IoT-based multi-welding robot collaborative control system

Abstract

The present application relates to the technical field of robot control, and especially relates to a multi-welding robot collaborative control system based on the Internet of Things, which comprises a welding collaborative control center, a collaborative work unit, a collaborative performance unit, a control demand unit, an interference evaluation unit, a distribution control unit and a collaborative management unit; the present application preliminarily analyzes product welding quality and collaborative control performance of the multi-welding robot to determine whether the collaborative control stability and the collaborative control efficiency of the multi-welding robot meet the standards, and then adjusts and manages the collaborative control of the multi-welding robot to improve the collaborative welding quality and the collaborative work efficiency of the multi-welding robot; whether the welding defects are caused by abnormal parameters is determined from the perspective of welding parameters, and then the abnormal parameters are adjusted and managed accordingly; meanwhile, the faulty welding robot is replaced through task re-distribution control to ensure the continuity of the entire welding operation.

Description

Technical Field

[0001] This invention relates to the field of robot control technology, and in particular to a collaborative control system for multiple welding robots based on the Internet of Things. Background Technology

[0002] In modern manufacturing, welding is an important processing technology. With the development of industrial automation, welding robots are widely used in various welding scenarios. However, in some complex welding tasks, a single welding robot often cannot meet production needs and multiple welding robots need to work together.

[0003] However, in the existing technology, it is difficult to supervise the collaborative control of multiple welding robots, which leads to a decrease in the stability of the collaborative control of multiple welding robots, as well as a decrease in the welding quality of multiple welding robots. Furthermore, it is difficult to self-inspect the causes of welding abnormalities of abnormal welding robots, resulting in a decrease in the management efficiency of welding robots. It is also difficult to replace abnormal welding robots in a reasonable way, leading to the rigidity of collaborative welding tasks and making it difficult to achieve autonomous task redistribution.

[0004] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide an IoT-based collaborative control system for multiple welding robots to address the aforementioned technical deficiencies. This invention initially analyzes the welding quality and collaborative control performance of the multiple welding robots to determine whether their collaborative control stability and efficiency meet standards. This allows for adjustments and management of the collaborative control of the multiple welding robots to improve their collaborative welding quality and operational efficiency. Furthermore, it assesses whether welding defects are caused by abnormal welding parameters and adjusts these parameters accordingly. Simultaneously, it replaces faulty welding robots through task redistribution control to ensure the continuity of the entire welding operation.

[0006] The objective of this invention can be achieved through the following technical solution: a multi-welding robot collaborative control system based on the Internet of Things, comprising a welding collaborative control center, a collaborative work unit, a collaborative performance unit, a control requirement unit, an interference assessment unit, an allocation control unit, and a collaborative management unit;

[0007] The welding collaborative control center is used to retrieve welding product information and welding operation information of multiple welding robots, and send the welding product information to the collaborative work unit for end-point collaborative quality evaluation and analysis. It performs discrimination processing on the obtained collaborative operation defect rate to obtain a stable signal or an early warning signal. It sends the welding operation information to the collaborative performance unit for collaborative control performance monitoring and analysis, and performs discrimination processing on the obtained collaborative performance index to obtain a collaborative qualified signal and a collaborative unqualified signal.

[0008] When a stable signal and a cooperative qualified signal are generated, the control demand unit is used to perform welding safety supervision and evaluation analysis on the collected welding feature images to obtain a normal welding signal or a welding abnormal signal.

[0009] When a welding anomaly signal is generated, the interference assessment unit is used to perform welding anomaly evaluation and feedback analysis on the collected welding parameter information to obtain a normal parameter signal or a parameter anomaly signal. The allocation control unit is used to perform collaborative task reassignment control analysis on the collected welding machine performance information and set the robot to be assigned the minimum value in the obtained welding replacement influence coefficient as the heavy replacement robot.

[0010] Preferably, the end-point collaborative quality assessment and analysis process is as follows:

[0011] The operation period of the welding robot is collected and set as a time threshold. Welding product information under the collaborative welding of multiple welding robots within the time threshold is obtained. The welding product information represents the weld feature image. At the same time, the standard weld feature image is retrieved and compared with the standard weld feature image. The difference between the weld feature image and the standard weld feature image is set as the collaborative welding deviation value. The collaborative welding deviation value is judged and processed to obtain qualified and unqualified signals.

[0012] The number of unqualified signals and the number of qualified signals generated within the time threshold are obtained. The ratio between the number of unqualified signals and the number of qualified signals is set as the collaborative operation defect rate. The collaborative operation defect rate is judged. If the collaborative operation defect rate is less than the preset collaborative operation defect rate threshold, a stable signal is generated. If the collaborative operation defect rate is greater than or equal to the preset collaborative operation defect rate threshold, an early warning signal is generated.

[0013] Preferably, the collaborative control performance monitoring and analysis process is as follows:

[0014] Welding operation information of multiple welding robots within a time threshold is obtained. The welding operation information includes synchronization duration value, cycle deviation value, and information interaction delay value.

[0015] Synchronization duration value represents the difference between the maximum and minimum values ​​of the duration between the start and end times of welding by the welding robot; cycle deviation value represents the value obtained by subtracting the preset production cycle from the actual production cycle of multiple welding robots cooperating to complete the entire welding task; and information interaction delay value represents the maximum value of the information interaction delay duration between each welding robot.

[0016] Preferably, the synchronization duration value, period deviation value, and information interaction delay value are processed to obtain the processing results of the synchronization duration value, period deviation value, and information interaction delay value. The processing results include control qualified and control unqualified. The number of control qualified is obtained and set as the coordination performance index. The coordination performance index is then processed: if the coordination performance index is equal to 3, a coordination qualified signal is generated; if the coordination performance index is not equal to 3, a coordination unqualified signal is generated.

[0017] Preferably, the welding safety supervision and evaluation analysis process is as follows:

[0018] The welding areas of each welding robot within a time threshold are acquired, and welding feature images of each welding area within the time threshold are acquired. The number of welding defect information corresponding to the welding feature images is obtained. Welding defect information includes cracks and bubbles. The number of welding defect information corresponding to the welding feature images is then processed. If the number of welding defect information corresponding to the welding feature images is equal to zero, a normal welding signal is generated. If the number of welding defect information corresponding to the welding feature images is not equal to zero, an abnormal welding signal is generated.

[0019] Preferably, the welding anomaly assessment and feedback analysis process is as follows:

[0020] The welding parameter information of the welding robot corresponding to the welding abnormal signal within the time threshold is obtained. The welding parameter information includes welding gas pressure, welding current, and welding angle. The maximum and minimum values ​​of the welding parameter information within the time threshold are obtained, and an actual parameter interval A is constructed based on the maximum and minimum values ​​of the welding parameter information. At the same time, a standard parameter interval B is obtained. The actual parameter interval A and the standard parameter interval B are compared and analyzed. If the actual parameter interval A is included in the standard parameter interval B, a normal parameter signal is generated. If the actual parameter interval A is not included in the standard parameter interval B, a parameter abnormal signal is generated.

[0021] Preferably, the collaborative task reallocation control analysis process is as follows:

[0022] The welding robot corresponding to the welding anomaly signal is set as the faulty welding robot. The welding machine performance information of the adjacent welding robots of the faulty welding robot is obtained. The welding machine performance information represents the historical welding error rate. The historical welding error rate is judged and processed. If the historical welding error rate is less than the preset historical welding error rate threshold, the corresponding welding robot is determined to be a robot to be assigned.

[0023] Preferably, the welding area of ​​the faulty welding robot is obtained, and the welding planning route of each robot to be assigned is obtained based on the welding area. The basic information of the welding planning route is obtained, including the running time, obstacle avoidance risk value and production capacity change value. The obstacle avoidance risk value represents the shortest straight-line distance between the robot to be assigned and the obstacle based on the welding planning route. The production capacity change value represents the value obtained by subtracting the production capacity value after replacement from the production capacity value before the faulty welding robot is replaced.

[0024] The capacity change value is processed. If the capacity change value is less than or equal to the preset capacity change value threshold, an available signal is generated. When an available signal is generated, the ratio between the obstacle avoidance risk value and the normalized value of the running time is set as the welding replacement influence coefficient. The minimum value of the welding replacement influence coefficient is obtained, and the robot to be assigned corresponding to the minimum value of the welding replacement influence coefficient is set as the heavy replacement robot.

[0025] The beneficial effects of this invention are as follows:

[0026] (1) This invention initially analyzes the product welding quality and collaborative control performance of the multi-welding robot to determine whether the collaborative control stability and collaborative control efficiency of the multi-welding robot meet the standards. The collaborative control evaluation results of the multi-welding robot are intuitively understood through text feedback, and then the collaborative control of the multi-welding robot is adjusted and managed to improve the collaborative welding quality and collaborative operation efficiency of the multi-welding robot.

[0027] (2) This invention performs welding safety supervision and evaluation analysis on welding feature images through information progression, so as to intuitively understand the operating status and welding quality of each welding robot, and judge whether welding defects are caused by abnormal parameters from the perspective of welding parameters, and then make targeted adjustments and management of abnormal parameters to improve the operating stability of welding robots. At the same time, the faulty welding robot is replaced through task redistribution control to ensure the continuity of the entire welding operation, and at the same time, it helps to realize autonomous task redistribution. Attached Figure Description

[0028] The invention will now be further described with reference to the accompanying drawings;

[0029] Figure 1 This is a flowchart of the system of the present invention;

[0030] Figure 2 This is a partial analysis diagram of Embodiment 2 of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments;

[0033] Example 1:

[0034] Please see Figures 1 to 2 As shown, the present invention is an IoT-based multi-welding robot collaborative control system, including a welding collaborative control center, a collaborative work unit, a collaborative performance unit, a control requirement unit, an interference assessment unit, an allocation control unit, and a collaborative management unit. The welding collaborative control center has a one-way communication connection with the collaborative work unit and the collaborative performance unit. The collaborative work unit and the collaborative performance unit have a one-way communication connection with the control requirement unit. The control requirement unit has a one-way communication connection with the interference assessment unit, the allocation control unit, and the collaborative management unit. The interference assessment unit and the allocation control unit have a one-way communication connection with the collaborative management unit.

[0035] The welding collaborative control center is used to retrieve welding product information and welding operation information of multiple welding robots, and send the welding product information to the collaborative work unit for end-point collaborative quality evaluation and analysis. It performs discrimination processing on the obtained collaborative operation defect rate to obtain a stable signal or an early warning signal. It sends the welding operation information to the collaborative performance unit for collaborative control performance monitoring and analysis, and performs discrimination processing on the obtained collaborative performance index to obtain a collaborative qualified signal and a collaborative unqualified signal.

[0036] The specific process for end-point collaborative quality assessment and analysis is as follows:

[0037] The system collects the operating time of the welding robot and sets this time as a time threshold. It then acquires welding product information from multiple welding robots working collaboratively within this time threshold. This welding product information represents weld feature images. Simultaneously, it retrieves standard weld feature images and compares them to obtain the difference value between the two images. This difference value is set as the collaborative welding deviation value. The system then performs discrimination processing on the collaborative welding deviation value. If the collaborative welding deviation value is less than a preset collaborative welding deviation value threshold, a qualified signal is generated; otherwise, a non-qualified signal is generated.

[0038] The system acquires the number of non-conforming signals and the number of conforming signals generated within a time threshold. The ratio between the number of non-conforming signals and the number of conforming signals is set as the collaborative operation defect rate. The collaborative operation defect rate is then processed. If the collaborative operation defect rate is less than the preset collaborative operation defect rate threshold, a stable signal is generated. If the collaborative operation defect rate is greater than or equal to the preset collaborative operation defect rate threshold, an early warning signal is generated. The collaborative management unit responds to the stable signal or the early warning signal and immediately performs the preset early warning operation corresponding to the stable signal or the early warning signal. That is, it analyzes whether the collaborative welding control of the multi-welding robot meets the standard from the perspective of welding quality results, so as to adjust and manage the current multi-welding robot collaborative welding operation, thereby improving the collaborative welding quality and collaborative operation efficiency of the multi-welding robot.

[0039] The specific process for monitoring and analyzing the performance of collaborative control is as follows:

[0040] Welding operation information of multiple welding robots within a time threshold is obtained. The welding operation information includes synchronization duration value, cycle deviation value, and information interaction delay value.

[0041] Among them, the synchronization duration value represents the difference between the maximum and minimum values ​​of the duration between the start and end of welding by the welding robot. It should be noted that the smaller the synchronization duration value, the lower the risk of quality problems at the weld joint in collaborative welding tasks.

[0042] The cycle deviation value represents the actual production cycle of multiple welding robots working together to complete the entire welding task minus the preset production cycle. It should be noted that the smaller the cycle deviation value, the higher the efficiency of collaborative work.

[0043] The information interaction delay value represents the maximum value of the information interaction delay time between each welding robot. It should be noted that during collaborative welding, welding robots need to exchange position information, welding parameters and other data in real time to achieve collaborative control. Therefore, the larger the value of the information interaction delay value, the greater the risk of collaborative control of welding robots.

[0044] The system performs discrimination processing on synchronization duration, period deviation, and information interaction delay values ​​to obtain the discrimination processing results. The discrimination processing results include control qualified and control unqualified. The number of control qualified values ​​is obtained and set as the collaborative performance index. The collaborative performance index is then processed: if the collaborative performance index is equal to 3, a collaborative qualified signal is generated; if the collaborative performance index is not equal to 3, a collaborative unqualified signal is generated. The collaborative management unit responds to the collaborative qualified and collaborative unqualified signals and immediately displays the preset warning text corresponding to the collaborative qualified and collaborative unqualified signals. That is, it analyzes whether the collaborative welding of multiple welding robots is qualified from the perspective of collaborative welding control performance, and intuitively understands the collaborative performance evaluation results of multiple welding robots through text feedback, so as to carry out collaborative control management of multiple welding robots and improve the collaborative control stability and collaborative control efficiency of multiple welding robots.

[0045] The judgment and processing results include control qualified and control unqualified: if the synchronization duration value is less than the preset synchronization duration value threshold, it is judged as control qualified; if the synchronization duration value is greater than or equal to the preset synchronization duration value threshold, it is judged as control unqualified; if the period deviation value is less than the preset period deviation value threshold, it is judged as control qualified; if the period deviation value is greater than or equal to the preset period deviation value threshold, it is judged as control unqualified; if the information interaction delay value is less than the preset information interaction delay value threshold, it is judged as control qualified; if the information interaction delay value is greater than or equal to the preset information interaction delay value threshold, it is judged as control unqualified.

[0046] Example 2:

[0047] When stable signals and cooperative qualified signals are generated, the control demand unit performs welding safety supervision and evaluation analysis on the acquired welding feature images to intuitively understand the operating status and welding quality of each welding robot. The specific welding safety supervision and evaluation analysis process is as follows:

[0048] The system acquires the welding areas of each welding robot within a time threshold, obtains welding feature images of each welding area within the time threshold, and obtains the number of welding defect information corresponding to the welding feature images. Welding defect information includes cracks, bubbles, etc. The system then performs discrimination processing on the number of welding defect information corresponding to the welding feature images. If the number of welding defect information corresponding to the welding feature images is equal to zero, a normal welding signal is generated. If the number of welding defect information corresponding to the welding feature images is not equal to zero, a welding abnormal signal is generated. The collaborative management unit responds to the normal welding signal or the welding abnormal signal by immediately marking the welding robot corresponding to the normal welding signal with text and marking the welding robot corresponding to the welding abnormal signal with text on the visual panel, so as to intuitively understand the operating status and welding quality of each welding robot.

[0049] When a welding anomaly signal is generated, the interference assessment unit performs welding anomaly evaluation and feedback analysis on the collected welding parameter information. This allows for a direct understanding of whether the welding defect is caused by parameter anomalies, enabling targeted adjustments and management of the abnormal parameters to improve the welding quality of the welding robot. The specific welding anomaly evaluation and feedback analysis process is as follows:

[0050] The system acquires welding parameter information of the welding robot corresponding to welding anomaly signals within a time threshold. This welding parameter information includes welding gas pressure, welding current, and welding angle. It also acquires the maximum and minimum values ​​of these welding parameters within the time threshold and constructs an actual parameter interval A based on these values. Simultaneously, it acquires a standard parameter interval B. The system compares and analyzes the actual parameter interval A with the standard parameter interval B. If the actual parameter interval A is contained within the standard parameter interval B, a normal parameter signal is generated. If the actual parameter interval A is not contained within the standard parameter interval B, a parameter anomaly signal is generated. The collaborative management unit responds to these signals by immediately marking the parameters in the welding robot's welding parameter information corresponding to the normal parameter signal in green and the parameters in the welding robot's welding parameter information corresponding to the parameter anomaly signal in red. This allows for a clear understanding of whether welding defects are caused by parameter anomalies, enabling targeted adjustments and management of abnormal parameters to improve the welding quality of the welding robot.

[0051] When a welding anomaly signal is generated, the allocation control unit performs collaborative task reallocation control analysis on the collected welding machine performance information to ensure the continuity of the entire welding operation and facilitate autonomous task reallocation. The specific collaborative task reallocation control analysis process is as follows:

[0052] The welding robot corresponding to the welding anomaly signal is set as the faulty welding robot. The welding machine performance information of the adjacent welding robots of the faulty welding robot is obtained. The welding machine performance information represents the historical welding error rate. The historical welding error rate is judged and processed. If the historical welding error rate is less than the preset historical welding error rate threshold, the corresponding welding robot is determined to be a robot to be assigned.

[0053] The welding area of ​​the faulty welding robot is obtained, and the welding planning route of each robot to be assigned is obtained based on the welding area. The basic information of the welding planning route is obtained, including the running time, obstacle avoidance risk value and production capacity change value. The obstacle avoidance risk value represents the shortest straight-line distance between the robot to be assigned and the obstacle based on the welding planning route. The production capacity change value represents the production capacity before the faulty welding robot is replaced minus the production capacity after replacement. It should be noted that the running time, obstacle avoidance risk value and production capacity change value are three parameters that reflect the feasibility and reliability of replacing the robots to be assigned.

[0054] The capacity change value is processed. If the capacity change value is less than or equal to the preset capacity change value threshold, an available signal is generated. When an available signal is generated, the ratio between the obstacle avoidance risk value and the normalized value of the runtime is set as the welding replacement influence coefficient. The minimum value of the welding replacement influence coefficient is obtained, and the robot to be assigned corresponding to the minimum value of the welding replacement influence coefficient is set as the replacement robot. The collaborative management unit responds to the replacement robot and immediately controls the replacement robot to perform welding operations according to the corresponding welding planning route to ensure the continuity of the entire welding operation and help to achieve autonomous task reallocation.

[0055] In summary, this invention initially analyzes the welding quality and collaborative control performance of multiple welding robots to determine whether their collaborative control stability and efficiency meet standards. The results of the collaborative control evaluation are intuitively understood through text feedback, allowing for adjustments and management of the collaborative control of the multiple welding robots to improve their collaborative welding quality and efficiency. Furthermore, a progressive information approach is used to analyze welding feature images for welding safety supervision, providing a clear understanding of the operating status and welding quality of each welding robot. Welding defects are also assessed from the perspective of welding parameters to determine if they are caused by abnormal parameters, enabling targeted adjustments and management of these abnormal parameters to improve the operational stability of the welding robots. Finally, task redistribution control is used to replace faulty welding robots, ensuring the continuity of the entire welding operation and facilitating autonomous task redistribution.

[0056] The threshold is set for comparative analysis of results to determine whether they are good or bad. The value of the threshold is determined by a combination of large-scale model analysis of sample data and human experience. It can also be adjusted appropriately based on seasonal or common-sense influencing factors.

[0057] The size of the coefficient is a specific value obtained by quantifying each parameter to facilitate subsequent comparison. The size of the coefficient depends on the amount of sample data and the corresponding operating coefficient initially set by those skilled in the art for each set of sample data; as long as it does not affect the proportional relationship between the parameter and the quantified value.

[0058] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A multi-welding robot collaborative control system based on Internet of Things, characterized in that, The welding collaborative control center, the collaborative work unit, the collaborative performance unit, the control demand unit, the interference evaluation unit, the distribution control unit and the collaborative management unit are included. The welding collaborative control center is used for calling welding product information and welding operation information of multiple welding robots, sending the welding product information to the collaborative work unit for end collaborative quality evaluation analysis, discriminating the obtained collaborative work defect rate, obtaining a stable signal or a warning signal, and sending the welding operation information to the collaborative performance unit for collaborative control performance monitoring analysis, discriminating the obtained collaborative performance index, and obtaining a collaborative qualified signal and a collaborative unqualified signal. When the stable signal and the collaborative qualified signal are generated, the control demand unit is used for welding safety supervision evaluation analysis on the collected welding feature images, to obtain a welding normal signal or a welding abnormal signal. When the welding abnormal signal is generated, the interference evaluation unit is used for welding abnormal evaluation feedback analysis on the collected welding parameter information, to obtain a parameter normal signal or a parameter abnormal signal, and the distribution control unit is used for collaborative task redistribution control analysis on the collected welding machine performance information, to set the minimum value in the welding replacement influence coefficient corresponding to the robot to be distributed as a heavy replacement robot.

2. The IoT-based multi-welding robot collaborative control system of claim 1, wherein, The end collaborative quality evaluation analysis process is as follows: The running period of the welding robot is collected, and the running period of the welding robot is set as a time threshold value, welding product information under the collaborative welding of multiple welding robots within the time threshold value is obtained, the welding product information represents a welding feature image, a standard welding feature image is called, the welding feature image and the standard welding feature image are compared and analyzed, a difference value between the welding feature image and the standard welding feature image is set as a collaborative welding deviation value, and the collaborative welding deviation value is discriminated to obtain a qualified signal and an unqualified signal. The number of unqualified signals and the number of qualified signals generated within the time threshold value are obtained, the ratio between the number of unqualified signals and the number of qualified signals is set as a collaborative work defect rate, and the collaborative work defect rate is discriminated, if the collaborative work defect rate is less than a preset collaborative work defect rate threshold value, a stable signal is generated, and if the collaborative work defect rate is greater than or equal to the preset collaborative work defect rate threshold value, a warning signal is generated.

3. The IoT-based multi-welding robot collaborative control system of claim 2, wherein, The collaborative control performance monitoring analysis process is as follows: The welding operation information of multiple welding robots within the time threshold value is obtained, the welding operation information includes a synchronization duration value, a cycle deviation value and an information interaction delay value. The synchronization duration value represents the difference between the maximum value and the minimum value in the duration between the start time and the end time of welding of the welding robot, the cycle deviation value represents the value obtained by subtracting a preset production cycle from the actual production cycle of the multiple welding robots in collaborative completion of the entire welding task, and the information interaction delay value represents the maximum value in the information interaction delay duration between the welding robots.

4. The IoT-based multi-welding robot collaborative control system of claim 3, wherein, The synchronization duration value, the cycle deviation value and the information interaction delay value are discriminated to obtain a discrimination result of the synchronization duration value, the cycle deviation value and the information interaction delay value, the discrimination result including control qualified and control unqualified, the number of control qualified is obtained and set as a collaborative performance index, and the collaborative performance index is discriminated: if the collaborative performance index is equal to 3, a collaborative qualified signal is generated, and if the collaborative performance index is not equal to 3, a collaborative unqualified signal is generated.

5. The IoT-based multi-welding robot collaborative control system of claim 2, wherein, The welding safety supervision evaluation analysis process is as follows: The welding area of each welding robot within the time threshold is obtained, the welding feature image of each welding area within the time threshold is obtained, the number of welding defect information in the welding feature image is obtained, the welding defect information includes cracks, bubbles, and the number of welding defect information in the welding feature image is discriminated: if the number of welding defect information in the welding feature image is equal to zero, a welding normal signal is generated, and if the number of welding defect information in the welding feature image is not equal to zero, a welding abnormal signal is generated.

6. The IoT-based multi-welding robot collaborative control system of claim 2, wherein, The welding abnormal evaluation feedback analysis process is as follows: The welding parameter information of the welding robot corresponding to the welding abnormal signal within the time threshold is obtained, the welding parameter information includes welding gas pressure, welding current, welding angle, the maximum and minimum values of the welding parameter information within the time threshold are obtained, and the actual parameter interval A is constructed based on the maximum and minimum values of the welding parameter information, while the standard parameter interval B is obtained. Compare and analyze actual parameter interval A and standard parameter interval B: if actual parameter interval A is contained in standard parameter interval B, a parameter normal signal is generated, and if actual parameter interval A is not contained in standard parameter interval B, a parameter abnormal signal is generated.

7. The IoT-based multi-welding robot collaborative control system of claim 1, wherein, The collaborative task reassignment control analysis process is as follows: The welding robot corresponding to the welding abnormal signal is set as a fault welding robot, the welding machine performance information of the adjacent welding robot of the fault welding robot is obtained, the welding machine performance information represents the historical welding error rate, and the historical welding error rate is discriminated: if the historical welding error rate is less than the preset historical welding error rate threshold, the corresponding welding robot is determined as a to-be-assigned robot. 8.The Internet of Things based multi-welding robot collaborative control system according to claim 7, wherein, The welding area of the fault welding robot is obtained, the welding planning route of each to-be-assigned robot is obtained based on the welding area, the basic information of the welding planning route is obtained, the basic information includes the running duration, the obstacle avoidance risk value and the capacity change value, the obstacle avoidance risk value represents the shortest straight line distance of the to-be-assigned robot based on the welding planning route from the obstacle, and the capacity change value represents the value obtained by subtracting the capacity value after replacement from the capacity value before replacement of the fault welding robot; The capacity change value is subjected to discrimination processing, and if the capacity change value is less than or equal to a preset capacity change value threshold, a usable signal is generated. When the usable signal is generated, the ratio between the obstacle avoidance risk value and the normalized value of the running time is set as a welding replacement influence coefficient, the minimum value in the welding replacement influence coefficient is obtained, and the minimum value in the welding replacement influence coefficient corresponding to the robot to be allocated is set as the heavy replacement robot.

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