A multi-dimensional information evaluation method and system for enterprise safety index

Abstract

The present invention discloses a multi-dimensional information evaluation method and system for an enterprise security index, and relates to the technical field of data analysis. The present invention retrieves business data from an enterprise database to obtain current real-time business project generation data; constructs a business project data set in combination with data normalization processing; performs business feature transformation based on the business project data set, performs development feature value analysis on the business project data, and constructs a business project development feature curve; retrieves business project data feature prediction curve data, performs comparative analysis in combination with the current real-time business project development feature curve, and performs abnormal feature point judgment and classification; performs abnormal risk analysis on local adjacent feature points in combination with feature point classification results; performs risk assessment on the current business project in combination with the abnormal risk analysis data of each local adjacent feature point; visually outputs the risk analysis data of the corresponding business project of the enterprise, and provides comprehensive feedback on the risk assessment results.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and in particular to a multi-dimensional information evaluation method and system for enterprise safety index. Background Art

[0002] Enterprises are the guarantee for the long-term stability of modern society, and enterprise security is the core competitiveness of enterprise development; enterprise safety development is an important benchmark for measuring the high-quality development, high-level security and high-quality ecology of enterprises; nowadays, technological progress has led to the rapid expansion of social demand, and the information society has made the supply of business needs between enterprises and users become complex in type and rapidly growing in number. Corresponding to this is whether the regulatory technology for business can be balanced with its growth rate; the types and numbers of enterprises are quite large at present, and the number of business processing to match them is also increasing exponentially. Traditional business regulatory analysis methods are obviously no longer applicable to the current environment. While manual business risk analysis is time-consuming and labor-intensive, its low-intelligence analysis methods cannot match the current rapid development of business. In the current environment with a large number of businesses, it is easy to cause potential risks of abnormal development of a large number of businesses. Summary of the invention

[0003] The purpose of the present invention is to provide a multi-dimensional information evaluation method and system for enterprise safety index to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A multi-dimensional information evaluation method for an enterprise safety index, the method comprising the following steps:

[0006] S100, by retrieving the business data of the enterprise database, obtaining the current real-time business project generation data of the corresponding enterprise; combining data normalization processing to construct a corresponding business project data set;

[0007] S200, based on the enterprise business project data set, performing business feature conversion on each business data, and performing time point development feature value analysis on the business project data; and constructing a business project development feature curve based on the time point feature conversion analysis results of the business project data;

[0008] S300, retrieve the data of the business project data feature prediction curve, compare and analyze it with the current real-time business project development feature curve, mark the abnormal risk feature points of the current real-time business project development feature curve by building a judgment window, and classify each feature point according to the attribute of the feature point; build an abnormal risk feature vector between adjacent feature points based on the feature point classification result, and perform abnormal risk analysis of local adjacent feature points based on the corresponding interval vector of the prediction curve; and conduct risk assessment on the current business project based on the abnormal risk analysis data of each local adjacent feature point;

[0009] S400: Visualize the risk analysis data of the enterprise's corresponding business projects and provide comprehensive feedback on the risk assessment results.

[0010] The S100 obtains the current real-time business project generation data of the corresponding enterprise by retrieving the business data from the enterprise database; and the specific steps of constructing the corresponding business project data set in combination with data normalization processing are as follows:

[0011] S101. Obtaining data access and retrieval authority for the enterprise database through the database management authority port; using the key search terms of the business project to perform a full data search on the target business project; obtaining the real-time business project generation data of the enterprise corresponding to the search project; the business project generation data includes business field, business type, business development data and business development time point; the business development data includes user data, transaction data, product data, market data, financial data, etc.;

[0012] S102, normalizing the retrieved target business data, respectively constructing a time point business project data matrix for the retrieved business project data corresponding to the business data at the time point; and constructing a corresponding target business project data set by coordinating the business project data matrices at each time point.

[0013] The S200 performs business feature conversion on each business data based on the enterprise business project data set, and performs time point development feature value analysis on the business project data; the specific steps of constructing a business project development feature curve based on the time point feature conversion analysis result of the business project data are as follows:

[0014] S201, retrieve the target business project data set, perform feature conversion analysis on the business project data corresponding to each time point in the set; perform development data feature value analysis based on the business project data matrix corresponding to each time point in the business project data set; analyze the safe development feature value and potential loss feature value of the business project data at each time point, and the calculation formulas are: ; ;

[0015] Among them, L(q,t) is the security development characteristic value of the business project data labeled q at the corresponding time point t; F(q,t) is the potential loss characteristic value of the business project data labeled q at the corresponding time point t; X(q,t) is the frequency rating of security incidents suffered by the business project data labeled q at the corresponding time point t; Y(q,t) is the rating of the stability of the development of security incidents on the business project data labeled q at the corresponding time point t; I(q,t) is the rating of the value loss of the business project data labeled q at the corresponding time point t due to security incidents; a, b, c, d are weight distribution coefficients; the rating of the stability of the development of security incidents on business data is analyzed and rated through the development trend of business data; value loss is rated through a comprehensive analysis of the total value loss degree of business data at the corresponding time point;

[0016] Combined with the business project data matrix corresponding to each time point in the business project data set, the development data eigenvalue analysis is performed to analyze the risk level of the business project corresponding to each time point. The calculation formula is: ;

[0017] Among them, R(q,t) is the risk level of the business project with number q at the corresponding time point t;

[0018] S202. Based on the analysis results of the time point characteristics of the business project data, each time point of the target business project data and the risk level corresponding to each time point are used to form a business development coordinate set. By mapping the coordinate system, the business development coordinate set corresponding to the target business project data is projected into the coordinate system. By using a smooth curve to fit the corresponding coordinates of each adjacent time point, a corresponding business project development characteristic curve is constructed.

[0019] The S300 retrieves the business project data feature prediction curve data, combines it with the current real-time business project development feature curve for comparison and analysis, marks the abnormal risk feature points of the current real-time business project development feature curve by constructing a judgment window, and classifies each feature point according to the feature point attributes; combines the feature point classification results, constructs abnormal risk feature vectors between adjacent feature points, and combines the corresponding interval vector of the prediction curve to perform abnormal risk analysis of local adjacent feature points; combines the abnormal risk analysis data of each local adjacent feature point to conduct risk assessment on the current business project. The specific steps are as follows:

[0020] S301. By retrieving the characteristic prediction curve data corresponding to the business project data stored in the database, the characteristic curve of the current business project development is compared and analyzed with the characteristic prediction curve of the corresponding business project through the same coordinate system mapping; by constructing a risk degree comparison interval window, the risk characteristic point judgment is performed on the current business project development characteristic curve value and the characteristic prediction curve value of the corresponding business project at each time point respectively; if M(q,t)∈[N(q,t)-U,N(q,t)+U], the current business project development characteristic curve value at the corresponding time point is judged to be a regular characteristic point; if M(q,t)∉[N(q,t)-U,N(q,t)+U], the coordinate point of the current business project development characteristic curve value at the corresponding time point is marked as an abnormal risk characteristic point; wherein M(q,t) corresponds to the current business project development characteristic curve; N(q,t) corresponds to the characteristic prediction curve; and U is an error parameter; by fully traversing the abnormal judgment of each coordinate point on the current business project development characteristic curve, the abnormal risk characteristics on the current business project development characteristic curve are fully traversed and marked;

[0021] Based on the abnormal risk feature points marked on the current business project development characteristic curve, the type is judged. If the corresponding abnormal risk feature points are located on the upper side of the characteristic prediction curve, they are judged as positive abnormal risk feature points; if the corresponding abnormal risk feature points are located on the lower side of the characteristic prediction curve, they are judged as negative abnormal risk feature points; traverse and select each abnormal risk feature point according to the time axis, and make continuous type judgments on adjacent abnormal risk feature points. If they are of the same type, a group of abnormal risk feature points of the same type is constructed; if they are not of the same type, the abnormal risk feature point on the right side of the adjacent abnormal risk feature points of different types is used as the new traversal starting point, and the adjacent abnormal risk feature points are continued to be traversed and judged of the same type; if there is an abnormal risk feature point that is not of the same type as the adjacent abnormal risk feature points on the left and right, it is marked as an abnormal risk feature singular point; according to the traversal results, the abnormal risk feature point group of the same type and the abnormal risk feature singular point are coordinated respectively;

[0022] S302, retrieve the curve values ​​corresponding to each abnormal risk feature point group of the same type on the current business project development characteristic curve, and perform a comprehensive risk analysis of the business project by combining the curve values ​​corresponding to the adjacent time points on the time axis corresponding to the characteristic prediction curve; perform risk assessment on the curve values ​​corresponding to each abnormal risk feature point group of the same type and the curve values ​​corresponding to the adjacent time points on the time axis corresponding to the characteristic prediction curve, respectively, and the calculation formula is: ;

[0023] Where Ag[j(t,t+1)] is the risk assessment value of the same type of abnormal risk feature point group corresponding to number j; α jVectorize the curve between the same type of abnormal risk feature point group numbered j on the current business project development feature curve and the curve corresponding to the adjacent time points on the time axis of the feature prediction curve, and overlap the two vectors by origin mapping to obtain the angle between the two vectors; M(j,t) is the value of the current business project development feature curve corresponding to the same type of abnormal risk feature point group numbered j; N(j,t) is the curve value of the same type of abnormal risk feature point group on the feature prediction curve; ave(N) is the mean value of the curve of the feature prediction curve; W(N) is the variance of the curve of the feature prediction curve;

[0024] The curve values ​​corresponding to the singular points of abnormal risk characteristics on the current business project development characteristic curve are respectively combined with the characteristic prediction curve to perform risk assessment. The calculation formula is: ;

[0025] Where Ag[k(t)] is the risk assessment value of the abnormal risk characteristic singular point numbered k; M(k,t) is the value of the characteristic curve corresponding to the abnormal risk characteristic singular point numbered k for the current business project development; N(k,t) is the value of the characteristic prediction curve corresponding to the abnormal risk characteristic singular point numbered k;

[0026] Combined with the risk assessment results of each abnormal risk feature point group and abnormal risk feature singular point of the same type, a comprehensive risk assessment is conducted on the current business project. The calculation formula is: ;

[0027] Where Ag(q) is the comprehensive risk assessment value of the business project numbered q; g and h are the risk assessment weight parameters of the same type of abnormal risk feature point group and abnormal risk feature singular point respectively; by introducing the risk assessment warning value Ag(th), the status of business development is judged; if Ag(q)≤Ag(th), the target business project data is continuously monitored for risks; if Ag(q)>Ag(th), the target business project data is warned of risks;

[0028] Based on the comprehensive risk assessment data of the corresponding business project, the real-time safety value of the business project is evaluated and analyzed; based on the risk assessment data of the historical business project data, a risk safety level classification form for the corresponding business project is constructed, and by inputting the comprehensive risk assessment value of the current business project, the risk level of the current business project is obtained, and the safety value index corresponding to the risk level of the current business project in the risk safety level classification form is obtained, and the real-time safety value of the business project is calculated, and the calculation formula is Sv(q)=η(q) / [E(q)*Ag(q)];

[0029] Among them, Sv(q) is the real-time security value assessment value of the business project numbered q; η(q) is the security value index of the business project numbered q; E(q) is the risk level of the business project numbered q.

[0030] The specific steps of S400 for visually outputting the risk analysis data of the enterprise's corresponding business projects and providing comprehensive feedback on the risk assessment results are as follows:

[0031] S401, visualizing the step data of risk analysis of the enterprise's target business project;

[0032] S402: Output the status of each enterprise business project and issue a monitoring port warning for abnormal risk status.

[0033] A multi-dimensional information evaluation system for enterprise safety index, the system comprising a database retrieval module, a data feature conversion module, a business comprehensive abnormal risk evaluation module and a data visualization module;

[0034] The database retrieval module obtains the data generated by the current real-time business project of the corresponding enterprise by retrieving the business data of the enterprise database; constructs the corresponding business project data set in combination with data normalization processing; the data feature conversion module performs business feature conversion on each business data based on the enterprise business project data set, and analyzes the time point development feature value of the business project data; constructs the business project development feature curve based on the time point feature conversion analysis result of the business project data; the business comprehensive abnormal risk assessment module retrieves the business project data feature prediction curve data, compares and analyzes it in combination with the current real-time business project development feature curve, marks the abnormal risk feature points of the current real-time business project development feature curve by constructing a judgment window, and classifies each feature point according to the feature point attributes; in combination with the feature point classification result, constructs abnormal risk feature vectors between adjacent feature points, and performs abnormal risk analysis of local adjacent feature points in combination with the corresponding interval vector of the prediction curve; conducts risk assessment on the current business project in combination with the abnormal risk analysis data of each local adjacent feature point; the data visualization module visualizes the risk analysis data of the corresponding business project of the enterprise, and provides comprehensive feedback on the risk assessment results.

[0035] The database retrieval module includes a business data retrieval unit and a business data set construction unit;

[0036] The business data retrieval unit obtains the enterprise database data access retrieval authority through the database management authority port; uses the business project key search terms to perform a full traversal search of the target business project data; obtains the real-time business project generation data corresponding to the search project of the enterprise;

[0037] The business data set construction unit performs data normalization processing on the retrieved target business data, and constructs a time point business project data matrix for the retrieved business project data corresponding to the business data at the time point; and constructs the corresponding target business project data set by coordinating the business project data matrices at each time point.

[0038] The data feature conversion module includes a business data feature value analysis unit and a business development feature curve construction unit;

[0039] The business data characteristic value analysis unit retrieves the target business project data set, and performs characteristic conversion analysis on the business project data corresponding to each time point in the set; performs development data characteristic value analysis based on the business project data matrix corresponding to each time point in the business project data set; analyzes the safe development characteristic value and potential loss characteristic value of the business project data at each time point respectively; performs development data characteristic value analysis in combination with the business project data matrix corresponding to each time point in the business project data set, and analyzes the risk level of the business project corresponding to each time point;

[0040] The business development characteristic curve construction unit is based on the analysis results of the time point characteristics of the business project data. By forming a business development coordinate set from each time point of the target business project data and the risk level corresponding to each time point, the business development coordinate set corresponding to the target business project data is projected into the coordinate system through mapping the coordinate system, and the corresponding business project development characteristic curve is constructed by using a smooth curve to fit the corresponding coordinates of each adjacent time point.

[0041] The business comprehensive abnormal risk assessment module includes a curve abnormal point traversal unit, a business comprehensive abnormal risk assessment unit and a safety value assessment unit;

[0042] The curve abnormal point traversal unit retrieves the characteristic prediction curve data corresponding to the business project data stored in the database, and compares the current business project development characteristic curve with the characteristic prediction curve of the corresponding business project through the same coordinate system mapping; by constructing a risk degree comparison interval window, the risk characteristic point judgment is performed on the current business project development characteristic curve value and the characteristic prediction curve value of the corresponding business project at each time point; the abnormal risk characteristic point is marked based on the judgment result; by performing an abnormal judgment full traversal on each coordinate point on the current business project development characteristic curve, the abnormal risk characteristics on the current business project development characteristic curve are fully traversed and marked; based on the abnormal risk characteristic points marked on the current business project development characteristic curve, the type judgment is performed; if the corresponding abnormal risk characteristic points are located at the corresponding positions, the abnormal risk characteristic points are marked; If the abnormal risk feature point is on the upper side of the characteristic prediction curve, it is judged as a positive abnormal risk feature point; if the corresponding abnormal risk feature point is on the lower side of the characteristic prediction curve, it is judged as a negative abnormal risk feature point; each abnormal risk feature point is traversed and selected according to the time axis, and the adjacent abnormal risk feature points are judged to be of the same type. If they are of the same type, a group of abnormal risk feature points of the same type is constructed; if they are not of the same type, the abnormal risk feature point on the right side of the adjacent abnormal risk feature points of different types is used as the new traversal starting point, and the adjacent abnormal risk feature points are continued to be traversed and judged to be of the same type; if there is an abnormal risk feature point that is not of the same type as the adjacent abnormal risk feature points on the left and right, it is marked as an abnormal risk feature singular point; according to the traversal results, the abnormal risk feature point group of the same type and the abnormal risk feature singular point are coordinated respectively;

[0043] The business comprehensive abnormal risk assessment unit retrieves the curve values ​​corresponding to each abnormal risk feature point group of the same type on the current business project development characteristic curve, and performs a comprehensive risk analysis of the business project by combining the curve values ​​corresponding to the adjacent time points on the time axis corresponding to the characteristic prediction curve; respectively, risk assessment is performed on the curve values ​​corresponding to each abnormal risk feature point group of the same type and the curve values ​​corresponding to the adjacent time points on the time axis corresponding to the characteristic prediction curve; respectively, risk assessment is performed on the curve values ​​corresponding to each abnormal risk characteristic singular point on the current business project development characteristic curve in combination with the characteristic prediction curve; a comprehensive risk assessment is performed on the current business project in combination with the risk assessment results of each abnormal risk feature point group of the same type and the abnormal risk characteristic singular point; by introducing a risk assessment early warning value, the state of business development is judged, and the business state is warned based on the judgment result;

[0044] The security value assessment unit assesses and analyzes the real-time security value of the business project based on the comprehensive risk assessment data of the corresponding business project; constructs a risk security level classification form for the corresponding business project based on the risk assessment data of historical business project data, obtains the risk level of the current business project by inputting the comprehensive risk assessment value of the current business project, obtains the security value index corresponding to the risk level of the current business project in the risk security level classification form, and calculates the real-time security value of the business project.

[0045] The data visualization module includes a data visualization unit and an abnormal business warning unit;

[0046] The data visualization unit performs a visual port display on the step data of the risk analysis of the enterprise target business project;

[0047] The abnormal business warning unit outputs the risk status and security value of each enterprise business project, and issues a monitoring port warning for abnormal risk status.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention retrieves data from an associated database, and uses a series of data processing methods to perform feature conversion analysis, curve comparison analysis, and abnormal risk assessment analysis on enterprise data, thereby realizing supervision and abnormal judgment of the entire development cycle of enterprise business projects; performs risk value analysis on each time point by retrieving real-time business development data, performs business curve development fitting using risk value analysis at each point, and annotates abnormal points of current business by comparing and analyzing predicted data; coordinates abnormal points by classifying abnormal points and traversing and judging abnormal combinations, and realizes real-time risk monitoring of business projects based on abnormal point processing results; the present invention establishes a set of methods for evaluating enterprise business security risks, improves business security value, locates business risks, greatly improves the efficiency and accuracy of traditional business monitoring, and realizes timely monitoring of enterprise business security risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The present invention is a structural schematic diagram of a multi-dimensional information evaluation system for enterprise safety index. DETAILED DESCRIPTION

[0051] 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.

[0052] Example: Figure 1 As shown, the present invention provides a technical solution:

[0053] A multi-dimensional information evaluation method for an enterprise safety index, the method comprising the following steps:

[0054] S100, by retrieving the business data of the enterprise database, obtaining the current real-time business project generation data of the corresponding enterprise; combining data normalization processing to construct a corresponding business project data set;

[0055] S200, based on the enterprise business project data set, performing business feature conversion on each business data, and performing time point development feature value analysis on the business project data; and constructing a business project development feature curve based on the time point feature conversion analysis results of the business project data;

[0056] S300, retrieve the data of the business project data feature prediction curve, compare and analyze it with the current real-time business project development feature curve, mark the abnormal risk feature points of the current real-time business project development feature curve by building a judgment window, and classify each feature point according to the attribute of the feature point; build an abnormal risk feature vector between adjacent feature points based on the feature point classification result, and perform abnormal risk analysis of local adjacent feature points based on the corresponding interval vector of the prediction curve; and conduct risk assessment on the current business project based on the abnormal risk analysis data of each local adjacent feature point;

[0057] S400: Visualize the risk analysis data of the enterprise's corresponding business projects and provide comprehensive feedback on the risk assessment results.

[0058] The S100 obtains the current real-time business project generation data of the corresponding enterprise by retrieving the business data from the enterprise database; and the specific steps of constructing the corresponding business project data set in combination with data normalization processing are as follows:

[0059] S101. Obtaining data access and retrieval authority for the enterprise database through the database management authority port; performing a full traversal search of data for the target business project using the key search terms for the business project; obtaining real-time business project generation data corresponding to the search project of the enterprise;

[0060] S102, normalizing the retrieved target business data, respectively constructing a time point business project data matrix for the retrieved business project data corresponding to the business data at the time point; and constructing a corresponding target business project data set by coordinating the business project data matrices at each time point.

[0061] The S200 performs business feature conversion on each business data based on the enterprise business project data set, and performs time point development feature value analysis on the business project data; the specific steps of constructing a business project development feature curve based on the time point feature conversion analysis result of the business project data are as follows:

[0062] S201, retrieve the target business project data set, perform feature conversion analysis on the business project data corresponding to each time point in the set; perform development data feature value analysis based on the business project data matrix corresponding to each time point in the business project data set; analyze the safe development feature value and potential loss feature value of the business project data at each time point, and the calculation formulas are: ; ;

[0063] Among them, L(q,t) is the security development characteristic value of the business project data labeled q at the corresponding time point t; F(q,t) is the potential loss characteristic value of the business project data labeled q at the corresponding time point t; X(q,t) is the frequency rating of security incidents suffered by the business project data labeled q at the corresponding time point t; Y(q,t) is the rating of the stability of the development of security incidents on the business project data labeled q at the corresponding time point t; I(q,t) is the rating of the value loss of the business project data labeled q at the corresponding time point t due to security incidents; a, b, c, d are weight distribution coefficients;

[0064] Combined with the business project data matrix corresponding to each time point in the business project data set, the development data eigenvalue analysis is performed to analyze the risk level of the business project corresponding to each time point. The calculation formula is: ;

[0065] Among them, R(q,t) is the risk level of the business project with number q at the corresponding time point t;

[0066] S202. Based on the analysis results of the time point characteristics of the business project data, each time point of the target business project data and the risk level corresponding to each time point are used to form a business development coordinate set. By mapping the coordinate system, the business development coordinate set corresponding to the target business project data is projected into the coordinate system. By using a smooth curve to fit the corresponding coordinates of each adjacent time point, a corresponding business project development characteristic curve is constructed.

[0067] The S300 retrieves the business project data feature prediction curve data, combines it with the current real-time business project development feature curve for comparison and analysis, marks the abnormal risk feature points of the current real-time business project development feature curve by constructing a judgment window, and classifies each feature point according to the feature point attributes; combines the feature point classification results, constructs abnormal risk feature vectors between adjacent feature points, and combines the corresponding interval vector of the prediction curve to perform abnormal risk analysis of local adjacent feature points; combines the abnormal risk analysis data of each local adjacent feature point to conduct risk assessment on the current business project. The specific steps are as follows:

[0068] S301. By retrieving the characteristic prediction curve data corresponding to the business project data stored in the database, the characteristic curve of the current business project development is compared and analyzed with the characteristic prediction curve of the corresponding business project through the same coordinate system mapping; by constructing a risk degree comparison interval window, the risk characteristic point judgment is performed on the current business project development characteristic curve value and the characteristic prediction curve value of the corresponding business project at each time point respectively; if M(q,t)∈[N(q,t)-U,N(q,t)+U], the current business project development characteristic curve value at the corresponding time point is judged to be a regular characteristic point; if M(q,t)∉[N(q,t)-U,N(q,t)+U], the coordinate point of the current business project development characteristic curve value at the corresponding time point is marked as an abnormal risk characteristic point; wherein M(q,t) corresponds to the current business project development characteristic curve; N(q,t) corresponds to the characteristic prediction curve; and U is an error parameter; by fully traversing the abnormal judgment of each coordinate point on the current business project development characteristic curve, the abnormal risk characteristics on the current business project development characteristic curve are fully traversed and marked;

[0069] Based on the abnormal risk feature points marked on the current business project development characteristic curve, the type is judged. If the corresponding abnormal risk feature points are located on the upper side of the characteristic prediction curve, they are judged as positive abnormal risk feature points; if the corresponding abnormal risk feature points are located on the lower side of the characteristic prediction curve, they are judged as negative abnormal risk feature points; traverse and select each abnormal risk feature point according to the time axis, and make continuous type judgments on adjacent abnormal risk feature points. If they are of the same type, a group of abnormal risk feature points of the same type is constructed; if they are not of the same type, the abnormal risk feature point on the right side of the adjacent abnormal risk feature points of different types is used as the new traversal starting point, and the adjacent abnormal risk feature points are continued to be traversed and judged of the same type; if there is an abnormal risk feature point that is not of the same type as the adjacent abnormal risk feature points on the left and right, it is marked as an abnormal risk feature singular point; according to the traversal results, the abnormal risk feature point group of the same type and the abnormal risk feature singular point are coordinated respectively;

[0070] S302, retrieve the curve values ​​corresponding to each abnormal risk feature point group of the same type on the current business project development characteristic curve, and perform a comprehensive risk analysis of the business project by combining the curve values ​​corresponding to the adjacent time points on the time axis corresponding to the characteristic prediction curve; perform risk assessment on the curve values ​​corresponding to each abnormal risk feature point group of the same type and the curve values ​​corresponding to the adjacent time points on the time axis corresponding to the characteristic prediction curve, respectively, and the calculation formula is: ;

[0071] Where Ag[j(t,t+1)] is the risk assessment value of the same type of abnormal risk feature point group corresponding to number j; α j Vectorize the curve between the same type of abnormal risk feature point group numbered j on the current business project development feature curve and the curve corresponding to the adjacent time points on the time axis of the feature prediction curve, and overlap the two vectors by origin mapping to obtain the angle between the two vectors; M(j,t) is the value of the current business project development feature curve corresponding to the same type of abnormal risk feature point group numbered j; N(j,t) is the curve value of the same type of abnormal risk feature point group on the feature prediction curve; ave(N) is the mean value of the curve of the feature prediction curve; W(N) is the variance of the curve of the feature prediction curve;

[0072] The curve values ​​corresponding to the singular points of abnormal risk characteristics on the current business project development characteristic curve are respectively combined with the characteristic prediction curve to perform risk assessment. The calculation formula is: ;

[0073] Where Ag[k(t)] is the risk assessment value of the abnormal risk characteristic singular point numbered k; M(k,t) is the value of the characteristic curve corresponding to the abnormal risk characteristic singular point numbered k for the current business project development; N(k,t) is the value of the characteristic prediction curve corresponding to the abnormal risk characteristic singular point numbered k;

[0074] Combined with the risk assessment results of each abnormal risk feature point group and abnormal risk feature singular point of the same type, a comprehensive risk assessment is conducted on the current business project. The calculation formula is: ;

[0075] Where Ag(q) is the comprehensive risk assessment value of the business project numbered q; g and h are the risk assessment weight parameters of the same type of abnormal risk feature point group and abnormal risk feature singular point respectively; by introducing the risk assessment warning value Ag(th), the status of business development is judged; if Ag(q)≤Ag(th), the target business project data is continuously monitored for risks; if Ag(q)>Ag(th), the target business project data is warned of risks;

[0076] Based on the comprehensive risk assessment data of the corresponding business project, the real-time safety value of the business project is evaluated and analyzed; based on the risk assessment data of the historical business project data, a risk safety level classification form for the corresponding business project is constructed, and by inputting the comprehensive risk assessment value of the current business project, the risk level of the current business project is obtained, and the safety value index corresponding to the risk level of the current business project in the risk safety level classification form is obtained, and the real-time safety value of the business project is calculated, and the calculation formula is Sv(q)=η(q) / [E(q)*Ag(q)];

[0077] Among them, Sv(q) is the real-time security value assessment value of the business project numbered q; η(q) is the security value index of the business project numbered q; E(q) is the risk level of the business project numbered q.

[0078] The specific steps of S400 for visually outputting the risk analysis data of the enterprise's corresponding business projects and providing comprehensive feedback on the risk assessment results are as follows:

[0079] S401, visualizing the step data of risk analysis of the enterprise's target business project;

[0080] S402. Output the risk status and security value data of each enterprise business project, and issue monitoring port warnings for abnormal risk status.

[0081] A multi-dimensional information evaluation system for enterprise safety index, the system comprising a database retrieval module, a data feature conversion module, a business comprehensive abnormal risk evaluation module and a data visualization module;

[0082] The database retrieval module obtains the data generated by the current real-time business project of the corresponding enterprise by retrieving the business data of the enterprise database; constructs the corresponding business project data set in combination with data normalization processing; the data feature conversion module performs business feature conversion on each business data based on the enterprise business project data set, and analyzes the time point development feature value of the business project data; constructs the business project development feature curve based on the time point feature conversion analysis result of the business project data; the business comprehensive abnormal risk assessment module retrieves the business project data feature prediction curve data, compares and analyzes it in combination with the current real-time business project development feature curve, marks the abnormal risk feature points of the current real-time business project development feature curve by constructing a judgment window, and classifies each feature point according to the feature point attributes; in combination with the feature point classification result, constructs abnormal risk feature vectors between adjacent feature points, and performs abnormal risk analysis of local adjacent feature points in combination with the corresponding interval vector of the prediction curve; conducts risk assessment on the current business project in combination with the abnormal risk analysis data of each local adjacent feature point; the data visualization module visualizes the risk analysis data of the corresponding business project of the enterprise, and provides comprehensive feedback on the risk assessment results.

[0083] The database retrieval module includes a business data retrieval unit and a business data set construction unit;

[0084] The business data retrieval unit obtains the enterprise database data access retrieval authority through the database management authority port; uses the business project key search terms to perform a full traversal search of the target business project data; obtains the real-time business project generation data corresponding to the search project of the enterprise;

[0085] The business data set construction unit performs data normalization processing on the retrieved target business data, and constructs a time point business project data matrix for the retrieved business project data corresponding to the business data at the time point; and constructs the corresponding target business project data set by coordinating the business project data matrices at each time point.

[0086] The data feature conversion module includes a business data feature value analysis unit and a business development feature curve construction unit;

[0087] The business data characteristic value analysis unit retrieves the target business project data set, and performs characteristic conversion analysis on the business project data corresponding to each time point in the set; performs development data characteristic value analysis based on the business project data matrix corresponding to each time point in the business project data set; analyzes the safe development characteristic value and potential loss characteristic value of the business project data at each time point respectively; performs development data characteristic value analysis in combination with the business project data matrix corresponding to each time point in the business project data set, and analyzes the risk level of the business project corresponding to each time point;

[0088] The business development characteristic curve construction unit is based on the analysis results of the time point characteristics of the business project data. By forming a business development coordinate set from each time point of the target business project data and the risk level corresponding to each time point, the business development coordinate set corresponding to the target business project data is projected into the coordinate system through mapping the coordinate system, and the corresponding business project development characteristic curve is constructed by using a smooth curve to fit the corresponding coordinates of each adjacent time point.

[0089] The business comprehensive abnormal risk assessment module includes a curve abnormal point traversal unit, a business comprehensive abnormal risk assessment unit and a safety value assessment unit;

[0090] The curve abnormal point traversal unit retrieves the characteristic prediction curve data corresponding to the business project data stored in the database, and compares the current business project development characteristic curve with the characteristic prediction curve of the corresponding business project through the same coordinate system mapping; by constructing a risk degree comparison interval window, the risk characteristic point judgment is performed on the current business project development characteristic curve value and the characteristic prediction curve value of the corresponding business project at each time point; the abnormal risk characteristic point is marked based on the judgment result; by performing an abnormal judgment full traversal on each coordinate point on the current business project development characteristic curve, the abnormal risk characteristics on the current business project development characteristic curve are fully traversed and marked; based on the abnormal risk characteristic points marked on the current business project development characteristic curve, the type judgment is performed; if the corresponding abnormal risk characteristic points are located at the corresponding positions, the abnormal risk characteristic points are marked; If the abnormal risk feature point is on the upper side of the characteristic prediction curve, it is judged as a positive abnormal risk feature point; if the corresponding abnormal risk feature point is on the lower side of the characteristic prediction curve, it is judged as a negative abnormal risk feature point; each abnormal risk feature point is traversed and selected according to the time axis, and the adjacent abnormal risk feature points are judged to be of the same type. If they are of the same type, a group of abnormal risk feature points of the same type is constructed; if they are not of the same type, the abnormal risk feature point on the right side of the adjacent abnormal risk feature points of different types is used as the new traversal starting point, and the adjacent abnormal risk feature points are continued to be traversed and judged to be of the same type; if there is an abnormal risk feature point that is not of the same type as the adjacent abnormal risk feature points on the left and right, it is marked as an abnormal risk feature singular point; according to the traversal results, the abnormal risk feature point group of the same type and the abnormal risk feature singular point are coordinated respectively;

[0091] The business comprehensive abnormal risk assessment unit retrieves the curve values ​​corresponding to each abnormal risk feature point group of the same type on the current business project development characteristic curve, and performs a comprehensive risk analysis of the business project by combining the curve values ​​corresponding to the adjacent time points on the time axis corresponding to the characteristic prediction curve; respectively, risk assessment is performed on the curve values ​​corresponding to each abnormal risk feature point group of the same type and the curve values ​​corresponding to the adjacent time points on the time axis corresponding to the characteristic prediction curve; respectively, risk assessment is performed on the curve values ​​corresponding to each abnormal risk characteristic singular point on the current business project development characteristic curve in combination with the characteristic prediction curve; a comprehensive risk assessment is performed on the current business project in combination with the risk assessment results of each abnormal risk feature point group of the same type and the abnormal risk characteristic singular point; by introducing a risk assessment early warning value, the state of business development is judged, and the business state is warned based on the judgment result;

[0092] The security value assessment unit assesses and analyzes the real-time security value of the business project based on the comprehensive risk assessment data of the corresponding business project; constructs a risk security level classification form for the corresponding business project based on the risk assessment data of historical business project data, obtains the risk level of the current business project by inputting the comprehensive risk assessment value of the current business project, obtains the security value index corresponding to the risk level of the current business project in the risk security level classification form, and calculates the real-time security value of the business project.

[0093] The data visualization module includes a data visualization unit and an abnormal business warning unit;

[0094] The data visualization unit performs a visual port display on the step data of the risk analysis of the enterprise target business project;

[0095] The abnormal business warning unit outputs the risk status and security value data of each enterprise business project, and issues a monitoring port warning for abnormal risk status;

[0096] In the example:

[0097] At present, a certain enterprise adopts a multi-dimensional information evaluation system of enterprise security index of the present invention to conduct real-time business risk monitoring of its enterprise business; obtains the enterprise database data access and retrieval authority through the database management authority port; uses the business project key search terms to perform a full traversal search of the target business project data; obtains the real-time business project generation data of the enterprise corresponding to the search project; performs data normalization processing on the retrieved target business data, and respectively constructs the time point business project data matrix for the retrieved business project data corresponding to the time point business data; and constructs the corresponding target business project data set by coordinating the business project data matrices at each time point;

[0098] Retrieve the target business project data set, and perform feature conversion analysis on the business project data corresponding to each time point in the set; perform development data feature value analysis based on the business project data matrix corresponding to each time point in the business project data set; analyze the safe development feature value and potential loss feature value of the business project data at each time point, and the calculation formulas are as follows: ; ;

[0099] Combined with the business project data matrix corresponding to each time point in the business project data set, the development data eigenvalue analysis is performed to analyze the risk level of the business project corresponding to each time point. The calculation formula is: ;

[0100] Based on the results of the transformation and analysis of the time point characteristics of the business project data, the business development coordinate set is formed by the target business project data at each time point and the risk level corresponding to each time point. The business development coordinate set corresponding to the target business project data is projected into the coordinate system by mapping the coordinate system. The corresponding business project development characteristic curve is constructed by curve fitting the corresponding coordinates of each adjacent time point using a smooth curve.

[0101] By retrieving the characteristic prediction curve data of the corresponding business project data stored in the database, the characteristic curve of the current business project development is compared with the characteristic prediction curve of the corresponding business project through the same coordinate system mapping; by constructing a risk level comparison interval window, the risk characteristic point judgment is performed on the current business project development characteristic curve value and the characteristic prediction curve value of the corresponding business project at each time point; if M(q,t)∈[N(q,t)-U,N(q,t)+U], the current business project development characteristic curve value at the corresponding time point is judged to be a regular characteristic point; if M(q,t)∉[N(q,t)-U,N(q,t)+U], the coordinate point of the current business project development characteristic curve value at the corresponding time point is marked as an abnormal risk characteristic point; by fully traversing the abnormal judgment of each coordinate point on the current business project development characteristic curve, the abnormal risk characteristics on the current business project development characteristic curve are fully traversed and marked;

[0102] Based on the abnormal risk feature points marked on the current business project development characteristic curve, the type is judged. If the corresponding abnormal risk feature points are located on the upper side of the characteristic prediction curve, they are judged as positive abnormal risk feature points; if the corresponding abnormal risk feature points are located on the lower side of the characteristic prediction curve, they are judged as negative abnormal risk feature points; traverse and select each abnormal risk feature point according to the time axis, and make continuous type judgments on adjacent abnormal risk feature points. If they are of the same type, a group of abnormal risk feature points of the same type is constructed; if they are not of the same type, the abnormal risk feature point on the right side of the adjacent abnormal risk feature points of different types is used as the new traversal starting point, and the adjacent abnormal risk feature points are continued to be traversed and judged of the same type; if there is an abnormal risk feature point that is not of the same type as the adjacent abnormal risk feature points on the left and right, it is marked as an abnormal risk feature singular point; according to the traversal results, the abnormal risk feature point group of the same type and the abnormal risk feature singular point are coordinated respectively;

[0103] Retrieve the curve values ​​corresponding to each group of abnormal risk feature points of the same type on the current business project development characteristic curve, and perform a comprehensive risk analysis of the business project by combining the curve values ​​corresponding to the adjacent time points on the time axis corresponding to the characteristic prediction curve; perform risk assessment on the curve values ​​corresponding to each group of abnormal risk feature points of the same type and the curve values ​​corresponding to the adjacent time points on the time axis corresponding to the characteristic prediction curve, respectively. The calculation formula is: ;

[0104] The curve values ​​corresponding to the singular points of abnormal risk characteristics on the current business project development characteristic curve are respectively combined with the characteristic prediction curve to perform risk assessment. The calculation formula is: ;

[0105] Combined with the risk assessment results of each abnormal risk feature point group and abnormal risk feature singular point of the same type, a comprehensive risk assessment is conducted on the current business project. The calculation formula is: ;

[0106] By introducing the risk assessment warning value Ag(th), the status of business development is judged; if Ag(q)≤Ag(th), the target business project data is continuously monitored for risk; if Ag(q)>Ag(th), the target business project data is warned for risk; based on the comprehensive risk assessment data of the corresponding business project, the real-time safety value of the business project is evaluated and analyzed; based on the risk assessment data of the historical business project data, a risk safety level classification form for the corresponding business project is constructed, and by inputting the comprehensive risk assessment value of the current business project, the risk level of the current business project is obtained, and the safety value index corresponding to the risk level of the current business project in the risk safety level classification form is obtained, and the real-time safety value of the business project is calculated, and the calculation formula is Sv(q)=η(q) / [E(q)*Ag(q)];

[0107] The step data of risk analysis of the enterprise's target business projects are displayed in a visual port; the risk status and safety value data of each enterprise's business project are output, and monitoring port warnings are issued for abnormal risk status.

[0108] 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. A multi-dimensional information evaluation method for enterprise safety index, characterized by: The method comprises the following steps: S100, by retrieving the business data of the enterprise database, obtaining the current real-time business project generation data of the corresponding enterprise; combining data normalization processing to construct a corresponding business project data set; S200, based on the enterprise business project data set, performing business feature conversion on each business data, and performing time point development feature value analysis on the business project data; and constructing a business project development feature curve based on the time point feature conversion analysis results of the business project data; The specific steps of S200 are as follows: S201, retrieve the target business project data set, perform feature conversion analysis on the business project data corresponding to each time point in the set; perform development data feature value analysis based on the business project data matrix corresponding to each time point in the business project data set; analyze the safe development feature value and potential loss feature value of the business project data at each time point, and the calculation formulas are: ; ; Among them, L(q,t) is the security development characteristic value of the business project data labeled q at the corresponding time point t; F(q,t) is the potential loss characteristic value of the business project data labeled q at the corresponding time point t; X(q,t) is the frequency rating of security incidents suffered by the business project data labeled q at the corresponding time point t; Y(q,t) is the rating of the stability of the development of security incidents on the business project data labeled q at the corresponding time point t; I(q,t) is the rating of the value loss of the business project data labeled q at the corresponding time point t due to security incidents; a, b, c, d are weight distribution coefficients; Combined with the business project data matrix corresponding to each time point in the business project data set, the development data eigenvalue analysis is performed to analyze the risk level of the business project corresponding to each time point. The calculation formula is: ; Among them, R(q,t) is the risk level of the business project with number q at the corresponding time point t; S202, based on the result of the transformation and analysis of the time point characteristics of the business project data, by forming a business development coordinate set from each time point of the target business project data and the risk level corresponding to each time point, by mapping the coordinate system, projecting the business development coordinate set corresponding to the target business project data into the coordinate system, and constructing a corresponding business project development characteristic curve by curve fitting the corresponding coordinates of each adjacent time point using a smooth curve; S300, retrieve the data of the business project data feature prediction curve, compare and analyze it with the current real-time business project development feature curve, mark the abnormal risk feature points of the current real-time business project development feature curve by building a judgment window, and classify each feature point according to the attribute of the feature point; build an abnormal risk feature vector between adjacent feature points based on the feature point classification result, and perform abnormal risk analysis of local adjacent feature points based on the corresponding interval vector of the prediction curve; and conduct risk assessment on the current business project based on the abnormal risk analysis data of each local adjacent feature point; S400: Visualize the risk analysis data of the enterprise's corresponding business projects and provide comprehensive feedback on the risk assessment results.

2. The multi-dimensional information evaluation method of enterprise safety index according to claim 1 is characterized by: The specific steps of S100 are as follows: S101. Obtaining data access and retrieval authority for the enterprise database through the database management authority port; performing a full traversal search of data for the target business project using the key search terms for the business project; obtaining real-time business project generation data corresponding to the search project of the enterprise; S102, normalizing the retrieved target business data, respectively constructing a time point business project data matrix for the retrieved business project data corresponding to the business data at the time point; and constructing a corresponding target business project data set by coordinating the business project data matrices at each time point.

3. The multi-dimensional information evaluation method of enterprise safety index according to claim 2 is characterized by: The specific steps of S300 are as follows: S301. By retrieving the characteristic prediction curve data corresponding to the business project data stored in the database, the characteristic curve of the current business project development is compared and analyzed with the characteristic prediction curve of the corresponding business project through the same coordinate system mapping; by constructing a risk degree comparison interval window, the risk characteristic point judgment is performed on the corresponding characteristic curve value of the current business project development and the characteristic prediction curve value of the corresponding business project at each time point; if M(q,t)∈[N(q,t)-U,N(q,t)+U], the value of the current business project development characteristic curve at the corresponding time point is judged to be a regular characteristic point; If M(q,t)∉[N(q,t)-U,N(q,t)+U], then the coordinate point of the current business project development characteristic curve value at the corresponding time point is marked as an abnormal risk characteristic point; where M(q,t) corresponds to the current business project development characteristic curve; N(q,t) corresponds to the characteristic prediction curve; U is the error parameter; by fully traversing the abnormal judgment of each coordinate point on the current business project development characteristic curve, the abnormal risk characteristics on the current business project development characteristic curve are fully traversed and marked; Based on the abnormal risk feature points marked on the current business project development characteristic curve, the type is judged. If the corresponding abnormal risk feature points are located on the upper side of the characteristic prediction curve, they are judged as positive abnormal risk feature points; if the corresponding abnormal risk feature points are located on the lower side of the characteristic prediction curve, they are judged as negative abnormal risk feature points; traverse and select each abnormal risk feature point according to the time axis, and make continuous type judgments on adjacent abnormal risk feature points. If they are of the same type, a group of abnormal risk feature points of the same type is constructed; if they are not of the same type, the abnormal risk feature point on the right side of the adjacent abnormal risk feature points of different types is used as the new traversal starting point, and the adjacent abnormal risk feature points are continued to be traversed and judged of the same type; if there is an abnormal risk feature point that is not of the same type as the adjacent abnormal risk feature points on the left and right, it is marked as an abnormal risk feature singular point; according to the traversal results, the abnormal risk feature point group of the same type and the abnormal risk feature singular point are coordinated respectively; S302, retrieve the curve values ​​corresponding to each abnormal risk feature point group of the same type on the current business project development characteristic curve, and perform a comprehensive risk analysis of the business project by combining the curve values ​​corresponding to the adjacent time points on the time axis corresponding to the characteristic prediction curve; perform risk assessment on the curve values ​​corresponding to each abnormal risk feature point group of the same type and the curve values ​​corresponding to the adjacent time points on the time axis corresponding to the characteristic prediction curve, respectively, and the calculation formula is: ; Where Ag[j(t,t+1)] is the risk assessment value of the same type of abnormal risk feature point group corresponding to number j; α j Vectorize the curve between the same type of abnormal risk feature point group numbered j on the current business project development feature curve and the curve corresponding to the adjacent time points on the time axis of the feature prediction curve, and overlap the two vectors by origin mapping to obtain the angle between the two vectors; M(j,t) is the value of the current business project development feature curve corresponding to the same type of abnormal risk feature point group numbered j; N(j,t) is the curve value of the corresponding same type of abnormal risk feature point group on the feature prediction curve; ave(N) is the mean value of the curve of the feature prediction curve; W(N) is the variance of the curve of the feature prediction curve; The curve values ​​corresponding to the singular points of abnormal risk characteristics on the current business project development characteristic curve are respectively combined with the characteristic prediction curve to perform risk assessment. The calculation formula is: ; Where Ag[k(t)] is the risk assessment value of the abnormal risk characteristic singular point numbered k; M(k,t) is the value of the characteristic curve corresponding to the abnormal risk characteristic singular point numbered k for the current business project development; N(k,t) is the value of the characteristic prediction curve corresponding to the abnormal risk characteristic singular point numbered k; Combined with the risk assessment results of each abnormal risk feature point group and abnormal risk feature singular point of the same type, a comprehensive risk assessment is conducted on the current business project. The calculation formula is: ; Where Ag(q) is the comprehensive risk assessment value of the business project numbered q; g and h are the risk assessment weight parameters of the same type of abnormal risk feature point group and abnormal risk feature singular point respectively; by introducing the risk assessment warning value Ag(th), the status of business development is judged; if Ag(q)≤Ag(th), the target business project data is continuously monitored for risks; if Ag(q)>Ag(th), the target business project data is warned of risks; Based on the comprehensive risk assessment data of the corresponding business project, the real-time safety value of the business project is evaluated and analyzed; based on the risk assessment data of the historical business project data, a risk safety level classification form for the corresponding business project is constructed, and by inputting the comprehensive risk assessment value of the current business project, the risk level of the current business project is obtained, and the safety value index corresponding to the risk level of the current business project in the risk safety level classification form is obtained, and the real-time safety value of the business project is calculated, and the calculation formula is Sv(q)=η(q) / [E(q)*Ag(q)]; Among them, Sv(q) is the real-time security value assessment value of the business project numbered q; η(q) is the security value index of the business project numbered q; E(q) is the risk level of the business project numbered q.

4. The multi-dimensional information evaluation method of enterprise safety index according to claim 3 is characterized by: The specific steps of S400 are as follows: S401, displaying the step data of the risk analysis of the enterprise's target business project in a visual port; S402. Output the risk status and security value data of each enterprise business project, and issue monitoring port warnings for abnormal risk status.

5. A multi-dimensional information evaluation system for enterprise safety index, using a multi-dimensional information evaluation method for enterprise safety index as claimed in any one of claims 1 to 4, characterized in that: The system includes a database retrieval module, a data feature conversion module, a business comprehensive abnormal risk assessment module and a data visualization module; The database retrieval module obtains the data generated by the current real-time business project of the corresponding enterprise by retrieving the business data of the enterprise database; constructs the corresponding business project data set in combination with data normalization processing; the data feature conversion module performs business feature conversion on each business data based on the enterprise business project data set, and analyzes the time point development feature value of the business project data; constructs the business project development feature curve based on the time point feature conversion analysis result of the business project data; the business comprehensive abnormal risk assessment module retrieves the business project data feature prediction curve data, compares and analyzes it in combination with the current real-time business project development feature curve, marks the abnormal risk feature points of the current real-time business project development feature curve by constructing a judgment window, and classifies each feature point according to the feature point attributes; in combination with the feature point classification result, constructs abnormal risk feature vectors between adjacent feature points, and performs abnormal risk analysis of local adjacent feature points in combination with the corresponding interval vector of the prediction curve; conducts risk assessment on the current business project in combination with the abnormal risk analysis data of each local adjacent feature point; the data visualization module visualizes the risk analysis data of the corresponding business project of the enterprise, and provides comprehensive feedback on the risk assessment results.

6. The multi-dimensional information evaluation system of enterprise safety index according to claim 5 is characterized by: The database retrieval module includes a business data retrieval unit and a business data set construction unit; The business data retrieval unit obtains the enterprise database data access retrieval authority through the database management authority port; uses the business project key search terms to perform a full traversal search of the target business project data; obtains the real-time business project generation data corresponding to the search project of the enterprise; The business data set construction unit performs data normalization processing on the retrieved target business data, and constructs a time point business project data matrix for the retrieved business project data corresponding to the business data at the corresponding time points; and constructs the corresponding target business project data set by coordinating the business project data matrices at each time point.

7. The multi-dimensional information evaluation system of enterprise safety index according to claim 6 is characterized by: The data feature conversion module includes a business data feature value analysis unit and a business development feature curve construction unit; The business data characteristic value analysis unit retrieves the target business project data set, and performs characteristic conversion analysis on the business project data corresponding to each time point in the set; performs development data characteristic value analysis based on the business project data matrix corresponding to each time point in the business project data set; analyzes the safe development characteristic value and potential loss characteristic value of the business project data at each time point respectively; performs development data characteristic value analysis in combination with the business project data matrix corresponding to each time point in the business project data set, and analyzes the risk level of the business project corresponding to each time point; The business development characteristic curve construction unit is based on the analysis results of the time point characteristics of the business project data. By forming a business development coordinate set from each time point of the target business project data and the risk level corresponding to each time point, the business development coordinate set corresponding to the target business project data is projected into the coordinate system through mapping the coordinate system, and the corresponding business project development characteristic curve is constructed by using a smooth curve to fit the corresponding coordinates of each adjacent time point.

8. The multi-dimensional information evaluation system of enterprise safety index according to claim 7 is characterized by: The business comprehensive abnormal risk assessment module includes a curve abnormal point traversal unit, a business comprehensive abnormal risk assessment unit and a safety value assessment unit; The curve abnormal point traversal unit retrieves the characteristic prediction curve data corresponding to the business project data stored in the database, and compares the current business project development characteristic curve with the characteristic prediction curve of the corresponding business project through the same coordinate system mapping; by constructing a risk degree comparison interval window, the risk characteristic point judgment is performed on the current business project development characteristic curve value corresponding to each time point and the characteristic prediction curve value of the corresponding business project; the abnormal risk characteristic point is marked based on the judgment result; by performing a full traversal of abnormal judgments on each coordinate point on the current business project development characteristic curve, the abnormal risk characteristics on the current business project development characteristic curve are fully traversed and marked; based on the abnormal risk characteristic points marked on the current business project development characteristic curve, the type judgment is performed, and if the corresponding abnormal risk characteristic points are located on the upper side of the characteristic prediction curve, they are judged as positive abnormal risk characteristic points; If the corresponding abnormal risk feature points are located on the lower side of the characteristic prediction curve, they are judged as negative abnormal risk feature points; traverse and select each abnormal risk feature point according to the time axis, and make continuous type judgments on adjacent abnormal risk feature points. If they are of the same type, a group of abnormal risk feature points of the same type is constructed; if they are not of the same type, the abnormal risk feature point on the right side of the adjacent abnormal risk feature points of different types is used as the new traversal starting point, and the adjacent abnormal risk feature points are continued to be traversed and judged of the same type; if there is an abnormal risk feature point that is not of the same type as the adjacent abnormal risk feature points on the left and right, it is marked as an abnormal risk feature singular point; According to the traversal results, the same type of abnormal risk feature point groups and abnormal risk feature singular points are coordinated respectively; The business comprehensive abnormal risk assessment unit retrieves the curve values ​​corresponding to each abnormal risk feature point group of the same type on the current business project development characteristic curve, and performs a comprehensive risk analysis of the business project by combining the curve values ​​corresponding to the adjacent time points on the time axis corresponding to the characteristic prediction curve; performs risk assessment on the curve values ​​corresponding to each abnormal risk feature point group of the same type and the curve values ​​corresponding to the adjacent time points on the time axis corresponding to the characteristic prediction curve; performs risk assessment on the curve values ​​corresponding to each abnormal risk characteristic singular point on the current business project development characteristic curve in combination with the characteristic prediction curve; performs a comprehensive risk assessment on the current business project in combination with the risk assessment results of each abnormal risk feature point group of the same type and the abnormal risk characteristic singular point; By introducing risk assessment warning values, the business development status is judged and the business status is warned based on the judgment results; The security value assessment unit assesses and analyzes the real-time security value of the business project based on the comprehensive risk assessment data of the corresponding business project; constructs a risk security level classification form for the corresponding business project based on the risk assessment data of historical business project data, obtains the risk level of the current business project by inputting the comprehensive risk assessment value of the current business project, obtains the security value index corresponding to the risk level of the current business project in the risk security level classification form, and calculates the real-time security value of the business project.

9. The multi-dimensional information evaluation system for enterprise safety index according to claim 8, characterized in that: The data visualization module includes a data visualization unit and an abnormal business warning unit; The data visualization unit performs a visual port display on the step data of the risk analysis of the enterprise target business project; The abnormal business warning unit outputs the risk status and security value data of each enterprise business project, and issues a monitoring port warning for abnormal risk status.

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