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CN119130163B - Enterprise risk intelligent management and control system and method based on data analysis technology - Google Patents

Enterprise risk intelligent management and control system and method based on data analysis technology Download PDF

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CN119130163B
CN119130163B CN202411641465.5A CN202411641465A CN119130163B CN 119130163 B CN119130163 B CN 119130163B CN 202411641465 A CN202411641465 A CN 202411641465A CN 119130163 B CN119130163 B CN 119130163B
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CN119130163A (en
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赵里海
王艺
葛靓
汪嘉旻
李广宇
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Cloud Accounting Room Network Technology Co ltd
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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 steps of classifying all risk assessment records in any risk assessment log, determining risk characteristic indexes of each type of risk assessment record and normal deviation amplitude of each risk characteristic index, calculating risk values of the risk assessment records, determining risk threshold values for judging that abnormality exists in the risk assessment records, analyzing relevance among various types of data, setting a plurality of types of high risk data, directly carrying out risk assessment on the new risk assessment records if the high risk data exists in the new risk assessment records, carrying out abnormality marking on the new risk assessment records if the obtained risk values exceed the risk threshold values, and carrying out warning on the risk assessment log if abnormality marks exist in a plurality of continuous risk assessment records in the certain risk assessment log.

Description

Enterprise risk intelligent management and control system and method based on data analysis technology
Technical Field
The invention relates to the technical field of risk management and control, in particular to an enterprise risk intelligent management and control system and method based on a data analysis technology.
Background
The enterprise risk assessment is a process of identifying, analyzing and assessing potential risks faced by enterprises and formulating coping strategies, and aims to find and describe enterprise risks, evaluate the influence degree and risk value of various identified risks on the realization of targets by the enterprises and the like,
The effective risk assessment can help enterprises prevent and reduce the influence of risks, ensure continuous and stable operation of the business, is of great importance for effective management and control of risks for timely discovering risks of the enterprises, and can cause continuous and subsequent risks to the enterprises if timely discovering the risks of the enterprises and finding the source causing the abnormality is impossible.
Disclosure of Invention
The invention aims to provide an enterprise risk intelligent management and control system and method based on a data analysis technology, so as to solve the problems in the prior art.
In order to achieve the purpose, the invention provides the technical scheme that the enterprise risk intelligent management and control method based on the data analysis technology comprises the following steps:
Step 100, in an enterprise risk assessment system, generating corresponding risk assessment logs for risk assessments of all enterprises, carrying out risk assessment on any enterprise once every unit period, generating one risk assessment record in the risk assessment logs, classifying all risk assessment records in any risk assessment log, and determining risk characteristic indexes of each type of risk assessment record and normal deviation amplitude of each risk characteristic index;
Step 200, constructing a risk assessment model, obtaining the offset amplitude of any risk assessment record and a corresponding risk characteristic index, and calculating to obtain the risk value of the risk assessment record;
Step 300, randomly selecting a certain risk assessment log, acquiring all data stored in each risk assessment log, analyzing sources of various data to obtain relevance among various data, obtaining probability of occurrence of abnormality of various data according to abnormality of each risk assessment log and relevance among various data, and setting a plurality of types of high risk data;
Step 400, when a certain risk assessment log exists, generating a new risk assessment record in real time, if high risk data exists in the new risk assessment record, directly carrying out risk assessment on the new risk assessment record, if the obtained risk value exceeds a risk threshold, carrying out abnormal marking on the new risk assessment record, and if a plurality of continuous risk assessment records exist in the certain risk assessment log, carrying out warning on the risk assessment log.
Further, step S100 includes the steps of:
Step S101, acquiring an ith risk assessment record in a certain risk assessment log, and extracting data information stored in the ith risk assessment record; acquiring data sources of all data information, classifying all the data information according to the data sources, and generating a data set of an ith risk assessment record;
step S102, obtaining an evaluation result in the ith risk evaluation record, comparing the data type of the evaluation result with some data in the data set in a similarity way, and setting a similarity threshold value Wherein N i is the number of data categories in the ith risk assessment record; if the obtained similarity is larger than a similarity threshold, taking the certain type of data as characteristic data of the ith risk assessment record, and taking the type of data with the highest similarity as a risk characteristic index of the ith risk assessment record;
step S103, selecting all risk assessment records with the same risk characteristic index as the ith risk assessment record, and dividing the risk assessment records into a normal record set and an abnormal record set according to whether abnormality exists; setting the offset of a certain risk evaluation record on a risk characteristic index as P, and respectively extracting the offset of each risk evaluation record in a normal record set and an abnormal record set to obtain a normal offset range in the normal record set as P 1,P2 and an abnormal offset range in the abnormal record set as P 3,P4;
Step S104, continuously selecting a value P ' from small to large in an error range, counting the number of risk assessment records with the offset between (P 3,P') in a normal record set as M 3, counting the number of risk assessment records with the offset between (P ',P4) in an abnormal record set as M 4 until M 3＞M12-M3 and M 4＞M34-M4 are met, and taking the value P ' as the characteristic offset of the divided normal record set and the abnormal record set to obtain a normal amplitude range of a risk characteristic index of an ith risk assessment record as (0, P ');
when the offset range of the normal record set overlaps with the offset range of the abnormal record set, the overlapping range is subdivided by the distribution condition of the two record sets within the overlapping range, so that the offset evaluation range can be as accurate as possible.
Further, step S200 includes the steps of:
Step S201, obtaining the offset of the ith risk assessment record in the certain risk assessment log as P i, obtaining the normal range of the risk characteristic index of the ith risk assessment record as (0, P '), and constructing a risk assessment model:
N i is the number of data categories in the ith risk assessment record, and a and b are constant coefficients;
the evaluation of the risk value is to consider the offset with the risk characteristic index and the data quantity influenced by various data, and effectively reflect the magnitude of the risk value by taking the actual offset as a basic value and combining the offset degree;
Step S202, randomly selecting a plurality of risk assessment records in each risk assessment log, training a risk assessment model to obtain risk values of each risk assessment record in the plurality of risk assessment records, dividing the plurality of risk assessment records according to whether abnormality exists to obtain a normal record set and an abnormal record set, respectively obtaining risk value ranges of the normal record set and the abnormal record set, and determining values of a and b so that the risk values of each risk assessment record in the normal record set are smaller than the risk values of each risk assessment record in the abnormal record set;
Step 203, taking the rest risk assessment records as a test set, inputting the test set into a risk assessment model, and if the risk value of the risk assessment record in the normal record set is larger than the risk value of one risk assessment record in the abnormal record set, selecting a plurality of risk assessment records in the rest risk assessment records to retrain the risk assessment model;
Step S204, acquiring risk values of all risk assessment records with abnormality, and selecting the risk value with the smallest value as a risk threshold for judging that the risk assessment record has abnormality.
Further, step S300 includes the steps of:
Step 301, acquiring a data set of an ith risk assessment record in the certain risk assessment log to obtain a data source of each type of data information in the data set, setting the certain type of data information as derivative data if the data source of the certain type of data information is the same as one type of data information in the data set of any other risk assessment record, otherwise, setting the certain type of data information as original data to obtain a plurality of derivative data sets and original data sets;
step S302, randomly selecting one type of derivative data, and when the data source of the derivative data is another type of derivative data, acquiring the data source of the another type of derivative data again until the data source is original data, and generating a generation path of the derivative data;
Step S303, acquiring a risk characteristic index of an ith risk assessment record in the certain risk assessment log to obtain a plurality of generation paths for generating the risk characteristic index, setting the number of paths of the plurality of generation paths as L i, wherein the number of data types in the jth generation path is H j, and according to the formula:
The calculation of the degree of association G a;Li-(Ka between the class a data and the risk characteristic index reflects that among the generated paths, the paths exist because of different initial data, but the middle derived data are the same, the generated derived data are obtained through the common of multiple classes of initial data, and the paths are actually the same path and need to be eliminated;
Step S304, obtaining the number of records with abnormality in the risk assessment records corresponding to the risk characteristic indexes in the enterprise risk assessment system, obtaining the abnormality proportion beta of abnormality of the risk characteristic indexes, setting an abnormality proportion threshold beta max, if beta > beta max, selecting the data with the highest degree of association with the risk characteristic indexes, and setting the data as high risk data.
Further, step S400 includes the steps of:
step S401, when a certain type of high risk data is stored in the new risk assessment record, inputting a risk assessment model into the new risk assessment record, and calculating to obtain a risk value F new of the new risk assessment record;
Step S402, setting a risk threshold for judging that the risk assessment record is abnormal as F max, and when F new＞Fmax, performing abnormal marking on the new risk assessment record;
Step S403, acquiring one risk assessment record closest to the generation time of the new risk assessment record in the risk assessment log corresponding to the new risk assessment record, and warning the risk assessment log if an abnormal mark exists in the latest one risk assessment record.
In order to better realize the method, an enterprise risk intelligent management and control system based on a data analysis technology is also provided, and the management and control system comprises a history analysis module, an enterprise risk analysis module, a risk data analysis module and a risk real-time analysis module;
The history analysis module is used for generating corresponding risk assessment logs for risk assessment of each enterprise in an enterprise risk assessment system, carrying out risk assessment on any enterprise once every other unit period, and generating one risk assessment record in the risk assessment logs;
The enterprise risk analysis module is used for constructing a risk assessment model, obtaining the offset amplitude of any risk assessment record and a corresponding risk characteristic index, and calculating to obtain the risk value of the risk assessment record;
The risk data analysis module is used for arbitrarily selecting a certain risk assessment log, acquiring all data stored in each risk assessment log, analyzing sources of various data, and obtaining relevance among various data;
The risk real-time analysis module is used for generating a new risk assessment record in real time when a certain risk assessment log exists, directly carrying out risk assessment on the new risk assessment record if high risk data exists in the new risk assessment record, carrying out abnormal marking on the new risk assessment record if an obtained risk value exceeds a risk threshold, and carrying out warning on the risk assessment log if abnormal marks exist in a plurality of continuous risk assessment records in the certain risk assessment log.
Further, the history analysis module comprises an evaluation record setting unit and an evaluation record dividing unit;
The evaluation record setting unit is used for generating corresponding risk evaluation logs for risk evaluation of each enterprise in the enterprise risk evaluation system, carrying out risk evaluation on any enterprise every other unit period, generating one risk evaluation record in the risk evaluation logs, and the evaluation record dividing unit is used for classifying all the risk evaluation records in any risk evaluation log to determine risk characteristic indexes of each type of risk evaluation record and normal deviation amplitude of each risk characteristic index.
Further, the enterprise risk analysis module comprises an evaluation model analysis unit and a risk threshold setting unit;
the evaluation model analysis unit is used for constructing a risk evaluation model, acquiring the offset amplitude of any risk evaluation record and a corresponding risk characteristic index, and calculating to obtain a risk value of the risk evaluation record; the risk threshold setting unit is used for extracting risk values of all risk assessment records with abnormality and determining a risk threshold for judging that the risk assessment records have abnormality.
Further, the enterprise risk analysis module comprises an evaluation model analysis unit and a risk threshold setting unit;
the evaluation model analysis unit is used for constructing a risk evaluation model, acquiring the offset amplitude of any risk evaluation record and a corresponding risk characteristic index, and calculating to obtain a risk value of the risk evaluation record; the risk threshold setting unit is used for extracting risk values of all risk assessment records with abnormality and determining a risk threshold for judging that the risk assessment records have abnormality.
Further, the risk data analysis module comprises a data association evaluation unit and a data abnormality analysis unit;
The data abnormality analysis unit is used for obtaining the probability of abnormality of various data according to the abnormality condition of various risk assessment records and the relevance between various data, and setting a plurality of types of high risk data.
Further, the risk real-time analysis module comprises a risk real-time assessment unit and an abnormality mark analysis unit;
The risk real-time evaluation unit is used for generating a new risk evaluation record in real time when a certain risk evaluation log exists, directly performing risk evaluation on the new risk evaluation record if high risk data exist in the new risk evaluation record, performing abnormal marking on the new risk evaluation record if an obtained risk value exceeds a risk threshold value, and warning the risk evaluation log if a plurality of continuous risk evaluation records exist in the certain risk evaluation log.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, by evaluating the risk value of the enterprise, effective management and risk control of the enterprise are realized, and abnormal enterprises are timely found and reminded, so that the normal operation of the enterprise is ensured;
2. The risk assessment records are classified according to the data stored in different risk assessment records and the risk characteristic indexes for risk assessment at last, and abnormal conditions of various risk assessment records can be reasonably reflected through different offsets;
3. According to the method, the degree of influence of various data on the final risk characteristic index is obtained by analyzing the relevance among various data, the situation that the same risk characteristic index continuously appears abnormal is considered, a plurality of types of high risk data are screened, when the generated risk assessment record records the high risk data, the risk assessment is directly carried out, the risk of the high risk data on an enterprise is avoided, the abnormality can be found and processed in time, and the loss of the enterprise is reduced.
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FIG. 1 is a schematic diagram of steps of an enterprise risk intelligent management and control method based on a data analysis technology;
fig. 2 is a schematic structural diagram of an enterprise risk intelligent management and control system based on a data analysis technology.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
1-2, The invention provides an enterprise risk intelligent management and control method based on a data analysis technology, which comprises the following steps:
Step 100, in an enterprise risk assessment system, generating corresponding risk assessment logs for risk assessments of all enterprises, carrying out risk assessment on any enterprise once every unit period, generating one risk assessment record in the risk assessment logs, classifying all risk assessment records in any risk assessment log, and determining risk characteristic indexes of each type of risk assessment record and normal deviation amplitude of each risk characteristic index;
wherein, step S100 includes the following steps:
Step S101, acquiring an ith risk assessment record in a certain risk assessment log, and extracting data information stored in the ith risk assessment record; acquiring data sources of all data information, classifying all the data information according to the data sources, and generating a data set of an ith risk assessment record;
step S102, obtaining an evaluation result in the ith risk evaluation record, comparing the data type of the evaluation result with some data in the data set in a similarity way, and setting a similarity threshold value If the obtained similarity is larger than a similarity threshold, taking the certain type of data as the characteristic data of the ith risk assessment record, and taking the type of data with the highest similarity as the risk characteristic index of the ith risk assessment record;
step S103, selecting all risk assessment records with the same risk characteristic index as the ith risk assessment record, and dividing the risk assessment records into a normal record set and an abnormal record set according to whether abnormality exists; setting the offset of a certain risk evaluation record on a risk characteristic index as P, and respectively extracting the offset of each risk evaluation record in a normal record set and an abnormal record set to obtain a normal offset range in the normal record set as P 1,P2 and an abnormal offset range in the abnormal record set as P 3,P4;
Step S104, continuously selecting a value P ' from small to large in an error range, counting the number of risk assessment records with offset between (P 3,P') in a normal record set as M 3, counting the number of risk assessment records with offset between (P ',P4) in an abnormal record set as M 4 until M 3＞M12-M3 and M 4＞M34-M4 are met, and taking the value P ' as the characteristic offset of the divided normal record set and the abnormal record set to obtain a normal amplitude range of risk characteristic indexes of an ith risk assessment record as (0, P ');
The method includes the steps of setting a normal offset range in a normal record set to be (0, 20%), setting an abnormal offset range in an abnormal record set to be (15%, 50%), obtaining an error range to be (15%, 20%), selecting an offset from the error range to be 17%, obtaining more offsets in the normal record set in the (15%, 17%), and obtaining an offset 17% as a characteristic offset when the offsets in the abnormal record set in the (17%, 20%) range are more, and obtaining a normal amplitude range of a risk characteristic index to be (0,17%);
Step 200, constructing a risk assessment model, obtaining the offset amplitude of any risk assessment record and a corresponding risk characteristic index, and calculating to obtain the risk value of the risk assessment record;
wherein, step S200 includes the following steps:
Step S201, obtaining the offset of the ith risk assessment record in the certain risk assessment log as P i, obtaining the normal range of the risk characteristic index of the ith risk assessment record as (0, P '), and constructing a risk assessment model:
N i is the number of data categories in the ith risk assessment record, and a and b are constant coefficients;
Setting the offset of the ith risk assessment record to be 10%, and setting the normal amplitude range to be (0,17%), wherein the number of data categories in the ith risk assessment record is 3, a=50%, and b=100, and obtaining a risk value of F i =10% × (1-0.7×50%) ×3+100= 100.195;
Step S202, randomly selecting a plurality of risk assessment records in each risk assessment log, training a risk assessment model to obtain risk values of each risk assessment record in the plurality of risk assessment records, dividing the plurality of risk assessment records according to whether abnormality exists to obtain a normal record set and an abnormal record set, respectively obtaining risk value ranges of the normal record set and the abnormal record set, and determining values of a and b so that the risk values of each risk assessment record in the normal record set are smaller than the risk values of each risk assessment record in the abnormal record set;
Step 203, taking the rest risk assessment records as a test set, inputting the test set into a risk assessment model, and if the risk value of the risk assessment record in the normal record set is larger than the risk value of one risk assessment record in the abnormal record set, selecting a plurality of risk assessment records in the rest risk assessment records to retrain the risk assessment model;
Step S204, acquiring risk values of all risk assessment records with abnormality, and selecting the risk value with the smallest value as a risk threshold for judging that the risk assessment record has abnormality.
Step 300, randomly selecting a certain risk assessment log, acquiring all data stored in each risk assessment log, analyzing sources of various data to obtain relevance among various data, obtaining probability of occurrence of abnormality of various data according to abnormality of each risk assessment log and relevance among various data, and setting a plurality of types of high risk data;
wherein, step S300 includes the following steps:
Step 301, acquiring a data set of an ith risk assessment record in the certain risk assessment log to obtain a data source of each type of data information in the data set, setting the certain type of data information as derivative data if the data source of the certain type of data information is the same as one type of data information in the data set of any other risk assessment record, otherwise, setting the certain type of data information as original data to obtain a plurality of derivative data sets and original data sets;
Step S302, randomly selecting one type of derivative data, and when the data source of the derivative data is another type of derivative data, acquiring the data source of the another type of derivative data again until the data source is original data, and generating a generation path of the derivative data;
Step S303, acquiring a risk characteristic index of an ith risk assessment record in the certain risk assessment log to obtain a plurality of generation paths for generating the risk characteristic index, setting the number of paths of the plurality of generation paths as L i, wherein the number of data types in the jth generation path is H j, and according to the formula:
K a is the same category number of the rest data as the derivative data generated by the class a data, and the degree of association G a between the class a data and the risk characteristic index is calculated;
The method comprises the steps of obtaining 5 generation paths for generating b-th data, wherein 3 generation paths have different original data to generate the same derivative data, so K a =2, and the number of data types of each generation path is 3;
Step S304, obtaining the number of records with abnormality in the risk assessment records corresponding to the risk characteristic indexes in the enterprise risk assessment system, obtaining the abnormality proportion beta of abnormality of the risk characteristic indexes, setting an abnormality proportion threshold beta max, if beta > beta max, selecting the data with the highest degree of association with the risk characteristic indexes, and setting the data as high risk data.
Step 400, when a certain risk assessment log exists, generating a new risk assessment record in real time, if high risk data exists in the new risk assessment record, directly carrying out risk assessment on the new risk assessment record, if the obtained risk value exceeds a risk threshold value, carrying out abnormal marking on the new risk assessment record, and if abnormal marks exist in a plurality of continuous risk assessment records in the certain risk assessment log, carrying out warning on the risk assessment log;
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, inputting a risk assessment model into the new risk assessment record, and calculating to obtain a risk value F new of the new risk assessment record;
Step S402, setting a risk threshold for judging that the risk assessment record is abnormal as F max, and when F new＞Fmax, performing abnormal marking on the new risk assessment record;
Step S403, acquiring one risk assessment record closest to the generation time of the new risk assessment record in the risk assessment log corresponding to the new risk assessment record, and warning the risk assessment log if an abnormal mark exists in the latest one risk assessment record.
The enterprise risk intelligent management and control system based on the data analysis technology comprises a history analysis module, an enterprise risk analysis module, a risk data analysis module and a risk real-time analysis module;
The history analysis module is used for generating corresponding risk assessment logs for risk assessment of each enterprise in an enterprise risk assessment system, carrying out risk assessment on any enterprise once every other unit period, and generating one risk assessment record in the risk assessment logs;
The enterprise risk analysis module is used for constructing a risk assessment model, obtaining the offset amplitude of any risk assessment record and a corresponding risk characteristic index, and calculating to obtain the risk value of the risk assessment record;
The risk data analysis module is used for arbitrarily selecting a certain risk assessment log, acquiring all data stored in each risk assessment log, analyzing sources of various data, and obtaining relevance among various data;
The risk real-time analysis module is used for generating a new risk assessment record in real time when a certain risk assessment log exists, directly carrying out risk assessment on the new risk assessment record if high risk data exists in the new risk assessment record, carrying out abnormal marking on the new risk assessment record if an obtained risk value exceeds a risk threshold, and carrying out warning on the risk assessment log if abnormal marks exist in a plurality of continuous risk assessment records in the certain risk assessment log.
The history analysis module comprises an evaluation record setting unit and an evaluation record dividing unit;
The evaluation record setting unit is used for generating corresponding risk evaluation logs for risk evaluation of each enterprise in the enterprise risk evaluation system, carrying out risk evaluation on any enterprise every other unit period, generating one risk evaluation record in the risk evaluation logs, and the evaluation record dividing unit is used for classifying all the risk evaluation records in any risk evaluation log to determine risk characteristic indexes of each type of risk evaluation record and normal deviation amplitude of each risk characteristic index.
The enterprise risk analysis module comprises an assessment model analysis unit and a risk threshold setting unit;
the evaluation model analysis unit is used for constructing a risk evaluation model, acquiring the offset amplitude of any risk evaluation record and a corresponding risk characteristic index, and calculating to obtain a risk value of the risk evaluation record; the risk threshold setting unit is used for extracting risk values of all risk assessment records with abnormality and determining a risk threshold for judging that the risk assessment records have abnormality.
The risk data analysis module comprises a data association evaluation unit and a data abnormality analysis unit;
The data abnormality analysis unit is used for obtaining the probability of abnormality of various data according to the abnormality condition of various risk assessment records and the relevance between various data, and setting a plurality of types of high risk data.
The risk real-time analysis module comprises a risk real-time assessment unit and an abnormality mark analysis unit;
The risk real-time evaluation unit is used for generating a new risk evaluation record in real time when a certain risk evaluation log exists, directly performing risk evaluation on the new risk evaluation record if high risk data exist in the new risk evaluation record, performing abnormal marking on the new risk evaluation record if an obtained risk value exceeds a risk threshold value, and warning the risk evaluation log if a plurality of continuous risk evaluation records exist in the certain risk evaluation log.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.

Claims (6)

1.一种基于数据分析技术的企业风险智能管控方法，其特征在于：所述管控方法包括以下步骤：1. A method for intelligent enterprise risk management and control based on data analysis technology, characterized in that the management and control method comprises the following steps: 步骤S100：在企业风险评估系统中，对各个企业的风险评估生成对应的风险评估日志；对任意企业每隔一个单位周期进行一次风险评估，将所述风险评估的过程生成风险评估日志中的一条风险评估记录；对任意风险评估日志中的所有风险评估记录进行分类，确定每一类风险评估记录的风险特征指标和每个风险特征指标的正常偏移幅度；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; 步骤S200：构建一个风险评估模型，获取任意一条风险评估记录与对应的风险特征指标的偏移幅度，计算得到所述风险评估记录的风险值；提取所有存在异常的风险评估记录的风险值，确定判断风险评估记录存在异常的风险阈值；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; 步骤S300：任意选取某个风险评估日志，获取其中每一条风险评估记录中存储的所有数据，分析各类数据的来源，得到各类数据之间的关联性；根据各条风险评估记录的异常情况和各类数据之间的关联性，得到各类数据出现异常的概率，并设定若干类高风险数据；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; 步骤S400：当存在某个风险评估日志中，实时生成一条新的风险评估记录；若所述新的风险评估记录中存在高风险数据，则直接对所述新的风险评估记录进行风险评估；若得到的风险值超过风险阈值，则对所述新的风险评估记录进行异常标记；若所述某个风险评估日志中，连续若干条风险评估记录均存在异常标记时，则对所述风险评估日志进行警告；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; 所述步骤S100包括以下步骤：The step S100 includes the following steps: 步骤S101：获取某一风险评估日志中的第i条风险评估记录，提取所述第i条风险评估记录中存储的数据信息；获取所有数据信息的数据来源，按照数据来源对所述所有数据信息进行分类，并生成第i条风险评估记录的数据集；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; 步骤S102：获取第i条风险评估记录中的评估结果，将所述评估结果的数据类型与数据集中的某类数据进行相似度比对，设定一个相似度阈值θ＝1/Ni，其中，Ni为第i条风险评估记录中的数据类别数量；若得到的相似度大于相似度阈值，则将所述某类数据作为第i条风险评估记录的特征数据，将相似度最高的一类数据作为第i条风险评估记录的风险特征指标；Step S102: Obtain the assessment result in the ith risk assessment record, compare the data type of the assessment result with a certain type of data in the data set for similarity, and set a similarity threshold θ=1/N i , where N i is the number of data categories in the ith risk assessment record; if the obtained similarity is greater than the similarity threshold, use the certain type of data as the characteristic data of the ith risk assessment record, and use the type of data with the highest similarity as the risk characteristic indicator of the ith risk assessment record; 步骤S103：选取第i条风险评估记录的风险特征指标相同的所有风险评估记录，并按照是否存在异常划分为正常记录集和异常记录集；设定某一风险评估记录在风险特征指标上的偏移量为P，分别提取正常记录集和异常记录集中各条风险评估记录的偏移量，得到正常记录集中的正常偏移范围为(P1,P2)和异常记录集中的异常偏移范围为(P3,P4)；若P3＜P2，则得到误差范围为(P3,P2)；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 (P 1 , P 2 ) and the abnormal offset range in the abnormal record set as (P 3 , P 4 ); if P 3 <P 2 , then obtain the error range as (P 3 , P 2 ); 步骤S104：在误差范围中按照数值从小到大连续选取一个数值P’，统计在正常记录集中偏移量处于(P3,P’)之间的风险评估记录数量为M3，在异常记录集中偏移量处于(P’,P4)之间的风险评估记录数量为M4，直到满足M3＞M12-M3并且M4＞M34-M4为止，将所述数值P’作为划分的正常记录集和异常记录集的特征偏移量，得到第i条风险评估记录的风险特征指标的风险特征指标的正常幅度范围为(0,P’)；Step S104: continuously select a value P ' in the error range from small to large, count the number of risk assessment records whose offsets in the normal record set are between ( P3 , P ' ) as M3 , and the number of risk assessment records whose offsets in the abnormal record set are between (P ' , P4 ) as M4 , until M3 > M12 - M3 and M4 > M34 - M4 are satisfied, and use the value P ' as the characteristic offset of the normal record set and the abnormal record set, and obtain the normal amplitude range of the risk characteristic indicator of the risk characteristic indicator of the i-th risk assessment record as (0, P ' ); 所述步骤S200包括以下步骤：The step S200 includes the following steps: 步骤S201：获取所述某一风险评估日志中，第i条风险评估记录的偏移量为Pi，获取所述第i条风险评估记录的风险特征指标的正常幅度范围为(0,P’)，构建风险评估模型：Step S201: Obtain the offset of the ith risk assessment record in the risk assessment log as P i , obtain the normal amplitude range of the risk characteristic indicator of the ith risk assessment record as (0, P ' ), and construct a risk assessment model: 其中，Ni为第i条风险评估记录中的数据类别数量，a、b为常数系数；计算得到第i条风险评估记录的风险值为Fi；Wherein, Ni 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 ; 步骤S202：任意选取各个风险评估日志中的若干条风险评估记录，对风险评估模型进行训练，得到所述若干条风险评估记录中，各条风险评估记录的风险值；将所述若干条风险评估记录按照是否存在异常进行划分得到正常记录集和异常记录集，分别得到正常记录集和异常记录集的风险值范围，确定a和b的值，使得正常记录集中各条风险评估记录的风险值均小于异常记录集中各条风险评估记录的风险值；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; 步骤S203：将其余风险评估记录作为测试集，输入风险评估模型中，若正常记录集中存在风险评估记录的风险值大于异常记录集中某条风险评估记录的风险值，则选取所述其余风险评估记录中的若干条风险评估记录对风险评估模型重新进行训练；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. 步骤S204：获取所有存在异常的风险评估记录中，各条风险评估记录的风险值，选取数值最小的风险值作为判断风险评估记录存在异常的风险阈值；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; 所述步骤S300包括以下步骤：The step S300 includes the following steps: 步骤S301：获取所述某一风险评估日志中，第i条风险评估记录的数据集，得到所述数据集中每一类数据信息的数据来源；若某类数据信息的数据来源与其余任意风险评估记录的数据集中的一类数据信息相同时，则将所述某类数据信息设定为衍生数据，否则，将所述某类数据信息设定为原始数据；得到若干个衍生数据集和原始数据集；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; 步骤S302：任意选取一类衍生数据，当所述衍生数据的数据来源为另一类衍生数据时，则再次获取所述另一类衍生数据的数据来源，直到数据来源为原始数据为止，生成所述衍生数据的生成路径；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; 步骤S303：获取所述某一风险评估日志中，第i条风险评估记录的风险特征指标，得到生成所述风险特征指标的若干条生成路径；设定所述若干条生成路径的路径数量为Li，其中第j条生成路径中的数据类型数量为Hj，根据公式：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 be Li , wherein the number of data types in the j-th generation path is Hj , according to the formula: 其中，Ka为与第a类数据生成的衍生数据相同的，其余数据的类别数量；计算得到第a类数据与所述风险特征指标之间的关联度Ga；Wherein, Ka 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 Ga between the a-th data and the risk characteristic indicator is calculated; 步骤S304：获取在企业风险评估系统中，所述风险特征指标对应的风险评估记录存在异常的记录数量，得到所述风险特征指标出现异常的异常占比β；设定异常占比阈值βmax，若β＞βmax，则选取与所述风险特征指标之间关联度最高的一类数据，并设定为高风险数据；Step S304: obtaining the number of abnormal risk assessment records corresponding to the risk characteristic indicator in the enterprise risk assessment system, and obtaining the abnormality ratio β of the abnormal risk characteristic indicator; setting an abnormality ratio threshold β max , if β>β max , selecting a type of data with the highest correlation with the risk characteristic indicator and setting it as high-risk data; 所述步骤S400包括以下步骤：The step S400 includes the following steps: 步骤S401：设定所述新的风险评估记录中存储了某类高风险数据时，则对所述新的风险评估记录输入风险评估模型，计算得到所述新的风险评估记录的风险值Fnew；Step S401: When a certain type of high-risk data is stored in the new risk assessment record, a risk assessment model is input into the new risk assessment record to calculate a risk value F new of the new risk assessment record; 步骤S402：设定判断风险评估记录存在异常的风险阈值为Fmax；当Fnew＞Fmax时，则对所述新的风险评估记录进行异常标记；Step S402: setting the risk threshold for judging whether the risk assessment record is abnormal to F max ; when F new > F max , marking the new risk assessment record as abnormal; 步骤S403：获取所述新的风险评估记录所对应的风险评估日志中，距离所述新的风险评估记录的生成时间最近的一条风险评估记录，若所述最近的一条风险评估记录中存在异常标记，则对所述风险评估日志进行警告。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. 2.一种企业风险智能管控系统，用于执行权利要求1所述的一种基于数据分析技术的企业风险智能管控方法，其特征在于：所述管控系统包括了历史记录分析模块、企业风险分析模块、风险数据分析模块和风险实时分析模块；2. An enterprise risk intelligent management and control system, used to execute the enterprise risk intelligent management and control method based on data analysis technology according to claim 1, 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; 所述历史记录分析模块，用于在企业风险评估系统中，对各个企业的风险评估生成对应的风险评估日志；对任意企业每隔一个单位周期进行一次风险评估，将所述风险评估的过程生成风险评估日志中的一条风险评估记录；对任意风险评估日志中的所有风险评估记录进行分类，确定每一类风险评估记录的风险特征指标和每个风险特征指标的正常偏移幅度；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; 所述企业风险分析模块，用于构建一个风险评估模型，获取任意一条风险评估记录与对应的风险特征指标的偏移幅度，计算得到所述风险评估记录的风险值；提取所有存在异常的风险评估记录的风险值，确定判断风险评估记录存在异常的风险阈值；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; 所述风险数据分析模块，用于任意选取某个风险评估日志，获取其中每一条风险评估记录中存储的所有数据，分析各类数据的来源，得到各类数据之间的关联性；根据各条风险评估记录的异常情况和各类数据之间的关联性，得到各类数据出现异常的概率，并设定若干类高风险数据；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; 所述风险实时分析模块，用于当存在某个风险评估日志中，实时生成一条新的风险评估记录；若所述新的风险评估记录中存在高风险数据，则直接对所述新的风险评估记录进行风险评估；若得到的风险值超过风险阈值，则对所述新的风险评估记录进行异常标记；若所述某个风险评估日志中，连续若干条风险评估记录均存在异常标记时，则对所述风险评估日志进行警告。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. 3.根据权利要求2所述的一种企业风险智能管控系统，其特征在于：所述历史记录分析模块包括评估记录设定单元和评估记录划分单元；3. An enterprise risk intelligent management and control system according to claim 2, characterized in that: the historical record analysis module includes an evaluation record setting unit and an evaluation record division unit; 所述评估记录设定单元，用于在企业风险评估系统中，对各个企业的风险评估生成对应的风险评估日志；对任意企业每隔一个单位周期进行一次风险评估，将所述风险评估的过程生成风险评估日志中的一条风险评估记录；所述评估记录划分单元，用于对任意风险评估日志中的所有风险评估记录进行分类，确定每一类风险评估记录的风险特征指标和每个风险特征指标的正常偏移幅度。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. 4.根据权利要求2所述的一种企业风险智能管控系统，其特征在于：所述企业风险分析模块包括评估模型分析单元和风险阈值设定单元；4. An enterprise risk intelligent management and control system according to claim 2, characterized in that: the enterprise risk analysis module includes an evaluation model analysis unit and a risk threshold setting unit; 所述评估模型分析单元，用于构建一个风险评估模型，获取任意一条风险评估记录与对应的风险特征指标的偏移幅度，计算得到所述风险评估记录的风险值；所述风险阈值设定单元，用于提取所有存在异常的风险评估记录的风险值，确定判断风险评估记录存在异常的风险阈值。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. 5.根据权利要求2所述的一种企业风险智能管控系统，其特征在于：所述风险数据分析模块包括数据关联评估单元和数据异常分析单元；5. An enterprise risk intelligent management and control system according to claim 2, characterized in that: the risk data analysis module includes a data association evaluation unit and a data anomaly analysis unit; 所述数据关联评估单元，用于任意选取某个风险评估日志，获取其中每一条风险评估记录中存储的所有数据，分析各类数据的来源，得到各类数据之间的关联性；所述数据异常分析单元，用于根据各条风险评估记录的异常情况和各类数据之间的关联性，得到各类数据出现异常的概率，并设定若干类高风险数据。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. 6.根据权利要求2所述的一种企业风险智能管控系统，其特征在于：所述风险实时分析模块包括风险实时评估单元和异常标记分析单元；6. An enterprise risk intelligent management and control system according to claim 2, characterized in that: the risk real-time analysis module includes a risk real-time assessment unit and an abnormality mark analysis unit; 所述风险实时评估单元，用于当存在某个风险评估日志中，实时生成一条新的风险评估记录；若所述新的风险评估记录中存在高风险数据，则直接对所述新的风险评估记录进行风险评估；所述异常标记分析单元，用于若得到的风险值超过风险阈值，则对所述新的风险评估记录进行异常标记；若所述某个风险评估日志中，连续若干条风险评估记录均存在异常标记时，则对所述风险评估日志进行警告。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.
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