Object falling risk detection method, device, equipment and medium

Abstract

The present application is suitable for the technical field of machine vision, and provides an object falling risk detection method, device, equipment and medium, the method comprising: acquiring image and wind speed and direction information of the aerial work platform area; determining the edge of the aerial work platform area and the position and posture of the object on the aerial work platform area according to the image; detecting whether the object has falling risk according to the position and posture of the object, the wind speed and direction information and the edge of the aerial work platform area; and if the object has falling risk, giving a warning prompt. The present application can detect the safety risk of the object on the aerial work platform in real time and accurately.

Description

Technical Field

[0001] This invention belongs to the field of machine vision technology, and in particular relates to a method, device, equipment and medium for detecting the risk of falling objects. Background Technology

[0002] Aerial work platforms are common workplaces in construction, maintenance, and industrial sectors, often used to store tools, materials, and equipment. Improper placement of these objects can lead to safety hazards such as collisions or tipping over, and even serious risks like falls.

[0003] In related technologies, the safety management of aerial work platforms mainly relies on manual inspections and experience-based judgment. For example, operators visually inspect the placement and stability of objects or use simple securing devices to reduce risks. However, this method has significant limitations: first, it lacks real-time capability and cannot adapt to dynamic environmental changes; second, it is highly subjective and susceptible to human factors, easily overlooking potential hazards; and third, it lacks risk analysis, making it impossible to accurately assess the risk status of objects, resulting in untimely warnings. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method, apparatus, equipment and medium for detecting the risk of falling objects, so as to detect the safety risks of objects on high-altitude work platforms in real time and accurately.

[0005] A first aspect of this invention provides a method for detecting the risk of an object falling, comprising: Acquire images and wind speed and direction information for the area of ​​the aerial work platform; Based on the image, determine the edge of the aerial work platform area and the position and orientation of objects on the aerial work platform area; Based on the object's position and orientation, the wind speed and direction information, and the edge of the aerial work platform area, detect whether the object poses a risk of falling. If the object is at risk of falling, a warning will be issued.

[0006] In one possible implementation, detecting whether the object poses a fall risk based on the object's position and orientation, the wind speed and direction information, and the edge of the aerial work platform area includes: Based on the object's position and orientation, and the wind speed and direction information, it is determined whether the object is at risk of tipping over. If the object is at risk of tipping over, the tipping range is determined; wherein, the tipping range is the area covered by the object after it tipps over. If the tipping area of ​​the object exceeds the edge of the aerial work platform area, the object is determined to have a risk of falling; if the tipping area of ​​the object does not exceed the edge of the aerial work platform area, the object is determined to have no risk of falling.

[0007] In one possible implementation, detecting whether the object is at risk of tipping over based on its position and orientation and the wind speed and direction information includes: Based on the position and orientation of the object and the wind speed and direction information, determine the wind force value on the windward surface of the object and the overturning moment of the object. If the wind force value is greater than or equal to the overturning moment, the object is determined to be at risk of tipping over; if the wind force value is less than the overturning moment, the object is determined not to be at risk of tipping over.

[0008] In one possible implementation, determining the tipping range of the object includes: rotating and projecting the windward side of the object along the tipping axis onto the area of ​​the aerial work platform to obtain the tipping range of the object.

[0009] In one possible implementation, after determining that the object poses no risk of falling, the method further includes: Monitor whether the object has tipped over; If the object tilts, the step of detecting whether the object is at risk of falling is performed again based on the object's position and orientation, the wind speed and direction information, and the edge of the aerial work platform area. Furthermore, if the object is detected to have a risk of tipping over a preset number of times, then it is determined that the object has a risk of falling.

[0010] In one possible implementation, determining the edge of the aerial work platform area and the position and orientation of objects on the aerial work platform area based on the image includes: The image is input into a preset edge recognition model to obtain the edge of the aerial work platform area output by the edge recognition model; The image is input into a pre-trained object recognition model to obtain the shape parameters of the object on the aerial work platform area output by the object recognition model. Based on the shape parameters of the object, the position and orientation of the object are determined through geometric analysis.

[0011] In one possible implementation, after issuing a warning if the object is at risk of falling, the method further includes: Detect whether there is any information indicating that people are approaching the location of the object; If the proximity of the person is detected, the movement information of the object is monitored; If the object moves, its position and orientation are reacquired after the movement stops, and based on the reacquired position and orientation, the risk of the object falling is detected.

[0012] A second aspect of the present invention provides an object fall risk detection device, comprising: The acquisition module is used to acquire images and wind speed and direction information of the aerial work platform area; The processing module is used to determine the edge of the aerial work platform area and the position and orientation of objects on the aerial work platform area based on the image. The detection module is used to detect whether the object has a risk of falling based on the object's position and orientation, the wind speed and direction information, and the edge of the high-altitude work platform area. The early warning module is used to issue an early warning if the object is at risk of falling.

[0013] In one possible implementation, the detection module is used to: Based on the object's position and orientation, and the wind speed and direction information, it is determined whether the object is at risk of tipping over. If the object is at risk of tipping over, the tipping range is determined; wherein, the tipping range is the area covered by the object after it tipps over. If the tipping area of ​​the object exceeds the edge of the aerial work platform area, the object is determined to have a risk of falling; if the tipping area of ​​the object does not exceed the edge of the aerial work platform area, the object is determined to have no risk of falling.

[0014] In one possible implementation, the detection module is used to: Based on the position and orientation of the object and the wind speed and direction information, determine the wind force value on the windward surface of the object and the overturning moment of the object. If the wind force value is greater than or equal to the overturning moment, the object is determined to be at risk of tipping over; if the wind force value is less than the overturning moment, the object is determined not to be at risk of tipping over.

[0015] In one possible implementation, the detection module is used to: rotate and project the windward surface of the object along the overturning axis onto the area of ​​the aerial work platform to obtain the overturning range of the object.

[0016] In one possible implementation, after determining that the object poses no risk of falling, the detection module is further configured to: Monitor whether the object has tipped over; If the object tilts, the step of detecting whether the object is at risk of falling is performed again based on the object's position and orientation, the wind speed and direction information, and the edge of the aerial work platform area. Furthermore, if the object is detected to have a risk of tipping over a preset number of times, then it is determined that the object has a risk of falling.

[0017] In one possible implementation, the processing module is used to: The image is input into a preset edge recognition model to obtain the edge of the aerial work platform area output by the edge recognition model; The image is input into a pre-trained object recognition model to obtain the shape parameters of the object on the aerial work platform area output by the object recognition model. Based on the shape parameters of the object, the position and orientation of the object are determined through geometric analysis.

[0018] In one possible implementation, after issuing a warning if the object is at risk of falling, the detection module is further configured to: Detect whether there is any information indicating that people are approaching the location of the object; If the proximity of the person is detected, the movement information of the object is monitored; If the object moves, its position and orientation are reacquired after the movement stops, and based on the reacquired position and orientation, the risk of the object falling is detected.

[0019] A third aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method of the first aspect or any possible implementation thereof.

[0020] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect or any possible implementation thereof.

[0021] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows: This invention, through acquiring images and wind speed and direction information of the aerial work platform area, accurately defines the edge of the platform area based on the images and identifies the real-time position and posture of objects. It then comprehensively assesses the risk of object fall by combining multiple dimensions of factors, including object position and posture, environmental wind speed and direction, and the platform area edge, effectively improving the comprehensiveness, accuracy, and reliability of aerial object fall risk detection. Simultaneously, it triggers timely warnings when a fall risk is detected. This embodiment can monitor the status of objects on the aerial work platform in real time, automatically and accurately determining whether objects are properly placed, effectively reducing construction risks. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating the object fall risk detection method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of an application scenario provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of the object fall risk detection device provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0024] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0025] To illustrate the technical solution described in this invention, specific embodiments are described below.

[0026] Figure 1 This is a schematic diagram illustrating the implementation process of the object fall risk detection method provided in this embodiment of the invention. See also... Figure 1 As shown, the method includes: Step S101: Obtain images and wind speed and direction information for the area of ​​the aerial work platform.

[0027] In this embodiment, the camera acquisition device can capture and collect real-time images of the working area where the aerial work platform is located; at the same time, the meteorological sensor device can collect real-time meteorological data such as wind speed and wind direction in the working area, thereby obtaining the visual image of the platform and the on-site wind environment parameters simultaneously, providing original basic data support for subsequent determination of the platform area edge, identification of object position and posture, and assessment of object fall risk.

[0028] Step S102: Based on the image, determine the edge of the aerial work platform area and the position and orientation of objects on the aerial work platform area.

[0029] For example, based on the collected images of the aerial work platform area, a preset edge recognition model such as YOLO can be used to identify the various feature vertices of the aerial work platform, connect adjacent feature vertices in sequence, and fit and outline the four boundary lines of the aerial work platform, thereby accurately determining the contour edge of the aerial work platform area.

[0030] Simultaneously, images can be input into a pre-trained object recognition model to obtain the shape parameters of objects on the aerial work platform area, as output by the model. Based on these shape parameters, the object's position and orientation can be determined through geometric analysis. For example, the vertex coordinates of a cuboid object can be identified, and the top boundary of a cylindrical object can be identified. Furthermore, by combining the identified vertex coordinates or top boundary features, the spatial position of the object within the work area, as well as its tilt, orientation, and other posture states, can be calculated, achieving accurate determination of the object's position and orientation on the aerial work platform area.

[0031] For example, if the object is a cuboid, its pose can be determined by analyzing the three sides formed by the object's four vertices in the image and the included angles therein. If the object is a cylinder, the top surface of the cylinder can be identified using depth images, and the positional relationship between the top surface of the cylinder and the platform can be analyzed to determine the object's pose.

[0032] Step S103: Based on the object's position and orientation, wind speed and direction information, and the edge of the aerial work platform area, detect whether the object poses a risk of falling.

[0033] This embodiment analyzes the stability of an object by combining its position, posture, wind speed, and direction information. It also determines whether the object's current spatial position is close to the platform edge or has a tendency to exceed the platform boundary. By combining the object's posture (internal instability factor), wind speed and direction (external interference factor), and platform edge (spatial constraint factor), it comprehensively captures various potential hazards that may cause the object to fall, avoiding the one-sidedness of single-dimensional detection and significantly improving the comprehensiveness of risk detection.

[0034] Step S104: If there is a risk of the object falling, a warning is issued.

[0035] When an object's own posture, real-time wind speed and direction, and information on the edge of the aerial work platform area are considered to indicate that the object has a tendency to fall or poses a risk of falling, an early warning can be issued through at least one of the following methods: audible and visual alarm, terminal pop-up notification, voice broadcast, or background platform alarm push notification. This will prompt relevant personnel to intervene in a timely manner, eliminate such safety hazards, or reduce the probability of such safety accidents.

[0036] This invention, through acquiring images and wind speed and direction information of the aerial work platform area, accurately defines the edge of the platform area based on the images and identifies the real-time position and posture of objects. It then comprehensively assesses the risk of object fall by combining multiple dimensions of factors, including object position and posture, environmental wind speed and direction, and the platform area edge, effectively improving the comprehensiveness, accuracy, and reliability of aerial object fall risk detection. Simultaneously, it triggers timely warnings when a fall risk is detected. This embodiment can monitor the status of objects on the aerial work platform in real time, automatically and accurately determining whether objects are properly placed, effectively reducing construction risks.

[0037] In one possible implementation, step S103 above, which involves detecting whether an object poses a risk of falling based on the object's position and orientation, wind speed and direction information, and the edge of the aerial work platform area, may include: Step S1031: Based on the object's position, orientation, wind speed, and direction information, detect whether the object has a risk of tipping over. If the object has a risk of tipping over, determine the tipping range of the object; where the tipping range is the area covered after the object tipps over.

[0038] Here, based on the object's position, orientation, wind speed, and direction, the wind force on the object's windward surface and the object's overturning moment can be determined. If the wind force is greater than or equal to the overturning moment, the object is at risk of tipping over; if the wind force is less than the overturning moment, the object is at no risk of tipping over.

[0039] In this method, the object's outline is projected vertically into the windward direction; the effective windward area formed by this projection is the wind-receiving surface. The wind force received by the wind-receiving surface is... The calculation formula is: ; in, Let be the air density, v be the wind speed, S be the area of ​​the windward surface, and c be the drag coefficient.

[0040] Calculate the overturning moment based on the overturning axis (when an object is pushed over, it will rotate along the ground around the outermost bottom edge, which is the overturning axis). The calculation formula is: ; In the formula,h The vertical height of the body center from the overturning axis. m For the mass of the object, g It is the acceleration due to gravity. l This is the horizontal distance from the center of the body to the overturning axis.

[0041] In step S102, if the tilting range of the object exceeds the edge of the aerial work platform area, it is determined that the object poses a risk of falling; if the tilting range of the object does not exceed the edge of the aerial work platform area, it is determined that the object does not pose a risk of falling.

[0042] Here, the windward side of the object can be rotated and projected onto the aerial work platform area along the object's overturning axis to obtain the object's overturning range. For example... Figure 2 As shown.

[0043] In one possible implementation, to identify scenarios where an object exceeds the edge of the aerial work platform area without tipping over and poses a potential fall hazard, when it is determined that the object does not pose a tipping risk, it can be further detected whether the bottom outline of the object exceeds the edge of the aerial work platform area. If the bottom outline of the object exceeds the edge of the aerial work platform area, it is determined that the object poses a fall hazard; if the bottom outline of the object does not exceed the edge of the aerial work platform area, it is determined that the object does not pose a fall hazard.

[0044] In one possible implementation, after determining in step S103 that there is no risk of the object falling, the following may also be included: Monitor whether the object is tipping over; If the object tilts, the process is repeated to check for the risk of falling based on the object's position and orientation, wind speed and direction, and the edge of the aerial work platform area. Furthermore, if an object is detected to be at risk of tipping over after a preset number of consecutive tests, then the object is determined to be at risk of falling.

[0045] In this embodiment, the object's position and posture changes can be collected in real time to continuously monitor whether the object is tipping over.

[0046] If an object tipps over, the process is repeated to check for a risk of falling based on the object's position and orientation, wind speed and direction, and the edge of the aerial work platform area (i.e., if the tipping area of ​​the object exceeds the edge of the aerial work platform area, the object is at risk of falling).

[0047] If an object is detected to have a tipping risk multiple times consecutively, but the tipping range does not exceed the edge of the aerial work platform area, then when a preset threshold number of times is reached, the object is considered to be continuously rolling, and a fall risk is determined. For example, if an object shows a tipping tendency three times consecutively in the same direction, it can be determined that the object has become unstable and a fall risk is directly identified, triggering an alarm to remind relevant personnel to adjust their posture and take safe measures in a timely manner, thereby achieving more comprehensive risk monitoring.

[0048] In one possible implementation, if there is a risk of an object falling, after issuing a warning, the following may also be included: Detect whether there is information indicating that people are approaching the location of the object; If a person is detected approaching, the movement of the object is monitored. If the object moves, its position and orientation are reacquired after the movement stops, and based on the reacquired position and orientation, the risk of the object falling is detected.

[0049] In this embodiment, objects at risk of falling can be periodically re-inspected, and repeated alarms will be triggered if the object is not rectified in a timely manner. Additionally, this embodiment can identify actions by personnel approaching, moving, or addressing potential fall hazards. By tracking personnel's movements and adjustments to objects to eliminate hazards in real time, and after the adjustments are completed and the object's posture stabilizes, a second check is performed to confirm whether the object still poses a fall risk, ensuring the safe placement of the handled items.

[0050] The fall risk detection method in this embodiment is real-time and efficient, and supports monocular vision without the need for additional sensors. When a fall risk is detected, it can immediately trigger sound and visual alarms or remote notifications to ensure timely intervention by on-site personnel and effectively guarantee the safety of high-altitude operations.

[0051] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0052] Figure 3 This is a schematic diagram of the object fall risk detection device provided in an embodiment of the present invention. See also... Figure 3 As shown, the object fall risk detection device 30 includes: The acquisition module 31 is used to acquire images and wind speed and direction information of the aerial work platform area; The processing module 32 is used to determine the edge of the aerial work platform area and the position and orientation of objects on the aerial work platform area based on the image. Detection module 33 is used to detect whether there is a risk of falling from an object based on the object's position and orientation, wind speed and direction information, and the edge of the aerial work platform area. The early warning module 34 is used to issue an early warning if there is a risk of an object falling.

[0053] In one possible implementation, the detection module 33 is used for: Based on the object's position, orientation, wind speed, and direction, the system detects whether the object is at risk of tipping over. If the object is at risk of tipping over, the tipping range is determined; the tipping range is the area covered after the object tipps over. If the tipping area of ​​an object extends beyond the edge of the aerial work platform area, the object is considered to pose a risk of falling; if the tipping area of ​​an object does not extend beyond the edge of the aerial work platform area, the object is considered to pose no risk of falling.

[0054] In one possible implementation, the detection module 33 is used for: Based on the object's position, orientation, wind speed, and direction, determine the wind force on the object's windward surface and the object's overturning moment. If the wind force is greater than or equal to the overturning moment, the object is at risk of tipping over; if the wind force is less than the overturning moment, the object is at no risk of tipping over.

[0055] In one possible implementation, the detection module 33 is used to: rotate and project the windward side of the object along the overturning axis onto the area of ​​the aerial work platform to obtain the overturning range of the object.

[0056] In one possible implementation, after determining that there is no risk of the object falling, the detection module 33 is further configured to: Monitor whether the object is tipping over; If the object tilts, the process is repeated to check for the risk of falling based on the object's position and orientation, wind speed and direction, and the edge of the aerial work platform area. Furthermore, if an object is detected to be at risk of tipping over after a preset number of consecutive tests, then the object is determined to be at risk of falling.

[0057] In one possible implementation, processing module 32 is used for: The image is input into a preset edge recognition model to obtain the edge of the aerial work platform area output by the edge recognition model; The image is input into a pre-trained object recognition model to obtain the shape parameters of the objects on the aerial work platform area output by the object recognition model. Based on the shape parameters of the objects, the position and orientation of the objects are determined through geometric analysis.

[0058] In one possible implementation, after issuing a warning if there is a risk of the object falling, the detection module 33 is further configured to: Detect whether there is information indicating that people are approaching the location of the object; If a person is detected approaching, the movement of the object is monitored. If the object moves, its position and orientation are reacquired after the movement stops, and based on the reacquired position and orientation, the risk of the object falling is detected.

[0059] This invention, through acquiring images and wind speed and direction information of the aerial work platform area, accurately defines the edge of the platform area based on the images and identifies the real-time position and posture of objects. It then comprehensively assesses the risk of object fall by combining multiple dimensions of factors, including object position and posture, environmental wind speed and direction, and the platform area edge, effectively improving the comprehensiveness, accuracy, and reliability of aerial object fall risk detection. Simultaneously, it triggers timely warnings when a fall risk is detected. This embodiment can monitor the status of objects on the aerial work platform in real time, automatically and accurately determining whether objects are properly placed, effectively reducing construction risks.

[0060] Figure 4 This is a schematic diagram of an electronic device 40 provided in an embodiment of the present invention. Figure 4 As shown, the electronic device 40 of this embodiment includes: a processor 41, a memory 42, and a computer program 43 stored in the memory 42 and executable on the processor 41. When the processor 41 executes the computer program 43, it implements the steps in the various method embodiments described above. Alternatively, when the processor 41 executes the computer program 43, it implements the functions of each module / unit in the various device embodiments described above.

[0061] For example, the computer program 43 may be divided into one or more modules / units, which are stored in the memory 42 and executed by the processor 41 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 43 in the electronic device 40.

[0062] The electronic device 40 may include, but is not limited to, a processor 41 and a memory 42. Those skilled in the art will understand that... Figure 4 This is merely an example of electronic device 40 and does not constitute a limitation on electronic device 40. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 40 may also include input / output devices, network access devices, buses, etc.

[0063] The processor 41 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0064] The memory 42 can be an internal storage unit of the electronic device 40, such as a hard disk or memory of the electronic device 40. The memory 42 can also be an external storage device of the electronic device 40, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 40. Furthermore, the memory 42 can include both internal and external storage units of the electronic device 40. The memory 42 is used to store the computer program and other programs and data required by the electronic device 40. The memory 42 can also be used to temporarily store data that has been output or will be output.

[0065] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0066] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0067] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0068] In the embodiments provided by this invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0069] The units described as separate components may or may not be physically separate. 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0070] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0071] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0072] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for detecting the risk of an object falling, characterized in that, include: Acquire images and wind speed and direction information for the area of ​​the aerial work platform; Based on the image, determine the edge of the aerial work platform area and the position and orientation of objects on the aerial work platform area; Based on the object's position and orientation, the wind speed and direction information, and the edge of the aerial work platform area, detect whether the object poses a risk of falling. If the object is at risk of falling, a warning will be issued.

2. The object fall risk detection method as described in claim 1, characterized in that, The step of detecting whether the object poses a fall risk based on the object's position and orientation, wind speed and direction information, and the edge of the aerial work platform area includes: Based on the object's position and orientation, and the wind speed and direction information, it is determined whether the object is at risk of tipping over. If the object is at risk of tipping over, the tipping range is determined; wherein, the tipping range is the area covered by the object after it tipps over. If the tipping area of ​​the object exceeds the edge of the aerial work platform area, the object is determined to have a risk of falling; if the tipping area of ​​the object does not exceed the edge of the aerial work platform area, the object is determined to have no risk of falling.

3. The object fall risk detection method as described in claim 2, characterized in that, The step of detecting whether the object is at risk of tipping over based on its position and orientation and the wind speed and direction information includes: Based on the position and orientation of the object and the wind speed and direction information, determine the wind force value on the windward surface of the object and the overturning moment of the object. If the wind force value is greater than or equal to the overturning moment, the object is determined to be at risk of tipping over; if the wind force value is less than the overturning moment, the object is determined not to be at risk of tipping over.

4. The object fall risk detection method as described in claim 2, characterized in that, Determining the tipping range of the object includes: rotating and projecting the windward side of the object along the tipping axis onto the area of ​​the aerial work platform to obtain the tipping range of the object.

5. The object fall risk detection method as described in claim 2, characterized in that, After determining that the object poses no risk of falling, the process further includes: Monitor whether the object has tipped over; If the object tilts, the step of detecting whether the object is at risk of falling is performed again based on the object's position and orientation, the wind speed and direction information, and the edge of the aerial work platform area. Furthermore, if the object is detected to have a risk of tipping over a preset number of times, then it is determined that the object has a risk of falling.

6. The object fall risk detection method as described in any one of claims 1 to 5, characterized in that, The step of determining the edge of the aerial work platform area and the position and orientation of objects on the aerial work platform area based on the image includes: The image is input into a preset edge recognition model to obtain the edge of the aerial work platform area output by the edge recognition model; The image is input into a pre-trained object recognition model to obtain the shape parameters of the object on the aerial work platform area output by the object recognition model. Based on the shape parameters of the object, the position and orientation of the object are determined through geometric analysis.

7. The object fall risk detection method as described in any one of claims 1 to 5, characterized in that, After issuing a warning if the object is at risk of falling, the system further includes: Detect whether there is any information indicating that people are approaching the location of the object; If the proximity of the person is detected, the movement information of the object is monitored; If the object moves, its position and orientation are reacquired after the movement stops, and based on the reacquired position and orientation, the risk of the object falling is detected.

8. A device for detecting the risk of falling objects, characterized in that, include: The acquisition module is used to acquire images and wind speed and direction information of the aerial work platform area; The processing module is used to determine the edge of the aerial work platform area and the position and orientation of objects on the aerial work platform area based on the image. The detection module is used to detect whether the object has a risk of falling based on the object's position and orientation, the wind speed and direction information, and the edge of the high-altitude work platform area. The early warning module is used to issue an early warning if the object is at risk of falling.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

Images