Dynamic perception adjustment system and method for robot multi-axis mechanical arm

Abstract

The present invention relates to the technical field of multi-axis robot arms, specifically to a dynamic perception adjustment system and method for multi-axis robot arms, which collects state data and characterization data of the multi-axis robot arms, obtains the execution difficulty of the robot arms in executing adjustment instructions by calculation; classifies the levels according to the difficulty level set, and establishes an abnormal warning model for the j-th state data of various difficulty levels for executing each adjustment instruction; when the multi-axis robot arm completes the execution of the adjustment instruction, the multi-axis robot arm is evaluated, the difficulty level of the robot arm in executing the adjustment instruction is calculated, and the state data of the multi-axis robot arm is input into various state data abnormal warning models with the same difficulty level for executing the same adjustment instruction according to the difficulty level for calculation, and the abnormal situation of the multi-axis robot arm is evaluated. The present invention can provide fault warning for the multi-axis robot arm.

Description

Technical Field

[0001] The present invention relates to the technical field of robot multi-axis mechanical arms, and in particular to a robot multi-axis mechanical arm dynamic sensing adjustment system and method. Background Art

[0002] A multi-axis robot arm is a robot arm with multiple degrees of freedom, usually composed of several interconnected joints, each of which can achieve independent movement. This design enables the multi-axis robot arm to complete complex motion tasks in three-dimensional space, such as grasping, handling, and assembly.

[0003] Dynamic perception and regulation means that the system can obtain information about the external environment or the internal state of the system in real time, and dynamically adjust the system parameters or control strategies based on this information to achieve the system's adaptability and stability to external changes, thereby ensuring the safe and efficient movement of the robotic arm.

[0004] The adaptive dynamic perception adjustment of the robot's multi-axis manipulator arm is achieved by controlling the manipulator arm through a controller and performing positioning through the controller's adjustment instructions. During long-term use, internal components may wear out, resulting in inaccurate dynamic perception adjustment positioning, reduced performance of the manipulator arm, and deviations in the accuracy of clamping objects to the assembly position. Therefore, the robot's multi-axis manipulator arm is maintained and repaired to improve its performance and ensure that its dynamic perception adjustment can operate accurately. Summary of the invention

[0005] The purpose of the present invention is to provide a robot multi-axis mechanical arm dynamic perception adjustment system and method to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solution: a dynamic sensing adjustment method for a multi-axis robot arm, the method comprising the following steps:

[0007] Step S100: when the robot multi-axis manipulator is working, the adjustment instructions issued by the controller are collected, and whenever the robot multi-axis manipulator executes the adjustment instructions, the state data generated by the robot multi-axis manipulator in the process of executing the adjustment instructions is collected, and the characterization data presented by the manipulator in the process of executing the adjustment instructions is collected;

[0008] Step S200: obtaining the execution difficulty level of the adjustment instruction executed by the robot arm by calculating the characterization data of the robot arm, and then obtaining the execution difficulty level set;

[0009] Step S300: Classify the execution difficulty set to obtain a difficulty level set, obtain state data of the same difficulty level according to the difficulty level set, establish an abnormal warning model for the j-th state data of various difficulty levels for executing each adjustment instruction, and obtain deviation thresholds of the various state data;

[0010] Step S400: When the robot multi-axis robotic arm executes the adjustment instruction issued by the controller, the robot multi-axis robotic arm is evaluated, and the difficulty of the robotic arm executing the adjustment instruction is calculated. According to the difficulty level, the state data of the robot multi-axis robotic arm is input into various state data abnormal warning models with the same difficulty level for executing the same adjustment instruction for calculation. When the robot multi-axis robotic arm has an abnormal warning, an alarm is issued to prompt the staff to perform maintenance.

[0011] Further, step S100 includes the following steps:

[0012] Step S101: collecting adjustment instructions issued by a controller of a multi-axis robot arm in operation to obtain a set of adjustment instructions issued by the controller;

[0013] Step S102: Whenever the robot multi-axis mechanical arm is monitored to execute the i-th adjustment instruction, the state data generated in the process from the beginning of the execution of the i-th adjustment instruction to the completion of the execution of the i-th adjustment instruction by the robot multi-axis mechanical arm is collected to obtain a state data set corresponding to the i-th adjustment instruction, wherein the state data includes: the execution time of the adjustment instruction from the beginning of the execution of the adjustment instruction issued by the controller to the completion of the execution of the adjustment instruction by the robot multi-axis mechanical arm, the average temperature of the robot multi-axis mechanical arm, and the average current of the robot multi-axis mechanical arm;

[0014] Step S103: Whenever the robot multi-axis manipulator is monitored to execute the i-th adjustment instruction, the characterization data generated in the process from the manipulator executing the i-th adjustment instruction to the completion of the execution of the i-th adjustment instruction is collected to obtain a characterization data set corresponding to the i-th adjustment instruction, wherein the characterization data includes the manipulator arm movement distance and the manipulator arm total movement angle, the manipulator arm movement distance is the distance moved by the manipulator arm from the position before the execution of the i-th adjustment instruction to the position after the execution of the i-th adjustment instruction, the manipulator arm total movement angle is the sum of the absolute angles of each servo rotation from the start of the execution of the adjustment instruction to the completion of the adjustment instruction, through the formula: y = y1 + y2 + ... + y n , where y is the total angle of movement of the robot arm, y1, y2, ..., y n It is the absolute angle of rotation of the 1st, 2nd, ..., nth servos from the beginning of executing the adjustment command to the completion of the adjustment command, with the initial position of each servo as the reference point.

[0015] The above steps are equivalent to a robot multi-axis manipulator generally used to grip large parts to be assembled, and send the parts to the assembly position for assembly. The adaptive dynamic perception adjustment of the manipulator is to control the manipulator through a controller to ensure that the manipulator can accurately position the parts it grips. The various adjustment instructions issued by the controller and the values ​​of various state data are obtained. A sensor device is placed on the manipulator to collect characterization data of the manipulator's posture adjustment when the manipulator executes the adjustment instruction, calculate the characterization data of the manipulator's posture adjustment, and obtain the manipulator's arm movement distance and total angle of movement from the start of the adjustment instruction to the completion of the adjustment instruction, which supports subsequent analysis.

[0016] Further, step S200 includes the following steps:

[0017] Step S201: Retrieve the characterization data set corresponding to the execution of the i-th adjustment instruction, and use the formula: C=a*x+b*y, where a represents the weight of the robot arm's moving distance x, and b represents the weight of the robot arm's total moving angle y, to calculate the execution difficulty C corresponding to the robot arm's execution of the i-th adjustment instruction and collect them to obtain the execution difficulty set corresponding to the i-th adjustment instruction.

[0018] The above steps are equivalent to calculating the execution difficulty of the adjustment instructions through a formula, establishing an execution difficulty set, and supporting subsequent analysis.

[0019] Further, step S300 includes the following steps:

[0020] Step S301: Retrieve the execution difficulty set corresponding to the execution of the i-th adjustment instruction, obtain the average value of the execution difficulty set by calculation, set the level dividing line of the execution difficulty set according to the average value, set the execution difficulty less than or equal to the average value as the first difficulty level and collect them together to obtain the first difficulty level set when executing the i-th adjustment instruction, set the execution difficulty greater than the average value as the second difficulty level and collect them together to obtain the second difficulty level set when executing the i-th adjustment instruction;

[0021] Step S302: collecting the j-th state data of the robot multi-axis manipulator corresponding to each execution difficulty level in the r-th difficulty level set when executing the i-th adjustment instruction to obtain the j-th state data set;

[0022] Step S303: Calculate according to the j-th state data set to obtain the average value and standard deviation of the j-th state data, establish an abnormal warning model for the j-th state data with a difficulty level of r for executing the i-th adjustment instruction, and obtain the deviation value of the j-th state data with a difficulty level of r for executing the i-th adjustment instruction, according to the formula:

[0023]

[0024] Where Z irj It is represented as the deviation value of the jth state data with difficulty level r for executing the i-th adjustment instruction, W irj Represented as the value in the j-th state data set, W' irj It is expressed as the average value of the j-th state data, σ irj Expressed as the standard deviation of the j-th state data;

[0025] Step S304: The deviation values ​​are collected and arranged in ascending order, with the minimum value being the minimum value of the deviation threshold and the maximum value being the maximum value of the deviation threshold, to obtain the deviation threshold of the jth state data with a difficulty level of r for executing the i-th adjustment instruction.

[0026] The above steps are equivalent to setting a level dividing line for the difficulty of executing the adjustment instruction, dividing the difficulty of executing the adjustment instruction according to the difficulty level, summarizing various status data of the same difficulty level, and establishing an abnormal warning model for the j-th status data with a difficulty level of r for executing the ith adjustment instruction according to the formula. A deviation threshold is obtained through calculation to support subsequent analysis.

[0027] Further, step S400 includes the following steps:

[0028] Step S401: when the robot multi-axis mechanical arm executes the current adjustment instruction, various state data of the robot multi-axis mechanical arm and the characterization data of the mechanical arm are collected respectively, the execution difficulty of executing the current adjustment instruction is calculated, and then the difficulty level is obtained, and the values ​​of the various state data are input into various state data abnormal warning models with the same difficulty level for executing the same adjustment instruction, so as to obtain the deviation values ​​of various state data of the current robot multi-axis mechanical arm;

[0029] Step S402: Retrieve the deviation threshold of various status data with the same difficulty level for executing the same adjustment instruction, and compare the deviation value with the deviation threshold. If the deviation value of one status data of the robot multi-axis robotic arm exceeds the deviation threshold, an abnormal warning will appear in the status data of the current robot multi-axis robotic arm, and an alarm will be issued to prompt the staff to repair the robot multi-axis robotic arm.

[0030] The above steps are equivalent to a post-use evaluation of the robot multi-axis manipulator. When the manipulator executes the adjustment instruction, the adjustment instruction and characterization data of the robot multi-axis manipulator are obtained, and the difficulty of executing the current adjustment instruction is calculated, and then the difficulty level is obtained. The state data of the robot multi-axis manipulator is input into various state data abnormal warning models with the same difficulty level for executing the same adjustment instruction, and the deviation values ​​of various state data are obtained. The deviation values ​​are compared with the deviation threshold. When an abnormal warning occurs in one state data, it means that an abnormal warning occurs in the robot multi-axis manipulator, and an alarm is issued to prompt the staff to perform maintenance.

[0031] In order to better implement the above method, a robot multi-axis manipulator dynamic perception adjustment system is also proposed, which is characterized in that the system includes a robot multi-axis manipulator analysis module, a calculation difficulty module, an abnormal warning model module, and a state evaluation warning module:

[0032] Robot multi-axis manipulator analysis module: When the robot multi-axis manipulator is working, the adjustment instructions issued by the controller are collected. Whenever the robot multi-axis manipulator executes the adjustment instructions, the state data generated by the robot multi-axis manipulator in the process of executing the adjustment instructions are collected, and the characterization data presented by the manipulator in the process of executing the adjustment instructions are collected;

[0033] Difficulty calculation module: by calculating the representation data of the robot arm, the difficulty of the robot arm executing the adjustment instruction is obtained, and then the execution difficulty set is obtained;

[0034] Abnormal warning model module: according to the execution difficulty set, the difficulty level set is divided into levels to obtain the difficulty level set, and the state data of the same difficulty level is obtained according to the difficulty level set, and the abnormal warning model is established for the j-th state data of various difficulty levels for executing each adjustment instruction to obtain the deviation threshold value of the various state data;

[0035] State evaluation and early warning module: When the robot multi-axis robotic arm executes the adjustment command issued by the controller, the robot multi-axis robotic arm is evaluated, and the difficulty of the robotic arm executing the adjustment command is calculated. According to the difficulty level, the state data of the robot multi-axis robotic arm is input into various state data abnormal early warning models with the same difficulty level for executing the same adjustment command for calculation. When the robot multi-axis robotic arm has an abnormal early warning, an alarm is issued to prompt the staff to perform maintenance.

[0036] Further, the robot multi-axis mechanical arm analysis module includes a controller adjustment instruction acquisition unit and a robot multi-axis mechanical arm operation parameter acquisition unit;

[0037] Obtaining controller adjustment instruction unit: obtaining the adjustment instruction issued by the controller to realize the operation of the robot multi-axis manipulator;

[0038] A robot multi-axis manipulator data acquisition unit is used to acquire state data and numerical values ​​of characterization data of the robot multi-axis manipulator when the robot multi-axis manipulator is performing adjustment instructions.

[0039] Further, the module for calculating the degree of difficulty includes a unit for analyzing the characterization data of the robot arm and a unit for calculating the degree of difficulty of executing the adjustment instruction;

[0040] Analyzing robot arm characterization data unit: analyzing the characterization data of the robot arm;

[0041] A unit for calculating the difficulty of executing an adjustment instruction: calculating the difficulty of the robot arm executing the adjustment instruction.

[0042] Further, the abnormal warning model module includes a difficulty level analysis unit and an abnormal warning establishment unit;

[0043] Difficulty level analysis unit: sets difficulty level dividing lines for difficulty levels and aggregates adjustment instructions of the same difficulty level;

[0044] Establish an abnormal warning unit: establish an abnormal warning model for the j-th state data of various difficulty levels for executing various adjustment instructions, and calculate the deviation threshold.

[0045] Further, the status assessment and early warning module includes a status assessment unit and an analysis and early warning unit;

[0046] State evaluation unit: when the robot arm completes executing the current adjustment instruction, the state data and characterization data of the executed adjustment instruction and the robot multi-axis robot arm are obtained, the difficulty level of the adjustment instruction is calculated, and various state data values ​​of the robot multi-axis robot arm are input into various state data abnormal warning models with the same difficulty level for executing the same adjustment instruction;

[0047] Analysis and early warning unit: Analyze the relationship between the current deviation value and the deviation threshold. When an abnormal warning appears in the status data of a robot multi-axis mechanical arm, an alarm is issued to prompt the staff to repair the robot multi-axis mechanical arm.

[0048] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention collects the state data of the robot multi-axis mechanical arm and the characterization data of the mechanical arm, obtains the execution difficulty of the mechanical arm executing the adjustment instruction by calculation; classifies the levels according to the difficulty level set, and establishes an abnormal warning model for the j-th state data of various difficulty levels for executing each adjustment instruction; when the robot multi-axis mechanical arm completes the execution of the adjustment instruction, the robot multi-axis mechanical arm is evaluated, the difficulty level of the mechanical arm executing the adjustment instruction is calculated, and the state data of the robot multi-axis mechanical arm is input into various state data abnormal warning models with the same difficulty level for executing the same adjustment instruction according to the difficulty level for calculation, and the abnormal situation of the robot multi-axis mechanical arm is evaluated. The present invention can provide fault warning for the robot multi-axis mechanical arm. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0050] Figure 1 It is a schematic flow chart of the dynamic sensing adjustment method of the robot multi-axis mechanical arm of the present invention;

[0051] Figure 2 It is a structural schematic diagram of the dynamic sensing and adjustment system of the robot multi-axis mechanical arm of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] See also Figure 1 The present invention provides a technical solution: a dynamic sensing adjustment method for a multi-axis robot arm, the method comprising the following steps:

[0054] Step S100: when the robot multi-axis manipulator is working, the adjustment instructions issued by the controller are collected, and whenever the robot multi-axis manipulator executes the adjustment instructions, the state data generated by the robot multi-axis manipulator in the process of executing the adjustment instructions is collected, and the characterization data presented by the manipulator in the process of executing the adjustment instructions is collected;

[0055] Wherein, step S100 includes the following steps:

[0056] Step S101: collecting adjustment instructions issued by a controller of a multi-axis robot arm in operation to obtain a set of adjustment instructions issued by the controller;

[0057] Step S102: Whenever the robot multi-axis mechanical arm is monitored to execute the i-th adjustment instruction, the state data generated in the process from the beginning of the execution of the i-th adjustment instruction to the completion of the execution of the i-th adjustment instruction by the robot multi-axis mechanical arm is collected to obtain a state data set corresponding to the i-th adjustment instruction, wherein the state data includes: the execution time of the adjustment instruction from the beginning of the execution of the adjustment instruction issued by the controller to the completion of the execution of the adjustment instruction by the robot multi-axis mechanical arm, the average temperature of the robot multi-axis mechanical arm, and the average current of the robot multi-axis mechanical arm;

[0058] Step S103: Whenever the robot multi-axis manipulator is monitored to execute the i-th adjustment instruction, the characterization data generated in the process from the manipulator executing the i-th adjustment instruction to the completion of the execution of the i-th adjustment instruction is collected to obtain a characterization data set corresponding to the i-th adjustment instruction, wherein the characterization data includes the manipulator arm movement distance and the manipulator arm total movement angle, the manipulator arm movement distance is the distance moved by the manipulator arm from the position before the execution of the i-th adjustment instruction to the position after the execution of the i-th adjustment instruction, the manipulator arm total movement angle is the sum of the absolute angles of each servo rotation from the start of the execution of the adjustment instruction to the completion of the adjustment instruction, through the formula: y = y1 + y2 + ... + y n , where y is the total angle of movement of the robot arm, y1, y2, ..., y n It is the absolute angle of rotation of the 1st, 2nd, ..., nth servos from the beginning of executing the adjustment command to the completion of the adjustment command, with the initial position of each servo as the reference point.

[0059] For example, when the robot multi-axis manipulator executes a grasping adjustment instruction, the robot multi-axis manipulator starts to execute the adjustment instruction issued by the controller to the status data of the completion of the execution of the adjustment instruction. The time used to execute the adjustment instruction is 45s, the average temperature of the robot multi-axis manipulator is 50°, and the average current of the robot multi-axis manipulator is 120A. The distance moved by the manipulator from the position before the execution of the grasping adjustment instruction to the position after the execution of the grasping adjustment instruction is 14m. The absolute angles of rotation of the 1st, 2nd, and 3rd servos of the manipulator are 20°, 32°, and 26°, respectively. Through calculation, it is obtained that the distance moved by the manipulator from the start of the execution of the adjustment instruction to the completion of the adjustment instruction is 14m and the total angle of movement of the manipulator is 78°.

[0060] Step S200: obtaining the execution difficulty level of the adjustment instruction executed by the robot arm by calculating the characterization data of the robot arm, and then obtaining the execution difficulty level set;

[0061] Wherein, step S200 includes the following steps:

[0062] Step S201: Retrieve the characterization data set corresponding to the execution of the i-th adjustment instruction, and use the formula: C=a*x+b*y, where a represents the weight of the robot arm's moving distance x, and b represents the weight of the robot arm's total moving angle y, to calculate the execution difficulty C corresponding to the robot arm's execution of the i-th adjustment instruction and collect them to obtain the execution difficulty set corresponding to the i-th adjustment instruction.

[0063] For example, the weight of the robot arm's moving distance is 0.3, and the weight of the robot arm's moving angle is 0.7. According to the formula, when the robot arm executes the grasping adjustment instruction, the execution difficulty of the robot arm is 58.8;

[0064] Step S300: Classify the execution difficulty set to obtain a difficulty level set, obtain state data of the same difficulty level according to the difficulty level set, establish an abnormal warning model for the j-th state data of various difficulty levels for executing each adjustment instruction, and obtain deviation thresholds of the various state data;

[0065] Wherein, step S300 includes the following steps:

[0066] Step S301: Retrieve the execution difficulty set corresponding to the execution of the i-th adjustment instruction, obtain the average value of the execution difficulty set by calculation, set the level dividing line of the execution difficulty set according to the average value, set the execution difficulty less than or equal to the average value as the first difficulty level and collect them together to obtain the first difficulty level set when executing the i-th adjustment instruction, set the execution difficulty greater than the average value as the second difficulty level and collect them together to obtain the second difficulty level set when executing the i-th adjustment instruction;

[0067] Step S302: collecting the j-th state data of the robot multi-axis manipulator corresponding to each execution difficulty level in the r-th difficulty level set when executing the i-th adjustment instruction to obtain the j-th state data set;

[0068] For example, a robot multi-axis manipulator executes a grasping adjustment instruction, and the difficulty of the manipulator executing the adjustment instruction is set according to the difficulty of the manipulator executing the grasping adjustment instruction, which is divided into level 1, level 2, and level 3;

[0069] For example, the set of durations used by the state data of the first-level robot multi-axis manipulator is {45s, 50s, 70s}, the set of average temperatures of the robot multi-axis manipulator is {50°, 65°, 70°}, and the set of average currents of the robot multi-axis manipulator is {120A, 165A, 180A}

[0070] Step S303: Calculate according to the j-th state data set to obtain the average value and standard deviation of the j-th state data, establish an abnormal warning model for the j-th state data with a difficulty level of r for executing the i-th adjustment instruction, and obtain the deviation value of the j-th state data with a difficulty level of r for executing the i-th adjustment instruction, according to the formula:

[0071]

[0072] Where Z irj It is represented as the deviation value of the jth state data with difficulty level r for executing the i-th adjustment instruction, W irj Represented as the value in the j-th state data set, W' irj It is expressed as the average value of the j-th state data, σ irj Expressed as the standard deviation of the j-th state data;

[0073] For example, the deviation values ​​of the duration of the state data of the multi-axis manipulator of the level 1 robot are -0.93, -0.46 and 1.39;

[0074] The deviation values ​​of the average temperature of the robot multi-axis manipulator arm status data of the robot multi-axis manipulator arm at level 1 are -1.38, 0.40 and 0.98;

[0075] The deviation values ​​of the average current of the robot multi-axis manipulator state data of the robot multi-axis manipulator at level 1 are -1.37, 0.39 and 0.98;

[0076] Step S304: The deviation values ​​are collected and arranged in ascending order, with the minimum value being the minimum value of the deviation threshold and the maximum value being the maximum value of the deviation threshold, to obtain the deviation threshold of the jth state data with a difficulty level of r for executing the i-th adjustment instruction.

[0077] For example, according to the obtained deviation values, the deviation threshold of the duration of the state data of the multi-axis robot arm of the first level is [-0.93, 1.39]

[0078] The deviation threshold of the average temperature of the robot multi-axis manipulator arm is [-1.38, 0.98]

[0079] State data of the robot multi-axis manipulator at level 1 The deviation threshold of the average current of the robot multi-axis manipulator is [-1.37, 0.98]

[0080] Step S400: When the robot multi-axis robotic arm executes the adjustment instruction issued by the controller, the robot multi-axis robotic arm is evaluated, and the difficulty of the robotic arm executing the adjustment instruction is calculated. According to the difficulty level, the state data of the robot multi-axis robotic arm is input into various state data abnormal warning models with the same difficulty level for executing the same adjustment instruction for calculation. When the robot multi-axis robotic arm has an abnormal warning, an alarm is issued to prompt the staff to perform maintenance.

[0081] Wherein, step S400 includes the following steps:

[0082] Step S401: when the robot multi-axis mechanical arm executes the current adjustment instruction, various state data of the robot multi-axis mechanical arm and the characterization data of the mechanical arm are collected respectively, the execution difficulty of executing the current adjustment instruction is calculated, and then the difficulty level is obtained, and the values ​​of the various state data are input into various state data abnormal warning models with the same difficulty level for executing the same adjustment instruction, so as to obtain the deviation values ​​of various state data of the current robot multi-axis mechanical arm;

[0083] Step S402: Retrieve the deviation threshold of various status data with the same difficulty level for executing the same adjustment instruction, and compare the deviation value with the deviation threshold. If the deviation value of one status data of the robot multi-axis robotic arm exceeds the deviation threshold, an abnormal warning will appear in the status data of the current robot multi-axis robotic arm, and an alarm will be issued to prompt the staff to repair the robot multi-axis robotic arm.

[0084] For example, when the robot multi-axis manipulator executes the grasping adjustment instruction, the state of the robot multi-axis manipulator is evaluated, and the characterization data of the manipulator's posture adjustment is collected. The result shows that the manipulator's moving distance is 16m, and the total moving angle of the manipulator is 73°. By calculating, when the manipulator executes the grasping adjustment instruction, the execution difficulty of the manipulator is 55.9, which belongs to the first difficulty level. After the manipulator executes the current grasping adjustment instruction, the state data of the robot multi-axis manipulator takes 150s, the average temperature of the robot multi-axis manipulator is 80°, and the average current of the robot multi-axis manipulator is 185A.

[0085] The duration is input into the first level duration abnormal warning model, and the deviation value is 9.90;

[0086] The average temperature of the robot multi-axis manipulator is input into the average temperature abnormal warning model of the first-level robot multi-axis manipulator, and the deviation value is 2.15;

[0087] The average current of the robot multi-axis robotic arm is input into the average current abnormal warning model of the first-level robot multi-axis robotic arm, and the deviation value is 0.58. The deviation values ​​of the real-time usage time and the average temperature of the robot multi-axis robotic arm exceed the threshold value. The status data of the robot multi-axis robotic arm is abnormal. Then, the robot multi-axis robotic arm has an abnormal warning, and the system issues an alarm to remind the staff to repair and inspect the robot multi-axis robotic arm.

[0088] See also Figure 2 , also provides a robot multi-axis mechanical arm dynamic perception adjustment system, the system includes: a robot multi-axis mechanical arm analysis module, a calculation difficulty module, an abnormal warning model module, a state evaluation warning module:

[0089] Robot multi-axis manipulator analysis module: When the robot multi-axis manipulator is working, the adjustment instructions issued by the controller are collected. Whenever the robot multi-axis manipulator executes the adjustment instructions, the state data generated by the robot multi-axis manipulator in the process of executing the adjustment instructions are collected, and the characterization data presented by the manipulator in the process of executing the adjustment instructions are collected;

[0090] Among them, the robot multi-axis mechanical arm analysis module includes a controller adjustment instruction acquisition unit and a robot multi-axis mechanical arm operation parameter acquisition unit;

[0091] Obtaining controller adjustment instruction unit: obtaining the adjustment instruction issued by the controller to realize the operation of the robot multi-axis manipulator;

[0092] A robot multi-axis manipulator data acquisition unit is used to acquire state data and numerical values ​​of characterization data of the robot multi-axis manipulator when the robot multi-axis manipulator is performing adjustment instructions.

[0093] Difficulty calculation module: by calculating the representation data of the robot arm, the difficulty of the robot arm executing the adjustment instruction is obtained, and then the execution difficulty set is obtained;

[0094] The module for calculating the degree of difficulty includes a unit for analyzing the characterization data of the robot arm and a unit for calculating the degree of difficulty of executing the adjustment instruction;

[0095] Analyzing robot arm characterization data unit: analyzing the characterization data of the robot arm;

[0096] A unit for calculating the difficulty of executing an adjustment instruction: calculating the difficulty of the robot arm executing the adjustment instruction.

[0097] Abnormal warning model module: according to the execution difficulty set, the difficulty level set is divided into levels to obtain the difficulty level set, and the state data of the same difficulty level is obtained according to the difficulty level set, and the abnormal warning model is established for the j-th state data of various difficulty levels for executing each adjustment instruction to obtain the deviation threshold value of the various state data;

[0098] Among them, the abnormal warning model module includes a difficulty level analysis unit and an abnormal warning establishment unit;

[0099] Difficulty level analysis unit: sets difficulty level dividing lines for difficulty levels and aggregates adjustment instructions of the same difficulty level;

[0100] Establish an abnormal warning unit: establish an abnormal warning model for the j-th state data of various difficulty levels for executing various adjustment instructions, and calculate the deviation threshold.

[0101] State evaluation and early warning module: When the robot multi-axis robotic arm executes the adjustment command issued by the controller, the robot multi-axis robotic arm is evaluated, and the difficulty of the robotic arm executing the adjustment command is calculated. According to the difficulty level, the state data of the robot multi-axis robotic arm is input into various state data abnormal early warning models with the same difficulty level for executing the same adjustment command for calculation. When the robot multi-axis robotic arm has an abnormal early warning, an alarm is issued to prompt the staff to perform maintenance.

[0102] Among them, the status assessment and early warning module includes a status assessment unit and an analysis and early warning unit;

[0103] State evaluation unit: when the robot arm completes executing the current adjustment instruction, the state data and characterization data of the executed adjustment instruction and the robot multi-axis robot arm are obtained, the difficulty level of the adjustment instruction is calculated, and various state data values ​​of the robot multi-axis robot arm are input into various state data abnormal warning models with the same difficulty level for executing the same adjustment instruction;

[0104] Analysis and early warning unit: Analyze the relationship between the current deviation value and the deviation threshold. When an abnormal warning appears in the status data of a robot multi-axis mechanical arm, an alarm is issued to prompt the staff to repair the robot multi-axis mechanical arm.

[0105] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0106] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for dynamic perception and adjustment of a multi-axis robot arm, characterized in that: The method comprises: Step S100: when the robot multi-axis manipulator is working, the adjustment instructions issued by the controller are collected, and whenever the robot multi-axis manipulator executes the adjustment instructions, the state data generated by the robot multi-axis manipulator in the process of executing the adjustment instructions is collected, and the characterization data presented by the manipulator in the process of executing the adjustment instructions is collected; Step S200: obtaining the execution difficulty level of the adjustment instruction executed by the robot arm by calculating the characterization data of the robot arm, and then obtaining the execution difficulty level set; Step S300: Classify the execution difficulty set to obtain a difficulty level set, obtain state data of the same difficulty level according to the difficulty level set, establish an abnormal warning model for the j-th state data of various difficulty levels for executing each adjustment instruction, and obtain deviation thresholds of the various state data; Step S400: When the robot multi-axis robotic arm executes the adjustment instruction issued by the controller, the robot multi-axis robotic arm is evaluated, and the difficulty of the robotic arm executing the adjustment instruction is calculated. According to the difficulty level, the state data of the robot multi-axis robotic arm is input into various state data abnormal warning models with the same difficulty level for executing the same adjustment instruction for calculation. When the robot multi-axis robotic arm has an abnormal warning, an alarm is issued to prompt the staff to perform maintenance.

2. The method for dynamic perception and adjustment of a multi-axis robot arm according to claim 1, characterized in that: The step S100 includes the following steps: Step S101: collecting adjustment instructions issued by a controller of a multi-axis robot arm in operation to obtain a set of adjustment instructions issued by the controller; Step S102: Whenever the robot multi-axis mechanical arm is monitored to execute the i-th adjustment instruction, the state data generated in the process from the beginning of the execution of the i-th adjustment instruction to the completion of the execution of the i-th adjustment instruction by the robot multi-axis mechanical arm is collected to obtain a state data set corresponding to the i-th adjustment instruction, wherein the state data includes: the execution time of the adjustment instruction from the beginning of the execution of the adjustment instruction issued by the controller to the completion of the execution of the adjustment instruction by the robot multi-axis mechanical arm, the average temperature of the robot multi-axis mechanical arm, and the average current of the robot multi-axis mechanical arm; Step S103: Whenever the robot multi-axis manipulator is monitored to execute the i-th adjustment instruction, the characterization data generated in the process from the manipulator executing the i-th adjustment instruction to the completion of the execution of the i-th adjustment instruction is collected to obtain a characterization data set corresponding to the i-th adjustment instruction, wherein the characterization data includes the manipulator arm movement distance and the manipulator arm total movement angle, the manipulator arm movement distance is the distance moved by the manipulator arm from the position before the execution of the i-th adjustment instruction to the position after the execution of the i-th adjustment instruction, the manipulator arm total movement angle is the sum of the absolute angles of each servo rotation from the start of the execution of the adjustment instruction to the completion of the adjustment instruction, through the formula: y = y1 + y2 + ... + y n , where y is the total angle of movement of the robot arm, y1, y2, ..., y n It is the absolute angle of rotation of the 1st, 2nd, ..., nth servos from the beginning of executing the adjustment command to the completion of the adjustment command, with the initial position of each servo as the reference point.

3. The method for dynamic perception and adjustment of a multi-axis robot arm according to claim 2, characterized in that: The step S200 includes the following steps: Step S201: Retrieve the characterization data set corresponding to the execution of the i-th adjustment instruction, and use the formula: C=a*x+b*y, where a represents the weight of the robot arm's moving distance x, and b represents the weight of the robot arm's total moving angle y, to calculate the execution difficulty C corresponding to the robot arm's execution of the i-th adjustment instruction and collect them to obtain the execution difficulty set corresponding to the i-th adjustment instruction.

4. The method for dynamic perception and adjustment of a multi-axis robot arm according to claim 3, characterized in that: The step S300 includes the following steps: Step S301: Retrieve the execution difficulty set corresponding to the execution of the i-th adjustment instruction, obtain the average value of the execution difficulty set by calculation, set the level dividing line of the execution difficulty set according to the average value, set the execution difficulty less than or equal to the average value as the first difficulty level and collect them together to obtain the first difficulty level set when executing the i-th adjustment instruction, set the execution difficulty greater than the average value as the second difficulty level and collect them together to obtain the second difficulty level set when executing the i-th adjustment instruction; Step S302: collecting the j-th state data of the robot multi-axis manipulator corresponding to each execution difficulty level in the r-th difficulty level set when executing the i-th adjustment instruction to obtain the j-th state data set; Step S303: Calculate according to the j-th state data set to obtain the average value and standard deviation of the j-th state data, establish an abnormal warning model for the j-th state data with a difficulty level of r for executing the i-th adjustment instruction, and obtain the deviation value of the j-th state data with a difficulty level of r for executing the i-th adjustment instruction, according to the formula: Where Z irj It is represented as the deviation value of the jth state data with difficulty level r for executing the i-th adjustment instruction, W irj Represented as the value in the j-th state data set, W' irj It is expressed as the average value of the j-th state data, σ irj Expressed as the standard deviation of the j-th state data; Step S304: The deviation values ​​are collected and arranged in ascending order, with the minimum value being the minimum value of the deviation threshold and the maximum value being the maximum value of the deviation threshold, to obtain the deviation threshold of the jth state data with a difficulty level of r for executing the i-th adjustment instruction.

5. The method for dynamic sensing and adjusting a multi-axis robot arm according to claim 4, characterized in that: The step S400 includes the following steps: Step S401: when the robot multi-axis mechanical arm executes the current adjustment instruction, various state data of the robot multi-axis mechanical arm and the characterization data of the mechanical arm are collected respectively, the execution difficulty of executing the current adjustment instruction is calculated, and then the difficulty level is obtained, and the values ​​of the various state data are input into various state data abnormal warning models with the same difficulty level for executing the same adjustment instruction, so as to obtain the deviation values ​​of various state data of the current robot multi-axis mechanical arm; Step S402: Retrieve the deviation threshold of various status data with the same difficulty level for executing the same adjustment instruction, and compare the deviation value with the deviation threshold. If the deviation value of one status data of the robot multi-axis robotic arm exceeds the deviation threshold, an abnormal warning will appear in the status data of the current robot multi-axis robotic arm, and an alarm will be issued to prompt the staff to repair the robot multi-axis robotic arm.

6. A robot multi-axis mechanical arm dynamic perception adjustment system, used to implement the robot multi-axis mechanical arm dynamic perception adjustment method according to any one of claims 1 to 5, characterized in that: The system includes a robot multi-axis manipulator analysis module, a calculation difficulty module, an abnormal warning model module, and a state assessment warning module: Robot multi-axis manipulator analysis module: When the robot multi-axis manipulator is working, the adjustment instructions issued by the controller are collected. Whenever the robot multi-axis manipulator executes the adjustment instructions, the state data generated by the robot multi-axis manipulator in the process of executing the adjustment instructions are collected, and the characterization data presented by the manipulator in the process of executing the adjustment instructions are collected; Difficulty calculation module: by calculating the representation data of the robot arm, the difficulty of the robot arm executing the adjustment instruction is obtained, and then the execution difficulty set is obtained; Abnormal warning model module: according to the execution difficulty set, the difficulty level set is divided into levels to obtain the difficulty level set, and the state data of the same difficulty level is obtained according to the difficulty level set, and the abnormal warning model is established for the j-th state data of various difficulty levels for executing each adjustment instruction to obtain the deviation threshold value of the various state data; State evaluation and early warning module: When the robot multi-axis robotic arm executes the adjustment command issued by the controller, the robot multi-axis robotic arm is evaluated, and the difficulty of the robotic arm executing the adjustment command is calculated. According to the difficulty level, the state data of the robot multi-axis robotic arm is input into various state data abnormal early warning models with the same difficulty level for executing the same adjustment command for calculation. When the robot multi-axis robotic arm has an abnormal early warning, an alarm is issued to prompt the staff to perform maintenance.

7. The robot multi-axis mechanical arm dynamic perception and adjustment system according to claim 6, characterized in that: The robot multi-axis mechanical arm analysis module includes a controller adjustment instruction acquisition unit and a robot multi-axis mechanical arm operation parameter acquisition unit; Obtaining controller adjustment instruction unit: obtaining the adjustment instruction issued by the controller to realize the operation of the robot multi-axis manipulator; A robot multi-axis manipulator data acquisition unit is used to acquire state data and numerical values ​​of characterization data of the robot multi-axis manipulator when the robot multi-axis manipulator is performing adjustment instructions.

8. The robot multi-axis mechanical arm dynamic perception and adjustment system according to claim 6, characterized in that: The difficulty calculation module includes a unit for analyzing the characterization data of the robot arm and a unit for calculating the difficulty of executing the adjustment instruction; Analyzing robot arm characterization data unit: analyzing the characterization data of the robot arm; A unit for calculating the difficulty of executing an adjustment instruction: calculating the difficulty of the robot arm executing the adjustment instruction.

9. The robot multi-axis mechanical arm dynamic perception and adjustment system according to claim 6, characterized in that: The abnormal warning model module includes a difficulty level analysis unit and an abnormal warning establishment unit; Difficulty level analysis unit: sets difficulty level dividing lines for difficulty levels and aggregates adjustment instructions of the same difficulty level; Establish an abnormal warning unit: establish an abnormal warning model for the j-th state data of various difficulty levels for executing various adjustment instructions, and calculate the deviation threshold.

10. The robot multi-axis mechanical arm dynamic perception and adjustment system according to claim 6, characterized in that: The status assessment and early warning module includes a status assessment unit and an analysis and early warning unit; State evaluation unit: when the robot arm completes executing the current adjustment instruction, the state data and characterization data of the executed adjustment instruction and the robot multi-axis robot arm are obtained, the difficulty level of the adjustment instruction is calculated, and various state data values ​​of the robot multi-axis robot arm are input into various state data abnormal warning models with the same difficulty level for executing the same adjustment instruction; Analysis and early warning unit: Analyze the relationship between the current deviation value and the deviation threshold. When an abnormal warning appears in the status data of a robot multi-axis mechanical arm, an alarm is issued to prompt the staff to repair the robot multi-axis mechanical arm.

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