Bank operation and maintenance event processing method and device, computer device and readable storage medium

Abstract

The application provides a bank operation and maintenance event processing method and device, computer equipment and a readable storage medium, the method comprising: collecting multi-source data related to a bank operation and maintenance event; based on the multi-source data, calculating four-dimensional scores in parallel, the four-dimensional scores including complexity scores, efficiency scores, quality scores and business impact scores; aggregating the four-dimensional scores into a current comprehensive score of the bank operation and maintenance event according to the weights corresponding to each score; and automatically generating a suggestion report for the bank operation and maintenance event based on the current comprehensive score and historical score data of the bank operation and maintenance event.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular to a method, apparatus, computer equipment, and readable storage medium for processing bank operation and maintenance events. Background Technology

[0002] Operational event tickets (O&M event tickets) serve as the core recorder of IT system failures, changes, requests, and other activities. The efficiency and quality of their processing directly impact the stability of business systems and the level of IT services. Currently, the analysis and management of O&M event tickets primarily focus on process tracking and manual quality inspection, aiming to ensure that tickets flow according to established processes and meet basic compliance requirements. However, with the increasing complexity of IT systems and the surge in the number of events, traditional methods face significant challenges in achieving refined, quantitative management and continuous improvement of event handling. Existing technical solutions mainly suffer from the following limitations: (1) Problem of one-sided evaluation: Existing methods can only quantify and score a single indicator for an event, which is difficult to reflect the overall quality and complexity of event handling in a true and comprehensive way.

[0003] (2) High cost of solutions: Some automation solutions rely on complex AI models, which leads to high consumption of computing resources and high barriers to implementation and maintenance, making it difficult to implement and promote in cost-sensitive environments.

[0004] (3) The problem of disconnect between management and improvement: Most technical solutions focus on real-time anomaly alarms, and their analysis results cannot be directly applied to the statistical analysis of historical event records. They fail to effectively transform the evaluation results into specific insights and action suggestions to drive the optimization of operation and maintenance processes. Summary of the Invention

[0005] In view of this, this application provides a method, apparatus, computer equipment, and readable storage medium for handling bank operation and maintenance events, in order to solve the above problems.

[0006] Firstly, embodiments of this application provide a method for handling bank operation and maintenance events, including: Collect multi-source data related to bank operation and maintenance events; Based on the multi-source data, scores in four dimensions are calculated in parallel, including complexity score, efficiency score, quality score, and business impact score. The scores of the four dimensions are aggregated according to the weights corresponding to each score to form the current comprehensive score of the bank's operation and maintenance event; Based on the current comprehensive score and historical score data of the bank's operation and maintenance event, a recommendation report is automatically generated for the bank's operation and maintenance event.

[0007] Secondly, embodiments of this application provide a device for processing bank operation and maintenance events, including: The data acquisition module is used to collect multi-source data related to bank operation and maintenance events; The preliminary scoring module is used to calculate scores in parallel across four dimensions based on the multi-source data. These four dimensions include complexity score, efficiency score, quality score, and business impact score. The comprehensive scoring module is used to aggregate the scores of the four dimensions according to the weights corresponding to each score into a current comprehensive score for the bank's operation and maintenance event. The report generation module is used to automatically generate a recommended report for the bank's operation and maintenance event based on the current comprehensive score and historical score data.

[0008] Thirdly, embodiments of this application provide a computer device including a processor and a memory, the memory storing programs or instructions that can run on the processor, the programs or instructions implementing the steps of the method as described in the first aspect when executed by the processor.

[0009] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method as described in the first aspect.

[0010] The bank operation and maintenance event processing method, apparatus, computer equipment, and readable storage medium of this application achieve three key technical effects by constructing a multi-dimensional, lightweight automated scoring and insight scheme: First, it realizes a comprehensive quantitative evaluation of operation and maintenance events from a partial to a full-scale approach, making the scoring results more accurate and fair by introducing multi-dimensional indicators such as complexity and business impact; second, it replaces complex large models with a lightweight rule engine, significantly reducing system implementation and maintenance costs while ensuring automation levels, and improving the feasibility and universality of the solution; third, it establishes a closed loop from evaluation to improvement, automatically converting scoring data into targeted optimization suggestions, driving continuous improvement of operation and maintenance processes, and effectively enhancing the refinement and intelligence of operation and maintenance management.

[0011] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0012] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart illustrating one of the methods for handling bank operation and maintenance events according to an embodiment of this application; Figure 2 This is a second flowchart illustrating a method for handling bank operation and maintenance events according to an embodiment of this application; Figure 3 This paper shows a structural block diagram of a bank operation and maintenance event processing device according to an embodiment of this application; Figure 4 A structural block diagram of a computer device according to an embodiment of this application is shown. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0014] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0015] The existing technical solutions have the following limitations: (1) Reliance on human experience and insufficient automation and intelligence: Some existing technologies attempt to introduce automation methods, and score the accuracy of fault location, effectiveness of feedback and timeliness of handling by operation and maintenance personnel by semantic analysis of work order content. However, the core purpose of this method is to assess the performance of "people". Its evaluation model is closely focused on the behavior of "operation and maintenance personnel" in a single event, rather than conducting multi-dimensional and traceable quality assessment and root cause insight of the "event" itself.

[0016] (2) The technical model is complex, and the implementation cost and threshold are high: Some existing technologies rely on a complex combination of a well-trained Large Language Model (LLM) and a Pre-trained Language Model (PLM). This solution first uses LLM to extract entity information from work orders, then performs feature concatenation and model processing, and finally outputs the quality inspection score. Although this method is effective, it relies on large-scale labeled data and complex model training, consumes a lot of computing resources, and has high implementation and maintenance costs, which is too high for many enterprises seeking lightweight and efficient solutions.

[0017] (3) Focusing on anomaly detection, disconnected from event management process: Some existing technologies score and alarm grade anomalies in KPI time series data. This method achieves more accurate alarms by establishing a probability distribution of anomaly deviation and duration. However, this solution is applicable to real-time monitoring indicators, and its technical logic serves the "anomaly detection" scenario, rather than performing statistical analysis, quality scoring, and improvement point mining on "historical event records" that have already occurred and contain rich text information and processing procedures.

[0018] In summary, existing technologies have three main limitations: First, the evaluation dimensions are singular, focusing only on isolated indicators such as resolution time, which cannot achieve comprehensive quantitative evaluation; second, some automation solutions rely on complex large language models, resulting in high implementation costs and difficulty in implementation; and third, most solutions focus on real-time anomaly detection, which is seriously out of touch with the actual management needs of statistical analysis, quality evaluation, and improvement mining of historical event records.

[0019] The following description, in conjunction with the accompanying drawings, details the method, apparatus, computer equipment, and readable storage medium for handling bank operation and maintenance events provided in this application, through specific embodiments and application scenarios. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0020] This application provides an intelligent scoring and insight solution for operation and maintenance (O&M) event tickets based on multi-dimensional quantitative analysis. Its core lies in building an automated, configurable scoring engine. This engine transforms raw event ticket data into quantifiable scores and actionable improvement suggestions through four stages: data collection, multi-dimensional score calculation, comprehensive score generation, and intelligent insight. It aims to overcome the shortcomings of existing technologies, achieve automated, multi-dimensional quantitative evaluation of O&M event tickets, and automatically generate actionable improvement insights, thereby enhancing the precision and continuous improvement capabilities of O&M management.

[0021] This application provides a method for handling bank operation and maintenance events, such as... Figure 1 and Figure 2 As shown, the method includes: Step 101: Collect multi-source data related to bank operation and maintenance events.

[0022] This step involves the collection and preprocessing of multi-source data, automatically collecting structured and unstructured data related to event tickets from the IT system via an interface.

[0023] Step 102: Based on multi-source data, calculate the scores in parallel for four dimensions: complexity score, efficiency score, quality score, and business impact score.

[0024] In this step, the scoring engine calculates scores for four dimensions in parallel. (1) Complexity score: A rule-based event complexity quantification model is adopted, and a lightweight and accurate difficulty assessment is achieved by matching rules with a predefined classification weight table and a keyword library. (2) Efficiency score: The concept of "timeout coefficient" is introduced, and the actual processing time is compared with the standard time dynamically set based on complexity to obtain a relative efficiency score. (3) Quality score: The recurrence rate of the event and the standardization of the resolution process are considered comprehensively. Points are deducted for recurring events and points are added for behaviors that meet the standards. (4) Business impact score: By associating the event with weighted business services, the technical downtime is converted into a quantified business impact score.

[0025] In one embodiment of this application, the complexity score is calculated as follows: Based on multi-source data, the event classification rules of the event complexity quantification model are used to classify bank operation and maintenance events, and the basic score is determined according to the classification results. In addition, the keywords of bank operation and maintenance events are determined, and the keyword scoring rules of the event complexity quantification model are used to assign additional scores to the keywords. The complexity score is determined based on the base score and the bonus score.

[0026] In this embodiment, the first step is to build a rule-based event complexity quantification model. A core rule base is predefined; that is, event classification rules and keyword scoring rules are formulated in advance, taking into account business scenarios such as IT failures, customer service tickets, and project tasks. General scoring rules are also defined. These three elements together constitute the rule system of the event complexity quantification model, ensuring a unified standard for subsequent scoring. The event classification rules can be understood as an event classification weight table, and the keyword scoring rules can be understood as a keyword scoring library.

[0027] (1) Develop an event classification weight table Event classification is a primary qualitative division of events based on business attributes. The core of the classification weight table is to assign a base score to each category. The base score is the core benchmark for event complexity, reflecting the inherent difficulty of that type of event.

[0028] Based on the actual needs of the business scenario, the events requiring evaluation should be exhaustively and non-overlappingly categorized. The categorization dimensions should align with the understanding of business personnel, ensuring that each event can be matched with a unique primary category. Secondary categories can also be set for further refinement without affecting the core logic of the base score. Combining historical processing costs, the scope of the failure's impact, technical difficulty, and required professional skills, each category should be assigned a base score of 0-10 points, or a custom range. Categories with higher difficulty will receive higher base scores. It is recommended that the score range align with the final scoring ceiling for ease of subsequent calculations.

[0029] For example, the primary categories of events include: core application failures, network link failures, peripheral system failures, hardware device failures, and basic environment failures, with corresponding base scores of 8, 6, 4, 3, and 2, respectively. The scoring criteria for core application failures are: impacting core business, requiring R&D intervention, and high technical difficulty. The scoring criteria for network link failures are: impacting business access, requiring network engineer troubleshooting, and medium difficulty. The scoring criteria for peripheral system failures are: impacting non-core business and can be resolved independently by operations and maintenance. The scoring criteria for hardware device failures are: mostly hardware replacement, standardized operation, and low difficulty. The scoring criteria for basic environment failures are: no business impact, and can be restored with simple troubleshooting.

[0030] Establish a unique matching principle between events and categories, meaning an event can only match one primary category to avoid score distortion caused by the accumulation of base scores from multiple categories. If an event has cross-category characteristics, priority rules need to be established; for example, if it involves both core applications and network failures, priority should be given to matching core application failures.

[0031] (2) Construct a keyword bonus database Keywords are feature words extracted from the titles, descriptions, notes, etc. of multi-source event data. They reflect details such as the severity of the event, the scope of its impact, and the specific problem type. The core of the keyword scoring library is to assign additional points to each keyword / keyword group, so as to achieve fine-tuning of the base score.

[0032] Extract keywords positively correlated with event complexity. Keywords must be semantically clear, unambiguous, and relevant to the actual scenario. For example, keywords related to the scope of impact include: large area, full volume, entire network, multiple regions, core users, batch; keywords related to the severity of the problem include: timeout, stuck, crash, paralysis, data loss, abnormal permissions; keywords related to technical issues include: connection pool full, memory overflow, database deadlock, interface circuit breaker, cache breakdown; keywords related to the urgency of handling include: urgent, immediate, deadline, cannot be rolled back. Assign bonus points to keywords, assigning 1 to N points to each keyword based on the degree of complexity increase they correspond to. Keywords that more accurately reflect the difficulty / severity of the event receive higher bonus points. Additionally, keyword group rules can be set to prevent duplicate scoring when multiple related keywords appear simultaneously, using the highest value to avoid excessive scoring.

[0033] (3) Formulate scoring rules Score stacking rule: Complexity score = base score + ∑ additional score.

[0034] To avoid confusion in score calculation, clear general rules should be established for score aggregation, upper and lower limits, and handling of special cases to ensure consistent scoring logic across all events. For example, score upper and lower limit rules: set a minimum score (e.g., 0 points) and a maximum score (e.g., 10 points). If the calculated final score exceeds the upper limit, use the upper limit; if it falls below the lower limit, use the lower limit. Keyword matching rule: if an event does not match any keywords, the additional score is 0, and the complexity score equals the base score. Category matching rule: set a default category and assign a default base score (e.g., 2 points) to events that do not match any category to avoid missing scores.

[0035] In one embodiment of this application, the efficiency score is calculated as follows: Based on multi-source data, the actual processing time of bank operation and maintenance events is determined; The actual processing time is compared with the standard processing time to obtain an efficiency score. The standard processing time is determined based on at least one of the following: complexity score and alarm priority of bank operation and maintenance events.

[0036] In this embodiment, a standard processing time is preset. The standard processing time is set based on complexity and priority, so that the standard processing time of the event is more in line with the urgent handling requirements of the business. This achieves dual-dimensional control of "difficulty as the basis and urgency as the scale", and ultimately the timeout coefficient calculated by the actual processing time / standard processing time can accurately reflect the processing efficiency.

[0037] The following section first clarifies the definition and classification of priorities, and then details the core methods and rules for setting standard processing time by combining complexity scores and priorities.

[0038] (1) Core definition and hierarchy of priority Event priority is determined by the urgency of business impact, risk of loss escalation, and Service Level Agreement (SLA) requirements, outlining the order of event handling. It primarily reflects how quickly an event needs to be processed, complementing the complexity of the event and jointly determining the standard processing resources and time thresholds. Prioritization can employ a four-level grading system, based on the scope of business impact, degree of loss, risk of escalation, and SLA requirements. It is not directly tied to complexity; low complexity can also be high priority, such as a core system login password error (low complexity) or a complete network login failure (high priority). For example, priority levels include: P1, P2, P3, and P4. P1 indicates urgent priority, representing a complete interruption of core business operations, large-scale loss, and rapidly escalating risk, requiring immediate handling (minutes). P2 indicates high priority, representing a partial interruption of core business operations, localized loss, manageable risk, but requiring rapid handling (hours). P3 indicates medium priority, representing a non-core business interruption, minor loss, no risk of escalation, requiring timely handling (working day level). P4 indicates low priority, meaning there is no actual business loss and it only needs to be processed on demand (weekly / time-limited level).

[0039] Priority labeling rules can be manually assigned, such as selected by the operator when the event is submitted, or automatically assigned based on rules, such as matching using a keyword database. For example, "nationwide," "immediate," and "outage" match P1, while "partial" and "specific region" match P2. Each event is labeled with only one priority level to avoid multiple priorities overlapping and causing confusion in the standard processing time. Furthermore, priority escalation or downgrading is supported. Priority is upgraded as the event spreads; for example, a P2 failure evolves into a network-wide outage, which is then upgraded to P1. After the upgrade, the standard processing time needs to be recalculated.

[0040] (2) Set standard processing time based on complexity score + priority Combining the complexity scores and the priorities of P1-P4 mentioned above, the standard processing time is set according to the core logic of "base processing time determined by complexity, compression coefficient determined by priority," that is: Standard processing time (T) = Complexity base processing time (T0) × Priority compression coefficient (K). Complexity base processing time (T0): A basic processing time threshold determined based on the event's 10-point complexity score, reflecting the normal time required to process events of that difficulty. The higher the complexity score, the longer the base processing time. Priority compression coefficient (K): A time compression ratio determined based on the event's priority. The higher the priority, the smaller the coefficient, and the more significant the compression of the base processing time. For example, a P1 coefficient of 0.2 compresses the base processing time to 20%, reflecting that urgent events need to be processed in a shorter time. The advantages of this method are its lightweight nature, interpretability, and ease of adjustment. It requires no complex algorithms; only the rules corresponding to the T0 score and the priority rules corresponding to K need to be defined in advance to achieve automated calculation.

[0041] Based on actual business processing data, that is, counting the average processing duration of historical events with different complexity levels, stepped basic durations are delimited for 10-point complexity scores. The higher the score, the longer T0 is, following the principle of dense steps for low score ranges and sparse steps for high score ranges. The following is a general correspondence rule adapted to mainstream application scenarios, with the unit being minutes: the complexity score ranges: 0 to 2 points, 3 to 5 points, 6 to 7 points, 8 to 9 points, and 10 points, correspond to basic durations (T0 / minutes): 30, 60, 120, 240, 360 respectively.

[0042] The compression coefficient K is a decimal satisfying 0 < K ≤ 1. The higher the priority, the smaller the K value, and the more obvious the compression of the basic duration. The following is the general compression coefficient rule: priority level P1 (urgent) corresponds to 0.2, which is extreme compression; priority level P2 (high) corresponds to 0.5, which is substantial compression; priority level P3 (medium) corresponds to 1.0, which is no compression; priority level P4 (low) corresponds to 1.5, which is moderate relaxation.

[0043] In an embodiment of the present application, the calculation method of the quality score comprises: Determining the repetition rate score of bank operation and maintenance events and the standardization score of the resolution process of bank operation and maintenance events based on multi-source data; Obtaining the quality score according to the repetition rate score and the standardization score.

[0044] In this embodiment, the repetition rate score is obtained by clustering and identifying repetition for similar operation and maintenance problems, and deducting the basic score according to the number of repetitions, which reflects the radical cure effect of problems and operation and maintenance stability. For example, a basic score is set, and a proportional deduction is made for each repeated event, finally obtaining the repetition rate score that reflects the repetition degree of the problem. The standardization score is obtained by performing compliance check on the event processing process based on operation and maintenance process specifications, including root cause analysis completeness, processing record standardization, association with change orders and problem tickets, etc. Points are added for behaviors conforming to process specifications to form the standardization score.

[0045] Further, the repetition rate score and the standardization score are weighted or directly summed to obtain the quality score of the current operation and maintenance event.

[0046] In an embodiment of the present application, the calculation method of the business impact score comprises: Calculating the product of the business service weight of the bank operation and maintenance event and the downtime duration as the business impact score.

[0047] In this embodiment, the business service weight is a quantitative score of the core value, importance, and scope of influence of each business service of an enterprise. It is the core bridge that transforms downtime at the technical level into a business dimension impact score. Its value is not set arbitrarily, but is based on a comprehensive evaluation and quantification of multiple dimensions such as business value, user scope, revenue correlation, and operational importance. Finally, it is formulated and calibrated through a standardized process to ensure that the weight can truly reflect the degree of impact of a single business service anomaly on the enterprise as a whole.

[0048] For business service weighting, the evaluation dimensions revolve around four core aspects: business value, user scope, revenue relevance, and operational importance. Each dimension is assigned a different evaluation weight, and the initial weight is finally obtained through weighted calculation. Further, the business impact score is obtained by multiplying the business service weight by the downtime.

[0049] In one embodiment of this application, the method further includes: Generate and display visual profiles that demonstrate the relative strengths and weaknesses of scores across four dimensions.

[0050] In this embodiment, a visual profile is generated and displayed, which can be a radar chart. This visual profile transforms four abstract indicators—complexity, efficiency, quality, and business impact—into intuitive, comparable, and quantifiable graphical views, enabling multi-dimensional comprehensive evaluation, horizontal comparison, and trend analysis of operational events. This provides data support for operational optimization, root cause identification, resource scheduling, and performance evaluation.

[0051] Step 103: Aggregate the scores from the four dimensions according to the weights corresponding to each score to form the current comprehensive score of the bank's operation and maintenance event.

[0052] In this step, the scores from the four dimensions are aggregated into a single comprehensive score using a formula with configurable weights.

[0053] In one embodiment of this application, the method further includes: determining the weight corresponding to each score according to the target information; the target information includes at least one of the following: event type, event priority, and business time period.

[0054] In this embodiment, the weights corresponding to each score can be adjusted according to event type, event priority, or business time period. For example: (1) Adjust the weighting percentages according to the specific type of bank maintenance events. For example, when the bank maintenance event type is a core transaction system failure, the weights for complexity, efficiency, quality, and business impact are 20%, 25%, 15%, and 40%, respectively, which are the key concerns regarding business impact (transaction interruption directly affects funds and customers); when the bank maintenance event type is a network link anomaly, the weights for complexity, efficiency, quality, and business impact are 30%, 30%, 20%, and 20%, respectively, which emphasize complexity (difficulty in troubleshooting) and efficiency (rapid link recovery).

[0055] (2) Based on the P1-P4 priorities defined above, adjust the weights of events with different levels of urgency. For example, when the event priority is P1, the weights of complexity, efficiency, quality, and business impact are 10%, 40%, 10%, and 40%, respectively, with a focus on handling efficiency and business impact, and quick loss mitigation as the primary goal; when the event priority is P2, the weights of complexity, efficiency, quality, and business impact are 20%, 30%, 20%, and 30%, respectively, with an emphasis on efficiency and business impact, while taking other factors into account; when the event priority is P3, the weights of complexity, efficiency, quality, and business impact are 25%, 20%, 30%, and 25%, respectively, with an emphasis on quality (quality of problem-solving); when the event priority is P4, the weights of complexity, efficiency, quality, and business impact are 30%, 10%, 40%, and 20%, respectively, with a focus on quality and complexity, and no urgent handling requirements.

[0056] (3) Configuration by business hours: Adjust the weights for peak, off-peak, and quiet periods of the bank's transactions. Peak periods include: weekdays 8:30-10:00, 12:00-13:30, 18:00-20:00, holidays, end of month, end of quarter, and year-end closing period. Off-peak periods include non-transactional hours on weekdays. Quiet periods include 0:00-6:00. For example, during peak periods, the weights of complexity, efficiency, quality, and business impact are 15%, 30%, 15%, and 40%, respectively, with a strong focus on business impact and processing efficiency. During off-peak periods, the weights of complexity, efficiency, quality, and business impact are 25%, 25%, 25%, and 25%, respectively, with all four dimensions equally distributed for comprehensive evaluation. During quiet periods, the weights of complexity, efficiency, quality, and business impact are 30%, 20%, 40%, and 10%, respectively, with an emphasis on quality (inspection / upgrade quality) and complexity (problem localization), and minimal business impact.

[0057] Step 104: Based on the current comprehensive score and historical score data of bank operation and maintenance events, automatically generate a recommendation report for bank operation and maintenance events.

[0058] In this step, trend and correlation analysis is performed on the scoring results, and actionable improvement suggestion reports such as "System A has a high rate of repeated failures" are automatically generated, forming a management closed loop from "measurement" to "insight" to "action", driving continuous optimization of operation and maintenance.

[0059] The following is a specific example of how to handle and evaluate a "transaction anomaly alarm on a certain platform" failure event within the industry: 1. Scene Background On Monday morning, during peak trading hours, a large number of alerts suddenly appeared on a certain platform within the bank regarding widespread "mobile banking transfer transaction timeouts." The system automatically assigned event tickets, and the operations and maintenance team responded immediately upon receiving the alerts.

[0060] 2. Data Input and Preprocessing The system automatically collected the following information from various data sources: (1) From ITSM (IT Service Management) system: Event Ticket B, titled "Large-scale timeout of mobile banking transfer transactions", the event source is "monitoring", the event reporting time is 09:05, the event occurrence time is 09:03, and the resolution time is 10:30.

[0061] (2) Data from the root cause localization system: The root cause localization is “database connection pool full”, and the associated microservice is “transaction core service”.

[0062] (3) From CMDB (Configuration Management Database): The configuration item affected by this event is "Database Cluster-C".

[0063] (4) From the business catalog: The affected business service is "personal online banking transfer", and the business weight is preset to 9 (out of 10).

[0064] 3. Execution process of the multi-dimensional scoring engine 3.1 Complexity Score The category "core application failure" is mapped to 8 points in the weight table.

[0065] In the title and description, the keywords "large area", "timeout", and "connection pool full" were matched by the keyword database, adding 2 points and 1 point respectively.

[0066] Complexity score calculation: 8 (basic) + 2 (large area) + 1 (connection pool full) = 11 points. Since the system's maximum score is 10, the complexity score is 10.

[0067] 3.2 Efficiency Score Standard processing time setting: Based on its complexity (10 points) and priority (high), the system sets its standard processing time to 60 minutes.

[0068] Actual processing time: from 09:05 to 10:30, a total of 85 minutes.

[0069] Efficiency score calculation: Standard duration / Actual duration = 60 / 85 ≈ 0.71.

[0070] 3.3 Quality Score Duplicate Check: The system detected two similar incidents caused by the "database connection pool" issue within the past week. According to the rules, 20% of the base score (let's say 10 points) is deducted for each duplicate, so the duplicate score is 10 - 10 × 0.2 × 2 = 6 points.

[0071] Compliance Check: The personnel handling the incident completed the root cause analysis in the incident form and linked it to the corresponding change order, which complies with the standards and earns +2 bonus points.

[0072] Quality score calculation: 6 + 2 = 8 points.

[0073] 3.4 Business Impact Score Calculation: Business impact score = Downtime (85 minutes) × Business service weight (9) = 765 points.

[0074] Note: This is a negative indicator and will be deducted from the overall score.

[0075] 4. Comprehensive scoring and visualization The overall score is calculated, assuming the weights of each score are 0.2, 0.3, 0.3, and 0.2 respectively. Current overall score = (10×0.2) + (0.71×0.3) + (8×0.3) - (765×0.2) = 2 +0.21 + 2.4 - 153 = -148.39 Normalization: The system maps the overall score to a 0-100 point scale. This case's score is significantly low, and the final score is 25 points.

[0076] Visual profile: Generate a radar chart that clearly shows that the event performs well in terms of "complexity" and "business impact", but poorly in terms of "efficiency" and "quality" (due to recurrence).

[0077] 5. Intelligent Insights and Improvements Based on the current comprehensive score and historical score data, the system automatically generates the following insight report: Root cause remediation recommendations: The "Transaction Core Service" has experienced three failures (including this one) in the past week due to the "Database Connection Pool" issue. The recurrence rate of failures is high, and it is strongly recommended to initiate radical changes, such as optimizing the connection pool configuration strategy or expanding the architecture.

[0078] Efficiency improvement suggestions: The processing time for this alarm fault exceeded the standard by 25 minutes. It is recommended to review the emergency response process and optimize the fault location script to improve the efficiency of resolving critical faults.

[0079] This application's embodiments achieve three key technical effects by constructing a multi-dimensional, lightweight automated scoring and insight solution: First, it enables a comprehensive quantitative evaluation of operational events, moving from a partial to a complete picture. By introducing multi-dimensional indicators such as complexity and business impact, the scoring results are more accurate and fair. Second, by replacing complex large models with a lightweight rule engine, the system implementation and maintenance costs are significantly reduced while maintaining automation levels, improving the feasibility and universality of the solution. Third, it establishes a closed loop from evaluation to improvement, automatically converting scoring data into targeted optimization suggestions, driving continuous improvement of operational processes, and effectively enhancing the refinement and intelligence of operational management.

[0080] As a specific implementation of the aforementioned method for handling bank operation and maintenance events, this application provides a device for handling bank operation and maintenance events. For example... Figure 3 As shown, the bank operation and maintenance event processing device 300 includes: a data acquisition module 301, a scoring module 302, a comprehensive scoring module 303, and a report generation module 304.

[0081] Among them, the data acquisition module 301 is used to collect multi-source data related to bank operation and maintenance events; The preliminary scoring module 302 is used to calculate scores in parallel across four dimensions based on multi-source data. The four dimensions include complexity score, efficiency score, quality score, and business impact score. The comprehensive scoring module 303 is used to aggregate the scores of the four dimensions according to the weights of each score into a current comprehensive score for the bank's operation and maintenance events. The report generation module 304 is used to automatically generate a recommendation report for bank operation and maintenance events based on the current comprehensive score and historical score data of the events.

[0082] Furthermore, the device also includes: The display module is used to generate and display a visual profile, which is used to show the strength of the scores in four dimensions.

[0083] Furthermore, the complexity score is calculated as follows: Based on the multi-source data, the event classification rules of the event complexity quantification model are used to classify the bank operation and maintenance events, and a basic score is determined according to the classification results. Also, the keywords of the bank operation and maintenance events are determined, and additional scores are assigned to the keywords using the keyword scoring rules of the event complexity quantification model. The complexity score is determined based on the base score and the additional score.

[0084] Furthermore, the efficiency score is calculated as follows: Based on the multi-source data, the actual processing time of the bank's operation and maintenance events is determined; The actual processing time is compared with the standard processing time to obtain the efficiency score. The standard processing time is determined based on at least one of the complexity score and the alarm priority of the bank operation and maintenance event.

[0085] Furthermore, the calculation method for the quality score includes: Based on the multi-source data, the recurrence rate score of the bank's operation and maintenance events and the standardization score of the bank's operation and maintenance event resolution process are determined. The quality score is obtained based on the repetition rate score and the normality score.

[0086] Furthermore, the calculation method for the business impact score includes: The business service weight of the bank's operation and maintenance event is calculated as the product of the downtime and the business impact score.

[0087] Furthermore, the device also includes: The weight adjustment module is used to determine the weight corresponding to each score according to the target information; The target information includes at least one of the following: event type, event priority, and business time period.

[0088] The bank operation and maintenance event processing device 300 in this application embodiment can be a computer device or a component in the computer device, such as an integrated circuit or a chip. The bank operation and maintenance event processing device 300 provided in this application embodiment can achieve... Figure 1 and Figure 2 The various processes implemented in the method for handling bank operation and maintenance events will not be described in detail here to avoid repetition.

[0089] This application also provides a computer device, such as... Figure 4As shown, the computer device 400 includes a processor 401 and a memory 402. The memory 402 stores programs or instructions that can run on the processor 401. When the program or instructions are executed by the processor 401, they implement the various steps of the above-described bank operation and maintenance event handling method embodiment and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0090] The memory 402 can be used to store software programs and various data. The memory 402 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 402 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 402 in this embodiment includes, but is not limited to, these and any other suitable types of memory.

[0091] Processor 401 may include one or more processing units; optionally, processor 401 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 401.

[0092] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described bank operation and maintenance event handling method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0093] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0094] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for handling bank operation and maintenance events, characterized in that, include: Collect multi-source data related to bank operation and maintenance events; Based on the multi-source data, scores in four dimensions are calculated in parallel, including complexity score, efficiency score, quality score, and business impact score. The scores of the four dimensions are aggregated according to the weights corresponding to each score to form the current comprehensive score of the bank's operation and maintenance event; Based on the current comprehensive score and historical score data of the bank's operation and maintenance event, a recommendation report is automatically generated for the bank's operation and maintenance event.

2. The method for handling bank operation and maintenance events according to claim 1, characterized in that, The method further includes: Generate and display a visual profile, which is used to show the strength of the scores in four dimensions.

3. The method for handling bank operation and maintenance events according to claim 1, characterized in that, The complexity score is calculated in the following ways: Based on the multi-source data, the event classification rules of the event complexity quantification model are used to classify the bank operation and maintenance events, and a basic score is determined according to the classification results. Also, the keywords of the bank operation and maintenance events are determined, and additional scores are assigned to the keywords using the keyword scoring rules of the event complexity quantification model. The complexity score is determined based on the base score and the additional score.

4. The method for handling bank operation and maintenance events according to claim 1, characterized in that, The efficiency score is calculated in the following ways: Based on the multi-source data, the actual processing time of the bank's operation and maintenance events is determined; The actual processing time is compared with the standard processing time to obtain the efficiency score. The standard processing time is determined based on at least one of the complexity score and the alarm priority of the bank operation and maintenance event.

5. The method for handling bank operation and maintenance events according to claim 1, characterized in that, The calculation method for the quality fraction includes: Based on the multi-source data, the recurrence rate score of the bank's operation and maintenance events and the standardization score of the bank's operation and maintenance event resolution process are determined. The quality score is obtained based on the repetition rate score and the normality score.

6. The method for handling bank operation and maintenance events according to claim 1, characterized in that, The calculation method for the business impact score includes: The business service weight of the bank's operation and maintenance event is calculated as the product of the downtime and the business impact score.

7. The method for handling bank operation and maintenance events according to claim 1, characterized in that, The method further includes: Determine the weight of each score based on the target information; The target information includes at least one of the following: event type, event priority, and business time period.

8. A device for handling bank operation and maintenance events, characterized in that, include: The data acquisition module is used to collect multi-source data related to bank operation and maintenance events; The preliminary scoring module is used to calculate scores in parallel across four dimensions based on the multi-source data. These four dimensions include complexity score, efficiency score, quality score, and business impact score. The comprehensive scoring module is used to aggregate the scores of the four dimensions according to the weights corresponding to each score into a current comprehensive score for the bank's operation and maintenance event. The report generation module is used to automatically generate a recommended report for the bank's operation and maintenance event based on the current comprehensive score and historical score data.

9. A computer device, characterized in that, It includes a processor and a memory, the memory storing a program or instructions that run on the processor, the program or instructions being executed by the processor to implement the steps of the information transmission method as described in any one of claims 1 to 8.

10. A readable storage medium having a program or instructions stored thereon, characterized in that, When the program or instructions are executed by the processor, they implement the steps of the method for handling bank operation and maintenance events as described in any one of claims 1 to 8.

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