A cloud computing-based software development application data processing method

Abstract

This invention discloses a cloud computing-based software development application data processing method, belonging to the field of software development data processing technology. The method includes: dividing an edge preprocessing dataset into data of different priorities, calculating the routing decision value for each path, allocating transmission paths for data of different priorities, and summarizing to obtain an intelligent data flow transmission plan; executing the intelligent data flow transmission plan, triggering a dynamic adjustment mechanism to dynamically adjust the transmission paths, and allocating storage nodes according to data priorities, summarizing the stored edge preprocessing dataset into an optimized storage dataset; modifying the optimized storage dataset and performing conflict assessment on the modified content to obtain conflict assessment values, simultaneously performing priority assessment on the modified content to obtain priority assessment values, calculating synchronization decision values, and judging the synchronization decision values ​​to obtain synchronization reports and rollback reports. This invention improves the stability of the development environment and collaborative efficiency.

Description

Technical Field

[0001] This invention relates to the field of software development data processing technology, and in particular to a cloud computing-based software development application data processing method. Background Technology

[0002] In the field of industrial cloud computing, efficient processing of software development application data is crucial for supporting continuous integration and continuous deployment processes and improving development efficiency. Currently, conventional data processing methods are typically based on centralized or partially distributed cloud computing architectures, using standardized data collection, network transmission, and storage mechanisms to aggregate and manage multi-source heterogeneous data generated during development. For example, conventional methods collect raw development data such as code changes and performance metrics from multiple sources, including version control systems, build servers, and monitoring tools. This data is then initially cleaned and formatted in the cloud or at edge nodes. The processed data is then transmitted to designated cloud storage resources via pre-configured network paths and centrally stored according to a specific data model for subsequent analysis, retrospective analysis, and collaborative development. This approach fully leverages the elastic computing and storage capabilities of cloud platforms, providing fundamental support for the automation of software development and data-driven decision-making.

[0003] However, existing conventional methods still have certain limitations when adapting to the dynamic and complex data scenarios of software development. On the one hand, at the data transmission level, conventional methods usually rely on static or semi-static routing strategies and priority allocation mechanisms, making it difficult to ensure low-latency, high-reliability differentiated transmission of high-priority data based on real-time network conditions. This may lead to the accumulation or delay of critical information during transmission, affecting the development team's real-time response to problems. On the other hand, at the data modification and synchronization level, conventional methods mostly adopt post-event conflict detection or simple timestamp sorting mechanisms, lacking intelligent mechanisms for quantitatively assessing and linking the conflict risk and business priority of modified content. This can easily lead to data inconsistency or over-synchronization problems, increasing the instability of the development environment and maintenance costs. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a cloud computing-based software development application data processing method to solve the problems of insufficient high-priority data transmission guarantees and the lack of intelligent balance between conflict risks and business priorities during data modification synchronization in existing technologies.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides a cloud computing-based software development application data processing method, comprising: collecting an original development dataset; preprocessing the original development dataset to obtain an edge preprocessing dataset; dividing the edge preprocessing dataset into data of different priorities, calculating the routing decision value for each path, and allocating transmission paths for data of different priorities, and summarizing to obtain an intelligent data flow transmission plan; executing the intelligent data flow transmission plan, triggering a dynamic adjustment mechanism, dynamically adjusting the transmission paths, and allocating storage nodes according to data priorities, and summarizing the stored edge preprocessing dataset into an optimized storage dataset; modifying the optimized storage dataset and performing conflict assessment on the modified content to obtain a conflict assessment value, and simultaneously performing priority assessment on the modified content to obtain a priority assessment value, and calculating a synchronization decision value; and judging the synchronization decision value to obtain a synchronization report and a rollback report.

[0007] As a preferred embodiment of the cloud computing-based software development application data processing method of the present invention, the original development dataset includes code repository changes, error logs, warning messages, debugging logs, historical records, and real-time performance data.

[0008] As a preferred embodiment of the cloud computing-based software development application data processing method of the present invention, the steps for obtaining the edge preprocessing dataset are as follows: the original development dataset is collected through an event-driven method, and the original development dataset is initially prioritized; the original development data is standardized to obtain a high-quality dataset; the duplicate fields and duplicate structures of the high-quality dataset are scanned, and after deduplication and compression, a compressed high-quality dataset is obtained; the compressed high-quality dataset is summarized into an edge preprocessing dataset.

[0009] As a preferred embodiment of the cloud computing-based software development application data processing method of the present invention, the step of prioritizing the edge preprocessing dataset includes the following steps: extracting feature values ​​of the edge preprocessing dataset one by one; further prioritizing the edge preprocessing dataset according to the feature values, calculating priority evaluation values, and dividing the range of priority evaluation values ​​into three equal parts; and dividing the edge preprocessing dataset into high-priority data, medium-priority data, and low-priority data according to the range of priority evaluation values.

[0010] As a preferred embodiment of the cloud computing-based software development application data processing method of the present invention, the steps for obtaining the intelligent data stream transmission plan are as follows: extract the effective network bandwidth, total path delay, and load balancing coefficient respectively; calculate the routing decision value and record the transmission time of each transmission path; set transmission path allocation rules to allocate transmission paths for high-priority data, medium-priority data, and low-priority data simultaneously; and summarize the priority evaluation value, routing decision value, transmission path allocation rules, and path allocation results to obtain the intelligent data stream transmission plan.

[0011] As a preferred embodiment of the cloud computing-based software development application data processing method of the present invention, the steps of executing the intelligent data stream transmission plan and triggering the dynamic adjustment mechanism to dynamically adjust the transmission path are as follows: Execute the intelligent data stream transmission plan and start data transmission; monitor the bandwidth utilization, total path delay, and network load of each transmission path in real time; when the bandwidth utilization exceeds the threshold, the delay exceeds the maximum threshold, or the network load exceeds the maximum capacity, trigger the dynamic adjustment mechanism to reselect the transmission path and continue data transmission.

[0012] As a preferred embodiment of the cloud computing-based software development application data processing method of the present invention, the aggregation into an optimized storage dataset comprises the following steps: allocating storage nodes to the transmitted edge preprocessing datasets according to data priority, and storing the transmission path, bandwidth utilization, and transmission time in the storage nodes; and aggregating all stored edge preprocessing datasets to generate an optimized storage dataset.

[0013] As a preferred embodiment of the cloud computing-based software development application data processing method of the present invention, the steps for modifying the optimized storage dataset and evaluating the conflict of the modified content to obtain a conflict evaluation value are as follows: 1) Statistically calculate the total number of bytes of the modified content, the modification difference, the modification difference degree, and the historical modification difference degree; 2) Calculate the ratio of the modification difference to the modification difference degree to obtain the conflict evaluation value; 3) Set an initial conflict evaluation threshold and experimentally adjust the initial conflict evaluation threshold to obtain a conflict evaluation threshold; 4) When the conflict evaluation value is greater than the conflict evaluation threshold, it is determined that the modified content has a potential conflict, and a rollback mechanism is initiated, generating a rollback report; 5) When the conflict evaluation value is not greater than the conflict evaluation threshold, it indicates that the risk of conflict of the modified content is relatively small, and the process enters the priority evaluation stage.

[0014] As a preferred embodiment of the cloud computing-based software development application data processing method of the present invention, the steps for prioritizing the modified content and obtaining the priority evaluation value of the modified content are as follows: extracting the degree of difference between the modified content and the content before the last modification, and calculating the priority evaluation value of the modified content; and classifying the priority of the modified content according to the priority evaluation value of the modified content.

[0015] As a preferred embodiment of the cloud computing-based software development application data processing method of the present invention, the steps of calculating the synchronization decision value and judging the synchronization decision value to obtain a synchronization report and a rollback report are as follows: the time factor is obtained by proportionally mapping the priority evaluation value of the modified content, and the synchronization decision value is calculated with the conflict evaluation value and the priority evaluation value of the modified content; the synchronization decision value is used to make a synchronization judgment and generate a synchronization report and a rollback report.

[0016] The beneficial effects of this invention are as follows: by summarizing to obtain an intelligent data flow transmission plan, dynamic intelligent matching of data transmission path and data priority is realized, which improves the transmission efficiency of high-priority data flow; by judging the synchronization decision value to obtain synchronization report and rollback report, high-value modification synchronization is accurately and automatically driven, which effectively improves the stability of development environment and collaborative efficiency. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a data processing method for cloud-based software development applications.

[0019] Figure 2 A flowchart for obtaining the intelligent data stream transmission plan.

[0020] Figure 3 This is a flowchart summarizing the optimized storage of the dataset.

[0021] Figure 4 A flowchart for obtaining synchronous and rollback reports.

[0022] Figure 5 This is a comparison chart of transmission times. Detailed Implementation

[0023] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0024] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0025] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0026] Reference Figures 1-5 This is one embodiment of the present invention, which provides a cloud computing-based software development application data processing method, including the following steps: S1. Collect the original development dataset, preprocess the original development dataset to obtain the edge preprocessing dataset.

[0027] The original development dataset was collected using an event-driven approach, and a preliminary priority classification was performed on the original development dataset.

[0028] It should be noted that the event-driven approach is based on data collection that is automatically triggered by events in the development environment, ensuring the timeliness and accuracy of the data.

[0029] It should be noted that the original development dataset includes codebase changes, error logs, warning messages, debug logs, and real-time performance data.

[0030] The original development dataset is initially prioritized, with error logs and warning messages classified as high-priority data, and codebase changes, debug logs, historical records, and real-time performance data classified as low-priority data.

[0031] The original data is standardized to obtain a high-quality dataset.

[0032] Furthermore, the original development dataset is scanned to identify format differences among various data types. Format unification is performed on different data formats, standardizing the units and dimensions of numerical data, converting time data to a unified time format, and unifying the character encoding of text data while removing invalid characters to obtain a standard development dataset. A mean filtering algorithm is then used to smooth the standard development dataset, removing abnormal fluctuations and supplementing the data with interpolated values ​​at the points where fluctuations were removed, ensuring data integrity and resulting in a high-quality dataset.

[0033] Scan high-quality datasets for duplicate fields and structures, and then deduplicate and compress them to obtain compressed high-quality datasets.

[0034] Furthermore, the high-quality dataset undergoes data compression. All high-quality datasets are scanned by field, and a hash value is generated for each field using hash calculation. The hash values ​​of all fields are compared to determine if duplicate hash values ​​exist. If duplicate hash values ​​are found, it indicates the existence of a duplicate field, and these duplicate fields are marked as duplicates. A pattern matching algorithm is used to detect the structure in the high-quality dataset, and duplicate structures are marked as duplicates. All fields and structures marked as duplicates are summarized to obtain a pattern mapping dictionary, and a numeric index is added to each duplicate field and structure. Based on the pattern mapping dictionary, duplicate fields and structures in the high-quality dataset are replaced with their corresponding numeric indices, resulting in the compressed high-quality dataset.

[0035] Compress the high-quality dataset and aggregate it into an edge preprocessing dataset.

[0036] S2. Divide the edge preprocessing dataset into data of different priorities, calculate the routing decision value for each path, and assign transmission paths to data of different priorities. The results are then summarized to obtain the intelligent data flow transmission plan.

[0037] Extract the feature values ​​of the edge preprocessing dataset one by one.

[0038] Furthermore, the time span from the start of generating the edge preprocessing dataset to the current time is recorded to obtain the historical duration, which serves as the historical record feature value. The number of codebase changes is recorded, and the ratio of this number to the historical duration is calculated to obtain the codebase change frequency, which serves as the codebase change feature value. The number of errors in the error log is recorded, and the ratio of this number to the historical duration is calculated to obtain the error frequency, which serves as the error log feature value. The number of warnings in the warning messages is recorded, and the ratio of this number to the historical duration is calculated to obtain the warning frequency, which serves as the warning message feature value. The number of debugging sessions in the debug log is recorded, and the ratio of this number to the historical duration is calculated to obtain the debug density, which serves as the debug log feature value. Response time is extracted from the real-time performance data as the real-time performance feature value. The codebase change feature value, error log feature value, warning message feature value, debug log feature value, and real-time performance feature value are then processed to obtain dimensionless codebase change feature values, error log feature values, warning message feature values, debug log feature values, and real-time performance feature values.

[0039] The edge preprocessing dataset is further prioritized, priority evaluation values ​​are calculated, and the range of priority evaluation values ​​is divided into three equal parts. Based on the range of priority evaluation values, the edge preprocessing dataset is divided into high-priority data, medium-priority data, and low-priority data.

[0040] The number of feature values ​​for each data type in the edge preprocessing dataset is counted. The mean and standard deviation of each feature value are calculated, and a priority evaluation value is calculated. The expression is as follows: ; in, This is a priority evaluation value. The number of eigenvalues. For the first 1 eigenvalue, The average of the eigenvalues. The standard deviation of the eigenvalues. For feature value index.

[0041] It should be noted that the priority evaluation value is used to evaluate the priority of the edge preprocessing dataset.

[0042] The resulting priority evaluation value is between 0 and 1. The interval between 0 and 1 is divided into three equal parts. Data with a priority evaluation value between 0 and 0.33 is set as low priority data; data with a priority evaluation value between 0.33 and 0.67 is set as medium priority data; and data with a priority evaluation value between 0.67 and 1 is set as high priority data.

[0043] Extract the effective network bandwidth, total path delay, and load balancing coefficient respectively, calculate the routing decision value, and record the transmission time of each transmission path.

[0044] Furthermore, the maximum bandwidth of all transmission paths and the available bandwidth of the current node are extracted, and the minimum value between the maximum bandwidth of all transmission paths and the available bandwidth of the current node is selected as the effective network bandwidth. The transmission delay of each transit node in each transmission path is extracted, and the transmission delays of all transit nodes in each path are summed to obtain the total path delay for each transmission path. The load data and processing capacity data of the current transmission path are extracted, and the ratio between the load data and the processing capacity data is calculated to obtain the load balancing coefficient. The average and standard deviation of the effective network bandwidth, total path delay, and load balancing coefficient for all transmission paths are calculated, and the routing decision value is calculated, expressed as: ; in, For routing decision values, Where D is the effective network bandwidth and D is the total path delay. This is the load balancing coefficient. This represents the average effective bandwidth of the network. The standard deviation of the network's effective bandwidth. This represents the average of the total path delay. The standard deviation of the total path delay. This represents the average value of the load balancing coefficient. This represents the standard deviation of the load balancing coefficient.

[0045] It should be noted that the routing decision value is used to select the most suitable transmission path. The larger the routing decision value, the faster the transmission speed. The routing decision value is determined by the effective network bandwidth, total path delay, and load balancing coefficient. The effective network bandwidth affects the maximum data transmission speed; the larger the effective network bandwidth, the faster the data transmission speed. The total path delay affects the time delay of data transmission; the smaller the path delay, the shorter the response time of data transmission. The load balancing coefficient reflects the load of the transmission path; transmission paths with excessive load will experience data congestion and increased latency.

[0046] A higher routing decision value is more suitable for transmitting high-priority data, while the transmission time of each transmission path is recorded in real time.

[0047] Set transmission path allocation rules to allocate transmission paths for high-priority, medium-priority, and low-priority data.

[0048] Furthermore, transmission path allocation rules are set, selecting the transmission path with the highest routing decision value as the high-priority data transmission path. The transmission time of this high-priority path is extracted, and its transmission time is used to determine if it will cause delays. If delays are expected, a new transmission path is added for high-priority data transmission, and the remaining path with the highest routing decision value is selected for high-priority data transmission, shortening the transmission time. Among the unused transmission paths, the path with the shortest delay is selected as the medium-priority data transmission path. If paths with the same delay exist, the path with the largest effective bandwidth is selected as the medium-priority path. When network space is available, the path with the smallest effective bandwidth and the highest delay is selected as the low-priority data transmission path; when network bandwidth is limited, low-priority data transmission is stopped. Transmission paths are allocated to high-priority, medium-priority, and low-priority data according to the transmission path allocation rules.

[0049] The priority evaluation value, routing decision value, transmission path allocation rules, and path allocation results are summarized to obtain the intelligent data flow transmission plan.

[0050] Figure 5This graph illustrates the latency variations of three data priorities during different transmission processes. The red line represents high-priority data, the green line represents medium-priority data, and the blue line represents low-priority data. The horizontal axis represents the sequence number of the data packets, and the vertical axis represents the transmission latency (in milliseconds). As can be seen from the graph, the transmission latency of high-priority data is significantly lower than that of low-priority data, and the latency of the three types of data exhibits different fluctuation trends as the sequence number of the data packets increases. High-priority data demonstrates a more stable and lower latency throughout the entire transmission process, showcasing its priority transmission characteristic.

[0051] S3. Execute the intelligent data stream transmission plan, trigger the dynamic adjustment mechanism, dynamically adjust the transmission path, allocate storage nodes according to data priority, and aggregate the stored edge preprocessed dataset into an optimized storage dataset.

[0052] Execute the intelligent data stream transmission plan and initiate data transmission.

[0053] The system monitors the bandwidth utilization, total path latency, and network load of each transmission path in real time. When the bandwidth utilization exceeds the threshold, the latency exceeds the maximum threshold, or the network load exceeds the maximum capacity, a dynamic adjustment mechanism is triggered to reselect a transmission path to continue data transmission.

[0054] Furthermore, during data transmission, parameters such as bandwidth usage, latency, and load for each transmission path are monitored in real time. When the edge preprocessing dataset passes through a transmission path, the current bandwidth usage, latency data, and load are recorded in real time and updated and stored accordingly. When the effective network bandwidth of a transmission path reaches its limit, the total path latency exceeds expectations, or the network load becomes too heavy, a dynamic adjustment mechanism is triggered. This mechanism calculates the ratio of the current data transmission volume to the effective network bandwidth to obtain the bandwidth utilization rate of the current transmission path, and also extracts the total path latency and network load for the current transmission path. A bandwidth assessment is performed on the transmission path, comparing the bandwidth utilization rate with a bandwidth utilization threshold. If the bandwidth utilization rate is greater than the bandwidth utilization threshold, the current transmission path cannot continue to be used. If the bandwidth utilization rate is not greater than the bandwidth utilization threshold, a latency assessment is performed, comparing the total path latency with a maximum latency threshold. If the total path latency is greater than the maximum latency threshold, the current transmission path cannot continue to be used. If the total path latency is not greater than the maximum latency threshold, a network load assessment is performed, comparing the network load with the maximum load capacity. If the network load is greater than the maximum load capacity, the current transmission path cannot continue to be used. If the total path latency is not greater than the maximum latency threshold, the current transmission path can continue to be used.

[0055] It should be noted that the bandwidth utilization threshold is set to 90% to prevent transmission path overload and reserve bandwidth redundancy; the historical maximum latency value of each transmission path is extracted, and to avoid the influence of extreme values, the 95th percentile of the historical maximum latency value is selected as the maximum latency threshold; the maximum load capacity is set to 70% of the path bandwidth to ensure network latency and quality.

[0056] Through a dynamic adjustment mechanism, abnormal situations such as excessive bandwidth utilization, excessive latency, and excessive network load on the transmission path can be quickly identified during real-time monitoring. The triggered dynamic adjustment mechanism will re-evaluate and select a suitable transmission path, thereby avoiding data loss or delay due to unstable transmission paths.

[0057] Storage nodes are allocated to the edge preprocessing dataset to be transmitted based on data priority, and the transmission path, bandwidth utilization and transmission time are stored in the storage nodes.

[0058] Furthermore, the transmitted edge preprocessing dataset is stored. During storage, the edge preprocessing dataset is allocated to corresponding storage nodes according to data priority. High-priority data is stored on storage nodes with large network bandwidth and fast response speed to ensure that high-priority data can be accessed and processed quickly. After the high-priority data is stored, medium-priority data is stored on the remaining network storage nodes with large network bandwidth and fast response speed. Low-priority data is stored on idle nodes to ensure reasonable utilization of resources.

[0059] It should be noted that response time refers to the time required to receive the edge preprocessing dataset, begin processing, and return the results.

[0060] The edge preprocessing dataset is recorded in detail according to the transmission path and bandwidth usage. The transmission path, bandwidth utilization and transmission time are stored in the storage node together with the corresponding high-priority data, medium-priority data or low-priority data.

[0061] All stored edge preprocessed datasets are aggregated to generate an optimized stored dataset.

[0062] S4. Modify the optimized storage dataset and perform conflict assessment on the modified content to obtain conflict assessment values. At the same time, perform priority assessment on the modified content to obtain priority assessment values. Calculate synchronization decision values ​​and judge the synchronization decision values ​​to obtain synchronization reports and rollback reports.

[0063] The total number of bytes modified, the difference in modification, the degree of difference in modification, and the degree of difference in historical modifications are statistically analyzed. The ratio of the difference in modification to the degree of difference in modification is calculated to obtain the conflict assessment value.

[0064] Furthermore, during software development, when code or data is modified, modified content is generated. A conflict assessment is performed on the optimized storage dataset and the modified content. The total number of bytes of the modified content is counted to obtain the total amount of modified content. The modified content and the original content are analyzed, and the content identical to the original content is extracted to obtain duplicate modified content. The total number of bytes of the duplicate modified content is counted to obtain the total amount of duplicate modified content. The difference between the total amount of modified content and the total amount of duplicate modified content is calculated to obtain the modification difference. The ratio of the modification difference to the total amount of modified content is calculated to obtain the modification difference degree. All historical modification records of the current modified content are extracted, the number of historical modifications is counted, the original content of each historical modification is extracted, the total number of bytes is counted, and the summation is performed to obtain the range of each historical modification. The summation of all historical modification ranges is performed to obtain the total historical modification range. The content identical to the original content of each historical modification is extracted, the historical modification difference is calculated for each historical modification, the total number of bytes of the historical modification difference is counted, and the summation is performed to obtain the total historical modification difference. The ratio of the total historical modification difference to the total historical modification range is calculated to obtain the historical modification difference degree. Finally, a conflict assessment value is calculated, expressed as: ; in, This is a conflict assessment value. To correct the differences, To modify the degree of difference, Let the historical modification difference be the degree of the j-th historical modification. This represents the number of historical modifications. This is an index of the number of historical modifications.

[0065] It should be noted that the conflict assessment value is used to quantify the potential conflict between the modified content and the previous modifications; the modification difference is used to measure the magnitude of change in the current modification. The greater the modification difference, the higher the possibility of potential conflict; the ratio of the modification difference to the historical modification difference represents the difference between the current modification and the previous modification. The greater the difference, the greater the possibility of conflict.

[0066] A preliminary conflict assessment threshold was randomly set, and the threshold was experimentally adjusted. The threshold was dynamically adjusted based on the number of false positives and missed conflicts. When the number of false positives was greater than the number of missed conflicts, the threshold was lowered and the experiment continued. When the number of false positives was less than the number of missed conflicts, the threshold was raised and the experiment continued until there were no obvious false positives or missed conflicts, thus obtaining the final conflict assessment threshold.

[0067] It should be noted that false positives refer to data that is not actually conflicted being incorrectly labeled as conflicted. This usually occurs when the assessment process is overly sensitive, causing some data that does not actually need to be rolled back to be mistakenly labeled as potentially conflicted, thereby triggering unnecessary rollbacks or interventions, reflecting the error in the assessment.

[0068] If the conflict assessment value is greater than the conflict assessment threshold, it is determined that the modified content has a potential conflict, and the rollback mechanism is initiated to restore the data to its state before the modification and generate a rollback report; if the conflict assessment value is not greater than the conflict assessment threshold, it indicates that the risk of conflict in the modified content is relatively small, and it enters the priority assessment stage.

[0069] It should be noted that the rollback mechanism refers to automatically restoring the data to its state before the modification when the modified content conflicts with existing data or has a high potential risk, in order to avoid the introduction of inconsistent or erroneous data.

[0070] Extract the degree of difference between the modified content and the content before the last modification, and calculate the priority evaluation value of the modified content. The expression is: ; in, To modify the content priority evaluation value, This refers to the degree of difference between the modified content and the content before the last modification.

[0071] It should be noted that the content modification priority evaluation value indicates the priority of the current content being modified.

[0072] The priority evaluation value of the modified content is between 0 and 1. The range of the priority evaluation value of the modified content is divided into three equal parts: the range of 0 to 0.33 is classified as low priority modified content, the range of 0.33 to 0.67 is classified as medium priority modified content, and the range of 0.67 to 1 is classified as high priority modified content.

[0073] The content modification priority assessment value is used as a time factor, and the ratio of the conflict assessment value and the content modification priority assessment value is multiplied by the time factor to obtain the synchronization decision value.

[0074] It should be noted that the time factor indicates the urgency of data modification.

[0075] If the synchronization decision value is not less than 1, the modified content will be synchronized to the development environment and a synchronization report will be generated; if the synchronization decision value is less than 1, the rollback mechanism will be activated to restore the data to its state before the modification and a rollback report will be generated.

[0076] In summary, this invention achieves dynamic and intelligent matching of data transmission paths and data priorities by summarizing intelligent data flow transmission plans, thereby improving the transmission efficiency of high-priority data flows; and by judging synchronization decision values ​​to obtain synchronization reports and rollback reports, it accurately and automatically drives the synchronization of high-value modifications, effectively improving the stability of the development environment and collaborative efficiency.

[0077] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A data processing method for software development applications based on cloud computing, characterized in that: include, Collect the original development dataset, preprocess the original development dataset to obtain the edge preprocessing dataset; The edge preprocessing dataset is divided into data of different priorities, and the routing decision value for each path is calculated. At the same time, transmission paths are assigned to data of different priorities, and the intelligent data flow transmission plan is obtained by summarizing the results. The system executes an intelligent data stream transmission plan, triggers a dynamic adjustment mechanism, dynamically adjusts the transmission path, allocates storage nodes according to data priority, and aggregates the stored edge preprocessed dataset into an optimized storage dataset. The optimized storage dataset is modified, and the modifications are evaluated for conflict, resulting in a conflict evaluation value. Simultaneously, the modifications are evaluated for priority, resulting in a priority evaluation value. A synchronization decision value is calculated, and the synchronization decision value is used to determine the synchronization report and rollback report.

2. The cloud computing-based software development application data processing method as described in claim 1, characterized in that: The original development dataset includes codebase changes, error logs, warning messages, debug logs, historical records, and real-time performance data.

3. The cloud computing-based software development application data processing method as described in claim 2, characterized in that: The steps to obtain the edge preprocessing dataset are as follows: The original development dataset was collected using an event-driven approach, and a preliminary priority classification was performed on the original development dataset. Standardize the original development data to obtain a high-quality dataset; Scan the high-quality dataset for duplicate fields and structures, and then deduplicatize and compress it to obtain a compressed high-quality dataset. Compress the high-quality dataset and aggregate it into an edge preprocessing dataset.

4. The data processing method for software development applications based on cloud computing as described in claim 3, characterized in that: The steps for prioritizing the edge preprocessing dataset are as follows: Extract the feature values ​​of the edge preprocessing dataset one by one; Based on the feature values ​​of the edge preprocessing dataset, the edge preprocessing dataset is further prioritized, priority evaluation values ​​are calculated, and the range of priority evaluation values ​​is divided into three equal parts. Based on the range of priority evaluation values, the edge preprocessing dataset is divided into high-priority data, medium-priority data, and low-priority data.

5. The cloud computing-based software development application data processing method as described in claim 4, characterized in that: The aggregation yields the intelligent data stream transmission plan, and the steps are as follows: Extract the effective network bandwidth, total path delay, and load balancing coefficient respectively, calculate the routing decision value, and record the transmission time of each transmission path; Set transmission path allocation rules to allocate transmission paths for high-priority data, medium-priority data, and low-priority data; The priority evaluation value, routing decision value, transmission path allocation rules, and path allocation results are summarized to obtain the intelligent data flow transmission plan.

6. The cloud computing-based software development application data processing method as described in claim 5, characterized in that: The execution of the intelligent data stream transmission plan, triggering a dynamic adjustment mechanism to dynamically adjust the transmission path, involves the following steps. Execute the intelligent data stream transmission plan and initiate data transmission; The system monitors the bandwidth utilization, total path latency, and network load of each transmission path in real time. When the bandwidth utilization exceeds the threshold, the latency exceeds the maximum threshold, or the network load exceeds the maximum capacity, a dynamic adjustment mechanism is triggered to reselect a transmission path to continue data transmission.

7. The cloud computing-based software development application data processing method as described in claim 6, characterized in that: The aggregation process for optimizing the stored dataset involves the following steps. Based on data priority, storage nodes are allocated to the edge preprocessing dataset to be transmitted, and the transmission path, bandwidth utilization and transmission time are stored in the storage nodes. All stored edge preprocessed datasets are aggregated to generate an optimized stored dataset.

8. The cloud computing-based software development application data processing method as described in claim 7, characterized in that: The steps for modifying the optimized storage dataset and evaluating the conflict of the modifications to obtain conflict evaluation values ​​are as follows. The total number of bytes modified, the difference in modification, the degree of difference in modification, and the degree of difference in historical modification are counted. The ratio of the difference in modification to the degree of difference in modification is calculated to obtain the conflict assessment value. An initial conflict assessment threshold is set, and the initial conflict assessment threshold is experimentally adjusted to obtain the conflict assessment threshold. When the conflict assessment value exceeds the conflict assessment threshold, it is determined that the modified content has a potential conflict, and the rollback mechanism is initiated, generating a rollback report. When the conflict assessment value is not greater than the conflict assessment threshold, it indicates that the risk of conflict in modifying the content is relatively small, and it enters the priority assessment stage.

9. The cloud computing-based software development application data processing method as described in claim 8, characterized in that: The steps for prioritizing the modified content and obtaining its priority value are as follows. Extract the degree of difference between the modified content and the content before the last modification, and calculate the priority evaluation value of the modified content; The priority of the modified content is determined based on the priority evaluation value of the modified content.

10. The cloud computing-based software development application data processing method as described in claim 9, characterized in that: The steps for calculating the synchronization decision value, judging the synchronization decision value to obtain the synchronization report and rollback report are as follows. The time factor is obtained by proportionally mapping the priority assessment value of the modified content, and then the synchronous decision value is calculated by combining it with the conflict assessment value and the priority assessment value of the modified content. Based on the synchronized decision value, a synchronized judgment is made, and a synchronized report and a rollback report are generated.

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