A group tower operation construction simulation method and system for preventing tower crane collision
Patent Information
- Application Number
- CN202611158886.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-31
- Publication Date
- 2026-10-16
AI Technical Summary
[0004]为了解决现有系统往往只能感知固定障碍物，动态变化的感知能力严重不足，塔吊碰撞可能性误差较大，碰撞预防有效性较差的技术问题，本发明的目的在于提供一种预防塔吊碰撞的群塔作业施工模拟方法及系统，所采用的技术方案具体如下：
本发明根据每个塔吊在每个统计周期的不同目标类型的预设初始障碍程度分布、以及不同目标在不同时刻的速度三维向量和加速度三维向量的分布，获得每个塔吊在每个统计周期的周边障碍真实复杂程度，反映塔吊作业环境中，目标类型的综合混乱度；根据不同统计周期之间的周边障碍真实复杂程度的偏差程度分布，获得每个塔吊在最新统计周期的碰撞风险程度，反映塔吊周边动态环境的危险情况；根据每个塔吊和不同目标的在实时时刻的三维位置坐标、位置运动特征以及碰撞风险程度，获得每个塔吊的碰撞可能性，反映塔吊与目标之间发生物理接触的概率或危险程度；根据不同塔吊的碰撞可能性，获得碰撞风险等级，进行群塔作业模拟预演。本发明通过准确获取动态变化下的碰撞可能性，提高预防塔吊碰撞的模拟有效性。
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Abstract
Description
Technical Field
[0001] This invention relates to the field of tower crane construction data processing technology, specifically to a method and system for simulating multi-tower operation construction to prevent tower crane collisions. Background Technology
[0002] With the increasing scale of modern construction projects, multi-tower operation has become an unavoidable norm in large-scale project construction. The cross-operation of multiple tower cranes in a confined space greatly increases the risk of collisions between tower arms, hooks, wire ropes, and between tower cranes and buildings, posing a severe challenge to construction safety and efficiency. However, the traditional management model that relies on manual coordination and signalmen's command has inherent defects such as blind spots, delayed response, and misjudgment when dealing with dynamic and complex multi-tower operation environments.
[0003] In existing technologies, digital twin technology is used to perform real-time, two-way data mapping between the physical entities of the construction site and the digital models of the virtual space, thereby enabling pre-rehearsals and intelligent interventions for tower crane operations in a virtual environment. However, since existing systems can often only sense fixed obstacles, their ability to sense dynamically changing scaffolding, steel pipes, and ongoing concrete pouring processes is severely insufficient, resulting in a large error in the probability of tower crane collisions and poor effectiveness in collision prevention. Summary of the Invention
[0004] To address the technical problems of existing systems that often only detect fixed obstacles and have insufficient ability to detect dynamic changes, resulting in large errors in the probability of tower crane collisions and poor effectiveness in collision prevention, the present invention aims to provide a method and system for simulating multi-tower operation construction to prevent tower crane collisions. The specific technical solution adopted is as follows: This invention proposes a method for simulating multi-tower operation construction to prevent tower crane collisions, the method comprising: Obtain multiple target types for each tower crane in each statistical period, as well as the three-dimensional position coordinates, three-dimensional velocity vector, and three-dimensional acceleration vector of the tower crane and the target at each moment; Based on the preset initial obstacle degree distribution of different target types for each tower crane in each statistical period, and the distribution of velocity three-dimensional vectors and acceleration three-dimensional vectors of different targets at different times, the true complexity of the surrounding obstacles for each tower crane in each statistical period is obtained. Based on the distribution of the actual complexity of the surrounding obstacles for each tower crane in different statistical periods, the degree of increase in the risk of surrounding obstacles for each tower crane in the latest statistical period is obtained, as well as the degree of collision risk for each tower crane in the latest statistical period; based on the three-dimensional position coordinates, position movement characteristics, and collision risk degree of each tower crane and different targets at real time, the probability of collision for each tower crane is obtained. Based on the collision probability of different tower cranes, a collision risk level is obtained, and a simulation exercise of multiple tower crane operations is conducted.
[0005] Furthermore, the method for obtaining the true complexity of the surrounding obstacles includes: Based on the preset initial obstacle degree distribution of different target types for each tower crane in each statistical period, the initial complexity of the surrounding obstacles for each tower crane in each statistical period is obtained. Obtain the magnitude of the three-dimensional acceleration vector of each target at each moment, and obtain the fluctuation characteristics of the vector magnitude of each target at different moments as the acceleration fluctuation coefficient; obtain the angle between the three-dimensional velocity vector of each target and the horizontal plane at each moment, and obtain the angle fluctuation characteristics of each target at different moments as the velocity fluctuation coefficient. Based on the preset initial obstacle level, target acceleration fluctuation coefficient, velocity fluctuation coefficient, and initial complexity of surrounding obstacles for each tower crane in each statistical period, the true complexity of surrounding obstacles for each tower crane in each statistical period is obtained.
[0006] Furthermore, the method for obtaining the initial complexity of the surrounding obstacles includes: The preset initial obstacle levels of all target types for each tower crane in each statistical period are accumulated and normalized to form the initial obstacle complexity of each tower crane in each statistical period.
[0007] Furthermore, the method for obtaining the true complexity of the surrounding obstacles includes: The preset initial obstacle level for each target type is normalized and used as the initial obstacle weight; Obtain the first product between the preset first weight and the normalized acceleration fluctuation coefficient, and obtain the second product between the preset second weight and the normalized velocity fluctuation coefficient; obtain the sum of the first product and the second product as the trajectory complexity of each target, and the sum of the preset first weight and the preset second weight is 1; The product of the initial obstacle weights for different target types for each tower crane in each statistical period and the average trajectory complexity of all targets within the corresponding target type is accumulated. The product of the accumulated value and the initial complexity of the surrounding obstacles is calculated as the true complexity of the surrounding obstacles for each tower crane in each statistical period.
[0008] Furthermore, the method for obtaining the degree of increased risk from surrounding obstacles includes: The difference between the actual complexity of the surrounding obstacles for each tower crane in the latest statistical period and the adjacent previous statistical period is obtained and normalized to represent the degree of increase in the surrounding obstacle risk for each tower crane in the latest statistical period.
[0009] Furthermore, the method for obtaining the degree of collision risk includes: The product of the increase in surrounding obstacle risk and the actual complexity of surrounding obstacles for each tower crane in the latest statistical period is obtained as the collision risk level of each tower crane in the latest statistical period.
[0010] Furthermore, the method for obtaining the collision probability includes: Based on the real-time three-dimensional position coordinates and position movement characteristics of each tower crane and different targets, the degree of change in collision risk for each tower crane is obtained; The product of the degree of change in collision risk and the degree of collision risk for each tower crane is obtained as the collision probability for each tower crane.
[0011] Furthermore, the method for obtaining the degree of change in collision risk includes: Obtain the relative distance between each tower crane and each target at different three-dimensional position coordinates in real time, and select the minimum relative distance between each tower crane and different targets as the first safety factor; For a tower crane or any target, based on the three-dimensional position coordinates, velocity vector, and acceleration vector at real time, a contour prediction algorithm is used to obtain the three-dimensional predicted position coordinates at the next time step; the minimum relative distance between each tower crane and different targets at the next time step is selected as the second safety factor. The difference between the first and second safety factors is obtained and normalized to represent the degree of change in collision risk for each tower crane.
[0012] Furthermore, the method for obtaining the collision risk level includes: If the probability of a tower crane colliding with another tower crane is less than a preset first threshold, the tower crane is determined to be at the first collision risk level. If the probability of a tower crane colliding with another tower crane is greater than or equal to a preset first threshold, but less than a preset second threshold, the tower crane is determined to be at the second collision risk level. If the probability of a tower crane colliding with another tower crane is greater than or equal to a preset second threshold, and less than or equal to a preset third threshold, the tower crane is judged to be at the third collision risk level. If the probability of a tower crane colliding with another tower crane is greater than the preset third threshold, the tower crane is determined to be at the fourth collision risk level; the preset first threshold, preset second threshold, and preset third threshold show an increasing trend.
[0013] The present invention also proposes a multi-tower operation construction simulation system for preventing tower crane collisions, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of any one of the multi-tower operation construction simulation methods for preventing tower crane collisions.
[0014] The present invention has the following beneficial effects: This invention obtains the true complexity of the surrounding obstacles for each tower crane in each statistical period based on the preset initial obstacle severity distribution for different target types and the distribution of the velocity and acceleration vectors of different targets at different times. This reflects the overall disorder of target types in the tower crane operating environment. Based on the deviation distribution of the true complexity of the surrounding obstacles between different statistical periods, it obtains the collision risk level of each tower crane in the latest statistical period, reflecting the dangerous situation of the dynamic environment surrounding the tower crane. Based on the real-time three-dimensional position coordinates, positional motion characteristics, and collision risk level of each tower crane and different targets, it obtains the collision probability of each tower crane, reflecting the probability or degree of danger of physical contact between the tower crane and the target. Based on the collision probabilities of different tower cranes, it obtains the collision risk level and conducts simulation exercises for multiple tower crane operations. This invention improves the simulation effectiveness of tower crane collision prevention by accurately obtaining the collision probability under dynamic changes. Attached Figure Description
[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.
[0016] Figure 1 A flowchart illustrating a method for simulating multi-tower operation to prevent tower crane collisions, provided as an embodiment of the present invention; Figure 2 A flowchart illustrating a method for obtaining the true complexity of surrounding obstacles according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating a method for obtaining the degree of change in collision risk, as provided in one embodiment of the present invention. Detailed Implementation
[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a multi-tower operation simulation method and system for preventing tower crane collisions proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.
[0018] 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.
[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of a multi-tower operation simulation method and system for preventing tower crane collisions provided by this invention.
[0020] Please see Figure 1 It illustrates a flowchart of a method for simulating multi-tower operation construction to prevent tower crane collisions, according to an embodiment of the present invention, specifically including: Step S1: Obtain multiple target types for each tower crane in each statistical cycle, as well as the three-dimensional position coordinates, three-dimensional velocity vector, and three-dimensional acceleration vector of the tower crane and the target at each moment.
[0021] In the embodiments of the present invention, for each tower crane, multiple cameras are installed above the crane's cab, at the front end of the tower arm, and around the top of the crane to form a panoramic vision camera array. Based on the data acquired by the camera array in the sensor layer, a lightweight real-time detection model such as YOLOv8 is used on the edge computing terminal to identify target types such as pedestrians, vehicles, scaffolding, steel pipes, hoisting boxes, and concrete pouring areas in the images captured by the cameras. Different targets under each target type are identified and analyzed. The specific means are well known to those skilled in the art and will not be described in detail here.
[0022] It should be noted that, in the embodiments of the present invention, in order to more accurately reflect the motion, the two-dimensional image information acquired by the camera and the three-dimensional point cloud data acquired by the lidar are registered using existing methods, thereby mapping the identified two-dimensional target to three-dimensional space and generating a three-dimensional point cloud with semantic labels, which can obtain the three-dimensional position coordinates of different locations; for dynamic targets such as pedestrians, vehicles, and hoisting boxes, based on Kalman filtering, the three-dimensional coordinate change sequence within consecutive frames is used as the observation input of Kalman filtering for iterative differentiation to estimate the velocity and acceleration of each target, obtaining the velocity three-dimensional vector and acceleration three-dimensional vector; for static entity targets such as scaffolding and concrete pouring areas, their velocity and acceleration three-dimensional vectors are set to zero vectors; specific methods can be set according to specific circumstances, and will not be elaborated here.
[0023] It should be noted that in the embodiments of the present invention, a statistical cycle is performed every 10 seconds, and the data is acquired at a time interval of 1 second. In other embodiments of the present invention, the size of the statistical cycle and the time interval can be set according to specific circumstances, and will not be limited or elaborated here.
[0024] Step S2: Based on the preset initial obstacle degree distribution of different target types for each tower crane in each statistical period, and the distribution of velocity three-dimensional vectors and acceleration three-dimensional vectors of different targets at different times, obtain the true complexity of the surrounding obstacles for each tower crane in each statistical period.
[0025] The preset initial obstacle level reflects the inherent danger level and safety protection priority of the target. The higher the preset initial obstacle level, the higher the inherent danger level and the greater the actual complexity. The distribution of the three-dimensional velocity vector and the three-dimensional acceleration vector reflects the complexity of the target's trajectory, combined with the quantification of the actual complexity of the surrounding obstacles.
[0026] Preferably, in one embodiment of the present invention, the method for obtaining the true complexity of surrounding obstacles is described in [reference needed]. Figure 2 It illustrates a flowchart of a method for obtaining the true complexity of surrounding obstacles, including: Step S201: Based on the preset initial obstacle degree distribution of different target types for each tower crane in each statistical period, obtain the initial complexity of the surrounding obstacles for each tower crane in each statistical period.
[0027] Preferably, in one embodiment of the present invention, the method for obtaining the initial complexity of surrounding obstacles includes: The preset initial obstacle levels of all target types for each tower crane in each statistical period are accumulated and normalized to form the initial obstacle complexity of each tower crane.
[0028] It should be noted that, in the embodiments of the present invention, the preset initial obstacle level can be manually assigned by the implementer according to the safety priority and the severity of the collision consequences; for example, based on relevant historical experience, pedestrians are set to 10, mobile hoisting objects are set to 8, temporary facilities such as scaffolding are set to 5, and fixed buildings are set to 2.
[0029] It should be noted that in some embodiments of the present invention, normalization mapping can be performed by linear normalization or normalization function, such as maximum and minimum value normalization, that is, all tower cranes are analyzed, and the maximum and minimum values in their cumulative results are obtained and normalized to the range of 0-1. The specific means are well known to those skilled in the art and will not be described in detail here.
[0030] Step S202: Obtain the magnitude of the three-dimensional acceleration vector of each target at each moment, and obtain the fluctuation characteristics of the vector magnitude of each target at different moments as the acceleration fluctuation coefficient; obtain the angle between the three-dimensional velocity vector of each target and the horizontal plane at each moment, and obtain the angle fluctuation characteristics of each target at different moments as the velocity fluctuation coefficient.
[0031] It should be noted that, in one embodiment of the present invention, the fluctuation characteristics can be characterized by calculating the variance to obtain the fluctuation coefficient. The larger the variance, the larger the fluctuation characteristics and the larger the fluctuation coefficient; the smaller the variance, the smaller the fluctuation characteristics and the smaller the fluctuation coefficient. In other embodiments of the present invention, the fluctuation characteristics can also be characterized by calculating the standard deviation. The specific means are well known to those skilled in the art and will not be described in detail here.
[0032] Step S203: Based on the preset initial obstacle level, acceleration fluctuation coefficient, velocity fluctuation coefficient and initial complexity of surrounding obstacles of different target types for each tower crane in each statistical period, obtain the true complexity of surrounding obstacles for each tower crane in each statistical period.
[0033] It should be noted that the preset initial obstacle level reflects the inherent threat level of the target type. The higher the preset initial obstacle level, the greater the impact on the actual complexity of the surrounding obstacles. The acceleration fluctuation coefficient reflects the drastic change of the target's acceleration over time, and the velocity fluctuation coefficient reflects the drastic change of the target's velocity over time. Both the acceleration fluctuation coefficient and the velocity fluctuation coefficient reflect the target's motion stability. The worse the stability, the greater the actual complexity. The initial complexity of the surrounding obstacles reflects the initial spatial distribution disorder. The higher the initial complexity, the greater the actual complexity. Therefore, the preset initial obstacle level, the target's acceleration fluctuation coefficient, velocity fluctuation coefficient, and the initial complexity of the surrounding obstacles are all positively correlated with the actual complexity of the surrounding obstacles.
[0034] In one embodiment of the present invention, the method for obtaining the true complexity of surrounding obstacles includes: The first step is to normalize the preset initial obstacle level for each target type, which will be used as the initial obstacle weight. It should be noted that, in the embodiments of the present invention, the average of the preset initial obstacle levels of all target types for each tower crane in each statistical period is obtained as the overall obstacle level; the ratio of the preset initial obstacle level of each target type to the overall obstacle level is calculated, that is, the preset initial obstacle level of each target type is normalized to obtain the initial obstacle weight, which reflects the degree of safety threat of the target type to the operation of the tower crane. The larger the initial obstacle weight, the greater the degree of safety threat; considering that the overall obstacle level may be 0, a very small positive number with consistent dimensions is added to the denominator when calculating the ratio. The value of the number can be specifically set according to the range of values of the denominator, which will not be elaborated here.
[0035] The second step is to obtain the first product between the preset first weight and the normalized acceleration fluctuation coefficient, and the second product between the preset second weight and the normalized velocity fluctuation coefficient; the sum of the first product and the second product is obtained as the trajectory complexity of each target, and the sum of the preset first weight and the preset second weight is 1. It should be noted that, in order to avoid the risk of the overall trajectory complexity being directly cleared to zero when the feature value of a single motion dimension is zero, a weighted summation method is adopted. In the embodiments of the present invention, the specific values of the preset first weight and the preset second weight are manually set by the implementer according to the relative influence of the target motion features on the trajectory complexity. For example, when the contribution of speed and acceleration to the trajectory complexity is equally important, they are set to 0.5 respectively.
[0036] It should be noted that in some embodiments of the present invention, normalization can be performed by linear normalization or a normalization function, such as maximum and minimum value normalization, that is, all targets are analyzed, and the maximum and minimum values of their coefficients are selected for maximum and minimum value normalization to the range of 0-1; the specific means are well known to those skilled in the art and will not be described in detail here.
[0037] The third step is to obtain the cumulative value of the product between the initial obstacle weights of different target types for each tower crane in each statistical period and the average trajectory complexity of all targets within the corresponding target type. The product of the cumulative value and the initial complexity of the surrounding obstacles is then calculated as the true complexity of the surrounding obstacles for each tower crane in each statistical period.
[0038] Based on this, the greater the initial obstacle weight, the greater the trajectory complexity, the greater the reliability of the initial complexity of the surrounding obstacles, the greater the actual complexity of the surrounding obstacles, and the greater the overall chaos of the target type in the tower crane operation environment.
[0039] The formula for expressing the true complexity of surrounding obstacles is as follows: ;in, Indicates the first The actual complexity of the obstacles surrounding the tower crane; Indicates the first The initial complexity of the surrounding obstacles of each tower crane; Indicates the first Preset initial obstacle level for each target type; Indicates the calculation of the first The average of the preset initial obstacle levels for all target types of each tower crane in each statistical period, i.e., the overall obstacle level; Represents a very small positive number; Indicates the first The initial obstacle level of each target type is normalized, i.e., the initial obstacle weight; Indicates the number of target types; Indicates the first Number of targets within each target type; Indicates the first The trajectory complexity of each target.
[0040] Step S3: Based on the distribution of the actual complexity of the surrounding obstacles for each tower crane in different statistical periods, obtain the degree of increase in the risk of surrounding obstacles for each tower crane in the latest statistical period, and obtain the degree of collision risk for each tower crane in the latest statistical period; based on the three-dimensional position coordinates, position movement characteristics and collision risk degree of each tower crane and different targets at real time, obtain the probability of collision for each tower crane.
[0041] The true complexity of surrounding obstacles reflects the overall disorder of target types in the tower crane operating environment. The greater the overall disorder, the greater the possibility of risk. Therefore, analyzing the true complexity of surrounding obstacles in different statistical periods helps to quantify the degree of increase in surrounding obstacle risk. Based on the distribution of the true complexity of surrounding obstacles for each tower crane in different statistical periods, the degree of increase in surrounding obstacle risk for each tower crane in the latest statistical period can be obtained.
[0042] Preferably, in one embodiment of the present invention, the method for obtaining the degree of increased risk from surrounding obstacles includes: The difference between the actual complexity of the surrounding obstacles for each tower crane in the latest statistical period and the adjacent previous statistical period is obtained and normalized to represent the degree of increase in the surrounding obstacle risk for each tower crane in the latest statistical period.
[0043] Based on this, the greater the actual complexity of the surrounding obstacles in the latest statistical period compared to the adjacent previous statistical period, the more complex the surrounding obstacles become, and the greater the increase in the risk of surrounding obstacles.
[0044] It should be noted that in some embodiments of the present invention, normalization mapping can be performed by linear normalization or normalization function, such as maximum and minimum value normalization, that is, all tower cranes are analyzed, and the maximum and minimum values in their difference results are obtained and normalized to the range of 0-1. The specific means are well known to those skilled in the art and will not be described in detail here.
[0045] The increase in the risk of surrounding obstacles reflects the rate at which the complexity of the tower crane's operating environment increases in each statistical period compared to the previous statistical period. The greater the increase in the risk of surrounding obstacles, the greater the possibility of a collision. The collision risk level of each tower crane in the latest statistical period is quantified.
[0046] Preferably, in one embodiment of the present invention, the method for obtaining the degree of collision risk includes: The product of the increase in surrounding obstacle risk and the actual complexity of surrounding obstacles for each tower crane in the latest statistical period is obtained as the collision risk level of each tower crane in the latest statistical period.
[0047] The three-dimensional position coordinates reflect the current position of the target, and the collision risk level reflects the dangerous situation of the dynamic environment around the tower crane; the position motion characteristics reflect the degree of disorder in the target's movement; by combining the characteristics, the collision probability of the target is quantified; based on the three-dimensional position coordinates, position motion characteristics, and collision risk level of each tower crane and different targets at real time, the collision probability of each tower crane is obtained.
[0048] Preferably, in one embodiment of the present invention, the method for obtaining the collision probability includes: The first step is to obtain the degree of change in collision risk for each tower crane based on the real-time three-dimensional position coordinates and position movement characteristics of each tower crane and different targets. Preferably, in one embodiment of the present invention, the method for obtaining the degree of change in collision risk is described in [reference needed]. Figure 3 It illustrates a flowchart of a method for obtaining the degree of change in collision risk, including: Step S301: Obtain the relative distance between each tower crane and each target at different three-dimensional position coordinates in real time, and select the minimum relative distance between each tower crane and different targets as the first safety factor.
[0049] It should be noted that the smaller the relative distance, the closer the tower crane and the target are, the greater the possibility of a collision, and the smaller the initial safety factor.
[0050] It should be noted that in some embodiments of the present invention, the relative distance is obtained by Euclidean distance or Manhattan distance calculation methods. The specific means can be set according to the specific situation, and will not be elaborated here.
[0051] Step S302: For a tower crane or any target, based on the three-dimensional position coordinates, velocity three-dimensional vector and acceleration three-dimensional vector at real time, the contour prediction algorithm is used to obtain the three-dimensional predicted position coordinates at the next time; the minimum relative distance between each tower crane and different targets at the next time is selected as the second safety factor.
[0052] It should be noted that, in one embodiment of the present invention, the three-dimensional predicted position coordinates of the next moment are obtained by contour prediction using the Kalman filter algorithm; in other embodiments of the present invention, the position coordinate sequence formed by historical moments can be obtained, and the motion trajectory curve can be fitted by polynomial fitting or spline interpolation method, and the time point corresponding to the next moment can be substituted into the trajectory curve to obtain the predicted position coordinates; the specific means are well known to those skilled in the art and will not be described in detail here.
[0053] Step S303: Obtain the difference between the first safety factor and the second safety factor, and normalize it as the degree of change in collision risk for each tower crane.
[0054] Based on this, the larger the second safety factor at the next moment is than the first safety factor at the real time, the smaller the difference, the farther the tower crane is from the target at the next moment, and the lower the risk of collision; the smaller the second safety factor at the next moment is than the first safety factor at the real time, the larger the difference, the closer the tower crane is to the target at the next moment, and the greater the change in the risk of collision.
[0055] It should be noted that in some embodiments of the present invention, normalization mapping can be performed by linear normalization or normalization function, such as maximum and minimum value normalization, that is, all tower cranes are analyzed, and the maximum and minimum values in their difference results are obtained and normalized to the range of 0-1. The specific means are well known to those skilled in the art and will not be described in detail here.
[0056] The second step is to obtain the product of the degree of change in collision risk and the degree of collision risk for each tower crane, which is used as the collision probability for each tower crane.
[0057] Step S4: Based on the collision probability of different tower cranes, obtain the collision risk level and conduct a simulation exercise of group tower crane operations.
[0058] Collision probability reflects the likelihood or degree of danger of physical contact between the tower crane and the target. The higher the collision probability, the greater the probability or degree of danger of contact, which helps to judge the collision risk level.
[0059] Preferably, in one embodiment of the present invention, the method for obtaining the collision risk level includes: If the probability of a tower crane colliding with another tower crane is less than a preset first threshold, the tower crane is determined to be at the first collision risk level. If the probability of a tower crane colliding with another tower crane is greater than or equal to a preset first threshold, but less than a preset second threshold, the tower crane is determined to be at the second collision risk level. If the probability of a tower crane colliding with another tower crane is greater than or equal to a preset second threshold, and less than or equal to a preset third threshold, the tower crane is judged to be at the third collision risk level. If the probability of a tower crane colliding with another tower crane is greater than the preset third threshold, the tower crane is judged to be at the fourth collision risk level; the preset first threshold, preset second threshold, and preset third threshold show an increasing trend.
[0060] It should be noted that the greater the probability of collision, the greater the collision risk level. The third collision risk level is greater than the second collision risk level, and the second collision risk level is greater than the first collision risk level. The preset first threshold, preset second threshold, and preset third threshold show an increasing trend. In one embodiment of the present invention, the thresholds are determined by the implementers based on historical safety simulation data of tower crane operations and statistical distribution of collision accidents. That is, the preset first threshold is set to 0.3, the preset second threshold is set to 0.6, and the preset third threshold is set to 0.8. In other embodiments of the present invention, the values of the preset first threshold, preset second threshold, and preset third threshold are set according to specific circumstances, and are not limited or elaborated here.
[0061] It should be noted that, in another embodiment of the present invention, a group tower crane operation simulation is performed based on the collision risk level of each tower crane, including: importing the prepared group tower crane operation plan into the digital twin system, setting preset operating parameters for each tower crane, and marking the high-risk operation period and high-risk operation area in the simulation process based on the collision risk level. For identified high-risk points, the system automatically generates optimization plans based on a pre-configured rule mapping table of risk levels and intervention strategies. These plans include adjusting tower crane operation time windows to avoid multiple tower cranes operating simultaneously in the same area, optimizing hoisting routes to avoid areas with dense dynamic obstacles, adjusting tower crane operating parameters to reduce operating speed in high-risk areas, and optimizing dynamic construction procedures, such as staggering scaffolding erection and tower crane hoisting. The optimized operation plans are then imported back into the system for simulation and rehearsal until the collision risk level of all tower cranes is controlled below the second collision risk level, effectively preventing tower crane collisions.
[0062] In summary, this invention obtains the true complexity of the surrounding obstacles for each tower crane in each statistical period based on the preset initial obstacle severity distribution for different target types and the distribution of the velocity and acceleration vectors of different targets at different times. Based on the deviation distribution of the true obstacle complexity between different statistical periods, it obtains the collision risk level of each tower crane in the latest statistical period. Based on the real-time three-dimensional position coordinates, positional motion characteristics, and collision risk level of each tower crane and different targets, it obtains the collision probability of each tower crane. Based on the collision probabilities of different tower cranes, it obtains the collision risk level and conducts simulation exercises for multiple tower crane operations. This invention improves the simulation effectiveness of tower crane collision prevention by accurately obtaining the collision probability under dynamic changes.
[0063] The present invention also proposes a multi-tower operation construction simulation system for preventing tower crane collisions, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the steps of a multi-tower operation construction simulation method for preventing tower crane collisions.
[0064] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0065] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
Claims
1. A method for simulating multi-tower operation construction to prevent tower crane collisions, characterized in that, The method includes: Obtain multiple target types for each tower crane in each statistical period, as well as the three-dimensional position coordinates, three-dimensional velocity vector, and three-dimensional acceleration vector of the tower crane and the target at each moment; Based on the preset initial obstacle degree distribution of different target types for each tower crane in each statistical period, and the distribution of velocity three-dimensional vectors and acceleration three-dimensional vectors of different targets at different times, the true complexity of the surrounding obstacles for each tower crane in each statistical period is obtained. Based on the distribution of the actual complexity of the surrounding obstacles for each tower crane in different statistical periods, the degree of increase in the risk of surrounding obstacles for each tower crane in the latest statistical period is obtained, as well as the degree of collision risk for each tower crane in the latest statistical period; based on the three-dimensional position coordinates, position movement characteristics, and collision risk degree of each tower crane and different targets at real time, the probability of collision for each tower crane is obtained. Based on the collision probability of different tower cranes, a collision risk level is obtained, and a simulation exercise of multiple tower crane operations is conducted.
2. The method for simulating multi-tower operation construction to prevent tower crane collisions according to claim 1, characterized in that, The methods for obtaining the true complexity of the surrounding obstacles include: Based on the preset initial obstacle degree distribution of different target types for each tower crane in each statistical period, the initial complexity of the surrounding obstacles for each tower crane in each statistical period is obtained. Obtain the magnitude of the three-dimensional acceleration vector of each target at each moment, and obtain the fluctuation characteristics of the vector magnitude of each target at different moments as the acceleration fluctuation coefficient; obtain the angle between the three-dimensional velocity vector of each target and the horizontal plane at each moment, and obtain the angle fluctuation characteristics of each target at different moments as the velocity fluctuation coefficient. Based on the preset initial obstacle level, target acceleration fluctuation coefficient, velocity fluctuation coefficient, and initial complexity of surrounding obstacles for each tower crane in each statistical period, the true complexity of surrounding obstacles for each tower crane in each statistical period is obtained.
3. The method for simulating multi-tower operation construction to prevent tower crane collisions according to claim 2, characterized in that, The method for obtaining the initial complexity of the surrounding obstacles includes: The preset initial obstacle levels of all target types for each tower crane in each statistical period are accumulated and normalized to form the initial obstacle complexity of each tower crane in each statistical period.
4. The method for simulating multi-tower operation construction to prevent tower crane collisions according to claim 2, characterized in that, The methods for obtaining the true complexity of the surrounding obstacles include: The preset initial obstacle level for each target type is normalized and used as the initial obstacle weight; Obtain the first product between the preset first weight and the normalized acceleration fluctuation coefficient, and obtain the second product between the preset second weight and the normalized velocity fluctuation coefficient; obtain the sum of the first product and the second product as the trajectory complexity of each target, and the sum of the preset first weight and the preset second weight is 1; The product of the initial obstacle weights for different target types for each tower crane in each statistical period and the average trajectory complexity of all targets within the corresponding target type is accumulated. The product of the accumulated value and the initial complexity of the surrounding obstacles is calculated as the true complexity of the surrounding obstacles for each tower crane in each statistical period.
5. A method for simulating multi-tower operation construction to prevent tower crane collisions according to claim 1, characterized in that, The methods for obtaining the degree of increased risk from surrounding obstacles include: The difference between the actual complexity of the surrounding obstacles for each tower crane in the latest statistical period and the adjacent previous statistical period is obtained and normalized to represent the degree of increase in the surrounding obstacle risk for each tower crane in the latest statistical period.
6. The method for simulating multi-tower operation construction to prevent tower crane collisions according to claim 1, characterized in that, The methods for obtaining the degree of collision risk include: The product of the increase in surrounding obstacle risk and the actual complexity of surrounding obstacles for each tower crane in the latest statistical period is obtained as the collision risk level of each tower crane in the latest statistical period.
7. A method for simulating multi-tower operation construction to prevent tower crane collisions according to claim 1, characterized in that, The method for obtaining the collision probability includes: Based on the real-time three-dimensional position coordinates and position movement characteristics of each tower crane and different targets, the degree of change in collision risk for each tower crane is obtained; The product of the degree of change in collision risk and the degree of collision risk for each tower crane is obtained as the collision probability for each tower crane.
8. A method for simulating multi-tower operation construction to prevent tower crane collisions according to claim 7, characterized in that, The methods for obtaining the degree of change in collision risk include: Obtain the relative distance between each tower crane and each target at different three-dimensional position coordinates in real time, and select the minimum relative distance between each tower crane and different targets as the first safety factor; For a tower crane or any target, based on the three-dimensional position coordinates, velocity vector, and acceleration vector at real time, a contour prediction algorithm is used to obtain the three-dimensional predicted position coordinates at the next time step; the minimum relative distance between each tower crane and different targets at the next time step is selected as the second safety factor. The difference between the first and second safety factors is obtained and normalized to represent the degree of change in collision risk for each tower crane.
9. A method for simulating multi-tower operation construction to prevent tower crane collisions according to claim 1, characterized in that, The methods for obtaining the collision risk level include: If the probability of a tower crane colliding with another tower crane is less than a preset first threshold, the tower crane is determined to be at the first collision risk level. If the probability of a tower crane colliding with another tower crane is greater than or equal to a preset first threshold, but less than a preset second threshold, the tower crane is determined to be at the second collision risk level. If the probability of a tower crane colliding with another tower crane is greater than or equal to a preset second threshold, and less than or equal to a preset third threshold, the tower crane is judged to be at the third collision risk level. If the probability of a tower crane colliding with another tower crane is greater than the preset third threshold, the tower crane is determined to be at the fourth collision risk level; the preset first threshold, preset second threshold, and preset third threshold show an increasing trend.
10. A multi-tower operation construction simulation system for preventing tower crane collisions, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the multi-tower operation construction simulation method for preventing tower crane collisions as described in any one of claims 1 to 9.