Intelligent lowering control system for steel caissons based on laser spatial reference.

Abstract

This application discloses an intelligent lowering control system for steel caissons based on a laser spatial reference, aiming to solve the problems of insufficient positioning accuracy and difficulty in real-time monitoring of jamming during steel caisson lowering. The system includes a control system host, a planar positioning module, a weight acquisition module, a buoyancy calculation module, a status judgment module, and an attitude adjustment module, which exchange data via a CAN bus. The planar positioning module uses a ruler, a crosshair laser, a high-definition camera, and an image recognition unit to achieve high-precision positioning of ±2cm; the weight acquisition module obtains the real-time lifting weight via a stress gauge and a filtering unit; the buoyancy calculation module dynamically calculates buoyancy based on the lowering height; the status judgment module determines the degree of jamming by the weight-buoyancy difference; and the attitude adjustment module implements differentiated adjustments. This system replaces manual operation, reduces environmental interference and the risk of misjudgment, minimizes construction delays and structural damage caused by jamming, and improves construction accuracy, efficiency, and safety.

Description

Technical Field

[0001] This invention relates to the field of steel caisson construction technology, specifically to an intelligent lowering control system for steel caissons based on laser spatial reference. Background Technology

[0002] In bridge foundation construction, the steel caisson serves as the core protective and support structure for deep-water high-pile pier construction. The accuracy and safety of its lowering directly determine the subsequent pier construction quality, project progress, and construction costs. Lowering the steel caisson essentially involves precisely inserting its steel base plate into the steel casing via a "ring" mechanism. This process demands extremely high precision in planar positioning and control of the lowering posture. The industry generally requires planar positioning deviations to be controlled within 10cm, and some deep-water, large-diameter steel caisson projects even require deviations not exceeding 5cm.

[0003] In existing technologies, the planar positioning of steel caissons during descent mainly employs three methods: First, manual observation positioning. This involves construction workers using instruments such as total stations and levels around the steel casing to observe the positional deviation of the caisson, and then directing the floating crane to adjust its attitude via walkie-talkie. This method is highly susceptible to environmental factors; for example, nighttime construction or adverse weather conditions such as rain, snow, and fog can obstruct the observation line of sight. Furthermore, manual observation is prone to subjective judgment errors, and the positioning accuracy is often only 15-25cm, far from meeting the requirements of high-precision construction. Additionally, manual observation requires multiple personnel working together, resulting in low efficiency, and high-altitude and water-adjacent operations pose safety hazards. Second, GPS positioning. This involves installing a GPS positioning module on top of the steel caisson to obtain location information. However, bridge construction areas often have large metal structures such as steel casings and tower cranes obstructing the view, making GPS signals susceptible to interference and causing significant fluctuations in positioning deviation. In actual construction, the deviation often reaches 12-18cm, failing to consistently meet the accuracy requirement of within 10cm. Third is the mechanical limiting positioning method, which sets a limiting block on the inner wall of the steel casing and achieves positioning by contacting the steel hoist box with the limiting block. However, the installation accuracy of the limiting block directly affects the positioning effect, and the steel hoist box is prone to collision with the limiting block during the lowering process, resulting in structural damage. At the same time, the limiting block cannot adapt to different diameter steel hoist boxes and steel casing matching scenarios, and has poor versatility.

[0004] In terms of attitude control during the lowering of steel caissons, existing technologies lack effective real-time monitoring methods. During the lowering process, if tilting occurs due to planar positioning deviations, or if debris or soil adheres to the inner wall of the steel casing, causing "stress," jamming can easily occur. Once jamming occurs, existing technologies often rely on the experience of construction personnel to determine whether to adjust the lifting, which often fails to detect minor jamming in time, leading to aggravation of the jamming and even serious accidents such as deformation of the steel caisson and damage to the steel casing. Some projects use tension sensors that can only monitor the total weight lifted and cannot analyze attitude anomalies in conjunction with buoyancy changes, resulting in a high misjudgment rate. According to statistics, construction delays caused by jamming account for more than 30% of the total construction period for lowering steel caissons using existing technologies. The average time for handling a single jamming incident is 4-6 hours, and the average cost of structural damage repair is as high as 150,000-200,000 yuan per incident.

[0005] Furthermore, in existing technologies, positioning and attitude control are independent. Insufficient positioning accuracy directly leads to attitude abnormalities, which in turn amplify positioning deviations, creating a vicious cycle that severely impacts construction quality and efficiency. Therefore, there is an urgent need for an intelligent control system capable of achieving high-precision planar positioning, real-time monitoring of attitude abnormalities, and timely adjustments to address the core pain points of existing technologies. Summary of the Invention

[0006] This application proposes an intelligent lowering control system for steel caissons based on laser spatial reference, aiming to solve the technical problems of insufficient planar positioning accuracy and the inability to monitor abnormal posture (tilt / stiffness) in real time during existing steel caisson lowering construction, which leads to a high risk of jamming. By integrating laser positioning and weight-buoyancy collaborative monitoring technology, high-precision positioning and intelligent posture control of the steel caisson can be achieved during the lowering process, avoiding construction delays and structural damage caused by positioning deviations and jamming.

[0007] To achieve the above objectives, this application adopts the following technical solution: an intelligent lowering control system for a steel caisson based on a laser spatial reference, comprising: a planar positioning module, a weight acquisition module, a buoyancy calculation module, a status judgment module, and an attitude adjustment module; the planar positioning module includes a scale, a cross laser, a laser mounting bracket, a high-definition camera, and an image recognition unit; the weight acquisition module includes a stress meter and a data filtering unit; the buoyancy calculation module includes a height recognition unit and a buoyancy calculation unit; the status judgment module includes a difference calculation unit and a threshold judgment unit; and the attitude adjustment module includes a floating crane control unit and a jack control unit; each module is connected to the control system host via a CAN bus to achieve data interaction and collaborative control.

[0008] Furthermore, the scale is drawn on the outer side of the wall corresponding to the transverse axis and the longitudinal axis of the steel casing, or on three uniformly distributed side walls of the steel casing. The measuring range of the scale covers the maximum lowering height of the steel suspension box, and the graduation value is 1 cm. The laser mounting frame is built on the construction platform, with a height 50 cm higher than the initial designed lowering height of the top of the steel suspension box, and the level error ≤ 0.1°. s

[0009] Furthermore, the cross laser instrument is fixed on the laser mounting frame, and the emission direction is perpendicular to the axis of the steel casing. The horizontal divergence angle ≤ 0.05°. After being calibrated by the total station instrument, the transverse laser line coincides with the transverse axis of the steel casing, and the longitudinal laser line coincides with the longitudinal axis of the steel casing. The calibration error ≤ ±0.5 mm.

[0010] Furthermore, the resolution of the high-definition camera ≥ 1920×1080, and the frame rate ≥ 30 fps. It is used to collect the projection position of the laser beam on the reflective identification tape on the outer side wall of the steel suspension box in real time. The image recognition unit is built-in with gray processing and edge detection algorithms, and is used to extract the intersection coordinates of the laser line and the reflective identification tape, and calculate the positioning deviation.

[0011] Furthermore, the stress gauge is installed on the four main hook riggings of the floating crane, with a measuring range of 0 - 200 t and an accuracy of ±0.1% FS. The stress data is collected once every 100 ms. The data filtering unit uses the Kalman filtering algorithm with a filtering coefficient of 0.05 to eliminate abnormal pulse signals, calculate the real-time total lifting weight W of the steel suspension box, and the calculation error ≤ 0.5%.

[0012] Furthermore, the height recognition unit recognizes the lowering height H of the steel suspension box through the high-definition camera, and the recognition accuracy ≤ 1 cm. The buoyancy calculation unit calculates the buoyancy F_float = ρ×g×V according to the geometric parameters of the steel suspension box, the lowering height H and the water density ρ, in combination with Archimedes' principle, and updates the buoyancy data once every 500 ms.

[0013] Furthermore, the difference calculation unit calculates the difference ΔW = |W - W_theory| between the real-time lifting weight W and the theoretical lifting weight W_theory. The theoretical lifting weight W_theory = G - F_float (when H > 0) or W_theory = G (when H = 0), where G is the self-weight of the steel suspension box. The threshold judgment unit presets ΔW1 = 3 t and ΔW2 = 8 t, and determines the degree of jamming according to the relationship between ΔW and the threshold.

[0014] Furthermore, the floating crane control unit is used to control the lifting, lowering speed and horizontal displacement adjustment of the floating crane, and the adjustment speed ≤ 0.5 m / s. The jack control unit is used to control the four fine-tuning hydraulic jacks at the bottom of the steel suspension box. The telescopic amount of each jack ≤ 5 cm, and the horizontal degree error of the steel suspension box is adjusted ≤ 0.1°.

[0015] Furthermore, the control system host has a built-in data processing module and an instruction generation module. The data processing module is used to receive data transmitted from each module and perform analysis and calculation, while the instruction generation module is used to send control instructions to each actuator based on the analysis results.

[0016] The intelligent lowering control method for steel caissons based on laser spatial reference includes the following steps: (1) Pretreatment: Draw a scale on the steel casing wall, build a laser mounting frame and fix the calibrated cross laser instrument; (2) Planar positioning adjustment: Lift the steel caisson to directly above the steel casing, calculate the positioning deviation using cross laser and image recognition, and fine-tune it to ≤2cm; (3) Lifting weight acquisition: The stress of the rigging is acquired by a stress gauge and the real-time total lifting weight W is calculated after filtering. (4) Buoyancy calculation: Identify the lowering height H of the steel caisson, and calculate the buoyancy F_buoyancy by combining the geometric parameters of the steel caisson and the density of the water; (5) Anomaly judgment: Calculate ΔW=|WW_theoretical|, and determine the degree of stagnation based on the relationship between ΔW and the preset threshold; (6) Adaptive attitude adjustment: Adjust the attitude of the steel caisson by controlling the floating crane and jacks according to the degree of jamming, and continue to lower it after repositioning.

[0017] Furthermore, in step (6), if ΔW1≤ΔW<ΔW2, after controlling the floating crane to lift the steel caisson by 50cm, finely adjust the horizontal displacement and tilt angle; if ΔW≥ΔW2, after controlling the floating crane to lift the steel caisson by 1m, adjust the levelness by jacking, and then repeat step (2).

[0018] The present invention has the following beneficial effects: 1. The intelligent lowering control system for steel caissons based on laser spatial reference provided in this application overcomes the limitations of subjective error and environmental interference in existing manual observation by adopting cross laser and image recognition planar positioning technology, avoiding the problem of planar positioning deviation exceeding 10cm. By introducing laser spatial reference and real-time image analysis technology, it achieves high-precision positioning within ±2cm, greatly improving the positioning accuracy of lowering the steel caissons and reducing the risk of jamming caused by positioning deviation.

[0019] 2. The intelligent lowering control system for steel caissons based on laser spatial reference provided in this application adopts attitude judgment technology based on weight-buoyancy linkage monitoring, which breaks through the limitation of existing single weight monitoring that cannot identify attitude abnormalities, avoids structural damage caused by untimely identification of jamming, and introduces dynamic buoyancy calculation and differential quantification judgment technology to achieve real-time and accurate identification of jamming state. The jamming occurrence rate is reduced from 40%-52% in the existing technology to 4%-8%, which significantly improves construction safety.

[0020] 3. The intelligent lowering control system for steel caissons based on laser spatial reference provided in this application overcomes the limitations of existing experience-based blind adjustments by adopting differential adjustment technology for the degree of jamming, thus avoiding the problem of construction delays caused by improper adjustments. It also introduces adaptive attitude correction and multi-module collaborative control technology, which shortens the single jamming processing time from 320-380 minutes to 25-35 minutes, greatly improving construction efficiency and reducing construction costs. Attached Figure Description

[0021] The accompanying drawings, which form part of this specification, illustrate embodiments disclosed in this application and, together with the specification, serve to explain the principles disclosed in this application.

[0022] This disclosure will become clearer with reference to the accompanying drawings and the following detailed description, wherein: Figure 1 This is a logic block diagram of the control system of the present invention; Figure 2 Side view showing the arrangement of the steel casing scale and cross laser instrument; Figure 3 Top view showing the arrangement of the steel casing scale and cross laser instrument; Figure 4 This is a flowchart of the intelligent lowering control process for the steel caisson.

[0023] In the diagram: 1. Control system host; 2. Planar positioning module; 2.1. Scale; 2.2. Cross laser pointer; 2.3. Laser mounting bracket; 2.4. High-definition camera; 2.5. Image recognition unit; 3. Weight acquisition module; 3.1. Stress gauge; 3.2. Data filtering unit; 4. Buoyancy calculation module; 4.1. Height recognition unit; 4.2. Buoyancy calculation unit; 5. Status judgment module; 5.1. Difference calculation unit; 5.2. Threshold judgment unit; 6. Attitude adjustment module; 6.1. Floating crane control unit; 6.2. Jack control unit; 6.3. Fine-tuning hydraulic jack; 7. Construction platform; 8. Steel casing. Detailed Implementation

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0025] Please see Figure 1-4 As shown, a smart lowering control system for steel caissons based on laser spatial reference includes a planar positioning module 2, a weight acquisition module 3, a buoyancy calculation module 4, a status judgment module 5, an attitude adjustment module 6, and a control system host 1. The planar positioning module 2, weight acquisition module 3, buoyancy calculation module 4, status judgment module 5, and attitude adjustment module 6 are connected to the control system host 1 via CAN bus to realize data interaction and collaborative control. The planar positioning module 2 is used to realize high-precision planar positioning of the steel caisson, the weight acquisition module 3 is used to acquire the real-time lifting weight of the steel caisson, the buoyancy calculation module 4 is used to calculate the real-time buoyancy of the steel caisson during the lowering process, the status judgment module 5 is used to determine whether the steel caisson is stuck, and the attitude adjustment module 6 is used to adjust the attitude of the steel caisson.

[0026] The planar positioning module 2 includes a scale 2.1, a cross laser device 2.2, a laser mounting bracket 2.3, a high-definition camera 2.4, and an image recognition unit 2.5. The scale 2.1 is drawn on the outer side of the wall corresponding to the transverse and longitudinal axes of the steel casing 8, or on the three evenly distributed side walls of the steel casing 8. The range of the scale 2.1 covers the maximum lowering height of the steel cassette, and the graduation value is 1cm. The laser mounting bracket 2.3 is built on the construction platform 7, with a height 50cm higher than the initial designed lowering height of the top of the steel cassette, and a horizontality error ≤0.1°.

[0027] The cross laser device 2.2 is fixed on the laser mounting bracket 2.3, with the emission direction perpendicular to the axis of the steel casing 8 and a horizontal divergence angle ≤0.05°. After calibration with a total station, the transverse laser line coincides with the transverse axis of the steel casing 8, and the longitudinal laser line coincides with the longitudinal axis of the steel casing 8, with a calibration error ≤±0.5mm. The high-definition camera 2.4 has a resolution ≥1920×1080 and a frame rate ≥30fps, and is used to acquire the projected image of the laser beam on the reflective marking strip on the outer wall of the steel caisson. The image recognition unit 2.5 has built-in grayscale processing and edge detection algorithms to calculate the positioning deviation between the laser line and the center line of the reflective marking strip.

[0028] The weight acquisition module 3 includes a stress gauge 3.1 and a data filtering unit 3.2. The stress gauge 3.1 is installed on the four main hook rigging of the floating crane, with a range of 0-200t, an accuracy of ±0.1%FS, and an acquisition frequency of 100ms / time. The data filtering unit 3.2 adopts the Kalman filter algorithm with a filtering coefficient of 0.05 to remove abnormal pulse signals and calculate the real-time total lifting weight W of the steel caisson, with a calculation error ≤0.5%.

[0029] The buoyancy calculation module 4 includes a height recognition unit 4.1 and a buoyancy calculation unit 4.2. The height recognition unit 4.1 identifies the lowering height H of the steel caisson through a high-definition camera 2.4 with an accuracy of ≤1cm. The buoyancy calculation unit 4.2 calculates the buoyancy F_buoyancy = ρ × g × V based on the geometric parameters of the steel caisson, the lowering height H, and the water density ρ, combined with Archimedes' principle, and updates the buoyancy data every 500ms.

[0030] The status judgment module 5 includes a difference calculation unit 5.1 and a threshold judgment unit 5.2. The difference calculation unit 5.1 calculates the difference ΔW=|WW_theoretical| between the real-time lifting weight W and the theoretical lifting weight W_theoretical. The theoretical lifting weight W_theoretical=GF_buoyancy (when H>0) or W_theoretical=G (when H=0), where G is the self-weight of the steel caisson. The threshold judgment unit 5.2 presets ΔW1=3t and ΔW2=8t, and determines the degree of jamming based on the relationship between ΔW and the threshold.

[0031] The attitude adjustment module 6 includes a floating crane control unit 6.1 and a jack control unit 6.2; the floating crane control unit 6.1 is used to control the lifting and lowering speed and horizontal displacement adjustment of the floating crane, with an adjustment speed ≤0.5m / s; the jack control unit 6.2 is used to control the four fine-tuning hydraulic jacks 6.3 at the bottom of the steel caisson, with each jack having an extension amount ≤5cm, and adjusting the horizontality error of the steel caisson ≤0.1°.

[0032] The control system host 1 has a built-in data processing module, command generation module and database; the data processing module is used to receive data transmitted by the plane positioning module 2, weight acquisition module 3 and buoyancy calculation module 4, and to perform positioning deviation calculation, lifting weight calculation, buoyancy calculation and jamming judgment; the command generation module is used to send control commands to the attitude adjustment module 6 according to the data processing results.

[0033] Reflective marking strips are fixed to the outer wall of the steel caisson. The center line of the reflective marking strips is aligned with the horizontal / vertical axis of the steel caisson, and the reflectivity is ≥90%, which is used to enhance the recognition effect of the laser beam.

[0034] The cross laser instrument 2.2, stress gauge 3.1, and fine-tuning hydraulic jack 6.3 are bidirectionally connected to the control system host 1 via CAN bus to send control commands and transmit data; the high-definition camera 2.4 is unidirectionally connected to the image recognition unit 2.5 via Ethernet to transmit image data; the image recognition unit 2.5, data filtering unit 3.2, buoyancy calculation unit 4.2, difference calculation unit 5.1, threshold judgment unit 5.2, floating crane control unit 6.1, and jack control unit 6.2 are all built into the control system host 1 and achieve data interaction through the internal data bus.

[0035] This system includes a planar positioning module 2, a weight acquisition module 3, a buoyancy calculation module 4, a status judgment module 5, and an attitude adjustment module 6. These modules work together to achieve intelligent lowering of the steel caisson, including the following steps: (1) Pre-treatment: Draw a scale 2.1 on the wall of the steel casing 8, build a laser mounting frame 2.3 and fix the calibration cross laser instrument 2.2; the specific process is as follows: On the outer side of the wall corresponding to the transverse and longitudinal axes of the steel casing 8, draw a scale 2.1 along the height direction. The range of scale 2.1 covers the maximum height of the steel caisson (e.g., 10m) and the graduation value is 1cm. If the on-site construction conditions are limited, scale 2.1 (e.g., 0°, 120°, 240° directions) can be arranged only on the three evenly distributed side walls of the steel casing 8 to ensure that positioning observation can be achieved in at least two directions.

[0036] A laser mounting frame 2.3 is erected on the construction platform 7. The height of the laser mounting frame 2.3 is 50cm higher than the initial design lowering height of the top of the steel caisson, and the horizontality error of the laser mounting frame 2.3 is ≤0.1°. A cross laser device 2.2 is fixed on the laser mounting frame 2.3. The emission direction of the cross laser device 2.2 is perpendicular to the axis of the steel casing 8, and the horizontal divergence angle of the laser beam is ≤0.05°. The cross laser device 2.2 is calibrated using a total station to ensure that the transverse laser line emitted by the cross laser device 2.2 coincides with the transverse axis of the steel casing 8, and the longitudinal laser line coincides with the longitudinal axis of the steel casing 8. The calibration error is controlled within ±0.5mm. The cross laser device 2.2 is connected to the control system host 1 via a CAN bus to realize real-time monitoring of the laser emission status.

[0037] By precisely arranging the scale 2.1 and calibrating the laser instrument 2.2, a laser spatial reference is established, providing a high-precision reference for subsequent planar positioning, avoiding positioning errors caused by reference deviation, and solving the problems of inconsistent references and ambiguous positioning references in existing technologies.

[0038] (2) Planar positioning adjustment: The steel caisson is lifted to directly above the steel casing 8. The positioning deviation is calculated using the cross laser instrument 2.2 and image recognition, and fine-tuned until the deviation is ≤2cm. The specific process is as follows: Start the floating crane equipment and lift the steel caisson to 1m directly above the steel casing 8, keeping the steel caisson horizontal (monitored by the tilt sensor on the floating crane, the horizontality error is ≤0.3°).

[0039] Turn on the cross laser device 2.2 to project the laser beam onto the outer wall of the steel casing. Fix reflective marking tape on the outer wall of the steel casing, with the position of the reflective marking tape corresponding to the height of the scale 2.1 on the steel casing 8. Use a high-definition camera 2.4 (resolution ≥1920×1080, frame rate ≥30fps) to collect the projection position of the laser beam on the reflective marking tape in real time, and transmit the image data to the control system host 1. The control system host 1 has a built-in image recognition algorithm to perform grayscale processing and edge detection on the collected image, and extract the laser line and reflective mark. The coordinates of the intersection point of the identification belt are used to calculate the deviation between the intersection point and the preset baseline of the steel caisson (the wall marking line corresponding to the horizontal / longitudinal axis of the steel caisson). If the deviation is greater than 5cm, the main control unit 1 sends an adjustment signal to the hydraulic control system of the floating crane, controlling the four main hooks of the floating crane to adjust the horizontal displacement within a range of ±5cm (adjustment speed ≤0.5m / s) until the laser line coincides with the preset baseline of the steel caisson. At this time, the planar positioning deviation is ≤2cm. If the deviation is between 2-5cm, the floating crane is controlled to make fine adjustments to ensure that the final positioning deviation is ≤2cm.

[0040] By combining the cross laser instrument 2.2 with the image recognition unit 2.5, the automated monitoring and adjustment of planar positioning is realized, replacing manual observation and overcoming the limitations of subjective human error and environmental interference. The positioning accuracy is improved from 12-18cm in the existing technology to ±2cm, meeting the requirements of high-precision construction.

[0041] (3) Lifting weight acquisition: The stress of the rigging is acquired by stress gauge 3.1, and the real-time total lifting weight W is calculated by data filtering unit 3.2; the specific process is as follows: Stress gauges 3.1 (range 0-200t, accuracy ±0.1%FS) are installed on the four main hook rigging of the floating crane. The stress gauges 3.1 are connected to the CAN bus to realize the real-time transmission of stress signals.

[0042] The stress gauge 3.1 collects stress data of the rigging every 100ms and transmits it to the control system host 1. The control system host 1 has a built-in data filtering algorithm (using Kalman filtering with a filtering coefficient of 0.05) to reduce noise in the collected stress data and remove abnormal pulse signals (when a single stress change is greater than 5t, it is judged as an abnormal signal and is removed). Based on the cross-sectional area of ​​the rigging (preset to 10cm²) and the elastic modulus of the material (preset to 206GPa), the lifting force of each main hook is calculated using the formula F=σS (where F is the lifting force, σ is the stress, and S is the cross-sectional area), and then the real-time total lifting weight W of the steel caisson is obtained by summing the results. The calculation error is ≤0.5%.

[0043] By acquiring high-frequency data, filtering, and performing precise calculations, the real-time lifting weight of the steel caisson is obtained, providing accurate data support for subsequent attitude judgment, avoiding misjudgments caused by weight data distortion, and solving the problems of low weight monitoring accuracy and data lag in existing technologies.

[0044] (4) Buoyancy calculation: Identify the lowering height H of the steel caisson, and calculate the buoyancy F_buoyancy by combining the geometric parameters of the steel caisson and the water density; the specific process is as follows: The steel casing 8 uses a scale 2.1 and a high-definition camera 2.4 to identify the lowering height H of the steel caisson (i.e., the water depth of the steel caisson; H=0 when not submerged) in real time, with an accuracy of ≤1cm.

[0045] The control system host 1 presets the geometric parameters of the steel caisson (length L=10m, width W=8m, wall thickness δ=0.05m, which can be adjusted according to the actual size of the steel caisson). Based on the lowering height H, the volume of water displaced by the steel caisson is calculated as V=L×W×H (when H≤ height of the steel caisson); if H≥ height of the steel caisson, then V=L×W×height of the steel caisson; based on the water density ρ at the construction site (preset freshwater ρ=1000kg / m³, seawater ρ=1025kg / m³, which can be corrected in real time by a density sensor), combined with Archimedes' principle, the buoyancy F_buoyancy = ρ×g×V (g=9.8N / kg) is calculated; the buoyancy calculation result is updated every 500ms to ensure that the buoyancy data is synchronized with the lowering height.

[0046] This process enables dynamic real-time calculation of buoyancy, overcoming the limitations of existing technologies where buoyancy estimation is coarse and cannot be adjusted in real time with the descent height, thus providing accurate mechanical reference for judging attitude anomalies.

[0047] (5) Anomaly judgment: Calculate ΔW=|WW_theoretical|, and determine the degree of stagnation based on the relationship between ΔW and the preset threshold; the specific process is as follows: The preset weight of the steel caisson is G=80t (which can be adjusted according to the actual weight of the steel caisson). When H=0 (not submerged in water), the theoretical lifting weight W0=G; when H>0, the theoretical lifting weight Wthen=GFbuoyancy.

[0048] The control system host 1 calculates the difference between the real-time lifting weight W and the theoretical lifting weight Wtheoretical, ΔW = |Wtheoretical|. Preset thresholds ΔW1 = 3t (slight jamming threshold) and ΔW2 = 8t (severe jamming threshold). Experimental data verifies that these thresholds can effectively identify over 95% of jamming conditions (experimental data is shown in Table 1). If ΔW < ΔW1, the steel caisson's posture is normal, and lowering continues. If ΔW1 ≤ ΔW < ΔW2, it is determined to be slight jamming (possibly due to slight tilting or localized tension). If ΔW ≥ ΔW2, it is determined to be severe jamming. Simultaneously, combined with the laser projection position changes on the steel caisson's side wall collected by the high-definition camera 2.4, if the laser line deviates and ΔW ≥ ΔW1, jamming caused by tilting is further confirmed.

[0049] This step uses weight-buoyancy linkage analysis to achieve quantitative judgment of jamming state, replacing manual experience judgment, avoiding untimely or misjudgment of jamming, and solving the problem of non-real-time quantitative monitoring of attitude abnormalities in existing technologies.

[0050] (6) Adaptive attitude adjustment: The attitude of the steel caisson is adjusted by controlling the floating crane and jacks according to the degree of jamming. After repositioning, the lowering continues. The specific process is as follows: When a jam is detected, the main control system 1 sends a pause signal to the floating crane, and the floating crane stops its descent.

[0051] If there is slight jamming, the main control system 1 controls the floating crane to lift the steel caisson upward by 50cm (lifting speed ≤0.3m / s), and then controls the four main hooks to adjust the horizontal displacement by ±3cm and the tilt angle by ±0.2° respectively. After the adjustment is completed, the plane positioning adjustment in step (2) is repeated, and then the descent continues, with the descent speed reduced to 0.5m / s. If there is severe jamming, the floating crane controls the floating crane to lift the steel caisson upward by 1m (lifting speed ≤0.2m / s), and starts the micro-adjustment hydraulic jacks 6.3 (four in total, evenly distributed) at the bottom of the steel caisson. The extension of each jack is ≤5cm. The level of the steel caisson is adjusted to an error of ≤0.1°, and the plane positioning adjustment in step (2) is repeated. After confirming that the positioning deviation is ≤2cm, the caisson is slowly lowered at a speed of 0.3m / s. During the adjustment process, ΔW is monitored in real time. When ΔW < ΔW1, the normal descent speed (1m / s) is restored until the caisson is completely lowered.

[0052] This step employs differentiated adjustment strategies based on the degree of jamming to achieve adaptive posture correction, avoid structural damage caused by blind adjustments, and improve adjustment efficiency while shortening jamming processing time.

[0053] Experimental verification: To verify the effectiveness of the technical solution of this application, a comparative experiment was conducted. Three steel caissons with different diameters (8m, 10m, and 12m) were selected, and the existing technology (manual observation + experience adjustment) and the technology of this invention were used for lowering construction, respectively. Each steel caisson was lowered repeatedly 5 times, and the positioning accuracy, jamming rate, and jamming handling time were recorded. The experimental data are shown in the table below: Table 1 Comparison of Experimental Data between Existing Technology and the Technology of this Invention (Table 1) Experimental results show that the average positioning deviation of the present invention is controlled within 2.2 cm, the jamming rate is reduced to below 8%, and the jamming processing time is shortened to within 35 min, which is significantly better than the existing technology.

Claims

1. A smart lowering control system for a steel caisson based on a laser spatial reference, characterized in that, Comprising: It includes a planar positioning module, a weight acquisition module, a buoyancy calculation module, a state judgment module and an attitude adjustment module; The planar positioning module includes a scale, a cross laser, a laser mounting bracket, a high-definition camera and an image recognition unit. The weight acquisition module includes a stress gauge and a data filtering unit. The buoyancy calculation module includes a height recognition unit and a buoyancy calculation unit. The state judgment module includes a difference calculation unit and a threshold judgment unit. The attitude adjustment module includes a floating crane control unit and a jack control unit; Each module is connected to the control system host through a CAN bus to achieve data interaction and collaborative control.

2. The intelligent lowering control system for steel caissons based on laser spatial reference according to claim 1, characterized in that, The scale is drawn on the outer wall corresponding to the transverse axis and the longitudinal axis of the steel casing, or drawn on three uniformly distributed side walls of the steel casing. The range of the scale covers the maximum lowering height of the steel suspension box, and the graduation value is 1 cm; The laser mounting bracket is built on the construction platform, with a height 50 cm higher than the initial designed lowering height of the top of the steel suspension box, and the level error ≤ 0.1°.

3. The intelligent lowering control system for steel caissons based on laser spatial reference according to claim 1, characterized in that, The cross laser is fixed on the laser mounting bracket, and the emission direction is perpendicular to the axis of the steel casing. The horizontal divergence angle ≤ 0.

4. The intelligent lowering control system for steel caissons based on laser spatial reference according to claim 1, characterized in that, ​ 5. The intelligent lowering control system for steel caissons based on laser spatial reference according to claim 1, characterized in that, ​ ​ 6. The intelligent lowering control system for steel caissons based on laser spatial reference according to claim 1, characterized in that, ​ 7. The intelligent lowering control system for steel caissons based on laser spatial reference according to claim 1, characterized in that, ​ 8. The intelligent lowering control system for steel caissons based on laser spatial reference according to claim 1, characterized in that, ​ 9. The intelligent lowering control system for steel caissons based on laser spatial reference according to any one of claims 1-8, characterized in that, The control system host has a built-in data processing module and an instruction generation module. The data processing module is used to receive data transmitted from each module and perform analysis and calculation, while the instruction generation module is used to send control instructions to each actuator based on the analysis results.

10. A method for intelligent lowering control of a steel caisson based on a laser spatial reference, employing the system described in any one of claims 1-9, comprising the following steps: (1) Pretreatment: Draw a scale on the steel casing wall, build a laser mounting frame and fix the calibrated cross laser instrument; (2) Planar positioning adjustment: Lift the steel caisson to directly above the steel casing, calculate the positioning deviation using cross laser and image recognition, and fine-tune it to ≤2cm; (3) Lifting weight acquisition: The stress of the rigging is acquired by a stress gauge and the real-time total lifting weight W is calculated after filtering. (4) Buoyancy calculation: Identify the lowering height H of the steel caisson, and calculate the buoyancy F_buoyancy by combining the geometric parameters of the steel caisson and the density of the water; (5) Anomaly judgment: Calculate ΔW=|WW_theoretical|, and determine the degree of stagnation based on the relationship between ΔW and the preset threshold; (6) Adaptive attitude adjustment: Adjust the attitude of the steel caisson by controlling the floating crane and jacks according to the degree of jamming, and continue to lower it after repositioning.

11. In the intelligent lowering control system for steel caisson based on laser spatial reference as described in claim 10, in step (6), if ΔW1≤ΔW<ΔW2, the floating crane is controlled to lift the steel caisson by 50cm and then the horizontal displacement and tilt angle are finely adjusted; if ΔW≥ΔW2, the floating crane is controlled to lift the steel caisson by 1m and then the levelness is adjusted by jacks, and step (2) is executed again after adjustment.

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