Comprehensive financial IT operation and maintenance management system and method based on artificial intelligence

Abstract

The present invention provides a comprehensive financial IT operation and maintenance management system and method based on artificial intelligence, including: an intelligent monitoring and predictive maintenance module, which is used to realize real-time monitoring, anomaly identification and performance prediction of IT systems by collecting multi-dimensional time series data, and trigger an intelligent early warning when an abnormal situation is monitored or the prediction result does not meet the preset requirements; an AI-driven fault diagnosis and self-healing module, which is used to perform intelligent fault analysis and repair based on the detected abnormal situation in combination with IT system logs and historical fault data; an adaptive resource management module, which is used to predict future load trends and future performance trends, and perform adaptive resource management based on the predicted future load trends and future performance trends in combination with cost and energy consumption factors; a security operation and maintenance automation module, which is used to monitor IT system security threats in real time and automatically execute security policies.

Description

Technical Field

[0001] The present invention relates to the field of IT operation and maintenance automation technology in the field of financial technology, and specifically to a comprehensive financial IT operation and maintenance management system and method based on artificial intelligence. Background Art

[0002] With the rapid development of financial technology, the IT systems of financial institutions are becoming increasingly complex, and the requirements for IT operations and maintenance are becoming increasingly stringent. However, existing financial IT operations and maintenance solutions have the following major limitations:

[0003] Insufficient intelligence: Existing systems have some capabilities in anomaly detection and basic alarms, but their intelligence level in predictive maintenance, automatic fault diagnosis, and self-healing is still limited.

[0004] Incomplete consideration of security and compliance: Insufficient consideration is given to the stringent security and compliance requirements unique to the financial industry, and there is a lack of comprehensive security risk assessment and automated security operations and maintenance capabilities.

[0005] Lack of adaptive capabilities and dynamic optimization: Most existing solutions are relatively static and lack the ability to automatically adjust to environmental changes and business needs.

[0006] Insufficient operation and maintenance knowledge management and continuous learning: The lack of systematic knowledge management and continuous learning mechanisms makes it difficult to effectively accumulate and utilize long-term operation and maintenance experience.

[0007] Insufficient cross-system collaboration and business relevance: There is a lack of effective support for cross-team and cross-system collaboration, as well as the ability to closely link IT operations with business impact.

[0008] According to IDC, global financial institutions' spending on IT operations and maintenance will reach $200 billion by 2025, with a compound annual growth rate of approximately 8%. However, traditional operations and maintenance methods are no longer able to cope with the increasingly complex financial IT environment. Therefore, developing an intelligent financial IT operations and maintenance management system that can overcome these limitations has significant practical significance and market value. Summary of the Invention

[0009] In response to the deficiencies in the prior art, the present invention aims to provide a comprehensive financial IT operation and maintenance management system and method based on artificial intelligence.

[0010] According to the present invention, a comprehensive financial IT operation and maintenance management system based on artificial intelligence is provided, comprising:

[0011] The intelligent monitoring and predictive maintenance module is used to achieve real-time monitoring, anomaly identification, and performance prediction of IT systems by collecting multi-dimensional time series data. When an abnormal situation is monitored or the prediction results do not meet the preset requirements, an intelligent early warning is triggered;

[0012] An AI-driven fault diagnosis and self-healing module, which performs intelligent fault analysis and repair based on detected anomalies and combines IT system logs and historical fault data.

[0013] Adaptive resource management module, used to predict future load trends and future performance trends, and perform adaptive resource management based on the predicted future load trends and future performance trends, combined with cost and energy consumption factors;

[0014] The security operation and maintenance automation module is used to monitor IT system security threats in real time and automatically execute security policies.

[0015] Preferably, the intelligent monitoring and predictive maintenance module includes:

[0016] Data collection submodule: collects multi-dimensional time series data including performance indicators, logs, and events through distributed collection modules;

[0017] Data preprocessing submodule: preprocesses the collected multi-dimensional time series data, including data cleaning, normalization, and feature extraction;

[0018] Anomaly detection submodule: uses the isolation forest algorithm to identify anomalies in pre-processed multi-dimensional time series data; uses graph neural networks to build network behavior models and uses network behavior models to detect abnormal activities;

[0019] Trend prediction submodule: Use LSTM network to analyze multi-dimensional time series data to predict data change trends and obtain trend prediction results;

[0020] Dynamic threshold adjustment submodule: Dynamically adjusts the alarm threshold based on the statistical process control method, and compares the obtained trend prediction results with the alarm threshold. When the trend prediction results exceed the alarm threshold, it is considered an abnormality;

[0021] Warning generation submodule: triggers intelligent warnings based on detected abnormal situations.

[0022] Preferably, the AI-driven fault diagnosis and self-healing module includes:

[0023] Knowledge graph engine submodule: Builds knowledge graphs of IT system topology, component relationships, and failure modes;

[0024] Based on rule-based regular expression matching and the BERT entity recognition model, we extract device entities, logical components, and failure modes from IT system configuration items and logs. We build static dependencies through dependency parsing and generate dynamic relationships using Prometheus monitoring data. We use the Neo4j graph database to store triples and define topological constraints.

[0025] Inference engine submodule: performs fault propagation analysis and root cause location based on the constructed knowledge graph;

[0026] Integrate the Apache Jena rule engine to implement fault propagation path reasoning; align the ITIL fault database with real-time log events based on TF-IDF and GloVe word vectors, and use the TransE algorithm for knowledge embedding;

[0027] The NLP engine submodule uses a multimodal deep learning model based on the Transformer architecture to analyze unstructured logs and unstructured text information related to IT systems or business processes to extract key diagnostic information. Comprehensive analysis results are obtained based on the extracted key diagnostic information, fault propagation analysis, and root cause location.

[0028] Define the learnable parameter matrix , used to learn adaptation weights in the space after modal splicing and perform feature alignment on log text, performance indicators and topology maps:

[0029]

[0030] Among them, softmax is used to normalize the output and obtain the attention weight of each modality , indicating the importance of each mode in the final decision at the current moment; Represents the feature vector of the i-th log text, Represents the performance indicator feature vector related to the log, A node graph vector representing the event in the system topology;

[0031] Reinforcement learning agent submodule: selects the optimal repair strategy based on comprehensive analysis results and historical experience;

[0032] Among them, a dynamic attention mechanism is introduced into the multimodal deep learning model based on the Transformer architecture, and the importance weights of different data sources are adaptively adjusted according to actual conditions based on the dynamic attention mechanism.

[0033] Preferably, the adaptive resource management module includes:

[0034] Load forecasting submodule: uses the Prophet model to predict future load and performance trends based on historical load data;

[0035] Multi-objective optimization submodule: Based on the predicted future load and performance trends, the NSGA-II algorithm is used to balance multiple objectives including resource performance, cost, and energy consumption;

[0036] Resource scheduling submodule: implements dynamic scaling and migration at the container level through Kubernetes.

[0037] Preferably, the security operation and maintenance automation module includes:

[0038] DevSecOps Integration Submodule: Embeds automated security scanning and compliance checks into the CI / CD process to obtain inspection results;

[0039] Network behavior analysis submodule: The inspection results use graph neural networks to build network behavior models and detect abnormal activities;

[0040] Threat intelligence analysis submodule: Analyzes abnormal activities based on the MITRE ATT&CK framework and correlates them with security incidents;

[0041] Automatic response submodule: Automatically triggers security defense and repair based on predefined policies based on associated security events.

[0042] Preferably, the system further comprises: an intelligent customer service module;

[0043] The intelligent customer service module is used to provide intelligent IT support services, including: intelligent question and answer, automatic classification and routing of work orders, and intelligent knowledge recommendation.

[0044] Preferably, the intelligent customer service module includes:

[0045] Intent recognition submodule: uses the BERT model to identify the intent and entities of user queries;

[0046] Intelligent question-answering submodule: Based on the identified intent and entities of user queries, it uses knowledge graphs to perform semantic understanding and generate answers;

[0047] Ticket routing submodule: Uses deep reinforcement learning to optimize ticket assignment strategies during customer service.

[0048] Knowledge recommendation submodule: During the customer service process, it intelligently recommends relevant knowledge and solution strategies based on the context.

[0049] Preferably, the system further comprises: an open architecture module;

[0050] The open architecture module is used to provide standardized APIs and integration interfaces to achieve flexible integration, expansion and interoperability of IT systems.

[0051] Preferably, the open architecture module includes: a RESTful API and a GraphQL interface;

[0052] The RESTful API is used to provide a standardized HTTP interface and support CRUD operations on resources;

[0053] The GraphQL interface is used to support data query and aggregation;

[0054] The RESTful API and the GraphQL interface set up message queues, WebSockets, and security authentication;

[0055] The message queue is used to implement asynchronous communication using message middleware;

[0056] The WebSocket is used to support real-time data push and two-way communication;

[0057] The security authentication is used to implement API authentication and authorization mechanisms based on OAuth 2.0 and JWT.

[0058] According to the present invention, a comprehensive financial IT operation and maintenance management method based on artificial intelligence includes:

[0059] Step S1: The intelligent monitoring and predictive maintenance module collects multi-dimensional time series data to achieve real-time monitoring, anomaly identification, and performance prediction of IT systems. When an abnormal situation is monitored or the prediction result does not meet the preset requirements, an intelligent warning is triggered;

[0060] Step S2: The AI-driven fault diagnosis and self-healing module performs intelligent fault analysis and repair based on the detected anomalies, combined with IT system logs and historical fault data;

[0061] Step S3: using the adaptive resource management module to predict future load trends and future performance trends, and performing adaptive resource management based on the predicted future load trends and future performance trends in combination with cost and energy consumption factors;

[0062] Step S4: Use the security operation and maintenance automation module to monitor IT system security threats in real time and automatically execute security policies.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] 1. This invention significantly improves the accuracy of fault prediction and shortens the mean time to repair by moving from passive response to active prediction. Compared with traditional methods, the accuracy of fault prediction is improved and the mean time to repair is shortened.

[0065] 2. This invention can better adapt to the complex and changing IT operation and maintenance environment and provide more accurate, real-time and explainable diagnostic results;

[0066] 3. This invention uses reinforcement learning and multi-objective optimization algorithms to achieve continuous optimization of system configuration, resource allocation, and security policies, adapting to dynamically changing business needs and technical environments. Compared with static rules, resource utilization is improved and energy consumption is reduced.

[0067] 4. This invention combines knowledge graphs and machine learning technologies to build an intelligent fault diagnosis and problem-solving system, accelerating knowledge accumulation and experience inheritance; compared with traditional knowledge bases, the problem-solving efficiency is improved by 50%.

[0068] 5. This invention deeply embeds security protection and compliance management into the DevOps process, achieving "security left shift." It uses advanced algorithms such as graph neural networks to improve the ability to detect advanced threats in complex network environments. Compared with traditional security operations and maintenance, the threat detection accuracy is improved and the response time is shortened.

[0069] 6. This invention is based on microservices and API design, supporting flexible integration with existing IT systems and tools to achieve end-to-end process automation. Through open interfaces, it supports financial institutions to customize and expand according to their own needs.

[0070] 7. This invention significantly reduces manual intervention through AI-driven automation, improving the efficiency of operations and maintenance personnel by 200%. Through predictive maintenance and intelligent fault diagnosis, the system's mean time between failures (MTBF) is increased by 50%. Through adaptive resource management, IT infrastructure utilization is increased by 25%, reducing the total cost of ownership (TCO). Through intelligent security analysis and automated response, the average processing time for security incidents is shortened by 70%. Through intelligent customer service and knowledge management, customer satisfaction is increased by 30%. Through an open architecture, it supports the rapid integration of new technologies and services, shortening the innovation cycle by 40%.

[0071] 8. This invention can effectively address the growing complexity and uncertainty of financial IT systems, significantly improve operational efficiency and system reliability, and provide strong support for the digital transformation of financial institutions;

[0072] 9. Through machine learning and deep learning technologies, the present invention can continuously learn from operation and maintenance data, optimize decision-making strategies, and adapt to the ever-changing IT environment and business needs; at the same time, its open architecture design enables the platform to have good scalability and integration capabilities, and can seamlessly connect with existing IT systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0074] Figure 1 This is the overall system architecture diagram of the present invention;

[0075] Figure 2 This is a schematic diagram of the intelligent monitoring and predictive maintenance module;

[0076] Figure 3 This is a schematic diagram of the AI-driven fault diagnosis and self-healing module;

[0077] Figure 4 This is a schematic diagram of the adaptive resource management module;

[0078] Figure 5 This is a schematic diagram of the security operation and maintenance automation module;

[0079] Figure 6 This is a schematic diagram of the intelligent customer service module. DETAILED DESCRIPTION

[0080] The present invention will be described in detail below with reference to specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those skilled in the art, several changes and improvements can be made without departing from the scope of the present invention. These all fall within the scope of protection of the present invention.

[0081] Example 1

[0082] According to the present invention, a comprehensive financial IT operation and maintenance management system based on artificial intelligence is provided, such as Figure 1 As shown, including:

[0083] Intelligent monitoring and predictive maintenance module: By collecting multi-dimensional time series data, it enables real-time monitoring of IT systems, anomaly identification, and performance prediction, enabling early detection of potential problems and preventive maintenance.

[0084] Specific functions include but are not limited to: real-time collection of system performance indicators, logs, and event data; anomaly detection using machine learning algorithms (such as Isolation Forest); and prediction of system performance trends using deep learning models (such as LSTM networks).

[0085] AI-driven fault diagnosis and self-healing module: Based on detected anomalies, combined with system logs and historical fault data, it conducts intelligent fault analysis and repair, reducing system downtime and manual intervention.

[0086] Specific functions include but are not limited to: using knowledge graphs and other technologies to analyze fault propagation, applying machine learning algorithms to locate root causes, automatically generating repair strategies based on analysis results, and triggering and executing automatic repair operations when appropriate;

[0087] Adaptive resource management module: This module predicts the system's future load and performance trends and performs intelligent resource management based on cost and energy consumption factors to improve resource utilization and application performance.

[0088] Specific functions include but are not limited to: using time series forecasting models (such as Prophet) to predict future load and performance trends, adopting multi-objective optimization algorithms (such as NSGA-II) to balance factors such as performance, cost and energy consumption, and realizing dynamic allocation and optimized scheduling of resources.

[0089] Security operation and maintenance automation module: responsible for real-time monitoring of system security threats and automatically executing corresponding security policies to enhance system security and compliance;

[0090] Specific functions include but are not limited to: using machine learning algorithms to detect abnormal behavior, automatically implementing security defense measures, and integrating automated security checks into DevOps processes.

[0091] Intelligent customer service module: provides intelligent IT support services to improve customer service efficiency and satisfaction;

[0092] Specific functions include but are not limited to: intelligent question-answering system based on natural language processing technology, automatic classification and routing of work orders using machine learning algorithms, and intelligent knowledge recommendation based on user context and historical data.

[0093] Open architecture module: By providing standardized APIs and integration interfaces, the system can be flexibly expanded and seamlessly connected with other systems to support business innovation and technological evolution.

[0094] Specific functions include but are not limited to: providing RESTful API and GraphQL interface, supporting microservice architecture, and integrating with third-party systems and tools.

[0095] The present invention is based on the above modules to form a comprehensive, intelligent and adaptive financial IT operation and maintenance management system, which can effectively respond to the challenges of complex financial IT environments.

[0096] Specifically, if Figure 2 As shown, the intelligent monitoring and predictive maintenance module includes:

[0097] Data collection submodule: collects multi-dimensional time series data such as system performance indicators, logs, and events through distributed collection agents;

[0098] Data preprocessing submodule: performs data cleaning, normalization and feature extraction.

[0099] Anomaly detection submodule: uses the isolation forest algorithm to identify anomalies in multidimensional data; uses graph neural networks to build network behavior models and use network behavior models to detect abnormal activities;

[0100] Trend prediction submodule: This module uses an LSTM network to analyze time series data and predict performance trends. By analyzing historical data and predicting future trends, this embodiment can identify potential performance bottlenecks or system failures early, reducing unexpected downtime. It can also more rationally allocate computing resources to avoid resource waste or shortages, helping to maintain stable system performance and improve user experience.

[0101] This embodiment uses the LSTM network to analyze these multi-dimensional time series data to capture complex nonlinear patterns and long-term dependencies, thereby providing more accurate performance trend predictions and supporting more intelligent IT operation and maintenance decisions.

[0102] Dynamic threshold adjustment submodule: Dynamically adjusts the alarm threshold based on statistical process control methods.

[0103] In this embodiment, the dynamic adjustment of the alarm threshold based on the statistical process control method includes:

[0104] Collect historical data and calculate the mean μ and standard deviation σ;

[0105] Set initial control limits:

[0106] Upper control limit (UCL) = μ + 3σ;

[0107] Lower control limit (LCL) = μ - 3σ;

[0108] Real-time monitoring of new data:

[0109] If the data points fall within the control limits, the process is considered to be in control;

[0110] If a number of consecutive points (usually 7-8) show a monotonic trend, or are slightly outside the control limits, the process is considered out of control;

[0111] When the process is detected to be out of control, μ and σ are recalculated and the control limits are updated;

[0112] Use the new control limits as dynamic alarm thresholds.

[0113] Warning generation submodule: Generates intelligent warnings based on the results of anomaly detection and trend prediction.

[0114] In this embodiment, the integrated anomaly detection and trend prediction results generate intelligent warnings, including:

[0115] If the current point is detected as abnormal and the predicted trend shows that the problem may persist or worsen, a high-priority warning is generated;

[0116] If the current point is normal, but the forecast trend shows that there may be problems in the future, a low priority warning is generated;

[0117] If the current point is normal and the forecast trend is good, no warning is generated;

[0118] This embodiment provides early warning based on both anomaly detection and trend prediction. Anomaly detection focuses on the current status, while trend prediction helps assess future risks. The combination of the two can provide more comprehensive early warning information.

[0119] Specifically, if Figure 3 As shown, the AI-driven fault diagnosis and self-healing module includes:

[0120] Knowledge graph engine sub-module: Builds a knowledge graph of IT system topology, component relationships, and failure modes.

[0121] First, model the topology and component relationships:

[0122] Use rule-based regular matching and the BERT entity recognition model to extract device entities (servers, switches), logical components (microservices, database instances), and failure modes (CPU overload, network packet loss) from IT system configuration items (CMDB) and logs.

[0123] Static dependencies (such as "server A hosts service B") are constructed through dependency syntax analysis, while dynamic associations (such as "service call latency is positively correlated with database query volume") are generated using Prometheus monitoring data.

[0124] The Neo4j graph database is used to store triples and define topology-level constraints (such as "a switch must be connected to at least two servers").

[0125] Integrate the Apache Jena rule engine to implement fault propagation path reasoning (example rule: `If service S depends on database D and D's response time is > 1s, then the probability of S's failure increases by 30%`).

[0126] Inference engine submodule: performs fault propagation analysis and root cause location based on knowledge graphs;

[0127] The ITIL fault database and real-time log events are aligned based on TF-IDF and GloVe word vectors, and knowledge embedding is performed using the TransE algorithm.

[0128] NLP engine submodule: This module uses a multimodal deep learning model based on the Transformer architecture to analyze unstructured logs and text information, extracting key diagnostic information and integrating the extracted key diagnostic information with fault propagation analysis and root cause location results.

[0129] Reinforcement learning agent submodule: Based on the integrated results, it selects the optimal repair solution according to historical experience.

[0130] The multimodal deep learning model based on the Transformer architecture includes:

[0131] Multimodal Fusion Learning: We designed a multimodal deep learning model based on the Transformer architecture, capable of simultaneously processing multiple data types, including system logs, performance metrics, and network traffic. We also introduced a dynamic attention mechanism to adaptively adjust the importance of different data sources based on the current situation, improving the model's adaptability and accuracy.

[0132] Online Incremental Learning: We developed a streaming data processing module based on Reservoir Sampling to achieve consistent data distribution. We introduced elastic windowing technology to dynamically balance the impact of historical and new data, improving model stability and real-time performance. We also used Elastic Weight Consolidation (EWC) technology to prevent important information from being forgotten during model updates.

[0133] Explainable AI: Integrates an explanation framework based on SHAP (SHapley Additive exPlanations) to provide understandable explanations for each prediction result. Develops an interactive visualization module based on D3.js to intuitively display feature importance and decision paths, enhancing the interpretability and credibility of the model.

[0134] Transfer learning and few-shot learning: Implementing a meta-learning framework based on MAML (Model-Agnostic Meta-Learning) improves the model's ability to quickly adapt to new fault types. Introducing the SimCLR (Simple Framework for Contrastive Learning of Visual Representations) contrastive learning method enhances feature representation capabilities and improves the model's generalization ability in small-sample situations.

[0135] Through the above improvements, the AI-driven fault diagnosis and self-healing module of the present invention can better adapt to the complex and changeable IT operation and maintenance environment, provide more accurate, real-time, and explainable prediction and diagnosis results, significantly improve the accuracy of fault prediction and shorten the mean time to repair.

[0136] Specifically, if Figure 4 As shown, the adaptive resource management module includes:

[0137] Load prediction submodule: Uses the Prophet model to predict future load and performance trends based on historical load data. In this embodiment, the historical load data includes time series data reflecting system load, such as CPU usage, memory usage, network traffic, number of requests, and transaction volume. These data are collected over a long period of time by the monitoring system.

[0138] Multi-objective optimization submodule: uses the NSGA-II algorithm to balance multiple objectives such as performance, cost and energy consumption.

[0139] Resource scheduling submodule: implements dynamic scaling and migration at the container level through Kubernetes;

[0140] In this embodiment, the Horizontal Pod Autoscaler (HPA) monitors container resource usage; automatically adjusts the number of pods according to predefined policies; and uses a scheduler to migrate pods between nodes to balance the load.

[0141] Feedback optimization submodule: collects actual operation data and continuously optimizes the prediction model and scheduling strategy. The scheduling strategy includes: container deployment strategy, load balancing strategy, resource allocation strategy, failover strategy, etc.

[0142] Specifically, if Figure 5 As shown, the security operation and maintenance automation module includes:

[0143] Network behavior analysis submodule: uses graph neural networks to build network behavior models and detect abnormal activities;

[0144] Threat intelligence analysis submodule: Analyzes and correlates security events based on the MITRE ATT&CK framework.

[0145] In this embodiment, the analysis and correlation of security events based on the MITRE ATT&CK framework includes:

[0146] Event collection and standardization: Collect raw security event logs from various security devices and systems; standardize logs in different formats and extract key fields such as timestamp, source IP, destination IP, user, operation, etc.

[0147] Event mapping: Mapping standardized events to corresponding techniques in the MITRE ATT&CK framework;

[0148] Context enrichment: Adding additional contextual information to events, such as asset information and threat intelligence, helps better understand the impact and severity of events.

[0149] Pattern recognition: Using machine learning or rules engines to identify attack patterns in a sequence of events; for example, identifying a series of events that may constitute a complete "lateral movement" tactic.

[0150] Correlation analysis: Correlate related events based on attributes such as time, IP, and user to identify events that may belong to different stages of the same attack chain.

[0151] Tactical reconstruction: Mapping the associated event sequence to MITRE ATT&CK tactics to reconstruct the attacker's possible actions.

[0152] Threat Score: Assigns a score to the entire attack chain based on the number and severity of observed techniques and tactics.

[0153] Visualization: Use the MITRE ATT&CK matrix to visualize detected techniques and tactics; generate an attack chain timeline to show the evolution of the attack.

[0154] Automated response: Based on the identified ATT&CK techniques, trigger corresponding automated response actions.

[0155] Continuous learning: Feedback analysis results into detection rules to continuously optimize and update detection capabilities.

[0156] Automatic response submodule: automatically performs security defense and repair operations according to predefined strategies.

[0157] In this embodiment, when a high-risk vulnerability is detected, patches or temporary mitigation measures are automatically deployed. When malware is discovered, the infected system is isolated and malware removal is initiated. When abnormal login behavior is detected, accounts are temporarily locked and multi-factor authentication is required. When a data exfiltration attempt is identified, suspicious connections are blocked and sensitive data is encrypted. When unauthorized configuration changes are detected, the changes are rolled back to a secure configuration, logged, and reported. When a DDoS attack is detected, traffic scrubbing services are activated and firewall rules are adjusted. When lateral movement is identified, the affected network segment is isolated and network segmentation is strengthened. When abnormal access to sensitive data is detected, user access rights are revoked and data access audits are initiated. When a brute force attack is detected, the attack source IP address is temporarily blocked and password policies are enforced. When a SQL injection attempt is identified, web application firewall rules are updated and vulnerabilities are patched. When abnormal process behavior is detected, suspicious processes are terminated and analyzed. When unauthorized API access is detected, API keys are revoked and API authentication mechanisms are strengthened. These automated response policies are typically graded based on the severity and potential impact of the threat to ensure appropriate responses. At the same time, the system retains an interface for manual intervention, allowing security teams to manually adjust or cancel automated responses when necessary. By implementing this automated response mechanism, the system can take action as soon as a threat is detected, significantly reducing response time and improving the efficiency and effectiveness of security defenses.

[0158] DevSecOps integration submodule: Embeds automated security scanning and compliance checks into the CI / CD process.

[0159] Specifically, if Figure 6 As shown, the intelligent customer service module includes:

[0160] Intent recognition submodule: Use the BERT model to identify the intent and entities of user queries.

[0161] Intelligent question-answering submodule: semantic understanding and answer generation based on knowledge graph.

[0162] In this embodiment, the semantic understanding and answer generation based on the knowledge graph includes: mapping the user query to the entities and relationships of the knowledge graph, reasoning in the knowledge graph to find relevant information nodes, scoring and sorting the candidate answers, and converting the information in the graph into natural language answers.

[0163] Work order routing submodule: Use deep reinforcement learning to optimize the work order assignment strategy.

[0164] In this embodiment, the method of optimizing the ticket assignment strategy using deep reinforcement learning includes: encoding information such as the current ticket queue and customer service status into a state vector; defining possible assignment actions (such as assigning a ticket to a specific customer service representative); designing a reward function based on factors such as processing time and customer satisfaction; training a deep neural network to select the optimal assignment action based on the current state; storing and reusing historical assignment experience to improve learning efficiency; and continuously optimizing the assignment strategy through interaction with the environment.

[0165] Knowledge recommendation submodule: intelligently recommends relevant knowledge and solutions based on context.

[0166] Specifically, the open architecture module includes:

[0167] RESTful API: Provides a standardized HTTP interface and supports CRUD operations on resources.

[0168] GraphQL interface: supports flexible data query and aggregation.

[0169] Message queue: Use message middleware such as Kafka to implement asynchronous communication.

[0170] WebSocket: supports real-time data push and two-way communication.

[0171] Security Authentication: Implement API authentication and authorization mechanisms based on OAuth 2.0 and JWT.

[0172] The present invention also provides an artificial intelligence-based comprehensive financial IT operation and maintenance management system, which can be implemented by executing the process steps of the artificial intelligence-based comprehensive financial IT operation and maintenance management method, that is, those skilled in the art can understand the artificial intelligence-based comprehensive financial IT operation and maintenance management method as an optimal implementation method of the artificial intelligence-based comprehensive financial IT operation and maintenance management system.

[0173] This invention realizes the intelligence, automation and collaboration of financial IT operation and maintenance, can effectively cope with the growing complexity and uncertainty of financial IT systems, significantly improve operation and maintenance efficiency and system reliability, and provide strong support for the digital transformation of financial institutions.

[0174] Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, and units provided by the present invention in purely computer-readable program code, it is entirely possible to implement the same functions of the system and its various devices, modules, and units provided by the present invention in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; the devices, modules, and units for implementing various functions can also be considered as both software modules implementing the method and structures within the hardware component.

[0175] The above describes specific embodiments of the present invention. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art may make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. The embodiments of this application and the features in the embodiments may be combined with each other in any manner unless there is a conflict.

Claims

1. A comprehensive financial IT operation and maintenance management system based on artificial intelligence, characterized by: include: The intelligent monitoring and predictive maintenance module is used to achieve real-time monitoring, anomaly identification, and performance prediction of IT systems by collecting multi-dimensional time series data. When an abnormal situation is monitored or the prediction results do not meet the preset requirements, an intelligent early warning is triggered; An AI-driven fault diagnosis and self-healing module, which performs intelligent fault analysis and repair based on detected anomalies and combines IT system logs and historical fault data. Adaptive resource management module, used to predict future load trends and future performance trends, and perform adaptive resource management based on the predicted future load trends and future performance trends, combined with cost and energy consumption factors; Security operation and maintenance automation module, used to monitor IT system security threats in real time and automatically execute security policies; The AI-driven fault diagnosis and self-healing module includes: Knowledge graph engine submodule: Builds knowledge graphs of IT system topology, component relationships, and failure modes; Based on rule-based regular expression matching and the BERT entity recognition model, we extract device entities, logical components, and failure modes from IT system configuration items and logs. We build static dependencies through dependency parsing and generate dynamic relationships using Prometheus monitoring data. We use the Neo4j graph database to store triples and define topological constraints. Inference engine submodule: performs fault propagation analysis and root cause location based on the constructed knowledge graph; Integrate the Apache Jena rule engine to implement fault propagation path reasoning; align the ITIL fault database with real-time log events based on TF-IDF and GloVe word vectors, and use the TransE algorithm for knowledge embedding; The NLP engine submodule uses a multimodal deep learning model based on the Transformer architecture to analyze unstructured logs and unstructured text information related to IT systems or business processes to extract key diagnostic information. Comprehensive analysis results are obtained based on the extracted key diagnostic information, fault propagation analysis, and root cause location. Define the learnable parameter matrix , used to learn adaptation weights in the space after modal splicing and perform feature alignment on log text, performance indicators and topology maps: Among them, softmax is used to normalize the output and obtain the attention weight of each modality , indicating the importance of each mode in the final decision at the current moment; Represents the feature vector of the i-th log text, Represents the performance indicator feature vector related to the log, A node graph vector representing the event in the system topology; Reinforcement learning agent submodule: selects the optimal repair strategy based on comprehensive analysis results and historical experience; Among them, a dynamic attention mechanism is introduced into the multimodal deep learning model based on the Transformer architecture, and the importance weights of different data sources are adaptively adjusted according to actual conditions based on the dynamic attention mechanism.

2. The comprehensive financial IT operation and maintenance management system based on artificial intelligence according to claim 1 is characterized in that: The intelligent monitoring and predictive maintenance module includes: Data collection submodule: collects multi-dimensional time series data including performance indicators, logs, and events through distributed collection modules; Data preprocessing submodule: preprocesses the collected multi-dimensional time series data, including data cleaning, normalization, and feature extraction; Anomaly detection submodule: uses the isolation forest algorithm to identify anomalies in pre-processed multi-dimensional time series data; uses graph neural networks to build network behavior models and uses network behavior models to detect abnormal activities; Trend prediction submodule: Use LSTM network to analyze multi-dimensional time series data to predict data change trends and obtain trend prediction results; Dynamic threshold adjustment submodule: Dynamically adjusts the alarm threshold based on the statistical process control method, and compares the obtained trend prediction results with the alarm threshold. When the trend prediction results exceed the alarm threshold, it is considered an abnormality; Warning generation submodule: triggers intelligent warnings based on detected abnormal situations.

3. The comprehensive financial IT operation and maintenance management system based on artificial intelligence according to claim 1 is characterized in that: The adaptive resource management module includes: Load forecasting submodule: uses the Prophet model to predict future load and performance trends based on historical load data; Multi-objective optimization submodule: Based on the predicted future load and performance trends, the NSGA-II algorithm is used to balance multiple objectives including resource performance, cost, and energy consumption; Resource scheduling submodule: implements dynamic scaling and migration at the container level through Kubernetes.

4. The comprehensive financial IT operation and maintenance management system based on artificial intelligence according to claim 1 is characterized in that: The security operation and maintenance automation module includes: DevSecOps Integration Submodule: Embeds automated security scanning and compliance checks into the CI / CD process to obtain inspection results; Network behavior analysis submodule: The inspection results use graph neural networks to build network behavior models and detect abnormal activities; Threat intelligence analysis submodule: Analyzes abnormal activities based on the MITRE ATT&CK framework and correlates them with security incidents; Automatic response submodule: Automatically triggers security defense and repair based on predefined policies based on associated security events.

5. The comprehensive financial IT operation and maintenance management system based on artificial intelligence according to claim 1 is characterized in that: The system also includes: an intelligent customer service module; The intelligent customer service module is used to provide intelligent IT support services, including: intelligent question and answer, automatic classification and routing of work orders, and intelligent knowledge recommendation.

6. The comprehensive financial IT operation and maintenance management system based on artificial intelligence according to claim 5 is characterized in that: The intelligent customer service module includes: Intent recognition submodule: uses the BERT model to identify the intent and entities of user queries; Intelligent question-answering submodule: Based on the identified intent and entities of user queries, it uses knowledge graphs to perform semantic understanding and generate answers; Ticket routing submodule: Uses deep reinforcement learning to optimize ticket assignment strategies during customer service. Knowledge recommendation submodule: During the customer service process, it intelligently recommends relevant knowledge and solution strategies based on the context.

7. The comprehensive financial IT operation and maintenance management system based on artificial intelligence according to claim 1 is characterized in that: The system further comprises: an open architecture module; The open architecture module is used to provide standardized APIs and integration interfaces to achieve flexible integration, expansion and interoperability of IT systems.

8. The comprehensive financial IT operation and maintenance management system based on artificial intelligence according to claim 7 is characterized in that: The open architecture module includes: RESTful API and GraphQL interface; The RESTful API is used to provide a standardized HTTP interface and support CRUD operations on resources; The GraphQL interface is used to support data query and aggregation; The RESTful API and the GraphQL interface set up message queues, WebSockets, and security authentication; The message queue is used to implement asynchronous communication using message middleware; The WebSocket is used to support real-time data push and two-way communication; The security authentication is used to implement API authentication and authorization mechanisms based on OAuth 2.0 and JWT.

9. A comprehensive financial IT operation and maintenance management method based on artificial intelligence, characterized by: include: Step S1: The intelligent monitoring and predictive maintenance module collects multi-dimensional time series data to achieve real-time monitoring, anomaly identification, and performance prediction of IT systems. When an abnormal situation is monitored or the prediction result does not meet the preset requirements, an intelligent warning is triggered; Step S2: The AI-driven fault diagnosis and self-healing module performs intelligent fault analysis and repair based on the detected anomalies, combined with IT system logs and historical fault data; Step S3: using the adaptive resource management module to predict future load trends and future performance trends, and performing adaptive resource management based on the predicted future load trends and future performance trends in combination with cost and energy consumption factors; Step S4: Use the security operation and maintenance automation module to monitor IT system security threats in real time and automatically execute security policies; The AI-driven fault diagnosis and self-healing module includes: Knowledge graph engine submodule: Builds knowledge graphs of IT system topology, component relationships, and failure modes; Based on rule-based regular expression matching and the BERT entity recognition model, we extract device entities, logical components, and failure modes from IT system configuration items and logs. We build static dependencies through dependency parsing and generate dynamic relationships using Prometheus monitoring data. We use the Neo4j graph database to store triples and define topological constraints. Inference engine submodule: performs fault propagation analysis and root cause location based on the constructed knowledge graph; Integrate the Apache Jena rule engine to implement fault propagation path reasoning; align the ITIL fault database with real-time log events based on TF-IDF and GloVe word vectors, and use the TransE algorithm for knowledge embedding; The NLP engine submodule uses a multimodal deep learning model based on the Transformer architecture to analyze unstructured logs and unstructured text information related to IT systems or business processes to extract key diagnostic information. Comprehensive analysis results are obtained based on the extracted key diagnostic information, fault propagation analysis, and root cause location. Define the learnable parameter matrix , used to learn adaptation weights in the space after modal splicing and perform feature alignment on log text, performance indicators and topology maps: Among them, softmax is used to normalize the output and obtain the attention weight of each modality , indicating the importance of each mode in the final decision at the current moment; Represents the feature vector of the i-th log text, Represents the performance indicator feature vector related to the log, A node graph vector representing the event in the system topology; Reinforcement learning agent submodule: selects the optimal repair strategy based on comprehensive analysis results and historical experience; Among them, a dynamic attention mechanism is introduced into the multimodal deep learning model based on the Transformer architecture, and the importance weights of different data sources are adaptively adjusted according to actual conditions based on the dynamic attention mechanism.

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