Exterior insulation system hollow detection robot, method, device, medium and product

Abstract

This application relates to a robot, method, equipment, medium, and product for detecting hollow areas in external insulation systems. The robot includes: a robot body, a negative pressure adsorption module, a walking mechanism, a striking mechanism, a sound acquisition module, an acceleration sensing module, a flight module, and a control module. The negative pressure adsorption module is used to adsorb the robot body onto the external wall surface; the walking mechanism is used to control the movement of the robot body on the external wall surface; the striking mechanism is used to calculate the striking force and send a striking excitation to the external wall surface based on the striking force; the sound acquisition module is used to collect the sound signal generated by the external wall surface when receiving the striking excitation; the acceleration sensing module is used to collect the acceleration signal generated by the external wall surface when receiving the vibration of the striking excitation; the flight module is used to provide fall protection when the robot body falls; and the control module is used to perform hollow area detection based on the sound signal and acceleration signal. The technical solution of this application can improve the efficiency of hollow area detection.

Description

Technical Field

[0001] This application relates to the field of testing technology, specifically to a robot, method, equipment, medium, and product for detecting hollow areas in external insulation systems. Background Technology

[0002] As the service life of building exterior wall insulation systems continues to increase, the safety risks associated with these systems are becoming increasingly prominent. Under the combined effects of long-term temperature and humidity cycles, wind loads, and material aging, incoordination and deformation can easily occur between the various structural layers of the insulation system, leading to continuous degradation of adhesion and the gradual formation of voids. These defects are highly concealed and difficult to detect through routine visual inspections in their early stages. As the voids expand, the overall mechanical properties of the insulation system gradually decrease, potentially causing large-scale bulging or even detachment of the finishing layer or insulation layer. This not only affects the normal functionality of the building but also poses a serious threat to the safety of people in the surrounding area.

[0003] Existing wall-climbing robots based on suspended safety ropes only identify hollow defects in external insulation systems based on collected acoustic signals. Due to the limitations of the safety ropes, the mobility of these wall-climbing robots is restricted, resulting in low detection efficiency. Summary of the Invention

[0004] This application provides a robot, method, equipment, medium, and product for detecting hollow areas in external insulation systems, which can improve the efficiency and accuracy of hollow area detection.

[0005] According to a first aspect of the embodiments of this application, a robot for detecting hollow areas in an external insulation system is provided, comprising: a robot body, a negative pressure adsorption module, a walking mechanism, a striking mechanism, a sound acquisition module, an acceleration sensing module, a flight module, and a control module; wherein, the negative pressure adsorption module is used to adsorb the robot body onto the exterior wall surface; the walking mechanism is used to control the movement of the robot body on the exterior wall surface; the striking mechanism is used to calculate the striking force corresponding to different types of external insulation systems and to issue a striking excitation to the exterior wall surface according to the striking force; the sound acquisition module is used to acquire the sound signal generated by the exterior wall surface when receiving the striking excitation; the acceleration sensing module is used to acquire the acceleration signal generated by the exterior wall surface when receiving the vibration of the striking excitation; the flight module is used to provide fall protection when the robot body falls; and the control module is used to perform hollow area detection based on the sound signal and the acceleration signal.

[0006] In one embodiment, fall protection is provided when the robot body falls, including: attitude control and fall control when the robot body falls, wherein attitude control is used to adjust the robot body to a horizontal attitude when the robot body falls, and fall control is used to control the robot to land on the ground when the robot body is in a horizontal attitude.

[0007] In one implementation, attitude control includes: in the first stage, adjusting the rotational speed of the two top rotors and the rotational speed of the two bottom rotors, such that the total lift F1 generated by the two top rotors is less than the total lift F2 generated by the two bottom rotors.

[0008] In the second stage, the rotational speeds of the two top rotors and the two bottom rotors are adjusted so that the total lift F1 generated by the two top rotors is greater than the total lift F2 generated by the two bottom rotors.

[0009] In one implementation, during attitude control, the total lift F1 generated by the two top rotors and the total lift F2 generated by the two bottom rotors, as well as the overturning torque corresponding to F1 and F2, satisfy the following conditions:

[0010] (F1+F2)×sin ≤m r ×g

[0011] M = |(F2 - F1)| × L

[0012] in, Let m be the angle between the robot body and the vertical direction. r Let g be the mass of the robot body, g be the acceleration due to gravity, and L be the lever arm from the line of action of the lift force of each rotor to the flip axis.

[0013] In one implementation, during fall control, the four rotors rotate at the same speed, and the total lift F3 generated by the four rotors satisfies the following condition:

[0014] F3=β(m r g+(m r v r 2 )⁄2hr)

[0015] Where β is the correction coefficient, with a value range of [0.8, 0.95]; m r v is the mass of the robot body. r hr represents the robot's falling speed, and hr represents the distance between the robot and the ground.

[0016] In one implementation, hollow detection based on acoustic signals and acceleration signals includes: extracting feature parameters of the acoustic signals and the acceleration signals; inputting the feature parameters into a first hollow detection model to determine whether a hollow exists at the current detection point and recording the hollow information at the detection point; inputting the feature parameters into a second hollow detection model to obtain the size and type of the hollow, where the hollow type includes shallow hollow and deep hollow; locating the hollow according to the building elevation drawing and storing the detection results.

[0017] According to a second aspect of the embodiments of this application, a method for detecting hollow areas in an external thermal insulation system is provided. The method includes: extracting feature parameters of acoustic signals and acceleration signals; inputting the feature parameters into a first hollow area identification model to determine whether a hollow area exists at the current detection point and recording the location information of the hollow area; inputting the feature parameters into a second hollow area identification model to obtain the size and type of the hollow area, wherein the hollow area type includes shallow hollow areas and deep hollow areas; locating the hollow area according to the building elevation drawing and storing the detection results.

[0018] According to a third aspect of the embodiments of this application, an electronic device is provided, including a memory and a processor. The memory is used to store a computer program executable by the processor; the processor is used to execute the computer program in the memory to implement the external insulation system hollow detection method of the second aspect.

[0019] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the executable computer program in the storage medium is executed by a processor, the method for detecting hollow areas in an external insulation system as described in the second aspect is implemented.

[0020] According to a fifth aspect of the embodiments of this application, a computer program product is provided, including a computer program that, when executed by a processor, implements the external insulation system hollow detection method of the second aspect.

[0021] The robot and method for detecting hollow areas in external insulation systems presented in this application have the following beneficial effects:

[0022] 1. The robot and method for detecting hollow areas in external insulation systems in this application can achieve synchronous acquisition and fusion analysis of sound signals and acceleration signals under impact excitation through the onboard sound acquisition module and acceleration sensing module, overcoming the limitation of insufficient basis for single physical signal discrimination and improving the accuracy and reliability of external insulation hollow area detection.

[0023] 2. The robot and method for detecting hollow areas in external insulation systems in this application can calculate the corresponding impact force based on the elastic modulus of different types of external insulation systems, thereby improving the accuracy of acoustic signal and acceleration signal acquisition. Furthermore, a weighted acoustic-vibration signal fusion analysis method is proposed based on the characteristics of external insulation systems, which further improves the accuracy and reliability of detecting hollow areas in external insulation systems.

[0024] 3. This application uses a flight module to replace the traditional safety rope to provide fall protection for the robot, and proposes a two-stage design method of attitude adjustment and fall control. By reasonably designing the rotor speed, the robot's aerial attitude adjustment and smooth landing are realized, which improves the safety and mobility of the inspection operation and the inspection efficiency. Attached Figure Description

[0025] The above and other objects, features, and advantages of exemplary embodiments of this application will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of this application are illustrated in the drawings by way of example and not limitation, in which:

[0026] Figure 1 This is a schematic diagram illustrating the structural composition of a robot for detecting hollow areas in an external insulation system, according to an exemplary embodiment.

[0027] Figure 2 This is a schematic diagram of the overall structure of a robot for detecting hollow areas in an external insulation system, according to an exemplary embodiment.

[0028] Figure 3 This is a bottom view schematic diagram of a robot for detecting hollow areas in an external insulation system, according to an exemplary embodiment.

[0029] Figure 4 This is a schematic diagram of the striking module structure of a robot for detecting hollow areas in an external insulation system, according to an exemplary embodiment.

[0030] Figure 5 This is a schematic diagram illustrating a two-stage fall prevention design process for a robot for detecting hollow areas in an external insulation system, according to an exemplary embodiment.

[0031] Figure 6 This is a force diagram illustrating the attitude adjustment of a robot for detecting hollow areas in an external insulation system during a fall, according to an exemplary embodiment.

[0032] Figure 7 This is a flowchart illustrating a method for detecting voids in an external insulation system according to an exemplary embodiment.

[0033] Figure 8 This is a flowchart illustrating another method for detecting voids in an external insulation system according to an exemplary embodiment.

[0034] Figure 9 This is a block diagram illustrating an electronic device according to an exemplary embodiment.

[0035] Explanation of markings in the attached diagram:

[0036] 1. Robot body, 2. Negative pressure adsorption module, 3. Sealing component, 4. Sealed chamber, 5. Drive motor, 6. Omnidirectional rotating wheel, 7. Striking hammer, 8. Striking drive component, 9. Microphone, 10. Fixed bracket, 11. Accelerometer, 12. Telescopic transmission mechanism, 13. Drive motor, 14. Rotor, 15. Attitude sensor, 16. Fall detection sensor, 17. Distance sensor, 18. Negative pressure sensor. Detailed Implementation

[0037] It should be understood that in the embodiments of this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0038] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0039] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0040] One embodiment of this application provides a robot for detecting voids in external insulation systems, which can achieve efficient and intelligent detection and identification of voids in external insulation, improving detection efficiency and accuracy. See also Figure 1 The external insulation system hollow detection robot may include: robot body, negative pressure adsorption module, walking mechanism, tapping mechanism, sound acquisition module, acceleration sensing module, flight module and control module.

[0041] The robot's body has an approximate rectangular shape, with the side closest to the wall as the bottom and the side furthest from the wall as the top. A negative pressure adsorption module is used to stably attach the robot to the building's exterior wall. A walking mechanism drives the robot to move along the exterior wall, enabling omnidirectional free movement via omnidirectional wheels. A tapping mechanism applies tapping excitation to the test area of ​​the external insulation system, collecting the acoustic signals generated by the system under tapping excitation via a sound acquisition module and the acceleration signals via an acceleration sensor module. The tapping mechanism can calculate the appropriate tapping force for different types of external insulation systems to ensure data acquisition quality and safety. A flight module activates in case of a fall, providing fall protection to ensure a smooth landing. The flight module can adjust the robot's attitude and control its fall trajectory through flight control and structural stress analysis. The control module is used to coordinate the operation of other modules in the external insulation system hollow detection robot, and to perform fusion analysis of acoustic signals and acceleration signals to identify hollow defects in the external insulation system.

[0042] In some embodiments, reference Figure 2 and Figure 3 The negative pressure adsorption module includes a negative pressure generator 2, a sealing component 3, and a sealing chamber 4. When the negative pressure generator 2 is activated, it extracts air from the sealing chamber 4 to create a negative pressure, causing the robot body 1 to adhere to the wall surface. The sealing component 3, made of rubber and positioned around the perimeter of the sealing chamber 4, compensates for unevenness on the exterior wall surface to improve the negative pressure sealing effect.

[0043] In some embodiments, reference Figure 3 The walking mechanism includes a drive motor 5 and four omnidirectional rotating wheels 6. The omnidirectional rotating wheels 6 can be Mecanum wheels, arranged in a rectangle at the four corners of the robot body 1. The walking mechanism controls the speed and direction of the four omnidirectional rotating wheels 6 through the drive motor 5, enabling the robot body 1 to move longitudinally, laterally, and rotate in place on the exterior wall of the building.

[0044] In some embodiments, reference Figure 4 The striking mechanism includes striking hammers 7 and striking drive components 8. To improve detection efficiency, three striking hammers 7 and corresponding drive components 8 can be set. The striking hammers 7 are arranged at lateral intervals along the robot body 1. The control module controls the energizing time and frequency of the striking drive components 8 to make the striking hammers 7 reciprocate according to a preset striking frequency and force. The preset striking frequency can be flexibly adjusted according to detection requirements and the type of external insulation system. The striking force can be adjusted by the driving current of the striking drive components 8, and the striking force range is controlled between 50 and 150 N. Furthermore, to prevent aliasing of striking response signals from different striking hammers 7, a preset time interval can be maintained between adjacent striking hammers 7 for reciprocating motion.

[0045] It is understandable that controlling the impact force is crucial for detecting hollow defects in external insulation systems. If the impact force is too large, it may damage the external insulation system; if the impact force is too small, the collected sound and acceleration signals will not be able to effectively reflect the actual situation of hollow defects in the external insulation system. External insulation systems include various types, such as EPS board thin plaster external insulation systems and inorganic thermal insulation mortar external insulation systems. The structural composition and material properties of different types of external insulation systems vary greatly, that is, the material parameters such as the elastic modulus of different types of external insulation systems vary significantly. For external insulation systems with different elastic moduli, the amplitude, frequency, energy, and other parameters of the sound and acceleration signals generated by the same impact force vary greatly, which in turn has a significant impact on the data processing method and the accuracy of defect identification. Therefore, in order to improve the quality of data acquisition and the accuracy of defect identification, the corresponding impact force can be calculated according to the characteristics of different types of external insulation systems. The formula for calculating the impact force can be found in formulas (1) and (2), which are:

[0046] F=mv / (∆t) Formula (1)

[0047] v = 2π × p × Lq Formula (2)

[0048] Where m is the mass of the striking hammer 7. v is the contact velocity between the striking hammer 7 and the exterior wall surface. ∆t is the contact time between the striking hammer 7 and the exterior wall surface, which is mainly related to the material and diameter of the striking hammer and the elastic modulus of the exterior wall surface (including the finishing layer and the external insulation system). The value range of ∆t is [0.05ms, 0.5ms]. For example, if the exterior wall surface is ceramic tile and the striking hammer head is steel, the ∆t of the EPS board thin plaster external insulation system can be taken as 0.1ms, and the ∆t of the inorganic thermal insulation mortar external insulation system can be taken as 0.2ms. p is the striking frequency of the striking hammer. Lq is the total length of the striking hammer.

[0049] To prevent damage to the external insulation system from excessive impact force, an upper limit for the impact force can be set.

[0050] The formula for calculating the upper limit of the striking force is as shown in formula (3):

[0051] F f(v,m,A,E1,E2,h1) Formula (3)

[0052] Where F is the striking force, f is the fitting function, v is the contact velocity between the hammer 7 and the exterior wall, m is the mass of the hammer, A is the contact area between the hammer 7 and the wall, E1 and E2 are the elastic moduli of the hammer and the external insulation system, respectively, and h1 is the thickness of the external insulation system.

[0053] In some embodiments, the fitting function f can be obtained based on a knocking test device, such as the knocking test device designed in CN122468837A, which can conduct a large number of knocking tests on the walls of different types of external insulation systems in a laboratory testing environment to obtain test data of external insulation systems under different hollow sizes and hollow types. By analyzing the hollow test data under different hollow sizes and hollow types, and fitting the formula (3), the fitting function f can be obtained, thereby realizing a reasonable calculation of the upper limit of the knocking force. The fitting method can be one of multiple linear fitting or machine learning method, which is not specifically limited here.

[0054] In some embodiments, reference Figure 4The sound acquisition module in the data acquisition module can include three microphones 9 and corresponding fixed brackets 10. Each microphone 9 is positioned close to the striking position of each striking hammer 7, and the relative distance between the microphone 9 and the striking hammer 7 can be adjusted by the fixed brackets 10. The sound acquisition module can collect the sound signal generated by the exterior wall surface under the excitation of the strike. The acceleration sensing module in the data acquisition module includes an acceleration sensor 11, a telescopic transmission mechanism 12, and a drive motor 13. The telescopic transmission mechanism 12 is used to drive the acceleration sensor 11 to extend and contact the exterior wall surface after the robot body 1 reaches the detection position, and to drive the acceleration sensor 11 to retract and separate from the exterior wall surface after the detection is completed. The measurement range of the acceleration sensor 11 can be set to ±10g to ±50g according to the hollow detection data of the external insulation system. The drive motor 13 drives the acceleration sensor 11 to extend and closely fit the exterior wall surface when it reaches the detection point, collect the acceleration signal generated by the vibration of the exterior wall surface under the excitation of the strike, and retract and separate from the wall surface during the movement of the robot body 1.

[0055] In some embodiments, reference Figure 2 and Figure 3 The flight module includes rotors 14, attitude sensors 15, fall detection sensors 16, and distance sensors 17. The four rotors 14 are arranged in a quadcopter configuration, symmetrically mounted on the top surface of the robot body 1 near the four corners. The attitude sensors 15 employ inertial measurement units (IMUs) to detect the robot body 1's attitude in real time. The fall detection sensors 16 detect the robot body 1's acceleration in the direction of gravity in real time. The distance sensors 17 are used to obtain the distance between the robot body and obstacles below. When the absolute value of the robot body 1's acceleration in the direction of gravity exceeds a preset acceleration threshold, it is determined that the robot body 1 has fallen. At this time, the control module immediately activates and adjusts the rotational speed differences of the four rotors 14, first pulling the robot body 1 away from the wall, and then adjusting the robot body 1's attitude to a horizontal position. (Reference) Figure 5 When the distance sensor 17 on the bottom surface of the robot body 1 detects that the distance between the robot body 1 and the obstacle below is less than the preset safe distance, the control module gradually increases the rotation speed of the four rotors 14 to achieve a smooth landing of the robot body 1.

[0056] In some embodiments, when the absolute value of the acceleration of the robot body 1 in the direction of gravity exceeds a preset acceleration threshold or the negative pressure sensor 18 detects that the pressure in the sealed chamber 4 is lower than a safety threshold, it is determined that the robot body 1 has fallen, and the four rotors 14 can be activated to provide fall protection for the robot body 1.

[0057] In some embodiments, the fall protection process is the process from the start of the fall to a smooth landing of the robot body 1, which may include two stages: attitude adjustment and fall control of the robot body 1. (Refer to...) Figure 5 .

[0058] S1: During the attitude control phase:

[0059] During this stage, the control module immediately activates the four rotors 14 and adjusts the speed difference between them. For example, the speed of the two bottom rotors 14 (closest to the ground) can be controlled to be greater than that of the two top rotors 14. The total lift generated by the four rotors 14 will move the robot body 1 away from the wall. At the same time, the lift difference generated by the speed difference between the bottom and top rotors will create a tilting torque on the robot body 1. This tilting torque can adjust the robot body 1 from a vertical posture to a horizontal posture, specifically including the following two steps:

[0060] S11: Pull robot body 1 away from the wall to prevent it from colliding with it. (Reference) Figure 6 Assuming the total lift generated by the two top rotors 14 is F1, and the total lift generated by the two bottom rotors 14 is F2, if F1 = F2, the resultant force of F1 and F2, in the component perpendicular to the wall, Fout, can pull the robot body 1 away from the wall. The calculation formula is as follows:

[0061] Formula (4)

[0062] Where Fout is greater than 0, the robot body 1 can be pulled away from the wall; if the robot body 1 still has residual adsorption, then Fout should be greater than the residual adsorption force, which can be estimated by the negative pressure detected by the negative pressure sensor 18.

[0063] F1 and F2 can be calculated using the following formulas:

[0064] F1=F11+F12 formula (5)

[0065] F2=F21+F22 formula (6)

[0066] F11=F12=F21=F22=C T ×ρ×n 2 ×D 4 Formula (7)

[0067] F11 and F12 are the lift generated by the two top rotors 14 respectively. F21 and F22 are the lift generated by the two bottom rotors 14 respectively. T ρ is the thrust coefficient. ρ is the air density. D is the diameter of rotor 14. n is the corresponding rotor speed.

[0068] It's understandable, C T It is a dimensionless empirical coefficient that takes into account the design parameters of rotor 14, such as airfoil, angle of attack, and number, and its value ranges from [0.005, 0.02].

[0069] S12: Adjust the robot body 1 from a vertical posture to a horizontal posture.

[0070] When the distance sensor 17 detects that the distance between the robot body 1 and the wall reaches the preset safe distance, the total lift F1 is set to be less than the total lift F2 by adjusting the rotation speed of each rotor. The lift difference between F1 and F2 will generate a flipping torque M on the horizontal axis of the robot body 1. The calculation formula for the flipping torque M is as shown in formula (8):

[0071] M = |(F2 - F1)| × L Formula (8)

[0072] Where L is the lever arm from the line of action of the lift force of each rotor 14 to the flip axis. The four rotors of this robot are arranged symmetrically, so the L of the four rotors is the same.

[0073] Under the action of the overturning torque M, the robot body 1 overturns around the horizontal axis. The formula for calculating the rotational angular acceleration α is as shown in formula (9):

[0074] α=M / I Formula (9)

[0075] Where M is the overturning torque and I is the moment of inertia of the robot body 1 about the horizontal axis.

[0076] During the flipping process of robot body 1, assuming that the angular acceleration α is constant, the formula for calculating the time t during which it has rotated is as follows: Formula (10):

[0077] Formula (10)

[0078] in, The rotation angle of robot body 1 can be obtained by measuring the attitude sensor 15. The angle γ that has been rotated needs to be less than or equal to 90°.

[0079] To prevent excessive rotational speed from causing the robot body 1 to continue flipping after adjusting its posture to a horizontal position, the robot body 1 can be controlled to accelerate its rotation in the first stage and decelerate its rotation in the second stage. This ensures that the rotational angular velocity of the robot body 1 is exactly 0 or close to 0 when it reaches a horizontal position after rotating 90°. The segmentation of the first and second stages of posture adjustment is based on the robot body 1 reaching the critical angle γ. 0， It can be detected by attitude sensor 15. The critical angle γ0 is related to the initial rotational angular velocity of the robot body 1.

[0080] Taking a critical angle γ0 of 45° as an example, in the first stage, when the attitude sensor 15 detects that the robot body 1's flip angle is between 0° and 45°, the rotation speeds of the two top rotors 14 can be adjusted to be less than the rotation speeds of the two bottom rotors 14, so that the total lift F1 formed by the two top rotors 14 is less than the total lift F2 formed by the two bottom rotors 14. The resulting lift difference causes the robot body 1 to accelerate its flip. At this time, the flipping torque M can be calculated according to formula (8). The rotation speeds of the two top rotors 14 can be equal to each other, and the rotation speeds of the two bottom rotors 14 can be equal to each other.

[0081] In the second stage, when the attitude sensor 15 detects that the robot body 1 has a tilt angle of 45° to 90°, the rotational speeds of the two top rotors 14 can be adjusted to be greater than those of the two bottom rotors 14. This results in the total lift F2 generated by the two bottom rotors 14 being less than the total lift F1 generated by the two top rotors 14. The resulting lift difference creates a tilting torque opposite to that in the first stage, thereby reducing the rotational angular velocity of the robot body. The rotational speeds of the two top rotors 14 and the two bottom rotors 14 can be equal.

[0082] Assuming the overturning torque M is the same during the first acceleration and second deceleration stages, and there is no air resistance, the total time T1 for the robot body 1 to change from a vertical to a horizontal state can be calculated according to formulas (8)-(10), as shown in formula (11). The calculation of the total time T2 for the vertical fall of the robot body 1 under gravity is shown in formula (12), and the relationship between the two formulas should satisfy formula (13):

[0083] T1= Formula (11)

[0084] T2= Formula (12)

[0085] Formula (13)

[0086] Where hr is the distance between robot body 1 and the ground. r Let be the mass of robot body 1. The safety factor is used to ensure that the robot body 1 completes its attitude adjustment before falling, and its value ranges from [0.5, 0.8]. The value of T1 can be obtained from formula (10) and the angle γ=90° that the robot body 1 has rotated.

[0087] Furthermore, during the flipping process of the robot body 1, the vertical lift of the four rotors 14 should satisfy formula (14) to prevent excessive lift from causing the robot body 1 to fly upwards and away from the ground. Formula (14) is:

[0088] (F1+F2)×sin ≤m r ×g formula (14)

[0089] in, Let m be the angle between the robot body 1 and the vertical direction. r Let F1 be the mass of robot body 1. g is the acceleration due to gravity. (F1 + F2) × sin This is the component of the total lift of the four rotors 14 in the vertical upward direction.

[0090] Therefore, the allowable rotor lift F1 and F2| can be solved in reverse according to formulas (11) to (14). Furthermore, the corresponding rotational speed can be calculated according to formulas (5) to (7), thereby realizing the attitude adjustment of the robot body 1.

[0091] S2: During the fall control phase:

[0092] S21: Maintain the attitude of the robot body 1. When the attitude sensor 15 detects that all four rotors 14 on the back of the robot body 1 are completely horizontal, the control module adjusts the rotation speed of the four rotors 14 to be consistent to prevent the robot body 1 from tilting again.

[0093] S22: Control the robot body 1 to land smoothly. To ensure that the robot body 1 can land smoothly, the total lift F3 generated by the four rotors 14 should meet the requirements of equation (15):

[0094] F3=β(m r g+(m r v r 2 )⁄2hr) Formula (15)

[0095] Where F3 = F1 + F2. β is a correction factor; considering that the air resistance experienced by the robot body 1 during its descent can partially offset the effect of gravity, a value of [0.8, 0.95] is recommended. r Let v be the mass of robot body 1. r Let be the falling speed of robot body 1.

[0096] In some embodiments, during the descent of the robot body 1, the fall detection sensor 16 can acquire the acceleration of the robot body 1 in real time and integrate it to obtain the falling velocity v. rThe distance sensor 17 can monitor the height hr of the robot body 1 above the ground in real time and feed the above parameters back to the control module, thereby adjusting the rotation speed of the rotor 14 in real time.

[0097] In some embodiments, the control module is used to coordinate and control the operation of other functional modules. The memory in the control module can be used to store the acquired acoustic signals, acceleration signals, and various thresholds (including preset acceleration threshold, preset robot body rotation angular velocity threshold, and preset safety time threshold) and discrimination model parameters, for the purpose of intelligent identification of hollow defects in the external insulation system.

[0098] Another embodiment of this application provides a method for detecting hollow areas in an external insulation system.

[0099] In some embodiments, a hollow detection robot for external insulation systems can be placed on the exterior wall of a building. A negative pressure adsorption module is activated to adhere the robot to the wall, and a control module controls the walking mechanism to move the robot to a preset detection point. Upon reaching the detection point, the control module controls the accelerometer to ensure it is firmly attached to the wall. Then, a tapping mechanism applies a tapping excitation to the area of ​​the external insulation system to be tested. Simultaneously, a sound acquisition module and an accelerometer sensor module acquire sound and acceleration signals, respectively, achieving synchronous acquisition of sound and vibration signals. The control module preprocesses the acquired sound and acceleration signals, extracting and fusing the characteristic parameters of each signal. Based on a pre-trained first hollow detection model, it determines whether a hollow exists at the current detection point and records the location information of the detection point. Based on a pre-trained second hollow detection model, it obtains the size and type of the hollow, including shallow and deep hollows. Finally, based on the building elevation drawing, the hollows are located, resulting in the location and quantity statistics of the hollow areas on the wall. After the current detection point is completed, the control module controls the accelerometer to retract to its original position and drives the robot body to move to the next detection point. The above detection steps are repeated until the detection task of all preset detection points is completed.

[0100] Please see Figure 7 and Figure 8 The method for detecting hollow areas in the external insulation system may include the following steps:

[0101] S601 extracts the characteristic parameters of the acoustic signal and acceleration signal.

[0102] In some embodiments, the external insulation system hollow detection robot can acquire the dimensions of the exterior wall and plan the inspection path; it can preprocess the collected acoustic and acceleration signals, including cleaning and noise reduction. Considering that hollow areas in the external insulation system may exist at the bonding interface with the surface layer (shallow hollow areas) or the bonding interface with the base wall (deep hollow areas), and that the reflection signal characteristics of the impact vibration are significantly different at different interfaces, low-frequency and high-frequency filters can be designed separately.

[0103] In some embodiments, a Fast Fourier Transform is performed on the preprocessed acoustic signal and acceleration signal to extract the corresponding feature parameters and normalize them, including the main peak frequency, band energy, and Mel-frequency cepstral coefficients. Based on the structural characteristics of the external insulation system under test, different weights are assigned to the feature parameters of the acoustic signal and the vibration acceleration signal, and the weighted acoustic-vibration features are then fused and analyzed.

[0104] S602, input the feature parameters into the first hollow drum recognition model, determine whether there is a hollow drum at the current detection point and record the location information of the hollow drum.

[0105] In some embodiments, based on a pre-trained first hollow drum recognition model, feature parameters (such as fused feature parameters) of acoustic signals and acceleration signals can be input into the first hollow drum recognition model respectively to determine whether a hollow drum exists at each detection point and mark its position information.

[0106] S603, input the feature parameters into the second hollow recognition model to obtain the hollow size and hollow type, which includes shallow hollow and deep hollow.

[0107] In some embodiments, based on a pre-trained second hollow recognition model, feature parameters (such as fused feature parameters) of acoustic signals and acceleration signals can be input into the second hollow recognition model to calculate the hollow size and hollow type (shallow or deep).

[0108] It is understandable that for voids at different locations in the external insulation system, the low-frequency characteristics of deep voids are more pronounced than their high-frequency characteristics, making acceleration signals a more suitable characterization method. Conversely, the high-frequency characteristics of shallow voids are more pronounced than their low-frequency characteristics, making acoustic signals a more suitable characterization method. Therefore, when training the first and second void recognition models, different weighting coefficients can be assigned to the feature parameters of the acceleration and acoustic signals for different types of voids to improve the accuracy of void recognition.

[0109] S604, locate the hollow areas based on the building elevation drawings and store the test results.

[0110] It is understandable that after the current detection point is completed, the telescopic transmission mechanism 12 can drive the three accelerometers 11 to retract and separate from the wall. The walking mechanism can then drive the robot body 1 to move to the next detection point.

[0111] In some embodiments, the robot body 1 can first move longitudinally along the height of the wall and detect hollow areas; the distance between each measuring line is adjustable. After all points on this longitudinal detection path have been detected, the control module drives the rotating wheel 6 to turn, causing the robot body 1 to move laterally. The movement distance L is calculated as follows:

[0112] L=b+d

[0113] Where b is the width of the robot body 1, and d is the distance between two adjacent striking hammers 7.

[0114] It is understandable that the above-described method for detecting hollow areas in the external insulation system can be repeated at each monitoring point until the testing task at all monitoring points is completed. After the testing at each monitoring point is completed, the control module stores and outputs the testing results, including the point coordinates, characteristic parameter values, and hollow area identification results.

[0115] The method embodiments and robot embodiments of this application can complement each other.

[0116] Embodiments of this application also propose an electronic device, including a processor and a memory; the memory is used to store a computer program executable by the processor; the processor is used to execute the computer program in the memory to implement the external insulation system hollow detection method of any of the above embodiments.

[0117] The embodiments of this application also propose a computer-readable storage medium that, when an executable computer program in the storage medium is executed by a processor, can implement the external insulation system hollow detection method of any of the above embodiments.

[0118] Embodiments of this application also propose a computer program product, including a computer program that, when executed by a processor, implements the external insulation system hollow detection method of any of the above embodiments.

[0119] Regarding the apparatus in the above embodiments, the specific manner in which the processor performs the operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0120] Embodiments of this application also provide an electronic device 600, such as... Figure 6As shown, the electronic device 600 includes a memory 601 and a processor 602. The memory 601 is used to store computer programs executable by the processor 602; the processor 602 is used to execute the computer programs in the memory 601 to implement the external insulation system hollow detection method provided in any of the above embodiments.

[0121] The electronic device 600 also includes a communication interface 603. The processor 602, memory 601, and communication interface 603 are connected via a communication bus and communicate with each other.

[0122] Processor 602 may be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of programs in the above scheme.

[0123] Communication interface 603 is used to communicate with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Networks (WLAN), etc.

[0124] The memory 601 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory may exist independently and be connected to the processor via a bus. The memory may also be integrated with the processor.

[0125] In this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The term "multiple" refers to two or more unless otherwise expressly defined.

[0126] The above description of the embodiments is intended to enable those skilled in the art to understand and apply this application. It will be apparent to those skilled in the art that various modifications can be easily made to these embodiments, and the general principles described herein can be applied to other embodiments without creative effort. Therefore, this application is not limited to the embodiments described herein, and any improvements and modifications made by those skilled in the art based on the disclosure of this application without departing from the scope and spirit of this application are within the scope of this application.

Claims

1. A robot for detecting hollow areas in an external insulation system, characterized in that, The external insulation system hollow detection robot includes: robot body, negative pressure adsorption module, walking mechanism, tapping mechanism, sound acquisition module, acceleration sensing module, flight module and control module; The system comprises the following components: a negative pressure adsorption module for adsorbing the robot body onto the exterior wall surface; a walking mechanism for controlling the movement of the robot body on the exterior wall surface; a striking mechanism for calculating the striking force corresponding to different types of external insulation systems and emitting striking excitation to the exterior wall surface based on the striking force; a sound acquisition module for acquiring the sound signal generated by the exterior wall surface when receiving the striking excitation; an acceleration sensing module for acquiring the acceleration signal generated by the exterior wall surface when receiving the vibration of the striking excitation; a flight module for providing fall protection when the robot body falls; and a control module for detecting hollow areas based on the sound signal and the acceleration signal.

2. The robot for detecting hollow areas in an external insulation system according to claim 1, characterized in that, The provision of fall protection in the event of a fall by the robot body includes: When the robot body falls, attitude control and fall control are performed. The attitude control is used to adjust the robot body to a horizontal position when it falls, and the fall control is used to control the robot to land on the ground when it is in a horizontal position.

3. The robot for detecting hollow areas in an external insulation system according to claim 2, characterized in that, The attitude control includes: In the first stage, the rotational speeds of the two top rotors and the two bottom rotors are adjusted so that the total lift F1 generated by the two top rotors is less than the total lift F2 generated by the two bottom rotors. In the second stage, the rotational speeds of the two top rotors and the two bottom rotors are adjusted so that the total lift F1 generated by the two top rotors is greater than the total lift F2 generated by the two bottom rotors.

4. The robot for detecting hollow areas in an external insulation system according to claim 2, characterized in that, During the attitude control process, the total lift F1 generated by the two top rotors and the total lift F2 generated by the two bottom rotors, as well as the overturning torque corresponding to F1 and F2, satisfy the following conditions: (F1+F2) × sin ≤m r xg M = |(F2 - F1)| × L in, Let m be the angle between the robot body and the vertical direction. r Let g be the mass of the robot body, g be the acceleration due to gravity, and L be the lever arm from the line of action of the lift force of each rotor to the flip axis.

5. The robot for detecting hollow areas in an external insulation system according to claim 2, characterized in that, During the fall control process, the rotational speed of the four rotors is the same, and the total lift F3 generated by the four rotors satisfies the following condition: F3=β(m r g+(m r v r 2 )⁄2hr) Where β is the correction coefficient, m r v is the mass of the robot body. r hr represents the robot's falling speed, and hr represents the distance between the robot and the ground.

6. The robot for detecting hollow areas in an external insulation system according to claim 1, characterized in that, The hollow detection based on the acoustic signal and the acceleration signal includes: Extract the characteristic parameters of the acoustic signal and the acceleration signal; The feature parameters are input into the first hollow drum recognition model to determine whether there is a hollow drum at the current detection point and to record the hollow drum information of the detection point. The feature parameters are input into the second hollow recognition model to obtain the hollow size and hollow type, wherein the hollow type includes shallow hollow and deep hollow. The hollow areas are located based on the building elevation drawings, and the test results are stored.

7. A method for detecting hollow areas in an external insulation system, characterized in that, The method for detecting hollow areas in the external insulation system includes: Extract characteristic parameters from acoustic and acceleration signals; The feature parameters are input into the first hollow drum recognition model to determine whether there is a hollow drum at the current detection point and record the location information of the hollow drum. The feature parameters are input into the second hollow recognition model to obtain the hollow size and hollow type, which includes shallow hollow and deep hollow. Locate the hollow areas based on the building elevation drawings and store the test results.

8. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory is used to store computer programs executable by the processor; the processor is used to execute the computer programs in the memory to implement the external insulation system hollow detection method as described in claim 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the hollow detection method for the external insulation system as described in claim 7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for detecting hollow areas in an external insulation system as described in claim 7.

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