Enterprise operation risk assessment method and system

Abstract

The invention provides an enterprise operation risk assessment method and system. The method comprises the following steps: acquiring a multivariate operation data set of an enterprise; obtaining enterprise review opinions, calculating a review score of each risk factor in the risk factor set, and sorting the risk factor set; determining a second review weight based on the trapezoidal fuzzy number and the first review weight; calculating the relative relevancy between each risk factor and the enterprise operation risk set, and sorting the risk factor set based on the relative relevancy; according to the method, the balance and effectiveness of the enterprise review opinions of each reviewer are fully considered, and the review weight can be adaptively adjusted according to the specific risk factors, so that the accuracy of the evaluation result is improved, and the risk of the enterprise is evaluated based on the important influence factor set and the basic operation data set. Meanwhile, risk assessment is performed through the important influence factor set, so that the accuracy of the assessment result can be further improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of risk assessment, and specifically relates to a method and system for assessing business operation risks. Background Art

[0002] Risk assessment refers to the work of quantitatively evaluating the impact and possibility of loss caused by a risk event on people's lives, lives, property, etc. before or after the risk event occurs (but not yet over). Risk assessment is to quantitatively evaluate the possible extent of the impact or loss caused by an event or thing. For enterprises, some potential risks may occur in the process of their business activities. Therefore, it is necessary to conduct a series of risk assessment operations on the business activities of the enterprises and specify corresponding execution strategies for the potential risks. In the existing technology, it is usually achieved by obtaining expert scoring information on various risk factors and risks of various enterprises, and conducting risk assessment on the business conditions of the enterprises based on the scoring information. However, for the above method, the gap between the scoring results of different experts is large and the subjectivity is strong. If the scoring data is directly used for risk assessment, it ignores the balance of risk factor assessment information and cannot reflect the effectiveness of the assessment information, which leads to poor accuracy of the assessment results. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a method and system for enterprise operation risk assessment, which are used to solve the technical problems in the prior art.

[0004] On the one hand, the present invention provides the following technical solution, a method for assessing business risk, comprising: Acquire a multi-operation data set of an enterprise, wherein the multi-operation data set includes a basic operation data set, an enterprise operation risk set, and a risk factor set; Obtaining enterprise review opinions based on the enterprise operation risk set and the risk factor set, converting the enterprise review opinions into an intuitive fuzzy set, determining a first review weight based on the intuitive fuzzy set, calculating a review score for each risk factor in the risk factor set based on the first review weight, and sorting the risk factor set based on the review score to obtain a first sorting factor set; Converting the enterprise review opinion into a trapezoidal fuzzy number, and determining a second review weight based on the trapezoidal fuzzy number and the first review weight; Calculating a third review weight based on the second review weight, calculating a relative correlation between each risk factor in the risk factor set and the enterprise operation risk set based on the third review weight, and sorting the risk factor set based on the relative correlation to obtain a second sorting factor set; An important influencing factor set is determined based on the first ranking factor set and the second ranking factor set, and a risk assessment is performed on the enterprise based on the important influencing factor set and the basic operating data set.

[0005] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention first obtains a multivariate business data set of the enterprise, and the multivariate business data set includes a basic business data set, an enterprise business risk set and a risk factor set; then, based on the enterprise business risk set and the risk factor set, the enterprise review opinion is obtained, the enterprise review opinion is converted into an intuitive fuzzy set, and a first review weight is determined based on the intuitive fuzzy set, and a review score of each risk factor in the risk factor set is calculated based on the first review weight, and the risk factor set is sorted based on the review score to obtain a first sorting factor set; then, the enterprise review opinion is converted into a trapezoidal fuzzy number, and a second review weight is determined based on the trapezoidal fuzzy number and the first review weight; then, a third review weight is calculated based on the second review weight, and a correlation between each risk factor in the risk factor set and the enterprise business is calculated based on the third review weight. The relative correlation between risk sets is used, and the risk factor sets are sorted based on the relative correlation to obtain a second sorting factor set; finally, the important influencing factor set is determined based on the first sorting factor set and the second sorting factor set, and the enterprise risk assessment is performed based on the important influencing factor set and the basic operating data set. The present invention fully considers the balance and effectiveness of each reviewer's enterprise review opinion, and determines the first, second, and third review weights respectively, and the third review weight can be adaptively adjusted according to specific risk factors, which effectively expands the weight distribution method, thereby improving the accuracy of the assessment result. At the same time, the important influencing factor set is further determined by cross-validation between the first sorting factor set and the second sorting factor set. Risk assessment is performed through the important influencing factor set, which can further improve the accuracy of the assessment result.

[0006] Preferably, the steps of obtaining enterprise review opinions based on the enterprise operating risk set and the risk factor set, converting the enterprise review opinions into an intuitive fuzzy set, determining a first review weight based on the intuitive fuzzy set, calculating a review score for each risk factor in the risk factor set based on the first review weight, and sorting the risk factor set based on the review score to obtain a first sorted factor set include: Based on the enterprise operating risk set and the risk factor set, the enterprise review opinions are obtained, and the enterprise review opinions of each reviewer are aggregated into a review matrix And determine the review matrix The corresponding intuitionistic fuzzy set, based on the review matrix The corresponding intuitive fuzzy set corresponds to the first weight ; Based on the review matrix The corresponding intuitionistic fuzzy set determines the second weight ; Based on the first weight With the second weight Calculate the first review weight , based on the first review weight Determine a review score for each risk factor in the risk factor set , and sorting the risk factor set based on the review score to obtain a first sorting factor set.

[0007] Preferably, the enterprise review opinions are obtained based on the enterprise operation risk set and the risk factor set, and the enterprise review opinions of each reviewer are aggregated into a review matrix And determine the review matrix The corresponding intuitionistic fuzzy set, based on the review matrix The corresponding intuitive fuzzy set corresponds to the first weight The steps include: Based on the enterprise operating risk set and the risk factor set, the enterprise review opinions are obtained, and the enterprise review opinions of each reviewer are aggregated to obtain several review matrices. ,in, Indicates Review matrix for each reviewer; The review matrix Transformed into corresponding intuitionistic fuzzy sets, and based on the review matrix The corresponding intuitionistic fuzzy set is calculated The first weight of the reviewer : ; ; In the formula, Indicates Reviewers on The business risk of an enterprise Intuitive fuzzy sets of risk factors, Indicates Reviewers on The risk factor belongs to The degree of affiliation of an enterprise's operating risk, Indicates Reviewers on The risk factor belongs to The non-subordinate degree of the business risk of an enterprise Indicates The overall hesitation of each reviewer.

[0008] Preferably, the review matrix based The corresponding intuitionistic fuzzy set determines the second weight The steps include: Based on the review matrix The corresponding intuitionistic fuzzy set is calculated Reviewers on Intuitionistic fuzzy sets of risk factors : ; In the formula, Indicates The weight corresponding to the business risk of each enterprise is Indicates Reviewers on The business risk of an enterprise Intuitive fuzzy sets of risk factors, Indicates Reviewers on The risk factor belongs to The degree of affiliation of an enterprise's operating risk, Indicates Reviewers on The risk factor belongs to The non-subordinate degree of business risk of an enterprise; Based on the intuitionistic fuzzy set Calculate the The intimacy of the reviewers : ; In the formula, Indicates Reviewers on Intuitive fuzzy sets of risk factors; Based on The intimacy of the reviewers Calculate the second weight : .

[0009] Preferably, the first weight With the second weight Calculate the first review weight , based on the first review weight Determine a review score for each risk factor in the risk factor set , and sorting the risk factor set based on the review score to obtain a first sorting factor set includes: Based on the first weight With the second weight Calculate the first review weight : ; In the formula, is the first scale factor; Based on the first review weight Determine the intuitionistic fuzzy set of each risk factor in the risk factor set : ; In the formula, For the Reviewers on Intuitive fuzzy sets of risk factors; Based on intuitionistic fuzzy sets Calculate the review score for each risk factor in the risk factor set : ; Based on review scores Arrange each risk factor in the risk factor set in descending order to obtain a first sorting factor set.

[0010] Preferably, the step of converting the enterprise review opinion into a trapezoidal fuzzy number and determining the second review weight based on the trapezoidal fuzzy number and the first review weight comprises: Integrate the enterprise review opinions into a decision matrix based on the first review weights The elements in the decision matrix are converted into trapezoidal fuzzy numbers and the converted trapezoidal fuzzy numbers are weighted to obtain a conversion decision matrix : ; In the formula, , are the four parameters in the basic trapezoidal fuzzy number, Indicates the decision matrix Line The elements of the column, represents the four parameters of the converted trapezoidal fuzzy number before weight adjustment; The conversion decision matrix Perform standardization to obtain a standardized matrix , based on the standardized matrix Calculate the trapezoidal fuzzy number of the second review weight : ; In the formula, , Represents a standardized matrix Middle Line Elements of a column; Trapezoidal fuzzy number based on the second review weight Solving for the second review weight : ; In the formula, is a trapezoidal fuzzy number The four parameters.

[0011] Preferably, the step of calculating the third review weight based on the second review weight, calculating the relative correlation between each risk factor in the risk factor set and the enterprise operation risk set based on the third review weight, and sorting the risk factor set based on the relative correlation to obtain the second sorting factor set includes: Based on the second review weight Calculate initial weights : ; In the formula, is the second scale factor; Based on the initial weight Calculate the third review weight : ; In the formula, The decision matrix Line The elements of the column, is the balance coefficient, ; Based on the conversion decision matrix Determine the first factor sequence With the second factor sequence : ; ; In the formula, the transformation decision matrix Middle Line The elements of the column correspond to the four parameters in the trapezoidal fuzzy number; Based on the first factor sequence With the second factor sequence Determine relative relevance , based on the relative correlation The risk factors in the risk factor set are arranged in ascending order to obtain a second sorting factor set.

[0012] Preferably, the first factor sequence With the second factor sequence Determine relative relevance , based on the relative correlation The step of arranging the risk factors in the risk factor set in ascending order to obtain a second ranking factor set comprises: Based on the first factor sequence With the second factor sequence Calculate the first intimacy Second degree of intimacy : ; ; In the formula, , The first factor sequence With the second factor sequence The data, is the intimacy coefficient; Based on the first intimacy The second intimacy Calculate the first correlation The second degree of association : ; ; Based on the first correlation The second degree of association Calculate relative correlation : ; Based on the relative correlation The risk factors in the risk factor set are arranged in ascending order to obtain a second sorting factor set.

[0013] Preferably, the step of determining a set of important influencing factors based on the first ranking factor set and the second ranking factor set includes: In the first ranking factor set, select risk factors to obtain the first screening factor set; In the second ranking factor set, select risk factors to obtain the second screening factor set; The first screening factor set and the second screening factor set are intersected to obtain a set of important influencing factors.

[0014] In a third aspect, the present invention provides the following technical solution: a computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the enterprise operation risk assessment method as described above when executing the computer program.

[0015] In a fourth aspect, the present invention provides the following technical solution: a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the enterprise operation risk assessment method as described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flow chart of a method for assessing business risk provided by Embodiment 1 of the present invention; Figure 2 A structural block diagram of an enterprise operation risk assessment system provided in Embodiment 2 of the present invention; Figure 3 A schematic diagram of the hardware structure of a computer provided in another embodiment of the present invention.

[0018] The embodiments of the present invention will be further described below with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the embodiments of the present invention, and should not be construed as limiting the present invention.

[0020] In the description of the embodiments of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0021] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0022] In the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

[0023] Embodiment 1 In the first embodiment of the present invention, Figure 1 As shown, a method for assessing business risk includes: S1. Obtain a multi-operation data set of an enterprise, wherein the multi-operation data set includes a basic operation data set, an enterprise operation risk set, and a risk factor set; Specifically, in this step, the multivariate business data set includes a basic business data set, an enterprise business risk set and a risk factor set. The basic business data set includes enterprise financial statements, enterprise type, enterprise annual tax payment, enterprise headcount, enterprise debt repayment ability, enterprise litigation situation, etc., while the enterprise business risk set includes market risk, financial risk, team risk, etc., and the risk factor set includes product demand, product price and cost, personnel turnover, enterprise financing decisions, investment situation, etc.

[0024] S2. Obtaining enterprise review opinions based on the enterprise operation risk set and the risk factor set, converting the enterprise review opinions into an intuitive fuzzy set, and determining a first review weight based on the intuitive fuzzy set, calculating a review score for each risk factor in the risk factor set based on the first review weight, and sorting the risk factor set based on the review score to obtain a first sorting factor set; Among them, for the enterprise operating risk set and risk factor set, which include several medium enterprise operating risks and various influencing factors that may cause enterprise operating risks, therefore, by selecting several reviewers from different fields and obtaining the reviewers' review opinions on various enterprise operating risks for each risk influencing factor, the review opinions can be specifically very high impact, high impact, medium impact, low impact, very low impact, no impact, and for direct fuzziness, the reviewer's hesitation can be divided into certain, uncertain, and uncertain. Therefore, based on the above review opinions, a corresponding review matrix can be constructed, and then the elements in the review matrix are converted into corresponding intuitive fuzzy numbers. The specific conversion process is to convert the semantic information of the enterprise review opinions into corresponding intuitive fuzzy numbers, and then determine the corresponding first ranking factor set based on the intuitive fuzzy numbers.

[0025] Wherein, the step S2 comprises: S21, obtaining enterprise review opinions based on the enterprise operation risk set and the risk factor set, and aggregating the enterprise review opinions of each reviewer into a review matrix And determine the review matrix The corresponding intuitionistic fuzzy set, based on the review matrix The corresponding intuitive fuzzy set corresponds to the first weight .

[0026] Wherein, the step S21 comprises: S211, based on the enterprise operation risk set and the risk factor set, obtain enterprise review opinions, aggregate the enterprise review opinions of each reviewer to obtain several review matrices ,in, Indicates Review matrix for each reviewer.

[0027] S212: The review matrix Transformed into corresponding intuitionistic fuzzy sets, and based on the review matrix The corresponding intuitionistic fuzzy set is calculated The first weight of the reviewer : ; ; In the formula, Indicates Reviewers on The business risk of an enterprise Intuitive fuzzy sets of risk factors, Indicates Reviewers on The risk factor belongs to The degree of affiliation of an enterprise's operating risk, Indicates Reviewers on The risk factor belongs to The non-subordinate degree of the business risk of an enterprise Indicates The overall hesitation of each reviewer; Specifically, the overall hesitation of the reviewer represents the uncertainty of the corresponding reviewer's evaluation of the risk influencing factors of the enterprise's operating risk. The greater the uncertainty, the lower the reliability of the reviewer. Therefore, when calculating the actual weight, the corresponding weight is smaller, which can further improve the accuracy of the final risk assessment and fully consider the balance and effectiveness of the enterprise review opinions of different reviewers.

[0028] S22. Based on the review matrix The corresponding intuitionistic fuzzy set determines the second weight .

[0029] Wherein, the step S22 comprises: S221, based on the review matrix The corresponding intuitionistic fuzzy set is calculated Reviewers on Intuitionistic fuzzy sets of risk factors : ; In the formula, Indicates The weight corresponding to the business risk of each enterprise is Indicates Reviewers on The business risk of an enterprise Intuitive fuzzy sets of risk factors, Indicates Reviewers on The risk factor belongs to The degree of affiliation of an enterprise's operating risk, Indicates Reviewers on The risk factor belongs to The non-subordinate degree of business risk of an enterprise.

[0030] S222, based on the intuitionistic fuzzy set Calculate the The intimacy of the reviewers : ; In the formula, Indicates Reviewers on Intuitive fuzzy sets of risk factors; Specifically, the review intimacy value here Specific instructions The reviewer and The intimacy between reviewers. Since there are certain differences between different reviewers, that is, differences in fields and professional knowledge, their review opinions are quite different. Therefore, by determining the intimacy between two different reviewers, the greater the intimacy between one reviewer and the other reviewers, the higher the credibility of the reviewer, and therefore the greater the weight ratio.

[0031] S223, based on The intimacy of the reviewers Calculate the second weight : .

[0032] S23, based on the first weight With the second weight Calculate the first review weight , based on the first review weight Determine a review score for each risk factor in the risk factor set , and sorting the risk factor set based on the review score to obtain a first sorting factor set.

[0033] Wherein, the step S23 comprises: S231: Based on the first weight With the second weight Calculate the first review weight : ; In the formula, is the first scale factor; Specifically, since the first weight represents the weight of the reviewer's hesitation, and the second weight represents the weight of the reviewer's intimacy, the first review weight is determined by weighted fusion based on the first weight and the second weight. , the first review weight The hesitation of each reviewer and the intimacy between reviewers are fully considered to improve the accuracy of the ranking of risk influencing factors.

[0034] S232: Based on the first review weight Determine the intuitionistic fuzzy set of each risk factor in the risk factor set : ; In the formula, For the Reviewers on Intuitive fuzzy sets of risk factors.

[0035] S233, based on intuitionistic fuzzy sets Calculate the review score for each risk factor in the risk factor set : ; S234, based on review scores Arrange each risk factor in the risk factor set in descending order to obtain a first sorting factor set; Specifically, with regard to the review score, the higher the review score, the greater the impact of the risk factor on the business risk of the enterprise. Therefore, in the first ranking factor set, the risk factor ranked first is the factor with the greatest risk impact, and the same goes for the remaining influencing factors.

[0036] S3. Convert the enterprise review opinion into a trapezoidal fuzzy number, and determine a second review weight based on the trapezoidal fuzzy number and the first review weight.

[0037] Wherein, the step S3 comprises: S31, integrating the enterprise review opinions into a decision matrix, based on the first review weights The elements in the decision matrix are converted into trapezoidal fuzzy numbers and the converted trapezoidal fuzzy numbers are weighted to obtain a conversion decision matrix : ; In the formula, , are the four parameters in the basic trapezoidal fuzzy number, Indicates the decision matrix Line The elements of the column, represents the four parameters of the converted trapezoidal fuzzy number before weight adjustment; Specifically, the decision matrix is ​​similar to the review matrix in the above steps. A corresponding decision matrix is ​​established for each reviewer, and then each element in the decision matrix is ​​represented by a trapezoidal fuzzy number.

[0038] S32, the conversion decision matrix Perform standardization to obtain a standardized matrix , based on the standardized matrix Calculate the trapezoidal fuzzy number of the second review weight : ; In the formula, , Represents a standardized matrix Middle Line Elements of a column; Specifically, for the conversion decision matrix The purpose of standardization is to unify the units of different influencing factors for easy calculation. Then, the standardized matrix Determine the trapezoidal fuzzy number for the second review weight Here, the second review weight is expressed in the form of trapezoidal fuzzy numbers. Therefore, in the subsequent steps, the corresponding second weight can be reversely solved based on the trapezoidal fuzzy numbers.

[0039] S33, trapezoidal fuzzy number based on the second review weight Solving for the second review weight : ; In the formula, is a trapezoidal fuzzy number The four parameters.

[0040] S4, calculating a third review weight based on the second review weight, calculating a relative correlation between each risk factor in the risk factor set and the enterprise operation risk set based on the third review weight, and sorting the risk factor set based on the relative correlation to obtain a second sorting factor set; Wherein, the step S4 comprises: S41, based on the second review weight Calculate initial weights : ; In the formula, is the second scale factor; Specifically, the initial weights calculated here are It is equal to the second review weight, so for a risk factor, it is a specific value, so it is also a constant weight.

[0041] S42, based on the initial weight Calculate the third review weight : ; In the formula, The decision matrix Line The elements of the column, is the balance coefficient, ; Specifically, according to the third review weight calculated in step S42, it is a weight that can change according to the difference between risk factors and business risks of the enterprise, so that adaptive adjustment of the weight can be achieved, which is beneficial to the accuracy of subsequent risk assessment results. The weight not only takes into account the relative importance of risk factors, but also takes into account the balance between risk factor assessment information, and represents the assessment information of risk factors with trapezoidal fuzzy numbers, thereby overcoming the subjectivity of review opinions and improving the accuracy of risk assessment.

[0042] S43, based on the conversion decision matrix Determine the first factor sequence With the second factor sequence : ; ; In the formula, the transformation decision matrix Middle Line The elements of the column correspond to the four parameters in the trapezoidal fuzzy number.

[0043] S44. Based on the first factor sequence With the second factor sequence Determine relative relevance , based on the relative correlation The risk factors in the risk factor set are arranged in ascending order to obtain a second sorting factor set.

[0044] Wherein, the step S44 comprises: S441. Based on the first factor sequence With the second factor sequence Calculate the first intimacy Second degree of intimacy : ; ; In the formula, , The first factor sequence With the second factor sequence The data, The intimacy coefficient.

[0045] S442: Based on the first intimacy The second intimacy Calculate the first correlation The second degree of association : ; .

[0046] S443: Based on the first degree of association The second degree of association Calculate relative correlation : ; S444: Based on the relative correlation Arrange the risk factors in the risk factor set in ascending order to obtain a second sorting factor set; Specifically, relative relevance It reflects the relative criticality between the business risk and the risk factors. The higher the relative correlation, the lower the importance, so the corresponding risk factor has a smaller impact. Therefore, it is necessary to classify the risk factors in the risk factor set according to the correlation. They are arranged in ascending order from low to high, so in the second ranking factor set, the risk factor ranked first has the greatest impact, and so on.

[0047] S5. Determine a set of important influencing factors based on the first ranking factor set and the second ranking factor set, and perform risk assessment on the enterprise based on the set of important influencing factors and the basic operating data set.

[0048] The step S5 comprises: S51, selecting the first ranking factor set risk factors to obtain the first set of screening factors.

[0049] S52, selecting the first risk factors to obtain the second set of screening factors.

[0050] S53, taking the intersection of the first screening factor set and the second screening factor set to obtain a set of important influencing factors; Specifically, in the present application, the top 10 risk factors are generally selected from the first ranking factor set, so there are 10 elements in the first screening factor set, and the top 8 risk factors are selected from the second ranking factor set, so there are 8 elements in the second screening factor set. After that, the first screening factor set and the second screening factor set are cross-validated to obtain a specific set of important influencing factors, and the cross-validation method is to take the intersection of the first screening factor set and the second screening factor set.

[0051] It should also be noted that after obtaining the set of important influencing factors, the enterprise specifies the corresponding risk management strategy from the direction corresponding to the set of important influencing factors, and extracts the data from the corresponding basic operating data set according to the elements in the set of important influencing factors, and then normalizes the data to obtain a normalized data set. After that, the normalized data is input into the trained evaluation model to output the corresponding risk assessment score, and the risk assessment score of the enterprise is compared with the score threshold. If it is higher than the score threshold, an early warning is issued to indicate the existence of risks. If it is not higher than the score threshold, the enterprise is informed to prepare for risk management. The specific evaluation model is: ; In the formula, ,and , The first bits of data, To score the risk assessment, for The data weights can be fitted during the model training process.

[0052] The enterprise operation risk assessment method provided in the first embodiment of the present invention first obtains a multivariate operation data set of the enterprise, and the multivariate operation data set includes a basic operation data set, an enterprise operation risk set and a risk factor set; then, based on the enterprise operation risk set and the risk factor set, the enterprise review opinion is obtained, the enterprise review opinion is converted into an intuitive fuzzy set, and a first review weight is determined based on the intuitive fuzzy set, and a review score of each risk factor in the risk factor set is calculated based on the first review weight, and the risk factor set is sorted based on the review score to obtain a first sorted factor set; then, the enterprise review opinion is converted into a trapezoidal fuzzy number, and a second review weight is determined based on the trapezoidal fuzzy number and the first review weight; then, a third review weight is calculated based on the second review weight, and a correlation between each risk factor in the risk factor set and the enterprise operation is calculated based on the third review weight. The relative correlation between risk sets is used, and the risk factor sets are sorted based on the relative correlation to obtain a second sorting factor set; finally, the important influencing factor set is determined based on the first sorting factor set and the second sorting factor set, and the enterprise risk assessment is performed based on the important influencing factor set and the basic operating data set. The present invention fully considers the balance and effectiveness of each reviewer's enterprise review opinion, and determines the first, second, and third review weights respectively, and the third review weight can be adaptively adjusted according to specific risk factors, which effectively expands the weight distribution method, thereby improving the accuracy of the assessment result. At the same time, the important influencing factor set is further determined by cross-validation between the first sorting factor set and the second sorting factor set. Risk assessment is performed through the important influencing factor set, which can further improve the accuracy of the assessment result.

[0053] Embodiment 2 like Figure 2 As shown, in the second embodiment of the present invention, a business risk assessment system is provided, and the system includes: An acquisition module 1 is used to acquire a multi-operation data set of an enterprise, wherein the multi-operation data set includes a basic operation data set, an enterprise operation risk set and a risk factor set; A first sorting module 2 is used to obtain enterprise review opinions based on the enterprise operation risk set and the risk factor set, convert the enterprise review opinions into an intuitive fuzzy set, determine a first review weight based on the intuitive fuzzy set, calculate a review score for each risk factor in the risk factor set based on the first review weight, and sort the risk factor set based on the review score to obtain a first sorting factor set; A conversion module 3, configured to convert the enterprise review opinion into a trapezoidal fuzzy number, and determine a second review weight based on the trapezoidal fuzzy number and the first review weight; A second sorting module 4 is used to calculate a third review weight based on the second review weight, calculate a relative correlation between each risk factor in the risk factor set and the enterprise operation risk set based on the third review weight, and sort the risk factor set based on the relative correlation to obtain a second sorting factor set; The evaluation module 5 is used to determine a set of important influencing factors based on the first ranking factor set and the second ranking factor set, and to perform risk evaluation on the enterprise based on the set of important influencing factors and the basic operating data set.

[0054] The first sorting module comprises: The first weight determination submodule is used to obtain enterprise review opinions based on the enterprise operation risk set and the risk factor set, and to aggregate the enterprise review opinions of each reviewer into a review matrix And determine the review matrix The corresponding intuitionistic fuzzy set, based on the review matrix The corresponding intuitive fuzzy set corresponds to the first weight ; The second weight determination submodule is used to determine the weight based on the review matrix The corresponding intuitionistic fuzzy set determines the second weight ; A first sorting submodule is used to sort the With the second weight Calculate the first review weight , based on the first review weight Determine a review score for each risk factor in the risk factor set , and sorting the risk factor set based on the review score to obtain a first sorting factor set.

[0055] The first weight determination submodule includes: A matrix determination unit is used to obtain enterprise review opinions based on the enterprise operation risk set and the risk factor set, and aggregate the enterprise review opinions of each reviewer to obtain a number of review matrices. ,in, Indicates Review matrix for each reviewer; The first weight unit is used to weight the review matrix Transformed into corresponding intuitionistic fuzzy sets, and based on the review matrix The corresponding intuitionistic fuzzy set is calculated The first weight of the reviewer : ; ; In the formula, Indicates Reviewers on The business risk of an enterprise Intuitive fuzzy sets of risk factors, Indicates Reviewers on The risk factor belongs to The degree of affiliation of an enterprise's operating risk, Indicates Reviewers on The risk factor belongs to The non-subordinate degree of the business risk of an enterprise Indicates The overall hesitation of each reviewer.

[0056] The second weight determination submodule includes: Intuitionistic fuzzy set determination unit for review matrix based The corresponding intuitionistic fuzzy set is calculated Reviewers on Intuitionistic fuzzy sets of risk factors : ; In the formula, Indicates The weight corresponding to the business risk of each enterprise is Indicates Reviewers on The business risk of an enterprise Intuitive fuzzy sets of risk factors, Indicates Reviewers on The risk factor belongs to The degree of affiliation of an enterprise's operating risk, Indicates Reviewers on The risk factor belongs to The non-subordinate degree of business risk of an enterprise; A review intimacy determination unit is used to determine the review intimacy based on the intuitionistic fuzzy set Calculate the The intimacy of the reviewers : ; In the formula, Indicates Reviewers on Intuitive fuzzy sets of risk factors; The second weight unit is used to The intimacy of the reviewers Calculate the second weight : .

[0057] The first sorting submodule includes: A first review weight determination unit is used to determine the first review weight based on the first review weight. With the second weight Calculate the first review weight : ; In the formula, is the first scale factor; A risk intuitive fuzzy set determination unit is used to determine the risk based on the first review weight Determine the intuitionistic fuzzy set of each risk factor in the risk factor set : ; In the formula, For the Reviewers on Intuitive fuzzy sets of risk factors; Review scoring unit for intuitionistic fuzzy sets Calculate the review score for each risk factor in the risk factor set : ; Based on review scores Arrange each risk factor in the risk factor set in descending order to obtain a first sorting factor set.

[0058] The conversion module 3 comprises: An adjustment submodule is used to integrate the enterprise review opinions into a decision matrix based on the first review weights. The elements in the decision matrix are converted into trapezoidal fuzzy numbers and the converted trapezoidal fuzzy numbers are weighted to obtain a conversion decision matrix : ; In the formula, , are the four parameters in the basic trapezoidal fuzzy number, Indicates the decision matrix Line The elements of the column, represents the four parameters of the converted trapezoidal fuzzy number before weight adjustment; The trapezoidal fuzzy number determination submodule is used to determine the conversion decision matrix Perform standardization to obtain a standardized matrix , based on the standardized matrix Calculate the trapezoidal fuzzy number of the second review weight : ; In the formula, , Represents a standardized matrix Middle Line Elements of a column; The second review weight submodule is used to calculate the trapezoidal fuzzy number based on the second review weight. Solving for the second review weight : ; In the formula, is a trapezoidal fuzzy number The four parameters.

[0059] The second sorting module 4 comprises: Initial weight submodule, for Calculate initial weights : ; In the formula, is the second scale factor; The third review weight submodule is used to Calculate the third review weight : ; In the formula, The decision matrix Line The elements of the column, is the balance coefficient, ; A sequence submodule for making a decision based on the transformation matrix Determine the first factor sequence With the second factor sequence : ; ; In the formula, the transformation decision matrix Middle Line The elements of the column correspond to the four parameters in the trapezoidal fuzzy number; The second arrangement submodule is used to arrange the sequence of the first factors based on the With the second factor sequence Determine relative relevance , based on the relative correlation The risk factors in the risk factor set are arranged in ascending order to obtain a second sorting factor set.

[0060] The second arrangement submodule includes: A sequence closeness unit is used to determine the closeness of the first factor sequence. With the second factor sequence Calculate the first intimacy Second degree of intimacy : ; ; In the formula, , The first factor sequence With the second factor sequence The data, is the intimacy coefficient; A relevance unit, configured to: The second intimacy Calculate the first correlation The second degree of association : ; ; A relative association unit is used to The second degree of association Calculate relative correlation : ; The second sorting unit is used to sort the The risk factors in the risk factor set are arranged in ascending order to obtain a second sorting factor set.

[0061] The evaluation module includes: The first screening submodule is used to select the top risk factors to obtain the first screening factor set; The second screening submodule is used to select the top risk factors to obtain the second screening factor set; The verification submodule is used to take the intersection of the first screening factor set and the second screening factor set to obtain an important influencing factor set.

[0062] In some other embodiments of the present invention, the embodiments of the present invention provide the following technical solutions: a computer, comprising a memory 102, a processor 101, and a computer program stored in the memory 102 and executable on the processor 101; the processor 101 implements the enterprise operation risk assessment method as described above when executing the computer program.

[0063] Specifically, the processor 101 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiment of the present invention.

[0064] Among them, the memory 102 may include a large-capacity memory for data or instructions. By way of example and not limitation, the memory 102 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 102 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 102 may be inside or outside the data processing device. In a specific embodiment, the memory 102 is a non-volatile memory. In a specific embodiment, the memory 102 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0065] The memory 102 may be used to store or cache various data files that need to be processed and / or used for communication, as well as possible computer program instructions executed by the processor 101 .

[0066] The processor 101 implements the above-mentioned enterprise operation risk assessment method by reading and executing the computer program instructions stored in the memory 102.

[0067] In some embodiments, the computer may further include a communication interface 103 and a bus 100. Figure 3 As shown, the processor 101, the memory 102, and the communication interface 103 are connected via a bus 100 and communicate with each other.

[0068] The communication interface 103 is used to implement communication between the modules, devices, units and / or equipment in the embodiment of the present invention. The communication interface 103 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0069] The bus 100 includes hardware, software or both, and couples the components of the computer device to each other. The bus 100 includes but is not limited to at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 100 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, bus 100 may include one or more buses. Although embodiments of the present invention describe and illustrate a particular bus, the present invention contemplates any suitable bus or interconnect.

[0070] The computer can execute the enterprise operation risk assessment method of the present invention based on the enterprise operation risk assessment system obtained, thereby realizing the recommendation of student exercises for smart teaching.

[0071] In some further embodiments of the present invention, in combination with the above-mentioned enterprise operation risk assessment method, the embodiments of the present invention provide the following technical solutions: a storage medium having a computer program stored thereon, and the computer program implements the above-mentioned enterprise operation risk assessment method when executed by a processor.

[0072] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0073] More specific examples of readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0074] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0075] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0076] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for assessing business risk, characterized in that: include: Acquire a multi-operation data set of an enterprise, wherein the multi-operation data set includes a basic operation data set, an enterprise operation risk set, and a risk factor set; Obtaining enterprise review opinions based on the enterprise operation risk set and the risk factor set, converting the enterprise review opinions into an intuitive fuzzy set, determining a first review weight based on the intuitive fuzzy set, calculating a review score for each risk factor in the risk factor set based on the first review weight, and sorting the risk factor set based on the review score to obtain a first sorting factor set; Converting the enterprise review opinion into a trapezoidal fuzzy number, and determining a second review weight based on the trapezoidal fuzzy number and the first review weight; Calculating a third review weight based on the second review weight, calculating a relative correlation between each risk factor in the risk factor set and the enterprise operation risk set based on the third review weight, and sorting the risk factor set based on the relative correlation to obtain a second sorting factor set; An important influencing factor set is determined based on the first ranking factor set and the second ranking factor set, and a risk assessment is performed on the enterprise based on the important influencing factor set and the basic operating data set.

2. The enterprise operation risk assessment method according to claim 1, characterized in that: The steps of obtaining enterprise review opinions based on the enterprise operation risk set and the risk factor set, converting the enterprise review opinions into an intuitive fuzzy set, determining a first review weight based on the intuitive fuzzy set, calculating a review score for each risk factor in the risk factor set based on the first review weight, and sorting the risk factor set based on the review score to obtain a first sorted factor set include: Based on the enterprise operating risk set and the risk factor set, the enterprise review opinions are obtained, and the enterprise review opinions of each reviewer are aggregated into a review matrix And determine the review matrix The corresponding intuitionistic fuzzy set, based on the review matrix The corresponding intuitive fuzzy set corresponds to the first weight ; Based on the review matrix The corresponding intuitionistic fuzzy set determines the second weight ; Based on the first weight With the second weight Calculate the first review weight , based on the first review weight Determine a review score for each risk factor in the risk factor set , and sorting the risk factor set based on the review score to obtain a first sorting factor set.

3. The enterprise operation risk assessment method according to claim 2, characterized in that: The enterprise review opinions are obtained based on the enterprise operation risk set and the risk factor set, and the enterprise review opinions of each reviewer are aggregated into a review matrix And determine the review matrix The corresponding intuitionistic fuzzy set, based on the review matrix The corresponding intuitive fuzzy set corresponds to the first weight The steps include: Based on the enterprise operating risk set and the risk factor set, the enterprise review opinions are obtained, and the enterprise review opinions of each reviewer are aggregated to obtain several review matrices. ,in, Indicates Review matrix for each reviewer; The review matrix Transformed into corresponding intuitionistic fuzzy sets, and based on the review matrix The corresponding intuitionistic fuzzy set is calculated The first weight of the reviewer : ； ； In the formula, Indicates Reviewers on The business risk of an enterprise Intuitive fuzzy sets of risk factors, Indicates Reviewers on The risk factor belongs to The degree of affiliation of an enterprise's operating risk, Indicates Reviewers on The risk factor belongs to The non-subordinate degree of the business risk of an enterprise Indicates The overall hesitation of each reviewer.

4. The enterprise operation risk assessment method according to claim 2, characterized in that: Based on the review matrix The corresponding intuitionistic fuzzy set determines the second weight The steps include: Based on the review matrix The corresponding intuitionistic fuzzy set is calculated Reviewers on Intuitionistic fuzzy sets of risk factors : ； In the formula, Indicates The weight corresponding to the business risk of each enterprise is Indicates Reviewers on The business risk of an enterprise Intuitive fuzzy sets of risk factors, Indicates Reviewers on The risk factor belongs to The degree of affiliation of an enterprise's operating risk, Indicates Reviewers on The risk factor belongs to The non-subordinate degree of business risk of an enterprise; Based on the intuitionistic fuzzy set Calculate the The intimacy of the reviewers : ； In the formula, Indicates Reviewers on Intuitive fuzzy sets of risk factors; Based on The intimacy of the reviewers Calculate the second weight : 。 5. The enterprise operation risk assessment method according to claim 2, characterized in that: based on the first weight With the second weight Calculate the first review weight , based on the first review weight Determine a review score for each risk factor in the risk factor set , and sorting the risk factor set based on the review score to obtain a first sorting factor set includes: Based on the first weight With the second weight Calculate the first review weight : ； In the formula, is the first scale factor; Based on the first review weight Determine the intuitionistic fuzzy set of each risk factor in the risk factor set : ； In the formula, For the Reviewers on Intuitive fuzzy sets of risk factors; Based on intuitionistic fuzzy sets Calculate the review score for each risk factor in the risk factor set : ； Based on review scores Arrange each risk factor in the risk factor set in descending order to obtain a first sorting factor set.

6. The enterprise operation risk assessment method according to claim 1, characterized in that: The step of converting the enterprise review opinion into a trapezoidal fuzzy number and determining a second review weight based on the trapezoidal fuzzy number and the first review weight comprises: Integrate the enterprise review opinions into a decision matrix based on the first review weights The elements in the decision matrix are converted into trapezoidal fuzzy numbers and the converted trapezoidal fuzzy numbers are weighted to obtain a conversion decision matrix : ； In the formula, , are the four parameters in the basic trapezoidal fuzzy number, Indicates the decision matrix Line The elements of the column, represents the four parameters of the converted trapezoidal fuzzy number before weight adjustment; The conversion decision matrix Perform standardization to obtain a standardized matrix , based on the standardized matrix Calculate the trapezoidal fuzzy number of the second review weight : ； In the formula, , Represents a standardized matrix Middle Line Elements of a column; Trapezoidal fuzzy number based on the second review weight Solving for the second review weight : ； In the formula, is a trapezoidal fuzzy number The four parameters.

7. The enterprise operation risk assessment method according to claim 1, characterized in that: The steps of calculating a third review weight based on the second review weight, calculating a relative correlation between each risk factor in the risk factor set and the enterprise operation risk set based on the third review weight, and sorting the risk factor set based on the relative correlation to obtain a second sorting factor set include: Based on the second review weight Calculate initial weights : ； In the formula, is the second scale factor; Based on the initial weight Calculate the third review weight : ； In the formula, The decision matrix Line The elements of the column, is the balance coefficient, ; Based on the conversion decision matrix Determine the first factor sequence With the second factor sequence : ； ； In the formula, the transformation decision matrix Middle Line The elements of the column correspond to the four parameters in the trapezoidal fuzzy number; Based on the first factor sequence With the second factor sequence Determine relative relevance , based on the relative correlation The risk factors in the risk factor set are arranged in ascending order to obtain a second sorting factor set.

8. The enterprise operation risk assessment method according to claim 1, characterized in that: The first factor sequence With the second factor sequence Determine relative relevance , based on the relative correlation The step of arranging the risk factors in the risk factor set in ascending order to obtain a second ranking factor set comprises: Based on the first factor sequence With the second factor sequence Calculate the first intimacy Second degree of intimacy : ； ； In the formula, , The first factor sequence With the second factor sequence The data, is the intimacy coefficient; Based on the first intimacy The second intimacy Calculate the first correlation The second degree of association : ； ； Based on the first correlation The second degree of association Calculate relative correlation : ； Based on the relative correlation The risk factors in the risk factor set are arranged in ascending order to obtain a second sorting factor set.

9. The enterprise operation risk assessment method according to claim 1, characterized in that: The step of determining a set of important influencing factors based on the first ranking factor set and the second ranking factor set comprises: In the first ranking factor set, select risk factors to obtain the first screening factor set; In the second ranking factor set, select risk factors to obtain the second screening factor set; The first screening factor set and the second screening factor set are intersected to obtain a set of important influencing factors.

10. An enterprise operation risk assessment system, characterized in that: The system comprises: An acquisition module, used to acquire a multi-operation data set of an enterprise, wherein the multi-operation data set includes a basic operation data set, an enterprise operation risk set and a risk factor set; A first sorting module is used to obtain enterprise review opinions based on the enterprise operation risk set and the risk factor set, convert the enterprise review opinions into an intuitive fuzzy set, determine a first review weight based on the intuitive fuzzy set, calculate a review score for each risk factor in the risk factor set based on the first review weight, and sort the risk factor set based on the review score to obtain a first sorting factor set; A conversion module, used for converting the enterprise review opinion into a trapezoidal fuzzy number, and determining a second review weight based on the trapezoidal fuzzy number and the first review weight; A second sorting module is used to calculate a third review weight based on the second review weight, calculate a relative correlation between each risk factor in the risk factor set and the enterprise operation risk set based on the third review weight, and sort the risk factor set based on the relative correlation to obtain a second sorting factor set; An evaluation module is used to determine a set of important influencing factors based on the first ranking factor set and the second ranking factor set, and to perform risk evaluation on the enterprise based on the set of important influencing factors and the basic operating data set.

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