A monitoring method and device for the deformation of a long-span and out-of-limit beam structure

Abstract

A method and device for monitoring the deformation of a long-span and over-limit beam structure, which relates to the technical field of long-span and over-limit beam structures. In this method, first monitoring data is received, and the first monitoring data is collected by a target sensor for the long-span and over-limit beam structure to be measured; a first position is determined according to the first monitoring data; the first position is removed from the long-span and over-limit beam structure to be measured to obtain a plurality of second positions; target scan data is received, and the target scan data is data obtained by a wall-mounted crawling robot going to the second positions for ultrasonic guided wave scanning; the target scan data and the first monitoring data are input into a digital twin model to obtain a first analysis result; according to the first analysis result, it is determined that the long-span and over-limit beam structure to be measured has first deformation information, and the first deformation information is displayed to a target user. Implementing the technical solution provided by this application can significantly improve the accuracy and comprehensiveness of the deformation monitoring of the long-span and over-limit beam structure.

Description

Technical Field

[0001] This application relates to the technical field of long-span and out-of-limit beam structures, and particularly relates to a method and device for monitoring the deformation of long-span and out-of-limit beam structures. Background Art

[0002] With the development of society, people's requirements for the functionality, comfort, and building space layout of large public buildings are getting higher and higher. Long-span and out-of-limit beam structures are increasingly widely used in public buildings such as hotels, convention centers, and stadiums.

[0003] During the construction stage and the whole-life cycle operation process of long-span and out-of-limit beam structures, as a key mechanical response parameter, the evolution law of structural deformation has a significant impact on the structural health state. Deformation will change the internal force distribution of the structure, leading to secondary damages such as stress concentration and crack propagation, which will further threaten the safety of infrastructure. By real-time monitoring the structural deformation, the pre-warning of risks can be realized, and major economic losses and social impacts caused by sudden accidents (such as collapse and instability) can be avoided. At present, the deformation monitoring of long-span and out-of-limit beam structures mainly relies on distributed sensors for data collection. Through point sensors such as strain gauges and displacement gauges, mechanical parameters such as strain and displacement in local areas are obtained, and then the data collected by the sensors are analyzed manually to determine the health state of the beam structure, but the evaluation results obtained by manual analysis are inaccurate.

[0004] Therefore, there is an urgent need for a method and device for monitoring the deformation of long-span and out-of-limit beam structures that can solve the above technical problems. Summary of the Invention

[0005] This application provides a method and device for monitoring the deformation of long-span and out-of-limit beam structures. By combining sensor data with ultrasonic guided wave scanning data of wall-mounted crawling robots and using digital twin models for comprehensive analysis, the accuracy and comprehensiveness of the deformation monitoring of long-span and out-of-limit beam structures can be significantly improved.

[0006] In a first aspect, the present application provides a method for monitoring the deformation of a long-span and over-limit beam structure. The method includes: receiving first monitoring data, which is collected from the long-span and over-limit beam structure to be measured using a target sensor, and the target sensor is a sensor pre-deployed in the long-span and over-limit beam structure to be measured; determining a first position based on the first monitoring data, where the first position is the position of the target sensor in the long-span and over-limit beam structure to be measured; removing the first position from the long-span and over-limit beam structure to be measured to obtain a plurality of second positions, where the second positions are the positions in the long-span and over-limit beam structure to be measured other than the first position; receiving target scan data, which is data obtained by a wall-mounted crawling robot going to the second positions for ultrasonic guided wave scanning, and the target scan data includes vibration signals, acceleration signals, temperature data, humidity data, and propagation speed; inputting the target scan data and the first monitoring data into a digital twin model to obtain a first analysis result; determining that the long-span and over-limit beam structure to be measured has first deformation information based on the first analysis result, and displaying the first deformation information to a target user.

[0007] By adopting the above technical solution, first, data is collected from the long-span and over-limit beam structure to be measured according to the target sensor to obtain first monitoring data, and the first monitoring data provides mechanical parameters of a local area. Then, ultrasonic guided wave scanning is performed by the wall-mounted crawling robot, which can cover multiple positions of the beam structure, and global data such as vibration signals, acceleration signals, temperature, humidity, and propagation speed are obtained, making up for the monitoring blind area of the target sensor. The target scan data provides global structural state information. The combination of the two can more comprehensively reflect the health state of the long-span and over-limit beam structure to be measured. Inputting the first monitoring data and the target scan data into the digital twin model to obtain a first analysis result, the first analysis result can accurately simulate and analyze the deformation of the long-span and over-limit beam structure to be measured. Automatically processing and analyzing data through the digital twin model reduces human intervention, reduces human errors, and improves the objectivity and accuracy of the analysis result.

[0008] Optionally, before receiving the target scan data, the method further includes: obtaining environmental data and material information. The environmental data is the environmental value corresponding to the long-span and over-limit beam structure to be measured, and the environmental value includes air volume value, temperature value, humidity value, and vibration value. The material information is the information of the material used on the surface of the long-span and over-limit beam structure to be measured; determining whether the environmental data exists in a preset environmental table and whether the material information is consistent with the preset material information. The preset environmental table is the information summarized for the normal working environment of the wall-mounted crawling robot, and the preset material information is the material information that the wall-mounted crawling robot can normally adsorb; when the environmental data exists in the preset environmental table and the material information is consistent with the preset material information, it is determined to receive the target scan data obtained by scanning the wall-mounted crawling robot.

[0009] By adopting the above technical solution, judging whether the current environmental data exists in the preset environment table can ensure that the wall-mounted crawling robot works under suitable environmental conditions, avoid operating the robot in unsuitable environments, reduce the risk of failures caused by environmental factors, and extend the service life of the equipment. Then, by judging whether the material information is consistent with the preset material information, it is ensured that the wall-mounted crawling robot can stably adsorb on the surface of the large-span ultra-limit beam structure to be measured. The surface characteristics of different materials will affect the adsorption effect of the robot. Selecting appropriate materials can ensure the safe and stable operation of the robot, avoid adsorption failure caused by material mismatch, reduce the risk of the robot falling or being damaged, and ensure the safety of the equipment and personnel. Only when both the environmental conditions and the material information meet the requirements, the target scan data obtained by scanning the wall-mounted crawling robot is received, reducing the influence of external interference factors on data acquisition and improving the data quality.

[0010] Optionally, after judging whether the environmental data exists in the preset environment table and whether the material information is consistent with the preset material information, the method further includes: when the environmental data does not exist in the preset environment table and the material information is inconsistent with the preset material information, determining that the wall-mounted crawling robot is in a mismatched state with the large-span ultra-limit beam structure to be measured; receiving a first image according to the mismatched state, where the first image is an image obtained by the target unmanned aerial vehicle carrying the target camera to photograph the large-span ultra-limit beam structure to be measured; extracting second monitoring data from the first image, where the second monitoring data includes spectral feature information, spatial geometric information, vibration signals, and motion trajectory information; inputting the second monitoring data and the first monitoring data into the digital twin model to obtain a second analysis result; determining that the large-span ultra-limit beam structure to be measured has second deformation information according to the second analysis result, and displaying the second deformation information to the target user.

[0011] By adopting the above technical solution, when the wall-mounted crawling robot cannot work properly due to environmental or material mismatch, the target unmanned aerial vehicle can quickly intervene and carry a high-precision camera to comprehensively photograph the large-span ultra-limit beam structure to be measured, making up for the limitations of robot monitoring, ensuring that the monitoring task is not restricted by a single device. The target unmanned aerial vehicle can flexibly adjust the flight path and shooting angle to obtain image data of different positions and angles of the beam structure, improving the comprehensiveness and accuracy of monitoring. Extracting second monitoring data from the images taken by the target unmanned aerial vehicle, where the second monitoring data includes spectral feature information, spatial geometric information, vibration signals, and motion trajectory information, and then inputting the second monitoring data and the first monitoring data into the digital twin model to obtain a second analysis result can timely detect potential changes or damages in the large-span ultra-limit beam structure to be measured. According to the second analysis result, the second deformation information can be quickly displayed to the target user. The combination of the unmanned aerial vehicle and the digital twin model reduces the need for manual monitoring and analysis and lowers the labor cost.

[0012] Optionally, before receiving the first image according to the mismatch status, the method further includes: obtaining the initial position of the target unmanned aerial vehicle (UAV); dividing the large-span and over-limit beam structure to be measured into multiple sub-regions, and obtaining multiple third positions corresponding to the multiple sub-regions, where one sub-region corresponds to one third position; obtaining multiple target distances, where the multiple target distances are the distances between the initial position and each of the third positions; sorting the multiple target distances from smallest to largest to obtain a target sorting result; performing path planning on the multiple sub-regions according to the target sorting result to obtain a first inspection route, and sending the first inspection route to the target UAV so that the target UAV can take pictures of the large-span and over-limit beam structure to be measured according to the first inspection route.

[0013] By adopting the above technical solution, first obtain the initial position of the target UAV, then calculate the distances between the initial position of the target UAV and the third positions of each sub-region, and sort them in ascending order to generate a first inspection route. This path planning method can minimize the flight distance and time of the UAV, improve the inspection efficiency, divide the large-span and over-limit beam structure to be measured into multiple sub-regions, and ensure that each sub-region corresponds to a third position. This division method can ensure that the UAV covers the entire structure during the inspection process and avoid missing any key areas. Through path planning, the target UAV can approach each sub-region at the shortest distance to achieve close-range shooting, thereby obtaining clearer and more accurate image data.

[0014] Optionally, after determining that there is second deformation information in the large-span and over-limit beam structure to be measured according to the second analysis result, the method further includes: obtaining the abnormal position from the large-span and over-limit beam structure to be measured at intervals of a preset time, where the abnormal position is the position corresponding to the second deformation information in the large-span and over-limit beam structure to be measured; generating a second inspection route based on the abnormal position, where the second inspection route is an inspection route starting from the abnormal position; sending the second inspection route to the target UAV so that the target UAV can take pictures of the abnormal position according to the second inspection route to obtain a second image.

[0015] By adopting the above technical solution, dynamically obtaining the abnormal position at intervals of a preset time can quickly lock the deformed area in the large-span and over-limit beam structure to be measured, avoid the monitoring lag caused by a fixed inspection route, generate a second inspection route starting from the abnormal position, the UAV can give priority to inspecting the abnormal position to ensure the safety monitoring of key areas, and the UAV replaces manual inspection in high-risk areas to reduce the risk of casualties.

[0016] Optionally, after sending the second inspection route to the target UAV so that the target UAV captures the abnormal position according to the second inspection route to obtain a second image, the method further includes: processing the second image to obtain a first deformation value, where the first deformation value is the deformation value corresponding to the abnormal position in the second image; obtaining the second deformation value corresponding to the abnormal position from the second deformation information; determining whether the first deformation value is less than or equal to the second deformation value; when the first deformation value is greater than the second deformation value, determining that the abnormal position is marked as high risk, selecting a processing measure according to the high risk, and sending the processing measure and the high risk to the target user so that the target user can repair the abnormal position according to the processing measure.

[0017] By adopting the above technical solution, extracting the first deformation value from the second image and comparing it with the second deformation value can quantify the change in the deformation degree of the abnormal position, avoid the error of subjective judgment. When the first deformation value exceeds the second deformation value, it is automatically marked as high risk to ensure that high-risk areas are not missed and improve the accuracy of risk assessment. After the image data captured by the UAV is transmitted in real time, the deformation value calculation and comparison can be carried out immediately to quickly identify high-risk areas and shorten the time from abnormal discovery to risk response. Then, the high-risk areas and processing measures are intuitively displayed to the target user to enhance the user's perception of the structural health status.

[0018] Optionally, the first deformation information is displayed to the target user, which specifically includes: obtaining multiple key points from the large-span and over-limit beam structure to be measured, mapping the multiple key points into a virtual environment to obtain a first virtual image; determining a risk position from the first deformation information, mapping the risk position in the first virtual image to obtain a second virtual image; sending the second virtual image to the target AR device so that the target user can view the second virtual image corresponding to the risk position by wearing the target AR device.

[0019] By adopting the above technical solution, mapping multiple key points of the large-span and over-limit beam structure to be measured into a virtual environment to generate a first virtual image enables the target user to intuitively perceive the overall shape and key positions of the structure. Further mapping the risk position in the first virtual image to generate a second virtual image, highlighting the risk area by means of color, marking or animation, etc., to enhance the target user's perception of the risk position. Through the target AR device, the target user can view the second virtual image corresponding to the risk position in real time, converting the abstract structural deformation data into a visual risk position, reducing the target user's difficulty in understanding professional data. Through the target AR device, the target user can remotely view the risk position, reducing the frequency and cost of on-site inspections.

[0020] In the second aspect of the present application, a monitoring device for the deformation of a long-span and over-limit beam structure is provided. The device includes a receiving unit, a processing unit, and a transmitting unit. The receiving unit receives first monitoring data, which is the data collected by a target sensor for the long-span and over-limit beam structure to be measured. The target sensor is a sensor previously deployed in the long-span and over-limit beam structure to be measured. The processing unit determines a first position according to the first monitoring data, where the first position is the position of the target sensor in the long-span and over-limit beam structure to be measured. The first position is removed from the long-span and over-limit beam structure to be measured to obtain a plurality of second positions, where the second positions are the positions in the long-span and over-limit beam structure to be measured other than the first position. The target scan data is received, which is the data obtained by the wall-mounted crawling robot going to the second position for ultrasonic guided wave scanning. The target scan data includes vibration signals, acceleration signals, temperature data, humidity data, and propagation speed. The target scan data and the first monitoring data are input into the digital twin model to obtain a first analysis result. The transmitting unit determines that there is first deformation information in the long-span and over-limit beam structure to be measured according to the first analysis result, and displays the first deformation information to the target user.

[0021] Optionally, the receiving unit is used to obtain environmental data and material information. The environmental data is the environmental value corresponding to the long-span and over-limit beam structure to be measured, and the environmental value includes air volume value, temperature value, humidity value, and vibration value. The material information is the information of the material used on the surface of the long-span and over-limit beam structure to be measured. The processing unit is used to determine whether there is environmental data in the preset environmental table and whether the material information is consistent with the preset material information. The preset environmental table is the information summarized for the normal working environment of the wall-mounted crawling robot, and the preset material information is the material information that the wall-mounted crawling robot can normally adsorb. The receiving unit is used to determine to receive the target scan data scanned by the wall-mounted crawling robot when there is environmental data in the preset environmental table and the material information is consistent with the preset material information.

[0022] Optionally, when there is no environmental data in the preset environmental table and the material information is inconsistent with the preset material information, the processing unit determines that the wall-mounted crawling robot and the long-span and over-limit beam structure to be measured are in a mismatched state. The receiving unit is used to receive a first image according to the mismatched state, where the first image is an image obtained by the target unmanned aerial vehicle carrying the target camera to photograph the long-span and over-limit beam structure to be measured. The processing unit is used to extract second monitoring data from the first image, where the second monitoring data includes spectral feature information, spatial geometric information, vibration signals, and motion trajectory information. The second monitoring data and the first monitoring data are input into the digital twin model to obtain a second analysis result. The transmitting unit determines that there is second deformation information in the long-span and over-limit beam structure to be measured according to the second analysis result, and displays the second deformation information to the target user.

[0023] Optionally, the receiving unit is configured to obtain the initial position corresponding to the target UAV; the processing unit is configured to divide the large-span and over-limit beam structure to be measured into multiple sub-regions, and obtain multiple third positions corresponding to the multiple sub-regions, where one sub-region corresponds to one third position; the receiving unit is configured to obtain multiple target distances, and the multiple target distances are the distances between the initial position and each of the third positions; the processing unit is configured to sort the multiple target distances from smallest to largest to obtain a target sorting result; the sending unit is configured to perform path planning on the multiple sub-regions according to the target sorting result to obtain a first inspection route, and send the first inspection route to the target UAV, so that the target UAV takes pictures of the large-span and over-limit beam structure to be measured according to the first inspection route.

[0024] Optionally, the receiving unit is configured to obtain an abnormal position from the large-span and over-limit beam structure to be measured at preset time intervals, where the abnormal position is the position corresponding to the second deformation information in the large-span and over-limit beam structure to be measured; the processing unit is configured to generate a second inspection route according to the abnormal position, and the second inspection route is an inspection route starting from the abnormal position; the sending unit is configured to send the second inspection route to the target UAV, so that the target UAV takes pictures of the abnormal position according to the second inspection route to obtain a second image.

[0025] Optionally, the processing unit is configured to process the second image to obtain a first deformation value, where the first deformation value is the deformation value corresponding to the abnormal position in the second image; the receiving unit is configured to obtain the second deformation value corresponding to the abnormal position from the second deformation information; the processing unit is configured to determine whether the first deformation value is less than or equal to the second deformation value; the sending unit is configured to, when the first deformation value is greater than the second deformation value, determine that the abnormal position is marked as high risk, select a treatment measure according to the high risk, and send the treatment measure and the high risk to the target user, so that the target user repairs the abnormal position according to the treatment measure.

[0026] Optionally, the receiving unit is configured to obtain multiple key points from the large-span and over-limit beam structure to be measured, and map the multiple key points into a virtual environment to obtain a first virtual picture; the processing unit is configured to determine a risk position from the first deformation information, and map the risk position in the first virtual picture to obtain a second virtual picture; the sending unit is configured to send the second virtual picture to the target AR device, so that the target user can view the second virtual picture corresponding to the risk position by wearing the target AR device.

[0027] In a third aspect of the present application, an electronic device is provided. The electronic device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. The user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory, so that an electronic device executes the method according to any one of the above in the present application.

[0028] In the fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions that, when executed, perform the method of any one of the above in the present application.

[0029] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0030] 1. First, data collection is performed on the large-span over-limit beam structure to be measured according to the target sensor to obtain the first monitoring data. The first monitoring data provides mechanical parameters of a local area. Then, ultrasonic guided wave scanning is performed by the wall-mounted crawling robot, which can cover multiple positions of the beam structure to obtain global data such as vibration signals, acceleration signals, temperature, humidity, and propagation speed, making up for the monitoring blind spots of the target sensor. The target scanning data provides global structural state information. The combination of the two can more comprehensively reflect the health state of the large-span over-limit beam structure to be measured. The first monitoring data and the target scanning data are input into the digital twin model to obtain the first analysis result. The first analysis result can accurately simulate and analyze the deformation of the large-span over-limit beam structure to be measured. By automatically processing and analyzing data through the digital twin model, human intervention is reduced, human errors are lowered, and the objectivity and accuracy of the analysis result are improved.

[0031] 2. When the wall-mounted crawling robot cannot work properly due to environmental or material mismatch, the target unmanned aerial vehicle can quickly intervene and carry a high-precision camera to comprehensively photograph the large-span over-limit beam structure to be measured, making up for the limitations of robot monitoring and ensuring that the monitoring task is not restricted by a single device. The target unmanned aerial vehicle can flexibly adjust the flight path and shooting angle to obtain image data of different positions and angles of the beam structure, improving the comprehensiveness and accuracy of monitoring. The second monitoring data is extracted from the images taken by the target unmanned aerial vehicle. The second monitoring data includes spectral feature information, spatial geometric information, vibration signals, and motion trajectory information. Then, the second monitoring data and the first monitoring data are input into the digital twin model to obtain the second analysis result, which can timely detect potential changes or damages in the large-span over-limit beam structure to be measured. According to the second analysis result, the second deformation information can be quickly displayed to the target user. The combination of the unmanned aerial vehicle and the digital twin model reduces the need for manual monitoring and analysis and lowers the labor cost. Description of the Drawings

[0032] Figure 1 is a schematic flowchart of a method for monitoring the deformation of a large-span over-limit beam structure provided by an embodiment of the present application;

[0033] Figure 2 is a schematic structural diagram of a device for monitoring the deformation of a large-span over-limit beam structure provided by an embodiment of the present application;

[0034] Figure 3It is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application.

[0035] Explanation of reference numerals in the drawings: 201, receiving unit; 202, processing unit; 203, confirmation unit; 300, electronic device; 301, processor; 302, memory; 303, user interface; 304, network interface; 305, communication bus. Detailed implementation manners

[0036] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments.

[0037] In the description of the embodiments of the present application, words such as "for example" or "for instance" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "for example" or "for instance" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "for example" or "for instance" is intended to present relevant concepts in a specific manner.

[0038] In the description of the embodiments of the present application, the meaning of the term "plural" refers to two or more. For example, a plurality of systems refers to two or more systems, and a plurality of screen terminals refers to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly indicating the technical features indicated. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "include", "comprise", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0039] With the rapid development of the social economy and the acceleration of the urbanization process, the public has put forward higher requirements for the functionality, spatial comfort and rationality of spatial layout of large public buildings. In this context, large-span and ultra-limit beam structures have been widely used in large public buildings such as hotels, convention centers, and stadiums due to their excellent mechanical properties and space expansion capabilities. However, the mechanical behavior of such structures is complex, and the rationality of their design parameters and construction techniques is directly related to the safety, economy and functional applicability of the building.

[0040] During the construction phase and throughout the lifecycle of large-span, over-limit beam structures, structural deformation, as a key mechanical response parameter, significantly influences the structural health. Deformation alters the distribution of internal forces, leading to secondary damage such as stress concentration and crack propagation, which in turn threatens infrastructure safety. Real-time monitoring of structural deformation can provide early warning of risks and prevent significant economic losses and social impacts caused by unexpected accidents such as collapse and instability. Currently, deformation monitoring of large-span, over-limit beam structures primarily relies on distributed sensors for data acquisition. Point sensors such as strain gauges and displacement meters are used to obtain mechanical parameters such as strain and displacement in local areas. Manual analysis of the sensor data is then performed to determine the health of the beam structure, but manual analysis yields inaccurate results.

[0041] Therefore, how to solve the problem of inaccurate manual assessment of the health status of large-span over-limit beam structures. The embodiment of the present application provides a method for monitoring the deformation of large-span over-limit beam structures, which is applied to a server. The server of the present application can be a platform that provides deformation monitoring services for large-span over-limit beam structures in large public buildings. Figure 1 This is a flow chart of a method for monitoring deformation of a large-span over-limit beam structure provided by an embodiment of the present application, with reference to Figure 1 The method includes the following steps S101-S106.

[0042] S101: Receive first monitoring data, where the first monitoring data is collected using a target sensor for a large-span over-limit beam structure to be measured.

[0043] In S101 above, during or after construction of a large-span, over-limit beam structure, sensors must be deployed on or within the beam structure. In this case, the beam structure refers to the large-span, over-limit beam structure. Because large-span, over-limit beam structures have a wide range of applications, the deployment locations must be determined based on the specific application. Because the stress characteristics, functional use, and environmental conditions of large-span, over-limit beam structures vary across different application scenarios, the sensor deployment locations must be tailored. For example, large-span, over-limit beam structures in hotel buildings primarily support floor slabs and provide large activity areas, such as banquet halls and conference rooms. Sensors should be deployed primarily at the beam's mid-span, near supports, and in locations likely to generate significant bending moments and shear forces. Large-span, over-limit beam structures in bridges primarily withstand vehicle loads, wind loads, temperature effects, and seismic forces. Sensors should be deployed at key sections of the beam, such as the mid-span, quarter-span, and three-quarter-span, as well as at supports and piers. Therefore, before deploying sensors, a detailed structural analysis of the large-span over-limit beam structure is required. Based on the results of the structural analysis, a targeted sensor deployment plan should be formulated to clarify the type, quantity, deployment location, and measurement parameters of the sensors.

[0044] In addition, this application is described with a hotel building as the application scenario. First, various sensors are pre-installed in the key parts of the large-span over-limit beam structure to be measured. The key parts refer to the mid-span, beam ends, and straight seats, etc. The sensors refer to strain gauges, accelerometers, displacement sensors, etc. The large-span over-limit beam structure to be measured refers to the large-span over-limit beam structure in the hotel building being monitored this time. The target sensors collect structural response data, such as strain, acceleration, displacement, etc., in real time or regularly, and transmit the data to the server by wired or wireless means. The server receives and stores these sensor data to form the first monitoring data.

[0045] For example, a strain gauge sensor is installed at the mid-span position of a large over-limit beam in a hotel. This sensor collects strain data every 10 minutes and sends the data to the server of the hotel management center through LoRa wireless communication technology.

[0046] S102: Determine the first position according to the first monitoring data. The first position is the position of the target sensor in the large-span over-limit beam structure to be measured.

[0047] In the above S102, the received first monitoring data is parsed to identify the sensor ID or position identifier corresponding to each data point. According to the records during sensor deployment, the sensor ID or position identifier is mapped to the specific position on the large-span over-limit beam structure to be measured to form the first position. Since there is more than one sensor deployed in the large-span over-limit beam structure, multiple sensors are deployed, and each sensor corresponds to a first position respectively. Therefore, the first position can be understood as the position corresponding to any one sensor. For example, the server receives data from the mid-span strain gauge sensor and determines that the data corresponds to the mid-span position of the large over-limit beam in the hotel according to the sensor deployment record.

[0048] S103: Remove the first position from the large-span over-limit beam structure to be measured to obtain multiple second positions.

[0049] In the above S103, first, all the positions where sensors are deployed, that is, the first positions, are identified from the large-span over-limit beam structure to be measured, and then the first positions where sensors have been installed are excluded from the large-span over-limit beam structure to be measured. The remaining positions are the multiple second positions. The second position can also be understood as the position corresponding to the part where no sensor is installed in the large-span over-limit beam structure to be measured, that is, the second position is the position other than the first position in the large-span over-limit beam structure to be measured. For example, after determining that the mid-span position is the first position, all other positions of the large over-limit beam in the hotel (such as the beam ends, 1 / 4 span position, etc.) are used as the second positions.

[0050] S104: Receive the target scan data. The target scan data is the data obtained by the wall-mounted crawling robot performing ultrasonic guided wave scanning at the second position.

[0051] In the above S104, after receiving the first monitoring data collected by the target sensor, since the first monitoring data only represents the monitoring of a local area in the large-span and over-limit beam structure to be measured, in order to comprehensively monitor the large-span and over-limit beam structure to be measured, it is also necessary to use a robot or a drone to collect data in the areas where sensors are not deployed, so as to determine whether there is deformation in the large-span and over-limit beam structure to be measured. When choosing whether to use a robot or a drone to collect data for the large-span and over-limit beam structure to be measured, it is necessary to decide according to the environment where the large-span and over-limit beam structure to be measured is located and the material used on the surface. Next, it will be described in detail under what circumstances a robot is selected to collect data.

[0052] In addition, obtain environmental data and material information. The environmental data is the environmental value corresponding to the large-span and out-of-limit beam structure to be measured, and the environmental value includes air volume value, temperature value, humidity value, and vibration value. The material information is the information of the material used on the surface of the large-span and out-of-limit beam structure to be measured. Determine whether there is environmental data in the preset environmental table and whether the material information is consistent with the preset material information. The preset environmental table is the information summarized for the normal working environment of the wall-mounted crawling robot, and the preset material information is the material information that the wall-mounted crawling robot can normally adsorb. When there is environmental data in the preset environmental table and the material information is consistent with the preset material information, determine to receive the target scan data scanned by the wall-mounted crawling robot. Specifically, environmental sensors can be installed at the key positions (such as the mid-span, supports, beam ends, etc.) of the large-span and out-of-limit beam structure to be measured, including: air volume sensor, temperature sensor, humidity sensor, and vibration sensor. The air volume sensor is used to measure the wind speed, such as an ultrasonic anemometer. The temperature sensor is used to measure the environmental temperature, such as a thermocouple or a thermistor. The humidity sensor is used to measure the air humidity, such as a capacitive humidity sensor. The vibration sensor is used to measure the structural vibration, such as an accelerometer. The sensors collect data in real time and transmit it to the server by wired or wireless means. Conduct a field survey of the structure to be measured in advance and record the surface material information. Use a portable detection device (such as a spectrometer) to analyze the composition of the surface material. Record the material information (such as concrete, steel, composite materials, etc.) in the structure information table. First, determine the model of the robot used. Simulate the working environment of the wall-mounted crawling robot in the laboratory and test its performance under different wind speed, temperature, humidity, and vibration conditions. Record the range of environmental parameters for the normal operation of the robot (such as wind speed 0 - 10 m / s, temperature -10°C - 50°C, humidity 20% - 90%, etc.). Test the adsorption ability of the robot to different materials (such as concrete, steel, glass, etc.) and record the material types for normal operation. Organize the above data into a preset environmental table and establish a corresponding relationship with the model of the robot for subsequent use. First, obtain the environmental data corresponding to the large-span and out-of-limit beam structure to be measured, and then extract the current air volume, temperature, humidity, and vibration values from the environmental data. First, check whether the current environmental data is within the range of the preset environmental table and check whether the material information is consistent with the preset material information. At this time, the preset material information is the material information that the robot can normally adsorb. For example, the preset environmental table stipulates that the environmental conditions for the normal operation of the robot are: wind speed ≤ 8 m / s, temperature 5°C - 40°C, humidity 30% - 80%, vibration ≤ 0.5g, and the normal adsorption material is concrete. The currently collected environmental data is: wind speed 6 m / s, temperature 25°C, humidity 70%, vibration 0.3g, and the material information is concrete. Determine that the current environmental data exists in the preset environmental table and the material information is consistent with the normal adsorption material information, so it is determined that the robot can be adapted to perform a secondary scan on the large-span and out-of-limit beam structure to be measured.When both the environmental data and the material information meet the preset conditions, a start command is sent to the wall-mounted crawling robot. First, an inspection route is planned based on multiple second positions, and then the inspection route is sent to the robot so that the robot can move to the second positions according to the inspection route and scan the second positions. The robot uses devices such as ultrasonic guided wave sensors and laser scanners to scan the structural surface and collect target scan data such as vibration signals, acceleration signals, temperature data, humidity data, and propagation speed. The robot transmits the collected data to the server wirelessly. Through the above steps, it is possible to ensure that the wall-mounted crawling robot works under safe and suitable environmental conditions, avoiding robot failures or inaccurate data caused by environmental factors or material mismatches.

[0053] Furthermore, after determining that the environmental data and material information of the large-span and ultra-limit beam structure to be measured match the normal working environment of the robot, the wall-mounted crawling robot is used to collect data from the large-span and ultra-limit beam structure to be measured. First, it is necessary to determine that the wall-mounted crawling robot has been deployed on the large-span and ultra-limit beam structure to be measured and the ultrasonic guided wave scanning device is configured. According to multiple second positions, a scanning path is planned for the robot to ensure that the robot can cover all second positions. The robot moves along the planned scanning path and performs ultrasonic guided wave scanning at each second position to collect target scan data such as vibration signals, acceleration signals, temperature data, humidity data, and propagation speed. The robot transmits the collected target scan data to the server wirelessly.

[0054] S105: Input the target scan data and the first monitoring data into the digital twin model to obtain the first analysis result.

[0055] In the above S105, after receiving the first monitoring data and the target scan data, the target scan data and the first monitoring data need to be input into the digital twin model to obtain the first analysis result. Before inputting the target scan data and the first monitoring data into the digital twin model to obtain the first analysis result, it is necessary to first construct the digital twin model. Since the digital twin model monitors the health status of the long-span and over-limit beam structure, it then outputs whether the current long-span and over-limit beam structure is in a healthy state or an abnormal state. Based on the mechanical properties, material properties, etc. of the long-span and over-limit beam structure, establish its physical model. This includes determining parameters such as the stiffness, mass, and damping of the structure, as well as the response law of the structure under different loadings. Using 3D scanning technology, establish the geometric model of the long-span and over-limit beam structure. The geometric model should accurately reflect the shape, size, connection relationship, etc. of the structure, providing a basis for subsequent finite element analysis or mesh generation. Through technologies such as laser scanning and photogrammetry, obtain information such as the geometric shape and surface defects of the long-span and over-limit beam structure. These data can be used to verify the accuracy of the geometric model or as supplementary information for structural health monitoring. Through sensors installed on the long-span and over-limit beam structure, collect parameters such as the displacement, strain, and temperature of the structure in real time. These data are the main inputs of the digital twin model and are used to reflect the real-time state of the structure. Process the collected data through cleaning, denoising, interpolation, etc. to improve the quality and usability of the data. According to the characteristics and requirements of the long-span and over-limit beam structure, select an appropriate modeling method. Common modeling methods include finite element analysis (FEA), discrete element analysis (DEM), computational fluid dynamics (CFD), etc. Based on the geometric model, perform mesh generation and element type selection to construct a finite element model. The finite element model should be able to accurately reflect the mechanical properties and response law of the structure. Associate the parameters (such as stiffness, mass, damping, etc.) in the physical model with the element attributes in the finite element model to achieve parametric modeling. In this way, when the physical parameters change, the finite element model can be automatically updated to reflect the latest state of the structure. Then integrate the first monitoring data into the finite element model as the input or boundary condition of the model. In this way, the finite element model can simulate the real-time response and state changes of the structure according to the real-time monitoring data. After obtaining the analysis result from the finite element model, compare the analysis result with the actual monitoring data to verify the accuracy of the finite element model. If it is determined that there is an error between the analysis result and the actual monitoring data, it is necessary to optimize the model to obtain an optimized digital twin model. Then input the target scan data and the first monitoring data into the optimized digital twin model to obtain the first analysis result. At this time, the first analysis result refers to the health status assessment of the beam structure, such as showing that there is a large stress concentration, structural deformation, component corrosion, damage, and crack conditions in a certain part.

[0056] S106: Determine that the large-span and over-limit beam structure to be measured has first deformation information according to the first analysis result, and display the first deformation information to the target user.

[0057] In the above S106, according to the first analysis result, it is judged whether there is the first deformation information in the large-span and over-limit beam structure to be measured, and the characteristics such as the degree and position of the deformation are determined. Then, the obtained first deformation information is displayed to the target user. When displaying the first deformation information to the target user, the first deformation information can be visualized and the deformation information can be intuitively displayed to the target user through an AR device, so as to improve the efficiency of structural monitoring and evaluation. Specifically, it includes: obtaining multiple key points from the large-span and over-limit beam structure to be measured, and mapping the multiple key points into a virtual environment to obtain the first virtual image; determining the risk positions from the first deformation information, and mapping the risk positions in the first virtual image to obtain the second virtual image; sending the second virtual image to the target AR device so that the target user can view the second virtual image corresponding to the risk position by wearing the target AR device. Specifically, according to the design drawings and actual structural characteristics of the large-span and over-limit beam structure, representative structural feature points are determined, such as the support positions, mid-span positions, variable cross-section positions of the beam body, etc. Measuring instruments such as total stations and levels can also be used, and professional surveyors conduct on-site measurements on the key points to obtain their spatial coordinates. Select a suitable virtual environment construction platform according to needs, such as Unity3D, etc. These platforms provide rich tools and resources and can conveniently create virtual scenes. According to the design drawings of the large-span and over-limit beam structure, use 3D modeling software (such as 3ds Max, Maya) to create a 3D model of the structure. Import the created 3D model into the virtual environment construction platform and build it according to the position and posture of the actual structure to construct a virtual large-span and over-limit beam structure scene. Convert the actual spatial coordinates of the key points into coordinates in the virtual environment. This requires conversion according to the scale relationship and coordinate system between the virtual environment and the actual structure. In the virtual environment, use markers (such as small balls, highlighted lines, etc.) to display the key points at the corresponding positions. For example, place red small balls at positions such as the center point of the main tower foundation and the mid-span point of the main beam for marking. Preprocess the first deformation information (such as displacement, strain and other data), including data cleaning (removing noise and outliers), data smoothing (using filtering algorithms), etc. For example, use the moving average filtering algorithm to smooth the displacement data. Calculate the deformation amount of the structure according to the deformation information, such as the deflection of the beam body, the inclination of the main tower, etc. For example, by calculating the displacement data of multiple monitoring points on the beam body, obtain the deflection curve of the beam body. If it is determined that the deflection of the beam body is abnormal in the first deformation information, it is determined that there is a risk in the beam body, and then the corresponding position of the beam body is determined as the risk position. According to the actual spatial coordinates of the risk position, convert them into coordinates in the virtual environment. This requires using the same coordinate conversion method as the mapping of the key points. In the first virtual image, use eye-catching markers (such as flashing red light points, highlighted red areas, etc.) to display the risk positions at the corresponding positions. For example, place a flashing red light point at the risk position and display the risk level information of this position.The virtual image after mapping the risk location is used as the second virtual image. The dynamic display of the risk location can be achieved by updating the marker information in the virtual environment in real time. Select appropriate AR devices according to the application scenarios and requirements, such as AR glasses, AR helmets, etc. These devices have functions such as displaying virtual information and tracking the user's head movement. Ensure that the format and transmission method of the second virtual image are compatible with the target AR device. Select an appropriate data transmission protocol, such as Wi-Fi, Bluetooth, 4G / 5G, etc., to send the second virtual image to the target AR device. For example, the second virtual image is transmitted in real time to the AR glasses through the Wi-Fi network. To improve the transmission efficiency, data compression can be performed on the second virtual image. After receiving the second virtual image, the target AR device uses the built-in rendering engine of the device to render the image and superimpose it on the user's actual field of view. Provide interactive functions for the user, such as zooming, rotating, clicking to view detailed information, etc., so that the user can better view and analyze the risk location.

[0058] For example, the second virtual image is sent to a pair of AR glasses through the Wi-Fi network. After receiving the image, the AR glasses perform rendering and superimpose the information of the risk location on the user's actual field of view in the form of virtual light points and text prompts. The user can view the risk information at different positions by turning the head and view the specific risk level and deformation data of that position by clicking on the virtual light point.

[0059] Furthermore, compare the environmental data and material information of the large-span and over-limit beam structure to be measured with the parameters of the normal working environment of the robot. When the environmental data and material information of the large-span and over-limit beam structure to be measured do not match the parameters of the normal working environment of the robot, trigger the subsequent UAV-assisted detection process, that is, use the UAV to collect data on the large-span and over-limit beam structure to be measured, and combine and analyze the data collected by the UAV with the data collected by the sensor, and then discover the structural deformation information, specifically including: when the environmental data does not exist in the preset environment table and the material information is inconsistent with the preset material information, determine that the wall-mounted crawling robot and the large-span and over-limit beam structure to be measured are in a mismatched state; receive the first image according to the mismatched state, and the first image is an image obtained by the target UAV carrying the target camera to photograph the large-span and over-limit beam structure to be measured; extract the second monitoring data from the first image, and the second monitoring data includes spectral feature information, spatial geometric information, vibration signals, and motion trajectory information; input the second monitoring data and the first monitoring data into the digital twin model to obtain the second analysis result; determine that the large-span and over-limit beam structure to be measured has the second deformation information according to the second analysis result, and display the second deformation information to the target user.

[0060] Specifically, first, environmental sensors deployed on the large-span and over-limit beam structure to be measured are used to collect environmental data in real time. The real-time environmental data is compared with the ranges in the environmental table. If any item exceeds the range, it is determined to be mismatched. Then, a portable material detection device (such as a spectral analyzer, hardness tester) is used to detect the surface material of the structure, and information such as the composition, hardness, and surface roughness of the material is obtained. Then, the material information detected on-site is compared with the preset material information. If they are inconsistent, it is determined to be mismatched. If any one of the environmental data or material information is mismatched, it is determined that the wall-mounted crawling robot is in a mismatched state with the structure to be measured. The mismatched state means that the wall-mounted crawling robot cannot work properly due to environmental or material mismatch. According to the mismatched state, a drone is selected to collect data on the large-span and over-limit beam structure to be measured. The drone is equipped with a high-precision camera to comprehensively photograph the beam structure, making up for the limitations of robot monitoring and ensuring that the monitoring task is not restricted by a single device. The target drone is equipped with a high-resolution camera (such as a multispectral camera, lidar, etc.). When the target drone scans the large-span and over-limit beam structure to be measured, it is necessary to first plan the first inspection route of the target drone so that the beam structure can be scanned according to the first inspection route subsequently. The construction of the first inspection route is as follows: obtain the initial position corresponding to the target drone; divide the large-span and over-limit beam structure to be measured into multiple sub-regions, and obtain multiple third positions corresponding to the multiple sub-regions, with one sub-region corresponding to one third position; obtain multiple target distances, where the multiple target distances are the distances between the initial position and each third position; sort the multiple target distances from smallest to largest to obtain the target sorting result; perform path planning on the multiple sub-regions according to the target sorting result to obtain the first inspection route, and send the first inspection route to the target drone so that the target drone can photograph the large-span and over-limit beam structure to be measured according to the first inspection route. Specifically, the target drone is usually equipped with a GPS module, through which the longitude and latitude coordinate information of the drone, that is, the initial position, can be obtained in real time. The obtained initial position of the drone is sent to the server. Then, a detailed structural analysis of the large-span and over-limit beam structure to be measured is carried out. According to factors such as its geometric shape, structural characteristics, and key parts, the entire structure is divided into multiple sub-regions. A unique identifier is assigned to each sub-region, and the central position or representative position of the sub-region is determined as the third position. The third position can be obtained through measurement, design drawings, or estimated according to the actual situation. The identifier of each sub-region and the corresponding third position information are recorded in a database or data file for subsequent use. According to the initial position of the drone and the third positions of each sub-region, a suitable distance calculation algorithm is used to calculate the distance between them. Commonly used algorithms include the Euclidean distance formula (applicable to plane coordinates) or the great circle distance formula (applicable to longitude and latitude coordinates on the earth's surface).Store the calculated multiple target distances, and then use a suitable sorting algorithm (such as quicksort, mergesort, etc.) to sort the calculated multiple target distances from smallest to largest. According to the target sorting result, use a path planning algorithm (such as greedy algorithm, genetic algorithm, ant colony algorithm, etc.) to connect multiple sub-regions to form the first inspection route. The optimal criterion can be the shortest path, the shortest time, the least energy consumption, etc., which is determined according to actual requirements. Send the generated first inspection route to the target UAV through wireless communication technology. After receiving the first inspection route data, the target UAV automatically flies according to the preset flight control program and uses the equipped shooting device to shoot the large-span and over-limit beam structure to be measured. The target UAV flies along the first inspection route and captures the image data of the beam structure to be measured in real time. During the shooting process, it is necessary to ensure that the image coverage is comprehensive and unobstructed, and record metadata such as the shooting time and location. The UAV transmits the collected first image data to the server through wireless communication technology. After obtaining the first image, extract the second monitoring data from the first image. The second monitoring data includes spectral feature information, spatial geometric information, vibration signals, and motion trajectory information. Use a multi-spectral analysis algorithm to extract spectral features (such as reflectivity, absorptivity) in different bands from the first image for analyzing the aging and corrosion conditions of the structure surface material. Use 3D reconstruction technology (such as structured light scanning, stereo vision) to extract the spatial geometric information (such as dimensions, shapes, curvatures) of the structure to be measured from the image. Through image processing technology (such as optical flow method, phase correlation method), analyze the micro-vibrations on the structure surface in the image and extract parameters such as vibration frequency and amplitude. Perform target tracking on the consecutive images captured by the UAV to extract the motion trajectories of specific points or regions on the structure to be measured for analyzing the dynamic deformation of the structure. Then input the first monitoring data and the second monitoring data into the digital twin model to generate the second analysis result. Since the digital twin model has been constructed in advance, the digital twin model is a virtual model constructed based on the physical characteristics, material properties, historical monitoring data, etc. of the structure to be measured. Input the second monitoring data (spectral features, spatial geometry, vibration signals, motion trajectories) and the first monitoring data (such as data collected by target sensors) into the digital twin model. The digital twin model simulates the current state of the structure according to the input data, and then determines the health state of the beam structure, that is, the second analysis result. The second analysis result includes structural stress distribution, deformation trend, and fatigue damage, etc. Then extract the second deformation information (such as deformation amount, deformation position, and deformation rate) of the beam structure to be measured from the second analysis result. Then use 3D visualization technology to overlay the deformation information on the digital model of the structure to be measured to generate an intuitive visualization report. Then display the visualization report to the target user through the user interface.

[0061] For example, the target distance corresponding to sub-region 1 and the initial position is 0.3 km, the target distance corresponding to sub-region 2 and the initial position is 0.54 km, the target distance corresponding to sub-region 3 and the initial position is 0.4 km, and the target distance corresponding to sub-region 4 and the initial position is 0.7 km. Sort the above four target distances, and the sorting result is 0.3 km, 0.4 km, 0.54 km, and 0.7 km. Then, use the greedy algorithm for path planning. According to the target sorting result, connect sub-region 1, sub-region 3, sub-region 2, and sub-region 4 in sequence to form the first inspection route.

[0062] In a possible implementation, when comprehensive analysis is performed on the data collected by the sensor and the UAV, and it is obtained that there is second deformation information in the large-span and ultra-limit beam structure to be measured, it is necessary to determine the position of the second deformation information in the large-span and ultra-limit beam structure to be measured. When subsequent deformation monitoring is carried out on the large-span and ultra-limit beam structure to be measured, the inspection route of the UAV is adjusted, that is, the deformed position is preferentially inspected in order to timely identify the current health state corresponding to the deformed position. Specifically, at preset time intervals, abnormal positions are obtained from the large-span and ultra-limit beam structure to be measured, and the abnormal positions are the positions corresponding to the second deformation information in the large-span and ultra-limit beam structure to be measured; a second inspection route is generated according to the abnormal positions, and the second inspection route is an inspection route starting from the abnormal positions; the second inspection route is sent to the target UAV so that the target UAV can take pictures of the abnormal positions according to the second inspection route to obtain a second image. Specifically, the preset time refers to the interval duration of the data acquisition frequency of the beam structure, and the interval duration can be set to 24 hours, 1 week, 15 days, etc., and the interval duration can be set based on the actual situation. After it is determined in advance that there is second deformation information in the ultra-limit beam structure to be measured, the deformed component or area corresponding to the second deformation information is determined, that is, it is determined that there is an initial crack with a width of 2 mm on the bottom surface of the large-span and ultra-limit beam structure to be measured. At this time, the second deformation information refers to the initial crack with a width of 2 mm. Then, the specific position of the second deformation information in the large-span and ultra-limit beam structure to be measured is determined, and at this time, the abnormal position is used to represent it. For example, the abnormal position refers to the bottom surface of the beam structure. First, the abnormal positions are obtained, and a suitable path planning algorithm can be selected according to the number, distribution of the abnormal positions and the performance parameters of the target UAV. The target UAV first starts from the current position, and then uses the abnormal position as the starting position. At this time, the starting position refers to the position that the target UAV arrives at next after departure and is called the starting position, until all positions in the beam structure are traversed to obtain the second inspection route. The second inspection route can be understood as arranging the abnormal positions at the forefront of the inspection route and preferentially inspecting the abnormal positions. For example, the first inspection route is A - B - C - D - E - F. After data collection is performed on the beam structure according to the first inspection route, the collected data is analyzed, and it is obtained that there is an initial crack with a width of 2 mm on the bottom surface of C. Therefore, C is determined to be the abnormal position. Then, C is preferentially set before the inspection route, that is, adjusted to C - A - B - D - E - F. At this time, the second inspection route is C - A - B - D - E - F. Then, the second inspection route is sent to the target UAV so that the target UAV can fly automatically according to the second inspection route. The target UAV adjusts its own position, height and speed in real time according to the coordinate sequence and flight parameters in the second inspection route. When the target UAV reaches each position (abnormal position and normal position) in the second inspection route, pictures are taken of each position using the mounted photographing equipment (such as a camera, a video camera). Thus, a second image is obtained.Then, the second image is transmitted back to the server through the network. The target UAV reaches the corresponding position according to the second inspection route and completes the corresponding shooting task, finally obtaining the second images at each position. If the data acquisition frequency is set relatively frequently, that is, the deformation information of the large-span and over-limit beam structure to be measured at different time points is monitored, the deformation information of the beam structure at different time points can be obtained first. When abnormalities appear in the deformation information, the abnormal positions of the large-span and over-limit beam structure to be measured at different time points can be obtained one by one.

[0063] In a possible implementation, continuously monitor the same abnormal position, compare the values obtained from continuous monitoring, determine the risk level based on the comparison result, then formulate a treatment measure based on the risk level, and promptly notify the target user to carry out maintenance work to ensure the safe operation of the beam structure: Process the second image to obtain a first deformation value, where the first deformation value is the deformation value corresponding to the abnormal position in the second image; Obtain the second deformation value corresponding to the abnormal position from the second deformation information; Determine whether the first deformation value is less than or equal to the second deformation value; When the first deformation value is greater than the second deformation value, mark the abnormal position as high risk, select a treatment measure according to the high risk, and send the treatment measure and the high risk to the target user so that the target user can repair the abnormal position according to the treatment measure. Specifically, specifically, an image filtering algorithm (such as Gaussian filtering, median filtering) can be used to remove the noise in the second image and improve the image quality. Enhance the contrast and details of the image through image enhancement techniques (such as histogram equalization, contrast stretching) to make the deformation characteristics of the abnormal position more obvious. Use an edge detection algorithm (such as Canny edge detection) to extract the edge information of the abnormal position in the image. Taking the crack of the bridge beam as an example, edge detection can accurately locate the contour of the crack. Analyze the shape characteristics of the extracted edge information, such as calculating parameters such as the length, width, and curvature of the crack. For example, by calculating the length and width of the crack, the severity of the crack can be initially judged. Establish the conversion relationship between the image coordinate system and the actual physical coordinate system, and calibrate through a calibration object with a known size (such as a calibration plate). Then, according to the calibration result and the extracted characteristic parameters, calculate the actual deformation value of the abnormal position, that is, the first deformation value. Obtain the second deformation value corresponding to the abnormal position from the second deformation information, where the second deformation information is the result of the most recent deformation monitoring of the beam structure, and determine the second deformation value corresponding to the abnormal position in the most recent judgment, that is, the value corresponding to the deformation of the beam structure detected in the most recent monitoring. Compare the first deformation value with the second deformation value, and output the corresponding judgment information according to the comparison result. If the first deformation value is less than or equal to the second deformation value, it is determined that the deformation situation corresponding to the abnormal position has not changed, and the abnormal position needs to be continuously monitored. When the first deformation value is greater than the second deformation value and the first deformation value exceeds the high-risk threshold, mark the abnormal position as high risk. The high-risk threshold is set according to the design parameters, historical data, and relevant specifications of the beam structure. Then formulate corresponding treatment measures according to the high risk, and corresponding treatment measures can also be matched from the treatment measures according to the risk level of the abnormal position. Select a suitable communication method to send the treatment measure and the high-risk information according to the communication method of the target user (such as mobile phone text message, email, mobile application push, etc.). For example, if the beam structure is a bridge, for a high-risk abnormal position, the treatment measures can include immediately stopping traffic, conducting on-site investigation, formulating a repair plan, etc.Corresponding processing measures can be selected according to the actual application scenario of the beam structure. Here is just an example and no specific limitation is made.

[0064] An embodiment of the present application further provides a monitoring device for the deformation of a long-span over-limit beam structure. Figure 2 It is a schematic structural diagram of a monitoring device for the deformation of a long-span over-limit beam structure provided by an embodiment of the present application. Refer to Figure 2 The device includes a receiving unit 201, a processing unit 202, and a transmitting unit 203.

[0065] The receiving unit 201 receives first monitoring data. The first monitoring data is obtained by using a target sensor to collect data from the to-be-measured long-span over-limit beam structure. The target sensor is a sensor pre-deployed in the to-be-measured long-span over-limit beam structure.

[0066] The processing unit 202 determines a first position according to the first monitoring data. The first position is the position of the target sensor in the to-be-measured long-span over-limit beam structure; the first position is removed from the to-be-measured long-span over-limit beam structure to obtain a plurality of second positions. The second positions are the positions in the to-be-measured long-span over-limit beam structure except the first position; the target scan data is received. The target scan data is the data obtained by the wall-mounted crawling robot going to the second position for ultrasonic guided wave scanning. The target scan data includes vibration signals, acceleration signals, temperature data, humidity data, and propagation speed; the target scan data and the first monitoring data are input into the digital twin model to obtain a first analysis result.

[0067] The transmitting unit 203 determines that there is first deformation information in the to-be-measured long-span over-limit beam structure according to the first analysis result, and displays the first deformation information to the target user.

[0068] In a possible implementation manner, the receiving unit 201 is used to obtain environmental data and material information. The environmental data is the environmental value corresponding to the to-be-measured long-span over-limit beam structure. The environmental value includes air volume value, temperature value, humidity value, and vibration value. The material information is the information of the material used on the surface of the to-be-measured long-span over-limit beam structure; the processing unit 202 is used to judge whether there is environmental data in the preset environmental table and whether the material information is consistent with the preset material information. The preset environmental table is the information summarized for the normal working environment of the wall-mounted crawling robot. The preset material information is the material information that the wall-mounted crawling robot can normally adsorb; the receiving unit 201 is used to determine to receive the target scan data scanned by the wall-mounted crawling robot when there is environmental data in the preset environmental table and the material information is consistent with the preset material information.

[0069] In a possible implementation, the processing unit 202 is configured to determine that the wall-mounted crawling robot is in a mismatched state with the large-span and over-limit beam structure to be measured when there is no environmental data in the preset environment table and the material information is inconsistent with the preset material information; the receiving unit 201 is configured to receive a first image according to the mismatched state, where the first image is an image obtained by the target unmanned aerial vehicle carrying the target camera to photograph the large-span and over-limit beam structure to be measured; the processing unit 202 is configured to extract second monitoring data from the first image, where the second monitoring data includes spectral feature information, spatial geometric information, vibration signals, and motion trajectory information; input the second monitoring data and the first monitoring data into the digital twin model to obtain a second analysis result; the sending unit 203 is configured to determine that there is second deformation information in the large-span and over-limit beam structure to be measured according to the second analysis result, and display the second deformation information to the target user.

[0070] In a possible implementation, the receiving unit 201 is configured to obtain the initial position corresponding to the target unmanned aerial vehicle; the processing unit 202 is configured to divide the large-span and over-limit beam structure to be measured into multiple sub-regions, and obtain multiple third positions corresponding to the multiple sub-regions, where one sub-region corresponds to one third position; the receiving unit 201 is configured to obtain multiple target distances, where the multiple target distances are the distances between the initial position and each of the third positions; the processing unit 202 is configured to sort the multiple target distances from smallest to largest to obtain a target sorting result; the sending unit 203 is configured to perform path planning on the multiple sub-regions according to the target sorting result to obtain a first inspection route, and send the first inspection route to the target unmanned aerial vehicle, so that the target unmanned aerial vehicle photographs the large-span and over-limit beam structure to be measured according to the first inspection route.

[0071] In a possible implementation, the receiving unit 201 is configured to obtain an abnormal position from the large-span and over-limit beam structure to be measured at preset time intervals, where the abnormal position is the position corresponding to the second deformation information in the large-span and over-limit beam structure to be measured; the processing unit 202 is configured to generate a second inspection route according to the abnormal position, where the second inspection route is an inspection route starting from the abnormal position; the sending unit 203 is configured to send the second inspection route to the target unmanned aerial vehicle, so that the target unmanned aerial vehicle photographs the abnormal position according to the second inspection route to obtain a second image.

[0072] In a possible implementation, the processing unit 202 is configured to process the second image to obtain a first deformation value, where the first deformation value is the deformation value corresponding to the abnormal position in the second image; the receiving unit is configured to obtain the second deformation value corresponding to the abnormal position from the second deformation information; the processing unit 201 is configured to determine whether the first deformation value is less than or equal to the second deformation value; the sending unit 203 is configured to, when the first deformation value is greater than the second deformation value, determine that the abnormal position is marked as a high risk, select a processing measure according to the high risk, and send the processing measure and the high risk to the target user, so that the target user can repair the abnormal position according to the processing measure.

[0073] In a possible implementation, the receiving unit 201 is configured to obtain a plurality of key points from the large-span and over-limit beam structure to be measured, map the plurality of key points into a virtual environment to obtain a first virtual screen; the processing unit 202 is configured to determine a risk position from the first deformation information, map the risk position in the first virtual screen to obtain a second virtual screen; the sending unit 203 is configured to send the second virtual screen to the target AR device, so that the target user can view the second virtual screen corresponding to the risk position by wearing the target AR device.

[0074] It should be noted that: when the device provided in the above embodiment implements its functions, only the above-mentioned division of each functional module is used for illustration. In actual application, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process can be seen in the method embodiment, which will not be elaborated here.

[0075] This application also discloses an electronic device. Refer to Figure 3 , Figure 3 FIG. is a schematic structural diagram of an electronic device provided by an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 302, and at least one communication bus 305.

[0076] Among them, the communication bus 305 is used to realize the connection and communication between these components.

[0077] Among them, the user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may further include a standard wired interface and a wireless interface.

[0078] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0079] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire server through various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 302, and by invoking the data stored in the memory 302, it executes various functions of the server and processes data. Optionally, the processor 301 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 301 may integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application requests, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301 and may be implemented separately by a single chip.

[0080] Among them, the memory 302 may include random access memory (RAM) and may also include read-only memory. Optionally, the memory 302 includes a non-transitory computer-readable storage medium. The memory 302 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 302 may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store the data involved in the above-mentioned various method embodiments. Optionally, the memory 302 may also be at least one storage device located far from the aforementioned processor 301.

[0081] As Figure 3 shown, the memory 302, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for monitoring the deformation of large-span and ultra-long beams.

[0082] In Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an interface for the user to input and obtain the data input by the user; and the processor 301 can be used to call the application program stored in the memory 302 for monitoring the deformation of the large-span and over-limit beam structure. When executed by one or more processors, the electronic device is enabled to execute one or more of the methods as described in the above embodiments.

[0083] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0084] In the above embodiments, the descriptions of the respective embodiments have their own focuses. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0085] In several embodiments provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0086] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0087] In addition, in each embodiment of this application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0088] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media that can store program codes, such as USB flash drives, mobile hard disks, magnetic disks, or optical discs.

[0089] The foregoing are only exemplary embodiments of the present disclosure and should not be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. After considering the specification and the practice of the present disclosure, those skilled in the art will readily think of other implementation manners of the present disclosure. This application aims to cover any variations, uses, or adaptive changes of the present disclosure, and these variations, uses, or adaptive changes follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure.

Claims

1. A monitoring method for the deformation of a long-span and out-of-limit beam structure, characterized in that, The method includes: Receiving first monitoring data, where the first monitoring data is collected for a large-span and over-limit beam structure to be measured using a target sensor, and the target sensor is a sensor pre-deployed in the large-span and over-limit beam structure to be measured; Determining a first position according to the first monitoring data, where the first position is the position of the target sensor in the large-span and over-limit beam structure to be measured; Removing the first position from the large-span and over-limit beam structure to be measured to obtain a plurality of second positions, where the second positions are the positions in the large-span and over-limit beam structure to be measured other than the first position; Receiving target scan data, where the target scan data is data obtained by a wall-mounted crawling robot going to the second positions for ultrasonic guided wave scanning, and the target scan data includes vibration signals, acceleration signals, temperature data, humidity data, and propagation speed; before receiving the target scan data, the method further includes: obtaining environmental data and material information, where the environmental data is the environmental value corresponding to the large-span and over-limit beam structure to be measured, and the environmental value includes air volume value, temperature value, humidity value, and vibration value, and the material information is the information of the material used on the surface of the large-span and over-limit beam structure to be measured; determining whether the environmental data exists in a preset environmental table and whether the material information is consistent with preset material information, where the preset environmental table is the information summarized for the normal working environment of the wall-mounted crawling robot, and the preset material information is the material information that the wall-mounted crawling robot can normally adsorb; when the environmental data exists in the preset environmental table and the material information is consistent with the preset material information, determining to receive the target scan data scanned by the wall-mounted crawling robot; Inputting the target scan data and the first monitoring data into a digital twin model to obtain a first analysis result; Determining that the large-span and over-limit beam structure to be measured has first deformation information according to the first analysis result, and displaying the first deformation information to a target user.

2. The method according to claim 1, wherein After determining whether the environmental data exists in the preset environmental table and whether the material information is consistent with the preset material information, the method further includes: When the environmental data does not exist in the preset environmental table and the material information is inconsistent with the preset material information, determining that the wall-mounted crawling robot and the large-span and over-limit beam structure to be measured are in a mismatched state; Receiving a first image according to the mismatched state, where the first image is an image obtained by a target unmanned aerial vehicle carrying a target camera to photograph the large-span and over-limit beam structure to be measured; Extracting second monitoring data from the first image, where the second monitoring data includes spectral feature information, spatial geometric information, vibration signals, and motion trajectory information; Inputting the second monitoring data and the first monitoring data into the digital twin model to obtain a second analysis result; Determining that the large-span and over-limit beam structure to be measured has second deformation information according to the second analysis result, and displaying the second deformation information to the target user.

3. The method according to claim 2, wherein Before receiving the first image according to the mismatched state, the method further includes: Obtain the initial position corresponding to the target UAV; divide the large-span and over-limit beam structure to be measured into multiple sub-regions, and obtain multiple third positions corresponding to the multiple sub-regions, where one sub-region corresponds to one third position; Obtain multiple target distances, where the multiple target distances are the distances between the initial position and each of the third positions; Sort the multiple target distances from smallest to largest to obtain a target sorting result; perform path planning on the multiple sub-regions according to the target sorting result to obtain a first inspection route, and send the first inspection route to the target UAV so that the target UAV takes pictures of the large-span and over-limit beam structure to be measured according to the first inspection route.

4. The method according to claim 2, wherein After determining that the large-span and over-limit beam structure to be measured has second deformation information according to the second analysis result, the method further includes: At preset time intervals, obtain an abnormal position from the large-span and over-limit beam structure to be measured, where the abnormal position is the position corresponding to the second deformation information in the large-span and over-limit beam structure to be measured; generate a second inspection route according to the abnormal position, where the second inspection route is an inspection route starting from the abnormal position; Send the second inspection route to the target UAV so that the target UAV takes pictures of the abnormal position according to the second inspection route to obtain a second image.

5. The method according to claim 4, wherein After sending the second inspection route to the target UAV so that the target UAV takes pictures of the abnormal position according to the second inspection route to obtain a second image, the method further includes: Process the second image to obtain a first deformation value, where the first deformation value is the deformation value corresponding to the abnormal position in the second image; Obtain the second deformation value corresponding to the abnormal position from the second deformation information; Judge whether the first deformation value is less than or equal to the second deformation value; When the first deformation value is greater than the second deformation value, determine that the abnormal position is marked as high risk, select a treatment measure according to the high risk, and send the treatment measure and the high risk to the target user so that the target user repairs the abnormal position according to the treatment measure.

6. The method according to claim 1, characterized in that, The step of displaying the first deformation information to the target user specifically includes: Obtain multiple key points from the large-span and over-limit beam structure to be measured, and map the multiple key points into a virtual environment to obtain a first virtual picture; Determine a risk position from the first deformation information, and map the risk position in the first virtual picture to obtain a second virtual picture; Send the second virtual picture to the target AR device so that the target user can view the second virtual picture corresponding to the risk position by wearing the target AR device.

7. A monitoring device for the deformation of a long-span and out-of-limit beam structure, characterized in that, The device includes a receiving unit (201), a processing unit (202) and a sending unit (203); The receiving unit (201) receives first monitoring data, where the first monitoring data is collected by a target sensor for the large-span over-limit beam structure to be measured, and the target sensor is a sensor pre-deployed in the large-span over-limit beam structure to be measured; the processing unit (202) determines a first position according to the first monitoring data, where the first position is the position of the target sensor in the large-span over-limit beam structure to be measured; the first position is removed from the large-span over-limit beam structure to be measured to obtain a plurality of second positions, where the second positions are the positions in the large-span over-limit beam structure to be measured other than the first position; receives target scan data, where the target scan data is data obtained by a wall-mounted crawling robot going to the second positions for ultrasonic guided wave scanning, and the target scan data includes vibration signals, acceleration signals, temperature data, humidity data, and propagation speed; before receiving the target scan data, it further includes: obtaining environmental data and material information, where the environmental data is the environmental value corresponding to the large-span over-limit beam structure to be measured, and the environmental value includes air volume value, temperature value, humidity value, and vibration value, and the material information is the information of the material used on the surface of the large-span over-limit beam structure to be measured; determining whether the environmental data exists in a preset environmental table and whether the material information is consistent with preset material information, where the preset environmental table is information summarized for the normal working environment of the wall-mounted crawling robot, and the preset material information is the material information that the wall-mounted crawling robot can normally adsorb; when the environmental data exists in the preset environmental table and the material information is consistent with the preset material information, it is determined to receive the target scan data scanned by the wall-mounted crawling robot; Input the target scan data and the first monitoring data into a digital twin model to obtain a first analysis result; The sending unit (203) determines that the large-span over-limit beam structure to be measured has first deformation information according to the first analysis result, and displays the first deformation information to a target user.

8. An electronic device, characterized in that, It includes a processor (301), a memory (302), a user interface (303), and a network interface (304). The memory (302) is used to store instructions. The user interface (303) and the network interface (304) are used to communicate with other devices. The processor (301) is used to execute the instructions stored in the memory (302) so that the electronic device (300) executes the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1-6 is executed.

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