higher the correlation, the lower the correlation warning evaluation result, and the higher the warning level, improving the accuracy of target tracking based on the bridge monitoring system in the later stage.

[0053] Furthermore, as Figure 4 shown, step S600 of this application further includes:

[0054] Step S640: Configure the traversal path of the UAV monitoring according to the basic design data, and collect the image data of the bridge to be monitored through the traversal path to construct an image set with position coordinate association;

[0055] Step S650: Perform crack feature matching on the image set to obtain a crack feature set with position coordinate association;

[0056] Step S660: Generate a newly added tracking target according to the crack feature set, and perform monitoring management on the bridge to be monitored based on the newly added tracking target.

[0057] Furthermore, step S630 of this application includes:

[0058] Step S661: Call the position coordinates corresponding to the crack feature set, and perform additional data collection of the sensor on the position coordinates;

[0059] Step S662: Record the additional data collection result, and perform feature compensation on the crack feature set through the additional data collection result;

[0060] Step S663: Regenerate a new tracking target according to the feature compensation result.

[0061] Specifically, to ensure the accuracy of detecting and warning the target monitoring bridge, the target monitoring bridge can be scanned and detected by a drone. The traversal path of the drone monitoring is configured according to the basic design data of the target monitoring bridge, which means that the load-bearing part of the bridge is key-checked based on the structural design data of the bridge. Then, each checked point is connected and recorded as the traversal path, and image data collection is performed on the bridge to be monitored through the traversal path. Then, an image set associated with position coordinates is constructed through the collected image data. The image set is matched with the bridge crack features included in the big data. The position coordinates of the images with crack matching are extracted, and the extracted images are integrated and recorded as a crack feature set associated with position coordinates. Further, a new tracking target is generated according to the crack feature set, which means that first, the position coordinates corresponding to the crack feature set are called, and additional data collection of the sensor is performed on the position coordinates, which means that sensors for measuring the safety of the bridge are arranged based on the position coordinates. Then, the data collected by the arranged sensors is used as the additional data set, and at the same time, the collected additional data collection result is recorded. Finally, feature compensation is performed on the crack feature set through the additional data collection result collected by the sensor, so that the crack features collected on the target bridge are more accurate. A new tracking target is regenerated according to the performed feature compensation result, and at the same time, the new tracking target is used as the target data of the bridge to be monitored for the monitoring management of the bridge to be monitored, achieving the technical effect of providing an important basis for target tracking of the new tracking target in the later stage.

[0062] In summary, a target tracking method for a bridge monitoring system provided by an embodiment of the present application has at least the following technical effects: realizing precise positioning, tracking, and control of bridge defects, and improving the low safety of the bridge.

[0063] Embodiment 2

[0064] Based on the same inventive concept as the target tracking method for a bridge monitoring system in the foregoing embodiment, as Figure 5 shown, the present application provides a target tracking system for a bridge monitoring system. The system includes:

[0065] Observation Focus Analysis Module 1, which is used to interactively obtain the basic design data of the bridge to be monitored, and perform observation focus analysis based on the basic design data to generate an observation focus distribution result;

[0066] Monitoring Module 2, which is used to configure the monitoring accuracy of the bridge to be monitored, distribute monitoring attention according to the monitoring accuracy and the observation focus distribution result, and generate preset monitoring points;

[0067] Data Acquisition Module 3, which is used to collect regional data within a predetermined area of the bridge to be monitored, and configure a reference point group based on the regional data and the preset monitoring points. Among them, the reference point group has a mapping relationship with the preset monitoring points, and each reference point group includes at least three reference points;

[0068] Reference Point Verification Module 4, which is used to perform reference point verification on the reference point group before monitoring the bridge to be monitored. After the verification passes, a monitoring data set based on the reference point group is generated, where the monitoring data set includes a static deformation set and a dynamic deformation set;

[0069] Target Configuration Module 5, which is used to configure a tracking target through the static deformation set and the dynamic deformation set;

[0070] Monitoring Attention Module 6, which is used to redistribute the monitoring attention of the tracking target and generate a monitoring warning message according to the tracking monitoring result.

[0071] Furthermore, the system further includes:

[0072] Deviation Certification Module, which is used to use any one of the reference points in the reference point group as a calibration reference point to perform deviation certification on other reference points in the current reference point group;

[0073] Traversal Module, which is used to traverse all the reference points in the reference point group as standard reference points and integrate to obtain a deviation certification set;

[0074] Association Certification Module, which is used to locate the associated reference point group of the reference point group and perform association certification on the reference point group through the associated reference point group to obtain an associated deviation result;

[0075] Verification Module, which is used to perform reference point verification through the deviation certification set and the associated deviation result.

[0076] Furthermore, the system further includes:

[0077] The first data interaction module, which is used to interact with the bridge passing data of the bridge to be monitored and synchronously determine the weather environment data;

[0078] The second data interaction module, which is used to set a monitoring data interaction window, and within the monitoring data interaction window, perform monitoring sensor data interaction based on the bridge passing data and the weather environment data to obtain a data interaction result;

[0079] The third data interaction module, which is used to evaluate the settlement and deformation of the bridge to be monitored through the data interaction result;

[0080] The first deformation module, which is used to generate the static deformation set based on the settlement and deformation evaluation results.

[0081] Furthermore, the system further includes:

[0082] The positioning module, which is used to locate dynamic vehicles according to the bridge passing data;

[0083] The speed measurement module, which is used to measure the speed of the dynamic vehicle through a speed measurement device and record the speed data;

[0084] The weight linear fitting module, which is used to determine the weight of the dynamic vehicle based on a weight measurement sensor and perform weight linear fitting according to the weight measurement result and the speed data to generate weight data;

[0085] The deformation monitoring module, which is used to perform dynamic deformation monitoring during the passage of the dynamic vehicle through the bridge to be monitored and generate a dynamic deformation monitoring result;

[0086] The second deformation module, which is used to map and identify the speed data, the weight data, and the dynamic deformation data, and generate a dynamic deformation set according to the mapping identification result.

[0087] Furthermore, the system further includes:

[0088] The set construction module, which is used to configure the traversal path of the UAV monitoring according to the basic design data and perform image data acquisition of the bridge to be monitored through the traversal path to construct an image set with position coordinate associations;

[0089] The feature matching module, which is used to perform crack feature matching on the image set to obtain a crack feature set with position coordinate associations;

[0090] The first new module, which is used to generate a new tracking target according to the crack feature set and perform monitoring and management of the bridge to be monitored based on the new tracking target.

[0091] Furthermore, the system further includes:

[0092] A position coordinate module, which is used to call the position coordinates corresponding to the crack feature set and perform additional data acquisition of sensors on the position coordinates;

[0093] A feature compensation module, which is used to record the results of additional data acquisition and perform feature compensation on the crack feature set through the results of additional data acquisition;

[0094] The second new module, which is used to regenerate a new tracking target according to the feature compensation result.

[0095] Furthermore, the system further includes:

[0096] An association analysis module, which is used to perform position association analysis on the new tracking target and the tracking target to determine the association coefficient;

[0097] An association warning module, which is used to perform an association warning evaluation on the new tracking target and the tracking target according to the association coefficient;

[0098] A warning management module, which is used to perform warning management of the bridge to be monitored according to the association warning evaluation result.

[0099] Through the foregoing detailed description of a target tracking method for a bridge monitoring system in this specification, those skilled in the art can clearly know a target tracking system for a bridge monitoring system in this embodiment. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For the relevant parts, reference may be made to the description in the method section.

[0100] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A target tracking method for a bridge monitoring system, characterized in that, The method includes: Interactively obtain the basic design data of the bridge to be monitored, and perform observation focus analysis based on the basic design data to generate an observation focus distribution result; Configure the monitoring accuracy of the bridge to be monitored, and distribute monitoring attention according to the monitoring accuracy and the observation focus distribution result to generate preset monitoring points. The monitoring attention mechanism refers to concentrating visual attention on different regions of an image. This attention mechanism is a special structure embedded in a machine learning model, used to automatically learn and calculate the contribution of the monitoring accuracy and the observation focus distribution result to the monitoring point data. The monitoring points with large data contributions are recorded as preset monitoring points; Collect the regional data within the predetermined area of the bridge to be monitored, and configure a reference point group based on the regional data and the preset monitoring points. Among them, the reference point group has a mapping relationship with the preset monitoring points, and each reference point group includes at least three reference points; Before monitoring the bridge to be monitored, perform reference point verification on the reference point group. After passing the verification, generate a monitoring data set based on the reference point group, where the monitoring data set includes a static deformation set and a dynamic deformation set; Configure a tracking target through the static deformation set and the dynamic deformation set; And redistribute the monitoring attention of the tracking target, and generate a monitoring warning message according to the tracking monitoring result.

2. The method according to claim 1, characterized in that, The method further includes: Use any one reference point within the reference point group as a calibration reference point to perform deviation authentication on other reference points within the current reference point group; Traverse all the reference points within the reference point group as standard reference points, and integrate to obtain a deviation authentication set; Locate the associated reference point group of the reference point group, and perform associated authentication of the reference point group through the associated reference point group to obtain an associated deviation result; Perform reference point verification through the deviation authentication set and the associated deviation result.

3. The method according to claim 1, characterized in that, The method further includes: Interactively obtain the bridge passing data of the bridge to be monitored, and synchronously determine the weather environment data; Set a monitoring data interaction window, and within the monitoring data interaction window, perform monitoring sensor data interaction according to the bridge passing data and the weather environment data to obtain a data interaction result; Perform settlement and deformation evaluation of the bridge to be monitored through the data interaction result; Generate the static deformation set based on the settlement and deformation evaluation results.

4. The method according to claim 3, characterized in that, The method further includes: Locate dynamic vehicles according to the bridge passing data; Measure the speed of the dynamic vehicle through a speed measuring device and record the speed data; Perform weight determination of the dynamic vehicle based on a weighing sensor, and perform weight linear fitting according to the weight determination result and the speed data to generate weight data; Perform dynamic deformation monitoring during the period when the dynamic vehicle passes through the bridge to be monitored to generate a dynamic deformation monitoring result; Map and identify the speed data, the weight data, and the dynamic deformation monitoring result, and generate a dynamic deformation set according to the mapping identification result.

5. The method according to claim 1, wherein The method further includes: Configure the traversal path for UAV monitoring according to the basic design data, and collect the image data of the bridge to be monitored through the traversal path to construct an image set associated with position coordinates; Perform crack feature matching on the image set to obtain a crack feature set associated with position coordinates; Generate new tracking targets according to the crack feature set, and perform monitoring and management of the bridge to be monitored based on the new tracking targets.

6. The method according to claim 5, wherein The method further includes: Call the position coordinates corresponding to the crack feature set, and perform additional data collection of sensors on the position coordinates; Record the results of additional data collection, and perform feature compensation on the crack feature set through the results of additional data collection; Regenerate new tracking targets according to the feature compensation results.

7. The method according to claim 6, wherein The method further includes: Perform position association analysis on the new tracking target and the tracking target to determine the correlation coefficient; Perform correlation early warning evaluation on the new tracking target and the tracking target according to the correlation coefficient; Perform early warning management of the bridge to be monitored according to the results of the correlation early warning evaluation.

8. A target tracking system for a bridge monitoring system, characterized in that, The system includes: An observation focus analysis module, which is used to interactively obtain the basic design data of the bridge to be monitored, and perform observation focus analysis based on the basic design data to generate an observation focus distribution result; A monitoring module, which is used to configure the monitoring accuracy of the bridge to be monitored, distribute monitoring attention according to the monitoring accuracy and the observation focus distribution result, and generate preset monitoring points. The monitoring attention mechanism refers to concentrating visual attention on different regions of the image. This attention mechanism is a special structure embedded in the machine learning model, used to automatically learn and calculate the contribution of the monitoring accuracy and the observation focus distribution result to the monitoring point data, and record the monitoring points with large data contributions as preset monitoring points; A data collection module, which is used to collect regional data within a predetermined area of the bridge to be monitored, and configure a reference point group based on the regional data and the preset monitoring points. Among them, the reference point group has a mapping relationship with the preset monitoring points, and each reference point group includes at least three reference points; A reference point verification module, which is used to verify the reference points of the reference point group before monitoring the bridge to be monitored. After passing the verification, generate a monitoring data set based on the reference point group, where the monitoring data set includes a static deformation set and a dynamic deformation set; A target configuration module, which is used to configure tracking targets through the static deformation set and the dynamic deformation set; A monitoring attention module, which is used to redistribute the monitoring attention of the tracking targets and generate monitoring early warning information according to the tracking monitoring results.

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