CN116610821B - Knowledge graph-based enterprise risk analysis method, system and storage medium - Google Patents
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Abstract
The application discloses an enterprise risk analysis method, system and storage medium based on a knowledge graph, wherein the method comprises the following steps: acquiring enterprise data information; extracting keywords in enterprise data information; constructing a knowledge graph of the enterprise according to the keywords in the enterprise data information; comparing and analyzing the knowledge graph of the enterprise with a preset enterprise risk graph to obtain a first similarity value; judging whether the first similarity value is larger than a preset first similarity threshold value, and if so, obtaining risk information of the enterprise; if not, determining that the enterprise has no risk; and sending the enterprise risk information to a preset management terminal for display. According to the method, the key words in the enterprise data information are classified, the knowledge patterns of a plurality of knowledge pattern areas are constructed, the enterprise risk is judged by calculating the knowledge patterns and the first similar value of the preset enterprise risk pattern, and the accuracy of enterprise risk analysis is improved.
Description
Technical Field
The application relates to the technical field of data processing and data analysis, in particular to an enterprise risk analysis method, system and storage medium based on a knowledge graph.
Background
With the progress of science and technology, the production capacity of each industry is effectively improved, the competitive pressure among enterprises is continuously improved, and when enterprises are at risk, if the anti-risk capacity of the enterprises is insufficient, the corresponding enterprises face the possibility of fund difficulty and even closing. At present, the risk analysis of enterprises mainly depends on the smell of enterprise operators to the enterprise risk, and large personal factors and errors exist.
Accordingly, there is a need for improvement in the art.
Disclosure of Invention
In view of the above problems, the present application aims to provide an enterprise risk analysis method, system and storage medium based on a knowledge graph, which can improve the accuracy of enterprise risk analysis.
The first aspect of the application provides an enterprise risk analysis method based on a knowledge graph, which comprises the following steps:
acquiring enterprise data information;
extracting keywords in enterprise data information;
constructing a knowledge graph of the enterprise according to the keywords in the enterprise data information;
comparing and analyzing the knowledge graph of the enterprise with a preset enterprise risk graph to obtain a first similarity value;
judging whether the first similarity value is larger than a preset first similarity threshold value, and if so, obtaining risk information of the enterprise; if not, determining that the enterprise has no risk;
and sending the enterprise risk information to a preset management terminal for display.
In this solution, after extracting the keywords in the enterprise data information, the method further includes:
comparing and analyzing the keywords in the enterprise data information with keywords in a preset type library to obtain a second similarity value;
judging whether the second similarity value is larger than a preset second similarity threshold value, if so, extracting keywords corresponding to the second similarity value and keywords in a preset type library;
setting the types of the keywords corresponding to the second similar value and the types of the keywords in a preset type library to be consistent;
classifying the keywords corresponding to the second similar value according to the corresponding types to obtain keywords of different types, and extracting the number of the keywords of the corresponding types;
judging whether the number of the keywords of the corresponding type is larger than a preset number threshold of the corresponding type, if so, the number of the keywords of the corresponding type meets the requirement; if not, obtaining information that the number of the keywords of the corresponding type does not meet the requirements;
and sending the information that the number of the type keywords does not meet the requirement to a preset management terminal for displaying.
In this scheme, still include:
acquiring a quantity value of the type of the keyword;
dividing the knowledge graph of the enterprise according to the number value of the type of the keyword to obtain a plurality of knowledge graph areas;
and marking the knowledge graph area according to the type of the keyword, and sending the keyword to the correspondingly marked knowledge graph area for storage.
In this scheme, still include:
comparing and analyzing the knowledge graph region and a preset enterprise risk graph to obtain a third similarity value;
inquiring in a preset weight coefficient table according to the marks of the knowledge graph areas to obtain weight coefficients of the corresponding knowledge graph areas;
multiplying the third similar value by the weight coefficient of the corresponding knowledge-graph region to obtain a fourth similar value;
and accumulating the fourth similar values to obtain a first similar value.
In this scheme, still include:
judging whether the fourth similar value is larger than a preset fourth similar threshold value, if so, triggering risk prompt information of the corresponding knowledge graph region;
and sending the risk prompt information of the knowledge graph region to a preset management terminal for display.
In this scheme, still include:
acquiring enterprise historical data information;
extracting historical keywords in enterprise data information;
dividing historical keywords in enterprise data information at any time according to a preset time period to obtain historical keywords in different time periods;
according to the historical keywords of different time periods, constructing historical knowledge maps of different time periods of the enterprise;
comparing and analyzing the historical knowledge patterns of different time periods of the enterprise with a preset enterprise risk pattern to obtain a fifth similarity value;
performing difference calculation on fifth similar values of the historical knowledge patterns of adjacent time periods to obtain first data;
judging whether the first data is larger than a preset first data threshold value, if so, triggering enterprise risk early warning information;
and sending the enterprise risk early warning information to a preset management terminal for display.
The second aspect of the present application provides an enterprise risk analysis system based on a knowledge graph, which comprises a memory and a processor, wherein the memory stores an enterprise risk analysis method program based on the knowledge graph, and the processor executes the enterprise risk analysis method program based on the knowledge graph to realize the following steps:
acquiring enterprise data information;
extracting keywords in enterprise data information;
constructing a knowledge graph of the enterprise according to the keywords in the enterprise data information;
comparing and analyzing the knowledge graph of the enterprise with a preset enterprise risk graph to obtain a first similarity value;
judging whether the first similarity value is larger than a preset first similarity threshold value, and if so, obtaining risk information of the enterprise; if not, determining that the enterprise has no risk;
and sending the enterprise risk information to a preset management terminal for display.
In this solution, after extracting the keywords in the enterprise data information, the method further includes:
comparing and analyzing the keywords in the enterprise data information with keywords in a preset type library to obtain a second similarity value;
judging whether the second similarity value is larger than a preset second similarity threshold value, if so, extracting keywords corresponding to the second similarity value and keywords in a preset type library;
setting the types of the keywords corresponding to the second similar value and the types of the keywords in a preset type library to be consistent;
classifying the keywords corresponding to the second similar value according to the corresponding types to obtain keywords of different types, and extracting the number of the keywords of the corresponding types;
judging whether the number of the keywords of the corresponding type is larger than a preset number threshold of the corresponding type, if so, the number of the keywords of the corresponding type meets the requirement; if not, obtaining information that the number of the keywords of the corresponding type does not meet the requirements;
and sending the information that the number of the type keywords does not meet the requirement to a preset management terminal for displaying.
In this scheme, still include:
acquiring a quantity value of the type of the keyword;
dividing the knowledge graph of the enterprise according to the number value of the type of the keyword to obtain a plurality of knowledge graph areas;
and marking the knowledge graph area according to the type of the keyword, and sending the keyword to the correspondingly marked knowledge graph area for storage.
In this scheme, still include:
comparing and analyzing the knowledge graph region and a preset enterprise risk graph to obtain a third similarity value;
inquiring in a preset weight coefficient table according to the marks of the knowledge graph areas to obtain weight coefficients of the corresponding knowledge graph areas;
multiplying the third similar value by the weight coefficient of the corresponding knowledge-graph region to obtain a fourth similar value;
and accumulating the fourth similar values to obtain a first similar value.
In this scheme, still include:
judging whether the fourth similar value is larger than a preset fourth similar threshold value, if so, triggering risk prompt information of the corresponding knowledge graph region;
and sending the risk prompt information of the knowledge graph region to a preset management terminal for display.
In this scheme, still include:
acquiring enterprise historical data information;
extracting historical keywords in enterprise data information;
dividing historical keywords in enterprise data information at any time according to a preset time period to obtain historical keywords in different time periods;
according to the historical keywords of different time periods, constructing historical knowledge maps of different time periods of the enterprise;
comparing and analyzing the historical knowledge patterns of different time periods of the enterprise with a preset enterprise risk pattern to obtain a fifth similarity value;
performing difference calculation on fifth similar values of the historical knowledge patterns of adjacent time periods to obtain first data;
judging whether the first data is larger than a preset first data threshold value, if so, triggering enterprise risk early warning information;
and sending the enterprise risk early warning information to a preset management terminal for display.
A third aspect of the present application provides a computer storage medium having stored therein a knowledge-based enterprise risk analysis method program, which when executed by a processor, implements the steps of a knowledge-based enterprise risk analysis method as described in any one of the above.
The application discloses an enterprise risk analysis method, system and storage medium based on a knowledge graph, which are used for classifying keywords in enterprise data information to construct knowledge graphs of a plurality of knowledge graph areas, and judging enterprise risk by calculating the knowledge graphs and a first similar value of a preset enterprise risk graph, so that the accuracy of enterprise risk analysis is improved.
Drawings
FIG. 1 shows a flow chart of an enterprise risk analysis method based on knowledge graph of the present application;
FIG. 2 shows a block diagram of an enterprise risk analysis system based on knowledge-graph of the present application.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, however, the present application may be practiced in other ways than those described herein, and therefore the scope of the present application is not limited to the specific embodiments disclosed below.
FIG. 1 shows a flow chart of an enterprise risk analysis method based on a knowledge graph of the present application.
As shown in fig. 1, the application discloses an enterprise risk analysis method based on a knowledge graph, which comprises the following steps:
s101, acquiring enterprise data information;
s102, extracting keywords in enterprise data information;
s103, constructing a knowledge graph of the enterprise according to the keywords in the enterprise data information;
s104, comparing and analyzing the knowledge graph of the enterprise with a preset enterprise risk graph to obtain a first similarity value;
s105, judging whether the first similarity value is larger than a preset first similarity threshold value, and if so, obtaining risk information of the enterprise; if not, determining that the enterprise has no risk;
and S106, the enterprise risk information is sent to a preset management terminal for display.
It should be noted that the enterprise data information includes data information such as funds of an enterprise, markets of the enterprise, enterprise personnel, etc., keywords in the corresponding enterprise data information are extracted, and a knowledge graph of the enterprise is constructed through the corresponding keywords, and the preset enterprise risk graph is set by a person skilled in the art according to the actual situation of the corresponding enterprise.
According to this embodiment, after extracting the keywords in the enterprise data information, the method further includes:
comparing and analyzing the keywords in the enterprise data information with keywords in a preset type library to obtain a second similarity value;
judging whether the second similarity value is larger than a preset second similarity threshold value, if so, extracting keywords corresponding to the second similarity value and keywords in a preset type library;
setting the types of the keywords corresponding to the second similar value and the types of the keywords in a preset type library to be consistent;
classifying the keywords corresponding to the second similar value according to the corresponding types to obtain keywords of different types, and extracting the number of the keywords of the corresponding types;
judging whether the number of the keywords of the corresponding type is larger than a preset number threshold of the corresponding type, if so, the number of the keywords of the corresponding type meets the requirement; if not, obtaining information that the number of the keywords of the corresponding type does not meet the requirements;
and sending the information that the number of the type keywords does not meet the requirement to a preset management terminal for displaying.
It should be noted that, the keywords in the preset type library all have type identifiers, and when the second similarity value is greater than the preset second similarity threshold value, the types of the keywords in the enterprise data information and the keywords in the corresponding preset type library are the same. And reminding the user terminal to continuously acquire the enterprise data information when the number of the keywords of the same type does not meet the requirement, until the number of the keywords of the same type meets the requirement.
According to an embodiment of the present application, further comprising:
acquiring a quantity value of the type of the keyword;
dividing the knowledge graph of the enterprise according to the number value of the type of the keyword to obtain a plurality of knowledge graph areas;
and marking the knowledge graph area according to the type of the keyword, and sending the keyword to the correspondingly marked knowledge graph area for storage.
It should be noted that, keywords in the enterprise data information are classified and sent to the corresponding knowledge-graph areas according to types, and different knowledge-graph areas are constructed by different types of keywords.
According to an embodiment of the present application, further comprising:
comparing and analyzing the knowledge graph region and a preset enterprise risk graph to obtain a third similarity value;
inquiring in a preset weight coefficient table according to the marks of the knowledge graph areas to obtain weight coefficients of the corresponding knowledge graph areas;
multiplying the third similar value by the weight coefficient of the corresponding knowledge-graph region to obtain a fourth similar value;
and accumulating the fourth similar values to obtain a first similar value.
It should be noted that different knowledge graph regions have different effects on enterprise risk analysis, the labels of the knowledge graph regions are types of keywords, weight coefficients of the corresponding knowledge graph regions are determined according to the types of the keywords, and the relationship between the types of the keywords and the weight coefficients of the knowledge graph regions is stored in the preset weight coefficient table.
According to an embodiment of the present application, further comprising:
judging whether the fourth similar value is larger than a preset fourth similar threshold value, if so, triggering risk prompt information of the corresponding knowledge graph region;
and sending the risk prompt information of the knowledge graph region to a preset management terminal for display.
It should be noted that, the preset fourth similar threshold values of the different knowledge-graph regions are different, and when the fourth similar value is greater than the preset fourth similar threshold value, it is indicated that the enterprise data corresponding to the corresponding knowledge-graph region has an independent risk, such as that the market region has a risk and the fund region has a risk, and the preset fourth similar threshold value is set by a person skilled in the art.
According to an embodiment of the present application, further comprising:
acquiring enterprise historical data information;
extracting historical keywords in enterprise data information;
dividing historical keywords in enterprise data information at any time according to a preset time period to obtain historical keywords in different time periods;
according to the historical keywords of different time periods, constructing historical knowledge maps of different time periods of the enterprise;
comparing and analyzing the historical knowledge patterns of different time periods of the enterprise with a preset enterprise risk pattern to obtain a fifth similarity value;
performing difference calculation on fifth similar values of the historical knowledge patterns of adjacent time periods to obtain first data;
judging whether the first data is larger than a preset first data threshold value, if so, triggering enterprise risk early warning information;
and sending the enterprise risk early warning information to a preset management terminal for display.
It should be noted that, the first data is a difference value obtained by subtracting a fifth similar value of the historical knowledge graph of a previous time period from a fifth similar value of the historical knowledge graph of a next time period in the adjacent time period, when the first data is a positive number, it is indicated that the enterprise risk coefficient of the corresponding enterprise between the adjacent time periods is increased, if the first data is greater than a preset first data threshold, it is indicated that the corresponding enterprise risk coefficient is excessively increased, enterprise risk early warning information is triggered, the fifth similar value is smaller than a preset first similar threshold, and the preset first data threshold is set by a person skilled in the art.
According to an embodiment of the present application, further comprising:
when the first data is smaller than or equal to a preset first data threshold value, extracting a preset number of first data by taking the current time as a reference;
judging whether the preset number of first data are in a preset first data range or not, if so, triggering enterprise risk early warning information;
and sending the enterprise risk early warning information to a preset management terminal for display.
It should be noted that, the first data is obtained by performing a difference calculation on the fifth similar value of the historical knowledge graph of the adjacent time period, so that the preset number of first data needs to be added with one fifth similar value, the preset number of first data is extracted based on the current time, for example, the preset number is 3, the fifth similar value of the historical knowledge graph of the adjacent 4 time periods is extracted based on the current time, the fifth similar value of the historical knowledge graph of the adjacent time period is subjected to a difference calculation, and corresponding first data is obtained, the preset first data range is greater than zero and smaller than a preset first data threshold, and when the preset number of first data is in the preset first data range, it is indicated that the risk coefficient existing in the corresponding enterprise continuously increases, so that the enterprise risk early warning information is triggered.
According to an embodiment of the present application, further comprising:
when the first similarity value is larger than a preset first similarity threshold value, extracting all fourth similarity values;
all the fourth similar values are ordered in the order from big to small, and the largest fourth similar value is extracted;
obtaining a knowledge graph region corresponding to the maximum fourth similarity value according to the maximum fourth similarity value;
matching the keywords of the knowledge graph region corresponding to the maximum fourth similarity value with a preset enterprise risk management and control scheme to obtain a correlation degree;
and judging whether the association degree is larger than a preset association degree threshold value, if so, setting a preset enterprise risk management scheme corresponding to the association degree as a current enterprise risk management scheme.
It should be noted that, various enterprise risk management and control schemes are stored in the preset enterprise risk management and control scheme, and the greater the association degree between the keywords of the knowledge graph region and the preset enterprise risk management and control scheme is, the more the corresponding preset enterprise risk management and control scheme is adapted to the risk management and control of the corresponding enterprise. The preset association threshold is set by a person skilled in the art.
According to an embodiment of the present application, further comprising:
when a plurality of risk management and control schemes of the current enterprises exist, numbering the risk management and control schemes of the current enterprises according to the sequence from the higher degree of association to the lower degree of association;
and sequentially sending the risk management and control schemes of the current enterprises to a preset management terminal for display according to the ordered numbers.
It should be noted that, the earlier the number is, the earlier the risk management scheme is at the display position of the preset management end.
FIG. 2 shows a block diagram of an enterprise risk analysis system based on knowledge-graph of the present application.
As shown in fig. 2, a second aspect of the present application provides an enterprise risk analysis system 2 based on a knowledge graph, including a memory 21 and a processor 22, where the memory stores an enterprise risk analysis method program based on the knowledge graph, and the processor executes the enterprise risk analysis method program based on the knowledge graph to implement the following steps:
acquiring enterprise data information;
extracting keywords in enterprise data information;
constructing a knowledge graph of the enterprise according to the keywords in the enterprise data information;
comparing and analyzing the knowledge graph of the enterprise with a preset enterprise risk graph to obtain a first similarity value;
judging whether the first similarity value is larger than a preset first similarity threshold value, and if so, obtaining risk information of the enterprise; if not, determining that the enterprise has no risk;
and sending the enterprise risk information to a preset management terminal for display.
It should be noted that the enterprise data information includes data information such as funds of an enterprise, markets of the enterprise, enterprise personnel, etc., keywords in the corresponding enterprise data information are extracted, and a knowledge graph of the enterprise is constructed through the corresponding keywords, and the preset enterprise risk graph is set by a person skilled in the art according to the actual situation of the corresponding enterprise.
According to this embodiment, after extracting the keywords in the enterprise data information, the method further includes:
comparing and analyzing the keywords in the enterprise data information with keywords in a preset type library to obtain a second similarity value;
judging whether the second similarity value is larger than a preset second similarity threshold value, if so, extracting keywords corresponding to the second similarity value and keywords in a preset type library;
setting the types of the keywords corresponding to the second similar value and the types of the keywords in a preset type library to be consistent;
classifying the keywords corresponding to the second similar value according to the corresponding types to obtain keywords of different types, and extracting the number of the keywords of the corresponding types;
judging whether the number of the keywords of the corresponding type is larger than a preset number threshold of the corresponding type, if so, the number of the keywords of the corresponding type meets the requirement; if not, obtaining information that the number of the keywords of the corresponding type does not meet the requirements;
and sending the information that the number of the type keywords does not meet the requirement to a preset management terminal for displaying.
It should be noted that, the keywords in the preset type library all have type identifiers, and when the second similarity value is greater than the preset second similarity threshold value, the types of the keywords in the enterprise data information and the keywords in the corresponding preset type library are the same. And reminding the user terminal to continuously acquire the enterprise data information when the number of the keywords of the same type does not meet the requirement, until the number of the keywords of the same type meets the requirement.
According to an embodiment of the present application, further comprising:
acquiring a quantity value of the type of the keyword;
dividing the knowledge graph of the enterprise according to the number value of the type of the keyword to obtain a plurality of knowledge graph areas;
and marking the knowledge graph area according to the type of the keyword, and sending the keyword to the correspondingly marked knowledge graph area for storage.
It should be noted that, keywords in the enterprise data information are classified and sent to the corresponding knowledge-graph areas according to types, and different knowledge-graph areas are constructed by different types of keywords.
According to an embodiment of the present application, further comprising:
comparing and analyzing the knowledge graph region and a preset enterprise risk graph to obtain a third similarity value;
inquiring in a preset weight coefficient table according to the marks of the knowledge graph areas to obtain weight coefficients of the corresponding knowledge graph areas;
multiplying the third similar value by the weight coefficient of the corresponding knowledge-graph region to obtain a fourth similar value;
and accumulating the fourth similar values to obtain a first similar value.
It should be noted that different knowledge graph regions have different effects on enterprise risk analysis, the labels of the knowledge graph regions are types of keywords, weight coefficients of the corresponding knowledge graph regions are determined according to the types of the keywords, and the relationship between the types of the keywords and the weight coefficients of the knowledge graph regions is stored in the preset weight coefficient table.
According to an embodiment of the present application, further comprising:
judging whether the fourth similar value is larger than a preset fourth similar threshold value, if so, triggering risk prompt information of the corresponding knowledge graph region;
and sending the risk prompt information of the knowledge graph region to a preset management terminal for display.
It should be noted that, the preset fourth similar threshold values of the different knowledge-graph regions are different, and when the fourth similar value is greater than the preset fourth similar threshold value, it is indicated that the enterprise data corresponding to the corresponding knowledge-graph region has an independent risk, such as that the market region has a risk and the fund region has a risk, and the preset fourth similar threshold value is set by a person skilled in the art.
According to an embodiment of the present application, further comprising:
acquiring enterprise historical data information;
extracting historical keywords in enterprise data information;
dividing historical keywords in enterprise data information at any time according to a preset time period to obtain historical keywords in different time periods;
according to the historical keywords of different time periods, constructing historical knowledge maps of different time periods of the enterprise;
comparing and analyzing the historical knowledge patterns of different time periods of the enterprise with a preset enterprise risk pattern to obtain a fifth similarity value;
performing difference calculation on fifth similar values of the historical knowledge patterns of adjacent time periods to obtain first data;
judging whether the first data is larger than a preset first data threshold value, if so, triggering enterprise risk early warning information;
and sending the enterprise risk early warning information to a preset management terminal for display.
It should be noted that, the first data is a difference value obtained by subtracting a fifth similar value of the historical knowledge graph of a previous time period from a fifth similar value of the historical knowledge graph of a next time period in the adjacent time period, when the first data is a positive number, it is indicated that the enterprise risk coefficient of the corresponding enterprise between the adjacent time periods is increased, if the first data is greater than a preset first data threshold, it is indicated that the corresponding enterprise risk coefficient is excessively increased, enterprise risk early warning information is triggered, the fifth similar value is smaller than a preset first similar threshold, and the preset first data threshold is set by a person skilled in the art.
According to an embodiment of the present application, further comprising:
when the first data is smaller than or equal to a preset first data threshold value, extracting a preset number of first data by taking the current time as a reference;
judging whether the preset number of first data are in a preset first data range or not, if so, triggering enterprise risk early warning information;
and sending the enterprise risk early warning information to a preset management terminal for display.
It should be noted that, the first data is obtained by performing a difference calculation on the fifth similar value of the historical knowledge graph of the adjacent time period, so that the preset number of first data needs to be added with one fifth similar value, the preset number of first data is extracted based on the current time, for example, the preset number is 3, the fifth similar value of the historical knowledge graph of the adjacent 4 time periods is extracted based on the current time, the fifth similar value of the historical knowledge graph of the adjacent time period is subjected to a difference calculation, and corresponding first data is obtained, the preset first data range is greater than zero and smaller than a preset first data threshold, and when the preset number of first data is in the preset first data range, it is indicated that the risk coefficient existing in the corresponding enterprise continuously increases, so that the enterprise risk early warning information is triggered.
According to an embodiment of the present application, further comprising:
when the first similarity value is larger than a preset first similarity threshold value, extracting all fourth similarity values;
all the fourth similar values are ordered in the order from big to small, and the largest fourth similar value is extracted;
obtaining a knowledge graph region corresponding to the maximum fourth similarity value according to the maximum fourth similarity value;
matching the keywords of the knowledge graph region corresponding to the maximum fourth similarity value with a preset enterprise risk management and control scheme to obtain a correlation degree;
and judging whether the association degree is larger than a preset association degree threshold value, if so, setting a preset enterprise risk management scheme corresponding to the association degree as a current enterprise risk management scheme.
It should be noted that, various enterprise risk management and control schemes are stored in the preset enterprise risk management and control scheme, and the greater the association degree between the keywords of the knowledge graph region and the preset enterprise risk management and control scheme is, the more the corresponding preset enterprise risk management and control scheme is adapted to the risk management and control of the corresponding enterprise. The preset association threshold is set by a person skilled in the art.
According to an embodiment of the present application, further comprising:
when a plurality of risk management and control schemes of the current enterprises exist, numbering the risk management and control schemes of the current enterprises according to the sequence from the higher degree of association to the lower degree of association;
and sequentially sending the risk management and control schemes of the current enterprises to a preset management terminal for display according to the ordered numbers.
It should be noted that, the earlier the number is, the earlier the risk management scheme is at the display position of the preset management end.
A third aspect of the present application provides a computer storage medium having stored therein a knowledge-based enterprise risk analysis method program, which when executed by a processor, implements the steps of a knowledge-based enterprise risk analysis method as described in any one of the above.
The application discloses an enterprise risk analysis method, system and storage medium based on a knowledge graph, wherein the method comprises the following steps: acquiring enterprise data information; extracting keywords in enterprise data information; constructing a knowledge graph of the enterprise according to the keywords in the enterprise data information; comparing and analyzing the knowledge graph of the enterprise with a preset enterprise risk graph to obtain a first similarity value; judging whether the first similarity value is larger than a preset first similarity threshold value, and if so, obtaining risk information of the enterprise; if not, determining that the enterprise has no risk; and sending the enterprise risk information to a preset management terminal for display. According to the method, the key words in the enterprise data information are classified, the knowledge patterns of a plurality of knowledge pattern areas are constructed, the enterprise risk is judged by calculating the knowledge patterns and the first similar value of the preset enterprise risk pattern, and the accuracy of enterprise risk analysis is improved.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present application may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present application may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
Claims (5)
1. An enterprise risk analysis method based on a knowledge graph is characterized in that,
comprising the following steps:
acquiring enterprise data information;
extracting keywords in enterprise data information;
constructing a knowledge graph of the enterprise according to the keywords in the enterprise data information;
comparing and analyzing the knowledge graph of the enterprise with a preset enterprise risk graph to obtain a first similarity value;
judging whether the first similarity value is larger than a preset first similarity threshold value, and if so, obtaining risk information of the enterprise;
if not, determining that the enterprise has no risk;
the enterprise risk information is sent to a preset management terminal for display;
after the keyword in the enterprise data information is extracted, the method further comprises the following steps:
comparing and analyzing the keywords in the enterprise data information with keywords in a preset type library to obtain a second similarity value;
judging whether the second similarity value is larger than a preset second similarity threshold value, if so, extracting keywords corresponding to the second similarity value and keywords in a preset type library;
setting the types of the keywords corresponding to the second similar value and the types of the keywords in a preset type library to be consistent;
classifying the keywords corresponding to the second similar value according to the corresponding types to obtain keywords of different types, and extracting the number of the keywords of the corresponding types;
judging whether the number of the keywords of the corresponding type is larger than a preset number threshold of the corresponding type, if so, the number of the keywords of the corresponding type meets the requirement; if not, obtaining information that the number of the keywords of the corresponding type does not meet the requirements;
the information that the number of the type keywords does not meet the requirement is sent to a preset management terminal for display;
further comprises:
acquiring a quantity value of the type of the keyword;
dividing the knowledge graph of the enterprise according to the number value of the type of the keyword to obtain a plurality of knowledge graph areas;
marking the knowledge graph area according to the type of the keyword, and sending the keyword to the correspondingly marked knowledge graph area for storage;
further comprises:
comparing and analyzing the knowledge graph region and a preset enterprise risk graph to obtain a third similarity value;
inquiring in a preset weight coefficient table according to the marks of the knowledge graph areas to obtain weight coefficients of the corresponding knowledge graph areas;
multiplying the third similar value by the weight coefficient of the corresponding knowledge-graph region to obtain a fourth similar value;
and accumulating the fourth similar values to obtain a first similar value.
2. The method for analyzing enterprise risk based on the knowledge graph according to claim 1, wherein,
further comprises:
judging whether the fourth similar value is larger than a preset fourth similar threshold value, if so, triggering risk prompt information of the corresponding knowledge graph region;
and sending the risk prompt information of the knowledge graph region to a preset management terminal for display.
3. The method for analyzing enterprise risk based on the knowledge graph according to claim 1, wherein,
further comprises:
acquiring enterprise historical data information;
extracting historical keywords in enterprise data information;
dividing historical keywords in enterprise data information at any time according to a preset time period to obtain historical keywords in different time periods;
according to the historical keywords of different time periods, constructing historical knowledge maps of different time periods of the enterprise;
comparing and analyzing the historical knowledge patterns of different time periods of the enterprise with a preset enterprise risk pattern to obtain a fifth similarity value;
performing difference calculation on fifth similar values of the historical knowledge patterns of adjacent time periods to obtain first data;
judging whether the first data is larger than a preset first data threshold value, if so, triggering enterprise risk early warning information;
and sending the enterprise risk early warning information to a preset management terminal for display.
4. An enterprise risk analysis system based on a knowledge graph is characterized in that,
the enterprise risk analysis method based on the knowledge graph comprises a memory and a processor, wherein the memory stores an enterprise risk analysis method program based on the knowledge graph, and the enterprise risk analysis method program based on the knowledge graph realizes the following steps when being executed by the processor:
acquiring enterprise data information;
extracting keywords in enterprise data information;
constructing a knowledge graph of the enterprise according to the keywords in the enterprise data information;
comparing and analyzing the knowledge graph of the enterprise with a preset enterprise risk graph to obtain a first similarity value;
judging whether the first similarity value is larger than a preset first similarity threshold value, and if so, obtaining risk information of the enterprise;
if not, determining that the enterprise has no risk;
the enterprise risk information is sent to a preset management terminal for display;
after the keyword in the enterprise data information is extracted, the method further comprises the following steps:
comparing and analyzing the keywords in the enterprise data information with keywords in a preset type library to obtain a second similarity value;
judging whether the second similarity value is larger than a preset second similarity threshold value, if so, extracting keywords corresponding to the second similarity value and keywords in a preset type library;
setting the types of the keywords corresponding to the second similar value and the types of the keywords in a preset type library to be consistent;
classifying the keywords corresponding to the second similar value according to the corresponding types to obtain keywords of different types, and extracting the number of the keywords of the corresponding types;
judging whether the number of the keywords of the corresponding type is larger than a preset number threshold of the corresponding type, if so, the number of the keywords of the corresponding type meets the requirement; if not, obtaining information that the number of the keywords of the corresponding type does not meet the requirements;
the information that the number of the type keywords does not meet the requirement is sent to a preset management terminal for display;
further comprises:
acquiring a quantity value of the type of the keyword;
dividing the knowledge graph of the enterprise according to the number value of the type of the keyword to obtain a plurality of knowledge graph areas;
marking the knowledge graph area according to the type of the keyword, and sending the keyword to the correspondingly marked knowledge graph area for storage;
further comprises:
comparing and analyzing the knowledge graph region and a preset enterprise risk graph to obtain a third similarity value;
inquiring in a preset weight coefficient table according to the marks of the knowledge graph areas to obtain weight coefficients of the corresponding knowledge graph areas;
multiplying the third similar value by the weight coefficient of the corresponding knowledge-graph region to obtain a fourth similar value;
and accumulating the fourth similar values to obtain a first similar value.
5. A computer storage medium, wherein a knowledge-graph-based enterprise risk analysis method program is stored in the computer storage medium, and when the knowledge-graph-based enterprise risk analysis method program is executed by a processor, the steps of a knowledge-graph-based enterprise risk analysis method according to any one of claims 1 to 3 are implemented.
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