A bank system research and development project management system

Abstract

The application discloses a bank system R&D project management system, which comprises an intelligent management platform, a risk prediction module, a blockchain storage and traceability module, a task coordination module and a security control module.The intelligent management platform generates compliance check lists and difference reports by real-time interfacing with a supervision database.The risk prediction module predicts the risk levels of R&D cycle delays and resource overruns, triggers early warnings and suggests allocation schemes.The blockchain storage and traceability module stores the hash values and operation records of key node documents on a chain, and solidifies time stamps and responsible persons.The task coordination module uses NLP to analyze requirement documents, automatically disassembles them into executable task trees, analyzes manpower and equipment conflicts through a resource occupation graph, and intelligently generates an allocation scheme.The security control module implements hierarchical permissions based on roles and data sensitivity, and requires biological authentication and dynamic token double verification for sensitive operations to prevent unauthorized access.The system has strong risk resistance, high overall R&D efficiency, fast data management and auditing, and can meet the needs in the R&D process of a bank system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bank system development, and particularly relates to a bank system research and development project management system. BACKGROUND

[0002] In the wave of financial informatization, bank system research and development project management has undergone a long evolution. In the early stage, bank system development relies on manual recording and manual experience, and the management of project progress, resource allocation and other aspects is scattered and disordered. Document management is also in disorder, and compliance check basically relies on manual comparison of policy documents, which is low in efficiency and easy to make mistakes. With the expansion of bank business and the preliminary penetration of information technology, basic project management tools such as simple progress tracking software and resource management table are introduced, which completes the transformation from disorder to preliminary standardization, but these tools are single in function and poor in integration, and it is difficult to meet the increasing complexity of bank system research and development.

[0003] Currently, a variety of mainstream technologies are widely used in the field of bank system research and development project management. On the one hand, mature project management software such as Jira and Trello provides visual board, agile development process support, task allocation and other functions to help team efficient collaboration; on the other hand, agile development methodology is popular, which emphasizes iterative development and rapid response to demand changes, so that the bank system can keep pace with the market; at the same time, data warehouse and business intelligence tools are combined to preliminarily realize the collection and simple analysis of project data, providing certain basis for decision-making. These technologies indeed play an important role in improving project management efficiency and promoting team collaboration, making the promotion of bank system research and development project more orderly and transparent.

[0004] However, the existing mainstream technologies have exposed many problems in the deep application in the financial field. Firstly, the compliance guarantee ability is limited, and it is difficult to cope with the increasingly complex regulatory policies by manual screening, which easily leads to compliance risks; secondly, risk early warning is lagging, and the existing tools are mostly passive in response after the problem appears, and cannot accurately predict the hidden troubles such as research and development cycle delay and resource overspending in advance; thirdly, the data island phenomenon is serious, and the project management system, human resource system and other systems are independent, and the data is difficult to share and integrate, causing information fragmentation and affecting the comprehensiveness of decision-making; fourthly, the resource allocation is not intelligent, and is only based on simple rules, without fully considering dynamic factors such as personnel skills and equipment status, resulting in frequent waste of resources and conflicts. These problems restrict the further improvement of bank system research and development project management level, and it is difficult to meet the strict requirements of modern banks for high efficiency, low risk and strong compliance. SUMMARY

[0005] The present application provides a bank system research and development project management system to solve the technical problems of poor risk resistance of existing management system, low overall research and development efficiency, slow data management and audit.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] The present application provides a bank system R&D project management system, comprising:

[0008] The intelligent management platform connects the supervision database in real time, scans sensitive words in project documents by combining NLP technology, generates compliance check lists and difference reports, marks high-risk tasks and recommends rectification schemes;

[0009] The risk prediction module analyzes historical project data based on machine learning to predict the risk level of R&D cycle delay, resource overruns, etc., triggers an early warning and suggests a deployment plan;

[0010] The blockchain storage and traceability module stores the hash value and operation record of the key node document on the chain, solidifies the timestamp and responsible person, and supports the regulatory authorities to quickly review the whole-process audit log through the private key;

[0011] The task coordination module uses NLP to analyze the demand document, automatically disassembles it into an executable task tree, analyzes human and equipment conflicts through a resource occupation graph, and intelligently generates a deployment plan;

[0012] The security control module implements hierarchical permissions based on roles and data sensitivity, and requires biological authentication and dynamic token double verification for sensitive operations to prevent unauthorized access.

[0013] Further, an implementation process of a bank system R&D project management system, comprising the following steps,

[0014] Initialize system parameters, configure the functions and permissions of each module, including connecting the supervision database account information, setting the basic permissions of each role, and initializing the blockchain node;

[0015] The intelligent management platform connects the supervision database in real time, uses the preset data collection rules and NLP algorithm to analyze and scan the sensitive words of the obtained policy update information, generates compliance check lists and difference reports based on the rule engine, and sends the to-do list notification to the relevant project managers through the built-in message push service of the system;

[0016] The risk prediction module is used to evaluate the project risk, the model loads historical project data for self-training and optimization, and outputs the risk level prediction result in real time according to the current project progress, when the risk reaches the warning threshold, the early warning process is triggered, and the deployment suggestion scheme based on the current resource state is provided;

[0017] The key node document is notarized by the blockchain notarization and traceability module, the document hash value is automatically calculated and the relevant information is stored on the chain, and the operation log is recorded, so that each key operation has a traceable record; the task coordination module is used for analyzing, disassembling and path recommending the demand document, the project management personnel confirms the recommended task tree and development path in the system, the task is allocated to the specific executor, the resource analysis unit monitors the resource occupation in real time, and if a conflict occurs, the system automatically adjusts or prompts manual intervention;

[0018] The security control module implements security control in the whole process, biological recognition and dynamic token verification are carried out when personnel log in the system, sensitive resources are accessed again, all operation requests are checked by the security strategy and then released, and the system security is guaranteed;

[0019] The visual display unit obtains data from each business module in real time, renders and displays according to the preset visual rules, and assists the management personnel in decision-making;

[0020] According to the feedback and data analysis results of the system, the project team continuously optimizes the execution process of the project until the completion of the project, and realizes the efficient management and smooth delivery of the bank system development project.

[0021] The technical scheme provided by the application has at least the following beneficial effects:

[0022] The following are 3 beneficial effects according to the content of the claims:

[0023] The intelligent management platform of the application can real-time interface with the supervision database, automatically capture and analyze policy updates, scan sensitive words in project documents combined with NLP technology, and can timely find compliance risk points in the project, generate compliance check lists and difference reports, mark high-risk tasks and recommend rectification schemes, ensure that the bank system development project meets the regulatory requirements, avoid huge fines, business restrictions and other risks due to violations, and ensure the stable operation of the bank, the risk prediction module can predict the risk level of the development cycle delay, resource overruns and other risks based on machine learning analysis of historical project data, real-time monitor resource occupation conflicts and trigger early warning, provide sufficient time for project management to take measures to mitigate risks, reduce the possibility of project failure, and enhance the risk resistance of the bank in the system development process.

[0024] The task coordination module of the application utilizes NLP to parse the requirement document, automatically disassembles it into an executable task tree, and combines with the bank business rules to recommend a development path, which can accurately convert the requirement into specific development tasks, reduce the errors and omissions of manual task disassembly, recommend the optimal development path to avoid detours in the research and development process, shorten the project development cycle, analyze the human and equipment conflicts through the resource occupation graph, intelligently generate a deployment scheme, realize fine management and reasonable deployment of research and development resources, improve the utilization rate of resources, avoid the idling and waste of resources, enable the bank to complete more research and development tasks with limited resources, and improve the overall research and development efficiency.

[0025] In the task coordination module, the resource analysis unit can evaluate the resource matching degree of outsourcing tasks, analyze the skill level, available time window and cost benefit of the outsourcing team, and ensure that the resource demand of the outsourcing task is met. This integrated outsourcing risk control function can effectively reduce the uncertainty and potential risks in the outsourcing process, ensure the smooth execution of the outsourcing task, and improve the overall controllability of the project.

[0026] The security control module implements hierarchical permissions based on roles and data sensitivity, and sensitive operations need to be verified by biometric authentication and dynamic token, which can effectively prevent unauthorized access, protect the confidentiality and integrity of core data assets such as customer information and transaction data in the bank system research and development process, reduce the risk of data leakage, maintain the reputation and customer trust of the bank, and the blockchain storage and traceability module stores the hash value and operation record of the key node document on the chain, solidifies the timestamp and responsible person, supports the regulatory agencies to quickly review the full-process audit log through the private key, ensures the transparency and traceability of the research and development process, facilitates the quick positioning of responsibilities when problems occur, and also provides convenience for the bank's internal audit work, improves the audit efficiency, and enhances the compliance and credibility of the bank in data management and audit. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creating laborious work.

[0028] Figure 1 The system block diagram of the bank system research and development project management system provided by the embodiments of the application. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical scheme and advantages of the application more clear, the following will further describe the embodiments of the application in combination with the drawings.

[0030] Please refer to Figure 1 The bank system research and development project management system system block diagram is shown. The embodiment provides a bank system research and development project management system, which comprises:

[0031] 1. Intelligent management platform

[0032] By real-time docking of the supervision database, combining NLP technology to scan sensitive words in project documents, generating compliance check lists and difference reports, marking high-risk tasks and recommending rectification schemes.

[0033] It includes data acquisition unit, policy analysis unit, sensitive word scanning unit and report generation unit. The data acquisition unit adopts a distributed crawler architecture based on the open API standard protocol of the supervision database, uses multi-threading technology to ensure that the incremental data is captured within the first time window of the supervision data update, and stores the data through the message queue Kakfa to prevent data loss due to sudden increase in data volume, and ensures the continuity of data acquisition. The policy analysis unit uses a deep learning language model based on the Transformer architecture to pre-train the complex structure and professional terms of financial policy text, and then fine-tune it according to the business characteristics of the bank, and the model can accurately identify the key elements in the policy and convert the policy provisions into structured data to provide accurate basis for subsequent compliance checks. The sensitive word scanning unit constructs a sensitive word knowledge graph based on the bank business scenario, stores sensitive words and their associated relationships in a graph database Neo4j, uses graph neural network technology, and dynamically evaluates the risk level of sensitive words based on the context semantics of the document, avoiding false positives caused by simple keyword matching. The report generation unit compares the compliance requirements, sensitive word risk assessment results and project documents based on the rule engine Drools, uses pre-set rule templates to automatically generate compliance check lists containing difference details, risk distribution heat maps, and uses deep learning text generation models to customize rectification schemes for each high-risk task, assisting project teams in efficiently responding to policy changes.

[0034] 2. Risk prediction module

[0035] Based on machine learning analysis of historical project data, the risk level of development cycle delay and resource overruns is predicted, triggering an early warning and suggesting a deployment plan.

[0036] It includes data storage unit, machine learning analysis unit, risk assessment unit and early warning unit. The data storage unit adopts distributed columnar storage system HBase, and classifies and stores the structured and unstructured parts in the historical project data. For unstructured data such as requirement documents and meeting minutes, the text vectorization technology Word2Vec is used to convert them into analyzable feature vectors, which are stored uniformly with structured data for subsequent model training, fast retrieval and batch reading. The machine learning analysis unit uses long short-term memory network to process project time series data, captures the fluctuation law of R&D cycle, and combines gradient boosting tree GBDT algorithm to fit multi-dimensional features of resource consumption, builds a hybrid model, and continuously optimizes model hyperparameters through cross-validation to improve prediction accuracy. The risk assessment unit builds a risk assessment model based on Bayesian network, integrates the risk factors and their correlation of R&D cycle delay and resource overrun into the probabilistic graph model, dynamically updates the prior probability according to the current project status, and calculates the posterior probability of risk level in real time to provide quantitative basis for early warning. The early warning unit uses real-time stream computing framework Flink to monitor project resource occupation indicators in real time, sets multi-level early warning thresholds, and when the resource conflict degree reaches the threshold, sends early warning information to the relevant person in charge through the message push service RabbitMQ, and calls the resource allocation algorithm to provide project team with scheme suggestions including manpower allocation and equipment sharing based on the current resource occupation graph, to ensure the smooth progress of the project.

[0037] 3. Blockchain storage and traceability module

[0038] The hash value and operation record of the key node document are stored on the chain, and the time stamp and responsible person are fixed. The supervisory authority can quickly review the whole process audit log through the private key.

[0039] It includes a hash calculation unit, a blockchain storage unit and an audit log management unit. The hash calculation unit adopts SHA-256 algorithm combined with Merkle tree structure to perform multi-layer hash operation on the key node document, generates a document fingerprint with high security, ensures that any minor tampering can be discovered in time, and uses GPU acceleration technology to improve the efficiency of hash calculation, meeting the performance requirements of high-frequency updating of bank project documents. The blockchain storage unit is based on the consortium chain architecture Hyperledger Fabric, and builds a multi-party participation evidence storage network including regulatory agencies, bank management and project teams. Each block records the hash value of the key node document, operation record, timestamp and the information of the digital signature of the responsible person. The legality of the transaction is automatically verified by the smart contract to ensure that the data on the chain is real and reliable and cannot be tampered with, providing a solid foundation for subsequent audits. The audit log management unit designs an encryption index mechanism, encrypts the relevant indexes of the audit log using the private key of the regulatory agency, ensures that only the regulatory agency can quickly locate the required log information within the authorized range, and uses zero-knowledge proof technology to protect the sensitive information of the project while enabling the regulatory agency to verify the integrity and authenticity of the log, achieving efficient audit supervision.

[0040] 4. Task coordination module

[0041] The NLP is used to analyze the requirement document, which is automatically disassembled into an executable task tree, and the resource occupation graph is used to analyze the conflict between manpower and equipment, and an intelligent deployment scheme is generated.

[0042] The system comprises a demand analysis unit, a task decomposition unit, a path recommendation unit and a resource analysis unit. The demand analysis unit uses an improved BERT model to perform deep semantic analysis on demand documents, conducts special training on common business logic and terminology in bank demand documents, accurately identifies functional points, non-functional constraints and associated business rules in the demand, and provides accurate semantic understanding for subsequent task decomposition. The task decomposition unit decomposes the parsed demand into an executable task tree according to the hierarchical structure of the bank business process based on the HTN (Hierarchical Task Network) planning algorithm. Each task node contains clear input and output parameters, preconditions and postconditions, and estimated workload. The dependency graph is used to clearly show the complex dependency relationship between tasks, ensuring that the task execution order is reasonable. The path recommendation unit constructs a knowledge base containing multiple bank business architecture templates, combines the current project demand characteristics and the bank technology stack status, and uses reinforcement learning algorithm to recommend the optimal development path for the task tree. The algorithm constantly optimizes the recommendation strategy based on the feedback of historical project path execution effect, and improves the development efficiency and quality. The resource analysis unit uses graph neural network technology to construct a resource occupation graph, treats human and equipment resources as graph nodes, and treats the demand relationship of tasks for resources as graph edges. The graph convolutional neural network (GCN) is used to analyze the resource conflict propagation path, predict the potential conflict range, intelligently generate a deployment scheme, and comprehensively consider the factors such as personnel skill matching degree and equipment available time window to achieve efficient resource utilization.

[0043] 5. Security control module

[0044] Hierarchical permissions are implemented based on roles and data sensitivity. Sensitive operations require dual verification of biometric authentication and dynamic token to prevent unauthorized access.

[0045] The system includes a permission management unit, an authentication unit, and an access control unit. The permission management unit extends the data sensitivity dimension based on the role-based access control (RBAC) hierarchical model, constructs a multi-level permission system, sets access levels according to sensitivity levels for resources of different business systems and project documents in the bank, combines dynamic permission adjustment strategies, and updates permission configurations in real time according to project phases and personnel position changes to ensure accurate and adaptive permission allocation. The authentication unit uses multi-modal biometric recognition technology, integrates multiple biometric features such as fingerprints, faces, and voiceprints, uses deep learning feature extraction models to improve biometric recognition accuracy and anti-forgery capabilities, and generates dynamic verification codes with high security using time-synchronized one-time password (TOTP) dynamic token technology. The dual verification mechanism ensures the security of sensitive operations and prevents unauthorized personnel from operating. The access control unit uses software-defined security (SDS) technology to construct a virtual security boundary, dynamically adjusts network access policies based on permission management and authentication results, encrypts sensitive data transmission channels, uses an intrusion detection system (IDS) to monitor access behavior in real time, and uses machine learning anomaly detection algorithms to detect and block abnormal access requests, providing comprehensive system security.

[0046] It should be noted that the modules interact through data transmission interfaces based on the message middleware RocketMQ and the API gateway. The data transmission interface uses RESTful API specifications to ensure the simplicity and universality of module calls, uses the API gateway for request routing, load balancing, and authentication management to ensure data exchange security and reliability. To achieve data sharing and collaborative work, the modules follow a unified data exchange format, JSON, and use a data dictionary to standardize data fields. Asynchronous communication is achieved through message middleware to ensure data transmission timeliness and integrity in high-concurrency scenarios, avoiding blocking caused by inter-module dependencies, ensuring the overall functionality of the system, and improving the efficiency and quality of bank system development project management.

[0047] It should be noted that the intelligent management platform is integrated with other management systems within the bank, including a project management system (based on the agile development framework Jira) and a human resource management system (based on the SAPHANA platform). A unified data integration layer is constructed, and the ETL tool Informatica PowerCenter is used for data extraction, transformation, and loading to achieve data interconnection. Taking project progress as an example, the intelligent management platform obtains task status update information from the project management system, combines personnel skills and scheduling data from the human resource management system, uses the critical path method (CPM) in operations research to re-evaluate the overall project progress, timely identify potential delay risks, and feedback to relevant systems to trigger corresponding adjustment processes, further improving the comprehensiveness and accuracy of bank system development project management.

[0048] It should be noted that the system also includes a visualization display unit, which uses a micro-frontend architecture to integrate visualization components of different business dimensions in the form of plugins from AntV data visualization components. For project progress display, a Gantt chart is used to visually present the comparison between task planning and actual execution, combined with a traffic light color warning mechanism to highlight progress deviations; for risk display, a risk heat map is constructed, dynamically colored according to risk levels and impact ranges to help management personnel quickly locate key risk points; for resource occupation display, a Sankey diagram is used to clearly present resource flow and allocation status, and WebSocket real-time push technology is used to ensure timely updating of display data, facilitating management personnel to make decisions and monitor, and timely identify problems and take appropriate measures.

[0049] It should be noted that the implementation process is introduced below.

[0050] 1. Initialization preparation phase

[0051] The professional system configuration team is responsible for initializing system parameters, including accurate configuration of interfacing regulatory database account information to ensure that the system can successfully connect to the regulatory database and obtain the latest regulatory policy data. At the same time, according to the organizational structure and business needs of the bank system, set up the basic permissions of each role, and clearly define the operation range and permission level of different roles in the system, such as project managers, developers, and testers having different data access and operation permissions. In addition, complete the initialization work of the blockchain node, determine the initial architecture and participating nodes of the blockchain network, and lay the foundation for subsequent evidence storage and traceability functions. In this phase, the system's various basic parameters also need to be optimized and set to adapt to the actual business scale and complexity of the bank system, ensuring that the system works efficiently and stably during operation.

[0052] 2. Data collection and compliance check phase

[0053] The data collection unit of the intelligent management platform is activated and real-time interfaces with the regulatory database according to the preset data collection rules and frequency. By utilizing the advantages of multi-threading technology and distributed crawler architecture, the incremental update part of the regulatory data is quickly captured, and data caching is performed through the message queue Kafka to ensure the continuity and integrity of data collection and avoid data loss due to excessive data volume or network fluctuations. The collected data will be transmitted to the policy analysis unit, which uses a deep learning language model based on the Transformer architecture to deeply analyze financial policy texts, accurately identify key elements, and store them as structured data. At the same time, the sensitive word scanning unit uses graph neural network technology combined with document context semantics to comprehensively scan project documents based on the constructed bank business scenario sensitive word knowledge graph, dynamically assessing the risk level of sensitive word occurrence. The report generation unit uses the rule engine Drools and deep learning text generation model to automatically generate detailed compliance check lists and difference reports based on compliance requirements, sensitive word risk assessment results, and project document content, which include difference point details, risk distribution heat maps, and customized rectification plans for each high-risk task. The rectification plan details the rectification steps, responsible departments, and key information such as expected time consumption, providing clear rectification direction and operation guidelines for project teams to efficiently address compliance challenges brought by policy changes. The system's built-in message push service will send a to-do notification to the relevant project leader after the report is generated, reminding them to pay attention to compliance issues and take appropriate rectification measures.

[0054] 3. Risk assessment and early warning phase

[0055] The risk prediction module starts a comprehensive risk assessment of the project. The data storage unit stores historical project data in a classified manner, where unstructured data such as requirement documents and meeting minutes are converted into feature vectors through the text vectorization technology Word2Vec and stored in the distributed columnar storage system HBase together with structured data, so that the machine learning analysis unit can quickly retrieve and batch read these data for model training. The machine learning analysis unit uses a hybrid model constructed by combining long short-term memory networks and gradient boosting tree (GBDT) algorithms to analyze the time series data and multi-dimensional features of resource consumption of the project, capture the fluctuation patterns of the research and development cycle and resource consumption patterns. During the model training process, the model hyperparameters are continuously optimized through cross-validation to improve the accuracy of risk prediction. The risk assessment unit builds a risk assessment model based on Bayesian networks, integrates various risk factors and their associated relationships of research and development cycle delay and resource overspending into the probabilistic graph model, dynamically updates the prior probability according to the current project status, and calculates the posterior probability of the risk level in real time to provide quantitative basis for early warning. When the risk level reaches the early warning threshold, the early warning unit quickly starts, uses the real-time stream computing framework Flink to monitor the project resource occupation indicators in real time, and once the resource conflict degree reaches the set multi-level early warning threshold, sends early warning information to the relevant responsible persons through the message push service RabbitMQ, reminding them to pay attention to the project risks in time. At the same time, the early warning unit calls the resource allocation algorithm and provides suggestions for the project team based on the current resource occupation graph, including manpower allocation, equipment sharing and other schemes, to help them adjust the resource allocation reasonably and ensure the smooth progress of the project, reducing the adverse effects of risks on the project.

[0056] 4. Evidence storage and traceability phase

[0057] The blockchain storage and traceability module plays a role at the key nodes of the project. When the key node document is generated or updated, the hash calculation unit adopts the SHA-256 algorithm combined with the Merkle tree structure to perform multi-layer hash operation on the document to generate a document fingerprint with high security. Using GPU acceleration technology, it ensures that the hash calculation can be completed efficiently in the environment of high-frequency updating of bank project documents, and any minor tampering behavior can be detected in time. The blockchain storage unit records the calculated document hash value, operation record, timestamp, and information signed by the responsible person in the block based on the consortium chain architecture Hyperledger Fabric, and automatically verifies the legality of the transaction through the smart contract to ensure that the data on the chain is real and reliable and cannot be tampered with, providing a solid and reliable data foundation for subsequent audit work. The audit log management unit designs an encrypted index mechanism, encrypts the relevant index of the audit log using the private key of the supervisory agency, and ensures that only the supervisory agency can quickly and accurately locate the required log information within the authorized range. At the same time, with the help of zero-knowledge proof technology, the supervisory agency can effectively verify the integrity and authenticity of the log while protecting the sensitive information of the project from being leaked, achieving efficient audit supervision and enhancing the transparency and credibility of the management of the bank system development project.

[0058] 5. Task coordination and resource allocation phase

[0059] The task coordination module deeply processes the requirement document. The requirement analysis unit uses an improved BERT model to conduct special training on the business logic and professional terminology in the bank requirement document, thereby accurately identifying the functional points, non-functional constraints, and associated business rules in the requirements, providing accurate semantic understanding for subsequent task decomposition. The task decomposition unit is based on the hierarchical task network (HTN) planning algorithm, and according to the hierarchical structure of the bank business process, it gradually decomposes the parsed requirements into an executable task tree. Each task node defines the input and output parameters, preconditions and postconditions, and estimated workload information, and uses a dependency graph to clearly show the complex dependency relationships between tasks, ensuring the rationality of the task execution order and avoiding project delays or resource waste due to incorrect task order. The path recommendation unit combines a knowledge base containing multiple bank business architecture templates, and according to the current project requirement characteristics and the bank's technology stack status, it uses a reinforcement learning algorithm to recommend the optimal development path for the task tree. This algorithm will continuously optimize the recommendation strategy based on feedback data from historical project path execution effects to improve development efficiency and project quality, ensuring that the project can be completed smoothly within the specified time and meet the bank's business requirements and technical standards. The resource analysis unit uses graph neural network technology to construct a resource occupation graph, representing human and equipment resources as graph nodes and task resource demand relationships as graph edges. By analyzing the propagation path of resource conflicts using graph convolutional neural networks (GCN), it can predict potential resource conflict ranges in advance, thereby intelligently generating resource allocation schemes. During scheme development, various factors such as personnel skill matching, equipment availability time windows are considered to achieve efficient resource utilization and reasonable allocation, improve overall project execution efficiency, and reduce project risks caused by resource shortages or conflicts. In the task coordination and resource allocation stage, the system also has a management function for outsourcing tasks. The resource analysis unit can evaluate the allocation of internal resources and also assess the resource matching degree of outsourcing teams. By analyzing factors such as skill level, available time window, past project experience, and cost-effectiveness, it ensures that the resource requirements of outsourcing tasks are accurately matched.

[0060] 6. Security control phase

[0061] The security control module runs throughout the entire bank system R&D project management system, and guarantees the security of the system and the confidentiality of the data. When the personnel log in the system, the authentication unit first starts the multi-modal biometric recognition technology, fuses multiple biometric features such as fingerprints, faces, and voiceprints for identity verification. The deep learning feature extraction model is used to improve the accuracy and anti-forgery ability of biometric recognition, effectively preventing illegal personnel from bypassing system authentication through forged biometric features. After the biometric recognition passes, the system will also generate a dynamic verification code with high security by combining the time-synchronized one-time password (TOTP) dynamic token technology, and require the user to input it to complete the double verification process, further enhancing the security of sensitive operations and ensuring that only authorized legal users can access and operate the sensitive resources of the system. The access control unit uses software-defined security (SDS) technology to build a flexible virtualized security boundary. According to the permission management and authentication results, the network access strategy is dynamically adjusted, and the sensitive data transmission channel is protected by an encrypted tunnel to prevent data from being stolen or tampered with during transmission. At the same time, the intrusion detection system (IDS) is used to monitor the access behavior of the user in real time, and the machine learning anomaly detection algorithm is used to analyze the access request in depth, and timely discover and block abnormal access requests, such as frequent access from unknown IP addresses, batch data download that does not conform to the user's normal operation habits, and comprehensively guard the security of the system, prevent data leakage and malicious attack events, and ensure the stable operation of the bank system R&D project management system and the security and reliability of the business data.

[0062] 7. Visualization and decision support stage

[0063] The visualization display unit obtains data from each business module in real time, renders and displays according to the preset visualization rules, and provides a visual and comprehensive project management view for managers. For project progress, the system uses a Gantt chart to visually present the comparison between task planning and actual execution, clearly displays the progress deviation of tasks through different colored bar charts, and highlights tasks with progress lag through a traffic light color warning mechanism, making it easy for managers to quickly locate problem areas and take timely measures to adjust and optimize. In terms of risk display, a risk heat map is constructed, and risks are dynamically colored based on key dimensions such as risk level and impact range. The color from light to dark intuitively reflects the severity of the risk, helping managers quickly focus on key risk points, develop risk response strategies in advance, and reduce the impact of risks on the project. For resource occupation, a Sankey diagram is used for display, with flowing lines intuitively presenting the allocation and flow of resources between different tasks and departments, helping managers clearly understand resource utilization efficiency and bottlenecks, and thus making reasonable resource allocation and optimization. For outsourcing risk control, the progress, resource occupation, and risk situation of outsourcing tasks are also displayed, providing managers with an intuitive view of outsourcing management and helping them quickly grasp the execution status of outsourcing tasks and adjust strategies in a timely manner. Through WebSocket real-time push technology, the display data can be updated in a timely manner, reflecting the latest status of project management, making it easy for managers to keep abreast of project dynamics and make scientific and reasonable decisions, ensuring the smooth progress of the bank system development project and timely delivery of high-quality system results.

[0064] 8. Continuous optimization phase

[0065] According to the feedback information and data analysis results of the system, the project team continuously optimizes the execution process of the project. For example, based on the compliance check list and difference report generated by the intelligent management platform, the project documents are updated and improved in a timely manner to ensure that the project always meets regulatory requirements; referring to the risk level prediction results and allocation suggestion scheme provided by the risk prediction module, the project plan is dynamically adjusted, resources are reasonably arranged, and project risks are reduced; combined with the task execution and resource occupation analysis data in the task collaboration module, the task priority and resource allocation scheme are optimized to improve project execution efficiency and quality; by monitoring the execution of outsourcing tasks and changes in risks in real time, the project team can adjust outsourcing strategies in a timely manner, optimize the allocation and management of outsourcing tasks, and ensure the smooth execution of outsourcing tasks, reducing the impact of outsourcing risks on the overall project. Throughout the project cycle, monitoring, evaluation, feedback, and optimization are continuously repeated to form a continuous improvement closed-loop management mechanism, ensuring that the bank system development project can be efficiently, stably, and compliantly advanced, and ultimately successfully delivered, providing strong support for the bank's digital transformation and business development.

[0066] Through the above implementation process, the bank system research and development project management system can give full play to the functional advantages of each module, realize all-around and fine management of the bank system research and development project, effectively improve the management efficiency and quality of the project, reduce the project risk, and guarantee the stable development and reliable delivery of the bank system.

[0067] In addition, it should be noted that the present application can be provided as a method, apparatus or computer program product. Therefore, the embodiments of the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including a computer-usable program of instructions) containing computer-usable program code.

[0068] The embodiments of the present application are described with reference to flowcharts and / or block diagrams of the method, terminal device (system) and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, embedded processor or other programmable data processing terminal device to produce a machine, so that the instructions executed by the computer or other programmable data processing terminal device produce a device implemented in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in the flow(s) or block(s).

[0069] These computer program instructions can also be stored in a computer-readable memory capable of causing the computer or other programmable data processing terminal device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in the flow(s) or block(s). These computer program instructions can also be loaded into a computer or other programmable data processing terminal device, so that a series of operation steps are performed on the computer or other programmable terminal device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable terminal device provide a process for implementing the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in the flow(s) or block(s). These computer program instructions can also be loaded into a computer or other programmable data processing terminal device, so that a series of operation steps are performed on the computer or other programmable terminal device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable terminal device provide a process for implementing the flowcharts and / or block diagrams.

[0070] It is also noted that, as used herein, the terms "first", "second", and the like, merely designate a relationship or order of importance and do not necessarily indicate any such actual relationship or order of importance. The terms "comprises", "comprising", or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0071] Finally, it is noted that the foregoing description is of a preferred embodiment of the application, and that numerous changes and modifications can be made thereto without departing from the spirit and scope of the application as set forth in the appended claims.

Claims

1. A bank system development project management system characterized by comprising: Comprise: Intelligent management platform, through real-time docking supervision database, combined with NLP technology scanning sensitive words in project document, generate compliance check list and difference report, mark high-risk task and recommend rectification scheme; Risk prediction module, based on machine learning analysis of historical project data, predict the risk level of R&D cycle delay, resource overruns, trigger early warning and suggest allocation scheme; Blockchain storage and traceability module, the hash value and operation record of key node document are stored on the chain, the time stamp and the responsible person are fixed, and the supervision agency can quickly access the whole process audit log through the private key; Task coordination module, use NLP to analyze demand document, automatically decompose into executable task tree, analyze manpower and equipment conflict through resource occupation graph, and intelligently generate allocation scheme; Security control module, based on role and data sensitivity to implement hierarchical permission, sensitive operation needs biological authentication and dynamic token double verification to prevent unauthorized access; The intelligent management platform comprises a data acquisition unit, a policy analysis unit, a sensitive word scanning unit and a report generation unit; Data acquisition unit, ensure that in the first time window of supervision data update, complete the capture of incremental data, and store the data in message queue Kakfa, prevent data loss due to sudden increase of data volume, and ensure data acquisition continuity; Policy analysis unit, pretrain for the complex structure and professional terms of financial policy text, and fine-tune combined with the business characteristics of bank, the model can accurately identify the key elements in the policy, and convert the policy provisions into structured data, providing accurate basis for subsequent compliance check; Sensitive word scanning unit, construct sensitive word knowledge graph based on bank business scenario, dynamically evaluate the risk level of sensitive words to avoid false judgment caused by simple keyword matching; Report generation unit, compare compliance requirements, sensitive word risk assessment results and project documents, use preset rule template to automatically generate compliance check list containing difference point details, risk distribution heat map, and customize rectification scheme for each high-risk task to assist project team to efficiently respond to policy changes; The blockchain storage and traceability module comprises a hash calculation unit, a blockchain storage unit and an audit log management unit; Hash calculation unit, responsible for multi-layer hash operation on key node documents, generate highly secure document fingerprint, ensure that any minor tampering can be discovered in time, and use GPU acceleration technology to improve hash calculation efficiency, meet the performance requirements of high-frequency update of bank project documents; Blockchain storage unit, by constructing a storage network involving supervision agencies, bank management and project team, each block records the hash value, operation record, timestamp and digital signature of the responsible person, through automatic verification of transaction legality, ensure that the data on the chain is real and reliable and cannot be tampered with, provide a solid foundation for subsequent audit; The audit log management unit is designed with an encrypted indexing mechanism to ensure that only regulatory agencies can quickly locate the required log information within the authorized scope. At the same time, it uses zero-knowledge proof technology to enable regulatory agencies to verify the integrity and authenticity of the logs while protecting sensitive project information, thereby achieving efficient audit supervision.

2. The bank system R&D project management system as described in claim 1, characterized in that: The risk prediction module includes a data storage unit, a machine learning analysis unit, a risk assessment unit, and an early warning unit; The data storage unit categorizes and stores the structured and unstructured portions of historical project data, converting unstructured data into analyzable feature vectors and storing them together with structured data for easy retrieval and batch reading during subsequent model training. The machine learning analysis unit captures the fluctuation patterns of the R&D cycle and fits multidimensional features of resource consumption to build a hybrid model. Through cross-validation, it continuously optimizes the model hyperparameters to improve prediction accuracy. The risk assessment unit constructs a risk assessment model based on Bayesian networks, integrating risk factors such as R&D cycle delays and resource overruns and their correlations into a probabilistic graphical model. It dynamically updates the prior probability based on the current project status and calculates the posterior probability of the risk level in real time, providing a quantitative basis for early warning. The early warning unit is responsible for real-time monitoring of project resource occupancy indicators and setting multi-level early warning thresholds. When the resource conflict level reaches the threshold, it immediately sends early warning information to the relevant responsible persons through message push service and calls the resource allocation algorithm to provide suggestions to the project team, including manpower allocation and equipment sharing solutions, based on the current resource occupancy map, to ensure the smooth progress of the project.

3. The bank system R&D project management system as described in claim 1, characterized in that: The task collaboration module includes a requirement analysis unit, a task decomposition unit, a path recommendation unit, and a resource analysis unit; The requirement parsing unit is specifically trained on common business logic and terminology in bank requirement documents to accurately identify functional points, non-functional constraints, and related business rules in the requirements, providing precise semantic understanding for subsequent task decomposition. The task decomposition unit is responsible for breaking down the parsed requirements into an executable task tree according to the hierarchical structure of the bank's business process. Each task node contains clear input and output parameters, preconditions and postconditions, and estimated workload. At the same time, it uses a dependency graph to clearly show the complex dependencies between tasks, ensuring that the task execution order is reasonable. The path recommendation unit constructs a knowledge base containing various banking business architecture templates, combines the characteristics of current project requirements with the current status of the bank's technology stack, and uses reinforcement learning algorithms to recommend the optimal development path for the task tree; The resource analysis unit constructs a resource occupancy graph, treating human and equipment resources as graph nodes and the resource demand relationship of tasks as graph edges. It analyzes the resource conflict propagation path through a graph convolutional neural network (GCN), predicts the potential conflict range, intelligently generates allocation plans, and comprehensively considers factors such as personnel skill matching degree and equipment availability time window to achieve efficient resource utilization.

4. The bank system R&D project management system of claim 1, wherein: the security control module comprises a permission management unit, an authentication unit and an access control unit; the permission management unit sets access levels according to sensitivity levels by constructing a multi-level permission system, and updates the permission configuration in real time according to the project phase and personnel post changes based on dynamic permission adjustment strategies, to ensure accurate adaptation of permission allocation; the authentication unit generates dynamic verification codes with high security by using multi-modal biometric recognition technology combined with dynamic token technology, and the dual verification mechanism jointly ensures the security of sensitive operations and prevents unauthorized operations by illegal personnel; the access control unit is responsible for constructing a virtualized security boundary, dynamically adjusting network access strategies based on permission management and authentication results, encrypting tunnel protection for sensitive data transmission channels, using an intrusion detection system to monitor access behavior in real time, and using machine learning anomaly detection algorithms to timely detect and block abnormal access requests, to comprehensively guard system security.

5. The bank system R&D project management system of claim 1, wherein: the modules interact with each other through data transmission interfaces; the data transmission interface uses RESTful API specifications to ensure the simplicity and universality of module calls, uses API gateways for request routing, load balancing and authentication management, and ensures the security and reliability of data interaction; to realize data sharing and collaborative work, the modules follow a unified data exchange format during data transmission, standardize data fields with a data dictionary, and use message middleware for asynchronous communication to ensure data transmission timeliness and integrity in high-concurrency scenarios and avoid blocking phenomena caused by inter-module dependencies.

6. The bank system R&D project management system of claim 1, wherein: the intelligent management platform is also integrated with the bank's internal project management system and human resource management system; a unified data integration layer is constructed to realize data extraction, conversion and loading, ensuring data interconnection.

7. The bank system R&D project management system of claim 1, wherein: the system also includes a visual display unit for integrating visual components of different business dimensions in the form of plug-ins; for project progress display, a Gantt chart is used to visually present the comparison between task planning and actual execution, combined with a traffic light color warning mechanism to highlight progress deviations; for risk display, a risk heat map is constructed, dynamically colored according to risk levels and impact ranges, to help managers quickly locate key risk points; for resource occupation display, a Sankey diagram is used to clearly present resource flow and allocation, and WebSocket real-time push technology is used to ensure timely updating of display data, facilitating managers to make decisions and monitor, and timely identify problems and take appropriate measures.

8. The implementation procedure of a bank system development project management system according to any one of claims 1-7, characterized in that, including the following steps: initialize system parameters, configure the functions and permissions of each module, including interfacing with regulatory database account information, setting basic permissions for each role, and initializing blockchain nodes; Through the intelligent management platform, real-time docking with the regulatory database is realized. By using preset data collection rules and NLP algorithms, the obtained policy update information is analyzed and scanned for sensitive words. Based on the rule engine, a compliance check list and a difference report are generated, and a to-do list notification is sent to the relevant project leader through the system's built-in message push service. The risk prediction module is used to assess the risks of the project. The model loads historical project data for self-training and optimization. Based on the current project progress, it outputs real-time risk level prediction results. When the risk reaches the warning threshold, the warning process is triggered, and a deployment suggestion based on the current resource status is provided. The blockchain storage and traceability module is used to store key node documents. The document hash value is automatically calculated and stored on the chain, and the operation log is recorded to ensure that each key operation can be traced. The task coordination module is used to analyze, disassemble, and recommend paths for requirement documents. Project managers confirm the recommended task tree and development path in the system, and the tasks are assigned to specific personnel. The resource analysis unit monitors the resource occupation in real time, and if a conflict occurs, it automatically adjusts or prompts manual intervention. The security control module implements security control throughout the process. When personnel log in to the system, biometric recognition and dynamic token verification are performed. When accessing sensitive resources, authentication is performed again. All operation requests are checked by security policies before being released, ensuring system security. The visual display unit obtains data from various business modules in real time and renders and displays them according to preset visualization rules, assisting managers in decision-making. Based on the feedback and data analysis results of the system, the project team continuously optimizes the execution process of the project until its completion, achieving efficient management and smooth delivery of the bank system development project.

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