Multi-brand robot program data processing method and system

Abstract

The invention discloses a multi-brand robot program data processing method and system. The method comprises the following steps: acquiring program data of different brands of robots; analyzing the program data of the robots of different brands to obtain an intermediate data structure; converting the intermediate data structure into an instruction tree and a data set which can be identified by the target brand robot, and generating a program file and a data file of the target brand robot according to the instruction tree and the data set; and storing the program data and the intermediate data structure of the robots of different brands, the instruction tree and the data set which can be identified by the robot of the target brand, and the program file and the data file of the robot of the target brand in a pre-constructed database. The problem that when a traditional program data processing method focusing on a single-brand robot is used for processing program data of robots of different brands, due to the fact that unified analysis and conversion standards are lacked, data conversion is not smooth, the data utilization rate is low, and the cooperative work efficiency of the robots of the multiple brands is limited is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial robot program data processing, and in particular to a multi-brand robot program data processing method and system. Background Art

[0002] Amid the rapid development of industrial robotics, the diversity of robot programming languages and brand differences have become significant challenges in industrial applications. Currently, most industrial robot program data processing methods focus on a single brand or specific model of robot. This approach lacks unified parsing and conversion standards when processing program data from robots of different brands, resulting in inefficient data conversion and low data utilization, limiting the efficiency of multi-brand robot collaboration. Summary of the Invention

[0003] In response to the above-mentioned defects, the present invention proposes a multi-brand robot program data processing method and system, aiming to solve the problem that the traditional program data processing method focusing on a single brand of robots lacks unified parsing and conversion standards when processing program data of robots of different brands, resulting in poor data conversion and low data utilization, thereby limiting the collaborative work efficiency of multi-brand robots.

[0004] To achieve this object, the present invention adopts the following technical solutions:

[0005] A multi-brand robot program data processing method comprises the following steps:

[0006] Step S1: Obtain program data of robots of different brands and build a database;

[0007] Step S2: Parsing the program data of robots of different brands to obtain an intermediate data structure, wherein the intermediate data structure is an abstract syntax tree;

[0008] Step S3: Convert the intermediate data structure into an instruction tree and data set recognizable by the target brand robot;

[0009] Step S4: Generate program files and data files of the target brand robot based on the instruction tree and data set recognizable by the target brand robot;

[0010] Step S5: The program data, intermediate data structures, instruction trees and data sets recognizable by the target brand robot, and program files and data files of the target brand robot of different brands are stored in a database.

[0011] Preferably, step S3 specifically includes the following sub-steps:

[0012] Step S31: using the class inheritance mechanism of object-oriented programming (OOP) to convert the instructions in the intermediate data structure into equivalent instructions for the target brand robot;

[0013] Step S32: extracting metadata in the instruction from the intermediate data structure;

[0014] Step S33: Generate an instruction tree and data set recognizable by the target brand robot based on the equivalent instructions of the target brand robot and the metadata within the instructions in the intermediate data structure;

[0015] Step S34: Based on the program and data text format requirements of the target brand robot, the text format in the instruction tree and data set that can be recognized by the target brand robot is standardized.

[0016] Preferably, in step S3, the process of converting the intermediate data structure into an instruction tree and data set recognizable by the target brand robot further includes the following steps:

[0017] According to the parameter naming requirements of different brands of robots, the parameter names of the corresponding brands of robots are standardized;

[0018] It is determined whether the program data of robots of different brands contain Chinese content. If so, the Chinese content is translated into English or other target languages. If not, no translation operation is performed.

[0019] Preferably, in step S4, a program file of the target brand robot is generated according to the instruction tree recognizable by the target brand robot, which specifically includes the following sub-steps:

[0020] Extract the text content of each instruction block in the instruction tree recognizable by the target brand robot, and organize the text content of each instruction block in sequence according to the program text format requirements of the target brand robot to obtain the program file of the target brand robot;

[0021] Generate the data file of the target brand robot based on the dataset that the target brand robot can recognize. This includes the following sub-steps:

[0022] According to the data set that can be recognized by the target brand robot, a corresponding data model is constructed, and according to the data text format requirements of the target brand robot, the format of the corresponding data model is standardized to obtain the data file of the target brand robot.

[0023] Another aspect of the present application provides a multi-brand robot program data processing system, the system comprising:

[0024] Acquisition module, used to obtain program data of robots of different brands;

[0025] Building modules, used to build databases;

[0026] The parsing module is used to parse the program data of robots of different brands to obtain an intermediate data structure, where the intermediate data structure is an abstract syntax tree;

[0027] A conversion module, used to convert the intermediate data structure into an instruction tree and data set that can be recognized by the target brand robot;

[0028] A generation module is used to generate program files and data files of the target brand robot based on the instruction tree and data set that can be recognized by the target brand robot;

[0029] The storage module is used to store the program data, intermediate data structures, instruction trees and data sets recognizable by the target brand robot, and the program files and data files of the target brand robot of different brands in a database.

[0030] Preferably, the conversion module includes: a conversion submodule, which is used to convert the instructions in the intermediate data structure into equivalent instructions of the target brand robot by using the class inheritance mechanism of object-oriented programming OOP; a first extraction submodule, which is used to extract the metadata in the instructions in the intermediate data structure; a generation submodule, which is used to generate an instruction tree and data set recognizable by the target brand robot based on the equivalent instructions of the target brand robot and the metadata in the instructions in the intermediate data structure; a first standardization processing submodule, which is used to standardize the text format in the instruction tree and data set recognizable by the target brand robot according to the program and data text format requirements of the target brand robot.

[0031] Preferably, the conversion module also includes: a second standardization processing submodule, which is used to standardize the parameter names of robots of corresponding brands according to the parameter naming requirements of robots of different brands; a judgment submodule, which is used to judge whether the program data of robots of different brands contain Chinese content, and if so, execute the language translation submodule; if not, do not execute the language translation submodule; a language translation submodule, which is used to translate Chinese content into English or other target languages.

[0032] Preferably, the generation module includes: a second extraction sub-module, used to extract the text content of each instruction block in the instruction tree recognizable by the target brand robot; a text content organization sub-module, used to organize the text content of each instruction block in sequence according to the program text format requirements of the target brand robot, and obtain the program file of the target brand robot; a construction sub-module, used to construct a corresponding data model based on the data set recognizable by the target brand robot; a third standardization processing sub-module, used to standardize the format of the corresponding data model according to the data text format requirements of the target brand robot, and obtain the data file of the target brand robot.

[0033] The technical solutions provided by the embodiments of the present application may have the following beneficial effects:

[0034] This solution parses program data collected from robots of different brands into an intermediate data structure, converts this intermediate data structure into an instruction tree and dataset recognizable by the target brand robot, and generates program and data files for the target brand robot based on this structure. This enables efficient and automated conversion of program data for robots from multiple brands. This approach effectively overcomes the limitations of traditional program data processing models that focus on a single brand of robot, avoiding the issues of unsmooth data conversion and inefficient utilization in a multi-brand robot environment, thereby improving the efficiency of multi-brand robot collaboration. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a step flow chart of a multi-brand robot program data processing method. DETAILED DESCRIPTION

[0036] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and are not to be construed as limiting the present invention.

[0037] A multi-brand robot program data processing method comprises the following steps:

[0038] Step S1: Obtain program data of robots of different brands and build a database;

[0039] Step S2: Parsing the program data of robots of different brands to obtain an intermediate data structure, wherein the intermediate data structure is an abstract syntax tree;

[0040] Step S3: Convert the intermediate data structure into an instruction tree and data set recognizable by the target brand robot;

[0041] Step S4: Generate program files and data files of the target brand robot based on the instruction tree and data set recognizable by the target brand robot;

[0042] Step S5: The program data, intermediate data structures, instruction trees and data sets recognizable by the target brand robot, and program files and data files of the target brand robot of different brands are stored in a database.

[0043] This solution is a multi-brand robot program data processing method, such as Figure 1As shown, the first step is to obtain the program data of robots of different brands and to build a database. In this embodiment, the program data of robots of different brands include program files in JSON format, program files in XML format, and program files in program file formats exclusive to each brand. By obtaining the program data of robots of different brands, a data basis is provided for subsequent data parsing. By building a database, subsequent data storage and management are facilitated. The second step is to parse the program data of robots of different brands to obtain an intermediate data structure, wherein the intermediate data structure is an abstract syntax tree. Specifically, by parsing the program data of robots of different brands into an intermediate data structure, it is ensured that the program data of robots of different brands can be uniformly managed and processed. In this embodiment, the intermediate data structure is an abstract syntax tree, which is the core data form of structured program storage. It organizes and represents the logical structure of the robot program in a hierarchical manner. The robot program is composed of different types of instructions, which can be divided into single-step instructions and block-level instructions according to the execution characteristics of the instructions. In the abstract syntax tree structure, each instruction consists of basic attributes, instruction data, and instruction functions. Basic attributes include instruction keywords and instruction descriptions, which identify the instruction type and function. Instruction data contains instruction parameter information, such as target coordinates, variable names, and logical conditions. Instruction functions include operations such as parameter renaming, instruction translation, metadata extraction, instruction mapping, and instruction format output, ensuring that the parsed data can be seamlessly adapted to robots of different brands. In other embodiments, in addition to parsing robot program data, configuration information such as signals, workpieces, and tools must also be processed. This configuration information is typically stored in independent configuration files. The data format and organization of this configuration information may vary significantly across different robot systems. Therefore, during parsing, this configuration information must be converted to a unified format based on a custom standard data structure to facilitate subsequent data storage, management, conversion, and use. The third step is to convert the intermediate data structure into an instruction tree and dataset recognizable by the target brand robot. In this embodiment, by converting the intermediate data structure into an instruction tree and dataset recognizable by the target brand robot, the subsequently generated program files and data files are ensured to meet the execution requirements of the target brand robot. The fourth step is to generate the target brand robot's program files and data files based on the target brand robot's recognizable instruction tree and data set. In this embodiment, generating the target brand robot's program files and data files helps ensure the smooth operation of the target brand robot. Specifically, the target brand robot's data files include configuration files and log files.The fifth step is to store the program data, intermediate data structures, instruction trees and data sets that can be recognized by the target brand robot, and the program files and data files of the target brand robot of different brands of robots in the database. In this embodiment, by storing the program data, intermediate data structures, instruction trees and data sets that can be recognized by the target brand robot, and the program files and data files of the target brand robot of different brands of robots in the database, these data can be efficiently managed and stored to ensure the reliability and consistency of the data.

[0044] This solution parses program data collected from robots of different brands into an intermediate data structure, converts this intermediate data structure into an instruction tree and dataset recognizable by the target brand robot, and generates program and data files for the target brand robot based on this structure. This enables efficient and automated conversion of program data for robots from multiple brands. This approach effectively overcomes the limitations of traditional program data processing models that focus on a single brand of robot, avoiding the issues of unsmooth data conversion and inefficient utilization in a multi-brand robot environment, thereby improving the efficiency of multi-brand robot collaboration.

[0045] Preferably, step S3 specifically includes the following sub-steps:

[0046] Step S31: using the class inheritance mechanism of object-oriented programming (OOP) to convert the instructions in the intermediate data structure into equivalent instructions for the target brand robot;

[0047] Step S32: extracting metadata in the instruction from the intermediate data structure;

[0048] Step S33: Generate an instruction tree and data set recognizable by the target brand robot based on the equivalent instructions of the target brand robot and the metadata within the instructions in the intermediate data structure;

[0049] Step S34: Based on the program and data text format requirements of the target brand robot, the text format in the instruction tree and data set that can be recognized by the target brand robot is standardized.

[0050] In this embodiment, in step S31, since the conversion of the intermediate data structure into an instruction tree and dataset recognizable by the target brand robot is based on instruction mapping, the instructions in the intermediate data structure need to be converted into equivalent instructions for the target brand robot. This process is achieved through the class inheritance mechanism of object-oriented programming (OOP). The core concept is: first, a base class is defined to encapsulate all robot instructions, and then subclasses are created to extend and customize them according to the needs of different brands of robots. By utilizing the OOP class inheritance mechanism, the instructions in the intermediate data structure are converted into equivalent instructions for the target brand robot, ensuring that the converted target brand robot instructions are logically consistent with the instructions in the intermediate data structure. In step S32, metadata, such as variables, global configurations, and tool parameters, are important components of the program and are typically stored within the instructions in the intermediate data structure. However, in the target program, this metadata may need to be stored separately or referenced through various means. Therefore, it is necessary to automatically extract this metadata and accurately insert it when generating the target program to ensure the integrity and readability of the target program. In step S33, based on the target brand robot's equivalent instructions and the metadata within the instructions in the intermediate data structure, the generated instruction tree and dataset strictly adhere to the target brand robot's instruction specifications and format requirements. This ensures that the target brand robot can correctly parse and execute the generated instructions. In step S34, the text formatting of the instruction tree and dataset recognizable by the target brand robot is standardized, including formatting such as indentation, line breaks, and comments. This helps improve the readability and maintainability of the instruction tree and dataset recognizable by the target brand robot.

[0051] Preferably, in step S3, the process of converting the intermediate data structure into an instruction tree and data set recognizable by the target brand robot further includes the following steps:

[0052] According to the parameter naming requirements of different brands of robots, the parameter names of the corresponding brands of robots are standardized;

[0053] It is determined whether the program data of robots of different brands contain Chinese content. If so, the Chinese content is translated into English or other target languages. If not, no translation operation is performed.

[0054] In this embodiment, by standardizing the parameter names of robots of different brands and translating the Chinese content in the program data of robots of different brands, not only can the compatibility problem between robots of different brands be solved, but also the accuracy of data conversion can be improved.

[0055] Preferably, in step S4, a program file of the target brand robot is generated according to the instruction tree recognizable by the target brand robot, which specifically includes the following sub-steps:

[0056] Extract the text content of each instruction block in the instruction tree recognizable by the target brand robot, and organize the text content of each instruction block in sequence according to the program text format requirements of the target brand robot to obtain the program file of the target brand robot;

[0057] Generate the data file of the target brand robot based on the dataset that the target brand robot can recognize. This includes the following sub-steps:

[0058] According to the data set that can be recognized by the target brand robot, a corresponding data model is constructed, and according to the data text format requirements of the target brand robot, the format of the corresponding data model is standardized to obtain the data file of the target brand robot.

[0059] In this embodiment, the target brand robot's recognizable instruction tree consists of a series of instruction blocks. The text content of each instruction block contains the specific instructions for the robot to perform the corresponding operation. Extracting the text content of each instruction block provides the data foundation for subsequent program file generation. By organizing the text content of each instruction block in sequence according to the target brand robot's program text format requirements, the generated program file is ensured to be correctly recognized and executed by the target brand robot.

[0060] The dataset recognizable by the target brand's robots contains all the data necessary for robot operation, such as environmental information, mission parameters, and sensor data. By constructing a corresponding data model, this data can be organized into a coherent whole for easy subsequent processing and use. By standardizing the format of the constructed data model according to the target brand's data text format requirements, the generated data file can be correctly read and processed by the target brand's robots.

[0061] Another aspect of the present application provides a multi-brand robot program data processing system, the system comprising:

[0062] Acquisition module, used to obtain program data of robots of different brands;

[0063] Building modules, used to build databases;

[0064] The parsing module is used to parse the program data of robots of different brands to obtain an intermediate data structure, where the intermediate data structure is an abstract syntax tree;

[0065] A conversion module, used to convert the intermediate data structure into an instruction tree and data set that can be recognized by the target brand robot;

[0066] A generation module is used to generate program files and data files of the target brand robot based on the instruction tree and data set that can be recognized by the target brand robot;

[0067] The storage module is used to store the program data, intermediate data structures, instruction trees and data sets recognizable by the target brand robot, and the program files and data files of the target brand robot of different brands in a database.

[0068] A multi-brand robot program data processing system in this solution realizes efficient conversion and management of program data of robots of different brands through the mutual cooperation of acquisition modules, construction modules, parsing modules, conversion modules, generation modules and storage modules. It effectively breaks through the limitations of the traditional program data processing mode focusing on a single brand of robots, avoids the situation of unsmooth data conversion and low utilization efficiency in a multi-brand robot environment, and thus improves the efficiency of collaborative work of multi-brand robots.

[0069] Preferably, the conversion module includes: a conversion submodule, which is used to convert the instructions in the intermediate data structure into equivalent instructions of the target brand robot by utilizing the class inheritance mechanism of object-oriented programming OOP; a first extraction submodule, which is used to extract the metadata within the instructions in the intermediate data structure; a generation submodule, which is used to generate an instruction tree and data set recognizable by the target brand robot based on the equivalent instructions of the target brand robot and the metadata within the instructions in the intermediate data structure; a first standardization processing submodule, which is used to standardize the text format in the instruction tree and data set recognizable by the target brand robot according to the program and data text format requirements of the target brand robot.

[0070] In this embodiment, by providing a conversion submodule, the converted target brand robot instructions are logically consistent with the instructions in the intermediate data structure. By providing a first extraction submodule, metadata is accurately inserted when generating the target program, ensuring the integrity and readability of the target program. The provision of a generation submodule helps ensure that the target brand robot can correctly parse and execute the generated instructions. The provision of a first standardization processing submodule helps improve the readability and maintainability of the instruction tree and dataset recognizable by the target brand robot.

[0071] Preferably, the conversion module also includes: a second standardization processing submodule for standardizing the parameter names of robots of different brands according to the parameter naming requirements of robots of different brands; a judgment submodule for determining whether the program data of robots of different brands contains Chinese content, and if so, executing the language translation submodule; otherwise, not executing the language translation submodule; and a language translation submodule for translating the Chinese content into English or another target language. In this embodiment, the provision of the second standardization processing submodule, the judgment submodule, and the language translation submodule not only resolves compatibility issues between robots of different brands but also improves the accuracy of data conversion.

[0072] Preferably, the generation module includes: a second extraction submodule, used to extract the text content of each instruction block in the instruction tree recognizable by the target brand robot; a text content organization submodule, used to organize the text content of each instruction block in sequence according to the program text format requirements of the target brand robot, and obtain the program file of the target brand robot; a construction submodule, used to construct a corresponding data model based on the data set recognizable by the target brand robot; a third standardization processing submodule, used to standardize the format of the corresponding data model according to the data text format requirements of the target brand robot, and obtain the data file of the target brand robot.

[0073] In this embodiment, the second extraction submodule provides a data foundation for subsequent program file generation. The text content component submodule ensures that the generated program file can be correctly recognized and executed by the target brand robot. The construction submodule organizes the individual data in the dataset into a cohesive whole, facilitating subsequent processing and use. The third standardization processing submodule ensures that the generated data file can be correctly read and processed by the target brand robot.

[0074] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0075] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for processing multi-brand robot program data, characterized by: The following steps are involved: Step S1: Obtain program data of robots of different brands and build a database; Step S2: Parsing the program data of robots of different brands to obtain an intermediate data structure, wherein the intermediate data structure is an abstract syntax tree; Step S3: Convert the intermediate data structure into an instruction tree and data set recognizable by the target brand robot; Step S4: Generate program files and data files of the target brand robot based on the instruction tree and data set recognizable by the target brand robot; Step S5: The program data, intermediate data structures, instruction trees and data sets recognizable by the target brand robot, and program files and data files of the target brand robot of different brands are stored in a database.

2. The multi-brand robot program data processing method according to claim 1, characterized in that: In step S3, the following sub-steps are specifically included: Step S31: using the class inheritance mechanism of object-oriented programming (OOP) to convert the instructions in the intermediate data structure into equivalent instructions for the target brand robot; Step S32: extracting metadata in the instruction from the intermediate data structure; Step S33: Generate an instruction tree and data set recognizable by the target brand robot based on the equivalent instructions of the target brand robot and the metadata within the instructions in the intermediate data structure; Step S34: Based on the program and data text format requirements of the target brand robot, the text format in the instruction tree and data set that can be recognized by the target brand robot is standardized.

3. The multi-brand robot program data processing method according to claim 1, characterized in that: In step S3, the process of converting the intermediate data structure into an instruction tree and data set recognizable by the target brand robot further includes the following steps: According to the parameter naming requirements of different brands of robots, the parameter names of the corresponding brands of robots are standardized; It is determined whether the program data of robots of different brands contain Chinese content. If so, the Chinese content is translated into English or other target languages. If not, no translation operation is performed.

4. The multi-brand robot program data processing method according to claim 1, characterized in that: In step S4, a program file for the target brand robot is generated based on the instruction tree recognizable by the target brand robot, which specifically includes the following sub-steps: Extract the text content of each instruction block in the instruction tree recognizable by the target brand robot, and organize the text content of each instruction block in sequence according to the program text format requirements of the target brand robot to obtain the program file of the target brand robot; Generate the data file of the target brand robot based on the dataset that the target brand robot can recognize. This includes the following sub-steps: According to the data set that can be recognized by the target brand robot, a corresponding data model is constructed, and according to the data text format requirements of the target brand robot, the format of the corresponding data model is standardized to obtain the data file of the target brand robot.

5. A multi-brand robot program data processing system, using the multi-brand robot program data processing method according to any one of claims 1 to 4, characterized in that: The system comprises: Acquisition module, used to obtain program data of robots of different brands; Building modules, used to build databases; The parsing module is used to parse the program data of robots of different brands to obtain an intermediate data structure, where the intermediate data structure is an abstract syntax tree; A conversion module, used to convert the intermediate data structure into an instruction tree and data set that can be recognized by the target brand robot; A generation module is used to generate program files and data files of the target brand robot based on the instruction tree and data set that can be recognized by the target brand robot; The storage module is used to store the program data, intermediate data structures, instruction trees and data sets recognizable by the target brand robot, and the program files and data files of the target brand robot of different brands in a database.

6. The multi-brand robot program data processing system according to claim 5, characterized in that: The conversion module includes: A conversion submodule, used to convert the instructions in the intermediate data structure into equivalent instructions of the target brand robot by utilizing the class inheritance mechanism of object-oriented programming (OOP); A first extraction submodule is used to extract metadata within the instruction in the intermediate data structure; A generation submodule is used to generate an instruction tree and a data set that can be recognized by the target brand robot based on the equivalent instructions of the target brand robot and the metadata within the instructions in the intermediate data structure; The first standardization processing submodule is used to standardize the text format of the instruction tree and data set that can be recognized by the target brand robot according to the program and data text format requirements of the target brand robot.

7. The multi-brand robot program data processing system according to claim 5, characterized in that: The conversion module also includes: The second standardization processing submodule is used to standardize the parameter names of robots of different brands according to the parameter naming requirements of robots of different brands; The judgment submodule is used to judge whether the program data of different brands of robots contain Chinese content. If so, the language translation submodule is executed; if not, the language translation submodule is not executed; The language translation submodule is used to translate Chinese content into English or other target languages.

8. The multi-brand robot program data processing system according to claim 5, characterized in that: The generation module includes: The second extraction submodule is used to extract the text content of each instruction block in the instruction tree that can be recognized by the target brand robot; The text content organization submodule is used to organize the text content of each instruction block in sequence according to the program text format requirements of the target brand robot to obtain the program file of the target brand robot; Build a submodule to build a corresponding data model based on the dataset that the target brand robot can recognize; The third standardization processing submodule is used to standardize the format of the corresponding data model according to the data text format requirements of the target brand robot to obtain the data file of the target brand robot.

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