A method and apparatus for welding time prediction

Abstract

The purpose of this application is to provide a method and apparatus for predicting welding time. The method includes: acquiring welding data corresponding to a target weld, wherein the welding data includes welding process data corresponding to the target weld and image information of the target weld before welding; and based on the welding data, using a welding time prediction model to obtain predicted welding time information corresponding to the target weld, wherein the welding time prediction model is trained based on welding sample data, and the welding sample data includes sample image information, sample welding process data, and sample welding IoT data corresponding to sample welds. This application combines image information to predict welding time, fully considering the complexity of weld appearance and avoiding reliance on a single welding process data. Furthermore, the model can be optimized based on actual welding conditions, making it more suitable for current industrial scenarios and improving prediction accuracy.

Description

Technical Field

[0001] This application relates to the field of welding process technology, and in particular to a technology for predicting welding time. Background Technology

[0002] Welding time refers to the time spent completing a weld. Accurate assessment of welding time is helpful for enterprise production management. In existing technologies, welding time is typically estimated by relevant process personnel based on past production experience; alternatively, a welding production case database is established, and matching cases are searched based on welding methods and processes to obtain corresponding welding time calculation parameters. However, welding time determined by these methods is easily affected by the subjective factors of process personnel or the richness of data accumulation, and the accuracy of welding time prediction cannot be guaranteed. Summary of the Invention

[0003] One object of this application is to provide a method and apparatus for predicting welding time.

[0004] According to one aspect of this application, a method for predicting welding time is provided, the method comprising:

[0005] Obtain the weld welding data corresponding to the target weld, wherein the weld welding data includes the welding process data corresponding to the target weld and the image information of the target weld before welding;

[0006] Based on the weld seam welding data, the welding time prediction model is used to obtain the welding prediction time information corresponding to the target weld seam. The welding time prediction model is trained based on welding sample data, which includes sample image information, sample welding process data, and sample welding IoT data corresponding to the sample weld seam.

[0007] According to one aspect of this application, a computer device for predicting welding time is provided, comprising a memory, a processor, and a computer program stored in the memory, characterized in that the processor executes the computer program to implement the steps of any of the methods described above.

[0008] According to one aspect of this application, a computer-readable storage medium is provided having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of any of the methods described above.

[0009] According to one aspect of this application, a computer program product is provided, comprising a computer program, characterized in that, when executed by a processor, the computer program implements the steps of any of the methods described above.

[0010] According to one aspect of this application, an apparatus for predicting welding time is provided, the apparatus comprising:

[0011] The module is used to acquire the weld data corresponding to the target weld, wherein the weld data includes the welding process data corresponding to the target weld and the image information of the target weld before welding;

[0012] The first and second modules are used to obtain the predicted welding time information corresponding to the target weld by using a welding time prediction model based on the weld welding data. The welding time prediction model is trained based on welding sample data, which includes sample image information, sample welding process data, and sample welding IoT data corresponding to the sample weld.

[0013] Compared with existing technologies, this application obtains weld welding data corresponding to the target weld, wherein the weld welding data includes welding process data corresponding to the target weld and image information of the target weld before welding; based on the weld welding data, a welding time prediction model is used to obtain the predicted welding time information corresponding to the target weld, wherein the welding time prediction model is trained based on welding sample data, and the welding sample data includes sample image information, sample welding process data, and sample welding IoT data corresponding to sample welds. This application predicts welding time by combining weld welding data with image information corresponding to the target weld, fully considering the complexity of weld appearance, avoiding reliance on a single welding process data, and improving the accuracy of welding time prediction. Furthermore, this application can also optimize the welding time prediction model based on the predicted welding time of the target weld and the actual welding condition of the target weld, thereby making the welding time prediction model more suitable for current industrial scenarios and improving prediction accuracy. Attached Figure Description

[0014] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0015] Figure 1 A flowchart of a method for predicting welding time according to an embodiment of this application is shown;

[0016] Figure 2 A structural diagram of an apparatus for predicting welding time according to an embodiment of this application is shown.

[0017] Figure 3 Exemplary systems that can be used to implement the various embodiments described in this application are shown.

[0018] The same or similar reference numerals in the accompanying drawings represent the same or similar parts. Detailed Implementation

[0019] The present application will now be described in further detail with reference to the accompanying drawings.

[0020] In a typical configuration of this application, the terminal, the device of the service network, and the trusted party all include one or more processors (e.g., a central processing unit (CPU)), input / output interfaces, network interfaces, and memory.

[0021] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory. Memory is an example of computer-readable media.

[0022] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PCM), programmable random access memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0023] The devices referred to in this application include, but are not limited to, user equipment, network equipment, or devices composed of user equipment and network equipment integrated through a network. The user equipment includes, but is not limited to, any mobile electronic product capable of human-computer interaction (e.g., via a touchpad), such as smartphones and tablets. These mobile electronic products can use any operating system, such as Android or iOS. The network equipment includes an electronic device capable of automatically performing numerical calculations and information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), and embedded devices. The network equipment includes, but is not limited to, computers, network hosts, single network servers, multiple network server sets, or clouds composed of multiple servers. Here, a cloud consists of a large number of computers or network servers based on cloud computing, where cloud computing is a type of distributed computing, consisting of a virtual supercomputer composed of a group of loosely coupled computer sets. The network includes, but is not limited to, the Internet, wide area network, metropolitan area network, local area network, VPN network, and wireless ad hoc network. Preferably, the device can also be a program running on the user equipment, network device, or a device formed by integrating user equipment and network device, network device, touch terminal, or network device and touch terminal through a network.

[0024] Of course, those skilled in the art should understand that the above-described devices are merely examples, and other existing or future devices that are applicable to this application should also be included within the scope of protection of this application, and are hereby incorporated by reference.

[0025] In the description of this application, "multiple" means two or more, unless otherwise expressly and specifically defined.

[0026] Figure 1A flowchart of a method for predicting welding time according to an embodiment of this application is shown. The method includes steps S11 and S12. In step S11, device 1 acquires welding data corresponding to a target weld, wherein the welding data includes welding process data corresponding to the target weld and image information of the target weld before welding; in step S12, device 1 uses a welding time prediction model based on the welding data to obtain predicted welding time information corresponding to the target weld, wherein the welding time prediction model is trained based on welding sample data, and the welding sample data includes sample image information, sample welding process data, and sample welding IoT data corresponding to sample welds.

[0027] In step S11, device 1 acquires weld welding data corresponding to the target weld, wherein the weld welding data includes welding process data corresponding to the target weld and image information of the target weld before welding. In some embodiments, device 1 includes, but is not limited to, user equipment or network equipment with information processing or computing capabilities, such as tablet computers, computers, servers, etc. In some embodiments, the welding process data includes, but is not limited to, the welding method, welding current, welding voltage, part thickness, part material, and weld length corresponding to the target weld.

[0028] In step S12, device 1 obtains the predicted welding time information corresponding to the target weld based on the weld welding data and using a welding time prediction model. The welding time prediction model is trained based on welding sample data, which includes sample image information, sample welding process data, and sample welding IoT data corresponding to the sample weld. In some embodiments, device 1 determines the weld contour complexity information based on the image information of the target weld before welding. The corresponding predicted welding time information is determined based on the welding process data and the weld contour complexity information, combined with the welding formula prediction model. In some embodiments, device 1 extracts feature information corresponding to the target weld (e.g., structural features such as the length, width, and area of ​​the target weld) from the image information of the target weld before welding, and then determines the weld contour complexity information based on the feature information.

[0029] In some embodiments, the welding time prediction model is trained by device 2 based on welding sample data. Device 2 and device 1 can be the same device or different devices. The welding sample data includes sample image information corresponding to the sample weld, sample welding process data, and sample welding IoT data. The sample welding process data includes, but is not limited to, the welding method, welding current, welding voltage, part thickness, part material, and weld length corresponding to the sample weld. The sample welding IoT data includes, but is not limited to, welding data acquired during the welding process of the sample weld, such as welding voltage time series data and welding current time series data. In some embodiments, device 2 determines the welding time information corresponding to the sample weld based on the sample welding IoT data. For example, if the welding voltage value or welding current value corresponding to the welding voltage time series data or welding current time series data in the sample welding IoT data is less than the corresponding voltage threshold or current threshold, it can be determined that the welding machine is not working at the time corresponding to the welding voltage value or welding current value; otherwise, the welding machine is working at the time corresponding to the welding voltage value or welding current value. Furthermore, the welding time information corresponding to the sample weld can be determined based on the working time of the welding machine. In some embodiments, the device 2 determines the weld contour complexity information corresponding to the sample weld based on the sample image information. For example, the device 2 preprocesses the sample image information and then extracts the feature information corresponding to the sample weld (e.g., structural features such as the length, width, and area of ​​the target weld) from the preprocessed sample image information. The preprocessing includes, but is not limited to, windowing, Gaussian filtering, grayscale stretching, edge detection, and boundary extraction. The weld contour complexity information corresponding to the sample weld = (weld length × weld width) / weld area. In some embodiments, the device 2 establishes a welding time prediction model based on the weld contour complexity information, welding time information, and welding time information corresponding to the sample weld, using machine learning algorithms. The machine learning algorithms include, but are not limited to, algorithms such as XGBoost, Random Forest, AdaBoost, and CatBoost that can be used to solve regression problems. After the welding time prediction model is trained, it can be deployed on the device 1, so that the device 1 can predict the welding prediction time information corresponding to the target weld based on the acquired welding process data and weld contour complexity information during actual welding.

[0030] In some embodiments, the method further includes: step S13 (not shown), whereby device 1 acquires welding IoT data during the welding process of the target weld; and step S14 (not shown), whereby device 1 updates the welding time prediction model based on the weld welding data and the welding IoT data. In some embodiments, to make the welding time prediction model more accurate, device 1 can also update the welding time prediction model based on relevant welding data acquired during actual welding production. Device 1 can acquire welding IoT data during the welding process of the target weld through a high-frequency data acquisition gateway or various sensors (e.g., voltage sensors, current sensors, or airflow sensors) deployed in the actual welding production environment. The welding IoT data includes, but is not limited to, welding data acquired during the target welding process, such as welding voltage timing data and welding current timing data.

[0031] In some embodiments, step S14 includes: step S141 (not shown), where device 1 determines the actual welding time information corresponding to the target weld seam based on the welding IoT data; step S142 (not shown), where device 1 updates the welding time prediction model based on the actual welding time information and the weld seam welding data. In some embodiments, device 1 determines the working status of the welding machine based on the welding IoT data, and then determines the actual welding time information corresponding to the target weld seam. If the welding voltage value or welding current value corresponding to the welding voltage time series data or welding current time series data in the welding IoT data is less than the corresponding voltage threshold or current threshold, it can be determined that the welding machine is not working at the time corresponding to the welding voltage value or welding current value; otherwise, the welding machine is working at the time corresponding to the welding voltage value or welding current value. The actual welding time information corresponding to the target weld seam is determined based on the welding machine working time. Then, the welding time prediction model is updated based on the actual welding time information combined with the weld seam welding data.

[0032] In some embodiments, step S142 includes: step S1421 (not shown), where device 1 determines welding time deviation information based on the actual welding time information and the predicted welding time information; step S1422 (not shown), where if the welding time deviation information meets the corresponding time deviation condition, device 1 updates the welding time prediction model. In some embodiments, device 1 uses the difference between the actual welding time information and the predicted welding time information as the welding time deviation information. By using the welding time deviation information to determine whether to use the weld data corresponding to the target weld and the corresponding actual welding time information in updating the welding time prediction model, suitable data can be selected for updating the welding time prediction model, eliminating the need to invest significant manpower and time in obtaining the data required for model updates. In some embodiments, if the welding time deviation information is greater than a preset welding time deviation threshold, it can be determined that the welding time deviation information meets the corresponding time deviation condition, and the predicted welding time information is invalid. The welding data corresponding to the target weld and the corresponding actual welding time information can be used to update the welding time prediction model to improve its accuracy. The welding time deviation threshold can be pre-set based on the acceptable time tolerance range of the production line or enterprise to which the welding time prediction model is applied, thereby making the trained model more suitable for the needs of the corresponding production line or enterprise. If the welding time deviation information is less than or equal to the preset welding time deviation threshold, it can be determined that the welding time deviation information does not meet the corresponding time deviation condition, and the welding data of that weld can be ignored.

[0033] In some embodiments, step S1421 includes: device 1 acquiring image information of the target weld after welding is completed; determining welding correction time information corresponding to the target weld based on the image information of the target weld after welding, the image information of the target weld before welding, and the welding prediction time information; and determining welding time deviation information based on the actual welding time information and the welding correction time information. In some embodiments, due to the possibility of abnormal situations during actual welding (e.g., welding is terminated prematurely before the weld is completed; or, the weld position changes significantly before and after welding, resulting in changes in weld length, etc.), the welding prediction time information acquired based on the image information of the target weld before welding is no longer accurate. Device 1 can acquire the image information of the target weld after welding is completed. Based on the image information of the target weld before and after welding, the welding prediction time information is corrected to reduce the impact of abnormal situations on subsequent updates to the welding time prediction model. The device 1 can determine the first weld length before welding and the second weld length after welding of the target weld based on the image information before welding and the image information after welding of the target weld. Based on the first weld length, the second weld length, and the predicted welding time information, it determines the corrected welding time information. The corrected welding time information T = (L × T0) / L0, where L is the second weld length, L0 is the first weld length, and T0 is the predicted welding time information. The device 1 uses the difference between the corrected welding time information and the actual welding time information as the welding time deviation information, and uses this difference to determine whether the time deviation conditions are met.

[0034] In some embodiments, step S1422 includes: if the welding time deviation information meets the corresponding time deviation conditions, device 1 records the weld welding data and the welding IoT data into a deviation data set; if the deviation data set meets the corresponding data set conditions, the welding time prediction model is updated based on the deviation data set. In some embodiments, device 1 can update the welding time prediction model based on the weld welding data and related welding IoT data of the current target weld when it is determined that the welding time deviation information meets the corresponding time deviation conditions, or it can update the welding time prediction model based on these data after accumulating a sufficient number of weld welding data and welding IoT data. Device 1 can first record the weld welding data and welding IoT data that meet the time deviation conditions into the deviation data set for storage. If the number of weld welding data or corresponding welding IoT data in the deviation data set is greater than or equal to the corresponding data quantity threshold, it can be determined that the deviation data set meets the corresponding data set conditions. The device 1 can update the welding time prediction model based on the data in the deviation data set, and after the update is completed, delete the data in the deviation data set to start a new round of data accumulation. If the number of weld data or corresponding welding IoT data in the deviation data set is less than the corresponding data quantity threshold, it can be determined that the deviation data set does not meet the corresponding data set conditions, and the device 1 can continue to accumulate data.

[0035] In some embodiments, step S142 includes: device 1 determining the weld contour complexity information based on the image information of the target weld before welding; and updating the welding time prediction model based on the weld contour complexity information, the actual welding time information, and the welding process data. In some embodiments, determining the weld contour complexity information based on the image information of the target weld before welding includes: determining the feature information corresponding to the target weld based on the image information of the target weld before welding; and determining the weld contour complexity information based on the feature information. In some embodiments, the feature information corresponding to the target weld includes, but is not limited to, structural features such as the length, width, and area of ​​the target weld. The weld contour complexity information = (weld length of the target weld × weld width of the target weld) / weld area of ​​the target weld.

[0036] Figure 2This diagram illustrates a device structure for predicting welding time according to an embodiment of this application. The device 1 includes a primary module 11 and a secondary module 12. The primary module 11 acquires welding data corresponding to a target weld, wherein the welding data includes welding process data corresponding to the target weld and image information of the target weld before welding. The secondary module 12, based on the welding data, uses a welding time prediction model to obtain predicted welding time information corresponding to the target weld, wherein the welding time prediction model is trained based on welding sample data, and the welding sample data includes sample image information, sample welding process data, and sample welding IoT data corresponding to sample welds. Here, the... Figure 2 The specific implementation methods corresponding to module 11 and module 12 shown are the same as or similar to the specific embodiments of steps S11 and S12 described above, so they will not be repeated here, but are included by reference.

[0037] In some embodiments, the device 1 further includes a third module 13 (not shown) and a fourth module 14 (not shown). The third module 13 acquires welding IoT data during the welding process of the target weld; the fourth module 14 updates the welding time prediction model based on the weld welding data and the welding IoT data. Here, the specific implementations of the third module 13 and the fourth module 14 are the same as or similar to the specific implementations of steps S13 and S14 described above, and therefore will not be repeated here, but are incorporated herein by reference.

[0038] In some embodiments, the four-module 14 includes a four-first unit 141 (not shown) and a four-second unit 142 (not shown). The four-first unit 141 determines the actual welding time information corresponding to the target weld based on the welding IoT data; the four-second unit 142 updates the welding time prediction model based on the actual welding time information and the weld welding data. Here, the specific implementations of the four-first unit 141 and the four-second unit 142 are the same as or similar to the specific embodiments of steps S141 and S142 described above, and therefore will not be repeated here, but are incorporated herein by reference.

[0039] In some embodiments, the first four-two unit 142 includes a first four-two-one subunit 1421 (not shown) and a first four-two-two subunit 1422 (not shown). The first four-two-one subunit 1421 determines welding time deviation information based on the actual welding time information and the predicted welding time information; the first four-two-two subunit 1422 updates the welding time prediction model if the welding time deviation information meets the corresponding time deviation conditions. Here, the specific implementations of the first four-two-one subunit 1421 and the first four-two-two subunit 1422 are the same as or similar to the specific embodiments of steps S1421 and S1422 described above, and therefore will not be repeated here, but are included by reference.

[0040] Figure 3 Exemplary systems that can be used to implement the various embodiments described in this application are shown; such as Figure 3 As shown in some embodiments, system 300 can function as any of the devices described in each of the embodiments. In some embodiments, system 300 may include one or more computer-readable media having instructions (e.g., system memory or NVM / storage device 320) and one or more processors (e.g., one or more processors 305) coupled to the one or more computer-readable media and configured to execute the instructions to implement the module and thus perform the actions described in this application.

[0041] In one embodiment, the system control module 310 may include any suitable interface controller to provide any suitable interface to at least one of the processors 305 and / or any suitable device or component communicating with the system control module 310.

[0042] The system control module 310 may include a memory controller module 330 to provide an interface to the system memory 315. The memory controller module 330 may be a hardware module, a software module, and / or a firmware module.

[0043] System memory 315 can be used, for example, to load and store data and / or instructions for system 300. In one embodiment, system memory 315 may include any suitable volatile memory, such as suitable DRAM. In some embodiments, system memory 315 may include double data rate type quad synchronous dynamic random access memory (DDR4 SDRAM).

[0044] In one embodiment, the system control module 310 may include one or more input / output (I / O) controllers to provide interfaces to the NVM / storage device 320 and (one or more) communication interfaces 325.

[0045] For example, NVM / storage device 320 may be used to store data and / or instructions. NVM / storage device 320 may include any suitable non-volatile memory (e.g., flash memory) and / or may include any suitable (one or more) non-volatile storage devices (e.g., one or more hard disk drives (HDDs), one or more optical disc drives (CDs), and / or one or more digital universal optical disc (DVD) drives).

[0046] NVM / storage device 320 may include storage resources that are physically part of a device on which system 300 is mounted, or that can be accessed by the device without necessarily being part of it. For example, NVM / storage device 320 may be accessed via a network through one or more communication interfaces 325.

[0047] One or more communication interfaces 325 may provide the system 300 with an interface to communicate over one or more networks and / or with any other suitable device. The system 300 may wirelessly communicate with one or more components of a wireless network in accordance with any of one or more wireless network standards and / or protocols.

[0048] In one embodiment, at least one of the processors 305 may be logically packaged with one or more controllers of the system control module 310 (e.g., memory controller module 330). In one embodiment, at least one of the processors 305 may be logically packaged with one or more controllers of the system control module 310 to form a system-in-package (SiP). In one embodiment, at least one of the processors 305 may be integrated with the logic of one or more controllers of the system control module 310 on the same die. In one embodiment, at least one of the processors 305 may be integrated with the logic of one or more controllers of the system control module 310 on the same die to form a system-on-a-chip (SoC).

[0049] In various embodiments, system 300 may be, but is not limited to, a server, workstation, desktop computing device, or mobile computing device (e.g., laptop computing device, handheld computing device, tablet computer, netbook, etc.). In various embodiments, system 300 may have more or fewer components and / or different architectures. For example, in some embodiments, system 300 includes one or more cameras, a keyboard, a liquid crystal display (LCD) screen (including a touchscreen display), a non-volatile memory port, multiple antennas, a graphics chip, an application-specific integrated circuit (ASIC), and a speaker.

[0050] In addition to the methods and devices described in the above embodiments, this application also provides a computer-readable storage medium storing computer code that, when executed, performs the method described in any of the preceding embodiments.

[0051] This application also provides a computer program product that, when executed by a computer device, performs the method described in any of the preceding claims.

[0052] This application also provides a computer device, the computer device comprising:

[0053] One or more processors;

[0054] Memory, used to store one or more computer programs;

[0055] When the one or more computer programs are executed by the one or more processors, the one or more processors cause the one or more processors to perform the method as described in any of the preceding methods.

[0056] It should be noted that this application can be implemented in software and / or a combination of software and hardware, for example, using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In one embodiment, the software program of this application can be executed by a processor to implement the steps or functions described above. Similarly, the software program of this application (including related data structures) can be stored in a computer-readable recording medium, such as RAM memory, magnetic or optical drives, floppy disks, and similar devices. Furthermore, some steps or functions of this application can be implemented in hardware, for example, as circuitry that cooperates with a processor to perform the various steps or functions.

[0057] Furthermore, a portion of this application can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to this application through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0058] Communication media include media through which communication signals containing, for example, computer-readable instructions, data structures, program modules, or other data are transmitted from one system to another. Communication media can include guided transmission media (such as cables and wires (e.g., optical fibers, coaxial cables, etc.)) and wireless (unguided transmission) media capable of propagating energy waves, such as sound, electromagnetic, RF, microwave, and infrared. Computer-readable instructions, data structures, program modules, or other data can be embodied as modulated data signals in, for example, wireless media (such as carrier waves or similar mechanisms embodied as part of spread spectrum technology). The term "modulated data signal" refers to a signal whose one or more characteristics are altered or set in a manner that encodes information in the signal. Modulation can be analog, digital, or a hybrid modulation technique.

[0059] By way of example and not limitation, computer-readable storage media may include volatile and non-volatile, removable and non-removable media implemented by any method or technique for storing information such as computer-readable instructions, data structures, program modules or other data. For example, computer-readable storage media include, but are not limited to, volatile memories such as random access memory (RAM, DRAM, SRAM); and non-volatile memories such as flash memory, various read-only memories (ROM, PROM, EPROM, EEPROM), magnetic and ferromagnetic / ferroelectric memories (MRAM, FeRAM); and magnetic and optical storage devices (hard disks, magnetic tapes, CDs, DVDs); or other media now known or hereafter developed capable of storing computer-readable information / data for use by a computer system.

[0060] Herein, one embodiment of this application includes an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the apparatus is triggered to run a method and / or technical solution based on the foregoing embodiments of this application.

[0061] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within this application. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in the apparatus claims may also be implemented by a single unit or device in software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

Claims

1. A method for predicting welding time, wherein, The method includes: Obtain the weld welding data corresponding to the target weld, wherein the weld welding data includes the welding process data corresponding to the target weld and the image information of the target weld before welding; Based on the weld seam welding data, the welding time prediction model is used to obtain the welding prediction time information corresponding to the target weld seam. The welding time prediction model is trained based on welding sample data, which includes sample image information, sample welding process data and sample welding IoT data corresponding to the sample weld seam. The step of obtaining the predicted welding time information of the target weld based on the weld welding data and using the welding time prediction model includes: determining the weld contour complexity information based on the image information of the target weld before welding, wherein the weld contour complexity information is determined based on the feature information corresponding to the target weld extracted from the image information of the target weld before welding; and determining the corresponding predicted welding time information based on the welding process data and the weld contour complexity information, combined with the welding time prediction model.

2. The method according to claim 1, wherein, The method further includes: Acquire welding IoT data during the welding process of the target weld; The welding time prediction model is updated based on the weld seam welding data and the welding IoT data.

3. The method according to claim 2, wherein, The step of updating the welding time prediction model based on the weld seam welding data and the welding IoT data includes: Based on the welding IoT data, the actual welding time information corresponding to the target weld is determined; The welding time prediction model is updated based on the actual welding time information and the weld data.

4. The method according to claim 3, wherein, The step of updating the welding time prediction model based on the actual welding time information and the weld data includes: Based on the actual welding time information and the predicted welding time information, welding time deviation information is determined; If the welding time deviation information meets the corresponding time deviation conditions, the welding time prediction model is updated.

5. The method according to claim 4, wherein, The step of determining the welding time deviation information based on the actual welding time information and the predicted welding time information includes: Acquire image information of the target weld after welding is completed; Based on the image information after the target weld is completed, the image information before the target weld is welded, and the predicted welding time information, the welding correction time information corresponding to the target weld is determined. Based on the actual welding time information and the welding correction time information, the welding time deviation information is determined.

6. The method according to claim 4 or 5, wherein, If the welding time deviation information meets the corresponding time deviation conditions, updating the welding time prediction model includes: If the welding time deviation information meets the corresponding time deviation conditions, the weld welding data and the welding IoT data are recorded in the deviation data set; If the deviation data set meets the corresponding data set conditions, the welding time prediction model is updated based on the deviation data set.

7. The method according to claim 3, wherein, The step of updating the welding time prediction model based on the actual welding time information and the weld data includes: The weld contour complexity information is determined based on the image information of the target weld before welding; The welding time prediction model is updated based on the weld contour complexity information, the actual welding time information, and the welding process data.

8. The method according to claim 7, wherein, The step of determining the weld contour complexity information based on the image information of the target weld before welding includes: Based on the image information of the target weld before welding, determine the feature information corresponding to the target weld; Based on the aforementioned feature information, the weld contour complexity information is determined.

9. A computer device for predicting welding time, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method as described in any one of claims 1 to 8.

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