CN117312438B

CN117312438B - Data processing methods, electronic devices and readable storage media

Info

Publication number: CN117312438B
Application number: CN202311279860.9A
Authority: CN
Inventors: 李凯; 李宜泽; 刘浩; 潘展; 何苗
Original assignee: Zhongke Yungu Technology Co Ltd
Current assignee: Zhongke Yungu Technology Co Ltd
Priority date: 2023-09-29
Filing date: 2023-09-29
Publication date: 2025-12-02
Anticipated expiration: 2043-09-29
Also published as: CN117312438A

Abstract

This application discloses a data processing method, an electronic device, and a readable storage medium. The data processing method includes: creating a front-end view layer object based on business code, the front-end view layer object including at least one type of natural language; executing rule-defined operations based on the front-end view layer object, generating JSON format data based on the operation results; and parsing the JSON format data to construct a SpEL expression. In this way, SpEL expressions can be automatically generated based on business code, achieving rapid conversion between natural language and SpEL expressions, with simple operation and high visualization.

Description

Data processing method, electronic device and readable storage medium

Technical Field

The present invention relates to the field of computer technologies, and in particular, to a data processing method, an electronic device, and a readable storage medium.

Background

SpEL (Spring Expression Language) is an expression language in Spring Framework. It is a powerful expression language for dynamically accessing and manipulating object diagrams at runtime. SpEL can be used for various purposes such as attribute access, method calls, mathematical operations, logical operations, conditional expressions, and the like. SpEL is similar to the expression languages of other programming languages, and also references the characteristics of some XML and XPath. It allows the values of the expressions to be calculated dynamically at run-time, enabling a more flexible and dynamic programming approach. In Spring Framework SpEL is widely used in configuration files such as XML configuration, annotation configuration, java configuration, etc. By SpEL, expressions can be used in the configuration file to dynamically configure the behavior of the application according to the runtime conditions. Spring Framework is an open source application Framework for building enterprise-level JAVA applications. It provides a series of solutions and tools for simplifying the development process of JAVA applications, which can help developers build maintainable, extensible and efficient applications.

The inventors found in the study that in the prior art, specific rules need to be defined by SpEL expressions for rule execution, spEL expressions need to be input when rules and actions are defined on pages, and natural language input parsing is not supported. However, spEL expressions generally require mastering a variety of keywords and grammar rules, are not easily mastered by ordinary users or programming novices, and are difficult to learn. The SpEL expression is generally a sentence composed of some symbols and keywords, which is not easily understood by non-professionals and clients, and the code readability is poor. The structure and syntax of SpEL expressions are relatively complex, requiring a degree of skill and experience if there are errors that require modification or debugging.

Disclosure of Invention

The application aims to provide a data processing method, a device, a system and a computer readable storage medium, which can automatically generate SpEL expressions based on service codes, realize quick conversion between natural language and SpEL expressions, and have simple operation and high visualization degree.

To achieve the above object:

in a first aspect, an embodiment of the present application provides a data processing method, where the method includes:

creating a front end view layer object according to the business codes, wherein the front end view layer object comprises at least one type of natural language;

performing rule definition operation based on the front end view layer object, and generating JSON format data according to an operation result;

parsing the JSON format data to construct a SpEL expression.

In an embodiment, the creating a front end view layer object according to the business code, wherein the front end view layer object comprises at least one kind of natural language, and the method comprises the following steps:

Acquiring first data and second data in a service code, wherein the first data comprises information of a class in the service code, and the second data comprises Chinese annotation information in the service code;

Generating third data according to the first data and the second data;

generating a front end view layer object according to the third data.

In one embodiment, the method further comprises:

and acquiring the first data and the second data in the service codes through a JAVA reflection mechanism.

In one embodiment, the anomalies in the process of acquiring the first data and the second data in the business code are captured and processed.

In an embodiment, the rule definition operation is performed based on the front end view layer object, and JSON format data is generated according to the operation result, including:

creating a rule tree according to the front end view layer object, wherein the rule tree comprises a definable rule frame;

executing rule definition operation through the definable rule frame to receive business rule data, wherein the business rule data comprises first business rule data and second business rule data, the first business rule data is acquired through the front end view layer object, and the second business rule data is input through a user;

JSON format data is generated based on the front end hierarchy object and the business rule data.

In an embodiment, the rule definition operation includes one or more of an initial value setting, a comparator selection, a selection value type setting, and a match result input.

In an embodiment, the parsing the JSON format data to construct a SpEL expression includes:

Analyzing a data structure according to the JSON data;

Extracting a numerical value in the JSON data according to the data structure;

And splicing the numerical values in a character string splicing mode to construct and form SpEL expression.

In an embodiment, after parsing the JSON format data to construct a SpEL expression, the method further includes:

Parsing the SpEL expression using a SpEL parser and compiling into an evaluable expression object;

And evaluating the expression object to obtain an evaluation result of the expression.

In a second aspect, the embodiment of the application also provides a data processing system, which comprises a first data processing module, a second data processing module and a third data processing module, wherein,

The first data processing module is used for creating a front end view layer object according to the service codes, wherein the front end view layer object comprises at least one type of natural language;

The second data processing module is used for receiving input data based on the front end view layer object to generate JSON format data;

The third data processing module is configured to parse the JSON format data to construct and form SpEL expressions.

In a third aspect, an embodiment of the present application further provides an electronic device, where the electronic device includes a processor, a memory, and a computer program stored on the memory and executable on the processor, where the computer program is executed by the processor to implement the steps of the data processing method described above.

In a fourth aspect, embodiments of the present application provide a computer readable storage medium having a computer program stored therein, which when executed by a processor, implements the steps of the data processing method described above.

The data processing method, device and system and computer readable storage medium provided by the embodiment of the application comprise the steps of creating a front end view layer object according to service codes, wherein the front end view layer object comprises at least one type of natural language, executing rule definition operation based on the front end view layer object, generating JSON format data according to an operation result, and analyzing the JSON format data to construct and form SpEL expression. Therefore, the SpEL expression can be automatically generated based on the service code, the rapid conversion between the natural language and the SpEL expression is realized, the operation is simple, and the visualization degree is high.

Drawings

FIG. 1 is a schematic diagram of a data processing system according to an embodiment of the present invention;

FIG. 2 is a schematic flow chart of data processing performed by the first data processing module according to the embodiment of the present invention;

FIG. 3 is a schematic view of a main interface of a front end view layer object provided by an embodiment of the present invention;

FIG. 4 is a schematic diagram of an operation interface of a front end view layer object according to an embodiment of the present invention;

FIG. 5 is a second schematic view of an operation interface of a front end view layer object according to an embodiment of the present invention;

FIG. 6 is a third view of an operator interface of a front end view layer object according to an embodiment of the present invention;

FIG. 7 is a diagram illustrating an operation interface of a front end view layer object according to an embodiment of the present invention;

FIG. 8 is a fifth view of an operator interface for a front end view layer object according to an embodiment of the present invention;

FIG. 9 is a schematic diagram of a structure of stored data according to an embodiment of the present invention;

Fig. 10 is a flowchart of a data processing method according to an embodiment of the present invention.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the element(s) defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other like elements in different embodiments of the application having the same meaning as may be defined by the same meaning as they are explained in this particular embodiment or by further reference to the context of this particular embodiment.

It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope herein. The term "if" as used herein may be interpreted as "at..once" or "when..once" or "in response to a determination", depending on the context. Furthermore, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes," and/or "including" specify the presence of stated features, steps, operations, elements, components, items, categories, and/or groups, but do not preclude the presence, presence or addition of one or more other features, steps, operations, elements, components, items, categories, and/or groups. The terms "or" and/or "as used herein are to be construed as inclusive, or meaning any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of A, B, C, A and B, A and C, B and C, A, B and C". An exception to this definition will occur only when a combination of elements, functions, steps or operations are in some way inherently mutually exclusive.

It should be understood that, although the steps in the flowcharts in the embodiments of the present application are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the figures may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily occurring in sequence, but may be performed alternately or alternately with other steps or at least a portion of the other steps or stages.

It should be noted that, in this document, step numbers such as S210 and S220 are adopted, and the purpose of the present application is to more clearly and briefly describe the corresponding content, and not to constitute a substantial limitation on the sequence, and those skilled in the art may execute S220 first and then S210 when implementing the present application, which is within the scope of protection of the present application.

It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

In the following description, suffixes such as "module", "part" or "unit" for representing elements are used only for facilitating the description of the present application, and have no specific meaning per se. Thus, "module," "component," or "unit" may be used in combination.

First embodiment

Referring to fig. 1, a data processing system provided in the present application includes a first data processing module, a second data processing module, and a third data processing module. The main functions of the first data processing module are business code culture based on a reflection mechanism and automatic construction of front end view layer objects. The main function of the second data processing module is to edit an image interface and store data for the front end view layer object constructed by the first data processing module. The main function of the third data processing module is to convert the data stored by the second data processing module into SpEL expressions.

In the first data processing module, performing a middle culture process on all classes, attributes, methods and the like in the business codes, and automatically constructing a front end view layer object. It should be noted that the front end view layer is the portion of the Web or mobile application with which the user interacts directly. A front end view object refers to a collection of elements and components that make up a visual interface. In the front end framework or library, the developer uses components, elements, etc. to build the various parts of the page. These view layer objects may be buttons, input boxes, images, paragraphs, and the like.

As shown in FIG. 2, in a first data processing module, a developer can use annotations to mark and specify Chinese names, such as @ ZBomType ("Chinese names"), on classes, attributes, methods, parameters. During system initialization, class information (i.e., first data) may be obtained through a reflection mechanism, including, but not limited to, class names, attributes, methods, and constructors. Chinese annotation information (i.e., second data) in the business code may also be obtained through a reflection mechanism. In Java, the reflection mechanism allows a program to obtain type information and manipulate classes, interfaces, fields, methods, constructors, etc. at runtime. Objects, calling methods, access attributes, obtaining information of classes, etc. can be dynamically created at runtime through reflection mechanisms.

Then, after the custom annotation is completed and the first data and the second data are acquired through the reflection mechanism, the acquired data are subjected to a middle culture process. Specifically, the names of the classes, attributes, methods may be added to the corresponding chinese names according to the chinese names specified in the annotations. Thus, the first data processing module dynamically acquires information such as class, attribute, method and the like in the service code by using a reflection mechanism, and automatically performs Chinese culture processing on the information according to the notes added on the class, the method and the parameters to obtain third data.

And finally, constructing a front end view layer object according to the third data such as the class, the attribute, the method and the like after the Chinese culture, and converting the third data into a JSON format to be transmitted to the front end.

In an embodiment, the first data processing module may also implement exception handling. By capturing and handling anomalies that may occur in reflection operations, such as class absence, method absence, etc., more friendly error cues and a more stable and robust operating environment are provided, providing reliability of the code.

In one embodiment, the first data processing module employs a caching mechanism to improve performance. After the class information is acquired and the middle culture processing is performed, the processing result can be cached. When the same class is used next time, the information of the class of the culture can be directly obtained from the cache, and repeated execution of the reflecting operation is avoided, so that the response speed and the performance of the system are improved.

In one embodiment, the first data processing module can dynamically acquire the currently callable method and the accessible attribute information through the reflection mechanism and the annotation, and provide the currently callable method and the accessible attribute information to the front-end object so as to display and execute the corresponding operation.

Therefore, the class, the attribute, the method and the like in the business code can be easily processed in a Chinese culture mode through the first data processing module, and a corresponding front end view layer object is automatically generated. Meanwhile, the first data processing module can dynamically return the current callable method and the accessible attribute information to the front-end object according to the request content of the front-end, and provide corresponding parameter options for the operating method, so that efficient interaction with the front-end is realized. Meanwhile, through automatic construction and Chinese culture processing of the front end view layer object, service data can be displayed to a user in a friendly and understandable mode.

Referring to fig. 3, in the second data processing module, the association relationship of elements and components in the front end view layer object may be presented using a regular tree form. For example, for the parent rule box homemade-out of process agreement, two child rule boxes 1100 and 1103 are associated. The 1100 rule boxes are associated with rule boxes such as H10 and A18, and the 1103 rule boxes are associated with rule boxes such as H20, H21, H39 and A21. Clicking on any of the rule boxes may enter the rule definition operation interface as shown in fig. 4. The rule definition operation mainly consists of four parts, namely initial value setting, comparator selection, selection value type and matching result.

For the operation of initial value setting, initial values are mainly classified into several types of attributes, objects, sets, and methods. The attribute is a single, unchangeable value, such as the utility. The Object is translated from an Object (Object) in JAVA, and attribute selection, such as an extended view, is possible. An object in JAVA refers to an instance of a class. An object is a data structure that exists in memory and has attributes (member variables) and methods (member functions) that can store data and perform operations. The collection is translated by Map in JAVA, and value, such as material version attribute, can be obtained by inputting key value. Map is a data structure in Java for storing key-value pairs (keys-value pairs). Each key is unique and the corresponding value can be accessed through the key. The method is translated by a Service layer in JAVA, and can be used for selecting the method to be called. The Service layer, namely the Service logic layer, is a middle layer between a Controller (Controller) and a data access layer (DAO layer), and is mainly responsible for processing Service logic, coordinating different Service operations and providing a high-level interface for an upper layer.

For the operation of selecting the comparer, the comparer mainly comprises a plurality of types of operations of more than, less than, equal to, not equal to, in a collection, empty and the like. For the selection value type, an input value, a selection variable, and the like may be selected. For the matching result, a custom value may be entered through an input box.

Four specific cases are exemplified below.

As shown in fig. 5, when the initial value is selected as the object "extended view", the next-level object attribute selection may be performed, and the initial value is filled into the box after the selection is completed. Such as "extended view. Material coding", the hierarchical relationship is represented by "". When multiple judgment is needed, the method can be used forTo add conditions, and or logical decisions are made. The comparator selection in FIG. 5 is equal to selecting an input value in the selection value type box that matches the definition rule, such as "26594512532154416" or "123456789", and defining an example result in the result box when the match was successful. Multiple logical decisions for each case are similar.

As shown in fig. 6, when the initial value is selected as the set "material version attribute", the input can be customized on the lower line, and the corresponding value is matched from the set through the key value. Such as "Material version property PQN. 3 CZ". Where "," is used to denote a hierarchical relationship, other conditional choices are consistent with the examples described above.

As shown in fig. 7, when the initial value is selected as the method "extended view service class", the next level selection may be performed to select a method in the service class. For example, "extend service attempt class, acquire earthwork ()", fill input parameters in brackets, input parameters can select the attribute of background input, and input character string can be customized. In fig. 7, the input parameters "material version", "factory" and "process attribute" are the attributes transmitted in the background, and "GY9MaterialPreRevision" and "pgy9_ GROOVECUTTING" are the custom input strings.

As shown in fig. 8, when the initial value is selected as the attribute "business", other condition selections are identical to those of the above example.

In the third data processing module, the JSON format data returned by the front end is analyzed and converted into a JAVA object. Then the SpEL expressions are spliced. As shown in fig. 9, JSON format data mainly includes three parts. initValue, result and map. Wherein initValue denotes an initial value, such as "utility". result represents a result, such as an "example result". map represents the connection logic between the comparator, input value and condition, such as "map", "map1", "map2", "map3" and "map4".

In actual implementation, the JSON data returned by the front end is firstly analyzed into a corresponding data structure. Values for the three parts initValue, result and map are then extracted from the parsed data structure. These values are obtained using a corresponding method according to the structure of the JSON data. The map has four keys, namely a comparator, an input value, an and or, and a value of the and or as an array structure, and a set name, such as map1, is stored for representing a logical relationship among a plurality of conditions. Next, based on the extracted data, a SpEL expression string is constructed in a string concatenation manner. The specific construction process depends on the form of the data and the required expression logic. The expression may be defined using comparators, operators, functions, and the like. Ensuring that the generated expression strings conform to the grammar rules of SpEL. For map median conversion splicing, logic connection among all conditions is realized through depth-first search, and the specific operation is as follows:

a. And acquiring the value of the map key from the analyzed JSON data. And obtaining the value of the map in a corresponding mode according to the method and the data structure of the parsing library.

And b, extracting the comparer and the input value from the map. According to the structure of the JSON data, the comparator and the input value are acquired using corresponding methods. The comparer and the input value can be of the types of character strings, numbers and the like, and corresponding type conversion is carried out according to actual conditions.

C. And extracting and or values. And acquiring the value of the sum or according to the structure of the JSON data. Since their values are array structures, they can be extracted as sets or lists using corresponding methods. Each element represents a name of a condition or combination of conditions, such as "map1".

D. A conditional logical expression is constructed. And constructing an expression representing the conditional logic according to the extracted comparer, the input value and the value of the sum and the or. The conditions are combined using logical operators (e.g., & &, ||) depending on the specific requirements and programming language used.

E. Using the extracted comparator, input value, and conditional logical expression, combined into SpEL expression, as the condition in fig. 4, the splice is converted into SpEL expression as:

Condition：{(#extendpandView.getItemId()＝＝"26594512532154416"&&#extendpandView.getItemId()＝＝"123456789")||#extendpandView.getItemId()＝＝"987654321"}

action # { # result [ 'result' ] = "example result" }

The Expression string is parsed using a parser (ExpressionParser) of SpEL and compiled into an evaluable Expression object (Expression). And calling a getValue () method of the expression object to obtain an evaluation result of the expression, and when the result is true/false, indicating that the expression conversion is correct. The result of the expression is processed or the next operation is performed as needed. Depending on the outcome of the expression, it may be decided to execute different logic.

Second embodiment

Referring to fig. 10, a data processing method provided by an embodiment of the present application may be implemented by a data processing system provided by an embodiment of the present application, where the data processing system may be implemented by software and/or hardware, and the data processing method provided by the embodiment of the present application includes:

Step S210, creating a front end view layer object according to the business codes, wherein the front end view layer object comprises at least one type of natural language.

In one embodiment, a front end view layer object is created according to a business code, wherein the front end view layer object comprises at least one type of natural language and comprises:

Generating third data according to the first data and the second data;

a front end view layer object is generated from the third data.

In an implementation manner, the data processing method of the embodiment of the application further comprises the step of acquiring the first data and the second data in the service code through a JAVA reflection mechanism.

This step may be performed by a first data processing module in the data processing system.

Optionally, the data processing method further comprises capturing and processing anomalies in the process of acquiring the first data and the second data in the business code. Here, the first data processing module may also implement exception handling. By capturing and handling anomalies that may occur in reflection operations, such as class absence, method absence, etc., more friendly error cues and a more stable and robust operating environment are provided, providing reliability of the code.

Optionally, the first data processing module employs a caching mechanism to improve performance. After the class information is acquired and the middle culture processing is performed, the processing result can be cached. When the same class is used next time, the information of the class of the culture can be directly obtained from the cache, and repeated execution of the reflecting operation is avoided, so that the response speed and the performance of the system are improved.

Optionally, the first data processing module can dynamically acquire the currently callable method and the accessible attribute information through the reflection mechanism and the annotation and provide the currently callable method and the accessible attribute information to the front-end object so as to display and execute the corresponding operation.

And step S220, performing rule definition operation based on the front end view layer object, and generating JSON format data according to an operation result.

In an embodiment, performing rule definition operation based on the front end view layer object, generating JSON format data according to the operation result, including:

Executing rule definition operation through a definable rule frame to receive business rule data, wherein the business rule data comprises first business rule data and second business rule data, the first business rule data is acquired through a front end view layer object, and the second business rule data is input through a user;

JSON format data is generated based on the front end hierarchy object and business rule data.

In one embodiment, the rule definition operation includes one or more of an initial value setting, a comparator selection, a selection value type setting, and a match result input.

This step may be performed by a second data processing module in the data processing system.

As shown in fig. 3, in the second data processing module, the association of elements and components in the front end view layer object may be exposed using a regular tree form. For example, for the parent rule box homemade-out of process agreement, two child rule boxes 1100 and 1103 are associated. The 1100 rule boxes are associated with rule boxes such as H10 and A18, and the 1103 rule boxes are associated with rule boxes such as H20, H21, H39 and A21. Clicking on any of the rule boxes may enter the rule definition operation interface as shown in fig. 4. The rule definition operation mainly consists of four parts, namely initial value setting, comparator selection, selection value type and matching result.

Four specific cases are exemplified below.

And S230, analyzing the JSON format data to construct and form SpEL expression.

In one embodiment, parsing JSON format data to construct SpEL expressions includes:

analyzing a data structure according to the JSON data;

extracting numerical values in the JSON data according to the data structure;

In one embodiment, after parsing the JSON format data to construct the SpEL expression, further comprising:

The parser of SpEL is used to parse SpEL expression and compile it into an evaluable expression object;

This step may be performed by a third data processing module in the data processing system.

action # { # result [ 'result' ] = "example result" }

The specific implementation of the data processing method according to the embodiment of the present application may refer to the data processing system according to the first embodiment.

In summary, in the method provided in the above embodiment, a front end view layer object is created according to a service code, where the front end view layer object includes at least one type of natural language, a rule definition operation is performed based on the front end view layer object, JSON format data is generated according to an operation result, and the JSON format data is parsed to construct and form SpEL expressions. Therefore, the SpEL expression can be automatically generated based on the service code, the rapid conversion between the natural language and the SpEL expression is realized, the operation is simple, and the visualization degree is high.

Based on the same inventive concept as the previous embodiments, the present embodiment further provides an electronic device, including a processor, a memory, and a computer program stored on the memory and executable on the processor, where the computer program implements the above data processing method when executed by the processor. The specific step flow implemented when the computer program is executed by the processor is described with reference to the embodiment shown in fig. 1, and will not be described herein.

Based on the same inventive concept as the previous embodiments, the present embodiment further provides a computer readable storage medium, in which a computer program is stored, and the computer readable storage medium may be a Memory such as a magnetic random access Memory (FRAM, ferromagnetic random access Memory), a Read Only Memory (ROM), a programmable Read Only Memory (PROM, programmable Read-Only Memory), an erasable programmable Read Only Memory (EPROM, erasable Programmable Read-Only Memory), an electrically erasable programmable Read Only Memory (EEPROM, ELECTRICALLY ERASABLE PROGRAMMABLE READ-Only Memory), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical disc, or a compact disc Read Only Memory (CD-ROM, compact Disc Read-Only Memory), or various devices including one or any combination of the above, such as a mobile phone, a computer, a tablet device, a personal digital assistant, and the like. The computer program stored in the computer readable storage medium realizes the data processing method when being executed by a processor. The specific step flow implemented when the computer program is executed by the processor is described with reference to the embodiment shown in fig. 1, and will not be described herein.

The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

In this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a list of elements is included, and may include other elements not expressly listed.

The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A data processing method, characterized in that the method comprises:

A front-end view layer object is created based on the business code, and the front-end view layer object includes at least one type of natural language;

Based on the front-end view layer object, perform the rule definition operation and generate JSON format data according to the operation result;

Parse the JSON format data to construct a SpEL expression;

The step of performing rule-defined operations based on the front-end view layer object and generating JSON format data based on the operation results includes:

A rule tree is created based on the front-end view layer object, the rule tree including a definable rule box;

The rule definition operation is performed through the definable rule box to receive business rule data, which includes first business rule data and second business rule data. The first business rule data is obtained through the front-end view layer object, and the second business rule data is obtained through user input.

Generate JSON format data based on the front-end view layer object and the business rule data;

The rule definition operation includes one or more of the following: initial value setting, comparison operator selection, selection value type setting, and matching result input.

2. The method according to claim 1, characterized in that, in the step of creating a front-end view layer object based on business code, the front-end view layer object includes at least one type of natural language, including:

Obtain first data and second data from the business code, wherein the first data includes class information in the business code, and the second data includes Chinese annotation information in the business code;

Generate third data based on the first data and the second data;

A front-end view layer object is generated based on the third data.

3. The method according to claim 2, characterized in that the method further comprises:

The first and second data in the business code are obtained through the Java reflection mechanism.

4. The method according to claim 2 or 3, characterized in that the method further comprises:

Capture and handle exceptions during the process of obtaining the first and second data in the business code.

5. The method according to claim 1, wherein parsing the JSON format data to construct a SpEL expression comprises:

The data structure is parsed from the JSON format data;

Extract the numerical values from the JSON format data according to the data structure;

The numerical values are concatenated using string concatenation to construct a SpEL expression.

6. The method according to claim 1 or 5, characterized in that, after parsing the JSON format data to construct the SpEL expression, it further includes:

The SpEL expression is parsed using the SpEL parser and compiled into an evaluable expression object;

The expression object is evaluated to obtain the evaluation result of the expression.

7. An electronic device, characterized in that it comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the data processing method as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that it stores a computer program, which, when executed by a processor, implements the steps of the data processing method according to any one of claims 1 to 6.