Software development application data processing method based on cloud computing

Abstract

The invention relates to the technical field of data processing, in particular to a cloud computing-based software development application data processing method, which comprises the following steps of: collecting original data generated by software development application from a plurality of heterogeneous data sources in real time through a distributed data acquisition module; cleaning, filtering and formatting the original data, removing noise data and redundant information, and generating a standardized data set; the standardized data set is fragmented according to a preset fragmentation strategy; analyzing the stored data in real time by using the parallel processing capability of cloud computing to generate an analysis result; a result storage and updating step; a data visualization step; a security and authority management step; and an anomaly detection and fault-tolerant processing step. According to the method, the data processing speed and the throughput of the system are improved, and the requirement for real-time feedback in an agile development environment is met.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method for processing software development application data based on cloud computing. Background Art

[0002] With the rapid development of cloud computing technology, the data processing model of software development applications has also undergone profound changes. Traditional software development usually relies on local computing resources, which are limited by hardware performance and storage capacity and cannot meet the needs of large-scale data processing. In recent years, emerging technologies such as microservice architecture, containerized deployment and DevOps have brought new vitality to software development, but also triggered complex technical challenges.

[0003] In large-scale software development projects, data from different teams, different tool chains, and different environments need to be efficiently integrated and processed. For example, version control data of code repositories, build logs of continuous integration systems, monitoring indicators of performance tests, and error reports from users, etc., these data types are different, the formats are diverse, and the data volume is huge. Existing processing methods usually adopt a decentralized management method and lack a unified data processing platform, resulting in data silos and information asymmetry.

[0004] In addition, with the promotion of agile development and continuous delivery concepts, the software development cycle has been greatly shortened, and the demand for real-time data analysis and feedback is growing. Traditional data processing methods are unable to provide effective decision support in a timely manner due to insufficient processing speed and real-time performance. At the same time, in a multi-cloud environment and hybrid cloud architecture, cross-cloud migration and consistency maintenance of data have also become urgent issues to be solved.

[0005] In terms of security, the software development process involves a large amount of sensitive data, such as source code, design documents, and user information. Existing technologies are not perfect in terms of data encryption, access control, and security auditing, which can easily lead to data leaks and security vulnerabilities. Summary of the invention

[0006] The present invention provides a method for processing software development application data based on cloud computing.

[0007] A method for processing software development application data based on cloud computing, comprising the following steps:

[0008] S1, data collection step: using a distributed data collection module to collect raw data generated by software development applications from multiple heterogeneous data sources in real time, the data sources including log files, databases, message queues, and third-party APIs;

[0009] S2, data preprocessing step: cleaning, filtering and formatting the raw data to remove noise data and redundant information and generate a standardized data set;

[0010] S3, data storage step: Store the standardized data set in a distributed database in the cloud according to the preset sharding strategy to support high-concurrency access and large-scale data processing;

[0011] S4, data analysis step: using the parallel processing capability of cloud computing to perform real-time analysis on the stored data and generate analysis results;

[0012] S5, result storage and update step: store the analysis results in the distributed cache system, and regularly refresh the cache data according to the preset update strategy;

[0013] S6, data visualization step: using visualization tools, the analysis results are presented to users in the form of charts or reports to support interactive data exploration;

[0014] S7, security and authority management step: perform authority control and encryption on data access and processing to ensure data security and privacy;

[0015] S8, anomaly detection and fault-tolerant processing steps: monitor anomalies in the data processing process, and use automatic retry and load balancing mechanisms to ensure high availability and stability of the system.

[0016] Optionally, the data collection step includes:

[0017] Dynamic collection strategy adjustment: adjust the frequency and method of data collection in real time according to changes in data sources and network conditions to optimize data transmission efficiency;

[0018] Data caching mechanism: cache temporary data locally to reduce frequent access to data sources and reduce network load;

[0019] The data preprocessing step specifically includes:

[0020] Outlier detection: using statistical methods to identify and process outliers in data;

[0021] Data completion: intelligently complete missing data to improve data integrity;

[0022] Feature extraction: Extract key features from raw data to reduce data dimension.

[0023] Optionally, the data storage step uses a distributed file system and a NoSQL database, supporting:

[0024] Data sharding and replication: Data is sharded according to the hash algorithm and replicated among multiple nodes to enhance data reliability;

[0025] Horizontal expansion: By increasing the number of nodes, linear expansion of storage capacity and processing power can be achieved.

[0026] Optionally, the data analysis step utilizes a real-time stream processing framework, specifically including:

[0027] Parallel computing: Decompose the analysis task into subtasks and assign them to different computing nodes for parallel processing;

[0028] In-memory computing: using memory to store data and intermediate results to increase computing speed;

[0029] Extensible algorithm library: supports a variety of data analysis algorithms, including statistical analysis and machine learning algorithms.

[0030] Optionally, the result storage and update step adopts a multi-level cache strategy, specifically including:

[0031] Local cache: Cache frequently used data on the client or application server to reduce access to remote caches;

[0032] Distributed cache: Use cache systems such as Redis or Memcached to store hot data and improve data reading speed;

[0033] Cache expiration policy: Set the lifetime of cached data and adjust it dynamically based on the frequency of data changes.

[0034] Optionally, the anomaly detection and fault tolerance processing steps adopt:

[0035] Real-time monitoring: Use monitoring tools to monitor the status and performance indicators of each system component in real time;

[0036] Automatic failover: Automatically switch tasks to a standby node when a component failure is detected;

[0037] Load balancing: Through scheduling algorithms, the load of each node is balanced to prevent performance bottlenecks.

[0038] Optionally, the load of each node is balanced through a scheduling algorithm to prevent performance bottlenecks, and a load balancing mechanism is introduced to evenly distribute tasks and data among the computing nodes, specifically including:

[0039] Real-time monitoring of node status: Use monitoring tools to continuously collect key performance indicators of each node, including CPU usage, memory usage, network bandwidth, and disk I / O, to obtain the real-time load status of each node.

[0040] The intelligent scheduling algorithm uses a polling algorithm to assign new tasks to each node in turn. It is simple and easy to implement, but it does not take into account the actual load of the node. A task queue is maintained in the central scheduler, and new data processing tasks will be placed in the queue. The scheduler takes tasks from the queue and assigns them to appropriate nodes based on the scheduling algorithm.

[0041] Optionally, the method further includes deployment of a multi-cloud environment, specifically including:

[0042] Cross-cloud data synchronization: realize data synchronization and consistency maintenance between different cloud service providers;

[0043] Seamless migration: supports migration of applications and data between multi-cloud environments;

[0044] Unified management platform: Provides a unified management interface for centralized management and scheduling of multi-cloud resources.

[0045] Beneficial effects of the present invention:

[0046] The present invention realizes efficient collection and cleaning of multi-source heterogeneous data through distributed data acquisition and preprocessing mechanism, performs outlier detection and data completion, improves data quality and integrity, solves the problems of data islands and inconsistent formats in traditional methods, and utilizes the parallel processing capabilities and real-time stream processing framework of cloud computing to perform real-time analysis of large-scale data. The application of parallel computing and in-memory computing technology greatly improves the speed of data processing and the throughput of the system, meeting the demand for real-time feedback in an agile development environment.

[0047] The present invention realizes load balancing of each computing node through intelligent scheduling algorithm to prevent performance bottlenecks. Real-time monitoring and dynamic adjustment of task allocation ensure that the system can maintain stable performance under high concurrency conditions, introduce role-based access control and data encryption transmission mechanism to ensure the security and privacy protection of sensitive data. The audit log function supports security auditing and problem tracing, enhances the security protection capability of the system, and adopts automatic failover and node health check mechanism to improve the reliability of the system. Multi-level cache strategy and data replication technology ensure high data availability and reduce service interruptions caused by single point failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 Schematic diagram of the method steps of an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0051] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0052] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0053] like Figure 1 As shown, a method for processing software development application data based on cloud computing includes the following steps:

[0054] S1, data collection step: through the distributed data collection module, the raw data generated by the software development application is collected in real time from multiple heterogeneous data sources, including log files, databases, message queues and third-party APIs;

[0055] S2, data preprocessing step: cleaning, filtering and formatting the raw data to remove noise data and redundant information and generate a standardized data set;

[0056] S3, data storage step: Store the standardized data set in a distributed database in the cloud according to the preset sharding strategy to support high-concurrency access and large-scale data processing;

[0057] S4, data analysis step: using the parallel processing capability of cloud computing to perform real-time analysis on the stored data and generate analysis results;

[0058] S5, result storage and update step: store the analysis results in the distributed cache system, and regularly refresh the cache data according to the preset update strategy;

[0059] S6, data visualization step: using visualization tools, the analysis results are presented to users in the form of charts or reports to support interactive data exploration;

[0060] S7, security and authority management step: perform authority control and encryption on data access and processing to ensure data security and privacy;

[0061] S8, anomaly detection and fault tolerance processing step: monitor anomalies in the data processing process, use automatic retry and load balancing mechanisms to ensure high availability and stability of the system;

[0062] Among them, each step is closely linked through data flow and control flow, realizing full-process automation from data collection to visual display.

[0063] The data collection steps include:

[0064] Dynamic collection strategy adjustment: adjust the frequency and method of data collection in real time according to changes in data sources and network conditions to optimize data transmission efficiency;

[0065] Data caching mechanism: cache temporary data locally to reduce frequent access to data sources and reduce network load;

[0066] The data preprocessing steps specifically include:

[0067] Outlier detection: using statistical methods to identify and process outliers in data;

[0068] Data completion: intelligently complete missing data to improve data integrity;

[0069] Feature extraction: Extract key features from raw data to reduce data dimension.

[0070] The data storage step uses a distributed file system and NoSQL database, supporting:

[0071] Data sharding and replication: Data is sharded according to the hash algorithm and replicated among multiple nodes to enhance data reliability;

[0072] Horizontal expansion: By increasing the number of nodes, linear expansion of storage capacity and processing power can be achieved.

[0073] The data analysis steps utilize the real-time stream processing framework, including:

[0074] Parallel computing: Decompose the analysis task into subtasks and assign them to different computing nodes for parallel processing;

[0075] In-memory computing: using memory to store data and intermediate results to increase computing speed;

[0076] Extensible algorithm library: supports a variety of data analysis algorithms, including statistical analysis and machine learning algorithms.

[0077] The result storage and update steps use a multi-level cache strategy, including:

[0078] Local cache: Cache frequently used data on the client or application server to reduce access to remote caches;

[0079] Distributed cache: Use cache systems such as Redis or Memcached to store hot data and improve data reading speed;

[0080] Cache expiration policy: Set the lifetime of cached data and adjust it dynamically based on the frequency of data changes.

[0081] The anomaly detection and fault tolerance processing steps are as follows:

[0082] Real-time monitoring: Use monitoring tools to monitor the status and performance indicators of each system component in real time;

[0083] Automatic failover: Automatically switch tasks to a standby node when a component failure is detected;

[0084] Load balancing: Through scheduling algorithms, the load of each node is balanced to prevent performance bottlenecks.

[0085] The scheduling algorithm is used to balance the load of each node, prevent performance bottlenecks, and introduce a load balancing mechanism to evenly distribute tasks and data among the computing nodes. Specifically, it includes:

[0086] Real-time monitoring of node status: Use monitoring tools to continuously collect key performance indicators of each node, including CPU usage, memory usage, network bandwidth, and disk I / O, to obtain the real-time load status of each node.

[0087] The intelligent scheduling algorithm uses a polling algorithm to assign new tasks to each node in turn. It is simple and easy to implement, but it does not take into account the actual load of the node. A task queue is maintained in the central scheduler, and new data processing tasks will be placed in the queue. The scheduler takes tasks from the queue and assigns them to appropriate nodes based on the scheduling algorithm.

[0088] The approach also includes the deployment of a multi-cloud environment, including:

[0089] Cross-cloud data synchronization: realize data synchronization and consistency maintenance between different cloud service providers;

[0090] Seamless migration: supports migration of applications and data between multi-cloud environments;

[0091] Unified management platform: Provides a unified management interface for centralized management and scheduling of multi-cloud resources.

[0092] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0093] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for processing software development application data based on cloud computing, characterized in that: The following steps are involved: S1, data collection step: using a distributed data collection module to collect raw data generated by software development applications from multiple heterogeneous data sources in real time, the data sources including log files, databases, message queues, and third-party APIs; S2, data preprocessing step: cleaning, filtering and formatting the raw data to remove noise data and redundant information and generate a standardized data set; S3, data storage step: Store the standardized data set in a distributed database in the cloud according to the preset sharding strategy to support high-concurrency access and large-scale data processing; S4, data analysis step: using the parallel processing capability of cloud computing to perform real-time analysis on the stored data and generate analysis results; S5, result storage and update step: store the analysis results in the distributed cache system, and regularly refresh the cache data according to the preset update strategy; S6, data visualization step: using visualization tools, the analysis results are presented to users in the form of charts or reports to support interactive data exploration; S7, security and authority management step: perform authority control and encryption on data access and processing to ensure data security and privacy; S8, anomaly detection and fault-tolerant processing steps: monitor anomalies in the data processing process, and use automatic retry and load balancing mechanisms to ensure high availability and stability of the system.

2. The method for processing software development and application data based on cloud computing according to claim 1, characterized in that: The data collection step comprises: Dynamic collection strategy adjustment: adjust the frequency and method of data collection in real time according to changes in data sources and network conditions to optimize data transmission efficiency; Data caching mechanism: cache temporary data locally to reduce frequent access to data sources and reduce network load; The data preprocessing step specifically includes: Outlier detection: using statistical methods to identify and process outliers in data; Data completion: intelligently complete missing data to improve data integrity; Feature extraction: Extract key features from raw data to reduce data dimension.

3. The method for processing software development and application data based on cloud computing according to claim 1, characterized in that: The data storage step uses a distributed file system and a NoSQL database to support: Data sharding and replication: Data is sharded according to the hash algorithm and replicated among multiple nodes to enhance data reliability; Horizontal expansion: By increasing the number of nodes, linear expansion of storage capacity and processing power can be achieved.

4. The method for processing software development application data based on cloud computing according to claim 1, characterized in that: The data analysis step utilizes a real-time stream processing framework, specifically including: Parallel computing: Decompose the analysis task into subtasks and assign them to different computing nodes for parallel processing; In-memory computing: using memory to store data and intermediate results to increase computing speed; Extensible algorithm library: supports a variety of data analysis algorithms, including statistical analysis and machine learning algorithms.

5. The method for processing software development and application data based on cloud computing according to claim 1, characterized in that: The result storage and update steps adopt a multi-level cache strategy, specifically including: Local cache: Cache frequently used data on the client or application server to reduce access to remote caches; Distributed cache: Use cache systems such as Redis or Memcached to store hot data; Cache expiration policy: Set the lifetime of cached data and adjust it dynamically based on the frequency of data changes.

6. The method for processing software development application data based on cloud computing according to claim 1, characterized in that: The anomaly detection and fault tolerance processing steps are as follows: Real-time monitoring: Use monitoring tools to monitor the status and performance indicators of each system component in real time; Automatic failover: Automatically switch tasks to a standby node when a component failure is detected; Load balancing: Balance the load of each node through scheduling algorithms.

7. A method for processing software development and application data based on cloud computing according to claim 6, characterized in that: The load balancing mechanism is introduced in the process of balancing the load of each node through the scheduling algorithm, so that tasks and data are evenly distributed among the computing nodes, which specifically includes: Real-time monitoring of node status: Use monitoring tools to continuously collect key performance indicators of each node, including CPU usage, memory usage, network bandwidth, and disk I / O, to obtain the real-time load status of each node. The intelligent scheduling algorithm uses a polling algorithm to assign new tasks to each node in turn. It is simple and easy to implement, but it does not take into account the actual load of the node. A task queue is maintained in the central scheduler, and new data processing tasks will be placed in the queue. The scheduler takes tasks from the queue and assigns them to appropriate nodes based on the scheduling algorithm.

8. The method for processing software development application data based on cloud computing according to claim 1, characterized in that: The method also includes deployment of a multi-cloud environment, specifically including: Cross-cloud data synchronization: realize data synchronization and consistency maintenance between different cloud service providers; Seamless migration: supports migration of applications and data between multi-cloud environments; Unified management platform: Provides a unified management interface for centralized management and scheduling of multi-cloud resources.

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