Simulation process testing method based on low-code platform

Abstract

The invention relates to the technical field of low-code development platforms, in particular to a simulation process testing method based on a low-code platform, which comprises the following steps: based on the low-code platform, adopting a K-means clustering algorithm and a decision tree classification method to analyze historical process data, performing mode and exception recognition, and generating a process mode and an exception condition. According to the method and the device, through a low-code platform and a machine learning algorithm, the flow mode and the abnormal condition can be identified more accurately, and the test efficiency and accuracy are improved. And test parameters are optimized by using reinforcement learning, and the strategy is adjusted in real time. And the subtasks are executed in parallel, and the parallel processing capability is improved by message transmission coordination. Visual tools and interfaces are provided, and the design and execution process is simplified. And a real-time editing and debugging function is realized, and the flow chart is dynamically modified. AI and NLP technologies are utilized to analyze a process model and requirements, an intelligent test strategy and result analysis are generated, and deep process insight and understanding are provided.

Description

Technical Field

[0001] The present invention relates to the technical field of low-code development platforms, and in particular to a simulation process testing method based on a low-code platform. Background Art

[0002] The low-code development platform technology field is an emerging software development method that uses a visual drag-and-drop interface and a small amount of code to quickly build applications and business processes. The low-code development platform can greatly improve software development efficiency, reduce development costs, and improve software quality.

[0003] Among them, the simulation process testing method based on the low-code platform is a method of using the low-code platform to perform simulation process testing. The purpose of this method is to improve the efficiency and accuracy of simulation process testing. Generally, the corresponding effect is achieved by using the low-code platform to quickly build a simulation process model, perform simulation process testing on the low-code platform, analyze the test results, and optimize the simulation process.

[0004] However, existing methods cannot accurately identify all process patterns and abnormal situations, resulting in incomplete or inaccurate test results. Secondly, existing methods have limitations when adjusting process test parameters and variables, and cannot fully utilize the advantages of reinforcement learning algorithms. In addition, although decomposing process tests into multiple subtasks and executing them in parallel can improve test efficiency, the lack of a coordination mechanism leads to problems with communication and synchronization between tasks. In addition, although visual process testing tools and interfaces are provided, they cannot meet the needs of all developers. Although real-time editing and debugging functions are provided, they cannot meet the modification and optimization needs of complex flowcharts. Finally, although artificial intelligence and natural language processing technologies are used for analysis and reasoning, they cannot fully mine the key information and requirements in process models and test requirements. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and propose a simulation process testing method based on a low-code platform.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a simulation process testing method based on a low-code platform, comprising the following steps:

[0007] S1: Based on the low-code platform, K-means clustering algorithm and decision tree classification method are used to analyze historical process data, identify patterns and anomalies, and generate process patterns and anomalies;

[0008] S2: Based on the process mode and abnormal conditions, the Q-learning reinforcement learning algorithm is used to optimize the process test strategy, and the test parameters are adjusted to generate an optimized process test strategy;

[0009] S3: Based on the optimized process testing strategy, multi-threaded parallel processing technology is used to decompose the process testing tasks, and multiple tasks are executed to generate process testing tasks to be executed in parallel;

[0010] S4: Based on the parallel execution process test tasks, a message queue synchronization mechanism is used to coordinate the communication between tasks and generate a coordinated process test task;

[0011] S5: Based on the coordinated process testing task, a visualization tool is developed using a graphical user interface design principle to generate a visualization process testing interface;

[0012] S6: Based on the visual process test interface, the real-time editing technology is used to adjust and optimize the flow chart to generate a debugged flow chart;

[0013] S7: Based on the debugged flowchart, natural language processing and machine learning technology are used to analyze the process model and test requirements, and generate model test requirement analysis results;

[0014] S8: Based on the test demand analysis results of the model, a collaborative filtering recommendation algorithm is used to make test suggestions and optimize the plan, and generate an intelligent test optimization plan;

[0015] S9: Based on the intelligent test optimization solution, the knowledge graph and logic reasoning engine are used to conduct in-depth analysis of test strategies and results to generate intelligent analysis results;

[0016] The process patterns and abnormal situations specifically refer to the identified regular process paths and abnormal activities that deviate from normal trajectories. The optimized process testing strategy includes test variable values ​​and optimal test paths. The parallel executed process testing tasks specifically refer to test subtasks that run simultaneously on multiple processors and computing nodes. The visual process testing interface specifically refers to providing a graphical operation interface and an interactive design environment. The post-debugging flowchart specifically refers to a process representation diagram that can be dynamically modified and debugged. The model test requirement analysis results include key information extraction and requirement understanding. The intelligent test optimization plan specifically refers to customized test strategies and improvement suggestions.

[0017] As a further solution of the present invention, based on the low-code platform, the K-means clustering algorithm and decision tree classification method are used to analyze historical process data, identify patterns and anomalies, and generate process patterns and anomalies. The specific steps are as follows:

[0018] S101: Based on the low-code platform, data preprocessing technology is used to clean and standardize historical process data to generate preprocessed historical process data;

[0019] S102: Based on the pre-processed historical process data, a K-means clustering algorithm is used to classify the process data to generate classified process data;

[0020] S103: Based on the classified process data, a decision tree classification method is used to generate differentiated process pattern data;

[0021] S104: Based on the differentiated process pattern data, the process pattern and abnormal situation are displayed using data visualization technology to generate the process pattern and abnormal situation.

[0022] As a further solution of the present invention, based on the process mode and abnormal conditions, the Q-learning reinforcement learning algorithm is used to optimize the process test strategy, and the test parameters are adjusted to generate the optimized process test strategy. The specific steps are as follows:

[0023] S201: Based on the process mode and abnormal situation, a reinforcement learning model training method is used to generate established Q-learning model parameters;

[0024] S202: Based on the established Q-learning model parameters, using a Q-learning reinforcement learning algorithm, performing model training optimization to generate optimized Q-learning model parameters;

[0025] S203: Based on the optimized Q-learning model parameters, perform testing in a simulation environment, adjust the test strategy according to model feedback, and generate an adjusted test strategy;

[0026] S204: Based on the adjusted test strategy, an optimized process test strategy is generated by using an excellent strategy selection and verification method.

[0027] As a further solution of the present invention, based on the optimized process testing strategy, multi-threaded parallel processing technology is used to decompose the process testing tasks, and multiple tasks are executed to generate the process testing tasks for parallel execution. The specific steps are as follows:

[0028] S301: Based on the optimization process test strategy, a load balancing algorithm is used to allocate test tasks and generate load balanced test task allocation;

[0029] S302: Based on the load-balanced test task allocation, use thread pool management technology to create and manage multi-threads, and generate a multi-thread environment under thread pool management;

[0030] S303: Based on the multi-threaded environment managed by the thread pool, a concurrency control mechanism is adopted to generate a test execution task under concurrency control;

[0031] S304: Based on the test execution tasks under the concurrent control, a task cutting and allocation strategy is implemented to refine the tasks and generate a process test task for parallel execution.

[0032] As a further solution of the present invention, based on the parallel execution process test task, a message queue synchronization mechanism is used to coordinate inter-task communication and generate the coordinated process test task in the following specific steps:

[0033] S401: Based on the parallel execution process test task, a basic task communication mechanism is generated by using message queue technology;

[0034] S402: Based on the basic task communication mechanism, a standardized message transmission format is generated by using data serialization and deserialization technology;

[0035] S403: Based on the standardized message transmission format, a synchronization and lock mechanism is adopted to generate a synchronized message processing flow;

[0036] S404: Based on the synchronized message processing flow, coordinate and integrate tasks to generate a post-coordination flow test task.

[0037] As a further solution of the present invention, based on the coordinated process test task, the specific steps of developing a visualization tool and generating a visualization process test interface by using the graphical user interface design principle are as follows:

[0038] S501: Based on the post-coordination process test task, a modular user interface layout is generated using a graphical interface design principle;

[0039] S502: Based on the modular user interface layout, using interactive design technology, generating interactive function modules;

[0040] S503: Based on the interactive function module, use data binding technology to display and interact with data and generate a dynamically bound user interface;

[0041] S504: Based on the dynamically bound user interface, style and theme design technology is used to beautify the interface to generate a visual process test interface.

[0042] As a further solution of the present invention, based on the visual process test interface, the real-time editing technology is used to adjust and optimize the flow chart, and the specific steps of generating the debugged flow chart are as follows:

[0043] S601: Based on the visual process test interface, using real-time editing technology, generate a real-time adjusted process interface;

[0044] S602: Based on the real-time adjusted process interface, a dynamically rendered process flow chart is generated using a chart rendering technology;

[0045] S603: Based on the dynamically rendered flowchart, using debugging and optimization algorithms, an optimized flowchart is generated;

[0046] S604: Based on the optimized flowchart, version control technology is used to perform version tracking management to generate a debugged flowchart.

[0047] As a further solution of the present invention, based on the debugged flowchart, natural language processing and machine learning technology are used to analyze the process model and test requirements, and the specific steps of generating the model test requirement analysis results are as follows:

[0048] S701: Based on the debugged flowchart, text content is extracted and analyzed using text mining technology to generate flowchart text content extraction results;

[0049] S702: Based on the flowchart text content extraction result, natural language processing technology is used to perform semantic analysis and information extraction to generate semantic analysis and key information extraction results;

[0050] S703: Based on the semantic analysis and key information extraction results, a process model and a test requirement in-depth analysis result are generated using a machine learning model;

[0051] S704: Based on the in-depth analysis results of the process model and test requirements, integration and induction are performed to generate model test requirement analysis results.

[0052] As a further solution of the present invention, based on the model test demand analysis results, a collaborative filtering recommendation algorithm is used to make test suggestions and formulate optimization plans. The specific steps of generating an intelligent test optimization plan are as follows:

[0053] S801: Based on the model test requirement analysis result, using data preprocessing technology to generate preliminary processed test requirement data;

[0054] S802: Based on the preliminarily processed test requirement data, a collaborative filtering algorithm is applied to generate a test case and requirement association analysis result;

[0055] S803: Based on the test case and requirement association analysis result, using an optimization algorithm, generate an optimized test case set;

[0056] S804: Based on the optimized test case set, integrate and adjust according to actual needs to generate an intelligent test optimization solution.

[0057] As a further solution of the present invention, based on the intelligent test optimization solution, the knowledge graph and logical reasoning engine are used to conduct in-depth analysis of test strategies and results, and the specific steps of generating intelligent analysis results are as follows:

[0058] S901: Based on the intelligent test optimization solution, using knowledge extraction technology, generate a test strategy and rule knowledge set;

[0059] S902: Based on the test strategy and rule knowledge set, a knowledge graph is constructed to generate a test strategy knowledge graph;

[0060] S903: Based on the test strategy knowledge graph, a logic reasoning engine is applied to perform in-depth analysis to generate logic analysis and optimization suggestion results;

[0061] S904: Based on the logic analysis and optimization suggestion results, an intelligent test strategy analysis result is generated through data visualization technology.

[0062] Compared with the prior art, the advantages and positive effects of the present invention are:

[0063] In the present invention, through the process pattern and abnormal situation identification based on the low-code platform and machine learning algorithm, common process patterns and possible problems can be identified more accurately, thereby improving test efficiency and accuracy. The reinforcement learning algorithm is used to optimize the process test parameters and variables, and the test strategy can be adjusted in real time according to the test results to further improve the test effect. The process test is decomposed into multiple subtasks for parallel execution and coordinated using a message passing mechanism, which can improve the test efficiency and parallel processing capabilities. Visual process testing tools and interfaces are provided, allowing developers to design and execute process tests more easily. Real-time editing and debugging functions enable developers to dynamically modify and optimize flowcharts during the test process. Analyzing process models and test requirements through artificial intelligence and natural language processing technology, and using knowledge graphs and reasoning engines to generate intelligent test strategies and result analysis, can provide deeper process insights and understanding. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0065] Figure 2 It is a schematic diagram of the refinement of S1 of the present invention;

[0066] Figure 3 It is a schematic diagram of the refinement of S2 of the present invention;

[0067] Figure 4 It is a schematic diagram of the refinement of S3 of the present invention;

[0068] Figure 5It is a schematic diagram of the refinement of S4 of the present invention;

[0069] Figure 6 It is a detailed schematic diagram of S5 of the present invention;

[0070] Figure 7 It is a detailed schematic diagram of S6 of the present invention;

[0071] Figure 8 It is a detailed schematic diagram of S7 of the present invention;

[0072] Fig. 9 It is a detailed schematic diagram of S8 of the present invention;

[0073] Fig.10 It is a detailed schematic diagram of S9 of the present invention. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0075] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0076] Embodiment 1

[0077] See also Figure 1 The present invention provides a technical solution: a simulation process testing method based on a low-code platform, comprising the following steps:

[0078] S1: Based on the low-code platform, K-means clustering algorithm and decision tree classification method are used to analyze historical process data, identify patterns and anomalies, and generate process patterns and anomalies;

[0079] S2: Based on process patterns and abnormal situations, the Q-learning reinforcement learning algorithm is used to optimize the process testing strategy.

[0080] And adjust the test parameters to generate an optimized process test strategy;

[0081] S3: Based on the optimization process testing strategy, multi-threaded parallel processing technology is used to decompose the process testing tasks, execute multiple tasks, and generate parallel execution process testing tasks;

[0082] S4: Based on the parallel execution of process test tasks, the message queue synchronization mechanism is used to coordinate the communication between tasks and generate the coordinated process test tasks;

[0083] S5: Based on the coordinated process testing task, the graphical user interface design principle is adopted to develop visualization tools and generate a visualization process testing interface;

[0084] S6: Based on the visual process test interface, real-time editing technology is used to adjust and optimize the flowchart and generate the debugged flowchart;

[0085] S7: Based on the post-debugging flowchart, natural language processing and machine learning technologies are used to analyze the process model and test requirements, and generate model test requirement analysis results;

[0086] S8: Based on the results of the model test requirements analysis, a collaborative filtering recommendation algorithm is used to make test recommendations and optimization plans, and generate intelligent test optimization plans;

[0087] S9: Based on the intelligent test optimization solution, the knowledge graph and logical reasoning engine are used to conduct in-depth analysis of test strategies and results, and generate intelligent analysis results;

[0088] Process patterns and abnormal situations specifically refer to the identified regular process paths and abnormal activities that deviate from normal trajectories. Optimized process testing strategies include test variable values ​​and optimal test paths. Parallel process testing tasks specifically refer to test subtasks that run simultaneously on multiple processors and computing nodes. Visual process testing interface specifically refers to the provision of a graphical operating interface and an interactive design environment. Post-debugging flowcharts specifically refer to process representation diagrams that can be dynamically modified and debugged. Model testing requirements analysis results include key information extraction and requirements understanding. Intelligent testing optimization solutions specifically refer to customized testing strategies and improvement suggestions.

[0089] By using K-means clustering algorithm and decision tree classification method to analyze historical process data, it is possible to accurately identify regular process paths and abnormal activities that deviate from normal trajectories. It helps to improve test efficiency and accuracy and reduce unnecessary testing work. The Q-learning reinforcement learning algorithm is used to optimize the process test strategy and adjust the test parameters, and the test strategy can be adjusted in real time according to the test results. This makes the test process more flexible and efficient and can better adapt to different test needs. The multi-threaded parallel processing technology is used to decompose the process test task into multiple subtasks for parallel execution, which can improve the test efficiency and parallel processing capabilities. At the same time, the message queue synchronization mechanism is used to coordinate inter-task communication to ensure the collaborative work between the various subtasks. The development of visual tools based on the graphical user interface design principle enables developers to design and execute process tests more easily. The application of real-time editing technology enables developers to dynamically modify and optimize the flowchart during the test process, further improving the test effect. The use of natural language processing and machine learning technology to analyze the process model and test requirements, and the use of knowledge graphs and reasoning engines to generate intelligent test strategies and result analysis can provide deeper process insights and understanding. This helps to discover potential problems and improvement points, and further improve the effect and quality of testing.

[0090] In summary, the simulation process testing method based on the low-code platform can improve testing efficiency and accuracy, optimize testing strategies and parameters, realize parallel processing and task coordination, provide visualization tools and interfaces, support real-time editing and debugging functions, and perform process analysis and result generation through artificial intelligence and natural language processing technology, making this method have broad application prospects in the field of process testing.

[0091] See also Figure 2 Based on the low-code platform, the K-means clustering algorithm and decision tree classification method are used to analyze historical process data and identify patterns and anomalies. The specific steps for generating process patterns and anomalies are as follows:

[0092] S101: Based on the low-code platform, data preprocessing technology is used to clean and standardize historical process data to generate preprocessed historical process data;

[0093] S102: Based on the pre-processed historical process data, a K-means clustering algorithm is used to classify the process data to generate classified process data;

[0094] S103: Based on the classified process data, a decision tree classification method is used to generate differentiated process pattern data;

[0095] S104: Based on the differentiated process pattern data, the process pattern and abnormal situation are displayed using data visualization technology to generate the process pattern and abnormal situation.

[0096] In S101, data preprocessing is performed on the low-code platform. This includes cleaning and standardizing historical process data to eliminate noise and outliers and ensure data consistency and comparability.

[0097] In S102, the pre-processed historical process data is classified using a K-means clustering algorithm, and the process data is divided into different categories or groups by calculating the distance and similarity between data points.

[0098] In S103, the classified process data is further differentiated using a decision tree classification method. According to the characteristics and conditions of different categories, a decision tree model is constructed to predict the category to which each data point belongs.

[0099] In S104, the generated process patterns and abnormal situations are displayed using data visualization technology, and the characteristics and distribution of the process patterns and abnormal situations are intuitively presented through charts, graphs or other visualization methods.

[0100] Through the above steps, the historical process data analysis method based on the low-code platform, K-means clustering algorithm and decision tree classification method can be implemented. First, data preprocessing is performed, then the clustering algorithm is used to classify the data, and then the decision tree classification method is used to further distinguish the process mode, and finally the results are displayed through visualization.

[0101] See also Figure 3 ,Based on the process mode and abnormal situations, the Q-learning reinforcement learning algorithm is used to optimize the process test strategy and adjust the test parameters. The specific steps to generate the optimized process test strategy are as follows:

[0102] S201: Based on the process mode and abnormal conditions, a reinforcement learning model training method is used to generate established Q-learning model parameters;

[0103] S202: Based on the established Q-learning model parameters, a Q-learning reinforcement learning algorithm is used to perform model training optimization to generate optimized Q-learning model parameters;

[0104] S203: Based on the optimized Q-learning model parameters, use the simulation environment to perform testing, adjust the testing strategy according to model feedback, and generate an adjusted testing strategy;

[0105] S204: Based on the adjusted test strategy, use the excellent strategy selection and verification method to generate an optimized process test strategy.

[0106] In S201, a Q-learning model is established based on the process pattern and abnormal conditions, which includes defining the state, action and reward functions, and initializing the model parameters.

[0107] In S202, the established Q-learning model is trained and optimized using a reinforcement learning algorithm. Through interaction with the environment, the model parameters are continuously updated to maximize the cumulative reward.

[0108] In S203, the simulation environment is used for testing. The testing strategy is adjusted according to the feedback results of the model. For example, the test is performed according to the optimal action recommended by the model, and the strategy is evaluated and adjusted according to the actual results.

[0109] In S204, an optimal strategy selection and verification method is used to generate an optimized process test strategy, which may include comparing performance indicators of different strategies, selecting the best strategy, and performing verification and iterative improvement.

[0110] Through the above steps, the process testing strategy optimization method based on process mode and abnormal situation can be implemented. First, the Q-learning model is established and trained and optimized, then the simulation environment is used to test and adjust the test strategy, and finally the excellent strategy selection and verification method is used to generate the optimized process testing strategy. It can effectively improve the efficiency and accuracy of process testing and provide support for subsequent process analysis and improvement.

[0111] See also Figure 4 ,Based on the optimization process testing strategy, the multi-threaded parallel processing technology is used to decompose the process testing tasks, and execute multiple tasks to generate parallel execution process testing tasks. The specific steps are as follows:

[0112] S301: Based on the optimization process test strategy, a load balancing algorithm is used to allocate test tasks and generate load balanced test task allocation;

[0113] S302: Based on load balancing test task allocation, use thread pool management technology to create and manage multi-threads, and generate a multi-thread environment under thread pool management;

[0114] S303: Based on the multi-threaded environment managed by the thread pool, a concurrent control mechanism is adopted to generate a test execution task under concurrent control;

[0115] S304: Based on the test execution tasks under concurrent control, the task cutting and allocation strategies are implemented to refine the tasks and generate parallel execution process test tasks.

[0116] In S301, according to the optimization process test strategy, the load balancing algorithm is used to distribute the test tasks. This can reasonably distribute the test tasks to different threads or executors according to factors such as task priority and resource requirements to achieve load balancing.

[0117] In S302, a multi-thread environment is created and managed using thread pool management technology. A certain number of threads are created in advance and the number of threads is dynamically adjusted when needed to improve the performance and responsiveness of the system.

[0118] In S303, in a multi-threaded environment, a concurrency control mechanism is used to ensure the orderly execution of the test tasks. For example, a lock or other synchronization mechanism is used to prevent multiple threads from accessing shared resources at the same time, thereby avoiding data competition and inconsistency problems.

[0119] In S304, according to the test execution tasks under concurrent control, the task cutting and allocation strategy is implemented to refine the tasks. The large task is decomposed into smaller subtasks, and they are allocated to different threads or executors for parallel execution according to certain rules.

[0120] Through the above steps, the multi-threaded parallel processing technology based on the optimization process test strategy can be implemented. First, the load balancing algorithm is used to allocate test tasks, then a multi-threaded environment is created and managed, and then a concurrent control mechanism is used to ensure the orderly execution of tasks. Finally, the task is refined through task cutting and allocation strategies to generate parallel execution process test tasks. The capabilities of multi-core processors can be fully utilized to improve the efficiency and speed of process testing.

[0121] See also Figure 5 Based on the parallel execution of process test tasks, the message queue synchronization mechanism is used to coordinate the communication between tasks. The specific steps of generating coordinated process test tasks are as follows:

[0122] S401: Based on the parallel execution process test task, the message queue technology is used to generate a basic task communication mechanism;

[0123] S402: Based on the basic task communication mechanism, a standardized message transmission format is generated by using data serialization and deserialization technology;

[0124] S403: Based on the standardized message transmission format, a synchronization and lock mechanism is adopted to generate a synchronized message processing flow;

[0125] S404: Based on the synchronized message processing flow, coordinate and integrate the tasks, and generate a post-coordination flow test task.

[0126] In S401, based on the parallel execution process test task, the message queue technology is used to establish the basic task communication mechanism, including creating a message queue, defining the message format and message content, etc.

[0127] In S402, data serialization and deserialization technology is used to convert the message into a standardized transmission format, which can ensure that the message can be accurately transmitted and parsed between different tasks.

[0128] In S403, with the support of synchronization and lock mechanisms, a synchronized message processing process is implemented. By locking key resources or using semaphores and other mechanisms, multiple tasks are ensured to access shared resources in a certain order, avoiding competition conditions and data inconsistency.

[0129] In S404, task coordination and integration are performed according to the synchronized message processing flow, which may include adjusting the execution order of tasks according to the content and sequence of the messages, or merging the results of multiple tasks into a comprehensive result.

[0130] Through the above steps, the message queue synchronization mechanism based on parallel execution of process test tasks can be implemented. First, the basic task communication mechanism is established, and then the standardized message transmission format is generated using data serialization and deserialization technology. Then, the synchronized message processing process is realized with the support of synchronization and lock mechanisms, and finally, the tasks are coordinated and integrated to generate coordinated process test tasks. Communication and coordination between tasks can be effectively realized, and the efficiency and accuracy of process testing can be improved.

[0131] See also Figure 6 ,Based on the post-coordination process testing task, the graphical user interface design principle is adopted to develop a visualization tool.,The specific steps for generating a visualization process testing interface are as follows:

[0132] S501: Based on the post-coordination process test task, a modular user interface layout is generated using graphical interface design principles;

[0133] S502: Based on the modular user interface layout, using interactive design technology, generate interactive function modules;

[0134] S503: Based on the interactive function module, data binding technology is used to display and interact with data, and a dynamically bound user interface is generated;

[0135] S504: Based on the dynamic binding user interface, the style and theme design technology is used to beautify the interface and generate a visual process test interface.

[0136] In S501, according to the requirements of the post-coordination process test task, the graphical interface design principle is used to generate a modular user interface layout, including dividing the interface into different functional modules and determining their positions and relative sizes.

[0137] In S502, interactive design technology is used to add interactive functions to each functional module. For example, a click event is added to a button, and data binding is added to an input box. This allows users to interact with the interface and implement specific operations and functions.

[0138] In S503, data binding technology is used to associate data with interface elements to achieve dynamic display and interaction of data. By binding the data source with the attributes of the interface elements, when the data changes, the interface will automatically update to display the corresponding content.

[0139] In S504, style and theme design techniques are used to beautify the interface. By selecting appropriate colors, fonts, icons and other elements, and setting a consistent layout and style, the interface looks more beautiful and easy to use.

[0140] Through the above steps, the development of visualization tools based on the coordinated process testing task can be implemented. First, a modular user interface layout is generated, then interactive functions are added to each functional module, and then data binding technology is used to realize dynamic display and interaction of data. Finally, the interface is beautified through style and theme design technology to generate a visual process testing interface. An intuitive, easy-to-use interface with good user experience can be provided, which is convenient for users to conduct process testing and related operations and analysis.

[0141] See also Figure 7 Based on the visual process test interface, the real-time editing technology is used to adjust and optimize the flowchart. The specific steps to generate the debugged flowchart are as follows:

[0142] S601: Based on the visual process test interface, using real-time editing technology, generate a real-time adjustment process interface;

[0143] S602: Based on the process interface adjusted in real time, a dynamically rendered process flow chart is generated by using a chart rendering technology;

[0144] S603: Based on the dynamically rendered flowchart, use debugging and optimization algorithms to generate an optimized flowchart;

[0145] S604: Based on the optimized flowchart, version control technology is used to perform version tracking management to generate a debugged flowchart.

[0146] In S601, the real-time editing technology is used on the visual process test interface to implement real-time adjustment of the flow chart, including operations such as adding, deleting and moving nodes and edges, and modifying the properties of nodes and edges.

[0147] In S602, the flowchart adjusted in real time is dynamically rendered using a chart rendering technology, and the flowchart can be displayed in real time according to the user's adjustment by updating the properties such as the position, style and color of the nodes and edges.

[0148] In S603, the dynamically rendered flowchart is optimized using debugging and optimization algorithms, including detecting and repairing potential problems, such as dead loops, redundant steps, etc., to improve the efficiency and accuracy of the process.

[0149] In S604, the optimized flowchart is tracked and managed using version control technology, and the changes and modifications of each version are recorded for subsequent backtracking and comparative analysis.

[0150] Through the above steps, the real-time editing technology based on the visual process test interface can be implemented. First, the real-time editing technology is used to generate a real-time adjusted process interface, then the chart rendering technology is used to dynamically render the flowchart, then the debugging and optimization algorithms are used to optimize the flowchart, and finally the version control technology is used to track the version and generate the debugged flowchart.

[0151] See also Figure 8 ,Based on the post-debugging flowchart, natural language processing and machine learning technology are used to analyze the process model and test requirements. The specific steps to generate the model test requirement analysis results are as follows:

[0152] S701: Based on the debugged flowchart, text content is extracted and analyzed using text mining technology to generate flowchart text content extraction results;

[0153] S702: Based on the flowchart text content extraction results, natural language processing technology is used to perform semantic analysis and information extraction to generate semantic analysis and key information extraction results;

[0154] S703: Based on the semantic analysis and key information extraction results, the machine learning model is used to generate the process model and test requirements in-depth analysis results;

[0155] S704: Based on the in-depth analysis results of the process model and test requirements, integrate and summarize to generate model test requirement analysis results.

[0156] In S701, text mining technology is used to extract and analyze text content of the debugged flowchart, including identifying text information in the flowchart, such as node names, edge descriptions, etc., and extracting them as a basis for subsequent analysis.

[0157] In S702, natural language processing technology is used to perform semantic analysis and key information extraction on the extracted flowchart text content. Through lexical analysis, syntactic analysis and other technologies, the meaning and context of the text are understood, and key information and entities are extracted.

[0158] In S703, the results of semantic analysis and key information extraction are deeply analyzed and modeled using a machine learning model. Based on existing annotation data or rules, the model is trained to automatically identify the features and test requirements of the process model, and generate in-depth analysis results of the process model and test requirements.

[0159] In S704, the in-depth analysis results of the process model and test requirements are integrated and summarized to generate the model test requirement analysis results. The analysis results of each step are integrated to form a complete test requirement analysis report, including the characteristics of the process model, potential problems and improvement suggestions.

[0160] Through the above steps, natural language processing and machine learning technologies based on debugged flowcharts can be implemented. First, text mining technology is used to extract the text content of the flowchart, then natural language processing technology is used for semantic analysis and key information extraction, and then machine learning models are used for in-depth analysis and modeling, and finally the results of model test requirements analysis are integrated and summarized. Valuable information can be extracted from the flowchart to help testers better understand and analyze process models and test requirements, and improve the efficiency and accuracy of testing.

[0161] See also Fig. 9 ,Based on the results of model test demand analysis, a collaborative filtering recommendation algorithm is used to make test suggestions and optimize the plan. The specific steps for generating intelligent test optimization plans are as follows:

[0162] S801: Based on the model test requirement analysis results, use data preprocessing technology to generate preliminary processed test requirement data;

[0163] S802: Based on the test requirement data after preliminary processing, a collaborative filtering algorithm is applied to generate a test case and requirement association analysis result;

[0164] S803: Based on the test case and requirement correlation analysis results, an optimized test case set is generated using an optimization algorithm;

[0165] S804: Based on the optimized test case set, integrate and adjust according to actual needs to generate an intelligent test optimization solution.

[0166] In S801, according to the model test requirement analysis results, data preprocessing technology is used to preliminarily process the test requirement data, including removing duplicate data, processing missing values ​​and outliers, etc., to ensure the quality and accuracy of the data.

[0167] In S802, a collaborative filtering algorithm is applied to analyze the preliminarily processed test requirement data, and a test case and requirement association analysis result is generated by comparing the similarities between different test cases and the degree of association with the requirements.

[0168] In S803, the optimization algorithm is used to further process the test case and requirement association analysis results. According to the association analysis results, the most relevant and effective test cases are selected to generate an optimized test case set.

[0169] In S804, the optimized test case set is integrated and adjusted according to actual needs to generate an intelligent test optimization plan, including determining the order, priority and coverage of the test to meet the requirements and goals of the project.

[0170] Through the above steps, the collaborative filtering recommendation algorithm based on the model test requirement analysis results can be implemented. First, the test requirement data is preliminarily processed using data preprocessing technology, and then the collaborative filtering algorithm is applied to generate the test case and requirement association analysis results. Then, the optimization algorithm is used to generate the optimized test case set, and finally, it is integrated and adjusted according to actual needs to generate an intelligent test optimization plan.

[0171] Please refer to the figure. Based on the intelligent test optimization solution, the knowledge graph and logical reasoning engine are used to conduct in-depth analysis of test strategies and results. The specific steps to generate intelligent analysis results are as follows:

[0172] S901: Based on the intelligent test optimization solution, use knowledge extraction technology to generate test strategies and rule knowledge sets;

[0173] S902: Based on the test strategy and rule knowledge set, a knowledge graph is constructed to generate a test strategy knowledge graph;

[0174] S903: Based on the test strategy knowledge graph, the logic reasoning engine is used for in-depth analysis to generate logic analysis and optimization suggestion results;

[0175] S904: Based on the logic analysis and optimization suggestion results, generate intelligent test strategy analysis results through data visualization technology.

[0176] In S901, according to the intelligent test optimization plan, knowledge extraction technology is used to extract test strategies and rules from existing test data and experience, including determining the scope, methods and steps of the test.

[0177] In S902, a knowledge graph is constructed based on the test strategy and rule knowledge set. Different test strategies and rules are used as nodes, and the relationships and dependencies between them are represented by edges to form a structured knowledge graph.

[0178] In S903, the logic reasoning engine is used to conduct in-depth analysis on the test strategy knowledge graph. Through reasoning and inference, potential problems and improvement points are discovered, and logic analysis and optimization suggestion results are generated.

[0179] In S904, data visualization technology is used to display the results of logic analysis and optimization suggestions in the form of charts or graphs, which can more intuitively present the analysis results of the test strategy and help testers better understand and apply these results.

[0180] Through the above steps, the knowledge graph and logic reasoning engine based on the intelligent test optimization solution can be implemented. First, the knowledge extraction technology is used to generate the test strategy and rule knowledge set, and then the knowledge graph is constructed. Then the logic reasoning engine is used for in-depth analysis to generate logic analysis and optimization suggestion results. Finally, the intelligent test strategy analysis results are generated through data visualization technology. It can improve the efficiency and accuracy of the test and help testers better understand the test strategy and optimize it.

[0181] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A simulation process testing method based on a low-code platform, characterized in that: The following steps are involved: Based on the low-code platform, K-means clustering algorithm and decision tree classification method are used to analyze historical process data, identify patterns and anomalies, and generate process patterns and anomalies; Based on the process pattern and abnormal conditions, a Q-learning reinforcement learning algorithm is used to optimize the process test strategy, and the test parameters are adjusted to generate an optimized process test strategy; Based on the optimized process testing strategy, multi-threaded parallel processing technology is used to decompose the process testing tasks, and multiple tasks are executed to generate process testing tasks that are executed in parallel; Based on the parallel execution process test tasks, a message queue synchronization mechanism is used to coordinate the communication between tasks and generate a coordinated process test task; Based on the post-coordination process testing task, a visualization tool is developed using the graphical user interface design principle to generate a visualization process testing interface; Based on the visual process test interface, real-time editing technology is used to adjust and optimize the flow chart to generate a debugged flow chart; Based on the debugged flowchart, natural language processing and machine learning techniques are used to analyze the process model and test requirements, and generate model test requirement analysis results; Based on the test demand analysis results of the model, a collaborative filtering recommendation algorithm is used to make test suggestions and optimize the plan, and generate an intelligent test optimization plan; Based on the intelligent test optimization solution, the knowledge graph and logical reasoning engine are used to conduct in-depth analysis of test strategies and results to generate intelligent analysis results; The process patterns and abnormal situations specifically refer to the identified regular process paths and abnormal activities that deviate from normal trajectories. The optimized process testing strategy includes test variable values ​​and optimal test paths. The parallel executed process testing tasks specifically refer to test subtasks that run simultaneously on multiple processors and computing nodes. The visual process testing interface specifically refers to providing a graphical operation interface and an interactive design environment. The post-debugging flowchart specifically refers to a process representation diagram that can be dynamically modified and debugged. The model test requirement analysis results include key information extraction and requirement understanding. The intelligent test optimization plan specifically refers to customized test strategies and improvement suggestions.

2. The simulation process testing method based on the low-code platform according to claim 1 is characterized in that: Based on the low-code platform, the K-means clustering algorithm and decision tree classification method are used to analyze historical process data and identify patterns and anomalies. The specific steps for generating process patterns and anomalies are as follows: Based on the low-code platform, data preprocessing technology is used to clean and standardize historical process data to generate preprocessed historical process data; Based on the pre-processed historical process data, a K-means clustering algorithm is used to classify the process data to generate classified process data; Based on the classified process data, a decision tree classification method is used to generate differentiated process pattern data; Based on the differentiated process pattern data, the process pattern and abnormal situation are displayed using data visualization technology to generate process patterns and abnormal situations.

3. The simulation process testing method based on the low-code platform according to claim 1 is characterized in that: Based on the process mode and abnormal conditions, the Q-learning reinforcement learning algorithm is used to optimize the process test strategy and adjust the test parameters. The specific steps to generate the optimized process test strategy are as follows: Based on the process pattern and abnormal conditions, a reinforcement learning model training method is used to generate established Q-learning model parameters; Based on the established Q-learning model parameters, a Q-learning reinforcement learning algorithm is used to perform model training optimization to generate optimized Q-learning model parameters; Based on the optimized Q-learning model parameters, testing is performed using a simulation environment, and the test strategy is adjusted according to model feedback to generate an adjusted test strategy; Based on the adjusted test strategy, an optimized process test strategy is generated using excellent strategy selection and verification methods.

4. The simulation process testing method based on the low-code platform according to claim 1 is characterized in that: Based on the optimization process test strategy, multi-threaded parallel processing technology is used to decompose the process test task, execute multiple tasks, and generate parallel execution process test tasks. The specific steps are as follows: Based on the optimization process test strategy, a load balancing algorithm is used to allocate test tasks and generate a load balanced test task allocation; Based on the load-balanced test task allocation, thread pool management technology is used to create and manage multi-threads, and a multi-thread environment under thread pool management is generated; Based on the multi-threaded environment managed by the thread pool, a concurrent control mechanism is adopted to generate test execution tasks under concurrent control; Based on the test execution tasks under the concurrent control, task cutting and allocation strategies are implemented to refine the tasks and generate parallel execution process test tasks.

5. The simulation process testing method based on the low-code platform according to claim 1 is characterized in that: Based on the parallel execution process test task, the message queue synchronization mechanism is used to coordinate the communication between tasks, and the specific steps of generating the coordinated process test task are as follows: Based on the parallel execution process test tasks, a basic task communication mechanism is generated by using message queue technology; Based on the basic task communication mechanism, a standardized message transmission format is generated by using data serialization and deserialization technology; Based on the standardized message transmission format, a synchronization and lock mechanism is adopted to generate a synchronized message processing flow; Based on the synchronized message processing flow, tasks are coordinated and integrated to generate post-coordination flow test tasks.

6. The simulation process testing method based on the low-code platform according to claim 1 is characterized in that: Based on the post-coordination process testing task, the specific steps of developing a visualization tool and generating a visualization process testing interface using the graphical user interface design principle are as follows: Based on the post-coordination process testing task, a modular user interface layout is generated using graphical interface design principles; Based on the modular user interface layout, using interactive design technology, generating interactive functional modules; Based on the interactive function module, data binding technology is used to display and interact with data to generate a dynamically bound user interface; Based on the dynamically bound user interface, style and theme design technology is used to beautify the interface and generate a visual process testing interface.

7. The simulation process testing method based on the low-code platform according to claim 1 is characterized in that: Based on the visual process test interface, the real-time editing technology is used to adjust and optimize the flow chart. The specific steps for generating the debugged flow chart are as follows: Based on the visual process test interface, a process interface that can be adjusted in real time is generated using real-time editing technology; Based on the real-time adjusted process interface, a dynamically rendered process flow chart is generated using a chart rendering technology; Based on the dynamically rendered flowchart, using debugging and optimization algorithms, generating an optimized flowchart; Based on the optimized flowchart, version control technology is used to perform version tracking management to generate a debugged flowchart.

8. The simulation process testing method based on the low-code platform according to claim 1 is characterized in that: Based on the debugged flowchart, natural language processing and machine learning technologies are used to analyze the process model and test requirements. The specific steps for generating the model test requirement analysis results are as follows: Based on the debugged flowchart, text content extraction and analysis are performed using text mining technology to generate flowchart text content extraction results; Based on the extraction results of the flowchart text content, natural language processing technology is used to perform semantic analysis and information extraction to generate semantic analysis and key information extraction results; Based on the semantic analysis and key information extraction results, a process model and in-depth analysis results of test requirements are generated using a machine learning model; Based on the in-depth analysis results of the process model and test requirements, integration and induction are carried out to generate model test requirement analysis results.

9. The simulation process testing method based on a low-code platform according to claim 1 is characterized in that: Based on the test demand analysis results of the model, a collaborative filtering recommendation algorithm is used to make test suggestions and optimize the plan. The specific steps for generating an intelligent test optimization plan are as follows: Based on the model test requirement analysis results, using data preprocessing technology to generate preliminary processed test requirement data; Based on the test requirement data after the preliminary processing, a collaborative filtering algorithm is applied to generate a test case and requirement association analysis result; Based on the test case and requirement association analysis results, an optimized test case set is generated using an optimization algorithm; Based on the optimized test case set, it is integrated and adjusted according to actual needs to generate an intelligent test optimization solution.

10. The simulation process testing method based on a low-code platform according to claim 1 is characterized in that: Based on the intelligent test optimization solution, the knowledge graph and logical reasoning engine are used to conduct in-depth analysis of test strategies and results. The specific steps for generating intelligent analysis results are as follows: Based on the intelligent test optimization solution, using knowledge extraction technology, generate test strategy and rule knowledge set; Based on the test strategy and rule knowledge set, a knowledge graph is constructed to generate a test strategy knowledge graph; Based on the test strategy knowledge graph, a logical reasoning engine is applied to perform in-depth analysis to generate logical analysis and optimization suggestion results; Based on the logical analysis and optimization suggestion results, intelligent test strategy analysis results are generated through data visualization technology.

Images