Early warning system in software development process

Abstract

The invention relates to an early warning system in a software development process, which belongs to the technical field of software development and comprises a data source, the output end of the data source is in signal connection with a base layer, the output end of the base layer is in signal connection with a core processing layer, and the output end of the core processing layer is in signal connection with an application layer. The input ends of the base layer, the core processing layer and the application layer are all in signal connection with a security permission module; the base layer comprises a data acquisition module and a data preprocessing module, and the output end of the data acquisition module is in signal connection with the input end of the data preprocessing module. According to the early warning system in the software development process, by continuously collecting and analyzing code quality related data, development strategies and resource investment are adjusted in time, and it is ensured that the software quality is always kept at a high level; and the user satisfaction and the loss risk are analyzed through the data analysis modeling module, so that the user satisfaction is improved, the competitiveness of the software is enhanced, and the advantage of enhancing the quality and stability of the software is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of software development, and in particular to an early warning system in a software development process. Background Art

[0002] In today's digital age, software development has become a core driver of innovation and growth across various industries. From internet applications to enterprise-level software, from mobile applications to cloud computing services, the quality and delivery efficiency of software products directly impact a company's competitiveness and market performance.

[0003] However, the software development process is fraught with complexity and uncertainty, presenting numerous challenges: 1. Frequent Requirements Changes: The ever-changing market environment and user needs lead to frequent adjustments to software requirements during development, complicating project management and potentially leading to schedule delays and cost overruns. 2. Increasing Technical Complexity: As software scale and functionality grow, the technology stack involved becomes increasingly broad, encompassing multiple programming languages, frameworks, and tools. Integration and compatibility issues between these technologies increase technical risks during development. 3. Difficulties in Team Collaboration: Large-scale software development projects are often completed by multiple teams, whose members may be geographically dispersed. This leads to high communication costs and low collaboration efficiency, making it easy for information to be untimely or inaccurately transmitted, impacting overall project progress and quality. 4. Intense Quality Assurance Pressure: Users are increasingly demanding software quality, demanding not only comprehensive functionality but also stable performance, security, and reliability. In a rapidly iterative development model, ensuring software quality becomes a major challenge, as potential defects and vulnerabilities can cause significant losses to businesses.

[0004] With the rapid development of information technology, emerging technologies are providing strong support for the construction of early warning systems for software development. The software development process generates massive amounts of data, including code submission records, defect reports, test data, and user feedback. Big data technologies can efficiently store, manage, and analyze this multi-source, heterogeneous data, uncovering the underlying patterns and value, and providing a foundation for risk identification and early warning.

[0005] In order to solve the problems existing in the above-mentioned prior art, improve the efficiency and quality of software development, and reduce development costs and risks, it is of great practical significance to develop an early warning system in the software development process. Summary of the Invention

[0006] In view of the deficiencies in the existing technology, the present invention provides an early warning system for the software development process, which has the advantages of high development efficiency, low development cost and high development quality, and solves the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions: An early warning system for software development comprises a data source, wherein an output terminal of the data source is signal-connected to a base layer, an output terminal of the base layer is signal-connected to a core processing layer, an output terminal of the core processing layer is signal-connected to an application layer, and input terminals of the base layer, the core processing layer, and the application layer are all signal-connected to a security permission module; The basic layer includes a data acquisition module and a data preprocessing module, and the output end of the data acquisition module is signal-connected to the input end of the data preprocessing module; The core processing layer includes a risk identification module and a data analysis and modeling module, wherein the output end of the risk identification module is signal-connected to the input end of the data analysis and modeling module; The application layer includes an early warning rule engine module and a visual reporting module, wherein the output end of the early warning rule engine module is signal-connected to the input end of the visual reporting module; The output and input of the warning rule engine module are both signal-connected to the warning notification module, the output of the warning notification module and the visual reporting module are both signal-connected to the feedback optimization module, the output of the feedback optimization module is signal-connected to the integrated extension module, and the output of the integrated extension module is signal-connected to the input of the security authority module; The data collection module is responsible for collecting various types of data during the software development process, including but not limited to code submission records, defect reports, test coverage, task completion progress, resource usage and user feedback; The data preprocessing module is used to clean, convert and standardize the collected raw data for subsequent analysis; The risk identification module is used to identify risk factors that may affect project progress, quality or cost; The data analysis and modeling module is used to conduct in-depth analysis of the pre-processed data and establish a prediction model; The warning rule engine module is used to define warning rules and trigger warnings based on data analysis results; The warning notification module is used to notify relevant personnel of the warning information in a timely manner. The warning information supports multiple notification methods, such as email, SMS, and instant messaging tools; The visual reporting module is used to provide an intuitive visual interface to display early warning information, risk trends and project status, and support report generation; The feedback optimization module is used to collect user feedback on the early warning system and continuously optimize the early warning rules and models; The integrated extension module is used to support integration with other development tools and provide an API interface for external system calls; The security authority management module is used to ensure data security and access control of the early warning system to prevent unauthorized access.

[0008] Furthermore, the risk identification module includes input data, feature engineering, model reasoning, risk assessment output and model version management. The output end of the input data is connected to the input end signal of the feature engineering, the output end of the feature engineering is connected to the input end signal of the model reasoning, the output end of the model reasoning is connected to the input end signal of the risk assessment output, and the output end of the model version management is connected to the input end signal of the feature engineering. When evaluating the risk identification module, it is necessary to comprehensively consider multiple dimensions such as code quality, progress, resources and user feedback to conduct a comprehensive risk assessment; support dynamic adjustment of risk identification rules according to the actual situation of the project to adapt to the risk identification needs in different stages and scenarios; and reveal potential risk chains and risk transmission paths by analyzing the correlation between different risk factors.

[0009] Furthermore, the notification methods of the early warning notification module include email notification, SMS push and pop-up windows within the system. The early warning notification module allows users to customize the content and format of the early warning notification to meet the needs of different users. By establishing a notification confirmation mechanism, it ensures that relevant personnel can receive and confirm the early warning notification in a timely manner; at the same time, it supports user feedback to optimize the early warning notification strategy.

[0010] Furthermore, the data preprocessing module includes an advanced data cleaning module, a data conversion and mapping module, and a data quality monitoring module. The advanced data cleaning module adopts a more complex data cleaning algorithm to improve data quality; the data conversion and mapping module supports the mapping and conversion of data fields so as to unify data from different data sources into the same format and structure; the data quality monitoring module establishes a data quality monitoring mechanism to monitor data quality indicators in the data preprocessing process in real time, such as the proportion of missing values ​​and the number of outliers.

[0011] Furthermore, the data analysis and modeling module provides a variety of machine learning algorithms and statistical analysis methods for selection, supports model parameter tuning and performance evaluation; ensures the timeliness and accuracy of the early warning system by establishing a real-time data analysis and prediction mechanism; and provides a visual display and explanation of the model decision-making process so that users can understand the model's prediction results.

[0012] Furthermore, the warning rule engine module supports users to flexibly configure warning rules, including threshold settings, condition combinations and triggering methods; by establishing a rule priority management mechanism, it ensures that high-priority rules can trigger warnings first; and by recording the change history of warning rules for traceability and auditing.

[0013] Furthermore, the visual reporting module includes a personalized dashboard, an interactive visualization module and a report automatic generation and distribution module. The personalized dashboard supports user-defined dashboards to display key indicators and risk trends. The interactive visualization module provides interactive visualization tools that allow users to explore data in depth through drag and drop and zoom operations. The report automatic generation and distribution module supports regular automatic generation of project health reports and distributes them to relevant personnel via email or file sharing.

[0014] Furthermore, the feedback optimization module includes user feedback collection channels, feedback analysis and mining, and a continuous optimization mechanism. The user feedback collection channel establishes multiple user feedback collection channels, such as online surveys, user forums, and customer service systems; the feedback analysis and mining uses text mining and sentiment analysis techniques to analyze and mine user feedback to extract valuable information; the continuous optimization mechanism establishes a continuous optimization mechanism to continuously adjust early warning rules and models based on user feedback and data analysis results.

[0015] Furthermore, the integrated extension module provides a deep integration solution, supports seamless docking and data sharing with mainstream development tools, and provides a rich RESTful API interface to support third-party systems to call the functions and data of the early warning system; the integrated extension module adopts a plug-in architecture design to facilitate the expansion of new data sources, analysis methods and early warning rules.

[0016] Furthermore, the security authority management module includes a data encryption and desensitization module, a fine-grained authority control module and a security audit log module; the data encryption and desensitization module is used to encrypt, store and transmit sensitive data; and at the same time supports data desensitization processing to protect user privacy. The fine-grained authority control module is used to establish a fine-grained authority control mechanism and support authority allocation in multiple dimensions based on roles, departments and projects; the security audit log module is used to record the system's security operation logs and audit information in order to trace and troubleshoot security issues.

[0017] Compared with the existing technology, the present invention provides an early warning system in the software development process, which has the following beneficial effects: 1. The early warning system in the software development process, the risk identification module uses static code analysis tools to detect potential defects and security vulnerabilities in the code, can discover problems in the early development stage, fix them in time, and avoid the accumulation and spread of defects in the later stage, thereby improving the quality and stability of the software and reducing system failures and security risks caused by defects; by continuously collecting and analyzing code quality-related data, the system can monitor the changing trends of software quality, adjust development strategies and resource investment in time, and ensure that software quality always remains at a high level; the system collects user feedback data, analyzes user satisfaction and churn risk through the data analysis modeling module, improves user satisfaction, enhances the competitiveness of the software, and achieves the advantages of enhancing software quality and stability.

[0018] 2. The early warning system in the software development process collects data from all aspects of software development in real time through the data acquisition module. After processing by the basic layer, the risk identification module of the core processing layer can quickly identify potential risks, so that project managers can grasp the project risk status in a timely manner without waiting for manual reports, formulate response strategies in advance, and avoid risks from evolving into actual problems, thereby improving the initiative and efficiency of project management; the data analysis and modeling module conducts in-depth analysis of the pre-processed data, establishes a prediction model, provides a scientific basis for project decision-making, and predicts the project completion time through time series analysis, helping managers to reasonably allocate resources, adjust plans, make more accurate decisions, and improve project success rates; the visual reporting module of the application layer provides an intuitive visual interface, displaying warning information, risk trends and project status in the form of dashboards, trend charts, etc., and supports report generation, which greatly shortens the time for obtaining and understanding information, helps to make decisions quickly, and achieves the advantage of high decision-making efficiency.

[0019] 3. The early warning system in the software development process monitors resource usage such as server load through the risk identification module, predicts resource bottlenecks, and triggers early warnings in a timely manner when resource usage approaches or exceeds the threshold, allowing managers to take measures in advance to avoid project delays and cost increases due to insufficient resources; the early warning rule engine module triggers early warnings based on the deviation between the actual progress and the planned progress, ensuring that the project is delivered on time and avoiding additional costs and customer churn due to progress delays; through the analysis of data from various links of the project, it helps to optimize resource allocation, improve resource utilization efficiency, and reduce project costs, thereby achieving the advantages of optimizing resource utilization and cost control. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a system block diagram of an early warning system in a software development process of the present invention; Figure 2 This is a system block diagram of a risk identification module of an early warning system in a software development process of the present invention; Figure 3The present invention provides a system block diagram of an early warning notification module of an early warning system in a software development process. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0022] See also Figures 1 to 3 In this embodiment, an early warning system for a software development process includes a data source, an output terminal of the data source is connected to a basic layer, an output terminal of the basic layer is connected to a core processing layer, an output terminal of the core processing layer is connected to an application layer, and input terminals of the basic layer, the core processing layer, and the application layer are all connected to a security permission module. The basic layer includes a data acquisition module and a data preprocessing module, and the output end of the data acquisition module is connected to the input end signal of the data preprocessing module; The core processing layer includes a risk identification module and a data analysis and modeling module. The output of the risk identification module is connected to the input of the data analysis and modeling module. The application layer includes an early warning rule engine module and a visual report module. The output end of the early warning rule engine module is connected to the input end signal of the visual report module; The output and input ends of the early warning rule engine module are both connected to the early warning notification module, the output ends of the early warning notification module and the visual report module are both connected to the feedback optimization module, the output end of the feedback optimization module is connected to the integrated extension module, and the output end of the integrated extension module is connected to the input end of the security authority module; The data collection module is responsible for collecting various data during the software development process, including but not limited to code submission records, defect reports, test coverage, task completion progress, resource usage, and user feedback; Specifically: monitor the frequency of code changes through Git repositories, track task status through project management tools such as Jira, and obtain build and test results through CI / CD tools such as Jenkins.

[0023] The data preprocessing module is used to clean, convert and standardize the collected raw data for subsequent analysis; Specifically: removing duplicate data, filling missing values, and converting unstructured data (such as logs) into a structured format.

[0024] The risk identification module is used to identify risk factors that may affect project progress, quality or cost; Specifically: Detect potential defects and security vulnerabilities in the code through static code analysis tools (such as SonarQube); identify task delay risks through Gantt charts or critical path analysis; monitor server load and predict resource bottlenecks.

[0025] The data analysis and modeling module is used to conduct in-depth analysis of pre-processed data and establish a prediction model; Specifically, based on historical defect data, the training model predicts possible defects in the future; through time series analysis, the project completion time is predicted; and user feedback is analyzed to predict user satisfaction or churn risk.

[0026] The warning rule engine module is used to define warning rules and trigger warnings based on data analysis results; Specifically, an early warning is triggered when the code complexity exceeds the set value; an early warning is triggered when the defect density of a module in the early warning system exceeds the threshold; and an early warning is triggered when the deviation between the actual progress and the planned progress exceeds a certain ratio.

[0027] The early warning notification module is used to notify relevant personnel of early warning information in a timely manner. Early warning information supports multiple notification methods, such as email, SMS, and instant messaging tools; Specifically, when a high-risk defect is detected, an email notification is automatically sent to the development team; when the risk of task delay is high, a reminder is sent to the project manager via WeChat for Business.

[0028] The visual reporting module is used to provide an intuitive visual interface to display early warning information, risk trends and project status, and support report generation; Specifically, the dashboard displays key indicators such as the number of defects, task completion rate, and resource utilization; trend charts show how risks change over time; and project health reports are generated regularly for management decision-making.

[0029] The feedback optimization module is used to collect user feedback on the early warning system and continuously optimize the early warning rules and models; Specifically, through user surveys or log analysis, understand the accuracy and practicality of early warnings; adjust early warning thresholds or optimize analysis models based on feedback.

[0030] The integrated extension module is used to support integration with other development tools and provide API interfaces for external system calls; Specifically, other development tools include IDE, CI / CD tools, and project management tools, which integrate with GitLab to monitor code changes in real time; and provide RESTful APIs for third-party systems to query warning information.

[0031] The security authority management module is used to ensure data security and access control of the early warning system to prevent unauthorized access.

[0032] Specifically, data is encrypted for storage and transmission; role-based access control (RBAC) limits the access rights of different users to warning information.

[0033] In this embodiment, the risk identification module uses static code analysis tools to detect potential defects and security vulnerabilities in the code, which can discover problems in the early stages of development and repair them in a timely manner to avoid the accumulation and spread of defects in the later stages, thereby improving the quality and stability of the software and reducing system failures and security risks caused by defects; by continuously collecting and analyzing code quality-related data, the system can monitor the changing trends of software quality, adjust development strategies and resource investment in a timely manner, and ensure that software quality always remains at a high level; the system collects user feedback data, analyzes user satisfaction and churn risk through the data analysis modeling module, improves user satisfaction, enhances the competitiveness of the software, and achieves the advantages of enhancing software quality and stability.

[0034] In this embodiment, data from various links of software development are collected in real time through the data acquisition module. After being processed by the basic layer, the risk identification module of the core processing layer can quickly identify potential risks, so that project managers can grasp the project risk status in a timely manner without waiting for manual reports, formulate response strategies in advance, and avoid risks from evolving into actual problems, thereby improving the initiative and efficiency of project management; the data analysis and modeling module conducts in-depth analysis of the pre-processed data, establishes a prediction model, provides a scientific basis for project decision-making, and predicts the project completion time through time series analysis, helping managers to reasonably allocate resources, adjust plans, make more accurate decisions, and improve project success rates; the visual reporting module of the application layer provides an intuitive visual interface, displays warning information, risk trends and project status in the form of dashboards, trend charts, etc., and supports report generation, which greatly shortens the time for obtaining and understanding information, helps to make decisions quickly, and achieves the advantage of high decision-making efficiency.

[0035] Specifically, the data preprocessing module includes an advanced data cleaning module, a data conversion and mapping module, and a data quality monitoring module. The advanced data cleaning module adopts more complex data cleaning algorithms, such as rule-based cleaning and machine learning-based anomaly detection, to improve data quality; the data conversion and mapping module supports the mapping and conversion of data fields so as to unify data from different data sources into the same format and structure; the data quality monitoring module establishes a data quality monitoring mechanism to monitor data quality indicators in the data preprocessing process in real time, such as the proportion of missing values ​​and the number of outliers.

[0036] Specifically, the data analysis and modeling module provides a variety of machine learning algorithms and statistical analysis methods to choose from, supports model parameter tuning and performance evaluation; ensures the timeliness and accuracy of the early warning system by establishing a real-time data analysis and prediction mechanism; and provides a visual display and explanation of the model decision-making process so that users can understand the model's prediction results.

[0037] Specifically, the warning rule engine module supports users to flexibly configure warning rules, including threshold settings, condition combinations and triggering methods; by establishing a rule priority management mechanism, it ensures that high-priority rules can trigger warnings first; and by recording the change history of warning rules for traceability and auditing.

[0038] Specifically, the visual reporting module includes a personalized dashboard, an interactive visualization module, and an automatic report generation and distribution module. The personalized dashboard supports user-defined dashboards to display key indicators and risk trends. The interactive visualization module provides interactive visualization tools that allow users to explore data in depth through dragging and zooming operations. The automatic report generation and distribution module supports the regular automatic generation of project health reports and distributes them to relevant personnel via email or file sharing.

[0039] Specifically, the feedback optimization module includes user feedback collection channels, feedback analysis and mining, and continuous optimization mechanisms. The user feedback collection channels establish multiple user feedback collection channels, such as online surveys, user forums, and customer service systems; feedback analysis and mining use text mining and sentiment analysis techniques to analyze and mine user feedback and extract valuable information; the continuous optimization mechanism establishes a continuous optimization mechanism to continuously adjust the early warning rules and models based on user feedback and data analysis results.

[0040] Specifically, the integrated extension module provides a deep integration solution that supports seamless docking and data sharing with mainstream development tools. The integrated extension module provides a rich RESTful API interface that supports third-party systems to call the functions and data of the early warning system. The integrated extension module adopts a plug-in architecture design to facilitate the expansion of new data sources, analysis methods and early warning rules.

[0041] Specifically, the security permission management module includes a data encryption and desensitization module, a fine-grained permission control module and a security audit log module; the data encryption and desensitization module is used to encrypt, store and transmit sensitive data; it also supports data desensitization processing to protect user privacy. The fine-grained permission control module is used to establish a fine-grained permission control mechanism and supports permission allocation in multiple dimensions based on roles, departments and projects; the security audit log module is used to record the system's security operation logs and audit information in order to trace and troubleshoot security issues.

[0042] See also Figure 2In this embodiment, the risk identification module includes input data, feature engineering, model reasoning, risk assessment output and model version management. The output end of the input data is connected to the input end signal of the feature engineering, the output end of the feature engineering is connected to the input end signal of the model reasoning, the output end of the model reasoning is connected to the input end signal of the risk assessment output, and the output end of the model version management is connected to the input end signal of the feature engineering. When evaluating the risk identification module, it is necessary to comprehensively consider multiple dimensions such as code quality, progress, resources and user feedback to conduct a comprehensive risk assessment; support dynamic adjustment of risk identification rules according to the actual situation of the project to adapt to the risk identification needs in different stages and scenarios; by analyzing the correlation between different risk factors, reveal the potential risk chain and risk transmission path.

[0043] See also Figure 3 In this embodiment, the notification methods of the early warning notification module include email notification, SMS push and pop-up window in the system. The early warning notification module allows users to customize the content and format of the early warning notification to meet the needs of different users. By establishing a notification confirmation mechanism, it ensures that relevant personnel can receive and confirm the early warning notification in a timely manner; at the same time, it supports user feedback to optimize the early warning notification strategy.

[0044] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0045] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An early warning system in a software development process, comprising a data source, characterized in that: The output end of the data source is signal-connected to the basic layer, the output end of the basic layer is signal-connected to the core processing layer, the output end of the core processing layer is signal-connected to the application layer, and the input ends of the basic layer, core processing layer and application layer are all signal-connected to the security permission module; The basic layer includes a data acquisition module and a data preprocessing module, and the output end of the data acquisition module is signal-connected to the input end of the data preprocessing module; The core processing layer includes a risk identification module and a data analysis and modeling module, wherein the output end of the risk identification module is signal-connected to the input end of the data analysis and modeling module; The application layer includes an early warning rule engine module and a visual reporting module, wherein the output end of the early warning rule engine module is signal-connected to the input end of the visual reporting module; The output and input of the warning rule engine module are both signal-connected to the warning notification module, the output of the warning notification module and the visual reporting module are both signal-connected to the feedback optimization module, the output of the feedback optimization module is signal-connected to the integrated extension module, and the output of the integrated extension module is signal-connected to the input of the security authority module; The data collection module is responsible for collecting various types of data during the software development process, including but not limited to code submission records, defect reports, test coverage, task completion progress, resource usage and user feedback; The data preprocessing module is used to clean, convert and standardize the collected raw data for subsequent analysis; The risk identification module is used to identify risk factors that may affect project progress, quality or cost; The data analysis and modeling module is used to conduct in-depth analysis of the pre-processed data and establish a prediction model; The warning rule engine module is used to define warning rules and trigger warnings based on data analysis results; The warning notification module is used to promptly notify relevant personnel of warning information. Warning information supports multiple notification methods, such as email, SMS, and instant messaging tools; The visual reporting module is used to provide an intuitive visual interface to display early warning information, risk trends and project status, and support report generation; The feedback optimization module is used to collect user feedback on the early warning system and continuously optimize the early warning rules and models; The integrated extension module is used to support integration with other development tools and provide an API interface for external system calls; The security authority management module is used to ensure data security and access control of the early warning system to prevent unauthorized access.

2. The early warning system for software development according to claim 1, characterized in that: The risk identification module includes input data, feature engineering, model reasoning, risk assessment output and model version management. The output end of the input data is connected to the input end signal of the feature engineering, the output end of the feature engineering is connected to the input end signal of the model reasoning, the output end of the model reasoning is connected to the input end signal of the risk assessment output, and the output end of the model version management is connected to the input end signal of the feature engineering. When evaluating the risk identification module, it is necessary to comprehensively consider multiple dimensions such as code quality, progress, resources and user feedback to conduct a comprehensive risk assessment; support dynamic adjustment of risk identification rules according to the actual situation of the project to adapt to the risk identification needs in different stages and scenarios; by analyzing the correlation between different risk factors, reveal potential risk chains and risk transmission paths.

3. The early warning system for software development according to claim 1, characterized in that: The notification methods of the early warning notification module include email notification, SMS push and pop-up window within the system. The early warning notification module allows users to customize the content and format of the early warning notification to meet the needs of different users. By establishing a notification confirmation mechanism, it ensures that relevant personnel can receive and confirm the early warning notification in a timely manner; at the same time, it supports user feedback to optimize the early warning notification strategy.

4. The early warning system for software development according to claim 1, characterized in that: The data preprocessing module includes an advanced data cleaning module, a data conversion and mapping module, and a data quality monitoring module. The advanced data cleaning module adopts a more complex data cleaning algorithm to improve data quality; the data conversion and mapping module supports the mapping and conversion of data fields so as to unify data from different data sources into the same format and structure; the data quality monitoring module establishes a data quality monitoring mechanism to monitor data quality indicators in the data preprocessing process in real time, such as the proportion of missing values ​​and the number of outliers.

5. The early warning system for software development according to claim 1, characterized in that: The data analysis and modeling module provides a variety of machine learning algorithms and statistical analysis methods to choose from, supports model parameter tuning and performance evaluation; and ensures the timeliness and accuracy of the early warning system by establishing a real-time data analysis and prediction mechanism. By providing a visual display and explanation of the model's decision-making process, users can understand the model's prediction results.

6. The early warning system for software development according to claim 1, characterized in that: The warning rule engine module supports users to flexibly configure warning rules, including threshold settings, condition combinations and triggering methods; by establishing a rule priority management mechanism, it ensures that high-priority rules can trigger warnings first; By recording the change history of warning rules, it is easy to trace and audit.

7. The early warning system for software development according to claim 1, characterized in that: The visual reporting module includes a personalized dashboard, an interactive visualization module, and an automatic report generation and distribution module. The personalized dashboard supports user-defined dashboards to display key indicators and risk trends. The interactive visualization module provides interactive visualization tools, allowing users to deeply explore data through drag and drop and zoom operations. The automatic report generation and distribution module supports regular automatic generation of project health reports and distributes them to relevant personnel via email or file sharing.

8. The early warning system for software development according to claim 1, characterized in that: The feedback optimization module includes user feedback collection channels, feedback analysis and mining, and a continuous optimization mechanism. The user feedback collection channel establishes multiple user feedback collection channels, such as online surveys, user forums, and customer service systems; the feedback analysis and mining uses text mining and sentiment analysis techniques to analyze and mine user feedback and extract valuable information; the continuous optimization mechanism establishes a continuous optimization mechanism to continuously adjust warning rules and models based on user feedback and data analysis results.

9. The early warning system for software development according to claim 1, characterized in that: The integrated extension module provides a deep integration solution, supports seamless docking and data sharing with mainstream development tools, and provides a rich RESTful API interface to support third-party systems to call the functions and data of the early warning system; the integrated extension module adopts a plug-in architecture design to facilitate the expansion of new data sources, analysis methods and early warning rules.

10. The early warning system in the software development process according to claim 1, characterized in that: The security authority management module includes a data encryption and desensitization module, a fine-grained authority control module and a security audit log module; the data encryption and desensitization module is used to encrypt, store and transmit sensitive data; it also supports data desensitization processing to protect user privacy. The fine-grained authority control module is used to establish a fine-grained authority control mechanism and support authority allocation in multiple dimensions based on roles, departments and projects; the security audit log module is used to record the system's security operation logs and audit information to trace and troubleshoot security issues.

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