Cable form reconstruction and distance compensation method and system based on visual-acoustic combined measurement

Abstract

This invention relates to the field of precision measurement technology, and discloses a method and system for cable morphology reconstruction and distance compensation based on visual-acoustic joint measurement. The method includes: acquiring geometric feature data of the building curtain wall's outline and receiving acoustic signals; measuring the horizontal distance and vertical velocity between the hook and the curtain wall based on the geometric feature data; obtaining the initial vertical distance based on the first acoustic wave, estimating the suspended weight, and calculating elastic elongation for compensation to obtain the true vertical distance; reconstructing the cable's spatial morphology based on the reflected signal of the second acoustic wave, and extracting swing and bending parameters; fusing the vertical velocity and acoustic velocity, and combining the true vertical and horizontal distances to determine the hook's three-dimensional spatial coordinates; comparing the hook's three-dimensional spatial coordinates, swing parameters, and bending parameters with preset thresholds, and outputting the comparison results. This invention achieves joint measurement of hook position and cable morphology during hoisting, providing measurement data reference for hoisting operations.

Description

Technical Field

[0001] This invention relates to the field of precision measurement technology, and in particular to a method and system for cable morphology reconstruction and distance compensation based on visual-acoustic joint measurement. Background Technology

[0002] In the construction industry, civil buildings with a height exceeding 100m are generally defined as super high-rise buildings. The installation of curtain wall units for such buildings has always been a key focus and challenge in construction monitoring due to its height, stringent precision requirements, and significant safety risks.

[0003] In existing technologies, there has been some research on the monitoring and control of curtain wall hoisting. For example, related technical solutions acquire real-time pose data of the curtain wall through multi-sensor fusion, construct a hoisting trajectory and compare it with a predetermined trajectory, and combine environmental interference factors to achieve dynamic correction of the hoisting process, which improves the pose monitoring accuracy and automation control level of curtain wall hoisting to a certain extent.

[0004] In the area of ​​cable condition monitoring, relevant technical solutions have also been proposed. For example, Chinese patent application CN119063647A discloses a real-time cable condition monitoring system based on 3D point cloud surface reconstruction. This system uses a 3D point cloud array camera to collect 3D point cloud data of the cable surface and analyzes changes in geometric parameters such as the cable's diameter and cross-sectional area to determine if the cable is abnormal. This solution provides a non-contact optical measurement method for measuring the geometric deformation of cables.

[0005] However, existing technologies, including the aforementioned solutions, primarily focus on the pose monitoring of the curtain wall units themselves or the static measurement of cable surface deformation. Improvements are still needed in areas such as real-time measurement of the dynamic spatial morphology of the cables during hoisting and active compensation for sensor ranging errors. The overall measurement solution's completeness and positioning accuracy cannot fully meet the stringent requirements of high-precision hoisting of curtain walls for ultra-high-rise buildings. Specifically, this manifests in the following aspects:

[0006] First, hoisting positioning has limitations in terms of field of vision and accuracy. Operators have blind spots during high-altitude hoisting operations, making it difficult to accurately measure the relative distance between the hoisting unit and the building structure. Especially when approaching the installation floor, inaccurate distance measurements can easily lead to collision accidents.

[0007] Secondly, there is a lack of dynamic measurement methods for the spatial morphology of cables. As a key component connecting the hook and the tower crane, the spatial morphology of the cable (such as swing amplitude and bending curvature) is an important geometric parameter for assessing the safety of hoisting. However, existing technologies (including static measurement schemes based on three-dimensional point clouds) are unable to reconstruct the dynamic spatial morphology of the cable in real time and accurately, nor can they extract the corresponding time-varying parameters for safety early warning.

[0008] Furthermore, the distance measurement error caused by cable deformation has not been effectively compensated for. Cables undergo elastic elongation when subjected to loads, especially under conditions of heavy lifting and high height, where deformation cannot be ignored. Simultaneously, changes in ambient temperature cause thermal expansion or contraction of the cable. Current technology has not established a measurement error compensation model that integrates elastic deformation and thermal deformation. These deformations directly lead to significant deviations in distance values ​​measured by acoustic waves or mechanical encoders, severely affecting the positioning accuracy of the lifting unit.

[0009] In addition, some existing monitoring systems consume a lot of power and mostly adopt a continuous sensor acquisition mode, which makes it difficult to meet the endurance requirements of long-term continuous hoisting operations of curtain walls of super high-rise buildings; and some technologies rely on positioning methods such as GNSS / RTK, which are prone to signal blockage in densely built areas, limiting their adaptability to different scenarios.

[0010] Therefore, how to achieve high-precision real-time measurement of the distance between the hook and curtain wall unit and the main building during hoisting, accurate reconstruction of the dynamic spatial morphology of the cable, and effective compensation for measurement errors caused by cable elastic deformation and thermal deformation are technical issues worthy of attention in this field. Summary of the Invention

[0011] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows:

[0012] According to a first aspect of the present invention, a method for cable morphology reconstruction and distance compensation based on visual-acoustic joint measurement is provided, comprising:

[0013] S100, acquires geometric feature data of the building curtain wall's outline and receives acoustic signals from the top of the tower crane; the acoustic signals include a first acoustic wave propagating along the cable and a second acoustic wave propagating through space.

[0014] S200, based on the geometric feature data of the outline, measure the horizontal distance between the hook and the curtain wall, and measure the vertical speed of the hook.

[0015] S300: Measure the initial vertical distance based on the propagation time of the first sound wave, estimate the current suspended weight, calculate the elastic elongation of the cable based on the elastic parameters of the cable, and use the elastic elongation to compensate for the initial vertical distance to obtain the compensated true vertical distance.

[0016] S400: Based on the reflected signal of the second sound wave, calculate and fit the spatial coordinates of multiple discrete reflection points on the cable to reconstruct the spatial shape of the cable and extract the swing and bending parameters of the cable.

[0017] S500, by integrating the vertical velocity and the acoustic velocity derived from the acoustic signal, and combining the actual vertical distance and the horizontal distance, the three-dimensional spatial coordinates of the hook are determined.

[0018] S600, compare the three-dimensional spatial coordinates of the hook, the swing parameters and bending parameters of the cable with the corresponding preset thresholds, and output the comparison results.

[0019] According to a second aspect of the present invention, a cable morphology reconstruction and distance compensation system based on visual-acoustic joint measurement is provided, comprising:

[0020] The detection terminal, fixedly installed on a hoisting hook, includes: an infrared vision sensor for collecting geometric feature data of the building curtain wall outline, an acceleration sensor for collecting acceleration data, a temperature sensor for collecting ambient temperature, a sound sensor array consisting of at least three microphones, and a processor.

[0021] The tower crane terminal, fixedly installed at the top of the tower crane, includes: an electromagnetic hammer for striking the cable to generate a first sound wave, at least one sound wave transmitter for emitting a second sound wave, and a control unit.

[0022] A communication unit is used to establish data communication between the detection terminal and the tower crane terminal.

[0023] The processor is configured to perform the steps of the method described in the first aspect.

[0024] This invention achieves high-precision joint measurement of hook position and cable morphology during hoisting through the collaborative processing of multi-source sensor data. Specifically, the method first uses collected geometric feature data of the building curtain wall outline to measure the horizontal distance between the hook and the curtain wall, and calculates the vertical velocity of the hook, providing a visual measurement benchmark for subsequent positioning. Simultaneously, the initial vertical distance is measured based on the propagation time of the first sound wave, and the elastic elongation is calculated by combining the estimated current hoisting weight and cable elastic parameters to compensate for errors in the initial vertical distance, thereby eliminating the influence of cable elastic deformation on the accuracy of distance measurement and obtaining a more accurate true vertical distance. Based on this, the method further utilizes the reflected signal of the second sound wave to calculate and fit the spatial coordinates of multiple discrete reflection points on the cable, reconstructing the spatial morphology of the cable and extracting its swing amplitude and curvature, achieving precise measurement of the dynamic geometric parameters of the cable. By fusing vertical velocity and sound wave velocity, and combining the true vertical distance and horizontal distance to determine the three-dimensional spatial coordinates of the hook, the method leverages both the advantages of sensitive dynamic response of vertical velocity and the good long-term stability of sound wave velocity, improving the reliability of velocity measurement. Finally, the measured three-dimensional spatial coordinates of the hook, the swing amplitude of the cable, and the bending curvature are compared with preset thresholds, and the comparison results are output, providing comprehensive measurement data support for hoisting operations.

[0025] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 The flowchart illustrates a cable morphology reconstruction and distance compensation method based on visual-acoustic joint measurement, as provided in an embodiment of the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0030] It should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the steps as sequential processes, many of these steps can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the steps can be rearranged. A process can be terminated when its operation is complete, but it may also have additional steps not included in the figures. A process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0031] Example 1

[0032] This invention provides a method for cable morphology reconstruction and distance compensation based on combined visual-acoustic measurement. It aims to solve metrological problems during the hoisting of curtain walls in super high-rise buildings, including accurate measurement of the distance between the hook and the curtain wall, real-time acquisition of the cable's spatial geometry, and compensation for distance measurement errors caused by cable elastic deformation. Through multi-sensor fusion and error compensation technology, high-precision measurement of the spatial position (three-dimensional coordinates) of the hoisting unit and the cable's geometric parameters (swing amplitude, curvature) is achieved, providing accurate measurement data for hoisting operations.

[0033] This method is primarily applied to real-time monitoring of tower crane hoisting operations for curtain wall units in super high-rise buildings exceeding 100m in height. It aims to solve technical problems such as low positioning accuracy, uncontrollable cable conditions, and large ranging errors caused by deformation in super high-rise hoisting scenarios. It is applicable to hoisting operation systems consisting of tower cranes, hoisting cables, hoisting hooks, and curtain wall units. Data acquisition and signal interaction are achieved through a multi-sensor detection terminal fixed to the hoisting hook, an electromagnetic hammer deployed at the top of the tower crane, and a sonic transmitter. Figure 1 As shown, the method specifically includes the following steps:

[0034] S100, Data Acquisition Steps: Acquire geometric feature data of the building's curtain wall outline and receive acoustic signals from the top of the tower crane.

[0035] This step involves acquiring geometric feature data of the building's curtain wall outline using a detection terminal installed on the hoisting hook, and receiving acoustic signals from the top of the tower crane. The acoustic signals include a first acoustic wave and a second acoustic wave. The first acoustic wave is generated by the electromagnetic hammer striking the cable, propagating along the cable's axis, and is used to measure the initial vertical distance and estimate the suspended weight. The second acoustic wave is emitted by a sound wave transmitter, propagating through the air, and is used for reconstructing the spatial shape of the cable.

[0036] In this article, curtain wall refers to the external envelope of a building, including the panels and their support system. Curtain wall unit refers to the basic assembly components that make up the curtain wall, including glass panels, metal panels and their attached frames or connectors. During the hoisting process, the curtain wall unit is suspended from the tower crane by cables and must be precisely positioned to the preset installation location.

[0037] To achieve the above data collection, the deployment and parameter calibration of the detection terminal and tower crane terminal must be completed in advance, as detailed below:

[0038] (I) Deployment and Calibration of Testing Terminals

[0039] The detection terminal is fixedly installed on a hoisting hook. Its built-in sensors include an infrared vision sensor, an accelerometer, a sound sensor array consisting of at least three microphones, and a temperature sensor. During installation, the detection terminal's orientation is adjusted so that the infrared vision sensor faces the curtain wall, and the angle between its optical axis and the curtain wall plane is no greater than a preset angle, such as 15 degrees. This ensures clear acquisition of the geometric features of the building's curtain wall, including the edges of the curtain wall units and glass gaps. The detection terminal is rigidly fixed to ensure that the relative positions of the built-in sensors remain unchanged during hoisting, avoiding measurement errors caused by shaking.

[0040] All microphones in the sound sensor array are arranged in an equilateral triangle geometry to collect multipath reflection signals of sound waves. They can simultaneously capture the direct wave of the sound wave and the echo signals reflected from different positions on the hoisting cable, providing the original signals for subsequent reconstruction of the cable's spatial morphology.

[0041] (II) Deployment and Calibration of Tower Crane Terminals

[0042] The tower crane terminal is fixedly installed at a predetermined position at the top of the tower crane and includes an electromagnetic hammer, at least three acoustic wave transmitters, and a control unit (an STM32F407 microcontroller in this embodiment). The three acoustic wave transmitters are arranged in an equilateral triangle. The spatial three-dimensional coordinates of each acoustic wave transmitter are accurately calibrated and recorded using a total station, serving as the reference for subsequent calculation of the spatial coordinates of the cable reflection point.

[0043] The electromagnetic hammer and each acoustic transmitter are electrically connected to the control unit, which controls the working sequence: the electromagnetic hammer periodically strikes the hoisting cable at a preset striking frequency (1Hz in this embodiment) to generate the first acoustic wave that propagates along the cable axis; each acoustic transmitter emits an ultrasonic pulse with a center frequency of 40kHz as the second acoustic wave at a preset emission sequence (50ms interval in this embodiment) to avoid mutual interference between the signals of each acoustic transmitter.

[0044] (III) System parameter calibration

[0045] System parameter calibration includes visual sensor calibration, accelerometer threshold setting, acoustic parameter setting, and cable elasticity parameter calibration.

[0046] (1) Visual sensor calibration: Set the resolution and frame rate of the visual sensor, measure and record the lens focal length; acquire images of the frame or glass gaps of the curtain wall unit with known actual spacing, and eliminate lens distortion error through multiple sampling calibrations; at the same time, spray reflective marking points on the cable at preset intervals (1m in this embodiment) as a benchmark for subsequent visual verification of elastic deformation. In this embodiment, the resolution of the infrared visual sensor is set to 1920×1080, and the acquisition frame rate is set to 30fps.

[0047] (2) Accelerometer threshold setting: Set the wake-up threshold of the accelerometer (1 m / s in this embodiment). 2 The detection terminal automatically wakes up and enters working state when it detects vertical movement of the hook, and automatically enters sleep state when it detects that the hook has been stationary for longer than the preset sleep trigger time, keeping only the accelerometer and temperature sensor working, so as to achieve low power consumption control of the detection terminal.

[0048] (3) Sound wave parameter setting: calibrate the reference propagation speed of sound waves in the hoisting cable (1200m / s in this embodiment); configure UWB wireless synchronization modules in both the detection terminal and the tower crane terminal to achieve microsecond-level time synchronization between each sound wave transmitter and the sound sensor array of the detection terminal, and ensure that the measurement accuracy of the sound wave multipath reflection time is controlled within ±1ms.

[0049] (4) Cable elasticity parameter calibration: Under no-load conditions, the initial spatial position of two fixed reflective markers on the hoisting cable is collected by an infrared vision sensor; then, starting from 0 kg, the standard lifting weight is gradually increased to the maximum design lifting weight of the tower crane in equal gradients. After each loading is completed, the actual displacement of the markers is measured by an infrared vision sensor, and the cable elongation under the corresponding lifting weight is calculated; a cable elastic elongation model is established based on Hooke's law, and the elastic modulus E and equivalent cross-sectional area A of the hoisting cable are obtained by fitting multiple sets of measured data of lifting weight and elongation; at the same time, the cable elongation at different ambient temperatures is recorded, and the linear expansion coefficient α and reference temperature T0 of the cable are calibrated (25℃ in this embodiment); the linear density ρ (mass of cable per unit length) of the hoisting cable is recorded simultaneously to provide basic parameters for subsequent lifting weight estimation.

[0050] (iv) Data acquisition and synchronization

[0051] After completing the deployment and fixing of the above-mentioned detection terminal and tower crane terminal, and the calibration of all system parameters, start the detection terminal and tower crane terminal, perform multi-source data acquisition according to the following rules, and complete the time synchronization of all acquired data to ensure the accuracy of subsequent calculations:

[0052] (1) Visual data acquisition: After the infrared visual sensor is activated, it continuously acquires the geometric feature data of the building curtain wall outline and the real-time image of the hoisting cable at a preset acquisition frame rate, accurately capturing the visual features of the curtain wall unit frame, glass gaps, and reflective markers on the cable. At the same time, it records the number of curtain wall frames or glass gaps passing through the visual sensor acquisition area per unit time, the number of visual samples, and the time interval between single samples, providing data support for subsequent calculation of the vertical speed of the hook; it also records the pixel spacing change of the reflective markers on the cable, providing data for the visual verification of subsequent cable elastic deformation compensation.

[0053] (2) Acoustic data acquisition: The control unit of the tower crane terminal triggers the electromagnetic hammer to strike the hoisting cable at a preset striking frequency, generating the first sound wave that propagates along the cable; the sound sensor array of the detection terminal synchronously records the propagation time t of the first sound wave from the top of the tower crane through the cable to the detection terminal. g This provides data support for subsequent vertical distance calculations and cable elastic deformation compensation. Simultaneously, each acoustic transmitter emits ultrasonic pulses as a second acoustic wave according to a preset transmission sequence. The sound sensor array synchronously records the direct wave of the second acoustic wave received by each microphone and the echo signals reflected from different positions on the cable, extracting the arrival time of each signal to provide data support for subsequent cable spatial morphology reconstruction.

[0054] (3) Auxiliary data acquisition: The temperature sensor of the detection terminal collects the ambient temperature T of the hoisting site in real time at a preset sampling frequency, which is used for subsequent real-time correction of air velocity and calculation of cable thermal deformation; the acceleration sensor synchronously and continuously collects the vertical acceleration a of the hoisting hook. z After filtering, the data provides support for subsequent load estimation.

[0055] (4) Data synchronization: Through timestamp alignment technology, all data collected by infrared vision sensor, sound sensor array, temperature sensor and acceleration sensor are synchronized in time to ensure that the collection time of each group of data is completely consistent, and to avoid errors in subsequent speed fusion calculation, cable shape reconstruction and deformation compensation due to time deviation.

[0056] S200, Distance and speed calculation steps: Based on the geometric feature data of the outline, measure the horizontal distance between the hook and the curtain wall, and measure the vertical speed of the hook.

[0057] This step, based on the geometric feature data of the building curtain wall outline collected by S100, calculates the horizontal distance between the hook and the curtain wall, as well as the vertical velocity of the hook, as follows:

[0058] (a) Calculation of horizontal distance

[0059] Horizontal distance calculation utilizes visual sensor-acquired curtain wall feature images, based on imaging geometry. Specifically, it includes the following sub-steps:

[0060] First, visual sensors are used to acquire images of the curtain wall features. These features include the frames of the curtain wall units or the gaps between the glass panes.

[0061] Next, the pixel difference corresponding to the curtain wall feature is extracted from the curtain wall feature image. Specifically, any clear curtain wall feature in the image is selected, and the number of pixels occupied by this feature in the image is identified, denoted as D. i The subscript i is used to distinguish different curtain wall features.

[0062] Then, based on the pixel difference, the pre-calibrated lens focal length, and the actual spacing of the curtain wall features, the horizontal distance between the hook and the curtain wall is calculated according to imaging geometry. According to the principle of similar triangles, the horizontal distance between the hook and the curtain wall satisfies a proportional relationship with the lens focal length, the actual spacing of the curtain wall features, and the image pixel difference. Let the pre-calibrated lens focal length be f, and the actual spacing of the curtain wall features be L. i (Obtained through prior calibration), then the horizontal distance D between the hook and the i-th curtain wall feature is... qi Calculate using the following formula: D qi =(L i ×f) / D i .

[0063] To improve measurement accuracy, multiple curtain wall features can be selected to calculate the horizontal distance separately, and the arithmetic mean of the calculation results can be taken as the final output.

[0064] (II) Calculation of Vertical Velocity

[0065] Vertical velocity calculation utilizes continuously acquired curtain wall feature images from a vision sensor. The vertical velocity of the hook is estimated by the number of curtain wall features passing through the visual acquisition area per unit time. Specifically, it includes the following sub-steps:

[0066] First, visual sensors are used to continuously acquire feature images of the curtain wall, and a unit time is set (in this embodiment, it is taken as the sampling time interval of the visual sensors, denoted as t).

[0067] Secondly, feature extraction is performed on the collected curtain wall feature images to identify the number of curtain wall features passing through the visual acquisition area per unit time, denoted as N.

[0068] Then, based on the product of the quantity and the actual spacing of the pre-calibrated curtain wall features, the displacement of the hook within the unit time is calculated, i.e., Let the sampling time be N×L.

[0069] Finally, the vertical velocity of the hook is calculated based on the ratio of the displacement to the unit time. To improve measurement stability, a sampling correction coefficient E is introduced. This coefficient is dynamically adjusted based on the number of samplings and the confidence level of feature recognition. The vertical velocity V... v Calculate using the following formula: V v =(E×N×L) / t. The sampling correction coefficient E is used to correct the measurement error caused by the mismatch between the visual sampling frequency and the feature motion speed. The value range is 0.8~1.2, and it can be determined through prior calibration.

[0070] The above calculations provide the horizontal distance between the hook and the curtain wall, as well as the vertical speed of the hook, providing basic data for subsequent steps.

[0071] S300, Vertical distance compensation step: Obtain the initial vertical distance based on the propagation time of the first sound wave, estimate the current suspended weight and calculate the elastic elongation of the cable based on the elastic parameters of the cable, and use the elastic elongation to compensate for the initial vertical distance to obtain the compensated true vertical distance.

[0072] This step, based on the first acoustic wave propagation time, acceleration data, and temperature data collected by the S100, eliminates the influence of cable elastic deformation and thermal deformation on ranging accuracy through load estimation and deformation compensation, as detailed below:

[0073] (a) Obtaining the initial vertical distance

[0074] The propagation time t of the first sound wave collected by S100 from the top of the tower crane through the cable to the detection terminal is based on... g Combined with the pre-calibrated speed of sound propagation V in the cable g Calculate the initial vertical distance L between the hook and the top of the tower crane. g :L g =V g ×t g .

[0075] (ii) Real-time lifting weight estimation

[0076] To accurately calculate the elastic elongation of the cable, the current load must first be estimated. This embodiment provides two methods for estimating the load, which can be used individually or combined for verification:

[0077] Method 1: Back-calculation method based on acceleration dynamics

[0078] The vertical acceleration 'a' of the hook is collected by an accelerometer. z The acceleration data is filtered to remove high-frequency noise, and then the velocity and displacement of the hook are obtained through integration. The dynamic equations of the hook and the suspended load as a whole are then established.

[0079] T-(m+m hook )×g=(m+m hook )×a z .

[0080] Where T is the cable tension, and m is the weight to be estimated. hook Let g be the weight of the hook (a known constant), and g be the acceleration due to gravity. By solving the above equations, the cable tension T can be derived, and then the real-time suspended weight m can be calculated:

[0081] m=T / gm hook -T×a z / g 2 .

[0082] Method 2: Sound wave speed reverse calculation method

[0083] The first sound wave generated by striking the cable with an electromagnetic hammer propagates along the cable's axis to the detection terminal. This is based on the cable's extended length L. rope (Read by the tower crane winch encoder) and the total sound wave propagation time t wave Calculate the average wave velocity V of the sound wave in the cable. s V s =L rope / t wave .

[0084] Based on the relationship between sound wave velocity and cable tension V s =(T / ρ) 1 / 2Where ρ is the linear density of the cable (obtained through prior calibration), the average tension of the cable T = ρ(V) is calculated. s ) 2 Substitute into the lifting weight calculation formula:

[0085] m=ρ×(V s ) 2 / gm hook .

[0086] To improve estimation accuracy, the average value (AvgV) of the wave velocity results from multiple strikes can be taken. s To reduce the error of a single measurement, the final estimated weight is: m = ρ × (AvgV) s ) 2 / gm hook .

[0087] (III) Calculation and Compensation of Deformation

[0088] Under the influence of a suspended load, the cable will undergo elastic elongation, and changes in ambient temperature will also cause thermal deformation, requiring comprehensive compensation for the initial vertical distance. Specifically, this includes the following steps:

[0089] (1) Obtain the length L of the cable released. rope and ambient temperature T.

[0090] (2) Calculate the elastic elongation ΔL based on the load, the length of the cable released, the elastic modulus E of the cable, and the equivalent cross-sectional area A. elastic .

[0091] According to Hooke's Law, elastic elongation is directly proportional to the suspended weight and the length of the cable released, and inversely proportional to the elastic modulus and equivalent cross-sectional area of ​​the cable. The calculation formula is as follows:

[0092] ΔL elastic =m×g×L rope / (E×A).

[0093] E and A were obtained through pre-loading calibration.

[0094] (3) Calculate the thermal deformation ΔL based on the linear expansion coefficient of the cable, the length released, and the difference between the ambient temperature and the reference temperature. thermal The amount of thermal deformation is directly proportional to the change in ambient temperature and the length of the cable released. The calculation formula is as follows:

[0095] ΔL thermal =α×L rope ×(T-T0).

[0096] Where α is the linear expansion coefficient of the cable, and T0 is the reference temperature (usually taken as 25℃). α is obtained through elongation tests at different temperatures in the early stages.

[0097] (4) Take the sum of the elastic elongation and the thermal deformation as the total compensation amount, and subtract the total compensation amount from the initial vertical distance to obtain the true vertical distance H. true :

[0098] H true =L g -(ΔL elastic +ΔL thermal ).

[0099] Furthermore, by combining the altitude H (in meters) of the top of the tower crane, the true vertical coordinate Z of the hook can be calculated. t Z t =HH true .

[0100] (iv) Visual-assisted verification and correction

[0101] To improve the long-term accuracy of deformation compensation, this embodiment also introduces a visual-assisted verification mechanism. Specifically, it includes the following steps:

[0102] First, reflective markers are sprayed onto the cable at predetermined intervals (e.g., one every 1 meter) as a reference for visual measurement. Images of the reflective markers on the cable are acquired in real time using a visual sensor. The actual displacement of the reflective markers is calculated using a pixel matching algorithm, and this actual displacement is used as the visually measured elongation, denoted as ΔL. vision .

[0103] Secondly, the visually measured elongation is compared with the calculated elastic elongation, and the absolute value of the difference between the two is calculated: |ΔL vision -ΔL elastic |

[0104] When the absolute value is greater than the preset error threshold ε th When this occurs, it indicates that the currently calibrated elastic modulus E and equivalent cross-sectional area A deviate from the actual working conditions, triggering the parameter correction mechanism.

[0105] The specific correction mechanism is as follows:

[0106] (1) Based on the currently acquired visual measurement of elongation ΔL vision , Lifting weight (m), Cable release length Lrope Given the gravitational acceleration g, we can deduce the equivalent elastic coefficient EA under the current working condition:

[0107] EA=mgL rope / ΔL vision .

[0108] Where EA is the product of the elastic modulus and the equivalent cross-sectional area.

[0109] (2) The elastic modulus and equivalent cross-sectional area are updated smoothly using a recursive filtering method. Let the estimated value of the equivalent elastic coefficient at the current moment be EA. c k The equivalent elasticity coefficient at the previous moment was EA. c k-1 The weighted update is performed using the following formula:

[0110] EA c k =(1-β)EA c k-1 +β×EA.

[0111] Wherein, β is the update weight coefficient, which ranges from 0 to 1 (e.g., 0.2), and is used to balance the contribution of historical values ​​and current measurements to avoid drastic fluctuations in parameters caused by noise from a single measurement.

[0112] (3) Based on the updated equivalent elastic coefficient, correct the elastic modulus E and the equivalent cross-sectional area A. In this embodiment, keep the equivalent cross-sectional area unchanged at the initial calibration value, and only correct the elastic modulus: E=EA c k / A.

[0113] Through the above feedback correction mechanism, the elastic parameters of the cable can be adaptively calibrated, making the subsequent calculation of elastic elongation more accurate and ensuring the compensation accuracy under different lifting weights and working conditions.

[0114] Through the above steps, comprehensive compensation for the elastic deformation and thermal deformation of the cable is achieved, eliminating the influence of deformation on the ranging accuracy and providing a high-precision true vertical distance for subsequent positioning steps.

[0115] S400, Cable spatial morphology reconstruction steps: Based on the reflection signal of the second sound wave, calculate the spatial coordinates of multiple discrete reflection points on the cable and fit them to reconstruct the spatial morphology of the cable, and extract the swing parameters and bending parameters of the cable.

[0116] This step, based on the second acoustic wave reflection signal acquired by the S100, solves for the spatial coordinates of discrete reflection points on the cable using the principle of multipath acoustic wave reflection and geometric constraints. It then fits the spatial curve of the cable and extracts characteristic parameters reflecting the cable's geometric shape, providing data support for subsequent measurement and comparison. Specifically, it includes the following sub-steps: (I) Reflected wave signal processing.

[0117] The sound sensor array consists of at least three microphones arranged in an equilateral triangle geometry to collect multipath reflection signals of sound waves. Three sound wave transmitters at the top of the tower crane sequentially emit ultrasonic pulses as secondary sound waves according to a preset timing sequence, and each microphone synchronously receives the direct wave and the echo signals reflected from different positions on the cable.

[0118] Matched filtering or cross-correlation algorithms are used to process the signals acquired by the sound sensor array, extracting the arrival time of the sound waves along each reflection path. Specifically, a preset ultrasonic pulse reference waveform w(t) (consistent with the waveform of the second sound wave emitted by the sound transmitter) is used, and the received signal r(t) actually acquired by the sound sensor array (including the direct wave and the echo signal reflected from different positions on the cable) is cross-correlated with the reference waveform.

[0119] .

[0120] The cross-correlation function value reflects the degree of matching between the received signal and the shifted reference waveform. The time offset (delay time) represents the amount by which the reference waveform w(t) is shifted along the time axis. By setting a reasonable amplitude threshold, spurious peaks caused by noise interference are removed, and the arrival time of the effective reflection path is preserved, denoted as t. ajb Where a is the sound wave transmitter number (a=1,2,3), j is the microphone number (j=1,2,3), b is the sequence number of the b-th reflection point, and t ajb This is the propagation time of the sound wave emitted by the a-th sound wave transmitter, after being reflected by the b-th reflection point, and received by the j-th microphone.

[0121] (ii) Real-time correction of air speed of sound

[0122] Since the speed of sound in air is affected by ambient temperature, the air velocity needs to be corrected based on the real-time ambient temperature T collected by a temperature sensor. The correction formula is as follows:

[0123] V air =331.3×(1+T / 273.15) 1 / 2 .

[0124] Where T is in degrees Celsius, and V is the corrected speed of sound in air. air The unit is meters per second. The corrected air speed of sound is used for subsequent calculations of the sound wave propagation path length.

[0125] (III) Calculation of Discrete Reflection Points of Cables

[0126] For each combination of sound wave transmitter and sound sensor, based on the sound wave arrival time t ajb Using the real-time corrected air velocity, calculate the total path length of the sound wave from the transmitter, through the cable reflection point, to the microphone:

[0127] L=V air ×t ajb .

[0128] The reflection point P(x,y,z) satisfies the geometric constraints of an ellipsoid with the sound wave transmitter and the sound sensor as foci, meaning the sum of the distances from the reflection point to the two foci equals the total path length of the sound wave propagation. [(xx[S a ])) 2 +(yy[S a ]) 2 +(zz[S a ]) 2 ] 1 / 2 +[(xx[M j ]) 2 +(yy[M j ]) 2 +(zz[M j ]) 2 ] 1 / 2 =L.

[0129] Among them, (x[S a ],y[S a ],z[S a ]) represents the spatial coordinates of the a-th acoustic transmitter (pre-calibrated using a total station), (x[M j ],y[M j ],z[M j ]) represents the spatial coordinates of the j-th microphone (determined by the geometry and installation location of the detection terminal).

[0130] Since a single transmitter-microphone pair can only determine one ellipsoid and cannot uniquely determine the location of the reflection point, it is necessary to construct an overdetermined system of equations by combining measurements from multiple transmitter-microphone pairs. Furthermore, considering the physical characteristics of the cable as a slender and flexible body, the coordinates of the reflection point at adjacent times or adjacent arc lengths should change continuously and smoothly, thus introducing a continuity constraint: ΔP ≤ Δ max Where ΔP is the change in spatial distance between adjacent reflection points, Δ max This is the preset maximum position change threshold.

[0131] The optimal spatial coordinates of the reflection point P are determined using the least squares method, with the objective function being:

[0132] .

[0133] in:

[0134] A1= A2= M represents the total number of effective reflection paths. By solving the above optimization problem, we obtain the spatial coordinate sequence {P1, P2, ..., Pn} of multiple discrete reflection points on the cable, where n is the total number of discrete reflection points.

[0135] (iv) Cable spatial morphology fitting

[0136] Taking the coordinates r0(x0,y0,z0) at the top of the tower crane as the starting point of the curve, the visual-sound fusion coordinates at the hook point are r hook (x hook ,y hook ,z hook The endpoint of the curve is s0, s1, s2, ..., s3. The discrete reflection point sequence obtained from the solution is arranged in order of cable length to obtain the arc length coordinates s0, s1, s2, ..., s4. q Where s0=0, s q =L rope q represents the total number of discrete points minus one, and L rope This represents the total length of the cable.

[0137] A curve fitting algorithm, such as a cubic spline curve, is used to fit discrete points to construct the parametric equation r(s) of the cable space curve, where s is the arc length parameter, representing the actual length along the cable from the top of the tower crane to the current position. The cubic spline curve satisfies the following within each segmented interval:

[0138] r(s) = a g ×s 3 +b g ×s 2 +c g ×s+d g ,s∈[s g-1 ,s g ].

[0139] Where the subscripts g=1 to q represent the index of the segmented interval, s g-1 and s g These are the starting and ending arc length coordinates of the g-th segment interval, respectively, a g b g c g d g Let be the coefficients of the cubic polynomial for the g-th segmented interval. These coefficients are solved by ensuring the continuity of the endpoints of adjacent intervals, the continuity of the first derivative, and the continuity of the second derivative, thus ensuring the smooth and continuous fitting curve. Finally, a complete spatial morphology model of the cable is obtained.

[0140] To improve fitting accuracy, the morphological model can be calibrated using cable images acquired by a visual sensor, and the spatial position of discrete points can be corrected using the visible outline of the cable in the image.

[0141] (v) Extraction of cable swing and bending parameters

[0142] The core parameters reflecting the dynamic characteristics of the cable are extracted from the fitted cable space curve r(s)=[x(s),y(s),z(s)], where x(s), y(s), and z(s) are the coordinates of a point at arc length s on the cable in the horizontal (x, y-axis) and vertical (z-axis) directions, respectively. The oscillation parameters include oscillation amplitude and oscillation frequency, and the bending parameters include bending curvature. The specific calculation methods for each parameter are as follows:

[0143] Swing amplitude: Calculate the maximum horizontal distance at which the cable deviates from its vertical equilibrium position at the hook point, i.e.:

[0144] A max =max s∈[0,Lrope] [(x(s)−x0) 2 +(y(s)−y0) 2 ] 1 / 2 .

[0145] This parameter reflects the maximum degree of horizontal deflection of the cable.

[0146] Oscillation frequency: By analyzing the hook point (i.e., s=L) rope The time-domain waveform of the horizontal displacement is obtained, and the number of times f of the cable completes reciprocating swing per unit time is calculated using peak detection or spectrum analysis methods.

[0147] Bending curvature: The curvature of a cable curve is used to characterize the degree of bending of the cable. The formula for calculating curvature is:

[0148] k(s)=||r′′(s)|| / (1+||r′(s)|| 2 ) 3 / 2 .

[0149] Where r′(s) is the first derivative of the cable's spatial curve, representing the tangential direction of the curve; and r′′(s) is the second derivative, representing the bending direction of the curve. Calculate the maximum value k of the curvature of the entire cable. max =max s k(s) serves as a key indicator for assessing the risk of cable bending.

[0150] Through the above steps, the spatial morphology of the cable was accurately reconstructed, and key parameters such as swing amplitude, swing frequency and bending curvature were extracted, providing reliable measurement data for subsequent comparison results.

[0151] S500, speed fusion and positioning steps: fuse the vertical speed and the sound wave speed derived from the sound wave signal, combine the actual vertical distance and the horizontal distance, and determine the three-dimensional spatial coordinates of the hook.

[0152] This step fuses the vertical velocity derived from the visual sensor and the acoustic velocity derived from the acoustic sensor using a weighted Kalman filter. The fused velocity is then used to dynamically correct the hook's position. Combined with the actual vertical and horizontal distances after elastic deformation compensation, the three-dimensional spatial coordinates of the hook are determined. Specifically, this includes the following sub-steps:

[0153] (I) Sensor speed measurement model

[0154] Vertical velocity measurement: Vertical velocity V calculated through step S200. v This is used as a measurement of vertical velocity. The velocity is calculated by identifying the number of curtain wall features passing through the visual acquisition area per unit time, based on images of the curtain wall unit frame or glass gaps acquired by a vision sensor. The measurement error of vertical velocity mainly comes from factors such as frame recognition accuracy and changes in illumination; it is assumed that its noise follows a Gaussian distribution N(0,σ1). 2 ). σ1 2 The observation noise variance of the vertical velocity was determined through prior static calibration and dynamic testing: Multiple sets of vertical velocity measurements were continuously collected while the hook was stationary, and their statistical variance was calculated as σ1. 2 The base value; during the actual hoisting process, it is dynamically adjusted based on the confidence level of visual features.

[0155] Sound wave velocity measurement: The sound wave velocity is measured by striking the cable with an electromagnetic hammer to generate the first sound wave. The velocity is then determined based on the sound wave's propagation time t within the cable. g and the pre-calibrated sound wave propagation speed V g The speed of sound V s =L g / t g Because after elastic deformation compensation, Lg=V g ×t g Therefore, V s =V g The velocity measurement has been corrected for deformation effects through elastic deformation compensation in step S300. Its measurement error mainly stems from the accuracy of the sound wave propagation time measurement, assuming that its noise follows a Gaussian distribution N(0,σ²). 2 ). σ2 2 The observation noise variance of sound wave velocity was determined through prior static calibration: under conditions of no external interference, multiple sets of sound wave velocity measurements were continuously collected, and their statistical variance was calculated as σ². 2 A fixed value.

[0156] (ii) Weighted Kalman filter fusion

[0157] A weighted Kalman filter is used to fuse vertical velocity and acoustic velocity. The fusion effect is optimized by dynamic weight allocation, while the long-term stability of acoustic velocity is used to suppress the cumulative drift of vertical velocity.

[0158] State equation: Define the vertical velocity of the hook as the state variable, denoted as V. v The state vector is x=[V v Let the state vector at time k (the current time) be x (i.e., the estimated vertical velocity at time k). k The system state transition equation is: x k =F·x k-1 +w k-1 .

[0159] Where F=[1] is the state transition matrix, x k-1 The state vector at time k-1 (the previous time step) represents the estimated vertical velocity at time k-1 (posterior estimate). k-1 The process noise at time k-1 is used to describe the random uncertainty during state transition. It is usually assumed to follow a Gaussian distribution with zero mean and covariance Q, i.e., w k-1 ~N(0,Q) represents the process noise. Q represents the process noise covariance, which reflects the degree of random fluctuation in velocity variation.

[0160] Prediction Phase: Based on the state estimate from the previous time step, predict the velocity state and covariance at the current time step.

[0161] x c- k =F·x c k-1 ;P c- k =F·P c k-1 ·F T +Q;

[0162] Where, x c- k Let P be the predicted velocity value at time k. c- k Let x be the covariance of the velocity prediction at time k. c k-1 The velocity estimate at time k-1 is the fused velocity P. c k-1 The covariance is calculated for the velocity estimate at time k-1.

[0163] Update phase: The predicted value is corrected using the current observations. Kalman gain K k Dynamically adjusted based on the observed noise covariance R:

[0164] K k =P c- k ·H T (H·P c-k ·H T +R) -1 .

[0165] Based on the observed value Z k (i.e., the current vertical velocity or sound wave velocity) Update state estimate:

[0166] x * k =x c- k +K k (Z) k -H·x c- k ).

[0167] Update the covariance matrix:

[0168] P k =(IK k ·H)·P c- k .

[0169] Where H=[1] is the observation matrix and I is the identity matrix.

[0170] Weighted correction mechanism: Weighting coefficients are introduced to dynamically correct the fused velocity (hereinafter referred to as fused velocity). During the filtering update phase, the observation noise covariance R is adjusted based on the real-time confidence levels of the vertical velocity and the acoustic velocity. Specifically:

[0171] (1) Quantification of confidence in visual features

[0172] The confidence level of visual feature recognition is comprehensively evaluated using the following indicators:

[0173] Edge strength index: In the feature image of the curtain wall acquired by the visual sensor, the edge pixels of the border or gap area are extracted, and the average value G of the edge gradient is calculated. avg When G avg Higher than the preset high threshold G high When, the edges are considered clear; when G avg Below the preset low threshold G low At that time, the edges were considered blurry.

[0174] Contrast ratio: The grayscale contrast C between the feature area of ​​the curtain wall and the background area in the image is calculated using the formula C = (I feature -I background ) / (I feature +I background ), where I feature I represents the average gray level of the feature region. background This represents the average gray level of the background area. Higher contrast results in clearer and more discernible features.

[0175] Feature matching index: The curtain wall features extracted from the current frame are matched with the template of the previous frame, and the normalized cross-correlation (NCC) coefficient h is calculated, with a value range of [-1, 1]. The closer h is to 1, the more reliable the feature matching and the more stable the tracking.

[0176] Overall confidence level C conf Defined as the weighted sum of the three indicators mentioned above:

[0177] C conf =w G ·G avg / G max +w C ·C+w h ·(h+1) / 2.

[0178] Among them, w G w C w h G represents the weighting coefficients (satisfying that the sum of the weights equals 1). max This represents the maximum possible value of the edge gradient. The overall confidence score ranges from [0,1], with values ​​closer to 1 indicating higher visual feature quality and higher recognition confidence.

[0179] (2) Dynamic adjustment of observation noise covariance

[0180] The observation noise covariance R of vertical velocity is dynamically adjusted based on the comprehensive confidence level of visual features. v :

[0181] R v =R v0 ·(1+k1·(1-C con f)).

[0182] Among them, R v0 The basic observation noise covariance for vertical velocity (obtained through prior calibration) is given by k1, which is an adjustment coefficient (ranging from 1 to 10). When C conf When it is high (e.g., greater than 0.8), R v When C is close to the baseline value, vertical velocity has a larger weight in the fusion process; conf When R is less than 0.3 (e.g., less than 0.3), v As the vertical velocity increases significantly, its weight decreases accordingly.

[0183] The observation noise covariance R of sound wave velocity s Relatively stable, mainly affected by environmental noise, a fixed value R was obtained through prior testing and calibration. s0 When the visual feature quality is extremely poor (C conf When R < 0.2, R can be temporarily increased. s To maintain filtering stability.

[0184] (3) Determining the fusion weights

[0185] In the update phase of the Kalman filter, the Kalman gain is calculated based on the current observation type (vertical velocity or acoustic velocity) and its observation noise covariance to achieve adaptive weighted fusion. When the vertical velocity observation noise covariance R... v When the Kalman gain is relatively small, such as less than 0.05, the calculated Kalman gain is relatively large (close to 1), indicating high reliability of the vertical velocity measurement. During filter updates, the vertical velocity is given a larger weight, and the fusion result mainly reflects the changes in vertical velocity. When the observation noise covariance of the vertical velocity is large (e.g., greater than 0.5), the Kalman gain is relatively small (close to 0), indicating that the vertical velocity measurement has high noise and low reliability. During filter updates, the correction effect of the vertical velocity is weakened, and the fusion result tends to rely more on historical predictions or acoustic velocity measurements.

[0186] By dynamically adjusting the observation noise covariance, an adaptive weighted fusion of vertical velocity and acoustic velocity is achieved.

[0187] (4) Fusion speed weighted correction

[0188] The posterior state estimate x is obtained by Kalman filtering update. c k Then, a weighting coefficient f is introduced to dynamically adjust the fusion speed, resulting in the initial fusion speed. The weighted adjustment formula is as follows:

[0189] V init =f·x c k +(1-f)·x c- k .

[0190] in:

[0191] The value of f ranges from 0 to 1 and is used to balance the contributions of the posterior estimate and the predicted value.

[0192] The weighting coefficient f can be dynamically adjusted according to the system state:

[0193] When the confidence level of the observation is high (e.g., clear visual features and stable acoustic signals), a larger value (e.g., 0.8~0.9) is taken to make the fusion speed more inclined to the posterior estimate of the Kalman filter;

[0194] When the confidence level of the observation is low (e.g., blurred visual features or large interference from acoustic signals), a smaller value (e.g., 0.2~0.3) should be used to make the fusion speed more inclined to the predicted value and avoid interference from abnormal observations.

[0195] (5) Long-term reference correction of sound wave velocity

[0196] To suppress potential cumulative drift in vertical velocity, a reference correction is performed using the long-term stability of the acoustic velocity. The specific method is as follows:

[0197] Establish a sliding window queue for the velocity of sound waves, storing the most recent W (e.g., W=50) velocity of sound waves Vs(t), and calculate the median within the window as the reference velocity V. sref :

[0198] V sref =median{Vs(t-W+1), Vs(t-W+2),…,Vs(t)}.

[0199] Where median{} represents taking the median, which can effectively eliminate occasional noise interference and obtain a stable reference speed. Every preset correction period T c (e.g., 30 seconds), compare the fusion speed v fusion With reference speed V sref The deviation Δv=|v fusion -V sre When the deviation exceeds a preset threshold, it indicates that the fusion speed may be drifting. In this case, the initial fusion speed is forcibly corrected towards the reference speed to obtain the final output fusion speed v. fusion :v fusion =V init +γ·(V sref -V init ).

[0200] Wherein, γ is the correction coefficient (ranging from 0.1 to 0.5), which controls the correction step size and avoids abrupt changes.

[0201] Through the above mechanism, weighted correction achieves real-time optimization of Kalman filtering results, while long-term benchmark correction periodically suppresses cumulative drift. The two have a clear division of labor and work together to output a stable and reliable fusion speed.

[0202] (III) Calculation of three-dimensional spatial coordinates

[0203] Vertical coordinate calibration: The true vertical coordinate Z after elastic deformation compensation using the S300 procedure. t According to the fusion speed v fusion Calculate the displacement increment and update the current height correction value in real time. Specifically, let the sampling time interval be Δt (0.1 seconds in this embodiment), then the current height value Z is calculated. t current The height value Z can be obtained from the previous moment. t previous Integrating with the fusion velocity (the velocity estimate at the current moment) yields:

[0204] Z t current=Z t previous +v fusion ·△t.

[0205] This formula is the Euler method (first-order integral) in numerical integration. Given a sufficiently small sampling time interval Δt, it can calculate displacement from velocity with high accuracy.

[0206] To improve vertical positioning accuracy, a periodic calibration mechanism is employed. The horizontal distance D is remeasured at preset intervals (e.g., 10 seconds). qi Then, the corrected vertical coordinate Zt′ is calculated using geometric relationships, and Zt′ is compared with the current fusion height value Z. t current We perform weighted fusion to obtain the final height value:

[0207] Z t final =λ·Z t current +(1-λ)·Zt′.

[0208] Where λ is a weighting coefficient, ranging from 0 to 1, reflecting the degree of confidence in the integral estimate, and can be determined based on the comprehensive confidence level C of the visual features. conf Dynamic adjustment: When C conf When C is higher (e.g., greater than 0.8), λ takes a larger value (e.g., 0.9), indicating greater confidence in the integral estimate; when C conf When the value is low (e.g., less than 0.3), λ takes a smaller value (e.g., 0.5), indicating greater trust in periodic measurements. When λ=1, the integral estimate is fully trusted; when λ=0, the periodic measurement is fully trusted.

[0209] Horizontal coordinate calculation: Based on the horizontal distance calculated in step S200, and combined with the curtain wall contour feature points collected by the visual sensor, the horizontal coordinates (X, Y) of the hook are solved using the principle of triangulation. Specifically, at least two curtain wall feature points are selected to construct a system of equations:

[0210] (X-X1) 2 +(Y-Y1) 2 =(D q1 ) 2 (X-X2) 2 +(Y-Y2) 2 =(D q2 ) 2 .

[0211] Where (X1,Y1) and (X2,Y2) represent the known horizontal coordinates of the first and second curtain wall feature points in the horizontal plane, respectively.

[0212] Solving this system of equations yields two possible horizontal coordinate solutions. The unique hook horizontal coordinate is determined by selecting the solution closest to the coordinates from the previous time step. To improve measurement accuracy, three or more curtain wall feature points can be selected to construct an overdetermined system of equations, and the optimal horizontal coordinate can be solved using the least squares method.

[0213] .

[0214] The obtained horizontal coordinates are processed by Kalman filtering to smooth the position changes.

[0215] 3D Coordinate Integration and Cable Shape Correction: The vertical and horizontal coordinates are integrated to obtain the 3D spatial coordinates (X, Y, Z) of the hook and curtain wall unit. t final ).

[0216] Based on the cable spatial morphology model r(s)=[x(s),y(s),z(s)] reconstructed in step S400, a secondary correction is performed on the horizontal coordinates of the hook. Correction is triggered when the angle between the tangent and vertical directions of the cable curve at the hook is greater than a preset threshold (e.g., 5°), or when the deviation between the horizontal displacement of the cable curve endpoint and the visually measured horizontal displacement is greater than a preset threshold (e.g., 0.1 meters).

[0217] The correction method employs a weighted fusion approach to obtain the final output hook horizontal coordinate (X). final Y final ):

[0218] X final =λ X ·X vision +(1-λ X )·x(L rope );Y final =λ Y ·Y vision +(1-λ Y )·y(L rope ).

[0219] Among them, (X) vision ,Y vision (x(L) represents the horizontal coordinate of the hook obtained by visual measurement) rope ),y(L rope )) represents the horizontal coordinate of the endpoint of the cable curve, with a weighting coefficient λ. X , λ Y The value of λ is dynamically adjusted based on the confidence level of visual measurements: when the visual features are clear and the confidence level is high, λ takes a larger value (e.g., 0.8); when the visual features are blurry and the confidence level is low, λ takes a smaller value (e.g., 0.2).

[0220] The final output is the corrected three-dimensional spatial coordinates (X). final Yfinal Z t final This enables high-precision three-dimensional positioning of the hook.

[0221] Through the above steps, the vertical velocity and sound wave velocity were fused and filtered. Combined with the distance data after elastic deformation compensation, high-precision and high-stability three-dimensional spatial coordinates of the hook were obtained, providing a reliable measurement basis for subsequent comparison results.

[0222] S600, Comparison result output step: Compare the three-dimensional spatial coordinates of the hook, the swing parameters and bending parameters of the cable with the corresponding preset thresholds, and output the comparison results.

[0223] This step compares the hook's three-dimensional spatial coordinates obtained in step S500 and the cable swing and curvature parameters extracted in step S400 with preset thresholds and outputs the comparison results. Specifically, it includes the following sub-steps: Threshold

[0224] (a) Setting the preset threshold

[0225] During the initialization phase, the following preset thresholds are used:

[0226] Height threshold: Set to a preset distance from the rooftop (e.g., 10 meters). Used to determine whether the actual vertical coordinates of the hook exceed this threshold.

[0227] Horizontal distance threshold: Set to a preset distance from the curtain wall (e.g., 0.5 meters). Used to determine whether the horizontal distance between the hook and the curtain wall is lower than this threshold.

[0228] Swing amplitude threshold: This threshold is set based on the physical characteristics of the cable and the hoisting conditions to determine whether the swing amplitude of the cable exceeds this threshold.

[0229] Bending curvature threshold: This threshold is set based on the cable's bending limit and material properties to determine whether the cable's maximum bending curvature exceeds this threshold.

[0230] The above thresholds can be determined through preliminary cable tensile tests, bending fatigue tests, or engineering experience. The specific values ​​can be adapted and adjusted according to different hoisting conditions and cable specifications.

[0231] (ii) Comparison Logic

[0232] The processor of the detection terminal compares each measurement parameter with the corresponding preset threshold in real time and records whether the following conditions are met:

[0233] Condition A: The actual vertical coordinate measurement of the hook is higher than the height threshold;

[0234] Condition B: The measured horizontal distance between the hook and the curtain wall is less than the horizontal distance threshold;

[0235] Condition C: The measured value of the cable's swing amplitude exceeds the swing amplitude threshold;

[0236] Condition D: The maximum bending curvature measurement of the cable exceeds the bending curvature threshold.

[0237] (III) Output of Comparison Results

[0238] The satisfaction of the above conditions will be output as the comparison result. The comparison result can be used to trigger subsequent safety warning operations such as audible and visual alarms and information push notifications. It should be noted that the above alarm and push notification operations are application examples of the comparison result and do not constitute a limitation on the steps of this measurement method. As an example, when any condition is met, the following operations can be performed:

[0239] Audible and visual alarm: The detection terminal activates the built-in buzzer and LED indicator;

[0240] Information push: The comparison results are sent to the tower crane terminal through the communication unit. The tower crane terminal then uploads the data to the server via the 4G network and pushes it to the operator's terminal.

[0241] The entire comparison response process (from parameter detection to result output) is controlled within a preset time, such as 0.5 seconds.

[0242] Furthermore, the comparison results can be recorded in local storage and a remote server. Each record includes: comparison time, measured values ​​of each parameter, each threshold, and items that meet the conditions. The server can perform statistical analysis on the records and generate reports for subsequent evaluation.

[0243] Example 2

[0244] This embodiment provides a cable morphology reconstruction and distance compensation system based on visual-acoustic joint measurement. This system is used to measure the distance between the hook and the curtain wall, the three-dimensional spatial coordinates of the hook, and the geometric parameters of the cable (swing amplitude, curvature, etc.) during the hoisting of curtain walls in super high-rise buildings. The system includes:

[0245] (a) Testing terminal

[0246] The testing terminal is fixedly mounted on a lifting hook. This rigid fixing method ensures that the relative positions of the built-in sensors remain unchanged during hoisting, preventing measurement errors caused by shaking. The testing terminal includes the following components:

[0247] Infrared vision sensor: Used to acquire geometric feature data of the building's curtain wall outline, including the frame of the curtain wall units and glass gaps. The vision sensor resolution can be set to 1920×1080, the frame rate to 30fps, and the lens focal length determined through prior calibration. During installation, adjust the terminal's orientation so that the vision sensor faces the curtain wall, and the angle between its optical axis and the curtain wall plane is no greater than a preset angle, such as 15 degrees, to ensure clear acquisition of the curtain wall outline data.

[0248] Accelerometer: Used to collect acceleration data of the hook, with a range of, for example, ±16g. The accelerometer is also used to detect the motion state of the hook. When the vertical acceleration of the hook exceeds a preset wake-up threshold (e.g., 1m / s²), the terminal is triggered to wake up. When the hook remains stationary for a longer period than a preset sleep trigger time (e.g., 5 minutes), the terminal is triggered to sleep, thus achieving low-power control.

[0249] The sound sensor array consists of at least three microphones arranged in an equilateral triangle geometry to acquire multipath reflection signals of sound waves. Each microphone is capable of simultaneously capturing the direct wave and the echo signal reflected by the cable. The sampling rate is set to an exemplary 44.1 kHz to provide raw sound wave data for reconstructing the spatial morphology of the cable.

[0250] Temperature sensor: Used to collect ambient temperature in real time. The collected temperature data is used for air sound speed correction and cable thermal deformation calculation.

[0251] Processor: Employs an ARM Cortex-M4 processor with a clock speed of 180MHz, used for data acquisition, signal processing, data fusion, and early warning judgment. The processor is configured to execute the steps of any of the methods described in the foregoing method embodiments.

[0252] Communication module: It adopts LoRa wireless transmission unit to establish data communication with tower crane terminal, with a transmission distance of up to 1 kilometer and communication delay controlled within 0.5 seconds.

[0253] (ii) Tower crane terminal

[0254] The tower crane terminal is fixedly installed at the top of the tower crane and includes the following components:

[0255] Electromagnetic hammer: Electrically connected to the control unit, it periodically strikes the cable at a preset frequency (e.g., 1Hz) to generate a first sound wave that propagates along the cable's axis, used for vertical distance measurement and load estimation.

[0256] Acoustic wave transmitters: At least three acoustic wave transmitters are set up in an equilateral triangle layout. Each acoustic wave transmitter is electrically connected to the control unit and transmits ultrasonic pulses with a center frequency of 40kHz as a second acoustic wave according to a preset timing sequence (50ms interval) for cable spatial morphology measurement. The spatial coordinates of each acoustic wave transmitter are pre-calibrated using a total station.

[0257] Control unit: An STM32F407 microcontroller is used to control the striking timing of the electromagnetic hammer and the emission timing of the sound wave transmitter, and to interact with the detection terminal through a communication unit.

[0258] Communication module: It adopts a LoRa wireless transmission unit to establish data communication with the detection terminal, and at the same time uploads the comparison results to the remote server and pushes them to the operator's terminal via the 4G network.

[0259] (III) Communication Unit

[0260] The communication unit includes LoRa wireless transmission modules respectively installed on the detection terminal and the tower crane terminal, used to establish bidirectional data communication between the two terminals. Simultaneously, the tower crane terminal connects to a remote server and operator terminals via a 4G network to enable real-time push of measurement data and comparison results.

[0261] (iv) System initialization and parameter calibration

[0262] During system startup, the following initialization operations are performed:

[0263] Hardware initialization: Start the detection terminal and tower crane terminal, and initialize each sensor module, processing module, and communication module. The processor of the detection terminal completes a self-test to ensure that the infrared vision sensor, acceleration sensor, sound sensor array, and temperature sensor are working properly; the control unit of the tower crane terminal completes the initialization of the electromagnetic hammer and sound wave transmitter to ensure that they can work stably according to the set frequency and timing.

[0264] Software initialization: Load the measurement program and align the data from the vision sensor, sound sensor array, and temperature sensor using timestamps to ensure consistent data acquisition timing. Initialize the weighted Kalman filter parameters, setting the process noise covariance, visual velocity measurement noise covariance, vertical velocity measurement noise covariance, and weighting coefficients. Initialize the relevant program modules for height measurement, horizontal distance measurement, cable spatial morphology measurement, elastic deformation compensation, and comparison result output. Load matched filtering, cross-correlation algorithm, least squares method, and cubic spline curve fitting algorithms for acoustic signal processing, reflection point calculation, and cable morphology fitting.

[0265] Baseline parameter input: The tower crane top height, acoustic transmitter coordinates, microphone array relative position, cable elastic parameters (elastic modulus, equivalent cross-sectional area, coefficient of linear expansion, linear density), and hook self-weight are input into the detection terminal processor as the baseline for subsequent calculations. Gravitational acceleration is also input to provide a basis for load estimation and elastic deformation calculation.

[0266] (V) Working principle of the device

[0267] When the device is working, the accelerometer of the detection terminal monitors the movement of the hook in real time. When the detected hook acceleration exceeds the wake-up threshold, the processor wakes up the detection terminal and activates the infrared vision sensor, sound sensor array, and temperature sensor to collect data.

[0268] The control unit of the tower crane terminal controls the electromagnetic hammer to strike the cable according to a preset timing sequence to generate the first sound wave, and simultaneously controls the sound wave transmitter to emit the second sound wave. The sound sensor array of the detection terminal synchronously collects the reflected signals of the first and second sound waves, the infrared vision sensor collects the geometric feature data of the curtain wall outline, and the temperature sensor collects the ambient temperature.

[0269] The processor processes the collected data as follows: it measures the horizontal distance and vertical velocity between the hook and the curtain wall based on visual data; it measures the initial vertical distance based on the propagation time of the first sound wave, and obtains the true vertical distance by combining the weight estimation and elastic deformation compensation; it reconstructs the spatial morphology of the cable based on the reflection signal of the second sound wave, and extracts the swing amplitude, swing frequency and bending curvature; it fuses the vertical velocity and sound wave velocity through a weighted Kalman filter, and determines the three-dimensional spatial coordinates of the hook by combining the true vertical distance and the horizontal distance; finally, it compares the three-dimensional coordinates of the hook, the swing amplitude and bending curvature of the cable with preset thresholds and outputs the comparison results.

[0270] The comparison results can be sent to the tower crane terminal, remote server, and operator terminal via the communication unit. As an example of the application of the comparison results, when the comparison results show that the measured value exceeds the threshold range, an audible and visual alarm and information push can be triggered so that the operator can adjust the lifting posture in time.

[0271] The method and apparatus provided in this invention achieve the following technical effects through the comprehensive application of technologies such as multi-sensor fusion, multipath reflection of sound waves, elastic deformation compensation, and weighted Kalman filtering:

[0272] (1) Improved distance measurement accuracy

[0273] By combining visual-sound ranging with an elastic deformation compensation mechanism, the elastic elongation of the cable under the load and the thermal deformation caused by changes in ambient temperature can be compensated, which can reduce the impact of deformation on the accuracy of distance measurement and help improve the measurement accuracy of the vertical and horizontal distances of the hook.

[0274] (2) Cable spatial morphology measurement capability

[0275] Based on the principle of multipath sound reflection, and utilizing multiple sound wave transmitters and an array of sound sensors, combined with ellipsoidal geometric constraints and least squares optimization algorithms, the spatial coordinates of discrete reflection points on a cable can be located. Through cubic spline curve fitting, a continuous representation of the cable's spatial curve can be obtained, from which geometric parameters such as swing amplitude, swing frequency, and curvature can be extracted.

[0276] (3) System power consumption control

[0277] The motion status of the hook is monitored by an accelerometer. The system adopts a motion wake-up and static sleep mechanism. When the hook is stationary for more than a preset time, it automatically enters sleep mode and shuts down unnecessary sensor modules, which can reduce the power consumption of the detection terminal and extend the battery life after a single charge.

[0278] (4) Speed ​​measurement stability

[0279] A weighted Kalman filter is used to fuse vertical velocity and acoustic velocity. Adaptive weighted fusion is achieved by dynamically adjusting the observation noise covariance, while the long-term stability of acoustic velocity is used for benchmark correction of the filtering result. The fused velocity signal combines the dynamic response characteristics of vertical velocity with the long-term stability of acoustic velocity, which helps to suppress measurement noise and cumulative drift from a single sensor.

[0280] (5) Measurement data comparison output

[0281] The measured three-dimensional spatial coordinates of the hook, the amplitude of the cable swing, and the curvature of the bend are compared with preset thresholds, and the comparison results are output. These comparison results can be used for subsequent judgment or early warning, providing measurement data references for hoisting operations.

[0282] (6) Environmental adaptability

[0283] Infrared vision sensors can collect curtain wall outline features under different weather and lighting conditions; wireless transmission units can support long-distance data transmission in super high-rise buildings; and acoustic multipath reflection technology has a certain degree of adaptability to working conditions such as high-altitude wind force and suspended weight swing.

[0284] (7) Hardware configuration

[0285] This solution utilizes visual sensors, acoustic sensors, and accelerometers, and achieves position and shape measurement through algorithm fusion, eliminating the need for additional high-cost measurement equipment.

[0286] This invention also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform the method described in this invention.

[0287] This invention also provides a computer-readable storage medium storing computer-executable instructions for performing the methods described in this invention.

[0288] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0289] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for cable morphology reconstruction and distance compensation based on visual-acoustic joint measurement, characterized in that, include: S100: Collect geometric feature data of the building curtain wall's outline and receive acoustic signals from the top of the tower crane; the acoustic signals include a first acoustic wave propagating along the cable and a second acoustic wave propagating through space. S200, based on the geometric feature data of the outline, measure the horizontal distance between the hook and the curtain wall, and measure the vertical speed of the hook; S300, the initial vertical distance is measured based on the propagation time of the first sound wave, the current suspended weight is estimated, and the elastic elongation of the cable is calculated based on the elastic parameters of the cable. The initial vertical distance is compensated using the elastic elongation to obtain the compensated true vertical distance. S400, based on the reflected signal of the second sound wave, calculate the spatial coordinates of multiple discrete reflection points on the cable and fit them to reconstruct the spatial shape of the cable, and extract the swing parameters and bending parameters of the cable. S500, by integrating the vertical velocity and the acoustic velocity derived from the acoustic signal, and combining the actual vertical distance and the horizontal distance, the three-dimensional spatial coordinates of the hook are determined; S600, compare the three-dimensional spatial coordinates of the hook, the swing parameters and bending parameters of the cable with the corresponding preset thresholds, and output the comparison results.

2. The method according to claim 1, characterized in that, In S200, measuring the horizontal distance between the hook and the curtain wall specifically includes: Visual sensors are used to acquire images of curtain wall features, including the frames of curtain wall units or glass gaps. Extract the pixel difference corresponding to the curtain wall feature from the curtain wall feature image; based on the pixel difference, the pre-calibrated lens focal length and the actual spacing of the curtain wall features, measure the horizontal distance between the hook and the curtain wall based on the imaging geometry.

3. The method according to claim 1, characterized in that, In S200, the vertical velocity of the hook is measured, specifically including: Use visual sensors to acquire feature images of the curtain wall; Feature extraction is performed on the acquired curtain wall feature images to identify the number of curtain wall features passing through the visual acquisition area per unit time. The displacement of the hook per unit time is calculated based on the product of the quantity and the actual spacing of the pre-calibrated curtain wall features. The vertical velocity of the hook is measured based on the ratio of the displacement to the unit time.

4. The method according to claim 1, characterized in that, In S300, the current load is estimated using any one or a combination of the following methods: Method 1: Infer the cable tension based on the collected vertical acceleration of the hook, and calculate the lifting weight based on the cable tension; Method 2: Calculate the average wave velocity based on the total propagation time of the first sound wave in the cable and the length of the cable released, calculate the average tension of the cable based on the relationship between wave velocity and tension, and calculate the suspended weight based on the average tension of the cable.

5. The method according to claim 1, characterized in that, The S300 also includes: Obtain the cable release length and ambient temperature; Calculate the elastic elongation based on the suspended weight, the released length of the cable, the elastic modulus of the cable, and the equivalent cross-sectional area. The thermal deformation is calculated based on the linear expansion coefficient of the cable, the length released, and the difference between the ambient temperature and the reference temperature. The sum of the elastic elongation and the thermal deformation is taken as the total compensation amount. The total compensation amount is then subtracted from the initial vertical distance to obtain the true vertical distance.

6. The method according to claim 5, characterized in that, S300 also includes a visual verification sub-step: Images of reflective markers on a cable are captured using a visual sensor, and the actual displacement of the reflective markers is calculated as the visual measurement of elongation. The visually measured elongation is compared with the elastic elongation. When the absolute value of the difference between the visually measured elongation and the elastic elongation is greater than a preset difference threshold, the elastic modulus and the equivalent cross-sectional area are corrected.

7. The method according to claim 1, characterized in that, The spatial morphology of the reconstruction cable specifically includes: The signals collected by the sound sensor array are processed to extract the arrival time of the sound waves in each reflection path; For each combination of sound wave transmitter and each sound sensor, the total path length of sound wave propagation is calculated based on the arrival time of the sound wave and the real-time corrected air velocity. Based on the geometric constraints of the ellipsoid with the acoustic transmitter and the sound sensor as the focus, combined with the measurement values ​​of multiple sets of acoustic transmitter and sound sensor combinations, and introducing the cable continuity constraint, the optimal spatial coordinates of multiple discrete reflection points on the cable are solved by the least squares method. Using the top of the tower crane and the hook as endpoints, a curve fitting algorithm is used to fit the discrete reflection points to construct the spatial curve of the cable, thereby reconstructing the spatial shape of the cable.

8. The method according to claim 1, characterized in that, In the S500, a weighted Kalman filter is used to fuse vertical velocity and acoustic velocity; the observation noise covariance of the filter is set according to the measurement noise characteristics of vertical velocity and acoustic velocity respectively.

9. The method according to claim 8, characterized in that, The output comparison results include at least one of the following: the actual vertical coordinate measurement of the hook is higher than the height threshold; the horizontal distance measurement is less than the horizontal distance threshold; the swing amplitude measurement of the cable exceeds the amplitude threshold; the bending curvature measurement of the cable exceeds the curvature threshold.

10. A cable morphology reconstruction and distance compensation system based on visual-acoustic joint measurement, characterized in that, include: The detection terminal, fixedly installed on a hoisting hook, includes: an infrared vision sensor for collecting geometric feature data of the building curtain wall outline, an acceleration sensor for collecting acceleration data, a temperature sensor for collecting ambient temperature, a sound sensor array consisting of at least three microphones, and a processor. The tower crane terminal, fixedly installed at the top of the tower crane, includes: an electromagnetic hammer for striking the cable to generate a first sound wave, at least one sound wave transmitter for emitting a second sound wave, and a control unit. A communication unit is used to establish data communication between the detection terminal and the tower crane terminal; The processor is configured to perform the steps of the method according to any one of claims 1-9.

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