CN114841598A - Decision method, device, equipment and program product for operation risk - Google Patents
Decision method, device, equipment and program product for operation risk Download PDFInfo
- Publication number
- CN114841598A CN114841598A CN202210557829.6A CN202210557829A CN114841598A CN 114841598 A CN114841598 A CN 114841598A CN 202210557829 A CN202210557829 A CN 202210557829A CN 114841598 A CN114841598 A CN 114841598A
- Authority
- CN
- China
- Prior art keywords
- node
- risk
- graph
- path
- weight
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 59
- 238000012544 monitoring process Methods 0.000 claims abstract description 91
- 238000003066 decision tree Methods 0.000 claims abstract description 71
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 35
- 238000005516 engineering process Methods 0.000 claims abstract description 20
- 238000011156 evaluation Methods 0.000 claims abstract description 12
- 238000004590 computer program Methods 0.000 claims description 19
- 230000005540 biological transmission Effects 0.000 claims description 7
- 238000012546 transfer Methods 0.000 claims description 7
- 238000011160 research Methods 0.000 claims description 5
- 238000001514 detection method Methods 0.000 abstract description 6
- 230000008569 process Effects 0.000 description 14
- 238000010586 diagram Methods 0.000 description 10
- 238000004891 communication Methods 0.000 description 8
- 238000012545 processing Methods 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 4
- 230000002776 aggregation Effects 0.000 description 3
- 238000004220 aggregation Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000013507 mapping Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 229910003460 diamond Inorganic materials 0.000 description 1
- 239000010432 diamond Substances 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/901—Indexing; Data structures therefor; Storage structures
- G06F16/9024—Graphs; Linked lists
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
- G06F21/57—Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
- G06F21/577—Assessing vulnerabilities and evaluating computer system security
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06398—Performance of employee with respect to a job function
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation or account maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/18—Legal services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F2221/03—Indexing scheme relating to G06F21/50, monitoring users, programs or devices to maintain the integrity of platforms
- G06F2221/033—Test or assess software
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F2221/03—Indexing scheme relating to G06F21/50, monitoring users, programs or devices to maintain the integrity of platforms
- G06F2221/034—Test or assess a computer or a system
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Educational Administration (AREA)
- Tourism & Hospitality (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Computer Security & Cryptography (AREA)
- Computer Hardware Design (AREA)
- Technology Law (AREA)
- Databases & Information Systems (AREA)
- Computing Systems (AREA)
- Computational Linguistics (AREA)
- Mathematical Physics (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a decision-making method, a decision-making device, equipment and a program product for operation risks. The method comprises the following steps: creating a graph database portrait model matched with the operation risk framework based on a graph database technology; performing parameter initialization setting on nodes and paths in the graph database image model by adopting a graph weight decision tree algorithm to obtain a graph weight decision tree framework model; acquiring an operation value of a target monitoring node, and determining a risk influence range of the target monitoring node by adopting a graph mode weight distance evaluation algorithm according to the operation value of the target monitoring node; and determining a risk decision mode according to the risk influence range. And obtaining a graph weight decision tree framework model through the constructed operation risk graph database and sketch model, determining an influence range of a target detection node according to the node attribute and the path relation when obtaining an operation numerical value of the target monitoring node, and determining a decision mode for controlling or slowly releasing the operation risk according to the influence range so as to realize effective control on the operation risk.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a program product for operation risk decision.
Background
Operational risk refers to risk caused by imperfect or problematic internal programs, personnel and information technology systems, and external events, including legal risk but not strategic risk and reputation risk.
At present, in the management process of the conventional operation risk of a bank, the identification, evaluation, detection, control and slow release of the related risk in the bank process are concerned. The content of interest basically includes elements such as internal, external and legal risks.
However, in the currently implemented operational risk management process, the influence range of the operational risk key index cannot be determined according to the relationship and the mutual influence between the elements, so that effective management and control on the operational risk cannot be realized.
Disclosure of Invention
The invention provides a decision-making method, a decision-making device, decision-making equipment and a decision-making program product for operation risks, so as to realize effective management and control of the operation risks.
According to an aspect of the present invention, there is provided a method for deciding operational risk, including: creating a graph database sketch model matched with an operation risk frame based on graph database technology, wherein nodes in the graph database sketch model represent key indexes in the operation risk frame, and paths in the graph database sketch model represent incidence relations of the key indexes;
performing parameter initialization setting on nodes and paths in the graph database and sketch model by adopting a graph weight decision tree algorithm to obtain a graph weight decision tree frame model, wherein the types of the nodes in the graph weight decision tree frame model comprise monitoring nodes, collecting nodes and decision nodes;
obtaining an operation value of a target monitoring node, and determining a risk influence range of the target monitoring node by adopting a graph mode weight distance evaluation algorithm according to the operation value of the target monitoring node;
and determining a risk decision mode according to the risk influence range.
According to another aspect of the present invention, there is provided a decision device for operational risk, comprising: the system comprises a database drawing model creating module, a database drawing model generating module and a database drawing model generating module, wherein the database drawing model creating module is used for creating a database drawing model matched with an operation risk frame based on a database technology, nodes in the database drawing model represent key indexes in the operation risk frame, and paths in the database drawing model represent incidence relations of the key indexes;
the graph weight decision tree framework model acquisition module is used for carrying out parameter initialization setting on nodes and paths in the graph database and sketch model by adopting a graph weight decision tree algorithm to acquire a graph weight decision tree framework model, wherein the types of the nodes in the graph weight decision tree framework model comprise monitoring nodes, collecting nodes and decision nodes;
the risk influence range determining module is used for acquiring the operation value of a target monitoring node and determining the risk influence range of the target monitoring node by adopting a graph mode weight distance evaluation algorithm according to the operation value of the target monitoring node;
and the wind direction decision mode determining module is used for determining a risk decision mode according to the risk influence range.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the method according to any of the embodiments of the invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium having stored thereon computer instructions for causing a processor to execute a method according to any one of the embodiments of the present invention.
According to the technical scheme, an operation risk graph database image model is constructed, parameters of nodes and paths in the graph database image model are initialized by adopting a graph weight decision tree algorithm, a graph weight decision tree framework model is obtained, when the operation numerical values of target monitoring nodes are obtained, leaf node index changes are comprehensively monitored and analyzed through backward calculation of distances and weights according to node attributes and path relations, the influence range of the target detection nodes is determined, and a decision mode for controlling or slowly releasing operation risks is determined according to the influence range, so that effective control over the operation risks is achieved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a decision method for operational risk according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a graph database representation model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an application scenario for providing a decision method for operational risk according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a graph weight decision tree framework model according to an embodiment of the present invention;
FIG. 5 is a flowchart of a decision method for operational risk according to a second embodiment of the present invention;
fig. 6 is a schematic structural diagram of a decision-making device for operational risk according to a third embodiment of the present invention;
FIG. 7 is a schematic structural diagram of an electronic device according to a fourth aspect of the present invention;
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a decision method for operational risk according to an embodiment of the present invention, which is applicable to a decision method for operational risk, and the method may be performed by a decision device for operational risk, which may be implemented in the form of hardware and/or software. As shown in fig. 1, the method includes:
step S110, creating a graph database portrait model matched with the operation risk frame based on graph database technology.
Optionally, creating a graph database sketch model matched with the operation risk framework based on a graph database technology includes: taking the operation risk frame as a root node and taking the key indexes as leaf nodes; connecting the root node and the leaf node by adopting a path to obtain a graph database portrait model; wherein, the key indexes at least comprise: employees, internal programs, information technology systems, external events, and legal risks.
Specifically, for an operation risk frame, a graph theory technology is utilized, on the basis of a graph database technology, the operation risk frame is abstracted into a root multipoint tree graph structure, the operation risk frame is used as a root node, key indexes such as employees, internal programs, an information technology system, external events and legal risks are used as leaf nodes, each leaf node is provided with multiple attributes, and each attribute is used as a next-layer leaf node of an associated leaf node. And connecting the root node and the leaf node by adopting a path to obtain a graph database representation model, wherein the node in the graph database representation model represents a key index in the operation risk frame, and the path in the graph database representation model represents an incidence relation of the key index, as shown in fig. 2, the graph database representation model is a schematic diagram of the graph database representation model in the embodiment. As shown in fig. 3, which is a schematic view of an application scenario of a decision method for operational risk, in this embodiment, a risk decision manner is determined based on the application scenario shown in fig. 3.
And step S120, carrying out parameter initialization setting on nodes and paths in the graph database image model by adopting a graph weight decision tree algorithm to obtain a graph weight decision tree framework model.
Optionally, the obtaining a graph weight decision tree framework model by performing parameter initialization setting on nodes and paths in the graph database image model by using a graph weight decision tree algorithm includes: initializing a graph database image model by adopting a graph weight decision tree algorithm, and determining the node weight and the node threshold of each node and the path weight of each path; constructing the weight transfer direction of each path; and determining a graph weight decision tree framework model according to the node weight, the node threshold, the path weight and the weight transmission direction.
Specifically, in the embodiment, when the graph weight decision tree framework model is obtained according to the graph weight decision tree algorithm, the graph weight decision tree algorithm is adopted to initialize the node weight, the node threshold value and the path weight in the graph database image model. The node weight represents a risk influence degree of the node on the whole system, the node threshold represents a maximum operand value set for each node and allowed when risk early warning occurs, the path weight represents an influence relationship between a child node and an upper-layer parent node, a weight transmission direction of each path is constructed, and a graph weight decision tree frame model is determined according to the information, as shown in fig. 4, the graph weight decision tree frame model is a schematic diagram of the graph weight decision tree frame model in the present embodiment. The types of the nodes in the graph weight decision tree framework model comprise monitoring nodes, collecting nodes and decision nodes. As shown in fig. 4, the square frame "1.1.2 with low professional skills" is a monitoring node, the oval frame "2.1 with defects in the business process" is a collection node, and the diamond frame "research and judgment on related influences" is a decision node, but the embodiment is only exemplified as above, and does not limit the specific display forms of the monitoring node, the collection node, and the decision node.
It is worth mentioning that the monitoring node weight is divided into a conventional convergence influence weight for the convergence node and an extension influence weight for influencing other nodes in a cross-path extension manner, and the initial probability distribution weight of the node in the overall graph weight decision tree framework model is attached. The collection node is a superior node of the monitoring node, a path exists between the collection node and the monitoring node, and the path weight represents the conventional attribute track relation and the related influence compactness of the monitoring node and the collection node. The path weights are divided into two categories, length and width: the length represents the influence conduction strength between the upper and lower nodes, and the width represents the tightness of the mutual influence between the upper and lower nodes. The collection node also has the weight of the epitaxial influence on the nodes on the cross path, the collection node is attached with a secondary node and a utility distribution mapping table of the subordinate node, and the decision node mainly means the weight decision expressing the correlation influence. The decision node is attached with a decision tree algorithm, and is divided into three types of weights of input, study and judgment and output on the whole: the input value is a critical early warning value or a trigger threshold value of the relevant node; determining input mapping relation values of influence degree, conductivity and convergence degree according to the weight; the output weights are selection mapping table values, usually defined as both normal and abnormal.
When a graph weight decision tree algorithm is adopted to initialize a problem database portrait model, the specific mode is to initialize the weight, the dependency relationship and the relationship path of a node; according to the node weight and the path weight, a weight decision function baseline is initially constructed, namely a monitoring node threshold value; constructing a weight transfer direction; and initially constructing an influence degree baseline of each decision node, namely a decision node threshold, wherein the baseline is used for carrying out influence relation analysis and decision path transmission analysis in a graph theory according to monitored key index changes. As shown in table 1 below, the overall view of the key indicators corresponding to the initialized graph weight decision tree framework model is shown:
TABLE 1
The quantitative early warning indicators in table 1 correspond to node thresholds, and also include information such as node weights, path weights, and weight transfer directions.
In the embodiment, according to an operation risk graph database image model and a graph weight decision tree framework model obtained based on the operation risk graph database image model, dynamic monitoring is carried out on five types of matters and subsequent indexes in the operation process of a commercial bank, when the monitoring indexes change, corresponding processes and weights are respectively provided, each risk is incorporated into the graph model to be subjected to qualitative and quantitative analysis integrally, then according to the evolution path of each risk, the minimum influence path and the optimal corresponding strategy are found in the graph, and threshold type early warning is carried out.
Step S130, obtaining an operation value of the target monitoring node, and determining a risk influence range of the target monitoring node by adopting a graph mode weight distance evaluation algorithm according to the operation value of the target monitoring node.
Specifically, the event monitoring and decision making of the operation risk is actually an event driving type, and the operation risk events are abstracted into indexes such as nodes, paths and node relations, node (event) attributes and weights, path weights, node relation weights and the like in a graph mode through graph database modeling and drawing. The node attributes represent various events and attributes of the events occurring in the operation risk field, for example, "1 human factor", "1.1 misoperation", "1.1.1 misoperation" in table 1. The service rule of the node is as follows: when the personnel is poor in quality in subjective factors of the personnel to handle the banking service, business operations, such as inputting loan interest rate into a high value aiming at a credit business process, cause the increase of capital risks of customers and the occurrence of credit violation risks, and cause the above matters to be not checked and avoided because a business process design department is reasonable. The attribute of the misoperation node 1.1.1 in the table 1 is provided with a relevant service attribute value of the service operation process, meanwhile, the node attribute and a loss income index and an allowable loss threshold value which are counted by months/seasons and contain the key index characteristics of the service, and when the threshold value enters an early warning value interval, relevant influence analysis is triggered. In the influence analysis process, relevant attributes, node relationships, path relationships, weights and the like are substituted into the deduction flow. Therefore, the core idea of the embodiment is to construct an operation risk frame graph database sketch model, comprehensively monitor, analyze and estimate the index change of leaf nodes by reversely calculating the distance and the weight according to the node attributes and the path relation rules, and control and slowly release the influence of operation risks on the organization operation by the influence range and the trend of the path on the weight of the root nodes.
Optionally, determining a risk influence range of the target monitoring node by using a graph mode weight distance evaluation algorithm according to the operand value of the target monitoring node includes: when the operand value is determined to be larger than the target monitoring node threshold value and smaller than the primary collection node threshold value connected with the primary path weight, determining the risk influence range of the target monitoring node as the primary collection node of the path; and when the operand value is determined to be larger than the threshold value of the primary collection node, studying and judging through the decision node of the path to determine the risk influence range of the target monitoring node.
Optionally, the studying and judging by the decision node of the path to determine the risk influence range of the target monitoring node includes: obtaining the attribute of a target monitoring node through a decision node of the path; judging whether the attribute is a designated attribute, if so, crossing the path to a first adjacent path, and carrying out research and judgment through decision nodes of the first adjacent path to determine the risk influence range of the target monitoring node; otherwise, determining the risk influence range of the target monitoring node as a secondary collection node connected with the secondary path weight.
Optionally, the studying and judging by the decision node of the first adjacent path to determine the risk influence range of the target monitoring node includes: obtaining the attribute of a target monitoring node through a decision node of a first adjacent path; judging whether the attribute is a designated attribute, if so, spanning the path to a second adjacent path, and determining that the risk influence range is a related node of the second adjacent path, wherein the related node of the second adjacent path comprises legal risk; otherwise, determining the risk influence range of the target monitoring node as the relevant node of the first connection path.
In one specific implementation, as shown in fig. 4, when the target monitoring node is a 1.1.1 misoperation node, and the misoperation weight may specifically refer to an influence ratio of the misoperation in the system risk, for example, 2%, the early warning threshold corresponding to the misoperation node is 3, the 1.1 misoperation may be referred to as a primary sink node, the connection relationship between the 1.1.1 misoperation node and the 1.1 misoperation node is referred to as a primary path weight, and the early warning threshold corresponding to the 1.1 misoperation node is 7. And detecting the misoperation node to obtain a misoperation value, and when the misoperation value is determined to be 4 times of misoperation, determining that the risk influence range of the 1.1.1 misoperation node is 1.1 misoperation node of the path because the misoperation number is more than 3 but less than 7.
In addition, the 1 artifact may be referred to as a secondary aggregation node of the path, the connection relationship between the 1.1 misoperation and the 1 artifact may be referred to as a secondary path weight, and the early warning threshold corresponding to the 1 artifact node is 9. When the operand value is determined to be misoperation for 8 times, because the early warning threshold value corresponding to the misoperation node is exceeded by 1.1, therefore, the decision node of the path is used for studying and judging, and when the decision node is used for studying and judging, the attribute of the misoperation node is obtained through the decision node, i.e., whether the performer who misoperates 8 times is an old employee or a new employee, since the risk tolerance for the new and old employees is different, the requirements for the old staff are stricter, and the weight values of the primary paths adopted by the new and old staff are different, thus, when it is determined that the executive is an old employee, i.e., specifying attributes, then the path is crossed to the first adjacent path, namely 2.1, the path corresponding to the defective node of the business process, and the decision node of the first adjacent path is used for studying and judging, when the executive is determined not to be the old man-hour, the risk influence range of the 1.1.1 misoperation node is determined to be 1 artifact of the present path.
It should be noted that the way of studying and judging the decision node of the first adjacent path to determine the risk influence range of the 1.1.1 faulty operation node after the path is crossed is substantially the same as the way of studying and judging the path, and therefore, the detailed description is omitted in this embodiment. And when the decision node of the first adjacent path is researched, when the decision node is determined to exceed the related early warning threshold of the first adjacent path and is the designated attribute, the decision node continues to cross the path to the second adjacent path, otherwise, the risk influence range of the misoperation node of 1.1.1 is determined to be the related node of the first adjacent path, for example, the internal program node of 2. Of course, in the present embodiment, only the faulty operation node 1.1.1 in fig. 4 is exemplified as the target monitoring node, and the specific type of the target detection node is not limited in the present embodiment.
And S140, determining a risk decision mode according to the risk influence range.
Optionally, determining a risk decision manner according to the risk influence range includes: when the risk influence range is determined to be the node of the path, determining that a risk decision mode is to carry out internal control on the path, wherein the node of the path comprises a primary collection node and a secondary collection node; when the risk influence range is determined to be the relevant node of the first adjacent path, determining that the risk decision mode is slow release in the first adjacent path; and when the risk influence range is determined to be the related node of the second adjacent path, determining that the risk decision mode is service continuity guarantee.
Specifically, as shown in fig. 4, when it is determined that the influence range of the 1.1.1 misoperation node is the node of the path, for example, 1.1 misoperation or 1 human factor, it may be determined that the risk decision manner is to perform internal control on the path. And when determining that the risk influence range is the related node of the first ringing path, for example, 2.1 service flow defect or 2 internal program, determining that the risk decision mode is to perform slow release on the first ringing path. And when the risk influence range is determined to be the related node of the second adjacent path, for example, 5 legal risks, determining the risk decision mode to be service continuity guarantee.
In the embodiment, an operation risk graph database image model is constructed, parameters of nodes and paths in the graph database image model are initialized by adopting a graph weight decision tree algorithm, a graph weight decision tree framework model is obtained, when the operation numerical values of target monitoring nodes are obtained, leaf node index changes are comprehensively monitored and analyzed through backward calculation of distances and weights according to node attributes and path relations, the influence range of the target detection nodes is determined, and a decision mode for controlling or slowly releasing operation risks is determined according to the influence range, so that effective management and control of operation risks are realized.
Example two
Fig. 5 is a flowchart of a decision method for operational risk according to a second embodiment of the present invention, where the present embodiment is based on the foregoing embodiment, and after determining a risk decision manner according to a risk influence range, the present embodiment further includes: and when the risk decision mode is determined to be slow release or service continuity guarantee, backtracking the graph weight decision tree framework model so as to adjust the parameters of the nodes and the paths. The method comprises the following steps:
step S210, creating a graph database portrait model matched with the operation risk frame based on graph database technology.
Optionally, creating a graph database sketch model matched with the operation risk framework based on a graph database technology includes: taking the operation risk frame as a root node and taking the key indexes as leaf nodes; connecting the root node and the leaf node by adopting a path to obtain a graph database portrait model; wherein, the key indexes at least comprise: employees, internal programs, information technology systems, external events, and legal risks.
Step S220, carrying out parameter initialization setting on nodes and paths in the graph database image model by adopting a graph weight decision tree algorithm to obtain a graph weight decision tree framework model.
Optionally, the obtaining a graph weight decision tree framework model by performing parameter initialization setting on nodes and paths in the graph database image model by using a graph weight decision tree algorithm includes: initializing a graph database image model by adopting a graph weight decision tree algorithm, and determining the node weight and the node threshold of each node and the path weight of each path; constructing the weight transfer direction of each path; and determining a graph weight decision tree framework model according to the node weight, the node threshold, the path weight and the weight transmission direction.
And step S230, obtaining the operation value of the target monitoring node, and determining the risk influence range of the target monitoring node by adopting a graph mode weight distance evaluation algorithm according to the operation value of the target monitoring node.
Optionally, determining a risk influence range of the target monitoring node by using a graph mode weight distance evaluation algorithm according to the operand value of the target monitoring node includes: when the operand value is determined to be larger than the target monitoring node threshold value and smaller than the primary collection node threshold value connected with the primary path weight, determining the risk influence range of the target monitoring node as the primary collection node of the path; and when the operand value is larger than the threshold value of the primary collection node, studying and judging through the decision node of the path to determine the risk influence range of the target monitoring node.
Optionally, the studying and judging by the decision node of the path to determine the risk influence range of the target monitoring node includes: obtaining the attribute of a target monitoring node through a decision node of the path; judging whether the attribute is a designated attribute, if so, crossing the path to a first adjacent path, and carrying out research and judgment through decision nodes of the first adjacent path to determine the risk influence range of the target monitoring node; otherwise, determining the risk influence range of the target monitoring node as a secondary collection node connected with the secondary path weight.
Optionally, the studying and judging by the decision node of the first adjacent path to determine the risk influence range of the target monitoring node includes: obtaining the attribute of a target monitoring node through a decision node of a first adjacent path; judging whether the attribute is a designated attribute, if so, spanning the path to a second adjacent path, and determining that the risk influence range is a related node of the second adjacent path, wherein the related node of the second adjacent path comprises legal risk; otherwise, determining the risk influence range of the target monitoring node as the relevant node of the first connection path.
And S240, when the risk decision mode is determined to be slow release or service continuity guarantee, backtracking the graph weight decision tree framework model so as to adjust the parameters of the nodes and the paths.
Specifically, in this embodiment, when it is determined that the risk decision manner is slow release or service continuity guarantee, it is determined that the risk influence of the target monitoring node on the service system is large, at this time, backtracking needs to be performed on the graph weight decision tree framework model, so as to implement adjustment of parameters of the node and the path, for example, for the graph weight decision tree framework model shown in fig. 4, when it is determined that the influence range of the 1.1.1 misoperated node is 5 legal risks, and the corresponding risk decision manner is service continuity guarantee, the misoperated weight of the 1.1.1 misoperated node may be correspondingly adjusted to 5%, which is only illustrated by way of example in this embodiment, and specific values of the adjustment are not limited.
In the embodiment, an operation risk graph database image model is constructed, parameters of nodes and paths in the graph database image model are initialized by adopting a graph weight decision tree algorithm, a graph weight decision tree framework model is obtained, when the operation numerical values of target monitoring nodes are obtained, leaf node index changes are comprehensively monitored and analyzed through backward calculation of distances and weights according to node attributes and path relations, the influence range of the target detection nodes is determined, and a decision mode for controlling or slowly releasing operation risks is determined according to the influence range, so that effective management and control of operation risks are realized. And the management and control of the operation risk can be further improved by adjusting the graph weight decision tree framework model.
EXAMPLE III
Fig. 6 is a schematic structural diagram of a decision device for operational risk according to a third embodiment of the present invention. As shown in fig. 6, the apparatus includes: the system comprises a graph database sketch model creating module 510, a graph weight decision tree framework model obtaining module 520, a risk influence range determining module 530 and a risk decision mode determining module 540.
A graph database representation model creating module 510 for creating a graph database representation model matched with the operation risk frame based on a graph database technology, wherein nodes in the graph database representation model represent key indexes in the operation risk frame, and paths in the graph database representation model represent association relations of the key indexes;
a graph weight decision tree framework model obtaining module 520, configured to perform parameter initialization setting on nodes and paths in the graph database image model by using a graph weight decision tree algorithm, so as to obtain a graph weight decision tree framework model, where the types of the nodes in the graph weight decision tree framework model include monitoring nodes, sink nodes, and decision nodes;
a risk influence range determining module 530, configured to obtain an operation value of the target monitoring node, and determine a risk influence range of the target monitoring node by using a graph mode weight distance evaluation algorithm according to the operation value of the target monitoring node;
and a risk decision manner determining module 540, configured to determine a risk decision manner according to the risk influence range.
Optionally, the graph database representation model creating module is configured to use the operation risk frame as a root node and use the key index as a leaf node;
connecting the root node and the leaf node by adopting a path to obtain a graph database portrait model;
wherein, the key indexes at least comprise: employees, internal programs, information technology systems, external events, and legal risks.
Optionally, the graph weight decision tree framework model obtaining module is configured to initialize the graph database image model by using a graph weight decision tree algorithm, and determine a node weight and a node threshold of each node and a path weight of each path;
constructing the weight transfer direction of each path;
and determining a graph weight decision tree framework model according to the node weight, the node threshold, the path weight and the weight transmission direction.
Optionally, the risk influence range determining module includes:
the first determining submodule is used for determining the risk influence range of the target monitoring node as a primary collection node of the path when the operand value is determined to be larger than the threshold value of the target monitoring node and smaller than the threshold value of the primary collection node connected with the primary path weight;
and the second determining submodule is used for judging through the decision node of the path to determine the risk influence range of the target monitoring node when the operand value is determined to be larger than the threshold value of the primary collection node.
Optionally, the second determining submodule is configured to obtain an attribute of the target monitoring node through the decision node of the path;
judging whether the attribute is a designated attribute, if so, crossing the path to a first adjacent path, and carrying out research and judgment through decision nodes of the first adjacent path to determine the risk influence range of the target monitoring node;
otherwise, determining the risk influence range of the target monitoring node as a secondary collection node connected with the secondary path weight.
Optionally, the second determining submodule is configured to obtain an attribute of the target monitoring node through the decision node of the first adjacent path;
judging whether the attribute is a designated attribute, if so, spanning the path to a second adjacent path, and determining that the risk influence range is a related node of the second adjacent path, wherein the related node of the second adjacent path comprises legal risk;
otherwise, determining the risk influence range of the target monitoring node as the related node of the first adjacent path.
Optionally, the risk decision making mode determining module is configured to determine that the risk decision making mode is internal control in the path when it is determined that the risk influence range is a node of the path, where the node of the path includes a primary aggregation node and a secondary aggregation node;
when the risk influence range is determined to be the relevant node of the first adjacent path, determining that the risk decision mode is slow release in the first adjacent path;
and when the risk influence range is determined to be the related node of the second adjacent path, determining that the risk decision mode is the service continuity guarantee.
Optionally, the apparatus further includes an adjusting module, configured to backtrack the graph weight decision tree framework model to adjust parameters of the node and the path when it is determined that the risk decision manner is slow release or service continuity guarantee.
The decision device for operation risk provided by the embodiment of the invention can execute the decision method for operation risk provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Example four
FIG. 7 illustrates a schematic diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 7, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM)12, a Random Access Memory (RAM)13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as decision-making methods for operational risk.
In some embodiments, the decision-making method for operational risk may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the above described decision method for operational risk may be performed. Alternatively, in other embodiments, the processor 11 may be configured in any other suitable way (e.g., by means of firmware) as a decision method for operational risk.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (15)
1. A method for decision-making for operational risk, comprising:
creating a graph database sketch model matched with an operation risk frame based on graph database technology, wherein nodes in the graph database sketch model represent key indexes in the operation risk frame, and paths in the graph database sketch model represent incidence relations of the key indexes;
performing parameter initialization setting on nodes and paths in the graph database and sketch model by adopting a graph weight decision tree algorithm to obtain a graph weight decision tree frame model, wherein the types of the nodes in the graph weight decision tree frame model comprise monitoring nodes, collecting nodes and decision nodes;
acquiring an operand value of a target monitoring node, and determining a risk influence range of the target monitoring node by adopting a graph mode weight distance evaluation algorithm according to the operand value of the target monitoring node;
and determining a risk decision mode according to the risk influence range.
2. The method of claim 1, wherein creating a graph database representation model that matches an operational risk framework based on graph database techniques comprises:
taking the operation risk frame as a root node and the key indexes as leaf nodes;
connecting the root node and the leaf node by adopting a path to obtain the graph database portrait model;
wherein the key indicators at least include: employees, internal programs, information technology systems, external events, and legal risks.
3. The method of claim 1, wherein said using graph weight decision tree algorithm to perform parameter initialization setting on nodes and paths in said graph database representation model to obtain a graph weight decision tree framework model comprises:
initializing the graph database portrait model by adopting a graph weight decision tree algorithm, and determining the node weight and the node threshold of each node and the path weight of each path;
constructing a weight transfer direction of each path;
and determining the graph weight decision tree framework model according to the node weight, the node threshold, the path weight and the weight transmission direction.
4. The method of claim 3, wherein the determining the risk impact range of the target monitoring node by using a graph mode weight distance evaluation algorithm according to the operand value of the target monitoring node comprises:
when the operand value is determined to be larger than the target monitoring node threshold value and smaller than a primary collection node threshold value connected with a primary path weight, determining the risk influence range of the target monitoring node as the primary collection node of the path;
and when the operand value is determined to be larger than the threshold value of the primary collection node, studying and judging through the decision node of the path to determine the risk influence range of the target monitoring node.
5. The method of claim 4, wherein the determining nodes through the path to determine risk impact ranges of the target monitoring nodes comprises:
obtaining the attribute of the target monitoring node through the decision node of the path;
judging whether the attribute is a designated attribute, if so, crossing a path to a first adjacent path, and carrying out research and judgment through a decision node of the first adjacent path to determine a risk influence range of the target monitoring node;
otherwise, determining the risk influence range of the target monitoring node as a secondary collection node connected with the secondary path weight.
6. The method of claim 5, wherein the developing decisions by the decision nodes of the first neighboring path to determine the risk impact range of the target monitoring node comprises:
obtaining the attribute of the target monitoring node through the decision node of the first adjacent path;
judging whether the attribute is a designated attribute, if so, spanning the path to a second adjacent path, and determining a risk influence range as a related node of the second adjacent path, wherein the related node of the second adjacent path comprises legal risk;
otherwise, determining the risk influence range of the target monitoring node as the related node of the first adjacent path.
7. The method according to claim 5 or 6, wherein determining a risk decision-making manner according to the risk impact range comprises:
when the risk influence range is determined to be a node of the path, determining that a risk decision mode is to perform internal control on the path, wherein the node of the path comprises the primary collection node and the secondary collection node;
when the risk influence range is determined to be the relevant node of the first adjacent path, determining that the risk decision mode is slow release in the first adjacent path;
and when the risk influence range is determined to be the related node of the second adjacent path, determining that the risk decision mode is service continuity guarantee.
8. The method of claim 1, wherein after determining a risk decision based on the risk impact range, further comprising:
and when the risk decision mode is determined to be slow release or service continuity guarantee, backtracking the graph weight decision tree framework model so as to adjust the parameters of the nodes and the paths.
9. A decision-making device for operational risk, comprising:
the system comprises a database drawing model creating module, a database drawing model generating module and a database drawing model generating module, wherein the database drawing model creating module is used for creating a database drawing model matched with an operation risk frame based on a database technology, nodes in the database drawing model represent key indexes in the operation risk frame, and paths in the database drawing model represent incidence relations of the key indexes;
the graph weight decision tree framework model acquisition module is used for carrying out parameter initialization setting on nodes and paths in the graph database and sketch model by adopting a graph weight decision tree algorithm to acquire a graph weight decision tree framework model, wherein the types of the nodes in the graph weight decision tree framework model comprise monitoring nodes, collecting nodes and decision nodes;
the risk influence range determining module is used for acquiring the operation value of a target monitoring node and determining the risk influence range of the target monitoring node by adopting a graph mode weight distance evaluation algorithm according to the operation value of the target monitoring node;
and the risk decision mode determining module is used for determining a risk decision mode according to the risk influence range.
10. The apparatus of claim 9, wherein the graph database representation model creation module is configured to take the operational risk frame as a root node and the key indicator as a leaf node;
based on a graph database portrait model, connecting the root node and the leaf nodes by adopting a path to obtain the graph database portrait model;
wherein the key indicators at least include: employees, internal programs, information technology systems, external events, and legal risks.
11. The apparatus of claim 9, wherein the graph weight decision tree framework model obtaining module is configured to initialize the graph database representation model using a graph weight decision tree algorithm, and determine node weights and node thresholds for each node and path weights for each path;
constructing a weight transfer direction of each path;
and determining the graph weight decision tree framework model according to the node weight, the node threshold, the path weight and the weight transmission direction.
12. The apparatus according to claim 9, further comprising an adjusting module, configured to backtrack the graph weight decision tree framework model to adjust parameters of the nodes and the paths when it is determined that the risk decision manner is slow release or service continuity guarantee.
13. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.
14. A computer-readable storage medium storing computer instructions for causing a processor to perform the method of any one of claims 1-8 when executed.
15. A computer program product, characterized in that the computer program product comprises a computer program which, when being executed by a processor, carries out the method according to any one of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210557829.6A CN114841598A (en) | 2022-05-19 | 2022-05-19 | Decision method, device, equipment and program product for operation risk |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210557829.6A CN114841598A (en) | 2022-05-19 | 2022-05-19 | Decision method, device, equipment and program product for operation risk |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114841598A true CN114841598A (en) | 2022-08-02 |
Family
ID=82571520
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210557829.6A Pending CN114841598A (en) | 2022-05-19 | 2022-05-19 | Decision method, device, equipment and program product for operation risk |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114841598A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115632840A (en) * | 2022-10-08 | 2023-01-20 | 北京天融信网络安全技术有限公司 | Risk processing method, device and equipment based on zero trust and storage medium |
| CN117670066A (en) * | 2024-01-31 | 2024-03-08 | 深圳市拜特科技股份有限公司 | Judicial management method, system, equipment and storage medium based on intelligent decision |
-
2022
- 2022-05-19 CN CN202210557829.6A patent/CN114841598A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115632840A (en) * | 2022-10-08 | 2023-01-20 | 北京天融信网络安全技术有限公司 | Risk processing method, device and equipment based on zero trust and storage medium |
| CN115632840B (en) * | 2022-10-08 | 2023-07-04 | 北京天融信网络安全技术有限公司 | Zero trust-based risk processing method, device, equipment and storage medium |
| CN117670066A (en) * | 2024-01-31 | 2024-03-08 | 深圳市拜特科技股份有限公司 | Judicial management method, system, equipment and storage medium based on intelligent decision |
| CN117670066B (en) * | 2024-01-31 | 2024-05-17 | 深圳市拜特科技股份有限公司 | Questor management method, system, equipment and storage medium based on intelligent decision |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114841598A (en) | Decision method, device, equipment and program product for operation risk | |
| CN113360358A (en) | Method and system for adaptively calculating IT intelligent operation and maintenance health index | |
| WO2017071369A1 (en) | Method and device for predicting user unsubscription | |
| WO2019142391A1 (en) | Data analysis assistance system and data analysis assistance method | |
| CN114638696A (en) | Credit risk prediction model training method and system | |
| CN115509784A (en) | Fault detection method and device for database instance | |
| CN115422028A (en) | Credibility evaluation method and device for label portrait system, electronic equipment and medium | |
| CN115766096A (en) | Network security protection system based on big data | |
| CN115438663A (en) | Risk analysis method, device, equipment and medium for power grid work bill | |
| CN113835947B (en) | Method and system for determining abnormality cause based on abnormality recognition result | |
| CN113361959A (en) | Method and device for calculating maturity of centralized operation of banking business | |
| Shen et al. | The relationship between supply chain resilience, supply chain integration, and supply chain performance: A MASEM analysis | |
| CN117273450A (en) | Power system risk assessment method, device, equipment and storage medium | |
| CN115759751A (en) | Enterprise risk prediction method and device, storage medium, electronic equipment and product | |
| CN115796585A (en) | Enterprise operation risk assessment method and system | |
| Yang et al. | Influential risk spreaders and systemic risk in Chinese financial networks | |
| CN114676266A (en) | Conflict identification method, device, equipment and medium based on multilayer relation graph | |
| CN114896418A (en) | Knowledge graph construction method and device, electronic equipment and storage medium | |
| Li | Credit card fraud identification based on unbalanced data set based on fusion model | |
| CN112613732A (en) | Method and device for supervising and rating of financial institution | |
| CN105844376A (en) | Method and device for identifying influence degree of factors on service system | |
| Li et al. | Fraud detection based on graph neural networks with self-attention | |
| CN116596651A (en) | Method, device, equipment and storage medium for managing overdue risk information of clients | |
| CN118674257A (en) | Method, device, equipment and storage medium for determining risk level of power grid enterprise | |
| CN117370655A (en) | Analysis method and device for user liveness, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |