A method and system for evaluating the operational status of a scaffolding robot

Abstract

This invention discloses a method and system for assessing the operational status of a scaffolding robot, relating to the field of scaffolding robot status assessment. It primarily addresses the problem that traditional scaffolding robot assessment systems have a single monitoring dimension, focusing only on the robot's own operational parameters and ignoring external environmental interference factors such as material accumulation at the construction site and scaffolding structural deformation. This invention utilizes multi-type sensor collaborative data acquisition, multi-dimensional parameter threshold comparison, dedicated tag classification and control, and precise fault area location to achieve dynamic monitoring of the entire scaffolding robot operation process, risk classification determination, and accurate fault tracing. Furthermore, the data from the three major units are interconnected and cross-validated, effectively reducing the probability of data misjudgment.

Description

Technical Field

[0001] This invention relates to the field of scaffolding robot technology, specifically to a method and system for evaluating the operational status of a scaffolding robot. Background Technology

[0002] In the context of the comprehensive advancement of intelligent construction, construction robots are gradually replacing traditional manual labor to complete high-risk and high-intensity construction operations, and have become the core direction for the transformation and upgrading of the construction industry. Among them, scaffolding robots, with their advantages of being able to autonomously complete scaffolding erection, dismantling, inspection, and maintenance, are widely used in various construction scenarios such as super high-rise buildings, large-scale infrastructure projects, shipbuilding, and industrial plant renovation.

[0003] The existing assessment system has a single monitoring dimension, focusing only on the robot's own operating parameters and ignoring external environmental interference factors such as material accumulation at the construction site and scaffolding structural deformation. It cannot achieve integrated monitoring of internal and external factors and has insufficient coverage of risk assessment. To address the aforementioned technical shortcomings, a method and system for assessing the operational status of scaffolding robots are proposed. This system creatively constructs a comprehensive operational status assessment framework that integrates three dimensions: external visual environment, equipment mechanical stress, and the robot's own operational status. This breaks through the technical barriers of traditional single-dimensional monitoring modes and adapts to the operational monitoring needs of scaffolding robots in various complex construction sites. Summary of the Invention

[0004] The purpose of this invention is to solve the problems mentioned above by proposing a method and system for evaluating the operating status of a scaffolding robot.

[0005] The objective of this invention can be achieved through the following technical solutions: A method for assessing the operational status of a scaffolding robot, comprising the following steps: Step 1: Collect visual images of the scaffolding robot's working area, extract image frames and determine the coverage area, obtain the spacing change parameters of the scaffolding structure connection points and the material accumulation area change parameters within the coverage area, compare the spacing change parameters with a first preset threshold, compare the area change parameters with a second preset threshold, and set visual data status labels based on the comparison results. Step 2: Determine the force-bearing nodes based on the visual data, collect the joint torque change parameters of each force-bearing node and the overall platform tilt angle change parameters, compare the joint torque change parameters with the third preset threshold, compare the tilt angle change parameters with the fourth preset threshold, and set the mechanical data status label according to the comparison results. Step 3: Collect the deviation parameters of the temperature change trajectory and the predicted trajectory during the execution of the operation, as well as the cumulative deviation distance parameters between the actual required position and the operation position. Compare the deviation parameters with the fifth preset threshold and the cumulative deviation distance parameters with the sixth preset threshold. Set the self-state data label according to the comparison results. Based on the visual data status label, mechanical data status label, and self-status data label, corresponding status control or maintenance measures are executed.

[0006] Furthermore, in step one, the spacing change parameter is the increase in span at the spacing of the scaffold structure connection, and the area change parameter is the increase in span at the material accumulation area.

[0007] Furthermore, the first preset threshold and the second preset threshold are the spacing increase span threshold and the area increase span threshold, respectively; the third preset threshold and the fourth preset threshold are the torque fluctuation threshold and the tilt angle change rate threshold, respectively; and the fifth preset threshold and the sixth preset threshold are the trajectory deviation threshold and the deviation distance accumulation threshold, respectively.

[0008] Furthermore, in step two, the joint torque change parameter is the joint torque fluctuation value, and the tilt angle change parameter is the overall platform tilt angle change rate.

[0009] Furthermore, in step two, the joint torque fluctuation value is obtained by a six-dimensional force / torque sensor, and the overall platform tilt angle change rate is obtained by a tilt angle sensor, wherein the tilt angle sensor is a combination of a MEMS gyroscope and an accelerometer.

[0010] Furthermore, in step three, the deviation parameter is the degree of deviation between the temperature change trend trajectory and the pre-predicted temperature trend trajectory, i.e., the deviation angle between the two trajectory curves; the cumulative deviation distance parameter is the cumulative value of the deviation distance between the actual required position and the operating position throughout the entire operation.

[0011] Furthermore, in step one, if the spacing change parameter exceeds a first preset threshold or the area change parameter exceeds a second preset threshold, the visual data status label is set as an interference label, and the status of the current coverage area is controlled; otherwise, it is set as a stable label, and the current coverage area is determined.

[0012] Furthermore, in step two, if the joint torque change parameter exceeds a third preset threshold or the tilt angle change parameter exceeds a fourth preset threshold, the mechanical data status label is set as an abnormal label, and the corresponding force-bearing node is maintained; otherwise, it is set as a normal label, and the force-bearing node is continuously monitored.

[0013] Furthermore, in step three, if the deviation parameter exceeds the fifth preset threshold or the cumulative deviation distance parameter exceeds the sixth preset threshold, then the self-state data label is set as an abnormal label, and a comprehensive review of the scaffolding robot is conducted; otherwise, it is set as a normal label, and continuous monitoring continues.

[0014] Furthermore, a scaffolding robot operation status evaluation system is provided, which includes a visual data acquisition and analysis unit, a mechanical data acquisition and analysis unit, and a self-state data acquisition and analysis unit. The visual data acquisition and analysis unit acquires visual images and sets visual data status labels based on image analysis. The mechanical data acquisition and analysis unit collects and analyzes force-bearing nodes and sets mechanical data status labels; The self-status data acquisition and analysis unit collects the deviation parameters between the temperature change trajectory and the predicted trajectory during the execution of the operation, as well as the cumulative deviation distance parameters between the actual required position and the operation position, and sets self-status data labels based on the comparison results; The system executes corresponding status control or maintenance measures based on the visual data status labels, mechanical data status labels, and its own status data labels.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. Through multi-frame image comparison technology, the coverage area of ​​each image frame is accurately delineated, and two core quantitative indicators—the increase in span at scaffold structure connection points and the increase in span of material accumulation area—are quantitatively analyzed. This abandons the traditional subjective judgment mode and achieves quantitative assessment of environmental risks. Differentiated status labels are set up: stable states are labeled with a visual data stability label, and risk states are labeled with a visual data interference label. Abnormal coverage areas are simultaneously identified, allowing staff to intuitively distinguish the risk level and abnormal location of the work area, significantly narrowing the scope of troubleshooting and reducing manual troubleshooting costs. This unit can simultaneously monitor the structural integrity of the scaffolding work platform and the environmental monitoring of the robot's movement area, taking into account both the equipment's work platform and the surrounding work environment. This solves the problem of the one-sidedness of traditional visual monitoring, comprehensively avoiding safety risks such as loose members, structural deformation, and material obstruction and collision, ensuring the safety of scaffolding robot operations from the external environmental perspective.

[0016] 2. By setting preset torque fluctuation thresholds and tilt angle change rate thresholds, a dual-level parameter determination is achieved, accurately distinguishing different types of mechanical anomalies such as joint jamming, overload, platform tilting, and unilateral load imbalance. Simultaneously, a tiered handling strategy is implemented: routine continuous monitoring is performed on normal stress nodes, while abnormal stress nodes are quickly located and targeted maintenance is carried out. This not only promptly eliminates high-risk safety hazards such as overturning and joint damage but also avoids indiscriminate downtime for maintenance, resulting in project delays and resource waste, thus balancing safety management and construction efficiency. Furthermore, this unit can provide real-time feedback on the impact of external factors such as high-altitude wind loads, ground settlement, and heavy material loads on the robot's stress state, providing data support for dynamic adjustments to on-site operation plans and adapting to various high-risk and complex stress operation scenarios.

[0017] 3. By employing dual judgment criteria—trajectory deviation threshold and cumulative deviation distance threshold—standardized and quantitative assessment of the equipment's status is achieved, completely eliminating the limitations of subjective human judgment and reducing the probability of misjudgment and omission in status assessment. Differentiated handling solutions are configured for different abnormal states. Continuous monitoring requires no manual intervention under normal conditions, while triggering a comprehensive review command under abnormal conditions helps staff quickly troubleshoot multi-dimensional faults in hardware, software, and programs. This comprehensively covers the monitoring needs of the robot's internal hardware, temperature control system, navigation system, and control system, effectively extending the service life of the scaffolding robot, reducing equipment failure rate and subsequent maintenance costs, and ensuring the stability of the equipment during long-term continuous operation. Attached Figure Description

[0018] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0019] Figure 1 This is a system principle block diagram of the present invention. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0022] Please see Figure 1 As shown, a method for evaluating the operational status of a scaffolding robot includes the following steps: Step 1: Visual data acquisition and analysis; Visual images of the scaffolding robot are acquired using sensors, including industrial-grade RGB-D cameras (such as Intel RealSense D455) and wide-angle stabilized cameras. Image frames are extracted based on visual images, and each image frame is compared to determine the coverage area of ​​each image frame. The integrity of the scaffolding structure within the coverage area is then checked. The span of the distance between the scaffolding structure connections within the corresponding coverage area is collected in the image frame. At the same time, the span of the material accumulation area within the scaffolding robot's movement area is also obtained. Sampling frequency: ≥15Hz (satisfying human eye dynamic capture + key event backtracking). The increase in span of the spacing at the scaffold structure connections within the corresponding coverage area of ​​the image frame, and the increase in span of the material accumulation area within the scaffold robot's movement area, are compared with the span increase threshold for spacing and the span increase threshold for area, respectively. If the increase in the spacing of the scaffold structure connection within the corresponding coverage area of ​​the image frame exceeds the spacing increase threshold, or if the increase in the material accumulation area within the scaffold robot's movement area exceeds the area increase threshold, it is inferred that there is a risk in the analysis of the collected visual data. A visual data status interference label is set at the current moment, and the coverage area of ​​the current image frame is determined and status control is carried out together. If the increase in the span of the spacing at the connection of the scaffold structure within the corresponding coverage area of ​​the image frame does not exceed the threshold for the increase in the span of the spacing, and the increase in the span of the material accumulation area within the movement area of ​​the scaffold robot does not exceed the threshold for the increase in the span of the area, then it is inferred that there is no risk in the analysis of the collected visual data. The visual data status is set to stable at the current moment, and the coverage area of ​​the current image frame is determined. Step 2: Mechanical data acquisition and analysis; Based on visual data, the force nodes of the scaffolding robot are determined through sensors, specifically a six-dimensional force / torque sensor (installed on the joints or the bottom of the platform) and a tilt sensor (MEMS gyroscope + accelerometer combination). Obtain the joint torque fluctuation value corresponding to the force-bearing node of the scaffolding robot, and at the same time collect the overall platform tilt angle change rate of the scaffolding robot; The torque fluctuation values ​​of the joints corresponding to the force-bearing nodes of the scaffolding robot and the overall platform tilt angle change rate of the scaffolding robot were compared with the torque fluctuation threshold and the tilt angle change rate threshold, respectively. If the joint torque fluctuation value of the force-bearing node of the scaffolding robot exceeds the torque fluctuation threshold, or the tilt angle change rate of the overall platform of the scaffolding robot exceeds the tilt angle change rate threshold, it is inferred that there is a risk in the mechanical data analysis of the scaffolding robot. The mechanical data anomaly label is set at the current moment, the position of the force-bearing node is determined, and the force-bearing node is maintained. If the joint torque fluctuation value corresponding to the force-bearing node of the scaffolding robot does not exceed the torque fluctuation threshold, and the overall platform tilt angle change rate of the scaffolding robot does not exceed the tilt angle change rate threshold, it is inferred that there is no risk in the mechanical data analysis of the scaffolding robot. The mechanical data is set to normal at the current moment, the position of the force-bearing node is determined, and the force-bearing node is continuously monitored. Step 3: Self-status data collection and analysis; The deviation between the temperature change trend trajectory and the pre-predicted temperature trend trajectory during the operation of the scaffolding robot is obtained, i.e., the deviation angle between the two trajectory curves; at the same time, the cumulative value of the deviation distance between the actual required position and the operating position of the scaffolding robot during the operation is collected. The deviation between the temperature change trend trajectory of the scaffolding robot during operation and the pre-predicted temperature trend trajectory, as well as the cumulative deviation distance between the actual required position and the operating position of the scaffolding robot, are compared with the trajectory deviation threshold and the cumulative deviation distance threshold, respectively. If the temperature change trend trajectory of the scaffolding robot during operation deviates from the pre-predicted temperature trend trajectory by more than the trajectory deviation threshold, or if the cumulative deviation distance between the actual required position and the operating position of the scaffolding robot exceeds the cumulative deviation distance threshold, it is inferred that the scaffolding robot's own status data is abnormal. An abnormal status data label will be set for the current moment, and a comprehensive review of the scaffolding robot will be conducted. If the deviation between the temperature change trend trajectory and the pre-predicted temperature trend trajectory during the operation of the scaffolding robot does not exceed the trajectory deviation threshold, and the cumulative deviation distance between the actual required position and the operating position of the scaffolding robot does not exceed the cumulative deviation distance threshold, then it is inferred that the scaffolding robot's own status data is normal, the current moment is marked as normal, and continuous monitoring is performed.

[0023] A scaffolding robot operation status assessment system, the system is equipped with a visual data acquisition and analysis unit, a mechanical data acquisition and analysis unit, and a self-state data acquisition and analysis unit; The visual data acquisition and analysis unit acquires visual images and sets visual data status labels based on image analysis. The mechanical data acquisition and analysis unit collects and analyzes force-bearing nodes and sets mechanical data status labels; The self-status data acquisition and analysis unit collects the deviation parameters between the temperature change trajectory and the predicted trajectory during the execution of the operation, as well as the cumulative deviation distance parameters between the actual required position and the operation position, and sets self-status data labels based on the comparison results; The system executes corresponding status control or maintenance measures based on the visual data status labels, mechanical data status labels, and its own status data labels.

[0024] In summary, this invention integrates three independent units: visual environment monitoring, mechanical stress monitoring, and equipment status monitoring, to construct a three-in-one, data-interconnected, and cross-validated full-dimensional operational status assessment system for scaffolding robots. It comprehensively covers risk monitoring scenarios throughout the entire operation process of scaffolding robots from three levels: external working environment, equipment stress posture, and internal operating hardware.

[0025] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for evaluating the operational status of a scaffolding robot, characterized in that, Includes the following steps: Step 1: Collect visual images of the scaffolding robot's working area, extract image frames and determine the coverage area, obtain the spacing change parameters of the scaffolding structure connection points and the material accumulation area change parameters within the coverage area, compare the spacing change parameters with a first preset threshold, compare the area change parameters with a second preset threshold, and set visual data status labels based on the comparison results. Step 2: Determine the force-bearing nodes based on the visual data, collect the joint torque change parameters of each force-bearing node and the overall platform tilt angle change parameters, compare the joint torque change parameters with the third preset threshold, compare the tilt angle change parameters with the fourth preset threshold, and set the mechanical data status label according to the comparison results. Step 3: Collect the deviation parameters of the temperature change trajectory and the predicted trajectory during the operation, as well as the cumulative deviation distance parameters between the actual required position and the operation position. Compare the deviation parameters with the fifth preset threshold and the cumulative deviation distance parameters with the sixth preset threshold. Set the self-state data label according to the comparison results, and execute the corresponding state control or maintenance measures according to the visual data state label, mechanical data state label and self-state data label.

2. The method for evaluating the operating status of a scaffolding robot according to claim 1, characterized in that, In step one, the spacing change parameter is the increase in span at the spacing of the scaffold structure connection, and the area change parameter is the increase in span at the material accumulation area.

3. The method for evaluating the operating status of a scaffolding robot according to claim 1, characterized in that, The first preset threshold and the second preset threshold are the span increase threshold and the area increase threshold, respectively; The third and fourth preset thresholds are the torque fluctuation threshold and the tilt angle change rate threshold, respectively. The fifth and sixth preset thresholds are the trajectory deviation threshold and the cumulative deviation distance threshold, respectively.

4. The method for evaluating the operating status of a scaffolding robot according to claim 1, characterized in that, In step two, the joint torque change parameter is the joint torque fluctuation value, and the tilt angle change parameter is the overall platform tilt angle change rate.

5. The method for evaluating the operating status of a scaffolding robot according to claim 1, characterized in that, In step two, the joint torque fluctuation value is obtained through a six-dimensional force / torque sensor, and the overall platform tilt angle change rate is obtained through a tilt angle sensor, which is a combination of a MEMS gyroscope and an accelerometer.

6. The method for evaluating the operating status of a scaffolding robot according to claim 1, characterized in that, In step three, the deviation parameter is the degree of deviation between the temperature change trend trajectory and the pre-predicted temperature trend trajectory, i.e., the deviation angle between the two trajectory curves; the cumulative deviation distance parameter is the cumulative value of the deviation distance between the actual required position and the operating position throughout the entire operation.

7. The method for evaluating the operating status of a scaffolding robot according to claim 1, characterized in that, In step one, if the spacing change parameter exceeds the first preset threshold or the area change parameter exceeds the second preset threshold, the visual data status label is set as an interference label, and the status of the current coverage area is controlled; otherwise, it is set as a stable label, and the current coverage area is determined.

8. The method for evaluating the operating status of a scaffolding robot according to claim 1, characterized in that, In step two, if the joint torque change parameter exceeds the third preset threshold or the tilt angle change parameter exceeds the fourth preset threshold, the mechanical data status label is set as an abnormal label, and the corresponding force-bearing node is maintained. Otherwise, set it to the normal label and continuously monitor the stressed node.

9. The method for evaluating the operating status of a scaffolding robot according to claim 1, characterized in that, In step three, if the deviation parameter exceeds the fifth preset threshold or the cumulative deviation distance parameter exceeds the sixth preset threshold, the self-status data label is set as an abnormal label, and a comprehensive review of the scaffolding robot is conducted; otherwise, it is set as a normal label and continuous monitoring is performed.

10. A scaffolding robot operation status assessment system, used in the scaffolding robot operation status assessment method as described in any one of claims 1-9, characterized in that, The system is equipped with a visual data acquisition and analysis unit, a mechanical data acquisition and analysis unit, and a self-state data acquisition and analysis unit; The visual data acquisition and analysis unit acquires visual images and sets visual data status labels based on image analysis. The mechanical data acquisition and analysis unit collects and analyzes force-bearing nodes and sets mechanical data status labels; The self-status data acquisition and analysis unit collects the deviation parameters between the temperature change trajectory and the predicted trajectory during the execution of the operation, as well as the cumulative deviation distance parameters between the actual required position and the operation position, and sets self-status data labels based on the comparison results; The system executes corresponding status control or maintenance measures based on the visual data status labels, mechanical data status labels, and its own status data labels.

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