Efficient software component multiplexing method and system based on parallel computing

Abstract

The invention discloses a parallel computing-based efficient software component multiplexing method, which comprises the following steps of: retrieving matched software components in a component storage module by applying an intelligent search algorithm according to demand information input by a user, analyzing semantics and key features of demands, positioning the components meeting the demands by combining metadata tags of the components, and storing the components in a component storage module; transmitting the retrieved software components to a parallel processing module, and distributing multiplexing tasks to a plurality of computing nodes for simultaneous processing by adopting a parallel computing technology according to a dependency relationship and computing complexity among the components; in the aspect of load monitoring, the detailed load condition of each node is accurately monitored in real time, a reliable basis is provided for task scheduling, the performance evaluation module can accurately evaluate the system performance and the load balancing effect and provide visual reports and optimization suggestions, the configuration management module can adjust parameters according to different environments and requirements, automatic optimization is achieved, and the load balancing efficiency is improved. And the operation efficiency, the stability and the expandability of the parallel software are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of software component reuse methods, and more specifically, to an efficient software component reuse method and system based on parallel computing. Background Art

[0002] In today's software industry, the efficiency, quality and maintainability of software development face many challenges. With the continuous expansion of software scale and the increasing complexity of functional requirements, the traditional software development model has been unable to meet the rapidly developing business needs.

[0003] On the one hand, as an important part of software development, the reusability of software components has always been a key factor in improving development efficiency. However, there are many shortcomings in the existing software component management and reuse methods. In terms of component storage, traditional storage methods often lack effective classification and indexing mechanisms, resulting in low efficiency in finding and retrieving specific components. Developers often need to spend a lot of time looking for suitable components in a massive component library, which seriously affects the development progress. Moreover, the metadata description of the component is not detailed and accurate enough, which makes it difficult to accurately reflect the functional characteristics and applicable scenarios of the component, further increasing the difficulty of reuse.

[0004] On the other hand, in the process of reusing software components, the problem of computing efficiency has always been a bottleneck. Traditional reuse methods are usually serial processing, which cannot fully utilize the advantages of modern multi-core processors and distributed computing resources. When faced with large-scale software projects and complex component combinations, the reuse process takes too long, which seriously affects the iteration speed of software development. Especially in some time-sensitive software development projects, such as the development of real-time systems and large-scale enterprise applications, this inefficient reuse method may cause the project to be unable to be delivered on time, causing huge economic losses to the enterprise.

[0005] In addition, the diversity of software systems and the ever-changing operating environment also make it difficult to reuse software components. Different software systems have different architectures, programming languages, and operating platforms. How to ensure that the reused components can be smoothly integrated into the target system and perform at their best is an urgent problem to be solved. Traditional adaptation methods often require a lot of manual intervention and debugging, which not only increases development costs, but also easily introduces new errors and risks.

[0006] At the same time, as users' requirements for software quality and performance continue to increase, how to optimize the performance of software systems during reuse has also become a key issue. Traditional methods lack effective performance monitoring and optimization mechanisms, and it is difficult to automatically adjust the parameters and configuration of components according to actual operating conditions, resulting in the inability to fully improve the performance of the reused software system.

[0007] In summary, the existing software component reuse technology has obvious deficiencies in storage management, computing efficiency, adaptability and performance optimization. There is an urgent need for an efficient software component reuse method and system based on parallel computing to solve these problems, so as to improve the efficiency and quality of software development and meet the ever-changing market needs. Summary of the invention

[0008] The present invention proposes an efficient software component reuse method and system based on parallel computing to solve the problems mentioned in the above-mentioned prior art.

[0009] In order to achieve the above object, the present invention adopts the following technical solution: an efficient software component reuse method based on parallel computing, comprising the following steps:

[0010] S1. Based on the requirement information input by the user, the intelligent search algorithm is used to retrieve matching software components in the component storage module, analyze the semantics and key features of the requirements, and locate the components that meet the requirements in combination with the metadata tags of the components;

[0011] S2, the retrieved software components are transferred to the parallel processing module, and according to the dependencies between components and the computational complexity, the parallel computing technology is used to distribute the reused tasks to multiple computing nodes for simultaneous processing, and the task allocation optimization function is used Where T represents the completion time of the entire reuse task, n represents the number of components involved in reuse, and t i represents the processing time of the i-th component, r i represents the proportion of computing resources allocated to the i-th component) dynamically adjusts the allocation of computing resources to speed up the reuse process;

[0012] S3, transferring the components in the reuse process to the adaptation optimization module, adjusting the adaptability and performance of the components according to the architecture and operating environment of the target software system, and automatically adjusting the parameters and interfaces of the components to integrate them into the target system;

[0013] S4. Use machine learning algorithms to monitor and analyze the performance of the reused software system, and automatically adjust component configuration parameters based on the analysis results;

[0014] S5. Output the reused result and display it to the user.

[0015] Furthermore, when using intelligent search algorithms for component retrieval, a natural language processing model based on deep learning is used to perform semantic analysis and feature extraction on user requirement text. The model is trained with software component description text and user requirement samples.

[0016] Furthermore, in the adaptation and optimization process, a reinforcement learning algorithm is used to automatically learn the optimal adaptation and optimization scheme by continuously trying different parameter configurations and adjustment strategies. The reward function of the reinforcement learning algorithm is comprehensively determined based on the degree of improvement in system performance and the improvement in stability. The reward function formula is R=γ1×P+γ2×S, where R represents the reward value, γ1 and γ2 are weight coefficients, P represents the degree of improvement in system performance, and S represents the system.

[0017] Furthermore, when the results are output, the relevant information of the reused software system or component is displayed to the user in the form of interactive charts and reports. The user can view detailed component information and performance parameters by clicking on the component or data point in the chart.

[0018] Furthermore, during the reuse process, a data redundant storage strategy is adopted for the distributed file system in the component storage module. The proportion of data redundant storage is dynamically adjusted according to the importance and usage frequency of the component. The adjustment formula is R=α×F+β×I, where R represents the proportion of data redundant storage, α and β are adjustment coefficients, F represents the usage frequency of the component, and I represents the importance index of the component.

[0019] Furthermore, in the reuse process, we collect user feedback on the reuse results, analyze the feedback text using sentiment analysis technology, and automatically extract user satisfaction and improvement suggestions to optimize the search algorithm and adapt the optimization strategy. The accuracy evaluation formula of the sentiment analysis algorithm is: Where A represents the accuracy of the sentiment analysis algorithm, N correct Indicates the number of correctly analyzed feedback texts, N total Indicates the total number of feedback texts.

[0020] Further, an efficient software component reuse system based on parallel computing includes a user feedback module, a component storage module, a component retrieval module, a parallel processing module, an adaptation optimization module and a result output module;

[0021] The user feedback module is used to collect users' evaluations and suggestions on the reuse results, and transmit the user's feedback information to the component retrieval module and the adaptation optimization module for improving and optimizing the search algorithm and optimization strategy. The user feedback module uses sentiment analysis technology to analyze the user's feedback text and automatically extract user satisfaction and improvement suggestions.

[0022] Furthermore, the component storage module of the system also has a component version management function, which can store and manage different versions of software components. The version management adopts a timeline-based management method, and users can view and switch components of different versions. At the same time, when reusing components, the system will automatically select the most appropriate component version for reuse according to the needs and compatibility requirements of the target software system. The version selection algorithm is based on a compatibility evaluation function Where C represents the compatibility evaluation value, n represents the number of factors affecting compatibility, ω i Represents the weight coefficient of the factor, V i Represents the evaluation value of the th factor. By calculating the compatibility evaluation values ​​of components of different versions, the version with the highest compatibility is selected for reuse.

[0023] Furthermore, the parallel processing module of the system also considers the load of the computing nodes and the network bandwidth when allocating tasks. A load balancing factor is introduced into the task allocation algorithm, and its calculation formula is: Where L represents the load balancing factor, m represents the number of components currently assigned to the computing node, and r i represents the computing resource ratio of the i-th component, t i represents the estimated processing time of the i-th component, B represents the network bandwidth of the computing node, and by adjusting r i The value of makes the load balancing factors of each computing node close, achieving balanced utilization of computing resources and efficient execution of tasks.

[0024] Furthermore, it also includes a security authentication module for authenticating and authorizing the user's identity. The security authentication module adopts multi-factor authentication technology, including passwords, fingerprints, digital certificates and other authentication methods to ensure that only legitimate users can access and reuse software components. The security authentication module also encrypts data transmission and storage during the reuse process to ensure the security of the system and the privacy of the data. The strength evaluation indicators of its encryption algorithm include key length, complexity of the encryption algorithm and difficulty of cracking.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. Efficient component retrieval and reuse process:

[0027] By using intelligent search algorithms, especially natural language processing models based on deep learning to analyze user needs, matching software components can be found accurately and quickly from a huge component repository. This greatly reduces the time developers spend on finding suitable components and improves development efficiency. At the same time, the similar component recommendation function also provides developers with more choices and references, which helps to develop better software.

[0028] The application of parallel computing technology in the reuse process can distribute complex reuse tasks to multiple computing nodes for simultaneous processing, greatly shortening the completion time of reuse tasks. This is of great significance for the development of large-scale software projects, and can accelerate the software iteration cycle, enabling enterprises to bring products to market faster.

[0029] 2.Excellent adaptation and optimization capabilities:

[0030] The adaptation and optimization module can automatically adjust the parameters and interfaces of the components according to the architecture and operating environment of the target software system, ensuring that the components can be seamlessly integrated into the target system. This reduces the workload and error rate of manual adaptation and improves the stability and reliability of the software. At the same time, the machine learning algorithm is used to monitor and analyze the performance of the reused system, and the component configuration parameters can be automatically adjusted according to the actual operating conditions, so that the software system always maintains the best performance state.

[0031] The application of reinforcement learning algorithms in adaptation and optimization can continuously learn and explore the optimal adaptation and optimization solutions, and continuously improve the performance and adaptability of the software system as time goes by and the system runs.

[0032] 3. Reliable data management and security:

[0033] The application of data redundancy storage strategy in the component storage module ensures the security and reliability of component data. The redundant storage ratio is dynamically adjusted according to the importance and usage frequency of the component, which not only ensures the high availability of key component data, but also makes rational use of storage resources.

[0034] The multi-factor authentication technology and encryption processing measures of the security authentication module ensure data security and user privacy during the reuse of software components. Only legitimate users can access and reuse components, effectively preventing data leakage and illegal use, and providing a safe environment for enterprise software development.

[0035] 4. Good user experience and feedback mechanism:

[0036] The results are output in the form of interactive charts and reports, allowing users to intuitively understand the reuse results and system performance parameters. Users can easily view detailed component information and export the reuse results into files in multiple formats for easy archiving and sharing. This facilitates collaboration and knowledge sharing among developers.

[0037] The establishment of the user feedback module can collect users' evaluation and suggestions on the reuse results. Automatic extraction of user satisfaction and improvement suggestions through sentiment analysis technology will help to continuously improve the search algorithm and adaptation optimization strategy, so that the system can better meet user needs and improve user satisfaction and usage experience.

[0038] 5. Flexible version management and adaptability:

[0039] The component version management function enables the system to effectively manage different versions of software components. Users can view and switch different versions of components at any time, meeting the needs of different projects for component versions. At the same time, the system can automatically select the most appropriate component version for reuse based on the needs and compatibility requirements of the target software system, improving the success rate of reuse and the quality of the software system.

[0040] In summary, the software component reuse method and system of the present invention provides an efficient, safe and reliable solution for software development, which can greatly promote the development and innovation of the software industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a schematic block diagram of the efficient software component reuse method based on parallel computing proposed by the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0044] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, and it can be the internal connection of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0045] Reference Figure 1 : An efficient software component reuse method based on parallel computing, comprising the following steps:

[0046] S1. Based on the requirement information input by the user, the intelligent search algorithm is used to retrieve matching software components in the component storage module, analyze the semantics and key features of the requirements, and locate the components that meet the requirements in combination with the metadata tags of the components;

[0047] S2, the retrieved software components are transferred to the parallel processing module, and according to the dependencies between components and the computational complexity, the parallel computing technology is used to distribute the reused tasks to multiple computing nodes for simultaneous processing, and the task allocation optimization function is used Where T represents the completion time of the entire reuse task, n represents the number of components involved in reuse, and t i represents the processing time of the i-th component, r i represents the proportion of computing resources allocated to the i-th component) dynamically adjusts the allocation of computing resources to speed up the reuse process;

[0048] S3, transferring the components in the reuse process to the adaptation optimization module, adjusting the adaptability and performance of the components according to the architecture and operating environment of the target software system, and automatically adjusting the parameters and interfaces of the components to integrate them into the target system;

[0049] S4. Use machine learning algorithms to monitor and analyze the performance of the reused software system, and automatically adjust component configuration parameters based on the analysis results;

[0050] S5. Output the reused result and display it to the user.

[0051] In the present invention, when using an intelligent search algorithm for component retrieval, a natural language processing model based on deep learning is used to perform semantic analysis and feature extraction on user demand text, and the model is trained through software component description text and user demand samples.

[0052] In the present invention, in the adaptation and optimization process, a reinforcement learning algorithm is used to automatically learn the optimal adaptation and optimization scheme by continuously trying different parameter configurations and adjustment strategies. The reward function of the reinforcement learning algorithm is comprehensively determined based on the degree of improvement in system performance and the improvement in stability. The reward function formula is R=γ1×P+γ2×S, where R represents the reward value, γ1 and γ2 are weight coefficients, P represents the degree of improvement in system performance, and S represents the system.

[0053] In the present invention, when the results are output, the relevant information of the reused software system or component is displayed to the user in the form of interactive charts and reports. The user can view detailed component information and performance parameters by clicking on the component or data point in the chart.

[0054] In the present invention, during the reuse process, a data redundant storage strategy is adopted for the distributed file system in the component storage module, and the proportion of data redundant storage is dynamically adjusted according to the importance and usage frequency of the component. The adjustment formula is R=α×F+β×I, where R represents the proportion of data redundant storage, α and β are adjustment coefficients, F represents the usage frequency of the component, and I represents the importance index of the component.

[0055] In the present invention, in the reuse process, user feedback information on the reuse results is collected, and the feedback text is analyzed using sentiment analysis technology to automatically extract user satisfaction and improvement suggestions to optimize the search algorithm and adapt the optimization strategy. The accuracy evaluation formula of the sentiment analysis algorithm is: Where A represents the accuracy of the sentiment analysis algorithm, N correct Indicates the number of correctly analyzed feedback texts, N total Indicates the total number of feedback texts.

[0056] In the present invention, an efficient software component reuse system based on parallel computing includes a user feedback module, a component storage module, a component retrieval module, a parallel processing module, an adaptation optimization module and a result output module;

[0057] The user feedback module is used to collect users' evaluations and suggestions on the reuse results, and transmit the user's feedback information to the component retrieval module and the adaptation optimization module for improving and optimizing the search algorithm and optimization strategy. The user feedback module uses sentiment analysis technology to analyze the user's feedback text and automatically extract user satisfaction and improvement suggestions.

[0058] In the present invention, the component storage module of the system also has a component version management function, which can store and manage different versions of software components. The version management adopts a timeline-based management method, and users view and switch components of different versions. At the same time, when the system reuses components, it will automatically select the most appropriate component version for reuse according to the requirements and compatibility requirements of the target software system. The version selection algorithm is based on a compatibility evaluation function. Where C represents the compatibility evaluation value, n represents the number of factors affecting compatibility, ω i Represents the weight coefficient of the factor, V i Represents the evaluation value of the th factor. By calculating the compatibility evaluation values ​​of components of different versions, the version with the highest compatibility is selected for reuse.

[0059] In the present invention, the parallel processing module of the system also considers the load of the computing nodes and the network bandwidth when performing task allocation. A load balancing factor is introduced into the task allocation algorithm, and its calculation formula is: Where L represents the load balancing factor, m represents the number of components currently assigned to the computing node, and r i represents the computing resource ratio of the i-th component, t i represents the estimated processing time of the i-th component, B represents the network bandwidth of the computing node, and by adjusting r i The value of makes the load balancing factors of each computing node close, achieving balanced utilization of computing resources and efficient execution of tasks.

[0060] The present invention also includes a security authentication module for authenticating and authorizing the identity of the user. The security authentication module adopts multi-factor authentication technology, including multiple authentication methods such as passwords, fingerprints, digital certificates, etc., to ensure that only legitimate users can access and reuse software components. The security authentication module also encrypts data transmission and storage during the reuse process to ensure the security of the system and the privacy of the data. The strength evaluation indicators of its encryption algorithm include key length, complexity of the encryption algorithm and difficulty of cracking.

[0061] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and inventive concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An efficient software component reuse method based on parallel computing, characterized in that: The following steps are involved: S1. Based on the requirement information input by the user, the intelligent search algorithm is used to retrieve matching software components in the component storage module, analyze the semantics and key features of the requirements, and locate the components that meet the requirements in combination with the metadata tags of the components; S2, the retrieved software components are transferred to the parallel processing module, and according to the dependencies between components and the computational complexity, the parallel computing technology is used to distribute the reused tasks to multiple computing nodes for simultaneous processing, and the task allocation optimization function is used Where T represents the completion time of the entire reuse task, n represents the number of components involved in reuse, and t i represents the processing time of the i-th component, r i represents the proportion of computing resources allocated to the i-th component) dynamically adjusts the allocation of computing resources to speed up the reuse process; S3, transferring the components in the reuse process to the adaptation optimization module, adjusting the adaptability and performance of the components according to the architecture and operating environment of the target software system, and automatically adjusting the parameters and interfaces of the components to integrate them into the target system; S4. Use machine learning algorithms to monitor and analyze the performance of the reused software system, and automatically adjust component configuration parameters based on the analysis results; S5. Output the reused result and display it to the user.

2. The method for efficiently reusing software components based on parallel computing according to claim 1, characterized in that: When using intelligent search algorithms for component retrieval, a natural language processing model based on deep learning is used to perform semantic analysis and feature extraction on user requirement text. The model is trained using software component description text and user requirement samples.

3. The method for efficiently reusing software components based on parallel computing according to claim 1, characterized in that: In the adaptation and optimization process, the reinforcement learning algorithm is used to automatically learn the optimal adaptation and optimization scheme by continuously trying different parameter configurations and adjustment strategies. The reward function of the reinforcement learning algorithm is determined comprehensively based on the degree of improvement in system performance and the improvement in stability. The reward function formula is R=γ1×P+γ2×S, where R represents the reward value, γ1 and γ2 are weight coefficients, P represents the degree of improvement in system performance, and S represents the system.

4. The method for efficiently reusing software components based on parallel computing according to claim 1, characterized in that: When the results are output, the relevant information of the reused software system or component is displayed to the user in the form of interactive charts and reports. The user can view detailed component information and performance parameters by clicking on the component or data point in the chart.

5. The method for efficiently reusing software components based on parallel computing according to claim 1, characterized in that: During the reuse process, a data redundant storage strategy is adopted for the distributed file system in the component storage module. The proportion of data redundant storage is dynamically adjusted according to the importance and usage frequency of the component. The adjustment formula is R=α×F+β×I, where R represents the proportion of data redundant storage, α and β are adjustment coefficients, F represents the usage frequency of the component, and I represents the importance index of the component.

6. The method for efficiently reusing software components based on parallel computing according to claim 1, characterized in that: In the reuse process, collect user feedback on the reuse results, use sentiment analysis technology to analyze the feedback text, automatically extract user satisfaction and improvement suggestions, optimize the search algorithm and adapt the optimization strategy. The accuracy evaluation formula of the sentiment analysis algorithm is: Where A represents the accuracy of the sentiment analysis algorithm, N correct Indicates the number of correctly analyzed feedback texts, N total Indicates the total number of feedback texts.

7. A system for implementing the efficient software component reuse method based on parallel computing as described in any one of claims 1 to 6, characterized in that: It includes a user feedback module, a component storage module, a component retrieval module, a parallel processing module, an adaptation optimization module and a result output module; The user feedback module is used to collect users' evaluations and suggestions on the reuse results, and transmit the user's feedback information to the component retrieval module and the adaptation optimization module for improving and optimizing the search algorithm and optimization strategy. The user feedback module uses sentiment analysis technology to analyze the user's feedback text and automatically extract user satisfaction and improvement suggestions.

8. The efficient software component reuse system based on parallel computing according to claim 7, characterized in that: The component storage module of the system also has a component version management function, which can store and manage different versions of software components. The version management adopts a timeline-based management method. Users can view and switch components of different versions. At the same time, when reusing components, the system will automatically select the most appropriate component version for reuse based on the needs and compatibility requirements of the target software system. The version selection algorithm is based on a compatibility evaluation function Where C represents the compatibility evaluation value, n represents the number of factors affecting compatibility, ω i Represents the weight coefficient of the factor, V i Represents the evaluation value of the th factor. By calculating the compatibility evaluation values ​​of components of different versions, the version with the highest compatibility is selected for reuse.

9. The efficient software component reuse system based on parallel computing according to claim 7, characterized in that: The parallel processing module of the system also considers the load of computing nodes and network bandwidth when allocating tasks. A load balancing factor is introduced into the task allocation algorithm, and its calculation formula is: Where L represents the load balancing factor, m represents the number of components currently assigned to the computing node, and r i represents the computing resource ratio of the i-th component, t i represents the estimated processing time of the i-th component, B represents the network bandwidth of the computing node, and by adjusting r i The value of makes the load balancing factors of each computing node close, achieving balanced utilization of computing resources and efficient execution of tasks.

10. The efficient software component reuse system based on parallel computing according to claim 7, characterized in that: It also includes a security authentication module for authenticating and authorizing the user's identity. The security authentication module uses multi-factor authentication technology, including passwords, fingerprints, digital certificates and other authentication methods to ensure that only legitimate users can access and reuse software components. The security authentication module also encrypts data transmission and storage during the reuse process to ensure the security of the system and the privacy of the data. The strength evaluation indicators of its encryption algorithm include key length, complexity of the encryption algorithm and difficulty of cracking.

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