Industrial robot multi-machine collaborative interaction method and system

Abstract

The present invention discloses a method and system for industrial robot multi-machine collaborative interaction, which relates to the field of automobile manufacturing technology. By pre-acquiring a distribution map of workstations of an industrial production line, the work area and the robot are marked; the system is enabled to monitor and record the robot collaborative state data in real time, thereby improving the system's flexibility in allocating work areas and tasks to the robot; through real-time monitoring and fault detection of the robot collaborative state, the robot in a faulty or inefficient collaborative state is quickly located, and its fault recovery time Tfx and collaborative recovery capability Fhx are evaluated, and a robot replacement evaluation value Plz is generated. The system can accurately determine whether it is necessary to start a replacement robot for task replacement, thereby solving the problem of insufficient fault management and recovery capability in traditional systems, ensuring continuous operation of the production line and reducing production stagnation.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile manufacturing, and in particular to a method and system for multi-machine collaborative interaction of industrial robots. Background Art

[0002] The industrial robot multi-machine collaborative interaction system is a technical system that uses multiple robots to work together to improve production efficiency and flexibility; the origin of industrial robot technology can be traced back to the 1950s, when the United States' Unimate robots were first used in the automotive manufacturing industry. With the advancement of computer technology and the increasing demand for industrial automation, robotics technology has continued to develop. By the 1980s, with the improvement of sensor technology and computing power, robots began to have a higher level of intelligence. During this period, the concept of multiple robots working together gradually took shape and was used in complex manufacturing tasks.

[0003] Industrial robot multi-machine collaboration systems are widely used in various fields, including automobile manufacturing. However, when applied to automobile manufacturing, although industrial robot multi-machine collaboration systems have the advantages of improving production efficiency and quality, they also have some technical disadvantages. The specific disadvantages are as follows:

[0004] 1. Lack of flexibility: The production requirements of the automotive manufacturing industry often change, especially when switching between different models. The current multi-machine collaboration system is not flexible enough to adapt to different tasks or reprogram, and it takes a lot of time and resources to adjust and configure;

[0005] 2. Weak fault management and recovery capabilities: If a robot fails, the entire collaborative system may be affected. In existing technologies, the robot's fault management and system recovery mechanisms are not mature enough, which may cause production line interruptions or reduced efficiency.

[0006] 3. Insufficient collaboration accuracy: In automobile manufacturing, especially for precision operations such as welding, assembly and painting, collaboration between robots requires extremely high synchronization and accuracy. Currently, when multi-machine collaboration systems handle complex and non-standardized tasks, errors may accumulate, resulting in inaccurate assembly or inconsistent quality. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention provides an industrial robot multi-machine collaborative interaction method and system, which solves the technical shortcomings of the background technology such as insufficient flexibility, weak fault management and recovery capabilities, and insufficient collaborative accuracy.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for multi-machine collaborative interaction of industrial robots, comprising the following steps:

[0009] S1. Obtain the distribution map of the workstations of the industrial production line in advance, lock several groups of robot work areas, mark several groups of robot work areas and several groups of robots with serial numbers, and record the robot type, task category and status information;

[0010] S2. Real-time monitoring of the collaborative status data between several groups of robots, generating a collaborative status data set, determining and locating the robots currently in a faulty and inefficient collaborative state from among several groups of robots based on the collaborative status data set, and preliminarily determining the working area and impact range of the robots currently in a faulty and inefficient collaborative state;

[0011] S3. Based on the information data of several groups of robots in faulty or inefficient collaborative states obtained in step S2, a task state data set is constructed, and according to the task state data set, a collaborative precision requirement evaluation value Zpd of the current robot is generated by calculation and evaluated, and finally, based on the collaborative precision requirement evaluation value Zpd, it is determined whether the current robot can continue to complete the current task normally;

[0012] S4, simultaneously monitoring and evaluating the workload status and task progress information of adjacent robots, including the robot with the generated sequence number i and the adjacent robot with the generated sequence number n; if the robot with the sequence number i cannot effectively cooperate with the adjacent robot with the sequence number n, it is preliminarily determined that the robot needs to enter a standby or fault handling mode;

[0013] S5. According to the result of S4, collect and calculate the fault recovery time Tfx and collaborative recovery capability Fhx of the robot with serial number i, and fit the collaborative recovery capability Fhx with the collaborative recovery capability Fhx to generate the robot replacement evaluation value Plz and evaluate it to determine whether the replacement robot should intervene.

[0014] Preferably, the collaborative status data set includes the task completion degree Xrw, load rate Lhz and working status code Ztm of several groups of robots;

[0015] Then, after extracting the task completion degree Xrw, load rate Lhz and work status code Ztm and performing dimensionless processing, the collaborative reliability coefficient Kxl is calculated using the following formula:

[0016] ;

[0017] The preset collaboration reliability threshold Q and collaboration reliability coefficient Kxl are compared and evaluated, and the comparison and evaluation contents are used to determine whether several groups of robots are in an inefficient collaboration or failure state. The specific comparison and evaluation contents are as follows:

[0018] When the collaboration reliability coefficient Kxl ≤ the collaboration reliability threshold Q, the corresponding robot is determined to be a faulty or inefficient collaborative robot, and the corresponding robot is marked;

[0019] When the collaborative reliability coefficient Kxl>the collaborative reliability threshold Q, the corresponding robot is determined to be a normal collaborative robot, and no fault processing is performed at this time.

[0020] Preferably, the task status data set includes the importance coefficient Jyx and the remaining workload Rwl of each robot task;

[0021] Then extract the importance coefficient Jyx and the remaining workload Rwl, perform fitting and obtain the collaborative accuracy requirement evaluation value Zpd. The specific calculation formula is as follows:

[0022] ;

[0023] Where a is the correction coefficient of the importance coefficient Jyx, and a is a constant.

[0024] Preferably, a preset accuracy requirement threshold W is compared with the collaborative accuracy requirement evaluation value Zpd to determine whether the robot can continue to complete the current task normally; the determination conditions are as follows:

[0025] When the collaborative accuracy requirement evaluation value Zpd ≥ the accuracy requirement threshold W, it means that the robot meets the task requirements under the current collaborative accuracy, and continues to complete the task normally;

[0026] When the collaborative accuracy requirement evaluation value Zpd is less than the accuracy requirement threshold W, it means that the robot cannot meet the task requirements under the current collaborative accuracy. At this time, adjust the collaborative strategy or perform task adjustments.

[0027] Preferably, the workload status and task progress information of the adjacent robot include a workload value Gfh and a task progress value Rjz;

[0028] The collaborative state matching coefficient Cp is obtained by fitting the workload value Gfh and the task progress value Rjz of the robot with sequence number i and the adjacent robot with sequence number n;

[0029] The specific calculation formula of the collaboration state matching coefficient Cp is as follows:

[0030] ;

[0031] Among them, Gfh i is the workload value of the robot with serial number i, Gfh n is the workload value of the robot with serial number n; Rjz i is the task progress value of the robot with serial number i, Rjzn is the task progress value of the robot with serial number n;

[0032] s represents the weight value of the workload value Gfh, d represents the weight value of the task progress value Rjz, and , , .

[0033] Preferably, a preset matching threshold E is compared with the cooperation state matching coefficient Cp for evaluation, and it is determined whether the robot with sequence number i needs to enter the standby or fault handling mode based on the evaluation content. The specific evaluation and judgment content is as follows:

[0034] When the preset matching threshold E> the collaborative state matching coefficient Cp, the robot with serial number i enters the standby or fault handling mode, waiting for the system to be repaired or a replacement robot to intervene;

[0035] When the preset matching threshold E≤cooperative state matching coefficient Cp, the robot with serial number i keeps working normally and does not enter the fault handling mode.

[0036] Preferably, by collecting relevant data of robot failure events, the fault recovery time Tfx and the collaborative recovery capability Fhx of the faulty robot are calculated and obtained respectively, and the specific calculation formula is as follows:

[0037] ;

[0038] ;

[0039] Among them, Gwc represents the time point when the fault recovery is completed in the relevant data, Gfs represents the time point when the fault occurs in the relevant data, Xwc represents the amount of tasks completed during the collaborative recovery in the relevant data, and Xhf represents the time required to restore the collaboration in the relevant data.

[0040] Preferably, the robot replacement evaluation value Plz is obtained by fitting the fault recovery time Tfx and the collaborative recovery capability Fhx of the faulty robot; specifically, it is obtained by the following formula:

[0041] ;

[0042] Next, the standard threshold of the robot replacement evaluation value Plz is set to 1 and then evaluated, and whether the replacement robot needs to be started is determined based on the evaluation content. The specific contents are as follows:

[0043] When the path planning evaluation value Plz is less than 1, the substitute robot does not intervene;

[0044] When the path planning evaluation value Plz is greater than or equal to 1, the replacement robot is started to perform task replacement and plan its path.

[0045] Preferably, after the replacement robot intervenes, it is determined whether the original robot enters the emergency maintenance mode; the emergency maintenance start value Rqx is obtained by calculation according to the path planning evaluation value Plz; the specific calculation formula of the emergency maintenance start value Rqx is as follows:

[0046] ;

[0047] Among them, R is the preset emergency maintenance threshold;

[0048] Next, the standard threshold of the emergency maintenance start value Rqx is also set to 1 for evaluation, and whether the original robot enters the emergency maintenance mode is determined based on the evaluation content. The specific content is as follows:

[0049] When the emergency maintenance start value Rqx is greater than 1, the emergency maintenance mode is started;

[0050] When the emergency maintenance start value Rqx is less than or equal to 1, the robot does not need to enter the emergency maintenance mode, but remains in normal state or enters the low-load operation mode.

[0051] An industrial robot multi-machine collaborative interaction system, comprising a preparation module, a fault detection module, a task analysis module, a collaborative state evaluation module, a recovery capability evaluation module and an intervention decision module;

[0052] The preparation module obtains the distribution map of the workstations of the industrial production line in advance, locks several groups of robot work areas, and marks these work areas and robots with serial numbers; at the same time, it records the type, task category and status information of each robot to establish a basic data model and area identification system;

[0053] The fault detection module is used to monitor the collaborative state data between several groups of robots in real time, generate a collaborative state data set, determine and locate the robots currently in a faulty and inefficient collaborative state from among several groups of robots according to the collaborative state data set, and preliminarily determine the working area and impact range of the robots currently in a faulty and inefficient collaborative state;

[0054] The task analysis module constructs a task status data set based on the information data of several groups of robots in faulty or inefficient collaborative states acquired by the fault detection module; based on the task status data set, generates a collaborative precision requirement evaluation value Zpd of the robot with sequence number i by calculation and evaluation to determine whether the robot can continue to complete the current task normally;

[0055] The collaborative state evaluation module is used to simultaneously monitor the workload state and task progress information of adjacent robots; generate a collaborative state matching coefficient Cp between the robot with sequence number i and the adjacent robot with sequence number n; and determine whether the robot needs to enter a standby or fault handling mode;

[0056] The recovery capability evaluation module recalculates the robot replacement evaluation value Plz of the robot with the evaluation sequence number i according to the result of the collaborative status evaluation module; determines whether a replacement robot needs to be involved according to the evaluation content of the robot replacement evaluation value Plz, and finally determines whether the replacement robot needs to intervene in the current task in combination with the existing operation status information;

[0057] The intervention decision module is used to integrate the information from the recovery capability assessment module and the intervention decision logic, and output the final decision result; including deciding the intervention of the replacement robot, whether the original robot enters the emergency maintenance mode, and related scheduling and path planning instructions.

[0058] The present invention provides a method and system for multi-machine collaborative interaction of industrial robots, which has the following beneficial effects:

[0059] (1) A method and system for industrial robot multi-machine collaborative interaction, by pre-acquiring the distribution map of workstations of an industrial production line and marking the work areas and robots, establishes a basic data model and area identification system; in this way, the system can monitor and record the collaborative status data between robots in real time and generate a detailed collaborative status data set; compared with traditional methods, this method improves the system's flexibility in allocating work areas and tasks to robots; whether facing changes in production line tasks or dynamic adjustments to robot status, the system can quickly adapt, thereby optimizing overall work efficiency and reducing potential problems caused by delayed information updates.

[0060] (2) This method and system for industrial robot multi-machine collaborative interaction can quickly locate robots in a faulty or inefficient collaborative state and evaluate their fault recovery time and collaborative recovery capability through real-time monitoring of the robot collaborative state and fault detection; by calculating the fault recovery time Tfx and the collaborative recovery capability Fhx and generating a robot replacement evaluation value Plz, the system can accurately determine whether it is necessary to start a replacement robot for task replacement; this method effectively solves the problems of insufficient fault management and recovery capability in traditional systems, ensures the continuous operation of the production line, and minimizes production stagnation caused by faults.

[0061] (3) This method and system for industrial robot multi-machine collaborative interaction can accurately evaluate the performance of robots in collaboration by extracting task completion degree Xrw, load rate Lhz and working status code Ztm, and calculating the collaborative reliability coefficient Kxl, as well as the further collaborative accuracy requirement evaluation value Zpd. This evaluation mechanism solves the problem of insufficient collaborative accuracy in traditional systems, and ensures the efficiency and accuracy of each robot in collaboration by dynamically adjusting collaborative strategies and task allocation, thereby improving the overall collaborative accuracy. Through timely adjustment and optimization, the operating efficiency of the production line and the quality of task completion are effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A schematic diagram of the steps of a multi-machine collaborative interaction method of industrial robots according to the present invention;

[0063] Figure 2 The present invention is a schematic diagram of the framework structure of an industrial robot multi-machine collaborative interaction system. DETAILED DESCRIPTION

[0064] 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.

[0065] Example 1

[0066] See also Figure 1 , a method for multi-machine collaborative interaction of industrial robots, characterized in that it includes the following steps:

[0067] S1. Obtain the distribution map of the workstations of the industrial production line in advance, lock several groups of robot work areas, mark several groups of robot work areas and several groups of robots with serial numbers, and record the robot type, task category and status information;

[0068] S2. Real-time monitoring of the collaborative status data between several groups of robots, generating a collaborative status data set, determining and locating the robots currently in a faulty and inefficient collaborative state from among several groups of robots based on the collaborative status data set, and preliminarily determining the working area and impact range of the robots currently in a faulty and inefficient collaborative state;

[0069] S3. Based on the information data of several groups of robots in faulty or inefficient collaborative states obtained in step S2, a task state data set is constructed, and according to the task state data set, a collaborative precision requirement evaluation value Zpd of the current robot is generated by calculation and evaluated, and finally, based on the collaborative precision requirement evaluation value Zpd, it is determined whether the current robot can continue to complete the current task normally;

[0070] S4, simultaneously monitoring and evaluating the workload status and task progress information of adjacent robots, including the robot with the generated sequence number i and the adjacent robot with the generated sequence number n; if the robot with the sequence number i cannot effectively cooperate with the adjacent robot with the sequence number n, it is preliminarily determined that the robot needs to enter a standby or fault handling mode;

[0071] S5. According to the result of S4, collect and calculate the fault recovery time Tfx and collaborative recovery capability Fhx of the robot with serial number i, and fit the collaborative recovery capability Fhx with the collaborative recovery capability Fhx to generate the robot replacement evaluation value Plz and evaluate it to determine whether the replacement robot should intervene.

[0072] In this embodiment, by pre-acquiring the distribution map of the workstations of the industrial production line and marking the working areas of the robots, a detailed basic data model and an area identification system are established, which significantly improves the flexibility of the system for robot task allocation and area management; by real-time monitoring of the robot collaboration status data, the system can quickly identify faulty or inefficiently collaborative robots, accurately locate their working areas and impact ranges, and effectively solve the problem of insufficient fault management and recovery capabilities in traditional systems; at the same time, the system calculates the collaborative accuracy requirement evaluation value Zpd to accurately judge whether the robot can continue to complete the task normally, and monitors the workload status and task progress information between adjacent robots, and determines whether it is necessary to enter the standby or fault handling mode through evaluation, thereby ensuring the smooth completion of the collaborative accuracy and task; finally, by collecting and calculating the fault recovery time Tfx and the collaborative recovery capacity Fhx, the robot replacement evaluation value Plz is generated, and it is evaluated whether the replacement robot needs to be started, which not only improves the system's fault handling capability, but also enhances the dynamic adjustment capability of the robot collaboration accuracy, thereby greatly improving the overall operation efficiency of the production line and the quality of task completion.

[0073] Example 2

[0074] The collaborative state data set contains the task completion degree Xrw, load rate Lhz and working state code Ztm of several groups of robots;

[0075] Then, after extracting the task completion degree Xrw, load rate Lhz and work status code Ztm and performing dimensionless processing, the collaborative reliability coefficient Kxl is calculated using the following formula:

[0076] ;

[0077] The preset collaboration reliability threshold Q and collaboration reliability coefficient Kxl are compared and evaluated, and the comparison and evaluation contents are used to determine whether several groups of robots are in an inefficient collaboration or failure state. The specific comparison and evaluation contents are as follows:

[0078] When the collaboration reliability coefficient Kxl ≤ the collaboration reliability threshold Q, the corresponding robot is determined to be a faulty or inefficient collaborative robot, and the corresponding robot is marked;

[0079] When the collaborative reliability coefficient Kxl>the collaborative reliability threshold Q, the corresponding robot is determined to be a normal collaborative robot, and no fault processing is performed at this time.

[0080] In this embodiment, the collaborative status data of several groups of robots on the industrial production line are monitored and recorded in real time to generate a collaborative status data set including task completion Xrw, load rate Lhz and working status code Ztm, which can ensure that the system has a comprehensive grasp of the working status of the robot; by extracting these data and performing dimensionless processing, the collaborative reliability coefficient Kxl is obtained through formula calculation. This process not only improves the ability to accurately evaluate the collaborative status of the robot, but also solves the problem of insufficient collaborative reliability evaluation in traditional methods; further, by comparing and evaluating the collaborative reliability coefficient Kxl with the preset collaborative reliability threshold Q, the system can accurately determine whether the robot is in an inefficient collaborative or faulty state, thereby timely marking the faulty robot or confirming the normal collaborative robot. This mechanism greatly optimizes the fault identification and processing process, improves the system's response speed to the robot status and the overall production line operation efficiency.

[0081] Example 3

[0082] The task status data set includes the importance coefficient Jyx and the remaining workload Rwl of each robot task;

[0083] Then extract the importance coefficient Jyx and the remaining workload Rwl, perform fitting and obtain the collaborative accuracy requirement evaluation value Zpd. The specific calculation formula is as follows:

[0084] ;

[0085] Where a is the correction coefficient of the importance coefficient Jyx, and a is a constant.

[0086] By comparing and evaluating the preset accuracy requirement threshold W with the collaborative accuracy requirement evaluation value Zpd, it is determined whether the robot can continue to complete the current task normally; the judgment conditions are as follows:

[0087] When the collaborative accuracy requirement evaluation value Zpd ≥ the accuracy requirement threshold W, it means that the robot meets the task requirements under the current collaborative accuracy, and continues to complete the task normally;

[0088] When the collaborative accuracy requirement evaluation value Zpd is less than the accuracy requirement threshold W, it means that the robot cannot meet the task requirements under the current collaborative accuracy. At this time, adjust the collaborative strategy or perform task adjustments.

[0089] In this embodiment, by extracting the task importance coefficient Jyx and the remaining workload Rwl of each robot, and fitting these data to obtain the collaborative accuracy requirement evaluation value Zpd, this method can effectively evaluate the collaborative accuracy of the robot in completing the task; by comparing the collaborative accuracy requirement evaluation value Zpd with the preset accuracy requirement threshold W, it can be clearly judged whether the robot can meet the task requirements under the current accuracy, thereby optimizing the task allocation and collaboration strategy; when the collaborative accuracy requirement evaluation value Zpd≥the accuracy requirement threshold W, the robot can continue to complete the task normally; when Zpd<the accuracy requirement threshold W, the system can adjust the collaboration strategy or task arrangement in time to ensure the smooth operation of the production line. This method improves the accuracy and flexibility of task allocation and ensures efficient collaboration in the production process.

[0090] Example 4

[0091] The workload status and task progress information of the adjacent robots, including the workload value Gfh and the task progress value Rjz;

[0092] The collaborative state matching coefficient Cp is obtained by fitting the workload value Gfh and the task progress value Rjz of the robot with sequence number i and the adjacent robot with sequence number n;

[0093] The specific calculation formula of the collaboration state matching coefficient Cp is as follows:

[0094] ;

[0095] Among them, Gfh i is the workload value of the robot with serial number i, Gfh n is the workload value of the robot with serial number n; Rjz i is the task progress value of the robot with serial number i, Rjz n is the task progress value of the robot with serial number n;

[0096] s represents the weight value of the workload value Gfh, d represents the weight value of the task progress value Rjz, and , , .

[0097] The preset matching threshold E is compared with the collaborative state matching coefficient Cp for evaluation, and based on the evaluation content, it is determined whether the robot with sequence number i needs to enter the standby or fault handling mode. The specific evaluation and judgment contents are as follows:

[0098] When the preset matching threshold E> the collaborative state matching coefficient Cp, the robot with serial number i enters the standby or fault handling mode, waiting for the system to be repaired or a replacement robot to intervene;

[0099] When the preset matching threshold E≤cooperative state matching coefficient Cp, the robot with serial number i keeps working normally and does not enter the fault handling mode.

[0100] In this embodiment, the collaborative state matching coefficient Cp is calculated by fitting the load value Gfh and the task progress value Rjz of the robot. This method can accurately evaluate the collaborative state between the robot and the adjacent robot; by setting a preset matching threshold E and comparing and evaluating the collaborative state matching coefficient Cp, it can effectively determine whether the robot needs to enter the standby or fault handling mode; when the preset matching threshold E>the collaborative state matching coefficient Cp, the robot enters the fault handling mode, waiting for the system to be repaired or a replacement robot to intervene; when the preset matching threshold E≤the collaborative state matching coefficient Cp, the robot maintains a normal working state. This method improves the reliability and fault response capability of the robot system by real-time monitoring and precise evaluation of the collaborative state, which helps to maintain efficient operation of the production line and reduce downtime.

[0101] Example 5

[0102] By collecting relevant data of robot failure events, the fault recovery time Tfx and collaborative recovery capability Fhx of the faulty robot are calculated respectively. The specific calculation formula is as follows:

[0103] ;

[0104] ;

[0105] Among them, Gwc represents the time point when the fault recovery is completed in the relevant data, Gfs represents the time point when the fault occurs in the relevant data, Xwc represents the amount of tasks completed during the collaborative recovery in the relevant data, and Xhf represents the time required to restore the collaboration in the relevant data.

[0106] The robot replacement evaluation value Plz is obtained by fitting the fault recovery time Tfx and the collaborative recovery capability Fhx of the faulty robot; it is obtained specifically through the following formula:

[0107] ;

[0108] Next, the standard threshold of the robot replacement evaluation value Plz is set to 1 and then evaluated, and whether the replacement robot needs to be started is determined based on the evaluation content. The specific contents are as follows:

[0109] When the path planning evaluation value Plz is less than 1, the substitute robot does not intervene;

[0110] When the path planning evaluation value Plz is greater than or equal to 1, the replacement robot is started to perform task replacement and plan its path.

[0111] After the replacement robot intervenes, it is determined whether the original robot enters the emergency maintenance mode; the emergency maintenance start value Rqx is obtained by calculation based on the path planning evaluation value Plz; the specific calculation formula of the emergency maintenance start value Rqx is as follows:

[0112] ;

[0113] Among them, R is the preset emergency maintenance threshold;

[0114] Next, the standard threshold of the emergency maintenance start value Rqx is also set to 1 for evaluation, and whether the original robot enters the emergency maintenance mode is determined based on the evaluation content. The specific content is as follows:

[0115] When the emergency maintenance start value Rqx is greater than 1, the emergency maintenance mode is started;

[0116] When the emergency maintenance start value Rqx is less than or equal to 1, the robot does not need to enter the emergency maintenance mode, but remains in normal state or enters the low-load operation mode.

[0117] In this embodiment, by collecting data related to robot failure events, calculating the failure recovery time Tfx and the collaborative recovery capability Fhx, the robot recovery capability and replacement needs can be accurately evaluated; by fitting Tfx and Fhx, the robot replacement evaluation value Plz is obtained, and Plz is compared with the standard threshold to decide whether to start the replacement robot for task replacement; when Plz is greater than or equal to 1, the replacement robot will be started and its path will be planned; in addition, it is judged whether the original robot needs to enter the emergency maintenance mode, by calculating the emergency maintenance start value Rqx, and comparing Rqx with the standard threshold, when Rqx is greater than 1, the emergency maintenance mode is started; this method ensures that the system can respond quickly when the robot fails through precise data collection and evaluation, reduces production interruption time and optimizes maintenance and replacement strategies, thereby improving overall production efficiency and system reliability.

[0118] Example 6

[0119] See also Figure 2, an industrial robot multi-machine collaborative interaction system, including a preparation module, a fault detection module, a task analysis module, a collaborative state evaluation module, a recovery capability evaluation module and an intervention decision module;

[0120] The preparation module obtains the distribution map of the workstations of the industrial production line in advance, locks several groups of robot work areas, and marks these work areas and robots with serial numbers; at the same time, it records the type, task category and status information of each robot to establish a basic data model and area identification system;

[0121] The fault detection module is used to monitor the collaborative state data between several groups of robots in real time, generate a collaborative state data set, determine and locate the robots currently in a faulty and inefficient collaborative state from among several groups of robots according to the collaborative state data set, and preliminarily determine the working area and impact range of the robots currently in a faulty and inefficient collaborative state;

[0122] The task analysis module constructs a task status data set based on the information data of several groups of robots in faulty or inefficient collaborative states acquired by the fault detection module; based on the task status data set, generates a collaborative precision requirement evaluation value Zpd of the robot with sequence number i by calculation and evaluation to determine whether the robot can continue to complete the current task normally;

[0123] The collaborative state evaluation module is used to simultaneously monitor the workload state and task progress information of adjacent robots; generate a collaborative state matching coefficient Cp between the robot with sequence number i and the adjacent robot with sequence number n; and determine whether the robot needs to enter a standby or fault handling mode;

[0124] The recovery capability evaluation module recalculates the robot replacement evaluation value Plz of the robot with the evaluation sequence number i according to the result of the collaborative status evaluation module; determines whether a replacement robot needs to be involved according to the evaluation content of the robot replacement evaluation value Plz, and finally determines whether the replacement robot needs to intervene in the current task in combination with the existing operation status information;

[0125] The intervention decision module is used to integrate the information from the recovery capability assessment module and the intervention decision logic, and output the final decision result; including deciding the intervention of the replacement robot, whether the original robot enters the emergency maintenance mode, and related scheduling and path planning instructions.

[0126] In this embodiment, the system establishes a basic data model and an area identification system through a preparation module, and realizes real-time monitoring and fault location of the robot's collaborative state through a fault detection module, thereby ensuring timely identification of faults or inefficient collaborative states; the task analysis module generates a collaborative precision requirement assessment value Zpd based on the data of the fault detection module, and assesses whether the robot can continue to complete the task normally, thereby optimizing task allocation and collaborative efficiency; the collaborative state assessment module monitors the workload state and task progress information between adjacent robots, and determines whether the robot needs to enter standby or fault handling mode by calculating the collaborative state matching coefficient Cp, thereby improving the accuracy and response speed of fault management; the recovery capability assessment module calculates the robot replacement assessment value Plz based on the results of the collaborative state assessment module, determines whether it is necessary to intervene with a replacement robot, and optimizes task execution in combination with the operation state information; the intervention decision module integrates the information of all modules, outputs the final decision result, determines the intervention of the replacement robot, whether the original robot enters the emergency maintenance mode, and provides scheduling and path planning instructions, thereby improving the overall efficiency and reliability of the system.

[0127] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for multi-machine collaborative interaction of industrial robots, characterized in that: The following steps are involved: S1. Obtain the distribution map of the workstations of the industrial production line in advance, lock several groups of robot work areas, mark several groups of robot work areas and several groups of robots with serial numbers, and record the robot type, task category and status information; S2. Real-time monitoring of the collaborative status data between several groups of robots, generating a collaborative status data set, including the corresponding task completion Xrw, load rate Lhz and working status code Ztm of several groups of robots; determining and locating the robots currently in a faulty and inefficient collaborative state from several groups of robots, and preliminarily determining the working area and impact range of the robots currently in a faulty and inefficient collaborative state; extracting the task completion Xrw, load rate Lhz and working status code Ztm and performing dimensionless processing, and using the following formula to calculate the collaborative reliability coefficient Kxl: ； The preset collaboration reliability threshold Q and collaboration reliability coefficient Kxl are compared and evaluated, and the comparison and evaluation contents are used to determine whether several groups of robots are in an inefficient collaboration or failure state. The specific comparison and evaluation contents are as follows: When the collaboration reliability coefficient Kxl ≤ the collaboration reliability threshold Q, the corresponding robot is determined to be a faulty or inefficient collaborative robot, and the corresponding robot is marked; When the collaboration reliability coefficient Kxl> the collaboration reliability threshold Q, the corresponding robot is determined to be a normal collaborative robot, and no fault processing is performed at this time; S3. Based on the information data of several groups of robots in faulty or inefficient collaborative states obtained in step S2, a task status data set is constructed, including the importance coefficient Jyx and the remaining workload Rwl of each robot task; and the collaborative precision requirement evaluation value Zpd of the current robot is generated and evaluated by calculation, and finally, based on the collaborative precision requirement evaluation value Zpd, it is determined whether the current robot can continue to complete the current task normally; wherein, the specific calculation formula of the collaborative precision requirement evaluation value Zpd is as follows: ； In the formula, a represents the correction coefficient of the importance coefficient Jyx, and a is a constant; S4, simultaneously monitoring and evaluating the workload status and task progress information of adjacent robots, including the robot with the generated sequence number i and the adjacent robot with the generated sequence number n; if the robot with the sequence number i cannot effectively cooperate with the adjacent robot with the sequence number n, it is preliminarily determined that the robot needs to enter a standby or fault handling mode; S5. According to the result of S4, collect and calculate the fault recovery time Tfx and collaborative recovery capability Fhx of the robot with serial number i, and fit the collaborative recovery capability Fhx with the collaborative recovery capability Fhx to generate the robot replacement evaluation value Plz and evaluate it to determine whether the replacement robot should intervene.

2. The method for multi-machine collaborative interaction of industrial robots according to claim 1, characterized in that: By comparing and evaluating the preset accuracy requirement threshold W with the collaborative accuracy requirement evaluation value Zpd, it is determined whether the robot can continue to complete the current task normally; the judgment conditions are as follows: When the collaborative accuracy requirement evaluation value Zpd ≥ the accuracy requirement threshold W, it means that the robot meets the task requirements under the current collaborative accuracy, and continues to complete the task normally; When the collaborative accuracy requirement evaluation value Zpd is less than the accuracy requirement threshold W, it means that the robot cannot meet the task requirements under the current collaborative accuracy. At this time, adjust the collaborative strategy or perform task adjustments.

3. The method for multi-machine collaborative interaction of industrial robots according to claim 1, characterized in that: The workload status and task progress information of the adjacent robots include a workload value Gfh and a task progress value Rjz; The collaborative state matching coefficient Cp is obtained by fitting the workload value Gfh and the task progress value Rjz of the robot with sequence number i and the adjacent robot with sequence number n; The specific calculation formula of the collaboration state matching coefficient Cp is as follows: ； Among them, Gfh i is the workload value of the robot with serial number i, Gfh n is the workload value of the robot with serial number n; Rjz i is the task progress value of the robot with serial number i, Rjz n is the task progress value of the robot with serial number n; s represents the weight value of the workload value Gfh, d represents the weight value of the task progress value Rjz, and , ， 。 4. The method for multi-machine collaborative interaction of industrial robots according to claim 3, characterized in that: The preset matching threshold E is compared with the collaborative state matching coefficient Cp for evaluation, and based on the evaluation content, it is determined whether the robot with sequence number i needs to enter the standby or fault handling mode. The specific evaluation and judgment contents are as follows: When the preset matching threshold E> the collaborative state matching coefficient Cp, the robot with serial number i enters the standby or fault handling mode, waiting for the system to be repaired or a replacement robot to intervene; When the preset matching threshold E≤cooperative state matching coefficient Cp, the robot with serial number i keeps working normally and does not enter the fault handling mode.

5. The method for multi-machine collaborative interaction of industrial robots according to claim 1, characterized in that: By collecting relevant data of robot failure events, the fault recovery time Tfx and collaborative recovery capability Fhx of the faulty robot are calculated respectively. The specific calculation formula is as follows: ； ； Among them, Gwc represents the time point when the fault recovery is completed in the relevant data, Gfs represents the time point when the fault occurs in the relevant data, Xwc represents the amount of tasks completed during the collaborative recovery in the relevant data, and Xhf represents the time required to restore the collaboration in the relevant data.

6. The method for multi-machine collaborative interaction of industrial robots according to claim 5, characterized in that: The robot replacement evaluation value Plz is obtained by fitting the fault recovery time Tfx and the collaborative recovery capability Fhx of the faulty robot; it is obtained specifically through the following formula: ； Next, the standard threshold of the robot replacement evaluation value Plz is set to 1 and then evaluated, and whether the replacement robot needs to be started is determined based on the evaluation content. The specific contents are as follows: When the path planning evaluation value Plz is less than 1, the substitute robot does not intervene; When the path planning evaluation value Plz is greater than or equal to 1, the replacement robot is started to perform task replacement and plan its path.

7. The method for multi-machine collaborative interaction of industrial robots according to claim 6, characterized in that: After the replacement robot intervenes, it is determined whether the original robot enters the emergency maintenance mode; the emergency maintenance start value Rqx is obtained by calculation based on the path planning evaluation value Plz; the specific calculation formula of the emergency maintenance start value Rqx is as follows: Among them, R is the preset emergency maintenance threshold; Next, the standard threshold of the emergency maintenance start value Rqx is also set to 1 for evaluation, and whether the original robot enters the emergency maintenance mode is determined based on the evaluation content. The specific content is as follows: When the emergency maintenance start value Rqx is greater than 1, the emergency maintenance mode is started; When the emergency maintenance start value Rqx is less than or equal to 1, the robot does not need to enter the emergency maintenance mode, but remains in normal state or enters the low-load operation mode.

8. An industrial robot multi-machine collaborative interaction system, applied to an industrial robot multi-machine collaborative interaction method according to any one of claims 1 to 7, characterized in that: It includes preparation module, fault detection module, task analysis module, collaboration status assessment module, recovery capability assessment module and intervention decision module; The preparation module obtains the distribution map of the workstations of the industrial production line in advance, locks several groups of robot work areas, and marks these work areas and robots with serial numbers; at the same time, it records the type, task category and status information of each robot to establish a basic data model and area identification system; The fault detection module is used to monitor the collaborative state data between several groups of robots in real time, generate a collaborative state data set, determine and locate the robots currently in a faulty and inefficient collaborative state from among several groups of robots according to the collaborative state data set, and preliminarily determine the working area and impact range of the robots currently in a faulty and inefficient collaborative state; The task analysis module constructs a task status data set based on the information data of several groups of robots in faulty or inefficient collaborative states acquired by the fault detection module; based on the task status data set, generates a collaborative precision requirement evaluation value Zpd of the robot with sequence number i by calculation and evaluation to determine whether the robot can continue to complete the current task normally; The collaborative state evaluation module is used to simultaneously monitor the workload state and task progress information of adjacent robots; generate a collaborative state matching coefficient Cp between the robot with sequence number i and the adjacent robot with sequence number n; and determine whether the robot needs to enter a standby or fault handling mode; The recovery capability evaluation module recalculates the robot replacement evaluation value Plz of the robot with the evaluation sequence number i according to the result of the collaborative status evaluation module; determines whether a replacement robot needs to be involved according to the evaluation content of the robot replacement evaluation value Plz, and finally determines whether the replacement robot needs to intervene in the current task in combination with the existing operation status information; The intervention decision module is used to integrate the information from the recovery capability assessment module and the intervention decision logic, and output the final decision result; This includes deciding on the intervention of a replacement robot, whether the original robot enters emergency maintenance mode, and related scheduling and path planning instructions.

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