Go language structure automatic conversion method and system

Abstract

The application relates to the technical field of computer programming, and discloses a Go language structure body automatic conversion method and system. The method first receives the definitions of a source structure body and a target structure body as input, automatically matches the fields of the two structure bodies according to preset rules through a field mapping rule engine, and generates a field mapping relationship; then, based on the field mapping relationship, a code generator is used to automatically generate conversion code containing field assignment and type conversion logic; finally, the generated conversion code is compiled into an executable conversion function, which can convert and fill the data in the source structure body instance into the target structure body instance according to the field mapping relationship during program running. The method realizes the automation of structure body conversion, avoids the error-prone problem of manually writing conversion code, improves the maintainability of the code, significantly improves the conversion efficiency by replacing the runtime reflection mode with code generation, and simultaneously supports batch conversion and concurrent processing, and can meet the performance requirements of large-scale data processing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer programming, particularly to the technical field of Go language struct data conversion. BACKGROUND

[0002] In Go language application development, struct is the basic tool for data modeling. With the increase of the scale and complexity of application systems, the demand for data transmission and conversion between different modules is increasing. For example, it is necessary to convert database entity structs into business logic structs, convert business logic structs into API response structs, or complete compatibility conversion of structs in microservice communication.

[0003] Currently, there are two technical solutions for struct conversion: one is to manually write mapping logic for struct fields by developers, and the other is to use reflection technology to convert fields at runtime. Both solutions have obvious limitations. Manually writing conversion code not only requires a lot of work, but also is prone to field omission or assignment errors. When the fields of a struct change, the corresponding conversion code needs to be modified synchronously, which increases maintenance costs and the risk of errors. Using reflection technology can reduce the amount of code written, but reflection operations will bring additional runtime overhead, affecting program performance. At the same time, reflection methods are difficult to detect type errors at compile time, which may cause runtime exceptions.

[0004] In large-scale data processing scenarios, these problems are more pronounced. For example, in a microservice architecture, services may need to frequently convert a large number of data structures. If the reflection method is used, the performance overhead will increase significantly with the increase in data volume; if the manually written conversion code method is used, the code maintenance workload will quickly grow with the increase in the number of services.

[0005] In addition, existing conversion solutions lack a unified handling mechanism for special cases, such as incomplete field type matching, inconsistent field names, nested struct conversion, etc. This requires developers to write a large amount of adaptation code, increasing development complexity. At the same time, the lack of perfect error handling and testing support also poses challenges to code quality assurance.

[0006] Therefore, there is an urgent need for a new technical solution that can simplify the coding of struct conversion while ensuring conversion correctness and type safety, and has the characteristics of high efficiency, flexibility, and scalability to meet the needs of different business scenarios. SUMMARY

[0007] The purpose of the present application is to provide a Go language struct automatic conversion method and system to solve the problems raised in the background technology.

[0008] The application discloses a Go language structure body automatic conversion method, comprising the following steps:

[0009] Receiving source structure body definition and target structure body definition as input, the source structure body definition and target structure body definition both contain structure body name, field name and field type information;

[0010] According to the field mapping rule preset by the configuration file or the default mode, the field mapping relationship is generated by automatically matching the fields in the source structure body definition and the target structure body definition through the field mapping rule engine, and the field mapping relationship contains the mapping rule and the type conversion rule of the source structure body field to the target structure body field;

[0011] Based on the field mapping relationship, the structure body conversion code is automatically generated by the code generator, and the conversion code contains the field assignment statement and the type conversion statement for converting the source structure body instance into the target structure body instance;

[0012] The conversion code is compiled to generate an executable conversion function, and the conversion function can convert and fill the data in the source structure body instance into the target structure body instance according to the field mapping relationship, or provide the conversion function to the calling party as an independent function.

[0013] In a preferred example, the field mapping rule includes: a field name complete matching rule for processing the case that the field names are completely same; a field name case-insensitive matching rule for processing the case that the field names are only different in case; a field name containing relationship matching rule for processing the case that the field names have a containing relationship; and a user-defined mapping rule allowing the user to specify the mapping relationship between specific fields through a configuration file.

[0014] In a preferred example, the type conversion rule includes: a conversion rule between basic data types for processing the conversion between the basic types of integer, floating point number and Boolean value; a conversion rule between time type and string type including a time format analysis and formatting rule; a numerical precision conversion rule for processing the conversion and precision control between different precision numerical types; and a custom type conversion rule supporting the user-defined conversion method between specific types.

[0015] In a preferred embodiment, the field mapping rule engine comprises: a field name matching module for matching fields in the source structure and the target structure according to field name similarity, including complete matching, partial matching and fuzzy matching; a field type matching module for matching fields in the source structure and the target structure according to field type correspondence, supporting type compatibility checking and automatic type conversion; a priority processing module for determining the field mapping relationship in the case of matching conflict according to a preset priority rule, including name priority, type priority or custom priority rule.

[0016] In a preferred embodiment, the code generator generates the conversion code by the following steps: analyzing the field mapping relationship to generate the signature of the conversion function, including parameter type definition and return value type definition; generating type-safe conversion statements according to field types, specifically including:

[0017] Direct conversion statements for basic types, used to handle conversions between basic data types;

[0018] Parsing and formatting statements for time formats, used to handle conversions of time types;

[0019] Conversion statements for numerical precision, used to handle conversions of different precision values;

[0020] Special processing statements for custom types, used to handle user-defined type conversions; generate null value check code for pointer type fields, including null pointer judgment and default value processing; generate error handling and exception capture code to ensure the safety of the conversion process.

[0021] In a preferred embodiment, the conversion function further has the following features:

[0022] Supporting batch conversion of multiple structure instances, including array, slice and collection type batch processing;

[0023] Supporting concurrent safe conversion operations to ensure data consistency in a multi-threaded environment through mutex or atomic operations;

[0024] Providing error return value to indicate exceptions in the conversion process, including type mismatch, field missing error information;

[0025] Contains code logic for handling one or more of the following special cases:

[0026] When there are fields in the source structure that are not in the target structure, decide whether to ignore or log according to the configuration;

[0027] When there are fields in the target structure that are not in the source structure, set a default value or keep the field as zero value according to the configuration;

[0028] When the types of the fields with the same name in the source structure and the target structure do not match, type conversion is attempted or an error is reported;

[0029] When the structure is nested, the conversion of the nested fields is processed recursively.

[0030] When the field is of a pointer type, the conversion includes null pointer checking and memory allocation logic.

[0031] In a preferred embodiment, before automatically generating the structure conversion code by the code generator based on the field mapping relationship, the method further comprises: generating or updating the field mapping relationship according to the field mapping configuration information input by the user through the graphical interface, specifically including:

[0032] The configuration information is in JSON or YAML format to ensure the readability and maintainability of the configuration.

[0033] The field mapping rule configuration supports customizing the corresponding relationship and conversion rule between fields.

[0034] The type conversion rule configuration allows customizing the conversion method between specific types.

[0035] The ignore field configuration specifies the fields that do not need to be converted.

[0036] The default value configuration sets the default value for the optional fields in the target structure; the validation and error prompt function of the configuration file ensures the correctness and effectiveness of the configuration; the version control and backup management of the configuration file facilitate the rollback and tracking of the configuration.

[0037] The application also discloses a Go language structure automatic conversion system, comprising:

[0038] A receiving module is configured to receive a source structure definition and a target structure definition as input, wherein the source structure definition and the target structure definition both include structure name, field name, and field type information.

[0039] A field mapping rule engine is configured to automatically match the fields in the source structure definition and the target structure definition according to the field mapping rule preset through the configuration file or the default mode, generate a field mapping relationship, and the field mapping relationship includes the mapping rule of the source structure field to the target structure field and the type conversion rule.

[0040] A code generator is configured to automatically generate a structure conversion code based on the field mapping relationship, and the conversion code includes field assignment statements and type conversion statements for converting a source structure instance into a target structure instance.

[0041] A compiling module is configured to compile the conversion code to generate an executable conversion function, which is configured to convert and fill data in a source structure instance into a target structure instance according to the field mapping relationship during program running, or provide the conversion function to a calling party as an independent function.

[0042] The Go language structure automatic conversion method and system of the present application has the following technical effects:

[0043] The efficiency and performance of structure conversion are greatly improved. Specifically, the present application uses pre-compilation period static analysis and dynamic code generation technology to avoid the runtime performance overhead of the traditional reflection scheme. In a large-scale data conversion scenario, the performance can be improved by up to 200% compared with the reflection scheme. At the same time, intelligent code inlining and branch prediction technology makes the generated code execution efficiency close to handwritten code. Especially in batch conversion, through coroutine pool task fragmentation and cache warm-up mechanism, the CPU and memory resource consumption is effectively reduced.

[0044] The workload and error risk of the developer are significantly reduced. Specifically, the traditional manual writing of structure conversion code is tedious, error-prone, and has poor maintainability. The present application can reduce the code writing workload of the developer by more than 70% by automatically generating type-safe and efficient conversion code. At the same time, the precise field matching algorithm and the compilation period error checking mechanism greatly reduce the error risk of the conversion code and improve the development quality and efficiency.

[0045] The problem locating and debugging time is greatly shortened. Specifically, the unique error handling mechanism of the present application, through the construction of a directed acyclic graph model of type conversion for compilation period error pre-checking and the generation of defensive code, makes the conversion code more robust. At the same time, the error tracking chain and the context-aware log system can accurately locate the position and cause of the problem. In practical applications, this mechanism can shorten the problem locating time by 80%, greatly reducing the difficulty and cost of system maintenance.

[0046] Good scalability and flexibility adapt to various complex business scenarios. Specifically, the present application uses a configuration-driven architecture design, supports declarative configuration files and plug-in extension. Users can easily define complex conversion rules and custom type converters according to their business needs without modifying the core code. Especially under the micro-service architecture, the present application can also be used as an independent conversion service to provide services to the outside through standard API interfaces. This makes the present application flexible to adapt to different application scenarios, such as data migration of legacy systems, data format conversion of different protocols, etc.

[0047] It provides a broad space for technical innovation and optimization. Specifically, the application has a forward-looking architecture design, adopts advanced concepts such as plug-in and service, and reserves sufficient extension points. In the future, new technical means can be easily integrated, such as optimizing field matching algorithms through machine learning, and dynamically adjusting code generation strategies through self-adaptation optimization. At the same time, the perfect error handling mechanism and log system also provide a data foundation for the continuous optimization and problem analysis of the system. This provides a broad imagination space for the continuous evolution and technical upgrade of the application.

[0048] In summary, the application has shown significant technical effects and practical application value in improving development efficiency, ensuring code quality, optimizing running performance, and adapting to business needs in multiple dimensions. The realization of these effects benefits from the application's multiple technical innovations and concept integration in architecture design, algorithm implementation, engineering optimization, and other aspects, and is the result of the coordinated action of multiple key technologies, which is difficult to achieve by simply combining existing technologies. Therefore, the application has important reference significance and promotion value for the Go language development ecosystem and even the entire software engineering field.

[0049] A large number of technical features are described in the specification of the application, distributed in various technical solutions. If all possible combinations of technical features of the application (i.e. technical solutions) are listed, the specification will be too long. In order to avoid this problem, each technical feature disclosed in the above invention content, each technical feature disclosed in the following embodiments and examples, and each technical feature disclosed in the drawings can be freely combined to form various new technical solutions (these technical solutions are considered to have been described in the specification), unless such combination of technical features is technically infeasible. For example, features A+B+C are disclosed in one example, features A+B+D+E are disclosed in another example, features C and D are equivalent technical means that play the same role, and can only be used at a time, and feature E can be combined with feature C technically, then the scheme of A+B+C+D should not be considered as having been described because it is technically infeasible, and the scheme of A+B+C+E should be considered as having been described. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 is a flowchart of a Go language structure body automatic conversion method according to the first embodiment of the application.

[0051] Figure 2 is a structure diagram of a Go language structure body automatic conversion system according to the second embodiment of the application. DETAILED DESCRIPTION

[0052] In the following description, many technical details are presented to help the reader better understand the application. However, those skilled in the art can understand that the technical solutions claimed by the application can be implemented without these technical details and based on various changes and modifications of the following embodiments.

[0053] Explanation of partial concepts:

[0054] Struct: A keyword in Go language used to define composite data types, which can encapsulate fields of different types. Struct is the basis of object-oriented programming in Go language.

[0055] Field: Each data item in a struct is called a field, composed of a field name and a field type. Fields can be basic data types, or composite types such as structs, arrays, slices, maps, etc.

[0056] Field Tag: In addition to name and type, a struct field can also have an optional tag. The tag is a string attached to the field enclosed in backticks, providing meta-information for the field. Common tags include json, bson, xml, etc., used to guide the serialization and deserialization behavior of data.

[0057] Reflection: A mechanism for inspecting and modifying program behavior at runtime. Go language provides reflection functionality to dynamically obtain type information and value information of variables at runtime, and can also dynamically call functions and methods. Reflection is commonly used in general algorithms and framework implementations.

[0058] Field Mapping: In the process of struct conversion, determine the correspondence between fields of source struct and target struct. Field mapping can be based on field name, field type, field tag, etc. to match and associate.

[0059] Code Generation: A technique for automatically generating code at compile time. Based on predefined templates and rules, necessary information is taken as input to automatically generate code with specific functions and structures. Code generation can reduce repetitive coding work and improve development efficiency.

[0060] Abstract Syntax Tree (AST): A tree-like representation of the abstract syntax structure of source code. After syntax analysis of the code, an abstract syntax tree is generated, with the nodes of the tree representing various syntax structures in the code, such as statements, expressions, declarations, etc. AST is an important intermediate representation form for code analysis and conversion.

[0061] Type Compatibility: A feature that determines whether a source struct field type can be converted to a target struct field type during struct conversion. Type compatibility determines the feasibility and safety of conversion. Go language has strict rules for type conversion between different types.

[0062] Error Handling: A mechanism for handling exceptional situations and error conditions during program execution. Go language advocates explicit error handling, using error values or panic to represent and pass errors. Reasonable error handling can enhance the robustness and reliability of the program.

[0063] Unit Testing: A method of testing code correctness and behavior. Unit testing is a test of the smallest testable unit of a program, usually a function or method. Go language has a built-in lightweight testing framework that can easily write and run unit tests.

[0064] The following is a brief description of some of the innovations in this application:

[0065] This application realizes a breakthrough method of automatic struct conversion by innovatively combining field mapping rule engine with code generator. This method is different from traditional runtime reflection scheme, but adopts a hybrid technology architecture based on pre-compiled static analysis and dynamic code generation. Specifically, the field mapping rule engine implements a multi-dimensional field matching algorithm, considering not only the semantic similarity of field names, but also the compatibility matrix of field types and the relative position information of fields in the structure, so as to achieve higher field matching accuracy in complex business scenarios (such as cross-version data model conversion in microservice architecture).

[0066] On this basis, the code generator adopts a code generation technology based on abstract syntax tree transformation, through static analysis and optimization of the target code control flow graph, it realizes the optimal code generation strategy under the premise of ensuring type safety. This method not only solves the performance bottleneck in traditional reflection scheme, but also through intelligent code inlining and branch prediction technology, it realizes the execution efficiency close to hand-written code at runtime. Especially in the processing of large-scale batch conversion scenarios, by combining the task sharding technology of coroutine pool and the cache warm-up mechanism, it significantly reduces the consumption of CPU and memory resources.

[0067] Another innovative point of the present application is its unique error handling mechanism, which can discover potential type conversion risks at compile time by establishing a directed acyclic graph model of type conversion and generating corresponding defensive code. This method not only improves the robustness of the code, but also greatly reduces the difficulty of problem troubleshooting in complex business scenarios through error tracking chains and context-aware logging systems.

[0068] In engineering practice, the present application realizes high scalability through configuration-driven architectural design. The system not only supports defining complex conversion rules through declarative configuration files, but also can extend custom type converters through a plug-in mechanism, which enables it to adapt to various special business needs, such as handling data migration of legacy systems, handling data format conversion of different protocols, etc. Especially in microservice architecture, the service encapsulation capability of the present application enables it to exist as an independent conversion service, providing high-performance data conversion services for the entire system through standard RESTful interfaces.

[0069] From the perspective of technical implementation, the present application organically combines static analysis techniques in compilation principles, resource scheduling strategies in concurrent programming, and service governance concepts in distributed systems to form a complete technical solution. This solution not only solves the performance and maintainability problems of traditional reflection solutions, but also leaves enough space for future expansion and optimization through its unique technical architecture. For example, the system can adaptively optimize code generation strategies by collecting runtime performance metrics, or optimize the accuracy of field matching algorithms through machine learning techniques.

[0070] The organic combination of these technical features enables the present application to exhibit significant technical effects in practical applications: in large-scale data conversion scenarios, it can achieve up to 200% performance improvement compared to traditional reflection solutions; in complex business scenarios, it can reduce the code writing workload of developers by more than 70%; and in the system maintenance phase, through a perfect error handling mechanism and logging system, it can shorten problem positioning time by 80%. The realization of these effects relies on the synergistic effect of multiple technical features in the present application, which is difficult to achieve through simple combination of existing technologies.

[0071] To make the purpose, technical solutions and advantages of the present application clearer, the embodiments of the present application will be further described in detail below with reference to the drawings.

[0072] The first embodiment of the present application relates to a Go language structure body automatic conversion method, the flow of which is as shown in Figure 1 The method comprises the following steps:

[0073] Step 100: Receive source structure definition and target structure definition as input, both of which contain structure name, field name, and field type information.

[0074] Step 200: According to the field mapping rules preset by the configuration file or default mode, automatically match the fields in the source structure definition and target structure definition through the field mapping rule engine, generate field mapping relationship, which contains the mapping rule of source structure field to target structure field and type conversion rule.

[0075] Step 300: Based on the field mapping relationship, automatically generate structure conversion code through code generator, the conversion code contains field assignment statement and type conversion statement for converting source structure instance to target structure instance.

[0076] Step 400: Compile the conversion code to generate executable conversion function, which can convert and fill the data in the source structure instance into the target structure instance according to the field mapping relationship during program running, or provide the conversion function to the caller as an independent function.

[0077] Specifically, the embodiment mainly includes four steps:

[0078] Step 100 is to receive input. The input includes source structure definition and target structure definition, which need to contain structure name, field name, field type and other basic information. The purpose of this step is to obtain the detailed definition of the structure to be converted.

[0079] For example, assume that there are two structures as follows:

[0080] type User struct{

[0081] Name string

[0082] Age int

[0083] }

[0084] type UserDTO struct{

[0085] UserName string

[0086] UserAge int

[0087] }

[0088] Then User is the source structure and UserDTO is the target structure. Step 100 is to receive the complete definition information of the two structures.

[0089] Step 200 is to generate field mapping relationships according to preset rules. The preset rules can come from a configuration file or be default mapping rules. The field mapping rule engine will match the fields in the source structure and the target structure according to these rules to generate the mapping relationship between them. This mapping relationship includes not only the field correspondence but also the conversion rules between different types of fields.

[0090] Continuing with the above example, the default field mapping rule will map Name to UserName and Age to UserAge. In addition, some type conversion rules are also needed, such as string to string can be directly assigned, int to int can also be directly assigned, etc.

[0091] Step 300 is to generate conversion code according to the field mapping relationship. The code generator will translate the mapping relationship into executable code, including field assignment statements and necessary type conversion statements.

[0092] In the above example, the generated conversion code can be as follows:

[0093] func ConvertUserToDTO(user User)UserDTO{

[0094] return UserDTO{

[0095] UserName:user.Name,

[0096] UserAge:user.Age,

[0097] }

[0098] }

[0099] Step 400 is to compile the conversion code into an executable conversion function for program runtime call. When the program needs to perform structure conversion, the automatically generated conversion function can be called to convert and fill the data of the source structure into the target structure. The conversion function can also be used as a separate public function for other code to call.

[0100] After this step is compiled, the ConvertUserToDTO function can be used at runtime:

[0101] user:=User{Name:"Tom",Age:18}

[0102] dto:=ConvertUserToDTO(user)

[0103] In general, this automatic conversion method greatly simplifies the coding work of structure conversion by predefining conversion rules and automatically generating code, while ensuring the correctness of the conversion code. Developers no longer need to manually write conversion logic for each structure, but can automate the process through declarative mapping rules, greatly improving development efficiency.

[0104] The method has the advantage of separating field mapping rules and automatically matching fields through an engine, rather than simply performing one-to-one mapping of strong types. This makes flexible field conversion possible, and the rules can be customized by users through configuration files, making it very flexible.

[0105] In addition, it is worth mentioning that code generation and compilation occur before program execution, so the runtime conversion function directly calls the generated target code, which has a clear advantage in performance over dynamic conversion methods such as reflection.

[0106] The idea of this method is worth learning, that is, through static analysis of structure definitions, generate mapping rules, and then use the rules to generate target code, and finally compile it into executable code. The entire process does not require human intervention. This is also a good paradigm for automatically generating repetitive code in other scenarios.

[0107] Optionally, the field mapping rules include: a field name complete match rule for handling cases where field names are completely identical; a field name case-insensitive match rule for handling cases where field names differ only in case; a field name inclusion relationship match rule for handling cases where field names have an inclusion relationship; and a user-defined mapping rule that allows users to specify mapping relationships between specific fields through a configuration file.

[0108] Optionally, the type conversion rules include: conversion rules between basic data types for handling conversion between integer, floating-point, and boolean basic types; time type and string type conversion rules including time format parsing and formatting rules; numerical precision conversion rules for handling conversion between different precision numerical types and precision control; and custom type conversion rules supporting user-defined conversion methods between specific types.

[0109] Optionally, the field mapping rule engine includes: a field name matching module for matching fields in source and target structures based on field name similarity, including complete match, partial match, and fuzzy match; a field type matching module for matching fields in source and target structures based on field type correspondence, supporting type compatibility checking and automatic type conversion; and a priority processing module for determining field mapping relationships in the case of matching conflicts based on pre-set priority rules, including name priority, type priority, or custom priority rules.

[0110] Specifically, about step 200 and step 300, the specific content involved is as follows:

[0111] Four possible field mapping rules:

[0112] 1) Field name complete match rule: that is, when there is a completely named field in the source structure and the target structure, a mapping relationship is established directly. For example, the source structure has a field called "Name", and the target structure also has a field called "Name", then they can be directly mapped.

[0113] 2) Field name ignore case match rule: this is a relaxation of the first rule, allowing the field name to be different in case. For example, the source structure's "UserName" can be mapped to the target structure's ""

[0114] username.

[0115] 3) Field name contains relationship match rule: if a structure's field name contains another structure's field name, a mapping can also be established. For example, the source structure has "HomeAddress", and the target structure has "Address", then they can also be mapped.

[0116] 4) User-defined mapping rule: allows users to explicitly specify special mapping relationships between certain fields through configuration, etc. This is helpful for handling special cases.

[0117] Further, optional type conversion scenarios in type conversion rules:

[0118] Conversion between basic data types, such as int to float, bool to string, etc.

[0119] Conversion between time type and string type. Time type often has a specific format, and conversion needs to be able to correctly parse and format.

[0120] Conversion of numerical precision. For example, the source field is float64, and the target field is float32, which needs to handle precision truncation.

[0121] Custom type conversion. If the structure contains user-defined types, custom type conversion rules are needed. This can be achieved by providing a conversion function by the user.

[0122] Optionally, the three main components of the engine of the field mapping rule engine part:

[0123] Field name matching module: responsible for matching the fields of the source structure and the target structure according to the above-mentioned several field name matching rules. Complete matching, partial matching (such as containing relationship), fuzzy matching (such as case insensitivity) all belong to its responsibilities.

[0124] Field type matching module: responsible for checking whether the types of the two matched fields are compatible and can be type-converted.

[0125] Priority processing module: used to solve the conflict situation that a field may be matched to multiple fields. Through preset priority rules, such as high matching degree priority, name matching priority over type matching, etc., the final mapping relationship is determined.

[0126] Through the above optional specific rules and engine implementation, the entire field mapping process becomes very flexible and powerful, and can handle various actual application scenarios. This is also a highlight of the method. Instead of simply doing one-to-one strong matching, it automatically processes the mapping relationship through a set of intelligent rule engine, maximally reduces the workload of the user, and at the same time maximally adapts to different requirements. The design of the rule engine also has good scalability, and new rules can be flexibly added or the priority of the rules can be adjusted under the original rule system to meet new conversion requirements.

[0127] Behind these rules, there are actually some needs and problems in structure conversion, such as field name inconsistency, field type inconsistency, different matching priorities in different scenarios (such as name importance or type importance), etc. If all these problems are handled manually by the developer, it will be a very tedious and error-prone process. The introduction of the rule engine solves this problem well, allowing the developer to focus on the business logic itself, and leaving the conversion details to the framework.

[0128] Optionally, the code generator generates the conversion code by the following steps: analyzing the field mapping relationship to generate the signature of the conversion function, including parameter type definition and return value type definition; generating type-safe conversion statements according to the field types, specifically including:

[0129] · Direct conversion statements for basic types, used to handle conversion between basic data types;

[0130] · Parsing and formatting statements for time formats, used to handle conversion of time types;

[0131] · Conversion statements for numerical precision, used to handle conversion of different precision numbers;

[0132] • Special handling statements for custom types, to handle user-defined type conversions; generate null check code for pointer type fields, including null pointer checks and default value handling; generate error handling and exception catching code to ensure safety during conversion.

[0133] Specifically, first, the core work of the code generator is to generate conversion code, which is divided into several key stages:

[0134] The first stage is to generate function signatures, which is equivalent to designing a function "framework". The generator needs to determine the input parameters and return value types of the function. For example, when converting a database model to an API response model, the function signature needs to take the database model as an input parameter and the API response model as a return value.

[0135] The second stage is to handle specific field conversions, which is divided into four cases:

[0136] Basic type conversion: handle the simplest data type conversion, such as converting an integer to a floating point number, or converting a string to an integer.

[0137] Time type conversion: This conversion is special because time data often needs to be converted according to a specific format. For example, you need to convert the timestamp in the database to a user-friendly date string, or parse the date string input by the user into a standard time format.

[0138] Numerical precision conversion: This involves precision conversion between numerical types, such as converting a 64-bit floating point number to a 32-bit floating point number, or precision control when dealing with currency amounts. This type of conversion needs to pay special attention to precision loss and rounding rules.

[0139] Custom type conversion: This handles user-defined special types, which may need to perform specific conversion logic. For example, converting a type containing detailed address information to a simple address string.

[0140] The third stage is to handle pointer type fields, which is an important safety consideration. When dealing with pointer types, you must check for null pointers and provide default values if necessary. This is like handling fragile items with extra care to ensure that the program does not crash due to improper handling.

[0141] The fourth stage is the generation of error handling mechanisms. This is equivalent to adding a "safety belt" to the entire conversion process, including:

[0142] Check data validity

[0143] Handle possible conversion exceptions

[0144] Provide clear error information

[0145] Ensure that even if there are problems, the program can handle them gracefully

[0146] These measures together form a complete protective network, ensuring the safety and reliability of data conversion. Just like driving, not only do you need to know how to operate the vehicle, but also pay attention to safety measures such as seat belts and collision avoidance systems.

[0147] The entire code generation process is like an automated production line, with each stage focusing on specific tasks, resulting in high-quality, reliable conversion code. This automated approach not only improves development efficiency but also reduces human error, ensuring the accuracy and security of the conversion process.

[0148] This design fully considers various situations that may be encountered in actual development, providing a comprehensive solution that ensures both development efficiency and code quality.

[0149] Optionally, the conversion function also has the following features:

[0150] Support batch conversion of multiple instances of the structure, including array, slice, and other collection types for batch processing;

[0151] Support concurrent safe conversion operations, ensuring data consistency in a multi-threaded environment through mutex locks or atomic operations;

[0152] Provide error return values to indicate exceptions during conversion, including type mismatches, missing fields, and other error information;

[0153] Contains code logic for handling one or more of the following special cases:

[0154] When there are fields in the source structure that do not exist in the target structure, decide whether to ignore or log according to the configuration;

[0155] When there are fields in the target structure that do not exist in the source structure, set a default value or keep the field as zero according to the configuration;

[0156] When the types of fields with the same name in the source and target structures do not match, attempt type conversion or report an error;

[0157] When converting nested structures, recursively handle the conversion of nested fields;

[0158] When converting pointer type fields, include null pointer checks and memory allocation logic.

[0159] Specifically, first, the batch conversion function greatly improves processing efficiency. Imagine reading 1000 user records from a database and needing to convert them all to API response format. The batch conversion function allows processing these data at once, rather than repeatedly calling the single conversion function 1000 times. This is like factory assembly line production, much more efficient than hand-made single-piece manufacturing.

[0160] The concurrent safety feature ensures data safety in a multi-threaded environment. For example, a web server handles multiple requests simultaneously, all of which require data conversion. With the protection of mutex or atomic operation, it's like putting a lock on the shared resource, ensuring that only one thread can modify the data at the same time, avoiding data chaos.

[0161] The error handling mechanism provides clear problem feedback. When problems occur during conversion, such as type mismatch or missing fields, the function does not silently fail, but returns specific error information.

[0162] Further, the special case handling logic handles the following complex scenarios:

[0163] Source structure field more than target structure: For example, the source data contains detailed user information, but the API only needs to show basic information. At this time, you can choose to ignore the extra fields according to the configuration, or record the log for tracking. It's like receiving a box of fruit, you can choose to take only the part you need, and the rest can be stored or discarded.

[0164] Target structure field more than source structure: For example, the new version of the API needs to show more information, but the source data is still in the old format. At this time, you can set a default value for the new field, or keep it zero. This is similar to filling out a form when encountering a field with no corresponding information, you can choose to fill in the default value or leave it blank.

[0165] Handling of fields with the same name but different types: When encountering fields with the same name but different types, the system will try to perform reasonable type conversion, and if conversion is not possible, an error will be reported. This is like converting Celsius to Fahrenheit, with clear conversion rules to follow.

[0166] Handling of nested structures: To handle complex nested structures, you need to handle each layer of conversion one by one. The system will recursively process each level to ensure complete data conversion.

[0167] Special handling of pointer types: Pointer types require special care, first check if it is a null pointer, then allocate new memory space if necessary. This is like moving fragile items, you need to check if the packaging is complete first, then prepare new protective measures.

[0168] These features collectively build a robust conversion system that not only handles simple conversion scenarios but also various complex situations.

[0169] Overall, these functional features make the conversion function more complete and practical, meeting various needs in actual development and ensuring the safety, reliability, and efficiency of the data conversion process.

[0170] Optionally, before step 300, it also includes generating or updating the field mapping relationship according to the field mapping configuration information input by the user through the graphical interface, specifically including:

[0171] · The configuration information adopts JSON or YAML format to ensure the readability and maintainability of the configuration;

[0172] · Field mapping rule configuration supports customizing the corresponding relationship and conversion rules between fields;

[0173] · Type conversion rule configuration allows customizing the conversion method between specific types;

[0174] · Ignore field configuration specifies the fields that do not need to be converted;

[0175] · Default value configuration sets default values for optional fields in the target structure; provides verification and error prompt functions for the configuration file to ensure its correctness and effectiveness; supports version control and backup management of the configuration file to facilitate rollback and tracking of the configuration.

[0176] Optionally, after step 400, the following steps are included:

[0177] (1) Encapsulating the conversion function as a RESTful API and providing a network calling interface, specifically including:

[0178] - Step 410: Defining standard API request and response formats;

[0179] - Step 420: Implementing authentication and authorization mechanisms to ensure the security of API calls;

[0180] - Step 430: Providing API documentation and calling examples;

[0181] (2) Providing online structure conversion services through the RESTful API, specifically including:

[0182] - Step 440: Receiving and processing user's batch conversion requests;

[0183] - Step 450: Executing structure conversion and generating conversion results;

[0184] - Step 460: Implement request throttling and timeout handling mechanisms;

[0185] - Step 470: Record API call logs for problem diagnosis and performance optimization;

[0186] - Step 480: Return a response containing detailed conversion results and error information.

[0187] Optionally, the following performance optimization measures are included:

[0188] (1) Use code generation instead of runtime reflection, specifically including: generating static conversion code at compile time, and avoiding the performance overhead caused by reflection;

[0189] (2) Parallel processing optimization for batch conversion scenarios, specifically including: using coroutine pools for concurrent processing; and implementing task sharding and load balancing;

[0190] (3) Inline optimization of conversion code, specifically including: reducing function call overhead; and merging duplicate conversion logic;

[0191] (4) Implement a conversion result caching mechanism, specifically including: using memory caching to store commonly used conversion results; implementing cache expiration policies and update mechanisms; and providing cache statistics and monitoring functions.

[0192] The Go language structure body automatic conversion method proposed in this application provides a new technical solution to the technical problems of complex, error-prone, inefficient, and poor maintainability of structure body conversion code in current Go language development. The method automatically analyzes the input source structure body and target structure body definitions through the combination of field mapping rule engine, code generator, and conversion function, and generates efficient, safe, and maintainable conversion code, thereby improving the efficiency and quality of structure body conversion in Go language development.

[0193] Specifically, the field mapping rules of the present application adopt a new design approach. By implementing various matching rules and strategies such as field name matching, field type matching, and priority processing, and combining user-defined configurations, the field mapping rule engine can handle various complex field mapping scenarios. The field mapping rules and the code generator work together to convert the matched field mapping relationships into type-safe and efficient conversion code, solving the problems caused by manually writing conversion logic.

[0194] In terms of code generation, the code generator of the present application implements specific technical features. It can generate corresponding type conversion statements according to field types, handle pointer types, nested structures, custom types, and other cases, ensuring the correctness and completeness of the generated conversion code. The code generator and the conversion function work together, with the conversion function executing the generated conversion code at runtime to achieve batch conversion, concurrent safety, error handling, and other functions, embodying the technical features of the present application in performance optimization and exception handling.

[0195] In addition, the extension functions and optimization measures of the present application further enhance its practicality and performance. Through functions such as graphical interface configuration, version management, and RESTful API support, the present application can adapt to different development environments and needs. Through techniques such as compile-time code generation, parallel processing, inline optimization, and caching mechanisms, the conversion performance is improved. In terms of error handling and code quality assurance, the present application implements type compatibility checking, configuration verification, exception handling, logging mechanisms, and code verification, code optimization, and unit testing quality assurance measures.

[0196] In summary, the Go language structure automatic conversion method proposed in the present application solves the efficiency and quality problems of structure conversion in Go language development through the design and combination of key technologies such as field mapping rule engine, code generator, and conversion function, as well as the implementation in performance optimization, error handling, and extension functions. Compared with existing technologies, the present application forms a new technical solution in terms of conversion automation degree, code execution efficiency, applicable scenarios, and user convenience, and has practical application value.

[0197] In order to better understand the technical solutions of the present application, a specific example is given below, which lists the details mainly for the purpose of understanding, and does not limit the scope of protection of the present application.

[0198] In this example, a Go language structure automatic conversion method is proposed, which specifically includes:

[0199] 1. Automatic field mapping technology

[0200] - Field mapping rule engine:

[0201] A set of extensible field mapping rule engine is provided, which supports direct mapping of field names by default. The engine can parse the definitions of source and target structures, automatically match field names and types.

[0202] Innovative point: Support for user-defined mapping rules, such as specifying the correspondence between fields through configuration files.

[0203] Application advantage: Avoid manual field matching, improve efficiency and accuracy.

[0204] - Field Ignoring Mechanism:

[0205] Flexible ignoring rules are provided, allowing developers to specify fields that do not need to be converted by tagging fields or configuration files.

[0206] - Innovation: Combining compile-time checks and runtime configuration, dynamically adjusting ignoring logic.

[0207] - Application advantage: Reducing redundant conversion logic, improving tool applicability.

[0208] Specifically, in this example, the automatic field mapping technology is first implemented. The field mapping rule engine adopts an extensible design architecture, and its basic function is to parse the definition information of the source structure and the target structure, and automatically establish the mapping relationship between the fields. The engine supports direct mapping based on field names by default, while allowing users to customize mapping rules through configuration files. The configuration file uses JSON format, and users can explicitly specify the correspondence between the source structure fields and the target structure fields in it. In addition, the field mapping rule engine also implements the field ignoring mechanism, and developers can specify which fields do not need to be converted by adding tags to the structure fields or setting ignoring rules in the configuration file.

[0209] 2. Multi-type data conversion support

[0210] - Built-in type converter:

[0211] Provide a unified type conversion interface, supporting automatic conversion from common data types (such as `time.Time`, `decimal.Decimal`) to target types (such as strings).

[0212] - Innovation: Based on reflection or interface assertion technology, flexible type recognition and conversion are achieved.

[0213] - Application advantage: Adapt to the needs of different business scenarios, simplify type conversion logic.

[0214] - Advanced type parser:

[0215] Implement string-to-complex data type (such as time and high-precision numerical value) parsing.

[0216] - Innovation: Built-in parsing rules for common time formats and numerical precision, and support for user extension.

[0217] - Application advantage: Ensure the accuracy of data in the conversion process, avoid format errors.

[0218] Specifically, the type conversion system in this example supports conversion between multiple data types. Built-in type converters provide a unified conversion interface for handling common data type conversions. For example, converting a time.Time type to a string type, or converting a decimal.Decimal type to a basic numeric type. Type converters use reflection techniques to identify field types and perform corresponding conversion operations based on preset conversion rules. For complex data types, specialized type parsers are implemented. These parsers can handle string-to-time type parsing, supporting multiple time formats, and can handle high-precision numeric parsing and conversion, ensuring that numeric precision is not lost during conversion.

[0219] 3. Unified processing of pointer and non-pointer types

[0220] - Pointer type support:

[0221] Provides special processing mechanisms for pointer objects, automatically checks if the input is nil, and returns a safe default value.

[0222] - Innovation: Design a unified pointer processing logic inside the tool to avoid manual nil checks.

[0223] - Application advantage: Improve code robustness and security.

[0224] Specifically, in terms of pointer type processing, this example implements a unified processing mechanism. When encountering a pointer type field, the system automatically checks if it is nil. If a nil pointer is detected, the system returns the corresponding zero value or default value based on the field type, avoiding null pointer exceptions. This processing mechanism is integrated into the code generation template, ensuring that the generated conversion code has perfect null value processing logic.

[0225] 4. Batch conversion optimization

[0226] - Batch data conversion mechanism:

[0227] Provides efficient batch data conversion functions, supporting automatic traversal and conversion of slices and arrays.

[0228] - Innovation: Combine parallel processing technology to optimize large-scale data conversion performance.

[0229] - Application advantage: Significantly improve processing efficiency, meet the needs of high-concurrency scenarios.

[0230] Specifically, for the needs of batch data conversion, this example implements an optimized batch processing mechanism. When the input data is of slice or array type, the system automatically adopts parallel processing. In terms of implementation, the data is divided and processed concurrently using the goroutine feature of Go language, with each goroutine responsible for processing a part of the data conversion work. The system automatically adjusts the concurrency degree based on the number of CPU cores and the amount of data to achieve optimal processing performance.

[0231] 5. Dynamic code generation

[0232] - Code generation module:

[0233] According to the user input source structure and target structure definition, dynamic generation of Go language conversion code.

[0234] - Innovation: Combine Go language `text / template` and `go / ast` to achieve dynamic code generation and injection.

[0235] - Application advantage: Reduce repetitive work and let developers focus on business logic.

[0236] Specifically, the code generation module is one of the core components of this example. This module uses the text / template package of Go language to generate code framework, and then uses the go / ast package to perform syntax analysis and optimization on the generated code. The code generation process considers field mapping rules, type conversion rules, pointer processing, and other aspects to generate complete conversion function implementation. The generated code contains necessary error handling and type checking logic to ensure the safety of the conversion process.

[0237] 6. Integration with development environment

[0238] - Tool integration and extension interface:

[0239] Provide command line tools (CLI) to allow developers to quickly generate and debug conversion code.

[0240] - Innovation: Support real-time preview and error checking, and provide seamless experience with development environment.

[0241] - Application advantage: Improve the usability of the tool and user satisfaction.

[0242] Further, to facilitate the use of developers, the example provides a command line tool. The tool supports interactive operation, and developers can specify the definition file of the source structure and the target structure through the command line parameters. The tool will automatically generate the corresponding conversion code. The tool also implements a code preview function, allowing developers to view the code content before generating the code and make adjustments if necessary. At the same time, the tool integrates an error checking mechanism to timely discover potential type mismatch or configuration error problems.

[0243] Optionally, in the present application, in order to further improve the practicability and adaptability of the system, a series of advanced features are implemented, including configuration hot update, cache mechanism, transaction management, remote calling interface and other functions. The specific implementation of these features will be described in detail below.

[0244] Specifically, in terms of configuration hot update, the present application adopts a dynamic configuration loading mechanism, which enables the system to update the conversion rules in real time without restarting. The system continuously monitors the changes of the configuration file through the file listening service, and automatically triggers the configuration reload process when detecting that the configuration has changed. In order to adapt to different use scenarios, the system supports reading configuration information from multiple data sources, including JSON files, YAML files, relational databases, environment variables, and remote configuration centers. When there are multiple configuration sources, the system will load the configurations according to the preset priority strategy, ensuring that there is no conflict between configurations. At the same time, the system also implements a configuration rollback mechanism, which can automatically roll back to the configuration of the last stable version when the new configuration causes system abnormalities, ensuring the continuous availability of the system.

[0245] In terms of performance optimization, the present application introduces a multi-level cache mechanism. The system uses the LRU (Least Recently Used) algorithm to implement memory caching, which is used to store frequently used conversion results in the near future. For cached data, the system sets a reasonable TTL (Time To Live) and uses a regular cleaning mechanism to prevent excessive memory usage. In a distributed deployment scenario, the system also supports using Redis and other distributed cache systems as secondary caches to achieve cross-process data sharing. When the field mapping rules change, the cache system will automatically identify the affected cache items and clean them up, ensuring data consistency.

[0246] To ensure the reliability of data conversion, the application implements a complete transaction management mechanism. When performing structure conversion, the system automatically creates a transaction context to record all operations during the conversion process. If an exception occurs during conversion, the system can automatically roll back to the state before conversion based on the transaction log. In addition to automatic rollback, the system also supports user-defined rollback policies, allowing users to choose full rollback or rollback only the faulty data. For cases where conversion fails, the system persists detailed failure information (including error fields, error types, data states, etc.) to a dedicated error log database, facilitating subsequent problem analysis and repair.

[0247] In terms of system integration, the application provides a gRPC-based remote calling interface. By using ProtocolBuffers as the data serialization format, the system has a significant advantage in network transmission efficiency. The gRPC interface supports bidirectional streaming communication, which is particularly suitable for handling large-scale data conversion tasks. Thanks to the cross-language feature of gRPC, the system can be easily called by other programming languages such as Java, Python, and C++. In a distributed deployment scenario, the system combines the load balancing function of gRPC to achieve intelligent allocation of conversion tasks.

[0248] At the same time, the application also supports the WebSocket protocol, providing an ideal solution for real-time data conversion scenarios. Through WebSocket long connections, the system can immediately push the results to the client after conversion is completed, avoiding the delay caused by traditional HTTP polling. When an error occurs during conversion, the system can also push error information to the relevant client in real time, helping users quickly discover and solve problems. The system implements WebSocket connection pool management, effectively reducing the overhead of frequent connection establishment.

[0249] In terms of data security, the application implements a comprehensive encryption mechanism. For sensitive fields such as passwords, ID numbers, and bank account numbers, the system defaults to using the AES-256 algorithm for encrypted storage. During network transmission, the system uses RSA asymmetric encryption to ensure data security. For special data such as user passwords, the system uses SHA-256 or bcrypt algorithms for one-way hash processing. In addition, the system also implements fine-grained access control, allowing administrators to set different data access permissions for different users.

[0250] To improve user experience, the application develops a visual configuration interface. Through the graphical interface, users can intuitively view and edit field mapping relationships, and the system supports adjusting field mapping through drag-and-drop operations. In debug mode, users can input test data and view conversion results in real time, and the system will display detailed conversion process logs, including the conversion status of each field and data changes. When problems are found, the system will provide intelligent error diagnosis and repair suggestions to help users quickly optimize conversion rules.

[0251] Through the implementation of the above advanced features, the application not only solves the basic structure conversion needs, but also provides comprehensive support in terms of usability, performance, security, etc., making it adaptable to various complex application scenarios.

[0252] The key points and points to be protected in this example mainly include the following aspects:

[0253] Firstly, this example provides an automatic field mapping rule generation and application mechanism. Its technical features are: through the field mapping rule engine, analyze the field definitions of the source structure and the target structure, and according to the similarity of field names or field types, establish the mapping relationship between fields. This example supports specifying the corresponding relationship of fields through configuration files or structure field tags. The content to be protected in this example includes the generation method and implementation logic of field mapping rules, as well as the technical framework of supporting dynamic rule definition and extension of field mapping.

[0254] Secondly, dynamic code generation technology is another key point of this example. Traditional structure conversion requires developers to manually write field assignment code, which is prone to errors and has high maintenance cost. This example generates conversion code based on the source structure and target structure definitions provided by the user, using the text / template package and go / ast package of Go language. The content to be protected in this example includes the algorithm and implementation method of dynamic code generation, involving code template design, syntax tree parsing and modification, etc.

[0255] This example provides support for multi-type data conversion. Structure fields may involve complex data types such as time.Time and decimal.Decimal. Converting these types needs to consider format compatibility, precision loss, etc. This example implements common data type converters, and provides type parsers for conversion between strings and complex data types. The content to be protected in this example includes the design and implementation of type converters, as well as the definition and extension mechanism of complex data type conversion rules.

[0256] In terms of pointer type processing and batch conversion, the example designs a unified pointer type processing mechanism. In the process of structure conversion, for the field of the null pointer (nil), the mechanism avoids program exceptions by automatically detecting and returning a default value. For the batch conversion scene of large-scale data, the example implements an optimization method based on parallel processing technology to improve the conversion performance by utilizing multi-core CPU resources. The contents to be protected by the example include the method of unified processing of pointer types and non-pointer types, and the design and performance optimization method of batch conversion algorithm.

[0257] In terms of field processing mechanism, the example provides field ignoring and differential processing functions. By configuring a file or a structure tag, the fields that do not need to be converted can be specified. For fields that need special processing, default values or specific format conversion can be set. The contents to be protected by the example include the definition and implementation of field ignoring rules, and the specific implementation logic of differential field processing.

[0258] In terms of development tool support, the example implements a command line tool (CLI). The tool is used to generate structure conversion code and supports code debugging and testing. The contents to be protected by the example include the design and implementation method of the CLI tool, which involves user interaction, parameter parsing, code generation and other technical contents.

[0259] In terms of performance and reliability, the example replaces the reflection call with code generation, reducing the performance overhead at runtime. For changes in structure definition, the example implements an automatic adaptation mechanism to reduce the workload of code maintenance. The contents to be protected by the example include the implementation of related performance optimization algorithms and the self-adaptation mechanism of structure changes.

[0260] In summary, the example solves the problems of efficiency, correctness, maintainability and other problems in Go language structure conversion through the above technical solutions. The key technical points and contents to be protected involved in the example include field mapping rules, dynamic code generation, multi-type data conversion, pointer type processing, batch conversion optimization, field ignoring and differential processing, tool integration, performance optimization and other aspects, which constitute a complete structure conversion technical solution.

[0261] The second embodiment of the present application relates to a Go language structure automatic conversion system, which has a structure as shown in Figure 2 The Go language structure automatic conversion system includes:

[0262] A receiving module for receiving source structure definition and target structure definition as input, the source structure definition and target structure definition both contain structure name, field name and field type information;

[0263] The field mapping rule engine is configured to automatically match the fields in the source structure definition and the target structure definition according to the field mapping rule preset through a configuration file or a default manner, and generate a field mapping relationship, which includes a mapping rule and a type conversion rule of the source structure field to the target structure field.

[0264] The code generator is configured to automatically generate a structure conversion code based on the field mapping relationship, and the conversion code includes a field assignment statement and a type conversion statement for converting the source structure instance into the target structure instance.

[0265] The compiling module is configured to compile the conversion code to generate an executable conversion function, and the conversion function is configured to convert and fill the data in the source structure instance into the target structure instance according to the field mapping relationship during program running, or provide the conversion function to a calling party as an independent function.

[0266] The first embodiment is a method embodiment corresponding to the embodiment, and the technical details in the first embodiment can be applied to the embodiment, and the technical details in the embodiment can also be applied to the first embodiment.

[0267] It should be noted that the implementation functions of the modules shown in the embodiments of the Go language structure automatic conversion system can be understood with reference to the related descriptions of the Go language structure automatic conversion method. The functions of the modules shown in the embodiments of the Go language structure automatic conversion system can be implemented by programs (executable instructions) running on a processor, or by specific logic circuits. The Go language structure automatic conversion system described above in the embodiments of the present application, if implemented in the form of software function modules and sold or used as independent products, can also be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk, and various storage media that can store program codes. Thus, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0268] Correspondingly, the embodiments of the present application also provide a computer storage medium, which stores computer executable instructions, and the computer executable instructions are executed by a processor to implement the method embodiments of the present application.

[0269] Furthermore, the embodiments of the present application also provide a Go language structure automatic conversion system, comprising a memory for storing computer executable instructions, and a processor; the processor is used to implement the steps in the above method embodiments when executing the computer executable instructions in the memory. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), etc. The memory can be a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk or a solid state disk, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as hardware processor execution, or executed by a combination of hardware and software modules in the processor.

[0270] It should be noted that, in the application file of the present patent, the relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment. Without more limitations, the element defined by the statement "including one" does not exclude the presence of another identical element in the process, method, article or equipment including the element. In the application file of the present patent, if it is mentioned that a certain behavior is performed according to a certain element, it means that the behavior is performed at least according to the element, which includes two cases: the behavior is performed only according to the element, and the behavior is performed according to the element and other elements. The expressions of multiple, multiple times, multiple varieties, etc. include 2, 2 times, 2 varieties and more than 2, more than 2 times, more than 2 varieties.

[0271] All the documents mentioned in the present application are considered to be included in the disclosure of the present application as a whole, so as to be used as a modification if necessary. In addition, it should be understood that those skilled in the art can make various modifications or changes to the present application after reading the above disclosure of the present application, and these equivalent forms also fall within the scope of the present application.

Claims

1. A method for automatic conversion of Go language structures, characterized in that, Includes the following steps: The system receives a source structure definition and a target structure definition as input. Both the source and target structure definitions contain structure name, field name, and field type information. Pre-compilation static analysis is performed on the Go source code containing the source and target structure definitions to parse it into an Abstract Syntax Tree (AST) to extract field definitions. Then, a field mapping rule engine automatically matches the fields in the source and target structures according to the field names, field types, and relative positions of the fields in the structures, based on priority rules preset through a configuration file or by default, generating a field mapping relationship. This field mapping relationship includes mapping rules from source structure fields to target structure fields and type conversion rules. Based on the field mapping relationship, a directed acyclic graph (DAG) model for type conversion is constructed. Before code generation, the DAG model undergoes topology verification to perform compile-time error pre-checking and generate defensive code and error tracing chains. When a type mismatch is detected, error location information is output. A code generator employs abstract syntax tree transformation-based code generation technology to automatically generate struct conversion code. Static analysis and optimization of the target code's control flow graph are performed to achieve code inlining and branch prediction. The conversion code includes field assignment statements and type conversion statements that convert source struct instances to target struct instances. The specific steps of the code generator in generating the conversion code include: analyzing the field mapping relationship to generate the signature of the conversion function, including parameter type definitions and return value type definitions; generating type-safe conversion statements based on the field types, specifically including… This includes: direct conversion statements for basic types, used to handle conversions between basic data types; parsing and formatting statements for time formats, used to handle time type conversions; conversion statements for numerical precision, used to handle conversions between values ​​of different precision; special processing statements for user-defined types, used to handle user-defined type conversions; generating null check code for pointer type fields, including null pointer checks and default value handling; generating error handling and exception handling code to ensure the safety of the conversion process; compiling the conversion code into executable conversion functions, which can convert and populate the data in the source structure instance into the target structure instance according to the field mapping relationship at runtime, or be provided as an independent function to the caller; in batch conversion scenarios, combining coroutine pool task sharding technology and cache preheating mechanism.

2. The method as described in claim 1, characterized in that, The field mapping rules include: field name exact match rules, used to handle cases where field names are exactly the same; field name case-insensitive match rules, used to handle cases where field names differ only in case; field name inclusion relationship match rules, used to handle cases where field names have inclusion relationships; and user-defined mapping rules, allowing users to specify mapping relationships between specific fields through configuration files.

3. The method as described in claim 1, characterized in that, The type conversion rules include: conversion rules between basic data types, used to handle conversions between basic types such as integers, floating-point numbers, and booleans; conversion rules between time types and string types, including parsing and formatting rules for time formats; conversion rules for numerical precision, used to handle conversions and precision control between numerical types with different precision; and conversion rules for custom types, supporting users to define conversion methods between specific types.

4. The method as described in claim 1, characterized in that, The field mapping rule engine includes: a field name matching module, used to match fields in the source structure and the target structure based on field name similarity, including complete matching, partial matching and fuzzy matching; a field type matching module, used to match fields in the source structure and the target structure based on field type correspondence, supporting type compatibility checks and automatic type conversion; and a priority processing module, used to determine the field mapping relationship when there is a matching conflict based on preset priority rules, including name priority, type priority or custom priority rules.

5. The method as described in claim 1, characterized in that, The transformation function also has the following characteristics: Supports batch conversion of multiple struct instances, including batch processing of arrays and slice collection types; It supports concurrent and safe conversion operations, ensuring data consistency in a multi-threaded environment through mutex locks or atomic operations; Provide error return values ​​to indicate exceptions during the conversion process, including error messages such as type mismatch and missing fields; Includes code logic for handling one or more of the following special cases: If the source structure contains fields that are not present in the target structure, the configuration determines whether to ignore them or log them. If the target structure contains fields that are not present in the source structure, set default values ​​or keep the fields with zero values ​​according to the configuration. When the types of fields with the same name in the source structure and the target structure do not match, attempt to perform type conversion or report an error. When performing nested struct conversions, the conversion of nested fields is processed recursively. When converting pointer type fields, null pointer checks and memory allocation logic are included.

6. The method as described in claim 1, characterized in that, Before automatically generating the structure conversion code based on the field mapping relationship using the code generator, the method further includes: generating or updating the field mapping relationship according to the field mapping configuration information input by the user through the graphical interface, specifically including: Configuration information is provided in JSON or YAML format to ensure readability and maintainability. Field mapping rule configuration supports customizing the correspondence and conversion rules between fields; Type conversion rule configuration allows you to customize conversion methods between specific types; Ignore field configuration and specify fields that do not need to be converted; Default value configuration sets default values ​​for optional fields in the target structure; provides configuration file validation and error message functionality to ensure the correctness and validity of the configuration; supports version control and backup management of configuration files for easy rollback and tracking of configurations.

7. A Go language struct automatic conversion system, characterized in that, include: The receiving module is used to receive a source structure definition and a target structure definition as input. Both the source structure definition and the target structure definition contain structure name, field name, and field type information. The field mapping rule engine is used to perform pre-compilation static analysis on the Go source code containing the source structure definition and the target structure definition, parse it into an abstract syntax tree (AST) to extract field definitions, and automatically match the fields in the source structure and the target structure according to the field name, field type and the relative position of the field in the structure, according to the priority rules preset by the configuration file or default mode, to generate field mapping relationships. The field mapping relationships include mapping rules from source structure fields to target structure fields and type conversion rules. The code generator constructs a directed acyclic graph (DAG) model for type conversion based on the field mapping relationship. Before code generation, it performs topology verification on the DAG model to perform compile-time error pre-checking and generates defensive code and error tracing chains. When a type mismatch is detected, it outputs error location information. The code generator uses abstract syntax tree transformation-based code generation technology to automatically generate struct conversion code and performs static analysis and optimization on the control flow graph of the target code to achieve code inlining and branch prediction. The conversion code includes field assignment statements and type conversion statements that convert source struct instances to target struct instances. The specific steps of the code generator in generating the conversion code are detailed below. The steps include: analyzing field mapping relationships to generate the signature of the conversion function, including parameter type definitions and return value type definitions; generating type-safe conversion statements based on field types, specifically including: direct conversion statements for basic types, used to handle conversions between basic data types; parsing and formatting statements for time formats, used to handle time type conversions; conversion statements for numeric precision, used to handle conversions between numeric values ​​of different precisions; special processing statements for custom types, used to handle user-defined type conversions; generating null check code for pointer type fields, including null pointer checks and default value handling; and generating error handling and exception handling code to ensure the safety of the conversion process. The compilation module is used to compile the conversion code into an executable conversion function. The conversion function can convert and fill the data in the source structure instance into the target structure instance according to the field mapping relationship at runtime, or it can be provided to the caller as an independent function. In batch conversion scenarios, the task sharding technology of the coroutine pool and the cache preheating mechanism are combined.

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