Tower crane jacking monitoring method, device and system and medium

Abstract

The invention discloses a tower crane jacking monitoring method, device and system and a medium, and relates to the technical field of tower crane control. The monitoring method comprises the steps that in the jacking process of the tower crane, for each standard section column of the tower crane, the position relative relation between the standard section column and a tenon correspondingly connected with the standard section column is obtained; and according to the position relative relation, whether the tenon has horizontal deviation or not is determined. According to the embodiment of the invention, the position relative relationship between the standard section column and the correspondingly connected tenon is obtained in real time, and whether the tenon has horizontal deviation or not is determined based on the position relative relationship, so that the original visual judgment is replaced, and the accuracy of tenon detection is improved.

Description

Technical Field

[0001] This application relates to the technical field of tower crane control, and specifically relates to a monitoring method, device, system and medium for tower crane jacking. Background Art

[0002] During the jacking process of a tower crane (hereinafter referred to as a tower crane), the processes of raising and lowering the tower are the most dangerous and most prone to major accidents. If an accident occurs during the jacking process, it usually leads to very serious consequences, such as the collapse and damage of the tower crane, serious injuries or even deaths of workers, causing significant economic and personal safety losses, and at the same time having a bad social impact.

[0003] During the entire tower crane jacking process, the most critical step is the separation of the upper structure from the tower body, also known as "removing the tenons". To ensure safety, when removing the tenons, it is necessary to ensure that the four tenons are pulled out vertically at the same time. To achieve this, the upper structure needs to be in a balanced state, that is, the torques borne by the front boom and the counterweight arm are equal in magnitude and opposite in direction, so that the tower body does not bear additional torque. Therefore, the "removing the tenons" monitoring is often carried out during the upper structure balancing process.

[0004] However, in the current balancing operation, workers often only visually confirm whether the four tenons are pulled out synchronously and whether there is a horizontal deviation, which makes there are a large number of human errors in the "removing the tenons" monitoring, thereby affecting the safety of the entire tower crane jacking process.

[0005] Therefore, a new tower crane jacking monitoring scheme is needed. Summary of the Invention

[0006] The purpose of the embodiments of this application is to provide a monitoring method, device, system and medium for tower crane jacking to at least partially solve the above technical problems.

[0007] To achieve the above purpose, the first aspect of this application provides a monitoring method for tower crane jacking, including: during the tower crane jacking process, for each standard section column of the tower crane, obtaining the relative position relationship between the standard section column and the corresponding connected tenon; and determining whether there is a horizontal deviation in the tenon removal according to the relative position relationship.

[0008] In the embodiments of this application, the monitoring method further includes: before obtaining the relative position relationship, roughly balancing the upper structure of the tower crane based on the calibration data, where the calibration data is the balancing data of the tower crane in the historical balanced state.

[0009] In an embodiment of the present application, obtaining the relative position relationship between the standard section column and the corresponding tenon includes: obtaining an image of the standard section column and the corresponding tenon; processing the image based on an AI vision recognition algorithm to obtain a standard section column recognition frame and a tenon recognition frame; and calculating the difference in the horizontal pixel coordinates of the standard section column recognition frame and the tenon recognition frame. Wherein, the difference is used to characterize the relative position relationship between the standard section column and the corresponding tenon. When the difference exceeds a preset threshold, it is determined that the tenon has a horizontal offset, otherwise it is determined that the tenon has no horizontal offset.

[0010] In an embodiment of the present application, the monitoring method further includes: when the tenon is ejected and after the tenon is ejected, obtaining and determining whether the upper part of the tower crane is tilted according to the tilt angle of the upper part of the tower crane.

[0011] A second aspect of the present application provides a monitoring device for tower crane jacking, including: a memory configured to store instructions; and a processor configured to call the instructions from the memory and be able to implement any of the above monitoring methods when executing the instructions.

[0012] A third aspect of the present application provides a monitoring system for tower crane jacking, including any of the above monitoring devices.

[0013] In an embodiment of the present application, the monitoring system further includes: an image acquisition device for acquiring images of each standard section column of the tower crane and its corresponding tenon and providing them to the monitoring device for processing to determine whether the tenon has a horizontal offset; and / or a tilt angle acquisition device for acquiring the tilt angle of the upper part of the tower crane when the tenon is ejected and after the tenon is ejected and transmitting it to the monitoring device to determine whether the upper part of the tower crane is tilted.

[0014] In an embodiment of the present application, the image acquisition device is a pan-tilt camera, and / or the tilt angle acquisition device is an attitude sensor.

[0015] In an embodiment of the present application, the monitoring device or the image acquisition device integrates an AI vision recognition algorithm module, which is used to process the images acquired by the image acquisition device to obtain a standard section column recognition frame and a tenon recognition frame, and calculate the difference in the horizontal pixel coordinates of the standard section column recognition frame and the tenon recognition frame, where the difference is used to characterize the relative position relationship between the standard section column and the corresponding tenon.

[0016] A fourth aspect of the present application provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to cause a machine to execute any of the above monitoring methods.

[0017] Through the above technical solution, the embodiments of the present application can obtain the relative position relationship between the standard section column and the corresponding tenon in real time, and determine whether there is a horizontal offset of the tenon based on this relative position relationship, replacing the original visual judgment and improving the accuracy of tenon monitoring.

[0018] Other features and advantages of the embodiments of the present application will be described in detail in the following specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings are used to provide a further understanding of the embodiments of the present application, and constitute a part of the specification. Together with the following specific implementation, they are used to explain the embodiments of the present application, but do not constitute a limitation to the embodiments of the present application. In the drawings:

[0020] Figure 1 Schematically shows a flowchart of a monitoring method for tower crane jacking according to Embodiment 1 of the present application;

[0021] Figure 2 Schematically shows a flowchart of a preferred monitoring method for tower crane jacking according to Embodiment 1 of the present application;

[0022] Figure 3 Is a flowchart of obtaining the relative position relationship between the standard section column and the corresponding tenon in the example of the embodiments of the present application;

[0023] FIG. 4(a) and FIG. 4(b) are schematic diagrams of the positional relationship between the standard section column and the target recognition frame of the tenon in the example of the embodiments of the present application;

[0024] Figure 5 Schematically shows a structural block diagram of a monitoring device for tower crane jacking according to Embodiment 2 of the present application; and

[0025] Figure 6 Schematically shows a structural block diagram of a monitoring system for tower crane jacking according to Embodiment 3 of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiments of the present application, and is not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0027] It should be noted that the acquisition, transmission, storage, use, processing, etc. of data in the technical solution of this application all comply with the relevant provisions of laws and regulations. In the embodiments of this application, some existing solutions in the industry such as certain software, components, models, etc. may be mentioned. They should be regarded as exemplary, and their purpose is only to illustrate the feasibility in the implementation of the technical solution of this application, but it does not mean that the applicant has already or necessarily used this solution.

[0028] It should be noted that if there are directional indications (such as up, down, left, right, front, back...) involved in the embodiments of this application, then such directional indications are only used to explain the relative positional relationship, movement conditions, etc. between components in a certain specific posture (as shown in the drawings). If this specific posture changes, then the directional indications will also change accordingly.

[0029] In addition, the technical solutions between various embodiments can be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0030] Before specifically introducing the embodiments of this application, some of the terms involved will be introduced first, so that those skilled in the art can understand the embodiments of this application more clearly.

[0031] 1. Mortise extraction: It refers to pulling out the tenon of the standard section column or the detachment of the upper part of the tower crane from the tower body caused by this pulling-out action. The tenon refers to the part protruding from the four standard section columns, and the tenon is used to connect with the standard section column of the upper section.

[0032] 2. AI vision recognition algorithm: It refers to a technology that simulates the human visual system through a computer and uses mathematical and machine learning methods to automatically detect, classify, recognize, and understand objects in images or videos. Its core is to extract features from pixel data through deep learning (such as convolutional neural network CNN), traditional machine learning, or digital image processing technology, and then achieve tasks such as object detection, image classification, semantic segmentation, and face recognition. Common AI vision algorithms include the YOLO series (YOLOv3 - v10).

[0033] 3. Upper assembly trimming: It refers to the process of adjusting the center of gravity position of the upper slewing mechanism of the tower crane (including the tower head, boom, counterweight arm, etc.) to coincide or be close to coincide with the center line of the tower body. In the embodiment of the present application, the upper assembly being in a trimmed state is a prerequisite for performing the "tenon extraction" monitoring. Usually, the trimming operation is to move the counterweight to a rough position by workers according to a pre-compiled trimming table, so that the front lifting arm and the counterweight arm are roughly balanced. This kind of balance is usually relatively rough and often requires manual fine-tuning again, which is also called rough trimming. In such rough trimming operations, for "tenon extraction", workers can only visually confirm whether the extraction of the four tenons is synchronous and whether there is a horizontal deviation, and there are still a large number of human errors.

[0034] 4. Safety jacking monitoring system: It is an intelligent device used to monitor, control, and give early warnings of key parameters during the jacking process in real time, aiming to prevent safety accidents such as overturning, overloading, and skew during the jacking operation. This system usually integrates sensors, controllers, and alarms to ensure that the jacking process complies with safety specifications.

[0035] Embodiment 1

[0036] Figure 1 Schematically shows a flowchart of a monitoring method for tower crane jacking according to Embodiment 1 of the present application. As Figure 1 shown, the embodiment of the present application provides a monitoring method for tower crane jacking, and this monitoring method may include the following steps S100 - S200.

[0037] Step S100, during the tower crane jacking process, for each standard section column of the tower crane, obtain the relative position relationship between the standard section column and the corresponding tenon.

[0038] Step S200, determine whether there is a horizontal offset in the tenon extraction according to the relative position relationship.

[0039] Through the above steps S100 - S200, the embodiment of the present application obtains the relative position relationship between the standard section column and the corresponding connected tenon in real time, and determines whether there is a horizontal offset in the tenon extraction based on this relative position relationship, replacing the original visual judgment and improving the accuracy of tenon extraction monitoring.

[0040] Furthermore, aiming at the problem that the existing rough trimming scheme is limited by the trimming table, in a preferred embodiment, as Figure 2 shown, before the above step S100 is executed, the monitoring method of the embodiment of the present application may further include:

[0041] Before obtaining the relative position relationship, perform rough trimming on the upper assembly of the tower crane based on the calibration data.

[0042] Among them, the calibration data is the trimming data of the tower crane in the historical trimming state. For example, before the formal jacking, instead of using the trimming table, a relatively accurate trimming operation is manually completed first, and the calibration data in this trimming state is recorded, including the upper slewing angle, trimming load, and the horizontal movement distance of the luffing trolley (abbreviated as luffing), etc. During the formal jacking, the working condition data of this time is compared with the previously recorded data to determine whether the upper structure has reached the trimming state. Among them, the judgment conditions are, for example: 1) The difference in the slewing angle does not exceed ±5°; 2) The product of the load and the luffing does not exceed ±5%, in unit of ton-meter.

[0043] In the example, after it is determined that the trimming state is reached through the previous calibration data, the subsequent steps S100 and S200 can be carried out.

[0044] Furthermore, Figure 3 is a schematic flow chart of obtaining the relative position relationship between the standard section column and the corresponding tenon in the example of the embodiment of the present application. As Figure 3 shown, it may include the following steps S110 - S130.

[0045] Step S110, obtain images of the standard section column and the corresponding tenon.

[0046] In the example, 1 camera (such as a pan-tilt camera) can be used to monitor 4 standard section columns. Align the camera with the standard section column to obtain images of the standard section column and the corresponding tenon.

[0047] Step S120, process the images based on the AI vision recognition algorithm to obtain a standard section column recognition frame and a tenon recognition frame.

[0048] Continuing from the above example, an AI vision recognition algorithm module can be integrated into the camera. For example, after image processing using the Yolov10 object detection algorithm, the target recognition frames of the standard section column and the tenon as shown in Figures 4(a) and 4(b) can be obtained.

[0049] Step S130, calculate the difference in the horizontal pixel coordinates of the standard section column recognition frame and the tenon recognition frame.

[0050] Among them, the difference is used to characterize the relative position relationship between the standard section column and the corresponding tenon. When the difference exceeds the preset threshold, it is determined that the tenon has a horizontal offset, otherwise it is determined that the tenon has no horizontal offset.

[0051] Continuing to refer to Figures 4(a) and 4(b), calculate the horizontal pixel coordinates of the two frames. If the difference does not exceed 3px, it can be considered that the standard section column has no horizontal offset when the tenon exits, that is, Figure 4(a) has no horizontal offset and Figure 4(b) has a horizontal offset.

[0052] Further, since there are 4 standard column sections, after the vertical judgment of the tenon of one standard column section is completed, the pan-tilt camera will automatically align with the position where the next standard column section is located. After the tenons of all 4 standard column sections are judged, the tenon judgment in step S200 is completed.

[0053] After the tenon is out, the trimming state of the upper assembly of the tower crane may be damaged by interference such as wind blowing and slight movement, and at this time, it is no longer possible for the operator to continue to detect whether the trimming state is normal. In this regard, in a preferred embodiment, following Figure 1 steps S100 and S200, as Figure 2 shown, the monitoring method further includes:

[0054] Step S300, when the tenon is out and after the tenon is out, obtain and determine whether the upper assembly is tilted according to the tilt angle of the upper assembly of the tower crane.

[0055] For example, an attitude sensor is installed in the driver's cab of the upper assembly. When the upper assembly tilts by more than 5°, the safety jacking monitoring system will issue an alarm.

[0056] In summary, the monitoring method for tower crane jacking in the examples of the embodiments of the present application includes three parts. One is to achieve rough trimming through calibration data. The second is to use the AI vision recognition algorithm to identify whether there is a horizontal offset during tenon out. The third is to use the attitude sensor to identify and monitor whether there is tilt during and after tenon out. These three parts are integrated with each other to form redundancy, improving the safety of the entire tower crane jacking process.

[0057] Specifically, the monitoring method for tower crane jacking in the embodiments of the present application has at least the following advantages:

[0058] First, it eliminates the dependence on the trimming table during tower crane jacking trimming, and instead uses the working condition data matching method of one tower one calibration. Although the generality is sacrificed, the trimming accuracy is improved.

[0059] Second, the method of visual recognition is used to judge whether there is a horizontal offset during tenon out, replacing the original visual judgment and improving the accuracy of tenon out monitoring.

[0060] Third, only the AI algorithm for target detection is called in the algorithm, and the computing power requirement is small.

[0061] Fourth, after the tenon is out, the working condition trimming data and the attitude sensor can still continue to work, continuously monitoring the jacking trimming state of the tower crane, and solving the problem that it is impossible to monitor after the tenon is out.

[0062] Fifth, the monitoring method of the embodiments of the present application can be carried on the safety jacking monitoring system, so as to realize the accurate trimming recognition and trimming state monitoring of the tower crane jacking process.

[0063] Embodiment 2

[0064] Figure 5 Schematically shown is a structural block diagram of a monitoring device for tower crane jacking according to Embodiment 2 of the present application. As Figure 5 shown, an embodiment of the present application provides a monitoring device, which may include: a memory configured to store instructions; and a processor configured to call instructions from the memory and capable of implementing the monitoring method for tower crane jacking in Embodiment 1 above when executing the instructions.

[0065] The monitoring device of this Embodiment 2 is, for example, a controller integrated in a safety jacking monitoring system.

[0066] For more implementation details and effects of this Embodiment 2, reference may be made to the foregoing Embodiment 1, and details will not be elaborated herein.

[0067] Embodiment 3

[0068] Figure 6 Schematically shown is a structural block diagram of a monitoring system for tower crane jacking according to Embodiment 3 of the present application, where the dashed line represents the data flow direction. The monitoring system includes the monitoring device 100 for tower crane jacking in Embodiment 2 above, and in a preferred embodiment, may further include: an image acquisition device 200 for acquiring images of each standard section column of the tower crane and the tenon connected thereto, and providing them to the monitoring device for processing to determine whether there is a horizontal offset of the tenon; and / or an inclination angle acquisition device 300 for acquiring the inclination angle of the upper part of the tower crane when and after the tenon comes out, and transmitting it to the monitoring device to determine whether the upper part is inclined.

[0069] Among them, the image acquisition device 200 is, for example, a pan-tilt camera adapted to be installed at the position of the standard section column of the tower crane; and the inclination angle acquisition device 300 is, for example, an attitude sensor, which is, for example, installed in the driver's cab of the upper part, but is not limited to being installed at this position.

[0070] In a preferred embodiment, the monitoring device 100 or the image acquisition device 200 is integrated with an AI vision recognition algorithm module, which is used to process the images acquired by the image acquisition device to obtain a standard section column recognition frame and a tenon recognition frame, and calculate the difference in the horizontal pixel coordinates between the standard section column recognition frame and the tenon recognition frame, where the difference is used to characterize the relative position relationship between the standard section column and the corresponding connected tenon. For example, the AI vision recognition algorithm module is, for example, a YOLOv10 algorithm module.

[0071] In addition, in the example, the monitoring system of the third embodiment can be configured as a safe jacking monitoring system or integrated with a safe jacking monitoring system. For example, the image acquisition device 200 and the tilt angle acquisition device 300 in the monitoring system can directly use the cameras and tilt sensors already configured in part of the safe jacking monitoring system.

[0072] For more implementation details and effects of the third embodiment, reference can be made to the foregoing first or second embodiment, and details will not be elaborated herein.

[0073] The embodiment of the present application also provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to cause a machine to execute the monitoring method of the first embodiment above.

[0074] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0075] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0076] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 steps of the functions specified in one block or multiple blocks.

[0078] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0079] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0080] Computer-readable media includes permanent and non-permanent, removable and non-removable media and can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0081] It should also be noted that the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or also includes elements inherent in such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of another identical element in the process, method, commodity or device comprising the element.

[0082] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A monitoring method for tower crane jacking, characterized in that, Including: During the tower crane jacking process, for each standard section column of the tower crane, obtain the relative position relationship between the standard section column and the corresponding tenon connection; And According to the relative position relationship, determine whether there is a horizontal offset of the tenon.

2. The monitoring method according to claim 1, characterized in that The monitoring method further includes: Before obtaining the relative position relationship, roughly level the upper part of the tower crane based on calibration data, where the calibration data is the leveling data of the tower crane in the historical leveling state.

3. The monitoring method according to claim 1, wherein The obtaining of the relative position relationship between the standard section column and the corresponding tenon connection includes: Obtain images of the standard section column and the corresponding tenon connection; Process the images based on the AI vision recognition algorithm to obtain a standard section column recognition frame and a tenon recognition frame; and Calculate the difference in the horizontal pixel coordinates of the standard section column recognition frame and the tenon recognition frame; Wherein, the difference is used to characterize the relative position relationship between the standard section column and the corresponding tenon connection. When the difference exceeds a preset threshold, it is determined that there is a horizontal offset of the tenon, otherwise it is determined that there is no horizontal offset of the tenon.

4. The monitoring method according to any one of claims 1 to 3, characterized in that The monitoring method further includes: During and after the tenon is taken out, obtain and determine whether the upper part of the tower crane is tilted according to the tilt angle of the upper part of the tower crane.

5. A monitoring device for tower crane jacking, characterized in that, Including: A memory configured to store instructions; And A processor configured to call the instructions from the memory and be able to implement the monitoring method according to any one of claims 1 to 4 when executing the instructions.

6. A monitoring system for tower crane jacking, characterized in that, Including the monitoring device according to claim 5.

7. The monitoring system according to claim 6, characterized in that, The monitoring system further includes: An image acquisition device for acquiring images of each standard section column of the tower crane and its corresponding tenon connection and providing them to the monitoring device for processing to determine whether there is a horizontal offset of the tenon; and / or A tilt angle acquisition device for acquiring the tilt angle of the upper part of the tower crane during and after the tenon is taken out and transmitting it to the monitoring device to determine whether the upper part of the tower crane is tilted.

8. The monitoring system according to claim 7, wherein The image acquisition device is a pan-tilt camera, and / or the tilt angle acquisition device is an attitude sensor.

9. The monitoring system according to claim 7, characterized in that, The monitoring device or the image acquisition device integrates an AI vision recognition algorithm module, which is used to process the images acquired by the image acquisition device to obtain a standard section column recognition frame and a tenon recognition frame, and calculate the difference in the horizontal pixel coordinates of the standard section column recognition frame and the tenon recognition frame, where the difference is used to characterize the relative position relationship between the standard section column and the corresponding tenon connection.

10. A machine-readable storage medium, characterized in that, Instructions are stored on this machine-readable storage medium, and the instructions are used to cause the machine to execute the monitoring method according to any one of claims 1 to 4.

Images