Automatic installation system and method using industrial-grade mechanical arm

Abstract

The invention belongs to the technical field of smart factories. The invention provides an automatic installation system and method utilizing an industrial grade mechanical arm. The method comprises the steps that a plurality of assembly tasks to be implemented are obtained, and task attributes of all the assembly tasks are matched to obtain industrial-grade mechanical arms of corresponding types; according to the assembly efficiency data and the task attributes of the industrial-grade mechanical arms of the corresponding types, assembly difficulty evaluation is conducted, and corresponding task difficulty evaluation values are obtained; the task difficulty evaluation value corresponding to each assembly task, the regional assembly line length and the assembly line transmission speed are input into a Transform prediction model, and the arrangement number of industrial-grade mechanical arms corresponding to each assembly task is obtained; and all the industrial-grade mechanical arms are controlled to move to the area assembly line corresponding to all the assembly tasks, and all the assembly tasks of the motorcycle are cooperatively completed. According to the assembly task, the appropriate number of industrial-grade mechanical arms are determined for the assembly line of each area, and the motorcycle assembly efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart factories, and in particular to an automated installation system and method using an industrial-grade robotic arm. Background Art

[0002] With the development and progress of science and technology, motorcycle assembly lines have realized the use of industrial-grade robotic arms for automated assembly, greatly improving the production efficiency of motorcycle assembly. The existing motorcycle assembly line is equipped with multiple industrial-grade robotic arms in sequence, which can be assigned different assembly tasks, that is, each group of industrial-grade robotic arms is dedicated to completing a specific assembly process of the motorcycle, such as Group A is used to realize the assembly of vibration reduction, and Group B is used to realize the assembly of headlights. At the same time, each group includes a certain number of industrial-grade robotic arms, and these industrial-grade robotic arms in the group are used to assemble different motorcycles, which can further improve the assembly rate.

[0003] However, the layout position of the industrial-grade robotic arms on the existing motorcycle assembly line based on industrial-grade robotic arms is fixed, and the assembly tasks of the industrial-grade robotic arms are also fixed, while the motorcycle assembly tasks are variable. It can be seen that the existing motorcycle assembly line based on industrial-grade robotic arms cannot dynamically adjust the layout mode and task allocation of the industrial-grade robotic arms according to the changes in the motorcycle assembly tasks, resulting in that the motorcycle assembly efficiency cannot meet the actual needs and needs to be improved. Summary of the invention

[0004] In this regard, the present invention provides an automated installation method, system, electronic device, computer storage medium and computer program product using an industrial-grade robotic arm to solve the above-mentioned technical problems.

[0005] The present invention discloses an automated installation method using an industrial-grade robotic arm, the method comprising the following steps: Acquire a number of assembly tasks to be implemented, and match the task attributes of each of the assembly tasks to obtain an industrial-grade robotic arm of a corresponding type; Performing an assembly difficulty assessment based on the assembly efficiency data of the corresponding type of industrial-grade robotic arm and the task attributes to obtain a corresponding task difficulty assessment value; Input the task difficulty assessment value, regional assembly line length, and assembly line transmission speed corresponding to each assembly task into the Transformer prediction model to obtain the number of industrial-grade robotic arms corresponding to each assembly task predicted by the Transformer prediction model; The industrial-grade robotic arms of the corresponding type and number are controlled to go to the regional assembly lines corresponding to the respective assembly tasks, and collaboratively complete the respective assembly tasks for the motorcycle.

[0006] Preferably, the obtaining of a number of assembly tasks to be performed includes: Receiving assembly task data sent by an assembly task publishing server, and determining whether the assembly task data is structured data or unstructured data; If the assembly task data is structured data, each assembly task is directly parsed from the assembly task data; If the assembly task data is unstructured data, calling a semantic analysis module to perform semantic analysis on the assembly task data to obtain each of the assembly tasks; Wherein, each of the assembly tasks is arranged in order of assembly.

[0007] Preferably, matching the task attributes of each assembly task to obtain a corresponding type of industrial-grade robotic arm includes: Communicate with each industrial-grade robotic arm in an idle state to obtain each locally installed assembly function plug-in and assembly function description information configured corresponding to each assembly task plug-in; Semantically matching the task attributes of the assembly task with the assembly function description information corresponding to each of the assembly task plug-ins, and if the match is successful, associating the industrial-grade robot arm with the assembly task; Repeat the above steps to complete the association between all industrial-grade robot arms in the idle state and the assembly tasks.

[0008] Preferably, the performing assembly difficulty assessment according to the assembly efficiency data of the corresponding type of industrial-grade robot arm and the task attributes to obtain the corresponding task difficulty assessment value includes: Obtaining a motorcycle assembly structure diagram corresponding to the assembly task, identifying each assembly node in the motorcycle assembly structure diagram according to the task attribute, and extracting geometric features of each assembly node; wherein the geometric features include the depth of the assembly node, the entrance width of the assembly node, and the orientation of the assembly node; The assembly difficulty is evaluated based on the assembly efficiency data of the corresponding type of industrial-grade robotic arms and the geometric features to obtain a corresponding task difficulty evaluation value; wherein the assembly efficiency data is obtained based on historical data of the same type of industrial-grade robotic arms performing assembly tasks with the same task attributes.

[0009] Preferably, the task difficulty assessment value, regional assembly line length, and assembly line transmission speed corresponding to each assembly task are input into a Transformer prediction model to obtain the number of industrial-grade robotic arms corresponding to each assembly task predicted by the Transformer prediction model, including: Inputting the task difficulty evaluation value corresponding to each of the assembly tasks, the length of the regional assembly line, and the transmission speed of the assembly line into the Transformer prediction model, and the Transformer prediction model predicts the first layout quantity corresponding to the assembly task; Acquire the received record data of the assembly task, calculate the number of executions of similar assembly tasks corresponding to each assembly task according to the record data, and determine an adjustment weight according to the execution number; wherein the adjustment weight is a value greater than 1; A second placement quantity is calculated based on the adjustment weight and the first placement quantity.

[0010] Preferably, the controlling of the arranged number of industrial-grade robotic arms of corresponding types to go to the regional assembly lines corresponding to the respective assembly tasks comprises: Randomly select the deployed number of industrial-grade robotic arms from the industrial-grade robotic arms associated with the assembly task, and control the selected industrial-grade robotic arms to go to the regional assembly line corresponding to the assembly task; After arriving at the corresponding regional assembly line, each industrial-grade robotic arm is evenly spaced and combined with a ground fixture to achieve fixation.

[0011] The present invention also discloses an automated installation system using an industrial-grade robotic arm, the system comprising a control module and a storage module, the control module being electrically connected to the storage module and calling a program code in the storage module to implement the following processing steps: Acquire a number of assembly tasks to be implemented, and match the task attributes of each of the assembly tasks to obtain an industrial-grade robotic arm of a corresponding type; Performing an assembly difficulty assessment based on the assembly efficiency data of the corresponding type of industrial-grade robotic arm and the task attributes to obtain a corresponding task difficulty assessment value; Input the task difficulty assessment value, regional assembly line length, and assembly line transmission speed corresponding to each assembly task into the Transformer prediction model to obtain the number of industrial-grade robotic arms corresponding to each assembly task predicted by the Transformer prediction model; The industrial-grade robotic arms of the corresponding type and number are controlled to go to the regional assembly lines corresponding to the respective assembly tasks, and collaboratively complete the respective assembly tasks for the motorcycle.

[0012] The present invention also discloses an electronic device, characterized in that it is applied to the system as described in any of the preceding items, and comprises: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor.

[0013] The present invention further discloses a computer storage medium, characterized in that: it is applied to the system as described in any of the preceding items, and the computer storage medium stores a computer program.

[0014] The present invention also discloses a computer program product, characterized in that: it is applied to the system as described in any of the preceding items, and includes a computer program stored on a non-transitory computer-readable medium.

[0015] The beneficial effect is that the solution of the present invention changes the traditional way of fixedly setting industrial-grade robotic arms on the existing motorcycle assembly line, and can determine the appropriate number of industrial-grade robotic arms of corresponding types for each regional assembly line according to the specific assembly task, thereby ensuring that these industrial-grade robotic arms work together to complete the assembly of all motorcycles on the regional assembly line, which can significantly improve the efficiency of motorcycle assembly. Moreover, since the number of industrial-grade robotic arms arranged in each regional assembly line is determined based on the goal that the motorcycles entering the regional assembly line can just complete the corresponding assembly task before leaving the regional assembly line, this can also minimize the number of industrial-grade robotic arms arranged, thereby reducing the arrangement cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flow chart of an automated installation method using an industrial-grade robotic arm disclosed in an embodiment of the present invention; Figure 2 It is a schematic diagram of a process for determining a task difficulty evaluation value disclosed in an embodiment of the present invention; Figure 3 It is a structural schematic diagram of an automated installation system using an industrial-grade robotic arm disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following is a description of the implementation of the present application by specific specific embodiments. People familiar with the technology can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0019] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0020] The existing motorcycle assembly line is equipped with multiple industrial-grade robotic arms in sequence, which can be assigned different assembly tasks, that is, each group of industrial-grade robotic arms is dedicated to completing a specific assembly process of the motorcycle, for example, group A is used to realize the assembly of vibration reduction, group B is used to realize the assembly of headlights, etc. At the same time, each group includes a certain number of industrial-grade robotic arms, and these industrial-grade robotic arms in the group are used to assemble different motorcycles, which can further improve the assembly rate.

[0021] However, the layout position of the industrial-grade robotic arms on the existing motorcycle assembly line based on industrial-grade robotic arms is fixed, and the assembly tasks of the industrial-grade robotic arms are also fixed, while the motorcycle assembly tasks are variable. It can be seen that the existing motorcycle assembly line based on industrial-grade robotic arms cannot dynamically adjust the layout mode and task allocation of the industrial-grade robotic arms according to the changes in the motorcycle assembly tasks, resulting in that the motorcycle assembly efficiency cannot meet the actual needs and needs to be improved.

[0022] In response to the above technical issues, such as Figure 1 As shown, an embodiment of the present invention discloses an automated installation method using an industrial-grade robotic arm, the method comprising the following steps: Acquire a number of assembly tasks to be implemented, and match the task attributes of each of the assembly tasks to obtain an industrial-grade robotic arm of a corresponding type; Performing an assembly difficulty assessment based on the assembly efficiency data of the corresponding type of industrial-grade robotic arm and the task attributes to obtain a corresponding task difficulty assessment value; Input the task difficulty assessment value, regional assembly line length, and assembly line transmission speed corresponding to each assembly task into the Transformer prediction model to obtain the number of industrial-grade robotic arms corresponding to each assembly task predicted by the Transformer prediction model; The industrial-grade robotic arms of the corresponding type and number are controlled to go to the regional assembly lines corresponding to the respective assembly tasks, and collaboratively complete the respective assembly tasks for the motorcycle.

[0023] Compared with the existing motorcycle assembly line based on industrial-grade robotic arms, which uses fixed layout of industrial-grade robotic arms, the present invention sets the layout position of industrial-grade robotic arms to be dynamic, and dynamically adjusts the number of industrial-grade robotic arms based on the actual assembly task. The details are as follows: First, several assembly tasks to be performed this time are obtained, each of which corresponds to each assembly link of the motorcycle assembly, such as the vibration reduction assembly link, the headlight assembly link, etc., and the task attributes of each assembly task are matched to obtain the corresponding type of industrial-grade robotic arms. Among them, the present invention deploys multiple types of industrial-grade robotic arms in the assembly production workshop, and each type of industrial-grade robotic arm is adapted to a specific assembly task, such as a handling robotic arm, an installation robotic arm, a spraying robotic arm, a quality inspection robotic arm, etc.

[0024] Then, for each assembly task, the corresponding task difficulty assessment value is obtained based on the assembly efficiency data of each type of industrial-grade robotic arms and the task attribute evaluation of the assembly task. The task difficulty assessment value is used to characterize the difficulty of the corresponding type of industrial-grade robotic arms in implementing the assembly task. The task difficulty assessment value can be characterized by the assembly time.

[0025] Next, a motorcycle assembly line includes multiple regional assembly lines, each of which corresponds to the aforementioned assembly links one by one, and the length of each regional assembly line and the transmission speed of the conveyor are known. The task difficulty assessment value, regional assembly line length, and assembly line transmission speed corresponding to each assembly task are input into the pre-built Transformer prediction model, and the Transformer prediction model can analyze the number of industrial-grade robotic arms that should be deployed in the regional assembly line. When determining the number of industrial-grade robotic arms to be deployed, the Transformer prediction model is based on the assumption that the motorcycles entering the regional assembly line can just complete the corresponding assembly tasks before leaving the regional assembly line. This can also minimize the number of industrial-grade robotic arms deployed, thereby reducing the deployment cost.

[0026] Finally, the corresponding type of industrial-grade robotic arms are selected from the industrial-grade robotic arm storage library, and these industrial-grade robotic arms are controlled to go to the corresponding regional assembly lines to wait, and when the assembly operation starts, the motorcycles transferred to their operation areas are assembled to complete the corresponding assembly tasks. Several industrial-grade robotic arms of different types on multiple regional assembly lines can collaboratively complete various assembly tasks for motorcycles.

[0027] Therefore, the solution of the present invention changes the traditional way of fixing industrial-grade robotic arms on the existing motorcycle assembly line. Instead, it can determine the appropriate number of industrial-grade robotic arms of the corresponding type for each regional assembly line according to the specific assembly task, thereby ensuring that these industrial-grade robotic arms work together to complete the assembly of all motorcycles on the regional assembly line, which can significantly improve the efficiency of motorcycle assembly. Moreover, since the number of industrial-grade robotic arms deployed on each regional assembly line is determined based on the goal that the motorcycles entering the regional assembly line can just complete the corresponding assembly task before leaving the regional assembly line, this can also minimize the number of industrial-grade robotic arms deployed, thereby reducing the deployment cost.

[0028] Preferably, the obtaining of a number of assembly tasks to be performed includes: Receiving assembly task data sent by an assembly task publishing server, and determining whether the assembly task data is structured data or unstructured data; If the assembly task data is structured data, each assembly task is directly parsed from the assembly task data; If the assembly task data is unstructured data, calling a semantic analysis module to perform semantic analysis on the assembly task data to obtain each of the assembly tasks; Wherein, each of the assembly tasks is arranged in order of assembly.

[0029] In the embodiment of the present invention, an assembly task publishing server is deployed in the motorcycle manufacturer, and relevant personnel formulate corresponding assembly task data according to the production task of the corresponding motorcycle, and submit the assembly task data to the assembly task publishing server. Each assembly task data has a specific trigger time. When the trigger time is reached and the motorcycle assembly line has completed the previous round of assembly tasks, the assembly task publishing server sends the new assembly task data to the scheduling server of the motorcycle assembly line for execution.

[0030] At the same time, when formulating assembly task data, relevant personnel can choose appropriate data types by themselves, mainly including structured data and unstructured data. Among them, structured data mainly refers to the assembly task table format, and unstructured data refers to assembly task data in the format of pictures, texts, etc. For structured data, multiple assembly tasks involved in this assembly operation can be directly extracted from it; for unstructured data, multiple assembly tasks involved in this assembly operation cannot be directly extracted from it. At this time, it is necessary to call the semantic analysis module to perform semantic analysis on the unstructured assembly task data, so as to indirectly obtain the multiple assembly tasks involved in this assembly operation. The distinction between structured data and unstructured data can be judged based on the above-mentioned information format, for example.

[0031] Preferably, matching the task attributes of each assembly task to obtain a corresponding type of industrial-grade robotic arm includes: Communicate with each industrial-grade robotic arm in an idle state to obtain each locally installed assembly function plug-in and assembly function description information configured corresponding to each assembly task plug-in; Semantically matching the task attributes of the assembly task with the assembly function description information corresponding to each of the assembly task plug-ins, and if the match is successful, associating the industrial-grade robot arm with the assembly task; Repeat the above steps to complete the association between all industrial-grade robot arms in the idle state and the assembly tasks.

[0032] In an embodiment of the present invention, the industrial-grade robotic arm in the present invention is not a robotic arm with a single assembly function, but a universal type. Different types of assembly function plug-ins are downloaded and installed to the industrial-grade robotic arm in advance according to design requirements, and the industrial-grade robotic arm then has the corresponding assembly function. For example, if an industrial-grade robotic arm downloads and installs a rotation operation plug-in, it has the assembly functions of screwing and punching. Of course, after downloading and installing the corresponding assembly function plug-in (each assembly function plug-in should belong to an industrial robotic arm of the same functional type, such as an assembly function plug-in for screwing and an assembly function plug-in for knocking belonging to an assembly robotic arm, and an assembly function plug-in for painting and an assembly function plug-in for cleaning belonging to a spraying robotic arm), the industrial-grade robotic arm can implement or switch the corresponding assembly function by replacing the operating head.

[0033] On the basis of the above, the dispatch server of the motorcycle assembly line of the present invention communicates with each industrial-grade robotic arm in an idle state, so that each assembly function plug-in installed by each industrial-grade robotic arm and the assembly function description information of these assembly task plug-ins can be obtained. Among them, the assembly function description information is used to describe the type of assembly operation that can be achieved by the assembly function plug-in, such as screwing, knocking, etc. The task attributes of a specific assembly task are semantically analyzed and matched with the assembly function description information of a certain industrial-grade robotic arm, and the successfully matched industrial-grade robotic arm is associated with the corresponding assembly task. For example, if the task attribute is the screw fixing of the fuel tank, and it is found that the assembly function description information of a certain assembly function plug-in corresponds to the screwing operation, the two are associated. Then the association of each industrial-grade robotic arm in all idle states with the corresponding assembly tasks is completed one by one. At this point, each assembly task is associated with multiple adapted industrial-grade robotic arms.

[0034] Preferably, if Figure 2 As shown, the assembly difficulty evaluation is performed according to the assembly efficiency data of the corresponding type of industrial-grade robot arm and the task attributes to obtain the corresponding task difficulty evaluation value, including: Obtaining a motorcycle assembly structure diagram corresponding to the assembly task, identifying each assembly node in the motorcycle assembly structure diagram according to the task attribute, and extracting geometric features of each assembly node; wherein the geometric features include the depth of the assembly node, the entrance width of the assembly node, and the orientation of the assembly node; The assembly difficulty is evaluated based on the assembly efficiency data of the corresponding type of industrial-grade robotic arms and the geometric features to obtain a corresponding task difficulty evaluation value; wherein the assembly efficiency data is obtained based on historical data of the same type of industrial-grade robotic arms performing assembly tasks with the same task attributes.

[0035] In an embodiment of the present invention, the assembly task publishing server also sends a motorcycle assembly structure atlas to the scheduling server of the motorcycle assembly line, and the motorcycle assembly structure atlas includes motorcycle assembly structure diagrams corresponding to each assembly link. According to the assembly task, the motorcycle assembly structure diagram suitable for the assembly line in the area can be screened from the motorcycle assembly structure atlas, and each assembly node in the motorcycle assembly structure diagram is identified according to the task attribute, for example, the screwing operation corresponds to the screw hole, and the knocking operation corresponds to the protrusion. Then, the geometric features of the determined assembly nodes are extracted, including the depth of the assembly node, the entrance width of the assembly node, and the orientation of the assembly node. Among them, the deeper the depth of the assembly node, the more unfavorable it is for the assembly of the industrial-grade robot arm, the longer the assembly time is, and the larger the corresponding task difficulty evaluation value is, otherwise it is smaller; the wider the entrance width of the assembly node, the more conducive it is for the assembly of the industrial-grade robot arm, the less the assembly time is, and the smaller the corresponding task difficulty evaluation value is, otherwise it is larger; the more the orientation of the assembly node is facing the industrial-grade robot arm, the more conducive it is for the assembly of the industrial-grade robot arm, the less the assembly time is, and the smaller the corresponding task difficulty evaluation value is, otherwise it is larger; the more the orientation of the assembly node is facing the industrial-grade robot arm, the more conducive it is for the assembly of the industrial-grade robot arm, the less the assembly time is, and the smaller the corresponding task difficulty evaluation value is, otherwise it is larger.

[0036] Therefore, the assembly difficulty of the assembly task of the assembly line in the region is evaluated based on the assembly efficiency data of the corresponding type of industrial-grade robot arm and the above-mentioned geometric features to obtain the corresponding task difficulty evaluation value. The task difficulty evaluation value can be the above-mentioned assembly time.

[0037] Among them, a corresponding evaluation model can be configured for the evaluation process, and historical assembly operation data of the same type of industrial-grade robotic arms performing the same type of assembly tasks can be collected. A set of training data can be constructed based on the historical assembly operation data. Each piece of training data can be [the historical assembly efficiency of the same type of industrial-grade robotic arms performing a specific type of assembly task, the geometric features of each assembly node on the motorcycle, and the actual assembly time]. Among them, the actual assembly time is used as the label data of the training data. Therefore, the above-mentioned evaluation model is trained using this set of training data until the training goal is achieved. The evaluation model obtained after training can synchronously analyze the assembly efficiency data and geometric features of the corresponding type of industrial-grade robotic arms, so as to predict the assembly time required for the corresponding type of industrial-grade robotic arms to perform the current assembly task, that is, the time required to complete the assembly task of a motorcycle. The evaluation model is preferably constructed based on a graph convolutional network, and the present invention does not limit the specific structure of the evaluation model.

[0038] In addition, the assembly efficiency data of the corresponding type of industrial robot arm is obtained based on the historical data of the same type of industrial robot arm performing assembly tasks with the same task attributes. For example, the time consumed by the same type of industrial robot arms to perform screwing operations is statistically analyzed to obtain the corresponding average time consumed, and the average time consumed is used as the assembly efficiency data of the corresponding type of industrial robot arm.

[0039] Preferably, the task difficulty assessment value, regional assembly line length, and assembly line transmission speed corresponding to each assembly task are input into a Transformer prediction model to obtain the number of industrial-grade robotic arms corresponding to each assembly task predicted by the Transformer prediction model, including: Inputting the task difficulty evaluation value corresponding to each of the assembly tasks, the length of the regional assembly line, and the transmission speed of the assembly line into the Transformer prediction model, and the Transformer prediction model predicts the first layout quantity corresponding to the assembly task; Acquire the received record data of the assembly task, calculate the number of executions of similar assembly tasks corresponding to each assembly task according to the record data, and determine an adjustment weight according to the execution number; wherein the adjustment weight is a value greater than 1; A second placement quantity is calculated based on the adjustment weight and the first placement quantity.

[0040] In an embodiment of the present invention, the Transformer prediction model can perform a comprehensive analysis of the task difficulty assessment value, regional assembly line length, and assembly line transmission speed corresponding to each assembly task, and then predict the first layout quantity corresponding to the assembly task.

[0041] Since the above-mentioned first layout quantity is actually an ideal value, it is also necessary to consider the "capability" of the industrial-grade robot arm to appropriately increase the first layout quantity. Specifically, in the actual assembly operation process, engineers will evaluate the efficiency and accuracy of the industrial-grade robot arm when performing each assembly task, and optimize the assembly task plug-in of the industrial-grade robot arm based on the evaluation results, thereby improving its assembly efficiency and accuracy. Based on this, the present invention is further configured to obtain the record data of the assembly tasks received (i.e., executed) by the motorcycle assembly line, perform semantic similarity analysis on the current assembly task and each assembly task in the record data, determine the similarity higher than the similarity threshold as a similar assembly task, and then determine the adjustment weight according to the execution number of these similar assembly tasks. Among them, the greater the number of similar assembly tasks executed, the more times or the greater the probability that the corresponding assembly task plug-in of the industrial-grade robotic arm configured on the assembly line is optimized, and the greater the probability of completing the assembly task efficiently, the smaller the adjustment weight is set accordingly; conversely, the larger the adjustment weight is set accordingly, so as to ensure that all motorcycles on the assembly line in the area can complete the corresponding assembly tasks by appropriately increasing the number of industrial-grade robotic arms. It should be noted that the present invention does not need to specifically limit the conversion method between the adjustment weight and the number of similar assembly tasks executed. It can be in the form of a specific formula or a comparison table, as long as the adjustment weight and the number of similar assembly tasks executed are in a negative correlation.

[0042] Preferably, the controlling of the arranged number of industrial-grade robotic arms of corresponding types to go to the regional assembly lines corresponding to the respective assembly tasks comprises: Randomly select the deployed number of industrial-grade robotic arms from the industrial-grade robotic arms associated with the assembly task, and control the selected industrial-grade robotic arms to go to the regional assembly line corresponding to the assembly task; After arriving at the corresponding regional assembly line, each industrial-grade robotic arm is evenly spaced and combined with a ground fixture to achieve fixation.

[0043] In an embodiment of the present invention, after determining the number of industrial-grade robotic arms of the corresponding type required for each assembly task, the industrial-grade robotic arms of the number of deployments can be randomly selected from the aforementioned association relationship, and these industrial-grade robotic arms can be controlled to go to the regional assembly lines corresponding to the assembly tasks. After arriving at the corresponding regional assembly lines, they are arranged in a uniformly spaced manner, and combined with ground fixtures to achieve fixation, power supply, and communication, and then they can wait for subsequent formal assembly commands. Among them, the industrial-grade robotic arm is equipped with a self-propelled mechanism, which can be wheeled, tracked, or tracked, without specific limitation.

[0044] It should be noted that the ground fixing device has both a fixing mechanism and an electrical mechanism. The fixing mechanism can temporarily fix the industrial-grade robot arm in the corresponding position to prevent it from shaking or even collapsing during assembly operations, but the specific structural type of the fixing mechanism is not limited. The electrical mechanism also includes a power cord / plug and a communication network to realize the power supply to the industrial-grade robot arm and a wired communication connection with the dispatch server of the motorcycle assembly line (receiving relevant instructions from the dispatch server in a more reliable wired communication method during the assembly process).

[0045] like Figure 3 As shown, an embodiment of the present invention further discloses an automated installation system using an industrial-grade robotic arm, the system comprising a control module and a storage module, the control module being electrically connected to the storage module and calling a program code in the storage module to implement the following processing steps: Acquire a number of assembly tasks to be implemented, and match the task attributes of each of the assembly tasks to obtain an industrial-grade robotic arm of a corresponding type; Performing an assembly difficulty assessment based on the assembly efficiency data of the corresponding type of industrial-grade robotic arm and the task attributes to obtain a corresponding task difficulty assessment value; Input the task difficulty assessment value, regional assembly line length, and assembly line transmission speed corresponding to each assembly task into the Transformer prediction model to obtain the number of industrial-grade robotic arms corresponding to each assembly task predicted by the Transformer prediction model; The industrial-grade robotic arms of the corresponding type and number are controlled to go to the regional assembly lines corresponding to the respective assembly tasks, and collaboratively complete the respective assembly tasks for the motorcycle.

[0046] The present invention also discloses an electronic device, applied to the system as described in any of the preceding items, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor.

[0047] An embodiment of the present invention further discloses a computer storage medium, which is applied to the system as described in any of the preceding items, and the computer storage medium stores a computer program.

[0048] An embodiment of the present invention further discloses a computer program product, which is applied to the system as described in any of the preceding items, and includes a computer program stored on a non-transitory computer-readable medium.

[0049] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0050] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0051] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0052] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. An automated installation method using an industrial-grade robotic arm, characterized in that: The method comprises the following steps: Acquire a number of assembly tasks to be implemented, and match the task attributes of each of the assembly tasks to obtain an industrial-grade robotic arm of a corresponding type; Performing an assembly difficulty assessment based on the assembly efficiency data of the corresponding type of industrial-grade robotic arm and the task attributes to obtain a corresponding task difficulty assessment value; Input the task difficulty assessment value, regional assembly line length, and assembly line transmission speed corresponding to each assembly task into the Transformer prediction model to obtain the number of industrial-grade robotic arms corresponding to each assembly task predicted by the Transformer prediction model; The industrial-grade robotic arms of the corresponding type and number are controlled to go to the regional assembly lines corresponding to the respective assembly tasks, and collaboratively complete the respective assembly tasks for the motorcycle.

2. The automated installation method using an industrial-grade robotic arm according to claim 1, characterized in that: The obtaining of several assembly tasks to be performed includes: Receiving assembly task data sent by an assembly task publishing server, and determining whether the assembly task data is structured data or unstructured data; If the assembly task data is structured data, each assembly task is directly parsed from the assembly task data; If the assembly task data is unstructured data, calling a semantic analysis module to perform semantic analysis on the assembly task data to obtain each of the assembly tasks; Wherein, each of the assembly tasks is arranged in order of assembly.

3. The automated installation method using an industrial-grade robotic arm according to claim 2, characterized in that: The task attribute matching of each assembly task to obtain a corresponding type of industrial-grade robotic arm includes: Communicate with each industrial-grade robotic arm in an idle state to obtain each locally installed assembly function plug-in and assembly function description information configured corresponding to each assembly task plug-in; Semantically matching the task attributes of the assembly task with the assembly function description information corresponding to each of the assembly task plug-ins, and if the match is successful, associating the industrial-grade robot arm with the assembly task; Repeat the above steps to complete the association between all industrial-grade robotic arms in the idle state and the assembly tasks.

4. The automated installation method using an industrial-grade robotic arm according to claim 3, characterized in that: The step of performing assembly difficulty assessment according to the assembly efficiency data of the corresponding type of industrial-grade robot arm and the task attributes to obtain a corresponding task difficulty assessment value includes: Obtaining a motorcycle assembly structure diagram corresponding to the assembly task, identifying each assembly node in the motorcycle assembly structure diagram according to the task attribute, and extracting geometric features of each assembly node; wherein the geometric features include the depth of the assembly node, the entrance width of the assembly node, and the orientation of the assembly node; The assembly difficulty is evaluated based on the assembly efficiency data of the corresponding type of industrial-grade robotic arms and the geometric features to obtain a corresponding task difficulty evaluation value; wherein the assembly efficiency data is obtained based on historical data of the same type of industrial-grade robotic arms performing assembly tasks with the same task attributes.

5. The automated installation method using an industrial-grade robotic arm according to claim 4, characterized in that: Inputting the task difficulty evaluation value, regional assembly line length, and assembly line transmission speed corresponding to each assembly task into the Transformer prediction model, and obtaining the number of industrial-grade robot arms corresponding to each assembly task predicted by the Transformer prediction model, including: Inputting the task difficulty evaluation value corresponding to each of the assembly tasks, the length of the regional assembly line, and the transmission speed of the assembly line into the Transformer prediction model, and the Transformer prediction model predicts the first layout quantity corresponding to the assembly task; Acquire the received record data of the assembly task, calculate the number of executions of similar assembly tasks corresponding to each assembly task according to the record data, and determine an adjustment weight according to the execution number; wherein the adjustment weight is a value greater than 1; A second placement quantity is calculated based on the adjustment weight and the first placement quantity.

6. The automated installation method using an industrial-grade robotic arm according to claim 5, characterized in that: The controlling the arranged number of industrial-grade robotic arms of corresponding types to go to the regional assembly lines corresponding to the assembly tasks comprises: Randomly select the deployed number of industrial-grade robotic arms from the industrial-grade robotic arms associated with the assembly task, and control the selected industrial-grade robotic arms to go to the regional assembly line corresponding to the assembly task; After arriving at the corresponding regional assembly line, each industrial-grade robotic arm is evenly spaced and combined with a ground fixture to achieve fixation.

7. An automated installation system using an industrial-grade robotic arm, the system comprising a control module and a storage module, characterized in that: The control module is electrically connected to the storage module and calls the program code in the storage module to implement the following processing steps: Acquire a number of assembly tasks to be implemented, and match the task attributes of each of the assembly tasks to obtain an industrial-grade robotic arm of a corresponding type; Performing an assembly difficulty assessment based on the assembly efficiency data of the corresponding type of industrial-grade robotic arm and the task attributes to obtain a corresponding task difficulty assessment value; Input the task difficulty assessment value, regional assembly line length, and assembly line transmission speed corresponding to each assembly task into the Transformer prediction model to obtain the number of industrial-grade robotic arms corresponding to each assembly task predicted by the Transformer prediction model; The industrial-grade robotic arms of the corresponding type and number are controlled to go to the regional assembly lines corresponding to the respective assembly tasks, and collaboratively complete the respective assembly tasks for the motorcycle.

8. An electronic device, characterized in that: The system applied to any one of claims 1 to 6 comprises: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor.

9. A computer storage medium, characterized in that: Applied to the system according to any one of claims 1 to 6, the computer storage medium stores a computer program.

10. A computer program product, characterized in that: A system as claimed in any one of claims 1 to 6, comprising a computer program stored on a non-transitory computer readable medium.

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