CN111401777B - Enterprise risk assessment method, enterprise risk assessment device, terminal equipment and storage medium - Google Patents
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Abstract
The method comprises the steps of obtaining an enterprise knowledge graph and enterprise financial data, and obtaining a time sequence of an enterprise state according to the enterprise knowledge graph and the enterprise financial data, wherein the time sequence is a corresponding relation between a vector space and time, the vector space consists of at least one vector, and one vector corresponds to data information of one risk dimension; and obtaining an enterprise risk grade corresponding to the preset time according to the time sequence of the enterprise state and the preset risk assessment model, wherein the enterprise risk grade comprises a grade corresponding to at least one risk dimension. Therefore, the enterprise risk is estimated from multiple dimensions of production information, management information and financial information, and the accuracy of risk estimation is improved.
Description
Technical Field
The application belongs to the technical field of computers, and particularly relates to an enterprise risk assessment method, an enterprise risk assessment device, terminal equipment and a storage medium.
Background
The existing enterprise risk assessment method generally assesses enterprise risk according to basic financial data and experience, few factors are considered in the assessment process, and assessment is inaccurate.
Disclosure of Invention
In view of this, the embodiments of the present application provide an enterprise risk assessment method, apparatus, terminal device, and storage medium, so as to comprehensively assess enterprise risk 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 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;
and obtaining an enterprise risk grade corresponding to the preset time according to the time sequence of the enterprise state and a preset risk assessment model, wherein the enterprise risk grade comprises a grade corresponding to at least one risk dimension.
In one possible implementation manner, the enterprise risk assessment method 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 one possible implementation manner, the enterprise risk assessment method further includes:
acquiring a national region knowledge graph and economic data related to the national region;
counting the national region knowledge graph and the economic data related to the national region to obtain the economic risk level of the national region;
and outputting a comparison result of the enterprise risk level and the economic risk level of the national region.
In one possible implementation manner, the enterprise risk assessment method further includes:
calculating periodic information of 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 one possible implementation manner, the enterprise risk assessment method further includes:
The vector space is updated according to periodic information of the enterprise financial data.
In one possible implementation manner, the enterprise risk assessment method further includes:
acquiring alarm information related to enterprises;
converting the alarm information into text information;
and outputting enterprise risk levels corresponding to the alarm information according to the text information and a preset alarm analysis model.
In one possible implementation, after the acquiring the alert 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 an embodiment of the present application provides an enterprise risk assessment apparatus, including:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for 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 analysis module is used for obtaining a time sequence of the enterprise state according to the enterprise knowledge graph 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;
The assessment module is used for obtaining enterprise risk grades corresponding to preset time according to the time sequence of the enterprise state and a preset risk assessment model, wherein the enterprise risk grades comprise grades corresponding to at least one risk dimension.
In one possible implementation manner, the enterprise risk assessment apparatus 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 one possible implementation manner, the enterprise risk assessment apparatus 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 the national region;
counting the national region knowledge graph and the economic data related to the national region to obtain the economic risk level of the national region;
and outputting a comparison result of the enterprise risk level and the economic risk level of the national region.
In one possible implementation manner, the enterprise risk assessment apparatus further includes a calculation module, where the calculation module is configured to:
Calculating periodic information of 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 one possible implementation manner, the enterprise risk assessment apparatus further includes an update module, where the update module is configured to:
the vector space is updated according to periodic information of the enterprise financial data.
In one possible implementation manner, the enterprise risk assessment apparatus further includes an alarm module, where the alarm module is configured to:
acquiring alarm information related to enterprises;
converting the alarm information into text information;
and outputting enterprise risk levels corresponding to the alarm information according to the text information and a preset alarm analysis model.
In one possible implementation, the alarm 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 implements the method for evaluating an enterprise risk according to the first aspect.
A fourth aspect of the embodiments of the present application provides a computer readable storage medium storing a computer program, which when executed by a processor implements the method for assessing risk of an enterprise as described in the first aspect above.
A fifth aspect of the embodiments of the present application provides a computer program product, which when run on a terminal device, causes the terminal device to perform the method for assessing risk of an enterprise as described in the first aspect above.
Compared with the prior art, the embodiment of the application has the beneficial effects that: 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; and obtaining a time sequence of the enterprise state according to the enterprise knowledge graph 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, namely the vector space contains production information, management information and financial information of each period of the enterprise. And obtaining an enterprise risk grade corresponding to the preset time according to the time sequence of the enterprise state and a preset risk assessment model, wherein the enterprise risk grade comprises at least one grade corresponding to a risk dimension, and the enterprise risk can be assessed from multiple dimensions of production information, operation information, management information and financial information of each period of the enterprise 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 each period of the enterprise, 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 description of the embodiments or the prior art will be briefly described below.
FIG. 1 is a schematic flow chart of an implementation of an enterprise risk assessment method according to an embodiment of the present application;
FIG. 2 is a detailed flow chart of an enterprise risk assessment method provided by an embodiment of the present application;
FIG. 3 is a schematic diagram of an enterprise risk assessment apparatus according to an embodiment of the present application;
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 configurations, 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 illustrate the technical solutions described in the present application, the following description is made by specific examples.
It should 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 is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification 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 any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
The enterprise risk assessment method provided by the embodiment of the present application is applied to a terminal device, referring to fig. 1, and includes:
s101: and acquiring an enterprise knowledge graph and enterprise financial data, wherein the enterprise knowledge graph comprises production information, management information and management information of an enterprise.
The enterprise knowledge graph is a description of the basic form of the enterprise and is generated according to related data of the enterprise input by a user, and the enterprise knowledge graph comprises production information, management information and management information of the enterprise. In one possible implementation, the enterprise knowledge graph also includes basic information of the enterprise, such as name, location, business scope, industry of interest, time of establishment, registered funds, employee count, floor space, enterprise prospect, social responsibility, and the like.
The enterprise financial data includes sales data, profit data, investment data, seasonal or periodic information of the enterprise, and the like, wherein each category of financial data includes a plurality of indicators to obtain financial data for a plurality of aspects of the enterprise. For example, profitability includes sales returns and return on investment. The sales rewards include: the rate index of profit/sales, operational profit/sales, tax pre-tax profit/sales, net profit/total profit, etc. Return on investment includes total profit/total asset, EBT/total asset, net profit/total asset, pre-tax profit/fixed asset, net profit/fixed asset, pre-tax profit/total stock, net profit/total stock, pre-tax profit/current liabilities, and a profit/total profit equal rate indicator reserved for corporate development, where "/" represents division calculations.
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 information such as production, operation, management and finance of an enterprise continuously changes along with time, knowledge spectrums of the enterprise and enterprise financial data also change along with time, the knowledge spectrums of the enterprise and the change information of the enterprise financial data along with time are counted and classified, and a time sequence of enterprise states is generated, so that the change condition of factors related to various risks of the enterprise along with time is counted. The enterprise state comprises management, production, operation, time period, economic index, business index, non-financial risk, financial risk and other dimensions. Each dimension in turn includes a plurality of microscopic dimensions, for example, non-financial risks including market risks, product risks, business risks, investment risks, foreign exchange risks, personnel risks, regime risks, purchase and union 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 financial risk and non-financial risk corresponds to data characterizing an index of that dimension, e.g., the data corresponding to financial risk is cash due liability ratio, cash flow liability ratio, cash liability total ratio, cash interest multiplier. 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 using the vector space of the current time point, and the time sequence is formed by the change of the vector space along with time.
S103: and obtaining an enterprise risk grade corresponding to the preset time according to the time sequence of the enterprise state and a preset risk assessment model, wherein the enterprise risk grade comprises a grade corresponding to at least one risk dimension.
The preset risk assessment model is obtained by training the classification model by taking a historical enterprise state and a corresponding enterprise risk level as training samples and adopting a machine learning algorithm, and the risk assessment model is optimized by comparing the enterprise risk level output by the risk assessment model with the expert predicted result. By way of example and not limitation, the classification model may be an LSTM network model, which may describe the temporal continuity of financial risk, and thus may result in an efficient risk assessment model.
The enterprise risks include enterprise management risks, production risks, operation risks and financial risks, the financial risks include financing risks, investment risks, operation risks, inventory management risks, liquidity risks, profitability risks, compensation and the like, the enterprise risk levels can be levels corresponding to various dimensions such as enterprise management risks, production risks, operation risks, financing risks, investment risks, operation risks, inventory management risks, liquidity risks, profitability risks and repayment capability risks, the comprehensive index levels calculated according to the levels corresponding to the various dimensions can be levels classified extremely low, medium, high and extremely high, and the enterprise risk levels can be expressed by numbers between 0 and 1.
The enterprise risk level corresponding to the preset time can be the current risk level of the enterprise, can also be the historical risk level of the enterprise, can also be the future risk level of the enterprise, can generate a time sequence of the change of the enterprise risk level along with time according to the historical risk level corresponding to each time period, and can more intuitively reflect the change condition of the enterprise risk level.
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 the user, for example, in a form of a graph, a table, or a descriptive report.
In one possible implementation manner, the enterprise risk assessment method provided by the embodiment of the application further obtains the industry risk level by acquiring the industry knowledge graph and the industry financial data and counting the industry knowledge graph and the industry financial data.
The industry knowledge graph is a multidimensional knowledge network for describing industries, products, services and the like. The basic components of industry knowledge graph are subject, dimension and emotion. Where the principal includes the current enterprise, other enterprises, departments within the enterprise, a product, or some version of the product. The dimension includes describing an entity in a certain aspect, a certain attribute, or a certain topic of a product, service, marketing, brand and other angles, and emotion generally refers to the opinion or the opinion of a consumer on an industry or a product, and according to an industry knowledge graph, industry related production and operation information and a peer enterprise can be obtained.
Industry financial data includes financial information of commercial companies in industry, expert views of accounting professors, global historical economic composite data, chinese historical economic composite data, composite business intelligence, consumer behavior data, agency data, microscopic economic data, and marking data, etc. And counting and classifying the industry financial data to obtain financial data in a preset format, such as structured expert views, structured real-time business intelligence, structured digestion behavior data, structured case marking data, structured agency data and structured micro-economic data. And carrying out statistics and analysis on the industry knowledge graph and the preprocessed industry financial data to obtain the industry risk level.
In one possible implementation manner, after the industry knowledge graph and the industry financial data are classified, a vector space is generated according to a vector mode, and for the current time point, the vector space comprises dimensions of management, production, operation, time period, economic index, business index, real-time information alarm, non-financial risk, financial risk and the like of a peer enterprise of the current enterprise, and risk levels corresponding to the dimensions can be obtained through a statistical analysis mode or 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 relation between the enterprise and the current industry can be tracked and predicted, and a manager is helped to make a decision.
In one possible implementation, a business enterprise corresponding to the current enterprise or an enterprise designated by a user can be selected according to an industry knowledge graph, a corresponding vector space is generated, and a risk level of the business enterprise or the enterprise designated by the user is calculated. And comparing the risk level of the same business enterprise or the enterprise appointed by the user with the current risk level or the historical risk level of the current enterprise, so that information such as the strength of a competitor, the development prospect of the enterprise and the like can be obtained to help the enterprise 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 acquiring the national region knowledge graph and the economic data related to the national region, and calculates the national region knowledge graph and the economic data related to the national region to obtain the economic situation information of the national region.
The national region knowledge graph is a description of a certain country or region related information, and comprises basic information of enterprises and industries in the country region. The economic data related to the country region includes financial data of global marketing companies, financial data of Chinese marketing companies, expert views of accounting professors, global historical economic comprehensive data, chinese historical economic comprehensive data, comprehensive business intelligence, consumer behavior data, agency data, microscopic economic data, case marking data and the like. And carrying out statistics and classification on the economic data related to the national region to obtain financial data in a preset format, such as structured expert views, structured real-time business intelligence, structured digestion behavior data, structured case marking data, structured agency data and structured micro economic data. And carrying out statistics and analysis on the industrial knowledge graph and the economic data related to the pretreated country region, so that the economic situation information of the country region can be obtained.
In one possible implementation manner, the industry knowledge graph and the economic data related to the country and the region after pretreatment are classified to obtain three dimensions of macroscopic economic condition, macroscopic economic trend and macroscopic economic risk, the three dimensions are respectively represented by vectors to obtain vector space corresponding to the country economic condition, and the economic data corresponding to the vector space is the economic condition information of the country and the region. And comparing the enterprise risk level with the economic situation information of the national region, and outputting a comparison result, so that the relationship between the enterprise risk and the external change can be seen to predict the enterprise development.
In a possible implementation manner, the method for evaluating enterprise risk further calculates periodic information of enterprise financial data according to a preset time series analysis method and enterprise financial data, where the preset time series analysis method may be a Holt-windows method, and the enterprise financial data analyzed by the preset time series analysis method includes total liability, profit, sales, profit, and the like of the enterprise. Through the Holt-windows method, related enterprise financial data can be represented by linear trend, periodic and non-stationary sequences, so that periodic information of the enterprise financial data is analyzed, and further, the enterprise financial data can be predicted. And comparing the corresponding relation between the enterprise risk grade corresponding to the preset time and the periodic information of the enterprise financial data, and outputting according to a preset format to intuitively display the corresponding relation between the enterprise risk and the financial data.
In a possible implementation manner, the enterprise risk assessment method provided by 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, micro-letter, video, bar, knowledge, etc. type information. And converting various types of information into text information, inputting the text information into a preset alarm analysis model, and outputting enterprise risk levels corresponding to the alarm information. The preset alarm analysis model is obtained by training the classification model by taking text information and corresponding risk grades as training samples. The classification model may be a convolutional neural network model, and the text needs to be preprocessed before being input into a preset alarm analysis model, including punctuation marks and spaces being removed, word segmentation being performed on the text, indexing being created, and the like, so that the text is converted into a plurality of character strings each taking a number as an index, and the text is converted 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. The risk level can be represented by numbers between 0 and 1, the enterprise risk level corresponding to the alarm information and the enterprise risk level corresponding to the financial data are compared, the mutual authentication function can be achieved, and the accuracy rate of enterprise risk level assessment is improved. Optionally, an alarm prompt with a preset format may also be generated according to the alarm information, for example, the alarm prompt is displayed in a voice or video format, so as to remind the user to pay attention to the related alarm.
In one possible implementation manner, in the embodiment of the present application, the calculated enterprise risk level, the industry risk level, the economic risk level of the country region, the periodic information of the enterprise financial data, and the enterprise risk level corresponding to the alarm information are used as the enterprise financial data, the industry financial data, and the economic data related to the country region again, so as to update the vector space related to the enterprise state, the industry, and the country 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, and as shown in fig. 2, the input data includes financial data of global marketing companies, financial data of chinese marketing companies, expert views of accounting professors, global historical economic synthesis data, chinese historical economic synthesis data, comprehensive business information, consumer behavior data, agency data, microscopic economic data, case marking data, and the like. And carrying out statistics and classification on the input data to obtain macroscopic economic data, structured expert views, structured real-time business intelligence, structured digestion behavior data, structured case marking data, structured agency data and structured microscopic economic data, and generating vector space corresponding to the current enterprise, vector space corresponding to the industrial enterprise and vector space corresponding to economic situation information of the national region by combining basic information, enterprise knowledge graph, industry knowledge graph, national region knowledge graph and time seasonal and periodic information of the enterprise. And 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 country region into a preset risk assessment model, and outputting the current risk, the historical risk of the enterprise, the comparison result of the industry risk level and the enterprise risk level of the current enterprise, the comparison result of each enterprise risk level, the comparison result of the enterprise risk level and the economic situation of the country region, periodic information of financial data and alarm information of the enterprise risk and the economic situation of the country region to display the relationship 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 period, new output data are generated, and the enterprise risk level of the next time period is estimated, so that the accuracy of risk assessment is improved.
In the above embodiment, the enterprise knowledge graph and the enterprise financial data are obtained, where the enterprise knowledge graph includes production information, operation information and management information of an enterprise; and obtaining a time sequence of the enterprise state according to the enterprise knowledge graph 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, namely the vector space contains production information, management information and financial information of each period of the enterprise. And obtaining an enterprise risk grade corresponding to the preset time according to the time sequence of the enterprise state and a preset risk assessment model, wherein the enterprise risk grade comprises at least one grade corresponding to a risk dimension, and the enterprise risk can be assessed from multiple dimensions of production information, operation information, management information and financial information of each period of the enterprise 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 each period of the enterprise, so that the accuracy of risk assessment is improved.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.
Corresponding to the enterprise risk assessment method described in the above embodiments, fig. 3 shows a block diagram of the enterprise risk assessment apparatus provided in the embodiment of the present application, and for convenience of explanation, only the portions relevant to the embodiments of the present application are shown.
As shown in fig. 3, the enterprise risk assessment apparatus includes,
an obtaining module 10, configured to obtain an enterprise knowledge graph and enterprise financial data, where the enterprise knowledge graph includes production information, operation information, and management information of an enterprise;
the analysis module 20 is configured to obtain a time sequence of an enterprise state according to the enterprise knowledge graph and the enterprise financial data, where the time sequence of the enterprise state is a correspondence between a vector space and time, where the vector space is formed by 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 the 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 one possible implementation manner, the enterprise risk assessment apparatus 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 one possible implementation manner, the enterprise risk assessment apparatus 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 the national region;
counting the national region knowledge graph and the economic data related to the national region to obtain the economic risk level of the national region;
and outputting a comparison result of the enterprise risk level and the economic risk level of the national region.
In one possible implementation manner, the enterprise risk assessment apparatus further includes a calculation module, where the calculation module is configured to:
calculating periodic information of 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 one possible implementation manner, the enterprise risk assessment apparatus further includes an update module, where the update module is configured to:
The vector space is updated according to periodic information of the enterprise financial data.
In one possible implementation manner, the enterprise risk assessment apparatus further includes an alarm module, where the alarm module is configured to:
acquiring alarm information related to enterprises;
converting the alarm information into text information;
and outputting enterprise risk levels corresponding to the alarm information according to the text information and a preset alarm analysis model.
In one possible implementation, the alarm module is further configured to:
and generating an alarm prompt in a preset format according to the alarm information.
It should be noted that, because the content of information interaction and execution process between the above devices/units is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be 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 the memory 12 and executable on the processor 11. The steps of the above-described embodiments of the enterprise risk assessment method, such as steps S101 to S103 shown in fig. 1, are implemented when the processor 11 executes the computer program 13. Alternatively, the processor 11 may perform the functions of the modules/units of the apparatus embodiments described above, such as the functions of the modules 10 to 30 shown in fig. 3, when executing the computer program 13.
By way of example, the computer program 13 may be divided into one or more modules/units, which are stored in the memory 12 and executed by the processor 11 to complete the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program 13 in the terminal device.
The terminal equipment can be computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The terminal device may include, but is not limited to, a processor 11, a memory 12. It will be appreciated by those skilled in the art that fig. 4 is merely an example of a terminal device and is not meant to be limiting, and that more or fewer components than shown may be included, or certain components may be combined, or different components may be included, for example, the terminal device may also include input and output devices, network access devices, buses, etc.
The processor 11 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. 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) or the like, which are 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 as well as 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-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a 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 process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
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 manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown 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 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 exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each method embodiment described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
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 solution. 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 embodiments are only for illustrating the technical solution of the present application, and are not limiting; 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 scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (8)
1. A method for evaluating enterprise risk, 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;
counting and classifying the enterprise knowledge graph and the time-dependent change information in the enterprise financial data to generate a time sequence of enterprise states, wherein the time sequence of the enterprise states is a corresponding relation between a first vector space and time, the first vector space consists of at least one vector, one vector corresponds to the data information of one risk dimension, and the first vector space contains production information, management information and financial information of each period of the enterprise; enterprise states include management, production, operation, time periods, economic indicators, business indicators, non-financial risks, and financial risk dimensions; describing the enterprise state of the current time point by using a first vector space of the current time point, wherein the change of the first vector space along with time forms a time sequence;
obtaining an enterprise risk grade corresponding to preset time according to the time sequence of the enterprise state and a preset risk assessment model, wherein the enterprise risk grade comprises at least one grade corresponding to a risk dimension, and the preset risk assessment model is obtained by training a classification model by taking a historical enterprise state and the corresponding enterprise risk grade as training samples and adopting a machine learning algorithm;
The enterprise risk assessment method further comprises the following steps:
acquiring an industry knowledge graph and industry financial data, wherein the industry knowledge graph comprises production information, operation information and business enterprise information of an industry;
counting the industry knowledge graph and the industry financial data to obtain industry risk levels, wherein the industry financial data are counted and classified to obtain financial data in a preset format, after the industry knowledge graph and the industry financial data are classified, a second vector space is generated in a vector mode, and for the current time point, the second vector space comprises multiple dimensions of management, production, operation, time period, economic index, business index, real-time information alarm, non-financial risk and financial risk of the same industry of the current enterprise, and the risk levels corresponding to the dimensions are obtained through a statistical analysis mode or a preset risk assessment model, namely the industry risk levels;
outputting a comparison result of the enterprise risk level and the industry risk level;
acquiring a national region knowledge graph and economic data related to the national region, wherein the national region knowledge graph comprises enterprise information and industry information of the national region;
The national region knowledge graph and the economic data related to the national region are counted to obtain economic situation information of the national region, wherein the economic data related to the national region comprises financial data of global marketing companies, financial data of Chinese marketing companies, expert views of accounting professors, global historical economic comprehensive data, chinese historical economic comprehensive data, comprehensive business information, consumer behavior data, agency data, microscopic economic data and case marking data; classifying the industrial knowledge graph and the economic data related to the pretreated country region to obtain three dimensions of macroscopic economic condition, macroscopic economic trend and macroscopic economic risk, respectively representing the three dimensions by vectors to obtain a third vector space corresponding to the country economic condition, wherein the economic data corresponding to the third vector space is the economic condition information of the country region;
and outputting a comparison result of the enterprise risk level and the economic situation information of the country region.
2. The method for assessing a risk of an enterprise of claim 1, further comprising:
calculating periodic information of 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.
3. The method for assessing a risk of an enterprise of claim 2, further comprising:
the first vector space is updated according to periodic information of the enterprise financial data.
4. The method for assessing a risk of an enterprise of claim 1, further comprising:
acquiring alarm information related to enterprises, wherein the alarm information comprises one or more types of information of news, forums, blogs, microblogs, weChat, videos, bar sticks, knowledge and knowledge;
converting the alarm information into text information;
outputting enterprise risk grades corresponding to the alarm information according to the text information and a preset alarm analysis model, wherein the preset alarm analysis model is obtained after training a classification model by taking the text information and the corresponding risk grades as training samples.
5. The method of assessing a risk of an enterprise of claim 4, wherein after the acquiring alarm information related to the enterprise, the method further comprises:
And generating an alarm prompt in a preset format according to the alarm information.
6. An enterprise risk assessment apparatus, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for 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 analysis module is used for counting and classifying the change information with time in the enterprise knowledge graph and the enterprise financial data to generate a time sequence of the enterprise state, wherein the time sequence of the enterprise state is a corresponding relation between a first vector space and time, the first vector space consists of at least one vector, one vector corresponds to the data information of one risk dimension, and the first vector space contains production information, management information and financial information of each period of the enterprise; enterprise states include management, production, operation, time periods, economic indicators, business indicators, non-financial risks, and financial risk dimensions; describing the enterprise state of the current time point by using a first vector space of the current time point, wherein the change of the first vector space along with time forms a time sequence;
The assessment module is used for obtaining an enterprise risk grade corresponding to preset time according to the time sequence of the enterprise state and a preset risk assessment model, wherein the enterprise risk grade comprises at least one grade corresponding to a risk dimension, the preset risk assessment model is obtained by training a classification model by taking a historical enterprise state and the corresponding enterprise risk grade as training samples and adopting a machine learning algorithm;
the first contrast module is used for: acquiring an industry knowledge graph and industry financial data, wherein the industry knowledge graph comprises production information, operation information and business enterprise information of an industry; counting the industry knowledge graph and the industry financial data to obtain industry risk levels, wherein the industry financial data are counted and classified to obtain financial data in a preset format, after the industry knowledge graph and the industry financial data are classified, a second vector space is generated in a vector mode, and for the current time point, the second vector space comprises multiple dimensions of management, production, operation, time period, economic index, business index, real-time information alarm, non-financial risk and financial risk of the same industry of the current enterprise, and the risk levels corresponding to the dimensions are obtained through a statistical analysis mode or a preset risk assessment model, namely the industry risk levels; outputting a comparison result of the enterprise risk level and the industry risk level;
A second contrast module for: acquiring a national region knowledge graph and economic data related to the national region, wherein the national region knowledge graph comprises enterprise information and industry information of the national region; the national region knowledge graph and the economic data related to the national region are counted to obtain economic situation information of the national region, wherein the economic data related to the national region comprises financial data of global marketing companies, financial data of Chinese marketing companies, expert views of accounting professors, global historical economic comprehensive data, chinese historical economic comprehensive data, comprehensive business information, consumer behavior data, agency data, microscopic economic data and case marking data; classifying the industrial knowledge graph and the economic data related to the pretreated country region to obtain three dimensions of macroscopic economic condition, macroscopic economic trend and macroscopic economic risk, respectively representing the three dimensions by vectors to obtain a third vector space corresponding to the country economic condition, wherein the economic data corresponding to the third vector space is the economic condition information of the country region; and outputting a comparison result of the enterprise risk level and the economic situation information of the country region.
7. Terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method of assessing risk of an enterprise according to any one of claims 1 to 5 when executing the computer program.
8. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the enterprise risk assessment method according to any one of claims 1 to 5.
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