CN-121258223-B - Method and system for managing operation risk

CN121258223BCN 121258223 BCN121258223 BCN 121258223BCN-121258223-B

Abstract

The invention discloses a method and a system for managing operation risks, wherein the method comprises the steps of collecting store operation data according to a preset period, preprocessing the data to obtain structural risk information, quantitatively evaluating based on a dynamic dividing reference algorithm, calculating deviation degree of a business index relative to a space-time dynamic reference value, determining core index anomaly degree and outputting a risk grade, generating a gate shop atmosphere risk label according to the risk grade and the core index anomaly degree, constructing a risk conduction network and generating a target management scheme, and finally outputting a risk management report through closed loop verification. The invention realizes the dynamic quantitative evaluation and accurate targeted treatment of the operation risk, remarkably improves the accuracy and the treatment efficiency of risk identification and simultaneously reduces the manual intervention cost through the intelligent adjustment of the space-time dynamic reference value and the construction of the risk conduction network.

Inventors

LI LUN
CHENG LILI
YE WEI
PAN YUEYING
SONG JIANCHENG

Assignees

朴朴科技(福建)有限公司

Dates

Publication Date: 20260512
Application Date: 20251208

Claims (7)

1. A method for operational risk management, comprising: Store operation data are collected according to a preset period, wherein the store operation data comprise operation behavior logs, transaction flow records and system operation tracks; preprocessing the store operation data to obtain structured risk information, wherein the preprocessing comprises data cleaning, field standardization and operation link reconstruction; performing quantitative evaluation on the structured risk information based on a dynamic positioning reference algorithm, wherein the quantitative evaluation comprises: Calculating the deviation degree of each service index relative to a space-time dynamic reference value, wherein the space-time dynamic reference value is dynamically generated through a three-dimensional weight matrix of an area, a time period and a service type; determining the abnormality degree of the core index according to the deviation degree, and outputting a risk level by combining a preset risk ladder model; generating a door shop atmosphere risk tag based on the risk level and the core index anomaly degree; constructing a risk conduction network according to the gate shop atmosphere risk tag, and generating a target treatment scheme based on the risk conduction network; performing closed-loop verification on the targeted treatment scheme and real-time operation data, and outputting a risk treatment report containing treatment effect evaluation and scheme optimization suggestions; Preprocessing store operation data to obtain structured risk information, wherein the preprocessing comprises data cleaning, field standardization and operation link reconstruction, and the method comprises the following steps: Performing abnormal operation identification and compliance verification on the operation behavior log, removing illegal operation records, marking suspicious behavior fragments, and obtaining a preprocessed operation behavior log; Carrying out integrity check and monetary threshold filtering on the transaction flow record, repairing the missing transaction field and eliminating abnormal transactions exceeding the preset monetary threshold to obtain a preprocessed transaction flow record; performing time sequence reconstruction and operation chain analysis on the system operation track, restoring a complete operation path, and identifying discontinuous operation fragments to obtain a preprocessed system operation track; aligning the preprocessed operation behavior log, the transaction flow record and the system operation track according to a unified time reference, and performing standardized conversion to obtain structured risk information; calculating the deviation degree of each service index relative to a space-time dynamic reference value, wherein the space-time dynamic reference value is dynamically generated through a three-dimensional weight matrix of an area, a time period and a service type, and the method comprises the following steps: performing gridding coding on the region where the store is located to generate a region weight coefficient, and dynamically adjusting the region weight coefficient based on the distribution density of the historical risk event; carrying out service peak classification on the operation time period to generate a time period weight coefficient, wherein the time period weight coefficient is dynamically configured according to the fluctuation characteristics of the transaction amount; labeling the risk level of the service type, generating a service type weight coefficient, and presetting a reference value according to the supervision requirement; Constructing a three-dimensional weight matrix based on the regional weight coefficient, the time period weight coefficient and the service type weight coefficient; extracting a current service index from the structured risk information, and matching the current service index with a corresponding three-dimensional weight matrix; Calculating the deviation degree of the current business index value and the historical reference value of the same area, the same time period and the same business type; performing closed loop verification on the targeted governance scheme and the real-time operation data, including: Taking a risk conduction network map and a target treatment scheme as inputs, and establishing a closed loop verification model comprising a strategy execution module, an effect evaluation module and a scheme optimization module; injecting a real-time interception strategy into the wind control system through a strategy execution module, and outputting a strategy hit record and a false interception analysis report; collecting an early warning disposal log through an effect evaluation module, and outputting an early warning response aging matrix and a false alarm missing report statistical table; and receiving a strategy hit record, a false interception analysis report, an early warning response aging matrix and a false report missing statistical table through a scheme optimization module, and outputting the dynamically adjusted risk conduction network parameters and the treatment strategy weight coefficient.
2. The method for managing operation risk according to claim 1, wherein determining the core indicator anomaly degree according to the deviation degree and outputting a risk level in combination with a preset risk ladder model comprises: Normalizing the deviation degree, and converting the deviation degree into an abnormality degree scoring value in a range of 0-100; Dividing the abnormality degree scoring value into three dimensions of a transaction safety class, an operation compliance class and a system stability class according to the service type; Setting a transaction safety risk threshold, an operation compliance risk threshold and a system stability risk threshold; matching the degree of abnormality grading value of each dimension with a transaction safety risk threshold, an operation compliance risk threshold and a system stability risk threshold to generate a dimension risk level; And carrying out weighted aggregation on the dimension risk levels based on a preset risk ladder model, and outputting final risk levels, wherein the risk levels comprise normal, concerned, warning and serious four-level classification.
3. The method for operational risk management according to claim 2, wherein the weighting and aggregating the dimensional risk levels based on the preset risk ladder model, and outputting the final risk level, comprises: establishing a dimension risk mapping relation, and respectively converting the abnormality degree grading values of the transaction safety class, the operation compliance class and the system stability class into standard risk equivalents; Constructing a risk conduction matrix, and quantifying the mutual influence coefficients among standard risk equivalents of each dimension; The method for automatically adjusting the weight proportion of each dimension risk equivalent according to the current business scene features by adopting a dynamic weighting algorithm comprises the following steps: Collecting transaction amount fluctuation coefficients, operation behavior dispersion and system load rate in real time as scene characteristic parameters; Inputting the scene characteristic parameters into a weight response function to calculate dynamic weights of all dimensions, and restricting the total weight to be 1 and to be in a preset interval to obtain weighted standard risk equivalent; carrying out nonlinear superposition calculation on the weighted standard risk equivalent to obtain an aggregation result; setting a risk level judging rule, and determining a final risk level according to a risk interval in which an aggregation result is located; and triggering a cross-dimension risk re-assessment mechanism when the standard risk equivalent of any dimension breaks through the critical threshold.
4. The method for operational risk management according to claim 1, wherein generating a gate shop atmosphere risk tag based on the risk level and core indicator anomaly, comprises: Obtaining a risk level identifier according to the risk level, performing multidimensional decomposition on the core index anomaly degree, extracting a transaction safety anomaly factor, an operation compliance deviation factor and a system stability fluctuation factor, and generating an index anomaly characteristic vector; constructing a risk tag mapping rule base, carrying out combination matching on the risk grade identification and the index abnormal feature vector, and generating a logic output initial risk tag through a preset tag; And carrying out context verification on the initial risk tag, and generating a final door shop atmosphere risk tag by combining the store history risk record and the industry risk portrait correction tag confidence, and associating the final door shop atmosphere risk tag with a door shop atmosphere risk portrait library.
5. The method for operational risk remediation according to claim 1, wherein the targeted remediation scheme includes real-time intercept policies for critical risk nodes, early warning escalation mechanisms for potential conductive paths, and dynamic adjustment rules based on historical treatment effect feedback; constructing a risk conduction network according to the gate shop atmosphere risk tag, generating a targeted abatement scheme based on the risk conduction network, including: performing topology analysis on the gate shop atmosphere risk tag, extracting risk source nodes, conduction path nodes and risk convergence points, and constructing a three-dimensional risk conduction network map; Identifying key risk nodes based on the risk conduction network map, and generating a real-time interception strategy comprising forced blocking, secondary verification and manual rechecking according to the key risk nodes and real-time service scene characteristics; determining potential conduction paths through path sensitivity analysis, and establishing an early warning upgrading mechanism linked with the risk level; and introducing a historical treatment effect feedback loop, dynamically evaluating the interception success rate and the path early warning accuracy of each node, and generating a dynamic adjustment rule by adjusting the risk weight coefficient and the treatment response threshold value based on the evaluation result.
6. The method of claim 1, wherein outputting a risk management report containing treatment effect assessment and plan optimization suggestions comprises: Generating risk interception efficiency analysis based on the strategy hit record, outputting interception success rate, misjudgment rate and average response time index of key risk nodes, and recording the interception success rate, misjudgment rate and average response time index as first treatment information; Constructing an early warning treatment evaluation model based on the early warning response aging matrix, outputting early warning accuracy, treatment aging deviation degree and upgrading mechanism triggering frequency of each conduction path, and recording the early warning accuracy, the treatment aging deviation degree and the upgrading mechanism triggering frequency as second treatment information; Performing strategy sensitivity analysis based on the false alarm missing report statistical table, and outputting false alarm rate curves and missing report risk thermodynamic diagrams under different risk levels, wherein the false alarm rate curves and missing report risk thermodynamic diagrams are recorded as third treatment information; Outputting strategy optimization suggestions containing node importance sequencing and path sensitivity change trend according to the dynamically adjusted risk conduction network parameters and the treatment strategy weight coefficients, and recording the strategy optimization suggestions as fourth treatment information; and fusing the first governance information, the second governance information, the third governance information and the fourth governance information into the risk governance report.
7. A system for management of operational risk, characterized by being adapted for the method of any one of claims 1 to 6.

Description

Method and system for managing operation risk Technical Field The invention relates to the technical field of operation risk management, in particular to a method and a system for managing operation risk. Background In retail industry store operation management, risk management is an important link for guaranteeing business compliance and operation efficiency. Traditional risk monitoring relies primarily on manual inspection or single point data verification to identify problems through isolated analysis of specific scenarios (e.g., transaction anomalies, inventory variances, etc.). Along with the expansion of the scale of chain stores and the improvement of business complexity, the decentralized treatment mode gradually exposes the defects of response lag, unreasonable resource allocation and the like. Particularly, in the multi-region and multi-business operation environment, the risk performance of different stores often has significant dynamic changes due to the differences of geographic positions, guest group characteristics and traffic, and a unified and flexible risk assessment system is difficult to establish in the prior art. In addition, due to the lack of systematic integration and analysis of the operational data, the conventional methods cannot effectively identify the risk conduction paths, resulting in a passive situation in which the treatment measures are often in the form of "headache, foot pain, and foot pain. How to implement the transition from local risk management to global dynamic governance becomes a key challenge to improve the performance of operational risk management. Disclosure of Invention In view of the above problems, the invention provides a method and a system for managing operation risks, which quantizes the risk deviation degree through a dynamic positioning reference algorithm and constructs a conducting network to realize accurate targeted management, thereby solving the problems of low efficiency and resource dispersion of the traditional single-point monitoring. To achieve the above object, in a first aspect, the present application provides a method for management of operational risk, comprising: Store operation data are collected according to a preset period, wherein the store operation data comprise operation behavior logs, transaction flow records and system operation tracks; preprocessing store operation data to obtain structured risk information, wherein the preprocessing comprises data cleaning, field standardization and operation link reconstruction; carrying out quantitative evaluation on the structured risk information based on a dynamic positioning reference algorithm, wherein the quantitative evaluation comprises the following steps: Calculating the deviation degree of each service index relative to a space-time dynamic reference value, wherein the space-time dynamic reference value is dynamically generated through a three-dimensional weight matrix of an area, a time period and a service type; determining the core index anomaly degree according to the deviation degree, and outputting a risk grade by combining a preset risk ladder model; Generating a door shop atmosphere risk tag based on the risk level and the core index anomaly degree; constructing a risk conduction network according to the gate shop atmosphere risk tag, and generating a target treatment scheme based on the risk conduction network; and performing closed-loop verification on the targeted treatment scheme and the real-time operation data, and outputting a risk treatment report containing treatment effect evaluation and scheme optimization suggestions. Further, preprocessing the store operation data to obtain structured risk information, wherein the preprocessing comprises data cleaning, field standardization and operation link reconstruction, and the method comprises the following steps: Performing abnormal operation identification and compliance verification on the operation behavior log, removing illegal operation records, marking suspicious behavior fragments, and obtaining a preprocessed operation behavior log; Carrying out integrity check and monetary threshold filtering on the transaction flow record, repairing the missing transaction field and eliminating abnormal transactions exceeding the preset monetary threshold to obtain a preprocessed transaction flow record; performing time sequence reconstruction and operation chain analysis on the system operation track, restoring a complete operation path, and identifying discontinuous operation fragments to obtain a preprocessed system operation track; and aligning the preprocessed operation behavior log, the transaction flow record and the system operation track according to a unified time standard, and performing standardized conversion to obtain the structured risk information. Further, calculating the deviation degree of each service index relative to a space-time dynamic reference value, wherein the space-time dynamic reference value