Visual data analyzing and processing system

Abstract

The invention discloses a visual data analysis and processing system, which relates to the field of data analysis and processing and comprises a component generation unit, a component execution and display unit and a component dynamic expansion unit. By integrating front and back end technologies, a complex data analysis process is simplified into intuitive visual operation, the use threshold of non-technical users is greatly reduced, the users can quickly verify thoughts and adjust analysis processes in combination with an interactive computing platform, efficient data processing and result visualization are realized, and the user experience is improved. And the efficiency of data analysis work and the user experience are remarkably improved.

Description

Technical Field

[0001] The present invention relates to data analysis and processing, and in particular to a visual data analysis and processing system. Background Art

[0002] Data visualization refers to the process and technology of presenting data in the form of graphics, images, animations or other easy-to-understand visual forms, aiming to help people more intuitively understand, analyze and interpret the information, trends, patterns and relationships in the data. Although significant progress has been made in the field of data visualization, it still faces a series of technical challenges in the construction of data analysis platforms, the convenience of process execution and debugging, and the dynamic scalability of components. Specifically, the existing visual data analysis and processing systems have the following limitations:

[0003] 1) Lack of low-code capabilities: Many data analysis platforms on the current market lack low-code functions, forcing users to write complex logic codes themselves when performing data analysis, greatly increasing the usage threshold and limiting 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 entire platform. They cannot support real-time debugging functions, which makes it difficult for users to quickly locate and solve problems when they encounter them, thereby extending the data analysis cycle and reducing efficiency.

[0005] 3) Component scalability limitations: Traditional visualization platforms generally face the problem of component fixation. The components available to users are often limited by the presets and restrictions of the back-end system and cannot meet diverse needs through user customization or expansion. This limits the flexibility and scalability of the platform and cannot meet the users' growing personalized needs.

[0006] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

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

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

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

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

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

[0012] 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 plug-ins.

[0013] Further, the component generation unit includes: a code file writing module, a back-end component generation module and a front-end component generation module;

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

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

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

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

[0018] Use the loader to load the code file, analyze the code file and extract the logic features and operation sequence features of the code from the code file;

[0019] Combine logical features with operation sequence features to establish comprehensive features;

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

[0021] Establish a training data set based on the acquired backend component logic features and the codes corresponding to the backend component logic features;

[0022] Use training data sets to train deep learning models and build backend code generation models;

[0023] Input the logical features of the code file into the backend code generation model and output the backend code;

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

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

[0026] Collect historical backend data sets, which contain several sets of backend component logical features;

[0027] Use data query and matching technology to retrieve historical backend data sets, and match the logical features of backend components with the comprehensive features in the historical backend component data sets;

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

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

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

[0031]

[0032] Where 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 the backend components in the historical backend component dataset;

[0034] m represents comprehensive characteristics;

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

[0036] P(m) represents the number of retrievals of 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] Further, 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] A component integration module, used for receiving a front-end component and uploading a code file of the front-end component to an interactive computing platform;

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

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

[0042] Furthermore, the obtained execution results are processed using a clustering algorithm, and the processing results are visualized, including:

[0043] Extracting performance indicators from the obtained execution results, and combining the performance indicators to obtain a performance indicator data set;

[0044] The parameter configuration of the clustering algorithm is preset, and the clustering algorithm is used to perform clustering analysis on the performance indicator data set to obtain the set of all clusters;

[0045] Analyze the center point and number of members of each cluster in the collection, and identify and eliminate abnormal clusters based on preset standards;

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

[0047] Furthermore, the component dynamic expansion unit includes: a data encryption processing module, a user code processing and registration module, an execution file processing and packaging module, a backend loader processing module, a plug-in creation and calling module, and a component management and monitoring module;

[0048] The data encryption processing module is used to receive the user code and the execution file, and encrypt the user code and the execution file using the encryption algorithm to obtain the encrypted user code and the execution 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 execution file processing and packaging module is used to process the encrypted execution file, generate the execution file component based on the preset template, and register the execution file component through the backend loader;

[0051] The back-end loader processing module is used to load the encrypted user code and execution file through the back-end loader and execute the corresponding loading logic;

[0052] The plug-in creation and calling module is used to create a plug-in according to the registration information, and use the plug-in to call the user code component and the execution file component for processing;

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

[0054] Further, the user code and the execution file are received, and the user code and the execution file are encrypted using an encryption algorithm, and the encrypted user code and the execution file include:

[0055] Analyze user code files and execution files to obtain basic information of the files;

[0056] Determine the file segmentation strategy based on the basic information of the file;

[0057] Split the user code files and execution files into several data blocks based on the file segmentation strategy;

[0058] Generate a byte random number according to the key byte length required by the AES algorithm, and use the byte random number as the key;

[0059] The key is stored in the user code file and the execution file, and the encryption algorithm identifier is recorded;

[0060] The data block is encrypted in combination with the encryption algorithm identifier and the key to obtain the encrypted user code and execution file.

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

[0062] The beneficial effects of the present invention are:

[0063] 1. The present invention simplifies the complex data analysis process into intuitive visual operations by integrating front-end and back-end technologies, greatly reducing the usage threshold for non-technical users. Combined with the interactive computing platform, users can quickly verify ideas, adjust analysis processes, achieve efficient data processing and result visualization, and significantly improve the efficiency of data analysis work and user experience.

[0064] 2. The present invention introduces modular debugging and visual auxiliary means to make the debugging process more intuitive and efficient. Users can use the interactivity of the interactive computing platform to gradually execute the code and observe the output results to quickly locate the problem. At the same time, key data is displayed in the form of charts to help users better understand the data change trend and improve the accuracy and efficiency of debugging.

[0065] 3. The present invention adopts the technology of dynamic component loading and plug-in calling, which not only enhances the scalability of the system so that it can adapt to a wider range of application scenarios, but also provides users with a high degree of flexibility and can customize personalized data analysis solutions according to actual needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0067] Figure 1 It is a principle block diagram of a visual data analysis and processing system according to an embodiment of the present invention.

[0068] In the figure:

[0069] 1. Component generation unit; 2. Component execution and display unit; 3. Component dynamic expansion unit. DETAILED DESCRIPTION

[0070] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments. They can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention.

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

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

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

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

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

[0076] Specifically, the code files are analyzed to obtain the backend code, and the backend components generated based on the backend code include:

[0077] Use the loader to load the code file, analyze the code file and extract the logic features and operation sequence features of the code from the code file.

[0078] Combine logical features with operation sequence features to establish comprehensive features.

[0079] It should be noted that logical features refer to the functional description of the code, such as the purpose of the function, the flow of the algorithm, etc., and operation sequence features are the specific execution steps of the code, such as the function call order, loop structure, etc.; logical features and operation sequence features are converted into numerical vector representations, and then the logical features and operation sequence features are integrated together through some mechanism (such as neural network) to form a comprehensive feature vector.

[0080] The specific implementation is as follows:

[0081] Step 1: Load and analyze code files

[0082] Use the code loader to read the source code file. After loading, use static analysis tools or dynamic analysis tools to extract the logical features and operation sequence features in the code; for example, use the abstract syntax tree (AST) to capture the structural information of the code, or use the control flow graph (CFG) 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 algorithmic 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] Convert the above features into numerical vectors for subsequent processing; for example, use word embedding techniques (such as Word2Vec or BERT) to represent text features such as function names and variable names, and use one-hot encoding or other encoding techniques to represent non-text features such as control flow structures.

[0089] Step 5: Establish comprehensive features

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

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

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

[0093] Collect historical backend data sets, which contain several groups of backend component logical features.

[0094] Use data query and matching technology to retrieve historical backend data sets, and match the logical features of the backend components in the historical backend component data sets with the comprehensive features.

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

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

[0097]

[0098] Where 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 the backend components in the historical backend component dataset;

[0100] m represents comprehensive characteristics;

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

[0102] P(m) represents the number of retrievals of 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 components are exactly the same as the comprehensive features, P(n∩m) will be equal to the smaller value of P(n) and P(m). At this time, the value of D(n,m) is close to 1, indicating the maximum similarity; conversely, if the logical features of the backend components have no intersection with the comprehensive features, the similarity is 0.

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

[0106] A training data set is established based on the acquired backend component logical features and the codes corresponding to the backend component logical features.

[0107] Use the training dataset to train the deep learning model and build the backend code generation model.

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

[0109] After reviewing and optimizing the logic of 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 the back-end components.

[0111] It should be noted that the functions and data structure of the back-end components should be analyzed, and the layout and interaction logic of the front-end components should be designed; the front-end components should be created using the React framework, and the status and properties of the front-end components should be defined; the application program interface (API interface) should be designed; the loading status of the components should be controlled using the state management of the single-page application, and the update events of the back-end components should be listened to to update the status of the front-end components; unit tests should be written to verify the functions of the components, and integration tests should be performed to ensure that the front-end and back-end components work together; the front-end components should be deployed to the production environment, and user behavior should be monitored.

[0112] The component execution and display unit 2 is used to upload the front-end component to the interactive computing platform, obtain the execution result, and visualize the execution result.

[0113] Specifically, the 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 the 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 the interactive computing platform environment and obtain the execution results through the application program interface.

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

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

[0118] Specifically, the obtained execution results are processed using a clustering algorithm, and the processing results are visualized and displayed, including:

[0119] Performance indicators are extracted from the obtained execution results, and the performance indicators are combined to obtain a performance indicator data set.

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

[0121] Response time: Measures 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 time.

[0123] Resource utilization: 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 send requests to the system at the same time.

[0126] Latency: The delay in request processing.

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

[0128] Bandwidth usage: Network transfer rate.

[0129] Cache hit rate: The ratio of cache hits during cache access.

[0130] The parameter configuration of the clustering algorithm is preset, and the clustering algorithm is used to perform cluster analysis on the performance indicator data set to obtain the set of all clusters.

[0131] Analyze the center point and number of members of each cluster in the collection, and identify and eliminate abnormal clusters based on preset standards.

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

[0133] It should be noted that visualization tools include Fine Report, Fine Vis, Tableau, Power BI, Python's matplotlib and seaborn libraries, etc.; according to the characteristics of the clustering results and the analysis objectives, choose the appropriate chart type for visualization, including scatter plots, bubble charts, heat maps, dendrograms (for hierarchical clustering), etc.

[0134] Set the chart's axis, color, label and other properties to ensure that the chart information is clear and easy to read; map the clustering results to the chart, and for each data point, assign the corresponding color or shape in the chart according to its cluster label.

[0135] The component dynamic expansion unit 3 is used to encrypt and register the user code and executable file, and dynamically load and call the user code component and the executable file component through the back-end loader and the plug-in.

[0136] Specifically, the component dynamic expansion unit 3 includes: a data encryption processing module, a user code processing and registration module, an execution file processing and packaging module, a backend loader processing module, a plug-in creation and calling module, and a component management and monitoring module;

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

[0138] Specifically, the user code and the execution file are received, and the user code and the execution file are encrypted using an encryption algorithm, and the encrypted user code and the execution file include:

[0139] Analyze user code files and execution files to obtain basic information about the files.

[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 occupied by a file 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 types are usually classified according to the content, purpose or function of the file. File types can be defined based on file formats, but can also include broader classification standards.

[0142] Determine the file segmentation strategy based on the basic information of the file.

[0143] It should be noted that the segmentation strategy includes selecting appropriate segmentation points. Based on the basic information of the file, the appropriate segmentation points for the file are determined as follows:

[0144] Determine the file size, file format and file type of the user code file and the execution file, and perform statistical analysis on the user code file and the execution file; select appropriate segmentation points based on the analysis of the user code file and the execution file, and test the segmentation points; adjust unreasonable segmentation points, and re-test to obtain appropriate segmentation points.

[0145] The user code files and execution files are divided into several data blocks based on the file segmentation strategy.

[0146] According to the key byte length required by the AES algorithm, a byte random number is generated and used as the key.

[0147] It should be noted that, according to the key byte length required by the AES algorithm (Advanced Encryption Standard, symmetric encryption algorithm), a byte random number is generated, and the byte random number is used as a key including:

[0148] According to the key length required by the AES algorithm, a byte random number is generated by using a random number generator; the generated byte random number is converted into a binary representation to obtain a bit stream composed of 0 and 1; and the obtained bit stream is used as the key of the AES algorithm.

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

[0150] The data block is encrypted in combination with the encryption algorithm identifier and the key to obtain the encrypted user code and execution 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 execution file processing and packaging module is used to process the encrypted execution file, generate the execution file component in combination with the preset template, and register the execution file component through the backend loader.

[0153] Backend loader processing The backend loader module is used to load the encrypted user code and execution file through the backend loader and execute the corresponding loading logic;

[0154] The plug-in creation and calling module is used to create a plug-in according to the registration information, and use the plug-in to call the user code component and the execution file component for processing.

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

[0156] It should be noted that the corresponding registration information is displayed according to the user's permissions. The user finds the registration information management module in the system interface to view the user's registration information, including user ID, user name, registration time, role, permissions, etc.

[0157] User code component management: Allow users to upload their own code components to the location specified by the system. The administrator or the system automatically reviews the uploaded code components to ensure their security and compliance. Establish a version control mechanism for code components, 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 executable file components, ensures that all necessary dependencies are correctly loaded during execution, and sets access permissions for executable file components based on user roles and permissions.

[0159] During the execution of user code components and executable file components, the system records detailed execution logs in real time. The log content includes but is not limited to execution time, executor, execution results, error information, etc.; use performance monitoring tools (such as Prometheus, Grafana, etc.) to monitor the execution performance of code components and executable file components in real time. The monitoring indicators include CPU usage, memory usage, response time, etc.; set up an abnormal alarm mechanism. When an abnormal situation (such as error, timeout, etc.) is detected during the execution process, the system automatically sends an alarm message to the administrator. The alarm information includes the abnormal type, occurrence time, abnormal details, etc., so that the administrator can quickly locate the problem; perform security audits on the execution process of user code components and executable file components to prevent malicious code or unauthorized access. The audit content includes code execution permissions, data access permissions, system resource usage, etc.

[0160] In summary, with the help of the above technical solution of the present invention, by integrating front-end and back-end technologies, the complex data analysis process is simplified into intuitive visual operations, which greatly reduces the usage threshold for non-technical users. Combined with the interactive computing platform, users can quickly verify ideas, adjust the analysis process, and achieve efficient data processing and result visualization, which significantly improves the efficiency and user experience of data analysis work. By introducing modular debugging and visual auxiliary means, the debugging process is made more intuitive and efficient. Users can use the interactivity of the interactive computing platform to gradually execute the code and observe the output results to quickly locate the problem. At the same time, key data is displayed in the form of charts to help users better understand the data change trend and improve the accuracy and efficiency of debugging. By adopting the technology of dynamic component loading and plug-in calling, not only the scalability of the system is enhanced, enabling it to adapt to a wider range of application scenarios, but also a high degree of flexibility is provided to users, and personalized data analysis solutions can be customized 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 principle of the present invention should be included in 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 a backend component by writing a code file, and develop a corresponding frontend component based on the backend component; The component execution and display unit is used to upload the front-end component to the interactive computing platform, obtain the execution result, and visually display the execution result; The component dynamic expansion 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 a backend loader and a plug-in.

2. A visual data analysis and processing system according to claim 1, characterized in that: The component generation unit includes: a code file writing module, a back-end component generation module and a front-end component generation module; The code file writing module is used to write a code file using a programming language, wherein the code file includes component definition, logic code and application program interface, wherein the component definition includes component name, component grouping and component usage instructions; The backend component generation module is used to analyze the code file, obtain the backend code, and generate the backend component based on the backend code; The front-end component generation module is used to develop corresponding front-end components based on the back-end components.

3. A visual data analysis and processing system according to claim 2, characterized in that: The analyzing the code file to obtain the backend code and generating the backend component based on the backend code includes: Use the loader to load the code file, analyze the code file and extract the logic features and operation sequence features of the code from the code file; Combine logical features with operation sequence features to establish comprehensive features; Collect historical backend data sets, match comprehensive features with historical backend component data sets, and obtain backend component logical features; Establish a training data set based on the acquired backend component logic features and the codes corresponding to the backend component logic features; Use training data sets to train deep learning models and build backend code generation models; Input the logical features of the code file into the backend code generation model and output the backend code; After reviewing and optimizing the logic of the backend code, the processed backend code is packaged to generate backend components.

4. A visual data analysis and processing system according to claim 3, characterized in that: The collecting of historical backend data sets, matching of comprehensive features with historical backend component data sets, and obtaining of logical features of backend components include: Collecting a historical backend data set, wherein the historical backend data set includes several groups of backend component logical features; Use data query and matching technology to retrieve historical backend data sets, and match the logical features of backend components with the comprehensive features in the historical backend component data sets; Calculate the similarity between the logical features and the comprehensive features of the backend components in the historical backend component dataset; The calculated similarity is compared with a preset threshold, and the backend component logical features with a similarity higher than the preset threshold are screened out to obtain the backend component logical features.

5. A visual data analysis and processing system according to claim 4, characterized in that: The similarity calculation formula between the logical features and comprehensive features of the backend components in the historical backend component dataset is: Where 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 retrievals of the logical features of the backend components in the historical backend component dataset; P(m) represents the number of retrievals of the comprehensive features; P(n∩m) represents the number of simultaneous retrievals of the logical features and comprehensive features of the backend components in the historical backend component dataset.

6. A visual 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 the front-end component and upload the code file of the front-end component to the interactive computing platform; The execution and result acquisition module is used to execute the code file of the front-end component in the interactive computing platform environment and obtain the execution result through the application program interface; The result processing and display module is used to process the acquired execution results using a clustering algorithm and to visually display the processing results.

7. A visual data analysis and processing system according to claim 6, characterized in that: The method of processing the obtained execution results by using a clustering algorithm and visually displaying the processing results includes: Extracting performance indicators from the obtained execution results, and combining the performance indicators to obtain a performance indicator data set; The parameter configuration of the clustering algorithm is preset, and the clustering algorithm is used to perform clustering analysis on the performance indicator data set to obtain the set of all clusters; Analyze the center point and number of members of each cluster in the collection, and identify and eliminate abnormal clusters based on preset standards; Based on the eliminated clusters, the clustering results are visualized using visualization tools.

8. A visual 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 execution file processing and packaging module, a backend loader processing module, a plug-in creation and calling module and a component management and monitoring module; The data encryption processing module is used to receive the user code and the execution file, and encrypt the user code and the execution file using an encryption algorithm to obtain the encrypted user code and the execution file; The user code processing and registration module is used to process the encrypted user code, generate a user code component, and register the user code component through the backend loader; The execution file processing and packaging module is used to process the encrypted execution file, generate an execution file component in combination with a preset template, and register the execution file component through the backend loader; The back-end loader processing module is used to load the encrypted user code and execution file through the back-end loader and execute the corresponding loading logic; The plug-in creation and calling module is used to create a plug-in according to the registration information, and use the plug-in to call the user code component and the execution file component for processing; The component management and monitoring module is used to view registration information, manage user code components and execution file components according to the registration information, and monitor the execution process of the user code components and execution file components.

9. A visual data analysis and processing system according to claim 8, characterized in that: The receiving of the user code and the execution file, and encrypting the user code and the execution file using an encryption algorithm to obtain the encrypted user code and the execution file include: Analyze user code files and execution files to obtain basic information of the files; Determine the file segmentation strategy based on the basic information of the file; Split the user code files and execution files into several data blocks based on the file segmentation strategy; Generate a byte random number according to the key byte length required by the AES algorithm, and use the byte random number as the key; The key is stored in the user code file and the execution file, and the encryption algorithm identifier is recorded; The data block is encrypted in combination with the encryption algorithm identifier and the key to obtain the encrypted user code and execution file.

10. A visual data analysis and processing system according to claim 9, characterized in that: The basic information of the file includes: file size, file format and file type.

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