CN112668832A - Risk quantitative evaluation method and device based on index management system and electronic equipment - Google Patents
Risk quantitative evaluation method and device based on index management system and electronic equipment Download PDFInfo
- Publication number
- CN112668832A CN112668832A CN202011337048.3A CN202011337048A CN112668832A CN 112668832 A CN112668832 A CN 112668832A CN 202011337048 A CN202011337048 A CN 202011337048A CN 112668832 A CN112668832 A CN 112668832A
- Authority
- CN
- China
- Prior art keywords
- risk
- index
- level
- early warning
- fitting
- 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
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention discloses a risk quantitative evaluation method and device based on an index management system and electronic equipment, and belongs to the technical field of index query. The method is based on a risk evaluation system classification level, a risk is divided into two levels, and a risk level relation is established according to a first level risk and a second level risk; determining a risk evaluation index based on the risk classification, and establishing a mapping relation between the secondary risk and the risk index; setting a single-index early warning threshold value, and determining an early warning interval threshold value, an attention interval threshold value and a normal interval threshold value; establishing a risk quantization matrix model, and calculating each level and index weight; and quantifying the risk score of the single index, and establishing an evaluation model to quantify the risk level.
Description
Technical Field
The present disclosure relates to the field of index query, and in particular, to a risk quantitative evaluation method and apparatus based on an index management system, and an electronic device.
Background
In order to realize real-time monitoring, early warning and the like of major risk indexes of a company, further improve the intellectualization level of the company, establish the process control of systematicness and major risk, improve the risk early warning capability and the risk management level of the company, more efficiently and intelligently monitor the major risk indexes of the company, and further improve the business level of the company, based on the reason, a risk quantitative evaluation method based on an index management system is established.
The conventional risk assessment and early warning is that each index is processed independently, the indexes exceeding the early warning threshold value are early warned, a specific risk module is not standardized on the conventional business system, if the risk assessment is required to be carried out on the whole module, the indexes under the modules are required to be additionally used for carrying out additional manual calculation and assessment, a great amount of manpower and material resources are required, and the working efficiency of business departments is greatly reduced. Therefore, it is necessary to establish a risk quantitative evaluation method based on an index management system, perform risk evaluation on the indexes and the specific risk modules thereof from point to surface, and improve the business level.
Disclosure of Invention
This disclosure is provided to introduce concepts in a simplified form that are further described below in the detailed description. This disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
The embodiment of the disclosure provides a risk quantitative evaluation method and device based on an index management system and electronic equipment, which can evaluate the risk of an index and a specific risk module thereof from point to surface and improve the service level.
In a first aspect, an embodiment of the present disclosure provides a risk quantitative evaluation method based on an index management system, including the following steps:
s100: dividing the risk into two levels based on the classification levels of the risk evaluation system, and establishing a risk level relation according to the first level risk and the second level risk;
s200: determining a risk evaluation index based on the risk classification, and establishing a mapping relation between the secondary risk and the risk index;
s300: setting a single-index early warning threshold value, and determining an early warning interval threshold value, an attention interval threshold value and a normal interval threshold value;
s400: establishing a risk quantization matrix model, and calculating each level and index weight;
s500: and quantifying the risk score of the single index, and establishing an evaluation model to quantify the risk level. .
With reference to the embodiments of the first aspect, in some embodiments, the setting a single-indicator early warning threshold, and determining an early warning interval threshold, an attention interval threshold, and a normal interval threshold includes:
and respectively calculating the average value and the standard deviation of each index based on the index historical data, calculating an index early warning interval, an attention interval threshold value and a normal interval threshold value according to a positive-Taiyang distribution principle, and adjusting according to actual conditions.
4. With reference to the embodiments of the first aspect, in some embodiments, the setting a single-indicator early warning threshold, and determining an early warning interval threshold, an attention interval threshold, and a normal interval threshold includes:
and determining an index early warning interval, an attention interval threshold and a normal interval threshold by using the current year target value as the early warning interval threshold degree.
With reference to the embodiments of the first aspect, in some embodiments, the establishing a risk quantification matrix model, and calculating the levels and the index weights include:
respectively carrying out primary risk, secondary risk and single index weight calculation, quantitatively scoring each risk type, building a judgment matrix through scoring, dividing the score of longitudinal classification by the score of transverse classification by data in the matrix, and judging the importance comparison of various risks according to a scale value of the ratio;
coordinating the importance of each risk factor, and judging whether consistency is met through consistency check;
and carrying out normalization processing on the judgment matrix, and calculating the weight values of all factors.
With reference to the embodiments of the first aspect, in some embodiments, the quantifying a risk score of a single indicator and establishing an evaluation model to quantify a risk level includes:
calculating scores of all risk indexes according to a preset index threshold;
calculating the risk index score of the last 3-5 years, respectively setting 1 sigma and 2 sigma as an attention value and an early warning value according to an overall distribution curve, and setting a first-level risk index threshold and a second-level risk index threshold;
and fitting to form a first-level risk index and a second-level risk index according to the first-level risk weight and the second-level risk weight and by combining the scores of all indexes.
With reference to embodiments of the first aspect, in some embodiments, the fitting according to the first and second level risk weights and the index scores to form first and second level risk indexes includes:
according to the risk level score of each single index, an index risk level score matrix under each secondary risk is constructed, then the weight corresponding to each index is multiplied by the score matrix to obtain a secondary risk fitting matrix under each secondary risk, and a secondary risk fitting index is calculated; after all the second-level risk fitting matrixes are calculated, multiplying the second-level risk fitting matrixes obtained in the previous step by the second-level risk fitting matrixes obtained in the previous step according to the weight of the second-level risk under each first-level risk to obtain first-level risk fitting matrixes under each first-level risk, and calculating first-level risk fitting indexes; finally, multiplying the first-level risk fitting matrix obtained in the last step by the weight of each first-level risk to obtain each finally summarized risk fitting matrix, and calculating a total risk fitting index;
and acquiring primary and secondary risks and the overall early warning condition according to the set threshold range of the risk fitting index.
In a second aspect, an embodiment of the present disclosure provides an index management system-based risk quantitative evaluation device, including: the hierarchical module is used for classifying the hierarchy based on the risk evaluation system, dividing the risk into two hierarchies and establishing a risk hierarchical relationship according to the first and second risks;
the mapping module is used for determining a risk assessment index based on the risk classification and establishing a mapping relation between the secondary risk and the risk index;
the threshold value determining module is used for setting a single-index early warning threshold value and determining an early warning interval threshold value, an attention interval threshold value and a normal interval threshold value;
the weight module is used for establishing a risk quantization matrix model and calculating the weight of each level and index;
and the evaluation module is used for quantifying the risk score of the single index and establishing an evaluation model to quantify the risk level.
In combination with an embodiment of the second aspect, in some embodiments, the weighting module comprises:
respectively carrying out primary risk, secondary risk and single index weight calculation, quantitatively scoring each risk type, building a judgment matrix through scoring, dividing the score of longitudinal classification by the score of transverse classification by data in the matrix, and judging the importance comparison of various risks according to a scale value of the ratio;
coordinating the importance of each risk factor, and judging whether consistency is met through consistency check;
and carrying out normalization processing on the judgment matrix, and calculating the weight values of all factors.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method as described above in relation to the first aspect.
In a fourth aspect, the disclosed embodiments provide a computer readable medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method as described above in the first aspect.
According to the risk quantitative evaluation method based on the index management system, based on the classification level of the risk evaluation system, the risk is divided into two levels, and a risk level relation is established according to the first level risk and the second level risk; determining a risk evaluation index based on the risk classification, and establishing a mapping relation between the secondary risk and the risk index; setting a single-index early warning threshold value, and determining an early warning interval threshold value, an attention interval threshold value and a normal interval threshold value; establishing a risk quantization matrix model, and calculating each level and index weight; and quantifying the risk score of the single index, and establishing an evaluation model to quantify the risk level.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.
FIG. 1 is a flow diagram of one embodiment of a risk quantification assessment method based on an index management system according to the present disclosure;
fig. 2 is a schematic structural diagram of a risk quantitative evaluation device based on an index management system for lean items of the present disclosure;
FIG. 3 is a schematic diagram of a risk quantitative evaluation device based on an index management system according to an embodiment of the disclosure;
fig. 4 is a schematic structural diagram of an electronic device provided according to an embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The management of an index system is the basis of the whole system, all service data are expanded according to indexes, one service data corresponds to one index, and the indexes relate to each service department, a service module, display dimensions and the like, and have various service attributes; and the logic of inquiring the index data in each module of the service system is approximately the same, if the data of each module is independently inquired, code redundancy is caused, the system performance is influenced, and the workload of system development is increased. For this reason, it is necessary to create an index management method and system for index management.
Referring to fig. 1, a flow of an embodiment of a risk quantitative evaluation method based on an index management system according to the present disclosure is shown. As shown in fig. 1, the risk quantitative evaluation method based on the index management system includes the following steps:
and 100, dividing the risk into two levels based on the classification levels of the risk evaluation system, and establishing a risk level relation according to the first level risk and the second level risk.
Here, to set up a risk assessment system, according to business definition, the risk is divided into two levels, namely a primary risk and a secondary risk, wherein the primary risk can be strategic risk, supervised disciplinary risk, public relationship risk and the like, and the primary risk can be further divided into a plurality of secondary risks, for example, the strategic risk can be decomposed into secondary risks such as value viewing risk, company management risk, group management and control risk and the like. And establishing a risk hierarchical relation according to the first-level risk and the second-level risk.
And 200, determining a risk evaluation index based on the risk classification, and establishing a mapping relation between the secondary risk and the risk index.
Different risk classifications have different risk assessment indexes, and a mapping relation between the secondary risk and the risk indexes is established according to the service definition.
And step 300, setting a single-index early warning threshold value, and determining an early warning interval threshold value, an attention interval threshold value and a normal interval threshold value.
Different indexes have different threshold calculation modes, and the risk assessment method has two threshold calculation modes.
In some specific embodiments, step 300 may include calculating an average value and a standard deviation of each indicator based on the indicator historical data, calculating an indicator early warning interval, an attention interval threshold value and a normal interval threshold value according to the positive-too-distribution principle, and adjusting according to actual conditions.
In other specific embodiments, the current year target value may also be used as the early warning interval threshold degree in step 300 to determine the index early warning interval, the attention interval threshold value, and the normal interval threshold value. For example: and (4) performing special treatment on indexes of partial business modules, such as performance assessment and department assessment modules, and using the current-year target value as an early warning interval threshold.
Generally speaking, there are two types of early warning scenarios, namely, forward indicator monitoring early warning, the larger the indicator value is, the more excellent the indicator value is, the other is reverse indicator monitoring early warning, and the smaller the indicator value is, the more excellent the indicator value is.
And 400, establishing a risk quantization matrix model, and calculating each level and index weight.
Here, step 400 specifically includes
Wherein, the value of the scale value is shown in the table 1.
TABLE 1 table of correspondence between index value and meaning
The first order risk is used as an example for illustration below, see table 2.
TABLE 2 quantitative scoring table for each risk type of first-level risk
And step 402, coordinating the importance of each risk factor, and judging whether the consistency is met through consistency check.
When a plurality of factors need to be judged, the phenomenon of front and back inconsistency can occur. In order to prevent logical contradiction of the judgment matrix, consistency check is required to be carried out, such as: if the risk Z is judged to be more important than the risk X (i.e., Z > X), a logical error occurs, and the importance of each risk factor needs to be coordinated.
The accurate method needs to judge whether the consistency is satisfied through consistency check, and the calculation formula is that CR is CI/RI, CI is (λ -n)/(n-1), λ is the maximum characteristic root of the judgment matrix, and n is the number of rows and columns of the matrix. RI is obtained according to the value of n according to the following table 3, and finally the value of CR is calculated.
TABLE 3 random consistency index RI
| n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
| |
0 | 0 | 0.58 | 0.40 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 | 1.51 |
When CR <0.1, the task judgment matrix has satisfactory consistency, otherwise, the task judgment matrix should be considered to be corrected.
And 500, quantifying the risk score of the single index, and establishing an evaluation model to quantify the risk level. The method specifically comprises the following steps:
step 501, calculating scores of each risk index according to a preset index threshold.
And 502, calculating the risk index score of the last 3-5 years, respectively setting 1 sigma and 2 sigma as attention values and early warning values according to the overall distribution curve, and setting primary and secondary risk index thresholds.
Referring to fig. 2, it can be specifically expressed as:
the green light in the normal interval: when the index value falls within 1 standard deviation sigma;
yellow light on attention section: when the index value is out of 1 standard deviation sigma, two standard deviations sigma;
the early warning interval lights the red light: when the index value falls outside 2 standard deviations σ.
And 503, fitting to form a first-level risk index and a second-level risk index according to the first-level risk weight and the second-level risk weight and by combining the scores of all indexes.
The method specifically comprises the following steps:
according to the risk level score of each single index, an index risk level score matrix under each secondary risk is constructed, then the weight corresponding to each index is multiplied by the score matrix to obtain a secondary risk fitting matrix under each secondary risk, and a secondary risk fitting index is calculated; after all the second-level risk fitting matrixes are calculated, multiplying the second-level risk fitting matrixes obtained in the previous step by the second-level risk fitting matrixes obtained in the previous step according to the weight of the second-level risk under each first-level risk to obtain first-level risk fitting matrixes under each first-level risk, and calculating first-level risk fitting indexes; and finally, multiplying the first-level risk fitting matrix obtained in the last step by the weight of each first-level risk to obtain each finally summarized risk fitting matrix, and calculating a total risk fitting index.
And acquiring primary and secondary risks and the overall early warning condition according to the set threshold range of the risk fitting index.
According to the risk quantitative evaluation method based on the index management system, based on the classification level of the risk evaluation system, the risk is divided into two levels, and a risk level relation is established according to the first level risk and the second level risk; determining a risk evaluation index based on the risk classification, and establishing a mapping relation between the secondary risk and the risk index; setting a single-index early warning threshold value, and determining an early warning interval threshold value, an attention interval threshold value and a normal interval threshold value; establishing a risk quantization matrix model, and calculating each level and index weight; and quantifying the risk score of the single index, and establishing an evaluation model to quantify the risk level.
With further reference to fig. 3, as an implementation of the methods shown in the above diagrams, the present disclosure provides a risk quantitative evaluation device based on an index management system, where an embodiment of the device corresponds to the embodiment of the method shown in fig. 1, and the device may be specifically applied to various electronic devices.
As shown in fig. 3, the risk quantitative evaluation device based on the index management system of the present embodiment includes:
the hierarchical module 301 is configured to classify the hierarchy based on a risk evaluation system, divide the risk into two hierarchies, and establish a risk hierarchical relationship according to the first and second risks.
A mapping module 302, where the mapping module 302 is configured to determine a risk assessment indicator based on the risk classification, and establish a mapping relationship between the secondary risk and the risk indicator.
And the threshold determination module 303 is configured to set a single-index early warning threshold, and determine an early warning interval threshold, an attention interval threshold, and a normal interval threshold.
A weight module 304, wherein the weight module 304 is configured to establish a risk quantization matrix model and calculate weights of each level and index.
An evaluation module 305, wherein the evaluation module 305 is used for quantifying the risk score of the single index and establishing an evaluation model to quantify the risk level.
In some optional embodiments, the threshold determination module 303 is configured to calculate an average value and a standard deviation of each index based on the index historical data, calculate an index early warning interval, an attention interval threshold, and a normal interval threshold according to the positive-too distribution principle, and adjust the thresholds according to actual conditions.
In other optional embodiments, the threshold determination module 303 is configured to determine the index early warning interval, the attention interval threshold, and the normal interval threshold using the current year target value as the early warning interval threshold degree.
In some optional embodiments, the establishing a risk quantification matrix model, and calculating the levels and the index weights include:
respectively carrying out primary risk, secondary risk and single index weight calculation, quantitatively scoring each risk type, building a judgment matrix through scoring, dividing the score of longitudinal classification by the score of transverse classification by data in the matrix, and judging the importance comparison of various risks according to a scale value of the ratio;
coordinating the importance of each risk factor, and judging whether consistency is met through consistency check;
and carrying out normalization processing on the judgment matrix, and calculating the weight values of all factors.
In some optional embodiments, the quantifying the risk score of the single index and establishing the evaluation model to quantify the risk level includes:
calculating scores of all risk indexes according to a preset index threshold;
calculating the risk index score of the last 3-5 years, respectively setting 1 sigma and 2 sigma as an attention value and an early warning value according to an overall distribution curve, and setting a first-level risk index threshold and a second-level risk index threshold;
and fitting to form a first-level risk index and a second-level risk index according to the first-level risk weight and the second-level risk weight and by combining the scores of all indexes.
In some optional embodiments, the fitting according to the first and second level risk weights and combining the index scores to form the first and second level risk indexes includes:
according to the risk level score of each single index, an index risk level score matrix under each secondary risk is constructed, then the weight corresponding to each index is multiplied by the score matrix to obtain a secondary risk fitting matrix under each secondary risk, and a secondary risk fitting index is calculated; after all the second-level risk fitting matrixes are calculated, multiplying the second-level risk fitting matrixes obtained in the previous step by the second-level risk fitting matrixes obtained in the previous step according to the weight of the second-level risk under each first-level risk to obtain first-level risk fitting matrixes under each first-level risk, and calculating first-level risk fitting indexes; finally, multiplying the first-level risk fitting matrix obtained in the last step by the weight of each first-level risk to obtain each finally summarized risk fitting matrix, and calculating a total risk fitting index;
and acquiring primary and secondary risks and the overall early warning condition according to the set threshold range of the risk fitting index.
Referring now to FIG. 4, shown is a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 4, the electronic device may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 401 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage means 408 into a Random Access Memory (RAM) 403. In the RAM403, various programs and data necessary for the operation of the electronic apparatus 400 are also stored. The processing device 401, the ROM 402, and the RAM403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Generally, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. While fig. 4 illustrates an electronic device having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication device 409, or installed from the storage device 408, or installed from the ROM 6402. The computer program performs the above-described functions defined in the methods of the embodiments of the present disclosure when executed by the processing device 401.
It should be noted that the computer readable medium of the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having 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. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: dividing the risk into two levels based on the classification levels of the risk evaluation system, and establishing a risk level relation according to the first level risk and the second level risk; determining a risk evaluation index based on the risk classification, and establishing a mapping relation between the secondary risk and the risk index; setting a single-index early warning threshold value, and determining an early warning interval threshold value, an attention interval threshold value and a normal interval threshold value; establishing a risk quantization matrix model, and calculating each level and index weight; and quantifying the risk score of the single index, and establishing an evaluation model to quantify the risk level.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present disclosure may be implemented by software or hardware. The names of the modules do not form a limitation on the modules themselves in some cases, for example, a hierarchical module may also be described as a "module for classifying a hierarchy based on a risk evaluation system, dividing a risk into two hierarchies, and establishing a hierarchical relationship of the risk according to a first-level risk and a second-level risk".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable 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. 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.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (10)
1. A risk quantitative evaluation method based on an index management system is characterized by comprising the following steps:
s100: dividing the risk into two levels based on the classification levels of the risk evaluation system, and establishing a risk level relation according to the first level risk and the second level risk;
s200: determining a risk evaluation index based on the risk classification, and establishing a mapping relation between the secondary risk and the risk index;
s300: setting a single-index early warning threshold value, and determining an early warning interval threshold value, an attention interval threshold value and a normal interval threshold value;
s400: establishing a risk quantization matrix model, and calculating each level and index weight;
s500: and quantifying the risk score of the single index, and establishing an evaluation model to quantify the risk level.
2. The method of claim 1, wherein the setting of the single-index early warning threshold and the determining of the early warning interval threshold, the attention interval threshold and the normal interval threshold comprises:
and respectively calculating the average value and the standard deviation of each index based on the index historical data, calculating an index early warning interval, an attention interval threshold value and a normal interval threshold value according to a positive-Taiyang distribution principle, and adjusting according to actual conditions.
3. The method of claim 1, wherein the setting of the single-index early warning threshold and the determining of the early warning interval threshold, the attention interval threshold and the normal interval threshold comprises:
and determining an index early warning interval, an attention interval threshold and a normal interval threshold by using the current year target value as the early warning interval threshold degree.
4. The method according to claim 2 or 3, wherein the establishing a risk quantification matrix model, calculating each level and index weight, comprises:
respectively carrying out primary risk, secondary risk and single index weight calculation, quantitatively scoring each risk type, building a judgment matrix through scoring, dividing the score of longitudinal classification by the score of transverse classification by data in the matrix, and judging the importance comparison of various risks according to a scale value of the ratio;
coordinating the importance of each risk factor, and judging whether consistency is met through consistency check;
and carrying out normalization processing on the judgment matrix, and calculating the weight values of all factors.
5. The method of claim 4, wherein the quantifying the risk score of the single indicator and establishing the assessment model quantifies the risk level comprises:
calculating scores of all risk indexes according to a preset index threshold;
calculating the risk index score of the last 3-5 years, respectively setting 1 sigma and 2 sigma as an attention value and an early warning value according to an overall distribution curve, and setting a first-level risk index threshold and a second-level risk index threshold;
and fitting to form a first-level risk index and a second-level risk index according to the first-level risk weight and the second-level risk weight and by combining the scores of all indexes.
6. The method of claim 5, wherein fitting the first and second level risk indices according to the first and second level risk weights in combination with the respective indicator scores comprises:
according to the risk level score of each single index, an index risk level score matrix under each secondary risk is constructed, then the weight corresponding to each index is multiplied by the score matrix to obtain a secondary risk fitting matrix under each secondary risk, and a secondary risk fitting index is calculated; after all the second-level risk fitting matrixes are calculated, multiplying the second-level risk fitting matrixes obtained in the previous step by the second-level risk fitting matrixes obtained in the previous step according to the weight of the second-level risk under each first-level risk to obtain first-level risk fitting matrixes under each first-level risk, and calculating first-level risk fitting indexes; finally, multiplying the first-level risk fitting matrix obtained in the last step by the weight of each first-level risk to obtain each finally summarized risk fitting matrix, and calculating a total risk fitting index;
and acquiring primary and secondary risks and the overall early warning condition according to the set threshold range of the risk fitting index.
7. A risk quantitative evaluation device based on an index management system is characterized by comprising
The hierarchical module is used for classifying the hierarchy based on the risk evaluation system, dividing the risk into two hierarchies and establishing a risk hierarchical relationship according to the first and second risks;
the mapping module is used for determining a risk assessment index based on the risk classification and establishing a mapping relation between the secondary risk and the risk index;
the threshold value determining module is used for setting a single-index early warning threshold value and determining an early warning interval threshold value, an attention interval threshold value and a normal interval threshold value;
the weight module is used for establishing a risk quantization matrix model and calculating the weight of each level and index;
and the evaluation module is used for quantifying the risk score of the single index and establishing an evaluation model to quantify the risk level.
8. The apparatus of claim 7, wherein the weighting module comprises:
respectively carrying out primary risk, secondary risk and single index weight calculation, quantitatively scoring each risk type, building a judgment matrix through scoring, dividing the score of longitudinal classification by the score of transverse classification by data in the matrix, and judging the importance comparison of various risks according to a scale value of the ratio;
coordinating the importance of each risk factor, and judging whether consistency is met through consistency check;
and carrying out normalization processing on the judgment matrix, and calculating the weight values of all factors.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.
10. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011337048.3A CN112668832A (en) | 2020-11-25 | 2020-11-25 | Risk quantitative evaluation method and device based on index management system and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011337048.3A CN112668832A (en) | 2020-11-25 | 2020-11-25 | Risk quantitative evaluation method and device based on index management system and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112668832A true CN112668832A (en) | 2021-04-16 |
Family
ID=75404095
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011337048.3A Pending CN112668832A (en) | 2020-11-25 | 2020-11-25 | Risk quantitative evaluation method and device based on index management system and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112668832A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113723759A (en) * | 2021-07-30 | 2021-11-30 | 北京淇瑀信息科技有限公司 | Method and device for providing Internet service for equipment based on equipment intention degree and equipment risk degree |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20160090406A (en) * | 2014-12-31 | 2016-08-01 | 주식회사 효성 | System and method for evaluating risk of electric power equipment |
| CN106056314A (en) * | 2016-06-29 | 2016-10-26 | 李明洋 | Risk assessment method for intelligent distribution network |
| CN109685339A (en) * | 2018-12-11 | 2019-04-26 | 深圳市城市公共安全技术研究院有限公司 | Static-dynamic risk assessment method and system for traffic infrastructure construction engineering |
| CN110310031A (en) * | 2019-06-27 | 2019-10-08 | 国网江苏省电力有限公司南京供电分公司 | A kind of power distribution network multidimensional methods of risk assessment |
-
2020
- 2020-11-25 CN CN202011337048.3A patent/CN112668832A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20160090406A (en) * | 2014-12-31 | 2016-08-01 | 주식회사 효성 | System and method for evaluating risk of electric power equipment |
| CN106056314A (en) * | 2016-06-29 | 2016-10-26 | 李明洋 | Risk assessment method for intelligent distribution network |
| CN109685339A (en) * | 2018-12-11 | 2019-04-26 | 深圳市城市公共安全技术研究院有限公司 | Static-dynamic risk assessment method and system for traffic infrastructure construction engineering |
| CN110310031A (en) * | 2019-06-27 | 2019-10-08 | 国网江苏省电力有限公司南京供电分公司 | A kind of power distribution network multidimensional methods of risk assessment |
Non-Patent Citations (1)
| Title |
|---|
| 董红磊等: "基于模糊风险评估矩阵的汽车产品缺陷认定 ", 《汽车安全与节能学报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113723759A (en) * | 2021-07-30 | 2021-11-30 | 北京淇瑀信息科技有限公司 | Method and device for providing Internet service for equipment based on equipment intention degree and equipment risk degree |
| CN113723759B (en) * | 2021-07-30 | 2024-06-04 | 北京淇瑀信息科技有限公司 | Method and device for providing Internet service for equipment based on equipment intention degree and equipment risk degree |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111784508A (en) | Enterprise risk assessment method and device and electronic equipment | |
| US20150039624A1 (en) | Decision tree insight discovery | |
| CN111435463A (en) | Data processing method and related equipment and system | |
| CN111340611A (en) | Risk early warning method and device | |
| CN107392259B (en) | Method and device for constructing unbalanced sample classification model | |
| CN111768096A (en) | Rating method and device based on algorithm model, electronic equipment and storage medium | |
| CN113537370A (en) | Cloud computing-based financial data processing method and system | |
| WO2023134188A1 (en) | Index determination method and apparatus, and electronic device and computer-readable medium | |
| CN115599650A (en) | Health degree evaluation method and device of IT system, electronic equipment and storage medium | |
| CN112668832A (en) | Risk quantitative evaluation method and device based on index management system and electronic equipment | |
| CN114528916A (en) | Sample clustering processing method, device, equipment and storage medium | |
| CN113159538B (en) | Energy control index management method and device and electronic equipment | |
| CN117273450A (en) | Power system risk assessment method, device, equipment and storage medium | |
| CN116955413A (en) | Data query method, device, medium and equipment based on online analysis processing | |
| CN116739742A (en) | Monitoring method, device, equipment and storage medium of credit wind control model | |
| CN114418780B (en) | Method, device, computer equipment and storage medium for identifying cheating group partner | |
| CN116151470A (en) | Product quality prediction method, device, equipment and storage medium | |
| CN115147183A (en) | Chip resource management method, device, equipment and storage medium based on cloud platform | |
| CN113779335A (en) | Information generation method and device, electronic equipment and computer readable medium | |
| CN117093429B (en) | Method and system for evaluating stability of server | |
| TWI657393B (en) | Marketing customer group prediction system and method | |
| CN111490889A (en) | Method and device for estimating wireless service growth | |
| CN118052354A (en) | Method, system, medium and electronic equipment for acquiring performance index of oil-gas pipe network | |
| CN113902098A (en) | Multi-type data parallel learning method and device, computer equipment and medium | |
| CN115829368A (en) | Transaction strategy evaluation method based on multiple factors |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210416 |