An online monitoring method, system and device for working state of a live-line robot

Abstract

The application discloses a kind of live-line working robot working state on-line monitoring method, system and equipment, the application includes based on target tightening torque, target axial pretightening force and target nut angle and is tightened to nut, generates actual tightening torque, actual axial pretightening force and actual nut angle;Using the target tightening torque of nut, target axial pretightening force, target nut angle, actual tightening torque, actual axial pretightening force and actual nut angle, the tightening torque error, axial pretightening force error and nut angle error of nut are calculated;Using tightening torque error, axial pretightening force error and nut angle error, the degree of rotation of nut is calculated, and according to degree of rotation, nut is rotated, target monitoring information is generated and sent to man-machine interface.Solve the technical problems that nut tightening state does not have perfect monitoring method, and there is security risk.The application acquires nut working state in real time, and then improves the reliability of live-line working robot operation.

Description

Technical Field

[0001] This invention relates to the field of live-line working robot technology, and in particular to a method, system and device for online monitoring of the working status of a live-line working robot. Background Technology

[0002] With the development of modern technology, the level of production automation is constantly improving, and robots are beginning to be applied in some high-risk and labor-intensive technological fields. Live-line working is an engineering method for inspecting and maintaining power lines and equipment without interrupting power. Traditional manual live-line working requires direct human intervention, relying on the worker's experience to inspect the site conditions, screen for faults, and accurately locate and resolve power line faults. Manual live-line working requires workers to be in an environment of high altitude, high voltage, and strong electromagnetic fields for extended periods. The high labor intensity and prolonged work can lead to difficulty in controlling body posture, causing significant mental stress and physical exhaustion. Even with strict adherence to safety operating procedures and increased insulation protection measures, personal injury accidents are still possible. To make live-line working safer and more efficient, the use of robots to replace manual labor is urgently needed. High-voltage live-line working robots can not only liberate workers from dangerous, heavy, and mentally stressful work but also bring significant economic and social benefits. Therefore, high-voltage live-line working robots are an inevitable trend to replace manual labor in live-line work.

[0003] However, monitoring the working status of high-voltage live-line working robots is particularly important during their operation. Real-time tracking of the robot's working status is crucial to improving its reliability. However, existing technologies lack a comprehensive method for monitoring the tightening status of nuts during live-line working, resulting in ineffective monitoring of nut tightening and posing significant safety hazards. Summary of the Invention

[0004] This invention provides a method, system, and device for online monitoring of the working status of a live-line working robot. It solves the technical problem that existing technologies lack a perfect monitoring method for the tightening status of nuts during the operation of live-line working robots, resulting in ineffective monitoring of the nut tightening status and posing significant safety hazards.

[0005] The first aspect of this invention provides a method for online monitoring of the working status of a live-line working robot, comprising:

[0006] In response to a received online monitoring request, the system acquires an image of the nut corresponding to the live-line working robot that corresponds to the online monitoring request.

[0007] Based on the nut image, determine the target tightening torque, target axial preload, and target nut rotation angle corresponding to the nut image;

[0008] The nut is tightened based on the target tightening torque, the target axial preload, and the target nut rotation angle to generate the actual tightening torque, the actual axial preload, and the actual nut rotation angle.

[0009] Using the target tightening torque, target axial preload, target nut rotation angle, actual tightening torque, actual axial preload, and actual nut rotation angle of the nut, calculate the tightening torque error, axial preload error, and nut rotation angle error of the nut;

[0010] The rotation degree of the nut is calculated using the tightening torque error, the axial preload error, and the nut rotation angle error. The nut is then rotated according to the rotation degree to generate target monitoring information, which is then sent to the human-machine interface.

[0011] Optionally, the step of obtaining the nut image corresponding to the live-line working robot in response to the received online monitoring request includes:

[0012] In response to a received online monitoring request, the system acquires a video of the nuts on the angle tower corresponding to the live-line working robot.

[0013] The video frames of the nut video are extracted frame by frame to generate a nut image.

[0014] Optionally, the step of determining the target tightening torque, target axial preload, and target nut rotation angle of the nut corresponding to the nut image based on the nut image includes:

[0015] The nuts in the nut image are identified to determine their size and model characteristics;

[0016] Based on the size and model characteristics of the nut, the target tightening torque, target axial preload, and target nut rotation angle are determined.

[0017] Optionally, the step of tightening the nut based on the target tightening torque, the target axial preload, and the target nut rotation angle to generate the actual tightening torque, the actual axial preload, and the actual nut rotation angle includes:

[0018] Obtain the initial tightening torque, initial axial preload, and initial nut rotation angle of the nut;

[0019] The target tightening torque, target axial preload, target nut rotation angle, initial tightening torque, initial axial preload, and initial nut rotation angle of the nut are fed back to the drive end of the live-line working robot through a preset torque feedback system.

[0020] The nut is tightened by the drive end of the live-line working robot, generating actual tightening torque, actual axial preload, and actual nut rotation angle.

[0021] Optionally, the step of calculating the tightening torque error, axial preload error, and nut rotation angle error of the nut using the target tightening torque, target axial preload, target nut rotation angle, actual tightening torque, actual axial preload, and actual nut rotation angle includes:

[0022] Calculate the difference between the target tightening torque of the nut and the actual tightening torque to generate the tightening torque error of the nut;

[0023] Calculate the difference between the target axial preload of the nut and the actual axial preload to generate the axial preload error of the nut;

[0024] The difference between the target nut rotation angle and the actual nut rotation angle is calculated to generate the nut rotation angle error.

[0025] Optionally, the step of calculating the rotation degree of the nut using the tightening torque error, the axial preload error, and the nut rotation angle error, rotating the nut according to the rotation degree, generating target monitoring information, and sending it to the human-machine interface includes:

[0026] When the actual nut rotation angle is equal to the target nut rotation angle, calculate the difference between the actual axial preload and the preset initial axial preload, and generate the initial axial preload difference.

[0027] Calculate the ratio between the initial axial preload difference and the actual nut rotation angle to generate the actual rotation angle effect value per degree when the nut rotates.

[0028] Calculate the ratio between the axial preload error and the actual rotation angle effect value per degree to generate the required rotation degree of the nut;

[0029] Rotate the nut according to the rotation reading to generate initial monitoring information;

[0030] Using the tightening torque error, the axial preload error, the nut rotation angle error, and the initial monitoring information, target monitoring information is generated and sent to the human-machine interface for display.

[0031] Optionally, the step of generating target monitoring information using the tightening torque error, the axial preload error, the nut rotation angle error, and the initial monitoring information, and sending it to the human-machine interface for display, includes:

[0032] Target monitoring information is generated using the tightening torque error, the axial preload error, the nut rotation angle error, and the initial monitoring information;

[0033] The target monitoring information is sent to the 3D model of the nut in the human-computer interaction interface;

[0034] Determine whether the actual tightening torque is less than the initial tightening torque or greater than the target tightening torque;

[0035] If the force is less than the specified value, insufficient force information is generated and fed back to the 3D model of the nut in the human-computer interaction interface, so that the 3D model of the nut is displayed in blue.

[0036] If the load is greater than the specified value, overload information is generated and fed back to the 3D model of the nut in the human-computer interaction interface, so that the 3D model of the nut is displayed in red.

[0037] Optionally, it also includes:

[0038] Determine whether the nut rotation angle error is greater than the preset nut rotation angle error range;

[0039] If so, then activate the activation function of the preset torque feedback model corresponding to the preset torque feedback system, update the preset torque feedback model, and generate the target torque feedback model;

[0040] If not, then the current preset torque feedback model will be determined as the target torque feedback model.

[0041] The second aspect of this invention provides a method for online monitoring of the working status of a live-line working robot, comprising:

[0042] The nut image module is used to respond to a received online monitoring request and obtain the nut image corresponding to the live-line working robot corresponding to the online monitoring request;

[0043] The target nut rotation module is used to determine the target tightening torque, target axial preload, and target nut rotation angle of the nut corresponding to the nut image based on the nut image.

[0044] The actual nut rotation angle module is used to tighten the nut based on the target tightening torque, the target axial preload, and the target nut rotation angle, and to generate the actual tightening torque, the actual axial preload, and the actual nut rotation angle.

[0045] The error module is used to calculate the tightening torque error, axial preload error, and nut rotation angle error of the nut using the target tightening torque, target axial preload, target nut rotation angle, actual tightening torque, actual axial preload, and actual nut rotation angle.

[0046] The sending module is used to calculate the rotation degree of the nut using the tightening torque error, the axial preload error and the nut rotation angle error, and rotate the nut according to the rotation degree to generate target monitoring information and send it to the human-machine interface.

[0047] A third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the online monitoring method for the working status of a live-line working robot as described in any of the preceding claims.

[0048] As can be seen from the above technical solutions, the present invention has the following advantages:

[0049] This invention establishes a control model for monitoring the tightening status of nuts. By monitoring the working status of the live-line working robot through torque feedback, the tightness status of nuts is monitored online, and the working status of nuts during the operation of the live-line working robot is obtained in real time, thereby improving the reliability of the live-line working robot. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, 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.

[0051] Figure 1 This is a flowchart illustrating the steps of an online monitoring method for the working status of a live-line working robot according to Embodiment 1 of the present invention.

[0052] Figure 2 This is a flowchart illustrating the steps of an online monitoring method for the working status of a live-line working robot according to Embodiment 2 of the present invention.

[0053] Figure 3 This is a schematic diagram of the torque feedback process for a live-line working robot tightening a nut, provided in Embodiment 2 of the present invention.

[0054] Figure 4 This is a schematic diagram of the display interface structure for a live-line working robot tightening nuts, provided in Embodiment 2 of the present invention;

[0055] Figure 5This is a structural block diagram of an online monitoring system for the working status of a live-line working robot, provided in Embodiment 3 of the present invention. Detailed Implementation

[0056] This invention provides a method, system, and device for online monitoring of the working status of a live-line working robot, which addresses the technical problem that existing technologies lack a perfect monitoring method for the tightening status of nuts during the operation of live-line working robots, resulting in ineffective monitoring of the nut tightening status and posing significant safety hazards.

[0057] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0058] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of an online monitoring method for the working status of a live-line working robot, as provided in Embodiment 1 of the present invention.

[0059] The present invention provides an online monitoring method for the working status of a live-line working robot, comprising the following steps:

[0060] Step 101: In response to the received online monitoring request, obtain the nut image corresponding to the live-line working robot corresponding to the online monitoring request.

[0061] It should be noted that the online monitoring request refers to a request for real-time monitoring of the nut tightening status of the live-line working robot.

[0062] Nut images refer to monitoring images of nuts that need to be tightened by live-line working robots.

[0063] In practice, when an online monitoring request is received, the live-line working robot object for which the online monitoring request is received is identified, and an image of the nut that needs to be tightened by the live-line working robot is obtained.

[0064] Step 102: Based on the nut image, determine the target tightening torque, target axial preload, and target nut rotation angle corresponding to the nut image.

[0065] It should be noted that the target tightening torque, target axial preload, and target nut rotation angle refer to the tightening torque, axial preload, and nut rotation angle required for each nut to be in an ideal state, respectively.

[0066] In practice, the size and model characteristics of the nut are identified by the nut image. According to the specifications corresponding to the size and model characteristics of the nut, the target tightening torque, target axial preload, and target nut rotation angle that the nut needs to achieve under ideal conditions can be determined.

[0067] Step 103: Tighten the nut based on the target tightening torque, target axial preload, and target nut rotation angle to generate the actual tightening torque, actual axial preload, and actual nut rotation angle.

[0068] It should be noted that, for the tightening state of the nut, the actual tightening torque, actual axial preload, and actual nut rotation angle need to be measured in real time by the torque sensor, force sensor, and motor encoder of the torque feedback system.

[0069] In practice, when the target tightening torque, target axial preload, and target nut rotation angle of the nut are obtained, they are sent to the controller of the live-line working robot through the torque feedback system. The controller then issues corresponding operation commands to the drive end according to the target tightening torque, target axial preload, and target nut rotation angle. The drive end executes the tightening operation corresponding to the operation command and feeds back the actual tightening torque, actual axial preload, and actual nut rotation angle driven by the drive end.

[0070] Step 104: Using the target tightening torque, target axial preload, target nut rotation angle, actual tightening torque, actual axial preload, and actual nut rotation angle, calculate the tightening torque error, axial preload error, and nut rotation angle error of the nut.

[0071] In practice, when the actual tightening torque, actual axial preload, and actual nut rotation angle are received, the target tightening torque, target axial preload, and target nut rotation angle are compared and calculated with the actual tightening torque, actual axial preload, and actual nut rotation angle to determine the nut tightening torque error, axial preload error, and nut rotation angle error.

[0072] Step 105: Calculate the rotation degree of the nut using the tightening torque error, axial preload error, and nut rotation angle error, and rotate the nut according to the rotation degree to generate target monitoring information and send it to the human-machine interface.

[0073] It should be noted that the human-computer interaction interface includes a solid-color nut 3D model, which is used to achieve visual recognition and facilitates observation and analysis of the overall stress state of the bolt.

[0074] Specifically, a solid-color nut 3D model is generally a gray-toned bolt 3D stress model. When it displays blue, it indicates that the bolt is under insufficient stress, and when it displays red, it indicates that the bolt is overloaded.

[0075] In practical implementation, by applying tightening torque error, axial preload error, and nut rotation angle error, the remaining rotation degree of the nut can be calculated. The nut is then rotated according to the rotation degree, and target monitoring information is generated. Based on the various error values ​​in the target monitoring information, the color of the solid-color 3D model of the nut is displayed on the human-machine interface. This allows the operator to perform the next operation based on the color displayed on the solid-color 3D model of the nut, deciding whether to stop tightening the nut or continue to correct the drive end of the live-line working robot to tighten the nut again.

[0076] Please see Figure 2-4 , Figure 2 This is a flowchart illustrating the steps of an online monitoring method for the working status of a live-line working robot, as provided in Embodiment 2 of the present invention.

[0077] The present invention provides an online monitoring method for the working status of a live-line working robot, comprising the following steps:

[0078] Step 201: In response to the received online monitoring request, obtain the video of the nut of the angle tower corresponding to the live-line working robot.

[0079] It should be noted that when the response receives an online monitoring request, it identifies the live-line working robot corresponding to the online monitoring request and obtains the current nut video of the corner tower captured by the live-line working robot.

[0080] Step 202: Extract video frames from the nut video frame by frame to generate a nut image.

[0081] It should be noted that after acquiring the nut video, the nut image is obtained by extracting each video frame of the nut video frame by frame.

[0082] Step 203: Based on the nut image, determine the target tightening torque, target axial preload, and target nut rotation angle of the nut corresponding to the nut image.

[0083] Optionally, step 203 includes the following steps S11-S12:

[0084] S11. Identify the nuts in the nut image and determine their size and model characteristics;

[0085] S12. Based on the size and model characteristics of the nut, determine the target tightening torque, target axial preload, and target nut rotation angle corresponding to the nut.

[0086] It should be noted that by identifying / comparing the nut in the nut image with nuts of various specifications in the database, the size and model characteristics of the nut can be identified, and the specification requirements of the size and model characteristics of the nut in the database can be extracted, such as the set target tightening torque, target axial preload, and target nut rotation angle.

[0087] Step 204: Tighten the nut based on the target tightening torque, target axial preload, and target nut rotation angle to generate the actual tightening torque, actual axial preload, and actual nut rotation angle.

[0088] Optionally, step 204 includes the following steps S21-S23:

[0089] S21. Obtain the initial tightening torque, initial axial preload, and initial nut rotation angle of the nut;

[0090] S22. The target tightening torque, target axial preload, target nut rotation angle, initial tightening torque, initial axial preload, and initial nut rotation angle of the nut are fed back to the drive end of the live-line working robot through a preset torque feedback system.

[0091] S23. Tighten the nut through the drive end of the live-line working robot to generate the actual tightening torque, actual axial preload, and actual nut rotation angle.

[0092] It should be noted that the initial tightening torque, initial axial preload, and initial nut rotation angle refer to the initial values ​​of tightening torque, axial preload, and nut rotation angle in the nut's specification sheet.

[0093] The preset torque feedback system includes a torque sensor, a force sensor, and a motor encoder. It is mainly used to monitor the tightening torque, axial preload, and nut rotation angle of the nut itself, which are exerted by the drive end of the live-line working robot.

[0094] In practical implementation, based on the nut's size and model characteristics, the initial tightening torque, initial axial preload, and initial nut rotation angle are retrieved from the database from the corresponding specification table. The target tightening torque, target axial preload, target nut rotation angle, initial tightening torque, initial axial preload, and initial nut rotation angle are then fed back to the controller of the live-line working robot via a preset torque feedback system. (See also: [link to relevant documentation]). Figure 3 and Figure 4 The controller controls the drive end of the live-line working robot to execute the tightening torque within the range of the initial tightening torque minus the target tightening torque. The controller also controls the drive end of the live-line working robot to apply the axial preload within the range of the initial axial preload minus the target axial preload. Furthermore, the controller controls the drive end of the live-line working robot to rotate the nut within the range of the initial nut rotation angle minus the target nut rotation angle. The controller monitors the tightening status of the nut at the drive end in real time through torque sensors, force sensors, and motor encoders, generating data such as actual tightening torque, actual axial preload, and actual nut rotation angle.

[0095] Step 205: Using the target tightening torque, target axial preload, target nut rotation angle, actual tightening torque, actual axial preload, and actual nut rotation angle, calculate the tightening torque error, axial preload error, and nut rotation angle error of the nut.

[0096] Optionally, step 205 includes the following steps S31-S33:

[0097] S31. Calculate the difference between the target tightening torque and the actual tightening torque of the nut to generate the tightening torque error of the nut;

[0098] S32. Calculate the difference between the target axial preload and the actual axial preload of the nut to generate the axial preload error of the nut;

[0099] S33. Calculate the difference between the target nut rotation angle and the actual nut rotation angle to generate the nut rotation angle error.

[0100] It should be noted that the tightening torque error, axial preload error, and nut rotation angle error refer to the error values ​​between the target value and the actual value, respectively.

[0101] In practical implementation, the target tightening torque minus the actual tightening torque equals the tightening torque error; the target axial preload minus the actual axial preload equals the axial preload error; and the target nut rotation angle minus the actual nut rotation angle equals the nut rotation angle error.

[0102] Step 206: Calculate the rotation degree of the nut using the tightening torque error, axial preload error, and nut rotation angle error, and rotate the nut according to the rotation degree to generate target monitoring information and send it to the human-machine interface.

[0103] Optionally, step 206 includes the following steps S41-S45:

[0104] S41. When the actual nut rotation angle is equal to the target nut rotation angle, calculate the difference between the actual axial preload and the preset initial axial preload, and generate the initial axial preload difference.

[0105] S42. Calculate the ratio between the initial axial preload difference and the actual nut rotation angle to generate the actual rotation angle effect value per degree when the nut rotates.

[0106] S43. Calculate the ratio between the axial preload error and the actual rotation angle effect value per degree to generate the required rotation degree of the nut;

[0107] S44. Rotate the nut according to the rotation reading to generate initial monitoring information;

[0108] S45. Using tightening torque error, axial preload error, nut rotation angle error and initial monitoring information, target monitoring information is generated and sent to the human-machine interface for display.

[0109] It should be noted that, assuming the nut currently in use is an M8 type nut, the required target axial preload Fp1 is 120, and its preset initial axial preload Fp2 is 97.

[0110] In practical implementation, when the actual nut rotation angle reaches the target nut rotation angle of 120 degrees, the actual axial preload Fp3 monitored by the preset torque feedback system is 117, and the axial preload error between the actual axial preload and the target axial preload is 3.

[0111] Specifically, when the actual nut rotates to 120 degrees, the actual axial preload is 20 N·m (i.e., the initial axial preload difference). Therefore, by dividing the initial axial preload difference of 20 N·m by the actual nut rotation angle of 120 degrees, we can obtain the actual rotation effect value per degree of rotation of the nut as 1 / 6. Axial preload error (3) = target axial preload Fp1 (120) - actual axial preload Fp3 (117). Dividing the axial preload error (3) by the actual rotation effect value per degree (1 / 6) gives 18 degrees, which is the required rotation degree of the nut. By tightening the nut according to this rotation reading, initial monitoring information can be generated.

[0112] The target monitoring information is formed by combining the tightening torque error, axial preload error, nut rotation angle error and initial monitoring information, and then sent to the human-machine interface for display.

[0113] Optionally, step S45 includes the following steps S51-S55:

[0114] S51. Target monitoring information is generated by using tightening torque error, axial preload error, nut rotation angle error, and initial monitoring information.

[0115] S52. Send the target monitoring information to the 3D model of the nut in the human-computer interaction interface;

[0116] S53. Determine whether the actual tightening torque is less than the initial tightening torque or greater than the target tightening torque;

[0117] S54. If the force is less than the specified value, insufficient force information is generated and fed back to the 3D model of the nut in the human-computer interaction interface so that the 3D model of the nut is displayed in blue.

[0118] S55. If the value is greater than the specified value, overload information is generated and fed back to the 3D model of the nut in the human-computer interaction interface so that the 3D model of the nut is displayed in red.

[0119] It should be noted that the 3D model of the nut is specifically a solid-color 3D model. After visually recognizing the size and model characteristics of the nut, the nut and bolt model is retrieved from the database. The target solid-color 3D model is initially pure gray. At the initial moment of nut rotation, the solid-color 3D model is colored according to the feedback value of the preset torque feedback system. The color of the target solid-color 3D model increases with the increase of force. The color change of the nut is a spiral gradient change, which is divided into two directions: axial and radial. After overlapping, it is a spiral gradient change. The axial direction takes any corner of the nut as the initial point, and the radial direction takes the surface of the nut that is attached to the robotic arm gripper as the initial starting point. The color changes with the gradient during the rotation of the nut.

[0120] The feedback values ​​of the preset torque feedback system are target tightening torque, target axial preload, target nut rotation angle, initial tightening torque, initial axial preload, initial nut rotation angle, actual tightening torque, actual axial preload, actual nut rotation angle, tightening torque error, axial preload error, and nut rotation angle error.

[0121] In practice, when the actual tightening torque is less than the initial tightening torque, it indicates insufficient force and is fed back to the solid-color nut 3D model so that the nut 3D model is displayed in blue. When the actual tightening torque is greater than the target tightening torque, it indicates overload and is fed back to the nut 3D model on the human-machine interface so that the nut 3D model is displayed in red.

[0122] Similarly, when the actual axial preload is less than the initial axial preload, it indicates insufficient force and is fed back to the solid-color nut 3D model so that the nut 3D model is displayed in blue. When the actual axial preload is greater than the target axial preload, it indicates overload and is fed back to the nut 3D model in the human-machine interface so that the nut 3D model is displayed in red.

[0123] Optionally, this method further includes the following steps S61-S63:

[0124] S61. Determine whether the nut rotation angle error is greater than the preset nut rotation angle error range;

[0125] S62. If so, activate the activation function of the preset torque feedback model corresponding to the preset torque feedback system, update the preset torque feedback model, and generate the target torque feedback model.

[0126] S63. If not, then the current preset torque feedback model will be determined as the target torque feedback model.

[0127] It should be noted that a torque feedback model is established, in which the target tightening torque is set as M1, the target axial preload is set as Fp1, the target nut rotation angle is set as W1, the initial tightening torque is set as M2, the initial axial preload is set as Fp2, the initial nut rotation angle is set as W2, the actual tightening torque is set as M3, the actual axial preload is set as Fp3, the actual nut rotation angle is set as W3, and the actual working time is T. Therefore, the average rotation angle during the working time is W3 / T, and the activation function is set as follows:

[0128]

[0129] When the error between the actual nut rotation angle and the nut rotation angle increases, the function is activated. After the function is activated, the torque feedback model is updated to: W2=(W1*T) / W3, which is the target torque feedback model. If the error is insufficient to activate the function, the torque feedback model is set according to the predetermined initial value.

[0130] Specifically, a torque feedback model for monitoring the tightening status of nuts is established. The working status of the live-line working robot is monitored through the torque feedback system, and the tightening status of nuts is monitored online. The working status of nuts during the operation of the live-line working robot is obtained in real time, thereby improving the reliability of the live-line working robot.

[0131] After completing the docking operation, the live-line working robot begins executing operational commands. During the operation, it needs to overcome friction and distribute torque. Before tightening, the nut size and model are first obtained. Then, based on the corresponding size and model, the axial preload and nut rotation angle are determined. The relationship between the axial preload and nut rotation angle is then analyzed to obtain the relationship between the axial preload and the motor speed of the tightening nut. Finally, the relationship between the tightening torque and axial preload is analyzed. When the tightening torque reaches the reference value, the drive end of the tightening nut motor can stop rotating, and the preload reaches the set value. Therefore, for the initial state of the nut, the actual tightening torque, actual axial preload, and actual nut rotation angle need to be measured in real time using torque sensors, force sensors, and motor encoders. This data is then fed back to the ideal value, forming a new error. When the error meets the control performance requirements, the PID control dynamic adjustment process ends. The output tightening torque, axial preload, and nut rotation angle are used to control the movement of the robot's tightening nut device in real time, thereby achieving a better nut tightening state.

[0132] Please see Figure 5 , Figure 5 This is a structural block diagram of an online monitoring system for the working status of a live-line working robot, provided in Embodiment 3 of the present invention.

[0133] This invention provides an online monitoring system for the working status of a live-line working robot, comprising:

[0134] Nut image module 501 is used to obtain the nut image corresponding to the live-line working robot in response to the received online monitoring request;

[0135] The target nut rotation module 502 is used to determine the target tightening torque, target axial preload, and target nut rotation angle of the nut corresponding to the nut image based on the nut image.

[0136] The actual nut rotation angle module 503 is used to tighten the nut based on the target tightening torque, the target axial preload, and the target nut rotation angle, and to generate the actual tightening torque, the actual axial preload, and the actual nut rotation angle.

[0137] Error module 504 is used to calculate the tightening torque error, axial preload error and nut rotation angle error of the nut using the target tightening torque, target axial preload, target nut rotation angle, actual tightening torque, actual axial preload and actual nut rotation angle;

[0138] The sending module 505 is used to calculate the rotation degree of the nut by using the tightening torque error, axial preload error and nut rotation angle error, and rotate the nut according to the rotation degree to generate target monitoring information and send it to the human-machine interface.

[0139] Optionally, the nut image module 501 includes:

[0140] The nut video submodule is used to respond to received online monitoring requests and obtain the nut video of the angle tower corresponding to the live-line working robot.

[0141] The nut image submodule is used to extract video frames from the nut video frame by frame and generate a nut image.

[0142] Optionally, the target nut corner module 502 includes:

[0143] The size and model feature submodule is used to identify nuts in nut images and determine the size and model features of the nuts;

[0144] The target nut rotation submodule is used to determine the target tightening torque, target axial preload, and target nut rotation angle based on the nut's size and model characteristics.

[0145] Optionally, the actual nut corner module 503 includes:

[0146] The initial nut rotation angle submodule is used to obtain the initial tightening torque, initial axial preload, and initial nut rotation angle of the nut.

[0147] The drive terminal module is used to feed back the target tightening torque, target axial preload, target nut rotation angle, initial tightening torque, initial axial preload, and initial nut rotation angle of the nut to the drive end of the live-line working robot through a preset torque feedback system.

[0148] The submodule is used to tighten the nut through the drive end of the live-line working robot, generating the actual tightening torque, actual axial preload, and actual nut rotation angle.

[0149] Optionally, the error module 504 includes:

[0150] The tightening torque error submodule is used to calculate the difference between the target tightening torque and the actual tightening torque of the nut, and generate the tightening torque error of the nut.

[0151] The axial preload error submodule is used to calculate the difference between the target axial preload and the actual axial preload of the nut, and generate the axial preload error of the nut.

[0152] The nut rotation angle error submodule is used to calculate the difference between the target nut rotation angle and the actual nut rotation angle, and generate the nut rotation angle error.

[0153] Optionally, the transmitting module 505 includes:

[0154] The initial axial preload difference submodule is used to calculate the difference between the actual axial preload and the preset initial axial preload when the actual nut rotation angle is equal to the target nut rotation angle, and generate the initial axial preload difference value.

[0155] The Angle Effect Value submodule is used to calculate the ratio between the initial axial preload difference and the actual nut rotation angle, and to generate the actual rotation angle effect value per degree when the nut rotates.

[0156] The rotation degree submodule is used to calculate the ratio between the axial preload error and the actual rotation angle effect value per degree, and generate the required rotation degree of the nut.

[0157] The rotation submodule is used to rotate the nut according to the rotation reading and generate initial monitoring information;

[0158] The display submodule is used to generate target monitoring information by using tightening torque error, axial preload error, nut rotation angle error and initial monitoring information, and send it to the human-machine interface for display.

[0159] Optionally, the display submodule includes:

[0160] The target monitoring information submodule is used to generate target monitoring information by using tightening torque error, axial preload error, nut rotation angle error and initial monitoring information;

[0161] The 3D nut model submodule is used to send target monitoring information to the 3D nut model of the human-computer interaction interface;

[0162] The judgment submodule is used to determine whether the actual tightening torque is less than the initial tightening torque or greater than the target tightening torque;

[0163] The "less than" submodule is used to generate insufficient force information if the force is less than the specified value, and then feed this information back to the 3D model of the nut in the human-computer interaction interface so that the 3D model of the nut is displayed in blue.

[0164] The "greater than" submodule is used to generate overload information if the load is greater than the specified value, and then feed it back to the 3D model of the nut in the human-computer interaction interface so that the 3D model of the nut is displayed in red.

[0165] Optionally, this system also includes:

[0166] The error range submodule is used to determine whether the nut rotation angle error is greater than the preset nut rotation angle error range;

[0167] The update submodule is used to activate the activation function of the preset torque feedback model corresponding to the preset torque feedback system if the condition is met, and update the preset torque feedback model to generate the target torque feedback model.

[0168] The target torque feedback model submodule is used to determine the current preset torque feedback model as the target torque feedback model if no.

[0169] The present invention provides an electronic device in embodiment four, including a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor performs the steps of the online monitoring method for the working status of a live-line working robot as described in any of the above embodiments.

[0170] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0171] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of 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 an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0172] 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.

[0173] 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.

[0174] If the integrated 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, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0175] 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.

Claims

1. A method for online monitoring of the working status of a live-line working robot, characterized in that, include: In response to a received online monitoring request, the system acquires an image of the nut corresponding to the live-line working robot that corresponds to the online monitoring request. Based on the nut image, determine the target tightening torque, target axial preload, and target nut rotation angle corresponding to the nut image; The nut is tightened based on the target tightening torque, the target axial preload, and the target nut rotation angle to generate the actual tightening torque, the actual axial preload, and the actual nut rotation angle. Using the target tightening torque, target axial preload, target nut rotation angle, actual tightening torque, actual axial preload, and actual nut rotation angle of the nut, calculate the tightening torque error, axial preload error, and nut rotation angle error of the nut; Using the tightening torque error, the axial preload error, and the nut rotation angle error, the rotation degree of the nut is calculated, and the nut is rotated according to the rotation degree to generate target monitoring information and send it to the human-machine interface; The steps of calculating the rotation degree of the nut using the tightening torque error, the axial preload error, and the nut rotation angle error, rotating the nut according to the rotation degree, generating target monitoring information, and sending it to the human-machine interface include: When the actual nut rotation angle is equal to the target nut rotation angle, calculate the difference between the actual axial preload and the preset initial axial preload, and generate the initial axial preload difference. Calculate the ratio between the initial axial preload difference and the actual nut rotation angle to generate the actual rotation angle effect value per degree when the nut rotates. Calculate the ratio between the axial preload error and the actual rotation angle effect value per degree to generate the required rotation degree of the nut; The nut is rotated according to the stated rotation degree to generate initial monitoring information; Using the tightening torque error, the axial preload error, the nut rotation angle error, and the initial monitoring information, target monitoring information is generated and sent to the human-machine interface for display.

2. The online monitoring method for the working status of a live-line working robot according to claim 1, characterized in that, The step of obtaining the nut image corresponding to the live-line working robot in response to the received online monitoring request includes: In response to a received online monitoring request, the system acquires a video of the nuts on the angle tower corresponding to the live-line working robot. The video frames of the nut video are extracted frame by frame to generate a nut image.

3. The online monitoring method for the working status of a live-line working robot according to claim 1, characterized in that, The step of determining the target tightening torque, target axial preload, and target nut rotation angle of the nut corresponding to the nut image based on the nut image includes: The nuts in the nut image are identified to determine their size and model characteristics; Based on the size and model characteristics of the nut, the target tightening torque, target axial preload, and target nut rotation angle are determined.

4. The online monitoring method for the working status of a live-line working robot according to claim 1, characterized in that, The step of tightening the nut based on the target tightening torque, the target axial preload, and the target nut rotation angle to generate the actual tightening torque, the actual axial preload, and the actual nut rotation angle includes: Obtain the initial tightening torque, initial axial preload, and initial nut rotation angle of the nut; The target tightening torque, target axial preload, target nut rotation angle, initial tightening torque, initial axial preload, and initial nut rotation angle of the nut are fed back to the drive end of the live-line working robot through a preset torque feedback system. The nut is tightened by the drive end of the live-line working robot, generating actual tightening torque, actual axial preload, and actual nut rotation angle.

5. The online monitoring method for the working status of a live-line working robot according to claim 1, characterized in that, The steps of calculating the tightening torque error, axial preload error, and nut rotation angle error of the nut using the target tightening torque, target axial preload, target nut rotation angle, actual tightening torque, actual axial preload, and actual nut rotation angle include: Calculate the difference between the target tightening torque of the nut and the actual tightening torque to generate the tightening torque error of the nut; Calculate the difference between the target axial preload of the nut and the actual axial preload to generate the axial preload error of the nut; The difference between the target nut rotation angle and the actual nut rotation angle is calculated to generate the nut rotation angle error.

6. The online monitoring method for the working status of a live-line working robot according to claim 1, characterized in that, The step of generating target monitoring information by using the tightening torque error, the axial preload error, the nut rotation angle error, and the initial monitoring information, and sending it to the human-machine interface for display, includes: Target monitoring information is generated using the tightening torque error, the axial preload error, the nut rotation angle error, and the initial monitoring information; The target monitoring information is sent to the 3D model of the nut in the human-computer interaction interface; Determine whether the actual tightening torque is less than the initial tightening torque or greater than the target tightening torque; If the force is less than the specified value, insufficient force information is generated and fed back to the 3D model of the nut in the human-computer interaction interface, so that the 3D model of the nut is displayed in blue. If the load is greater than the specified value, overload information is generated and fed back to the 3D model of the nut in the human-computer interaction interface, so that the 3D model of the nut is displayed in red.

7. The online monitoring method for the working status of a live-line working robot according to claim 4, characterized in that, Also includes: Determine whether the nut rotation angle error is greater than the preset nut rotation angle error range; If so, then activate the activation function of the preset torque feedback model corresponding to the preset torque feedback system, update the preset torque feedback model, and generate the target torque feedback model; If not, then the current preset torque feedback model will be determined as the target torque feedback model.

8. An online monitoring system for the working status of a live-line working robot, characterized in that, include: The nut image module is used to respond to a received online monitoring request and obtain the nut image corresponding to the live-line working robot corresponding to the online monitoring request; The target nut rotation module is used to determine the target tightening torque, target axial preload, and target nut rotation angle of the nut corresponding to the nut image based on the nut image. The actual nut rotation angle module is used to tighten the nut based on the target tightening torque, the target axial preload, and the target nut rotation angle, and to generate the actual tightening torque, the actual axial preload, and the actual nut rotation angle. The error module is used to calculate the tightening torque error, axial preload error, and nut rotation angle error of the nut using the target tightening torque, target axial preload, target nut rotation angle, actual tightening torque, actual axial preload, and actual nut rotation angle. The sending module is used to calculate the rotation degree of the nut using the tightening torque error, the axial preload error and the nut rotation angle error, and rotate the nut according to the rotation degree to generate target monitoring information and send it to the human-machine interface; The sending module includes: The initial axial preload difference submodule is used to calculate the difference between the actual axial preload and the preset initial axial preload when the actual nut rotation angle is equal to the target nut rotation angle, and generate the initial axial preload difference value. The corner effect value submodule is used to calculate the ratio between the initial axial preload difference and the actual nut rotation angle, and generate the actual rotation angle effect value per degree when the nut rotates. The rotation degree submodule is used to calculate the ratio between the axial preload error and the actual rotation angle effect value per degree, and to generate the required rotation degree of the nut. The rotation submodule is used to rotate the nut according to the rotation degree and generate initial monitoring information; The display submodule is used to generate target monitoring information by using the tightening torque error, the axial preload error, the nut rotation angle error and the initial monitoring information, and send it to the human-machine interface for display.

9. An electronic device, characterized in that, The system includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the online monitoring method for the working status of a live-line working robot as described in any one of claims 1-7.

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