Low-code mechanical arm task configuration analysis method

Abstract

According to the low-code mechanical arm task configuration analysis method, task configuration information written by a user is read and converted into a structured format file through an analysis engine, and task description of a tree structure is formed; determining logic association among the state modules through semantic analysis and a hierarchical relationship recognition algorithm; a state module behavior is predicted by means of machine learning, a data model conforming to a tree topology structure is constructed, and dynamic adjustment of a task sequence is supported; an intelligent recommendation system is used for evaluating the importance of nodes and automatically supplementing default values, deep learning and natural language processing technologies are combined, errors in configuration information are automatically detected and corrected, a generated data structure is directly used for guiding a mechanical arm to execute a task, the execution state is allowed to be monitored through a visual tool, and the operation efficiency is improved. And tasks are ensured to be performed according to a preset sequence, and response can be quickly made in case of abnormal conditions. According to the method, the task configuration efficiency and accuracy are remarkably improved, and the intelligent level of a mechanical arm system is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of mechanical automation control technology, and specifically to a low-code robotic arm task configuration parsing method. Background Art

[0002] With the continuous improvement of industrial automation, robotic arms have been widely used in many fields such as manufacturing, warehousing and logistics. However, traditional robotic arm task configuration methods usually require writing complex programming codes, which not only increases the burden on users, but also limits non-professional programmers from participating in the design of robotic arm tasks. In addition, traditional methods lack intelligent support in the configuration process, resulting in inflexible task configuration, making it difficult to adapt to rapidly changing production needs, and prone to errors, which affects production efficiency.

[0003] Traditional robot arm task configuration technology mainly relies on programmers to directly write control logic through programming languages. Although this method is powerful, it is very complicated for operators who do not have programming skills. This means that every change to the robot arm task requires the intervention of professionals, which greatly increases time and cost overhead. In addition, due to the lack of an effective error detection mechanism, configuration errors are often difficult to detect in time, resulting in extended debugging cycles and low production efficiency. Although some graphical programming tools have attempted to simplify this process in recent years, they still have not been able to completely solve the difficulties faced by non-programmers in task configuration.

[0004] At present, with the development of artificial intelligence technology, some advanced robotic arm control systems have begun to introduce automated configuration and error detection functions to reduce human intervention. These systems usually use natural language processing technology to parse the user's intentions and generate control codes with the assistance of artificial intelligence. However, the intelligence of these systems is limited, and they can often only handle relatively simple task configurations, and are still insufficient for complex multi-step task configurations. In addition, when faced with hierarchical relationships and logical associations in task configurations, existing technologies often require a large amount of manual settings, which is not only time-consuming and labor-intensive, but also prone to human errors.

[0005] In response to the above problems, the present invention proposes a low-code robot arm task configuration parsing method, which allows users to design robot arm task processes in a more intuitive way through a graphical interface and natural language processing technology, significantly reducing the difficulty of task configuration. At the same time, with the help of artificial intelligence and machine learning technology, the method can automatically identify the hierarchical relationship in the configuration information during the parsing process, and predict the behavior pattern of each state module through a pre-trained model, thereby optimizing the task execution logic. In addition, the present invention also introduces an intelligent recommendation system and deep learning technology, which can detect and correct configuration errors during the parsing process to ensure the accuracy and reliability of task configuration. In general, this technical solution significantly improves the flexibility and robustness of robot arm task configuration, reduces the error rate, and improves production efficiency through an intelligent configuration parsing method. Summary of the invention

[0006] A low-code robotic arm task configuration parsing method includes the following steps:

[0007] S1: Read the low-code task configuration information written by the user through a graphical interface and natural language. The configuration information is automatically converted into a standardized YAML or JSON format file through a parsing engine based on semantic understanding and machine learning models. The file uses a tree structure to describe the robot arm task, and each node represents a state module;

[0008] S2: Identify semantic units through the semantic analysis module, parse the hierarchical structure in the configuration information using the hierarchical relationship recognition algorithm, identify the logical association between nodes, and confirm the specific content of the status module;

[0009] S3: Use machine learning models to predict the behavior patterns of state modules, build internal data structures in a recursive or iterative manner, map the hierarchical relationships in the configuration information to software data structures that satisfy the tree topology structure, and support dynamic adjustment of task sequences to enhance configuration flexibility;

[0010] S4: During the parsing process, an intelligent recommendation system is used to detect the necessity and optionality of nodes. For necessary nodes, the corresponding information is ensured to exist and be set. For optional nodes, the intelligent recommendation system automatically fills in reasonable default values ​​according to the context to ensure the robustness of the parsing process.

[0011] S5: Use deep learning technology to detect syntax errors and logical errors in configuration information, and combine natural language processing technology to automatically correct errors and provide correction suggestions to enhance the reliability of the parsing process;

[0012] S6: After the analysis is completed, the generated internal data structure is passed into the execution logic, which supports real-time monitoring of the task execution status through visualization tools to ensure that the robot arm can perform tasks in the predetermined order and dependencies. When encountering abnormal situations, the intelligent decision-making system can respond quickly and take corresponding processing measures.

[0013] Preferably, in S1, the graphical interface allows the user to intuitively design the robot arm task process by dragging and selecting, and the natural language processing engine understands and converts the natural language description input by the user, and converts it into a standard YAML or JSON format file, reducing the user's need for programming expertise.

[0014] Preferably, the S1 specifically includes the following steps:

[0015] S1.1: Start the natural language processing module to perform preliminary analysis on the natural language description input by the user, extract the key words "move to", "grab", "release" action instructions and their parameters in the description, and use them to build a preliminary task intention model;

[0016] S1.2: Using the semantic understanding model, the extracted key words are semantically analyzed to understand the meaning of the words in a specific context and map them to the corresponding robot arm movements; if the semantic understanding model does not accurately describe the key words, the correct meaning of the key words is inferred from the context;

[0017] S1.3: Integrate the parsing results into a standardized YAML or JSON format file. The nodes in the file contain complete status module information, and the hierarchical relationship is clear and unambiguous, which is convenient for subsequent parsing engines to perform further processing.

[0018] Preferably, the S2 specifically includes the following steps:

[0019] S2.1: Identify semantic units in the configuration information through the semantic analysis module;

[0020] S2.2: Analyze the hierarchical relationship in the configuration information through the hierarchical relationship recognition algorithm, automatically identify the logical relationship between the parent node and the child node, and ensure that the relationship between the state module and the upper and lower nodes is correct;

[0021] S2.3: Use context-aware algorithms to refine the context of the state module so that the description of the state module is grammatically and logically correct and conforms to the actual application scenario;

[0022] S2.4: Confirm the specific contents of the status module, including but not limited to TCP position, posture, speed, acceleration, path planning constraints, peripheral operations, and exception handling logic, to ensure that they are logically consistent and meet the requirements for the robot to perform tasks;

[0023] S2.5: Generate a semantic analysis report, which contains detailed information and hierarchical relationships of the state modules, so that subsequent steps can be accurately parsed based on the semantic analysis report.

[0024] Preferably, the machine learning model in S3 specifically includes the following steps:

[0025] S3.11: Collect and organize historical task configuration data, wherein the historical task configuration data includes successfully executed task configurations and actual operation results; the data set of the robot arm task behavior pattern is formed by the historical task configuration data as the basis for training the machine learning model;

[0026] S3.12: Train a machine learning model by a supervised learning method; the machine learning model input is the description information of the state module, and the output is the predicted behavior pattern; during the training process, the model learns the mapping relationship between different configuration information and the actual behavior pattern, and predicts the expected behavior pattern based on the input state module description;

[0027] S3.13: During the parsing process, the information of the state module is input into the trained machine learning model. The machine learning model predicts the behavior pattern of the state module based on experience and rules and converts it into part of the internal data structure.

[0028] Preferably, the S3 constructs the internal data structure specifically including the following steps:

[0029] S3.21: Initialize the internal data structure and create a root node as the starting point of the entire task configuration; the root node serves as the top-level container for all subsequent state modules; the creation of the root node marks the beginning of the construction of the internal data structure and is also the basis of the recursive or iterative process;

[0030] S3.22: For the state module, after using the machine learning model to predict its behavior pattern, create the corresponding node in the internal data structure according to the prediction result, and add the newly created node as a child node or sibling node of the existing node to the internal data structure according to the hierarchical relationship defined in the configuration file, and implement it through recursive call; the recursive call is when a new state module is parsed and its behavior pattern is predicted, a recursive function is called; the recursive function adds new nodes and recursively processes the sub-state modules in the state module until all state modules are parsed and added to the internal data structure;

[0031] S3.23: During the construction of the internal data structure, the hierarchical relationship is tracked to ensure that the parent-child relationship of the nodes is correct; at the same time, it supports dynamic adjustment of the task sequence, inserting or deleting state modules according to actual conditions during the construction process, and it is achieved by iteratively checking the dependencies of the state modules and updating the internal data structure when necessary, thereby enhancing the flexibility and adaptability of the configuration.

[0032] Preferably, the S4 essential node specifically includes the following steps:

[0033] S4.11: Define the necessary node categories, including but not limited to the key attributes of the state module: TCP position, posture, speed, acceleration; the necessary nodes form the basis for effective task configuration and correct execution of tasks; the intelligent recommendation system checks whether the state module contains necessary attributes to ensure that the attribute settings are reasonable;

[0034] S4.12: Implement the necessary node detection logic. The system will verify whether the status module contains necessary nodes one by one during the parsing of the configuration file, and check whether the necessary nodes have been correctly set. If it is detected that the necessary nodes are missing or the necessary nodes are not set completely, the parsing will be stopped immediately, and the user will be prompted with the specific missing items through a visual interface or message to guide the user to supplement or correct them.

[0035] Preferably, the S4 optional node specifically includes the following steps:

[0036] S4.21: Define optional node categories, including but not limited to speed ratio, acceleration ratio, path planning constraints, peripheral operations and exception handling logic; the optional nodes are optional for the state module, which can enhance the flexibility and robustness of the task in certain circumstances;

[0037] S4.22: Implement optional node detection logic; the intelligent recommendation system identifies which nodes are optional during the process of parsing the configuration file and checks whether the nodes are defined; if the checked node is not defined, the intelligent recommendation system will automatically fill in a reasonable default value based on the context;

[0038] S4.23: Using big data analysis and machine learning technology, the intelligent recommendation system can learn and recommend best practices based on successful cases of past task configurations; when it is detected that an optional node is not defined, the system can fill in the default value and provide optimization suggestions based on the characteristics of the current task.

[0039] Preferably, the S5 deep learning technology detection includes the following steps:

[0040] S5.1: Build an error detection model; use long short-term memory network deep learning technology to train the error detection model of low-code task configuration files; the error detection model is trained based on a large number of annotated correct configuration files and error configuration files, and can identify common syntax errors, logical errors and other potential problems; the training data set of the error detection model includes instances of missing necessary fields, unreasonable parameter settings, and logical sequence errors, so that the error detection model has comprehensive error recognition capabilities;

[0041] S5.2: Execute error detection. During the parsing of the configuration file, the configuration file is input into the trained LSTM network deep learning model line by line and paragraph by paragraph. The LSTM network deep learning model will analyze the text of the configuration file and detect any errors. If an error is detected, the error detection model will output the specific error type and its location.

[0042] S5.3: Perform error correction using natural language processing technology; when an error is detected, use natural language processing technology to automatically generate correction suggestions and perform correction actions;

[0043] S5.4: Provide a detailed correction report; after completing error detection and correction, generate a correction report that includes the detected errors and their locations, detailed correction suggestions for the errors, and the corrective actions that have been performed.

[0044] Preferably, the intelligent decision-making system of S6 includes an anomaly detection module;

[0045] The anomaly detection module uses machine learning algorithms to analyze real-time data generated during the execution of the robot arm's tasks, and can identify excessive position deviations from expected behavior, abnormal speed, and external interference situations to issue alarms;

[0046] When the anomaly detection module detects an abnormal situation, the intelligent decision-making system automatically selects appropriate response measures based on the type and severity of the anomaly, including but not limited to suspending task execution, rolling back to the last stable state, attempting automatic repair, requesting manual intervention, and executing corresponding commands.

[0047] Compared with the prior art, the technical solution of the present invention has the following technical effects:

[0048] The present invention solves the problem that it is difficult for non-programming professionals to write robot arm task configuration codes through a graphical interface and natural language processing technology. Users can intuitively design robot arm task processes by dragging and selecting, and convert natural language descriptions into standard YAML or JSON format files through a natural language processing engine, thereby reducing users' demand for programming expertise and enabling more users to quickly get started with robot arm task configuration.

[0049] The present invention solves the problems of unclear hierarchical relationships and unclear logical associations between parent nodes and child nodes in configuration information through artificial intelligence-assisted semantic analysis and hierarchical relationship recognition algorithms. By automatically identifying the hierarchical structure in the configuration information, the context environment of the state module is refined, and the relationship between the state module and the upper and lower nodes is ensured to be correct, thereby improving the accuracy and consistency of the configuration information and making the execution of subsequent tasks more reliable.

[0050] The present invention predicts the behavior pattern of the state module through a pre-trained machine learning model and constructs an internal data structure, thereby solving the problem of lack of intelligent prediction ability and dynamic adjustment of task sequence in the task configuration process. By using historical task configuration data to train the machine learning model, the behavior pattern of each state module is predicted and converted into a part of the internal data structure, which supports dynamic adjustment of task sequence, enhances the flexibility and adaptability of the configuration, and makes the robot arm task execution more efficient and controllable.

[0051] The present invention uses deep learning technology and natural language processing technology to perform error detection and correction, solves the common grammatical errors and logical errors in configuration information, uses long short-term memory network deep learning technology to train the error detection model, can identify and locate errors in the configuration file, and combines natural language processing technology to automatically generate correction suggestions and execute correction operations, thereby improving the correctness and reliability of the configuration file and reducing the risk of robot arm task execution failure due to configuration errors.

[0052] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application so that it can be implemented in accordance with the contents of the specification, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following is a detailed description of the preferred embodiments of the present application in conjunction with the accompanying drawings as follows.

[0053] Based on the detailed description of the specific embodiments of the present application in combination with the accompanying drawings below, those skilled in the art will become more aware of the above and other objects, advantages and features of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings without creative work. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, each element or part is not necessarily drawn according to the actual scale.

[0055] Figure 1 Parsing flow chart for the robotic arm;

[0056] Figure 2 Reading flow chart for configuration information;

[0057] Figure 3 Parse flowcharts for hierarchical relationships;

[0058] Figure 4 Predict flow charts for behavioral patterns;

[0059] Figure 5 Build flow charts for data structures;

[0060] Figure 6 Detection flow chart for necessary nodes;

[0061] Figure 7 Detection flow chart for optional nodes;

[0062] Figure 8 The following is the error detection flow chart. Specific embodiments

[0063] To make the purpose, technical scheme and advantages of the embodiment of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiment of the present application. Obviously, the described embodiment is a part of the embodiment of the present application, rather than all of the embodiments. In the following description, specific details such as specific configuration and components are provided only to help fully understand the embodiments of the present application. Therefore, it should be clear to those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. In addition, for clarity and brevity, the description of known functions and structures is omitted in the embodiment.

[0064] It should be understood that the references to "one embodiment" or "this embodiment" throughout the specification mean that the specific features, structures, or characteristics associated with the embodiment are included in at least one embodiment of the present application. Therefore, the references to "one embodiment" or "this embodiment" appearing throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0065] In addition, the present application may repeat reference numerals and / or letters in different examples. This repetition is for the purpose of simplicity and clarity, and does not in itself indicate the relationship between the various embodiments and / or settings discussed.

[0066] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist at the same time. The term " / and" in this article describes another type of association object relationship, indicating that there can be two relationships. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " in this article generally indicates that the previous and next associated objects are in an "or" relationship.

[0067] The term "at least one" in this article is merely a description of the association relationship of associated objects, indicating that there may be three relationships. For example, at least one of A and B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0068] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusions.

[0069] Example 1

[0070] This embodiment mainly describes a low-code robotic arm task configuration parsing method;

[0071] Further, if Figure 1 As shown, a low-code robotic arm task configuration parsing method includes the following steps:

[0072] S1: Read the low-code task configuration information written by the user through a graphical interface and natural language. The configuration information is automatically converted into a standardized YAML or JSON format file through a parsing engine based on semantic understanding and machine learning models. The file uses a tree structure to describe the robot arm task, and each node represents a state module;

[0073] The graphical interface allows users to intuitively design the robot arm task flow by dragging and selecting. The natural language processing engine understands and converts the natural language descriptions input by the user and converts them into standard YAML or JSON format files, reducing the user's need for programming expertise.

[0074] The graphical interface allows users to intuitively design the robot arm task flow, and the natural language processing engine converts the natural language description into a standard format file, thereby reducing the user's need for programming expertise and allowing non-technical personnel to easily configure the robot arm tasks.

[0075] S2: Identify semantic units through the semantic analysis module, parse the hierarchical structure in the configuration information using the hierarchical relationship recognition algorithm, identify the logical association between nodes, and confirm the specific content of the status module;

[0076] S3: Use machine learning models to predict the behavior patterns of state modules, build internal data structures in a recursive or iterative manner, map the hierarchical relationships in the configuration information to software data structures that satisfy the tree topology structure, and support dynamic adjustment of task sequences to enhance configuration flexibility;

[0077] S4: During the parsing process, an intelligent recommendation system is used to detect the necessity and optionality of nodes. For necessary nodes, the corresponding information is ensured to exist and be set. For optional nodes, the intelligent recommendation system automatically fills in reasonable default values ​​according to the context to ensure the robustness of the parsing process.

[0078] S5: Use deep learning technology to detect syntax errors and logical errors in configuration information, and combine natural language processing technology to automatically correct errors and provide correction suggestions to enhance the reliability of the parsing process;

[0079] S6: After the analysis is completed, the generated internal data structure is passed into the execution logic, which supports real-time monitoring of the task execution status through visualization tools to ensure that the robot arm can perform tasks in the predetermined order and dependencies. When encountering abnormal situations, the intelligent decision-making system can respond quickly and take corresponding processing measures.

[0080] By reading the low-code task configuration information written by the user in a graphical interface and natural language, and automatically converting it into standardized YAML or JSON format files using a parsing engine based on semantic understanding and machine learning models, users can easily define the tasks of the robotic arm without having to deeply understand the programming details, greatly lowering the threshold for robotic arm programming.

[0081] Further, if Figure 2 As shown, S1 includes the following steps:

[0082] S1.1: Start the natural language processing module to perform preliminary analysis on the natural language description input by the user, extract the key words "move to", "grab", "release" action instructions and their parameters in the description, and use them to build a preliminary task intention model;

[0083] S1.2: Using the semantic understanding model, the extracted key words are semantically analyzed to understand the meaning of the words in a specific context and map them to the corresponding robot arm movements; if the semantic understanding model does not accurately describe the key words, the correct meaning of the key words is inferred from the context;

[0084] S1.3: Integrate the parsing results into a standardized YAML or JSON format file. The nodes in the file contain complete status module information, and the hierarchical relationship is clear and unambiguous, which is convenient for subsequent parsing engines to perform further processing.

[0085] By starting the natural language processing module to extract key instructions input by the user and using the semantic understanding model to map these instructions to the robotic arm movements, even if the description is not precise enough, the correct meaning can be inferred through the context, thereby improving the flexibility and accuracy of task configuration.

[0086] Further, if Figure 3 As shown, S2 includes the following steps:

[0087] S2.1: Identify semantic units in the configuration information through the semantic analysis module;

[0088] S2.2: Analyze the hierarchical relationship in the configuration information through the hierarchical relationship recognition algorithm, automatically identify the logical relationship between the parent node and the child node, and ensure that the relationship between the state module and the upper and lower nodes is correct;

[0089] S2.3: Use context-aware algorithms to refine the context of the state module so that the description of the state module is grammatically and logically correct and conforms to the actual application scenario;

[0090] S2.4: Confirm the specific contents of the status module, including but not limited to TCP position, posture, speed, acceleration, path planning constraints, peripheral operations, and exception handling logic, to ensure that they are logically consistent and meet the requirements for the robot to perform tasks;

[0091] S2.5: Generate a semantic analysis report, which contains detailed information and hierarchical relationships of the state modules, so that subsequent steps can be accurately parsed based on the semantic analysis report.

[0092] By analyzing the hierarchical structure in the configuration information through the hierarchical relationship recognition algorithm, the logical association between the parent node and the child node is automatically identified to ensure that the relationship between the state modules is correct, thereby establishing a complete task execution flowchart and enhancing the logical consistency of the task configuration.

[0093] Further, if Figure 4 As shown in the figure, the machine learning model in S3 specifically includes the following steps:

[0094] S3.11: Collect and organize historical task configuration data, wherein the historical task configuration data includes successfully executed task configurations and actual operation results; the data set of the robot arm task behavior pattern is formed by the historical task configuration data as the basis for training the machine learning model;

[0095] S3.12: Train a machine learning model by a supervised learning method; the machine learning model input is the description information of the state module, and the output is the predicted behavior pattern; during the training process, the model learns the mapping relationship between different configuration information and the actual behavior pattern, and predicts the expected behavior pattern based on the input state module description;

[0096] S3.13: During the parsing process, the information of the state module is input into the trained machine learning model. The machine learning model predicts the behavior pattern of the state module based on experience and rules and converts it into part of the internal data structure.

[0097] The behavior pattern of each state module is predicted through a pre-trained machine learning model, and the internal data structure is built according to the prediction results, making the generated data structure more efficient and easy to expand, supporting dynamic adjustment of task sequences, and enhancing configuration flexibility.

[0098] Further, if Figure 5 As shown, building the internal data structure in S3 specifically includes the following steps:

[0099] S3.21: Initialize the internal data structure and create a root node as the starting point of the entire task configuration; the root node serves as the top-level container for all subsequent state modules; the creation of the root node marks the beginning of the construction of the internal data structure and is also the basis of the recursive or iterative process;

[0100] S3.22: For the state module, after using the machine learning model to predict its behavior pattern, create the corresponding node in the internal data structure according to the prediction result, and add the newly created node as a child node or sibling node of the existing node to the internal data structure according to the hierarchical relationship defined in the configuration file, and implement it through recursive call; the recursive call is when a new state module is parsed and its behavior pattern is predicted, a recursive function is called; the recursive function adds new nodes and recursively processes the sub-state modules in the state module until all state modules are parsed and added to the internal data structure;

[0101] S3.23: During the construction of the internal data structure, the hierarchical relationship is tracked to ensure that the parent-child relationship of the nodes is correct; at the same time, it supports dynamic adjustment of the task sequence, inserting or deleting state modules according to actual conditions during the construction process, and it is achieved by iteratively checking the dependencies of the state modules and updating the internal data structure when necessary, thereby enhancing the flexibility and adaptability of the configuration.

[0102] By initializing the internal data structure, creating the root node, and creating corresponding nodes based on the prediction results and hierarchical relationships, it ensures that the parent-child relationship of each node is correct and supports dynamic adjustment of the task sequence, thereby enhancing the flexibility and adaptability of the configuration.

[0103] Further, if Figure 6 As shown, the S4 necessary node specifically includes the following steps:

[0104] S4.11: Define the necessary node categories, including but not limited to the key attributes of the state module: TCP position, posture, speed, acceleration; the necessary nodes form the basis for effective task configuration and correct execution of tasks; the intelligent recommendation system checks whether the state module contains necessary attributes to ensure that the attribute settings are reasonable;

[0105] S4.12: Implement the necessary node detection logic. The system will verify whether the status module contains necessary nodes one by one during the parsing of the configuration file, and check whether the necessary nodes have been correctly set. If it is detected that the necessary nodes are missing or the necessary nodes are not set completely, the parsing will be stopped immediately, and the user will be prompted with the specific missing items through a visual interface or message to guide the user to supplement or correct them.

[0106] By defining necessary node categories and ensuring that each state module contains necessary attributes, and checking the rationality of the settings of these attributes through an intelligent recommendation system, the effectiveness and accuracy of task configuration are improved.

[0107] Further, if Figure 7 As shown, the S4 optional node specifically includes the following steps:

[0108] S4.21: Define optional node categories, including but not limited to speed ratio, acceleration ratio, path planning constraints, peripheral operations and exception handling logic; the optional nodes are optional for the state module, which can enhance the flexibility and robustness of the task in certain circumstances;

[0109] S4.22: Implement optional node detection logic; the intelligent recommendation system identifies which nodes are optional during the process of parsing the configuration file and checks whether the nodes are defined; if the checked node is not defined, the intelligent recommendation system will automatically fill in a reasonable default value based on the context;

[0110] S4.23: Using big data analysis and machine learning technology, the intelligent recommendation system can learn and recommend best practices based on successful cases of past task configurations; when it is detected that an optional node is not defined, the system can fill in the default value and provide optimization suggestions based on the characteristics of the current task.

[0111] By defining optional node categories, the intelligent recommendation system can identify optional type nodes when parsing the configuration file and automatically fill in reasonable default values ​​based on the context, reducing the user's manual debugging time and enhancing the robustness of the configuration process.

[0112] Further, if Figure 8 As shown, the S5 deep learning technology detection includes the following steps:

[0113] S5.1: Build an error detection model; use long short-term memory network deep learning technology to train the error detection model of low-code task configuration files; the error detection model is trained based on a large number of annotated correct configuration files and error configuration files, and can identify common syntax errors, logical errors and other potential problems; the training data set of the error detection model includes instances of missing necessary fields, unreasonable parameter settings, and logical sequence errors, so that the error detection model has comprehensive error recognition capabilities;

[0114] S5.2: Execute error detection. During the parsing of the configuration file, the configuration file is input into the trained LSTM network deep learning model line by line and paragraph by paragraph. The LSTM network deep learning model will analyze the text of the configuration file and detect any errors. If an error is detected, the error detection model will output the specific error type and its location.

[0115] S5.3: Perform error correction using natural language processing technology; when an error is detected, use natural language processing technology to automatically generate correction suggestions and perform correction actions;

[0116] S5.4: Provide a detailed correction report; after completing error detection and correction, generate a correction report that includes the detected errors and their locations, detailed correction suggestions for the errors, and the corrective actions that have been performed.

[0117] By building an error detection model, using deep learning technology to detect syntax and logic errors in configuration information, and combining natural language processing technology to provide detailed correction suggestions or directly perform correction operations, the robustness of the system and the reliability of the configuration process are improved.

[0118] Furthermore, the S6’s intelligent decision-making system includes an anomaly detection module;

[0119] The anomaly detection module uses machine learning algorithms to analyze real-time data generated during the execution of the robot arm's tasks, and can identify excessive position deviations from expected behavior, abnormal speed, and external interference situations to issue alarms;

[0120] When the anomaly detection module detects an abnormal situation, the intelligent decision-making system automatically selects appropriate response measures based on the type and severity of the anomaly, including but not limited to suspending task execution, rolling back to the last stable state, attempting automatic repair, requesting manual intervention, and executing corresponding commands.

[0121] By integrating an anomaly detection module and using machine learning algorithms to analyze real-time data, abnormal situations can be identified and appropriate countermeasures can be automatically taken, thus enhancing the reliability and controllability of the robot arm's task execution.

[0122] This embodiment describes a low-code robot arm task configuration parsing method, which simplifies the robot arm task configuration through a graphical interface and natural language processing technology, predicts behavior patterns through machine learning models and builds internal data structures, making the configuration process flexible and efficient. Combining intelligent recommendation systems and deep learning technologies, the present invention not only ensures the accuracy and completeness of configuration information, but also provides real-time monitoring and exception handling mechanisms, greatly improving the reliability and execution efficiency of robot arm task configuration. .

[0123] Example 2

[0124] This embodiment is based on Embodiment 1 and mainly describes a specific implementation method:

[0125] Factory engineers use a simple graphical interface and natural language description to set the task flow of the robotic arm and realize the automated installation of automobile door panels.

[0126] Specific steps:

[0127] Task design and configuration:

[0128] Engineers use the graphical interface to drag and drop basic action modules such as "move to", "grab", and "place", and describe the task details in natural language. The specific description is as follows:

[0129] “The robotic arm moves to the door panel storage area, grabs the door panel, and then moves to the body assembly area to install the door panel on the vehicle body.”

[0130] The natural language processing engine recognizes and extracts action instructions such as "move to", "grab", "place" and their parameters, and builds a preliminary task intent model.

[0131] Semantic parsing and standardization:

[0132] The semantic understanding model converts natural language descriptions into specific action instructions for the robotic arm. The specific conversion is as follows:

[0133] “Move to the door panel storage area” is interpreted as the target position (x=50, y=100, z=20) and posture (roll=0, pitch=0, yaw=0) of the robot TCP.

[0134] Generate a standardized YAML configuration file. Each node in the file contains complete status module information, TCP position, and posture.

[0135] Machine learning model prediction and internal data structure construction:

[0136] The machine learning model trained with historical task configuration data predicts the behavior pattern of each state module.

[0137] The specific predictions are as follows:

[0138] Based on historical data, the model predicts the optimal path and speed for "grabbing the door panel".

[0139] According to the prediction results, corresponding nodes are created in the internal data structure to build a data model that meets the tree topology structure and supports dynamic adjustment of task sequences.

[0140] Node detection and intelligent recommendation:

[0141] The necessary node detection logic checks whether each state module contains the key attributes of TCP position and posture. If missing, the user is prompted to supplement them.

[0142] Optional node detection logic automatically fills in default values, such as path planning constraints, to ensure flexibility and robustness of the task.

[0143] Error detection and correction:

[0144] Use deep learning technology to detect syntax and logic errors in configuration files and provide correction suggestions. The specific detection is as follows:

[0145] If the "grab" action between "move to" and "place" is found missing, the user is prompted to add this step.

[0146] Combine natural language processing technology to automatically generate correction suggestions and execute correction operations.

[0147] Task execution and monitoring:

[0148] The generated internal data structure is passed into the execution logic, and the robot performs tasks according to the predetermined order and dependencies.

[0149] The anomaly detection module uses machine learning algorithms to analyze the real-time data generated during the execution of the robot arm's tasks. If the position deviation is too large or the speed is abnormal, an alarm is triggered.

[0150] The intelligent decision-making system automatically selects appropriate response measures and suspends task execution based on the type and severity of the anomaly.

[0151] Further, the following operation is adopted:

[0152]

[0153] Through the above-mentioned implementation steps, this embodiment can easily configure complex assembly tasks for the robot arm without in-depth knowledge of programming, thereby improving the automation level and work efficiency of the production line.

[0154] The above are only preferred embodiments of the present invention, which do not limit the scope of protection of the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any changes, modifications, replacements, integrations and parameter changes to these embodiments within the spirit and principles of the present invention through conventional substitutions or without departing from the principles and spirit of the present invention fall within the scope of protection of the present invention.

Claims

1. A low-code robotic arm task configuration parsing method, characterized in that: The following steps are involved: S1: Read the low-code task configuration information written by the user through a graphical interface and natural language. The configuration information is automatically converted into a standardized YAML or JSON format file through a parsing engine based on semantic understanding and machine learning models. The file uses a tree structure to describe the robot arm task, and each node represents a state module; S2: Identify semantic units through the semantic analysis module, parse the hierarchical structure in the configuration information using the hierarchical relationship recognition algorithm, identify the logical association between nodes, and confirm the specific content of the status module; S3: Use machine learning models to predict the behavior patterns of state modules, build internal data structures in a recursive or iterative manner, map the hierarchical relationships in the configuration information to software data structures that satisfy the tree topology structure, and support dynamic adjustment of task sequences to enhance configuration flexibility; S4: During the parsing process, an intelligent recommendation system is used to detect the necessity and optionality of nodes. For necessary nodes, the corresponding information is ensured to exist and be set. For optional nodes, the intelligent recommendation system automatically fills in reasonable default values ​​according to the context to ensure the robustness of the parsing process. S5: Use deep learning technology to detect syntax errors and logical errors in configuration information, and combine natural language processing technology to automatically correct errors and provide correction suggestions to enhance the reliability of the parsing process; S6: After the analysis is completed, the generated internal data structure is passed into the execution logic, which supports real-time monitoring of the task execution status through visualization tools to ensure that the robot arm can perform tasks in the predetermined order and dependencies. When encountering abnormal situations, the intelligent decision-making system can respond quickly and take corresponding processing measures.

2. A low-code robotic arm task configuration parsing method according to claim 1, characterized in that: In S1, the graphical interface allows the user to intuitively design the robot arm task process by dragging and selecting, and the natural language processing engine understands and converts the natural language description input by the user, converting it into a standard YAML or JSON format file, reducing the user's need for programming expertise.

3. A low-code robotic arm task configuration parsing method according to claim 1 or 2, characterized in that: The S1 specifically includes the following steps: S1.1: Start the natural language processing module to perform preliminary analysis on the natural language description input by the user, extract the key words "move to", "grab", "release" action instructions and their parameters in the description, and use them to build a preliminary task intention model; S1.2: Using the semantic understanding model, the extracted key words are semantically analyzed to understand the meaning of the words in a specific context and map them to the corresponding robot arm movements; if the semantic understanding model does not accurately describe the key words, the correct meaning of the key words is inferred from the context; S1.3: Integrate the parsing results into a standardized YAML or JSON format file. The nodes in the file contain complete status module information, and the hierarchical relationship is clear and unambiguous, which is convenient for subsequent parsing engines to perform further processing.

4. A low-code robotic arm task configuration parsing method according to claim 1, characterized in that: The S2 specifically includes the following steps: S2.1: Identify semantic units in the configuration information through the semantic analysis module; S2.2: Analyze the hierarchical relationship in the configuration information through the hierarchical relationship recognition algorithm, automatically identify the logical relationship between the parent node and the child node, and ensure that the relationship between the state module and the upper and lower nodes is correct; S2.3: Use context-aware algorithms to refine the context of the state module so that the description of the state module is grammatically and logically correct and conforms to the actual application scenario; S2.4: Confirm the specific contents of the status module, including but not limited to TCP position, posture, speed, acceleration, path planning constraints, peripheral operations, and exception handling logic, to ensure that they are logically consistent and meet the requirements for the robot to perform tasks; S2.5: Generate a semantic analysis report, which contains detailed information and hierarchical relationships of the state modules, so that subsequent steps can be accurately parsed based on the semantic analysis report.

5. A low-code robotic arm task configuration parsing method according to claim 1, characterized in that: The machine learning model in S3 specifically includes the following steps: S3.11: Collect and organize historical task configuration data, wherein the historical task configuration data includes successfully executed task configurations and actual operation results; the data set of the robot arm task behavior pattern is formed by the historical task configuration data as the basis for training the machine learning model; S3.12: Train a machine learning model by a supervised learning method; the machine learning model input is the description information of the state module, and the output is the predicted behavior pattern; during the training process, the model learns the mapping relationship between different configuration information and the actual behavior pattern, and predicts the expected behavior pattern based on the input state module description; S3.13: During the parsing process, the information of the state module is input into the trained machine learning model. The machine learning model predicts the behavior pattern of the state module based on experience and rules and converts it into part of the internal data structure.

6. A low-code robotic arm task configuration parsing method according to claim 1 or 5, characterized in that: The S3 internal data structure construction specifically includes the following steps: S3.21: Initialize the internal data structure and create a root node as the starting point of the entire task configuration; the root node serves as the top-level container for all subsequent state modules; the creation of the root node marks the beginning of the construction of the internal data structure and is also the basis of the recursive or iterative process; S3.22: For the state module, after using the machine learning model to predict its behavior pattern, create the corresponding node in the internal data structure according to the prediction result, and add the newly created node as a child node or sibling node of the existing node to the internal data structure according to the hierarchical relationship defined in the configuration file, and implement it through recursive call; the recursive call is when a new state module is parsed and its behavior pattern is predicted, a recursive function is called; the recursive function adds new nodes and recursively processes the sub-state modules in the state module until all state modules are parsed and added to the internal data structure; S3.23: During the construction of the internal data structure, the hierarchical relationship is tracked to ensure that the parent-child relationship of the nodes is correct; at the same time, it supports dynamic adjustment of the task sequence, inserting or deleting state modules according to actual conditions during the construction process, and it is achieved by iteratively checking the dependencies of the state modules and updating the internal data structure when necessary, thereby enhancing the flexibility and adaptability of the configuration.

7. A low-code robotic arm task configuration parsing method according to claim 1, characterized in that: The S4 essential node specifically includes the following steps: S4.11: Define the necessary node categories, including but not limited to the key attributes of the state module: TCP position, posture, speed, acceleration; the necessary nodes form the basis for effective task configuration and correct execution of tasks; the intelligent recommendation system checks whether the state module contains necessary attributes to ensure that the attribute settings are reasonable; S4.12: Implement the necessary node detection logic. The system will verify whether the status module contains necessary nodes one by one during the parsing of the configuration file, and check whether the necessary nodes have been correctly set. If it is detected that the necessary nodes are missing or the necessary nodes are not set completely, the parsing will be stopped immediately, and the user will be prompted with the specific missing items through a visual interface or message to guide the user to supplement or correct them.

8. A low-code robotic arm task configuration parsing method according to claim 1, characterized in that: The S4 optional node specifically includes the following steps: S4.21: Define optional node categories, including but not limited to speed ratio, acceleration ratio, path planning constraints, peripheral operations and exception handling logic; the optional nodes are optional for the state module, which can enhance the flexibility and robustness of the task in certain circumstances; S4.22: Implement optional node detection logic; the intelligent recommendation system identifies which nodes are optional during the process of parsing the configuration file and checks whether the nodes are defined; if the checked node is not defined, the intelligent recommendation system will automatically fill in a reasonable default value based on the context; S4.23: Using big data analysis and machine learning technology, the intelligent recommendation system can learn and recommend best practices based on successful cases of past task configurations; when it is detected that an optional node is not defined, the system can fill in the default value and provide optimization suggestions based on the characteristics of the current task.

9. A low-code robotic arm task configuration parsing method according to claim 1, characterized in that: The S5 deep learning technology detection includes the following steps: S5.1: Build an error detection model; use long short-term memory network deep learning technology to train the error detection model of low-code task configuration files; the error detection model is trained based on a large number of annotated correct configuration files and error configuration files, and can identify common syntax errors, logic errors and other potential problems; the training data set of the error detection model includes instances of missing necessary fields, unreasonable parameter settings, and logical sequence errors, so that the error detection model has comprehensive error recognition capabilities; S5.2: Execute error detection. During the parsing of the configuration file, the configuration file is input into the trained LSTM network deep learning model line by line and paragraph by paragraph. The LSTM network deep learning model will analyze the text of the configuration file and detect any errors. If an error is detected, the error detection model will output the specific error type and its location. S5.3: Perform error correction using natural language processing technology; when an error is detected, use natural language processing technology to automatically generate correction suggestions and perform correction actions; S5.4: Provide a detailed correction report; after completing error detection and correction, generate a correction report that includes the detected errors and their locations, detailed correction suggestions for the errors, and the corrective actions that have been performed.

10. A low-code robotic arm task configuration parsing method according to claim 1, characterized in that: The intelligent decision-making system of S6 includes an abnormality detection module; The anomaly detection module uses machine learning algorithms to analyze real-time data generated during the execution of the robot arm's tasks, and can identify excessive position deviations from expected behavior, abnormal speed, and external interference situations to issue alarms; When the anomaly detection module detects an abnormal situation, the intelligent decision-making system automatically selects appropriate response measures based on the type and severity of the anomaly, including but not limited to suspending task execution, rolling back to the last stable state, attempting automatic repair, requesting manual intervention, and executing corresponding commands.

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