Data conversion method and device based on artificial intelligence, computer equipment and medium

Abstract

The invention belongs to the technical field of artificial intelligence, and relates to a data conversion method based on artificial intelligence, which is applied to the field of financial science and technology and comprises the following steps: judging whether a data conversion request triggered by a user in a preset interface is received or not; if yes, JSP page codes are extracted from the data conversion request; performing format conversion on the JSP page code to obtain target code data; performing conversion processing on the target code data based on a pre-trained conversion model to generate a Vue page code corresponding to the target code data; performing optimization processing on the Vue page code to obtain a target Vue page code; and sending the target Vue page code to the user. The invention further provides a data conversion device based on artificial intelligence, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the target Vue page code can be stored in a block chain. According to the method, the processing efficiency and the processing accuracy of page conversion of the JSP page code are effectively improved based on the use of the conversion model.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence development technology and financial technology, and in particular to artificial intelligence-based data conversion methods, devices, computer equipment and storage media. Background Art

[0002] In the process of informatization of the financial industry, the stable operation and continuous optimization of business systems are key factors in promoting the development of the industry. In recent years, with the rapid development of information technology and the continuous increase in demand for independent control, the localization of full-stack information technology has become an important trend in the informatization construction of the financial industry. This trend requires that the core business systems of financial institutions must not only achieve independent control of technology, but also continuously improve the performance, flexibility and scalability of the system.

[0003] Currently, many financial institutions are facing an urgent need to migrate old core systems to new architectures. Old core systems are often built based on traditional technology stacks, such as using Oracle databases and JSP page technology. However, with the continuous advancement of technology, the architecture of these old systems can no longer meet the needs of current business development. Therefore, financial institutions need to migrate these old systems to a more advanced and flexible new architecture to support the rapid development and changes of their businesses.

[0004] In the process of migrating the old system to the new architecture, the conversion of JSP page technology has become an urgent problem to be solved. As a back-end rendering page technology, JSP page technology has been widely used in early Web development. However, with the continuous development of front-end technology, modern front-end frameworks such as Vue have gradually become the mainstream choice for front-end development due to their advantages such as componentization, responsive data binding and simplicity of use.

[0005] However, it is not easy to convert a large number of JSP pages into Vue pages. At present, the commonly used conversion method is still manual conversion. Although manual conversion can ensure the flexibility of conversion, it has low processing efficiency, high cost, and it is difficult to guarantee the conversion quality. Summary of the invention

[0006] The purpose of the embodiments of the present application is to propose an artificial intelligence-based data conversion method, device, computer equipment and storage medium to solve the technical problems of low processing efficiency, high cost and difficulty in ensuring conversion quality in the existing manual conversion method used to convert JSP pages into Vue pages.

[0007] In order to solve the above technical problems, the present application embodiment provides a data conversion method based on artificial intelligence, which adopts the following technical solutions:

[0008] Determine whether a data conversion request triggered by a user in a preset interface is received; wherein the data conversion request carries the JSP page code to be converted;

[0009] If so, extracting the JSP page code from the data conversion request;

[0010] Performing format conversion processing on the JSP page code to obtain corresponding target code data;

[0011] The target code data is converted based on a pre-trained conversion model to generate a Vue page code corresponding to the target code data;

[0012] Optimize the Vue page code to obtain the corresponding target Vue page code;

[0013] The target Vue page code is sent to the user.

[0014] Furthermore, the step of optimizing the Vue page code to obtain the corresponding target Vue page code specifically includes:

[0015] Perform code structure optimization processing on the Vue page code to obtain corresponding first code data;

[0016] Performing component sequence adjustment processing on the first code data to obtain corresponding second code data;

[0017] Performing tuning processing on the second code data based on a preset tuning strategy to obtain corresponding third code data;

[0018] The third code data is used as the target Vue page code.

[0019] Furthermore, the step of sending the target Vue page code to the user specifically includes:

[0020] Review the target Vue page code based on a preset review strategy;

[0021] If the target Vue page code passes the review, the target Vue page code is tested based on a preset test strategy;

[0022] If the target Vue page code passes the test, the preset target sending method is obtained;

[0023] Based on the target sending mode, the target Vue page code is sent to the user.

[0024] Furthermore, the step of reviewing the target Vue page code based on a preset review strategy specifically includes:

[0025] Get preset code review rules and readability review rules;

[0026] Perform code review on the target Vue page code based on the code review rules;

[0027] If the target Vue page code passes the code review, then the readability review of the target Vue page code is performed based on the readability review rules;

[0028] If the target Vue page code passes the readability review, it is determined that the target Vue page code passes the review, otherwise it is determined that the target Vue page code fails the review.

[0029] Furthermore, the step of testing the target Vue page code based on a preset test strategy specifically includes:

[0030] Obtain preset functional test rules, compatibility test rules, and performance test rules;

[0031] Perform functional testing on the target Vue page code based on the functional testing rules;

[0032] If the target Vue page code passes the functional test, a compatibility test is performed on the target Vue page code based on the compatibility test rule;

[0033] If the target Vue page code passes the compatibility test, a performance test is performed on the target Vue page code based on the performance test rules;

[0034] If the target Vue page code passes the performance test, it is determined that the target Vue page code passes the test, otherwise it is determined that the target Vue page code fails the test.

[0035] Furthermore, before the step of converting the target code data based on the pre-trained conversion model to generate the Vue page code corresponding to the target code data, it also includes:

[0036] Acquire the pre-collected initial JSP page code data and the initial Vue page code data corresponding to the initial JSP page code data;

[0037] Parsing the initial JSP page code data and the initial Vue page code data to obtain corresponding JSP page information and Vue page information;

[0038] Perform feature vectorization processing on the JSP page information and the Vue page information to obtain corresponding sample data;

[0039] Call the preset initial model and obtain the preset optimization strategy;

[0040] Based on the optimization strategy, the initial model is trained using the sample data to obtain a specified model that meets the preset model building requirements;

[0041] The specified model is used as the conversion model.

[0042] Furthermore, the step of performing feature vectorization processing on the JSP page information and the Vue page information to obtain corresponding sample data specifically includes:

[0043] Preprocess the JSP page information and the Vue page information respectively to obtain corresponding specified JSP page information and specified Vue page information;

[0044] Get preset feature type information;

[0045] Based on the feature type information, feature selection processing is performed on the specified JSP page information and the specified Vue page information to obtain corresponding specified features;

[0046] Constructing a corresponding feature vector based on the specified feature;

[0047] The sample data is generated based on the feature vector.

[0048] In order to solve the above technical problems, the embodiment of the present application also provides a data conversion device based on artificial intelligence, which adopts the following technical solution:

[0049] A judgment module, used to judge whether a data conversion request triggered by a user in a preset interface is received; wherein the data conversion request carries the JSP page code to be converted;

[0050] An extraction module, for extracting the JSP page code from the data conversion request if yes;

[0051] The first processing module is used to perform format conversion processing on the JSP page code to obtain corresponding target code data;

[0052] A conversion module, used to convert the target code data based on a pre-trained conversion model to generate a Vue page code corresponding to the target code data;

[0053] An optimization module, used to optimize the Vue page code to obtain the corresponding target Vue page code;

[0054] A sending module is used to send the target Vue page code to the user.

[0055] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0056] Determine whether a data conversion request triggered by a user in a preset interface is received; wherein the data conversion request carries the JSP page code to be converted;

[0057] If so, extracting the JSP page code from the data conversion request;

[0058] Performing format conversion processing on the JSP page code to obtain corresponding target code data;

[0059] The target code data is converted based on a pre-trained conversion model to generate a Vue page code corresponding to the target code data;

[0060] Optimize the Vue page code to obtain the corresponding target Vue page code;

[0061] The target Vue page code is sent to the user.

[0062] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0063] Determine whether a data conversion request triggered by a user in a preset interface is received; wherein the data conversion request carries the JSP page code to be converted;

[0064] If so, extracting the JSP page code from the data conversion request;

[0065] Performing format conversion processing on the JSP page code to obtain corresponding target code data;

[0066] The target code data is converted based on a pre-trained conversion model to generate a Vue page code corresponding to the target code data;

[0067] Optimize the Vue page code to obtain the corresponding target Vue page code;

[0068] The target Vue page code is sent to the user.

[0069] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0070] This application determines whether a data conversion request triggered by a user in a preset interface is received; wherein the data conversion request carries the JSP page code to be converted; if so, the JSP page code is extracted from the data conversion request; then the JSP page code is formatted and processed to obtain the corresponding target code data; then the target code data is converted and processed based on a pre-trained conversion model to generate a Vue page code corresponding to the target code data; subsequently, the Vue page code is optimized and processed to obtain the corresponding target Vue page code; and finally, the target Vue page code is sent to the user. This application extracts the JSP page code from the data conversion request triggered by the user in the preset interface, and performs format conversion processing on the JSP page code to obtain the target code data, and then converts the target code data based on the use of the conversion model, so as to quickly and intelligently realize the automatic conversion of the JSP page to the Vue page, effectively improve the processing efficiency of the page conversion of the JSP page code, and save a lot of manpower costs. In addition, the Vue page code will be further optimized, so that the quality of the generated target Vue page code can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0072] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0073] Figure 2 A flowchart of an embodiment of an artificial intelligence-based data conversion method according to the present application;

[0074] Figure 3 is a structural schematic diagram of an embodiment of an artificial intelligence-based data conversion device according to the present application;

[0075] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0076] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0077] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0078] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0079] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0080] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0081] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to the laptop computer 1011, tablet computer 1012 or mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV), a laptop computer, a desktop computer, etc.

[0082] The server 103 may be a server that provides various services, such as a background server that provides support for a web page displayed on the terminal device 101 .

[0083] It should be noted that the artificial intelligence-based data conversion method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the artificial intelligence-based data conversion device is generally set in the server / terminal device.

[0084] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0085] Continue to refer Figure 2 , showing a flow chart of an embodiment of an artificial intelligence-based data conversion method according to the present application. According to different requirements, the order of the steps in the flow chart can be changed, and some steps can be omitted. The artificial intelligence-based data conversion method provided in the embodiment of the present application can be applied to any scenario where data conversion is required, and the artificial intelligence-based data conversion method can be applied to products in these scenarios, for example, page conversion in the financial insurance field. The artificial intelligence-based data conversion method comprises the following steps:

[0086] Step S201, determining whether a data conversion request triggered by a user in a preset interface is received; wherein the data conversion request carries the JSP page code to be converted.

[0087] In this embodiment, the data conversion method based on artificial intelligence is executed on the electronic device (e.g. Figure 1The server / terminal device shown in the figure) can obtain the data conversion request through a wired connection or a wireless connection. It should be noted that the above-mentioned wireless connection method may include but is not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future. The executor of the present application may be a business system with a page conversion function, or simply referred to as a system. The above-mentioned preset interface may be a system interface, such as a web form, a desktop application, etc. The above-mentioned user data conversion request may be a request triggered by the user in the preset interface to convert the input JSP page code to be converted into a corresponding Vue page code processing request. In the business scenario of finance and insurance, the above-mentioned business system may be an insurance system, a banking system, a payment system, and the like.

[0088] Step S202: If yes, extract the JSP page code from the data conversion request.

[0089] In this embodiment, the data conversion request may be parsed to extract the JSP page code from the data conversion request.

[0090] Step S203, performing format conversion processing on the JSP page code to obtain corresponding target code data.

[0091] In this embodiment, the format conversion process may refer to the encoding process of the conversion model, that is, the JSP page code is encoded by using the conversion model to convert it into an internal representation form, thereby obtaining the corresponding target code data. The internal representation form may be vector-based, such as word embedding.

[0092] Step S204: convert the target code data based on a pre-trained conversion model to generate a Vue page code corresponding to the target code data.

[0093] In this embodiment, the target code data in the internal representation can be decoded by using a pre-trained conversion model to generate the corresponding Vue page code. The decoding process involves a series of complex operations, such as syntax generation, semantic understanding, contextual reasoning, etc. During the decoding process, the conversion model uses the knowledge learned in the training phase to convert the structure, data binding, event processing and other features of the JSP page into the corresponding features of the Vue page. The specific construction process of the above-mentioned conversion model will be further described in detail in the subsequent specific embodiments of this application, and will not be elaborated on here.

[0094] Step S205, optimizing the Vue page code to obtain the corresponding target Vue page code.

[0095] In this embodiment, the above-mentioned optimization process of the Vue page code to obtain the corresponding target Vue page code will be further described in detail in the subsequent specific embodiments of this application, and will not be elaborated here.

[0096] Step S206, sending the target Vue page code to the user.

[0097] In this embodiment, the specific implementation process of sending the target Vue page code to the user will be further described in detail in subsequent specific embodiments of the present application, and will not be elaborated on here.

[0098] This application determines whether a data conversion request triggered by a user in a preset interface is received; wherein the data conversion request carries the JSP page code to be converted; if so, the JSP page code is extracted from the data conversion request; then the JSP page code is formatted and processed to obtain the corresponding target code data; then the target code data is converted and processed based on a pre-trained conversion model to generate a Vue page code corresponding to the target code data; subsequently, the Vue page code is optimized and processed to obtain the corresponding target Vue page code; and finally, the target Vue page code is sent to the user. This application extracts the JSP page code from the data conversion request triggered by the user in the preset interface, and performs format conversion processing on the JSP page code to obtain the target code data, and then converts the target code data based on the use of the conversion model, so as to quickly and intelligently realize the automatic conversion of the JSP page to the Vue page, effectively improve the processing efficiency of the page conversion of the JSP page code, and save a lot of manpower costs. In addition, the Vue page code will be further optimized, so that the quality of the generated target Vue page code can be effectively improved.

[0099] In some optional implementations, step S205 includes the following steps:

[0100] The Vue page code is subjected to code structure optimization processing to obtain corresponding first code data.

[0101] In this embodiment, the above code structure optimization process may include: structural optimization of the generated Vue page code, such as merging repeated styles, removing unnecessary JavaScript code, adjusting the nesting relationship of HTML tags, and the like.

[0102] The first code data is subjected to component sequence adjustment processing to obtain corresponding second code data.

[0103] In this embodiment, the above-mentioned component sequence adjustment process may include: sorting and grouping the generated components according to the component development principle of Vue to ensure the clarity and maintainability of the code.

[0104] The second code data is tuned based on a preset tuning strategy to obtain corresponding third code data.

[0105] In this embodiment, the policy content of the above-mentioned tuning policy may include performing code tuning processing by adding comments, formatting codes, using consistent naming conventions, and other methods.

[0106] The third code data is used as the target Vue page code.

[0107] The present application performs code structure optimization processing on the Vue page code to obtain corresponding first code data; then performs component order adjustment processing on the first code data to obtain corresponding second code data; then performs tuning processing on the second code data based on a preset tuning strategy to obtain corresponding third code data; and subsequently uses the third code data as the target Vue page code. The present application performs code structure optimization processing, component order adjustment processing, and tuning processing on the Vue page code, thereby achieving fast and accurate optimization processing of the Vue page code, effectively improving the code quality and readability of the generated target Vue page code.

[0108] In some optional implementations of this embodiment, step S205 includes the following steps:

[0109] The target Vue page code is reviewed based on a preset review strategy.

[0110] In this embodiment, the specific implementation process of reviewing the target Vue page code based on the preset review strategy will be further described in detail in subsequent specific embodiments of the present application, and will not be elaborated on here.

[0111] If the target Vue page code passes the review, the target Vue page code is tested based on a preset test strategy.

[0112] In this embodiment, the specific implementation process of testing the target Vue page code based on the preset test strategy will be further described in detail in subsequent specific embodiments of the present application, and will not be elaborated on here.

[0113] If the target Vue page code passes the test, the preset target sending method is obtained.

[0114] In this embodiment, there is no specific limitation on the selection of the above-mentioned target sending method, which can be determined according to actual business needs. For example, the sending methods such as interface display, SMS sending, and email sending can be used.

[0115] Based on the target sending mode, the target Vue page code is sent to the user.

[0116] In this embodiment, the above-mentioned target sending method can be used to send the target Vue page code to the user.

[0117] This application reviews the target Vue page code based on a preset review strategy; if the target Vue page code passes the review, the target Vue page code is tested based on a preset test strategy; if the target Vue page code passes the test, the preset target sending method is obtained; and the target Vue page code is subsequently sent to the user based on the target sending method. Before sending the target Vue page code to the user, this application will automatically and intelligently review the target Vue page code based on the use of the review strategy, and test the target Vue page code based on the use of the test strategy, effectively ensuring that the generated target Vue page code meets the expected quality and readability requirements. And later, after detecting that the target Vue page code has passed the review and test at the same time, the target Vue page code will be sent to the user, which improves the accuracy of sending the target Vue page code, which is conducive to improving user satisfaction and thus improving the success rate of the project.

[0118] In some optional implementations, the review process of the target Vue page code based on a preset review strategy includes the following steps:

[0119] Get the preset code review rules and readability review rules.

[0120] In this embodiment, the procedural content of the above-mentioned code review rules may include: conducting structural review, style review, logic review and security review. Among them, the structural review may include: checking whether the division of Vue components is reasonable and whether the dependencies between components are clear. The style review may include: ensuring that the CSS style complies with the coding specifications of the project and there are no redundant or conflicting styles. The logic review may include: verifying whether the JavaScript logic correctly implements the functions of the JSP page, including data binding, event handling, and routing navigation. The security review may include: checking whether the code has common security vulnerabilities, such as XSS, CSRF, etc.

[0121] The readability review rules may include: conducting naming standard review, comment review, and format review. Among them, the naming standard review may include: ensuring that the names of variables, functions, components, etc. are clear and easy to understand. The comment review may include: checking whether there are enough comments in the code to explain complex logic or algorithms. The format review may include: ensuring that the code format is unified and complies with the project's coding standards.

[0122] The target Vue page code is code reviewed based on the code review rules.

[0123] In this embodiment, the target Vue page code may be code reviewed according to the rule content of the above code review rules, thereby obtaining a corresponding code review result. The code review result may include that the target Vue page code passes the code review, or that the target Vue page code fails the code review.

[0124] If the target Vue page code passes the code review, a readability review is performed on the target Vue page code based on the readability review rules.

[0125] In this embodiment, the readability review of the target Vue page code can be performed according to the rule content of the above readability review rules, so as to obtain a corresponding readability review result. Among them, the readability review result may include: the target Vue page code passes the readability review, or the target Vue page code fails the readability review.

[0126] If the target Vue page code passes the readability review, it is determined that the target Vue page code passes the review, otherwise it is determined that the target Vue page code fails the review.

[0127] This application obtains preset code review rules and readability review rules; then performs code review on the target Vue page code based on the code review rules; if the target Vue page code passes the code review, then performs readability review on the target Vue page code based on the readability review rules; if the target Vue page code passes the readability review, then the target Vue page code is determined to have passed the review, otherwise the target Vue page code is determined to have failed the review. This application performs code review on the target Vue page code based on the use of code review rules, and performs readability review on the target Vue page code based on the use of readability review rules, so as to achieve fast and accurate completion of the review process for the target Vue page code, thereby improving the processing efficiency and accuracy of the review process. And only when it is detected that the target Vue page code has passed both the code review and the readability review, will the target Vue page code be determined to have passed the review, effectively ensuring the accuracy of the review result of the generated target Vue page code.

[0128] In some optional implementations, the testing process of the target Vue page code based on a preset testing strategy includes the following steps:

[0129] Get preset functional test rules, compatibility test rules, and performance test rules.

[0130] In this embodiment, the rule content of the above-mentioned functional test rules may include: using automated testing tools to write test cases and perform automated testing on Vue pages. The rule content of the above-mentioned compatibility test rules may include: testing browser compatibility and testing device compatibility. Among them, testing browser compatibility includes: testing Vue pages on different browsers to ensure that they can work properly in different environments. Testing device compatibility includes: testing Vue pages on different devices (such as mobile phones, tablets, desktop computers, etc.) to ensure that they can be displayed well under different screen sizes and resolutions. The above-mentioned performance testing rules may include: testing loading speed and testing resource consumption. Among them, testing loading speed may include: testing the loading speed of Vue pages to ensure that they are loaded within a reasonable time. Testing resource consumption may include: monitoring the consumption of resources such as CPU and memory by Vue pages during runtime to ensure that they do not consume excessive resources and cause device freezes or crashes.

[0131] Perform functional testing on the target Vue page code based on the functional testing rules.

[0132] In this embodiment, the target Vue page code may be functionally tested according to the rule content of the above functional test rule, thereby obtaining a corresponding functional test result. Among them, the content of the functional test result may include that the target Vue page code passes the functional test, or that the target Vue page code fails the functional test.

[0133] If the target Vue page code passes the functional test, a compatibility test is performed on the target Vue page code based on the compatibility test rules.

[0134] In this embodiment, the target Vue page code may be subjected to a compatibility test according to the content of the compatibility test rules, thereby obtaining a corresponding compatibility test result. The content of the functional test result may include that the target Vue page code passes the compatibility test, or that the target Vue page code fails the compatibility test.

[0135] If the target Vue page code passes the compatibility test, a performance test is performed on the target Vue page code based on the performance test rules.

[0136] In this embodiment, the target Vue page code may be subjected to a performance test according to the rule content of the above-mentioned performance test rule, thereby obtaining a corresponding performance test result. Among them, the content of the functional test result may include that the target Vue page code passes the performance test, or that the target Vue page code fails the performance test.

[0137] If the target Vue page code passes the performance test, it is determined that the target Vue page code passes the test, otherwise it is determined that the target Vue page code fails the test.

[0138] This application obtains preset functional test rules, compatibility test rules and performance test rules; then performs functional test on the target Vue page code based on the functional test rules; if the target Vue page code passes the functional test, the target Vue page code is subjected to compatibility test based on the compatibility test rules; if the target Vue page code passes the compatibility test, the target Vue page code is subjected to performance test based on the performance test rules; if the target Vue page code passes the performance test, the target Vue page code is determined to pass the test, otherwise the target Vue page code is determined to fail the test. This application performs functional test on the target Vue page code based on the use of functional test rules, performs compatibility test on the target Vue page code based on the use of compatibility test rules, and performs performance test on the target Vue page code based on the use of performance test rules, so as to achieve fast and accurate completion of the test processing for the target Vue page code, and improve the processing efficiency and processing accuracy of the test processing. And only when it is detected that the target Vue page code has passed the functional test, compatibility test and performance test, will the target Vue page code be determined to pass the test, effectively ensuring the accuracy of the test results of the generated target Vue page code.

[0139] In some optional implementations of this embodiment, before step S204, the electronic device may further perform the following steps:

[0140] Acquire the pre-collected initial JSP page code data and the initial Vue page code data corresponding to the initial JSP page code data.

[0141] In this embodiment, a reliable data source of JSP page code and Vue page code can be determined, which can be an internal enterprise project library, an open source project, a code hosting platform, etc. Then, representative JSP and Vue page code pairs (i.e., initial JSP page code data, and initial Vue page code data corresponding to the initial JSP page code data) are screened out from the reliable data source, and it is ensured that they can cover common page structures and functions.

[0142] The initial JSP page code data and the initial Vue page code data are parsed to obtain corresponding JSP page information and Vue page information.

[0143] In this embodiment, the JSP page code can be parsed using a suitable parsing tool to extract information such as page structure, data binding, event processing, etc., and JSP element recognition can be performed to obtain elements including HTML / CSS / JavaScript, JSP instructions, JSP script elements, JSP action elements, EL expressions, custom tags, etc., and then integrated to obtain corresponding JSP page information. And the Vue page code can be parsed using a suitable parsing tool to extract information such as component definition, data binding, routing configuration, etc., and then integrated to obtain corresponding Vue page information.

[0144] The JSP page information and the Vue page information are subjected to feature vectorization processing to obtain corresponding sample data.

[0145] In this embodiment, the above-mentioned feature vectorization processing of the JSP page information and the Vue page information to obtain the specific implementation process of the corresponding sample data will be further described in detail in the subsequent specific embodiments of this application, and will not be elaborated on here.

[0146] Call the preset initial model and obtain the preset optimization strategy.

[0147] In this embodiment, the above initial model can be constructed using machine learning or deep learning algorithms, preferably using a random forest algorithm, which is an ensemble learning algorithm based on decision trees. It combines multiple decision trees to improve the accuracy and stability of the model. It can be used to build a model for automatic conversion of JSP to Vue, and improve the accuracy of prediction by analyzing multiple factors related to it. The following is the formula of the random forest algorithm:

[0148] 1. Build a decision tree. F = {f1, f2, ..., fN}, where f1, f2, ..., fN are each decision tree in the decision tree, and each decision tree is based on the construction and extraction of features of other decision trees.

[0149] 2. Calculate the average and standard error. E = (1 / N) * sum (F^2), where E represents the average and standard error of the decision tree, 1 / N represents the number of calculations for each decision tree, and sum represents the summation operation.

[0150] 3. Random split. X_k = X*f_k / N, where X_k represents the input vector of the kth feature, f_k is the output vector of the kth decision tree, and N is the number of features.

[0151] 4. Calculate the model evaluation index. mse = sqrt(E / N), where mse represents the mean absolute error of the model. E / N represents the number of calculations for each feature divided by the number of features.

[0152] 5. Model training. M = (X_1, X_2, ..., X_N) * (X_1^T * X_2^T, X_1*X_2^T, ..., X_N*X_1^T), where M represents the input vector of the model. The random forest algorithm trains the model through random splits and uses the model to make predictions on the training set.

[0153] 6. Model evaluation. Y_pred = prediction (M), where prediction means using the model to make predictions on the test set.

[0154] In addition, the above optimization strategies may include: adjusting model parameters through back propagation algorithm, using regularization, Dropout and other techniques to prevent overfitting, and using the optimization algorithm Adam to optimize the training process and improve convergence speed and accuracy.

[0155] Based on the optimization strategy, the initial model is trained using the sample data to obtain a specified model that meets the preset model building requirements.

[0156] In this embodiment, the initial model can be trained by using sample data, and the model parameters can be adjusted by the back propagation algorithm so that the model can accurately convert the JSP page code into the Vue page code; regularization, Dropout and other technologies are used to prevent overfitting; the optimization algorithm Adam is used to optimize the training process, and the grid search or random search methods provided by scikit-learn are used to automatically adjust the model parameters to find the optimal model configuration. Then, the performance indicators in the training process, such as accuracy, loss function value, etc., and the performance changes on the validation set are monitored to ensure that the model is not overfitting or underfitting, so as to obtain a specified model that meets the model construction requirements.

[0157] The specified model is used as the conversion model.

[0158] In this embodiment, the converted Vue page can also be tested using a test data set to verify the correctness and completeness of its functions; then the test results are counted and analyzed to evaluate the conversion effect of the conversion model; and the conversion model is further optimized and improved based on the test results. The test data set can be composed of data of a specified proportion randomly selected from the sample data. There is no limitation on the selection of the specified proportion, for example, 0.3 can be used.

[0159] The present application obtains pre-collected initial JSP page code data and initial Vue page code data corresponding to the initial JSP page code data; then parses and processes the initial JSP page code data and the initial Vue page code data to obtain corresponding JSP page information and Vue page information; then performs feature vectorization processing on the JSP page information and the Vue page information to obtain corresponding sample data; subsequently calls a preset initial model and obtains a preset optimization strategy; finally, based on the optimization strategy, uses the sample data to train the initial model to obtain a specified model that meets the preset model building requirements, and uses the specified model as the conversion model, thereby automatically and quickly completing the construction process of the conversion model, effectively improving the construction efficiency of the conversion model, and effectively ensuring the model effect of the generated conversion model.

[0160] In some optional implementations of this embodiment, performing feature vectorization processing on the JSP page information and the Vue page information to obtain corresponding sample data includes the following steps:

[0161] The JSP page information and the Vue page information are preprocessed respectively to obtain corresponding designated JSP page information and designated Vue page information.

[0162] In this embodiment, the above-mentioned preprocessing may include: data cleaning, data labeling, data formatting and other processing. Among them, data cleaning may include: removing irrelevant information, such as comments, blank characters, etc., to ensure the neatness of the data. Data labeling may include: labeling the extracted information, such as labeling the EL expression in the JSP page as data binding, labeling the v-bind in the Vue page as data binding, etc. Data formatting may include: converting the extracted information into a unified format, such as JSON or XML, for subsequent processing.

[0163] Get the preset feature type information.

[0164] In this embodiment, the above-mentioned feature type information may at least include JSP page feature type and Vue page feature type. Among them, the JSP page feature type specifically includes the following features. 1) Page structure features: HTML tag type, tag nesting relationship, attribute type and quantity, etc. 2) Data binding features: EL expression position, binding variable name, binding type (such as text, attribute, event, etc.). 3) Event processing features: event type (such as click, input, etc.), event processing function name. 4) JSP element features: JSP instruction type, script element type, action element type, custom tag name, etc.

[0165] In addition, the Vue page feature types specifically include the following features. 1) Component features: component name, component type (global / local), component properties, slot usage, etc. 2) Data binding features: Vue data binding syntax (such as v-bind, v-model), binding variable name, binding expression. 3) Routing features: routing path, routing component mapping relationship. 4) Style and script features: CSS selector, JavaScript function name, variable name, etc.

[0166] Based on the feature type information, feature selection processing is performed on the specified JSP page information and the specified Vue page information to obtain corresponding specified features.

[0167] In this embodiment, feature selection processing can be performed on the specified JSP page information and the specified Vue page information according to the above feature type information, that is, features matching the feature type information are filtered out from the specified JSP page information and the specified Vue page information to obtain the above specified features.

[0168] A corresponding feature vector is constructed based on the specified feature.

[0169] In this embodiment, a corresponding feature vector may be constructed according to the obtained specified features for use as input to the model.

[0170] The sample data is generated based on the feature vector.

[0171] In this embodiment, the feature vector can be directly used as the sample data. The sample data is data containing a pair of JSP and Vue page codes.

[0172] The present application obtains the corresponding designated JSP page information and designated Vue page information by preprocessing the JSP page information and the Vue page information respectively; then obtains the preset feature type information; then performs feature selection processing on the designated JSP page information and the designated Vue page information based on the feature type information to obtain the corresponding designated features; subsequently constructs the corresponding feature vector based on the designated features; and finally generates the sample data based on the feature vector. The present application obtains the designated JSP page information and the designated Vue page information by preprocessing the JSP page information and the Vue page information respectively; then performs feature selection processing on the designated JSP page information and the designated Vue page information based on the acquired feature type information to obtain the designated features; and then based on the designated features, it is possible to quickly and intelligently construct high-quality sample data, effectively improving the efficiency of sample data construction, and providing reliable data support for subsequent model construction.

[0173] In some optional implementations, the user information obtained is subject to the user's consent and complies with relevant laws and policies.

[0174] In addition, the non-Company software tools or components that appear in the embodiments of the present application are merely examples and do not represent actual use.

[0175] This application provides a method for automatically converting JSP pages to Vue pages, which can greatly improve the efficiency and quality of front-end development. This method can be integrated into front-end development tools or platforms to provide developers with a one-click JSP page to Vue page function.

[0176] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0177] It should be emphasized that in order to further ensure the privacy and security of the above-mentioned target Vue page code, the above-mentioned target Vue page code can also be stored in a node of a blockchain.

[0178] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0179] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0180] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0181] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0182] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0183] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of a data conversion device based on artificial intelligence, and the device embodiment is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0184] like Figure 3 As shown, the artificial intelligence-based data conversion device 300 described in this embodiment includes: a judgment module 301, an extraction module 302, a first processing module 303, a conversion module 304, an optimization module 305 and a sending module 306. Among them:

[0185] The judgment module 301 is used to judge whether a data conversion request triggered by a user in a preset interface is received; wherein the data conversion request carries the JSP page code to be converted;

[0186] An extraction module 302 is used to extract the JSP page code from the data conversion request;

[0187] The first processing module 303 is used to perform format conversion processing on the JSP page code to obtain corresponding target code data;

[0188] The conversion module 304 is used to convert the target code data based on a pre-trained conversion model to generate a Vue page code corresponding to the target code data;

[0189] The optimization module 305 is used to optimize the Vue page code to obtain the corresponding target Vue page code;

[0190] The sending module 306 is used to send the target Vue page code to the user.

[0191] In this embodiment, the operations performed by the above modules or units correspond one-to-one to the steps of the artificial intelligence-based data conversion method in the aforementioned implementation mode, and are not repeated here.

[0192] In some optional implementations of this embodiment, the optimization module 305 includes:

[0193] A first processing submodule is used to perform code structure optimization processing on the Vue page code to obtain corresponding first code data;

[0194] A second processing submodule, configured to perform component sequence adjustment processing on the first code data to obtain corresponding second code data;

[0195] A third processing submodule, configured to perform tuning processing on the second code data based on a preset tuning strategy to obtain corresponding third code data;

[0196] Determine a submodule for using the third code data as the target Vue page code.

[0197] In some optional implementations of this embodiment, the sending module 306 includes:

[0198] The review submodule is used to review the target Vue page code based on a preset review strategy;

[0199] A test submodule, used for testing the target Vue page code based on a preset test strategy if the target Vue page code passes the review;

[0200] The first acquisition submodule is used to obtain a preset target sending method if the target Vue page code passes the test;

[0201] The sending submodule is used to send the target Vue page code to the user based on the target sending method.

[0202] In some optional implementations of this embodiment, the review submodule includes:

[0203] A first acquisition unit is used to acquire preset code review rules and readability review rules;

[0204] A first review unit, configured to perform code review on the target Vue page code based on the code review rule;

[0205] A second review unit is used to perform a readability review on the target Vue page code based on the readability review rule if the target Vue page code passes the code review;

[0206] The first determination unit is used to determine that the target Vue page code has passed the review if the target Vue page code has passed the readability review, and otherwise determine that the target Vue page code has not passed the review.

[0207] In some optional implementations of this embodiment, the test submodule includes:

[0208] A second acquisition unit, used to acquire preset functional test rules, compatibility test rules and performance test rules;

[0209] A first testing unit, used to perform a functional test on the target Vue page code based on the functional testing rule;

[0210] A second testing unit is used to perform a compatibility test on the target Vue page code based on the compatibility test rule if the target Vue page code passes the functional test;

[0211] A third testing unit is used to perform a performance test on the target Vue page code based on the performance testing rule if the target Vue page code passes the compatibility test;

[0212] The second determination unit is used to determine that the target Vue page code has passed the test if the target Vue page code has passed the performance test, and otherwise determine that the target Vue page code has failed the test.

[0213] In some optional implementations of this embodiment, the data conversion device based on artificial intelligence further includes:

[0214] An acquisition module, used to acquire pre-collected initial JSP page code data and initial Vue page code data corresponding to the initial JSP page code data;

[0215] A parsing module is used to parse the initial JSP page code data and the initial Vue page code data to obtain corresponding JSP page information and Vue page information;

[0216] The second processing module is used to perform feature vectorization processing on the JSP page information and the Vue page information to obtain corresponding sample data;

[0217] A calling module is used to call the preset initial model and obtain the preset optimization strategy;

[0218] A training module, used to train the initial model using the sample data based on the optimization strategy to obtain a specified model that meets the preset model building requirements;

[0219] A determination module is used to use the specified model as the conversion model.

[0220] In some optional implementations of this embodiment, the second processing module includes:

[0221] A preprocessing submodule is used to preprocess the JSP page information and the Vue page information respectively to obtain corresponding specified JSP page information and specified Vue page information;

[0222] The second acquisition submodule is used to acquire preset feature type information;

[0223] A selection submodule is used to perform feature selection processing on the specified JSP page information and the specified Vue page information based on the feature type information to obtain corresponding specified features;

[0224] A construction submodule, used to construct a corresponding feature vector based on the specified feature;

[0225] A generating submodule is used to generate the sample data based on the feature vector.

[0226] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0227] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0228] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0229] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of data conversion methods based on artificial intelligence, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0230] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run computer-readable instructions stored in the memory 41 or process data, such as computer-readable instructions for running the artificial intelligence-based data conversion method.

[0231] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0232] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the artificial intelligence-based data conversion method as described above.

[0233] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0234] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A data conversion method based on artificial intelligence, characterized in that: The steps include: Determine whether a data conversion request triggered by a user in a preset interface is received; wherein the data conversion request carries the JSP page code to be converted; If so, extracting the JSP page code from the data conversion request; Performing format conversion processing on the JSP page code to obtain corresponding target code data; The target code data is converted based on a pre-trained conversion model to generate a Vue page code corresponding to the target code data; Optimize the Vue page code to obtain the corresponding target Vue page code; The target Vue page code is sent to the user.

2. The artificial intelligence-based data conversion method according to claim 1, characterized in that: The step of optimizing the Vue page code to obtain the corresponding target Vue page code specifically includes: Perform code structure optimization processing on the Vue page code to obtain corresponding first code data; Performing component sequence adjustment processing on the first code data to obtain corresponding second code data; Performing tuning processing on the second code data based on a preset tuning strategy to obtain corresponding third code data; The third code data is used as the target Vue page code.

3. The data conversion method based on artificial intelligence according to claim 1 is characterized in that: The step of sending the target Vue page code to the user specifically includes: Review the target Vue page code based on a preset review strategy; If the target Vue page code passes the review, the target Vue page code is tested based on a preset test strategy; If the target Vue page code passes the test, the preset target sending method is obtained; Based on the target sending mode, the target Vue page code is sent to the user.

4. The artificial intelligence-based data conversion method according to claim 3, characterized in that: The step of reviewing the target Vue page code based on a preset review strategy specifically includes: Get preset code review rules and readability review rules; Perform code review on the target Vue page code based on the code review rules; If the target Vue page code passes the code review, then the readability review of the target Vue page code is performed based on the readability review rules; If the target Vue page code passes the readability review, it is determined that the target Vue page code passes the review, otherwise it is determined that the target Vue page code fails the review.

5. The artificial intelligence-based data conversion method according to claim 3, characterized in that: The step of testing the target Vue page code based on a preset test strategy specifically includes: Obtain preset functional test rules, compatibility test rules, and performance test rules; Perform functional testing on the target Vue page code based on the functional testing rules; If the target Vue page code passes the functional test, a compatibility test is performed on the target Vue page code based on the compatibility test rule; If the target Vue page code passes the compatibility test, a performance test is performed on the target Vue page code based on the performance test rules; If the target Vue page code passes the performance test, it is determined that the target Vue page code passes the test, otherwise it is determined that the target Vue page code fails the test.

6. The artificial intelligence-based data conversion method according to claim 1, characterized in that: Before the step of converting the target code data based on the pre-trained conversion model to generate a Vue page code corresponding to the target code data, the step further includes: Acquire the pre-collected initial JSP page code data and the initial Vue page code data corresponding to the initial JSP page code data; Parsing the initial JSP page code data and the initial Vue page code data to obtain corresponding JSP page information and Vue page information; Perform feature vectorization processing on the JSP page information and the Vue page information to obtain corresponding sample data; Call the preset initial model and obtain the preset optimization strategy; Based on the optimization strategy, the initial model is trained using the sample data to obtain a specified model that meets the preset model building requirements; The specified model is used as the conversion model.

7. The artificial intelligence-based data conversion method according to claim 6, characterized in that: The step of performing feature vectorization processing on the JSP page information and the Vue page information to obtain corresponding sample data specifically includes: Preprocess the JSP page information and the Vue page information respectively to obtain corresponding specified JSP page information and specified Vue page information; Get preset feature type information; Based on the feature type information, feature selection processing is performed on the specified JSP page information and the specified Vue page information to obtain corresponding specified features; Constructing a corresponding feature vector based on the specified feature; The sample data is generated based on the feature vector.

8. A data conversion device based on artificial intelligence, characterized in that: include: A judgment module, used to judge whether a data conversion request triggered by a user in a preset interface is received; wherein the data conversion request carries the JSP page code to be converted; An extraction module, for extracting the JSP page code from the data conversion request if yes; The first processing module is used to perform format conversion processing on the JSP page code to obtain corresponding target code data; A conversion module, used to convert the target code data based on a pre-trained conversion model to generate a Vue page code corresponding to the target code data; An optimization module, used to optimize the Vue page code to obtain the corresponding target Vue page code; A sending module is used to send the target Vue page code to the user.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the artificial intelligence-based data conversion method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the artificial intelligence-based data conversion method according to any one of claims 1 to 7.

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