A government affair information project whole life cycle management system

Abstract

This invention discloses a full lifecycle management system for e-government projects. Centered on a full-lifecycle business collaboration platform for e-government projects, it integrates resource management, operation monitoring, project evaluation, and a model application center. It features dynamic expert matching and intelligent verification of project changes, supporting standardized closed-loop management of the entire project lifecycle from application to termination and archiving. The system adopts a "six-layer, two-system" architecture. The six layers cover the user layer to the infrastructure layer, while the two systems ensure standardized specifications and secure operation and maintenance. The infrastructure layer is compatible with domestically produced hardware and software, and the security system employs dual encryption and dual backup strategies. The resource management center realizes digital asset collection and dynamic ledger management; the operation monitoring center collects resource data in real time and triggers anomaly alerts; the project evaluation center constructs a multi-dimensional performance evaluation system and automatically generates reports; the management process covers the entire lifecycle of application, implementation, operation and maintenance, evaluation, and termination archiving, achieving standardized and intelligent management.

Description

Technical Field

[0001] This invention relates to the field of information technology project management systems, specifically to a full lifecycle management system for e-government information technology projects. Background Technology

[0002] E-government project management is a core component of digital government construction, and its management efficiency and intelligence level directly impact the improvement of government service capabilities. Currently, the development trend of e-government project management technology is characterized by partial process digitization and fragmented overall management. Existing related systems and methods have many technical shortcomings: fragmented processes, with most systems only covering parts such as application and approval, lacking key stages such as operation and maintenance, termination and archiving, failing to form a closed-loop management of the entire lifecycle; insufficient information sharing, with independent operation of systems in various departments creating data silos, significant problems such as duplicate data entry and inconsistencies, and low efficiency in cross-departmental collaboration; weak risk control, with project changes lacking intelligent verification rules, frequent problems such as exceeding budgets and irregular adjustments, and the review process relying on manual processes with significant subjectivity, lacking objective verification mechanisms; insufficient decision support, lacking professional data visualization and analysis tools, with leadership decisions relying on outdated manual statistical reports, making it difficult to achieve data-driven scientific decision-making; and shortcomings in domestic adaptation, with some systems relying on foreign hardware and software at the underlying level. The products fail to meet the development requirements of security, autonomy, and controllability in the government sector; operation and maintenance support is weak, with cumbersome fault response processes and long response times; data backup strategies are simplistic, posing potential risks of data loss and security leaks; asset management is inadequate, with various assets such as hardware, software, and intangible assets generated by IT projects lacking unified digital ledgers, resulting in management challenges of "lack of understanding, sharing, and linkage"; operational monitoring is lacking, making it difficult to track project resource utilization and application status in real time, hindering the economical and refined management of government resources; the evaluation system is weak, focusing primarily on post-project evaluation and lacking a dynamic performance evaluation mechanism throughout the entire project lifecycle, resulting in poor linkage between evaluation results and subsequent project management and fund allocation; furthermore, intelligent applications are weak, failing to fully utilize big data and artificial intelligence technologies to build experience and early warning models, thus hindering the achievement of intelligent decision-making throughout the project process.

[0003] In existing technologies, some local government departments' e-government project management systems only meet basic filing requirements and have not yet achieved full-process digitalization. The intelligent supervision and evaluation method and system (CN202210896073.8) developed by Shenzhen Huahui Data Service Co., Ltd., based on digital government systems, focuses on post-evaluation analysis platforms, primarily emphasizing post-event evaluation and lacking full-cycle dynamic assessment and intelligent model support. The project management graphical user interface (CN202230667875.2) of Digital Guangdong Network Construction Co., Ltd. only implements basic data storage and progress reporting functions, lacking advanced functions such as intelligent verification and expert matching. A digital resource management system (CN202210980430.9) of Cloud Shaanxi Technology Operation Co., Ltd. mainly focuses on financial review and budget control, and has not yet formed a full-cycle business collaboration and intelligent model application system. None of the above-mentioned existing technologies can solve the technical pain points of e-government project management from all dimensions, urgently requiring a management system and method that covers the entire lifecycle and combines collaboration and intelligence. Summary of the Invention

[0004] To address the problems of existing technologies, this invention provides a full lifecycle management system for e-government projects.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A full lifecycle management system for e-government projects includes a full lifecycle business collaboration platform for e-government projects, and a resource management center, an operation monitoring center, a project evaluation center, and a model application center that are bidirectionally connected to the platform.

[0007] The government informatization project full-cycle business collaboration platform serves as the core hub, linking the four functional centers to achieve data interoperability and business collaboration. It provides the business foundation and data source for the entire project process, has dynamic expert matching and intelligent verification functions for project changes, and realizes standardized closed-loop management of the entire lifecycle of the project from application to termination and archiving.

[0008] The resource management center is responsible for the digital collection, multi-dimensional classification, and dynamic ledger management of assets;

[0009] The operation monitoring center is responsible for the real-time collection of project resource operation data and early warning of anomalies.

[0010] The project evaluation center is responsible for building a multi-dimensional performance evaluation system and automatically generating evaluation reports;

[0011] The model application center is responsible for building a model training library, introducing generative artificial intelligence technology to create multiple types of intelligent models, and providing intelligent decision support for the entire process.

[0012] The dynamic expert matching system allocates the number of experts according to the project budget and adopts an anonymous scoring and variance verification mechanism. When the scoring variance exceeds the preset threshold, a secondary verification process is initiated to remove abnormal scores and supplement experts until the variance meets the standard.

[0013] The variance check uses the following calculation formula: Where n is the number of experts, Rate the i-th expert. The average score The variance of the ratings.

[0014] The intelligent verification of project changes relies on a rule engine to automatically verify five dimensions: investment scale, fund adjustment, compliance, progress correlation, and asset correlation. Based on the verification results, it triggers interception, warning, or manual review processes.

[0015] The resource management center classifies assets based on four dimensions: affiliated unit, asset type, usage status, and value level, and constructs a dynamically updated digital asset ledger. When an asset is idle for a longer period than a preset value or malfunctions, an optimization prompt is automatically triggered.

[0016] The operation monitoring center collects CPU, memory, storage, and bandwidth utilization data at a frequency of 90 seconds per time. When the monitored data exceeds the preset threshold, an alarm message is pushed within 5 seconds, and the fault handling data is synchronized to the model application center for model iteration.

[0017] The performance evaluation system of the project evaluation center includes basic evaluation indicators and supplementary special indicators. The expert review adopts a variance verification mechanism, and the evaluation results are linked to the allocation of project operation and maintenance funds and the approval of the second phase of the project.

[0018] The model application center includes an experience model, an early warning model, and an intelligent AI model. It adopts a "fixed cycle and dynamic triggering" mode for model iteration. The early warning model triggers warnings for abnormal situations such as progress delays and investment exceeding the budget.

[0019] The aforementioned e-government project lifecycle management system adopts a "six-layer, two-system" architecture and two major support systems;

[0020] The six-layer architecture includes, in sequence, the user layer, system access layer, business application layer, application support layer, data support layer, and infrastructure layer;

[0021] The two major support systems are the standards and regulations and management system, and the security and operation and maintenance support system.

[0022] The infrastructure layer is compatible with domestically produced software and hardware and has passed the domestic IT innovation certification. The security and operation and maintenance guarantee system adopts a dual encryption method of AES-256 and SM4 national cryptographic algorithms and a local and off-site dual backup strategy.

[0023] Compared to existing technologies, the advantages of this invention are as follows: This invention takes a full-lifecycle business collaboration platform for e-government projects as its core, linking four major centers: resource management, operation monitoring, project evaluation, and model application, to achieve data interoperability and business collaboration. The platform has dynamic expert matching and intelligent verification functions for project changes, supporting standardized closed-loop management of the entire project process.

[0024] The Resource Management Center is responsible for the digital collection and multi-dimensional classification of assets and the establishment of dynamic ledgers; the Operation Monitoring Center collects resource data in real time and triggers early warnings in the event of any anomalies; the Project Evaluation Center constructs a multi-dimensional performance evaluation system that can automatically generate reports.

[0025] The system adopts a "six-layer, two-system" architecture. The six layers cover the user layer to the infrastructure layer, the two systems ensure standard specifications and secure operation and maintenance, the infrastructure layer is adapted to domestic software and hardware, and the security system adopts a dual encryption and dual backup strategy.

[0026] The management process covers the entire lifecycle of application, implementation, operation and maintenance, evaluation, and termination archiving: during the application stage, templates are intelligently generated and verified, and expert matching uses variance verification; during the implementation stage, assets are automatically collected; during the operation and maintenance stage, real-time monitoring and early warning are provided; during the evaluation stage, evaluation reports are automatically generated; and during the termination stage, assets are classified, disposed of, and archived in three dimensions, achieving standardization and intelligence in full lifecycle management. Attached Figure Description

[0027] Figure 1 This is the overall system architecture diagram of the present invention.

[0028] Figure 2 This is a flowchart of the system operation of the present invention.

[0029] Figure 3 This is the timing diagram of the core functions of this invention.

[0030] Figure 4 This is a schematic diagram of the mobile terminal function of the present invention.

[0031] Figure 5 This is the interactive logic diagram of the "platform + 4 centers" of this invention.

[0032] Figure 6 This is a diagram of the resource management center of this invention.

[0033] Figure 7 This is a diagram of the operation monitoring center of this invention. Detailed Implementation

[0034] The present invention will be further described in detail below through embodiments. These embodiments are only used to illustrate the present invention and do not limit the scope of the present invention.

[0035] A full lifecycle management system for e-government projects includes a full lifecycle business collaboration platform for e-government projects, as well as a resource management center, an operation monitoring center, a project evaluation center, and a model application center that are bidirectionally connected to the platform. The full lifecycle business collaboration platform for e-government projects serves as the core hub, linking the four functional centers—resource management center, operation monitoring center, project evaluation center, and model application center—to achieve data interoperability, business collaboration, and functional complementarity.

[0036] The aforementioned e-government project full-cycle business collaboration platform is configured to achieve cross-departmental business collaboration throughout the entire process of project application, implementation, operation and maintenance, evaluation, and termination archiving. It can trigger operations such as data synchronization, expert matching, and evaluation task initiation at key nodes. The platform has dynamic expert matching and intelligent verification functions for project changes, providing business foundation and data sources for the four major centers, realizing standardized processing of the entire project process from planning to termination, and solving the problem of process fragmentation. Through dynamic expert matching and intelligent verification, the objectivity and compliance of the review are improved, and the project change compliance rate has increased from 60% to 98%.

[0037] Dynamic expert matching allocates the number of experts based on the project budget, employing anonymous scoring and variance verification mechanisms. When anomalies are detected in the scoring, a secondary verification process is triggered to ensure the objectivity and impartiality of expert review, reduce human intervention, improve review accuracy, and enhance review fairness by over 90%.

[0038] Variance calculation formula:

[0039] in: The number of experts participating in the review. For the first Expert ratings The average of the expert scores. The variance of the ratings;

[0040] The dynamic expert matching is based on the project budget, with the number of experts dynamically configured: ≥3 experts for budgets below 10 million; ≥5 experts for budgets between 10 million and 50 million; and ≥7 experts for budgets above 50 million. The variance threshold is dynamically set based on the number of experts: ≤8 for budgets below 10 million; ≤6 for budgets between 10 million and 50 million; and ≤4 for budgets above 50 million. The secondary verification rule is as follows: remove the score with the largest deviation from the average, add one expert of the same specialty to re-evaluate, until the variance is ≤ the threshold; the passing score for expert evaluation is ≥80 points.

[0041] The intelligent verification of project changes relies on a rule engine to automatically verify five dimensions: investment scale, funding adjustments, compliance, schedule correlation, and asset correlation. If violations are found, interception, warnings, or manual review processes are triggered. This method automates and standardizes the verification of project changes, enabling timely detection of irregular changes, strengthening risk control, and reducing the number of over-budget projects from an average of 15 per year to 1. Regarding investment scale verification, the total investment after the change must not exceed 10% of the original approved budget; if it exceeds the budget, it will be automatically intercepted. Regarding funding adjustment verification, the adjustment of a single item must not exceed 15% of the original budget for that item, and the cumulative adjustment must not exceed 10% of the total budget; any violation will trigger a warning and rejection. Regarding schedule correlation verification, schedule adjustments must not lag behind the original plan by more than 20% or advance by more than 15%; any violation will trigger manual review. Regarding compliance verification, it is necessary to ensure that supporting materials are 100% complete and comply with the "National Government Information Technology Project Construction Management Measures." Regarding asset correlation verification, asset information is synchronized with project changes through linkage with the asset ledger. Platform general parameters: Interface type (7 types, including cross-center data interaction and departmental collaboration); Data format (mainly JSON, with core business data supplemented by XML backup); Encoding standard (UTF-8); Key node triggering conditions (100% material completeness, complete approval opinions, etc.).

[0042] The main functions of the resource management center are: to digitally collect hardware, software, intangible assets, data resources, network resources, and service resources generated by the project, and to classify them according to four dimensions: affiliated unit, asset type, usage status, and value level, while also conducting dynamic ledger management. Through these functions, cross-departmental asset sharing and status monitoring are achieved, triggering optimization prompts when assets are idle for more than 30 days. Specifically, the resource management center can achieve full lifecycle digital asset management, comprehensively collect various types of assets and classify them according to multiple dimensions, establish a dynamically updated digital asset ledger, and also enable asset sharing and status monitoring, as well as bidirectional data synchronization with the operation monitoring center and project evaluation center.

[0043] Corresponding effect: It solves the pain points of assets being "unclear, not shared, and not linked", realizes the traceability and management of the entire asset life cycle, and improves asset utilization efficiency and traceability efficiency, with asset utilization efficiency increasing by 30% and asset traceability efficiency increasing by 90%.

[0044] The core parameters include: asset classification dimensions, covering four dimensions: affiliated unit, asset type, usage status, and value level; asset types, including six categories: hardware, software, intangible assets, data resources, network resources, and service resources; abnormal trigger thresholds, such as assets being idle for more than 30 days or triggering fault alarms; ledger update mechanism, i.e., real-time synchronization when assets are changed, scrapped, or idle; interface types, including five types such as asset data access and status synchronization; and data formats, namely JSON, Excel, and XML.

[0045] The operation monitoring center is configured to interface with the government cloud and integrated government big data platform. It collects operational data on project computing, storage, data, and network resources every 10 seconds and displays the resource operation status in a visual format. Simultaneously, it triggers anomaly warnings and fault notifications based on preset thresholds such as CPU utilization ≥85% and memory utilization ≥90%. Specifically, the operation monitoring center monitors core indicators such as resource utilization and load from multiple dimensions and triggers fault warnings according to preset thresholds. Furthermore, it displays the operational status in a visual format and generates daily resource usage reports. This achieves real-time monitoring, visual display, and fault warnings of project resources, supporting the economical and refined management of government resources, shortening fault response time, achieving a resource idle warning accuracy rate of 95%, and reducing fault response time by 60%. Data collection frequency is set to 90 seconds / time; monitoring indicator thresholds are set as follows: CPU utilization ≥85%, memory utilization ≥90%, storage utilization ≥80%, and bandwidth utilization ≥90%; visualization formats include bar charts, line charts, and dashboards; report generation time is 00:30 every day; interface types cover 5 categories including government cloud resource integration and big data platform data access; timestamps adopt the UTC standard; fault alarm push time is within 5 seconds to push to maintenance personnel.

[0046] The project evaluation center constructs a performance evaluation system based on 28 basic evaluation indicators across 6 categories and supplementary specific indicators. This system automatically collects data throughout the project lifecycle, supports multi-stakeholder evaluation, generates standardized evaluation reports, and links evaluation results to project operation and maintenance fund allocation and second-phase project approval. Expert review employs the same variance verification mechanism as dynamic expert matching. Specifically, the project evaluation center has constructed a multi-dimensional performance evaluation system covering 28 basic evaluation indicators across 6 categories and supplementary specific indicators. It supports full-cycle dynamic evaluation and multi-stakeholder evaluation, automatically collects evaluation data, matches anonymous expert reviews, verifies the validity of reviews through variance verification, and ultimately automatically generates standardized evaluation reports. Evaluation results are linked to project fund allocation and second-phase approval. This addresses the pain points of a single evaluation system and ex-post evaluation, achieving full-cycle dynamic evaluation of projects, improving the efficiency of the evaluation process and the practicality of the results. The evaluation process efficiency is improved by 70%, and the support of evaluation results for subsequent management is improved by more than 80%. The same variance calculation formula as dynamic expert matching is used. Variance threshold ≤ 8 (number of matching assessment experts ≥ 3); Assessment indicator system (6 major categories and 28 basic indicators, with support for supplementary special indicators); Assessment type (phase assessment, final settlement assessment, operation and maintenance performance assessment); Triggering conditions for operation and maintenance performance assessment (operation and maintenance cycle of 6 months and completion of operation and maintenance report submission); Assessment report format (standardized PDF); Interface type (5 types including assessment data access and task triggering).

[0047] The model application center integrates big data technology to conduct in-depth data collection and analysis throughout the entire project lifecycle, constructing a comprehensive model training library. Combining generative artificial intelligence technology, it has developed experience models, early warning models, and intelligent AI models, providing intelligent decision support for each stage of project management. The application of these models has significantly improved the accuracy of project applications and the timeliness of risk warnings, both achieving improvements of over 80%. A "fixed cycle + dynamic trigger" model is used for model iteration, and the accuracy / fitness of each model is expressed by the following formula:

[0048] Early warning model accuracy = (Number of correct early warnings / Total number of early warnings) × 100%.

[0049] The accuracy rate of intelligent AI model application guidance = (number of correctly guided application cases / total number of guided cases) × 100%.

[0050] The prediction accuracy of the intelligent AI model evaluation results = (number of correctly predicted evaluation results / total number of predicted evaluation results) × 100%.

[0051] Experience model fit rate = (Number of applicable cases / Total number of cases) × 100%. Model training library (≥1000GB, ≥500 projects, 8 categories and 32 feature dimensions); Iteration mechanism (fixed cycle: once a month; dynamic trigger: ≥100 new data entries added to the model training library or the model's core evaluation indicators fail to meet the standards); Model target values ​​(early warning model accuracy ≥95%, application guidance accuracy ≥90%, evaluation prediction accuracy ≥88%, experience model fit rate ≥92%); Early warning trigger conditions (progress delay exceeding 10%, investment exceeding budget by 5%, early warning information pushed to relevant personnel within 5 seconds after triggering); Interface types (5 types including model training data access and invocation); Data format (JSON / TXT).

[0052] The government informatization project lifecycle management system adopts a "six-layer, two-system" architecture. Specifically, the six layers are the user layer, system access layer, business application layer, application support layer, data support layer, and infrastructure layer, which support each other at different levels. The two support systems are the standards and specifications and management system, and the security and operation and maintenance guarantee system.

[0053] The user layer covers six user categories: construction units, data bureaus, approval bureaus, finance bureaus, operation and maintenance units, and evaluation agencies. The system clearly defines the permission boundaries for each role within the platform and the four major centers, and permissions can be customized visually in the backend. This not only meets the needs of multi-entity collaborative management and clarifies the responsibilities and operational scope of each role, but also enables cross-departmental business collaboration, improving cross-departmental collaboration efficiency by 100%. There are six user roles, and permission configuration uses a customizable visual configuration mode in the backend.

[0054] The system access layer adopts a three-tier authorization mechanism of "role-department-function," with operation logs retained for at least one year, fully recording key elements such as the operator, time, content, result, and terminal information. This mechanism effectively ensures access security for the platform, the four major centers, and various business modules, achieving full traceability of the operation process, preventing unauthorized actions, and ensuring a 100% pass rate for security compliance testing. Authorization levels (role-department-function three levels); operation log retention period ≥ 1 year; logs contain information (operator, operation time, operation content, operation result, terminal information).

[0055] The business application layer integrates core modules such as project management, archiving, analysis and decision-making, and knowledge management. Through deep integration with the collaboration platform and the four major centers, it constructs a full-process digital business processing system, supports intelligent applications, and achieves bidirectional real-time data synchronization between modules and centers. By deeply integrating the entire lifecycle of e-government projects with the technical architecture, it builds an online and standardized business processing system covering all stages of the process, including application, approval, implementation, evaluation, and archiving, improving business processing efficiency by more than 70%. The analysis and decision-making module supports a maximum of one data update per day; the project archiving module has an average processing time of no more than 5 minutes per project; and the knowledge management module can store no fewer than 100 policy documents.

[0056] The application support layer includes components such as unified identity authentication, workflow, interface management, data exchange, and an AI algorithm engine. It supports cross-system integration, custom process configuration, and intelligent model invocation. The AI ​​algorithm engine is compatible with domestic frameworks. It provides underlying technical support for the business application layer and the four major centers, achieving cross-system data interoperability, flexible process configuration, and rapid invocation of intelligent models, thereby improving the system's scalability and intelligent foundation capabilities. Interface data transmission format (JSON / XML), encoding standard (UTF-8), and data format standard (GB / T21062-2007) are supported. The AI ​​algorithm engine is compatible with frameworks (TensorFlow, domestic versions of PyTorch).

[0057] The data support layer integrates eight core databases: a project basic database, a process database, an expert database, a supplier database, an asset database, a monitoring indicator database, an evaluation indicator database, and a model training database. Each database is uniquely identified and linked via a project ID. The model training database contains at least 1000GB of data and covers at least 500 projects. Some databases are further linked via asset IDs and approval process identifiers, ensuring full-process data traceability and multi-dimensional linkage. This support layer enables centralized storage, interconnected sharing, and full-process traceability of all data, including project information, asset data, monitoring data, and evaluation results. It provides unified data support for all levels and centers, effectively solving the data silo problem and achieving 100% data sharing. Key parameters are as follows: the model training database contains at least 1000GB of data, covers at least 500 municipal / county-level e-government projects, and includes 8 categories and 32 core dimensions; all database operation logs are retained for at least one year.

[0058] The infrastructure layer is adapted to domestically produced software and hardware, including the Kylin operating system, Eastcom / Baoland middleware, and Kingbase / DM database, all of which have passed the national information technology innovation product adaptation certification. Government cloud elastic resources are automatically allocated based on CPU utilization, with expansion thresholds ≥80% and reduction thresholds ≤30%, ensuring system availability ≥99.9%. This provides a stable, secure, and independently controllable hardware operating environment for the platform and four major centers, eliminating dependence on foreign software and hardware and ensuring the security and compliance of government systems. Core parameters: Government cloud elastic resource allocation thresholds (CPU utilization ≥80% triggers expansion, ≤30% triggers reduction); server core parameters (≥2 domestic Kunpeng 920 processors, ≥64GB memory, ≥1TB SSD storage); network support parameters (bandwidth ≥1000M, latency ≤20ms); system availability ≥99.9%.

[0059] Two major support systems provide compliance and security guarantees for the entire system operation, forming the foundation for stable operation. Based on the GB / T 21062.2-2007 standard, the e-government management system incorporates more than 10 policy documents, standardizing data formats and ensuring unified rules in key areas such as asset classification, monitoring indicators, evaluation processes, and model application. This ensures compliance and standardization of project management and the operation of the four major centers, aligning e-government project management with national policies and industry standards, achieving a project management standardization rate of ≥98%. The system incorporates ≥10 policy documents (including the "National E-government Project Construction Management Measures," etc.); data format standards (GB / T21062-2007); and encoding standards (UTF-8).

[0060] The security and operation and maintenance system employs dual encryption methods: AES-256 encryption (storage) and SM4 national cryptographic algorithm (transmission), along with firewalls, intrusion detection, and other protective measures. Sensitive information is masked and encrypted using a de-identification algorithm. A dual backup strategy of "daily full backup locally + weekly incremental backup off-site" is established. Fault response is executed according to preset times. This ensures the security of system data storage and transmission, prevents data leakage and unauthorized access, avoids data loss through the dual backup strategy, enables rapid fault response and handling, improves system stability, and achieves 100% de-identification coverage for sensitive information. De-identification processing time is ≤0.5 seconds per item; fault response time is ≤2 hours; backup strategy (daily full backup locally + weekly incremental backup off-site); security protection measures (firewall, intrusion detection, authentication).

[0061] The system is based on a "platform + 4 centers" architecture and consists of five core functional modules. It achieves deep integration and functional complementarity with each center. The characteristics, functions, and parameters of each module are as follows:

[0062] The project management module covers the entire process from project application to termination, integrating dynamic expert matching and intelligent project change verification functions. It is deeply integrated with the four major centers to achieve real-time synchronization of business data. By realizing online, standardized, and intelligent processing of the entire project lifecycle, the efficiency of the entire process has been significantly improved by 70%, and the project application cycle has been drastically shortened from 15 working days to 5 working days. The process covers eight key stages; data synchronization is time-efficient (in seconds).

[0063] The analysis and decision-making module effectively integrates the full data from the platform and four major centers. It not only provides leadership dashboards and regional map statistics, but also supports multi-dimensional data drill-down queries, with a data update frequency of ≤1 time / day. This provides management with data-driven scientific decision support, improving decision response speed by 85.7%, and reducing regional project distribution analysis time from 2 days to 10 minutes. Data update frequency ≤1 time / day; accuracy of resource allocation optimization suggestions ≥90%; and visualization formats (leadership dashboards, regional maps, multi-dimensional charts).

[0064] The project archiving module uses a three-dimensional classification method of "year-region-project type" for archiving, comprehensively covering all project data and supporting file download, online query, and full lifecycle traceability. It achieves standardized and intelligent management of project files, improving archiving efficiency and traceability capabilities. Archiving completion time is ≤5 minutes per project, and file traceability efficiency is improved by 95%. Archiving dimensions (year-region-project type 3-dimensional); archived data types (business data, asset ledgers, monitoring data, assessment reports, etc.); supported functions (download, query, traceability).

[0065] The knowledge management module stores at least 100 policy documents and standards for e-government management, integrating experience models and best practices, and supports keyword search, category-based browsing, and offline access. It provides users with policy and knowledge support to improve the standardization and professionalism of business processes. Policy documents are updated in real time, and knowledge retrieval efficiency is improved by 80%. It stores no fewer than 100 files, with search methods including keyword and category-based searches, and supports offline access with a cache validity period of no less than 24 hours.

[0066] The project management mobile app supports Android 8.0 and above, and iOS 12.0 and above. It is compatible with mainstream smart office platforms and achieves real-time data synchronization with the PC via the WebSocket protocol. Furthermore, it supports offline operation and is adaptable to various screen sizes and resolutions.

[0067] Its function is to enable mobile office work anytime and anywhere, fully meet the business processing needs of field work, business trips and other scenarios, and thus effectively improve the review efficiency. According to statistics, the review efficiency has been improved by 66.7%.

[0068] Parameters: System compatibility (Android 8.0+ / iOS 12.0+); Synchronization protocol (WebSocket); Offline cache duration ≥ 24 hours; Screen compatibility (4.7-12.9 inches); Resolution compatibility ≥ 720P; Core functions (task review, project query, monitoring and alert viewing, etc., 6 categories).

[0069] A method for managing e-government projects throughout their entire lifecycle

[0070] Specifically, it includes the following steps:

[0071] (1) Application Stage: The construction unit initiates the application through the collaboration platform. The model application center generates standardized application material templates based on historical project data and pushes out filling instructions. The intelligent AI model performs secondary verification of the application materials and pushes optimization suggestions. The platform dynamically matches experts according to the project budget and completes the technical review using anonymous scoring and variance verification mechanisms. After the review is passed, the project enters the approval stage. The variance calculation formula is: ;

[0072] This method effectively improves the efficiency and quality of project applications, reduces repeated applications, achieves a 95% pass rate on the first attempt, and shortens the application time by more than 70%.

[0073] Using the variance calculation formula Expert configuration standards and variance thresholds are the same as the platform's core parameters; the application template adaptation rate is ≥92% (experience model adaptation rate).

[0074] The variance threshold for expert matching is set according to the project budget as follows: less than 10 million ≤ 8, 10 million to 50 million ≤ 6, and more than 50 million ≤ 4. At the same time, the passing score for expert evaluation must be ≥ 80 points, the first-time verification pass rate for project application must be ≥ 95%, and the application cycle is shortened by more than 70% compared to the traditional model.

[0075] (2) Implementation phase: After the project is approved and tendered, the construction unit confirms the commencement of construction on the platform and uploads the asset purchase voucher. The resource management center automatically collects various assets and classifies them according to four dimensions: affiliated unit / asset type / usage status / value level, generating an initial digital asset ledger. This ledger is synchronized to the platform and the asset library of the data support layer. The platform synchronizes progress and funding data in real time, and the early warning model monitors anomalies in real time, realizing the automated and standardized collection of assets, avoiding errors and omissions in manual entry, improving asset collection efficiency by 90%, and achieving 100% accuracy of asset information; asset classification dimensions: 4 dimensions; asset collection trigger conditions (confirmation of commencement of construction + completion of purchase voucher upload); ledger synchronization time (second level); early warning trigger threshold (progress delay exceeding 10%, investment exceeding budget by 5%).

[0076] (3) Operation and Maintenance Phase: The operation monitoring center collects resource operation data every 90 seconds according to preset thresholds and displays it visually. When indicators exceed the standard / assets are abnormal, an early warning is automatically triggered and disposal suggestions are pushed. The disposal data is synchronized to the model training library of the model application center for model iteration. The average fault disposal time is ≤20 minutes. By optimizing the fault diagnosis and repair process, the fault response and repair time can be further shortened, and the overall fault troubleshooting efficiency can be improved. Specific steps and characteristics: The operation monitoring center collects resource operation data every 90 seconds, displays it visually, and triggers an early warning according to preset thresholds; operation and maintenance personnel provide feedback on the disposal results, and the disposal data is synchronized to the model training library for model iteration; the resource management center updates the asset ledger in real time. Real-time monitoring of resource operation and rapid fault disposal are achieved, improving operation and maintenance efficiency and system stability. The average fault disposal time is ≤20 minutes, and the early warning accuracy rate for similar scenarios subsequently reaches 95%.

[0077] Key parameters: Data acquisition frequency 90 seconds / time; monitoring indicator thresholds are the same as the core parameters of the operation monitoring center; fault push time is within 5 seconds; data is synchronized to the model training library in real time.

[0078] (4) Evaluation Phase: When the project reaches a preset phase milestone or the operation and maintenance cycle reaches 6 months, the project evaluation center will automatically call up a system containing 28 basic evaluation indicators in 6 categories and supplementary special indicators, integrating all data on project processes, assets, and monitoring. Through matching anonymous expert reviews and variance verification, the system can automatically generate a standardized evaluation report and synchronize the evaluation results to relevant units and model application centers. This process automates and standardizes the evaluation process, significantly improving evaluation efficiency and objectivity. For example, the efficiency of evaluation data collection has increased by 85.7%, and the efficiency of report generation has increased by 90%. This improvement is due to the application of big data technology, which can process and analyze large amounts of heterogeneous data, thereby providing evaluators with comprehensive and objective decision-making basis. Variance calculation formula is used. Evaluation indicator system (6 major categories, 28 items + special items); Evaluation trigger conditions (completion of stage nodes / 6 months of operation and maintenance).

[0079] (5) During the project termination phase, the construction unit needs to initiate a project termination application. After compliance verification by the intelligent AI model and review by the competent department, the project can be officially terminated. The resource management center will classify and dispose of assets, including reusable allocation, scrapping, and idle marking. Subsequently, the platform will automatically collect all process data according to the three-dimensional classification standard of "year-region-project type" and complete the archiving work. The archived data will be synchronized to the knowledge management module to ensure the integrity and accuracy of the archive. Achieve closed-loop management of project termination and asset disposal, improve archiving efficiency and asset utilization, increase asset allocation and reuse rate by 30%, and archive completion time ≤ 5 minutes / project. Asset disposal types (reusable, scrapped, idle 3 categories); archiving dimensions (year-region-project type 3 dimensions); full coverage of archived data; data synchronized to the knowledge management module in real time.

Claims

1. A full lifecycle management system for e-government projects, characterized in that, This includes a full-cycle business collaboration platform for e-government projects, as well as a resource management center, operation monitoring center, project evaluation center, and model application center that are bidirectionally connected to the platform. The government informatization project full-cycle business collaboration platform serves as the core hub, linking the four functional centers to achieve data interoperability and business collaboration. It provides the business foundation and data source for the entire project process, has dynamic expert matching and intelligent verification functions for project changes, and realizes standardized closed-loop management of the entire life cycle of the project from application to termination and archiving. The resource management center is responsible for the digital collection, multi-dimensional classification, and dynamic ledger management of assets; The operation monitoring center is responsible for the real-time collection of project resource operation data and early warning of anomalies. The project evaluation center is responsible for building a multi-dimensional performance evaluation system and automatically generating evaluation reports; The model application center is responsible for building a model training library, introducing generative artificial intelligence technology to create multiple types of intelligent models, and providing intelligent decision support for the entire process.

2. The e-government project lifecycle management system according to claim 1, characterized in that, The dynamic expert matching system allocates the number of experts according to the project budget and adopts an anonymous scoring and variance verification mechanism. When the scoring variance exceeds the preset threshold, a secondary verification process is initiated to remove abnormal scores and supplement experts until the variance meets the standard.

3. The e-government project lifecycle management system according to claim 2, characterized in that, The variance check uses the following calculation formula: Where n is the number of experts, Rate the i-th expert. The average score The variance of the ratings.

4. The e-government project lifecycle management system according to claim 3, characterized in that, The intelligent verification of project changes relies on a rule engine to automatically verify five dimensions: investment scale, fund adjustment, compliance, progress correlation, and asset correlation. Based on the verification results, it triggers interception, warning, or manual review processes.

5. The e-government project lifecycle management system according to claim 4, characterized in that, The resource management center classifies assets based on four dimensions: affiliated unit, asset type, usage status, and value level, and constructs a dynamically updated digital asset ledger. When an asset is idle for a longer period than a preset value or malfunctions, an optimization prompt is automatically triggered.

6. The e-government project lifecycle management system according to claim 5, characterized in that, The operation monitoring center collects CPU, memory, storage, and bandwidth utilization data at a frequency of 90 seconds per time. When the monitored data exceeds the preset threshold, an alarm message is pushed within 5 seconds, and the fault handling data is synchronized to the model application center for model iteration.

7. The e-government project lifecycle management system according to claim 6, characterized in that, The performance evaluation system of the project evaluation center includes basic evaluation indicators and supplementary special indicators. The expert review adopts a variance verification mechanism, and the evaluation results are linked to the allocation of project operation and maintenance funds and the approval of the second phase of the project.

8. The e-government project lifecycle management system according to claim 7, characterized in that, The model application center includes an experience model, an early warning model, and an intelligent AI model. It adopts a "fixed cycle and dynamic triggering" mode for model iteration. The early warning model triggers warnings for abnormal situations such as progress delays and investment exceeding the budget.

9. The e-government project lifecycle management system according to any one of claims 1-8, characterized in that... It adopts a "six-layer, two-system" architecture and two major support systems; The six-layer architecture includes, in sequence, the user layer, system access layer, business application layer, application support layer, data support layer, and infrastructure layer; The two major support systems are the standards and regulations and management system, and the security and operation and maintenance support system. The infrastructure layer is compatible with domestically produced software and hardware and has passed the domestic IT innovation certification. The security and operation and maintenance guarantee system adopts a dual encryption method of AES-256 and SM4 national cryptographic algorithms and a local and off-site dual backup strategy.

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