Building peripheral frame body construction management and control method and system based on unmanned aerial vehicle and storage medium

Abstract

The invention relates to the technical field of hoisting construction risk management and control, and discloses a building peripheral frame construction management and control method and system based on an unmanned aerial vehicle and a storage medium. Then determining a hoisting risk coefficient according to the obstacle type and the state parameter; and when the risk coefficient exceeds a threshold value, according to the obstacle state parameter, determining an estimated time length for the obstacle to arrive at the hoisting rope, and correcting the estimated time length in combination with the hoisting rope following motion parameter and the environment characteristic parameter to obtain a target pre-collision time length. And finally, implementing graded coping according to the target pre-collision duration: accelerating take-off and hovering if the pre-collision time is sufficient, and triggering a lifting hook to be separated from a lifting rope if the pre-collision time is urgent. According to the technical scheme, the actual requirements of efficient and intelligent construction can be met.

Description

Technical Field

[0001] This invention relates to the field of risk management technology for hoisting construction, specifically to a method, system, and storage medium for construction management of building perimeter scaffolding based on unmanned aerial vehicles (UAVs). Background Technology

[0002] As building construction becomes increasingly high-altitude and complex, traditional manual gantry installation methods face problems of low efficiency and significant safety risks. Unmanned aerial vehicle (UAV)-assisted hoisting technology, with its flexibility, mobility, and automation potential, is gradually becoming a research focus in the field of building exterior scaffolding construction. However, existing UAV hoisting control methods still have significant shortcomings.

[0003] In related technologies, when a system detects a risk, it can only trigger an early warning and wait for manual intervention, lacking the ability to autonomously adapt and automatically resolve the risk based on the actual risk level and working conditions on site. This passive waiting mode may not only amplify safety hazards due to response delays, but also cause frequent interruptions in the construction process due to insufficient timeliness of manual intervention, making it difficult to meet the actual needs of efficient and intelligent construction. Summary of the Invention

[0004] The main objective of this invention is to provide a method, system, and storage medium for construction management and control of building perimeter scaffolding based on unmanned aerial vehicles (UAVs), aiming to solve the technical problem that existing technologies cannot meet the needs of efficient and intelligent construction.

[0005] To achieve the above objectives, in a first aspect, this application provides a method for construction control of building perimeter scaffolding based on unmanned aerial vehicles (UAVs), used for hoisting control of building construction gantry installation by UAVs, the method comprising: During the pre-takeoff or initial takeoff phase of drone hoisting, acquire obstacle types and obstacle status parameters within a preset distance range of the hoisting rope; The risk coefficient of the hoisting operation is determined based on the type of obstacle and the obstacle state parameters. The risk coefficient represents the probability of collision between the hoisting rope and the obstacle. If the risk coefficient is greater than or equal to the risk coefficient threshold, the estimated time for the obstacle to reach the hoisting rope is determined based on the obstacle state parameters; The estimated time is corrected based on the following motion parameters of the hoisting rope and environmental characteristic parameters to obtain the target pre-collision time; If the target pre-collision duration is determined to be greater than or equal to the duration threshold, the drone is controlled to accelerate and take off above the gantry and remain in a hovering state so that the hoisting rope is lifted off the ground. If the target pre-collision time is determined to be less than the time threshold, the control system will detach the drone hook from the hoisting rope so that the drone is separated from the gantry.

[0006] In one possible implementation, obtaining the obstacle type and obstacle state parameters within a preset distance range of the hoisting rope includes: The drone uses at least one of the following: a visual sensor or a lidar sensor to scan an area within a preset distance range of the hoisting rope to obtain multi-frame image data or point cloud data. The obstacle type and obstacle state parameters are obtained by analyzing and processing the multi-frame image data or point cloud data. The obstacle type includes static obstacles or dynamic obstacles, and the obstacle state parameters include the size of the obstacle, the direction of movement, the speed of movement, and the distance between the obstacle and the hoisting rope.

[0007] In one possible implementation, determining the risk coefficient of the hoisting operation based on the obstacle type and obstacle state parameters includes: When the obstacle type is a dynamic obstacle and the obstacle's movement direction is towards the hoisting rope, the size parameters, interval distance parameters, and movement speed parameters of the dynamic obstacle are input into the hoisting operation risk prediction model to obtain the hoisting operation risk coefficient. The risk coefficient is positively correlated with the size of the obstacle, positively correlated with the movement speed, and negatively correlated with the interval distance of the obstacle. When the obstacle type is a static obstacle, or the direction of movement deviates from the hoisting rope, the risk coefficient of the hoisting operation is obtained by querying the pre-established risk mapping table.

[0008] In one possible implementation, after determining the risk coefficient of the hoisting operation based on the obstacle type and obstacle state parameters, the method further includes: The predicted trajectory is obtained by predicting the obstacle's trajectory based on the obstacle's historical state parameters; The motion envelope of the hoisting rope is generated based on the pre-flight trajectory of the UAV, and the spatiotemporal intersection probability of the predicted flight trajectory and the motion envelope is calculated using a spatial geometry algorithm. The risk coefficient threshold is dynamically adjusted based on the spatiotemporal intersection probability, wherein the spatiotemporal intersection probability is negatively correlated with the risk coefficient threshold.

[0009] In one possible implementation, determining the estimated time for the obstacle to reach the hoisting rope based on obstacle state parameters includes: The estimated time for an obstacle to reach the hoisting rope is determined based on the distance between the obstacles and the speed of the obstacles. The distance between the obstacles is the initial distance between the obstacles and the hoisting rope.

[0010] In one possible implementation, the step of correcting the estimated duration based on the following motion parameters of the hoisting rope and environmental characteristic parameters to obtain the target pre-collision duration includes: A first correction value is determined based on the following motion parameters of the hoisting rope, wherein the following motion parameters include the following motion distance of the hoisting rope toward the obstacle or the following motion distance away from the obstacle; The second correction value is determined based on environmental characteristic parameters, including wind speed, wind direction, and sensor visibility. The target correction value is obtained by superimposing the first correction value and the second correction value. The target pre-collision time is obtained based on the estimated duration and the target correction value.

[0011] In one possible implementation, determining the first correction value based on the following motion parameters of the hoisting rope includes: The duration error caused by the following motion is determined based on the following motion distance of the hoisting rope towards or away from the obstacle, and the speed of the obstacle. The duration error caused by the following motion is determined as the first correction value.

[0012] In one possible implementation, determining the second correction value based on environmental characteristic parameters includes: When the wind speed exceeds a preset wind speed threshold, a second correction value is obtained by correcting the estimated duration based on the wind speed vector; and / or, When the sensor visibility is lower than a preset visibility threshold, the estimated duration is negatively corrected to obtain a second correction value.

[0013] In one possible implementation, controlling the drone hook to detach from the lifting rope includes: The drone is controlled to trigger an unlocking command for the connection mechanism, which is either an electromagnetic release mechanism or a mechanical latch structure. Unlocking the connection mechanism separates the drone hook from the hoisting rope.

[0014] Secondly, embodiments of this application also provide a construction management and control system, including: a memory and a processor, wherein the memory is used to store program code; and the processor is used to call the program code to execute the method described in the first aspect.

[0015] Thirdly, embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in the first aspect.

[0016] Unlike existing technologies, this application provides a method for construction management and control of building perimeter scaffolding based on unmanned aerial vehicles (UAVs). This method achieves intelligent hoisting control through risk assessment and dynamic decision-making mechanisms. During the pre-takeoff or initial takeoff phase of UAV hoisting, the types and status parameters of obstacles within a preset distance range of the hoisting rope are first acquired. Then, a hoisting risk coefficient is determined based on the obstacle types and status parameters. When the risk coefficient exceeds a threshold, the estimated time for the obstacle to reach the hoisting rope is determined based on the obstacle status parameters. This estimated time is then corrected using hoisting rope following motion parameters and environmental characteristic parameters to obtain the target pre-collision time. Finally, a tiered response is implemented based on the target pre-collision time: if the pre-collision time is sufficient, the UAV accelerates and hovers; if time is tight, the hook is disengaged from the hoisting rope. This solution significantly improves the accuracy of risk prediction and the flexibility of response through multi-dimensional risk quantification, dynamic time correction, and tiered handling strategies. It reduces human intervention, enhances the system's adaptability in complex working conditions, ensures construction safety, prevents accidents, maintains operational continuity, and meets the actual needs of efficient and intelligent construction. Attached Figure Description

[0017] To more clearly illustrate the technical solutions 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 the structures shown in these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the construction process of hoisting the building's external frame in some embodiments of this application; Figure 2 This is a flowchart illustrating the construction control method for building perimeter scaffolding based on unmanned aerial vehicles (UAVs) in some embodiments of this application; Figure 3 This is a flowchart illustrating step S400 of the construction control method for building perimeter scaffolding based on unmanned aerial vehicles in some embodiments of this application; Figure 4 This is a schematic diagram of the hardware structure of the construction control system in some embodiments of this application.

[0019] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0022] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the term "and / or" throughout the text includes three solutions; taking A and / or B as an example, it includes technical solution A, technical solution B, and a technical solution that simultaneously satisfies A and B. Furthermore, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0023] like Figure 1 As shown, the construction control system provided in this application embodiment includes a drone landing pad 100, a drone 200 docked at the landing pad 100 and used for hoisting the gantry 600, a hoisting rope 300, a first hook 400, and a second hook 500. The first hook 400 is mounted on the lower end of the drone 200 and can be unlocked and detached from the hoisting rope 300 under controller command. The second hook 500 is located at the end of the hoisting rope 300 and is used for detachable connection to the gantry 600.

[0024] During hoisting operations, the hoisting rope 300 is securely connected to the lower end of the drone 200 via the first hook 400. The workers then connect the second hook 500 at the other end of the hoisting rope 300 to the auxiliary hanging rope of the gantry 600, thereby forming a stable integrated hoisting structure and providing a reliable connection foundation for gantry hoisting.

[0025] The aforementioned gantry 600 can be a scaffolding structure used for exterior construction. In one embodiment, exterior scaffolding construction may include the following steps: 1. Prepare sufficient gantry frames and arrange them neatly on a flat surface. Plan a drone landing pad near the gantry placement area for debugging, battery replacement, or charging. 2. Scaffolding workers install the hoisting ropes, check and confirm their secureness, and then leave, maintaining a safe distance. When preparing to lift, the drone maintains a safe distance from the gantry and other obstacles. 3. The drone flies above the gantry, slowly retracts the rope until taut, and then slowly rises. 4. The drone slowly flies above the scaffolding, constantly monitoring for obstacles during flight, and then slowly descends. With the assistance of scaffolding workers, the gantry is installed onto the scaffolding. 5. After the gantry is properly installed, the scaffolding workers carefully pull the hoisting rope at the bottom of the gantry to detach it from the gantry. 6. The drone flies back to the gantry installation position. 7. Repeat steps one through six to install the gantry, while another group of scaffolding workers installs low-level handrails between the installed gantry frames. 8. When installing bridge plates, handrails, crossbars, diagonal braces, and other components, the flight path of the drone should be avoided during the process.

[0026] During the aforementioned construction process, the pre-takeoff and initial takeoff phases of UAV hoisting often present obstacles such as intersecting vehicles, construction workers, and other hoisting UAVs, posing a significant threat to the safety of hoisting operations. Specifically, dynamic obstacles can easily cause collisions, pulling, and other risky actions to the hoisting ropes during the pre-takeoff and initial takeoff phases, thus affecting hoisting safety. Based on this, this application proposes a UAV-based construction perimeter scaffolding construction management method. This method utilizes risk assessment and dynamic decision-making mechanisms to achieve intelligent hoisting management during the UAV pre-takeoff and initial takeoff phases, ensuring construction safety and maintaining operational continuity.

[0027] like Figures 2-3 As shown, the following explanation uses the construction control system to execute the construction control method for the external scaffolding of a building based on drones. It should be noted that although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order. Please refer to the appendix. Figure 2 The method includes the following steps S100-S600: Step S100: During the pre-takeoff or initial takeoff phase of the UAV hoisting, obtain the obstacle types and obstacle status parameters within a preset distance range of the hoisting rope; The pre-takeoff phase refers to the period after the scaffolding workers connect the hooks to the gantry, preparing to control the drone for takeoff. At this time, the hoisting ropes are on the ground. The initial takeoff phase refers to the period after the drone has started the takeoff procedure, but is still in the early stages of flight. At this time, the hoisting ropes are still on the ground or at a relatively low height.

[0028] In the two stages mentioned above, because the hoisting ropes are at a low height when they touch the ground or are off the ground, they pose a threat to the safety of the moving object. Furthermore, the moving object is also very likely to collide with or pull on the hoisting ropes, which could have adverse safety effects on the drone or gantry.

[0029] Based on this, in the embodiments of this application, during the pre-takeoff or initial takeoff stage of UAV hoisting, the types of obstacles and obstacle status parameters within a preset distance range of the hoisting rope are acquired in real time, so as to provide basic data support for subsequent construction management.

[0030] In one embodiment, the step of acquiring obstacle types and obstacle state parameters within a preset distance range of the hoisting rope includes: scanning the area within the preset distance range of the hoisting rope using at least one of a visual sensor or a lidar mounted on the UAV to acquire multi-frame image data or point cloud data; analyzing and processing the multi-frame image data or point cloud data to obtain obstacle types and obstacle state parameters, wherein the obstacle types include static obstacles or dynamic obstacles, and the obstacle state parameters include the size, direction of movement, speed of movement of the obstacle, and the distance between the obstacle and the hoisting rope.

[0031] Specifically, a visual sensor (such as a high-definition camera or infrared camera, not shown in the figure) or a lidar mounted on the top of the drone can scan an area within a preset distance range of the hoisting rope. The preset distance range can be set according to the actual situation, for example, within a radius of 3m or 5m. The visual sensor can capture multiple frames of image data of the area, while the lidar can generate corresponding point cloud data.

[0032] Then, the acquired multi-frame image data or point cloud data is analyzed and processed. Through image recognition, point cloud segmentation, and other technologies, the type of obstacle is accurately determined. This type is mainly divided into static obstacles (such as stones on the ground, fixed scaffolding poles, and other objects whose positions do not change) and dynamic obstacles (such as construction workers, moving forklifts, or other operating drones, and other objects whose positions are in motion). At the same time, the analysis and processing of multi-frame data can also obtain the state parameters of the obstacle. The state parameters of the obstacle include the size of the obstacle (such as length, width, height, and other dimensional information), the direction of movement (for dynamic obstacles, the direction of movement is determined, such as left, right, approaching the rope, or moving away from the rope), the speed of movement (the distance the dynamic obstacle moves per unit time), and the distance between the obstacle and the hoisting rope (i.e., the straight-line distance between the obstacle and the rope).

[0033] In this way, the visual sensors or lidar installed on the top of the drone can comprehensively and accurately obtain key information about obstacles around the hoisting rope, laying the foundation for ensuring the safety of subsequent drone hoisting operations.

[0034] Step S200: Determine the risk coefficient of the hoisting operation based on the obstacle type and obstacle state parameters. The risk coefficient represents the probability of collision between the hoisting rope and the obstacle. Collision probability refers to the likelihood of a hoisting rope colliding with an obstacle. It's understandable that the type and state parameters of the obstacle significantly impact the risk factor of hoisting operations. Specifically, dynamic obstacles, due to their constantly changing position, have a higher probability of colliding with hoisting ropes compared to static obstacles with fixed positions. Among dynamic obstacles, those moving towards the hoisting rope have a further increased collision probability compared to those moving away from or parallel to the rope. Furthermore, the larger the obstacle, the wider its contact area with the hoisting rope, thus increasing the likelihood of a collision. Based on the above characteristics, when determining the risk coefficient, the obstacle type can be used as the basis (the basic risk weight of dynamic obstacles is higher than that of static obstacles), and then combined with state parameters (size, direction of movement, speed of movement, distance from rope, etc.) for dynamic correction. The risk coefficient that can accurately represent the probability of collision can be obtained through quantitative calculation, providing a basis for safety decision-making in hoisting operations.

[0035] In one embodiment, the step of determining the risk coefficient of the hoisting operation based on the obstacle type and obstacle state parameters includes: When the obstacle type is a dynamic obstacle and the obstacle's movement direction is towards the hoisting rope, the size parameters, interval distance parameters, and movement speed parameters of the dynamic obstacle are input into the hoisting operation risk prediction model to obtain the hoisting operation risk coefficient. The risk coefficient is positively correlated with the size of the obstacle, positively correlated with the movement speed, and negatively correlated with the interval distance of the obstacle. When the obstacle type is a static obstacle, or the direction of movement deviates from the hoisting rope, the risk coefficient of the hoisting operation is obtained by querying the pre-established risk mapping table.

[0036] Specifically, in this embodiment, when a dynamic obstacle (such as a moving worker or a moving forklift) is detected, and its movement direction is confirmed to be towards the hoisting rope through image recognition or point cloud analysis (i.e., the angle between the trajectory vector and the straight line where the rope is located is ≤30°, which is determined as "orientation"), a pre-trained hoisting operation risk prediction model is used to calculate the risk coefficient. This involves inputting the size parameters, interval distance parameters, and movement speed parameters of the dynamic obstacle into the hoisting operation risk prediction model to obtain the risk coefficient for the hoisting operation. When a static obstacle is detected, or a dynamic obstacle is detected but its movement direction deviates from (including parallel cases) the hoisting rope, the collision probability is low, and the risk characteristics are relatively fixed. In this case, a pre-established risk mapping table is queried to obtain the risk coefficient.

[0037] It should be noted that the risk coefficient is positively correlated with the size of the obstacle, positively correlated with the movement speed, and negatively correlated with the distance between obstacles. When constructing a risk prediction model for hoisting operations, an initial model can be built based on the above relationships, and then the model can be continuously iterated and optimized through training with historical data and scenario verification.

[0038] Thus, through the two targeted calculation methods mentioned above, we can accurately capture the real-time changes of high-risk dynamic scenarios (using dynamic calculation with models) and efficiently process the risk assessment of low-risk stable scenarios (using fast lookup with mapping tables), thereby achieving the scientific quantification and efficient output of risk coefficients.

[0039] Step S300: If the risk coefficient is greater than or equal to the risk coefficient threshold, determine the estimated time for the obstacle to reach the hoisting rope based on the obstacle state parameters; When the risk factor of a hoisting operation reaches the risk factor threshold, it indicates a high probability of collision and pulling between the obstacle and the hoisting rope, further suggesting that the obstacle is likely dynamic. At this point, the estimated time for the obstacle to reach the hoisting rope is determined based on the obstacle's state parameters.

[0040] In one embodiment, the estimated time for an obstacle to reach the hoisting rope can be determined based on the distance between the obstacles and the obstacle's movement speed. Specifically, the estimated time = distance between the obstacle and the hoisting rope / speed of the obstacle. Here, the distance between the obstacles is the initial distance between the obstacle and the hoisting rope, which refers to the distance between the obstacle and the hoisting rope after the scaffolding worker has straightened the rope.

[0041] The risk coefficient threshold can be a preset fixed value or an adaptive dynamic value obtained based on the actual situation. After determining the risk coefficient of the hoisting operation, to further improve the flexibility and accuracy of risk assessment, the risk coefficient threshold in this embodiment adopts a dynamic adjustment mechanism for adaptive adjustment. Specifically, in one embodiment, after determining the risk coefficient of the hoisting operation based on the obstacle type and obstacle state parameters, the step further includes: The predicted trajectory is obtained by predicting the obstacle's trajectory based on the obstacle's historical state parameters; The motion envelope of the hoisting rope is generated based on the pre-flight trajectory of the UAV, and the spatiotemporal intersection probability of the predicted flight trajectory and the motion envelope is calculated using a spatial geometry algorithm. The risk coefficient threshold is dynamically adjusted based on the spatiotemporal intersection probability, wherein the spatiotemporal intersection probability is negatively correlated with the risk coefficient threshold.

[0042] Specifically, drone sensors (visual sensors, LiDAR) can continuously record historical data of dynamic obstacles (such as a construction truck), including its position coordinates (e.g., from (x1, y1, z1) to (x10, y10, z10)), speed (e.g., 2 m / s), and direction of movement (e.g., towards the hoisting rope) over the past 10 seconds. Then, using trajectory prediction models (e.g., Kalman filtering, LSTM neural networks), the obstacle's path over a future period (e.g., the next 5 seconds) can be predicted based on this historical data, thus obtaining the predicted trajectory. For example, it can be predicted that the transport vehicle will continue to approach in a straight line, reaching coordinates (x15, y15, z15) in 5 seconds.

[0043] Then, based on the drone's pre-flight trajectory and the length of the hoisting rope (e.g., 5 meters), the spatial range that the hoisting rope may cover during its movement (i.e., the "motion envelope") can be calculated. For example, during the ascent, the rope forms a cylindrical envelope area with a diameter of 1 meter.

[0044] Next, using spatial geometry algorithms (such as collision detection algorithms), the predicted trajectory of the obstacle (time dimension: 0-5 seconds in the future; spatial dimension: from the current position to (x15, y15, z15)) is compared with the motion envelope of the rope (time dimension: 0-5 seconds after the drone takes off; spatial dimension: cylindrical region) to determine whether the two will overlap at a certain moment and at a certain position, and to quantify the probability of overlap (i.e., the probability of spatiotemporal intersection).

[0045] Finally, the risk coefficient threshold is dynamically adjusted based on the spatiotemporal intersection probability. The system defaults to an initial threshold of 0.6 (i.e., an alert is triggered when the risk coefficient is ≥ 0.6). If the spatiotemporal intersection probability is high, the risk coefficient threshold is lowered (to trigger an alert earlier); conversely, if the probability is low, the risk coefficient threshold is increased (to reduce false positives).

[0046] For example, if the spatiotemporal intersection probability is 70% (relatively high), the threshold is lowered from 0.6 to 0.4. Even if the calculated risk coefficient is 0.5 (lower than the initial threshold of 0.6, but higher than the adjusted threshold of 0.4), the system will still trigger a warning (such as accelerated takeoff or decoupling) to avoid a collision. If the spatiotemporal intersection probability is 20% (relatively low), the threshold is raised from 0.6 to 0.8. In this case, a warning will only be triggered if the risk coefficient is ≥0.8, preventing construction from being interrupted due to minor risks.

[0047] Step S400: Correct the estimated duration based on the following motion parameters of the hoisting rope and environmental characteristic parameters to obtain the target pre-collision duration; It is understood that the estimated time obtained in step S300 is calculated based on the initial distance between the obstacle and the hoisting rope after the scaffolding worker has straightened the hoisting rope. However, during flight, the initial position of the hoisting rope usually changes: on the one hand, due to the driving effect of the drone on the hoisting rope, the rope will be displaced with the movement of the drone; on the other hand, external environmental factors (such as wind and airflow) will also cause the rope position to shift. Therefore, in order to improve the accuracy of the pre-collision time, this embodiment of the application corrects the estimated time by using the following motion parameters of the hoisting rope and environmental characteristic parameters, thereby obtaining the target pre-collision time.

[0048] In one embodiment, step S400: correcting the estimated duration based on the following motion parameters of the hoisting rope and environmental characteristic parameters to obtain the target pre-collision duration includes: S410. Determine a first correction value based on the following motion parameters of the hoisting rope, wherein the following motion parameters include the following motion distance of the hoisting rope toward the obstacle or the following motion distance away from the obstacle; S420. Determine a second correction value based on environmental characteristic parameters, wherein the environmental characteristic parameters include wind speed, wind direction, and sensor visibility; S430. The first correction value and the second correction value are superimposed to obtain the target correction value; S440. The target pre-collision time is obtained based on the estimated time and the target correction value.

[0049] When assessing the impact of the following motion parameters of the hoisting rope on the estimated time, the first correction value can be determined as follows: First, based on the following motion distance of the hoisting rope towards or away from the obstacle, combined with the obstacle's speed, calculate the time error caused by the following motion. Specifically, the time error is equal to the change in distance between the rope and the obstacle divided by the obstacle's speed (distance change = real-time distance - initial distance, negative when facing the obstacle, positive when away); then, this time error is directly used as the first correction value to quantify the impact of the rope's own motion on the pre-collision time.

[0050] For example, if the hoisting rope moves towards the obstacle under the guidance of the drone (e.g., the rope moves forward with the drone, and the real-time distance to the obstacle is shortened by 2 meters compared to the initial distance), then the first correction value is negative (e.g., -1 second, the actual value is calculated based on the distance difference and speed), indicating that the actual collision time will be earlier than the estimated time, and the pre-collision time needs to be shortened. If the rope moves away from the obstacle (e.g., the drone moves backward, causing the rope to move away from the obstacle, increasing the distance by 1.5 meters), then the first correction value is positive (e.g., +0.8 seconds, the actual value is calculated based on the distance difference and speed), indicating that the collision risk is delayed, and the pre-collision time needs to be extended.

[0051] When assessing the impact of environmental characteristic parameters on the estimated duration, a second correction value can be obtained by adjusting the estimated duration based on wind speed. Specifically, when the wind speed is less than or equal to the wind speed threshold, the impact on the positional movement of the hoisting rope is negligible due to the weak wind force, and no correction is needed for the estimated duration; the second correction value is 0. When the wind speed is greater than the preset wind speed threshold, the estimated duration needs to be dynamically corrected based on the wind speed vector (wind speed magnitude and wind direction) to obtain the second correction value. If the wind direction causes the rope to deviate towards the obstacle, it will shorten the actual distance between them, and the second correction value will be negative (e.g., if the wind speed is 5 m / s and the wind direction is towards the obstacle, causing the rope to accelerate towards it, the correction value is -0.5 seconds), indicating an earlier collision time. If the wind direction causes the rope to deviate away from the obstacle, it will increase the actual distance between them, and the second correction value will be positive (e.g., if the wind speed is 6 m / s and the wind direction is away from the obstacle, causing the rope to move away from it, the correction value is +0.3 seconds), indicating a delayed collision time.

[0052] In other embodiments, a second correction value can be determined in conjunction with sensor visibility. When sensor visibility is higher than or equal to a preset visibility threshold, the position information of obstacles and hoisting ropes can be clearly captured, and the impact on the estimated duration is small, so no correction is required, and the second correction value is 0. When sensor visibility is lower than the preset visibility threshold, due to poor environmental visibility, the detection accuracy of the sensor for the position and movement state of obstacles and hoisting ropes will decrease, which may lead to a lag in the judgment of collision risk. In this case, the estimated duration needs to be negatively corrected to obtain a second correction value (e.g., a correction value of -0.4 seconds) to provide early warning of collision risk and compensate for the detection delay caused by low visibility.

[0053] After obtaining the first and second correction values, the target correction value is calculated by algebraic superposition, i.e., target correction value = first correction value + second correction value. This superposition method can comprehensively reflect the overall impact of the hoisting rope's following motion and environmental factors on the pre-collision time, so as to obtain a pre-collision time that is more in line with the actual situation and provide an accurate basis for subsequent UAV control decisions.

[0054] Step S500: Determine that the target pre-collision duration is greater than or equal to the duration threshold, and control the drone to accelerate and take off above the gantry and hover so that the hoisting rope is off the ground; When the pre-collision time is within a safe range (greater than or equal to the time threshold), it indicates a high risk of collision, but a low probability of danger in the short term. The drone is ready for accelerated takeoff. In this case, the drone is controlled to accelerate and take off above the gantry, hovering to allow the lifting ropes to detach from the ground. Accelerated takeoff reduces the time the lifting ropes spend near the ground, lowering the likelihood of collisions with ground obstacles. Furthermore, hovering above the gantry allows the drone to accurately locate the lifting position in advance, further ensuring the safety and smoothness of the lifting operation.

[0055] When the drone is hovering, its position can be controlled to keep the hoisting ropes slack. This reduces the impact of rope tension on the drone's hovering stability and provides flexible operating space for subsequent precise adjustment of rope length and smooth hoisting of the gantry, avoiding positional deviations or force imbalances caused by rope tension.

[0056] Step S600: Determine that the target pre-collision time is less than the time threshold, and control the drone hook to detach from the hoisting rope so that the drone is separated from the gantry.

[0057] When the pre-collision time is determined to be less than the time threshold, it indicates a high risk of collision and a high probability of danger in the short term. At this point, the system will control the drone hook to detach from the hoisting rope, separating the drone from the gantry. In this state, even if an obstacle collides and pulls on the hoisting rope, it will not necessarily have a safety impact on the gantry or the drone, or its safety impact will be greatly reduced.

[0058] For example, to control the drone hook to detach from the hoisting rope, the drone can trigger an unlocking command on the connecting mechanism. The connecting mechanism can be an electromagnetic release mechanism or a mechanical latch structure. Unlocking the connecting mechanism separates the drone hook from the hoisting rope.

[0059] Specifically, in this embodiment of the application, when the target pre-collision time is less than the time threshold, the first hook 400 is controlled to unlock and separate from the hoisting rope 300.

[0060] Based on this, this application provides a method for construction management and control of building perimeter scaffolding based on unmanned aerial vehicles (UAVs), which achieves intelligent hoisting management through risk assessment and dynamic decision-making mechanisms. During the pre-takeoff or initial takeoff phase of UAV hoisting, the types and status parameters of obstacles within a preset distance range of the hoisting rope are first acquired. Then, the hoisting risk coefficient is determined based on the obstacle types and status parameters. When the risk coefficient exceeds a threshold, the estimated time for the obstacle to reach the hoisting rope is determined based on the obstacle status parameters. The estimated time is then corrected by combining the hoisting rope's following motion parameters and environmental characteristic parameters to obtain the target pre-collision time. Finally, a tiered response is implemented based on the target pre-collision time: if the pre-collision time is sufficient, the takeoff and hover are accelerated; if time is tight, the hook is triggered to detach from the hoisting rope. This solution, through multi-dimensional risk quantification, dynamic time correction, and tiered handling strategies, significantly improves the accuracy of risk prediction and the flexibility of response, reduces human intervention, and enhances the system's adaptability under complex working conditions. It ensures construction safety, reduces accidents, maintains operational continuity, and meets the actual needs of efficient and intelligent construction.

[0061] like Figure 4 As shown, Figure 4 The diagram below shows the hardware structure of the construction control system in some embodiments of this application. The construction control system provided in this application includes a memory 1000 and a processor 2000. The memory 1000 is used to store computer-readable instructions, and the processor 2000 is used to call the computer-readable instructions to execute the construction control method for the building perimeter frame based on UAVs as described above.

[0062] The processor 2000 provides computing and control capabilities to control the construction management system to perform corresponding tasks. For example, it controls the construction management system to perform the UAV-based construction management method for building perimeter scaffolding in any of the above method embodiments. The method includes: during the pre-takeoff or initial takeoff phase of UAV hoisting, acquiring obstacle types and obstacle state parameters within a preset distance range of the hoisting rope; determining a risk coefficient for the hoisting operation based on the obstacle types and obstacle state parameters, wherein the risk coefficient represents the probability of collision between the hoisting rope and the obstacle; if the risk coefficient is greater than or equal to a risk coefficient threshold, determining the estimated time for the obstacle to reach the hoisting rope based on the obstacle state parameters; correcting the estimated time based on the following motion parameters of the hoisting rope and environmental characteristic parameters to obtain a target pre-collision time; determining that the target pre-collision time is greater than or equal to a time threshold, controlling the UAV to accelerate and take off above the gantry and hover so that the hoisting rope leaves the ground; determining that the target pre-collision time is less than the time threshold, controlling the UAV hook to detach from the hoisting rope so that the UAV is separated from the gantry.

[0063] The processor 2000 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a hardware chip, or any combination thereof; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The aforementioned PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0064] The memory 1000, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the UAV-based building perimeter scaffolding construction control method in the embodiments of this application. The processor 2000, by running the non-transitory software programs, instructions, and modules stored in the memory 1000, can implement the UAV-based building perimeter scaffolding construction control method in any of the above method embodiments.

[0065] Specifically, memory 1000 may include volatile memory (VM), such as random access memory (RAM); memory 1000 may also include non-volatile memory (NVM), such as read-only memory (ROM), flash memory, hard disk drive (HDD), solid-state drive (SSD), or other non-transitory solid-state storage devices; memory 1000 may also include combinations of the above types of memory.

[0066] In summary, the construction control system of this application adopts the technical solution of any of the above-mentioned embodiments of the construction control method for building perimeter scaffolding based on UAVs. Therefore, it has at least the beneficial effects brought about by the technical solutions of the above embodiments, which will not be elaborated here.

[0067] This application also provides a computer-readable storage medium, such as a memory including program code, which can be executed by a processor to complete the construction control method for the building perimeter frame based on UAVs in the above embodiments. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CDROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0068] This application also provides a computer program product, which includes one or more lines of program code stored in a computer-readable storage medium. The processor of the early warning system reads the program code from the computer-readable storage medium and executes the program code to complete the steps of the UAV-based construction perimeter scaffolding control method provided in the above embodiments.

[0069] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware, or by a program or program code related to hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0070] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0071] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software and a general-purpose hardware platform, or of course, using hardware. Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0072] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method for construction control of building perimeter scaffolding based on unmanned aerial vehicles (UAVs), used for hoisting control of building construction gantry installation by UAVs, characterized in that, The method includes: During the pre-takeoff or initial takeoff phase of drone hoisting, acquire obstacle types and obstacle status parameters within a preset distance range of the hoisting rope; The risk coefficient of the hoisting operation is determined based on the type of obstacle and the obstacle state parameters. The risk coefficient represents the probability of collision between the hoisting rope and the obstacle. If the risk coefficient is greater than or equal to the risk coefficient threshold, the estimated time for the obstacle to reach the hoisting rope is determined based on the obstacle state parameters; The estimated time is corrected based on the following motion parameters of the hoisting rope and environmental characteristic parameters to obtain the target pre-collision time; If the target pre-collision duration is determined to be greater than or equal to the duration threshold, the drone is controlled to accelerate and take off above the gantry and remain in a hovering state so that the hoisting rope is lifted off the ground. If the target pre-collision time is determined to be less than the time threshold, the drone hook is controlled to detach from the hoisting rope so that the drone is separated from the gantry.

2. The construction control method for building perimeter scaffolding based on unmanned aerial vehicles (UAVs) as described in claim 1, characterized in that, The acquisition of obstacle types and obstacle status parameters within a preset distance range of the hoisting rope includes: The drone uses at least one of the following: a visual sensor or a lidar sensor to scan an area within a preset distance range of the hoisting rope to obtain multi-frame image data or point cloud data. The obstacle type and obstacle state parameters are obtained by analyzing and processing the multi-frame image data or point cloud data. The obstacle type includes static obstacles or dynamic obstacles, and the obstacle state parameters include the size of the obstacle, the direction of movement, the speed of movement, and the distance between the obstacle and the hoisting rope.

3. The construction control method for building perimeter scaffolding based on unmanned aerial vehicles (UAVs) as described in claim 2, characterized in that, Determining the risk coefficient of the hoisting operation based on the obstacle type and obstacle state parameters includes: When the obstacle type is a dynamic obstacle and the obstacle's movement direction is towards the hoisting rope, the size parameters, interval distance parameters, and movement speed parameters of the dynamic obstacle are input into the hoisting operation risk prediction model to obtain the hoisting operation risk coefficient. The risk coefficient is positively correlated with the size of the obstacle, positively correlated with the movement speed, and negatively correlated with the interval distance of the obstacle. When the obstacle type is a static obstacle, or the direction of movement deviates from the hoisting rope, the risk coefficient of the hoisting operation is obtained by querying the pre-established risk mapping table.

4. The construction control method for building perimeter scaffolding based on unmanned aerial vehicles (UAVs) as described in claim 1, characterized in that, After determining the risk coefficient of the hoisting operation based on the obstacle type and obstacle state parameters, the method further includes: The predicted trajectory is obtained by predicting the obstacle's trajectory based on the obstacle's historical state parameters; The motion envelope of the hoisting rope is generated based on the pre-flight trajectory of the UAV, and the spatiotemporal intersection probability of the predicted flight trajectory and the motion envelope is calculated using a spatial geometry algorithm. The risk coefficient threshold is dynamically adjusted based on the spatiotemporal intersection probability, wherein the spatiotemporal intersection probability is negatively correlated with the risk coefficient threshold.

5. The construction control method for building perimeter scaffolding based on unmanned aerial vehicles (UAVs) as described in claim 1, characterized in that, The process of determining the estimated time for the obstacle to reach the hoisting rope based on the obstacle's state parameters includes: The estimated time for an obstacle to reach the hoisting rope is determined based on the distance between the obstacles and the speed of the obstacles. The distance between the obstacles is the initial distance between the obstacles and the hoisting rope.

6. The construction control method for building perimeter scaffolding based on unmanned aerial vehicles (UAVs) as described in claim 1, characterized in that, The step of correcting the estimated time based on the following motion parameters of the hoisting rope and environmental characteristic parameters to obtain the target pre-collision time includes: A first correction value is determined based on the following motion parameters of the hoisting rope, wherein the following motion parameters include the following motion distance of the hoisting rope toward the obstacle or the following motion distance away from the obstacle; The second correction value is determined based on environmental characteristic parameters, including wind speed, wind direction, and sensor visibility. The target correction value is obtained by superimposing the first correction value and the second correction value. The target pre-collision time is obtained based on the estimated duration and the target correction value.

7. The construction control method for building perimeter scaffolding based on unmanned aerial vehicles (UAVs) as described in claim 6, characterized in that, The determination of the first correction value based on the following motion parameters of the hoisting rope includes: The duration error caused by the following motion is determined based on the following motion distance of the hoisting rope towards or away from the obstacle, and the speed of the obstacle. The duration error caused by the following motion is determined as the first correction value.

8. The construction control method for building perimeter scaffolding based on unmanned aerial vehicles (UAVs) as described in claim 6, characterized in that, The step of determining the second correction value based on environmental characteristic parameters includes: When the wind speed exceeds a preset wind speed threshold, a second correction value is obtained by correcting the estimated duration based on the wind speed vector; and / or, When the sensor visibility is lower than a preset visibility threshold, the estimated duration is negatively corrected to obtain a second correction value.

9. The construction control method for building perimeter scaffolding based on unmanned aerial vehicles (UAVs) as described in claim 1, characterized in that, The control of the drone hook to detach from the hoisting rope includes: The drone is controlled to trigger an unlocking command for the connection mechanism, which is either an electromagnetic release mechanism or a mechanical latch structure. Unlocking the connection mechanism separates the drone hook from the hoisting rope.

10. A construction management and control system, characterized in that, include: Memory and processor, wherein the memory is used to store program code; The processor is used to call the program code to perform the method as described in any one of claims 1 to 9.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 9.

Images