A task scheduling system for industrial robots

Abstract

The present invention provides a task scheduling system for industrial robots, which determines the work tasks of each robot based on production demand and robot position, and dynamically adjusts the work tasks in real time based on demand changes and state changes, determines the work tasks for multiple robots to perform work at the same time, optimizes the initial execution steps of the work tasks, obtains the target execution steps, monitors the work status of the robots working according to the target execution steps in real time, obtains monitoring data, and adjusts the target execution steps in real time in combination with real-time tasks; displays the robot status, work tasks and target execution steps, and ensures that the industrial robots can perform tasks efficiently and accurately in a complex production environment. 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.

Description

Technical Field

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

[0002] The wide application of industrial robots not only improves production efficiency, reduces production costs, but also greatly reduces the labor intensity of workers and the incidence of production accidents, providing huge competitive advantages for enterprises. With the continuous progress of technology, industrial robots will continue to play an important role in promoting the development of various industries.

[0003] The task scheduling of industrial robots mainly involves reasonably allocating and scheduling the actions of industrial robots under given time and space conditions according to task requirements and resource limitations to achieve the optimal production effect. This process is particularly important in the industrial automation industry because it directly relates to production efficiency and product quality. The intelligent scheduling of industrial robots not only includes the task allocation of a single robot, but also includes the coordination and optimization in the case of multiple robots executing tasks simultaneously.

[0004] How to achieve the task scheduling of industrial robots, improve production efficiency and reduce production costs is an urgent problem to be solved at present. Summary of the Invention

[0005] The present invention provides a task scheduling system for industrial robots to solve the problems raised in the background art.

[0006] A task scheduling system for industrial robots includes:

[0007] A task allocation module, configured to determine the work tasks of each robot based on production requirements and robot positions, and perform real-time dynamic adjustment of the work tasks based on demand changes and status changes;

[0008] A step determination module, configured to determine the operation tasks for multiple robots to perform operations simultaneously, optimize the initial execution steps of the operation tasks to obtain target execution steps;

[0009] A step adjustment module, configured to monitor the operation status of the robots working according to the target execution steps in real time to obtain monitoring data, and perform real-time adjustment of the target execution steps in combination with real-time tasks;

[0010] A display module, configured to display the robot status, work tasks and target execution steps.

[0011] Preferably, the task allocation module includes:

[0012] A marking unit for determining a task list based on production requirements, performing a first marking on the tasks in the task list based on the sequence before and after the tasks, performing a second marking on the tasks in the task list based on the collaborative characteristics of task execution, and performing a third marking on the tasks in the task list based on the task execution scope;

[0013] A first matching unit for matching the tasks in the task list based on the third marking result and in combination with the robot positions to determine a set of robots for completing the tasks;

[0014] A second matching unit for determining a combination of robots for completing the tasks from the set of robots based on the second marking result;

[0015] A third matching unit for determining multiple robot sequences that meet the production requirements based on the first marking result and based on the robot combination, where the robot sequences determine the sorting of the robots in the sequence according to the order of the tasks;

[0016] A task determination unit for evaluating the task characteristics determined by the robot sequences based on a task evaluation model and selecting the tasks corresponding to the robot sequence with the best evaluation result to determine the work tasks of each robot.

[0017] Preferably, the task determination unit includes:

[0018] An index determination unit for setting performance evaluation indexes of robots, collaborative evaluation indexes for robots to complete tasks collaboratively, and task completion efficiency evaluation indexes based on the basic information and execution information of the robots, and determining the type weights of each evaluation index type and the index weights of each evaluation index;

[0019] A standard determination unit for determining initial evaluation standards for performance evaluation indexes, collaborative evaluation indexes, and task completion efficiency evaluation indexes based on the historical work task execution information of the robots, and performing weighted processing on the evaluation standards corresponding to the performance evaluation indexes, collaborative evaluation indexes, and task completion efficiency evaluation indexes based on the index weights and type weights to obtain intermediate evaluation standards, and performing comprehensive weighted processing on the intermediate evaluation standards based on the type weights to obtain target evaluation standards;

[0020] A model construction unit for constructing a task evaluation model based on the target evaluation standards;

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

[0022] An 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, select alternative robot sequences from the robot sequence where both the single-task evaluation score and the task connection evaluation score meet the 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 tasks of each robot based on the task assignment of the target robot sequence.

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

[0024] An analysis unit is used to obtain the demand change of the production demand and the status change of the robot, determine the first task to be adjusted in the work task based on the demand change, and determine the second task to be adjusted in the work task based on the status change;

[0025] An assignment unit is used to determine the real-time status of the robot based on the status change, reassign the first task based on the real-time status to obtain the assignment result of the robot for the first task, and reassign the second task based on the assignment result of the robot for the first task to obtain the assignment result of the robot for the second task;

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

[0027] Preferably, the step determination module includes:

[0028] A step determination unit is used to determine the robots that perform work tasks by multiple robots during the same time period from the work tasks as co-operating robots, and match the corresponding steps from the preset step database based on the operation tasks of the co-operating robots to obtain the initial execution steps;

[0029] An operation model construction unit is used to establish an operation model corresponding to the execution parameter type based on the execution parameters of the co-operating robots in the initial execution steps and the association relationship within the execution parameter type, where the operation model includes a path model, a manipulator motion model, a work space model, and a load-bearing model;

[0030] An optimization model construction unit is used to construct an operation optimization model of the operation model based on the operation model, combined with three evaluation indicators of operation efficiency, operation complexity, and operation resource consumption;

[0031] A multi-objective model construction unit is used to determine the parameter collaboration characteristics based on the association relationship between the execution parameter types, determine the model collaboration characteristics based on the association relationship between the operation optimization models, and construct a multi-type target optimization model by combining all the operation optimization models based on the parameter collaboration characteristics and the model collaboration characteristics;

[0032] A target determination unit, configured to generate a particle swarm based on parameter variables of a multi-type target optimization model, and target optimization values of three evaluation indicators including job efficiency, job complexity, and job resource consumption;

[0033] A strategy determination unit, configured to set the fitness value of the particle swarm and set 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 variables according to the search strategy;

[0034] An optimization unit, configured to optimize the initial execution steps according to the optimal solution to obtain the target execution steps.

[0035] Preferably, the optimization model construction unit includes:

[0036] An information determination unit, configured to determine the optimization objectives of each running model for three evaluation indicators including job efficiency, job complexity, and job resource consumption, and determine the type parameter variables of each running model;

[0037] A model construction unit, configured to establish an association feature between the type parameter variables and the optimization objectives, and based on the association feature, determine the adjustment parameter - result data of the running model, and construct a running optimization model of the running model based on the adjustment parameter - result data.

[0038] Preferably, the step adjustment module includes:

[0039] A judgment unit, configured to judge the monitoring data and the standard state data that meet the target execution steps. If so, it is determined that the robot is executing normally, otherwise, it is determined that the robot is executing abnormally;

[0040] A real-time adjustment unit, configured to perform real-time adjustment on the target execution steps based on real-time tasks and in combination with the robot state.

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

[0042] A first adjustment unit, configured to judge whether the real-time task has changed. If so, when the robot is executing normally, perform real-time adjustment on the target execution steps based on the step features corresponding to the task change characteristics. When the robot is executing abnormally, perform abnormal location based on the data difference between the monitoring data and the standard state data, and based on the abnormal location result, perform fault repair on the robot in combination with the repair plan. After the fault repair is completed, perform real-time adjustment on the target execution steps based on the step features corresponding to the task change characteristics;

[0043] The second adjustment unit is used to, after the real-time task has not changed, when the robot is executing normally, not perform execution step adjustment, and when the robot is executing abnormally, perform anomaly localization based on the data difference between the monitoring data and the standard state data, and based on the anomaly localization result, repair the robot failure in combination with the repair plan.

[0044] Preferably, the display module includes:

[0045] A status display unit for displaying the status of the robot;

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

[0047] A step display unit for displaying the target execution steps;

[0048] A switching unit for arbitrarily switching between the status display unit, the task display unit, and the step display unit.

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

[0050] The task management module includes:

[0051] A queue unit for forming a task queue of work tasks in chronological order and setting an optimization level and a predetermined execution time for each work task in the task queue;

[0052] A warning alarm unit for monitoring the execution of each work task in the task queue and giving a warning reminder when the set optimization level and the predetermined 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 requirements and the robot's position, and performing real-time dynamic adjustment of the work tasks based on demand changes and status changes, determining the operation tasks for multiple robots to perform operations simultaneously, optimizing the initial execution steps of the operation tasks to obtain the target execution steps, performing real-time monitoring of the operation status of the robots working according to the target execution steps to obtain monitoring data, and combining with the real-time task, performing real-time adjustment of the target execution steps; displaying the robot status, work tasks, and target execution steps to ensure that the industrial robot can efficiently and accurately perform tasks in a complex production environment. In addition, it also supports real-time feedback and adaptive adjustment to cope with dynamic changes on the production line, realizing precise task scheduling of the industrial robot, improving production efficiency, and reducing production costs.

[0055] Other features and advantages of the present invention will be set forth in the following description, and in part will be obvious from the description, or may be learned by practice of the present invention. The objectives and other advantages of the present invention may be realized and attained by the structure particularly pointed out in the specification of this application.

[0056] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Description of the Drawings

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

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

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

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

[0061] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for explaining and illustrating 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 an industrial robot, as Figure 1 shown, including:

[0064] A task allocation module, configured to determine the work tasks of each robot based on production requirements and robot positions, and perform real-time dynamic adjustment of the work tasks based on demand changes and status changes;

[0065] A step determination module, configured to determine the operation tasks for multiple robots to perform operations simultaneously, and optimize the initial execution steps of the operation tasks to obtain the target execution steps;

[0066] A step adjustment module, configured to monitor the operation status of the robots working according to the target execution steps in real time to obtain monitoring data, and perform real-time adjustment of the target execution steps in combination with real-time tasks;

[0067] A display module, configured to display the robot status, work tasks, and target execution steps.

[0068] In this embodiment, the production requirements are, for example, output requirements, operation requirements, task requirements, etc.

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

[0070] In this embodiment, the requirement change is the change of the generated requirements.

[0071] In this embodiment, the operation task is a task that requires multiple robots to cooperate to complete in the work tasks.

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

[0073] The beneficial effects of the above design are as follows: By determining the work tasks of each robot based on the production requirements and the robot positions, and based on the requirement changes and state changes, dynamically adjusting the work tasks in real time, determining the operation tasks for multiple robots to perform the tasks simultaneously, optimizing the initial execution steps of the operation tasks to obtain the target execution steps, monitoring the operation status of the robots working according to the target execution steps in real time to obtain the monitoring data, and combining the real-time tasks to adjust the target execution steps in real time; displaying the robot state, work tasks, and target execution steps to ensure that the industrial robots can efficiently and accurately execute tasks in a complex production environment. In addition, it also supports real-time feedback and adaptive adjustment to cope with the dynamic changes on the production line, realizing precise task scheduling of industrial robots, improving production efficiency, and reducing production costs.

[0074] Embodiment 2:

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

[0076] A marking unit for determining a task list based on production requirements, performing a first mark on the tasks in the task list based on the task sequence before and after, performing a second mark on the tasks in the task list based on the task execution cooperation characteristics, and performing a third mark on the tasks in the task list based on the task execution scope;

[0077] A first matching unit for matching the tasks in the task list based on the third marking result and combining the robot positions to determine the set of robots for completing the tasks;

[0078] A second matching unit for determining the combination of robots for completing the tasks from the set of robots based on the second marking result;

[0079] A third matching unit, configured to determine, based on the first marking result and the robot combination, multiple robot sequences that meet the production requirements, where the sorting of the robots in the robot sequence is determined according to the order of tasks;

[0080] A task determination unit, configured to evaluate the task features determined by the robot sequence based on a task evaluation model, and select the task assignment corresponding to the robot sequence with the best evaluation result to determine the work tasks of each robot.

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

[0082] In this embodiment, the robot set is all the robots capable of completing the task, and the final determination requires further analysis.

[0083] In this embodiment, the task features include task execution conditions, the conditions of the robots during the task completion process, etc.

[0084] The beneficial effects of the above design solution are as follows: By matching the robots and tasks from three aspects: the order before and after the task, the collaborative features of task execution, and the task execution scope, and finally evaluating the task features determined by the robot sequence based on the task evaluation model, selecting the task assignment corresponding to the robot sequence with the best evaluation result to determine the work tasks of each robot, the superiority of the work tasks of each determined robot is ensured, and the optimal scheduling of the robots is achieved.

[0085] Embodiment 3:

[0086] Based on Embodiment 2, an embodiment of the present invention provides a task scheduling system for industrial robots. The task determination unit includes:

[0087] An index determination unit, configured to set performance evaluation indexes for robots based on the basic information and execution information of the robots, collaborative evaluation indexes for robots to complete tasks collaboratively, and task completion efficiency evaluation indexes, and determine the type weights of each evaluation index type and the index weights of each evaluation index;

[0088] A standard determination unit, configured to determine initial evaluation standards for the performance evaluation index, collaborative evaluation index, and task completion efficiency evaluation index based on the historical work task execution information of the robots, and perform weighted processing on the evaluation standards corresponding to the performance evaluation index, collaborative evaluation index, and task completion efficiency evaluation index based on the index weights and type weights to obtain intermediate evaluation standards, and perform comprehensive weighted processing on the intermediate evaluation standards based on the type weights to obtain target evaluation standards;

[0089] A model construction unit, configured to construct a task evaluation model based on the target evaluation standards;

[0090] A fractional evaluation unit for separately inputting the robot sequence into a task evaluation model in sequence to obtain single-task evaluation scores corresponding to each robot task element in the robot sequence, and inputting the entire robot sequence into the task evaluation model to obtain the total task evaluation score of the robot sequence;

[0091] An evaluation and analysis unit for determining the task connection evaluation score of the robot sequence based on the single-task evaluation score and the total task evaluation score, selecting an alternative robot sequence in which both the single-task evaluation score and the task connection evaluation score meet preset requirements from the robot sequence, selecting the one with the highest total task evaluation score as the target robot sequence from the alternative robot sequences, and determining the work tasks of each robot based on the task assignment of the target robot sequence.

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

[0093] In this embodiment, the collaboration evaluation indicators include the difficulty of collaboration completion, resource consumption for collaboration completion, etc.

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

[0095] In this embodiment, the type weights of the evaluation indicator types are the performance type weight, the writing type weight, and the efficiency type weight. The specific weight sizes are determined according to the influence degree of each type on task completion determined based on historical information.

[0096] In this embodiment, the indicator weights of the evaluation indicators are the specific weights for degrees of freedom, precision, speed, and working range, the difficulty of collaboration completion, resource consumption for collaboration completion, and completion time. The greater the influence on the evaluation indicator type, for example, the greatest influence of speed on the evaluation indicators, the higher the indicator weight is given to speed.

[0097] In this embodiment, various evaluation criteria are, for example, determining the indicator values that need to be met for each type of evaluation indicator, and the task evaluation such as score changes brought about by changes in the indicator value numerical values.

[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 for completing the production requirements.

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

[0100] The beneficial effects of the above design solution are as follows: By evaluating the work tasks from three aspects, namely the performance evaluation indicators of the robot, the collaboration evaluation indicators for the robot to complete tasks collaboratively, and the task completion efficiency evaluation indicators, and comprehensively evaluating single tasks, task connection situations, and comprehensive tasks during the evaluation process, the work tasks of each robot are determined, ensuring the superiority of the work tasks of each determined robot and achieving the optimal scheduling of the robot.

[0101] Embodiment 4:

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

[0103] An analysis unit for obtaining the demand changes of production requirements and the state changes of the robot, determining the first task to be adjusted in the work task based on the demand changes, and determining the second task to be adjusted in the work task based on the state changes;

[0104] An allocation unit for determining the real-time state of the robot based on the state changes, reallocating the first task based on the real-time state to obtain the allocation result of the robot for the first task, and reallocating the second task based on the allocation result of the robot for the first task to obtain the allocation result of the robot for the second task;

[0105] An adjustment unit for dynamically adjusting the work task 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 demand changes, and the second task needs to be adjusted due to changes in the robot state. The changes in the robot state include changes in position, degrees of freedom, speed, etc.

[0107] The beneficial effects of the above design solution are as follows: By making real-time dynamic adjustments to the work task based on demand changes and state changes, real-time feedback and adaptive adjustment are achieved to cope with the dynamic changes on the production line.

[0108] Embodiment 5:

[0109] Based on Embodiment 1, an embodiment of the present invention provides a task scheduling system for industrial robots. The step determination module includes:

[0110] A step determination unit for determining the robots that perform work tasks during the same time period among the work tasks as the co-operating robots, and matching the corresponding steps from a preset step database based on the work tasks of the co-operating robots to obtain the initial execution steps;

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

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

[0113] A multi-objective model construction unit is used to determine the parameter collaboration characteristics based on the association relationship between the execution parameter types, determine the model collaboration characteristics based on the association relationship between the operation optimization models, and construct a multi-type objective optimization model by combining all the operation optimization models based on the parameter collaboration characteristics and the model collaboration characteristics;

[0114] A target determination unit is used to generate a particle swarm based on the parameter variables of the multi-type objective optimization model and the target optimization values of the three evaluation indicators of 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 set the inertia weight based on the target optimization value, and determine the search strategy of the particle swarm based on the fitness value and the inertia weight of the particle swarm, and obtain the optimal solution of the parameter variables according to the search strategy;

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

[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 collaborative robot within the path, within the manipulator motion, within the workspace, and within the load-bearing, such as path intersection, manipulator motion 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 change of 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 manipulator motion and the load-bearing, etc.

[0121] In this embodiment, the association relationship between the operation optimization models is the influence situation brought by one model optimization to another model.

[0122] In this embodiment, the multi-type target optimization model can achieve the neutral analysis and optimization of all execution parameters.

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

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

[0125] The beneficial effects of the above design solution are as follows: By analyzing the association relationships between and within the four types of path, manipulator movement, workspace, and load-bearing, establishing the optimization models of each type first and then integrating them to obtain a multi-type target optimization model, and finally using the particle swarm optimization algorithm to obtain the optimal solution to optimize the initial execution steps to obtain the target execution steps, ensuring the optimality of the collaborative work process of multiple robots, improving production efficiency, and reducing production costs.

[0126] Embodiment 6:

[0127] Based on Embodiment 5, an embodiment of the present invention provides a task scheduling system for industrial robots. The optimization model construction unit includes:

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

[0129] A model construction unit, configured to establish the association characteristics between the type parameter variables and the optimization objectives, and based on the association characteristics, determine the adjustment parameter - result data of the operation model, and construct an operation optimization model of the operation model based on the adjustment parameter - result data.

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

[0131] The beneficial effects of the above design solution are as follows: By establishing the association characteristics between the type parameter variables and the optimization objectives, and based on the association characteristics, determining the adjustment parameter - result data of the operation model, and constructing an operation optimization model of the operation model based on the adjustment parameter - result data, it provides a basis for the optimization of subsequent execution steps through the optimization models of each type.

[0132] Embodiment 7:

[0133] Based on Embodiment 1, an embodiment of the present invention provides a task scheduling system for industrial robots, as Figure 3 shown, the step adjustment module includes:

[0134] A judgment unit for judging the monitoring data and the standard state data that meet the requirements under the target execution step. If so, it is determined that the robot is executing normally; otherwise, it is determined that the robot is executing abnormally.

[0135] A real-time adjustment unit for making real-time adjustments to the target execution step based on the real-time task and in combination with the robot state.

[0136] The beneficial effects of the above design solution are as follows: By judging the monitoring data and the standard state data that meet the requirements under the target execution step, if so, it is determined that the robot is executing normally; otherwise, it is determined that the robot is executing abnormally. Based on the real-time task and in combination with the robot state, real-time adjustments are made to the target execution step, so as to achieve a rapid response to emergencies such as urgent tasks and robot failures, and realize scheduling flexibility.

[0137] Embodiment 8:

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

[0139] A first adjustment unit for judging whether the real-time task has changed. If so, when the robot is executing normally, real-time adjustments are made to the target execution step based on the step characteristics corresponding to the task change characteristics; when the robot is executing abnormally, abnormal positioning is performed based on the data difference between the monitoring data and the standard state data, and based on the abnormal positioning result, the robot is repaired for faults in combination with the repair plan. After the fault repair is completed, real-time adjustments are made to the target execution step based on the step characteristics corresponding to the task change characteristics.

[0140] A second adjustment unit for, when the real-time task has not changed, not making any adjustment to the execution step when the robot is executing normally, and when the robot is executing abnormally, performing abnormal positioning based on the data difference between the monitoring data and the standard state data, and repairing the robot for faults in combination with the repair plan based on the abnormal positioning result.

[0141] The beneficial effects of the above design solution are as follows: By setting different adjustment strategies for different situations, a rapid response to emergencies such as urgent tasks and robot failures is achieved, and scheduling flexibility is realized.

[0142] Embodiment 9:

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

[0144] A status display unit for displaying the robot status.

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

[0146] A step display unit for displaying target execution steps;

[0147] A switching unit for arbitrarily switching between a status display unit, a task display unit, and a step display unit.

[0148] The beneficial effect of the above design solution is that by switching the display of the robot status, work tasks, and target execution steps, the task status can be viewed in real time, facilitating the staff to timely understand the production situation.

[0149] Embodiment 10:

[0150] Based on Embodiment 1, an embodiment of the present invention provides a task scheduling system for an industrial robot, further including: a task management module for managing work tasks;

[0151] The task management module includes:

[0152] A queue unit for forming a task queue of work tasks in chronological order and setting an optimization level and a predetermined execution time for each work task in the task queue;

[0153] A warning and alarm unit for monitoring the execution of each work task in the task queue and giving a warning reminder when the set optimization level and predetermined execution time are not met.

[0154] The beneficial effect of the above design solution is that by managing and warning about tasks, when an abnormality occurs, the compilation staff can timely understand the situation 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 fall within the scope of this application document and its equivalent technologies, the present invention also intends to include these changes and modifications.

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 demand and robot position, and to dynamically adjust the work tasks in real time based on changes in production demand and robot status; The step determination module is used to determine the work tasks in which multiple robots perform work simultaneously in the work tasks, optimize the initial execution steps of the work tasks, and obtain the target execution steps, including: 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 obtain a target optimization value 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; An optimization unit, used to optimize the initial execution steps according to the optimal solution to obtain 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 is used to set the performance evaluation indicator of the robot, the collaborative evaluation indicator of the robot to complete the task and the task completion efficiency evaluation indicator according to the basic information and execution information of the robot, and determine the type weight of each evaluation indicator type and the indicator weight of each evaluation indicator; A standard determination unit is used to determine the initial evaluation standards corresponding to 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 initial evaluation standards corresponding to the performance evaluation index, the collaboration evaluation index and the task completion efficiency evaluation index based on the index weight to obtain the intermediate evaluation standard, and to perform comprehensive weighted processing on the intermediate evaluation standard based on the type weight 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 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.

6. 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 executes normally, otherwise, determine that the robot executes abnormally; 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.

7. A task scheduling system for industrial robots according to claim 6, 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.

8. 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.

9. 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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