Suspension bridge main cable intelligent erection monitoring method and system

Abstract

The present application relates to the technical field of main cable erection of suspension bridge, and particularly relates to a monitoring method and system for intelligent erection of main cable of suspension bridge, which comprises the following steps: collecting surface image data of the tensioned cable strand; collecting traction tension data; pre-processing the image data, identifying the abnormal features of the cable strand shape, aligning the abnormal features of the cable strand shape with the time stamp of the tension data, and generating a monitoring result; when the monitoring result meets the preset warning condition, an alarm is sent and a control instruction for adjusting the winch is generated; the monitoring method also monitors the traction synchronization and progress of the tensioned cable strand, the linear shape of the main cable in the embryonic stage, the linear shape of the catwalk, the tension of the catwalk load-bearing cable and the safety vision of the catwalk, and alarms when abnormalities occur; the monitoring method and system for intelligent erection of main cable of suspension bridge of the present application can assist in improving the construction efficiency and quality of the main cable erection and ensuring the construction safety.

Description

Technical Field

[0001] This invention relates to the field of suspension bridge main cable erection technology, specifically to a monitoring method and system for intelligent erection of suspension bridge main cables. Background Technology

[0002] As a long-span bridge structure, the erection of the main cable is a crucial step in the entire construction process of a suspension bridge. The traction quality of the main cable strands directly affects the alignment accuracy, load-bearing capacity, and structural safety of the completed bridge. In traditional suspension bridge main cable erection, strand traction mainly relies on manual operation in conjunction with winches. Construction workers need to communicate via walkie-talkies and rely on experience to judge the traction speed, tension, and whether twisting has occurred in the strands. This method of operation suffers from high labor intensity, high communication costs, and slow response. Furthermore, in the face of sudden strong winds or equipment failures, there is a lack of effective emergency control measures, which can easily lead to excessive twisting or even breakage of the strands, seriously affecting construction safety and efficiency.

[0003] With the development of automation technology, several solutions have emerged in existing technologies aimed at improving the automation level of traction. For example, Chinese invention patent application CN113774806A discloses a traction control system and method for the main cable strands of a suspension bridge. This prior art corrects the torsional attitude of the main cable strands by setting a torsion monitoring sensor and a torsion correction mechanism on the puller, utilizing the reverse torque generated by the acceleration and deceleration of the inertial wheel; simultaneously, it uses a LoRa base station and gateway in collaboration to obtain the planar coordinates of the puller, and combines this with encoder data to determine the position of the puller. This solution achieves automated monitoring and torsion correction of the traction process to a certain extent, reducing reliance on human experience.

[0004] However, in practical engineering applications and further technological development, it was found that the aforementioned existing technology (CN113774806A) still has significant defects: ① The existing technology uses a torsion monitoring sensor to monitor the torsional attitude of the strands and provides a counter-torque when torsion occurs; however, the data acquisition method is limited to a single torsion monitoring sensor, and the reliability of the monitoring results is relatively poor. ② The existing technology mainly focuses on the torsional posture of the cable strands. It lacks effective intelligent monitoring methods for the physical state of the cable strand surface (such as wire fraying and wire breakage) and positional state (such as derailment). It cannot comprehensively assess the health status of the cable strands, has monitoring blind spots, and its state perception is not comprehensive enough.

[0005] ③ This existing technology uses LoRa radio for positioning. Although LoRa has the advantage of long-distance communication, at the construction site of a suspension bridge, there are a large number of metal structures (such as main towers, cable saddles, catwalks, etc.), which easily cause multipath effects and shielding interference to the radio signals, resulting in jumps or loss of positioning data. Especially when the traction device is close to the top of the main tower or other critical parts such as the cable saddle, the problem of LoRa signal blockage is more serious, making it difficult to meet the requirements of high-precision positioning adjustment.

[0006] ④ There is a lack of monitoring of the catwalk alignment, catwalk load-bearing cable tension, catwalk safety visibility, tension cable strand traction synchronization and progress, and preliminary main cable alignment. Summary of the Invention

[0007] This application provides a monitoring method and system for intelligent erection of main cables of suspension bridges, which provides more reliable monitoring results and more comprehensive status perception of tension cable strands, thereby helping to improve the construction efficiency and quality of main cable erection and ensuring construction safety.

[0008] In a first aspect, embodiments of this application provide a monitoring method for the intelligent erection of the main cable of a suspension bridge, comprising the following steps: Image data of the tension cable strand surface is acquired by a first camera installed on the puller; traction tension data is acquired by a first tension sensor connected in series between the puller and the end of the traction rope. The image data is preprocessed, and abnormal features of the strand shape are identified from the preprocessed image data. The abnormal features of the strand shape are then time-stamped with the tension data to generate monitoring results. When the monitoring results meet the preset early warning conditions, an alarm signal is issued and a control command to adjust the winch is generated; the preset early warning conditions include the presence of abnormal strand morphology and / or a sudden change in tension data exceeding a set value.

[0009] In conjunction with the first aspect, in one embodiment, the preprocessing of the image data includes: The image data is preprocessed using an adaptive illumination enhancement algorithm; The process of identifying abnormal features of strand patterns from preprocessed image data includes: The abnormal features of the strand pattern are identified from the preprocessed image data using a deep learning semantic segmentation model; the abnormal features of the strand pattern include loose strand features, derailment risk features, and breakage features.

[0010] In conjunction with the first aspect, in one implementation, the step of identifying abnormal features of strand patterns from preprocessed image data using a deep learning semantic segmentation model includes: Identify the proportion of the area segmented by loose strands at the edge of the strand. If the proportion of pixels in the loose strand area to pixels in the cross section of the strand exceeds a set proportion, it is determined to be a loose strand feature. Image data of the time when passing the roller is selected from the preprocessed image data, and the pixel distance between the center line of the cable strand and the center line of the roller is identified. If the distance exceeds the set distance, the derailment feature is determined. The cross-section of the steel wire in the cable strand was identified and determined to be a fracture feature.

[0011] In conjunction with the first aspect, in one implementation, it further includes monitoring of catwalks, which includes: By deploying GNSS monitoring terminals at several key characteristic cross-sections of the catwalk's load-bearing cable, the catwalk alignment is automatically monitored in real time. At the same time, prisms are also set up at the same locations to further verify the catwalk alignment with a total station. Tension data at various points on the load-bearing cable is acquired using several second tension sensors bonded to the cable; image data of the catwalk and construction workers is acquired using several second cameras installed on the top of the catwalk gantry. The acquired data is analyzed, and if at least one of the following is found: abnormal catwalk alignment, abnormal catwalk load-bearing cable tension, or abnormal catwalk safety vision, an alarm is triggered accordingly. The abnormal catwalk safety vision includes abnormal catwalk structure loosening and unsafe behavior of construction personnel. The acquired data includes images from the second camera, catwalk load-bearing cable tension, and data from the total station and GNSS monitoring terminal.

[0012] In conjunction with the first aspect, in one implementation, it further includes monitoring the traction progress of the tension cable strands, which includes: Multiple winches are controlled synchronously through an integrated winch control platform. Combined with speed sensors installed on the corresponding traction ropes of the multiple winches, the puller is controlled to retract and extend synchronously on both sides. When the puller is pulled, RFID cards with unique identification information are deployed at several key points along the tension cable strand traction path, and card readers are installed on the puller to obtain the RFID card information that the card readers automatically read during the traction process and visualize it on the remote monitoring equipment.

[0013] In conjunction with the first aspect, in one implementation, it further includes monitoring of the prototype main cable profile, which includes: By deploying GNSS monitoring terminals at several key stress and alignment control points along the prototype main cable, the alignment of the prototype main cable was monitored in real time. At the same time, prisms were also set up in the same locations to further verify the alignment with a total station. If an abnormality is detected in the shape of the initial main cable, an alarm will be triggered accordingly.

[0014] In conjunction with the first aspect, in one embodiment, issuing the alarm signal and generating the control command to adjust the winch includes: The system issues audible and visual warnings and generates control commands to adjust the speed of the winch or stop it.

[0015] Secondly, this application provides a monitoring system for the intelligent erection of the main cable of a suspension bridge, including: a cable strand following monitoring subsystem, which includes a first camera with its optical axis aligned with the tensioned cable strand, and a first tension sensor connected in series between the puller and the end of the traction rope. The first camera and the first tension sensor are both integrated into the puller. The first camera is used to collect image data of the surface of the tensioned cable strand in real time, and the first tension sensor is used to collect traction tension data in real time. An edge computing device is used to receive the image data and the tension data, and also to preprocess the image data, identify abnormal features of the strand shape from the preprocessed image data, align the abnormal features of the strand shape with the tension data by timestamp, and generate monitoring results. An early warning execution device is used to issue an alarm signal and generate a control command to adjust the winch when the monitoring results meet preset early warning conditions; the preset early warning conditions include the presence of abnormal strand morphology and / or a sudden change in tension data exceeding a set value.

[0016] In conjunction with the second aspect, in one embodiment, the edge computing device incorporates an adaptive illumination enhancement algorithm and a deep learning semantic segmentation model; the adaptive illumination enhancement algorithm is used to preprocess the image data; the deep learning semantic segmentation model is used to identify the loose strand features, derailment features, and breakage features of the abnormal strand morphology from the preprocessed image data. The intelligent installation system also includes a catwalk monitoring subsystem, which includes: Several second tension sensors are attached to the load-bearing cable to acquire tension data at various points on the load-bearing cable; Several second cameras are installed on the top of the catwalk gantry to acquire image data of the catwalk and construction workers; Several GNSS monitoring terminals were deployed at several key characteristic sections of the catenary support cable. Several prisms were set up at the same locations as the GNSS monitoring terminals, and together with several total stations set up at known control points on both sides of the bridge or the top of the tower, the catwalk alignment monitored by several GNSS monitoring terminals was further precisely verified. The edge computing device analyzes the acquired data. If it detects abnormalities in the catwalk alignment, load-bearing cable tension, or catwalk safety visuals, the early warning execution device will issue an alarm accordingly. The catwalk safety visuals include abnormalities in the catwalk structure or unsafe behaviors of construction personnel. The acquired data includes images from the second camera, catwalk load-bearing cable tension, and data from the total station and GNSS monitoring terminal.

[0017] In conjunction with the second aspect, in one embodiment, the intelligent erection system further includes a tension cable strand traction synchronization and progress monitoring subsystem and a preliminary main cable alignment monitoring subsystem; The tension cable strand traction synchronization and progress monitoring subsystem includes an integrated winch control platform, several RFID cards, and a card reader. The integrated winch control platform synchronously controls multiple winches and, in conjunction with speed sensors installed on the corresponding traction ropes of the multiple winches, controls the synchronous winding and unwinding of both sides of the puller. Several RFID cards with unique identification information are deployed at several key points along the tension cable strand traction path. The card reader is installed on the puller, and when the puller is being pulled, the card reader automatically reads the RFID card information passing through during the traction process and displays it on the remote monitoring equipment. The prototype main cable alignment monitoring subsystem includes several GNSS monitoring terminals, several prisms, and several total stations. The GNSS monitoring terminals are deployed at several key stress and alignment control positions of the prototype main cable to monitor its alignment in real time. The prisms are deployed at the same positions as the GNSS monitoring terminals and, in conjunction with several total stations erected at known control points on both banks of the bridge or at the top of the towers, further verify the prototype main cable alignment monitored by the GNSS monitoring terminals. The edge computing device communicates with and acquires data from the total stations and GNSS monitoring terminals, then analyzes and obtains the prototype main cable alignment. When the prototype main cable alignment is abnormal, the early warning execution device issues an alarm.

[0018] The beneficial effects of the technical solutions provided in this application include: 1. The monitoring method for intelligent erection of main cable of suspension bridge in this application embodiment has more reliable monitoring results and more comprehensive state perception of tensioned cable strands. Compared with the prior art, which only focuses on the torsional posture of cable strands and ignores surface defects, this application uses a first camera to collect images of cable strand surface at close range, and then identifies abnormal features of cable strand shape, filling the gap in monitoring the physical state of cable strand surface and realizing more comprehensive state perception. Compared to the single data acquisition method of existing technologies, this application utilizes the first camera of the puller to acquire close-up surface images of the tensioned cable strands, and also collects traction tension data of the tensioned cable strands through the first tension sensor. It aligns the abnormal characteristics of the cable strand morphology with the tension data using timestamps, generating a more reliable monitoring result that combines appearance and internal stress. This achieves comprehensive perception of both appearance and internal stress. Through a multimodal monitoring mechanism, more reliable anomaly judgments are obtained, effectively solving the problem that a single sensor cannot comprehensively assess the health status of the cable strands, significantly improving monitoring accuracy and achieving more comprehensive status monitoring. Furthermore, when there are abnormal characteristics of the cable strand morphology and / or a sudden change in tension data exceeding a set value, an alarm is triggered, and the winch slows down or stops, greatly improving the safety of main cable erection. The monitoring method of this application helps improve the construction efficiency and quality of main cable erection, ensuring construction safety.

[0019] 2. The intelligent suspension bridge main cable erection monitoring method of this application embodiment demonstrates good environmental adaptability in the image preprocessing stage. It utilizes an adaptive illumination enhancement algorithm to automatically adjust image parameters based on the ambient light (backlight, nighttime, shadows), and performs noise reduction, defogging, and brightness adjustment on the original image data. This eliminates interference from dust, water mist, and uneven lighting at the construction site, ensuring high-quality preprocessing of image data and significantly improving the system's robustness in complex field environments. Furthermore, the intelligent suspension bridge main cable erection monitoring method of this application embodiment employs a deep learning semantic segmentation model that can identify loose cable features, derailment features, and breakage features according to set judgment criteria. By focusing on these three aspects of detail, it identifies abnormalities in the morphology of the tension cables, greatly improving the accuracy of cable condition perception.

[0020] 3. The monitoring method for intelligent erection of the main cable of the suspension bridge in this application embodiment, in addition to monitoring the cable strands, further monitors the initial main cable alignment, catwalk alignment, catwalk load-bearing cable tension, catwalk safety vision, tension cable strand traction synchronization and progress. This improves the safety of main cable erection from five detailed aspects, greatly reducing construction risks. If abnormalities are found in the catwalk alignment, catwalk load-bearing cable tension, catwalk safety vision (including abnormal catwalk structure loosening or unsafe behavior of construction personnel), tension cable strand traction synchronization and progress, or initial main cable alignment, an alarm is issued in a timely manner, providing more comprehensive protection for construction safety. Attached Figure Description To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating the monitoring method for intelligent erection of the main cable of a suspension bridge provided in this application embodiment; Figure 2 A block diagram of a monitoring system for the intelligent erection of the main cable of a suspension bridge, provided in an embodiment of this application. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0023] This application provides a monitoring method and system for intelligent erection of main cables of suspension bridges, which provides more reliable anomaly judgment results and more comprehensive status perception for tension cable strands, helping to improve the construction efficiency and quality of main cable erection and ensuring construction safety; it also monitors the catwalk alignment, catwalk load-bearing cable tension, catwalk safety vision, tension cable strand traction synchronization and progress, and preliminary main cable alignment, and alarms when anomalies occur, further improving construction safety.

[0024] Before introducing the intelligent erection system of this application, it is necessary to first introduce the conventional structure of the main cable erection system of a traditional suspension bridge: The traditional suspension bridge main cable erection system mainly includes catwalk, catwalk gantry, traction rope, winch, puller, tensioned cable strands, and tensioned cable strands.

[0025] A catwalk is a temporary construction passageway erected below the designed alignment of the main cable during the construction of a suspension bridge. It typically consists of load-bearing cables, surface netting, handrails, and transverse pathways. The catwalk provides a working platform for key construction personnel and also serves as a supporting foundation during the cable strand traction process.

[0026] The catwalk gantry is a rigid support structure installed at regular intervals (e.g., 10 to 20 meters) along the longitudinal direction of the catwalk, typically spanning the catwalk surface in a U-shape. Rollers are installed at the top of the catwalk gantry to support the cable strands, reduce frictional resistance during cable traction, and control the linear height of the cable strands.

[0027] The traction rope is a high-strength steel wire rope that runs between the winch and the puller to transmit traction force. One end is connected to the winch drum at the anchor point, and the other end is connected to the puller.

[0028] The traction device is a key mechanical device used in the erection of the main cable of a suspension bridge to clamp the cable strands and move them along the catwalk. The traction device is connected to the winch through a traction rope and moves along the catwalk under the drive of the winch, dragging a single precast cable strand across the bridge tower. Its bottom is usually equipped with traveling wheels or sliders.

[0029] The tensioned cable strands are in a static state, usually arranged in hexagonal bundles on the catwalk support rollers, located beside or below the strands being tensioned. As construction progresses, the number of tensioned cable strands gradually increases, forming the initial main cable.

[0030] It is worth noting that in this application, the cable strands that are being tensioned are referred to as tensioned cable strands; and the cable strands that have already been tensioned are referred to as prototype main cables.

[0031] Firstly, such as Figure 1 As shown, this application discloses an embodiment of a monitoring method for the intelligent erection of the main cable of a suspension bridge. The monitoring method includes the following steps: S1: Acquire image data of the tension cable strand surface through the first camera installed on the puller; acquire traction tension data through the first tension sensor connected in series between the puller and the end of the traction rope; S2: Preprocess the image data, identify abnormal features of the strand shape from the preprocessed image data, align the abnormal features of the strand shape with the tension data using timestamps, and generate monitoring results; such monitoring results are more reliable and feasible, thus making the anomaly judgment results more reliable.

[0032] S3: When the monitoring results meet the preset warning conditions, an alarm signal is issued and a control command to adjust the winch is generated. The preset warning conditions include the presence of abnormal strand morphology and / or a sudden change in tension data exceeding a set value. Specifically, if either abnormal strand morphology or a sudden change in tension data occurs, or both occur simultaneously, an alarm is triggered and the winch is slowed down or stopped.

[0033] Specifically, the optical axis of the first camera is aligned with the tensioned cable strand, and the first tension sensor is connected in series between the puller and the end of the traction rope. Specifically, one end of the first tension sensor is fixed to the puller, and the other end is connected to the winch's traction rope. The first camera is used to acquire real-time image data of the tensioned cable strand surface, and the first tension sensor is used to acquire real-time traction tension data.

[0034] Specifically, the first camera and the first tension sensor are both integrated and installed in the puller.

[0035] Preferably, the first tension sensor is a tension bar tension sensor, whose tension data stream is transmitted to a unified data server through a predefined data communication protocol (such as via 4G or Ethernet) and is fused and analyzed in real time with the abnormal characteristics of the strand pattern.

[0036] Specifically, the first camera is an industrial-grade camera with a resolution of no less than 2560×1440, with its optical axis aligned with the cable strand. Because the puller moves with the cable strand, the camera and the cable strand remain relatively stationary or move at a low speed relative to each other, solving the problems of long distance and changing viewing angle of the fixed camera. The first tension sensor is connected in series between the puller and the traction rope to collect tension data in real time during the traction process.

[0037] The monitoring method for intelligent erection of the main cable of the suspension bridge in this application follows and monitors the tensioned cable strands, resulting in more reliable monitoring results and more comprehensive state perception. Compared with the prior art, which only focuses on the torsional posture of the cable strands and ignores surface defects, this application uses a first camera to collect images of the cable strand surface at close range, thereby identifying abnormal features of the cable strand shape, filling the gap in monitoring the physical state of the cable strand surface, and realizing more comprehensive state perception. Compared to the single data acquisition method of existing technologies, this application utilizes the first camera of the puller to acquire close-up surface images of the tensioned cable strands, and also collects traction tension data of the tensioned cable strands through the first tension sensor. It aligns the abnormal characteristics of the cable strand morphology with the tension data using timestamps, generating a more reliable monitoring result that combines appearance and internal stress. This achieves comprehensive perception of both appearance and internal stress. Through a multimodal monitoring mechanism, more reliable anomaly judgments are obtained, effectively solving the problem that a single sensor cannot comprehensively assess the health status of the cable strands, significantly improving monitoring accuracy and achieving more comprehensive status monitoring. Furthermore, when there are abnormal characteristics of the cable strand morphology and / or a sudden change in tension data exceeding a set value, an alarm is triggered, and the winch slows down or stops, greatly improving the safety of main cable erection. The monitoring method of this application helps improve the construction efficiency and quality of main cable erection, ensuring construction safety.

[0038] Furthermore, in one embodiment, step S2, preprocessing the image data, includes: Image data is preprocessed using an adaptive lighting enhancement algorithm. Specifically, the adaptive lighting enhancement algorithm automatically adjusts image parameters based on the ambient lighting conditions (backlighting, nighttime, shadows), and performs noise reduction, defogging, and brightness adjustment on the original image data. This eliminates interference from dust, water mist, and uneven lighting at the construction site, ensuring high-quality preprocessing of the image data.

[0039] In step S2, identifying abnormal features of the strand pattern from the preprocessed image data includes: The abnormal features of strand morphology are identified from preprocessed image data using a deep learning semantic segmentation model. These abnormal features include loose strand features, derailment features, and breakage features.

[0040] Specifically, the "loose wire" characteristic refers to the looseness of the internal steel wires of the cable strand, protruding from the surface of the strand. The "derailment risk" characteristic refers to the overall deviation of the cable strand from the center of the support roller, posing a risk of jumping off the roller; a situation where the cable strand has already jumped off the roller also falls under the category of derailment risk. The "fracture" characteristic refers to the breakage of the outer ring of steel wires in the cable strand.

[0041] It is worth noting that the monitoring results of this application include torsional anomalies, but this application will not elaborate on them.

[0042] In this embodiment, the adaptive illumination enhancement algorithm refers to a type of image processing technology that dynamically adjusts the enhancement intensity and parameters based on the local brightness distribution, noise level, and scene characteristics of the image, in order to improve details in dark areas while suppressing overexposure, noise amplification, and color distortion. The adaptive illumination enhancement algorithm processes video images, establishes an image database for complex lighting conditions such as cloudy days, rainy days, and evenings, as well as equipment shaking scenarios, and dynamically adjusts image parameters through the algorithm to improve image clarity and monitoring reliability in complex environments.

[0043] In this embodiment, the deep learning semantic segmentation model is specifically a semantic segmentation network based on the YOLO architecture (e.g., YOLOv8-seg version). The YOLO architecture was chosen because it offers a good balance between inference speed and accuracy on edge computing devices. YOLOv8-seg is a type of deep learning segmentation model, more accurately called instance segmentation. It not only considers categories but also distinguishes individuals (e.g., this wire is red, that wire is blue). Specifically, YOLOv8-seg stands for You Only Look Once version 8 Segmentation, which is the 8th version of the "You Only Look Once" segmentation model.

[0044] The monitoring method for intelligent erection of the main cable of the suspension bridge in this application demonstrates good environmental adaptability in the image preprocessing stage. It uses an adaptive illumination enhancement algorithm to automatically adjust image parameters according to the ambient light (backlight, night, shadow), and performs noise reduction, defogging, and brightness adjustment on the original image data. This eliminates interference from dust, water mist, and uneven lighting at the construction site, ensuring high-quality preprocessing of image data and significantly improving the robustness of the system in complex field environments. Furthermore, a deep learning semantic segmentation model is used to identify abnormal features of the strand pattern from the preprocessed image data, including: The deep learning semantic segmentation model identifies the proportion of loose strand area at the edge of the strand from the preprocessed image data. If the proportion of pixels in the loose strand area to pixels in the strand cross section exceeds a set proportion, it is determined to be a loose strand feature.

[0045] The deep learning semantic segmentation model filters out the image data when the towed roller passes through the preprocessed image data, identifies the pixel distance between the center line of the cable strand and the center line of the towed roller, and determines the derailment feature if the distance exceeds the set distance.

[0046] By using a deep learning semantic segmentation model, the cross-section of the steel wire in the cable strand is identified from the preprocessed image data, and the fracture characteristics are determined.

[0047] Specifically, the first camera swings along a set trajectory, with its optical axis always aligned with the tension cable strand, illuminating from near to far.

[0048] It is worth noting that the sudden change in tension data in step S3 implicitly indicates the breakage of the internal steel wires of the cable strand.

[0049] The monitoring method for intelligent erection of main cables of suspension bridges in this application embodiment uses a deep learning semantic segmentation model to identify loose wire features, derailment features, and breakage features according to set judgment criteria. By focusing on these three aspects of details, it can identify abnormal morphology of tensioned cables, greatly improving the accuracy of cable status perception.

[0050] Furthermore, in one embodiment, the monitoring method further includes monitoring the catwalk, which includes: By deploying GNSS monitoring terminals at several key characteristic cross-sectional locations along the catwalk's load-bearing cables, the catwalk alignment is automatically monitored in real time. Simultaneously, prisms are placed at the same locations to further refine the catwalk alignment using a total station. Specifically, the GNSS monitoring terminals enable automatic real-time monitoring at a high frequency, while the total station, combined with prisms, offers high measurement accuracy but a lower monitoring frequency. The total station is used to manually verify the GNSS automatic monitoring equipment, achieving high-precision measurement of the catwalk alignment. Specifically, the alignment of the catwalk's load-bearing cables represents the overall catwalk alignment. Tension data at various points along the catwalk's load-bearing cables are obtained using several second tension sensors bonded to the load-bearing cables; image data of the catwalk and construction workers are obtained using several second cameras installed on the top of the catwalk gantry. The acquired data (including images from the second camera, the tension of the catwalk load-bearing cables, and data from the total station and GNSS monitoring terminal) is analyzed. If at least one of the following is found: abnormal catwalk alignment, abnormal catwalk load-bearing cable tension, or abnormal catwalk safety vision, an alarm is triggered accordingly. Abnormal catwalk safety vision includes abnormal catwalk structure loosening and unsafe behavior of construction personnel.

[0051] Specifically, the locations of several key characteristic sections include at least the mid-span, 1 / 4-span, and tower anchorage points.

[0052] The total station is set up on a stable bedrock observation pier on both sides of the bridge, or on the top of a cable tower, or on a known control point with an unobstructed view. The station's orientation is then determined by backsight of the known reference point.

[0053] Specifically, when there is sufficient signal (at least 4 satellites), the GNSS receiver calculates the three-dimensional coordinates (longitude, latitude, and elevation). When there is no obstruction between the total station and the prism, the receiver calculates the three-dimensional coordinates of the target point (north coordinate N, east coordinate E, and elevation Z).

[0054] Specifically, a deep learning semantic segmentation model is used to analyze the image data from the second camera. Specifically, the second tension sensor uses a stress sheet.

[0055] It is worth noting that the monitoring of the cat path is performed simultaneously with steps S1, S2 and S3, but is independent of each other.

[0056] Specifically, there are three situations: abnormal catwalk alignment, abnormal catwalk structure loosening, and unsafe behavior of construction personnel. Each situation triggers a different alarm, and you can determine which situation it is by observing the actual alarm.

[0057] Preferably, when the monitoring results meet the preset early warning conditions, a control command is generated to adjust the working state of the winch. This control command can be transmitted to the winch control system wirelessly or via wired means to adjust the working state of the winch (such as deceleration or stopping), thereby forming a closed-loop control and preventing the accident from escalating.

[0058] Specifically, several second tension sensors are attached to the catwalk's load-bearing cables to acquire tension data at various points on the cables, ensuring the safety of the catwalk's load-bearing cables. At least one second camera is installed on the top of each catwalk gantry, and each second camera simultaneously monitors the catwalk. The second cameras acquire image data of the catwalk and construction workers.

[0059] Specifically, abnormalities in catwalk alignment, abnormal tension in catwalk load-bearing cables, and abnormal visual safety features (including abnormal loosening of catwalk structure and unsafe behavior of construction workers) are identified by pre-set rules and extensive training. If any of these issues are detected, an alarm will be triggered accordingly.

[0060] The monitoring method for intelligent erection of the main cable of the suspension bridge in this application embodiment also includes monitoring of the catwalk, which is specifically divided into monitoring the catwalk alignment, the catwalk load-bearing cable tension, and the catwalk safety vision. Monitoring these three aspects ensures the safety of the catwalk and the safety of construction personnel. If any abnormality is detected, an alarm will be issued in a timely manner, thus providing more comprehensive protection for construction safety.

[0061] Furthermore, in one embodiment, the monitoring method further includes monitoring the traction of the tension cable strands, which includes: Multiple winches are synchronously controlled through an integrated winch control platform. Combined with speed sensors installed on the corresponding traction ropes of each winch, the release and retraction of the tension cable strands on both sides are synchronized. Specifically, the winches are divided into main and auxiliary winch units. The tension cable is released on one side and retrieved on the other, maintaining synchronization on both sides.

[0062] When the traction device is pulled, RFID cards with unique identification information are deployed at several key points along the tension cable strand traction path, and card readers are installed on the traction device to obtain the RFID card information that the card readers automatically read during the traction process and visualize it on remote monitoring equipment.

[0063] Specifically, RFID cards with unique identification information are pre-deployed at several key points along the tension cable strand traction path (including the corresponding positions of the gantry, cable saddle, and catwalk), and card readers are installed on the puller.

[0064] Specifically, the monitoring of the traction synchronization and progress of the tension cable strands is carried out simultaneously in steps S1, S2 and S3.

[0065] Specifically, multiple winches are connected to the same puller through multiple traction ropes, and the operation of multiple winches is uniformly controlled to ensure traction synchronization. RFID technology is used to obtain the traction progress of the tension cable strands by automatically reading the RFID card information of the passing RFID cards during the traction process. This allows for precise control of the speed of the tension cable strands as they pass through the gantry and saddle, thus making the erection process visible.

[0066] Further, in one embodiment, step S1, acquiring surface image data of the tension cable strands using a first camera disposed on the puller, includes: By embedding two SIM cards, one corresponding to 4G and the other to 5G communication standards, in the first camera, the system automatically switches to the other network when the signal strength of one network falls below a preset threshold.

[0067] In other embodiments, the first camera can also transmit image data through other communication methods.

[0068] Preferably, the first camera is battery powered and has an IP66-rated protective housing to ensure stable operation in complex construction environments.

[0069] To address the issue mentioned in the background technology where LoRa signals are susceptible to interference from metal structures such as main towers and cable saddles, the first camera incorporates two SIM cards, one for 4G and the other for 5G communication standards, to transmit monitoring results. This design ensures stable transmission of monitoring results even in construction sites with uneven signal coverage, such as canyons and mountainous areas, avoiding data loss due to communication interruptions, and outperforming the single communication link of existing technologies.

[0070] The monitoring method for intelligent erection of the main cable of a suspension bridge according to this application embodiment uses a first camera with two built-in SIM cards corresponding to 4G and 5G communication standards, respectively. This reduces the reliance on single absolute positioning data and instead enhances relative status monitoring. Existing technologies rely on LoRa for absolute coordinate positioning, which is easily shielded by metal structures. The use of 5G / 4G dual SIM cards has stronger penetration and anti-metal interference capabilities compared to LoRa, significantly improving the data transmission stability and monitoring continuity of the system in environments with strong metal interference (such as near the main tower and cable saddle). This avoids monitoring blind spots caused by signal loss and ensures the data reliability of critical construction stages.

[0071] Furthermore, in one embodiment, the monitoring method further includes monitoring the shape of the prototype main cable, which includes: By deploying GNSS monitoring terminals at several key stress and alignment control points along the prototype main cable, the alignment of the prototype main cable was monitored in real time. At the same time, prisms were also set up at the same locations to further verify the alignment with a total station. If an abnormality is detected in the shape of the initial main cable, an alarm will be triggered accordingly.

[0072] Specifically, GNSS monitoring terminals can achieve automatic real-time monitoring with a high monitoring frequency, while total stations, in conjunction with prisms, offer high measurement accuracy but have a lower monitoring frequency. Using a total station to manually and accurately verify the GNSS automatic monitoring equipment enables high-precision measurement of the catwalk alignment.

[0073] Specifically, the key stress and alignment control positions refer to the core positions that are prone to displacement and deformation during the main cable erection process, including at least the mid-span position of the main span, the key setting positions of the upstream and downstream reference cable strands, and the top position of the tower.

[0074] The total station is set up on known control points with unobstructed views, such as stable bedrock observation piers on both sides of the bridge or the top of the cable tower. The station's orientation is determined by backsight of the known reference points.

[0075] Specifically, when there is sufficient signal (greater than or equal to 4 satellites), the GNSS monitoring terminal calculates the three-dimensional coordinates (longitude, latitude, and elevation). When there is no obstruction between the total station and the prism, the calculation is the three-dimensional coordinates of the target point (i.e., north coordinate N, east coordinate E, and elevation Z).

[0076] Specifically, given the known design main cable profile, an anomaly in the prototype main cable profile refers to a deviation exceeding the set range compared to the design main cable profile. The monitoring method for intelligent erection of the main cable of the suspension bridge according to the embodiments of this application also includes monitoring the shape of the prototype main cable, which further assists in the erection of the main cable and improves the quality of the main cable erection.

[0077] Furthermore, in one embodiment, step S3, issuing an alarm signal and generating a control command to adjust the winch, includes: The system issues audible and visual warnings and generates control commands to adjust the speed of the winch or stop it.

[0078] Preferably, the deep learning semantic segmentation model is deployed using YOLOv5s plus Transformer model compression technology.

[0079] The sound and light alarm is installed on the outside of the puller or in a conspicuous position on the cat walkway. It is used to emit a high-decibel sound and flashing light warning signal to remind on-site inspection personnel to intervene immediately.

[0080] The control commands for deceleration or shutdown further control the hoist to decelerate or stop in an emergency. This dual execution mechanism ensures that on-site personnel are immediately aware of the situation and provides an automated safety backup to prevent accidents caused by insufficient human response.

[0081] Furthermore, in one embodiment, the deep learning semantic segmentation model is deployed using YOLOv5s plus Transformer model compression technology.

[0082] like Figure 2 As shown, in a second aspect, this application discloses an embodiment of a monitoring system for the intelligent erection of the main cable of a suspension bridge. The monitoring system includes a cable strand following monitoring subsystem, an edge computing device, and an early warning execution device.

[0083] The cable strand following monitoring subsystem includes a first camera and a first tension sensor. Both the first camera and the first tension sensor are integrated into the puller. The optical axis of the first camera is aligned with the tensioned cable strand, and the first tension sensor is connected in series between the puller and the end of the traction rope. The first camera is used to acquire real-time image data of the tensioned cable strand surface, and the first tension sensor is used to acquire real-time traction tension data.

[0084] The edge computing device is communicatively connected to the cable strand following monitoring subsystem. The edge computing device is used to receive image data and tension data, and also to preprocess the image data, identify abnormal features of the cable strand shape from the preprocessed image data, align the abnormal features of the cable strand shape with the tension data using timestamps, and generate monitoring results.

[0085] The early warning execution device is used to issue an alarm signal and generate control commands to adjust the winch when the monitoring results meet the preset early warning conditions; wherein, the preset early warning conditions include the presence of abnormal characteristics in the strand shape and / or a sudden change in tension data exceeding a set value.

[0086] Preferably, the edge computing device communicates with the cable tracking monitoring subsystem via a high-speed wired interface (such as Gigabit Ethernet or CAN bus). The edge computing device is preferably an industrial computer with a built-in GPU or NPU accelerator card, deployed inside the puller to ensure low latency.

[0087] Furthermore, in one embodiment, the edge computing device incorporates an adaptive illumination enhancement algorithm and a deep learning semantic segmentation model; the adaptive illumination enhancement algorithm is used to preprocess image data; the deep learning semantic segmentation model is a semantic segmentation network based on the YOLO architecture; the deep learning semantic segmentation model is used to identify abnormal features of strand morphology from the preprocessed image data; abnormal features of strand morphology include loose strand features, derailment features, and breakage features.

[0088] Among them, the deep learning semantic segmentation model is used to identify the proportion of the area of ​​loose wires on the edge of the cable strand from the preprocessed image data. If the proportion of pixels in the loose wire segmentation area to pixels in the cross section of the cable strand exceeds a set proportion, it is determined to be a loose wire feature. It is also used to filter out the image data when the cable strand passes the roller from the preprocessed image data and identify the pixel distance between the center line of the cable strand and the center line of the roller. If the distance exceeds a set distance, it is determined to be a derailment feature. It is also used to identify the cross section of the steel wire of the cable strand and determine it to be a breakage feature.

[0089] The intelligent erection system also includes a catwalk monitoring subsystem, which includes several second tension sensors, several second cameras, several GNSS monitoring terminals, several total stations, and several prisms.

[0090] Several second tension sensors are attached to the catwalk support cable to acquire tension data at various points on the support cable.

[0091] Several second cameras are installed on the top of the catwalk gantry, with at least one second camera installed on the top of each catwalk gantry. The several second cameras acquire image data of the catwalk and construction personnel. Several GNSS monitoring terminals were deployed at key characteristic cross-sectional locations along the catenary cable to acquire the three-dimensional coordinates of each location. Specifically, the key characteristic cross-sectional locations included at least the mid-span, quarter-span, and tower anchorage points.

[0092] Several prisms were deployed at the same locations as the GNSS monitoring terminals, and together with several total stations set up at known control points on both sides of the bridge or at the top of the tower, the catwalk alignment monitored by the several GNSS monitoring terminals was further precisely verified.

[0093] Specifically, several total stations are set up on known control points with unobstructed views, such as stable bedrock observation piers on both sides of the bridge or the top of the cable tower, and the station orientation is completed by backsight of the known reference points.

[0094] The edge computing device analyzes the acquired data (including images from the second camera, the tension of the catwalk load-bearing cables, and data from the total station and GNSS monitoring terminal). If abnormalities are found in the catwalk alignment, the tension of the catwalk load-bearing cables, or the safety visual abnormalities of the catwalk (including abnormalities in the catwalk structure and unsafe behaviors of construction personnel), the early warning execution device will issue the corresponding alarm for the detected situation.

[0095] The early warning execution device is used to issue an alarm signal and generate control commands to adjust the winch when the monitoring results meet the preset early warning conditions. The preset early warning conditions include pre-set abnormal characteristics of the catwalk alignment, abnormal threshold of the load-bearing cable tension, abnormal characteristics of the catwalk structure, and unsafe behaviors and postures of construction personnel.

[0096] Furthermore, the intelligent erection system also includes a tension cable strand traction synchronization and progress monitoring subsystem, which includes an integrated winch control platform, several RFID cards and card readers.

[0097] The integrated winch control platform synchronously controls multiple winches, and, in conjunction with speed and tension sensors installed on the corresponding traction ropes of these winches, controls the synchronous release and retraction on both sides of the puller. Specifically, the main and auxiliary winches release the rope on one side and retract it on the other, maintaining synchronization on both sides.

[0098] Several RFID cards with unique identification information were deployed at several key points along the tension cable strand traction path.

[0099] The card reader is installed on the puller. When the puller is pulled, the card reader automatically reads the RFID card information passing by during the pulling process and displays it on the remote monitoring equipment.

[0100] Furthermore, the intelligent erection system also includes a preliminary main cable alignment monitoring subsystem, which comprises several GNSS monitoring terminals, several prisms, and several total stations.

[0101] Among them, several GNSS monitoring terminals are deployed at several key stress and alignment control positions of the prototype main cable to monitor the alignment of the prototype main cable in real time.

[0102] Several prisms were deployed at the same locations as the GNSS monitoring terminals, and together with several total stations erected at known control points on both sides of the bridge or at the top of the pylon, the preliminary main cable alignment monitored by the several GNSS monitoring terminals was further precisely verified.

[0103] Edge computing devices communicate with and acquire data from total stations and GNSS monitoring terminals, then analyze and obtain the preliminary main cable alignment, and when the preliminary main cable alignment is abnormal, the early warning execution device will issue an alarm.

[0104] In the description of this application, it should be noted that the terms "upper," "lower," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two elements. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0105] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0106] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A monitoring method for the intelligent erection of the main cable of a suspension bridge, characterized in that, Includes the following steps: Image data of the tension cable strand surface is acquired by a first camera installed on the puller; traction tension data is acquired by a first tension sensor connected in series between the puller and the end of the traction rope. The image data is preprocessed, and abnormal features of the strand shape are identified from the preprocessed image data. The abnormal features of the strand shape are then time-stamped with the tension data to generate monitoring results. When the monitoring results meet the preset early warning conditions, an alarm signal is issued and a control command to adjust the winch is generated; the preset early warning conditions include the presence of abnormal strand morphology and / or a sudden change in tension data exceeding a set value.

2. The monitoring method for intelligent erection of the main cable of a suspension bridge according to claim 1, characterized in that: The preprocessing of the image data includes: The image data is preprocessed using an adaptive illumination enhancement algorithm; The process of identifying abnormal features of strand patterns from preprocessed image data includes: The abnormal features of the strand pattern are identified from the preprocessed image data using a deep learning semantic segmentation model; the abnormal features of the strand pattern include loose strand features, derailment risk features, and breakage features.

3. The monitoring method for intelligent erection of the main cable of a suspension bridge according to claim 2, characterized in that, The method of identifying abnormal features of the strand pattern from preprocessed image data using a deep learning semantic segmentation model includes: Identify the proportion of the area segmented by loose strands at the edge of the strand. If the proportion of pixels in the loose strand area to pixels in the cross section of the strand exceeds a set proportion, it is determined to be a loose strand feature. Image data of the time when passing the roller is selected from the preprocessed image data, and the pixel distance between the center line of the cable strand and the center line of the roller is identified. If the distance exceeds the set distance, the derailment feature is determined. The cross-section of the steel wire in the cable strand was identified and determined to be a fracture feature.

4. The monitoring method for intelligent erection of the main cable of a suspension bridge according to claim 1, characterized in that, It also includes monitoring of cat paths, which includes: By deploying GNSS monitoring terminals at several key characteristic cross-sections of the catwalk's load-bearing cable, the catwalk alignment is automatically monitored in real time. At the same time, prisms are also set up at the same locations to further verify the catwalk alignment with a total station. Tension data at various points on the load-bearing cable are obtained through several second tension sensors bonded to the load-bearing cable; image data of the catwalk and construction personnel are obtained through several second cameras installed on the top of the catwalk gantry. The acquired data is analyzed, and if at least one of the following is found: abnormal catwalk alignment, abnormal catwalk load-bearing cable tension, or abnormal catwalk safety vision, an alarm is triggered accordingly. The abnormal catwalk safety vision includes abnormal catwalk structure loosening and unsafe behavior of construction personnel. The acquired data includes images from the second camera, catwalk load-bearing cable tension, and data from the total station and GNSS monitoring terminal.

5. The monitoring method for intelligent erection of the main cable of a suspension bridge according to claim 1, characterized in that, It also includes monitoring the traction synchronization and progress of the tension cable strands, which includes: Multiple winches are controlled synchronously through an integrated winch control platform. Combined with speed sensors installed on the corresponding traction ropes of the multiple winches, the puller is controlled to retract and extend synchronously on both sides. When the puller is pulled, RFID cards with unique identification information are deployed at several key points along the tension cable strand traction path, and card readers are installed on the puller to obtain the RFID card information that the card readers automatically read during the traction process and visualize it on the remote monitoring equipment.

6. The monitoring method for intelligent erection of the main cable of a suspension bridge according to claim 1, characterized in that, It also includes monitoring of the initial main cable alignment, which includes: By deploying GNSS monitoring terminals at several key stress and alignment control points along the prototype main cable, the alignment of the prototype main cable was monitored in real time. At the same time, prisms were also set up at the same locations to further verify the alignment with a total station. If an abnormality is detected in the shape of the initial main cable, an alarm will be triggered accordingly.

7. The monitoring method for intelligent erection of the main cable of a suspension bridge according to claim 1, characterized in that, The step of issuing an alarm signal and generating control commands to adjust the winch includes: The system issues audible and visual warnings and generates control commands to adjust the speed of the winch or stop it.

8. A monitoring system for the intelligent erection of the main cable of a suspension bridge, characterized in that, include: The cable strand following monitoring subsystem includes a first camera whose optical axis is aligned with the tensioned cable strand, and a first tension sensor connected in series between the puller and the end of the traction rope. The first camera and the first tension sensor are both integrated into the puller. The first camera is used to collect real-time image data of the tensioned cable strand surface, and the first tension sensor is used to collect real-time traction tension data. An edge computing device is used to receive the image data and the tension data, and also to preprocess the image data, identify abnormal features of the strand shape from the preprocessed image data, align the abnormal features of the strand shape with the tension data by timestamp, and generate monitoring results. An early warning execution device is used to issue an alarm signal and generate a control command to adjust the winch when the monitoring results meet preset early warning conditions; the preset early warning conditions include the presence of abnormal strand morphology and / or a sudden change in tension data exceeding a set value.

9. A monitoring system for intelligent erection of the main cable of a suspension bridge according to claim 8, characterized in that: The edge computing device incorporates an adaptive illumination enhancement algorithm and a deep learning semantic segmentation model; the adaptive illumination enhancement algorithm is used to preprocess the image data; the deep learning semantic segmentation model is used to identify the loose strand features, derailment features, and breakage features of the strand morphology abnormality from the preprocessed image data; The intelligent installation system also includes a catwalk monitoring subsystem, which includes: Several second tension sensors are attached to the load-bearing cable to acquire tension data at various points on the load-bearing cable; Several second cameras are installed on the top of the catwalk gantry to acquire image data of the catwalk and construction workers; Several GNSS monitoring terminals were deployed at several key characteristic sections of the catenary support cable. Several prisms were set up at the same locations as the GNSS monitoring terminals, and together with several total stations set up at known control points on both sides of the bridge or the top of the tower, the catwalk alignment monitored by several GNSS monitoring terminals was further precisely verified. The edge computing device analyzes the acquired data. If it detects abnormalities in the catwalk alignment, load-bearing cable tension, or catwalk safety visuals, the early warning execution device will issue an alarm accordingly. The catwalk safety visuals include abnormalities in the catwalk structure or unsafe behaviors of construction personnel. The acquired data includes images from the second camera, catwalk load-bearing cable tension, and data from the total station and GNSS monitoring terminal.

10. A monitoring system for intelligent erection of the main cable of a suspension bridge according to claim 8, characterized in that: The intelligent erection system also includes a subsystem for monitoring the traction synchronization and progress of tension cable strands, and a subsystem for monitoring the shape of the prototype main cable; The tension cable strand traction synchronization and progress monitoring subsystem includes an integrated winch control platform, several RFID cards, and a card reader. The integrated winch control platform synchronously controls multiple winches and, in conjunction with speed sensors installed on the corresponding traction ropes of the multiple winches, controls the synchronous winding and unwinding of both sides of the puller. Several RFID cards with unique identification information are deployed at several key points along the tension cable strand traction path. The card reader is installed on the puller, and when the puller is being pulled, the card reader automatically reads the RFID card information passing through during the traction process and displays it on the remote monitoring equipment. The prototype main cable alignment monitoring subsystem includes several GNSS monitoring terminals, several prisms, and several total stations. The GNSS monitoring terminals are deployed at several key stress and alignment control positions of the prototype main cable to monitor its alignment in real time. The prisms are deployed at the same positions as the GNSS monitoring terminals and, in conjunction with several total stations erected at known control points on both banks of the bridge or at the top of the towers, further verify the prototype main cable alignment monitored by the GNSS monitoring terminals. The edge computing device communicates with and acquires data from the total stations and GNSS monitoring terminals, then analyzes and obtains the prototype main cable alignment. When the prototype main cable alignment is abnormal, the early warning execution device issues an alarm.

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