Welding data processing method and device, storage medium and processor

Abstract

Embodiments of the present application provide a welding data processing method and device, a storage medium and a processor. The method comprises: determining, through a user instruction, initial parameter values of each welding parameter, target acquisition parameters and welding scene data of a welding device when the welding device is welding a current area; controlling the welding device to start a welding operation according to the initial parameter values of each welding parameter; obtaining parameter values of each target acquisition parameter; importing a preset template file, and determining target parameter values of each welding parameter of the welding device for a next welding area according to the preset template file and the parameter values of the target acquisition parameters, wherein the number of historical acquisition parameters is greater than or equal to the number of target acquisition parameters. By obtaining different welding scenes through user interaction technology, the welding parameters are adaptively adjusted to achieve the best welding effect, and the development cost of software can be reduced.

Description

Technical Field

[0001] This application relates to the field of intelligent welding technology, specifically to a method, apparatus, storage medium, and processor for processing welding data. Background Technology

[0002] Conventional automated welding equipment uses pre-set welding parameters based on actual operating conditions, lacking external information sensing and real-time control functions during welding. During welding, various factors can affect weld quality. Current technologies limit parameter adjustments to specific scenarios, relying on data collection and adjustment of only a subset of parameters (such as gap). Welding data collected in other scenarios lacks value for adaptive parameter adjustments in those scenarios, resulting in significant data waste. Furthermore, adaptive functions for each scenario require redevelopment, incurring high economic and time costs. Summary of the Invention

[0003] The purpose of this application is to provide a method, apparatus, storage medium, and processor for processing welding data.

[0004] To achieve the above objectives, the first aspect of this application provides a method for processing welding data, comprising:

[0005] Get user input commands;

[0006] The initial parameter values, target acquisition parameters, and welding scene data for each welding parameter are determined based on user instructions when the welding equipment welds the current area.

[0007] The welding equipment is controlled to start welding operations on the current area based on the initial parameter values ​​of each welding parameter;

[0008] Obtain the parameter values ​​for each target acquisition parameter;

[0009] Import the preset template file;

[0010] Based on the historical parameter values ​​of the historical acquisition parameters included in the preset template file, and the parameter values ​​of each target acquisition parameter, determine the target parameter values ​​for each welding parameter of the welding equipment for the next welding area;

[0011] Among them, the number of historically acquired parameters is greater than or equal to the number of target acquired parameters.

[0012] In the embodiments of this application, the preset template file is generated based on multiple historical acquisition parameters, multiple historical welding parameters, multiple historical welding scene data, and the historical welding quality corresponding to each historical welding parameter.

[0013] In the embodiments of this application, when a preset template file matching the device model is found, determining the target parameter value of each welding parameter for the next welding area based on the historical parameter values ​​of the historical acquisition parameters included in the preset template file and the parameter value of each target acquisition parameter includes: if the preset template file is a text file, determining the Euclidean distance between the parameter values ​​of all target acquisition parameters and each historical acquisition parameter group in the text file, wherein the text file includes multiple historical acquisition parameter groups and each historical acquisition parameter group includes multiple historical parameter values; and determining each target historical parameter value included in the target historical acquisition parameter group corresponding to the smallest Euclidean distance among all Euclidean distances as the target parameter value of each corresponding welding parameter.

[0014] In the embodiments of this application, determining the target parameter value of each welding parameter for the next welding area based on the historical parameter values ​​of the historical acquisition parameters included in the preset template file and the parameter values ​​of each target acquisition parameter includes: when the preset template file is a preset model, inputting the parameter values ​​of each target acquisition parameter into the preset model, so as to output the target parameter value of each welding parameter through the preset model.

[0015] In the embodiments of this application, the processing method further includes: without importing a preset template file, saving the parameter value of each target acquisition parameter in a database, and determining the target parameter value for each welding parameter based on the initial parameter value of the welding parameter.

[0016] In this embodiment of the application, the processing method further includes: sending the parameter value of each target acquisition parameter, the target parameter value of each welding parameter, and the welding scene data to the cloud; updating the preset template file in the cloud according to the parameter values ​​of all target acquisition parameters, the target parameter values ​​of all welding parameters, and the welding scene data, and obtaining the updated preset template file to determine the target parameter value of each welding parameter through the updated preset template file.

[0017] A second aspect of this application provides a processor configured to execute the above-described welding data processing method.

[0018] A third aspect of this application provides a welding data processing apparatus, including a processor configured to perform the welding data processing method described above.

[0019] In embodiments of this application, the processing device further includes: multiple data acquisition threads, used to acquire parameter values ​​of acquisition target parameters when the welding equipment is welding the current area.

[0020] A fourth aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the aforementioned welding data processing method.

[0021] The above technical solution determines the initial parameter values, target acquisition parameters, and welding scene data for each welding parameter when welding the current area using user commands. It then controls the welding equipment to initiate the welding operation based on the initial parameter values ​​for each parameter. The system acquires the parameter values ​​for each target acquisition parameter, imports a preset template file, and determines the target parameter values ​​for each welding parameter for the next welding area based on the preset template file and the target acquisition parameter values. The number of historical acquisition parameters is greater than or equal to the number of target acquisition parameters. By using user interaction technology to adaptively adjust welding parameters in different welding scenarios, the system achieves optimal welding results and reduces software development costs.

[0022] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0023] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0024] Figure 1 A schematic flowchart of a welding data processing method according to an embodiment of this application is shown.

[0025] Figure 2 A schematic diagram illustrating the software architecture for welding data processing according to an embodiment of this application is shown.

[0026] Figure 3 A schematic diagram illustrating an adaptive thread according to an embodiment of this application is shown.

[0027] Figure 4 A schematic diagram illustrating the structure of a welding data processing apparatus according to an embodiment of this application is shown.

[0028] Figure 5 The diagram illustrates the internal structure of a computer device according to an embodiment of this application. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0030] Figure 1 A schematic flowchart illustrating a welding data processing method according to an embodiment of this application is shown. Figure 1 As shown in one embodiment of this application, a method for processing welding data is provided. This embodiment mainly illustrates the application of this method to a processor, and includes the following steps:

[0031] S102, Obtain user input commands.

[0032] S104, based on user instructions, determines the initial parameter values ​​of each welding parameter, target acquisition parameters, and welding scene data when the welding equipment welds the current area.

[0033] S106, control the welding equipment to start welding operations on the current area according to the initial parameter values ​​of each welding parameter.

[0034] S108, obtain the parameter values ​​of each target acquisition parameter.

[0035] S110, import the preset template file.

[0036] S112, based on the historical parameter values ​​of the historical acquisition parameters included in the preset template file and the parameter values ​​of each target acquisition parameter, determine the target parameter value of each welding parameter for the next welding area of ​​the welding equipment, wherein the number of historical acquisition parameters is greater than or equal to the number of target acquisition parameters.

[0037] In practice, the data acquisition sensors on welding equipment are relatively limited, only capable of collecting current and voltage, or temperature data related to plate gaps and the welding process. Therefore, based on the actual welding scenario, the user can input user commands into the software interface, including a first command and a second command. The first command is the user's instruction to select the target acquisition parameter to be collected. The software interface displays all the parameter types for the target acquisition parameter, and the user can select one or more different types. After receiving the first command, the processor can obtain the parameter values ​​of the target acquisition parameter for the welding equipment. Specifically, this can be achieved by mounting sensors on the welding equipment or installing sensors in the welding area. The second command is the welding scenario data input by the user into the software interface. Welding scenario data includes information such as plate thickness, material, bevel type, lap joint scheme, bevel angle, etc. The processor can directly obtain this welding scenario data through user input. The third command is the welding parameter selected by the user, along with the initial parameter values. The processor can then obtain the third command to determine the initial parameter values ​​for each welding parameter of the welding equipment. Then, the processor can control the welding equipment to start the welding operation on the current area according to the initial parameter value of each welding parameter.

[0038] Among them, target acquisition parameters are parameters collected by the welding equipment when welding the current area, reflecting the welding state during the welding operation. Target acquisition parameters can be one or more different types of parameters. Welding parameters refer to parameters that control the welding process and can affect welding quality. For example, they can be one or more parameters such as welding current, welding voltage, welding speed, and robot job number. When the welding equipment is welding the current area, the processor can acquire the parameter values ​​of each target acquisition parameter at the time of welding. For example, they can be one or more welding parameters such as welding current, welding voltage, molten pool temperature, preheating temperature, and plate gap. Users can collect different target acquisition parameters in different welding scenarios. Specifically, the processor can collect data through data acquisition threads corresponding to the target acquisition parameters.

[0039] Furthermore, after acquiring the parameter values ​​of the target acquisition parameters, the processor receives the imported preset template file. The preset template file is used to adjust the welding parameters of the welding equipment. It includes different historical parameter values ​​acquired at different historical times. The number of historical acquisition parameters in the preset template file is greater than or equal to the target acquisition parameter. During the welding process, to achieve the best welding effect, the processor can adjust the welding parameters in real time according to the preset template file. Therefore, upon receiving the imported preset template file, the processor can determine the target parameter values ​​for each welding parameter for the next welding area based on the preset template file and the parameter values ​​of each target acquisition parameter. The target parameter value refers to the predicted parameter value of one or more of the welding parameters such as welding current, welding voltage, molten pool temperature, preheating temperature, and plate gap for the next welding area. Based on the parameter values ​​of the target acquisition parameters for the current area, the welding equipment adaptively adjusts the target parameter values ​​to achieve the best welding effect for the next welding area. Furthermore, welding parameters can be adaptively adjusted in different welding scenarios, avoiding redundant software development for different scenarios and reducing overall development costs.

[0040] In one embodiment, determining the target parameter value for each welding parameter of the welding equipment for the next welding area based on the historical parameter values ​​of historical acquisition parameters included in the preset template file and the parameter value of each target acquisition parameter includes: if the preset template file is a text file, determining the Euclidean distance between the parameter values ​​of all target acquisition parameters and each historical acquisition parameter group in the text file, wherein the text file includes multiple historical acquisition parameter groups and each historical acquisition parameter group includes multiple historical parameter values; and determining each target historical parameter value included in the target historical acquisition parameter group corresponding to the smallest Euclidean distance among all Euclidean distances as the target parameter value of the corresponding welding parameter.

[0041] In one embodiment, the preset template file is generated based on multiple historical acquisition parameters, multiple historical welding parameters, multiple historical welding scene data, and the historical welding quality corresponding to each historical welding parameter.

[0042] The preset template file includes multiple historical acquisition parameter groups. Upon receiving the imported preset template file, the processor can calculate the Euclidean distance between the parameter values ​​of all target acquisition parameters and each historical acquisition parameter group. The processor can then determine the target historical acquisition parameter group corresponding to the minimum Euclidean distance. Each target historical parameter value included in this target historical acquisition parameter group is the target parameter value for the corresponding welding parameter.

[0043] Specifically, the preset template file is generated based on multiple historical acquisition parameters, multiple historical welding parameters, multiple historical welding scenario data, and the historical welding quality corresponding to each historical welding parameter. The preset template file can be a text file containing multiple historical acquisition parameter groups. The text file can also be a regular CSV file. The processor can obtain user-imported CSV files. The CSV file includes historical parameter values ​​for different historical acquisition parameters. The first line records the parameter categories of the historical welding parameters input to the welding equipment, and the historical acquisition parameters acquired when the welding equipment executes the historical welding parameters, including the acquired welding current, welding voltage, molten pool temperature, preheating temperature, plate gap, welding robot job number, bevel angle, welding position, and other different historical acquisition parameter categories. Each of the other lines records the historical parameter values ​​of the same welding point at the same time, and the historical parameter values ​​of the historical acquisition parameter group corresponding to the historical acquisition parameter group with the best welding quality when performing multiple welds using the historical parameter values ​​of that historical welding parameter group. The processor can then calculate the Euclidean distance between the parameter values ​​of all target acquisition parameters and each historical acquisition parameter group in the CSV file. The processor can then determine the target historical acquisition parameter group corresponding to the minimum Euclidean distance in the CSV file. Each target historical parameter value in this target historical acquisition parameter group is the target parameter value for each corresponding welding parameter. The processor can control the welding equipment to weld the next welding area based on the target historical parameter values ​​in the target historical acquisition parameter group. When the accumulated data collected in the database is weak, and there is insufficient data to support the AI's decision-making logic, the above solution can be used to allow the processor to obtain the text file imported by the user and adaptively adjust the target parameter values ​​of the welding parameters to achieve better welding results.

[0044] In one embodiment, determining the target parameter value of each welding parameter for the next welding area based on the historical parameter values ​​of the historical acquisition parameters included in the preset template file and the parameter values ​​of each target acquisition parameter includes: if the preset template file is a preset model, inputting the parameter values ​​of each target acquisition parameter into the preset model, so as to output the target parameter value of each welding parameter through the preset model.

[0045] In one embodiment, the preset template file is generated based on multiple historical acquisition parameters, multiple historical welding parameters, multiple historical welding scene data, and the historical welding quality corresponding to each historical welding parameter.

[0046] Specifically, the preset template file is generated based on multiple historical acquisition parameters, multiple historical welding parameters, multiple historical welding scene data, and the historical welding quality corresponding to each historical welding parameter. The preset template file can be a preset model. The processor can train the aforementioned multiple historical acquisition parameters, multiple historical welding parameters, and the historical welding quality corresponding to each historical welding parameter to obtain a trained preset model. Then, the processor can input the parameter value of each target acquisition parameter into the preset model to output the target parameter value of each welding parameter through the preset model. The preset model can be a .m or .pkl model file. Such model files are developed and saved using software such as Python. When data accumulation is massive, it is difficult for users to manually analyze / formulate decision rules. In this case, computer machine learning can be used to train the preset model, enabling the function of calling the model file and making predictions.

[0047] In one embodiment, the processing method further includes: when importing a preset template file, saving the parameter value of each target acquisition parameter in a database, and determining the target parameter value for each welding parameter based on the initial parameter value of the welding parameter.

[0048] The processor can acquire data through data acquisition threads corresponding to the target acquisition parameters to obtain the parameter values ​​of each target acquisition parameter when welding the current area. For example, this could be one or more welding parameters such as welding current, welding voltage, molten pool temperature, preheating temperature, and plate gap. After acquiring the parameter values ​​of the target acquisition parameters, the processor can obtain the equipment parameters or equipment number input by the user to confirm the equipment model. If no preset template file is found, the processor enters data acquisition mode. That is, the processor can save the parameter values ​​of each target acquisition parameter in a database. Furthermore, the processor can use the initial parameter values ​​set by the user as the target parameter values ​​for welding the next area to control the welding equipment to weld the next area. When the processor acquires the imported preset template file, it can adaptively adjust the welding parameters. Alternatively, when the acquired target acquisition parameter values ​​are sufficiently rich, the acquired data can be used to train a machine learning model to adaptively adjust the welding parameters.

[0049] In one embodiment, the processing method further includes: sending the parameter value of each target acquisition parameter, the target parameter value of each welding parameter, and the welding scene data to the cloud; updating the preset template file in the cloud according to the parameter values ​​of all target acquisition parameters, the target parameter values ​​of all welding parameters, and the welding scene data, and obtaining the updated preset template file to determine the target parameter value of each welding parameter through the updated preset template file.

[0050] The processor can send the parameter values ​​of each target acquisition parameter, the target parameter values ​​of each welding parameter, and the welding scene data to the cloud. The cloud can then update the preset template file based on the parameter values ​​of all target acquisition parameters and the target parameter values ​​of all welding parameters. Specifically, the cloud can insert the acquired target acquisition parameters, target parameter values, and welding scene data into a CSV file. Alternatively, the cloud can use the acquired target acquisition parameters, target parameter values, and welding scene data to retrain and optimize the preset model. Then, the cloud can send the updated preset template file to the processor. Upon receiving the updated preset template file, the processor can determine the target parameter values ​​for each welding parameter in the subsequent welding area.

[0051] In a specific embodiment, such as Figure 2 As shown, another method for processing welding data is provided, including the following steps:

[0052] Step 1: Initialize the software.

[0053] After the software runs, the processor initializes the cached data with default configurations, including: data storage status, raw data, adaptive status, adaptive configuration cache, and software configuration cache. The main interface thread can load data acquisition threads, including: a high-speed acquisition thread from the acquisition card (acquiring real-time current and voltage), a high-speed temperature acquisition thread (acquiring molten pool temperature, welding front temperature, and preheating temperature), a high-speed gap acquisition thread (acquiring plate gap, bevel angle, etc.), and a robot data acquisition thread (acquiring initial welding parameters input by the user). The software interface displays the parameter types of all welding parameters and the data types of welding scene data. Users can select one or more different types of welding parameters and welding scene data for settings. The processor can then receive the third-party instructions input by the user, determine the initial parameter values ​​for each welding parameter of the welding equipment, send them to the welding equipment, and control the welding equipment to start welding operations on the current area based on the parameter values ​​of each initial welding parameter.

[0054] Step 2: Users set various configurations for adaptive control according to the actual scenario, and the data is saved in the software configuration.

[0055] The processor can receive the robot device type selected by the user in the software interface, thereby automatically adjusting the data acquisition and transmission specifications. Furthermore, the processor can obtain the user's selection of a data acquisition mode and a software mode from the preset modes displayed in the software interface. The software mode includes an expert mode and a default mode. When the processor detects that the user has selected expert mode, it can configure the internal rules for adaptive adjustment of welding parameters; if the user has selected default mode, it cannot configure the internal rules for adaptive adjustment. When the processor detects that the user has selected data acquisition mode, it can use different data acquisition threads to acquire welding data. This prevents the adaptive adjustment program from being arbitrarily tampered with. Furthermore, the software presets multiple parameter types for different types of acquisition parameters, as well as parameter types for welding parameters. The processor can obtain the first instruction input by the user in the software interface to determine the target acquisition parameters for the welding equipment, and obtain welding scene data through the second instruction. The processor can also obtain the third instruction input by the user to determine the initial parameter values ​​for each welding parameter of the welding equipment. Furthermore, the processor can also acquire data input by the user through the software interface, such as the adaptive control interval of the welding equipment, the laser gap advance emission distance, and the distance between the laser and the welding position. Then, the processor can control the welding equipment to initiate welding operations on the current area based on the parameter values ​​of each initial welding parameter. At this time, the processor can control the data acquisition thread corresponding to the target acquisition parameters to perform data acquisition.

[0056] Step 3: Collect the parameter values ​​of the target acquisition parameters (e.g., Figure 2 (The original data shown). The collected data consists of historical parameter values ​​of the same welding parameter group at the same welding point and time, as well as historical parameter values ​​of the historical parameter group corresponding to the best welding quality when multiple welding operations are performed using the historical parameter values ​​of this historical welding parameter group.

[0057] Specifically, users can select plate thickness, material, bevel type, lap joint scheme, and bevel angle in the preset mode, and input the corresponding welding scenario data. After acquiring the above data, the processor saves it in the adaptive data. Further, when the processor receives the "Start Data Acquisition" command input by the user in the software interface, it starts the corresponding data acquisition thread to acquire the parameter values ​​of the target acquisition parameters and saves them. After the processor receives the "Start Adaptation" command input by the user in the software interface, if the processor has not received the imported preset template file, a confirmation box stating "No adaptive rules, send initial parameter values" will be displayed in the software interface. If the processor receives a confirmation command input by the user, it updates the initial parameter values ​​of the welding parameters and the parameter values ​​of the target acquisition parameters in the adaptive configuration cache, generating an adaptive detailed log. The initial parameter values ​​are then sent to the welding equipment, enabling the welding equipment to weld the next welding area according to the initial parameter values. If the processor receives a cancellation command input by the user, the adaptive adjustment of welding parameters will not run. The processor can save the above adaptive configuration cache and software configuration cache data uniformly in the adaptive data.

[0058] Step 4: Import the preset template file for the adaptive decision-making logic. There are two types of imported files: CSV files or model files such as .m or .pkl. After the processor receives the "Start Adaptation" command input by the user in the software interface, it can adaptively adjust the welding parameters through the preset template file to obtain the target parameter values.

[0059] refer to Figure 3The processor can retrieve a user-imported CSV file, which includes historical parameter values ​​for different historical acquisition parameters. The first row records the parameter categories of historical welding parameters input to the welding equipment, as well as the historical acquisition parameters acquired when the welding equipment executed these parameters. These parameters include the acquired welding current, welding voltage, molten pool temperature, preheating temperature, plate gap, welding robot job number, bevel angle, and welding position. Each subsequent row records the historical parameter values ​​of the same welding point at the same time, and the historical parameter values ​​of the historical acquisition parameter group that yielded the best welding quality when multiple welds were performed using those historical parameter values. The processor can normalize the data in the CSV file and then calculate the Euclidean distance between all target acquisition parameter values ​​and each historical acquisition parameter group in the CSV file. The processor can then determine the target historical acquisition parameter group corresponding to the minimum Euclidean distance in the CSV file. Each target historical parameter value in this target historical acquisition parameter group is the target parameter value for the corresponding welding parameter. The processor can use the target historical parameter values ​​of the target historical acquisition parameter group to control the welding equipment to weld the next welding area. When the accumulated data collected in the database is weak and there is not enough data to support it, AI cannot form decision-making logic. In this case, the above solution can be used to enable the processor to obtain the text file imported by the user and adaptively adjust the target parameter values ​​of the welding parameters to achieve better welding results.

[0060] The preset model can be a .m or .pkl model file. These model files are developed and saved using software such as Python. When data accumulation is massive, it becomes difficult for users to manually analyze and formulate decision rules. In such cases, computer machine learning can be used to train the preset model, enabling the function of calling and predicting from the model file. The processor can train on multiple historical acquisition parameters, multiple historical welding parameters, welding scene data, and the historical welding quality corresponding to each historical welding parameter to obtain a trained preset model. The processor can then input the parameter value of each target acquisition parameter into the preset model, and output the target parameter value for each welding parameter through the preset model. The processor can uniformly store the aforementioned adaptive configuration cache and software configuration cache data in adaptive data. Furthermore, the processor can uniformly distribute data to the edge through user authentication, data upload to the cloud, and model file cloud updates.

[0061] The above technical solution enables adaptive adjustment of welding parameters in different welding scenarios to achieve optimal welding results. It also reduces software development costs. Furthermore, adaptive adjustment of welding parameters in different welding scenarios enhances the value of real-time data acquisition, avoids redundant software development for different scenarios, and reduces overall development costs. Simultaneously, through user interaction technology, users can customize different data acquisition threads, adjust relevant configurations and data, adapt to different welding adaptive scenarios, and quickly change adaptive decision-making logic.

[0062] Figure 1 This is a flowchart illustrating a welding data processing method in one embodiment. It should be understood that, although... Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0063] In one embodiment, such as Figure 4 As shown, a welding data processing device is provided, including a data acquisition module, a file search module, and a target parameter value determination module, wherein:

[0064] The user interaction module 402 acquires user input commands; determines the initial parameter values ​​of each welding parameter, target acquisition parameters, and welding scene data of the welding equipment when welding the current area based on the user commands; and controls the welding equipment to start welding operations on the current area based on the initial parameter values ​​of each welding parameter.

[0065] The data acquisition module 404 is used to acquire the parameter values ​​of each target acquisition parameter when the welding equipment is welding the current area.

[0066] File import module 408 imports preset template files.

[0067] The target parameter value determination module 410 determines the target parameter value of each welding parameter for the next welding area based on the historical parameter values ​​of historically acquired parameters included in the preset template file and the parameter value of each target acquired parameter. The number of historically acquired parameters is greater than the number of target acquired parameters.

[0068] In one embodiment, the preset template file is generated based on multiple historical acquisition parameters, multiple historical welding parameters, multiple historical welding scene data, and the historical welding quality corresponding to each historical welding parameter.

[0069] In one embodiment, the target parameter value determination module 408 is further configured to determine the Euclidean distance between the parameter values ​​of all target acquisition parameters and each historical acquisition parameter group in the text file when the preset template file is a text file. The text file includes multiple historical acquisition parameter groups, and each historical acquisition parameter group includes multiple historical parameter values. The target historical parameter value included in the target historical acquisition parameter group corresponding to the minimum Euclidean distance among all Euclidean distances is determined as the target parameter value of each corresponding welding parameter.

[0070] In the embodiments of this application, the target parameter value determination module 408 is further used to input the parameter value of each target acquisition parameter into the preset model when the preset template file is a preset model, so as to output the target parameter value of each welding parameter through the preset model.

[0071] In one embodiment, the target parameter value determination module 408 is further configured to save the parameter value of each target acquisition parameter in a database without importing a preset template file, and determine the target parameter value for each welding parameter based on the initial parameter value of the welding parameter.

[0072] In one embodiment, the processing device further includes a cloud update module (not shown in the figure), which is used to send the parameter value of each target acquisition parameter, the target parameter value of each welding parameter, and the welding scene data to the cloud; after updating the preset template file in the cloud according to the parameter values ​​of all target acquisition parameters, the target parameter values ​​of all welding parameters, and the welding scene data, the updated preset template file is obtained, so as to determine the target parameter value of each welding parameter through the updated preset template file.

[0073] In one embodiment, the welding data processing apparatus further includes multiple data acquisition threads for acquiring parameter values ​​of target acquisition parameters when the welding equipment is welding the current area.

[0074] Specifically, the data acquisition threads include a high-speed acquisition thread for the acquisition card (acquiring real-time current, voltage, welding speed, etc.), a high-speed temperature acquisition thread (acquiring molten pool temperature, welding front temperature, preheating temperature, etc.), a high-speed gap acquisition thread (acquiring plate gap, bevel angle, real-time plate thickness, etc.), and a robot data acquisition thread (acquiring initial welding parameters input by the user, etc.).

[0075] The welding data processing device includes a processor and a memory. The aforementioned data acquisition module, file search module, and target parameter value determination module are all stored as program units in the memory, and the processor executes the aforementioned program modules stored in the memory to implement the corresponding functions.

[0076] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured, and the processing method for welding data can be implemented by adjusting the kernel parameters.

[0077] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0078] This application provides a storage medium storing a program that, when executed by a processor, implements the above-described welding data processing method.

[0079] This application provides a processor for running a program, wherein the program executes the above-described welding data processing method during runtime.

[0080] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor A01, a network interface A02, memory (not shown), and a database (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A04. The database stores data related to welding data processing methods. The network interface A02 communicates with external terminals via a network connection. When the processor A01 executes the computer program B02, it implements a welding data processing method.

[0081] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0082] This application provides an apparatus, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of a welding data processing method.

[0083] This application also provides a computer program product that, when executed on a data processing device, is suitable for executing the method steps of a processing method that initializes welding data.

[0084] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0085] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0086] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0087] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0088] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0089] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0090] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change 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, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0091] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0092] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for processing welding data, characterized in that, The processing method includes: Get user input commands; The user instructions determine the initial parameter values, target acquisition parameters, and welding scene data for each welding parameter when the welding equipment welds the current area. The welding parameters are parameters that control the welding operation. The welding equipment is controlled to start welding operations on the current area according to the initial parameter values ​​of each welding parameter; Obtain the parameter values ​​for each target acquisition parameter; Import the preset template file; Based on the historical parameter values ​​of the historical acquisition parameters included in the preset template file, and the parameter values ​​of each target acquisition parameter, the target parameter values ​​for each welding parameter of the welding equipment for the next welding area are determined, specifically including: When the preset template file is a text file, the Euclidean distance between the parameter values ​​of all target acquisition parameters and the historical acquisition parameter groups in the text file is determined. The text file includes multiple historical acquisition parameter groups, and each historical acquisition parameter group includes multiple historical parameter values. Each target historical parameter value in the target historical acquisition parameter group corresponding to the smallest Euclidean distance among all Euclidean distances is determined as the target parameter value of each corresponding welding parameter. When the preset template file is a preset model, the parameter values ​​of each target acquisition parameter are input into the preset model, so that the target parameter values ​​of each welding parameter are output through the preset model; The processing method further includes: sending the parameter value of each target acquisition parameter, the target parameter value of each welding parameter, and the welding scene data to the cloud; after the cloud updates the preset template file according to the parameter values ​​of all target acquisition parameters, the target parameter values ​​of all welding parameters, and the welding scene data, obtaining the updated preset template file, and determining the target parameter value of each welding parameter through the updated preset template file; Wherein, the number of historically acquired parameters is greater than or equal to the number of target acquired parameters; The welding scenario data includes: plate thickness, material, bevel type, lap joint scheme, and bevel angle; The welding parameters include: welding current, welding voltage, and welding speed; The target acquisition parameters include: molten pool temperature, preheating temperature, and plate gap; The target acquisition parameters are acquired based on the welding scenario, and the target acquisition parameters are the parameters acquired by the welding equipment when welding the current area.

2. The method for processing welding data according to claim 1, characterized in that, The preset template file is generated based on multiple historical acquisition parameters, multiple historical welding parameters, multiple historical welding scene data, and the historical welding quality corresponding to each historical welding parameter.

3. A processor, characterized in that, It is configured to perform the welding data processing method according to any one of claims 1 to 2.

4. A welding data processing device, characterized in that, Includes the processor as described in claim 3.

5. The welding data processing apparatus according to claim 4, characterized in that, Also includes: Multiple data acquisition threads are used to acquire the parameter values ​​of the target acquisition parameters when the welding equipment is welding the current area.

6. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform a method for processing welding data according to any one of claims 1 to 2.

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