Intelligent enterprise risk management and control system and method based on data analysis technology

Abstract

The invention discloses an enterprise risk intelligent management and control system and method based on a data analysis technology, and relates to the technical field of risk management and control. The management and control method comprises the following steps: classifying all risk assessment records in any risk assessment log, and determining a risk characteristic index of each type of risk assessment records and a normal offset amplitude of each risk characteristic index; calculating a risk value of the risk assessment record, and determining a risk threshold for judging that the risk assessment record is abnormal; analyzing relevance among various types of data, and setting a plurality of types of high-risk data; if the high-risk data exists in the new risk assessment record, directly performing risk assessment on the new risk assessment record; if the obtained risk value exceeds a risk threshold value, performing exception marking on the new risk assessment record; and if a plurality of continuous risk assessment records in the certain risk assessment log have abnormal marks, warning the risk assessment log.

Description

Technical Field

[0001] The present invention relates to the field of risk management technology, and in particular to an enterprise risk intelligent management and control system and method based on data analysis technology. Background Art

[0002] Enterprise risk assessment is the process of identifying, analyzing and evaluating the potential risks faced by the enterprise and formulating response strategies; its purpose is to find and describe enterprise risks, evaluate the impact of various risks identified on the achievement of enterprise goals and risk value, etc. Effective risk assessment can help enterprises prevent and reduce the impact of risks and ensure the continuous and stable operation of the business; therefore, timely discovery of enterprise risks is crucial to effective management and control of risks; if the risks of the enterprise cannot be discovered in time and the source of the abnormality cannot be found in time, it may cause the enterprise to continue to have risks in the future and cause a series of losses to the enterprise. Summary of the invention

[0003] The purpose of the present invention is to provide an enterprise risk intelligent management and control system and method based on data analysis technology to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solution: an enterprise risk intelligent management and control method based on data analysis technology, the management and control method comprising the following steps: Step S100: In the enterprise risk assessment system, a corresponding risk assessment log is generated for the risk assessment of each enterprise; a risk assessment is performed on any enterprise every unit period, and the risk assessment process is generated into a risk assessment record in the risk assessment log; all risk assessment records in any risk assessment log are classified, and the risk characteristic index of each type of risk assessment record and the normal deviation range of each risk characteristic index are determined; Step S200: construct a risk assessment model, obtain the deviation amplitude between any risk assessment record and the corresponding risk characteristic indicator, and calculate the risk value of the risk assessment record; extract the risk values ​​of all risk assessment records with abnormalities, and determine the risk threshold for judging whether the risk assessment record has abnormalities; Step S300: arbitrarily select a risk assessment log, obtain all data stored in each risk assessment record, analyze the source of each type of data, and obtain the correlation between each type of data; according to the abnormal situation of each risk assessment record and the correlation between each type of data, obtain the probability of abnormality of each type of data, and set several types of high-risk data; Step S400: When there is a certain risk assessment log, a new risk assessment record is generated in real time; if there is high-risk data in the new risk assessment record, the new risk assessment record is directly risk assessed; if the obtained risk value exceeds the risk threshold, the new risk assessment record is marked as abnormal; if there are several consecutive risk assessment records in the certain risk assessment log with abnormal marks, the risk assessment log is warned.

[0005] Furthermore, step S100 includes the following steps: Step S101: obtaining the ith risk assessment record in a certain risk assessment log, extracting the data information stored in the ith risk assessment record; obtaining the data source of all the data information, classifying all the data information according to the data source, and generating a data set for the ith risk assessment record; Step S102: Obtain the assessment result in the i-th risk assessment record, compare the data type of the assessment result with a certain type of data in the data set, and set a similarity threshold , where N i is the number of data categories in the i-th risk assessment record; if the obtained similarity is greater than the similarity threshold, the data of a certain category is used as the characteristic data of the i-th risk assessment record, and the data of the category with the highest similarity is used as the risk characteristic indicator of the i-th risk assessment record; the risk assessment of an enterprise is conducted through one of the categories of data, therefore, to set the risk characteristic indicator, it is only necessary to find the data with the highest similarity to the assessment result from the stored data; Step S103: select all risk assessment records with the same risk characteristic index of the i-th risk assessment record, and divide them into a normal record set and an abnormal record set according to whether there is an abnormality; set the offset of a certain risk assessment record on the risk characteristic index as P, extract the offset of each risk assessment record in the normal record set and the abnormal record set respectively, and obtain the normal offset range in the normal record set as (P1, P2) and the abnormal offset range in the abnormal record set as (P3, P4); if P3<P2, then obtain the error range as (P3, P2); Step S104: Continuously select a value P from small to large in the error range. ’ , statistics in the normal record set offset is (P3,P ’ ) is M3, and the offset in the abnormal record set is (P ’ ,P4) is M4, until M3>M 12 -M3 and M4>M 34 -M4, the value P ’As the characteristic offset of the normal record set and the abnormal record set, the normal amplitude range of the risk characteristic index of the risk characteristic index of the i-th risk assessment record is (0,P ’ ); When the offset range of a normal record set overlaps with the offset range of an abnormal record set, the overlapping range is divided again according to the distribution of the two record sets in the overlapping range, so that the offset evaluation range can be as accurate as possible.

[0006] Further, step S200 includes the following steps: Step S201: Obtain the offset of the i-th risk assessment record in the risk assessment log as P i , the normal range of the risk characteristic index of the i-th risk assessment record is (0,P ’ ), build a risk assessment model:

[0007] Among them, N i is the number of data categories in the i-th risk assessment record, a and b are constant coefficients; the risk value of the i-th risk assessment record is calculated to be F i ; The risk value assessment should take into account the deviation from the risk characteristic index and the amount of data affected by various types of data; the actual deviation is used as the basic value, and the deviation degree is combined to effectively reflect the size of the risk value; Step S202: arbitrarily select a number of risk assessment records in each risk assessment log, train the risk assessment model, and obtain the risk value of each risk assessment record in the number of risk assessment records; divide the number of risk assessment records into a normal record set and an abnormal record set according to whether there is an abnormality, obtain the risk value range of the normal record set and the abnormal record set respectively, and determine the values ​​of a and b, so that the risk value of each risk assessment record in the normal record set is less than the risk value of each risk assessment record in the abnormal record set; Step S203: The remaining risk assessment records are used as a test set and input into the risk assessment model. If the risk value of a risk assessment record in the normal record set is greater than the risk value of a risk assessment record in the abnormal record set, then a number of risk assessment records from the remaining risk assessment records are selected to retrain the risk assessment model. Step S204: Obtain the risk value of each risk assessment record in all risk assessment records with abnormalities, and select the risk value with the smallest value as the risk threshold for determining whether the risk assessment record has abnormalities.

[0008] Further, step S300 includes the following steps: Step S301: Obtain a data set of the i-th risk assessment record in the risk assessment log, and obtain the data source of each type of data information in the data set; if the data source of a certain type of data information is the same as a type of data information in the data set of any other risk assessment record, then set the certain type of data information as derived data, otherwise, set the certain type of data information as original data; and obtain a plurality of derived data sets and original data sets; Step S302: arbitrarily select a type of derived data, and when the data source of the derived data is another type of derived data, obtain the data source of the other type of derived data again until the data source is the original data, and generate a generation path for the derived data; by obtaining the generation path of the derived data, the relationship between the various types of data can be most directly reflected, which is conducive to the subsequent calculation of the correlation between the various types of data; Step S303: Obtain the risk characteristic index of the i-th risk assessment record in the risk assessment log, and obtain a number of generation paths for generating the risk characteristic index; set the number of the generation paths to L. i , where the number of data types in the jth generation path is H j , according to the formula:

[0009] Among them, K a is the number of categories of the remaining data that are the same as the derived data generated by the a-th data; the correlation degree G between the a-th data and the risk characteristic index is calculated a ; L i -(K a ) reflects that among the generated paths, there are several paths due to different initial data, but the intermediate derived data are the same, which means that the generated derived data are obtained through multiple types of initial data. In this case, several paths are actually the same path and need to be excluded; H j It reflects the degree of direct reflection between the data; Step S304: Obtain the number of abnormal risk assessment records corresponding to the risk characteristic indicator in the enterprise risk assessment system, and obtain the abnormality ratio β of the abnormal risk characteristic indicator; set the abnormality ratio threshold β max , if β>β max , then select the data with the highest correlation with the risk characteristic indicator and set it as high-risk data.

[0010] Further, step S400 includes the following steps: Step S401: When a certain type of high-risk data is stored in the new risk assessment record, the risk assessment model is input into the new risk assessment record to calculate the risk value F of the new risk assessment record. new ; Step S402: Setting the risk threshold for determining whether the risk assessment record is abnormal to F max When F new >F max , the new risk assessment record is marked as abnormal; Step S403: obtaining a risk assessment record in the risk assessment log corresponding to the new risk assessment record that is closest to the generation time of the new risk assessment record, and if there is an abnormal mark in the most recent risk assessment record, issuing a warning to the risk assessment log.

[0011] In order to better implement the above method, an enterprise risk intelligent management and control system based on data analysis technology is also proposed. The management and control system includes a historical record analysis module, an enterprise risk analysis module, a risk data analysis module and a risk real-time analysis module; The historical record analysis module is used to generate a corresponding risk assessment log for the risk assessment of each enterprise in the enterprise risk assessment system; conduct a risk assessment on any enterprise every unit period, and generate a risk assessment record in the risk assessment log for the process of the risk assessment; classify all risk assessment records in any risk assessment log, and determine the risk characteristic index of each type of risk assessment record and the normal deviation range of each risk characteristic index; The enterprise risk analysis module is used to build a risk assessment model, obtain the deviation amplitude between any risk assessment record and the corresponding risk characteristic indicator, and calculate the risk value of the risk assessment record; extract the risk values ​​of all risk assessment records with abnormalities, and determine the risk threshold for judging whether the risk assessment record has abnormalities; The risk data analysis module is used to select a risk assessment log at random, obtain all data stored in each risk assessment record, analyze the source of each type of data, and obtain the correlation between each type of data; according to the abnormal situation of each risk assessment record and the correlation between each type of data, obtain the probability of abnormality of each type of data, and set several types of high-risk data; The real-time risk analysis module is used to generate a new risk assessment record in real time when there is a risk assessment log; if there is high-risk data in the new risk assessment record, the new risk assessment record is directly subjected to risk assessment; if the obtained risk value exceeds the risk threshold, the new risk assessment record is marked as abnormal; if there are abnormal marks for several consecutive risk assessment records in the risk assessment log, the risk assessment log is warned.

[0012] Further, the historical record analysis module includes an evaluation record setting unit and an evaluation record division unit; The assessment record setting unit is used to generate a corresponding risk assessment log for the risk assessment of each enterprise in the enterprise risk assessment system; conduct a risk assessment on any enterprise once every unit period, and generate a risk assessment record in the risk assessment log for the risk assessment process; the assessment record classification unit is used to classify all risk assessment records in any risk assessment log, and determine the risk characteristic indicators of each type of risk assessment record and the normal deviation range of each risk characteristic indicator.

[0013] Further, the enterprise risk analysis module includes an assessment model analysis unit and a risk threshold setting unit; The assessment model analysis unit is used to construct a risk assessment model, obtain the deviation amplitude between any risk assessment record and the corresponding risk characteristic indicator, and calculate the risk value of the risk assessment record; the risk threshold setting unit is used to extract the risk values ​​of all risk assessment records with abnormalities, and determine the risk threshold for judging whether the risk assessment record has abnormalities.

[0014] Further, the enterprise risk analysis module includes an assessment model analysis unit and a risk threshold setting unit; The assessment model analysis unit is used to construct a risk assessment model, obtain the deviation amplitude between any risk assessment record and the corresponding risk characteristic indicator, and calculate the risk value of the risk assessment record; the risk threshold setting unit is used to extract the risk values ​​of all risk assessment records with abnormalities, and determine the risk threshold for judging whether the risk assessment record has abnormalities.

[0015] Further, the risk data analysis module includes a data association assessment unit and a data anomaly analysis unit; The data association assessment unit is used to arbitrarily select a risk assessment log, obtain all data stored in each risk assessment record, analyze the sources of various types of data, and obtain the correlation between various types of data; the data anomaly analysis unit is used to obtain the probability of anomalies in various types of data based on the abnormal conditions of each risk assessment record and the correlation between various types of data, and set several types of high-risk data.

[0016] Further, the real-time risk analysis module includes a real-time risk assessment unit and an abnormality marking analysis unit; The real-time risk assessment unit is used to generate a new risk assessment record in real time when it exists in a certain risk assessment log; if there is high-risk data in the new risk assessment record, the new risk assessment record is directly subjected to risk assessment; the abnormal marking analysis unit is used to mark the new risk assessment record as abnormal if the obtained risk value exceeds the risk threshold; if there are several consecutive risk assessment records in the certain risk assessment log with abnormal markings, the risk assessment log is warned.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention evaluates the risk value of enterprises, realizes effective management and risk control of enterprises, timely discovers and reminds enterprises with abnormalities, and ensures the normal operation of enterprises; 2. The present invention classifies risk assessment records according to the data stored in different risk assessment records and the risk characteristic indicators of the final risk assessment; and can reasonably reflect the abnormal conditions of various risk assessment records through different offsets; 3. The present invention obtains the degree of influence of various types of data on the final risk characteristic indicators by analyzing the correlation between various types of data; considers the situation where the same risk characteristic indicators appear abnormally continuously, and screens out several types of high-risk data. When the generated risk assessment record records high-risk data, the risk assessment is directly performed to avoid the risk of high-risk data to the enterprise, and can detect and handle abnormalities in time to reduce the loss of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of the steps of an enterprise risk intelligent management and control method based on data analysis technology of the present invention; Figure 2 This is a structural schematic diagram of an enterprise risk intelligent management and control system based on data analysis technology in the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Example: Figure 1 to Figure 2As shown, the present invention provides an enterprise risk intelligent management and control method based on data analysis technology, and the management and control method includes the following steps: Step S100: In the enterprise risk assessment system, a corresponding risk assessment log is generated for the risk assessment of each enterprise; a risk assessment is performed on any enterprise every unit period, and the risk assessment process is generated into a risk assessment record in the risk assessment log; all risk assessment records in any risk assessment log are classified, and the risk characteristic index of each type of risk assessment record and the normal deviation range of each risk characteristic index are determined; Wherein, step S100 includes the following steps: Step S101: obtaining the ith risk assessment record in a certain risk assessment log, extracting the data information stored in the ith risk assessment record; obtaining the data source of all the data information, classifying all the data information according to the data source, and generating a data set for the ith risk assessment record; Step S102: Obtain the assessment result in the i-th risk assessment record, compare the data type of the assessment result with a certain type of data in the data set, and set a similarity threshold , where N i is the number of data categories in the i-th risk assessment record; if the obtained similarity is greater than the similarity threshold, the data of a certain category is used as the characteristic data of the i-th risk assessment record, and the data of the category with the highest similarity is used as the risk characteristic indicator of the i-th risk assessment record; Step S103: select all risk assessment records with the same risk characteristic index of the i-th risk assessment record, and divide them into a normal record set and an abnormal record set according to whether there is an abnormality; set the offset of a certain risk assessment record on the risk characteristic index as P, extract the offset of each risk assessment record in the normal record set and the abnormal record set respectively, and obtain the normal offset range in the normal record set as (P1, P2) and the abnormal offset range in the abnormal record set as (P3, P4); if P3<P2, then obtain the error range as (P3, P2); Step S104: Continuously select a value P from small to large in the error range. ’ , statistics in the normal record set offset is (P3,P ’ ) is M3, and the offset in the abnormal record set is (P ’ ,P4) is M4, until M3>M 12 -M3 and M4>M 34 -M4, the value P ’As the characteristic offset of the normal record set and the abnormal record set, the normal amplitude range of the risk characteristic index of the i-th risk assessment record is (0,P ’ ); Example: Set the normal offset range in the normal record set to (0, 20%), and the abnormal offset range in the abnormal record set to (15%, 50%); get the error range to be (15%, 20%); select the offset of 17% from the error range, and get that the offset in the normal record set is more in the range of (15%, 17%), and the offset in the abnormal record set is more in the range of (17%, 20%), then get the offset of 17% as the characteristic offset, and get the normal amplitude range of the risk characteristic indicator to be (0, 17%); Step S200: construct a risk assessment model, obtain the deviation amplitude between any risk assessment record and the corresponding risk characteristic indicator, and calculate the risk value of the risk assessment record; extract the risk values ​​of all risk assessment records with abnormalities, and determine the risk threshold for judging whether the risk assessment record has abnormalities; Wherein, step S200 includes the following steps: Step S201: Obtain the offset of the i-th risk assessment record in the risk assessment log as P i , the normal range of the risk characteristic index of the i-th risk assessment record is (0,P ’ ), build a risk assessment model:

[0021] Among them, N i is the number of data categories in the i-th risk assessment record, a and b are constant coefficients; the risk value of the i-th risk assessment record is calculated to be F i ; Example: Set the offset of the i-th risk assessment record to 10%, and the normal range to (0, 17%), the number of data categories in the i-th risk assessment record to 3, a=50%, b=100, and the risk value is F i =10%×(1-0.7×50%)×3+100=100.195; Step S202: arbitrarily select a number of risk assessment records in each risk assessment log, train the risk assessment model, and obtain the risk value of each risk assessment record in the number of risk assessment records; divide the number of risk assessment records into a normal record set and an abnormal record set according to whether there is an abnormality, obtain the risk value range of the normal record set and the abnormal record set respectively, and determine the values ​​of a and b, so that the risk value of each risk assessment record in the normal record set is less than the risk value of each risk assessment record in the abnormal record set; Step S203: The remaining risk assessment records are used as a test set and input into the risk assessment model. If the risk value of a risk assessment record in the normal record set is greater than the risk value of a risk assessment record in the abnormal record set, then a number of risk assessment records from the remaining risk assessment records are selected to retrain the risk assessment model. Step S204: Obtain the risk value of each risk assessment record in all risk assessment records with abnormalities, and select the risk value with the smallest value as the risk threshold for determining whether the risk assessment record has abnormalities.

[0022] Step S300: arbitrarily select a risk assessment log, obtain all data stored in each risk assessment record, analyze the source of each type of data, and obtain the correlation between each type of data; according to the abnormal situation of each risk assessment record and the correlation between each type of data, obtain the probability of abnormality of each type of data, and set several types of high-risk data; Wherein, step S300 includes the following steps: Step S301: Obtain a data set of the i-th risk assessment record in the risk assessment log, and obtain the data source of each type of data information in the data set; if the data source of a certain type of data information is the same as a type of data information in the data set of any other risk assessment record, then set the certain type of data information as derived data, otherwise, set the certain type of data information as original data; and obtain a plurality of derived data sets and original data sets; Step S302: arbitrarily selecting a type of derived data, when the data source of the derived data is another type of derived data, obtaining the data source of the other type of derived data again until the data source is the original data, thereby generating a generation path for the derived data; Step S303: Obtain the risk characteristic index of the i-th risk assessment record in the risk assessment log, and obtain a number of generation paths for generating the risk characteristic index; set the number of the generation paths to L. i , where the number of data types in the jth generation path is H j , according to the formula:

[0023] Among them, K a is the number of categories of the remaining data that are the same as the derived data generated by the a-th data; the correlation degree G between the a-th data and the risk characteristic index is calculated a ; Example: The number of paths for generating data of type b is 5, of which 3 have different original data generating the same derived data, so K a=2, the number of data types in each generated path is 3; the calculated correlation G=1 / 3×1 / 3×5=5 / 9; Step S304: Obtain the number of abnormal risk assessment records corresponding to the risk characteristic indicator in the enterprise risk assessment system, and obtain the abnormality ratio β of the abnormal risk characteristic indicator; set the abnormality ratio threshold β max , if β>β max , then select the data with the highest correlation with the risk characteristic indicator and set it as high-risk data.

[0024] Step S400: when there is a risk assessment log, a new risk assessment record is generated in real time; if there is high-risk data in the new risk assessment record, the new risk assessment record is directly risk assessed; if the obtained risk value exceeds the risk threshold, the new risk assessment record is marked as abnormal; if there are several consecutive risk assessment records in the risk assessment log with abnormal marks, the risk assessment log is warned; Wherein, step S400 includes the following steps: Step S401: When a certain type of high-risk data is stored in the new risk assessment record, the risk assessment model is input into the new risk assessment record to calculate the risk value F of the new risk assessment record. new ; Step S402: Setting the risk threshold for determining whether the risk assessment record is abnormal to F max When F new >F max , the new risk assessment record is marked as abnormal; Step S403: obtaining a risk assessment record in the risk assessment log corresponding to the new risk assessment record that is closest to the generation time of the new risk assessment record, and if there is an abnormal mark in the most recent risk assessment record, issuing a warning to the risk assessment log.

[0025] An enterprise risk intelligent management and control system based on data analysis technology, the management and control system includes a historical record analysis module, an enterprise risk analysis module, a risk data analysis module and a risk real-time analysis module; The historical record analysis module is used to generate a corresponding risk assessment log for the risk assessment of each enterprise in the enterprise risk assessment system; conduct a risk assessment on any enterprise every unit period, and generate a risk assessment record in the risk assessment log for the process of the risk assessment; classify all risk assessment records in any risk assessment log, and determine the risk characteristic index of each type of risk assessment record and the normal deviation range of each risk characteristic index; The enterprise risk analysis module is used to build a risk assessment model, obtain the deviation amplitude between any risk assessment record and the corresponding risk characteristic indicator, and calculate the risk value of the risk assessment record; extract the risk values ​​of all risk assessment records with abnormalities, and determine the risk threshold for judging whether the risk assessment record has abnormalities; The risk data analysis module is used to select a risk assessment log at random, obtain all data stored in each risk assessment record, analyze the source of each type of data, and obtain the correlation between each type of data; according to the abnormal situation of each risk assessment record and the correlation between each type of data, obtain the probability of abnormality of each type of data, and set several types of high-risk data; The real-time risk analysis module is used to generate a new risk assessment record in real time when there is a risk assessment log; if there is high-risk data in the new risk assessment record, the new risk assessment record is directly subjected to risk assessment; if the obtained risk value exceeds the risk threshold, the new risk assessment record is marked as abnormal; if there are abnormal marks for several consecutive risk assessment records in the risk assessment log, the risk assessment log is warned.

[0026] Among them, the historical record analysis module includes an evaluation record setting unit and an evaluation record division unit; The assessment record setting unit is used to generate a corresponding risk assessment log for the risk assessment of each enterprise in the enterprise risk assessment system; conduct a risk assessment on any enterprise once every unit period, and generate a risk assessment record in the risk assessment log for the risk assessment process; the assessment record classification unit is used to classify all risk assessment records in any risk assessment log, and determine the risk characteristic indicators of each type of risk assessment record and the normal deviation range of each risk characteristic indicator.

[0027] Among them, the enterprise risk analysis module includes an assessment model analysis unit and a risk threshold setting unit; The assessment model analysis unit is used to construct a risk assessment model, obtain the deviation amplitude between any risk assessment record and the corresponding risk characteristic indicator, and calculate the risk value of the risk assessment record; the risk threshold setting unit is used to extract the risk values ​​of all risk assessment records with abnormalities, and determine the risk threshold for judging whether the risk assessment record has abnormalities.

[0028] Among them, the risk data analysis module includes a data association assessment unit and a data anomaly analysis unit; The data association assessment unit is used to arbitrarily select a risk assessment log, obtain all data stored in each risk assessment record, analyze the sources of various types of data, and obtain the correlation between various types of data; the data anomaly analysis unit is used to obtain the probability of anomalies in various types of data based on the abnormal conditions of each risk assessment record and the correlation between various types of data, and set several types of high-risk data.

[0029] Among them, the real-time risk analysis module includes a real-time risk assessment unit and an abnormal marking analysis unit; The real-time risk assessment unit is used to generate a new risk assessment record in real time when it exists in a certain risk assessment log; if there is high-risk data in the new risk assessment record, the new risk assessment record is directly subjected to risk assessment; the abnormal marking analysis unit is used to mark the new risk assessment record as abnormal if the obtained risk value exceeds the risk threshold; if there are several consecutive risk assessment records in the certain risk assessment log with abnormal markings, the risk assessment log is warned.

[0030] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An enterprise risk intelligent management and control method based on data analysis technology, characterized by: The control method comprises the following steps: Step S100: In the enterprise risk assessment system, a corresponding risk assessment log is generated for the risk assessment of each enterprise; a risk assessment is performed on any enterprise every unit period, and the risk assessment process is generated into a risk assessment record in the risk assessment log; all risk assessment records in any risk assessment log are classified, and the risk characteristic index of each type of risk assessment record and the normal deviation range of each risk characteristic index are determined; Step S200: construct a risk assessment model, obtain the deviation amplitude between any risk assessment record and the corresponding risk characteristic indicator, and calculate the risk value of the risk assessment record; extract the risk values ​​of all risk assessment records with abnormalities, and determine the risk threshold for judging whether the risk assessment record has abnormalities; Step S300: arbitrarily select a risk assessment log, obtain all data stored in each risk assessment record, analyze the source of each type of data, and obtain the correlation between each type of data; according to the abnormal situation of each risk assessment record and the correlation between each type of data, obtain the probability of abnormality of each type of data, and set several types of high-risk data; Step S400: When there is a certain risk assessment log, a new risk assessment record is generated in real time; if there is high-risk data in the new risk assessment record, the new risk assessment record is directly risk assessed; if the obtained risk value exceeds the risk threshold, the new risk assessment record is marked as abnormal; if there are several consecutive risk assessment records in the certain risk assessment log with abnormal marks, the risk assessment log is warned.

2. According to claim 1, a method for intelligent enterprise risk management and control based on data analysis technology is characterized by: The step S100 includes the following steps: Step S101: obtaining the ith risk assessment record in a certain risk assessment log, extracting the data information stored in the ith risk assessment record; obtaining the data source of all the data information, classifying all the data information according to the data source, and generating a data set for the ith risk assessment record; Step S102: Obtain the assessment result in the i-th risk assessment record, compare the data type of the assessment result with a certain type of data in the data set, and set a similarity threshold , where N i is the number of data categories in the i-th risk assessment record; if the obtained similarity is greater than the similarity threshold, the data of a certain category is used as the characteristic data of the i-th risk assessment record, and the data of the category with the highest similarity is used as the risk characteristic indicator of the i-th risk assessment record; Step S103: select all risk assessment records with the same risk characteristic index of the i-th risk assessment record, and divide them into a normal record set and an abnormal record set according to whether there is an abnormality; set the offset of a certain risk assessment record on the risk characteristic index as P, extract the offset of each risk assessment record in the normal record set and the abnormal record set respectively, and obtain the normal offset range in the normal record set as (P1, P2) and the abnormal offset range in the abnormal record set as (P3, P4); if P3<P2, then obtain the error range as (P3, P2); Step S104: Continuously select a value P from small to large in the error range. ’ , statistics in the normal record set offset is (P3,P ’ ) is M3, and the offset in the abnormal record set is (P ’ ,P4) is M4, until M3>M 12 -M3 and M4>M 34 -M4, the value P ’ As the characteristic offset of the normal record set and the abnormal record set, the normal amplitude range of the risk characteristic index of the risk characteristic index of the i-th risk assessment record is (0,P ’ ).

3. The enterprise risk intelligent management and control method based on data analysis technology according to claim 2 is characterized by: The step S200 includes the following steps: Step S201: Obtain the offset of the i-th risk assessment record in the risk assessment log as P i , the normal range of the risk characteristic index of the i-th risk assessment record is (0,P ’ ), build a risk assessment model: ； Among them, N i is the number of data categories in the i-th risk assessment record, a and b are constant coefficients; the risk value of the i-th risk assessment record is calculated to be F i ; Step S202: arbitrarily select a number of risk assessment records in each risk assessment log, train the risk assessment model, and obtain the risk value of each risk assessment record in the number of risk assessment records; divide the number of risk assessment records into a normal record set and an abnormal record set according to whether there is an abnormality, obtain the risk value range of the normal record set and the abnormal record set respectively, and determine the values ​​of a and b, so that the risk value of each risk assessment record in the normal record set is less than the risk value of each risk assessment record in the abnormal record set; Step S203: The remaining risk assessment records are used as a test set and input into the risk assessment model. If the risk value of a risk assessment record in the normal record set is greater than the risk value of a risk assessment record in the abnormal record set, then a number of risk assessment records from the remaining risk assessment records are selected to retrain the risk assessment model. Step S204: Obtain the risk value of each risk assessment record in all risk assessment records with abnormalities, and select the risk value with the smallest value as the risk threshold for determining whether the risk assessment record has abnormalities.

4. The enterprise risk intelligent management and control method based on data analysis technology according to claim 3 is characterized by: The step S300 includes the following steps: Step S301: Obtain a data set of the i-th risk assessment record in the risk assessment log, and obtain the data source of each type of data information in the data set; if the data source of a certain type of data information is the same as a type of data information in the data set of any other risk assessment record, then set the certain type of data information as derived data, otherwise, set the certain type of data information as original data; and obtain a plurality of derived data sets and original data sets; Step S302: arbitrarily selecting a type of derived data, when the data source of the derived data is another type of derived data, obtaining the data source of the other type of derived data again until the data source is the original data, thereby generating a generation path for the derived data; Step S303: Obtain the risk characteristic index of the i-th risk assessment record in the risk assessment log, and obtain a number of generation paths for generating the risk characteristic index; set the number of the generation paths to L. i , where the number of data types in the jth generation path is H j , according to the formula: ； Among them, K a is the number of categories of the remaining data that are the same as the derived data generated by the a-th data; the correlation degree G between the a-th data and the risk characteristic index is calculated a ; Step S304: Obtain the number of abnormal risk assessment records corresponding to the risk characteristic indicator in the enterprise risk assessment system, and obtain the abnormality ratio β of the abnormal risk characteristic indicator; set the abnormality ratio threshold β max , if β>β max , then select the data with the highest correlation with the risk characteristic indicator and set it as high-risk data.

5. The enterprise risk intelligent management and control method based on data analysis technology according to claim 4 is characterized by: The step S400 includes the following steps: Step S401: When a certain type of high-risk data is stored in the new risk assessment record, the risk assessment model is input into the new risk assessment record to calculate the risk value F of the new risk assessment record. new ; Step S402: Setting the risk threshold for determining whether the risk assessment record is abnormal to F max When F new >F max , the new risk assessment record is marked as abnormal; Step S403: obtaining a risk assessment record in the risk assessment log corresponding to the new risk assessment record that is closest to the generation time of the new risk assessment record, and if there is an abnormal mark in the most recent risk assessment record, issuing a warning to the risk assessment log.

6. An enterprise risk intelligent management and control system, used to execute the enterprise risk intelligent management and control method based on data analysis technology as described in any one of claims 1 to 5, characterized in that: The management and control system includes a historical record analysis module, an enterprise risk analysis module, a risk data analysis module and a risk real-time analysis module; The historical record analysis module is used to generate corresponding risk assessment logs for risk assessment of each enterprise in the enterprise risk assessment system; Conduct a risk assessment on any enterprise every unit period, and generate a risk assessment record in the risk assessment log for the risk assessment process; Classify all risk assessment records in any risk assessment log, determine the risk characteristic indicators of each type of risk assessment record and the normal deviation range of each risk characteristic indicator; The enterprise risk analysis module is used to construct a risk assessment model, obtain the deviation between any risk assessment record and the corresponding risk characteristic indicator, and calculate the risk value of the risk assessment record; Extract the risk values ​​of all risk assessment records with abnormalities and determine the risk threshold for judging whether the risk assessment records have abnormalities; The risk data analysis module is used to select a risk assessment log at random, obtain all data stored in each risk assessment record, analyze the source of each type of data, and obtain the correlation between each type of data; according to the abnormal situation of each risk assessment record and the correlation between each type of data, obtain the probability of abnormality of each type of data, and set several types of high-risk data; The risk real-time analysis module is used to generate a new risk assessment record in real time when there is a risk assessment log; If there is high-risk data in the new risk assessment record, directly perform risk assessment on the new risk assessment record; If the obtained risk value exceeds the risk threshold, the new risk assessment record is marked as abnormal; If several consecutive risk assessment records in the risk assessment log have abnormal marks, a warning is issued for the risk assessment log.

7. The enterprise risk intelligent management and control system according to claim 6 is characterized by: The historical record analysis module includes an evaluation record setting unit and an evaluation record division unit; The assessment record setting unit is used to generate corresponding risk assessment logs for risk assessment of each enterprise in the enterprise risk assessment system; A risk assessment is conducted on any enterprise every unit period, and the risk assessment process is generated into a risk assessment record in the risk assessment log; the assessment record classification unit is used to classify all risk assessment records in any risk assessment log, determine the risk characteristic indicators of each type of risk assessment record and the normal deviation range of each risk characteristic indicator.

8. The enterprise risk intelligent management and control system according to claim 6 is characterized by: The enterprise risk analysis module includes an evaluation model analysis unit and a risk threshold setting unit; The assessment model analysis unit is used to construct a risk assessment model, obtain the deviation amplitude between any risk assessment record and the corresponding risk characteristic index, and calculate the risk value of the risk assessment record; The risk threshold setting unit is used to extract the risk values ​​of all risk assessment records with abnormalities and determine the risk threshold for judging whether the risk assessment records have abnormalities.

9. The enterprise risk intelligent management and control system according to claim 6 is characterized by: The risk data analysis module includes a data association assessment unit and a data anomaly analysis unit; The data association evaluation unit is used to arbitrarily select a risk assessment log, obtain all data stored in each risk assessment record therein, analyze the sources of various types of data, and obtain the association between various types of data; The data anomaly analysis unit is used to obtain the probability of anomalies in various types of data according to the anomalies of various risk assessment records and the correlation between various types of data, and set several types of high-risk data.

10. The enterprise risk intelligent management and control system according to claim 6, characterized in that: The real-time risk analysis module includes a real-time risk assessment unit and an abnormality mark analysis unit; The real-time risk assessment unit is used to generate a new risk assessment record in real time when there is a risk assessment log; If there is high-risk data in the new risk assessment record, directly perform risk assessment on the new risk assessment record; The abnormal marking analysis unit is used to mark the new risk assessment record as abnormal if the obtained risk value exceeds the risk threshold; If several consecutive risk assessment records in the risk assessment log have abnormal marks, a warning is issued for the risk assessment log.

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