Risk indicator prediction method, device, electronic device and storage medium

Abstract

The present application is applicable to the field of computer technology and provides a risk indicator prediction method, device, electronic device and storage medium. The risk indicator prediction method includes: obtaining the industry category to which the enterprise belongs, the change information of macroeconomic data in the first preset period, and the operating indicators of the enterprise in the first preset period, the operating indicators including asset information and revenue change information, and the asset information including asset composition information and asset change information; inputting the industry category, the change information of macroeconomic data in the first preset period, and the operating indicators of the enterprise in the first preset period into a data analysis model to obtain the level of operating conditions output by the data analysis model; determining the indicator of the operating indicators that is most relevant to the level of operating conditions according to the level of operating conditions, and using the most relevant indicator as the risk indicator, so as to determine the risk indicator that has the greatest impact on the operating conditions of the enterprise.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, device, electronic device, and storage medium for predicting risk indicators. Background Art

[0002] There are many indicators that reflect a company's operating conditions. Some of these indicators have clear trends, and their presence can be determined by analyzing these trends. Other indicators have less clear trends, and their presence cannot be determined based on these trends. The operating indicators that have the greatest impact on a company's operating conditions may be risk indicators with less clear trends. Failure to promptly identify these risk indicators can severely impact the company's development. Summary of the Invention

[0003] In view of this, the embodiments of the present application provide a risk indicator prediction method, device, electronic device and storage medium, which can solve the problem in the prior art that the risk indicator with the greatest impact on the business conditions of the enterprise cannot be determined.

[0004] A first aspect of an embodiment of the present application provides a method for predicting a risk indicator, comprising:

[0005] Obtaining the industry category to which the enterprise belongs, information on changes in macroeconomic data for a first preset time period, and operating indicators of the enterprise for the first preset time period, wherein the operating indicators include asset information and revenue change information, and the asset information includes asset composition information and asset change information;

[0006] Inputting the industry category, information on changes in macroeconomic data during the first preset period, and the operating indicators of the enterprise during the first preset period into a data analysis model to obtain a level of operating status output by the data analysis model;

[0007] According to the level of the operating status, an indicator most relevant to the level of the operating status among the operating indicators is determined, and the most relevant indicator is used as a risk indicator.

[0008] In one possible implementation, the data analysis model is obtained by training a classification model using the operating indicators of multiple listed companies in historical periods, the change information of macroeconomic data in the historical periods, the industry categories to which each of the listed companies belongs, and the level of operating conditions of each of the listed companies in the historical periods as training samples.

[0009] In a possible implementation, determining, according to the level of the operating status, the indicator most relevant to the level of the operating status among the operating indicators includes:

[0010] Fitting a relationship function between the level of the operating conditions and the operating indicators according to the level of the operating conditions and the operating indicators;

[0011] The indicator most relevant to the level of the operating status among the operating indicators is determined according to the relationship function.

[0012] In a possible implementation, fitting a relationship function between the level of the operating condition and the operating indicator according to the level of the operating condition and the operating indicator includes:

[0013] Determining a fitting value of the level of the operating condition based on the initial function and the operating indicator;

[0014] Optimizing the parameters of the initial function according to the fitting value of the level of the operating condition and the level of the operating condition until the optimal parameters are obtained;

[0015] The relationship function is determined according to the optimal parameters.

[0016] In a possible implementation, fitting a relationship function between the level of the operating condition and the operating indicator according to the level of the operating condition and the operating indicator includes:

[0017] Normalizing the operating indicators;

[0018] According to the level of the operating conditions and the normalized operating indicators, a relationship function between the level of the operating conditions and the operating indicators is fitted.

[0019] In a possible implementation, the method further includes:

[0020] The influence weight of each of the operating indicators on the operating status is determined according to the relationship function.

[0021] In a possible implementation, determining, according to the level of the operating status, the indicator most relevant to the level of the operating status among the operating indicators includes:

[0022] Determining a first level according to a preset indicator among the operating indicators and a first relationship formula;

[0023] Determining a target operating condition level based on the first level, the operating condition level, and a preset weight distribution method;

[0024] According to the target operating condition level, an indicator most relevant to the operating condition level among the operating indicators is determined.

[0025] A second aspect of an embodiment of the present application provides a risk indicator prediction device, comprising:

[0026] an acquisition module, configured to acquire the industry category to which the enterprise belongs, information on changes in macroeconomic data during a first preset period, and operating indicators of the enterprise during the first preset period, wherein the operating indicators include asset information and revenue change information, and the asset information includes asset composition information and asset change information;

[0027] an analysis module, configured to input the industry category, information on changes in macroeconomic data during the first preset period, and the operating indicators of the enterprise during the first preset period into a data analysis model, and obtain a level of operating status output by the data analysis model;

[0028] The output module is used to determine the most relevant indicator among the operating indicators according to the level of the operating status, and use the most relevant indicator as the risk indicator.

[0029] A third aspect of an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the risk indicator prediction method as described in the first aspect above is implemented.

[0030] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the risk indicator prediction method as described in the first aspect above.

[0031] A fifth aspect of an embodiment of the present application provides a computer program product. When the computer program product is run on an electronic device, the electronic device executes the risk indicator prediction method described in any one of the first aspects above.

[0032] Compared with the prior art, the beneficial effect of the embodiments of the present application is as follows: by inputting the industry category to which the enterprise belongs, the change information of the macroeconomic data in the first preset time period, and the operating indicators of the enterprise in the first preset time period into the data analysis model, the level of the operating status is obtained, so that the industry situation and the macroeconomic situation can be comprehensively considered to obtain a more accurate level of the operating status, and then the most relevant operating indicators are determined according to the level of the operating status, and the most relevant operating indicators are used as risk indicators. Since the most relevant operating indicators have the greatest impact on the operating status of the enterprise, the risk indicators that have the greatest impact on the operating status of the enterprise can be determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art.

[0034] Figure 1 This is a schematic diagram of the implementation flow of a risk indicator prediction method provided in one embodiment of the present application;

[0035] Figure 2 Schematic diagram of a risk indicator prediction device provided in an embodiment of the present application;

[0036] Figure 3 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0038] In order to illustrate the technical solution described in this application, specific embodiments are provided below.

[0039] It will be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0040] There are many indicators that reflect the operating conditions of an enterprise. The operating indicators that have the greatest impact on the operating conditions of an enterprise may be those with unclear changing trends and are not easy to be discovered. If the risk indicators that have the greatest impact on the operating conditions cannot be determined, it will seriously affect the development of the enterprise.

[0041] To this end, the present application provides a method for predicting risk indicators. By inputting the industry category to which the enterprise belongs, the change information of the macroeconomic data in the first preset time period, and the operating indicators of the enterprise in the first preset time period into the data analysis model, the level of the operating conditions is obtained, so that the industry situation and the macroeconomic situation can be comprehensively considered to obtain a more accurate level of the operating conditions. The most relevant operating indicators are then determined based on the level of the operating conditions, and the most relevant operating indicators are used as risk indicators. Since the most relevant operating indicators have the greatest impact on the operating conditions of the enterprise, the risk indicators that have the greatest impact on the operating conditions of the enterprise can be determined.

[0042] The following is an illustrative description of the risk indicator prediction method provided in this application.

[0043] Please see the attached Figure 1 , a risk indicator prediction method provided in one embodiment of the present application includes:

[0044] S101: Obtain the industry category to which the enterprise belongs, change information of macroeconomic data in a first preset time period, and operating indicators of the enterprise in the first preset time period, wherein the operating indicators include asset information and revenue change information, and the asset information includes asset composition information and asset change information.

[0045] Among them, industry categories refer to manufacturing industry, construction industry, advertising industry, service industry, tourism industry, financial industry, communications industry, semiconductor industry and other categories. The industry category to which an enterprise belongs can be one or more.

[0046] Macroeconomic data can be global or national economic data, such as national income and consumption information. Changes in macroeconomic data can include monthly, quarterly, or annual growth rates. The growth rate can be a single value or multiple values. If the growth rate is multiple values, each growth rate corresponds to a time period.

[0047] Asset composition information includes information such as customer concentration, direct sales ratio, distribution ratio, accounts payable period, number of employees, and R&D investment ratio over time. Asset change information includes information on changes in assets and liabilities, cash flow, and income statements over time. Change information can include growth rates. In one embodiment, asset change information also includes information such as return on equity, net profit margin on total assets, operating profit margin, total asset turnover rate, equity multiplier, and debt-to-asset ratio calculated based on financial statements. Revenue change information includes information such as total revenue, growth rate, gross profit margin, and competitive concentration of products.

[0048] S102: Input the industry category, the change information of the macroeconomic data in the first preset period, and the operating indicators of the enterprise in the first preset period into a data analysis model to obtain the level of the operating status output by the data analysis model.

[0049] Among them, the data analysis model is obtained by training the classification model using training samples, and can output the level of business conditions.

[0050] In one embodiment, the change information of the macroeconomic data of the first preset time period input into the data analysis model and the operating indicators of the enterprise in the first preset time period are normalized data, that is, all operating indicators are converted into numerical values ​​between 0 and 1, so that each indicator is at the same level, which is more suitable for comprehensive analysis, thereby improving the accuracy of the output operating status level.

[0051] In one embodiment, before normalizing the operating indicators, data cleaning and data reduction are performed on the data to improve data quality and calculation accuracy.

[0052] In one embodiment, the data analysis model is obtained by training a classification model using the operating indicators of multiple listed companies over historical periods, information on changes in macroeconomic data over historical periods, the industry categories to which each listed company belongs, and the level of operating conditions of each listed company over historical periods as training samples. The level of operating conditions of each listed company over historical periods is determined based on the financial information or business information of each listed company over historical periods. For example, the level of operating conditions can be determined based on the profit growth rate or operating income growth rate of each listed company. The level of operating conditions can be a numerical value used to represent the level, such as 1, 2, 3, etc., or a score value determined based on the operating conditions. The data of multiple listed companies can better reflect the operating conditions of different industries under different macroeconomic situations. The greater the amount of data, the more universal, applicable, and accurate the resulting data analysis model.

[0053] Optionally, the data analysis model can also be obtained by training the classification model using the operating indicators, operating status levels of enterprises in the same industry category as the current enterprise in historical periods, and change information of macroeconomic data in historical periods as training samples.

[0054] In another embodiment, a data analysis model is obtained by training a classification model using the current enterprise's operating indicators over a historical period, information on changes in macroeconomic data over that historical period, and the current enterprise's operating status levels at different periods in the historical period as training samples. The current enterprise's operating status levels at different periods in the historical period are determined based on the current enterprise's financial or operating information over that historical period. Using the current enterprise's historical data as training samples allows the resulting data analysis model to better match the current enterprise.

[0055] S103: Determine, according to the level of the operating status, the most relevant indicator among the operating indicators, and use the most relevant indicator as a risk indicator.

[0056] In one embodiment, the operating status level and various operating indicators for a first preset time period are input into a prediction model to obtain the most relevant indicator output by the prediction model. The prediction model is trained using the operating status levels, operating indicators, and most relevant indicators from multiple listed companies over historical time periods as training samples to train a classification model. The most relevant indicator is obtained by analyzing long-term data from multiple listed companies. Using the pre-trained prediction model, the most relevant indicator can be quickly determined.

[0057] In another embodiment, a relationship function between the level of the operating status and the operating indicators is fitted based on the level of the operating status and the operating indicators, and the most relevant indicator among the operating indicators is determined based on the relationship function. For example, the function to be fitted is Among them, y represents the level of business conditions, x1, x2, x3…x n Represents one of the operating indicators, k1, k2, k3…k n , b, and n represent the coefficients to be fitted corresponding to each operating indicator. By determining the coefficients to be fitted based on the operating status level and the operating indicator, we can obtain the relationship function. It can be seen that x1 has the greatest impact on y, so the operating indicator corresponding to x1 is the most relevant indicator.

[0058] In one embodiment, initial values ​​for each parameter to be fitted in the function to be fitted are first determined. An initial function is obtained based on the initial values. A fitted value for the operating status grade is determined based on the initial function and the operating indicators. Based on the fitted value for the operating status grade and the operating status grade, the parameters of the initial function are optimized until optimal parameters are obtained. A relationship function is then determined based on the optimal parameters. Specifically, the optimal parameters are obtained when the difference between the fitted value for the operating status grade and the operating status grade reaches a preset value or the number of iterations. Exemplarily, the relationship function can be obtained using the least squares method. In other embodiments, a neural network-based method can also be used to fit the relationship function.

[0059] In one embodiment, before fitting the relationship function, the operating indicators are normalized, and the relationship function between the level of operating conditions and the operating indicators is fitted based on the level of operating conditions and the normalized operating indicators, so that each indicator can be at the same level, thereby improving the accuracy of the most relevant indicators output subsequently.

[0060] In a possible implementation, after determining the relationship function, the correlation between different operating indicators and the level of operating conditions is determined according to the relationship function, and the influence weight of each indicator on the operating conditions is determined according to the correlation. For example, the formula In the example, from x1 to x n The degree of correlation between each operating indicator and the level of operating conditions decreases in descending order. The weight of each indicator's impact on the operating conditions can be determined based on the coefficient corresponding to each operating indicator, or based on the number of operating indicators and a preset weighting rule. For example, the coefficient corresponding to each operating indicator can be used as the corresponding weight of the impact of each operating indicator. By calculating the weight of the impact of each operating indicator, a decision-making basis can be provided for business operators. In one embodiment, each operating indicator can also be sorted according to the order of its weight of impact or plotted and outputted into a chart according to preset rules, so as to intuitively display the risk factors in the business operation process to the user.

[0061] In one embodiment, after obtaining the operating status level output by the data analysis model, a first level is determined based on preset indicators in the operating indicators and a first relationship. The first relationship is a pre-set relationship for determining the operating status level and can be established by financial experts based on big data or empirical data. The preset indicators can be a subset of the operating indicators for a first preset time period, such as operating income, total profit, total assets, etc. After determining the first level, a target operating status level is determined based on the first level, the operating status level, and a preset weighting scheme. The preset weighting scheme can be a 1:1 relationship between the operating status level output by the data analysis model and the first level, or a 7:3 relationship. After calculating the target operating status level, the indicator among the operating indicators that is most correlated with the target operating status level is determined based on the target operating status level. This is the indicator most correlated with the operating status level. Determining the target operating status level by combining the operating status level output by the data analysis model and the first level determined by the manually-set first relationship improves the accuracy of the determined target operating status level.

[0062] In the above embodiment, the industry category to which the enterprise belongs, the change information of the macroeconomic data in the first preset time period, and the operating indicators of the enterprise in the first preset time period are input into the data analysis model to obtain the level of the operating status, so that a more accurate level of the operating status can be obtained by comprehensively considering the industry situation and the macroeconomic situation. The most relevant operating indicators are then determined according to the level of the operating status, and the most relevant operating indicators are used as risk indicators. Since the most relevant operating indicators have the greatest impact on the operating status of the enterprise, the risk indicators that have the greatest impact on the operating status of the enterprise can be determined.

[0063] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0064] Corresponding to the risk indicator prediction method described in the above embodiment, Figure 2 A structural block diagram of a risk indicator prediction device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0065] like Figure 2 As shown, the risk indicator prediction device includes,

[0066] An acquisition module 21 is configured to acquire the industry category to which the enterprise belongs, information on changes in macroeconomic data during a first preset period, and operating indicators of the enterprise during the first preset period, wherein the operating indicators include asset information and revenue change information, and the asset information includes asset composition information and asset change information;

[0067] An analysis module 22 is configured to input the industry category, information on changes in macroeconomic data during the first preset period, and the operating indicators of the enterprise during the first preset period into a data analysis model to obtain a level of operating status output by the data analysis model;

[0068] The output module 23 is configured to determine the most relevant indicator among the operating indicators according to the level of the operating status, and use the most relevant indicator as the risk indicator.

[0069] In one possible implementation, the data analysis model is obtained by training a classification model using the operating indicators of multiple listed companies in historical periods, the change information of macroeconomic data in the historical periods, the industry categories to which each of the listed companies belongs, and the level of operating conditions of each of the listed companies in the historical periods as training samples.

[0070] In a possible implementation, the output module 23 is specifically configured to:

[0071] Fitting a relationship function between the level of the operating conditions and the operating indicators according to the level of the operating conditions and the operating indicators;

[0072] The indicator most relevant to the level of the operating status among the operating indicators is determined according to the relationship function.

[0073] In a possible implementation, the output module 23 is further configured to:

[0074] Determining a fitting value of the level of the operating condition based on the initial function and the operating indicator;

[0075] Optimizing the parameters of the initial function according to the fitting value of the level of the operating condition and the level of the operating condition until the optimal parameters are obtained;

[0076] The relationship function is determined according to the optimal parameters.

[0077] In a possible implementation, the output module 23 is further configured to:

[0078] Normalizing the operating indicators;

[0079] According to the level of the operating conditions and the normalized operating indicators, a relationship function between the level of the operating conditions and the operating indicators is fitted.

[0080] In a possible implementation, the output module 23 is further configured to:

[0081] The influence weight of each of the operating indicators on the operating status is determined according to the relationship function.

[0082] In a possible implementation, the output module 23 is further configured to:

[0083] Determining a first level according to a preset indicator among the operating indicators and a first relationship formula;

[0084] Determining a target operating condition level based on the first level, the operating condition level, and a preset weight distribution method;

[0085] According to the target operating condition level, an indicator most relevant to the operating condition level among the operating indicators is determined.

[0086] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0087] Figure 3 Schematic diagram of the structure of the electronic device provided in the embodiment of the present application. The electronic device may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server. Figure 3 As shown, the electronic device of this embodiment includes: a processor 31, a memory 32, and a computer program 33 stored in the memory 32 and executable on the processor 31. When the processor 31 executes the computer program 33, the steps of the above-mentioned risk indicator prediction method embodiment are implemented, such as Figure 1 Alternatively, when the processor 31 executes the computer program 33, the functions of the modules / units in the above-mentioned device embodiments are realized, for example, Figure 2 The functions of the acquisition module 21 to the output module 23 are shown.

[0088] Exemplarily, the computer program 33 may be divided into one or more modules / units, which are stored in the memory 32 and executed by the processor 31 to implement the present application. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program 33 in the electronic device.

[0089] Those skilled in the art will understand that Figure 3 These are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0090] The processor 31 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0091] The memory 32 may be an internal storage unit of the electronic device, such as a hard disk or memory of the electronic device. The memory 32 may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device. Furthermore, the memory 32 may include both an internal storage unit of the electronic device and an external storage device. The memory 32 is used to store the computer program and other programs and data required by the electronic device. The memory 32 may also be used to temporarily store data that has been output or is about to be output.

[0092] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0093] In the embodiments provided in the present application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0094] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0095] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can 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 that can carry the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0096] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0097] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for predicting risk indicators, characterized in that: include: Obtaining the industry category to which the enterprise belongs, information on changes in macroeconomic data for a first preset time period, and operating indicators of the enterprise for the first preset time period, wherein the operating indicators include asset information and revenue change information, and the asset information includes asset composition information and asset change information; Inputting the industry category, information on changes in macroeconomic data during the first preset period, and the operating indicators of the enterprise during the first preset period into a data analysis model to obtain a level of operating status output by the data analysis model; determining, according to the level of the operating status, an indicator among the operating indicators that is most relevant to the level of the operating status, and using the most relevant indicator as a risk indicator; The step of determining the most relevant indicator among the operating indicators according to the level of the operating status includes: Determining a first level according to a preset indicator among the operating indicators and a first relationship formula; Determining a target operating condition level based on the first level, the operating condition level, and a preset weight distribution method; According to the level of the target operating condition, the indicator most relevant to the level of the operating condition among the operating indicators is determined. The first relationship is a pre-set relationship for determining the level of the operating condition, and the preset indicators are some indicators among the operating indicators of the first preset time period.

2. The method according to claim 1, characterized in that The data analysis model is obtained by training the classification model using the operating indicators of multiple listed companies in historical periods, the change information of macroeconomic data in the historical periods, the industry categories to which each listed company belongs, and the level of operating conditions of each listed company in the historical periods as training samples.

3. The method according to claim 1, characterized in that The step of determining the most relevant indicator among the operating indicators according to the level of the operating status includes: Fitting a relationship function between the level of the operating conditions and the operating indicators according to the level of the operating conditions and the operating indicators; The indicator most relevant to the level of the operating status among the operating indicators is determined according to the relationship function.

4. The method according to claim 3, characterized in that The step of fitting a relationship function between the level of the operating condition and the operating indicator according to the level of the operating condition and the operating indicator comprises: Determining a fitting value of the level of the operating condition based on the initial function and the operating indicator; Optimizing the parameters of the initial function according to the fitting value of the level of the operating condition and the level of the operating condition until the optimal parameters are obtained; The relationship function is determined according to the optimal parameters.

5. The method according to claim 3, characterized in that The step of fitting a relationship function between the level of the operating condition and the operating indicator according to the level of the operating condition and the operating indicator includes: Normalizing the operating indicators; According to the level of the operating conditions and the normalized operating indicators, a relationship function between the level of the operating conditions and the operating indicators is fitted.

6. The method according to claim 3, characterized in that The method further comprises: The influence weight of each of the operating indicators on the operating status is determined according to the relationship function.

7. A risk indicator prediction device, characterized in that: include: an acquisition module, configured to acquire the industry category to which the enterprise belongs, information on changes in macroeconomic data during a first preset period, and operating indicators of the enterprise during the first preset period, wherein the operating indicators include asset information and revenue change information, and the asset information includes asset composition information and asset change information; an analysis module, configured to input the industry category, information on changes in macroeconomic data during the first preset period, and the operating indicators of the enterprise during the first preset period into a data analysis model, and obtain a level of operating status output by the data analysis model; an output module, configured to determine, according to the level of the operating status, an indicator among the operating indicators that is most relevant to the level of the operating status, and use the most relevant indicator as a risk indicator; The output module is also used for: Determining a first level according to a preset indicator among the operating indicators and a first relationship formula; Determining a target operating condition level based on the first level, the operating condition level, and a preset weight distribution method; According to the level of the target operating condition, the indicator most relevant to the level of the operating condition among the operating indicators is determined. The first relationship is a pre-set relationship for determining the level of the operating condition, and the preset indicators are some indicators among the operating indicators of the first preset time period.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the risk indicator prediction method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the risk indicator prediction method according to any one of claims 1 to 6 is implemented.

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