CN111401777A - Enterprise risk assessment method and device, terminal equipment and storage medium - Google Patents
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Abstract
The enterprise risk assessment method comprises the steps of obtaining an enterprise knowledge map and enterprise financial data, and obtaining a time sequence of an enterprise state according to the enterprise knowledge map and the enterprise financial data, wherein the time sequence is a corresponding relation between a vector space and time, the vector space is composed of at least one vector, and one vector corresponds to data information of one risk dimension; and obtaining an enterprise risk level corresponding to preset time according to the time sequence of the enterprise state and a preset risk evaluation model, wherein the enterprise risk level comprises a level corresponding to at least one risk dimension. Therefore, enterprise risks are evaluated in multiple dimensions of production information, operation information, management information and financial information, and accuracy of risk evaluation is improved.
Description
Technical Field
The application belongs to the technical field of computers, and particularly relates to an enterprise risk assessment method and device, terminal equipment and a storage medium.
Background
The existing enterprise risk assessment method generally assesses enterprise risks according to basic financial data and experience, and factors considered in the assessment process are few, and assessment is inaccurate.
Disclosure of Invention
In view of this, embodiments of the present application provide an enterprise risk assessment method, an enterprise risk assessment device, a terminal device, and a storage medium, so as to perform comprehensive assessment on enterprise risks and improve accuracy of risk assessment.
A first aspect of an embodiment of the present application provides an enterprise risk assessment method, including:
acquiring an enterprise knowledge graph and enterprise financial data, wherein the enterprise knowledge graph comprises production information, operation information and management information of an enterprise;
obtaining a time sequence of an enterprise state according to the enterprise knowledge graph and enterprise financial data, wherein the time sequence of the enterprise state is a corresponding relation between a vector space and time, the vector space is composed of at least one vector, and one vector corresponds to data information of one risk dimension;
and obtaining an enterprise risk level corresponding to preset time according to the time sequence of the enterprise state and a preset risk assessment model, wherein the enterprise risk level comprises a level corresponding to at least one risk dimension.
In a possible implementation manner, the method for assessing risk of an enterprise further includes:
acquiring an industry knowledge graph and industry financial data;
counting the industry knowledge graph and the industry financial data to obtain an industry risk level;
and outputting a comparison result of the enterprise risk level and the industry risk level.
In a possible implementation manner, the method for assessing risk of an enterprise further includes:
acquiring a national region knowledge graph and economic data related to national regions;
counting the knowledge graph of the country and the region and the economic data related to the country and the region to obtain the economic risk grade of the country and the region;
and outputting a comparison result of the enterprise risk level and the economic risk level of the national region.
In a possible implementation manner, the method for assessing risk of an enterprise further includes:
calculating periodic information of the enterprise financial data according to a preset time sequence analysis method and the enterprise financial data;
and outputting the corresponding relation between the enterprise risk level corresponding to the preset time and the periodic information of the enterprise financial data.
In a possible implementation manner, the method for assessing risk of an enterprise further includes:
and updating the vector space according to the periodic information of the enterprise financial data.
In a possible implementation manner, the method for assessing risk of an enterprise further includes:
acquiring alarm information related to an enterprise;
converting the alarm information into text information;
and outputting the enterprise risk level corresponding to the alarm information according to the text information and a preset alarm analysis model.
In one possible implementation, after the obtaining of the alarm information related to the enterprise, the method further includes:
and generating an alarm prompt in a preset format according to the alarm information.
A second aspect of the embodiments of the present application provides an enterprise risk assessment apparatus, including:
the system comprises an acquisition module, a management module and a management module, wherein the acquisition module is used for acquiring an enterprise knowledge graph and enterprise financial data, and the enterprise knowledge graph comprises production information, management information and management information of an enterprise;
the analysis module is used for obtaining a time sequence of the enterprise state according to the enterprise knowledge map and the enterprise financial data, wherein the time sequence is a corresponding relation between a vector space and time, the vector space is composed of at least one vector, and one vector corresponds to data information of one risk dimension;
and the evaluation module is used for obtaining enterprise risk levels corresponding to preset time according to the time sequence of the enterprise state and a preset risk evaluation model, wherein the enterprise risk levels comprise levels corresponding to at least one risk dimension.
In a possible implementation manner, the apparatus for assessing risk of an enterprise further includes a first comparison module, where the first comparison module is configured to:
acquiring an industry knowledge graph and industry financial data;
counting the industry knowledge graph and the industry financial data to obtain an industry risk level;
and outputting a comparison result of the enterprise risk level and the industry risk level.
In a possible implementation manner, the apparatus for assessing risk of an enterprise further includes a second comparison module, where the second comparison module is configured to:
acquiring a national region knowledge graph and economic data related to national regions;
counting the knowledge graph of the country and the region and the economic data related to the country and the region to obtain the economic risk grade of the country and the region;
and outputting a comparison result of the enterprise risk level and the economic risk level of the national region.
In a possible implementation manner, the apparatus for assessing risk of an enterprise further includes a computing module, where the computing module is configured to:
calculating periodic information of the enterprise financial data according to a preset time sequence analysis method and the enterprise financial data;
and outputting the corresponding relation between the enterprise risk level corresponding to the preset time and the periodic information of the enterprise financial data.
In a possible implementation manner, the apparatus for assessing risk of an enterprise further includes an updating module, where the updating module is configured to:
and updating the vector space according to the periodic information of the enterprise financial data.
In a possible implementation manner, the apparatus for assessing risk of an enterprise further includes an alarm module, and the alarm module is configured to:
acquiring alarm information related to an enterprise;
converting the alarm information into text information;
and outputting the enterprise risk level corresponding to the alarm information according to the text information and a preset alarm analysis model.
In one possible implementation, the alert module is further configured to:
and generating an alarm prompt in a preset format according to the alarm information.
A third aspect of the embodiments of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor, when executing the computer program, implements the method for assessing risk of an enterprise as described in the first aspect.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the method for assessing risk of an enterprise as described in the first aspect above is implemented.
A fifth aspect of the embodiments of the present application provides a computer program product, which, when running on a terminal device, causes the terminal device to execute the method for assessing enterprise risk according to the first aspect.
Compared with the prior art, the embodiment of the application has the advantages that: the method comprises the steps of obtaining an enterprise knowledge graph and enterprise financial data, wherein the enterprise knowledge graph comprises production information, operation information and management information of an enterprise; and obtaining a time sequence of the state of the enterprise according to the knowledge graph of the enterprise and financial data of the enterprise, wherein the time sequence is a corresponding relation between a vector space and time, the vector space is composed of at least one vector, and one vector corresponds to data information of one risk dimension, namely the vector space comprises production information, operation information, management information and financial information of the enterprise in each period. And then obtaining enterprise risk levels corresponding to the preset time according to the time sequence of the enterprise state and the preset risk assessment model, wherein the enterprise risk levels comprise levels corresponding to at least one risk dimension, and because the time sequence of the enterprise state is related to the production information, the operation information, the management information and the financial information of the enterprise in each period, the enterprise risk can be assessed from multiple dimensions of the production information, the operation information, the management information and the financial information in each period, so that the accuracy of risk assessment is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the embodiments or the description of the prior art will be briefly described below.
FIG. 1 is a schematic flow chart of an implementation of a method for assessing risk of an enterprise according to an embodiment of the present application;
FIG. 2 is a detailed flowchart of an enterprise risk assessment method provided by an embodiment of the present application;
FIG. 3 is a schematic diagram of an apparatus for risk assessment of an enterprise according to an embodiment of the present disclosure;
fig. 4 is a schematic diagram of a terminal device provided in an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
In order to explain the technical solution described in the present application, the following description will be given by way of specific examples.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
The method for evaluating enterprise risk provided by the embodiment of the present application is applied to a terminal device, and referring to fig. 1, the method for evaluating enterprise risk provided by the embodiment of the present application includes:
s101: acquiring an enterprise knowledge graph and enterprise financial data, wherein the enterprise knowledge graph comprises production information, operation information and management information of an enterprise.
The enterprise knowledge graph is a description of the basic form of an enterprise, is generated according to relevant data of the enterprise input by a user, and comprises production information, operation information and management information of the enterprise. In one possible implementation, the enterprise knowledge graph further includes basic information of the enterprise, such as name, location, business scope, industry of the enterprise, establishment time, registered funds, employee number, floor space, enterprise prospects, social responsibility, and the like.
The enterprise financial data comprises categories of sales data, profit data, investment data, seasonal or periodic information of the enterprise, and the like, wherein the financial data of each category comprises a plurality of indexes, so that financial data of a plurality of aspects of the enterprise is obtained. For example, profitability includes sales returns and return on investment. The sales rewards include: profitability/sales, operational profitability/sales, pre-tax profit/sales, net profitability/gross profitability, etc. ratio indicators. The return on investment includes gross profit/gross asset, EBT/gross asset, net profit/gross asset, pre-tax profit/fixed asset, net profit/fixed asset, pre-tax profit/gross equity, net profit/gross equity, pre-tax profit/current debt, profit/gross profit, etc. ratio indices reserved for company development, where "/" represents a division calculation.
S102: and obtaining a time sequence of the enterprise state according to the enterprise knowledge graph and the enterprise financial data, wherein the time sequence of the enterprise state is a corresponding relation between a vector space and time, the vector space is composed of at least one vector, and one vector corresponds to data information of one risk dimension.
Specifically, because the information of production, operation, management, finance and the like of the enterprise continuously changes along with time, the knowledge map and the enterprise financial data of the enterprise also change along with time, the change information of the knowledge map and the enterprise financial data of the enterprise along with time is counted and classified, and the time sequence of the state of the enterprise is generated, so that the change condition of the factors related to each risk of the enterprise along with time is counted. The enterprise state comprises management, production, operation, time period, economic indexes, business indexes, non-financial risks, financial risks and other dimensions. Each dimension, in turn, includes a plurality of microscopic dimensions, e.g., non-financial risks including market risks, product risks, business risks, investment risks, foreign exchange risks, personnel risks, system risks, merger risks, natural disaster risks, public crisis risks, and the like. Financial risks include financing risks, investment risks, operational risks, inventory management risks, liquidity risks, and the like. Wherein each dimension of the financial risk and the non-financial risk corresponds to data representing indexes of the dimension, for example, the data corresponding to the financial risk is cash due debt ratio, cash flow liability ratio, cash debt total sum ratio and cash interest multiple. For the current time point, each dimension corresponds to a vector, the vectors corresponding to all the dimensions form a vector space, namely the enterprise state of the current time point is described by the vector space of the current time point, and the change of the vector space along with time forms a time sequence.
S103: and obtaining an enterprise risk level corresponding to preset time according to the time sequence of the enterprise state and a preset risk assessment model, wherein the enterprise risk level comprises a level corresponding to at least one risk dimension.
The preset risk assessment model is obtained by training a classification model by using a machine learning algorithm and taking historical enterprise states and corresponding enterprise risk levels as training samples, and the enterprise risk levels output by the risk assessment model are compared with the expert prediction results to optimize the risk assessment model.
The enterprise risk includes enterprise management risk, production risk, operation risk and financial risk, the financial risk includes financing risk, investment risk, operation risk, inventory management risk, liquidity risk, profitability risk and reducibility risk, etc., the enterprise risk level may be a level corresponding to each dimension of enterprise management risk, production risk, operation risk, financing risk, investment risk, operation risk, inventory management risk, liquidity risk, profitability risk and reducibility risk, etc., or a comprehensive index level calculated according to the level corresponding to each dimension, the enterprise risk level may be a level divided into an extremely low level, a medium level, a high level and a high level, or the enterprise risk level may be represented by a number between 0 and 1.
The enterprise risk level corresponding to the preset time can be the current risk level of the enterprise, the historical risk level of the enterprise and the future risk level of the enterprise, a time sequence of the change of the enterprise risk level along with the time can be generated according to the historical risk level corresponding to each time period, and the change situation of the enterprise risk level can be reflected more visually.
In one possible implementation, after calculating the enterprise risk level corresponding to the preset time, the enterprise risk level is output according to a format set by a user, for example, in the form of a graph, a table, or a narrative report.
In a possible implementation manner, the enterprise risk assessment method provided by the embodiment of the application further obtains the industry knowledge graph and the industry financial data by acquiring the industry knowledge graph and the industry financial data, and performs statistics on the industry knowledge graph and the industry financial data to obtain the industry risk level.
The industry knowledge graph is a multi-dimensional knowledge network used for describing industries, products, services and the like. The basic components of an industry knowledge graph are body, dimension, and emotion. The main body comprises the current enterprise, other enterprises, all departments in the enterprise, products or a certain version of the products. The dimension comprises a certain aspect, a certain attribute or a certain topic of describing the entity in the aspects of products, services, marketing, brands and the like, the emotion generally refers to the opinion or opinion of consumers on the industry or products, and production and operation information and enterprises in the same industry related to the industry can be obtained according to an industry knowledge graph.
The industry financial data comprises financial information of companies on market in the industry, expert opinions of accounting professors, global historical economic comprehensive data, Chinese historical economic comprehensive data, comprehensive business information, consumer behavior data, agency data, microscopic economic data, marking data and the like. And counting and classifying the industry financial data to obtain financial data in a preset format, such as structured expert opinions, structured real-time business intelligence, structured digestive behavior data, structured case marking data, structured agent data and structured micro economic data. And (4) carrying out statistics and analysis on the industry knowledge graph and the preprocessed industry financial data to obtain an industry risk grade.
In a possible implementation manner, after the industry knowledge graph and the industry financial data are classified, a vector space is generated in a vector manner, for a current time point, the vector space comprises the dimensions of management, production, operation, time period, economic indexes, business indexes, real-time information alarm, non-financial risk, financial risk and the like of a company enterprise of the current enterprise, and the risk level corresponding to each dimension can be obtained in a statistical analysis manner or can be obtained through a preset risk assessment model. The obtained industry risk level is compared with the enterprise risk level of the current enterprise, so that the change relationship between the enterprise and the current industry can be tracked and predicted, and a manager can make a decision.
In a possible implementation manner, a peer enterprise with the current enterprise or an enterprise designated by the user can be selected according to the industry knowledge graph, a corresponding vector space is generated, and the risk level of the peer enterprise or the enterprise designated by the user is calculated. The risk level of the enterprise appointed by the same-industry enterprise or the user is compared with the current risk level or the historical risk level of the current enterprise, so that the information such as the strength of a competitor, the development prospect of the enterprise and the like can be obtained, and the enterprise can be helped to make decisions.
In a possible implementation manner, the enterprise risk assessment method provided by the embodiment of the application further obtains the national region knowledge graph and the economic data related to the national region by obtaining the national region knowledge graph and the economic data related to the national region, and obtains the economic situation information of the national region by performing statistics on the national region knowledge graph and the economic data related to the national region.
The state and region knowledge map is a description of relevant information of a certain country or region, and comprises basic information of enterprises and industries of the country and region. The national and regional economic data comprises financial data of global marketing companies, financial data of Chinese marketing companies, expert opinions of accounting professors, global historical economic comprehensive data, Chinese historical economic comprehensive data, comprehensive business information, consumer behavior data, agency data, microscopic economic data, case marking data and the like. And counting and classifying the economic data related to the country and the region to obtain financial data in a preset format, such as structured expert opinions, structured real-time business intelligence, structured digestive behavior data, structured case marking data, structured agent data and structured micro economic data. And carrying out statistics and analysis on the industry knowledge graph and the economic data related to the preprocessed national regions to obtain the economic situation information of the national regions.
In a possible implementation mode, the industry knowledge graph and the preprocessed economic data related to the national regions are classified to obtain three dimensions of macroscopic economic conditions, macroscopic economic trends and macroscopic economic risks, the three dimensions are respectively expressed by vectors to obtain vector spaces corresponding to the national economic conditions, and the economic data corresponding to the vector spaces are the economic condition information of the national regions. And comparing the enterprise risk level with the economic situation information of the national region, and outputting a comparison result, so that the relation between the enterprise risk and external changes can be seen to predict enterprise development.
In a possible implementation manner, the method for evaluating enterprise risk provided in this embodiment of the present application further calculates periodic information of the enterprise financial data according to a preset time series analysis method and the enterprise financial data, where the preset time series analysis method may be a Holt-winter method, and the enterprise financial data analyzed by the preset time series analysis method includes a total amount of liabilities of the enterprise, a profit amount, a sales amount, a profit, and the like. Through the Holt-Winters method, the related enterprise financial data can be represented by linear trend, periodic and non-stationary sequences, so that the periodic information of the enterprise financial data is analyzed, and the enterprise financial data can be predicted. And comparing the corresponding relation between the enterprise risk level corresponding to the preset time and the periodic information of the enterprise financial data, and outputting according to a preset format to visually display the corresponding relation between the enterprise risk and the financial data.
In a possible implementation manner, the enterprise risk assessment method provided in the embodiment of the present application further obtains alarm information related to an enterprise, converts the alarm information into text information, and outputs an enterprise risk level corresponding to the alarm information according to the text information and a preset alarm analysis model. Specifically, the alert information related to the enterprise may be news, forum, blog, microblog, WeChat, video, post, know, etc. type information. And converting various types of information into text information, inputting the text information into a preset alarm analysis model, and outputting the enterprise risk level corresponding to the alarm information. The preset alarm analysis model is obtained by training the classification model by taking the text information and the corresponding risk grade as training samples. The classification model may be a convolutional neural network model, and before inputting a text into a preset alarm analysis model, the text needs to be preprocessed, including removing punctuation marks and spaces, performing word segmentation and index creation on the text, so as to convert the text into a plurality of character strings each using a number as an index, and convert the text into a matrix. And inputting the matrix corresponding to the text into a preset alarm analysis model, and outputting the enterprise risk level corresponding to the alarm information. Wherein, can use the grade of the number representation risk between 0 to 1, compare the enterprise risk level that corresponds with alarm information and the enterprise risk level that financial data corresponds, can play the effect of verifying each other, improve the rate of accuracy of enterprise risk level evaluation. Optionally, an alarm prompt in a preset format may also be generated according to the alarm information, for example, the alarm prompt is presented in a voice or video format to remind the user to pay attention to the relevant alarm.
In a possible implementation manner, in the embodiment of the application, the enterprise risk levels corresponding to the enterprise risk level, the industry risk level, the economic risk level of the national region, the periodicity information of the enterprise financial data, and the alarm information in each calculated time period are used as the enterprise financial data, the industry financial data, and the economic data related to the national region again to update the vector space related to the enterprise state, the industry, and the national region, so as to improve the accuracy of risk assessment.
Referring to fig. 2, the flow of the enterprise risk assessment method according to an embodiment of the present application is further described, as shown in fig. 2, the input data includes financial data of a global company on sale, financial data of a chinese company on sale, expert opinions of an accounting professor, global historical economic comprehensive data, chinese historical economic comprehensive data, comprehensive business information, consumer behavior data, agent data, micro economic data, case marking data, and the like. And carrying out statistics and classification on input data to obtain macroscopic economic data, structured expert opinions, structured real-time business information, structured digestive behavior data, structured case marking data, structured agent data and structured microscopic economic data, and generating a vector space corresponding to the current enterprise, a vector space corresponding to the industry enterprise and a vector space corresponding to the economic situation information of the national region by combining basic information, an enterprise knowledge map, an industry knowledge map, a national region knowledge map and time seasonal and periodic information of the enterprise. Inputting a vector space corresponding to the current enterprise, a vector space corresponding to the industry enterprise and a vector space corresponding to the economic situation information of the national region into a preset risk assessment model, and outputting a comparison result of the current risk of the enterprise, the historical risk of the enterprise, the industry risk level and the enterprise risk level of the current enterprise, a comparison result of each enterprise risk level, a comparison result of the enterprise risk level and the economic situation of the national region, periodic information of financial data and alarm information so as to show the relation between the enterprise risk and the external economic situation to a user. Meanwhile, the current output information is used as the input information of the next time interval to generate new output data, and the enterprise risk level of the next time interval is evaluated, so that the accuracy of risk evaluation is improved.
In the embodiment, the enterprise knowledge graph and the enterprise financial data are obtained, wherein the enterprise knowledge graph comprises production information, operation information and management information of an enterprise; and obtaining a time sequence of the state of the enterprise according to the knowledge graph of the enterprise and financial data of the enterprise, wherein the time sequence is a corresponding relation between a vector space and time, the vector space is composed of at least one vector, and one vector corresponds to data information of one risk dimension, namely the vector space comprises production information, operation information, management information and financial information of the enterprise in each period. And then obtaining enterprise risk levels corresponding to the preset time according to the time sequence of the enterprise state and the preset risk assessment model, wherein the enterprise risk levels comprise levels corresponding to at least one risk dimension, and because the time sequence of the enterprise state is related to the production information, the operation information, the management information and the financial information of the enterprise in each period, the enterprise risk can be assessed from multiple dimensions of the production information, the operation information, the management information and the financial information in each period, so that the accuracy of risk assessment is improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Fig. 3 shows a block diagram of an enterprise risk assessment device provided in the embodiment of the present application, and for convenience of explanation, only the parts related to the embodiment of the present application are shown.
As shown in fig. 3, the enterprise risk assessment device includes,
the system comprises an acquisition module 10, a management module and a management module, wherein the acquisition module is used for acquiring an enterprise knowledge graph and enterprise financial data, and the enterprise knowledge graph comprises production information, management information and management information of an enterprise;
the analysis module 20 is configured to obtain a time series of enterprise states according to the enterprise knowledge graph and the enterprise financial data, where the time series of enterprise states is a correspondence between a vector space and time, the vector space is composed of at least one vector, and one vector corresponds to data information of one risk dimension;
and the evaluation module 30 is configured to obtain an enterprise risk level corresponding to preset time according to the time sequence of the enterprise state and a preset risk evaluation model, where the enterprise risk level includes a level corresponding to at least one risk dimension.
In a possible implementation manner, the apparatus for assessing risk of an enterprise further includes a first comparison module, where the first comparison module is configured to:
acquiring an industry knowledge graph and industry financial data;
counting the industry knowledge graph and the industry financial data to obtain an industry risk level;
and outputting a comparison result of the enterprise risk level and the industry risk level.
In a possible implementation manner, the apparatus for assessing risk of an enterprise further includes a second comparison module, where the second comparison module is configured to:
acquiring a national region knowledge graph and economic data related to national regions;
counting the knowledge graph of the country and the region and the economic data related to the country and the region to obtain the economic risk grade of the country and the region;
and outputting a comparison result of the enterprise risk level and the economic risk level of the national region.
In a possible implementation manner, the apparatus for assessing risk of an enterprise further includes a computing module, where the computing module is configured to:
calculating periodic information of the enterprise financial data according to a preset time sequence analysis method and the enterprise financial data;
and outputting the corresponding relation between the enterprise risk level corresponding to the preset time and the periodic information of the enterprise financial data.
In a possible implementation manner, the apparatus for assessing risk of an enterprise further includes an updating module, where the updating module is configured to:
and updating the vector space according to the periodic information of the enterprise financial data.
In a possible implementation manner, the apparatus for assessing risk of an enterprise further includes an alarm module, and the alarm module is configured to:
acquiring alarm information related to an enterprise;
converting the alarm information into text information;
and outputting the enterprise risk level corresponding to the alarm information according to the text information and a preset alarm analysis model.
In one possible implementation, the alert module is further configured to:
and generating an alarm prompt in a preset format according to the alarm information.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
Fig. 4 is a schematic diagram of a terminal device provided in an embodiment of the present application. As shown in fig. 4, the terminal device of this embodiment includes: a processor 11, a memory 12 and a computer program 13 stored in said memory 12 and executable on said processor 11. The processor 11, when executing the computer program 13, implements the steps in the above-described embodiment of the enterprise risk assessment method, such as the steps S101 to S103 shown in fig. 1. Alternatively, the processor 11, when executing the computer program 13, implements the functions of each module/unit in each device embodiment described above, for example, the functions of the modules 10 to 30 shown in fig. 3.
Illustratively, the computer program 13 may be partitioned into one or more modules/units, which are stored in the memory 12 and executed by the processor 11 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 13 in the terminal device.
The terminal device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The terminal device may include, but is not limited to, a processor 11, a memory 12. Those skilled in the art will appreciate that fig. 4 is merely an example of a terminal device and is not limiting and may include more or fewer components than shown, or some components may be combined, or different components, for example, the terminal device may also include input output devices, network access devices, buses, etc.
The Processor 11 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 12 may be an internal storage unit of the terminal device, such as a hard disk or a memory of the terminal device. The memory 12 may also be an external storage device of the terminal device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the terminal device. Further, the memory 12 may also include both an internal storage unit and an external storage device of the terminal device. The memory 12 is used for storing the computer program and other programs and data required by the terminal device. The memory 12 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A method for assessing risk of an enterprise, comprising:
acquiring an enterprise knowledge graph and enterprise financial data, wherein the enterprise knowledge graph comprises production information, operation information and management information of an enterprise;
obtaining a time sequence of an enterprise state according to the enterprise knowledge graph and enterprise financial data, wherein the time sequence of the enterprise state is a corresponding relation between a vector space and time, the vector space is composed of at least one vector, and one vector corresponds to data information of one risk dimension;
and obtaining an enterprise risk level corresponding to preset time according to the time sequence of the enterprise state and a preset risk assessment model, wherein the enterprise risk level comprises a level corresponding to at least one risk dimension.
2. The method for enterprise risk assessment according to claim 1, wherein said method for enterprise risk assessment further comprises:
acquiring an industry knowledge graph and industry financial data;
counting the industry knowledge graph and the industry financial data to obtain an industry risk level;
and outputting a comparison result of the enterprise risk level and the industry risk level.
3. The method for enterprise risk assessment according to claim 1, wherein said method for enterprise risk assessment further comprises:
acquiring a national region knowledge graph and economic data related to national regions;
counting the knowledge graph of the country and the region and the economic data related to the country and the region to obtain the economic situation information of the country and the region;
and outputting a comparison result of the enterprise risk level and the economic situation information of the national region.
4. The method for enterprise risk assessment according to claim 1, wherein said method for enterprise risk assessment further comprises:
calculating periodic information of the enterprise financial data according to a preset time sequence analysis method and the enterprise financial data;
and outputting the corresponding relation between the enterprise risk level corresponding to the preset time and the periodic information of the enterprise financial data.
5. The method for enterprise risk assessment according to claim 4, wherein said method for enterprise risk assessment further comprises:
and updating the vector space according to the periodic information of the enterprise financial data.
6. The method for enterprise risk assessment according to claim 1, wherein said method for enterprise risk assessment further comprises:
acquiring alarm information related to an enterprise;
converting the alarm information into text information;
and outputting the enterprise risk level corresponding to the alarm information according to the text information and a preset alarm analysis model.
7. The method for assessing risk of a business as claimed in claim 6, wherein after said obtaining of business related alert information, said method further comprises:
and generating an alarm prompt in a preset format according to the alarm information.
8. An enterprise risk assessment device, comprising:
the system comprises an acquisition module, a management module and a management module, wherein the acquisition module is used for acquiring an enterprise knowledge graph and enterprise financial data, and the enterprise knowledge graph comprises production information, management information and management information of an enterprise;
the analysis module is used for obtaining a time sequence of an enterprise state according to the enterprise knowledge graph and enterprise financial data, wherein the time sequence of the enterprise state is a corresponding relation between a vector space and time, the vector space is composed of at least one vector, and one vector corresponds to data information of one risk dimension;
and the evaluation module is used for obtaining enterprise risk levels corresponding to preset time according to the time sequence of the enterprise state and a preset risk evaluation model, wherein the enterprise risk levels comprise levels corresponding to at least one risk dimension.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for assessing risk of an enterprise according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out a method for assessing risk of an enterprise according to any one of claims 1 to 7.
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