A multi-level verification-based cable structure icing monitoring and early warning method and system

Abstract

The present application relates to the technical field of civil engineering structure safety monitoring, in particular to a cable structure icing monitoring and early warning method and system based on multi-level verification, which fundamentally avoids false positives and false negatives caused by single sensor data distortion through mutual verification of environment-mechanics-vision triple data. It realizes a breakthrough from nothing to something, can discover and confirm danger in a very short time after icing occurs, and provides valuable time for active response. The visual recognition algorithm uses curve fitting technology, which can not only determine whether it is icing, but also accurately calculate the thickness of uneven ice, providing accurate data support for risk assessment. The progressive judgment logic reduces system power consumption, and the judgment condition has clear physical meaning and good engineering applicability, realizing intelligent and precise operation and management. Through the multi-level verification mechanism of software and hardware cooperation, high-reliability and intelligent early warning of the cable structure icing state is realized, providing strong technical support for bridge operation safety.

Description

Technical Field

[0001] This invention relates to the field of civil engineering structural safety monitoring technology, specifically a method and system for monitoring and early warning of icing in cable structures based on multi-level verification. Background Technology

[0002] The cable structure of a long-span bridge is its core load-bearing component, and its safety directly affects the operational safety of the entire bridge. In many cold and mid-latitude regions of my country, cable structures are highly susceptible to icing under specific meteorological conditions. This is because cable structures have a high slenderness ratio and specific orientation, causing their surface temperature to drop to freezing point first, resulting in the rapid condensation and accumulation of supercooled water droplets, freezing rain, or wet snow in the air.

[0003] Ice accumulation on cable-stayed structures poses three serious threats: First, the ice layer significantly increases the self-weight of the cable and the wind-blocking area, leading to a surge in static wind loads and threatening structural safety; second, ice accumulation alters the aerodynamic characteristics of the cable cross-section, easily inducing large-scale wind-induced vibrations and exacerbating structural fatigue damage; and finally, the random detachment of high-altitude ice floes poses a serious public safety risk to vehicles and personnel on the bridge deck.

[0004] Currently, industry response strategies generally focus on a "post-event handling" model, i.e., initiating de-icing or ice removal only after icing has formed. This method inherently has a time lag, and risks already exist within the "time window" between icing and intervention. Existing technologies lack real-time, reliable sensing capabilities for icing conditions: regional weather stations cannot reflect the microenvironment of the ice mass, and single video monitoring is limited by light and weather conditions and relies on manual interpretation. Therefore, developing a specialized monitoring system capable of early detection, accurate assessment, and proactive warning, enabling a shift from passive response to proactive defense, has significant engineering value and is urgently needed. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method and system for monitoring and early warning of icing in cable structures based on multi-level verification, so as to solve the problems in the background art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: The present invention provides a method for monitoring and early warning of icing in cable structures based on multi-level verification, comprising the following steps: The system acquires environmental data of the environment in which the bridge cable structure is located, vibration frequency data of the cable structure, and original images of the cable structure. The environmental data is collected based on environmental sensors, the vibration frequency is collected based on vibration sensors, and the original images are collected based on image acquisition units. Based on the environmental data and the preset reference icing environment data, it is determined whether there are icing conditions in the environment where the cable structure is located; when there are icing conditions in the environment where the cable structure is located, a first-level warning is triggered, and the vibration frequency and the preset reference frequency are used to determine whether the cable structure has been iced, wherein the reference frequency is the vibration frequency of the cable structure when it is not iced. When the cable structure has frozen, a level two warning is triggered, and the original image is analyzed to obtain the thickness of the ice on the cable structure. The ice thickness is compared with a preset thickness threshold, and a level three warning is triggered when the ice thickness is greater than or equal to the preset thickness threshold.

[0007] In one embodiment of this application, the environmental data includes ambient temperature and ambient humidity, wherein determining whether icing conditions exist in the environment where the cable structure is located based on the environmental data and preset reference icing environment data includes: The ambient temperature is compared with a preset ambient temperature threshold, and the ambient humidity is compared with a preset ambient humidity threshold. If the ambient temperature is less than or equal to the preset ambient temperature threshold, and the ambient humidity is greater than or equal to the preset ambient humidity threshold, it is determined that there are icing conditions in the environment where the cable structure is located; otherwise, it is determined that there are no icing conditions in the environment where the cable structure is located.

[0008] In one embodiment of this application, determining whether the cable structure has frozen based on the vibration frequency and a preset reference frequency includes: Calculate the rate of change of the vibration frequency relative to the preset reference frequency. ; The rate of change Compare with a preset rate of change threshold, and at the rate of change If the rate of change is greater than or equal to the preset threshold, the cable structure is determined to be frozen; otherwise, the cable structure is determined not to be frozen.

[0009] In one embodiment of this application, the original image is analyzed to obtain the icing thickness of the cable structure, including: The original image is preprocessed to obtain a preprocessed image; Extract the effective edge pixels from the preprocessed image, and merge the effective edge pixels into multiple detection line segments based on the probabilistic Hough transform; The left and right contour lines of the cable structure are constructed based on multiple detection line segments, and the icing thickness of the cable structure is calculated based on the left contour line, the right contour line, the pre-constructed left reference contour line, and the pre-constructed right reference contour line.

[0010] In one embodiment of this application, the original image is preprocessed to obtain a preprocessed image, including: The original image is converted to grayscale to obtain a grayscale image; The grayscale image is subjected to Gaussian filtering to obtain a filtered image; The filtered image is then contrast-enhanced to obtain a preprocessed image.

[0011] In one embodiment of this application, effective edge pixels are extracted from the preprocessed image, and these effective edge pixels are merged into multiple detection line segments based on the probabilistic Hough transform, including: Based on the Canny edge detection algorithm, strong edge pixels and weak edge pixels are extracted from the preprocessed image by combining high threshold and low threshold. The strong edge pixels are pixels with gradient magnitude greater than the high threshold, and the weak edge pixels are pixels with gradient magnitude between the low threshold and the high threshold. The strong edge pixels and the weak edge pixels connected to the strong edge pixels are taken as valid edge pixels. The effective edge pixels are combined into multiple detection line segments based on the probabilistic Hough transform.

[0012] In one embodiment of this application, the left and right contour lines of the cable structure are constructed based on multiple detection line segments, including: The positions of the endpoints of the detected line segment are compared with those of multiple pre-defined regions of interest, wherein the regions of interest are located on the left or right side of the cable structure; The detection line segments whose endpoints are located in the same region of interest are assigned to the same edge contour; and the Euclidean distance between the adjacent endpoints of any two adjacent detection line segments in the same edge contour is calculated. When the Euclidean distance is less than a preset distance threshold, the adjacent endpoints of the two adjacent detection line segments are connected to obtain the pixel chain of the left contour or the right contour. The left or right contour pixel chain is fitted with a quadratic polynomial to obtain the left or right contour line.

[0013] In one embodiment of this application, the icing thickness of the cable structure is calculated based on the left contour line, the right contour line, a pre-constructed left reference contour line, and a pre-constructed right reference contour line, including: The left contour line, the right contour line, the pre-constructed left reference contour line, and the pre-constructed right reference contour line are sampled respectively to obtain multiple sampling points of the left contour line, multiple sampling points of the right contour line, multiple sampling points of the left reference contour line, and multiple sampling points of the right reference contour line, wherein the multiple sampling points are distributed at equal intervals along the vertical direction of the cable structure; For the same height level Calculate the first distance between the sampling points of the left contour line and the sampling points of the right contour line. And calculate the second distance between the sampling points of the left reference contour line and the sampling points of the right reference contour line. , where the first distance and the second distance The mathematical expression is: In the formula, Indicates the height level of the right contour line Corresponding sampling points Indicates the height level of the left contour line Corresponding sampling points Indicates the height level of the right-side reference contour line. Corresponding sampling points Indicates the height level of the left reference contour line. The corresponding sampling points; Based on the first distance and the second distance Calculate height level Ice layer pixel thickness The ice layer pixel thickness The mathematical expression is: Calculate the average pixel thickness of the ice layer pixel thickness at multiple sampling heights. And extract the maximum pixel thickness from the ice layer pixel thickness at multiple sampling heights. ; The average pixel thickness is calculated based on a pre-calibrated scaling factor. and the maximum pixel thickness Converted to average true thickness respectively and maximum true thickness Wherein, the average true thickness and the maximum true thickness The mathematical expressions are as follows: In the formula, This is the scaling factor.

[0014] In one embodiment of this application, the method further includes: comparing the ice thickness with a preset thickness threshold, and triggering a three-level warning when the ice thickness is greater than or equal to the preset thickness threshold, including: The average true thickness The maximum true thickness is compared with a preset average thickness threshold. The thickness is compared with a preset maximum thickness threshold, wherein the maximum thickness threshold is greater than the average thickness threshold. The average true thickness Greater than or equal to a preset average thickness threshold, or the maximum true thickness. When the thickness exceeds the preset maximum thickness threshold, icing is confirmed, a Level 3 warning is triggered, and the Level 3 warning is issued.

[0015] This application also provides a cable structure icing monitoring and early warning system based on multi-level verification, including: The acquisition module is used to acquire environmental data of the environment where the bridge cable structure is located, vibration frequency data of the cable structure, and original images of the cable structure. The environmental data is acquired based on environmental sensors, the vibration frequency is acquired based on vibration sensors, and the original images are acquired based on image acquisition units. The first-level early warning module is used to determine whether there are icing conditions in the environment where the cable structure is located based on the environmental data and the preset reference icing environment data; when there are icing conditions in the environment where the cable structure is located, the first-level early warning is triggered, and the vibration frequency and the preset reference reference frequency are used to determine whether the cable structure has been iced, wherein the reference reference frequency is the vibration frequency of the cable structure when it is not iced. The secondary early warning module is used to trigger a secondary early warning when the cable structure has frozen, and to analyze the original image to obtain the icing thickness of the cable structure. The three-level early warning module is used to compare the ice thickness with a preset thickness threshold, and to trigger a three-level early warning when the ice thickness is greater than or equal to the preset thickness threshold.

[0016] The beneficial effects of this invention are as follows: This invention provides a multi-level verification-based method and system for monitoring and early warning of icing in cable structures. By cross-verifying environmental, mechanical, and visual data, this application fundamentally avoids false alarms and missed alarms caused by data distortion from a single sensor. This represents a breakthrough from scratch, enabling the detection and confirmation of potential hazards in a very short time after icing occurs, providing valuable time for proactive response. The visual recognition algorithm employs curve fitting technology, which not only determines whether icing has occurred but also accurately calculates the thickness of uneven icing, providing precise data support for risk assessment. The progressive judgment logic reduces system power consumption, and the judgment conditions have clear physical meaning, resulting in good engineering applicability and achieving intelligent and precise operation and maintenance management. Through the aforementioned multi-level verification mechanism combining hardware and software, this application achieves highly reliable and intelligent early warning of the icing status of cable structures, providing strong technical support for bridge operation safety. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart illustrating a cable structure icing monitoring and early warning method based on multi-level verification in one embodiment of this application; Figure 2 This is a flowchart illustrating the visual image verification process in one embodiment of this application; Figure 3 This is a structural diagram of a cable structure icing monitoring and early warning system based on multi-level verification, as shown in one embodiment of this application. Detailed Implementation

[0018] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0019] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the layers related to the present invention and are not drawn according to the actual number, shape and size of the layers in the actual implementation. In the actual implementation, the form, number and proportion of each layer can be arbitrarily changed, and the layer layout may also be more complex.

[0020] Numerous details are explored in the following description to provide a more thorough explanation of embodiments of the invention; however, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details.

[0021] Figure 1 This is a flowchart illustrating a multi-level verification-based method for monitoring and early warning of icing in cable structures, as shown in one embodiment of this application. Figure 1 As shown: This embodiment of the method for monitoring and early warning of icing in cable structures based on multi-level verification may include steps S110 to S140: S110, acquire environmental data of the environment where the bridge cable structure is located, vibration frequency data of the cable structure, and original image of the cable structure, wherein the environmental data is acquired based on environmental sensors, the vibration frequency is acquired based on vibration sensors, and the original image is acquired based on image acquisition unit. Specifically, the data acquisition module upon which step S110 depends includes an environmental monitoring unit, a cable force monitoring unit, and an image acquisition unit arranged around and on the surface of the cable structure. The environmental monitoring unit is used to collect environmental parameters around the cable structure, including at least ambient temperature and ambient humidity; the cable force monitoring unit is used to collect vibration frequency signals or cable force values ​​of the cable structure; and the image acquisition unit is used to collect video or image information of the surface of the cable structure.

[0022] S120, based on the environmental data and the preset reference icing environment data, determine whether there are icing conditions in the environment where the cable structure is located; when there are icing conditions in the environment where the cable structure is located, trigger a first-level warning, and determine whether the cable structure has been iced based on the vibration frequency and the preset reference frequency, wherein the reference frequency is the vibration frequency of the cable structure when it is not iced. Step S120 performs an environmental risk assessment by analyzing data from the environmental monitoring unit in real time to determine whether the environment in which the cable structure is located has icing conditions. Specifically, the ambient temperature is compared with a preset ambient temperature threshold, and the ambient humidity is compared with a preset ambient humidity threshold. If the ambient temperature is less than or equal to the preset ambient temperature threshold, and the ambient humidity is greater than or equal to the preset ambient humidity threshold, it is determined that the environment in which the cable structure is located has icing conditions; otherwise, it is determined that the environment in which the cable structure is located does not have icing conditions.

[0023] For example, when the conditions of "ambient temperature is lower than or equal to the first threshold T1=0℃" and "ambient humidity is higher than or equal to the second threshold H1=85%" are met simultaneously, a first-level warning (preliminary warning) is triggered, indicating that the meteorological conditions for icing are mature.

[0024] S130, when the cable structure has frozen, a secondary warning is triggered, and the original image is analyzed to obtain the icing thickness of the cable structure; Step S130 performs dynamic characteristic verification. After triggering the first-level warning, a special analysis of the cable force monitoring unit is initiated. This is done by calculating the rate of change between the vibration frequency and the preset reference frequency. Then the rate of change Compare with a preset rate of change threshold, and at the rate of change If the rate of change is greater than or equal to the preset threshold, the cable structure is determined to be frozen; otherwise, the cable structure is determined not to be frozen.

[0025] For example: Obtaining the fundamental frequency of the cable force when the current cable structure vibrates. And compare it with the reference frequency of the cable when it is not icing, which is stored in the database. Compare the observed frequency changes. When the threshold value F1 exceeds 5%, a secondary warning (confirmation warning) is triggered, indicating that the dynamic characteristics of the cable structure have become abnormal, and it is highly likely that the structure has become icy. This is because when a cable structure is icy, its vibration frequency will change significantly.

[0026] S140, compare the ice thickness with a preset thickness threshold, and trigger a level 3 warning when the ice thickness is greater than or equal to the preset thickness threshold.

[0027] Step S140 performs visual image verification. After triggering a level-two warning, the image acquisition unit is activated and an image recognition algorithm is used for specialized analysis. The algorithm employs an ice thickness estimation method based on edge detection and contour analysis. By comparing the real-time extracted cable contour with the baseline contour, the ice thickness is accurately calculated. When the calculated ice thickness exceeds the confirmation threshold, a level-three warning (final warning) is triggered.

[0028] Finally, based on the output of the early warning analysis module, different levels of early warning information are issued to the monitoring center or management personnel, and relevant response measures can be linked.

[0029] Figure 2 This is a flowchart illustrating the visual image verification process in one embodiment of this application, as shown below. Figure 2 As shown below, the specific implementation process of visual image verification is as follows: In one embodiment of this application, the original image is analyzed to obtain the icing thickness of the cable structure, including: S210, preprocess the original image to obtain a preprocessed image; Specifically, the preprocessing process includes: S211, perform grayscale conversion on the original image to obtain a grayscale image; Specifically, a weighted average method is used to convert a color image into a grayscale image. The formula for calculating the weighted average method is as follows: In the formula, Grayscale value The value for the red channel. This is the green channel value. This is the value for the blue channel.

[0030] S212, Perform Gaussian filtering on the grayscale image to obtain a filtered image; Specifically, using 5×5 pixels and standard deviation A Gaussian kernel is used for filtering and noise reduction.

[0031] S213, perform contrast enhancement on the filtered image to obtain a preprocessed image.

[0032] The CLAHE algorithm (Clip Limit=2.0, Tile Grid Size=8×8) is used to enhance contrast.

[0033] S220, extract the effective edge pixels in the preprocessed image, and merge the effective edge pixels into multiple detection line segments based on the probabilistic Hough transform; the specific process includes: S221, Based on the Canny edge detection algorithm, strong edge pixels and weak edge pixels are extracted from the preprocessed image by combining a high threshold and a low threshold. The strong edge pixels are pixels with a gradient magnitude greater than the high threshold, and the weak edge pixels are pixels with a gradient magnitude between the low threshold and the high threshold. Specifically, the Sobel operator (with a kernel size of 3) is used to calculate the gradients of the image in the X and Y directions, respectively. .

[0034] Gradient magnitude:

[0035] Gradient direction: And approximate the angles to four directions: 0°, 45°, 90°, and 135°.

[0036] Then, non-maximum suppression is used to refine the edges by traversing the gradient magnitude matrix and retaining only the local maximum points in the gradient direction of each pixel.

[0037] This application also uses dual threshold detection and edge connectivity to filter out valid edge pixels, for example: High threshold: 30 (based on gradient magnitude statistics, usually taking the 70-80th percentile of the image gradient magnitude distribution).

[0038] Low threshold: 0.4 times the high threshold (i.e., 12).

[0039] S222, the strong edge pixel and the weak edge pixel connected to the strong edge pixel are taken as effective edge pixel points; Specifically, pixels with gradient magnitudes greater than the high threshold are identified as strong edge pixels; those between the high and low thresholds are identified as weak edge pixels; and those less than the low threshold are suppressed. Through 8-neighborhood connectivity analysis, weak edge pixels connected to strong edge pixels are identified as valid edges, thus completing edge connectivity.

[0040] S223, Based on the probabilistic Hough transform, the effective edge pixels are combined into multiple detection line segments.

[0041] The parameters required for the probabilistic Hough transform are configured as follows: Resolution (distance resolution): 1 pixel.

[0042] Resolution (angular resolution): 1 degree (i.e.) radian).

[0043] Threshold: 50. This is the minimum number of intersections required to detect a straight line. This value is adjusted based on image size and edge density to filter out short, interfering line segments.

[0044] Minimum line length: 50 pixels. Lines shorter than this length will be ignored.

[0045] Maximum line spacing: 10 pixels. Broken line segments smaller than this spacing will be connected into a straight line.

[0046] S230, constructing the left and right contour lines of the cable structure based on multiple detection line segments, and calculating the icing thickness of the cable structure based on the left contour line, the right contour line, a pre-constructed left reference contour line, and a pre-constructed right reference contour line. Specifically, this includes: S231, compare the position of the endpoint of the detected line segment with the position of a plurality of predefined regions of interest, wherein the regions of interest are located on the left or right side of the cable structure; In this application, regions of interest (ROIs) are pre-defined on both sides of the cable structure, and the endpoints of the detection line segments are compared with the positions of the pre-defined regions of interest (ROIs).

[0047] S232, classify the detection line segments whose endpoints are located in the same region of interest into the same edge contour; and calculate the Euclidean distance between the adjacent endpoints of any two adjacent detection line segments in the same edge contour. When the Euclidean distance is less than a preset distance threshold, connect the adjacent endpoints of the two adjacent detection line segments to obtain the pixel chain of the left contour or the right contour. The initial filtering and merging is performed based on the spatial position of the endpoints of line segments within the preset regions of interest (ROIs) of the left and right contours. For example, the detected line segments are first filtered using ROIs, retaining those whose endpoints are located within ROIs. Then, they are grouped together; if the endpoints of multiple detected line segments are all located within the ROI on the left side of the cable structure, they are grouped into the same left contour.

[0048] Then, connectivity analysis is performed based on the Euclidean distance between endpoints (threshold 5-10 pixels) to connect spatially adjacent line segments, forming "pixel chains" that describe the direction of the left and right contours.

[0049] S233, perform quadratic polynomial fitting on the pixel chain of the left or right contour to obtain the left or right contour line.

[0050] A quadratic polynomial fitting algorithm is used to perform curve fitting on the pixel chains of the left and right contours respectively, to obtain the left contour line. and right outline This method can accurately describe the contour curvature caused by localized icing.

[0051] S234, sampling is performed on the left contour line, the right contour line, the pre-constructed left reference contour line, and the pre-constructed right reference contour line respectively to obtain multiple sampling points of the left contour line, multiple sampling points of the right contour line, multiple sampling points of the left reference contour line, and multiple sampling points of the right reference contour line, wherein the multiple sampling points are equally spaced along the vertical direction of the cable structure; The system database stores the baseline profile curves in the unfrozen state, specifically including the left baseline profile line of each cable structure. and the right baseline outline .

[0052] S235, for the same altitude level Calculate the first distance between the sampling points of the left contour line and the sampling points of the right contour line. And calculate the second distance between the sampling points of the left reference contour line and the sampling points of the right reference contour line. , where the first distance and the second distance The mathematical expression is: In the formula, Indicates the height level of the right contour line Corresponding sampling points Indicates the height level of the left contour line Corresponding sampling points Indicates the height level of the right-side reference contour line. Corresponding sampling points Indicates the height level of the left reference contour line. The corresponding sampling points; S236, based on the first distance and the second distance Calculate height level Ice layer pixel thickness The ice layer pixel thickness The mathematical expression is: S237, Calculate the average pixel thickness of the ice layer pixel thickness at multiple sampling heights. And extract the maximum pixel thickness from the ice layer pixel thickness at multiple sampling heights. ; S238, the average pixel thickness is calculated based on a pre-calibrated scaling factor. and the maximum pixel thickness Converted to average true thickness respectively and maximum true thickness Wherein, the average true thickness and the maximum true thickness The mathematical expressions are as follows: In the formula, This is the scaling factor.

[0053] Finally, the average true thickness will also be... The maximum true thickness is compared with a preset average thickness threshold. The thickness is compared with a preset maximum thickness threshold, wherein the maximum thickness threshold is greater than the average thickness threshold. The average true thickness Greater than or equal to a preset average thickness threshold, or the maximum true thickness. When the thickness exceeds the preset maximum thickness threshold, icing is confirmed, a Level 3 warning is triggered, and the Level 3 warning is issued.

[0054] For example: when or maximum local thickness At that time, confirm that it has frozen.

[0055] The above process, through the construction of a multi-level progressive verification logic of "preliminary environmental risk assessment - dynamic characteristic change verification - final visual image confirmation", aims to solve the technical bottlenecks in the current field of cable structure icing monitoring, such as poor real-time performance, low reliability, and insufficient automation, and to achieve early, accurate, and automatic early warning of icing phenomena.

[0056] This invention discloses a multi-level verification-based method for monitoring and early warning of icing in cable structures. This application utilizes a triple verification system of environmental, mechanical, and visual data to fundamentally avoid false alarms and missed alarms caused by data distortion from a single sensor. This represents a breakthrough, enabling the detection and confirmation of potential hazards within a very short time after icing occurs, providing valuable time for proactive response. The visual recognition algorithm employs curve fitting technology, which not only determines whether icing has occurred but also accurately calculates the thickness of uneven icing, providing precise data support for risk assessment. The progressive judgment logic reduces system power consumption, and the judgment conditions have clear physical meaning, resulting in good engineering applicability and achieving intelligent and precise operation and maintenance management. Through the aforementioned multi-level verification mechanism combining hardware and software, this application achieves highly reliable and intelligent early warning of icing conditions in cable structures, providing strong technical support for bridge operation safety.

[0057] like Figure 3 As shown, this application also provides a cable structure icing monitoring and early warning system based on multi-level verification, including: The acquisition module is used to acquire environmental data of the environment where the bridge cable structure is located, vibration frequency data of the cable structure, and original images of the cable structure. The environmental data is acquired based on environmental sensors, the vibration frequency is acquired based on vibration sensors, and the original images are acquired based on image acquisition units. The first-level early warning module is used to determine whether there are icing conditions in the environment where the cable structure is located based on the environmental data and the preset reference icing environment data; when there are icing conditions in the environment where the cable structure is located, the first-level early warning is triggered, and the vibration frequency and the preset reference reference frequency are used to determine whether the cable structure has been iced, wherein the reference reference frequency is the vibration frequency of the cable structure when it is not iced. The secondary early warning module is used to trigger a secondary early warning when the cable structure has frozen, and to analyze the original image to obtain the icing thickness of the cable structure. The three-level early warning module is used to compare the ice thickness with a preset thickness threshold, and to trigger a three-level early warning when the ice thickness is greater than or equal to the preset thickness threshold.

[0058] This invention discloses a multi-level verification-based cable structure icing monitoring and early warning system. This application utilizes a triple verification mechanism involving environmental, mechanical, and visual data to fundamentally avoid false alarms and missed alarms caused by data distortion from a single sensor. This represents a breakthrough from scratch, enabling the detection and confirmation of potential hazards within a very short time after icing occurs, providing valuable time for proactive response. The visual recognition algorithm employs curve fitting technology, which not only determines whether icing has occurred but also accurately calculates the thickness of uneven icing, providing precise data support for risk assessment. The progressive judgment logic reduces system power consumption, and the judgment conditions have clear physical meaning, resulting in good engineering applicability and achieving intelligent and precise operation and maintenance management. Through the aforementioned multi-level verification mechanism combining hardware and software, this application achieves highly reliable and intelligent early warning of cable structure icing conditions, providing strong technical support for bridge operation safety.

[0059] As will be understood by those skilled in the art, the computer-readable storage medium described in this embodiment allows for the implementation of all or part of the steps in the above method embodiments by computer program-related hardware. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0060] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver, and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication between them. The memory is used to store computer programs, the communication interface is used to perform communication, and the processor and the transceiver are used to run the computer programs, so that the electronic terminal performs the various steps of the above method.

[0061] In this embodiment, the memory may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0062] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0063] In the above embodiments, although the invention has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. The embodiments of the invention are intended to cover all such substitutions, modifications, and variations falling within the broad scope of the appended claims.

[0064] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for monitoring and early warning of icing in cable structures based on multi-level verification, characterized in that, Including the following steps: The system acquires environmental data of the environment in which the bridge cable structure is located, vibration frequency data of the cable structure, and original images of the cable structure. The environmental data is collected based on environmental sensors, the vibration frequency is collected based on vibration sensors, and the original images are collected based on image acquisition units. Based on the environmental data and the preset reference icing environment data, it is determined whether there are icing conditions in the environment where the cable structure is located; when there are icing conditions in the environment where the cable structure is located, a first-level warning is triggered, and the vibration frequency and the preset reference frequency are used to determine whether the cable structure has been iced, wherein the reference frequency is the vibration frequency of the cable structure when it is not iced. When the cable structure has frozen, a level two warning is triggered, and the original image is analyzed to obtain the thickness of the ice on the cable structure. The ice thickness is compared with a preset thickness threshold, and a level three warning is triggered when the ice thickness is greater than or equal to the preset thickness threshold.

2. The method for monitoring and early warning of icing in cable structures based on multi-level verification according to claim 1, characterized in that, The environmental data includes ambient temperature and ambient humidity. Determining whether icing conditions exist in the environment where the cable structure is located based on the environmental data and preset reference icing environment data includes: The ambient temperature is compared with a preset ambient temperature threshold, and the ambient humidity is compared with a preset ambient humidity threshold. If the ambient temperature is less than or equal to the preset ambient temperature threshold, and the ambient humidity is greater than or equal to the preset ambient humidity threshold, it is determined that there are icing conditions in the environment where the cable structure is located; otherwise, it is determined that there are no icing conditions in the environment where the cable structure is located.

3. The method for monitoring and early warning of icing in cable structures based on multi-level verification according to claim 1, characterized in that, Determining whether the cable structure has frozen based on the vibration frequency and a preset reference frequency includes: Calculate the rate of change of the vibration frequency relative to the preset reference frequency. ; The rate of change Compare with a preset rate of change threshold, and at the rate of change If the rate of change is greater than or equal to the preset threshold, the cable structure is determined to be frozen; otherwise, the cable structure is determined not to be frozen.

4. The method for monitoring and early warning of icing in cable structures based on multi-level verification according to claim 1, characterized in that, The original image was analyzed to obtain the icing thickness of the cable structure, including: The original image is preprocessed to obtain a preprocessed image; Extract the effective edge pixels from the preprocessed image, and merge the effective edge pixels into multiple detection line segments based on the probabilistic Hough transform; The left and right contour lines of the cable structure are constructed based on multiple detection line segments, and the icing thickness of the cable structure is calculated based on the left contour line, the right contour line, the pre-constructed left reference contour line, and the pre-constructed right reference contour line.

5. The method for monitoring and early warning of icing in cable structures based on multi-level verification according to claim 4, characterized in that, The original image is preprocessed to obtain a preprocessed image, including: The original image is converted to grayscale to obtain a grayscale image; The grayscale image is subjected to Gaussian filtering to obtain a filtered image; The filtered image is then contrast-enhanced to obtain a preprocessed image.

6. The method for monitoring and early warning of icing in cable structures based on multi-level verification according to claim 4, characterized in that, Extracting valid edge pixels from the preprocessed image and merging them into multiple detection line segments based on probabilistic Hough transform, including: Based on the Canny edge detection algorithm, strong edge pixels and weak edge pixels are extracted from the preprocessed image by combining high threshold and low threshold. The strong edge pixels are pixels with gradient magnitude greater than the high threshold, and the weak edge pixels are pixels with gradient magnitude between the low threshold and the high threshold. The strong edge pixels and the weak edge pixels connected to the strong edge pixels are taken as valid edge pixels. The effective edge pixels are combined into multiple detection line segments based on the probabilistic Hough transform.

7. The method for monitoring and early warning of icing in cable structures based on multi-level verification according to claim 4, characterized in that, The left and right contour lines of the cable structure are constructed based on multiple detection line segments, including: The positions of the endpoints of the detected line segment are compared with those of multiple pre-defined regions of interest, wherein the regions of interest are located on the left or right side of the cable structure; The detection line segments whose endpoints are located in the same region of interest are assigned to the same edge contour; and the Euclidean distance between the adjacent endpoints of any two adjacent detection line segments in the same edge contour is calculated. When the Euclidean distance is less than a preset distance threshold, the adjacent endpoints of the two adjacent detection line segments are connected to obtain the pixel chain of the left contour or the right contour. The left or right contour pixel chain is fitted with a quadratic polynomial to obtain the left or right contour line.

8. The method for monitoring and early warning of icing in cable structures based on multi-level verification according to claim 4, characterized in that, The icing thickness of the cable structure is calculated based on the left contour line, the right contour line, the pre-constructed left reference contour line, and the pre-constructed right reference contour line, including: The left contour line, the right contour line, the pre-constructed left reference contour line, and the pre-constructed right reference contour line are sampled respectively to obtain multiple sampling points of the left contour line, multiple sampling points of the right contour line, multiple sampling points of the left reference contour line, and multiple sampling points of the right reference contour line, wherein the multiple sampling points are distributed at equal intervals along the vertical direction of the cable structure; For the same height level Calculate the first distance between the sampling points of the left contour line and the sampling points of the right contour line. And calculate the second distance between the sampling points of the left reference contour line and the sampling points of the right reference contour line. , where the first distance and the second distance The mathematical expression is: In the formula, Indicates the height level of the right contour line Corresponding sampling points Indicates the height level of the left contour line Corresponding sampling points Indicates the height level of the right-side reference contour line. Corresponding sampling points Indicates the height level of the left reference contour line. The corresponding sampling points; Based on the first distance and the second distance Calculate altitude level Ice layer pixel thickness The ice layer pixel thickness The mathematical expression is: Calculate the average pixel thickness of the ice layer pixel thickness at multiple sampling heights. And extract the maximum pixel thickness from the ice layer pixel thickness at multiple sampling heights. ; The average pixel thickness is calculated based on a pre-calibrated scaling factor. and the maximum pixel thickness Converted to average true thickness respectively and maximum true thickness Wherein, the average true thickness and the maximum true thickness The mathematical expressions are as follows: In the formula, This is the scaling factor.

9. A method for monitoring and early warning of icing in cable structures based on multi-level verification according to claim 8, characterized in that, Also includes: The ice thickness is compared with a preset thickness threshold, and a level three warning is triggered when the ice thickness is greater than or equal to the preset thickness threshold, including: The average true thickness The maximum true thickness is compared with a preset average thickness threshold. The thickness is compared with a preset maximum thickness threshold, wherein the maximum thickness threshold is greater than the average thickness threshold. The average true thickness Greater than or equal to a preset average thickness threshold, or the maximum true thickness. When the thickness exceeds the preset maximum thickness threshold, icing is confirmed, a Level 3 warning is triggered, and the Level 3 warning is issued.

10. A cable structure icing monitoring and early warning system based on multi-level verification, characterized in that, include: The acquisition module is used to acquire environmental data of the environment where the bridge cable structure is located, vibration frequency data of the cable structure, and original images of the cable structure. The environmental data is acquired based on environmental sensors, the vibration frequency is acquired based on vibration sensors, and the original images are acquired based on image acquisition units. The first-level early warning module is used to determine whether there are icing conditions in the environment where the cable structure is located based on the environmental data and the preset reference icing environment data; when there are icing conditions in the environment where the cable structure is located, the first-level early warning is triggered, and the vibration frequency and the preset reference reference frequency are used to determine whether the cable structure has been iced, wherein the reference reference frequency is the vibration frequency of the cable structure when it is not iced. The secondary early warning module is used to trigger a secondary early warning when the cable structure has frozen, and to analyze the original image to obtain the icing thickness of the cable structure. The three-level early warning module is used to compare the ice thickness with a preset thickness threshold, and to trigger a three-level early warning when the ice thickness is greater than or equal to the preset thickness threshold.

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