A visual data analysis processing system

Abstract

This invention discloses a visual data analysis and processing system, relating to the field of data analysis and processing. The system includes a component generation unit, a component execution and display unit, and a component dynamic expansion unit. By integrating front-end and back-end technologies, this invention simplifies the complex data analysis process into intuitive visual operations, significantly lowering the barrier to entry for non-technical users. Combined with an interactive computing platform, users can quickly verify their ideas, adjust their analysis processes, and achieve efficient data processing and result visualization, significantly improving the efficiency of data analysis work and the user experience.

Description

Technical Field

[0001] This invention relates to data analysis and processing, and more specifically, to a visual data analysis and processing system. Background Technology

[0002] Data visualization refers to the process and techniques of presenting data in graphical, image, animation, or other easily understandable visual forms, aiming to help people more intuitively understand, analyze, and interpret information, trends, patterns, and relationships within the data. Although significant progress has been made in the field of data visualization, a series of technical challenges remain regarding the construction of data analysis platforms, the ease of process execution and debugging, and the dynamic scalability of components. Specifically, existing data visualization analysis and processing systems have the following limitations:

[0003] 1) Lack of low-code capabilities: Many data analysis platforms on the market lack low-code functionality, forcing users to write complex logic code themselves when conducting data analysis, which greatly increases the barrier to entry and limits the participation and efficiency of non-technical personnel.

[0004] 2) Insufficient debugging flexibility: Traditional data analysis platforms require users to complete the entire data analysis process before they can run the whole system. They cannot support real-time debugging, which makes it difficult for users to quickly locate and solve problems when they encounter them, thus prolonging the data analysis cycle and reducing efficiency.

[0005] 3) Component extensibility limitations: Traditional visualization platforms generally face the problem of fixed components. The components available to users are often limited by the presets and restrictions of the backend system. They cannot meet diverse needs through user customization or extension, which limits the platform's flexibility and scalability and fails to meet the growing personalized needs of users.

[0006] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0007] In view of the problems in related technologies, this invention proposes a visual data analysis and processing system to overcome the aforementioned technical problems existing in the existing related technologies.

[0008] Therefore, the specific technical solution adopted by the present invention is as follows:

[0009] A visual data analysis and processing system, comprising: a component generation unit, a component execution and display unit, and a component dynamic expansion unit;

[0010] The component generation unit is used to generate backend components by writing code files and to develop corresponding frontend components based on the backend components;

[0011] The component execution and display unit is used to upload front-end components to the interactive computing platform, obtain execution results, and visualize the execution results;

[0012] The component dynamic extension unit is used to encrypt and register user code and executable files. It dynamically loads and calls user code components and executable file components through the backend loader and plugins.

[0013] Furthermore, the component generation unit includes: a code file writing module, a backend component generation module, and a frontend component generation module;

[0014] The code file writing module is used to write code files using a programming language. The code files contain component definitions, logic code, and application interfaces. The component definitions include component names, component groups, and component usage instructions.

[0015] The backend component generation module is used to analyze code files, obtain backend code, and generate backend components based on the backend code.

[0016] The front-end component generation module is used to develop corresponding front-end components based on back-end components.

[0017] Furthermore, the code files are analyzed to obtain the backend code, and backend components are generated based on the backend code, including:

[0018] The code file is loaded using a loader, analyzed, and its logical and operational sequence features are extracted.

[0019] By combining logical features and operation sequence features, a comprehensive feature set is established.

[0020] Collect historical backend datasets, match the comprehensive features with historical backend component datasets, and obtain the logical features of backend components;

[0021] A training dataset is established based on the acquired backend component logical features and the corresponding code.

[0022] Train a deep learning model using the training dataset and build a backend code generation model;

[0023] The logical characteristics of the code file are input into the backend code generation model, and the backend code is output.

[0024] After reviewing and optimizing the backend code, the processed backend code is packaged to generate backend components.

[0025] Furthermore, historical backend datasets are collected, and the comprehensive features are matched with historical backend component datasets to obtain the logical features of the backend components, including:

[0026] Collect historical backend datasets, which contain several sets of logical features of backend components.

[0027] Data query and matching techniques are used to retrieve historical backend datasets, and the logical features and comprehensive features of backend components in the historical backend component datasets are matched.

[0028] Calculate the similarity between the logical features and comprehensive features of backend components in the historical backend component dataset;

[0029] The calculated similarity is compared with a preset threshold, and backend component logical features with similarity higher than the preset threshold are selected to obtain backend component logical features.

[0030] Furthermore, the similarity calculation formula between the logical features and comprehensive features of backend components in the historical backend component dataset is as follows:

[0031]

[0032] In the formula, D(n, m) represents the similarity between the logical features and comprehensive features of the backend components in the historical backend component dataset;

[0033] n represents the logical characteristics of backend components in the historical backend component dataset;

[0034] m represents the comprehensive feature;

[0035] P(n) represents the number of logical features of backend components retrieved from the historical backend component dataset;

[0036] P(m) represents the number of searches based on comprehensive features;

[0037] P(n∩m) represents the number of backend component logical features and comprehensive features retrieved simultaneously in the historical backend component dataset.

[0038] Furthermore, the component execution and display unit includes: a component integration module, an execution and result acquisition module, and a result processing and display module;

[0039] The component integration module is used to receive front-end components and upload the code files of the front-end components to the interactive computing platform;

[0040] The execution and result acquisition module is used to execute the code files of the front-end components in an interactive computing platform environment and obtain the execution results through the application programming interface.

[0041] The results processing and display module is used to process the obtained execution results using clustering algorithms and then visualize the results.

[0042] Furthermore, clustering algorithms are used to process the obtained execution results, and the processing results are visualized, including:

[0043] Extract performance metrics from the obtained execution results and combine the performance metrics to obtain a performance metric dataset;

[0044] The parameters of the pre-defined clustering algorithm are configured, and the clustering algorithm is used to perform cluster analysis on the performance index dataset to obtain a set of all clusters;

[0045] Analyze the centroid and number of members of each cluster in the set, and identify and remove abnormal clusters based on preset criteria;

[0046] Based on the clusters after the removal process, the clustering results are visualized using visualization tools.

[0047] Furthermore, the component dynamic extension unit includes: a data encryption processing module, a user code processing and registration module, an executable file processing and packaging module, a backend loader processing module, a plugin creation and invocation module, and a component management and monitoring module;

[0048] The data encryption processing module is used to receive user code and executable file, and use encryption algorithms to encrypt the user code and executable file to obtain encrypted user code and executable file;

[0049] The user code processing and registration module is used to process the encrypted user code, generate user code components, and register the user code components through the backend loader.

[0050] The executable file processing and packaging module is used to process the encrypted executable file, generate executable file components by combining them with a preset template, and register the executable file components through the backend loader;

[0051] The backend loader processing module is used to load encrypted user code and executable files through the backend loader and execute the corresponding loading logic;

[0052] The plugin creation and invocation module is used to create plugins based on registration information and use the plugins to call user code components and executable file components for processing.

[0053] The component management and monitoring module is used to view registration information, manage user code components and executable file components based on the registration information, and monitor the execution process of user code components and executable file components.

[0054] Furthermore, the user code and executable file are received, and encrypted using an encryption algorithm, resulting in encrypted user code and executable file, including:

[0055] Analyze user code files and executable files to obtain basic file information;

[0056] Based on the basic information of the file, determine the file segmentation strategy;

[0057] Based on the file splitting strategy, user code files and executable files are divided into several data blocks;

[0058] Based on the key byte length required by the AES algorithm, generate a byte-random number and use the byte-random number as the key;

[0059] Store the key in the user code file and the executable file, and record the encryption algorithm identifier;

[0060] By combining the encryption algorithm identifier and the key, the data block is encrypted to obtain the encrypted user code and executable file.

[0061] Furthermore, the basic information of the file includes: file size, file format, and file type.

[0062] The beneficial effects of this invention are as follows:

[0063] 1. By integrating front-end and back-end technologies, this invention simplifies the complex data analysis process into an intuitive visual operation, significantly reducing the barrier to entry for non-technical users. Combined with an interactive computing platform, users can quickly verify their ideas, adjust the analysis process, and achieve efficient data processing and result visualization, thereby significantly improving the efficiency of data analysis and the user experience.

[0064] 2. This invention introduces modular debugging and visualization assistance, making the debugging process more intuitive and efficient. Users can utilize the interactivity of the interactive computing platform to execute code step by step and observe the output results, quickly locating the problem. At the same time, key data is displayed in the form of charts to help users better understand data change trends and improve the accuracy and efficiency of debugging.

[0065] 3. By employing dynamic component loading and plugin invocation technology, this invention not only enhances the system's scalability, enabling it to adapt to a wider range of application scenarios, but also provides users with high flexibility, allowing them to customize personalized data analysis solutions according to actual needs. Attached Figure Description

[0066] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0067] Figure 1 This is a schematic diagram of a visualization data analysis and processing system according to an embodiment of the present invention.

[0068] In the picture:

[0069] 1. Component generation unit; 2. Component execution and display unit; 3. Component dynamic extension unit. Detailed Implementation

[0070] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention.

[0071] According to an embodiment of the present invention, a visualization data analysis and processing system is provided.

[0072] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 1 As shown, the visualization data analysis and processing system according to an embodiment of the present invention includes: a component generation unit 1, a component execution and display unit 2, and a component dynamic expansion unit 3;

[0073] Component generation unit 1 is used to generate backend components by writing code files and to develop corresponding frontend components based on the backend components. The components are divided into backend components and frontend components. Backend components refer to executable units that package user code, scripts, and executable files, while frontend components refer to executable units that present the backend components to the user for visual use through frontend development. Specifically, component generation unit 1 includes: a code file writing module, a backend component generation module, and a frontend component generation module;

[0074] The code file writing module is used to write code files using a programming language. The code files contain component definitions, logic code, and application interfaces. The component definitions include component names, component groups, and component usage instructions.

[0075] The backend component generation module is used to analyze code files, obtain backend code, and generate backend components based on the backend code.

[0076] Specifically, analyzing code files to obtain backend code and generating backend components based on the backend code includes:

[0077] The code file is loaded using a loader, analyzed, and its logical and operational sequence characteristics are extracted.

[0078] By combining logical features and operation sequence features, a comprehensive feature set is established.

[0079] It should be further explained that logical features refer to the functional description of the code, such as the purpose of a function or the flow of an algorithm, while operation sequence features are the specific execution steps of the code, such as the order of function calls or loop structures. The logical features and operation sequence features are converted into numerical vector representations, and then fused together through a mechanism (such as a neural network) to form a comprehensive feature vector.

[0080] The specific implementation method is as follows:

[0081] Step 1: Loading and analyzing code files

[0082] The code loader reads the source code file, and after loading, static analysis tools or dynamic analysis tools are used to extract the logical features and operation sequence features in the code; for example, an abstract syntax tree (AST) is used to capture the structural information of the code, or a control flow graph (CFG) is used to capture the execution path of the program.

[0083] Step 2: Extract logical features

[0084] Analyze function comments or names to infer their main functions, identify algorithm patterns (such as sorting, searching, etc.), and record their main steps; evaluate the dependencies between code modules to determine which modules are independent and which are closely related.

[0085] Step 3: Extract operation sequence features

[0086] Record the order of function calls and parameter passing; identify loop types (such as for, while) and their nesting levels; record the structure of if / else or switch / case statements.

[0087] Step 4: Convert to a numerical vector

[0088] The above features are converted into numerical vectors for subsequent processing. For example, word embedding techniques (such as Word2Vec or BERT) can be used to represent text features such as function names and variable names, while one-hot encoding or other encoding techniques can be used to represent non-text features such as control flow structures.

[0089] Step 5: Establish comprehensive features

[0090] By combining the extracted logical feature vectors with the operation sequence feature vectors, we can fuse this information through simple vector concatenation or more complex mechanisms such as attention mechanisms to form a comprehensive feature vector.

[0091] Collect historical backend datasets, match the comprehensive features with the historical backend component datasets, and obtain the logical features of the backend components.

[0092] Specifically, historical backend datasets are collected, and the comprehensive features are matched with historical backend component datasets to obtain the logical features of the backend components, including:

[0093] Collect historical backend datasets, which contain several sets of logical features of backend components.

[0094] Data query and matching techniques are used to retrieve historical backend datasets, and the logical features and comprehensive features of backend components in the historical backend component datasets are matched.

[0095] Calculate the similarity between the logical features and comprehensive features of backend components in the historical backend component dataset.

[0096] Specifically, the formula for calculating the similarity between the logical features and comprehensive features of backend components in the historical backend component dataset is as follows:

[0097]

[0098] In the formula, D(n, m) represents the similarity between the logical features and comprehensive features of the backend components in the historical backend component dataset;

[0099] n represents the logical characteristics of backend components in the historical backend component dataset;

[0100] m represents the comprehensive feature;

[0101] P(n) represents the number of logical features of backend components retrieved from the historical backend component dataset;

[0102] P(m) represents the number of searches based on comprehensive features;

[0103] P(n∩m) represents the number of backend component logical features and comprehensive features retrieved simultaneously in the historical backend component dataset.

[0104] It should be noted that when the logical features of the backend component are exactly the same as the comprehensive features, P(n∩m) will be equal to the smaller of P(n) and P(m). In this case, the value of D(n,m) is close to 1, which indicates the maximum similarity. Conversely, if the logical features of the backend component and the comprehensive features have no intersection, the similarity is 0.

[0105] The calculated similarity is compared with a preset threshold, and backend component logical features with similarity higher than the preset threshold are selected to obtain backend component logical features.

[0106] A training dataset is built based on the acquired backend component logical features and the corresponding code.

[0107] A deep learning model is trained using the training dataset to build a backend code generation model.

[0108] The logical characteristics of the code file are input into the backend code generation model, and the backend code is output.

[0109] After reviewing and optimizing the backend code, the processed backend code is packaged to generate backend components.

[0110] The front-end component generation module is used to develop corresponding front-end components based on back-end components.

[0111] It should be further explained that the process involves analyzing the functionality and data structure of backend components, designing the layout and interaction logic of frontend components, creating frontend components using the React framework, defining the state and properties of frontend components, designing application programming interfaces (APIs), using the state management of a single-page application to control the loading state of components, listening for update events of backend components, and updating the state of frontend components, writing unit tests to verify component functionality, performing integration tests to ensure that frontend and backend components work together, deploying the frontend components to the production environment, and monitoring user behavior.

[0112] Component execution and display unit 2 is used to upload front-end components to the interactive computing platform, obtain execution results, and visualize the execution results.

[0113] Specifically, component execution and display unit 2 includes: a component integration module, an execution and result acquisition module, and a result processing and display module;

[0114] The component integration module is used to receive front-end components and upload the code files of the front-end components to the interactive computing platform.

[0115] The execution and result acquisition module is used to execute the code files of the front-end components in an interactive computing platform environment and obtain the execution results through the application programming interface.

[0116] It should be noted that the execution result refers to the output data generated after the front-end component runs, such as the processed dataset, statistical information, etc.

[0117] The results processing and display module is used to process the obtained execution results using clustering algorithms and then visualize the results.

[0118] Specifically, clustering algorithms are used to process the obtained execution results, and the processing results are then visualized, including:

[0119] Performance metrics are extracted from the obtained execution results and combined to obtain a performance metric dataset.

[0120] It should be noted that performance metrics are key measures extracted from the execution results and used to evaluate the performance and efficiency of components. Performance metrics include: response time, throughput, resource utilization, error rate, number of concurrent users, latency, success rate, bandwidth usage, and cache hit rate.

[0121] Response time: The time interval between sending a request and receiving a response, usually measured in milliseconds (ms) or seconds (s).

[0122] Throughput: The number of requests processed per unit of time.

[0123] Resource utilization: The usage of system resources such as CPU, memory, and disk I / O.

[0124] Error rate: The proportion of errors or exceptions that occur during execution.

[0125] Concurrent users: The number of users who simultaneously send requests to the system.

[0126] Delay time: The delay in the request processing process.

[0127] Success rate: The ratio of successfully processed requests to total requests.

[0128] Bandwidth usage: Network transmission rate.

[0129] Cache hit rate: The percentage of cache hits when accessing the cache.

[0130] The parameters of the pre-defined clustering algorithm are configured, and the clustering algorithm is used to perform cluster analysis on the performance index dataset to obtain a set of all clusters.

[0131] Analyze the centroid and number of members of each cluster in the set, and identify and remove abnormal clusters based on preset criteria.

[0132] Based on the clusters after the removal process, the clustering results are visualized using visualization tools.

[0133] It should be noted that visualization tools include Fine Report, Fine Vis, Tableau, Power BI, and Python's matplotlib and seaborn libraries. Depending on the characteristics of the clustering results and the analysis objectives, appropriate chart types should be selected for visualization, including scatter plots, bubble charts, heatmaps, and dendrograms (used for hierarchical clustering).

[0134] Set the chart's axes, colors, labels, and other attributes to ensure the chart information is clear and easy to read; map the clustering results to the chart, and for each data point, assign a corresponding color or shape to the chart based on its cluster label.

[0135] The component dynamic extension unit 3 is used to encrypt and register user code and executable files, and dynamically load and call user code components and executable file components through the backend loader and plugins.

[0136] Specifically, the component dynamic extension unit 3 includes: a data encryption processing module, a user code processing and registration module, an executable file processing and packaging module, a backend loader processing module, a plugin creation and invocation module, and a component management and monitoring module;

[0137] The data encryption processing module is used to receive user code and executable files, and to encrypt the user code and executable files using an encryption algorithm to obtain encrypted user code and executable files.

[0138] Specifically, the process receives user code and executable files, and encrypts them using an encryption algorithm to obtain the encrypted user code and executable files, which include:

[0139] Analyze user code files and executable files to obtain basic file information.

[0140] Specifically, the basic information of a file includes: file size, file format, and file type.

[0141] It should be noted that file size refers to the amount of space a file occupies on a storage device, usually expressed in bytes; file format is the specific encoding method or structure used when a file is stored or transmitted in a computer; file type is usually classified according to the content, purpose or function of the file. File type can be defined based on file format, but it can also include broader classification criteria.

[0142] Based on the basic information of the file, determine the file segmentation strategy.

[0143] It should be further explained that the segmentation strategy includes selecting appropriate segmentation points. Determining appropriate segmentation points based on the file's basic information includes:

[0144] Determine the file size, format, and type of the user code file and executable file, and perform statistical analysis on them. Based on the analysis, select appropriate split points and verify them. Adjust any unreasonable split points and re-verify them to obtain suitable split points.

[0145] The user code file and the executable file are divided into several data blocks based on the file splitting strategy.

[0146] Generate a byte-based random number based on the key byte length required by the AES algorithm, and use the byte-based random number as the key.

[0147] It should be further explained that, based on the key byte length required by the AES algorithm (Advanced Encryption Standard, a symmetric encryption algorithm), a byte-based random number is generated, and this byte-based random number is used as the key.

[0148] Based on the key length required by the AES algorithm, a random number generator is used to generate byte random numbers; the generated byte random numbers are converted into binary representations to obtain a bit stream consisting of 0s and 1s; the obtained bit stream is used as the key for the AES algorithm.

[0149] The key is stored in the user code file and the executable file, and the encryption algorithm identifier is recorded.

[0150] By combining the encryption algorithm identifier and the key, the data block is encrypted to obtain the encrypted user code and executable file.

[0151] The user code processing and registration module is used to process the encrypted user code, generate user code components, and register the user code components through the backend loader.

[0152] The executable file processing and packaging module is used to process the encrypted executable file, generate executable file components by combining them with a preset template, and register the executable file components through the backend loader.

[0153] The backend loader module is used to load encrypted user code and executable files through the backend loader and execute the corresponding loading logic.

[0154] The plugin creation and invocation module is used to create plugins based on registration information and to use the plugins to invoke user code components and executable file components for processing.

[0155] The component management and monitoring module is used to view registration information, manage user code components and executable file components based on the registration information, and monitor the execution process of user code components and executable file components.

[0156] It should be noted that the system displays the corresponding registration information based on the user's permissions. Users can find the registration information management module in the system interface to view their registration information, including user ID, username, registration time, role, permissions, etc.

[0157] User code component management: Allows users to upload their own code components to a designated location on the system. Administrators or the system automatically review the uploaded code components to ensure their security and compliance. A version control mechanism is established for the code components to record the history of each modification and support rollback to a specific version.

[0158] Executable file component management: Users upload executable file components to the system, which stores them in a secure location, manages the dependencies of the executable file components, ensures that all necessary dependencies are correctly loaded during execution, and sets access permissions for the executable file components based on the user's role and permissions.

[0159] During the execution of user code components and executable file components, the system records detailed execution logs in real time. Log content includes, but is not limited to, execution time, executor, execution result, and error messages. Performance monitoring tools (such as Prometheus and Grafana) are used to monitor the execution performance of code components and executable file components in real time, with monitoring metrics including CPU utilization, memory usage, and response time. An anomaly alarm mechanism is set up so that when an anomaly (such as an error or timeout) is detected during execution, the system automatically sends an alarm to the administrator. The alarm information includes the anomaly type, occurrence time, and anomaly details, facilitating quick problem localization by the administrator. Security auditing is performed on the execution process of user code components and executable file components to prevent malicious code or unauthorized access. Audit content includes code execution permissions, data access permissions, and system resource usage.

[0160] In summary, by leveraging the technical solutions described above, the complex data analysis process is simplified into intuitive visualization through the integration of front-end and back-end technologies. This significantly lowers the barrier to entry for non-technical users. Combined with an interactive computing platform, users can quickly verify their ideas, adjust their analysis processes, and achieve efficient data processing and result visualization, significantly improving the efficiency of data analysis and the user experience. The introduction of modular debugging and visualization aids makes the debugging process more intuitive and efficient. Users can utilize the interactivity of the interactive computing platform to execute code step-by-step and observe the output results, quickly locating problems. Simultaneously, key data is displayed in chart form, helping users better understand data trends and improving the accuracy and efficiency of debugging. By employing dynamic component loading and plugin invocation technologies, the system's scalability is enhanced, enabling it to adapt to a wider range of application scenarios. Furthermore, it provides users with high flexibility, allowing them to customize personalized data analysis solutions according to actual needs.

[0161] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A visual data analysis and processing system, characterized in that, The system includes: a component generation unit, a component execution and display unit, and a component dynamic expansion unit; The component generation unit is used to generate backend components by writing code files, and to develop corresponding frontend components based on the backend components. The component generation unit includes a backend component generation module, used to analyze code files, obtain backend code, and generate backend components based on the backend code, specifically including: The code file is loaded using a loader, analyzed, and its logical and operational sequence features are extracted. By combining logical features and operation sequence features, a comprehensive feature set is established. Collect historical backend datasets, match the comprehensive features with historical backend component datasets, and obtain the logical features of backend components; A training dataset is established based on the acquired backend component logical features and the corresponding code. Train a deep learning model using the training dataset and build a backend code generation model; The logical characteristics of the code file are input into the backend code generation model, and the backend code is output. After reviewing and optimizing the backend code, the processed backend code is packaged to generate backend components. The process of collecting historical backend datasets and matching comprehensive features with historical backend component datasets to obtain the logical features of backend components includes: Collect historical backend datasets, which contain several sets of backend component logical features; Data query and matching techniques are used to retrieve historical backend datasets, and the logical features and comprehensive features of backend components in the historical backend component datasets are matched. Calculate the similarity between the logical features and comprehensive features of backend components in the historical backend component dataset; The calculated similarity is compared with a preset threshold, and backend component logical features with similarity higher than the preset threshold are selected to obtain backend component logical features. The component execution and display unit is used to upload the front-end component to the interactive computing platform, obtain the execution result, and visualize the execution result. The component dynamic extension unit is used to encrypt and register user code and executable files, and dynamically load and call user code components and executable file components through the backend loader and plugin.

2. The visualization data analysis and processing system according to claim 1, characterized in that, The component generation unit includes: a code file writing module, a backend component generation module, and a frontend component generation module; The code file writing module is used to write code files using a programming language. The code files include component definitions, logic code, and application programming interfaces. The component definitions include component names, component groups, and component usage instructions. The backend component generation module is used to analyze code files, obtain backend code, and generate backend components based on the backend code. The front-end component generation module is used to develop corresponding front-end components based on back-end components.

3. The visualization data analysis and processing system according to claim 1, characterized in that, The similarity calculation formula between the logical features and comprehensive features of backend components in the historical backend component dataset is as follows: In the formula, D(n,m) represents the similarity between the logical features and comprehensive features of the backend components in the historical backend component dataset; n represents the logical features of the backend components in the historical backend component dataset; m represents the comprehensive features; P(n) represents the number of logical features of the backend components in the historical backend component dataset; P(m) represents the number of comprehensive features; and P(n∩m) represents the number of times the logical features and comprehensive features of the backend components in the historical backend component dataset are retrieved simultaneously.

4. The visualization data analysis and processing system according to claim 1, characterized in that, The component execution and display unit includes: a component integration module, an execution and result acquisition module, and a result processing and display module; The component integration module is used to receive front-end components and upload the code files of the front-end components to the interactive computing platform; The execution and result acquisition module is used to execute the code file of the front-end component in an interactive computing platform environment and obtain the execution result through the application programming interface; The result processing and display module is used to process the obtained execution results using a clustering algorithm and then visualize the processing results.

5. The visualization data analysis and processing system according to claim 4, characterized in that, The process of using clustering algorithms to process the obtained execution results and then visualizing the results includes: Extract performance metrics from the obtained execution results and combine the performance metrics to obtain a performance metric dataset; The parameters of the pre-defined clustering algorithm are configured, and the clustering algorithm is used to perform cluster analysis on the performance index dataset to obtain a set of all clusters; Analyze the centroid and number of members of each cluster in the set, and identify and remove abnormal clusters based on preset criteria; Based on the clusters after the removal process, the clustering results are visualized using visualization tools.

6. The visualization data analysis and processing system according to claim 1, characterized in that, The component dynamic expansion unit includes: a data encryption processing module, a user code processing and registration module, an executable file processing and packaging module, a backend loader processing module, a plugin creation and invocation module, and a component management and monitoring module; The data encryption processing module is used to receive user code and executable file, and use an encryption algorithm to encrypt the user code and executable file to obtain encrypted user code and executable file; The user code processing and registration module is used to process the encrypted user code, generate user code components, and register the user code components through the backend loader. The executable file processing and packaging module is used to process the encrypted executable file, generate executable file components by combining them with a preset template, and register the executable file components through a backend loader. The backend loader processing module is used to load encrypted user code and executable files through the backend loader and execute the corresponding loading logic; The plugin creation and invocation module is used to create plugins based on registration information and use the plugins to call user code components and executable file components for processing. The component management and monitoring module is used to view registration information, manage user code components and executable file components based on the registration information, and monitor the execution process of user code components and executable file components.

7. The visualization data analysis and processing system according to claim 6, characterized in that, The process of receiving user code and executable file, and encrypting the user code and executable file using an encryption algorithm to obtain encrypted user code and executable file includes: Analyze user code files and executable files to obtain basic file information; Based on the basic information of the file, determine the file segmentation strategy; Based on the file splitting strategy, user code files and executable files are divided into several data blocks; Based on the key byte length required by the AES algorithm, generate a byte-random number and use the byte-random number as the key; Store the key in the user code file and the executable file, and record the encryption algorithm identifier; By combining the encryption algorithm identifier and the key, the data block is encrypted to obtain the encrypted user code and executable file.

8. The visualization data analysis and processing system according to claim 7, characterized in that, The basic information of the file includes: file size, file format, and file type.

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