Cooperative control method and safety monitoring system for double-crane lifting of large-span steel structure

Abstract

The invention discloses a cooperative control method for double-crane lifting of a large-span steel structure, which comprises the following steps of: pre-installing sensor modules on a crane hook and a steel structure key node before construction, acquiring load distribution and structural deformation data in real time through a multi-source sensor fusion technology, and establishing a unified coordinate system; and the cooperative control system is started to control the hoisting process. The invention further discloses a safety monitoring system for double-crane lifting of the large-span steel structure. According to the invention, the action matching height precision of the two cranes is ensured, the construction precision and the hoisting efficiency are improved, the safety of hoisting operation is guaranteed in all directions, and the accident rate is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of steel structure construction, and in particular to a collaborative control method and a safety monitoring system for double-machine lifting of a large-span steel structure. Background Art

[0002] In the current large-span steel structure hoisting, double crane hoisting uses two cranes to share the load collaboratively, breaking through the load limit of single crane hoisting, effectively reducing the risk of single crane overload, and making it easier to achieve dynamic balance adjustment under complex working conditions, reducing the risk of overturning. However, the following problems still exist:

[0003] 1) Traditional manual command relies on operator experience, has low synchronization accuracy, and can easily lead to structural deflection or uneven force;

[0004] 2) Lack of real-time dynamic monitoring and inability to predict the risk of structural deformation;

[0005] 3) Insufficient emergency response under unexpected conditions (such as sudden change in wind speed), posing safety risks. Summary of the invention

[0006] The purpose of the present invention is to provide a collaborative control method and safety monitoring system for dual-crane lifting of large-span steel structures, to ensure that the movements of the two cranes are coordinated with high precision, to improve construction accuracy and lifting efficiency, to comprehensively guarantee the safety of lifting operations under complex and changeable weather conditions, and to reduce the accident rate.

[0007] The technical solution to achieve the above purpose is:

[0008] A collaborative control method for dual-machine lifting of a large-span steel structure includes: pre-installing sensor modules on crane hooks and key nodes of the steel structure before construction, collecting load distribution and structural deformation data in real time through multi-source sensor fusion technology, establishing a unified coordinate system, and starting a collaborative control system to control the lifting process.

[0009] Preferably, the connection between the crane hook and the steel structure includes a retractable balance beam and an electromagnetic damper.

[0010] Preferably, the telescopic adjustment amount of the telescopic balance beam satisfies the formula Where W is the weight of the steel structure, E is the elastic modulus, and I is the section moment of inertia.

[0011] Preferably, the sensor module includes: a tilt sensor, a strain sensor and a pressure sensor.

[0012] Preferably, the collaborative control system establishes a nonlinear programming model based on the real-time offset of the center of gravity of the steel structure, and solves the optimal dual-machine counterweight ratio with the crane safety load rate as a constraint condition. The nonlinear programming model is as follows: Among them, Fi is the actual lifting force, F i,max is the rated load of the crane, F i,ideal represents the theoretical ideal load value of the i-th crane, i represents the crane number, and λ is the adjustment coefficient or weight factor used to balance the load distribution between the two cranes.

[0013] Preferably, the collaborative control system monitors the spatial relative positions of the two crane hooks in real time through a laser rangefinder with an accuracy of ±2mm;

[0014] A dual-machine synchronization control model based on fuzzy PID is adopted to dynamically adjust the crane lifting / slewing speed according to real-time data to achieve millimeter-level synchronization.

[0015] Preferably, the collaborative control system introduces a BIM model to preview the hoisting path, and combines the on-site data to realize digital twin comparison and correction;

[0016] The collaborative control system establishes a steel structure stress-deformation threshold database and triggers an early warning when the monitoring data exceeds the limit.

[0017] Preferably, the load difference between the two cranes is compared in real time by starting the collaborative control system. When it is detected that the local bending moment of the steel structure exceeds the limit, the action is automatically suspended and a prompt is given to adjust the force distribution of the lifting point.

[0018] A safety monitoring system for double crane lifting of large-span steel structures, comprising:

[0019] The data acquisition unit collects load distribution and structural deformation data in real time through multi-source sensor fusion technology;

[0020] The database construction unit processes the load distribution and structural deformation data, establishes a unified coordinate system and forms a steel structure stress-deformation threshold database;

[0021] The steel structure health assessment unit analyzes the data in the steel structure stress-deformation threshold database, uses Miner linear damage theory based on the accumulated strain data to assess the remaining life of the steel structure, and outputs a risk level report;

[0022] AR visualization unit displays the stress distribution of steel structures in real time.

[0023] The beneficial effects of the present invention are:

[0024] The present invention uses multi-sensor collaboration to accurately grasp key information such as load distribution and structural deformation of crane hooks and key nodes of steel structures in real time. The laser rangefinder monitors the relative position of the hook space with high precision, accurate to ±2mm, providing a reliable data basis for the coordinated operation of the two cranes, ensuring that the movements of the two cranes are highly precise, effectively avoiding risks such as collision and imbalance caused by position deviation and uneven load, greatly improving the accuracy and quality of hoisting construction, and enabling the installation of large components to be accurately positioned.

[0025] The present invention uses a fuzzy PID dual-machine synchronous control model to quickly and dynamically control the lifting and slewing speeds of the crane based on real-time collected data, achieving millimeter-level synchronization and greatly improving the lifting efficiency. At the same time, the BIM model is introduced to preview the lifting path, and the digital twin comparison and correction is realized in combination with real-time data on site. Operators can be familiar with the process in advance, accurately respond to changes in actual working conditions, reduce operational errors, and further optimize the construction process.

[0026] By establishing a stress-deformation threshold database for steel structures, once the monitoring data exceeds the limit, three levels of warning will be triggered in sequence, from sound and light alarms to give operators intuitive warnings, to automatic speed reduction to gain adjustment time, and then to emergency braking to avoid danger, providing layers of protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of the collaborative control method of the double-machine lifting of a large-span steel structure of the present invention;

[0028] Figure 2 is the safety warning logic decision tree of the present invention;

[0029] Figure 3 It is a structural diagram of the safety monitoring system of the present invention. DETAILED DESCRIPTION

[0030] The present invention will be further described below in conjunction with the accompanying drawings.

[0031] The collaborative control method for dual-machine lifting of large-span steel structures of the present invention mainly pre-installs sensor modules on crane hooks and key nodes of steel structures before construction, collects load distribution and structural deformation data in real time through multi-source sensor fusion technology, establishes a unified coordinate system, and starts the collaborative control system to control the lifting process.

[0032] See also Figure 1 and Figure 2 The collaborative control method for lifting a large-span steel structure with two machines of the present invention specifically comprises the following steps:

[0033] Step S1, pre-install the sensor module, determine the optimal lifting point position based on finite element analysis, and pre-install the wireless strain sensor module on the steel structure. Use high-precision and stable wireless strain sensors with a measurement accuracy of micro-strain level to ensure that the tiny strain changes of the steel structure during the lifting process can be accurately captured. Use special installation techniques, such as using high-strength, corrosion-resistant adhesives to firmly stick the sensor to the surface of the steel structure, while ensuring that the sensitive direction of the sensor is consistent with the force direction of the steel structure, to obtain the most accurate strain data.

[0034] The two cranes are equipped with high-precision GNSS positioning terminals. GNSS equipment with centimeter-level positioning accuracy is selected, which can receive signals from multiple satellite systems to ensure stable operation in complex construction environments. The positioning terminal is installed at the top of the crane's boom or other key parts that are easy to receive satellite signals and can accurately reflect the position of the crane to establish a unified coordinate system.

[0035] The multi-sensor collaborative monitoring system enables real-time and accurate grasp of key information such as load distribution and structural deformation of the crane hook and key nodes of the steel structure, ensuring highly precise coordination of the movements of the two cranes, effectively avoiding risks such as collision and imbalance caused by position deviation and uneven load, greatly improving the accuracy and quality of lifting construction, and enabling the installation of large components to be accurately positioned.

[0036] Step S2, path planning, simulate the hoisting path based on the BIM model, and generate key correction nodes. Construct a detailed hoisting construction model based on the actual layout of the construction site, the building design drawings and the parameters of the crane. In the model, the motion trajectories of the two cranes are accurately simulated, including the extension, rotation, lifting and lowering of the boom, as well as the movement path of the hoisted steel structure. By simulating different hoisting schemes multiple times, combined with actual construction conditions, such as site space limitations, distribution of surrounding obstacles and other factors, the most reasonable hoisting path is screened out. On the determined hoisting path, a series of key correction nodes are generated based on possible position deviations, structural force changes and other situations. These nodes serve as an important basis for subsequent real-time monitoring and adjustment to ensure the smooth progress of the hoisting process.

[0037] Define the sensor data weights: strain data (weight 0.4) > hook displacement (0.3) > wind speed (0.2) > crane hydraulic pressure (0.1), and use Kalman filtering to eliminate noise interference; Digital twin comparison: compare the on-site collected data with the BIM model prediction value in real time, and trigger the correction program when the deviation exceeds 5%.

[0038] Combined with real-time data from the site, digital twin comparison and correction is achieved. The data collected by the on-site sensors in real time is transmitted to the digital twin model, and compared with the values ​​predicted in advance by the BIM model in real time. Professional data processing software and visualization tools are used to intuitively display the difference between actual data and predicted values. Operators can familiarize themselves with the process in advance, accurately respond to changes in actual working conditions, reduce operational errors, and further optimize the construction process.

[0039] Step S3, execute the hoisting, start the collaborative control system, develop a dual-machine synchronous control model based on fuzzy PID, and during the operation of the system, dynamically adjust the lifting / slewing speed of the crane using the fuzzy PID algorithm according to the hook position, steel structure strain, load and other data collected by the sensor in real time. Through continuous adjustment and optimization, the movements of the two cranes can be synchronized at the millimeter level to ensure the stability and accuracy of the steel structure during the hoisting process.

[0040] A retractable lifting beam is designed with a built-in hydraulic adjustment device, which can automatically adjust the spacing between lifting points (adjustment range: 5-15m) according to the change of the center of gravity of the steel structure, solving the problem of lifting asymmetric structures. An electromagnetic damper is installed at the hook to suppress lifting shaking through reverse current. Experimental data shows that the swing amplitude can be reduced by 72%, which greatly improves the stability of the lifting process and reduces the impact of shaking on the steel structure and construction safety.

[0041] At the same time, the load difference between the two cranes is compared in real time (threshold ≤ 5% of rated load). A dynamic load distribution algorithm is used. Based on the real-time offset of the center of gravity of the steel structure, a nonlinear programming model is established. The optimal dual-machine counterweight ratio is solved with the crane safety load rate as a constraint condition. The nonlinear programming model is as follows:

[0042]

[0043] F i,ideal : represents the theoretical ideal load value of the i-th crane (such as crane A or B). This value is calculated by the dynamic load distribution algorithm, which aims to optimize the counterweight ratio of the two cranes and ensure that the safe load rate of the crane does not exceed the rated threshold.

[0044] i: represents the crane number (such as i=1 is the main crane A, i=2 is the auxiliary crane B), used to distinguish the parameters of different cranes.

[0045] λ: is the adjustment coefficient or weight factor used to balance the load distribution of the two cranes. In the nonlinear programming model, λ may be associated with the offset of the center of gravity of the steel structure or the influence of the external environment (such as wind speed), and dynamically adjusted to achieve the optimal load distribution.

[0046] When it is detected that the local bending moment of the steel structure exceeds the limit, the action will be automatically paused and a prompt will be given to adjust the force distribution of the hanging points.

[0047] For example, Example 1: Working conditions of lifting the main beam of a large-span arch bridge: The span of the steel arch bridge is 218m, and the weight of a single beam is 320 tons, which requires two machines to lift across the river.

[0048] Implementation steps: 1) Pre-embedded sensors: Fiber Bragg grating strain sensors (model OS3100, accuracy ±1με) are arranged at the key sections of the arch ribs. 2) Path planning: Based on the BIM model, the hoisting trajectory is simulated and 27 key deviation correction nodes are generated. 3) Implementation effect: The error of dual-machine synchronization is controlled within 15mm throughout the whole process, and the peak stress of the arch foot is reduced by 41% compared with the traditional method.

[0049] See also Figure 3 The large-span steel structure double-machine lifting safety monitoring system of the present invention comprises:

[0050] The data acquisition unit collects load distribution and structural deformation data in real time through multi-source sensor fusion technology;

[0051] The database construction unit processes the load distribution and structural deformation data, establishes a unified coordinate system and forms a steel structure stress-deformation threshold database;

[0052] The steel structure health assessment unit analyzes the data in the steel structure stress-deformation threshold database, and based on the cumulative strain data, uses Miner linear damage theory to evaluate the remaining life of the steel structure, and outputs a risk level report (for example, the inspection after the roof of an airport was hoisted showed that the damage degree in the high stress area was only 0.03, and the safety threshold was 0.2). When the monitoring data exceeds the limit, a three-level warning is triggered (sound and light alarm → automatic speed reduction → emergency braking), and the meteorological station data is integrated to dynamically correct the allowable wind speed threshold for hoisting.

[0053] AR visualization unit displays the stress distribution of steel structures in real time.

[0054] Scenario of emergency response to sudden strong winds: Sudden gusts of wind (wind speed 14m / s, lasting 20 seconds) occurred during the lifting process.

[0055] System response: Triggering the third-level warning, automatically reducing the lifting speed to 0.5m / min; starting the electromagnetic locking device of the hoist to limit the swing of the steel structure; balancing the torque difference of the cranes on both sides through the hydraulic compensation system to avoid single machine overload. This system fully considers environmental factors, comprehensively guarantees the safety of hoisting operations under complex and changeable weather conditions, reduces the accident rate, protects the lives and property of personnel, and ensures the smooth progress of the project.

[0056] The above embodiments are only used to illustrate the present invention, rather than to limit the present invention. Those skilled in the relevant technical field may make various changes or modifications without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions should also belong to the scope of the present invention and should be defined by the claims.

Claims

1. A collaborative control method for double-machine lifting of a large-span steel structure, characterized in that: include: Before construction, sensor modules are pre-installed on the crane hook and key nodes of the steel structure. Multi-source sensor fusion technology is used to collect load distribution and structural deformation data in real time, establish a unified coordinate system, and start a collaborative control system to control the lifting process.

2. The collaborative control method for dual-machine lifting of a large-span steel structure according to claim 1 is characterized in that: The connection between the crane hook and the steel structure includes a telescopic balance beam and an electromagnetic damper.

3. The collaborative control method for dual-machine lifting of a large-span steel structure according to claim 2 is characterized in that: The telescopic adjustment amount of the telescopic balance beam satisfies the formula Where W is the weight of the steel structure, E is the elastic modulus, and I is the section moment of inertia.

4. The collaborative control method for dual-machine lifting of a large-span steel structure according to claim 1 is characterized in that: The sensor module includes: a tilt sensor, a strain sensor and a pressure sensor.

5. The collaborative control method for dual-machine lifting of a large-span steel structure according to claim 1 is characterized in that: The collaborative control system establishes a nonlinear programming model based on the real-time offset of the center of gravity of the steel structure, and solves the optimal dual-machine counterweight ratio with the crane safety load rate as a constraint condition. The nonlinear programming model is as follows: Among them, F i is the actual lifting force, F i,max is the rated load of the crane, F i,ideal represents the theoretical ideal load value of the i-th crane, i represents the crane number, and λ is the adjustment coefficient or weight factor used to balance the load distribution between the two cranes.

6. The collaborative control method for dual-machine lifting of a large-span steel structure according to claim 1 is characterized in that: The collaborative control system monitors the spatial relative positions of the two crane hooks in real time through a laser rangefinder with an accuracy of ±2mm; A dual-machine synchronization control model based on fuzzy PID is adopted to dynamically adjust the crane lifting / slewing speed according to real-time data to achieve millimeter-level synchronization.

7. The collaborative control method for dual-machine lifting of a large-span steel structure according to claim 1 is characterized in that: The collaborative control system introduces the BIM model to preview the hoisting path and combines the on-site data to realize digital twin comparison and correction; The collaborative control system establishes a steel structure stress-deformation threshold database and triggers an early warning when the monitoring data exceeds the limit.

8. The collaborative control method for dual-machine lifting of a large-span steel structure according to claim 1 is characterized in that: By starting the collaborative control system to compare the load difference between the two cranes in real time, when it is detected that the local bending moment of the steel structure exceeds the limit, the action is automatically suspended and a prompt is given to adjust the force distribution of the lifting point.

9. A safety monitoring system for double-machine lifting of large-span steel structures, characterized in that: include: The data acquisition unit collects load distribution and structural deformation data in real time through multi-source sensor fusion technology; The database construction unit processes the load distribution and structural deformation data, establishes a unified coordinate system and forms a steel structure stress-deformation threshold database; The steel structure health assessment unit analyzes the data in the steel structure stress-deformation threshold database, uses Miner linear damage theory based on the accumulated strain data to assess the remaining life of the steel structure, and outputs a risk level report; AR visualization unit displays the stress distribution of steel structures in real time.

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