Methods, apparatus, devices, media, and products for creating a workstation

Abstract

A method for creating a workstation. The method comprises receiving a user input (102) to create a workstation, wherein the user input (102) comprises a prompt to a language model (108), the prompt representing a plurality of requirements for the workstation, and the workstation being used for picking up and placing items by at least one robot on at least one conveyor. The method further comprises obtaining a plurality of parameters for creating the workstation. The method further comprises creating the workstation for the plurality of requirements based on the plurality of parameters. In this way, the user experience for creating the workstation can be improved, and the time and effort costs for creating the workstation can be saved. An apparatus, an electronic device, a computer-readable storage device, and a computer program product for creating a workstation are also provided.

Description

Technical Field

[0001] Embodiments of this disclosure generally relate to the field of computer technology, and particularly to a method, apparatus, electronic device, computer-readable medium, and computer program product for creating a workstation. Background Technology

[0002] Pick-and-place robots are industrial robots designed to automatically pick up and place items. They are widely used in various industries, including manufacturing, e-commerce, pharmaceuticals, and food and beverage. They can perform repetitive and monotonous tasks efficiently and accurately. These robots use robotic arms and grippers to quickly and accurately pick up items and place them in designated locations, thereby increasing productivity and reducing manual labor.

[0003] The workstation associated with the pick-and-place robot is a work area that includes one or more pick-and-place robots, their control systems, auxiliary equipment, and other peripheral devices. These workstations are designed to be flexible and customizable to meet the specific needs of different applications and workpieces. Summary of the Invention

[0004] Generally, various exemplary embodiments of this disclosure provide a method, apparatus, electronic device, computer-readable storage device, and computer program product for creating a workstation.

[0005] In a first aspect, a method for creating a workstation is provided. The method includes: receiving user input to create the workstation, wherein the user input includes prompts for a language model representing multiple requirements for the workstation, and wherein the workstation is used for picking up and placing items onto at least one conveyor by at least one robot. The method further includes: obtaining, based on the user input, multiple parameters associated with at least one conveyor and at least one robot for creating the workstation from the language model. The method also includes: creating the workstation for multiple requirements based on the multiple parameters.

[0006] In a second aspect, an apparatus for creating a workstation is provided. The apparatus includes: a receiving module configured to receive user input to create a workstation, wherein the user input includes prompts for a language model representing multiple requirements for the workstation, and wherein the workstation is used for picking up and placing items onto at least one conveyor by at least one robot. The apparatus further includes: an obtaining module configured to obtain, based on the user input, multiple parameters associated with at least one conveyor and at least one robot for creating the workstation. The apparatus further includes: a creating module configured to create the workstation based on the multiple parameters for multiple requirements.

[0007] In a third aspect, an electronic device is provided. The electronic device includes a processor; and a memory coupled to the processor, wherein the memory has instructions stored therein, and the instructions, when executed by the processor, cause the device to perform the actions described in the first aspect.

[0008] In a fourth aspect, a computer-readable medium is provided. The computer-readable medium includes instructions stored thereon that, when executed by a processor, cause the processor to perform the method according to the first aspect.

[0009] In a fifth aspect, a computer program product is provided. The computer program product includes instructions stored therein, which, when executed by a processor, cause the processor to perform the method according to the first aspect.

[0010] It should be understood that the content of this disclosure is not intended to identify key or essential features of the embodiments thereof, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become apparent from the following description. Attached Figure Description

[0011] The above and other objects, features, and advantages of the exemplary embodiments disclosed herein will become more readily understood through the following detailed description with reference to the accompanying drawings. In the drawings, several exemplary embodiments disclosed herein are illustrated by way of example and non-limitation, wherein:

[0012] Figure 1 The illustration shows a schematic diagram of an example environment in which various embodiments of the present disclosure may be implemented;

[0013] Figure 2 Detailed diagrams are illustrated of example environments in which various embodiments of the present disclosure may be implemented;

[0014] Figure 3 The illustration shows an example layout of a workstation according to some embodiments of the present disclosure;

[0015] Figure 4 The illustration shows a schematic diagram of an example training process for a language model according to some embodiments of the present disclosure;

[0016] Figure 5 The illustration shows a flowchart of an example method for creating a workstation according to some embodiments of the present disclosure;

[0017] Figure 6 The illustration shows a block diagram of an example apparatus for creating a workstation according to some embodiments of the present disclosure; and

[0018] Figure 7 A block diagram of an electronic device according to some embodiments of the present disclosure is illustrated.

[0019] In all the accompanying drawings, the same or similar reference numerals denote the same or similar elements. Detailed Implementation

[0020] The principles of this disclosure will now be described with reference to several exemplary embodiments illustrated in the accompanying drawings. Although exemplary embodiments of this disclosure are illustrated in the drawings, it should be understood that these embodiments are described only to enable those skilled in the art to better understand and implement this disclosure, and not to limit the scope of this disclosure in any way.

[0021] The terms “comprise” or “includes” and variations thereof shall be interpreted as open-ended terms meaning “including but not limited to”. Unless the context explicitly indicates otherwise, the term “or” shall be interpreted as “and / or”. The term “based on” shall be interpreted as “at least partially based on”. The term “operable as” means a function, action, movement, or state that can be achieved by operation induced by a user or external mechanism. The terms “one embodiment” and “embodiment” shall be interpreted as “at least one embodiment”. The term “another embodiment” shall be interpreted as “at least one other embodiment”. The terms “first,” “second,” etc., may refer to different or the same objects. Further explicit or implicit definitions may be included below. Unless the context explicitly indicates otherwise, the definitions of terms shall remain consistent throughout the specification.

[0022] In one embodiment, the functionality or algorithm described herein can be implemented in software. This software can consist of computer-executable instructions stored on a computer-readable medium or computer-readable storage device, such as one or more non-transitory memories or other types of hardware-based storage devices, local or networked. Furthermore, this functionality corresponds to modules, which can be software, hardware, firmware, or any combination thereof. Multiple functions can be performed in one or more modules as needed, and the described embodiments are merely examples. The software can be executed on a computer system (such as a personal computer, server, or other computer system) operating on a digital signal processor, ASIC, microprocessor, or other type of processor, transforming such a computer system into a specially programmed machine.

[0023] This function can be configured to perform operations using, for example, software, hardware, firmware, etc. For example, the phrase "configured to" can refer to the logical circuit structure of a hardware element to implement the associated function. The phrase "configured to" can also refer to the logical circuit structure of a hardware element whose coded design implements the associated function of firmware or software. The phrase "module" refers to a structural element that can be implemented using any suitable hardware (e.g., processor, etc.), software (e.g., application, etc.), firmware, or any combination of hardware, software, and firmware. The phrase "logic" encompasses any function used to perform a task. For example, each operation illustrated in a flowchart corresponds to the logic that performs that operation. Operations can be performed using software, hardware, firmware, etc. The terms "component," "system," etc., can refer to computer-related entities, hardware, and software, firmware, or combinations thereof in operation. A component can be a process, object, executable file, program, function, subroutine, computer, or a combination of software and hardware running on a processor. The term "processor" can refer to a hardware component, such as the processing unit of a computer system.

[0024] The terms “a (a)” or “an (an)” as used herein are defined as one or more. Furthermore, the use of introductory phrases such as “at least one” and “one or more” in claims should not be construed as implying that, even if the same claim includes the introductory phrase “one or more” or “at least one” and an indefinite article such as “a (a)” or “an (an)”, introducing another claim element by the indefinite article “a (a)” or “an (an)” limits any particular claim containing such introduced claim element to disclosure containing only one such element. The same applies to the use of definite articles.

[0025] Furthermore, the claimed subject matter can be implemented as a method, apparatus, or article of manufacture that uses standard programming and engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computing device to implement the disclosed subject matter. Computer-readable storage media can include, but is not limited to, magnetic storage devices, such as hard disks, floppy disks, magnetic stripes, optical discs, compact discs (CDs), digital multifunction discs (DVDs), smart cards, flash memory devices, etc. In contrast, computer-readable media (i.e., non-storage media) may additionally include communication media, such as transmission media for wireless signals.

[0026] As discussed above, with the increasing use of pick-and-place robots across various industries, there is a growing demand for faster and more efficient initial design and maintenance of robots and conveyors, modification and alteration of pick / place workstations, and improved equipment performance. Using simulation applications to effectively simulate the construction and optimization of a set of real-world application scenarios is crucial. Prior to simulation, to achieve accurate results, users must create scenarios in a specified order and will need to input highly complex data based on actual production line requirements, such as numerous parameters regarding robots, conveyor belts, work areas, and other necessary components.

[0027] However, simulation requires collecting and inputting large amounts of data in a specific manner, which consumes a significant amount of the user's time. Furthermore, data collected on-site may not yield optimal results, necessitating repeated parameter adjustments and workstation modifications to achieve more satisfactory outcomes. The workload of debugging and modification depends heavily on the user's experience. This, in turn, leads to an exponential increase in the user's time cost.

[0028] Therefore, this disclosure proposes a scheme for creating workstations. By implementing the proposed scheme, it simplifies the way parameters are input for creating workstations and obtaining simulation results. It can automatically create and optimize the required simulation workstations according to user needs. To simplify simulations based on large amounts of data, a language model is used to accelerate the process. Based on the language model, users can create workstations using natural language and also obtain optimized workstation layouts.

[0029] Figure 1 The illustration shows an example environment 100 in which various embodiments of the present disclosure may be implemented. Example environment 100 is for illustration only and is not intended to imply any limitation on the scope or functionality of the embodiments described herein.

[0030] like Figure 1 As shown, example environment 100 includes computing device 104. Examples of computing device 104 may be a server or a computer. Computing device 104 may also include language model 108. Language model 108 may be a natural language processing (NLP) model. Language model 108 is designed to enable computers to understand, interpret, and generate human language content. Language model 108 contains a set of computer algorithms used to process and analyze large amounts of natural language data. Language model 108 can receive prompts from the user (e.g., user input 102) and understand the user's needs and output parameters used to create the workstation.

[0031] The computing device 104 may also include an application programming interface (API) 110. The computing device 104 can use the API 110 to create workstation layouts and workstation simulation results. The API 110 acts as a bridge between different software components and allows various programs to interact with each other. For example, the API 110 can access software products capable of creating and simulating workstations. The computing device 104 can output the workstation layout and workstation simulation results generated via the API 110 as output 106.

[0032] right Figure 2 For reference, a detailed schematic diagram of an example environment 200 in which various embodiments of the present disclosure may be implemented is shown. Figure 2 The input 202 in the middle can correspond to Figure 1 The input is 102. Figure 2 The output 206 in the middle can correspond to Figure 1 The output is 106. Figure 2 The NLP model 208 in the text can be Figure 1 Example of language model 108 in the example. Figure 2 API 230 in the middle can be Figure 1 Example of API 110.

[0033] The entire process can be divided into four stages. Stage 1 is the training stage for training the NLP model 208, where a large amount of data from different configurations and running results is collected as the foundational data for training the NLP model 208. This data can include natural language prompts, workstation settings, and running results.

[0034] In some example embodiments, workstation setup data may include robot, conveyor, camera, input / output (IO) sensor configurations, etc. For example, the positions of the sample robot, sample conveyor, sample camera, and sample IO sensor in the world coordinate system; the relative relationships between the sample robot and the sample conveyor, sample camera, and sample IO sensor; the sample robot speed; the sample conveyor speed; the sample trigger signal configuration; the sample working area settings, etc. Operating results (also known as performance parameters) may include the number of sample loops, sample pick-up rate, total sample pick-up count, total sample placement count, sample load per robot, sample load per conveyor, missed sample pick-up count, missed sample placement count, etc. The trained NLP 208 can output parameters 220 that include workstation setup or performance parameters.

[0035] At stage 2, a workstation can be created based on the output parameters using API 230. Software (e.g., pick master) is used to automatically create the workstation after receiving the output parameters extracted by NLP model 208.

[0036] At stage 3, workstations can be quickly created and optimized simply by inputting natural language prompts. For example, a user can say, "I want to create a workstation with two conveyors and one robot, with a pick rate of 100 pieces / minute." A number of optional workstations will then be automatically created. Output 206 can include the workstation layout 240 and performance parameters 250. Performance parameters 250 can include cycle count, pick rate, total pick count, total placement count, load per robot, load per conveyor, missed pick count, missed placement count, and so on.

[0037] If he / she is satisfied, the user can select one of them. If he / she is not satisfied (e.g., not satisfied with the layout or performance parameters), he / she can continue to enter more detailed parameters, and then the updated workstation will be displayed based on further input. For example, the update may include adjusting the position of the robot and / or conveyor, the starting position for picking up items, the robot's speed, and so on.

[0038] At stage 4, the status of the simulation results can also be directly displayed to the user. This will shorten the waiting time for viewing the simulation results and provide an overview of the workstation (e.g., layout, updated layout, and corresponding performance parameters) directly after inputting prompts for creating, updating, or optimizing the workstation.

[0039] right Figure 3 For reference, an example layout 300 of a workstation according to some embodiments of the present disclosure is illustrated. Figure 3 As shown, layout 300 includes a robot 302 and two conveyors 304 and 306. Robot 302 can pick up one or more items 310 (e.g., cookies) from conveyor 304 at a time. Robot 302 can place one or more items 310 into a container 312 on conveyor 306. Layout 300 may also include one or more cameras 320 and / or one or more I / O sensors.

[0040] In some example embodiments, the number of cycles can represent the number of times the robot picks up and places items within a time period (e.g., one minute). The pick-up rate can represent the number of items the robot picks up and places within a time period (e.g., one minute). For example, if the robot can pick up three items at a time, with a cycle count of 20 times per minute and a time period of one minute, then the pick-up rate could be 3 items per minute. 20 = 60.

[0041] The total pick-up count indicates how many items the robot picked up within a given period. The total placement count indicates how many items the robot placed within a given period. The load per robot indicates the actual number of items picked up and placed by the robot within a given period. The load per conveyor indicates the actual number of items carried by the conveyor within a given period. The missed pick-up count indicates the number of items that should have been picked up but were missed by the robot within a given period. The missed placement count indicates the number of items that should have been placed by the robot but were missed within a given period.

[0042] right Figure 4 For reference, the illustration shows a schematic diagram of an example training process 400 for a language model according to some embodiments of the present disclosure. Training data 402 can be fed into an NLP model 410. Training data 402 may include sample prompts 404, sample workstation settings 406, and sample execution results from workstation 408, which can be referenced. Figure 2 The discussion will not be repeated for the sake of simplicity.

[0043] NLP 410 can extract parameters associated with the robot and conveyor for creating the workstation based on training data 402, and can output these parameters as output 412. Backpropagation module 414 can compare output 412 with the corresponding reference output and calculate the loss function. The parameters of NLP 410 are adjusted via backpropagation module 414. These training processes are iterated in a loop until the loss function satisfies predetermined conditions.

[0044] In some examples, neural network architectures such as Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), Gated Recurrent Units (GRUs), or Transformer models can be used. Pre-trained models like BERT (a Transformer-based bidirectional encoder representation) can also be considered, as they have been trained on large-scale corpora and can be fine-tuned for specific tasks. In some examples, NLP frameworks or libraries such as TensorFlow, PyTorch, or NLTK can be used to build the original NLP model. The layers, activation functions, and parameters of the NLP model can be specified. An appropriate loss function can be used to avoid overfitting. After successful training and evaluation, the MLP model can be deployed to a production environment where it can process real-world data.

[0045] right Figure 5 For reference, a flowchart illustrating an example method 500 for creating a workstation according to some embodiments of the present disclosure is shown. Figure 5 Reference Figure 1 Describe it.

[0046] At point 502, computing device 104 receives user input 102 to create a workstation. User input 102 may include prompts for a language model, representing multiple requirements for the workstation. For example, such as... Figure 3 As described, the workstation can be used to pick up items and place them on at least one conveyor by at least one robot.

[0047] At point 504, computing device 104 obtains multiple parameters associated with at least one conveyor and at least one robot for creating a workstation. These multiple parameters associated with at least one conveyor and at least one robot for creating a workstation can be obtained from language model 108. These multiple parameters associated with at least one conveyor and at least one robot for creating a workstation can be extracted by language model 108 based on user input 102.

[0048] At point 506, computing device 104 can create workstations based on multiple parameters and for multiple needs. For example, computing device 104 can use API 110 to create workstations based on multiple parameters and for multiple needs. Computing device 104 can output the workstation layout and workstation performance parameters as output 106.

[0049] In some example embodiments, multiple requirements may include a first number of at least one conveyor. In some example embodiments, multiple requirements may include a second number of at least one robot. In some example embodiments, multiple requirements may include both the first number and the second number simultaneously.

[0050] In some example embodiments, computing device 104 may also input user input into language model 108. Computing device 104 may also receive output from the language model as multiple parameters associated with at least one conveyor and at least one robot for creating workstations. In some example embodiments, the language model may be trained using training data including sample workstation settings and at least one sample performance parameter of the workstation.

[0051] In some example embodiments, a sample workstation setup may include at least one sample conveyor configuration. In some example embodiments, a sample workstation setup may include at least one sample robot configuration. In some example embodiments, a sample workstation setup may include at least one sample camera configuration. In some example embodiments, a sample workstation setup may include at least one sample I / O sensor configuration. In some example embodiments, a sample workstation setup may include any combination of the foregoing.

[0052] In some example embodiments, the sample workstation setup can configure the relative relationship between at least one sample robot and at least one sample conveyor. In some example embodiments, the sample workstation setup can configure the speed of at least one sample robot. In some example embodiments, the sample workstation setup can configure the speed of at least one sample conveyor. In some example embodiments, the sample workstation setup can configure at least one sample trigger signal configuration. In some example embodiments, the sample workstation setup can configure the position of at least one sample robot.

[0053] In some example embodiments, the sample workstation setup can configure the location of at least one sample conveyor. In some example embodiments, the sample workstation setup can configure the location of at least one sample camera. In some example embodiments, the sample workstation setup can configure the location of at least one sample I / O sensor. In some example embodiments, the sample workstation setup can configure a sample working area. In some example embodiments, the sample workstation setup can configure any combination of the above.

[0054] In some example embodiments, at least one sample performance parameter of the workstation may include the number of cycles. In some example embodiments, at least one sample performance parameter of the workstation may include the pick-up rate. In some example embodiments, at least one sample performance parameter of the workstation may include the total pick-up count. In some example embodiments, at least one sample performance parameter of the workstation may include the total placement count. In some example embodiments, at least one sample performance parameter of the workstation may include the load of each of at least one sample robot.

[0055] In some example embodiments, at least one sample performance parameter of the workstation may include the load of each sample conveyor in at least one sample conveyor. In some example embodiments, at least one sample performance parameter of the workstation may include a missed pick-up count. In some example embodiments, at least one sample performance parameter of the workstation may include a missed placement count. In some example embodiments, at least one sample performance parameter of the workstation may include any combination of the foregoing.

[0056] In some example embodiments, the user input may be a first user input. In some example embodiments, the computing device 104 may receive a second user input to update the workstation in response to a simulation of the workstation's performance failing to meet at least one of a plurality of requirements. The computing device 104 may update at least one of the workstation's layout or workstation settings to meet at least one of the plurality of requirements. In some example embodiments, the computing device 104 may update the workstation based on at least one of the updated workstation layout or updated workstation settings.

[0057] By implementing an embodiment of method 500, it simplifies the way parameters are input for creating workstations and obtaining simulation results. It can automatically create and optimize the required simulation workstations according to user needs. To simplify simulations based on large amounts of data, a language model is used to accelerate the process. Based on the language model, users can create workstations using natural language and also obtain optimized workstation layouts.

[0058] right Figure 6 Referring to the illustration is a block diagram of an example apparatus 600 for creating a workstation according to some embodiments of the present disclosure. Apparatus 600 includes a receiving module 602 configured to receive user input for creating a workstation, wherein the user input includes prompts for a language model representing multiple requirements for the workstation, and wherein the workstation is used for picking up and placing items onto at least one conveyor by at least one robot. The apparatus also includes an obtaining module 604 configured to obtain, based on the user input, multiple parameters associated with at least one conveyor and at least one robot for creating the workstation from the language model. The apparatus also includes a creating module 606 configured to create the workstation for multiple requirements based on the multiple parameters.

[0059] In some example embodiments, the multiple requirements may include a first number of at least one conveyor. In some example embodiments, the multiple requirements may include a second number of at least one robot. In some example embodiments, the multiple requirements may include a first number and a second number.

[0060] In some example embodiments, the obtaining module 604 may further include a first module configured to input user input into the language model. The obtaining module 604 may also include a second module configured to receive output from the language model as multiple parameters associated with at least one conveyor and at least one robot for creating a workstation. In some example embodiments, the language model may be trained using training data including sample workstation settings and at least one sample performance parameter of the workstation.

[0061] In some example embodiments, a sample workstation setup may include at least one sample conveyor configuration. In some example embodiments, a sample workstation setup may include at least one sample robot configuration. In some example embodiments, a sample workstation setup may include at least one sample camera configuration. In some example embodiments, a sample workstation setup may include at least one sample I / O sensor configuration. In some example embodiments, a sample workstation setup may include any combination of the foregoing.

[0062] In some example embodiments, the sample workstation setup may be configured with at least one of the following: the relative relationship between at least one sample robot and at least one sample conveyor; the speed of at least one sample robot; the speed of at least one sample conveyor; the configuration of at least one sample trigger signal; the position of at least one sample robot; the position of at least one sample conveyor; the position of at least one sample camera; the position of at least one sample I / O sensor; or the sample work area setup.

[0063] In some example embodiments, at least one sample performance parameter of the workstation may include at least one of the following: number of cycles; pick rate; total pick count; total placement count; load of each sample robot in at least one sample robot; load of each sample conveyor in at least one sample conveyor; missed pick count; or missed placement count.

[0064] In some example embodiments, the user input may be a first user input. In some example embodiments, the apparatus 600 may further include a third module configured to receive a second user input to update the workstation in response to a simulation of the workstation's performance failing to meet at least one of a plurality of requirements. The apparatus 600 may further include a fourth module configured to update at least one of the workstation's layout or workstation settings to meet at least one of the plurality of requirements.

[0065] In some example embodiments, the apparatus 600 may further include a fifth module configured to update the workstation based on at least one of the updated layout or updated workstation settings.

[0066] By implementing Figure 6 The example implementation simplifies the way parameters are entered for creating workstations and obtaining simulation results. The required simulation workstations can be automatically created and optimized according to user needs. Therefore, the user experience for creating workstations can be improved, and the time and effort spent on workstation creation can be saved.

[0067] Figure 7A block diagram of an electronic device 700 according to some embodiments of the present disclosure is illustrated. As indicated, the device 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes based on computer program instructions stored in read-only memory (ROM) 702 or loaded from storage unit 708 into random access memory (RAM) 703. RAM 703 also stores various programs and data required to operate the electronic device 700. The CPU 701, ROM 702, and RAM 703 are interconnected via a bus 704, and an input / output (I / O) interface 705 is also connected to the bus 704.

[0068] Multiple components in device 700 are connected to I / O interface 705, including: input unit 706, such as a keyboard, mouse, etc.; output unit 707, such as various types of displays, speakers, etc.; storage unit 708, such as storage disks, optical disks, etc.; and communication unit 709, such as network cards, modems, wireless communication transceivers, etc. Communication unit 709 allows device 700 to exchange information / data with other devices through computer networks (such as the Internet and / or various telecommunications networks).

[0069] Each program and process described above (such as method 500) can be executed by processing unit 701. For example, in some embodiments, method 500 can be implemented as a computer software program, which is tangibly included in a machine-readable medium (such as storage unit 708). In some embodiments, the computer program can be partially or wholly loaded and / or installed into device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by CPU 701, one or more steps of method 500 described above are implemented. Alternatively, in other embodiments, CPU 701 can be configured in any suitable manner to implement the above-described processes / methods.

[0070] This disclosure can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of this disclosure.

[0071] The computer-readable storage medium can be a tangible device capable of holding and storing instructions for use by an instruction execution device. The computer-readable storage medium can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (not an exhaustive list) of computer-readable storage media include: portable computer floppy disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable optical disc read-only memory (CD-ROM), digital versatile optical disc (DVD), memory sticks, floppy disks, mechanical encoding devices (such as punched cards or raised structures in grooves on which instructions are recorded), and any suitable combination thereof. As used herein, a computer-readable storage medium should not be construed as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0072] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a suitable computing / processing device, or downloaded via a network (e.g., the Internet, a local area network, a wide area network, and / or a wireless network) to an external computer or external storage device. This network may include copper cables, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to a computer-readable storage medium within the suitable computing / processing device.

[0073] Computer-readable program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​(such as Smalltalk, C++, etc.) and conventional procedural programming languages ​​(such as C or similar programming languages). The computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as a standalone software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)) or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, by means of the status information of the computer-readable program instructions, an electronic circuit system, including a programmable logic circuit system (PLC), a field-programmable gate array (FPGA), or a programmable logic array (PLA), may be customized to execute the computer-readable program instructions, thereby implementing various aspects of this disclosure.

[0074] This document will describe various aspects of the disclosure with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0075] These computer-readable program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that the instructions (executed via the processor of the computer or other programmable data processing apparatus) create parts for implementing the functions / actions specified in one or more boxes of a flowchart and / or block diagram. These computer-readable program instructions may also be stored in a computer-readable storage medium that can instruct a computer, programmable data processing apparatus, and / or other device to function in a particular manner, such that the computer-readable storage medium having the instructions stored therein comprises an article of writing including the instructions which implement aspects of the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0076] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other device, such that the instructions, which execute on the computer, other programmable apparatus, or other device, perform the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0077] Flowcharts and block diagrams illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, code segment, or portion of code, including one or more executable instructions for implementing a specified logical function(s). In some alternative implementations, the functions indicated in the blocks may be implemented in an order different from that illustrated in the figures. For example, two blocks shown consecutively may actually be executed substantially simultaneously, or, depending on the functions involved, these blocks may sometimes be executed in reverse order. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware system performing the specified function or action, or by a combination of dedicated hardware and computer instructions.

[0078] Furthermore, although the operations are depicted in a specific order, this should not be construed as requiring these operations to be performed in the specific order or sequence shown, or requiring all illustrated operations to be performed to obtain the desired result. In some scenarios, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the foregoing discussion, these details should not be construed as limiting the scope of this disclosure, but rather as a description of specific features that may be present in particular embodiments. Certain features described in the context of a single embodiment may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented separately in multiple embodiments, or in any suitable sub-combination.

[0079] Those skilled in the art will recognize that the examples, units, and algorithm steps described in conjunction with the embodiments disclosed in this specification can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether a function is implemented by hardware or software depends on the design constraints of the specific application and technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0080] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the detailed working processes of the aforementioned systems, devices, and units should be referred to the corresponding processes in the foregoing method embodiments. Further details are not described herein.

[0081] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the described apparatus embodiments are merely examples. For example, the unit division is merely a logical functional division, and may be other divisions in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not performed. In addition, the mutual coupling, direct coupling, or communication connection shown or discussed can be implemented through some interface. Indirect coupling or communication connection between apparatuses or units can be implemented electronically, mechanically, or otherwise.

[0082] The units described as separate portions may or may not be physically separate, and the portions shown as units may or may not be physical units, may be located in the same location, or may be distributed across multiple network units. Some or all of the units can be selected based on actual needs to achieve the objectives of the embodiment.

[0083] Furthermore, the functional units in the embodiments of this application can be integrated into one processing unit, or each unit in the unit can exist physically independently, or two or more units can be integrated into one unit.

[0084] When functions are implemented as software functional units and sold or used as independent products, these functions can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions in this application, or those constituting part of the prior art, or some of these technical solutions, can be implemented in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for directing a computer device (which may be a personal computer, server, network device, etc.) to perform all or some of the steps of the methods described in the embodiments of this application. The aforementioned storage medium includes any medium capable of storing program code, such as a USB flash drive, a removable hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0085] The foregoing description is merely a specific implementation of this application and is not intended to limit the scope of protection of this application. Any variations or substitutions that can be readily conceived by those skilled in the art within the scope of the technology disclosed in this application should fall within the scope of protection of this application. Therefore, the scope of protection of this application should be bound by the scope of protection of the claims.

Claims

1. A method for creating a workstation, comprising: Receive user input to create the workstation, wherein the user input includes prompts for a language model, the prompts representing multiple requirements for the workstation, and wherein the workstation is used by at least one robot to pick up items and place them on at least one conveyor; The language model and the user input are used to obtain multiple parameters associated with the at least one conveyor and the at least one robot for creating the workstation. as well as The workstation is created based on the aforementioned parameters and to meet the aforementioned requirements.

2. The method of claim 1, wherein the plurality of requirements includes: The first number of at least one conveyor; as well as The second number of at least one robot.

3. The method of claim 1, wherein obtaining a plurality of parameters associated with the at least one conveyor and the at least one robot for creating the workstation, based on the language model and the user input, comprises: The user input is fed into the language model; as well as The output from the language model is received as the plurality of parameters associated with the at least one conveyor and the at least one robot for creating the workstation.

4. The method of claim 3, wherein the language model is trained using training data, the training data including sample workstation settings and at least one sample performance parameter of the workstation.

5. The method of claim 4, wherein the sample workstation setup comprises at least one of the following: At least one sample conveyor configuration; At least one sample robot configuration; At least one sample camera configuration; At least one sample input / output (IO) sensor configuration.

6. The method of claim 5, wherein the sample workstation is configured with at least one of the following: The relative relationship between the at least one sample robot and the at least one sample conveyor; At least one sample robot speed; At least one sample conveyor speed; At least one sample trigger signal configuration; The position of the at least one sample robot; The location of the at least one sample conveyor; The position of the at least one sample camera; The position of the at least one sample I / O sensor; or Sample working area settings.

7. The method of claim 4, wherein the at least one sample performance parameter of the workstation includes at least one of the following: Loop count; Pickup rate; Total number of items picked up; Total placement count; The load of each of the at least one sample robot; The load of each of the at least one sample conveyors; Missed pickup count; or Missing placement count.

8. The method of claim 1, wherein creating the workstation based on the plurality of parameters for the plurality of requirements comprises: Create the layout for the workstation; as well as Output a simulation of the workstation's performance.

9. The method of claim 8, wherein the user input is a first user input, and the method further comprises: In response to the simulation of the workstation's performance failing to meet at least one of the plurality of requirements, a second user input is received to update the workstation; as well as Update at least one of the layout or settings of the workstation to satisfy at least one of the plurality of requirements.

10. The method of claim 9, further comprising: The workstation is updated based on at least one of the updated layout or the updated workstation settings.

11. An apparatus for creating a workstation, comprising: A receiving module configured to receive user input to create the workstation, wherein the user input includes prompts for a language model, the prompts representing multiple requirements for the workstation, and wherein the workstation is used by at least one robot to pick up items and place them on at least one conveyor. A module for obtaining parameters associated with the at least one conveyor and the at least one robot for creating the workstation, configured by the language model and based on the user input; as well as A creation module is configured to create the workstation based on the plurality of parameters and the plurality of requirements.

12. An electronic device, comprising: processor; as well as A memory coupled to the processor, wherein the memory has instructions stored therein, and the instructions, when executed by the processor, cause the device to perform the action according to any one of claims 1-10.

13. A computer-readable medium having instructions stored therein, which, when executed by a processor, cause the processor to perform the method according to any one of claims 1-10.

14. A computer program product having instructions stored therein, which, when executed by a processor, cause the processor to perform the method according to any one of claims 1-10.

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