Dynamic module operation management system based on programming language

Abstract

The invention discloses a dynamic module operation management system based on a programming language, and the system comprises a module analyzer which manages the dependency relationship between modules in the system, applies a compiling optimization technology before the modules are loaded, and optimizes the code execution through code expansion and constant folding; operation environment establishment: optimizing execution speed and resource scheduling by integrating a just-in-time compiling JIT technology and an optimization measure during operation; the module warehouse is used for controlling the retrieval and loading process of the module by adopting an optimized organization and index strategy; data structure optimization: using a skip list to adjust data structure design, and controlling data processing efficiency; dynamic optimization: the system dynamically adjusts an optimization strategy according to performance feedback and an operation environment, and controls improvement of self-adaptive performance; garbage collection: dynamically adjusting a garbage collection strategy by adopting a self-adaptive garbage collection algorithm, and reducing the module retrieval time, accelerating the module loading process and improving the data access efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of computer software, and in particular to a dynamic module operation management system based on programming language. Background Art

[0002] As the complexity of software development increases, modular programming has become an important trend in software development. Existing module management systems have certain limitations in handling module dependencies, optimizing execution efficiency, and resource scheduling. In order to improve the operating efficiency and maintainability of software systems, a more advanced dynamic module operation management system is needed. Summary of the invention

[0003] In view of the deficiencies of the prior art, the technical solution adopted by the present invention to solve the technical problems is: a dynamic module operation management system based on programming language, which can effectively manage the dependencies between modules, optimize code execution, improve operation efficiency, and dynamically adjust the optimization strategy to adapt to different operating environments. To achieve the above purpose, the present invention provides the following technical solutions:

[0004] Module Resolver: The module resolver is responsible for managing dependencies between modules in the system and applying compile-time optimization techniques, including code unrolling and constant folding, before modules are loaded to optimize code execution.

[0005] Runtime environment establishment: By integrating just-in-time compilation (JIT) technology and runtime optimization measures, this system optimizes execution speed and resource scheduling.

[0006] Module warehouse: adopts optimized organization and indexing strategies to control the retrieval and loading process of modules, including the use of skip lists to adjust the data structure design to control data processing efficiency.

[0007] Dynamic optimization: The system dynamically adjusts the optimization strategy based on performance feedback and operating environment to achieve adaptive performance improvement.

[0008] Garbage collection: Adopt adaptive garbage collection algorithm, dynamically adjust garbage collection strategy, and optimize resource reuse.

[0009] Furthermore, the above technical solution is further expanded as follows:

[0010] Dynamic construction and optimization of module dependency graph:

[0011] The system can monitor the dependencies between modules in real time and build a dynamic dependency graph, which can be used to predict and optimize the module loading order, reduce dependency conflicts and loading time.

[0012] Module lifecycle management:

[0013] A module lifecycle management mechanism is introduced, including the stages of module creation, initialization, operation, update, and destruction, to ensure that the module can run efficiently and recycle resources in a timely manner throughout its lifecycle.

[0014] Security Enhancements:

[0015] Add a module security review mechanism to perform security checks on the source, content, and behavior of the module to prevent the execution of malicious code and improve system security.

[0016] Cross-Platform Compatibility:

[0017] Through the design of the abstract layer, the system can support multiple operating systems and hardware platforms, improving the portability of the module and the compatibility of the system.

[0018] Modular AI Integration:

[0019] The integrated modular artificial intelligence algorithm allows the system to automatically adjust the optimization strategy according to user behavior and system status, achieving more intelligent resource management and performance optimization.

[0020] User-defined optimization strategy:

[0021] Providing interfaces allows advanced users to customize optimization strategies, including but not limited to compilation options, memory allocation strategies, and garbage collection algorithms, to suit specific application requirements.

[0022] Module hot update mechanism:

[0023] Implement module hot update technology, allowing modules to be updated without restarting the system, reducing system downtime and improving system availability.

[0024] The beneficial effects of the present invention are as follows:

[0025] The present invention reduces module retrieval time, speeds up module loading process and improves data access efficiency. The dynamic module operation management system of the present invention realizes comprehensive optimization of software performance and resource management through its innovative technical architecture and optimization strategy. The system significantly improves the execution speed and response capability of the program through real-time compilation and dynamic optimization technology, ensuring that the best performance can be achieved in various computing environments. At the same time, the adaptive garbage collection and intelligent memory management mechanism effectively reduce resource waste and improve the operation efficiency and stability of the system.

[0026] The system's module lifecycle management and security review mechanisms further enhance the reliability and security of the software, and ensure the integrity and privacy of user data. The module hot update function reduces the complexity and downtime of system maintenance, and improves the user's continuous use experience.

[0027] In addition, the custom optimization strategies and modular AI integration provided by the system have greatly improved the flexibility and efficiency of development work, shortened the time from product design to market, and accelerated the iteration and innovation of technology. The cross-platform compatibility design enables the system to be seamlessly integrated into different hardware and operating systems, broadening the application scenarios and enhancing market competitiveness.

[0028] Ultimately, this system not only promotes technological progress in the field of software development, but also provides a solid foundation for the integration and innovation of future technologies, and promotes the sustainable development and intelligent transformation of the entire industry. Through these comprehensive improvements, the present invention has brought far-reaching positive impacts to users, developers and the entire industry. DETAILED DESCRIPTION

[0029] The present invention is further described in detail below in conjunction with specific embodiments. The embodiments of the present invention are provided for the purpose of illustration and description, and are not intended to be exhaustive or to limit the present invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described to better illustrate the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

[0030] Example:

[0031] A dynamic module operation management system based on programming language, comprising:

[0032] Module resolver: manages dependencies between modules in the system and applies compile-time optimization techniques before modules are loaded, using code unrolling and constant folding to optimize code execution;

[0033] Runtime environment establishment: optimize execution speed and resource scheduling by integrating JIT technology and runtime optimization measures;

[0034] Module repository: uses optimized organization and indexing strategies to control the module retrieval and loading process;

[0035] Data structure optimization: Use jump tables to adjust data structure design and control data processing efficiency;

[0036] Dynamic optimization: The system dynamically adjusts the optimization strategy based on performance feedback and operating environment to control adaptive performance improvement;

[0037] Garbage collection: Adopt adaptive garbage collection algorithm, dynamically adjust garbage collection strategy, and optimize resource reuse.

[0038] The process of establishing the operating environment includes the following steps:

[0039] Runtime optimization: Use just-in-time compilation technology to dynamically optimize code execution based on data in the runtime environment;

[0040] Compile-time optimization: static code analysis is used to perform source code level optimization before module loading to reduce the computational load at runtime;

[0041] Memory management: Optimize memory through memory allocation and dynamic garbage collection strategies;

[0042] Concurrent processing: Use concurrent and asynchronous processing techniques to optimize task scheduling and resource sharing between modules.

[0043] The specific optimization plan for the module warehouse is as follows:

[0044] The module warehouse includes organizational strategies and organizational algorithms, and uses a multi-layer hash table structure to build organizational modules;

[0045] Multi-layer hash table structure construction: First, use a multi-layer hash table to organize modules. The top-level hash table is classified by module type. Each type of table entry points to the second-level hash table. The second-level hash table is classified according to the module version number. Then, a standard information model is designed for each module, including the module type, version number, dependency, and functional description. This information will then form the basis of the module index. Finally, when a new module is added to the warehouse, the system automatically extracts the module information and updates the multi-layer hash table and inverted index.

[0046] The index building process needs to ensure data consistency and security of multi-threaded or multi-process operations;

[0047] Build a lazy loading mechanism: Integrate the lazy loading mechanism to load module data into memory only when needed, which can effectively reduce the system startup time and the memory space occupied during runtime.

[0048] Combined with the preloading strategy, background preloading technology is used for frequently accessed modules to improve the response speed when users access them;

[0049] Inverted index: For the metadata and key attributes of the module, the inverted index is used for indexing;

[0050] Quickly retrieve related modules based on module attributes, including functions, authors, and dependencies, especially to improve retrieval efficiency when performing complex queries;

[0051] For frequently executed module searches and dependency lookups, the query algorithm is optimized to reduce the amount of computation and the number of storage accesses;

[0052] Implement query caching. For repeated query requests, directly return cached results to reduce repeated calculations.

[0053] Regularly maintain, organize and optimize the index structure to deal with index fragmentation problems caused by module updates and deletions.

[0054] Use performance monitoring tools to regularly evaluate the effectiveness of indexing strategies and adjust optimization strategies based on actual conditions.

[0055] The specific construction method of the multi-layer hash table structure in the module warehouse is as follows:

[0056] Step 1: Design the top-level hash table, including defining key-value pairs and structure design, where:

[0057] Define key-value pairs: The key of the top-level hash table is the type of module, including language, function, and usage field classification basis;

[0058] Structural design: Each key, i.e. module type, corresponds to a value, which is a pointer to the second-layer hash table;

[0059] Step 2: Design the second-layer hash table, which also includes defining key-value pairs and structure design, where:

[0060] Define key-value pairs: The key of the second-layer hash table is the version number of the module, which is a semantic version number;

[0061] Structural design: Each key, i.e., version number, corresponds to the storage location of a specific module and directly stores the data structure of the module itself;

[0062] Step 3: Build a classification system, including module registration and version checking, where:

[0063] Module registration: When a new module is added to the system, first check whether its type already exists in the top-level hash table. If not, create a new second-level hash table for the type and add it to the top-level hash table. If it exists, directly use the existing second-level hash table.

[0064] Version check: After determining the type of module, check whether the version number of the module already exists in the corresponding second-layer hash table. If not, add a new version number and a mapping of the corresponding module. If it exists, update and retain the existing information as needed.

[0065] Step 4: Implement module retrieval, including retrieval request, access to the top-level hash table, and access to the second-level hash table, where:

[0066] Retrieval request: search based on the module type and version number provided in the request;

[0067] Access the top-level hash table: search for the specified module type in the top-level hash table;

[0068] Access the second-layer hash table: After obtaining the location of the second-layer hash table through the top-layer hash table, further retrieve the specific module based on the version number;

[0069] Step 5: Maintenance and optimization, including dynamic updates and performance monitoring, including:

[0070] Dynamic update: When a module is updated or deleted, the records in the hash table are updated accordingly to maintain the accuracy and integrity of the data;

[0071] Performance monitoring: Regularly check and optimize the performance of the hash table to resolve hash conflicts and expand the hash table size.

[0072] Top-level hash table access uses the following formula:

[0073] TopIndex=(h(moduleType))mod n

[0074] Among them, TopIndex represents the index value finally calculated; h represents a hash function, which accepts a key moduleType and generates an integer. ModuleType represents the key to be hashed, which can be any type of data, depending on the design of the hash function. Mod represents the modulus operator, which is used to calculate the remainder of the division of two integers. n represents the divisor of the modulus operation, which is a positive integer used to limit the range of TopIndex.

[0075] Once the second-level hash table access locates the specific index of the top-level hash table, the following formula is used to select the specific version number:

[0076] SecondIndex=(h(version))mod m

[0077] Among them, SecondIndex represents the final index value obtained by hashing and modulo operations, h represents the hash function, which accepts a key version and obtains an integer, versionversion represents the key to be hashed, which refers to a version identifier, mod: modulo operator, used to calculate the remainder of the division of two integers, m: divisor of modulo operation, is a positive integer, used to determine the range of SecondIndex, specifically between 0 and m-1.

[0078] In the process of establishing the operating environment, each step includes the following steps:

[0079] Runtime optimization:

[0080] Just-in-time compilation technology: Converts intermediate code into machine code during program execution, identifies performance bottlenecks by monitoring the behavior of the program at runtime, and then optimizes these hot code. For example, the JIT compiler may identify repeated calculations in the loop body and convert them into a more efficient code path.

[0081] Dynamic optimization strategy: The system dynamically adjusts the execution of code based on data collected at runtime. For example, if a method is found to be called very frequently, the system may use inline expansion technology to embed the method code directly into the code that calls it, thereby reducing the overhead of method calls.

[0082] Compile-time optimizations:

[0083] Static code analysis: Before the code is compiled, static analysis tools examine the source code to identify potential bugs, performance issues, or coding style issues. These tools can identify unused variables, redundant code, complex expressions, etc.

[0084] Source code level optimization: Based on the results of static analysis, the compiler can apply a variety of optimization techniques, such as constant folding, dead code elimination, loop optimization, etc. These optimizations can reduce the execution time and memory usage of the code without changing the program behavior.

[0085] Memory Management:

[0086] Memory allocation mechanism: The system needs to design an efficient memory allocation algorithm to quickly respond to the module's memory request while reducing memory fragmentation. This may include the use of memory pools, object caches, and other technologies.

[0087] Dynamic garbage collection: The garbage collector is responsible for identifying and reclaiming memory that is no longer in use. It usually uses algorithms such as mark-sweep, copying, or generational collection to ensure the efficiency of memory recycling and reduce the impact on program operation.

[0088] Concurrent processing:

[0089] Concurrency model: The system can adopt a variety of concurrency models, such as multi-threading, event-driven, coroutines, etc., to support high concurrent execution. These models allow the system to execute multiple tasks simultaneously on a single-core or multi-core processor.

[0090] Asynchronous programming: Asynchronous programming allows the program to continue executing other tasks while waiting for certain operations (such as network requests, file I / O) to complete. This is usually achieved through mechanisms such as callback functions, Promises, and Futures.

[0091] Task scheduling: The system requires an efficient task scheduler that can balance the execution of tasks and prevent some tasks from occupying CPU resources for a long time, thereby improving the responsiveness and throughput of the system.

[0092] Resource sharing and synchronization: The system needs to properly manage access to shared resources to avoid race conditions and deadlocks. This may include the use of synchronization mechanisms such as locks, semaphores, condition variables, etc.

[0093] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without creative work should fall within the scope of protection of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention are implemented according to the conventional means in the field unless otherwise specified and limited.

Claims

1. A dynamic module operation management system based on programming language, characterized in that: include: Module resolver: manages dependencies between modules in the system and applies compile-time optimization techniques before modules are loaded, using code unrolling and constant folding to optimize code execution; Runtime environment establishment: optimize execution speed and resource scheduling by integrating JIT technology and runtime optimization measures; Module repository: uses optimized organization and indexing strategies to control the module retrieval and loading process; Data structure optimization: Use jump tables to adjust data structure design and control data processing efficiency; Dynamic optimization: The system dynamically adjusts the optimization strategy based on performance feedback and operating environment to control adaptive performance improvement; Garbage collection: Adopt adaptive garbage collection algorithm, dynamically adjust garbage collection strategy, and optimize resource reuse.

2. A dynamic module operation management system based on programming language according to claim 1, characterized in that: The process of establishing the operating environment includes the following steps: Runtime optimization: Use just-in-time compilation technology to dynamically optimize code execution based on data in the runtime environment; Compile-time optimization: static code analysis is used to perform source code level optimization before module loading to reduce the computational load at runtime; Memory management: Optimize memory through memory allocation and dynamic garbage collection strategies; Concurrent processing: Use concurrent and asynchronous processing techniques to optimize task scheduling and resource sharing between modules.

3. The dynamic module operation management system based on programming language according to claim 1, characterized in that: The specific optimization plan for the module warehouse is as follows: The module warehouse includes organizational strategies and organizational algorithms, and uses a multi-layer hash table structure to build organizational modules; Multi-layer hash table structure construction: First, use a multi-layer hash table to organize modules. The top-level hash table is classified by module type. Each type of table entry points to the second-level hash table. The second-level hash table is classified according to the module version number. Then, a standard information model is designed for each module, including the module type, version number, dependency, and functional description. This information will then form the basis of the module index. Finally, when a new module is added to the warehouse, the system automatically extracts the module information and updates the multi-layer hash table and inverted index. Build lazy loading mechanism: Integrate lazy loading mechanism to load module data into memory only when needed; Combined with the preloading strategy, background preloading technology is used for frequently accessed modules; Inverted index: For the metadata and key attributes of the module, the inverted index is used for indexing; Quickly retrieve related modules based on module attributes, including functions, authors, and dependencies; Optimize query algorithms for frequently executed module searches and dependency lookups; Implement query caching and directly return cached results for repeated query requests; Regularly maintain, organize and optimize the index structure; Use performance monitoring tools to regularly evaluate the effectiveness of indexing strategies and adjust optimization strategies based on actual conditions.

4. The dynamic module operation management system based on programming language according to claim 3, characterized in that: The specific construction method of the multi-layer hash table structure in the module warehouse is as follows: Step 1: Design the top-level hash table, including defining key-value pairs and structure design, where: Define key-value pairs: The key of the top-level hash table is the type of module, including language, function, and usage field classification basis; Structural design: Each key, i.e. module type, corresponds to a value, which is a pointer to the second-layer hash table; Step 2: Design the second-layer hash table, which also includes defining key-value pairs and structure design, where: Define key-value pairs: The key of the second-layer hash table is the version number of the module, which is a semantic version number; Structural design: Each key, i.e., version number, corresponds to the storage location of a specific module and directly stores the data structure of the module itself; Step 3: Build a classification system, including module registration and version checking, where: Module registration: When a new module is added to the system, first check whether its type already exists in the top-level hash table. If not, create a new second-level hash table for the type and add it to the top-level hash table. If it exists, directly use the existing second-level hash table. Version check: After determining the type of module, check whether the module version number already exists in the corresponding second-layer hash table. If not, add a new version number and a mapping of the corresponding module. If it exists, update and retain the existing information as needed. Step 4: Implement module retrieval, including retrieval request, access to the top-level hash table, and access to the second-level hash table, where: Retrieval request: search based on the module type and version number provided in the request; Access the top-level hash table: search for the specified module type in the top-level hash table; Access the second-layer hash table: After obtaining the location of the second-layer hash table through the top-layer hash table, further retrieve the specific module based on the version number; Step 5: Maintenance and optimization, including dynamic updates and performance monitoring, including: Dynamic update: When a module is updated or deleted, the records in the hash table are updated accordingly; Performance monitoring: Regularly check and optimize the performance of the hash table to resolve hash conflicts and expand the hash table size.

5. A dynamic module operation management system based on programming language according to claim 4, characterized in that: Top-level hash table access uses the following formula: TopIndex=(h(moduleType))mod n Among them, TopIndex represents the index value finally calculated; h represents a hash function, which accepts a key moduleType and generates an integer. ModuleType represents the key to be hashed, which can be any type of data, depending on the design of the hash function. Mod represents the modulus operator, which is used to calculate the remainder of the division of two integers. n represents the divisor of the modulus operation, which is a positive integer used to limit the range of TopIndex. Once the second-level hash table access locates the specific index of the top-level hash table, the following formula is used to select the specific version number: SecondIndex=(h(version))mod m Among them, SecondIndex represents the final index value obtained by hashing and modulo operations, h represents the hash function, which accepts a key version and obtains an integer, versionversion represents the key to be hashed, which refers to a version identifier, mod: modulo operator, used to calculate the remainder of the division of two integers, m: divisor of modulo operation, is a positive integer, used to determine the range of SecondIndex, specifically between 0 and m_1.

6. The dynamic module operation management system based on programming language according to claim 2, characterized in that: In the process of establishing the operating environment, each step includes the following steps: Runtime optimization: Just-in-time compilation technology: During program execution, the intermediate code is converted into machine code. By monitoring the behavior of the program at runtime, performance bottlenecks are identified and then these hot code are optimized. Dynamic optimization strategy: The system dynamically adjusts the execution of code based on data collected during runtime; Compile-time optimizations: Static code analysis: Static analysis tools examine the source code before the code is compiled. Static analysis tools can identify unused variables, redundant code, and complex expressions. Source code optimization: Based on the results of static analysis, the compiler applies a variety of optimization techniques to reduce code execution time and memory usage without changing program behavior; Memory Management: Memory allocation mechanism: The system designs an efficient memory allocation algorithm to quickly respond to module memory requests while reducing memory fragmentation; Dynamic garbage collection: The garbage collector is responsible for identifying and reclaiming memory that is no longer in use, using a mark-sweep, copy algorithm; Concurrent processing: Concurrency model: The system can adopt multi-threaded, event-driven and coroutine comprehensive concurrency models, which allow the system to execute multiple tasks simultaneously on single-core and multi-core processors; Asynchronous programming: Asynchronous programming allows the program to continue executing other tasks while waiting for network requests and file I / O operations to complete. It is implemented through callback functions, Promises, and Futures mechanisms; Task scheduling: The system balances the execution of tasks through the task scheduler; Resource sharing and synchronization: The system manages access to shared resources through synchronization mechanisms using locks, semaphores, and condition variables.