Go language structural body automatic conversion method and system

Abstract

The invention relates to the technical field of computer programming, and discloses a Go language structural body automatic conversion method and system. The method comprises the following steps: firstly, receiving definitions of a source structure body and a target structure body as input, automatically matching fields of the two structure bodies through a field mapping rule engine according to a preset rule, and generating a field mapping relationship; then, based on the field mapping relation, a code generator is used for automatically generating a conversion code containing field assignment and type conversion logic; finally, the generated conversion codes are compiled into an executable conversion function, and the function can convert data in the source structure instance according to the field mapping relation during program running and fill the data into the target structure instance. According to the method, automation of structural body conversion is achieved, the problem that errors are prone to occurring when conversion codes are written manually is solved, and the maintainability of the codes is improved; the mode of reflection during operation is replaced by code generation, so that the conversion efficiency is remarkably improved; meanwhile, the method supports batch conversion and concurrent processing, and can meet the performance requirements of large-scale data processing.

Description

Technical Field

[0001] This application relates to the field of computer programming technology, and particularly to the field of Go language struct data conversion technology. Background Art

[0002] In Go language application development, structs are the basic tools for data modeling. With the increase in the scale and complexity of application systems, the need to transfer and convert data between different modules is growing. For example, it is necessary to convert database entity structs into business logic structs, business logic structs into API response structs, or complete compatibility conversions of structs in microservice communication.

[0003] Currently, there are mainly two technical solutions for struct conversion: one is for developers to manually write the mapping logic of struct fields, and the other is to use reflection technology to perform field conversion at runtime. Both of these solutions have obvious limitations. Manually writing conversion code not only involves a large amount of work, but also is prone to missing fields or assignment errors. When the fields of a struct change, the corresponding conversion code needs to be modified synchronously, which increases the maintenance cost and the risk of errors. Although using reflection technology can reduce the amount of code written, reflection operations bring additional runtime overhead and affect program performance. At the same time, it is difficult to detect type errors during compilation in the reflection method, which may lead to runtime exceptions.

[0004] In large-scale data processing scenarios, these problems are more prominent. For example, in a microservice architecture, services may need to frequently perform a large number of data structure conversions. If the reflection method is used, the performance overhead will increase significantly with the increase in the amount of data; if the method of manually writing conversion code is used, the workload of code maintenance will increase rapidly with the increase in the number of services.

[0005] In addition, the existing conversion schemes lack a unified processing mechanism for special cases, such as situations where field types do not exactly match, field names are inconsistent, and nested struct conversions. This requires developers to write a large amount of adaptation code, increasing the development complexity. At the same time, the lack of perfect error handling and test 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 work of struct conversion while ensuring conversion correctness and type safety, and at the same time has the characteristics of high efficiency, flexibility, and scalability to meet the requirements of different business scenarios. Summary of the Invention

[0007] The purpose of this application is to provide a method and system for automatic conversion of Go language structs to solve the problems raised in the above background art.

[0008] This application discloses a method for automatic conversion of Go language structs, including the following steps:

[0009] Receive the source struct definition and the target struct definition as inputs, where both the source struct definition and the target struct definition include the struct name, field name, and field type information;

[0010] According to the field mapping rules preset through a configuration file or default method, automatically match the fields in the source struct definition and the target struct definition through a field mapping rule engine to generate a field mapping relationship, where the field mapping relationship includes the mapping rule and type conversion rule from the source struct field to the target struct field;

[0011] Based on the field mapping relationship, automatically generate struct conversion code through a code generator, where the conversion code includes field assignment statements and type conversion statements for converting a source struct instance into a target struct instance;

[0012] Compile the conversion code to generate an executable conversion function, which can convert and fill the data in the source struct instance into the target struct instance according to the field mapping relationship during program runtime, or be provided to the caller as an independent function for use.

[0013] In a preferred example, the field mapping rules include: the field name exact match rule for handling cases where the field names are exactly the same; the field name case-insensitive match rule for handling cases where the field names only differ in case; the field name inclusion relationship match rule for handling cases where there is an inclusion relationship between the field names; and user-defined mapping rules that allow users to specify the mapping relationship between specific fields through a configuration file.

[0014] In a preferred example, the type conversion rules include: the conversion rules between basic data types for handling conversions between basic types such as integers, floating-point numbers, and booleans; the conversion rules between time types and string types, including time format parsing and formatting rules; the conversion rules for numerical precision for handling conversions between numerical types with different precisions and precision control; and the conversion rules for custom types that support users to define conversion methods between specific types.

[0015] In a preferred embodiment, the field mapping rule engine includes: a field name matching module for matching fields in the source structure and the target structure according to the similarity of field names, including exact match, partial match, and fuzzy match; a field type matching module for matching fields in the source structure and the target structure according to the corresponding relationship of field types, supporting type compatibility check and automatic type conversion; a priority processing module for determining the field mapping relationship in case of matching conflicts according to the preset priority rules, including name priority, type priority, or custom priority rules.

[0016] In a preferred embodiment, the code generator generates conversion code through 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 type, 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 numerical values with different precisions;

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

[0021] In a preferred embodiment, the conversion function also has the following characteristics:

[0022] Support batch conversion of multiple structure instances, including batch processing of collection types such as arrays and slices;

[0023] Support concurrent-safe conversion operations, ensuring data consistency in a multi-threaded environment through mutex locks or atomic operations;

[0024] Provide error return values to indicate exceptions during the conversion process, including error messages for type mismatches and missing fields;

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

[0026] 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;

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

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

[0029] When performing nested structure conversion, recursively process the conversion of nested fields;

[0030] When converting fields of pointer type, include null pointer check and memory allocation logic.

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

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

[0033] Field mapping rule configuration, supporting customizing the corresponding relationship and conversion rules between fields;

[0034] Type conversion rule configuration, allowing customizing the conversion method between specific types;

[0035] Ignored field configuration, specifying the fields that do not need to be converted;

[0036] Default value configuration, setting default values for optional fields in the target structure; providing verification and error prompt functions for the configuration file to ensure the correctness and effectiveness of the configuration; supporting version control and backup management of the configuration file for convenient rollback and tracking of the configuration.

[0037] This application also discloses a Go language structure automatic conversion system, including:

[0038] A receiving module, used to receive the source structure definition and the target structure definition as inputs, and both the source structure definition and the target structure definition include structure name, field name, and field type information;

[0039] A field mapping rule engine, used to automatically match the fields in the source structure definition and the target structure definition according to the field mapping rules preset through a configuration file or by default, and generate a field mapping relationship, where the field mapping relationship includes the mapping rules and type conversion rules from the source structure fields to the target structure fields;

[0040] A code generator, used to automatically generate 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 compilation module for compiling the conversion code into an 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 execution, or be provided to the caller as an independent function for use.

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

[0043] Greatly improve the efficiency and performance of structure conversion. Specifically, this application adopts pre-compilation period static analysis and dynamic code generation technology, avoiding the runtime performance overhead of traditional reflection schemes. In large-scale data conversion scenarios, compared with the reflection scheme, it can achieve a performance improvement of up to 200%. At the same time, intelligent code inlining and branch prediction technology make the generated code execution efficiency close to handwritten code. Especially during batch conversion, through the coroutine pool task sharding and cache warm-up mechanism, the consumption of CPU and memory resources is effectively reduced.

[0044] Significantly reduce the workload and error risk of developers. Specifically, the traditional way of manually writing structure conversion code is cumbersome, error-prone, and has poor maintainability. And this application can reduce the code writing workload of developers by more than 70% by automatically generating type-safe and efficient conversion code. At the same time, the precise field matching algorithm and compilation period error checking mechanism greatly reduce the error risk of the conversion code and improve the development quality and efficiency.

[0045] Greatly shorten the time for problem troubleshooting and debugging. Specifically, the unique error handling mechanism of this application pre-checks compilation period errors by constructing a directed acyclic graph model of type conversion and generates defensive code, making the conversion code more robust. At the same time, the error tracking chain and context-aware logging system can accurately locate the location and cause of the problem. In practical applications, this set of mechanisms can shorten the problem location time by 80%, greatly reducing the difficulty and cost of system maintenance.

[0046] Good scalability and flexibility, adapting to various complex business scenarios. Specifically, this application adopts a configuration-driven architecture design, supporting declarative configuration files and plug-in extensions. Users can easily define complex conversion rules and custom type converters according to their business needs without modifying the core code. Especially in the microservice architecture, this application can also be used as an independent conversion service to provide services externally through standard API interfaces. This enables this application to flexibly 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 technological innovation and optimization. Specifically, this application has forward-looking in architecture design, adopting advanced concepts such as plug-in and service-oriented, and leaving sufficient extension points. In the future, it can be very convenient to integrate new technical means, such as optimizing the field matching algorithm through machine learning, and dynamically adjusting the code generation strategy through adaptive optimization. At the same time, the perfect error handling mechanism and logging system also provide a data basis for the continuous optimization and problem analysis of the system. This provides a broad imagination space for the continuous evolution and technological upgrade of this application.

[0048] In summary, this application has demonstrated remarkable technical effects and practical application values in multiple dimensions such as improving development efficiency, ensuring code quality, optimizing operation performance, and adapting to business requirements. The realization of these effects benefits from a number of technological innovations and concept integrations in aspects such as architecture design, algorithm implementation, and engineering optimization of this application, and is the result of the synergistic effect of multiple key technologies, which is difficult to achieve through simple combination of existing technologies. Therefore, this 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 recorded in the specification of this application, distributed in various technical solutions. If all possible combinations of technical features (i.e., technical solutions) of this application are to be listed, it will make the specification too long. To avoid this problem, each technical feature disclosed in the above-mentioned invention content of this application, each technical feature disclosed in the following embodiments and examples, and each technical feature disclosed in the drawings can be freely combined with each other to form various new technical solutions (these technical solutions are all regarded as having been recorded in this specification), unless the combination of such technical features is technically infeasible. For example, in one example, features A+B+C are disclosed, and in another example, features A+B+D+E are disclosed, and features C and D are equivalent technical means that play the same role, and only one of them can be used technically and it is impossible to use both at the same time. Feature E can be combined with feature C technically. Then, the solution of A+B+C+D should not be regarded as having been recorded due to technical infeasibility, and the solution of A+B+C+E should be regarded as having been recorded. Brief Description of the Drawings

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

[0051] Figure 2 It is a schematic structural diagram of the Go language structure automatic conversion system according to the second embodiment of this application. Detailed Embodiments

[0052] In the following description, many technical details are presented to enable readers to better understand this application. However, those of ordinary skill in the art can understand that even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in this application can still be implemented.

[0053] Explanation of some concepts:

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

[0055] Field: Each data item in a struct is called a field, which consists 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 having a name and a type, a struct field can also have an optional tag. A tag is a string attached to the field, enclosed in backticks, which can provide meta-information for the field. Common tags include json, bson, xml, etc., which are used to guide the serialization and deserialization behavior of data.

[0057] Reflection: A mechanism for inspecting and modifying the behavior of a program at runtime. The Go language provides reflection capabilities, which can dynamically obtain type information and value information of variables at runtime, and can also dynamically call functions and methods. Reflection is often used in the implementation of general algorithms and frameworks.

[0058] Field Mapping: During the struct conversion process, it determines the correspondence between fields in the source struct and the target struct. Field mapping can be performed based on information such as field names, field types, and field tags for matching and association.

[0059] Code Generation: A technique for automatically generating code during compilation. Based on predefined templates and rules, it takes necessary information as input and automatically generates 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 performing syntax analysis on the code, an abstract syntax tree is generated, where the nodes of the tree represent various syntax structures in the code, such as statements, expressions, declarations, etc. The AST is an important intermediate representation form for code analysis and transformation.

[0061] Type Compatibility: When converting structs, it is a characteristic to determine whether the source struct field type can be converted to the target struct field type. Type compatibility determines the feasibility and safety of the conversion. Go has strict rules for converting between different types.

[0062] Error Handling: A mechanism for handling exceptional situations and error conditions during program execution. Go advocates explicit error handling, representing and passing errors by returning error values or panicking. Reasonable error handling can enhance the robustness and reliability of the program.

[0063] Unit Testing: A method for testing the correctness and behavior of code. Unit testing is performed on the smallest testable units of a program (usually functions or methods). Go has a built-in lightweight testing framework that makes it convenient to write and run unit tests.

[0064] The following outlines some of the innovative points of this application:

[0065] This application combines the field mapping rule engine and the code generator innovatively to implement a breakthrough method for automatic struct conversion. This method is different from the traditional runtime reflection scheme and adopts a hybrid technical architecture based on pre-compile-time static analysis and dynamic code generation. Specifically, the field mapping rule engine implements a multi-dimensional field matching algorithm. When processing struct field mapping, it not only considers the semantic similarity of field names but also combines the type compatibility matrix of fields and the relative position information of fields in the struct, thus achieving a higher field matching accuracy in complex business scenarios (such as cross-version data model conversion in a microservices architecture).

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

[0067] Another innovative point of this application lies in its unique error handling mechanism. By establishing a directed acyclic graph model for type conversion, it can pre-detect potential type conversion risks during the compilation period and generate 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 the error tracking chain and context-aware logging system.

[0068] In engineering practice, this application realizes high scalability through a configuration-driven architecture design. The system not only supports defining complex conversion rules through declarative configuration files but also can extend custom type converters through a plugin mechanism, enabling it to adapt to various special business requirements, such as handling data migration in legacy systems and data format conversion for different protocols. Especially in a microservices architecture, the service encapsulation ability of this 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, this application innovatively 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 in traditional reflection solutions but also reserves sufficient space for future expansion and optimization through its unique technical architecture. For example, the system can adaptively optimize the code generation strategy by collecting runtime performance metrics or optimize the accuracy of the field matching algorithm through machine learning techniques.

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

[0071] To make the purpose, technical solution, and advantages of this application clearer, the following will further describe the implementation manners of this application in detail with reference to the accompanying drawings.

[0072] The first implementation manner of this application relates to a method for automatic conversion of Go language structs, and its process is as Figure 1 shown. This method includes the following steps:

[0073] Step 100: Receive the source structure definition and the target structure definition as inputs. Both the source structure definition and the target structure definition contain structure name, field name, and field type information.

[0074] Step 200: According to the field mapping rules preset through a configuration file or in a default manner, automatically match the fields in the source structure definition and the target structure definition through a field mapping rule engine to generate a field mapping relationship. The field mapping relationship includes the mapping rule from the source structure field to the target structure field and the type conversion rule.

[0075] Step 300: Based on the field mapping relationship, automatically generate structure conversion code through a code generator. The conversion code includes field assignment statements and type conversion statements for converting a source structure instance into a target structure instance.

[0076] Step 400: Compile the conversion code to generate 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 during program runtime, or be provided to the calling party as an independent function for use.

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

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

[0079] For example, assume there are the following two structures:

[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 these two structures.

[0089] Step 200 is to generate a field mapping relationship according to a preset rule. The preset rule can come from a configuration file or be a default mapping rule. The field mapping rule engine will match the fields in the source structure and the target structure according to these rules and 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 required, such as directly assigning a value for string to string and directly assigning a value for int to int, 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 assignment statements between fields and necessary type conversion statements.

[0092] In the above example, the generated conversion code may be like this:

[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 the program to call during runtime. When the program needs to perform a structure conversion, it can call this automatically generated conversion function to convert and populate the data of the source structure into the target structure. The conversion function can also be used as an independent public function for other code to call.

[0100] After this step of compilation, the ConvertUserToDTO function can be used during runtime:

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

[0102] dto:=ConvertUserToDTO(user)

[0103] Generally speaking, this automatic conversion method simplifies the coding work of structure conversion by means of predefined conversion rules and automatic code generation, while ensuring the correctness of the conversion code. Developers no longer need to handwrite the conversion logic for each structure, but can automate this process through declarative mapping rules, greatly improving development efficiency.

[0104] The advantage of this method lies in separating the field mapping rules and automatically matching fields through the engine, rather than simply making a one-to-one mapping of strong types. This makes it possible for flexible field conversion, and at the same time, the rules can be customized by users through configuration files and other means, which is very flexible.

[0105] In addition, it is worth mentioning that code generation and compilation occur before the program runs. Therefore, the conversion function at runtime directly calls the already generated target code, and it has obvious advantages in performance compared with dynamic conversion methods such as reflection.

[0106] The idea of this method is very worthy of reference, that is, by statically analyzing the structure definition, generating mapping rules, then using the rules to generate target code, and finally compiling it into executable code, and the whole process does not require manual participation. This is also a good paradigm for automatically generating repetitive code in other scenarios.

[0107] Optionally, the field mapping rules include: a field name exact match rule for handling cases where the field names are exactly the same; a field name case-insensitive match rule for handling cases where the field names only differ in case; a field name inclusion relationship match rule for handling cases where there is an inclusion relationship between field names; and user-defined mapping rules that allow users to specify the mapping relationship between specific fields through a configuration file.

[0108] Optionally, the type conversion rules include: conversion rules between basic data types for handling conversions between basic types such as integers, floating-point numbers, and booleans; conversion rules between time types and string types, including time format parsing and formatting rules; conversion rules for numerical precision for handling conversions between numerical types with different precisions and precision control; and conversion rules for custom types that support users to define conversion methods between specific types.

[0109] Optionally, the field mapping rule engine includes: a field name matching module for matching fields in the source structure and the target structure according to the similarity of field names, including exact match, partial match, and fuzzy match; a field type matching module for matching fields in the source structure and the target structure according to the field type correspondence, supporting type compatibility checking and automatic type conversion; and a priority processing module for determining the field mapping relationship in case of matching conflicts according to the preset priority rules, including name priority, type priority, or user-defined priority rules.

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

[0111] Four possible field mapping rules:

[0112] 1) Field name exact match rule: That is, when there are fields with exactly the same name in the source structure and the target structure, a mapping relationship is directly established. For example, if the source structure has a field named "Name" and the target structure also has a field named "Name", then the two can be directly mapped.

[0113] 2) Field name case-insensitive match rule: This is a relaxation of the first rule, allowing different cases for field names. For example, "UserName" in the source structure can be mapped to "username" in the target structure.

[0114] username.

[0115] 3) Field name inclusion match rule: If the field name of one structure contains the field name of another structure, a mapping can also be established. For example, if the source structure has "HomeAddress" and the target structure has "Address", then the two can also be mapped.

[0116] 4) User-defined mapping rule: Allows users to explicitly specify special mapping relationships between certain fields through configuration or other means. This is very helpful for handling special cases.

[0117] Furthermore, the optional type conversion scenarios in the type conversion rule:

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

[0119] Conversion between time type and string type. The time type often has a specific format, and it needs to be correctly parsed and formatted during conversion.

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

[0121] Conversion of custom types. If the structure contains user-defined types, custom type conversion rules are required. This can be achieved by allowing users to provide a conversion function.

[0122] Optionally, the three main components of the engine in 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 several field name matching rules mentioned above. Full match, partial match (such as inclusion relationship), and fuzzy match (such as case-insensitivity) all fall within its responsibilities.

[0124] Field Type Matching Module: Responsible for checking whether the types of the two matched fields are compatible and whether type conversion can be performed.

[0125] Priority Processing Module: Used to resolve the conflict situation where a field may match multiple fields. By preset priority rules, such as higher matching degree first, name match prior to type match, etc., to determine the final mapping relationship.

[0126] Through these optional specific rules and engine implementations above, the entire field mapping process becomes very flexible and powerful, capable of handling various actual application scenarios. This is also a highlight of this method. It does not simply perform one-to-one strict matching, but instead automatically processes the mapping relationship through a set of intelligent rule engines, minimizing the workload of users and also maximizing the adaptation to different requirements. The design of the rule engine also has good scalability, allowing new rules to be flexibly added or the priority of rules to be adjusted under the original rule system to meet new conversion requirements.

[0127] Behind these rules, in fact, it reflects some requirements and problems during structure conversion, such as incomplete consistency of field names, incomplete sameness of field types, different matching emphases in different scenarios (such as whether name is important or type is important), etc. And if these problems all need to be handled manually by developers, it will be a very cumbersome and error-prone process. Introducing the rule engine well solves this problem, enabling developers to focus on the business logic itself and leaving these conversion details to the framework for processing.

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

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

[0130] · Parsing and formatting statements for time formats, used to handle time type conversions;

[0131] · Conversion statements for numerical precision, used to handle conversions of numerical values with different precisions;

[0132] · 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 security of the conversion process.

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

[0134] The first stage is to generate a function signature, which is equivalent to designing the "framework" of a function. 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 the return value.

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

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

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

[0138] Numeric precision conversion: This involves precision conversions between numeric 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. Such conversions require special attention to precision loss and rounding rules.

[0139] Custom type conversion: This handles user-defined special types and may require 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 security consideration. When dealing with pointer types, it is necessary to check whether it is a null pointer and provide a default value if necessary. This is like being extra careful when handling fragile items to ensure that the program does not crash due to improper handling.

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

[0142] Checking data validity

[0143] Handling possible conversion exceptions

[0144] Provide clear error messages

[0145] Ensure that the program can handle gracefully even when problems occur

[0146] These measures together constitute a complete protection net to ensure the security and reliability of the data conversion process. Just like when driving a car, one should not only 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. Each stage focuses on a specific task, and finally produces high-quality and reliable conversion code. This automated approach not only improves development efficiency but also reduces human errors, ensuring the accuracy and security of the conversion process.

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

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

[0150] Support batch conversion of multiple structure instances, including batch processing of collection types such as arrays and slices;

[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 the conversion process, including error information such as type mismatches and missing fields;

[0153] Contain code logic for handling one or more of the following special situations:

[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 default values or keep the fields as zero values according to the configuration;

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

[0157] · When performing nested structure conversion, recursively handle the conversion of nested fields;

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

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

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

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

[0162] Furthermore, the processing logic for special cases handles the following complex scenarios:

[0163] The case where the source struct has more fields than the target struct: For example, the source data contains detailed user information, but the API only needs to display basic information. In this case, you can choose to directly ignore the extra fields according to the configuration, or record logs for tracking. This is like receiving a box of fruits and choosing to only take the parts you need, and either storing the rest or simply discarding them.

[0164] The case where the target struct has more fields than the source struct: For example, the new version of the API needs to display more information, but the source data is still in the old format. In this case, you can set default values for the newly added fields, or keep them as zero values. This is similar to filling out a form and when encountering fields without corresponding information, you can choose to fill in the default values or leave them 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 attempt to perform reasonable type conversions. If the conversion fails, an error will be reported. This is like converting Celsius temperature to Fahrenheit temperature, where there are clear conversion rules to follow.

[0166] Handling of nested structs: To handle complex nested structures, each level of conversion needs to be processed in depth. 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, and then allocate new memory space when necessary. This is like when handling fragile items, you need to first check if the packaging is intact and then prepare new protective measures.

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

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

[0170] Optionally, before step 300, it 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:

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

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

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

[0174] · Ignored field configuration, specifying the fields that do not need to be converted;

[0175] · Default value configuration, setting default values for optional fields in the target structure; providing verification and error prompt functions for the configuration file to ensure the correctness and effectiveness of the configuration; supporting version control and backup management of the configuration file for convenient rollback and tracking of the configuration.

[0176] Optionally, after step 400, it further includes the following steps:

[0177] (1) Wrapping the conversion function into a RESTful API and providing a network call interface, specifically including:

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

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

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

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

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

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

[0184] - Step 460: Implement a request rate limiting and timeout handling mechanism;

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

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

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

[0188] (1) Use code generation to replace runtime reflection, specifically including: generating static conversion code during compilation and avoiding the performance overhead brought by reflection;

[0189] (2) Optimize parallel processing for batch conversion scenarios, specifically including: using a coroutine pool for concurrent processing; and implementing task sharding and load balancing;

[0190] (3) Perform inlining optimization on the conversion code, specifically including: reducing function call overhead; and merging duplicate conversion logic;

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

[0192] The automatic Go language structure conversion method proposed in this application provides a new technical solution for the technical problems of cumbersome, error-prone, inefficient, and poor maintainability of structure conversion code in current Go language development. Through the combination of technical means such as a field mapping rule engine, a code generator, and conversion functions, this application method automatically analyzes the definitions of the input source structure and target structure, generates efficient, secure, and maintainable conversion code, thereby improving the efficiency and quality of structure conversion in Go language development.

[0193] Specifically, the field mapping rules of this application adopt a new design method. 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 cooperate with the code generator to convert the obtained field mapping relationships into type-safe and efficient conversion code, solving the problems brought by manually writing conversion logic.

[0194] In terms of code generation, the code generator of this application implements specific technical features. It can generate corresponding type conversion statements according to field types, handle cases such as pointer types, nested structs, and custom types, ensuring the correctness and completeness of the generated conversion code. The code generator collaborates with the conversion function, and the conversion function executes the generated conversion code at runtime to implement functions such as batch conversion, concurrency safety, and error handling, reflecting the technical characteristics of this application in performance optimization and exception handling.

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

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

[0197] To better understand the technical solution of this application, the following will be described with a specific example. The details listed in this example are mainly for easy understanding and do not limit the protection scope of this application.

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

[0199] 1. Automatic field mapping technology

[0200] - Field mapping rule engine:

[0201] Provide an extensible field mapping rule engine that natively supports direct field name mapping. The engine can parse the definitions of the source struct and the target struct, and automatically match field names and types.

[0202] - Innovation point: Support user-defined mapping rules, such as specifying the correspondence between fields through a configuration file.

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

[0204] - Field Ignoring Mechanism:

[0205] Provide flexible ignoring rules, allowing developers to specify fields that do not need to be converted by marking fields or using configuration files.

[0206] - Innovation Point: Combine compile-time checking and runtime configuration to dynamically adjust the ignoring logic.

[0207] - Application Advantage: Reduce redundant conversion logic and improve the applicability of the tool.

[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 fields. This engine natively supports direct mapping based on field names, while also allowing users to customize mapping rules through configuration files. The configuration file uses the JSON format, and users can clearly specify the correspondence between source structure fields and target structure fields in it. In addition, the field mapping rule engine also implements a field ignoring mechanism. Developers can specify which fields do not need to be converted by adding tags to structure fields or setting ignoring rules in the configuration file.

[0209] 2. Support for Multi-Type Data Conversion

[0210] - Built-in Type Converters:

[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 Point: Implement flexible type recognition and conversion based on reflection or interface assertion technology.

[0213] - Application Advantage: Adapt to the requirements of different business scenarios and simplify the type conversion logic.

[0214] - Advanced Type Parser:

[0215] Implement the parsing of strings to complex data types (such as time and high-precision numerical values).

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

[0217] - Application Advantage: Ensure the accuracy of data during the conversion process and avoid format errors.

[0218] Specifically, the type conversion system in this example implements conversion support for multiple data types. The built-in type converter provides a unified conversion interface for handling conversions between common data types. For example, converting a time.Time type to a string type, or converting a decimal.Decimal type to a basic numeric type. The type converter uses reflection technology to identify field types and performs corresponding conversion operations according to preset conversion rules. For complex data types, specialized type parsers are implemented. These parsers can handle the parsing of strings to time types, supporting multiple time formats; they can also handle the parsing and conversion of high-precision numerical values to ensure that numerical precision is not lost during the conversion process.

[0219] 3. Unified Handling of Pointer Types and Non-Pointer Types**

[0220] - Pointer Type Support:

[0221] Provide a special processing mechanism for pointer objects, automatically check if the input is `nil`, and return a safe default value.

[0222] - Innovation Point: Design a unified pointer handling logic inside the tool to avoid manual `nil` checking.

[0223] - Application Advantage: Improve the robustness and security of the code.

[0224] Specifically, in terms of pointer type handling, this example implements a unified handling mechanism. When encountering a field of pointer type, the system will automatically check if it is nil. If a nil pointer is detected, the system will return the corresponding zero value or default value according to the field type, avoiding null pointer exceptions. This handling mechanism is integrated into the code generation template to ensure that the generated conversion code has a perfect null value handling logic.

[0225] 4. Optimization of Batch Conversion

[0226] - Batch Data Conversion Mechanism:

[0227] Provide an efficient batch data conversion function, supporting automatic traversal and conversion of slices and arrays.

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

[0229] - Application Advantage: Significantly improve the processing efficiency and meet the requirements of high-concurrency scenarios.

[0230] Specifically, to meet the requirements of batch data conversion, this example implements an optimized batch processing mechanism. When the input data is of slice or array type, the system will automatically adopt a parallel processing method. Specifically, using the goroutine feature of the Go language, the data is sliced and processed concurrently, and each goroutine is responsible for handling the conversion of a part of the data. The system will automatically adjust the concurrency degree according to the number of CPU cores and the data volume to achieve the optimal processing performance.

[0231] 5. Dynamic Code Generation

[0232] - Code Generation Module:

[0233] Dynamically generate Go language conversion code according to the source structure and target structure definitions input by the user.

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

[0235] - Application Advantage: Reduce repetitive labor and allow developers to 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 the Go language to generate a code framework, and then performs syntax analysis and optimization on the generated code through the `go / ast` package. The code generation process will consider various aspects such as field mapping rules, type conversion rules, and pointer handling to generate a complete implementation of the conversion function. The generated code contains necessary error handling and type checking logic to ensure the security of the conversion process.

[0237] 6. Integration with the Development Environment

[0238] - Tool Integration and Extension Interface:

[0239] Provide a command-line tool (CLI) that allows developers to quickly generate and debug conversion code.

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

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

[0242] Furthermore, for the convenience of developers, this example provides a command-line tool. This tool supports interactive operations. Developers can specify the definition files of the source structure and the target structure through command-line parameters, and 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, which can promptly detect potential type mismatch or configuration error problems.

[0243] Optionally, in this application, to further enhance the practicality and adaptability of the system, a series of advanced features are implemented, including functions such as configuration hot update, caching mechanism, transaction management, and remote call interfaces. The specific implementation methods of these features will be described in detail below.

[0244] Specifically, in terms of configuration hot update, this application adopts a dynamic configuration loading mechanism, enabling the system to update conversion rules in real time without restarting. The system continuously monitors changes in the configuration file through a file monitoring service. When a configuration change is detected, it automatically triggers the configuration reload process. To adapt to different usage scenarios, the system supports reading configuration information from multiple data sources, including JSON files, YAML files, relational databases, environment variables, and remote configuration centers, etc. When there are multiple configuration sources, the system will load the configurations in sequence according to the preset priority policy to ensure that there are no conflicts between the configurations. At the same time, the system also implements a configuration rollback mechanism. When the new configuration causes system exceptions, it can automatically roll back to the configuration of the previous stable version to ensure the continuous availability of the system.

[0245] In terms of performance optimization, this application introduces a multi-level caching mechanism. The system uses the LRU (Least Recently Used) algorithm to implement memory caching for storing recently frequently used conversion results. For cached data, the system sets a reasonable survival period (TTL) and avoids excessive memory occupation through a regular cleaning mechanism. In a distributed deployment scenario, the system also supports using distributed cache systems such as Redis as a secondary cache 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 to ensure data consistency.

[0246] To ensure the reliability of data conversion, this 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 the conversion, the system can automatically roll back to the state before the conversion according to the transaction log. In addition to automatic rollback, the system also supports user-defined rollback strategies, allowing users to choose full rollback or only roll back the data of the error part. For conversion failures, the system persists detailed failure information (including error fields, error types, data status, etc.) to a dedicated error log database for subsequent problem analysis and repair.

[0247] In terms of system integration, this application provides a remote call interface based on gRPC. By using ProtocolBuffers as the data serialization format, the system has significant advantages 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, this system can be easily called by other programming languages (such as Java, Python, C++, etc.). 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, this application also supports the WebSocket protocol, providing an ideal solution for real-time data conversion scenarios. Through the WebSocket long connection, the system can immediately push the results to the client after the conversion is completed, avoiding the latency caused by traditional HTTP polling. When an error occurs during the conversion, the system can also push the error information to the relevant clients in real time to help 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, this application implements a comprehensive encryption mechanism. For sensitive fields (such as passwords, ID numbers, bank account numbers, etc.), 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 algorithms such as SHA-256 or bcrypt for one-way hashing. In addition, the system also implements fine-grained access control, and administrators can set different data access permissions for different users.

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

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

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

[0253] First of all, this example provides an automated mechanism for generating and applying field mapping rules. Its technical characteristics are as follows: Through the field mapping rule engine, analyze the field definitions of the source structure and the target structure, and establish the mapping relationship between fields according to the similarity of field names or field types. This example supports specifying the corresponding relationship of fields through configuration files or tags of structure fields. The content to be protected in this example includes the generation method and implementation logic of field mapping rules, as well as the field mapping technology framework that supports dynamic rule definition and extension.

[0254] Secondly, the dynamic code generation technology is another key point in this example. Traditional structure conversion requires developers to manually write field assignment code, which is prone to errors and has a 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 the Go language. The content to be protected in this example includes the algorithm and implementation method of dynamic code generation, involving technologies such as the design of code templates, the parsing and modification of syntax trees.

[0255] This example provides support for multi-type data conversion. Structure fields may involve complex data types such as time.Time and decimal.Decimal. When converting these types, issues such as format compatibility and precision loss need to be considered. This example implements converters between common data types and also provides a type parser for converting 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, this example designs a unified pointer type processing mechanism. During the structure conversion process, for fields with a null pointer (nil), this mechanism avoids program exceptions by automatically detecting and returning default values. For the scenario of batch conversion of large-scale data, this example implements an optimization method based on parallel processing technology to improve the conversion performance by utilizing multi-core CPU resources. The content to be protected in this example includes the method for unified processing of pointer types and non-pointer types, as well as the design and performance optimization method of the batch conversion algorithm.

[0257] In terms of the field processing mechanism, this example provides field ignoring and differential processing functions. Through configuration files or structure tags, fields that do not need to be converted can be specified. For fields that require special processing, setting default values or performing specific format conversions is supported. The content to be protected in this example includes the definition and implementation of field ignoring rules, as well as the specific implementation logic of differential field processing.

[0258] In terms of development tool support, this example implements a command-line tool (CLI). This tool is used to generate structure conversion code and supports code debugging and testing. The content to be protected in this example includes the design and implementation method of the CLI tool, involving technical content such as user interaction, parameter parsing, and code generation.

[0259] In terms of performance and reliability, this example replaces reflection calls through code generation, reducing the performance overhead during runtime. For changes in structure definitions, this example implements an automatic adaptation mechanism, reducing the workload of code maintenance. The content to be protected in this example includes the implementation of relevant performance optimization algorithms, as well as the adaptive mechanism for structure changes.

[0260] In summary, through the above technical solutions, this example solves problems such as efficiency, correctness, and maintainability in Go language structure conversion. The key technical points and the content to be protected involved in this example, including 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, etc., constitute a complete set of structure conversion technical solutions.

[0261] The second embodiment of this application relates to a Go language structure automatic conversion system, and its structure is as Figure 2 shown. This Go language structure automatic conversion system includes:

[0262] A receiving module, used to receive the source structure definition and the target structure definition as inputs, where both the source structure definition and the target structure definition include structure name, field name, and field type information;

[0263] A field mapping rule engine is used to automatically match the fields in the source structure definition and the target structure definition according to the field mapping rules preset through a configuration file or in a default manner, and generate a field mapping relationship, where the field mapping relationship includes the mapping rules and type conversion rules from the source structure fields to the target structure fields;

[0264] A code generator is used to automatically generate structure conversion code based on the field mapping relationship, where the conversion code includes field assignment statements and type conversion statements for converting a source structure instance into a target structure instance;

[0265] A compilation module is used to compile the conversion code into an executable conversion function, and 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 during program execution, or be provided to the caller as an independent function for use.

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

[0267] It should be noted that those skilled in the art should understand that the implementation functions of the various modules shown in the above embodiments of the Go language structure automatic conversion system can be understood with reference to the relevant descriptions of the foregoing Go language structure automatic conversion method. The functions of the various modules shown in the above embodiments of the Go language structure automatic conversion system can be implemented by a program (executable instructions) running on a processor, or can also be implemented by specific logic circuits. If the above Go language structure automatic conversion system in the embodiments of the present application is implemented in the form of software function modules and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several 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 various embodiments of the present application. And the foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read Only Memory), magnetic disks, or optical discs that can store program codes. In this way, 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, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the method embodiments of the present application are implemented.

[0269] In addition, an embodiment of the present application further provides a Go language structure automatic conversion system, which includes a memory for storing computer-executable instructions, and a processor; the processor is configured to implement the steps in the above method embodiments when executing the computer-executable instructions in the memory. Among them, the processor may be a central processing unit (Central Processing Unit, abbreviated as "CPU"), or other general-purpose processors, digital signal processors (Digital Signal Processor, abbreviated as "DSP"), application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as "ASIC"), etc. The aforementioned memory may be a read-only memory (read-only memory, abbreviated as "ROM"), a random access memory (random access memory, abbreviated as "RAM"), a flash memory (Flash), a hard disk, or a solid-state drive, etc. The steps of the methods disclosed in the embodiments of the present application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.

[0270] It should be noted that in the application documents of this patent, relational 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 these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one" does not exclude the existence of another identical element in the process, method, article or device including the element. In the application documents of this patent, if it is mentioned that an act is performed according to a certain element, it means at least performing the act according to the element, including two cases: only performing the act according to the element, and performing the act according to the element and other elements. Expressions such as multiple, multiple times, multiple types, etc. include 2, 2 times, 2 types, and more than 2, more than 2 times, more than 2 types.

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

Claims

1. A Go language structure automatic conversion method, characterized in that: The following steps are involved: Receiving 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; According to the field mapping rules preset by the configuration file or the default mode, the fields in the source structure definition and the target structure definition are automatically matched by the field mapping rule engine to generate a field mapping relationship, wherein the field mapping relationship includes mapping rules and type conversion rules from the source structure field to the target structure field; Based on the field mapping relationship, a structure conversion code is automatically generated by a code generator, wherein the conversion code includes a field assignment statement and a type conversion statement for converting a source structure instance into a target structure instance; The conversion code is compiled to generate an executable conversion function, which can convert the data in the source structure instance according to the field mapping relationship and fill it into the target structure instance when the program is running, or provide it as an independent function for the caller to use.

2. The method according to claim 1, characterized in that The field mapping rules include: a field name complete matching rule, which is used to handle the situation where the field names are exactly the same; a field name case-ignoring matching rule, which is used to handle the situation where the field names are only different in case; a field name inclusion relationship matching rule, which is used to handle the situation where the field names have an inclusion relationship; and a user-defined mapping rule, which allows the user to specify the mapping relationship between specific fields through a configuration file.

3. The method according to claim 1, characterized in that The type conversion rules include: conversion rules between basic data types, which are used to process the conversion between basic types such as integers, floating-point numbers, and Boolean values; conversion rules between time types and string types, including the parsing and formatting rules of time formats; conversion rules for numerical precision, which are used to process the conversion and precision control between numerical types with different precisions; conversion rules for custom types, which support users to define conversion methods between specific types.

4. The method according to claim 1, characterized in that The field mapping rule engine includes: a field name matching module, which is used to match the fields in the source structure and the target structure according to the field name similarity, including full matching, partial matching and fuzzy matching; a field type matching module, which is used to match the fields in the source structure and the target structure according to the field type correspondence, and supports type compatibility checking and automatic type conversion; a priority processing module, which is used to determine the field mapping relationship when matching conflicts according to preset priority rules, including name priority, type priority or custom priority rules.

5. The method according to claim 1, characterized in that The code generator generates 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 a type-safe conversion statement according to the field type, specifically including: Direct conversion statements for basic types are used to handle conversions between basic data types; Parsing and formatting statements for time formats, used to handle time type conversion; Conversion statements for numerical precision are used to process conversions of numerical values ​​with different precisions; Special processing statements for user-defined types are used to handle user-defined type conversions; null value check code is generated for pointer type fields, including null pointer judgment and default value processing; error handling and exception catching code are generated to ensure the security of the conversion process.

6. The method according to claim 1, characterized in that The conversion function also has the following characteristics: Support batch conversion of multiple structure instances, including batch processing of collection types such as arrays and slices; Supports concurrent and safe conversion operations, and ensures data consistency in a multi-threaded environment through mutex locks or atomic operations; Providing error return values ​​to indicate exceptions in the conversion process, including type mismatch and field missing error information; Contains code logic to handle one or more of the following special cases: When there are fields in the source structure that are not in the target structure, the configuration determines whether to ignore or log them; When there are fields in the target structure that are not in the source structure, set the default value or keep the field as zero according to the configuration; When the types of the fields with the same name in the source and target structures do not match, try to perform type conversion or report an error; When converting nested structures, the conversion of nested fields is processed recursively; When converting pointer type fields, null pointer checking and memory allocation logic are included.

7. The method according to claim 1, characterized in that Before automatically generating the structure conversion code through the code generator based on the field mapping relationship, 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 uses JSON or YAML format to ensure readability and maintainability of the configuration; Field mapping rule configuration, supporting custom correspondence and conversion rules between fields; Type conversion rule configuration allows custom 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 prompt functions to ensure the correctness and effectiveness of the configuration; supports version control and backup management of configuration files to facilitate configuration rollback and tracking.

8. A Go language structure automatic conversion system, characterized in that: include: A receiving module, used for receiving a source structure definition and a target structure definition as input, wherein the source structure definition and the target structure definition both include a structure name, a field name and a field type information; A field mapping rule engine, for automatically matching the fields in the source structure definition and the target structure definition according to the field mapping rules preset by the configuration file or by default, and generating a field mapping relationship, wherein the field mapping relationship includes mapping rules and type conversion rules from the source structure field to the target structure field; A code generator, used to automatically generate a structure conversion code based on the field mapping relationship, wherein the conversion code includes a field assignment statement and a type conversion statement for converting a source structure instance into a target structure instance; A compiling module is used to compile the conversion code to generate an executable conversion function. The conversion function can convert the data in the source structure instance according to the field mapping relationship and fill it into the target structure instance when the program is running, or provide it as an independent function for the caller to use.

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