Task scheduling system for industrial robot

Abstract

According to the task scheduling system for the industrial robots, the work task of each robot is determined based on the production demand and the robot position, the work task is dynamically adjusted in real time based on the demand change and the state change, and the work task of the work task in which a plurality of robots work at the same time is determined; optimizing the initial execution step of the operation task to obtain a target execution step, monitoring the operation state of the robot working according to the target execution step in real time to obtain monitoring data, and adjusting the target execution step in real time in combination with a real-time task; the robot state, the work task and the target execution step are displayed, and it is ensured that the industrial robot can efficiently and accurately execute the task in the complex production environment. And in addition, real-time feedback and self-adaptive adjustment are supported to cope with dynamic changes on a production line, precise task scheduling of the industrial robot is achieved, the production efficiency is improved, and the production cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of robot scheduling, and in particular to a task scheduling system for industrial robots. Background Art

[0002] The widespread use of industrial robots has not only improved production efficiency and reduced production costs, but also greatly reduced the labor intensity of workers and the incidence of production accidents, providing companies with a huge competitive advantage. With the continuous advancement of technology, industrial robots will continue to play an important role and drive various industries forward.

[0003] Task scheduling of industrial robots mainly involves the reasonable allocation and scheduling of the actions of industrial robots under given time and space conditions according to task requirements and resource constraints to achieve the best production effect. This process is particularly important in the industrial automation industry because it is directly related to production efficiency and product quality. Intelligent scheduling of industrial robots includes not only the task allocation of a single robot, but also the coordination and optimization of multiple robots performing tasks at the same time.

[0004] How to realize task scheduling of industrial robots, improve production efficiency and reduce production costs is a problem that needs to be solved urgently. Summary of the invention

[0005] The present invention provides a task scheduling system for industrial robots, which is used to solve the problems raised in the background technology.

[0006] A task scheduling system for industrial robots, comprising:

[0007] The task allocation module is used to determine the work tasks of each robot based on production requirements and robot positions, and to dynamically adjust the work tasks in real time based on changes in requirements and status;

[0008] A step determination module is used to determine a work task in which multiple robots perform work tasks simultaneously, optimize the initial execution steps of the work task, and obtain the target execution steps;

[0009] The step adjustment module is used to monitor the operation status of the robot working according to the target execution steps in real time, obtain monitoring data, and adjust the target execution steps in real time in combination with real-time tasks;

[0010] The display module is used to display the robot status, work tasks and target execution steps.

[0011] Preferably, the task allocation module includes:

[0012] A marking unit, used to determine a task list based on production requirements, and to first mark the tasks in the task list based on the order of the tasks, to second mark the tasks in the task list based on task execution collaboration characteristics, and to third mark the tasks in the task list based on task execution scope;

[0013] A first matching unit is used to match the tasks in the task list based on the third marking result and the robot position, and determine a set of robots that complete the tasks;

[0014] A second matching unit, configured to determine a robot combination that completes the task from the robot set based on the second marking result;

[0015] A third matching unit is used to determine a plurality of robot sequences that meet the production requirements based on the first marking result and the robot combination, wherein the robot sequence determines the order of the robots in the sequence according to the order of tasks;

[0016] The task determination unit is used to evaluate the task characteristics determined by the robot sequence based on the task evaluation model, and select the task allocation corresponding to the robot sequence with the best evaluation result to determine the working task of each robot.

[0017] Preferably, the task determination unit comprises:

[0018] An indicator determination unit, which is used for basic information and execution information of the robot, setting the performance evaluation index of the robot, the collaborative evaluation index of the robot to complete the task and the task completion efficiency evaluation index, and determining the type weight of each evaluation index type and the indicator weight of each evaluation index;

[0019] A standard determination unit is used to determine the initial evaluation standards for the performance evaluation index, the collaboration evaluation index and the task completion efficiency evaluation index based on the historical work task execution information of the robot, and to perform weighted processing on the evaluation standards corresponding to the performance evaluation index, the collaboration evaluation index and the task completion efficiency evaluation index based on the indicator weight, so as to obtain the intermediate evaluation standard, and to perform comprehensive weighted processing on the intermediate evaluation standard based on the type weight, so as to obtain the target evaluation standard;

[0020] A model building unit, used to build a task evaluation model based on the target evaluation standard;

[0021] A score evaluation unit, used for inputting the robot sequence into the task evaluation model in order to obtain a single task evaluation score corresponding to each robot task element in the robot sequence, and inputting all the robot sequences into the task evaluation model to obtain a total task evaluation score of the robot sequence;

[0022] The evaluation and analysis unit is used to determine the task connection evaluation score of the robot sequence based on the single task evaluation score and the total task evaluation score, and select an alternative robot sequence from the robot sequence whose single task evaluation score and task connection evaluation score both meet preset requirements, select the one with the highest total task evaluation score from the alternative robot sequences as the target robot sequence, and determine the work task of each robot based on the task allocation of the target robot sequence.

[0023] Preferably, the task allocation module further includes:

[0024] An analysis unit, configured to obtain a demand change of a production demand and a state change of a robot, determine a first task to be adjusted among the work tasks based on the demand change, and determine a second task to be adjusted among the work tasks based on the state change;

[0025] an allocation unit, configured to determine a real-time state of the robot based on the state change, reallocate the first task based on the real-time state to obtain an allocation result of the robot to the first task, and reallocate the second task based on the allocation result of the robot to the first task to obtain an allocation result of the robot to the second task;

[0026] The adjustment unit is used to dynamically adjust the work tasks based on the allocation results of the first task and the second task.

[0027] Preferably, the step determination module includes:

[0028] a step determination unit, configured to determine, from the work tasks, a robot that is performed by multiple robots in the same time period as a common working robot, and to obtain an initial execution step by matching corresponding steps from a preset step database based on the work tasks of the common working robots;

[0029] An operation model building unit is used to establish an operation model corresponding to the execution parameter type based on the execution parameters of the common operation robot in the initial execution step and based on the association relationship within the execution parameter type, wherein the operation model includes a path model, a manipulator motion model, a workspace model and a load-bearing model;

[0030] An optimization model building unit is used to build an operation optimization model of the operation model based on the operation model and in combination with three evaluation indicators: operation efficiency, operation complexity and operation resource consumption;

[0031] A multi-objective model building unit, used to determine parameter coordination features based on the association relationship between execution parameter types, determine model coordination features based on the association relationship between operation optimization models, and build a multi-type objective optimization model based on the parameter coordination features and model coordination features in combination with all operation optimization models;

[0032] A target determination unit is used to generate a particle swarm based on the parameter variables of a multi-type target optimization model and target optimization values ​​based on three evaluation indicators: operation efficiency, operation complexity, and operation resource consumption;

[0033] A strategy determination unit is used to set the fitness value of the particle swarm and the inertia weight based on the target optimization value, and determine the search strategy of the particle swarm based on the fitness value and inertia weight of the particle swarm, and obtain the optimal solution of the parameter variable according to the search strategy;

[0034] The optimization unit is used to optimize the initial execution steps according to the optimal solution to obtain the target execution steps.

[0035] Preferably, the optimization model building unit comprises:

[0036] An information determination unit, used to determine the optimization target of each operation model for three evaluation indicators, namely, operation efficiency, operation complexity and operation resource consumption, and to determine the type parameter variables of each operation model;

[0037] The model building unit is used to establish the association characteristics between the type parameter variables and the optimization objectives, and based on the association characteristics, determine the adjustment parameters-result data of the operation model, and build the operation optimization model of the operation model based on the adjustment parameters-result data.

[0038] Preferably, the step adjustment module includes:

[0039] A judgment unit, used to judge whether the monitoring data meets the standard state data under the target execution step, and if so, determine that the robot execution is normal, otherwise, determine that the robot execution is abnormal;

[0040] The real-time adjustment unit is used to make real-time adjustments to the target execution steps based on the real-time tasks and the robot status.

[0041] Preferably, the real-time adjustment unit includes:

[0042] The first adjustment unit is used to determine whether the real-time task has changed. If so, when the robot performs normally, the target execution step is adjusted in real time based on the step characteristics corresponding to the task change characteristics. When the robot performs abnormally, the abnormality is located based on the data difference between the monitoring data and the standard state data. Based on the abnormality location result, the robot is repaired in combination with the repair plan. After the fault is repaired, the target execution step is adjusted in real time based on the step characteristics corresponding to the task change characteristics.

[0043] The second adjustment unit is used for not adjusting the execution steps when the real-time task has not changed and the robot executes normally, and for locating the abnormality based on the data difference between the monitoring data and the standard state data when the robot executes abnormally, and for repairing the robot based on the abnormality locating result and the repair plan.

[0044] Preferably, the display module comprises:

[0045] A status display unit, used to display the status of the robot;

[0046] A task display unit, used for displaying work tasks;

[0047] A step display unit, used to display the target execution steps;

[0048] The switching unit is used to switch any of the status display unit, task display unit and step display unit.

[0049] Preferably, it also includes: a task management module for managing work tasks;

[0050] The task management module comprises:

[0051] A queue unit, used to organize the work tasks into a task queue in chronological order, and to set an optimization level and a scheduled execution time for each work task in the task queue;

[0052] The early warning alarm unit is used to monitor the execution of each work task in the task queue, and issue an early warning reminder when the set optimization level and scheduled execution time are not met.

[0053] Compared with the prior art, the present invention has achieved the following beneficial effects:

[0054] By determining the work tasks of each robot based on production demand and robot position, and adjusting the work tasks in real time and dynamically based on demand changes and state changes, determining the work tasks that multiple robots perform simultaneously, optimizing the initial execution steps of the work tasks, obtaining the target execution steps, monitoring the work status of the robots working according to the target execution steps in real time, obtaining monitoring data, and adjusting the target execution steps in real time in combination with real-time tasks; displaying the robot status, work tasks and target execution steps to ensure that industrial robots can perform tasks efficiently and accurately in complex production environments. In addition, it also supports real-time feedback and adaptive adjustment to cope with dynamic changes on the production line, realize accurate task scheduling of industrial robots, improve production efficiency and reduce production costs.

[0055] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in this application document.

[0056] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0058] Figure 1 This is a structural diagram of a task scheduling system for industrial robots in an embodiment of the present invention;

[0059] Figure 2 is a structural diagram of the task allocation module described in an embodiment of the present invention;

[0060] Figure 3 It is a structural diagram of the step adjustment module described in the embodiment of the present invention. DETAILED DESCRIPTION

[0061] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0062] Embodiment 1:

[0063] The embodiment of the present invention provides a task scheduling system for industrial robots. Figure 1 As shown, including:

[0064] The task allocation module is used to determine the work tasks of each robot based on production requirements and robot positions, and to dynamically adjust the work tasks in real time based on changes in requirements and status;

[0065] A step determination module is used to determine a work task in which multiple robots perform work tasks simultaneously, optimize the initial execution steps of the work task, and obtain the target execution steps;

[0066] The step adjustment module is used to monitor the operation status of the robot working according to the target execution steps in real time, obtain monitoring data, and adjust the target execution steps in real time in combination with real-time tasks;

[0067] The display module is used to display the robot status, work tasks and target execution steps.

[0068] In this embodiment, the production demand includes, for example, output demand, operation demand, and task demand.

[0069] In this embodiment, the state change is a change in the robot state, such as position, action, etc.

[0070] In this embodiment, the demand change is a change in generating demand.

[0071] In this embodiment, the operation task is a task that requires multiple robots to collaborate to complete.

[0072] In this embodiment, the initial execution step is determined according to a preset setting.

[0073] The beneficial effects of the above design scheme are: by determining the work tasks of each robot based on production demand and robot position, and adjusting the work tasks in real time and dynamically based on demand changes and state changes, determining the work tasks for multiple robots to perform work at the same time, optimizing the initial execution steps of the work tasks, obtaining the target execution steps, monitoring the working status of the robots working according to the target execution steps in real time, obtaining monitoring data, and adjusting the target execution steps in real time in combination with real-time tasks; displaying the robot status, work tasks and target execution steps to ensure that industrial robots can perform tasks efficiently and accurately in complex production environments. In addition, it also supports real-time feedback and adaptive adjustment to cope with dynamic changes on the production line, realize accurate task scheduling of industrial robots, improve production efficiency and reduce production costs.

[0074] Embodiment 2:

[0075] Based on Example 1, the embodiment of the present invention provides a task scheduling system for industrial robots, wherein the task allocation module includes:

[0076] A marking unit, used to determine a task list based on production requirements, and to first mark the tasks in the task list based on the order of the tasks, to second mark the tasks in the task list based on task execution collaboration characteristics, and to third mark the tasks in the task list based on task execution scope;

[0077] A first matching unit is used to match the tasks in the task list based on the third marking result and the robot position, and determine a set of robots that complete the tasks;

[0078] A second matching unit, configured to determine a robot combination that completes the task from the robot set based on the second marking result;

[0079] A third matching unit is used to determine a plurality of robot sequences that meet the production requirements based on the first marking result and the robot combination, wherein the robot sequence determines the order of the robots in the sequence according to the order of tasks;

[0080] The task determination unit is used to evaluate the task characteristics determined by the robot sequence based on the task evaluation model, and select the task allocation corresponding to the robot sequence with the best evaluation result to determine the working task of each robot.

[0081] In this embodiment, the robot combination may be one or more.

[0082] In this embodiment, the robot set is all robots that can complete the task, and the final determination requires further analysis.

[0083] In this embodiment, the task characteristics include the task execution status, the status of the robot during the task completion process, etc.

[0084] The beneficial effects of the above design scheme are: by matching robots and tasks from three aspects: task sequence, task execution collaboration characteristics and task execution scope, and finally evaluating the task characteristics determined by the robot sequence based on the task evaluation model, the task allocation corresponding to the robot sequence with the best evaluation result is selected to determine the work task of each robot, thereby ensuring the superiority of the work task of each robot and achieving optimal scheduling of the robots.

[0085] Embodiment 3:

[0086] Based on Example 2, the embodiment of the present invention provides a task scheduling system for an industrial robot, wherein the task determination unit includes:

[0087] An indicator determination unit, which is used for basic information and execution information of the robot, setting the performance evaluation index of the robot, the collaborative evaluation index of the robot to complete the task and the task completion efficiency evaluation index, and determining the type weight of each evaluation index type and the indicator weight of each evaluation index;

[0088] A standard determination unit is used to determine the initial evaluation standards for the performance evaluation index, the collaboration evaluation index and the task completion efficiency evaluation index based on the historical work task execution information of the robot, and to perform weighted processing on the evaluation standards corresponding to the performance evaluation index, the collaboration evaluation index and the task completion efficiency evaluation index based on the indicator weight, so as to obtain the intermediate evaluation standard, and to perform comprehensive weighted processing on the intermediate evaluation standard based on the type weight, so as to obtain the target evaluation standard;

[0089] A model building unit, used to build a task evaluation model based on the target evaluation standard;

[0090] A score evaluation unit, used for inputting the robot sequence into the task evaluation model in order to obtain a single task evaluation score corresponding to each robot task element in the robot sequence, and inputting all the robot sequences into the task evaluation model to obtain a total task evaluation score of the robot sequence;

[0091] The evaluation and analysis unit is used to determine the task connection evaluation score of the robot sequence based on the single task evaluation score and the total task evaluation score, and select an alternative robot sequence from the robot sequence whose single task evaluation score and task connection evaluation score both meet preset requirements, select the one with the highest total task evaluation score from the alternative robot sequences as the target robot sequence, and determine the work task of each robot based on the task allocation of the target robot sequence.

[0092] In this embodiment, the performance evaluation indicators include indicators such as degree of freedom, accuracy, speed and working range.

[0093] In this embodiment, the collaboration evaluation index includes the difficulty of collaboration completion, the resource consumption of collaboration completion, etc.

[0094] In this embodiment, the completion efficiency evaluation index includes the completion time.

[0095] In this embodiment, the type weights of the evaluation indicator types are performance type weights, writing type weights and efficiency type weights, and the specific weights are determined based on the impact of each type on task completion as determined by historical information.

[0096] In this embodiment, the indicator weights of the evaluation indicators are specific weights for degree of freedom, accuracy, speed and scope of work, difficulty of collaborative completion, resource consumption of collaborative completion, and completion time. The greater the impact on the evaluation indicator type, for example, speed has the greatest impact on the evaluation indicator, then a higher indicator weight is assigned to the speed.

[0097] In this embodiment, various evaluation criteria include, for example, determining the indicator value that needs to be met for each type of evaluation indicator, and the change in task evaluation, such as the change in the score, caused by the change in the indicator value.

[0098] In this embodiment, the single task evaluation score is the evaluation of each work task, and the total task evaluation score is the evaluation of all work tasks that complete the production requirements.

[0099] In this embodiment, the task connection evaluation score is an evaluation of the process of switching between two adjacent work tasks.

[0100] The beneficial effects of the above design scheme are: by evaluating the work tasks from three aspects: the robot's performance evaluation index, the robot's collaborative evaluation index for completing tasks, and the task completion efficiency evaluation index, and in the evaluation process, a comprehensive evaluation is conducted on the three aspects of single tasks, task connection status, and comprehensive tasks, to determine the work tasks of each robot, ensure the superiority of the work tasks of each robot, and achieve optimal scheduling of robots.

[0101] Embodiment 4:

[0102] Based on Example 1, the present invention provides a task scheduling system for industrial robots. Figure 2 As shown, the task allocation module further includes:

[0103] An analysis unit, configured to obtain a demand change of a production demand and a state change of a robot, determine a first task to be adjusted among the work tasks based on the demand change, and determine a second task to be adjusted among the work tasks based on the state change;

[0104] an allocation unit, configured to determine a real-time state of the robot based on the state change, reallocate the first task based on the real-time state to obtain an allocation result of the robot to the first task, and reallocate the second task based on the allocation result of the robot to the first task to obtain an allocation result of the robot to the second task;

[0105] The adjustment unit is used to dynamically adjust the work tasks based on the allocation results of the first task and the second task.

[0106] In this embodiment, the first task needs to be adjusted due to changes in demand, and the second task needs to be adjusted due to changes in the robot state. The robot state change includes changes in position, degrees of freedom, speed, etc.

[0107] The beneficial effect of the above design scheme is: through real-time dynamic adjustment of work tasks based on demand changes and status changes, real-time feedback and adaptive adjustment are achieved to cope with dynamic changes on the production line.

[0108] Embodiment 5:

[0109] Based on Example 1, an embodiment of the present invention provides a task scheduling system for an industrial robot, wherein the step determination module includes:

[0110] a step determination unit, configured to determine, from the work tasks, a robot that is performed by multiple robots in the same time period as a common working robot, and to obtain an initial execution step by matching corresponding steps from a preset step database based on the work tasks of the common working robots;

[0111] An operation model building unit is used to establish an operation model corresponding to the execution parameter type based on the execution parameters of the common operation robot in the initial execution step and based on the association relationship within the execution parameter type, wherein the operation model includes a path model, a manipulator motion model, a workspace model and a load-bearing model;

[0112] An optimization model building unit is used to build an operation optimization model of the operation model based on the operation model and in combination with three evaluation indicators: operation efficiency, operation complexity and operation resource consumption;

[0113] A multi-objective model building unit, used to determine parameter coordination features based on the association relationship between execution parameter types, determine model coordination features based on the association relationship between operation optimization models, and build a multi-type objective optimization model based on the parameter coordination features and model coordination features in combination with all operation optimization models;

[0114] A target determination unit is used to generate a particle swarm based on the parameter variables of a multi-type target optimization model and target optimization values ​​based on three evaluation indicators: operation efficiency, operation complexity, and operation resource consumption;

[0115] A strategy determination unit is used to set the fitness value of the particle swarm and the inertia weight based on the target optimization value, and determine the search strategy of the particle swarm based on the fitness value and inertia weight of the particle swarm, and obtain the optimal solution of the parameter variable according to the search strategy;

[0116] The optimization unit is used to optimize the initial execution steps according to the optimal solution to obtain the target execution steps.

[0117] In this embodiment, the execution parameters include path, manipulator motion, workspace and load bearing.

[0118] In this embodiment, the association relationship within the execution parameter type is the association relationship between each common robot within the path, within the manipulator action, within the workspace and within the load-bearing capacity, such as path intersection, manipulator action avoidance, workspace intersection, load-bearing limit, etc.

[0119] In this embodiment, the operation optimization model is to optimize the three evaluation indicators of operation efficiency, operation complexity and operation resource consumption according to the changes in the execution parameters.

[0120] In this embodiment, the association relationship between the execution parameter types is, for example, the association between the path and the workspace, the association between the robot action and the load-bearing capacity, etc.

[0121] In this embodiment, the association relationship between the running optimization models refers to the impact of the optimization of one model on another model.

[0122] In this embodiment, the multi-type objective optimization model can realize the neutralization analysis and optimization of all execution parameters.

[0123] In this embodiment, the target determination unit and the strategy determination unit are used to implement the execution process of the particle swarm optimization algorithm.

[0124] In this embodiment, the initial execution steps are optimized according to the optimal solution to optimize the execution parameters to achieve optimization of the steps, and finally the target execution parameters are obtained.

[0125] The beneficial effects of the above design scheme are: by analyzing the correlation between the four types of path, manipulator movement, workspace and load-bearing, as well as within the type, first establish the optimization model of each type and then integrate them to obtain a multi-type target optimization model, and finally use the particle swarm optimization algorithm to obtain the optimal solution to optimize the initial execution steps and obtain the target execution steps, thereby ensuring the optimality of the collaborative working process of multiple robots, improving production efficiency and reducing production costs.

[0126] Embodiment 6:

[0127] Based on Example 5, an embodiment of the present invention provides a task scheduling system for industrial robots, wherein the optimization model building unit includes:

[0128] An information determination unit, used to determine the optimization target of each operation model for three evaluation indicators, namely, operation efficiency, operation complexity and operation resource consumption, and to determine the type parameter variables of each operation model;

[0129] The model building unit is used to establish the association characteristics between the type parameter variables and the optimization objectives, and based on the association characteristics, determine the adjustment parameters-result data of the operation model, and build the operation optimization model of the operation model based on the adjustment parameters-result data.

[0130] In this embodiment, the adjustment parameter-result data is the change of the evaluation index value caused by the change of the execution parameter.

[0131] The beneficial effect of the above design scheme is: by establishing the association characteristics between the type parameter variables and the optimization objectives, and based on the association characteristics, determining the adjustment parameters-result data of the operation model, and constructing the operation optimization model of the operation model based on the adjustment parameters-result data, the optimization of subsequent execution steps is based on various types of optimization models.

[0132] Embodiment 7:

[0133] Based on Example 1, the present invention provides a task scheduling system for industrial robots. Figure 3 As shown, the step adjustment module includes:

[0134] A judgment unit, used to judge whether the monitoring data meets the standard state data under the target execution step, and if so, determine that the robot execution is normal, otherwise, determine that the robot execution is abnormal;

[0135] The real-time adjustment unit is used to make real-time adjustments to the target execution steps based on the real-time tasks and the robot status.

[0136] The beneficial effect of the above design scheme is: by judging the monitoring data and satisfying the standard status data under the target execution step, if yes, it is determined that the robot execution is normal, otherwise, it is determined that the robot execution is abnormal. Based on real-time tasks and combined with the robot status, the target execution steps are adjusted in real time to achieve rapid response to emergencies such as emergency tasks and robot failures, thereby achieving scheduling flexibility.

[0137] Embodiment 8:

[0138] Based on Example 7, the embodiment of the present invention provides a task scheduling system for an industrial robot, wherein the real-time adjustment unit includes:

[0139] The first adjustment unit is used to determine whether the real-time task has changed. If so, when the robot performs normally, the target execution step is adjusted in real time based on the step characteristics corresponding to the task change characteristics. When the robot performs abnormally, the abnormality is located based on the data difference between the monitoring data and the standard state data. Based on the abnormality location result, the robot is repaired in combination with the repair plan. After the fault is repaired, the target execution step is adjusted in real time based on the step characteristics corresponding to the task change characteristics.

[0140] The second adjustment unit is used for not adjusting the execution steps when the real-time task has not changed and the robot executes normally, and for locating the abnormality based on the data difference between the monitoring data and the standard state data when the robot executes abnormally, and for repairing the robot based on the abnormality locating result and the repair plan.

[0141] The beneficial effect of the above design scheme is: by setting different adjustment strategies to deal with different situations, it is possible to respond quickly to emergencies such as emergency tasks and robot failures, thereby achieving scheduling flexibility.

[0142] Embodiment 9:

[0143] Based on Example 1, the embodiment of the present invention provides a task scheduling system for an industrial robot, wherein the display module includes:

[0144] A status display unit, used to display the status of the robot;

[0145] A task display unit, used for displaying work tasks;

[0146] A step display unit, used to display the target execution steps;

[0147] The switching unit is used to arbitrarily switch the status display unit, the task display unit and the step display unit.

[0148] The beneficial effect of the above design scheme is: by displaying and switching the robot status, work tasks and target execution steps, the task status can be viewed in real time, making it easier for staff to understand the production situation in a timely manner.

[0149] Embodiment 10:

[0150] Based on Example 1, the embodiment of the present invention provides a task scheduling system for industrial robots, further comprising: a task management module for managing work tasks;

[0151] The task management module comprises:

[0152] A queue unit, used to organize the work tasks into a task queue in chronological order, and to set an optimization level and a scheduled execution time for each work task in the task queue;

[0153] The early warning alarm unit is used to monitor the execution of each work task in the task queue, and issue an early warning reminder when the set optimization level and scheduled execution time are not met.

[0154] The beneficial effect of the above design is that by managing and warning the tasks, when an abnormality occurs, the compilation staff can understand the situation in time and take corresponding measures.

[0155] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of this application document and its equivalent technology, the present invention is also intended to include these changes and variations.

Claims

1. A task scheduling system for industrial robots, characterized in that: include: The task allocation module is used to determine the work tasks of each robot based on production requirements and robot positions, and to dynamically adjust the work tasks in real time based on changes in requirements and status; A step determination module is used to determine a work task in which multiple robots perform work tasks simultaneously, optimize the initial execution steps of the work task, and obtain the target execution steps; The step adjustment module is used to monitor the operation status of the robot working according to the target execution steps in real time, obtain monitoring data, and adjust the target execution steps in real time in combination with real-time tasks; The display module is used to display the robot status, work tasks and target execution steps.

2. A task scheduling system for industrial robots according to claim 1, characterized in that: The task allocation module comprises: A marking unit, used to determine a task list based on production requirements, and to first mark the tasks in the task list based on the order of the tasks, to second mark the tasks in the task list based on task execution collaboration characteristics, and to third mark the tasks in the task list based on task execution scope; A first matching unit is used to match the tasks in the task list based on the third marking result and the robot position, and determine a set of robots that complete the tasks; A second matching unit, configured to determine a robot combination that completes the task from the robot set based on the second marking result; A third matching unit is used to determine a plurality of robot sequences that meet the production requirements based on the first marking result and the robot combination, wherein the robot sequence determines the order of the robots in the sequence according to the order of tasks; The task determination unit is used to evaluate the task characteristics determined by the robot sequence based on the task evaluation model, and select the task allocation corresponding to the robot sequence with the best evaluation result to determine the working task of each robot.

3. A task scheduling system for industrial robots according to claim 2, characterized in that: The task determination unit comprises: An indicator determination unit, which is used for basic information and execution information of the robot, setting the performance evaluation index of the robot, the collaborative evaluation index of the robot to complete the task and the task completion efficiency evaluation index, and determining the type weight of each evaluation index type and the indicator weight of each evaluation index; A standard determination unit is used to determine the initial evaluation standards for the performance evaluation index, the collaboration evaluation index and the task completion efficiency evaluation index based on the historical work task execution information of the robot, and to perform weighted processing on the evaluation standards corresponding to the performance evaluation index, the collaboration evaluation index and the task completion efficiency evaluation index based on the indicator weight, so as to obtain the intermediate evaluation standard, and to perform comprehensive weighted processing on the intermediate evaluation standard based on the type weight, so as to obtain the target evaluation standard; A model building unit, used to build a task evaluation model based on the target evaluation standard; A score evaluation unit, used for inputting the robot sequence into the task evaluation model in order to obtain a single task evaluation score corresponding to each robot task element in the robot sequence, and inputting all the robot sequences into the task evaluation model to obtain a total task evaluation score of the robot sequence; The evaluation and analysis unit is used to determine the task connection evaluation score of the robot sequence based on the single task evaluation score and the total task evaluation score, and select an alternative robot sequence from the robot sequence whose single task evaluation score and task connection evaluation score both meet preset requirements, select the one with the highest total task evaluation score from the alternative robot sequences as the target robot sequence, and determine the work task of each robot based on the task allocation of the target robot sequence.

4. The task scheduling system for industrial robots according to claim 1, characterized in that: The task allocation module further includes: An analysis unit, configured to obtain a demand change of a production demand and a state change of a robot, determine a first task to be adjusted among the work tasks based on the demand change, and determine a second task to be adjusted among the work tasks based on the state change; an allocation unit, configured to determine a real-time state of the robot based on the state change, reallocate the first task based on the real-time state to obtain an allocation result of the robot to the first task, and reallocate the second task based on the allocation result of the robot to the first task to obtain an allocation result of the robot to the second task; The adjustment unit is used to dynamically adjust the work tasks based on the allocation results of the first task and the second task.

5. The task scheduling system for industrial robots according to claim 1, characterized in that: The step determination module comprises: a step determination unit, configured to determine, from the work tasks, a robot that is performed by multiple robots in the same time period as a common working robot, and to obtain an initial execution step by matching corresponding steps from a preset step database based on the work tasks of the common working robots; An operation model building unit is used to establish an operation model corresponding to the execution parameter type based on the execution parameters of the common operation robot in the initial execution step and based on the association relationship within the execution parameter type, wherein the operation model includes a path model, a manipulator motion model, a workspace model and a load-bearing model; An optimization model building unit is used to build an operation optimization model of the operation model based on the operation model and in combination with three evaluation indicators: operation efficiency, operation complexity and operation resource consumption; A multi-objective model building unit, used to determine parameter coordination features based on the association relationship between execution parameter types, determine model coordination features based on the association relationship between operation optimization models, and build a multi-type objective optimization model based on the parameter coordination features and model coordination features in combination with all operation optimization models; A target determination unit is used to generate a particle swarm based on the parameter variables of a multi-type target optimization model and target optimization values ​​based on three evaluation indicators: operation efficiency, operation complexity, and operation resource consumption; A strategy determination unit is used to set the fitness value of the particle swarm and the inertia weight based on the target optimization value, and determine the search strategy of the particle swarm based on the fitness value and inertia weight of the particle swarm, and obtain the optimal solution of the parameter variable according to the search strategy; The optimization unit is used to optimize the initial execution steps according to the optimal solution to obtain the target execution steps.

6. A task scheduling system for industrial robots according to claim 5, characterized in that: The optimization model building unit comprises: An information determination unit, used to determine the optimization target of each operation model for three evaluation indicators, namely, operation efficiency, operation complexity and operation resource consumption, and to determine the type parameter variables of each operation model; The model building unit is used to establish the association characteristics between the type parameter variables and the optimization objectives, and based on the association characteristics, determine the adjustment parameters-result data of the operation model, and build the operation optimization model of the operation model based on the adjustment parameters-result data.

7. The task scheduling system for industrial robots according to claim 1, characterized in that: The step adjustment module includes: A judgment unit, used to judge whether the monitoring data meets the standard state data under the target execution step, and if so, determine that the robot execution is normal, otherwise, determine that the robot execution is abnormal; The real-time adjustment unit is used to make real-time adjustments to the target execution steps based on the real-time tasks and the robot status.

8. The task scheduling system for industrial robots according to claim 7, characterized in that: The real-time adjustment unit comprises: The first adjustment unit is used to determine whether the real-time task has changed. If so, when the robot performs normally, the target execution step is adjusted in real time based on the step characteristics corresponding to the task change characteristics. When the robot performs abnormally, the abnormality is located based on the data difference between the monitoring data and the standard state data. Based on the abnormality location result, the robot is repaired in combination with the repair plan. After the fault is repaired, the target execution step is adjusted in real time based on the step characteristics corresponding to the task change characteristics. The second adjustment unit is used for not adjusting the execution steps when the real-time task has not changed and the robot executes normally, and for locating the abnormality based on the data difference between the monitoring data and the standard state data when the robot executes abnormally, and for repairing the robot based on the abnormality locating result and the repair plan.

9. The task scheduling system for industrial robots according to claim 1, characterized in that: The display module comprises: A status display unit, used to display the status of the robot; A task display unit, used for displaying work tasks; A step display unit, used to display the target execution steps; The switching unit is used to switch any of the status display unit, task display unit and step display unit.

10. The task scheduling system for industrial robots according to claim 1, characterized in that: Also includes: Task management module, used to manage work tasks; The task management module comprises: A queue unit, used to organize the work tasks into a task queue in chronological order, and to set an optimization level and a scheduled execution time for each work task in the task queue; The early warning alarm unit is used to monitor the execution of each work task in the task queue, and issue an early warning reminder when the set optimization level and scheduled execution time are not met.

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