Auxiliary positioning method and system for hoisting component in assembly type scene

Abstract

The invention relates to an auxiliary positioning method and system for a hoisting component in an assembly type scene, the system comprises hoisting positioning equipment and an image sensor, and the hoisting positioning equipment comprises a hoisting tool, a motor and a controller; the image sensor is used for acquiring a video of a hoisting target position of a construction site in real time; the controller is used for controlling operation of the motor to drive the lifting appliance to move, and the lifting appliance is used for lifting a target prefabricated part; and the control module is used for receiving a video transmitted by the image sensor at the same time, identifying and determining the component position and the hoisting target position of the target prefabricated component from the video based on a target identification model, and when the component position is aligned to the hoisting target position, installing the target prefabricated component so as to execute the installation operation of the next prefabricated component. Wherein the target recognition model is obtained by training a target detection YOLO model based on a sample video.

Description

Technical Field

[0001] The disclosed embodiments relate to the technical field of construction hoisting automation, and in particular to a method and system for auxiliary positioning of hoisting components in an assembled scenario. Background Art

[0002] Currently, about 70% of the time spent on constructing prefabricated buildings is spent on component assembly. Prefabricated structural components are usually transported by crane to the vicinity of the assembly location for preliminary positioning. Subsequently, several workers work together to adjust the horizontal posture of the hoisted components to align them with the target installation area, and cooperate with the crane to place the components in the correct position and direction. Due to the large size and weight of the components, and their susceptibility to factors such as insufficient rigidity and deformation of the crane boom and ropes, limited motion control accuracy of traditional cranes, and harsh outdoor environments, the positioning accuracy and stability of the components during the hoisting process are poor. Therefore, repeated lifting and lowering steps are usually required, which seriously affects construction efficiency. Summary of the invention

[0003] In order to solve the above technical problems or at least partially solve the above technical problems, an embodiment of the present disclosure provides a method and system for auxiliary positioning of hoisting components in an assembled scenario.

[0004] In a first aspect, an embodiment of the present disclosure provides an auxiliary positioning system for hoisting components in an assembled scene, the system comprising a hoisting positioning device and an image sensor, the hoisting positioning device comprising at least a hoist, a motor and a controller, and being pre-installed at a preset installation position at a construction site; The image sensor is used to obtain a video of the hoisting target position at the construction site in real time; The controller is used to control the operation of the motor to drive the movement of the hoist, and the hoist is used to hoist a target prefabricated component; at the same time, the video transmitted by the image sensor is received, and the component position and the hoisting target position of the target prefabricated component are identified and determined from the video based on the target recognition model, and when the component position is aligned to the hoisting target position, the target prefabricated component is installed to perform the installation operation of the next prefabricated component; wherein the target recognition model is obtained by training a target detection YOLO model based on sample videos.

[0005] In one embodiment, the controller is also used to calculate the working time of the motor according to the installation position of the lifting positioning device, the lifting target position and the output power of the motor before the component position is aligned to the lifting target position, generate a control instruction based on the working time, and control the operation of the motor based on the control instruction to drive the movement of the hoisting device, so that the target prefabricated component moves and is aligned to the lifting target position.

[0006] In one embodiment, the controller is also used to identify and determine the positioning error distance of the lifting target position from the video based on the target recognition model. When the positioning error distance is less than or equal to a preset error distance, the working time of the motor is calculated according to the installation position of the lifting positioning device, the lifting target position and the output power of the motor.

[0007] In one embodiment, the installation position of the hoisting positioning device is determined by: According to the construction design drawings of prefabricated buildings, the coordinates of the target lifting position of each prefabricated component are marked, and all the lifting target positions are grouped in the form of areas according to the maximum working range of the lifting positioning equipment. A point is found in each group of areas so that the sum of the distances to all the lifting target positions is the shortest, and the lifting positioning equipment is installed at this point. The installation order of the prefabricated components in each group of areas is determined according to the priority of the installation tasks; or the installation order of the component lifting is determined according to the order of the adjacent position relationship of the prefabricated components.

[0008] In one embodiment, the image sensor includes at least one camera and / or a depth sensor.

[0009] In one embodiment, the YOLO model includes a first backbone network, a second backbone network, a feature extraction network, and a detection head; the output ends of the first backbone network and the second backbone network are both connected to the input end of the feature extraction network; the training process of the target recognition model includes: Acquire a sample video, wherein the sample video includes multiple frames of two-dimensional images and multiple frames of depth images acquired by the at least one camera and the depth sensor, each frame of the two-dimensional image corresponds to a frame of the depth image and both are marked with a component position and a hoisting target position; The multiple frames of two-dimensional images are input into the first backbone network, and the multiple frames of depth images are input into the second backbone network, so as to iteratively train the YOLO model until the loss function of the YOLO model is less than or equal to a preset value and the training is terminated.

[0010] In one embodiment, there are multiple hoisting target positions, each of which is located at the construction site and is marked with at least one physical mark, each of which corresponds to a prefabricated component, and all prefabricated components are used to assemble to form an assembled building structure.

[0011] In a second aspect, an embodiment of the present disclosure provides a method for auxiliary positioning of a hoisted component in an assembled scene, the method being applied to an auxiliary positioning system for a hoisted component, the system comprising a hoisting positioning device and an image sensor, the hoisting positioning device comprising a hoist, a motor and a controller, and being pre-installed at a preset installation position on a construction site; the method is executed by the controller and comprises the following steps: Controlling the image sensor to obtain a video of a hoisting target position at the construction site in real time; The operation of the motor is controlled to drive the movement of the hoist, and the hoist is used to hoist a target prefabricated component; at the same time, the video transmitted by the image sensor is received, and the component position and the hoisting target position of the target prefabricated component are identified and determined from the video based on a target recognition model, and when the component position is aligned to the hoisting target position, the target prefabricated component is installed to perform the installation operation of the next prefabricated component; wherein the target recognition model is obtained by training a target detection YOLO model based on a sample video.

[0012] In a third aspect, an embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for auxiliary positioning of hoisting components in an assembled scenario of the above embodiment.

[0013] In a fourth aspect, an embodiment of the present disclosure provides an automatic lifting device, including: Image sensor; A hoisting and positioning device, the hoisting and positioning device comprising a hoist, a motor and a processor; and a memory for storing a computer program; Wherein, the processor is configured to execute the auxiliary positioning method of hoisting components in the assembly scenario of the above embodiment by executing the computer program.

[0014] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages: The embodiments of the present disclosure provide a method and system for auxiliary positioning of hoisted components in an assembled scenario. The system includes a hoisting positioning device and an image sensor. The hoisting positioning device includes at least a hoist, a motor and a controller, and is pre-installed at a preset installation position at a construction site. The image sensor acquires a video of a hoisting target position at the construction site in real time. The controller controls the operation of the motor to drive the movement of the hoist, and the hoist is used to hoist a target prefabricated component. At the same time, the video transmitted by the image sensor is received, and the component position and the hoisting target position of the target prefabricated component are identified and determined from the video based on a target recognition model. When the component position is aligned to the hoisting target position, the target prefabricated component is installed to perform the installation operation of the next prefabricated component. The target recognition model is obtained by training a target detection YOLO model based on a sample video. The solution of this embodiment provides a solution for auxiliary positioning of hoisting components in an assembled scene, using a motor to control the hoisting device to pull the hoisting components, and the target recognition model trained based on the algorithm model of video target detection and positioning, that is, the YOLO model, determines the component position and the hoisting target position in real time through the collected video, and accurately judges whether the component has completed precise positioning to carry out the installation of the prefabricated component. In this way, the posture and position positioning of the hoisted component can be automatically and accurately completed. The positioning accuracy of the component during the hoisting process is high, and the component assembly can be completed accurately and efficiently, that is, the efficiency and accuracy of the component hoisting positioning are improved, thereby improving the construction efficiency and quality of assembled buildings. In addition, this hoisting positioning auxiliary solution can replace manual high-intensity and high-risk hoisting tasks, avoid the risk of personnel operation, and ensure construction safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0017] Figure 1 It is a schematic diagram of an auxiliary positioning system for hoisting components in an assembled scenario according to an embodiment of the present disclosure; Figure 2 It is a schematic diagram of the auxiliary positioning process of the hoisting component in the assembly scene of the embodiment of the present disclosure; Figure 3 This is a flow chart of a method for processing video using a target recognition model according to an embodiment of the present disclosure; Figure 4The present invention is a flow chart of the auxiliary positioning method of the hoisting component in the assembly scene according to the embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0019] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0020] It should be understood that, in the following, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0021] Figure 1 The figure is a schematic diagram of an auxiliary positioning system for hoisting components in an assembled scene according to an embodiment of the present disclosure. The system may include a hoisting positioning device 20 and an image sensor 10. The hoisting positioning device 20 may include at least a hoisting device 201, a motor 202 and a controller 203, and is pre-installed at a preset installation position at the construction site. Exemplarily, the hoisting positioning device may be an automatic hoisting robot, such as a hoisting robot with 6 degrees of freedom, which includes at least a hoisting device such as a hook, a motor such as a servo motor and a controller such as a processor. The motor may be connected to the hook through an actuator (such as a transmission mechanism, a reducer, etc.), and the actuator (not shown) may be controlled to drive the hook to move. In this embodiment, there is no limitation on the specific structure of the hoisting positioning device, and the focus is on the improvement of its control method.

[0022] The image sensor 10 is used to obtain a video of the target lifting position at the construction site in real time. The target lifting position is the installation position of the target prefabricated component. In one embodiment, there are multiple target lifting positions, each of which corresponds to a prefabricated component, and all prefabricated components are used to assemble and form an assembled building structure. Each target lifting position is located at the construction site and is marked with at least one physical mark such as a reference frame mark. During the construction process, as each prefabricated component is gradually installed, a physical mark can be set on the installed prefabricated component as the target lifting position when the next prefabricated component is installed.

[0023] The controller 203 is used to control the operation of the motor 202 to drive the actuator to drive the movement of the hoisting device such as a hook, and the hoisting device is used to hoist the target prefabricated component; the controller 203 simultaneously receives the video transmitted by the image sensor 10, identifies and determines the component position and the hoisting target position of the target prefabricated component from the video based on the target recognition model, and when the component position is aligned to the hoisting target position, installs the target prefabricated component to perform the installation operation of the next prefabricated component; wherein the target recognition model is obtained by training the target detection YOLO model based on the sample video.

[0024] Exemplarily, after the target prefabricated component is hoisted on a hoist such as a hook, the controller 203 controls the operation of the motor 202 to drive the actuator to drive the movement of the hoist such as the hook, thereby moving the target prefabricated component. At the same time, the controller 203 receives the video transmitted by the image sensor 10, and identifies and determines the component position of the target prefabricated component such as the three-dimensional (3D) coordinate position and the hoisting target position such as the 3D coordinate position from the video based on the target recognition model, and monitors the matching of the determined component position and the hoisting target position in real time. When the component position is aligned with the hoisting target position, the movement of the hook is stopped to allow the installer to install the target prefabricated component, and then continues to perform the installation operation of the next prefabricated component. The operation process is the same as the previous prefabricated component hoisting and position recognition and matching process.

[0025] Among them, the YOLO model can be a YOLOv11 model, but it is not limited to this, and models such as YOLOv10 can also be used. The YOLOv11 model has shown significant performance improvements in many aspects, especially in target detection tasks. Its enhanced feature extraction capability: YOLOv11 introduces C3K2 and C2PSA modules through improved backbone networks and neck networks, which significantly improves the feature extraction capability, making the model perform better in complex tasks (such as multi-target detection, occlusion processing, etc.); in addition, it optimizes speed and efficiency. YOLOv11 adopts a more efficient architecture and training process, maintaining high accuracy while improving processing speed. Therefore, the target recognition model trained based on this can more accurately identify and determine the component position and hoisting target position of the target prefabricated component in the video, thereby improving the efficiency and accuracy of component hoisting positioning, and then improving the construction efficiency and quality of prefabricated buildings.

[0026] The solution of this embodiment provides a solution for auxiliary positioning of hoisting components in an assembled scene, using a motor to control the hoisting device to pull the hoisting components, and the target recognition model trained based on the algorithm model of video target detection and positioning, that is, the YOLO model, determines the component position and the hoisting target position in real time through the collected video, and accurately judges whether the component has completed precise positioning to carry out the installation of the prefabricated component. In this way, the posture and position positioning of the hoisted component can be automatically and accurately completed. The positioning accuracy of the component during the hoisting process is high, and the component assembly can be completed accurately and efficiently, that is, the efficiency and accuracy of the component hoisting positioning are improved, thereby improving the construction efficiency and quality of assembled buildings. In addition, this hoisting positioning auxiliary solution can replace manual high-intensity and high-risk hoisting tasks, avoid the risk of personnel operation, and ensure construction safety.

[0027] In order to further improve the construction efficiency and quality of prefabricated buildings, based on the above embodiments, in one embodiment, reference is made to Figure 2 As shown in , the controller 203 is also used to calculate the working time of the motor according to the installation position of the lifting positioning device, the lifting target position and the output power of the motor before the component position is aligned to the lifting target position, generate a control instruction based on the working time, and control the operation of the motor based on the control instruction to drive the movement of the hoisting device, so that the target prefabricated component moves and is aligned to the lifting target position.

[0028] Exemplarily, the controller 203 can calculate the working time required for the servo motor to control the hoist to pull the target prefabricated component to the hoisting target position based on the positional relationship between the installation position of the hoisting equipment itself and the hoisting target position, such as the distance relationship (such as the straight-line distance), the orientation relationship (such as the angle in the horizontal and vertical directions), the height difference relationship, and the spatial relative position relationship, in combination with the output power of the servo motor, and generate an action control instruction for equipment control based on the calculation result, that is, the working time, and control the operation of the servo motor based on the control instruction to drive the actuator to drive the movement of the hoist, so that the target prefabricated component moves and aligns to the hoisting target position. Among them, the movement trajectory can be generated according to the positional relationship, and the working time can be determined in combination with the power-time conversion method. In some cases, the adjustment time may be extended or shortened through incremental PID or adaptive control. In this way, the shortest time required for hoisting components can be accurately calculated in advance, thereby improving the efficiency and accuracy of component hoisting and positioning, and further improving the construction efficiency and quality of prefabricated buildings.

[0029] In one embodiment, the controller 203 is further used to identify and determine the positioning error distance of the hoisting target position from the video based on the target recognition model, and when the positioning error distance is less than or equal to the preset error distance, the working time of the motor is calculated according to the installation position of the hoisting positioning device, the hoisting target position and the output power of the motor. In this way, the influence of positioning error can be avoided, and the shortest time required for hoisting components can be calculated more accurately, thereby improving the efficiency and accuracy of component hoisting positioning, and further improving the construction efficiency and quality of prefabricated buildings.

[0030] Based on any one of the above embodiments, in one embodiment, in combination with reference Figure 2 As shown in , the installation position of the hoisting positioning device is determined by the following method: According to the construction design drawings of prefabricated buildings, the coordinates of the target lifting position of each prefabricated component are marked, and all the lifting target positions are grouped in the form of areas according to the maximum working range of the lifting positioning equipment. A point is found in each group of areas so that the sum of the distances to all the lifting target positions is the shortest, and the lifting positioning equipment is installed at this point. The installation order of the prefabricated components in each group of areas is determined according to the priority of the installation tasks; or the installation order of the component lifting is determined according to the order of the adjacent position relationship of the prefabricated components.

[0031] For example, there are 100 prefabricated components, corresponding to 100 lifting target positions. The maximum working range of the lifting positioning equipment such as the lifting robot is the maximum distance range that the lifting device can move. The maximum distance range is projected to the ground, and the 100 lifting target positions are grouped in the projection area. For example, 10 adjacent lifting target positions are grouped into a group and divided into a sub-area, and each sub-area includes 10 corresponding adjacent lifting target positions. Find a point in each group of areas, that is, a sub-area, so that the sum of the distances to all 100 lifting target positions is the shortest. This problem can be converted into a constrained geometric median problem, for example, by introducing boundary constraints through the Lagrange multiplier method to solve it. For details, please refer to the prior art for understanding, and no further description is given here. Installing the lifting positioning equipment at this point can make the lifting device on the lifting positioning equipment reach each lifting target position relatively short when moving, thereby reducing the time required for each lifting component, further improving the efficiency of component lifting and positioning, and further improving the construction efficiency of prefabricated buildings.

[0032] In one embodiment, the image sensor includes at least one camera and / or a depth sensor. The camera can capture RGB images, that is, two-dimensional images, and the depth sensor can capture depth images.

[0033] Accordingly, in one embodiment, in order to further improve the construction efficiency and quality of prefabricated buildings, in one embodiment, reference is made to Figure 3 As shown, improvements are made to the architecture and training method of the YOLO model such as YOLOv11. Specifically, the YOLO model includes a first backbone network (Backbone), a second backbone network (Backbone), a feature extraction network (such as a neck network Neck) and a detection head (Head); the output ends of the first backbone network and the second backbone network are connected to the input end of the feature extraction network. That is, a backbone network (Backbone) is added to the original architecture of the YOLO model. The training process of the target recognition model includes: Acquire a sample video, wherein the sample video includes multiple frames of two-dimensional images and multiple frames of depth images acquired by the at least one camera and the depth sensor, each frame of the two-dimensional image corresponds to a frame of the depth image and both are marked with a component position and a hoisting target position; The multiple frames of two-dimensional images are input into the first backbone network, and the multiple frames of depth images are input into the second backbone network, so as to iteratively train the YOLO model until the loss function of the YOLO model is less than or equal to a preset value and the training is terminated.

[0034] Exemplarily, in this embodiment, for the added second backbone network, since it processes depth images containing more feature information, the number of convolutional layers in the second backbone network is greater than the number of convolutional layers in the first backbone network, so as to better mine and extract the features of the depth image, and perform comprehensive training in combination with the features of the two-dimensional image extracted by the first backbone network, so that the position determination result of the trained model is more accurate, that is, the component position and lifting target position of the target prefabricated component in the video can be more accurately identified, thereby further improving the efficiency and accuracy of the component lifting and positioning, and further improving the construction efficiency and quality of prefabricated buildings.

[0035] Among them, the first backbone network extracts the first feature map of the two-dimensional image, and the second backbone network extracts the second feature map of the depth image. The feature extraction network such as the neck network extracts the feature maps of the first feature map and the second feature map, and then fuses the feature maps into the detection head to output the predicted component position of the prefabricated component and the corresponding predicted lifting target position. The loss function of the YOLO model is updated based on the difference between the predicted component position and the predicted lifting target position and the corresponding labeled component position and lifting target position.

[0036] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of a module or unit described above can be further divided into multiple modules or units for concretization. The components displayed as modules or units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. Those of ordinary skill in the art can understand and implement it without paying creative work.

[0037] The embodiment of the present disclosure provides a method for auxiliary positioning of a hoisting component in an assembled scene, the method is applied to an auxiliary positioning system for a hoisting component, the system includes a hoisting positioning device and an image sensor, the hoisting positioning device includes a hoist, a motor and a controller, and is pre-installed at a preset installation position on the construction site; Figure 4 As shown in , the method is executed by the controller and may include the following steps: Controlling the image sensor to obtain a video of a hoisting target position at the construction site in real time; The motor is controlled to drive the movement of the hoist, which is used to hoist the target prefabricated component; at the same time, the video transmitted by the image sensor is received, and the component position and the hoisting target position of the target prefabricated component are identified and determined from the video based on the target recognition model, and when the component position is aligned to the hoisting target position, the target prefabricated component is installed to perform the installation operation of the next prefabricated component. The target recognition model is obtained by training the target detection YOLO model based on the sample video.

[0038] In one embodiment, before the component position is aligned to the lifting target position, the controller calculates the working time of the motor according to the installation position of the lifting positioning device, the lifting target position and the output power of the motor, generates a control instruction based on the working time, and controls the operation of the motor based on the control instruction to drive the movement of the hoisting device, so that the target prefabricated component is moved and aligned to the lifting target position.

[0039] In one embodiment, the controller identifies and determines the positioning error distance of the lifting target position from the video based on the target recognition model. When the positioning error distance is less than or equal to a preset error distance, the operating time of the motor is calculated according to the installation position of the lifting positioning device, the lifting target position and the output power of the motor.

[0040] In one embodiment, the installation position of the hoisting positioning device is determined by: According to the construction design drawings of prefabricated buildings, the coordinates of the target lifting position of each prefabricated component are marked, and all the lifting target positions are grouped in the form of areas according to the maximum working range of the lifting positioning equipment. A point is found in each group of areas so that the sum of the distances to all the lifting target positions is the shortest, and the lifting positioning equipment is installed at this point. The installation order of the prefabricated components in each group of areas is determined according to the priority of the installation tasks; or the installation order of the component lifting is determined according to the order of the adjacent position relationship of the prefabricated components.

[0041] In one embodiment, the image sensor includes at least one camera and / or a depth sensor.

[0042] In one embodiment, the YOLO model includes a first backbone network, a second backbone network, a feature extraction network, and a detection head; the output ends of the first backbone network and the second backbone network are both connected to the input end of the feature extraction network; the training process of the target recognition model includes: Acquire a sample video, wherein the sample video includes multiple frames of two-dimensional images and multiple frames of depth images acquired by the at least one camera and the depth sensor, each frame of the two-dimensional image corresponds to a frame of the depth image and both are marked with a component position and a hoisting target position; The multiple frames of two-dimensional images are input into the first backbone network, and the multiple frames of depth images are input into the second backbone network, so as to iteratively train the YOLO model until the loss function of the YOLO model is less than or equal to a preset value and the training is terminated.

[0043] In one embodiment, the number of convolutional layers in the second backbone network is greater than the number of convolutional layers in the first backbone network. The first backbone network extracts a first feature map of a two-dimensional image, and the second backbone network extracts a second feature map of a depth image. The feature extraction network, such as the neck network, extracts feature maps of the first feature map and the second feature map, and then fuses the feature maps into the detection head to output the predicted component position of the prefabricated component and the corresponding predicted lifting target position. The loss function of the YOLO model is updated based on the difference between the predicted component position and the predicted lifting target position and the corresponding labeled component position and lifting target position.

[0044] In one embodiment, there are multiple hoisting target positions, each of which is located at the construction site and is marked with at least one physical mark, each of which corresponds to a prefabricated component, and all prefabricated components are used to assemble to form an assembled building structure.

[0045] It should be noted that, although the steps of the method in the present disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution, etc. In addition, it is also easy to understand that these steps may be, for example, executed synchronously or asynchronously in multiple modules / processes / threads.

[0046] Regarding the method in the above embodiment, the specific manner of executing the operation in each step and the corresponding technical effects brought about have been described in detail in the embodiment of the system, and will not be elaborated here.

[0047] The embodiments of the present disclosure also provide a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for auxiliary positioning of hoisting components in an assembled scenario described in any one of the embodiments above is implemented.

[0048] Exemplarily, the readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0049] The computer readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by an instruction execution system, an apparatus, or a device or used in combination with it. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0050] An embodiment of the present disclosure also provides an automatic lifting device such as an automatic lifting robot, comprising: an image sensor; a lifting positioning device, the lifting positioning device comprising a lifting device, a motor and a processor; and a memory for storing a computer program; wherein the processor is configured to execute the auxiliary positioning method for lifting components in the assembly scenario of the above embodiment by executing the computer program.

[0051] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0052] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An auxiliary positioning system for hoisting components in an assembled scene, characterized in that: The system includes a hoisting positioning device and an image sensor, wherein the hoisting positioning device includes at least a hoist, a motor and a controller, and is pre-installed at a preset installation position at the construction site; The image sensor is used to obtain a video of the hoisting target position at the construction site in real time; The controller is used to control the operation of the motor to drive the movement of the hoist, and the hoist is used to hoist a target prefabricated component; at the same time, the video transmitted by the image sensor is received, and the component position and the hoisting target position of the target prefabricated component are identified and determined from the video based on the target recognition model, and when the component position is aligned to the hoisting target position, the target prefabricated component is installed to perform the installation operation of the next prefabricated component; wherein the target recognition model is obtained by training a target detection YOLO model based on sample videos.

2. The system according to claim 1, characterized in that The controller is further used to calculate the working time of the motor according to the installation position of the lifting positioning device, the lifting target position and the output power of the motor before the component position is aligned to the lifting target position, generate a control instruction based on the working time, and control the operation of the motor based on the control instruction to drive the movement of the hoisting device, so that the target prefabricated component moves and is aligned to the lifting target position.

3. The system according to claim 2, characterized in that The controller is also used to identify and determine the positioning error distance of the lifting target position from the video based on the target recognition model. When the positioning error distance is less than or equal to the preset error distance, the working time of the motor is calculated according to the installation position of the lifting positioning device, the lifting target position and the output power of the motor.

4. The system according to any one of claims 1 to 3, characterized in that: The installation position of the hoisting positioning device is determined by the following method: According to the construction design drawings of prefabricated buildings, the coordinates of the lifting target position of each prefabricated component are marked, and all lifting target positions are grouped in the form of areas according to the maximum working range of the lifting positioning equipment. A point is found in each group of areas so that the sum of the distances to all lifting target positions is the shortest, and the lifting positioning equipment is installed at this point. Among them, the installation order of the prefabricated components in each group of areas is determined according to the priority of the installation task.

5. The system according to any one of claims 1 to 3, characterized in that: The image sensor includes at least one camera and / or a depth sensor.

6. The system according to claim 5, characterized in that The YOLO model includes a first backbone network, a second backbone network, a feature extraction network and a detection head; the output ends of the first backbone network and the second backbone network are both connected to the input end of the feature extraction network; The training process of the target recognition model includes: Acquire a sample video, wherein the sample video includes multiple frames of two-dimensional images and multiple frames of depth images acquired by the at least one camera and the depth sensor, each frame of the two-dimensional image corresponds to a frame of the depth image and both are marked with a component position and a hoisting target position; The multiple frames of two-dimensional images are input into the first backbone network, and the multiple frames of depth images are input into the second backbone network, so as to iteratively train the YOLO model until the loss function of the YOLO model is less than or equal to a preset value and the training is terminated.

7. The system according to any one of claims 1 to 3, characterized in that: There are multiple hoisting target positions, each of which is located at a construction site and is marked with at least one physical mark. Each of the hoisting target positions corresponds to a prefabricated component, and all prefabricated components are used to assemble to form an assembled building structure.

8. A method for auxiliary positioning of hoisting components in an assembled scene, characterized in that: The method is applied to a hoisting component auxiliary positioning system, which includes a hoisting positioning device and an image sensor. The hoisting positioning device includes a hoist, a motor and a controller, and is pre-installed at a preset installation position on the construction site. The method is executed by the controller and includes the following steps: Controlling the image sensor to obtain a video of a hoisting target position at the construction site in real time; The operation of the motor is controlled to drive the movement of the hoist, and the hoist is used to hoist a target prefabricated component; at the same time, the video transmitted by the image sensor is received, and the component position and the hoisting target position of the target prefabricated component are identified and determined from the video based on a target recognition model, and when the component position is aligned to the hoisting target position, the target prefabricated component is installed to perform the installation operation of the next prefabricated component; wherein the target recognition model is obtained by training a target detection YOLO model based on a sample video.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the method for auxiliary positioning of hoisting components in an assembled scenario as described in claim 8 is implemented.

10. An automatic lifting device, characterized in that: include: Image sensor; A hoisting and positioning device, the hoisting and positioning device comprising a hoist, a motor and a processor; and a memory for storing a computer program; Wherein, the processor is configured to execute the auxiliary positioning method of hoisting components in an assembled scenario as claimed in claim 8 by executing the computer program.

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