Robot safe operation control system based on artificial intelligence

Abstract

The invention relates to the technical field of robot control, in particular to a robot safe operation control system based on artificial intelligence, which comprises a robot operation management platform, a data calling unit, an operation and maintenance execution influence unit, a mechanical breakage analysis unit, a joint working condition analysis unit, a path planning unit and an operation management response unit. According to the method, two points of robot joint maintenance and joint mechanical damage are analyzed to know the influence condition of robot joints on robot safe operation control so as to provide data support for follow-up analysis, analysis is performed in an information feedback and information fusion mode to ensure the operation safety and stability of the robot, and the safety and reliability of the robot are improved. And meanwhile, the influence of robot joints on robot operation is reduced, when the robot operates normally, analysis is conducted from the angle of the robot driving path, the driving path of the robot is optimized and adjusted according to the information feedback condition, and then low-consumption safe operation of the robot is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of robot control, and particularly to a robot safe operation control system based on artificial intelligence. Background Art

[0002] Industrial robots play a crucial role in modern production and can complete many heavy, dangerous or highly repetitive tasks; and the monitoring system is the basis for the safe control of industrial robots; by using sensors, monitoring devices and corresponding software, parameters such as the movement, position and speed of the robot can be monitored in real time; once an abnormal or dangerous situation is detected, the monitoring system will immediately take measures.

[0003] However, in the prior art, it is impossible to comprehensively and safely supervise the operating conditions of the robot, which in turn increases the risk of abnormal operation of the robot, is not conducive to the stable operation and safety of the robot, and at the same time cannot conduct multi-angle analysis by combining the maintenance, operating parameters and historical workload of the robot, reducing the comprehensiveness of the analysis data, and cannot optimize the operating path of the robot, thereby increasing the risk of running collision and energy consumption of the robot.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a robot safe operation control system based on artificial intelligence to solve the above-mentioned technical defects. The present invention analyzes from two points: robot joint maintenance and joint mechanical damage to understand the impact of robot joints on the safe operation control of the robot, so as to provide data support for subsequent analysis. And analyze from two angles of operating parameters and historical workload in joint mechanical damage to understand the impact level of mechanical damage on subsequent operation. Analyze through the way of information feedback and information fusion to ensure the safe and stable operation of the robot, and at the same time help to reduce the impact of robot joints on robot operation. When the robot is operating normally, analyze from the perspective of the robot's driving path, that is, conduct path optimization supervision feedback analysis on path information to judge whether the current driving path needs to be optimized and adjusted, so as to optimize and adjust the driving path of the robot according to the information feedback situation, so as to reduce the potential impact of the driving path on the safe operation of the robot, and thus help to improve the low-consumption and safe operation of the robot.

[0006] The purpose of the present invention can be achieved by the following technical solutions: A robot safe operation control system based on artificial intelligence, including a robot operation and management platform, a data retrieval unit, an operation and maintenance execution impact unit, a mechanical damage analysis unit, a joint working condition analysis unit, a path planning unit and an operation and management response unit.

[0007] The data retrieval unit is used to retrieve the operation and maintenance data and damage risk data of the robot joints, and send the operation and maintenance data and damage risk data to the operation and maintenance execution impact unit and the mechanical damage analysis unit respectively;

[0008] After receiving the operation and maintenance data, the operation and maintenance execution impact unit immediately conducts a risk feedback analysis on the operation and maintenance data for joint operation interference to obtain the operation and maintenance interference index W of each joint;

[0009] After receiving the damage risk data, the mechanical damage analysis unit immediately conducts an analysis on the mechanical damage operation interference division of the damage risk data, and conducts a division analysis on the obtained damage assessment index S to obtain the mechanical damage index JZ;

[0010] The joint working condition analysis unit is used to collect the joint working condition information of the robot joints, conduct a supervision and evaluation analysis on the joint working condition information for the operation working condition, and conduct a comparison analysis on the obtained operation risk peak Jmax to obtain a normal signal and a risk signal;

[0011] The path planning unit is used to respond to the normal signal, collect the path information of the robot driving path, and conduct a path optimization supervision feedback analysis on the path information to obtain a path optimization signal.

[0012] Preferably, the process of the risk feedback analysis of the joint operation interference by the operation and maintenance execution impact unit is as follows:

[0013] Collect the operation period of the robot and set it as the time threshold, obtain the operation and maintenance data of each joint of the robot within the time threshold. The operation and maintenance data includes a correction ratio index and a maintenance deviation index. Label the correction ratio index and the maintenance deviation index as XB and WP respectively. According to the formula Obtain the operation and maintenance interference index of each joint. f1 and f2 are the preset weight factor coefficients of the correction ratio index and the maintenance deviation index respectively. Both f1 and f2 are greater than zero, and W is the operation and maintenance interference index of each joint.

[0014] Preferably, the analysis process of the correction ratio index is as follows: the proportion of the number of times when the correction error value in the total number of historical maintenance times is greater than the preset threshold. The correction error value represents the number of joints corresponding to the change value of the friction decibel before and after joint maintenance being less than the difference between the maximum friction decibel before joint maintenance and the initial joint friction decibel; the analysis process of the maintenance deviation index is as follows: obtain the number of delayed maintenance times in the total number of historical maintenance times of the robot, and then obtain the operation working hours of the robot during the corresponding delay duration of each delayed maintenance time. Then set the sum value of the operation working hours as the maintenance deviation index.

[0015] Preferably, the process of the mechanical damage operation interference division analysis by the mechanical damage analysis unit is as follows:

[0016] Obtain the damage risk data of each joint of the robot within the time threshold. The damage risk data includes mechanical damage value and load damage value. According to the formula, obtain the damage assessment index S of each joint, and compare and analyze the damage assessment index S with the preset damage assessment index range stored in it:

[0017] If the damage assessment index S is greater than the maximum value in the preset damage assessment index range, it is determined as a first-level damage; if the damage assessment index S belongs to the preset damage assessment index range, it is determined as a second-level damage; if the damage assessment index S is less than the minimum value in the preset damage assessment index range, it is determined as a third-level damage. Set the first-level damage, second-level damage, and third-level damage as the mechanical damage index JZ, JZ = 1, 2, 3.

[0018] Preferably, the mechanical damage value represents the sum value between the total continuous operation duration corresponding to the joint running speed exceeding the preset running speed and the duration corresponding to the joint temperature value exceeding the preset threshold after data normalization; the load damage value represents the product value obtained by multiplying the total joint running duration corresponding to the load weight exceeding the preset threshold and the total number of times during the historical operation after data normalization.

[0019] Preferably, the operation condition supervision and evaluation analysis process of the joint condition analysis unit is as follows:

[0020] Obtain the joint condition information of each joint of the robot within the time threshold. The joint condition information represents the working amplitude index and the trajectory deviation value. Label the working amplitude index and the trajectory deviation value as GF and GP respectively. According to the formula, obtain the operation risk assessment coefficient J of each joint, and then obtain the maximum value in the operation risk assessment coefficient J, and set the maximum value in the operation risk assessment coefficient J as the operation risk peak value Jmax. Compare and analyze the operation risk peak value Jmax with the preset operation risk peak value threshold stored in it to obtain a normal signal and a risk signal.

[0021] Preferably, the working amplitude index represents the part of the difference between the initial oil temperature value and the maximum oil temperature value of the joint exceeding the preset threshold during the process of each joint traveling along the set traveling path; the trajectory deviation value represents the difference value between the actual running trajectory and the set running trajectory of each joint.

[0022] Preferably, the path optimization supervision feedback analysis process of the path planning unit is as follows:

[0023] Obtain the path information of the robot within the time threshold. The path information includes the collision risk value and the actual operation consumption index. The collision risk value represents the minimum vertical distance between the robot and the collision object when the robot operates along the driving path. The actual operation consumption index represents the ratio between the total actual energy consumption corresponding to the robot's single completion of the driving path and the actual operation duration. Compare and analyze the collision risk value and the actual operation consumption index with the preset collision risk value threshold and the preset actual operation consumption index threshold stored internally:

[0024] If the collision risk value is greater than or equal to the preset collision risk value threshold and the actual operation consumption index is less than the preset actual operation consumption index threshold, then no signal is generated;

[0025] If the collision risk value is less than the preset collision risk value threshold or the actual operation consumption index is greater than or equal to the preset actual operation consumption index threshold, then a path optimization signal is generated.

[0026] The beneficial effects of the present invention are as follows:

[0027] (1) The present invention analyzes from two aspects: robot joint maintenance and joint mechanical damage, to understand the impact of the robot joint on the safe operation control of the robot, so as to provide data support for subsequent analysis. And analyze from two perspectives: operating parameters and historical workload in joint mechanical damage, to understand the impact level of mechanical damage on subsequent operation. Analyze through the method of information feedback and information fusion to determine whether the abnormal risk of the robot joint is too high, so as to conduct safety control on the robot joint according to the information feedback situation, to ensure the safety and stability of the robot operation, and at the same time help reduce the impact of the robot joint on the robot operation;

[0028] (2) When the robot is operating normally, the present invention analyzes from the perspective of the robot's driving path, that is, conducts path optimization supervision feedback analysis on the path information to determine whether the current driving path needs to be optimized and adjusted, so as to optimize and adjust the robot's driving path according to the information feedback situation, to reduce the potential impact of the driving path on the safe operation of the robot, and thus help improve the low-consumption and safe operation of the robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The following further describes the present invention with reference to the drawings;

[0030] Figure 1 is the system flowchart of the present invention;

[0031] Figure 2 is the partial analysis diagram of Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0032] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0033] Embodiment 1:

[0034] Please refer to Figures 1 to 2 As shown, the present invention is a robot safe operation control system based on artificial intelligence, including a robot operation and management platform, a data retrieval unit, an operation and maintenance execution impact unit, a mechanical damage analysis unit, a joint working condition analysis unit, a path planning unit, and an operation and management response unit. The robot operation and management platform is unidirectionally communicatively connected to the data retrieval unit. The data retrieval unit is unidirectionally communicatively connected to both the operation and maintenance execution impact unit and the mechanical damage analysis unit. The operation and maintenance execution impact unit and the mechanical damage analysis unit are both unidirectionally communicatively connected to the joint working condition analysis unit. The joint working condition analysis unit is unidirectionally communicatively connected to both the path planning unit and the operation and management response unit. The path planning unit is unidirectionally communicatively connected to the operation and management response unit;

[0035] When the robot operation and management platform monitors the operation of the robot, it generates an operation and management instruction and sends the operation and management instruction to the data retrieval unit. After receiving the operation and management instruction, the data retrieval unit immediately retrieves the operation and maintenance data and damage risk data of the robot joints, and sends the operation and maintenance data and damage risk data to the operation and maintenance execution impact unit and the mechanical damage analysis unit respectively. After receiving the operation and maintenance data, the operation and maintenance execution impact unit immediately performs a feedback analysis on the operation and maintenance data for the risk of joint operation interference, so as to analyze from the perspective of maintenance to understand the potential impact of maintenance on the robot joints. The specific process of the feedback analysis on the risk of joint operation interference is as follows:

[0036] Collect the operation period of the robot and set it as the time threshold. Obtain the operation and maintenance data of each joint of the robot within the time threshold. The operation and maintenance data includes a correction ratio index and a maintenance deviation index. Label the correction ratio index and the maintenance deviation index as XB and WP respectively. According to the formula Get the operation and maintenance interference index of each joint. f1 and f2 are the preset weight factor coefficients of the correction ratio index and the maintenance deviation index respectively. Both f1 and f2 are greater than zero. W is the operation and maintenance interference index of each joint;

[0037] In the embodiment of the present invention, the analysis process of the correction ratio index is as follows: the proportion of the number of times when the correction error value in the total number of historical maintenance times is greater than the preset threshold. The correction error value represents the number of joints corresponding to the change value of the friction decibel before and after joint maintenance being less than the difference between the maximum friction decibel before joint maintenance and the initial joint friction decibel. It should be noted that from the perspective of joint maintenance, an analysis is carried out to understand the potential impact of maintenance;

[0038] In the embodiment of the present invention, the analysis process of the maintenance deviation index is as follows: obtain the number of delayed maintenance times in the total number of historical maintenance times of the robot, and then obtain the operating working hours of the robot during the corresponding delay duration of each delayed maintenance time. Then, set the sum value of the operating working hours as the maintenance deviation index. It should be noted that from the perspective of maintenance delay, that is, when the robot still works under the premise of maintenance delay, the risk of damage to the robot is greater;

[0039] After receiving the damage risk data, the mechanical damage analysis unit immediately conducts an analysis on the mechanical damage operation interference division of the damage risk data, that is, an analysis is carried out from two perspectives of operating parameters and historical workloads to understand the influence level of mechanical damage on subsequent operations, so as to provide data support for subsequent analysis. The specific mechanical damage operation interference division analysis process is as follows:

[0040] Obtain the damage risk data of each joint of the robot within the time threshold. The damage risk data includes mechanical damage values and load damage values. According to the formula obtain the damage assessment index of each joint, where JX represents the mechanical damage value, ZA represents the load damage value, a1, a2, and a3 are respectively the preset proportional factor coefficients of the mechanical damage value, load damage value, and operation and maintenance interference index. The proportional factor coefficients are used to correct the deviations that occur in the formula calculation process of each parameter, so as to make the calculation result more accurate. a4 is the preset error correction factor coefficient. a1, a2, a3, and a4 are all greater than zero. S is the damage assessment index of each joint. Compare and analyze the damage assessment index S with the preset damage assessment index range stored in it:

[0041] If the damage assessment index S is greater than the maximum value in the preset damage assessment index range, it is determined as a first-level damage;

[0042] If the damage assessment index S belongs to the preset damage assessment index range, it is determined as a second-level damage;

[0043] If the damage assessment index S is less than the minimum value in the preset damage assessment index range, it is determined as a level-three damage. Among them, the potential damages corresponding to level-one damage, level-two damage, and level-three damage decrease in sequence. The level-one damage, level-two damage, and level-three damage are set as the mechanical damage index JZ, where JZ = 1, 2, 3. That is, when the mechanical damage index JZ = 1, it represents level-one damage; when the mechanical damage index JZ = 2, it represents level-two damage; when the mechanical damage index JZ = 3, it represents level-three damage;

[0044] In the embodiment of the present invention, the mechanical damage value represents the sum value between the total continuous operation duration corresponding to the operating speed of the joint exceeding the preset operating speed and the duration corresponding to the joint temperature value exceeding the preset threshold after data normalization processing. It should be noted that from the perspective of the operating parameters of the joint, the mechanical damage risk situation of the joint during the continuous operation process is analyzed;

[0045] In the embodiment of the present invention, the load damage value represents the product value obtained by multiplying the total joint operation duration corresponding to the load weight exceeding the preset threshold in the historical operation process by the total number of times after data normalization processing. It should be noted that from the perspective of the historical workload, the damage situation brought by the load to the joint is analyzed.

[0046] Embodiment Two:

[0047] The joint working condition analysis unit is used to collect the joint working condition information of the robot joint and conduct operating condition supervision and evaluation analysis on the joint working condition information, that is, analyze through the way of information feedback and information fusion to determine whether the abnormal risk of the robot joint is too high, so as to conduct safety control on the robot joint according to the information feedback situation to ensure the operation safety and stability of the robot, and at the same time help reduce the impact of the robot joint on the robot operation. The specific process of operating condition supervision and evaluation analysis is as follows:

[0048] Obtain the joint working condition information of each joint of the robot within the time threshold. The joint working condition information represents the working amplitude index and the trajectory deviation value. Label the working amplitude index and the trajectory deviation value as GF and GP respectively. According to the formula J = (GF × v1 + GP × v2) 2 ÷ (JZ × v3) × v4 to obtain the operation risk assessment coefficient of each joint, where v1, v2, and v3 are the preset correction proportional factors of the working amplitude index, the trajectory deviation value, and the mechanical damage index respectively, v4 is the preset fault tolerance factor coefficient, v1, v2, v3, and v4 are all greater than zero, J is the operation risk assessment coefficient of each joint. Then obtain the maximum value in the operation risk assessment coefficient J and set the maximum value in the operation risk assessment coefficient J as the operation risk peak value Jmax, and compare and analyze the operation risk peak value Jmax with the preset operation risk peak value threshold stored in it:

[0049] If the ratio between the peak operation risk Jmax and the preset peak operation risk threshold is less than 1, a normal signal is generated;

[0050] If the ratio between the peak operation risk Jmax and the preset peak operation risk threshold is greater than or equal to 1, a risk signal is generated and sent to the operation management response unit. After receiving the risk signal, the operation management response unit immediately performs the preset warning operation corresponding to the risk signal, so as to perform safety control on the robot joints according to the information feedback situation, ensure the operation safety and stability of the robot, and at the same time help reduce the impact of the robot joints on the robot operation;

[0051] In the embodiment of the present invention, the working amplitude index represents the part that the difference between the initial oil temperature value and the maximum oil temperature value of the joint exceeds the preset threshold during the process that each joint travels along the set travel path. It should be noted that the larger the value of the working amplitude index, the greater the risk of robot joint failure;

[0052] In the embodiment of the present invention, the trajectory deviation value represents the difference value between the actual operation trajectory and the set operation trajectory of each joint. It should be noted that from the perspective of the actual operation trajectory, the operation safety of the robot is analyzed;

[0053] When a normal signal is generated, the path planning unit is used to respond to the normal signal, collect the path information of the robot travel path, and perform path optimization supervision feedback analysis on the path information to determine whether the current travel path needs to be optimized and adjusted, so as to improve the operation safety of the robot and at the same time improve the supervision and control effect of the robot. The specific path optimization supervision feedback analysis process is as follows:

[0054] Obtain the path information of the robot within the time threshold. The path information includes the collision risk value and the actual operation consumption index. The collision risk value represents the minimum vertical distance between the robot and the collision object when the robot operates along the travel path. The actual operation consumption index represents the ratio between the total actual energy consumption corresponding to the single completion of the travel path of the robot and the actual operation duration. It should be noted that the smaller the value of the collision risk value, the higher the robot path optimization requirement, and the larger the value of the actual operation consumption index, the higher the robot path optimization requirement;

[0055] Compare and analyze the collision risk value and the actual operation consumption index with the preset collision risk value threshold and the preset actual operation consumption index threshold stored in its internal memory:

[0056] If the collision risk value is greater than or equal to the preset collision risk value threshold and the actual operation consumption index is less than the preset actual operation consumption index threshold, no signal is generated;

[0057] If the collision risk value is less than the preset collision risk value threshold, or the actual operation consumption index is greater than or equal to the preset actual operation consumption index threshold, a path optimization signal is generated and sent to the operation management response unit. After receiving the path optimization signal, the operation management response unit immediately performs the preset warning operation corresponding to the path optimization signal, so as to optimize and adjust the driving path of the robot according to the information feedback situation, reduce the potential impact of the driving path on the safe operation of the robot, and thus help improve the low-consumption and safe operation of the robot.

[0058] In summary, the present invention analyzes from two aspects: robot joint maintenance and joint mechanical damage, to understand the impact of robot joints on the safe operation control of the robot, so as to provide data support for subsequent analysis, that is, perform joint operation interference risk feedback analysis on operation and maintenance data, so as to analyze from the perspective of maintenance to understand the potential impact of maintenance on robot joints, and perform mechanical damage operation interference classification analysis on the loss risk data, that is, analyze from two aspects of operation parameters and historical workload to understand the impact level of mechanical damage on subsequent operation. Through information feedback and information fusion, it is analyzed to determine whether the abnormal risk of robot joints is too high, so as to perform safety control on robot joints according to the information feedback situation to ensure the safety and stability of the robot operation, and at the same time help reduce the impact of robot joints on robot operation. When the robot is operating normally, it analyzes from the perspective of the robot driving path, that is, performs path optimization supervision feedback analysis on path information to determine whether the current driving path needs to be optimized and adjusted, so as to optimize and adjust the driving path of the robot according to the information feedback situation, reduce the potential impact of the driving path on the safe operation of the robot, and thus help improve the low-consumption and safe operation of the robot.

[0059] The setting of the threshold size is for the convenience of comparison. Regarding the threshold size, it depends on the amount of sample data and the number of base numbers set by those skilled in the art for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantified value.

[0060] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formulas are set by those skilled in the art according to the actual situation. As described above, this is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes, should be covered by the protection scope of the present invention.

Claims

1. A robot safe operation control system based on artificial intelligence, characterized in that, It includes a robot operation and management platform, a data retrieval unit, an operation and maintenance execution impact unit, a mechanical wear analysis unit, a joint working condition analysis unit, a path planning unit, and an operation and management response unit; The data retrieval unit is used to retrieve the operation and maintenance data and wear risk data of the robot joints, and send the operation and maintenance data and wear risk data to the operation and maintenance execution impact unit and the mechanical wear analysis unit respectively; After receiving the operation and maintenance data, the operation and maintenance execution impact unit immediately conducts a feedback analysis on the operation and maintenance data for the risk of joint operation interference, and obtains the operation and maintenance interference index W of each joint; After receiving the wear risk data, the mechanical wear analysis unit immediately conducts an analysis on the wear risk data for the division of mechanical damage operation interference, and conducts a division analysis on the obtained damage assessment index S to obtain the mechanical damage index JZ; The joint working condition analysis unit is used to collect the joint working condition information of the robot joints, conduct a supervision and evaluation analysis on the joint working condition information for the operation working condition, and conduct a comparison analysis on the obtained operation risk peak Jmax to obtain a normal signal and a risk signal; The path planning unit is used to respond to the normal signal, collect the path information of the robot driving path, and conduct a path optimization supervision feedback analysis on the path information to obtain a path optimization signal.

2. The robot safety operation control system based on artificial intelligence according to claim 1, characterized in that, The process of the feedback analysis on the risk of joint operation interference by the operation and maintenance execution impact unit is as follows: Collect the operation period of the robot and set it as the time threshold, and obtain the operation and maintenance data of each joint of the robot within the time threshold. The operation and maintenance data includes a correction ratio index and a maintenance deviation index. Label the correction ratio index and the maintenance deviation index as XB and WP respectively. According to the formula Obtain the operation and maintenance interference index of each joint. f1 and f2 are the preset weight factor coefficients of the correction ratio index and the maintenance deviation index respectively. Both f1 and f2 are greater than zero. W is the operation and maintenance interference index of each joint.

3. The robot safety operation control system based on artificial intelligence according to claim 2, characterized in that, The analysis process of the correction ratio index is as follows: the proportion of the number of times when the correction error value in the total number of historical maintenance times is greater than the preset threshold, and the correction error value represents the number of joints corresponding to the change value of the friction decibel before and after joint maintenance being less than the difference between the maximum friction decibel before joint maintenance and the initial joint friction decibel; the analysis process of the maintenance deviation index is as follows: obtain the number of delayed maintenance times in the total number of historical maintenance times of the robot, and then obtain the operation working hours of the robot during the corresponding delay duration for each delayed maintenance time, and then set the sum value of the operation working hours as the maintenance deviation index.

4. A robot safe operation control system based on artificial intelligence according to claim 1, characterized in that, The process of the division analysis on the mechanical damage operation interference by the mechanical wear analysis unit is as follows: Obtain the wear risk data of each joint of the robot within the time threshold. The wear risk data includes mechanical damage value and load damage value. According to the formula, obtain the damage assessment index S of each joint, and conduct a comparison analysis on the damage assessment index S with the preset damage assessment index range stored in it: If the damage assessment index S is greater than the maximum value in the preset damage assessment index range, it is determined as a first-level damage; if the damage assessment index S belongs to the preset damage assessment index range, it is determined as a second-level damage; if the damage assessment index S is less than the minimum value in the preset damage assessment index range, it is determined as a third-level damage. Set the first-level damage, second-level damage, and third-level damage as the mechanical damage index JZ, JZ = 1, 2, 3.

5. An artificial intelligence-based robot safe operation control system according to claim 4, characterized in that, The mechanical damage value represents the sum value of the normalized values of the total continuous operation duration corresponding to the operating speed of the joint exceeding the preset operating speed and the duration corresponding to the joint temperature value exceeding the preset threshold; The load damage value represents the product value obtained by multiplying the total running duration and total number of times of joint operation corresponding to the load weight exceeding the preset threshold during the historical operation process after data normalization processing.

6. The robot safety operation control system based on artificial intelligence according to claim 1, characterized in that, The operation condition supervision and evaluation analysis process of the joint condition analysis unit is as follows: Obtain the joint condition information of each joint of the robot within the time threshold. The joint condition information represents the working amplitude index and the trajectory deviation value. Label the working amplitude index and the trajectory deviation value as GF and GP respectively. According to the formula, obtain the operation risk assessment coefficient J of each joint, and then obtain the maximum value in the operation risk assessment coefficient J, and set the maximum value in the operation risk assessment coefficient J as the operation risk peak value Jmax. Compare and analyze the operation risk peak value Jmax with the preset operation risk peak value threshold stored in it to obtain the normal signal and the risk signal.

7. An artificial intelligence-based robot safe operation control system according to claim 6, characterized in that, The working amplitude index represents the part of the difference between the initial oil temperature value and the maximum oil temperature value of the joint exceeding the preset threshold during the process of each joint traveling along the set traveling path; The trajectory deviation value represents the difference value between the actual running trajectory and the set running trajectory of each joint.

8. An artificial intelligence-based robot safe operation control system according to claim 1, characterized in that, The path optimization supervision feedback analysis process of the path planning unit is as follows: Obtain the path information of the robot within the time threshold. The path information includes the collision risk value and the actual operation consumption index. The collision risk value represents the minimum vertical distance between the robot and the collision object when the robot runs along the traveling path. The actual operation consumption index represents the ratio of the total actual energy consumption corresponding to the single completion of the traveling path of the robot to the actual running duration; Compare and analyze the collision risk value and the actual operation consumption index with the preset collision risk value threshold and the preset actual operation consumption index threshold stored in it: If the collision risk value is greater than or equal to the preset collision risk value threshold and the actual operation consumption index is less than the preset actual operation consumption index threshold, no signal is generated; If the collision risk value is less than the preset collision risk value threshold or the actual operation consumption index is greater than or equal to the preset actual operation consumption index threshold, a path optimization signal is generated.

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